Encyclopedia of Information Science and Technology, Fourth Edition [4ed.] 1522522557, 9781522522553

Table of contents :
Content: Volume 1. A-BU --
volume 2. BU-CU --
volume 3. CU-ED --
volume 4. ED-F --
volume 5. G-HO --
volume 6. HO-IT --
volume 7. IT-MA --
volume 8. MA-MU --
volume 9. MU-SO --
volume 10. SO-W.

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Encyclopedia of Information Science and Technology, Fourth Edition Mehdi Khosrow-Pour Information Resources Management Association, USA

Published in the United States of America by IGI Global Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA, USA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com Copyright © 2018 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Names: Khosrow-Pour, Mehdi, 1951- editor. Title: Encyclopedia of information science and technology / Mehdi Khosrow-Pour, editor. Description: Fourth edition. | Hershey, PA : Information Science Reference, [2018] | Includes bibliographical references and index. Identifiers: LCCN 2017000834| ISBN 9781522522553 (set : hardcover) | ISBN 9781522522560 (ebook) Subjects: LCSH: Information science--Encyclopedias. | Information technology--Encyclopedias. Classification: LCC Z1006 .E566 2018 | DDC 020.3--dc23 LC record available at https://lccn.loc.gov/2017000834

British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. For electronic access to this publication, please contact: [email protected].

This book is dedicated to the memory of my late father for the love and care that he always displayed for his family, and for teaching me the importance of humanitarianism. Also, to my wife, Olga, and our son, Darius, for filling my life with so much love, joy, and happiness.



Editorial Advisory Board Ari-Veikko Anttiroiko, University of Tampere, Finland S. Annie Becker, Florida Institute of Technology, USA Shirley M. Fedorovich, Embry-Riddle Aeronautical University, USA Wen-Chen Hu, University of North Dakota, USA Jerzy Kisielnicki, Warsaw University, Poland In Lee, Western Illinois University, USA James A. Rodger, Indiana University of Pennsylvania, USA Ari Sigal, Catawba Valley Community College, USA Lawrence A. Tomei, Robert Morris University, USA John Wang, Montclair State University, USA Liudong Xing, University of Massachusetts, Dartmouth, USA Jordi Vallverdú, Universitat Autònoma de Barcelona, Spain

Editorial Review Board Mifrah Ahmad, Universiti Teknologi PETRONAS, Malaysia Bashar Shahir Ahmed, University Abdelmalek Essaadi, Morocco Daniel Alemneh, University of North Texas, USA Salam Omar Ali, Brighton College, UAE Paul Ankomah, North Carolina A&T University, USA Andrea Atzeni, Politecnico di Torino, Italy Danilo Avola, Sapienza University, Italy Ihuoma Babatope, Delta State Univeristy Abraka/ Delta State College of Physical Education, Mosogar, Nigeria Mihaela Badea, Petroleum-Gas University of Ploiesti, Romania Surajit Bag, Tega Industries South Africa Pty Ltd, South Africa Kannan Balasubramanian, Mepco Schlenk Engineering College, India Neeta Baporikar, Namibia University of Science and Technology, Nigeria Nomathamsanqa (Thami) Batyashe, Cape Peninsula University of Technology, South Africa Clementina Bruno, University of Piemonte Orientale, Italy Claudia Cappelli, Universidade Federal do Estado do Rio de Janeiro, Brazil Calin Ciufudean, Stefan cel Mare University, Romania Ángela Coello, University of Vigo, Spain Eduardo Contreras, Autonomous University of Coahuila, Mexico Caroline Crawford, University of Houston-Clear Lake, United States Phil Crosby, Curtin University, Australia Nicoletta Dessì, Università degli Studi di Cagliari, Italy Nic Dunham, Unitec Institute of Technology, New Zealand  



Christophe Duret, Université de Sherbrooke, Canada Alexander Fridman, Russian Academy of Sciences, IIMM KSC RAS, Russia Dražena Gašpar, University of Mostar, Bosnia and Herzegovina Ciara Heavin, University College Cork, Ireland Masoud Hemmatpour, Politecnico di Torino, Italy Adamkolo Mohammed Ibrahim, University of Maiduguri, Nigeria Shigeru Ikuta, Otsuma Women’s University, Japan Giuseppe Iurato, University of Palermo, Italy George Jamil, Informações em Rede, Brazil Jurgen Janssens, TETRADE Consulting, Belgium Michael Josefowicz, Nemetics Institute Kolkata, USA Prateek Kalia, I.K Gujral Punjab Technical University, India Yefim Katz, Chestnut Hill College, USA Nadim Akhtar Khan, University of Kashmir, India Maria Koleva, Bulgarian Academy of Sciences, Bulgaria Raghuraman Koteeswaran, SASTRA University, India Priya Krishnamoorthy, SASTRA University, India Sefer Kurnaz, Istanbul Esenyurt University, Turkey Trent Larson, North Carolina A&T University, USA, Elvira Locuratolo, ISTI, Italy Ronald Lofaro, Embry-Riddle Aeronautical University, USA Geetha Mariappan, Kamaraj College of Engineering and Technology, India M.K. Marichelvam, Mepco Schlenk Engineering College, India Gilberto Marzano, Rezekne University of Applied Sciences, Latvia Lincy Mathews, M S Ramaiah Institute of Technology, India Karim Mezghani, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Tunisia Emilia Mikolajewska, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Poland Dariusz Mikolajewski, Kazimierz Wielki University, Poland Vardan Mkrttchian, HHH University, Australia Jessina Muthee, Kenyatta University, Kenya Antonio Osorio, University of Minho, Portugal Leo Paschoal, Federal University of Cruz Alta, Brazil Prantosh Paul, Raiganj University, India Danilo Piaggesi, FRAmericas, International Knowledge Economy Program (IKEP), USA Diana Presada, Petroleum-Gas University of Ploiesti, Romania Mohd Rahman, Aligarh Muslim University, India N. Raghavendra Rao, FINAIT Consultancy Services, India José Rascão, Polytechnic Institute of Setúbal, Portugal Filipa Ribeiro, University of Porto, Portugal Gerasimos Rigatos, Industrial Systems Institute, Greece John Robinson, Bishop Heber College, India Juan Rodriguez, UNICEN CONICET, Argentina Isabel Romero, University of Castilla-La Mancha, Spain



Roya Rouzbehani, University of Tehran, Iran John C. Sandvig, Western Washington University, USA Francesca Sgobbi, University of Brescia, Italy Huma Shah, Coventry University, UK Dolly Sharma, PEC University of Technology, India Teay Shawyun, King Saud University, Saudi Arabia Zeljko Stojanov, University of Novi Sad, Serbia Apostolos Syropoulos, Greek Molecular Computing Group, Greece Safiye Turgay, Sakarya University, Turkey Immanuel Umukoro, Lagos Business School, Pan Atlantic University, Nigeria Tugba Ucma Uysal, Mugla Sitki Koçman University, Turkey JT Velikovsky, University of Newcastle, Australia Laura Vettraino, Learning Community, Italy Revathi Viswanathan, B.S. Abdur Rahman University, India Ye Wang, Zhejiang Gongshang University, China Tingshao Zhu, Institute of Psychology, Chinese Academy of Sciences, China

List of Contributors

A, Vadivel / National Institute of Technology Trichy, India............................................................... 212 Abad, Lalaine P. / Department of Education, Philippines............................................................... 2466 Abdelhafez, Hoda Ahmed / Suez Canal University, Egypt................................................................ 406 Abdou, Tamer / Suez Canal University, Egypt................................................................................ 7399 Abdrakhmanova, Gulnara / National Research University Higher School of Economics, Russia.......................................................................................................................................... 4704 Aboud, Sattar J. / University of Bedfordshire, UK.......................................................................... 4898 Adhikari, Arnab / Indian Institute of Management Ranchi, India.................................................. 7234 Adibfar, Alireza / University of Florida, USA................................................................................... 539 Agrawal, Shaila / Maulana Abul Kalam Azad University of Technology, India............................. 4872 Agrey, Renae / The University of Queensland, Australia................................................................ 5433 Aguilar-Moreno, Estefanía / Universitat Jaume I of Castellón, Spain........................................... 3473 Agwae, Uruemu / Carleton University, Canada.............................................................................. 1684 Ahmad, Mifrah / Universiti Teknologi PETRONAS, Malaysia....................................................... 3337 Ahmed, Bashar Shahir / University Abdelmalek Essaadi (LEROSA Laborator), Morocco........... 1616 Ahmed, Rukhsana / University of Ottawa, Canada........................................................................ 6083 Akahane-Yamada, Reiko / Advanced Telecommunications Research Institute International (ATR), Japan............................................................................................................................... 3850 Akalan, Nazif Ekin / Istanbul University, Turkey.............................................................................. 298 Akhtar, Saliha / Seton Hall University, USA................................................................................... 7181 Akolkar, Rajshree Tushar / Zeal College of Engineering and Research, India.............................. 4099 Akyuz, Goknur Arzu / University of Turkish Aeronautical Association, Turkey............................ 5285 Alba-Elías, Fernando / Universidad de La Rioja, Spain................................................................ 1934 Alberg, Dima / Shamoon College of Engineering (SCE), Israel....................................................... 364 Alemneh, Daniel G. / University of North Texas, USA..................................................................... 6748 Alharbi, Ali H. / Qassim University, Saudi Arabia................................................................. 1249,3448 Ali, Salam Omar / Brighton Collage Al Ain, UAE........................................................................... 7647 Al-Khalifa, Hend S. / King Saud University, Saudi Arabia............................................................. 4652 Al-Marzouqi, Mohamed / UAE University, UAE........................................................................... 3043 Almeida, Fernando / Polytechnic Institute of Gaya, Portugal.......................................................... 800 Almeida, Gustavo de Oliveira / Federal University of the State of Rio de Janeiro, Brazil............... 754 Almunawar, Hasan Jawwad / P. T. Tegar Kupas Mediatama, Indonesia....................................... 1101 Almunawar, Mohammad Nabil / Universiti Brunei Darussalam, Brunei.................... 1101,5908,7369 Alnowibet, Khalid / King Saud University, Saudi Arabia............................................................... 1570 Al-Rousan, Thamer / Isra University, Pakistan.............................................................................. 7549 



Al-Salman, Abdulmalik / King Saud University, Saudi Arabia..................................................... 4652 Altun, Arif / Hacettepe University, Turkey...................................................................................... 1441 Alves, Domingos / University of São Paulo, Brazil.......................................................................... 3782 Amoros, Francisco / Miguel Hernandez University, Spain............................................................. 6894 Amroush, Fadi / Universidad de Salamanca, Spain........................................................................ 1616 Amuda, Caleb Okoro / University of Ibadan, Nigeria..................................................................... 2303 Anderson, Billie / Ferris State University, USA.............................................................................. 7140 Andrade, Maria Teresa / University of Porto, Portugal.................................................................. 6031 Andreev, Rumen D. / Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Bulgaria..................................................................................... 2188 Ankomah, Paul / North Carolina A&T State University, USA........................................................ 4113 Anshari, Muhammad / Universiti Brunei Darussalam, Brunei............................................ 5908,7369 Anton, Jose Luis Monroy / La Ribera University Hospital, Spain.................................................. 5787 Antonino, Ardilio / Fraunhofer Institute for Industrial Engineering, Germany............................. 4560 Aragaki, Madoka / Business Breakthrough University, Japan....................................................... 4962 Arana, Loredana / University of Phoenix, USA.............................................................................. 5650 Araujo, Andre / Texas A&M University, USA................................................................................... 720 Arias, Vladimir Santiago / Texas Tech University, USA................................................................. 7069 Arner, Tracy / Kent State University, USA...................................................................................... 6388 Arora, Richa / Regenesys Institute of Management, India.............................................................. 2882 Arshad, Noreen Izza / Universiti Teknologi PETRONAS, Malaysia............................................... 3337 Arslan, Yunus Ziya / Istanbul Univesity, Turkey............................................................... 298,492,7470 Artizzu, Valentino / University of Cagliari, Italy............................................................................ 3273 Arumugam, Sivasankar / Sri Venkateswara College of Education, India..................................... 2599 Askarany, Davood / University of Auckland, New Zealand............................................................ 2166 Atzeni, Andrea / Politecnico di Torino, Italy................................................................................... 5004 Atzori, Barbara / University of Florence, Italy............................................................................... 5955 Aubin, Verónica Inés / Universidad Nacional de La Matanza, Argentina...................................... 4643 Averweg, Udo Richard / eThekwini Municipality, South Africa...................................................... 7106 Ávila, Liliana / University of Aveiro, Portugal.................................................................................. 888 Avola, Danilo / University of Udine, Italy........................................................................................ 6195 Aymerich-Franch, Laura / CNRS-AIST JRL (Joint Robotics Laboratory), AIST, Japan.............. 4234 Azevedo, Ana / Polytechnic Institute of Porto, Portugal................................................................. 1907 Aziz, Abdul / Usman Institute of Technology, Pakistan..................................................................... 166 Babatope, Ihuoma Sandra / Delta State College of Physical Education, Nigeria.......................... 5252 Badea, Mihaela / Petroleum-Gas University of Ploiesti, Romania.................................................. 3945 Baeva, Liudmila Vladimirovna / Astrakhan State University, Russia............................................ 4189 Bag, Surajit / Tega Industries South Africa Pty Ltd., South Africa................................................. 3086 Bailetti, Tony / Carleton University, Canada.................................................................................. 1684 Balamurugan B / VIT University, India........................................................................................... 1075 Balasubramanian, Kannan / Mepco Schlenk Engineering College, India.................................... 4975 Balasubramanian, Senthilarasu / National Institute of Technology, India........................................ 49 Baldini, Gianmarco / European Commission – Joint Research Centre, Italy................................. 6136 Ballesteros, Carlos / Universidad Pontificia Comillas, Spain......................................................... 1638 Banaji, Shakuntala / LSE, UK........................................................................................................ 3667 Banihashemi, Saeed / University of Technology Sydney, Australia.................................................. 539



Baporikar, Neeta / Namibia University of Science and Technology, Namibia & University of Pune, India.................................................................................................................................. 2989 Barlow, Michael / University of New South Wales, Australia........................................................... 156 Barrett, Diane / Bloomsburg University of Pennsylvania, USA...................................................... 1356 Bartens, Yannick / University of Hamburg, Germany...................................................................... 601 Basham, Randy / University of Texas at Arlington, USA................................................................ 1407 Basu, Rounaq / Indian Institute of Technology Bombay, India....................................................... 7843 Bates, Paul / University of Southern Queensland, Australia........................................................... 7869 Battistella, Gaetano / ISPRA, Italy.................................................................................................. 3156 Batyashe, Thami / Cape Peninsula University of Technology, South Africa.................................... 810 Bayındır, Levent / Ataturk University, Turkey................................................................................. 6286 Bayousuf, Abeer / King Saud University, Saudi Arabia.................................................................. 4652 Bedesem, Pena L. / Kent State University, USA............................................................................... 6388 Belalem, Ghalem / University of Es Senia Oran1, Algeria............................................................. 1116 Belikov, Olga / Brigham Young University, USA............................................................................. 6779 Bellar, Wendi R. / Texas A&M University, USA............................................................................... 6161 Ben Hamida, Ahmed / University of Sfax, Tunisia........................................................................... 106 Ben Maati, Mohammed / University Abdelmalek Essaadi, Morocco............................................ 1616 Ben-Abdallah, Hanêne / King Abdulaziz University, Saudi Arabia.................................................. 765 Benazeer, Shahzada / University of Antwerp, Belgium................................................................... 5317 Bennett, Sue / University of Wollongong, Australia........................................................................ 2512 Bergenti, Federico / Università degli Studi di Parma, Italy............................................................ 6950 Bessette, Dustin / National Graduate School of Quality Management, USA.................................. 7898 Betts, Lucy R. / Nottingham Trent University, UK.................................................................. 4168,4245 Bhanot, Neeraj / Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India................ 587 Bharwani, Sonia / Indian School of Management and Entrepreneurship, India............................ 1529 Bhattacharya, Simanti / University of Kalyani, India...................................................................... 466 Bhuimali, A. / Raiganj University, India................................................................................ 4448,7201 Bhutkar, Ganesh D. / Vishwakarma Institute of Technology, India................................................. 4099 Biancofiore, Giovanni / giovannibiancofiore.com, Italy................................................................. 7671 Bielenia-Grajewska, Magdalena / University of Gdansk, Poland........................................ 2580,5085 Bihl, Trevor J. / Air Force Institute of Technology, USA.................................................................. 6642 Bisht, Pankaja / B. T. Kumaon Institute of Technology, India......................................................... 6253 Bisui, Sandip / Indian Institute of Technology (IIT) Kanpur, India................................................. 5901 Blanco, Beatriz / University of Cantabria, Spain............................................................................ 3870 Boccasini, Adele / Lega del Filo d’Oro – Termini Imerese, Italy...................................................... 287 Bocci, Elena / Sapienza University of Rome, Italy....................................... 4038,4064,4404,7014,7044 Bodea, Constanţa-Nicoleta / Bucharest University of Economic Studies, Romania............. 5158,6579 Boff, Felipe / Lutheran University of Brazil (ULBRA), Brazil......................................................... 4797 Bonaiuti, Federica / ISPRA, Italy.................................................................................................... 3156 Bond, Emma / University Campus Suffolk, UK............................................................................... 7312 Bose, Ahana / Indian Institute of Management Calcutta, India.......................................................... 61 Bouaziz, Rafik / University of Sfax, Tunisia........................................................................... 2043,2054 Bouck, Emily C. / Michigan State University, USA............................................................................ 266 Boudriga, Noureddine / University of Carthage, Tunisia............................................................... 4849 Boukadi, Khouloud / University of Sfax, Tunisia.............................................................................. 765



Bowers, Clint / University of Central Florida, USA................................................................. 995,3327 Braga, Adriana / Pontifical Catholic University of Rio de Janeiro, Brazil..................................... 1186 Brahmia, Zouhaier / University of Sfax, Tunisia................................................................... 2043,2054 Bratsas, Charalampos P. / Aristotle University of Thessaloniki, Greece........................................ 1196 Brewer, James C. / Texas Tech University, USA............................................................................... 1499 Bringula, Rex Perez / University of the East, Philippines............................................................... 2466 Bruno, Clementina / University of Piemonte Orientale, Italy........................................................ 3144 Bruno, Giorgio / Politecnico di Torino, Italy.................................................................................... 788 Bryan, Lisa Dotterweich / Upper Iowa University, USA................................................................. 3612 Bryl, Lukasz / Poznan University of Economics and Business, Poland.......................................... 5056 Bueno, José de Jesús Pérez / Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Mexico............................................................................................................... 1277 Burton, Sharon / Grand Canyon University, USA.......................................................................... 7898 Buss, Terry F. / Carnegie Mellon University, Australia................................................................... 3526 Cabrera, Edison / University of the East, Philippines.................................................................... 2466 Cahlik, Tomas / Charles University Prague, Czech Republic & University of Economics Prague, Czech Republic............................................................................................................................ 4920 Calabrese, Rita / University of Salerno, Italy.................................................................................. 1206 Calmerin, Princess B. / University of the East, Philippines............................................................ 2466 Campbell, Heidi A. / Texas A&M University, USA.......................................................................... 6161 Cappelli, Claudia / Federal University of the State of Rio de Janeiro, Brazil.................................. 754 Carlos-Mancilla, Miriam A. / CINVESTAV Unidad Guadalajara, Mexico.................................... 6522 Carlucci, Carlo / ISPRA, Italy......................................................................................................... 3156 Carneiro de Sousa, Catarina / Polytechnic Institute of Viseu, Portugal........................................ 4146 Carnero, María Carmen / University of Castilla-La Mancha, Spain & University of Lisbon, Portugal.............................................................................................................................. 3131,3698 Caron, Franco / Politecnico di Milano, Italy.................................................................................. 5679 Carrasco-Carrasco, Rocío / University of Huelva, Spain............................................................... 3349 Carvalho, Henrique Cyrne / Serviço de Cardiologia, Hospital de Santo António, Centro Hospitalar do Porto, Portugal..................................................................................................... 1006 Castello, Valentina / CIOFS FP, Italy............................................................................................. 1559 Castro, Ana / Universidade do Porto, Portugal............................................................................... 1006 Catacutan, Annaliza E. / National University, Philippines............................................................. 2466 Celebi, Numan / Sakarya University, Turkey.................................................................................. 5490 Cella, Francesca / University of Cagliari, Italy............................................................................... 3273 Chabukswar, Aniruddha R. / MAEER’s Maharashtra Institute of Pharmacy, India..................... 6767 Chakraborty, Aruna / Maulana Abul Kalam Azad University of Technology, India............ 4872,6961 Chamakiotis, Petros / University of Sussex, UK............................................................................. 1153 Chand, Trilok / PEC University of Technology, India...................................................................... 477 Chandan, Harish C. / Argosy University, USA................................................................................ 4358 Chang, Ni / Indiana University – South Bend, USA......................................................................... 2661 Chatterjee, D. / Seacom Skills University, India.................................................................... 4448,4723 Chaudhuri, Somnath / Maldives National University, Maldives.................................................... 3403 Chedid, Marcello Fernandes / University of Aveiro, Portugal........................................................ 3963 Chen, E. Jack / BASF Corporation, USA................................................................................ 1297,1856 Chen, Edward T. / University of Massachusetts – Lowell, USA...................................................... 3077



Chen, Gaowei / The University of Hong Kong, Hong Kong............................................................ 7969 Chen, Hsin-Liang (Oliver) / University of Massachusetts Boston, USA......................................... 7599 Chen, Jihui / Illinois State University, USA..................................................................................... 2841 Chen, Shiping / CSIRO Data61, Australia...................................................................................... 7563 Chen, Yangjun / University of Winnipeg, Canada................................................................. 4502,7995 Chen, Ye-Sho / Louisiana State University, USA............................................................ 930,2686,4822 Chen, Yibin / University of Winnipeg, Canada............................................................................... 4502 Chen, Yueguo / Renmin University of China, China....................................................................... 1947 Cheng, Po-Keng / State University of New York, Stony Brook University, USA................................. 71 Cheung, Benny C. F. / The Hong Kong Polytechnic University, Hong Kong.................................. 4774 Cheung, H. H. / The University of Hong Kong, Hong Kong.............................................................. 505 Chickerur, Satyadhyan / KLE Technological University, India....................................................... 847 Chiluwa, Innocent / Covenant University OTA, Nigeria................................................................. 6275 Chiu, Ming M / The Education University of Hong Kong, Hong Kong........................................... 7969 Cho, Kyong James / Texas A&M University, USA........................................................................... 6161 Choi, S. H. / The University of Hong Kong, Hong Kong.................................................................... 505 Chong, Alberto / Georgia State University, USA & Universidad del Pacifico, Peru...................... 7214 Chuang, Huey-Wen / National Taichung University of Education, Taiwan.................................... 3434 Chunpir, Hashim / German Climate Computing Centre (DKRZ), Germany................................... 601 Cinque, Luigi / Sapienza University, Italy....................................................................................... 6195 Ciufudean, Calin / Stefan cel Mare University, Romania............................................................... 1814 Cockrell, Susan / Austin Peay State University, USA............................................................. 5476,5516 Cohen, Yuval / Afeka Tel Aviv College of Engineering, Israel........................................................ 1627 Collins, Geri / Mercer University, USA........................................................................................... 1432 Contreras, Eduardo C. / Autonomous University of Coahuila, Mexico.......................................... 2431 Contreras, Isis I. / Saltillo Institute of Technology, Mexico............................................................. 2431 Copeland Jr., Robert L. / Howard University,USA......................................................................... 1794 Cordero-Gutiérrez, Rebeca / University of Salamanca, Spain...................................................... 3359 Cornelius, Herbert / Intel Corporation EMEA, Germany.............................................................. 4004 Correia, Eduardo / Christchurch Polytechnic Institute of Technology (CPIT), New Zealand........ 1026 Corrin, Linda / University of Melbourne, Australia....................................................................... 2512 Cosby, Missy D. / Michigan State University, USA............................................................................ 266 Cotza, Alessandro / University of Cagliari, Italy............................................................................ 3273 Coulson, Neil S. / University of Nottingham, UK............................................................................. 3767 Cox, Sharon A. / Birmingham City University, UK................................................................... 694,1384 Craigen, Dan / Carleton University, Canada.................................................................................. 1684 Crawford, Caroline M. / University of Houston – Clear Lake, USA............................. 1474,5149,7922 Crompton, Helen / Old Dominion University, USA........................................................................ 6347 Crosby, Phil / Curtin University, Australia...................................................................................... 5690 Crossan, Adam / Letterkenny Institute of Technology, Ireland....................................................... 6216 Cruz-Jimenez, Miriam Guadalupe / Institute INAOE, Mexico...................................................... 6007 Cuff, Zianne / City University of New York, USA............................................................................ 7880 Cunningham, Tamrah D. / New York University, USA................................................................... 7880 Curcio, Davide / University of Cagliari, Italy................................................................................. 3273 Curcio, Giuseppe / University of L’Aquila, Italy.................................................................... 3296,6124 Curran, Kevin / Ulster University, UK............................................................................................ 6216



Czajkowski, Marcin / Bialystok University of Technology, Poland................................................ 2132 D’Abundo, Michelle Lee / Seton Hall University, USA.......................................................... 5810,5820 DaCosta, Boaventura / Solers Research Group, USA..................................................................... 6361 D’Agostino, Jerome V. / The Ohio State University, USA................................................................ 5183 Daidj, Nabyla / Telecom Ecole de Management, France................................................................. 2345 Dalcher, Darren / University of Hertfordshire, UK......................................................................... 5660 Damron, Terry Stringer / Austin Peay State University, USA................................................ 5476,5516 Dantsuji, Masatake / Kyoto University, Japan................................................................................ 3850 Darmont, Jérôme / Université de Lyon, Lyon 2, ERIC EA3083, France........................................ 1772 Das, Amit / University of Kalyani, India............................................................................................ 466 Dascalu, Maria-Iuliana / University Politehnica of Bucharest, Romania...................................... 5158 Dasso, Aristides / Universidad Nacional de San Luis, Argentina.......................................... 1919,7609 Dattakumar, Ambica / Nanyang Technological University, Singapore.......................................... 2280 David-West, Olayinka / Pan-Atlantic University, Nigeria.............................................................. 2724 Dawson, Maurice / University of Missouri – St. Louis, USA.......................................................... 7898 De Bruyn, Peter / University of Antwerp, Belgium......................................................................... 5317 de Matta, Renato / University of Iowa, USA................................................................................... 5503 de Mendoza, Cecilia / Ministry of Production of Argentina, Argentina......................................... 7214 de Oliveira, José Palazzo M. / Federal University of Rio Grande do Sul (UFRGS), Brazil............ 6424 DeCoito, Isha / Western University, Canada................................................................................... 1420 Delgado, José Carlos Martins / Universidade de Lisboa, Portugal................................................ 6566 Delgado-Pérez, Pedro / University of Cádiz, Spain......................................................................... 7459 DeLong, Ronald L. / University of Dayton, USA............................................................................. 1366 Deng, Shuyuan / Dakota State University, USA.............................................................................. 3861 Dessì, Nicoletta / Università degli Studi di Cagliari, Italy................................................................. 455 DeWitty Jr., Robert L. / Providence Hospital, USA........................................................................ 1794 Di Marco, Giuseppe / ISPRA, Italy................................................................................................. 3156 Diana, Barbara / Università di Milano-Bicocca, Italy.................................................................... 6223 Dias de Castro, Teresa Sofia Pereira / University of Minho, Portugal........................................... 7312 Dickson-Deane, Camille / University of Melbourne, Australia...................................................... 7599 Diković, Ljubica / Business Technical College, Serbia................................................................... 3689 Dillen, Nicole Belinda / St. Thomas’ College of Engineering and Technology, India..................... 6961 Dimitrova, Dimitrina / York University, Canada............................................................................ 7057 Dimoulas, Charalampos A. / Aristotle University of Thessaloniki, Greece........................... 2908,6476 Dinger, Michael / University of South Carolina Upstate, USA........................................................ 8036 Dobrilovic, Dalibor / University of Novi Sad, Serbia...................................................................... 7514 Đogatović, Vesna Radonjić / University of Belgrade, Serbia.......................................................... 6546 Dolecek, Gordana Jovanovic / Institute INAOE Puebla, Mexico................ 4746,6007,6043,6171,6234 Domínguez-Jiménez, Juan José / University of Cádiz, Spain......................................................... 7459 Doorn, Jorge Horacio / Universidad Nacional del Oeste, Argentina & Universidad Nacional de La Matanza, Argentina..................................................................................... 4643,5127,7411,7422 Dos Santos, Leandro Rocha / In3 Inteligência de Mercado, Brazil.................................................. 961 Dounias, Georgios / University of the Aegean, Greece..................................................................... 180 Dowell, Margaret-Mary Sulentic / Louisiana State University, USA............................................. 2326 Doyle, D. John / Cleveland Clinic, USA........................................................................................... 5829 Drivet, Alessio / GeoGebra Institute of Torino, Italy....................................................................... 4629



Dryjanska, Laura / Sapienza University of Rome, Italy...................................... 4038,4064,4404,7044 Dunham, Nicola / Massey University, New Zealand....................................................................... 3839 Duong, Linh Nguyen Khanh / Auckland University of Technology, New Zealand......................... 5335 Duret, Christophe / Université de Sherbrooke, Canada................................................................. 4296 Durkin, Keith F. / Ohio Northern University, USA.......................................................................... 1366 Duzgun, Sebnem / Middle East Technical University, Turkey........................................................ 3503 Edgeman, Rick / Utah State University, USA.................................................................................... 729 Edwards, John Steven / Aston University, UK................................................................................. 5046 Egedigwe, Eges / Dallas County Community College, USA............................................................ 1129 Eichler, Marcelo Leandro / Universidade Federal do Rio Grande do Sul, Brazil.......................... 6376 Endo, Hiroko / Rissho University, Japan........................................................................................ 7946 Erdt, Marius / Fraunhofer IDM@NTU, Singapore........................................................................... 381 Erener, Arzu / Kocaeli University, Turkey............................................................................. 3503,7831 Erickson, G. Scott / Ithaca College, USA.......................................................................................... 943 Eryılmaz, Mehmet Eymen / Uludağ University, Turkey................................................................. 2998 Esfahanipour, Akbar / Amirkabir University of Technology, Iran................................................. 4570 Esparza, Juan Carlos Moctezuma / Universidad Politécnica Metropolitana de Hidalgo, Mexico......................................................................................................................................... 1277 Espy, Deborah D. / Cleveland State University, USA....................................................................... 5941 Essila, Jean C. / Northern Michigan University, USA...................................................................... 5345 Eteokleous, Nikleia / Frederick University Cyprus, Cyprus.................................................. 2492,6859 Eustáquio, Luís / Universidade do Porto, Portugal........................................................................ 4146 Fadzil, Fadzlina Mohd / Universiti Sains Malaysia, Malaysia........................................................ 5378 Fahy, Patrick J. / Athabasca University, Canada............................................................................. 5829 Faily, Shamal / Bournemouth University, UK................................................................................. 5004 Fakhfakh, Sonda Bouattour / University of Tunis El-Manar, Tunisia............................................ 7190 Fan, Qiuyan / Western Sydney University, Australia....................................................................... 3602 Fancott, Terrill / Concordia University, Canada............................................................................ 7577 Fang, Edna Ho Chu / University of Malaya, Malaysia.................................................................... 4382 Farahneh, Hasan / Ryerson University, Canada............................................................................. 6672 Farinosi, Manuela / University of Udine, Italy................................................................................ 2064 Farmer, Lesley S. J. / California State University – Long Beach, USA........................................... 2477 Favre, Liliana Maria / Universidad Nacional Del Centro De La Provincia De Buenos Aires, Argentina.................................................................................................................................... 7447 Fazlollahtabar, Hamed / Sharif University of Technology, Iran & National Elites Foundation, Iran............................................................................................................................................. 6825 Feng, Haoxian / University of Ottawa, Canada............................................................................... 1757 Feraco, Antonio / Fraunhofer IDM@NTU, Singapore...................................................................... 381 Fernando, Xavier / Ryerson University, Canada............................................................................ 6672 Fernando, Yudi / Universiti Malaysia Pahang, Malaysia................................................................. 2802,5306,5327,5357,5378,5422,5446,5456,5465,5527,5550 Ferrari, Alberto / University of Parma, Italy.................................................................................. 2392 Ferrer, Gemma García / Rey Juan Carlos University, Spain.......................................................... 5767 Ferrero, Renato / Politecnico di Torino, Italy................................................................................. 3989 Fessi, Boutheina / University of Carthage, Tunisia......................................................................... 4849 Figg, Candace / Brock University, Canada...................................................................................... 3238



Figuerola, Paola / Cinvestav-IPN, Mexico...................................................................................... 3794 Filtenborg, Jacob / Aarhus University, Denmark............................................................................ 2920 Fino, Emanuele / Psychologist, Psychometrician, Italy.................................................................. 7014 Firdhous, Mohamed Fazil Mohamed / University of Moratuwa, Sri Lanka......................... 1174,6556 Fisher, Joel / Department of State, United States Government, USA.............................................. 1017 Fisher, Robert Leslie / Independent Researcher, USA..................................................................... 4136 Fister Jr., Iztok / University of Maribor, Slovenia.......................................................................... 7348 Fister, Iztok / University of Maribor, Slovenia................................................................................ 7348 Fitch-Hauser, Margaret / Auburn University, USA........................................................................ 6985 Flanagan, Patrick / St. John’s University, USA............................................................................... 4619 Flanagan, Sara M. / University of Kentucky, USA............................................................................. 266 Flor, Alexander G. / University of the Philippines, Philippines...................................................... 5077 Fokides, Emmanuel / University of the Aegean, Greece................................................................. 2616 Foresti, Gian Luca / University of Udine, Italy....................................................................... 2064,6195 Forge, John / Independent Researcher, Australia............................................................................ 3205 Forrest, Edward / University of Alaska Anchorage, USA............................................................... 5748 Fowler, Angela / Erikson Institute, USA.......................................................................................... 7623 Fox, William P. / Naval Postgraduate School, USA.......................................................................... 4594 Fragkogios, Antonios / University of Thessaly, Greece.................................................................. 5411 Franklin, Benjamin / Northumbria University, UK........................................................................ 1605 Freiman, Viktor / Université de Moncton, Canada............................................................... 2314,7248 Fridman, Alexander Yakovlevich / Institute for Informatics and Mathematical Modelling, Kola Science Centre of RAS, Russia.................................................................................................... 1995 Frydman, Antonia / The University of Texas at Austin, USA......................................................... 6685 Fuchs, Matthias / Mid-Sweden University, Sweden.......................................................................... 349 Fuji, Kei / University of Tsukuba, Japan.......................................................................................... 7946 Funes, Ana / Universidad Nacional de San Luis, Argentina.................................................. 1919,7609 Furlong, Shauneen / University of Ottawa, Canada & John Moores Liverpool University, UK............................................................................................................................................... 3621 Galés, Maria Nieves Lorenzo / The Transformation Society, Spain................................................ 6488 Gallo, Crescenzio / University of Foggia, Italy................................................................................. 440 Gallon, Ray / The Transformation Society, France.......................................................................... 6488 Galloni, Ruggero / Square Reply S.r.l., Italy................................................................................... 5004 Ganaie, Shabir Ahmad / University of Kashmir, India.......................................................... 2264,4515 Gandino, Filippo / Politecnico di Torino, Italy............................................................................... 3989 Ganguly, Shromona / Indian Institute of Management Calcutta, India & Reserve Bank of India, India................................................................................................................................... 6916,7234 Gani, Anisha Banu Dawood / Universiti Sains Malaysia, Malaysia............................................... 5306 García de Leaniz, Patricia Martínez / University of Cantabria, Spain.......................................... 3183 García, Diego Marroquín / Universidad Tecnológica de San Juan del Río, Mexico...................... 1277 Garcia, Felix Buendia / Polythecnical University of Valencia, Spain.............................................. 5787 García-Morales, Víctor / University of Granada, Spain................................................................ 5775 García-Vaquero, Martín / Nebrija University, Spain..................................................................... 3020 Garg, Nishu / VIT University, India................................................................................................. 4528 Garland, Virginia E. / University of New Hampshire, USA............................................................. 2503 Garrido-Moreno, Aurora / University of Malaga, Spain............................................................... 5775



Gaskins, Melvin / Howard University Hospital, USA..................................................................... 1794 Gasmelseed, Akram / Qassim University, Saudi Arabia....................................................... 1249,3448 Gašpar, Dražena / University of Mostar, Bosnia and Herzegovina................................................ 2521 Gasparini, Isabela / Santa Catarina State University (UDESC), Brazil......................................... 6424 Gavurová, Beáta / Technical University of Košice, Slovakia.......................................................... 5841 Geetha, Mariappan / Kamaraj College of Engineering and Technology, India............................. 4369 Ghasem, Nayef Mohamed / UAE University, UAE.......................................................................... 3043 Giaquinto-Cilliers, Maria / Kimberley Hospital Complex, South Africa....................................... 6147 Gibbs, William J. / Duquesne University, USA................................................................................ 4210 Gierl, Mark / University of Alberta, Canada................................................................................... 2369 Giomelakis, Dimitrios / Aristotle University of Thessaloniki, Greece............................................ 8046 Giuliani, Raimondo / European Commission – Joint Research Centre, Italy................................ 6136 Giuseffi, Frank G. / Lindenwood University, USA........................................................................... 2571 Gokhberg, Leonid / National Research University Higher School of Economics, Russia.............. 4704 Goldkind, Lauri / Fordham University, USA.................................................................................. 3569 Gomes, Jorge / ISEG, Universidade de Lisboa, Portugal....................................................... 3756,5714 Gómez, Andrés / University of Castilla-La Mancha, Spain............................................................ 3698 Gonsalves, Tad / Sophia University, Japan................................................................................ 144,229 González-Marcos, Ana / Universidad de La Rioja, Spain.............................................................. 1934 Gosney, Matthew W. / University of Colorado – Health, USA........................................................ 4326 Goudos, Sotirios K. / Aristotle University of Thessaloniki, Greece........................................ 5967,6595 Goulette, Elizabeth / Georgia State University, USA...................................................................... 7682 Govindarajan, M. / Annamalai University, India............................................................................. 373 Graham, Charles R. / Brigham Young University, USA.................................................................. 1487 Grahn, Kaj J. / Arcada University of Applied Sciences, Finland..................................................... 7715 Grandi, Fabio / University of Bologna, Italy.......................................................................... 2043,2054 Granell-Canut, Carlos / Universitat Jaume I of Castellón, Spain.................................................. 3473 Gretter, Sarah / Michigan State University, USA............................................................................ 2292 Grooms, Linda D. / Regent University, USA........................................................................... 2455,2588 Grotto, Rosapia Lauro / University of Florence, Italy.................................................................... 5955 Grover, Varun / Clemson University, USA...................................................................................... 8036 Grunwald, Armin / Karlsruhe Institute of Technology, Germany.................................................. 4267 Guan, Chong / SIM University, Singapore...................................................................................... 2280 Guedes, Rui / Faculdade de Medicina da Universidade do Porto, Portugal................................... 1006 Guerreiro, Sérgio Luís / University of Lisbon, Portugal.......................................................... 651,2154 Guglielman, Eleonora / Learning Community, Italy...................................................................... 1559 Guia, Angelita D. / University of the East, Philippines.................................................................... 2466 Gumussoy, Bilal / Shell and Turcas Petrol Inc., Turkey.................................................................... 530 Gumussoy, Cigdem Altin / Istanbul Technical University, Turkey.................................................... 530 Gupta, Yash / Maulana Abul Kalam Azad University of Technology, India................................... 4872 Gursoy, Guner / Okan University, Turkey....................................................................................... 5285 Guspini, Marco / educommunity – Educational Community, Italy................................................. 1559 Gutiérrez-Artacho, Juncal / University of Granada, Spain........................................................... 4471 Güzeldereli, Esra Ayça / Afyon Kocatepe University, Turkey.......................................................... 6789 Gwangwava, Norman / Botswana International University of Science and Technology, Botswana..................................................................................................................................... 2626



Hadad, Graciela Dora Susana / Universidad Nacional del Oeste, Argentina....................... 5127,7422 Haleem, P. P. Abdul / Farook College, India................................................................................... 7745 Halili, Munira / Universiti Sains Malaysia, Malaysia..................................................................... 5527 Hamam, Habib / University of Moncton, Canada............................................................................. 106 Hardin, J. Michael / Samford University, USA................................................................................ 7140 Hari, Seetha / Vellore Institute of Technology, India....................................................................... 1825 Hartsock, Ralph / University of North Texas, USA......................................................................... 6748 Hasan, Osman / National University of Sciences and Technology, Pakistan................ 3103,6872,6882 Hasan, Syed Rafay / Tennessee Technological University, USA...................................................... 3103 Hassan, Azizul / Cardiff Metropolitan University, UK.................................................................... 4031 Hassan, Md. Salleh Hj. / Universiti Putra Malaysia, Malaysia.............................................. 1704,2761 Hawkins, Donna Patterson / University of Phoenix, USA...................................................... 1463,3805 Heavin, Ciara / University College Cork, Ireland.................................................................. 2558,5864 Hemmatazad, Nolan / University of Nebraska at Omaha, USA..................................................... 7796 Hemmatpour, Masoud / Politecnico di Torino, Italy...................................................................... 3989 Henckell, Martha / Southeast Missouri State University, USA....................................................... 1451 Hendayani, Ratih / Telkom University, Indonesia........................................................................... 5456 Hernando-Gómez, Ángel / University of Huelva, Spain................................................................. 3976 Herpich, Fabrício / Federal University of Rio Grande do Sul, Brazil............................................. 7935 Herrera-Pérez, Gabriel / Instituto Tecnológico Superior de Irapuato, Mexico............................. 2897 Herschel, Richard T. / Saint Joseph’s University, USA..................................................................... 951 Highland, Sue Ann / Grand Canyon University, USA..................................................................... 5183 Hight, Craig / University of Newcastle, Australia........................................................................... 7539 Hirzalla, Fadi / Erasmus University Rotterdam, The Netherlands.................................................. 3667 Hlahla, Catherine / National University of Science and Technology, Zimbabwe........................... 2626 Hoanca, Bogdan / University of Alaska Anchorage, USA............................................................... 5748 Hoffman, Hunter G. / University of Washington, USA.................................................................... 5955 Holland, Barbara Jane / Brooklyn Public Library, USA................................................................... 899 Holmberg, Christopher / University of Gothenburg, Sweden........................................................ 6940 Hope, John K. / University of Auckland, New Zealand.................................................................... 2421 Höpken, Wolfram / University of Applied Sciences Ravensburg-Weingarten, Germany................. 349 Horne, Jeremy / The International Institute of Informatics and Systemics, USA........................... 1845 Hosseini, M. Reza / Deakin University, Australia.............................................................................. 539 Hough, Michelle / Pennsylvania State University, USA.................................................................. 7026 Hsieh, Wan-Chiang / National Taichung Girls’ Senior High School, Taiwan................................ 3434 Hsu, Jeffrey / Fairleigh Dickinson University, USA........................................................................ 5388 Hsueh, Ya-Hui / National Taichung University of Education, Taiwan............................................ 3434 Hu, Nan / University of Utah, USA.................................................................................................. 2213 Hu, Wen-Chen / University of North Dakota, USA......................................................................... 6057 Hude, Rahul U. / MAEER’s Maharashtra Institute of Pharmacy, India.......................................... 6767 Hughes, Claretha / University of Arkansas, USA............................................................................ 4326 Hui, Hoo Yee / Universiti Tunku Abdul Rahman, Malaysia............................................................. 2802 Hussain, Shabir / University of Kashmir, India.............................................................................. 5262 Huysmans, Philip / University of Antwerp, Belgium....................................................................... 5317 Ibrahim, Adamkolo Mohammed Mohammed / Universiti Putra Malaysia, Malaysia & University of Maiduguri, Nigeria....................................................................................... 1704,2761



Iengo, Giorgio Amedeo / University of Cagliari, Italy.................................................................... 3273 Igbinomwanhia, Osaro Rawlings / University of Benin, Nigeria.................................................... 5036 Iguisi, Osarumwense / University of Benin, Nigeria....................................................................... 5036 Ikahihifo, Tarah B. / Brigham Young University, USA.................................................................... 1487 Ikolo, Violet E. / Delta State University Library, Nigeria................................................................ 5726 Ikuta, Shigeru / Otsuma Women’s University, Japan...................................................................... 6464 Ilavarasan, P. Vigneswara / Indian Institute of Technology Delhi, India........................................ 7126 Iqbal, Badar Alam / Aligarh Muslim University, India................................................................... 6727 Iqbal, Mehree / North South University, Bangladesh...................................................................... 3579 Ishikawa, Yasushige / Kyoto University of Foreign Studies, Japan................................................ 3850 Iurato, Giuseppe / University of Palermo, Italy.............................................................................. 7704 Ivashov, Arslan / Kazakh Ablai Khan University of International Relations and World Languages, USA.......................................................................................................................... 5626 Iyamu, Tiko / Cape Peninsula University of Technology, South Africa.......................... 810,2943,4480 Jacques, Laura / Medical College of Wisconsin, USA.................................................................... 5800 Jafari, Mostafa / Regional Institute of Forest and Rangelands (RIFR), Iran......................... 3114,7815 Jagannathan, Veeraraghavan / National Institute of Technology, India........................................... 49 Jagdale, Swati C. / MAEER’s Maharashtra Institute of Pharmacy, India....................................... 6767 Jaipal-Jamani, Kamini / Brock University, Canada....................................................................... 3238 Jakobs, Kai / RWTH Aachen University, Germany......................................................................... 4679 Jamil, Cecília C. / Stockholm University, Sweden.............................................................................. 961 Jamil, George Leal / Informações em Rede, Brazil............................................................................ 961 Jamis, Marilou N. / National University, Philippines...................................................................... 2466 Jan, Rosy / University of Kashmir, India......................................................................................... 7912 Jana, Arnab / Indian Institute of Technology Bombay, India.......................................................... 7843 Janakova, Milena / Silesian University in Opava, Czech Republic................................................. 6907 Janczyk-Strzała, Elżbieta / Wroclaw School of Banking, Poland.................................................. 3910 Janeš, Aleksander / University of Primorska, Slovenia.................................................................. 5638 Janssens, Jurgen / QSpin, Belgium................................................................................................... 682 Jasmi, Fairuz / Universiti Sains Malaysia, Malaysia...................................................................... 5465 Jaume, Sylvain / Saint Peter’s University, USA............................................................................... 5388 Jayalakshmi, S. / Kumaraguru College of Technology (KCT), India................................................ 431 Jesus, Rui Alberto / CESPU, Instituto de Investigação e Formação Avançada em Ciências e Tecnologias da Saúde, Portugal.................................................................................................. 1548 Jhamb, Garvita / University of Delhi, India................................................................................... 4436 Jiang, Bo / Zhejiang Gongshang University, China......................................................................... 7588 Joe, Jennifer Ashley Wright / Western Kentucky University, USA................................................. 5204 Jones, James D. / Liberty University, USA....................................................................................... 4736 Jones-Lillie, Michelle / Lillie Pad Studios, USA............................................................................. 4692 Josefowicz, Michael / Nemetics Institute Kolkata, USA.................................................................. 6488 Julien, Heidi / State University of New York at Buffalo, USA.......................................................... 2243 K, John Singh / VIT University, India.............................................................................................. 1667 Kaabouch, Naima / University of North Dakota, USA.................................................................... 6057 Kabeil, Magdy M. / Al-Yamamah University, Saudi Arabia............................................................ 2006 Kabil, Ahmad M. / University of Wisconsin – Whitewater, USA..................................................... 2006 Kajan, Ejub / State University of Novi Pazar, Serbia...................................................................... 2773



Kalia, Prateek / I. K. Gujral Punjab Technical University, India................................................... 2882 Kalita, Manashee / NERIST, India.................................................................................................. 4985 Kaljo, Kristina / Medical College of Wisconsin, USA.................................................................... 5800 Kalliris, George / Aristotle University of Thessaloniki, Greece...................................................... 6476 Kalpić, Damir / University of Zagreb, Croatia.................................................................. 620,636,5607 Kamakula, Madhu Kishore Raghunath Raghunath / GVP College of Engineering (Autonomous), India.................................................................................................................... 3009 Kamariotou, Maria / University of Macedonia, Greece.......................................................... 912,3032 Kamat, R. K. / Shivaji University, India........................................................................................... 2411 Kamath, R. S. / Chatrapati Shahu Institute of Business Education and Research, India................ 2411 Kamel, Sherif H. / The American University in Cairo, Egypt................................................. 7223,7259 Kamthan, Pankaj / Concordia University, Canada............................................................... 7399,7577 Kanaan, Yasmine M. / Howard University, USA............................................................................. 1794 Kanellopoulos, Dimitris N. / University of Patras, Greece............................................................. 6435 Kang, Sunghyun Ryoo / Iowa State University, USA....................................................................... 8079 Karabulut, Ali Naci / Mugla Sitki Kocman University, Turkey........................................................ 5737 Karabulut, Derya / Halic University, Turkey.................................................................................... 492 Karagoz, Pinar / Middle East Technical University, Turkey........................................................... 1974 Karahan, Çetin / Directorate General of Civil Aviation, Turkey.................................................... 6789 Karahoca, Adem / Bahcesehir University, Turkey.......................................................................... 7168 Karasavvidis, Ilias / University of Thessaly, Greece....................................................................... 3287 Karpinski, Aryn C. / Kent State University, USA............................................................................ 5183 Kasemsap, Kijpokin / Suan Sunandha Rajabhat University, Thailand..................................................... 1584,1594,2253,2791,3591,3734,4199,4347,6412,7775 Katsaounidou, Anastasia N. / Aristotle University of Thessaloniki, Greece................................... 2908 Kaur, Sarabjot / IIT Kanpur, India........................................................................................ 7036,7161 Kavoura, Androniki / Technological Educational Institute of Athens, Greece............................................................................................................... 3827,4052,7002,7270 Kavurucu, Yusuf / Turkish Naval Research Center Command, Turkey.......................................... 1974 Kawata, Shigeo / Utsunomiya University, Japan............................................................................ 4583 Kaya, Galip / Havelsan Inc., Turkey................................................................................................ 1441 Kaye, Linda K. / Edge Hill University, UK...................................................................................... 3317 Kayikci, Yasanur / Turkish-German University, Turkey................................................................. 5367 Kefallonitis, Efstathios / State University of New York at Oswego, USA............................... 3827,4052 Kerkhoff, Shea N. / North Carolina State University, USA.............................................................. 2235 Khalifeh, Ala’ Fathi / German Jordan University, Jordan.............................................................. 6672 Khalil, Sadia / NUST School of Electrical Engineering and Computer Science, Pakistan............. 2869 Khan, Amadu Wurie / University of Edinburgh, UK...................................................................... 6695 Khan, Nadim Akhtar / University of Kashmir, India...................................................................... 4436 Khoo, Elaine / University of Waikato, New Zealand....................................................................... 7539 Kibret, Behailu / Victoria University, Australia.............................................................................. 6319 Kikumori, Mai / Ritsumeikan University, Japan............................................................................. 6019 Kilani, Amal / University of Gabes Tunisia, Tunisia......................................................................... 106 Kilburn, Michelle / Southeast Missouri State University, USA....................................................... 1451 Kilinç, Uğur / Ondokuz Mayıs University, Turkey........................................................................... 7338 Kille, Tarryn / Griffith University, Australia................................................................................... 7869



Kimmons, Royce / Brigham Young University, USA....................................................................... 6779 King, Samuel Olugbenga / Auburn University, USA....................................................................... 1880 Kinsell, Carolyn / Solers Research Group, USA............................................................................. 6361 Kiriakidis, Stavros / University of Crete, Greece............................................................................ 3827 Kitamura, Misato / Advanced Telecommunications Research Institute International (ATR), Japan........................................................................................................................................... 3850 Kitsios, Fotis / University of Macedonia, Greece..................................................................... 912,3032 Klock, Ana Carolina Tomé / Federal University of Rio Grande do Sul (UFRGS), Brazil.............. 6424 Knepper, Richard / Indiana University, USA................................................................................. 1063 Koleva, Maria K. / Institute of Catalysis, Bulgarian Academy of Sciences, Bulgaria....................... 220 Kolker, Alexander / GE Healthcare, USA.............................................................................. 2095,3711 Kondo, Mutsumi / Kyoto University of Foreign Studies, Japan...................................................... 3850 Konzack, Lars / University of Copenhagen, Denmark.................................................................... 8015 Kopeć, Katarzyna / Tischner European University, Poland............................................................. 563 Kopec, Rafal / Pedagogical University of Krakow, Poland............................................................. 7302 Köroğlu, Cemile Zehra / Uşak University, Turkey.......................................................................... 4715 Köroğlu, Muhammet Ali / Uşak University, Turkey........................................................................ 4715 Korstanje, Maximiliano Emanuel / University of Palermo, Argentina................................. 3637,4761 Kostic-Ljubisavljevic, Aleksandra / University of Belgrade, Serbia............................................. 1164 Koteeswaran, Raghuraman / SASTRA University, India................................................................... 77 Kováč, Viliam / Technical University of Košice, Slovakia............................................................... 5841 Krasniewicz, Jan / Birmingham City University, UK..................................................................... 1384 Kretowski, Marek / Bialystok University of Technology, Poland................................................... 2132 Krishnamoorthy, Priya / SASTRA University, India.......................................................................... 77 Krogstie, John / Norwegian University of Science and Technology, Norway................................. 4810 Kulik, Boris Alexandrovich / Institute of Problems in Mechanical Engineering RAS, Russia....... 1995 Kumar, Sameer / University of Malaya, Malaysia............................................... 2784,4382,6207,6993 Kumar, Sathish / VIT University, India........................................................................................... 1075 Kumaradjaja, Richard / Renaissance Consulting, Indonesia.......................................................... 862 Kundur, Avinash Reddy / Griffith University, Australia................................................................. 5853 Kurata, Noriko / Tokyo University of Science, Suwa, Japan.......................................................... 7327 Kurnaz, Sefer / Istanbul Esenyurt University, Turkey..................................................................... 7470 Kurt, Ganite / Gazi University, Turkey............................................................................................ 5737 Kuznik, Lea / University of Maribor, Slovenia................................................................................ 4077 Kyriafinis, Georgios / AHEPA University Hospital, Greece........................................................... 5886 Lagaris, Isaac E. / University of Ioannina, Greece.......................................................................... 7765 Lahuerta-Otero, Eva / University of Salamanca, Spain................................................................. 3359 Lai, Daniel T. H. / Victoria University, Australia............................................................................. 6319 Lai, Hollis / University of Alberta, Canada..................................................................................... 2369 Lakkaraju, Santhosh Kumar / Dakota State University, USA....................................................... 3861 Lakshika, Erandi / University of New South Wales, Australia......................................................... 156 Lansiquot, Reneta D. / City University of New York, USA.............................................................. 7880 Lao, Eduardo A. / University of the East, Philippines..................................................................... 2466 Larson, Trent / North Carolina A&T University, USA................................................................... 4113 Lau, Wilfred W. F. / The Chinese University of Hong Kong, China............................................... 3309 Law, Penny / Regenesys Business School, South Africa.................................................................. 2882



Lawless, Kimberly A. / University of Illinois at Chicago, USA....................................................... 7888 Lay, Ah-Nam / Institute of Teacher Education – Sultan Abdul Halim, Malaysia........................... 3248 Lazaro, Joan P. / University of the East, Philippines....................................................................... 2466 Leaning, Marcus / University of Winchester, UK............................................................................ 7106 Lecic, Dusanka Milorad / Levi9 Global Sourcing Balkan, Serbia.................................................. 2932 Ledesma, Viviana Alejandra / Universidad Nacional del Oeste, Argentina & Universidad Nacional de La Matanza. Argentina............................................................................................ 7422 Lee, D. Israel / Southern Illinois University, USA & University of Phoenix, USA........................... 1463 Lee, Wanbil William / The Computer Ethics Society, Hong Kong & Wanbil & Associates, Hong Kong................................................................................................................................... 4884,4909 Lekule, Chrispina S. / St. Augustine University of Tanzania, Tanzania.......................................... 2314 Lenzi, Valentina Bartalesi / ISTI-CNR, Italy................................................................................... 5067 Leonard, Awie C. / University of Pretoria, South Africa................................................................... 777 Leone, Sabrina / Università Politecnica delle Marche, Italy........................................ 1517,2545,7671 Leung, Carson K. / University of Manitoba, Canada........................................................................ 338 Lewis, John Kennedy / Salve Regina University, USA.................................................................... 5194 Lexhagen, Maria / Mid-Sweden University, Sweden......................................................................... 349 Li, Baobin / University of Chinese Academy of Sciences, China....................................................... 132 Li, Rowena / Bayside High School Library, USA................................................................... 4490,7956 Li, Shun / University of Chinese Academy of Sciences, China.......................................................... 132 Lillie, Jonathan / Loyola University Maryland, USA............................................................. 4692,6296 Link, Matthew R. / Indiana University, USA................................................................................... 1063 Litvak, Claudia S. / Universidad Nacional de La Matanza, Argentina & Universidad Nacional del Oeste, Argentina.................................................................................................................... 5127 Lockett, Nigel / University of Lancaster, UK................................................................................... 5775 Locuratolo, Elvira Immacolata / CNR ISTI, Italy.................................................................. 2020,5067 Lofaro, Ronald John / Embry-Riddle Aeronautical University, USA................................................ 662 Logan, Robert K. / University of Toronto, Canada.......................................................................... 1186 Loon, Donovan Peter Chan Wai / University of Malaya, Malaysia...................................... 2784,6207 Lopes, Hélder Silva / University of Minho, Portugal....................................................................... 3460 López, Maria Luisa Mendoza / Tecnológico Nacional de México, Instituto Tecnológico de Querétaro, Mexico....................................................................................................................... 1277 Lopez-Mellado, Ernesto / CINVESTAV Unidad Guadalajara, Mexico................................. 6522,7488 Lorenzi, Fabiana / Lutheran University of Brazil (ULBRA), Brazil................................................ 4797 Lu, Shen / University of South Florida, USA................................................................................... 1783 Lucchesi, Ivana Lima / Universidade Federal do Rio Grande do Sul, Brazil................................. 6376 Lun, Roanna / Cleveland State University, USA............................................................................. 5876 M, Naveenkumar / National Institute of Technology Trichy, India................................................... 212 Ma, Will W. K. / Hong Kong Shue Yan University, Hong Kong...................................................... 5093 Maamar, Zakaria / Zayed University, UAE...................................................................................... 765 Maciel, Cristiano / Federal University of Mato Grosso, Brazil........................................................ 754 Macis, Riccardo / University of Cagliari, Italy............................................................................... 3273 MacIver, Kristen / Northern Michigan University, USA................................................................. 3678 Macko, Marek / Kazimierz Weilki University, Poland...................................................................... 521 Mahmood, Awais / National University of Sciences and Technology, Pakistan............................. 3103 Mahmud, Nidhal / University of Hull, UK...................................................................................... 6847



Major, Debra A. / Old Dominion University, USA........................................................................... 3382 Majumder, Swanirbhar / NERIST, India........................................................................................ 4985 Malone, Lin G. / Nanyang Technological University, Singapore..................................................... 2280 Manne, Janga Reddy / Indian Institute of Technology Bombay, India.............................................. 239 Manrique, Cecilia G. / University of Wisconsin – La Crosse, USA................................................. 2861 Manrique, Gabriel G. / Winona State University, USA................................................................... 2861 Mantovani, Fabrizia / Università di Milano-Bicocca, Italy............................................................ 6223 Manuzzi, Raffaella / ISPRA, Italy................................................................................................... 3156 Manzanares, María Ángeles Rodriguez / Memorial University of Newfoundland, Canada......... 3900 Maphanyane, Joyce Gosata / University of Botswana, Botswana................................................... 3484 Maraver-López, Pablo / University of Huelva, Spain..................................................................... 3976 Maravilhas, Sérgio / Universidade Salvador, Brazil....................................................................... 5757 Marichelvam, Mariappan Kadarkarainadar / Mepco Schlenk Enginering College, India.......... 4369 Markkula, Jouni / University of Oulu, Finland..................................................................... 2108,4862 Marks, Madeline R. / University of Central Florida, USA................................................................ 995 Marmo, Roberto / University of Pavia, Italy................................................................................... 2851 Marques, Rui Pedro Figueiredo / Universidade de Aveiro, Portugal............................................... 820 Marras, Andrea / University of Cagliari, Italy............................................................................... 3273 Martinez, Liliana / Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina..................................................................................................................................... 7447 Martinez, Maria Isabel Asensio / La Ribera University Hospital, Spain....................................... 5787 Martinovic, Dragana / University of Windsor, Canada......................................................... 2314,7248 Marzano, Gilberto / Rezekne Academy of Technologies, Latvia........................................... 4157,5930 Masood, Rahat / NUST School of Electrical Engineering and Computer Science, Pakistan......... 2869 Masrom, Maslin / Universiti Teknologi Malaysia, Malaysia.......................................................... 7088 Mateos, Cristian / ISISTAN, UNICEN-CONICET, Argentina......................................................... 6658 Mathews, Lincy / M. S. Ramaiah Institute of Technology, India..................................................... 1825 Matuga, Julia M. / Bowling Green State University, USA............................................................... 3930 Mazur, Elizabeth / Pennsylvania State University, USA................................................................. 7026 McDaniel, Jeremy J / Principal Financial Group, USA.................................................................. 1656 McDowell Marinchak, Christina L. / University of Alaska Anchorage, USA................................ 5748 McGill, Tanya / Murdoch University, Australia.............................................................................. 4124 McKee, James / Independent Researcher, Australia......................................................................... 573 McKelvey, Nigel / Letterkenny Institute of Technology, Ireland...................................................... 6216 McKenna, Kelly / Colorado State University, USA......................................................................... 3954 McNeal, Ramona Sue / University of Northern Iowa, USA............................................................. 3612 McNeill, Fiona / Heriot-Watt University, UK.................................................................................. 2697 McNutt, John G. / University of Delaware, USA............................................................................. 3569 Medina-Bulo, Inmaculada / University of Cádiz, Spain................................................................ 7459 Medlock Paul, Casey / North Carolina State University, USA....................................................... 2235 Meghanathan, Natarajan / Jackson State University, USA.......................................... 1746,6507,6536 Mehta, Prashant / National Law University Jodhpur, India........................................................... 3066 Mekhiel, Christopher / Ryerson University, Canada..................................................................... 6672 Melendez, Thiago Troina / Instituto Federal Sul-Riograndense, Brazil.......................................... 6376 Mellott, Jennifer A. / Kent State University, USA............................................................................ 5183 Melnik, Mikhail I. / Kennesaw State University, USA..................................................................... 2833



Melro, Ana / University of Aveiro, Portugal.................................................................................... 4255 Melton, Amye M. / Austin Peay State University, USA........................................................... 5476,5516 Mentor, Dominic / Columbia University, USA................................................................................ 6184 Meroufel, Bakhta / University of Oran1, Algeria........................................................................... 1116 Messeri, Andrea / Meyer Children’s Hospital of Florence, Italy.................................................... 5955 Mezghani, Karim / Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia & University of Sfax, Tunisia........................................................................................................... 2965 Miaoui, Yosra / University of Carthage, Tunisia............................................................................. 4849 Mihaylova, Iva / University of St. Gallen, Switzerland.................................................................... 6631 Mikavica, Branka / University of Belgrade, Serbia........................................................................ 1164 Mikołajewska, Emilia / Nicolaus Copernicus University, Poland.................................................... 521 Mikołajewski, Dariusz / Kazimierz Wielki University, Poland......................................................... 521 Miller, Tan / Rider University, USA................................................................................................. 5503 Milosevic, Danijela / University of Kragujevac, Serbia.................................................................. 5538 Mishra, Vinod Kumar / B. T. Kumaon Institute of Technology, India................................... 6253,6266 Misra, Subhas Chandra / Indian Institute of Technology (IIT) Kanpur, India.............. 5901,7036,7161 Mitra, Ananda / Wake Forest University, USA................................................................................. 394 Mogos, Radu Ioan / Bucharest University of Economic Studies, Romania..................................... 5158 Mohamad, Mohammad Taqiuddin / University of Malaya, Malaysia.............................................. 36 Mohamed, Latifah Naina / Universiti Sains Malaysia, Malaysia................................................... 5527 Mohan, Fariel / University of Trinidad and Tobago, Trinidad and Tobago.................................... 2532 Mohanty, Radhakrishna N. / VIT University, India........................................................................ 3226 Montazemi, Ali Reza / McMaster University, Canada.................................................................... 4570 Montrucchio, Bartolomeo / Politecnico di Torino, Italy................................................................ 3989 Morales, Edith Obregón / Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Mexico......................................................................................................................................... 1277 Moreno-Ibarra, Marco Antonio / Instituto Politécnico Nacional, Mexico..................................... 6973 Morganti, Luca / Università di Milano-Bicocca, Italy.................................................................... 6223 Motwani, Jaideep / Grand Valley State University, USA.................................................................. 551 Mukhopadhyay, Prabir / Indian Institute of Information Technology Design and Manufacturing Jabalpur, India............................................................................................................................ 1260 Multisilta, Jari / Tampere University of Technology, Finland......................................................... 2641 Mupepi, Mambo Governor / Grand Valley State University, USA................................................... 551 Mupepi, Sylvia C. / Grand Valley State University, USA................................................................... 551 Murakami, Masayuki / Kyoto University of Foreign Studies, Japan............................................. 3850 Muralidhar, Krishnamurty / University of Oklahoma, USA......................................................... 5583 Muritala, Omotayo / Pan-Atlantic University, Nigeria................................................................... 2724 Murray, David / Murdoch University, Australia............................................................................. 4124 Murray, Patrick S. / University of Southern Queensland, Australia............................................... 7869 Murthy, Vasudeva / Creighton University, USA............................................................................. 8095 Murungi, Catherine G. / Kenyatta University, Kenya..................................................................... 1540 Musunuri, Durgamohan / Bhavan’s Usha and Lakshmi Mittal Institute of Management, India............................................................................................................................................ 1529 Muthee, Jessina Mukomunene / Kenyatta University, Kenya......................................................... 1540 Mutlu, Alev / Kocaeli University, Turkey........................................................................................ 1974 Nagaty, Khaled Ahmed / The British University in Egypt, Egypt................................................... 2810



Naidenova, Xenia Alexandre / Research Centre of Military Medical Academy – Saint Petersburg, Russia......................................................................................................................... 191 Nair, Jessy / PES University, India.................................................................................................. 2975 Najmaei, Arash / Australian Catholic University, Australia........................................................... 1141 Narayankar, Prashant M. / KLE Technological University, India.................................................... 847 Natarajan, Thamaraiselvan / National Institute of Technology, India............................................... 49 Nath, Ravi / Creighton University, USA.......................................................................................... 8095 Ndong, Celine / ISPRA, Italy........................................................................................................... 3156 Neamţu(Idoraşi), Alina / Bucharest University of Economic Studies, Romania............................ 6579 Nedelko, Zlatko / University of Maribor, Slovenia.......................................................................... 5559 Nedra, Bahri Ammari / IHEC of Carthage, USA.............................................................................. 707 Nesmachnow, Sergio / Universidad de la República, Uruguay...................................................... 7693 Neville, Karen / University College Cork, Ireland.......................................................................... 2558 Ng, Alex / Federation University, Australia..................................................................................... 7563 Ng, Artie W. / The Hong Kong Polytechnic University, Hong Kong................................................ 4774 Ng, Peggy M. L. / The Hong Kong Polytechnic University, Hong Kong.......................................... 4774 Ngafeeson, Madison N. / Northern Michigan University, USA............................................... 3678,3816 Ngan, Chun-Kit / The Pennsylvania State University, USA............................................................ 2142 Nganji, Julius T. / University of Ottawa, Canada.............................................................................. 314 Nguy, Yun-Mi / Griffith University, Australia................................................................................. 5853 Niaki, Seyed Taghi Akhavan / Sharif University of Technology, Iran............................................. 6825 Nicholas, Arlene J. / Salve Regina University, USA........................................................................... 740 Niemi, Hannele / University of Helsinki, Finland............................................................................ 2641 Nirmala, Nancy / Christ the King Matric Higher Secondary School, India................................... 2599 Nisar, Sajid / National University of Science and Technology, Pakistan................................ 6872,6882 Nisha, Nabila / North South University, Bangladesh....................................................................... 3579 Nunes, Felipe Becker / Federal University of Rio Grande do Sul, Brazil........................................ 7935 Nwankpa, Joseph K. / Miami University, USA................................................................................ 2953 Nyamundundu, Alice Violet / Skyway University, Malawi............................................................. 3393 O., Mohammad Mehdi Owrang / American University, USA........................................................ 1794 Obregón, Víctor Hugo Rodríguez / Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Mexico............................................................................................................... 1277 Ochs, Thomas / Villeroy & Boch, Germany...................................................................................... 873 O’Connor, Rory V. / Dublin City University, Ireland...................................................................... 6927 O’Connor, Yvonne / University College Cork, Ireland................................................................... 5864 O’Donnell, Eileen / Trinity College Dublin, Ireland....................................................................... 2380 O’Donnell, Liam / Dublin Institute of Technology, Ireland............................................................ 2380 Oghogho, Ikponmwosa / Delta State University, Abraka-Oleh Campus, Nigeria.......................... 6618 Okunoye, Adekunle O. / Xavier University, USA............................................................................ 3561 Oliboni, Barbara / University of Verona, Italy....................................................................... 2043,2054 Oliveira, Lídia / University of Aveiro, Portugal............................................................................... 4255 Olvera-Lobo, María-Dolores / University of Granada, Spain....................................................... 4471 Oncioiu, Ionica / Titu Maiorescu University, Romania................................................................... 5669 Oni, Aderonke A. / Covenant University, Nigeria............................................................................ 3561 Ono, Akinori / Keio University, Japan............................................................................................ 6019 Oravec, Jo Ann / University of Wisconsin – Whitewater, USA....................................... 1695,4306,4316



Ordieres-Meré, Joaquín / Universidad Politécnica de Madrid, Spain........................................... 1934 O’Riordan, Sheila / University College Cork, Ireland.................................................................... 2558 Ortes, Faruk / Istanbul University, Turkey........................................................................................ 492 Orwat, Carsten / Karlsruhe Institute of Technology, Germany...................................................... 4267 Osman, Kamisah / The National University of Malaysia, Malaysia...................................... 3248,3337 Osório, António / University of Minho, Portugal............................................................................ 7312 Otebolaku, Abayomi Moradeyo / Liverpool John Moores University, UK.................................... 6031 Otunla, Adekunle Olusola / University of Ibadan, Nigeria............................................................. 2303 Owen, Hazel / Ethos Consultancy NZ, New Zealand....................................................................... 3839 P, Nikhitha / VIT University, India.................................................................................................. 4528 Paciga, Kathleen A. / Columbia College Chicago, USA.................................................................. 7623 Pagel, James Frederic / University of Colorado School of Medicine, USA....................................... 202 Pallavicini, Federica / Università di Milano-Bicocca, Italy............................................................ 6223 Paluan, Francesca / Politecnico di Torino, Italy............................................................................. 5213 Pan, Weifeng / Zhejiang Gongshang University, China.................................................................. 7588 Pană, Laura L. / Polytechnic University of Bucharest, Romania................................................ 88,4459 Pantano, Eleonora / Middlesex University London, UK................................................................. 7805 Papadopoulos, Ioannis / Metropolitan College Thessaloniki, Greece............................................ 2337 Park, Young / Bradley University, USA.................................................................................. 1735,1869 Parker, Judith / Teachers College, Columbia University, USA....................................................... 5626 Parsopoulos, Konstantinos / University of Ioannina, Greece......................................................... 7765 Paschoal, Leo Natan / University of Cruz Alta, Brazil..................................................................... 7935 Passaris, Constantine E. / University of New Brunswick, Canada.................................................. 7980 Pathak, Tanuja / B. T. Kumaon Institute of Technology, India....................................................... 6266 Patro, Chandra Sekhar / GVP College of Engineering (Autonomous), India....................... 3009,4337 Pau, Iván / Universidad Politécnica de Madrid, Spain.................................................................... 7754 Paul, P. K. / Raiganj University, India............................................................................ 4448,4723,7201 Pavlidou, Fotini-Niovi / Aristotle University of Thessaloniki, Greece............................................ 3371 Pavlov, Yuri P. / Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Bulgaria................................................................................................................ 2188 Paya, Luis / Miguel Hernández University, Spain........................................................................... 6894 Pelet, Jean-Éric / ESCE International Business School, France..................................................... 6070 Pelfer, Giuliano / University of Florence, Italy............................................................................... 3419 Pendergrass, William Stanley / American Public University System, USA.................................... 7077 Peracchia, Sara / University of L’Aquila, Italy................................................................................ 3296 Pereira, Claudia Teresa / Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina..................................................................................................................................... 7447 Pérez, Arturo Trejo / Universidad Tecnológica de San Juan del Río, Mexico................................ 1277 Perilli, Viviana / Lega del Filo d’Oro – Molfetta, Italy..................................................................... 287 Perros, Harry G. / North Carolina State University, USA............................................................... 6609 Perry, Gabriela Trindade / Universidade Federal do Rio Grande do Sul, Brazil........................... 6376 Pes, Barbara / Università degli Studi di Cagliari, Italy..................................................................... 455 Piaggesi, Danilo / Framericas, USA................................................................................................ 5015 Picci, Simone / University of Cagliari, Italy.................................................................................... 3273 Piciarelli, Claudio / University of Udine, Italy................................................................................ 6195 Pierce, Marlon / Indiana University, USA....................................................................................... 1063



Pimenta, Marcelo Soares / Federal University of Rio Grande do Sul (UFRGS), Brazil................. 6424 Pino, José A. / Universidad de Chile, Chile...................................................................................... 3745 Planu, Michael / University of Cagliari, Italy................................................................................. 3273 Platt, Rebecca / Murdoch University, Australia.............................................................................. 7481 Poggi, Agostino / University of Parma, Italy.......................................................................... 2392,6950 Poirier, Sandra / Middle Tennessee State University, USA............................................................. 3930 Politis, Dionysios / Aristotle University of Thessaloniki, Greece..................................................... 5886 Popescu(Predescu), Oana Mădălina / Bucharest University of Economic Studies, Romania........ 6579 Potgieter, Tertius N. / Kimberley Hospital Complex, South Africa................................................. 6147 Potocan, Vojko / University of Maribor, Slovenia........................................................................... 5559 Pourshahbaz, Shakiba / Shahid Rajaee Teacher Training University, Iran................................... 7659 Prasanna, M. / VIT University, India............................................................................................... 7503 Presadă, Diana / Petroleum-Gas University of Ploiesti, Romania.................................................. 3945 Pulido, Ángela Coello / University of Vigo, Spain........................................................................... 2825 Pulkkis, Göran / Arcada University of Applied Sciences, Finland................................................. 7715 Punyanunt-Carter, Narissra Maria / Texas Tech University, USA................................................. 7069 Purushothaman, Aparna / Aalborg University, Denmark............................................................. 4178 Qin, Xiongpai / Renmin University of China, China....................................................................... 1947 Quest, Mary / Erikson Institute, USA.............................................................................................. 7623 R., Sooraj T. / VIT University, India................................................................................................. 3226 Rabuzin, Kornelije / University of Zagreb, Croatia........................................................................ 2031 Rada, Roy / University of Maryland – Baltimore County, USA............................................................ 1 Radenkovic, Bozidar / University of Belgrade, Serbia................................................................... 5398 Ragan, Mark A. / The University of Queensland, Australia.............................................................. 419 Rahim, Lukman Ab / Universiti Teknologi PETRONAS, Malaysia................................................ 3337 Rahim, Nihmiya Abdul / UAE University, UAE.............................................................................. 3043 Rahimi, Mehrak / Shahid Rajaee Teacher Training University, Iran.................................... 2442,7659 Rahman, Mohd Nayyer / Aligarh Muslim University, India........................................................... 6727 Rajakani, M. / Mepco Schlenk Engineering College, India............................................................ 4975 Rajib, Md. Salah Uddin / Jahangirnagar University, Bangladesh.................................................. 1896 Ramachandran, Muthu / Leeds Metropolitan University, UK....................................................... 7525 Ramayah, T. / University Sains Malaysia, Malaysia......................................................................... 831 Ramírez, Juan Manuel Olivares / Universidad Tecnológica de San Juan del Río, Mexico........... 1277 Ramsay, Judith / Manchester Metropolitan University, UK........................................................... 6398 Rao, N. Raghavendra / FINAIT Consultancy Services, India......................................................... 5705 Rascão, José Poças / Institute Polytechnic of Setúbal, Portugal....................................................... 4422 Rather, Mudasir Khazer / University of Kashmir, India........................................................ 2264,4515 Rathore, Ashish Kumar / Indian Institute of Technology Delhi, India........................................... 7126 Ray, Anirban / UNC Wilmington, USA........................................................................................... 2225 Ray, Nilanjan / Adamas University, India....................................................................................... 3403 Realdon, Olivia / University of Milano-Bicocca, Italy.................................................................... 6223 Rebaudengo, Maurizio / Politecnico di Torino, Italy..................................................................... 3989 Recupero, Diego Reforgiato / University of Cagliari, Italy............................................................. 3273 Reddy, D. Bhanusree / VIT University, India................................................................................... 2975 Reid-Martinez, Kathaleen / Oral Roberts University, USA........................................................... 2588 Reinoso, Oscar / Miguel Hernandez University, Spain................................................................... 6894



Reinthal, Ann / Cleveland State University, USA............................................................................ 5941 Remoaldo, Paula / University of Minho, Portugal.......................................................................... 3460 Reynoldson, Katelyn R. / Old Dominion University, USA.............................................................. 3382 Ribeiro, Filipa M. / University of Porto, Portugal........................................................................... 3922 Ribeiro, Vitor P. / University of Minho, Portugal............................................................................ 3460 Ridao, Marcela / Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina..................................................................................................................................... 7411 Riel, Jeremy / University of Illinois at Chicago, USA..................................................................... 7888 Riemann, Ute Anna / SAP SE, Germany........................................................................................... 873 Rifat, Afrin / North South University, Bangladesh.......................................................................... 3579 Rigatos, Gerasimos / Industrial Systems Institute, Greece.................................................................. 15 Rijo, Rui Pedro Charters Lopes / Polytechnic Institute of Leiria, Portugal................................... 3782 Robichaud, Xavier / Univesité de Moncton, Canada...................................................................... 7248 Robnik-Šikonja, Marko / University of Ljubljana, Slovenia.......................................................... 2085 Rodriguez, Alberto / Universidad Miguel Hernández de Elche, Spain.......................................... 4937 Rodriguez, Juan Manuel / ISISTAN, UNICEN-CONICET, Argentina............................................ 6658 Rodriguez-Perez, Edelma / CINVESTAV Unidad Guadalajara, Mexico....................................... 7488 Rohunen, Anna / University of Oulu, Finland................................................................................ 4862 Roig, Juan Ignacio / University of Castilla-La Mancha, Spain....................................................... 3698 Rojo, Lorenzo Fernández / Miguel Hernandez University, Spain................................................... 6894 Romão, Mário José Batista / ISEG, Universidade de Lisboa, Portugal................................. 3756,5714 Romero, David Ernesto Troncoso / CONACYT at ESCOM-IPN, Mexico............................. 6007,6234 Romero, Fernando Cabrita / University of Minho, Portugal.......................................................... 7150 Romero, Isabel / University of Castilla-La Mancha, Spain............................................................. 3698 Romero, Jorge A. / Towson University, USA...................................................................................... 923 Romero, Roberto Carlos Flores / Universidad Tecnológica de San Juan del Río, Mexico............ 1277 Rosen, Yigal / Harvard University, USA.......................................................................................... 2402 Rothberg, Helen N. / Marist College, USA........................................................................................ 943 Rouhani, Saeed / University of Tehran, Iran................................................................................... 2932 Roy, Loriene / The University of Texas at Austin, USA................................................................... 6685 Rubio, Alexis John M. / University of the East, Philippines............................................................ 2466 Ruffin, T. Ray / University of Phoenix, USA & Grand Canyon University, USA & Ashford University, USA & North Carolina Wesleyan College, USA.............................................. 1463,3805 Ruhi, Umar / University of Ottawa, Canada................................................................................... 4278 Ruiz, María Elena García / University of Cantabria, Spain........................................................... 3183 Ruiz-Martínez, Antonio / University of Murcia, Spain.................................................................. 2749 Saeed, Fauzan / Usman Institute of Technology, Pakistan................................................................ 166 Saeed, Munazza / University of Malaya, Malaysia............................................................................. 36 Safont, Gonzalo / Universitat Politècnica de València, Spain........................................................ 4937 Saharidis, Georgios K. D. / University of Thessaly, Greece............................................................ 5411 Saito, Nagayuki / Ochanomizu University, Japan........................................................................... 4962 Saiz-Alvarez, José Manuel / Tecnologico de Monterrey, Mexico.................................................... 3020 Sajib, Md. Qutub Uddin / China University of Geosciences (CUG), China................................... 1896 Salazar, Addisson / Universitat Politècnica de València, Spain..................................................... 4937 Saldana-Perez, Ana Maria Magdalena / Instituto Politecnico Nacional, Mexico......................... 6973 Sambhanthan, Arunasalam / Curtin University, Australia.............................................................. 609



Samuel, Anand A. / VIT University, India....................................................................................... 2975 Sánchez, Jorge Manjarrez / Instituto Tecnologico Superior de Jerez, Mexico............................... 1806 Sanchez-Ruiz, Lidia / University of Cantabria, Spain.................................................................... 3870 Sandvig, John Christopher / Western Washington University, USA............................................... 8087 Sanguinetti, Paola / University of Kansas, USA................................................................................ 983 Santos-Trigo, Manuel / Cinvestav-IPN, Mexico............................................................................. 3794 Sarathy, Rathindra / Oklahoma State University, USA.................................................................. 5583 Sarivougioukas, John / “G. Gennimatas” Athens General Hospital, Greece................................ 7765 Sarp, Gulcan / Suleyman Demirel University, Turkey..................................................................... 3503 Satterfield, Debra / California State University – Long Beach, USA............................................. 8079 Savaş, Kübra / Istanbul University, Turkey..................................................................................... 5490 Savastano, Marco / Sapienza University of Rome, Italy.................................................................. 7805 Scanzio, Stefano / CNR-IEIIT, Italy................................................................................................. 1227 Scasseddu, Riccardo / University of Cagliari, Italy........................................................................ 3273 Schnackenberg, Heidi Lee / SUNY Plattsburgh, USA..................................................................... 6245 Schulte, Frederik / University of Hamburg, Germany...................................................................... 601 Segall, Richard S. / Arkansas State University, USA........................................................................ 1783 Seidenstricker, Sven / Fraunhofer Institute for Industrial Engineering, Germany......................... 4560 Self, Lucy / University of Sussex, UK............................................................................................... 1153 Selvi, Ihsan Hakan / Sakarya University, Turkey............................................................................. 5490 Sembrano, Christian Gerard / University of the East, Philippines................................................ 2466 Sen, Devjani / University of Ottawa, Canada.................................................................................. 6083 Sengupta, Susmit / Maulana Abul Kalam Azad University of Technology, India........................... 4872 Seoane, Fernando / KI-Karolinska Institutet, Sweden & University of Borås, Sweden.................. 7754 Seok, Soonhwa / Korea University, South Korea............................................................................. 6361 Serpa, Nilo Sylvio / Universidade Paulista, Brazil........................................................................... 1040 Sezgin, Anil / Yildiz Technical University, Turkey........................................................................... 7470 Sgobbi, Francesca / University of Brescia, Italy............................................................................. 4785 Shaanika, Irja Naambo / Namibia University of Science and Technology – Windhoek, Namibia.............................................................................................................................. 2943,4480 Shafiq, Huma / University of Kashmir, India.................................................................................. 4535 Shah, Huma / Coventry University, UK............................................................................................ 251 Shahamiri, Seyed Reza / Manukau Institute of Technology, New Zealand..................................... 7392 Shaharudin, Muhammad Shabir / Universiti Sains Malaysia, Malaysia...................................... 5446 Shahmir, Nazlie / WestJet Airlines Limited, Canada...................................................................... 7399 Shao, Lan / University of Oulu, Finland.......................................................................................... 2108 Sharif, Muhammad Majid / National University of Sciences and Technology (NUST), Pakistan....................................................................................................................................... 5169 Sharma, Dolly / PEC University of Technology, India...................................................................... 477 Sharma, Ravi S. / Nanyang Technological University, Singapore................................................... 2280 Sharma, Supreeth / Akamai Technologies, India.............................................................................. 847 Sharmila Banu K / VIT University, India........................................................................................ 1835 Shawyun, Teay / King Saud University, Saudi Arabia.................................................................... 3881 Shibli, Muhammad Awais / VisionIT, USA..................................................................................... 2869 Shibuya, Kazuhiko / ROIS, Japan.................................................................................................. 3538 Siano, Pierluigi / University of Salerno, Italy...................................................................................... 15



Siddiqui, Adnan Ahmed / Hamdard University, Pakistan................................................................. 166 Sidman, Cara / Arizona State University, USA............................................................................... 5820 Signorella, Margaret L. / Pennsylvania State University, USA....................................................... 7026 Siller, Mario / CINVESTAV Unidad Guadalajara, Mexico............................................................. 6522 Silva, Fillipe Souza / University of Campinas, Brazil...................................................................... 1331 Silva, Sara Catarina Gomes / University of Minho, Portugal......................................................... 3460 Silvana de Rosa, Annamaria / Sapienza University of Rome, Italy................................................................................................. 4038,4064,4404,5237,7014,7044 Simões, Jorge / ISPGaya, Portugal.................................................................................................... 800 Sims, Cynthia M. / Clemson University, USA.................................................................................. 4326 Singh, Harwinder / Guru Nanak Dev Engineering College, India................................................... 587 Singh, Indu / Griffith University, Australia...................................................................................... 5853 Singh, Kuldeep / Thapar University, India...................................................................................... 6106 Singh, R. Arvind / Kumaraguru College of Technology (KCT), India............................................... 431 Singh, Shailendra / PEC University of Technology, India................................................................ 477 Siu, Kin Wai Michael / The Hong Kong Polytechnic University, Hong Kong................................. 7382 Skarmeta, Antonio F. / University of Murcia, Spain....................................................................... 2749 Skoll, Geoffrey / SUNY at Buffalo, USA.......................................................................................... 3637 Skoumpopoulou, Dimitra / Northumbria University, UK.............................................................. 1605 Slini, Theodora / Aristotle University of Thessaloniki, Greece....................................................... 3371 Šmahel, David / Masaryk University – Brno, Czech Republic........................................................ 4223 Smith, Alan D. / Robert Morris University, USA...................................................................... 4837,5273,5476,5516,5570,5595,5918,5981,6094 Smith, Craig / Kyoto University of Foreign Studies, Japan............................................................. 3850 Smith, Pete / University of Aberdeen, UK........................................................................................ 7815 Smith, Peter A. / University of Central Florida, USA...................................................................... 3327 Smith-Ditizio, Amber A. / Texas Woman’s University, USA........................................................................................ 4837,5273,5570,5595,5918,5981,6094 Sohrabzadeh, Sara / Tehran University of Medical Science, Iran.................................................. 4436 Sokolov, Alexander / National Research University Higher School of Economics, Russia............ 4704 Sokouti, Babak / Tabriz University of Medical Sciences, Iran........................................................ 1676 Sokouti, Massoud / Mashhad University of Medical Sciences, Iran............................................... 1676 Soltani, Pooya / University of Porto, Portugal................................................................................. 7358 Šoltés, Michal / Technical University of Košice, Slovakia............................................................... 5841 Sonnenberg, Christian / Florida Institute of Technology, USA...................................................... 3516 Soomarah, Garry / University of Trinidad and Tobago, Trinidad and Tobago.............................. 2532 Soriano, Juan Vicente Izquierdo / La Ribera University Hospital, Spain...................................... 5787 Sotiriadis, Marios D. / University of South Africa (UNISA), South Africa...................................... 4088 Spaanenburg, Lambert / Comoray AB, Sweden............................................................................. 6114 Spallone, Roberta / Politecnico di Torino, Italy.............................................................. 973,5213,7856 Speed, Chris / University of Edinburgh, UK................................................................................... 6695 Spenser, Karin / Nottingham Trent University, UK......................................................................... 4168 Spiegel, Thais / Rio de Janeiro State University, Brazil.................................................................. 2076 Spires, Hiller A. / North Carolina State University, USA................................................................. 2235 Spring, Kristian J. / Brigham Young University, USA..................................................................... 1487 Sreedhar, G. / Rashtriya Sanskrit Vidyapeetha (Deemed University), India................................... 8006



Srinivasan, Srividhya / University of Madras, India.......................................................................... 77 Stancu, Stelian / Bucharest University of Economic Studies, Romania................................. 5158,6579 Starrett, David / Columbia College, USA....................................................................................... 1451 Stasolla, Fabrizio / University of Bari, Italy...................................................................................... 287 Stavrianos, Athanasios T. / 2nd Technical Vocational School of Xanthi, Greece........................... 2651 Stefanovic, Nenad / University of Kragujevac, Serbia........................................................... 5398,5538 Steinfeld, Joshua M. / Old Dominion University, USA.................................................................... 6710 Steri, Gary / European Commission – Joint Research Centre, Italy............................................... 6136 Stewart, Craig A. / Indiana University, USA................................................................................... 1063 Steyn, Gert / Kimberley Hospital Complex, South Africa............................................................... 6147 Stojanov, Zeljko / University of Novi Sad, Serbia........................................................................... 7514 Suaste, Ernesto / Cinvestav-IPN, Mexico........................................................................................ 3794 Surmen, Hasan Kemal / Istanbul University, Turkey........................................................................ 298 Susanto, Heru / Indonesian Institute of Sciences, Indonesia & Tunghai University, Taiwan......... 7369 Svitek, Miroslav / Czech Technical University in Prague, Czech Republic.................................... 4391 Swaid, Samar I. / Philander Smith College, USA............................................................................ 8066 Swanson, Karen / Mercer University, USA..................................................................................... 1432 Swanson, Pete / Georgia State University, USA.............................................................................. 7682 Switzer, Jamie S. / Colorado State University, USA........................................................................ 2121 Switzer, Ralph V. / Colorado State University, USA........................................................................ 2121 Sylaiou, Stella / Aristotle University of Thessaloniki, Greece......................................................... 7002 Syropoulos, Apostolos / Greek Molecular Computing Group, Greece.................................. 2337,2651 Szczepański, Zbigniew / Kazimierz Wielki University, Poland......................................................... 521 Szeto, Kimmy / Baruch College, City University of New York, USA............................................... 6447 Szopa, Anna / Jagiellonian University, Poland................................................................................. 563 T, Tamilarasi / VIT University, India............................................................................................... 7503 T., Santhanamery / Universiti Teknologi MARA, Malaysia.............................................................. 831 Tabrizi, Aydin / University of Kansas, USA...................................................................................... 983 Tadj, Lotfi / Fairleigh Dickinson University, Canada..................................................................... 1570 Taieb, Basma / University of Cergy Pontoise, France..................................................................... 6070 Taiwo, Ayankunle A. / Texas A&M University – Commerce, USA.................................................. 3654 Tam, June Poh Kim / Universiti Sains Malaysia, Malaysia............................................................ 5357 Tambo, Torben / Aarhus University, Denmark............................................................................... 2920 Tan, Amanda C. / University of Central Florida, USA...................................................................... 995 Tani, Kawtar / UCOL, New Zealand............................................................................................... 3194 Tannous, Katia / University of Campinas, Brazil............................................................................ 1331 Tazhina, Gainiya / University of International Business, Kazakhstan............................................ 5626 Tchangani, Ayeley P. / Université Fédérale Toulouse Midi-Pyrénées, France................................. 7282 Tech, Deb / Dakota State University, USA....................................................................................... 3861 Tecpoyotl-Torres, Margarita / Universidad Autónoma del Estado de Morelos, Mexico............... 2897 Teik, Cheah Cheng / Universiti Sains Malaysia, Malaysia............................................................. 5422 Teixeira, Leonor / University of Aveiro, Portugal.................................................................... 888,3963 Terras, Melody M. / University of the West of Scotland, UK........................................................... 6398 Teshome, Assefa K. / Victoria University, Australia........................................................................ 6319 Thakare, Vaishali Ravindra / VIT University, India....................................................................... 1667 Thakur, R. S. / Maulana Azad National Institute of Technology, India........................................... 1376



Thompson, Nik / Curtin University, Australia....................................................................... 4124,7481 Tirado-Morueta, Ramon / University of Huelva, Spain................................................................. 3976 Titova, Svetlana / Far Eastern Federal University, Russia............................................................. 5116 Tiwari, Vivek / International Institute of Information Technology Naya Raipur, India.................. 1376 Tobin, Maryann Tatum / University of Miami, USA....................................................................... 1508 Tomaiuolo, Michele / University of Parma., Italy.................................................................. 2392,6950 Tommasel, Antonela / ISISTAN, UNICEN-CONICET, Argentina.................................................. 6658 Torkul, Orhan / Sakarya University, Turkey................................................................................... 2200 Torres-Ruiz, Miguel Jesus / Instituto Politécnico Nacional, Mexico.............................................. 6973 Tounekti, Oussama / University of Murcia, Spain.......................................................................... 2749 Trad, Antoine / Institute of Business and Information Systems Transformation Management, Switzerland.................................................................................................................... 620,636,5607 Tran, Ben / Alliant International University, USA........................................... 277,671,3173,3260,6756 Tran, Thomas / University of Ottawa, Canada............................................................................... 1757 Traxler, John / University of Wolverhampton, UK.......................................................................... 6347 Tremblay, Diane-Gabrielle / University of Quebec, Canada......................................................... 5027 Tripathy, B. K. / VIT University, India........................................................................... 1835,3226,4528 Tsalighopoulos, Miltiadis / Aristotle University of Thessaloniki, Greece....................................... 5886 Tsubota, Yasushi / Kyoto Institute of Technology, Japan................................................................ 3850 Tüfekci, Aslıhan / Gazi University, Turkey..................................................................................... 6789 Turel, Vehbi / The University of Bingol, Turkey.............................................................................. 2357 Turgay, Safiye / Sakarya University, Turkey.................................................................................... 2200 Turgay, Tahsin / Sakarya University, Turkey.................................................................................. 2200 Tzanis, George / Aristotle University of Thessaloniki, Greece........................................................ 1984 Ulloa, Juan Carlos Solís / Instituto Tecnológico Superior de Cintalapa, Mexico........................... 1277 Umair, Sajid / National University of Sciences and Technology (NUST), Pakistan........................ 5169 Umukoro, Immanuel Ovemeso / Pan-Atlantic University, Nigeria................................................ 2724 Ursyn, Anna / University of Northern Colorado, USA.................................................................... 5103 Usat, Selisa / Universiti Teknologi Malaysia, Malaysia................................................................... 7088 Uysal, Tugba Ucma / Mugla Sitki Kocman University, Turkey........................................................ 5737 V., Sriharsha K. / National Institute of Technology Trichy, India...................................................... 212 Vagelatos, Aristides / CTI&P, Greece............................................................................................. 7765 Vagnoli, Laura / Meyer Children’s Hospital of Florence, Italy...................................................... 5955 Van Zyl, D. H. / University of Pretoria, South Africa......................................................................... 777 Vargas-Bernal, Rafael / Instituto Tecnológico Superior de Irapuato, Mexico...................... 2673,2897 Vaterlaus, J. Mitchell / Montana State University, USA.................................................................. 7097 Vega-Barbas, Mario / KTH-Royal Institute of Technology, Sweden & ESNE-Universidad Camilo José Cela, Spain.......................................................................................................................... 7754 Veglis, Andreas A. / Aristotle University of Thessaloniki, Greece................................. 1196,6476,8046 Velikovsky, J. T. / University of Newcastle, Australia...................................................................... 4666 Verelst, Jan / University of Antwerp, Belgium................................................................................. 5317 Vergara, Luis / Universitat Politècnica de València, Spain............................................................ 4937 Verma, Anil Kumar / Thapar University, India.............................................................................. 6106 Vernier, Marco / University of Udine, Italy............................................................................ 2064,6195 Verteramo, Saverino / University of Calabria, Italy....................................................................... 7805 Vestias, Mário Pereira / Instituto Politécnico de Lisboa, Portugal............................... 4018,4549,4607



Véstias, Mário Pereira / Instituto Politecnico de Lisboa, Portugal................................................. 6307 Vettraino, Laura / Learning Community, Italy............................................................................... 1559 Vilas-Boas, João Paulo / University of Porto, Portugal................................................................... 7358 Vinzuda, Vipul / National Institute of Design Gandhinagar, India................................................ 1260 Viswanathan, Revathi / B. S. Abdur Rahman University, India..................................................... 7635 Vondrackova, Petra / Charles University in Prague, Czech Republic............................................ 4223 Vong, Yunny Meas / Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Mexico......................................................................................................................................... 1277 Vorndran, Brittany A. / Seton Hall University, USA....................................................................... 5810 Voskoglou, Michael / Graduate Technological Educational Institute (T.E.I.), Greece................... 3215 Voß, Stefan D. / University of Hamburg, Germany............................................................................ 601 Walczak, Steven / University of South Florida, USA................................................................... 98,120 Wang, Jingying / University of Chinese Academy of Sciences, China.............................................. 132 Wang, John / Montclair State University, USA............................................................................... 5388 Wang, Shun-Yung Kevin / University of South Florida – St. Petersburg, USA............................... 1656 Wang, Ye / Zhejiang Gongshang University, China........................................................................ 7588 Wani, Zahid Ashraf / University of Kashmir, India....................................................... 4535,5262,6739 Warwick, Kevin / Coventry University, UK...................................................................................... 251 Wasim, Muhammad / Usman Institute of Technology, Pakistan...................................................... 166 Wassan, Jyotsna Talreja / University of Delhi, India........................................................................ 326 Weaver, Kari D. / University of South Carolina – Aiken, USA........................................................ 7292 Weckman, Gary R. / Ohio University, USA..................................................................................... 6642 Weintraub, Eli / Afeka Tel Aviv College of Engineering, Israel...................................................... 1627 Weiss, Andrew Philip / California State University – Northridge, USA.......................................... 5226 Wellman, Barry / NetLab Network, Canada................................................................................... 7057 Wernert, Eric / Indiana University, USA........................................................................................ 1063 Westerlund, Mika / Carleton University, Canada.......................................................................... 1684 White, Jonathan R. / Högskolan Dalarna, Sweden......................................................................... 1217 Wiafe, Isaac / Ghana Institute of Management and Public Administration, Ghana....................... 7785 Wickramasinghe, Nilmini / Epworth HealthCare, Australia & Deakin University, Australia............................................................................................................................. 3725,6806 Widiyanesti, Sri / Telkom University, Indonesia............................................................................. 5550 Wierzchon, Slawomir T. / Polish Academy of Sciences, Poland..................................................... 1962 Wilkins-Diehr, Nancy / San Diego Supercomputer Center, USA................................................... 1063 Williams, Anne-Evan K. / Billings Middle School, USA................................................................. 5183 Williams, Idongesit / Aalborg University, Denmark....................................................................... 3549 Williams, James / University of Pittsburgh, USA............................................................................ 2707 Wimmer, Hayden / Georgia Southern University, USA........................................................................ 1 Winter, Jenifer Sunrise / University of Hawaii at Manoa, USA..................................................... 4951 Wodczak, Michal / Samsung Electronics, Poland........................................................................... 6499 Wolf, Frank / CSSTA L3C, USA...................................................................................................... 6813 Wong, Yi Lin / The Hong Kong Polytechnic University, Hong Kong............................................... 7382 Wood, Lincoln C. / University of Otago, New Zealand & Curtin University, Australia.................. 5335 Wooldridge, Deborah G. / Bowling Green State University, USA................................................... 3930 Worthington, Debra L. / Auburn University, USA.......................................................................... 6985 Wright, Jorja / University of Charleston, USA............................................................................... 7898



Wright, Michelle F. / Masaryk University, Czech Republic.................................................... 1723,7077 Wulansari, Puspita / Telkom University, Indonesia........................................................................ 5327 Xafopoulos, Alexandros / University College London, UK............................................................ 6336 Xiaojun (Jenny) Yuan / University of Albany, USA......................................................................... 3745 Xu, Henry / The University of Queensland, Australia..................................................................... 5433 Yadav, Aman / Michigan State University, USA.............................................................................. 2292 Yahya, Fadwa / University of Sfax, Tunisia....................................................................................... 765 Yang, Hung-Jen / National Kaohsiung Normal University, Taiwan............................................... 6057 Yang, Yuqi / University of Windsor, Canada................................................................................... 2314 Yasuda, Gen’ichi / Nagasaki Institute of Applied Science, Japan.......................................... 6836,7435 Yengin, İlker / A*STAR, Institute of High Performance Computing, Singapore............................. 7168 Yeomans, Julian Scott / York University, Canada........................................................................... 2178 Yfantis, Vasileios / Ionian University, Greece................................................................................. 1033 Yilmaz, Rahime / Istanbul University, Turkey................................................................................. 7470 Young, Richard A. / Driving Safety Consulting, LLC, USA............................................................ 5992 Young, William A. / Ohio University, USA...................................................................................... 6642 Younis, Eman / Minia University, Egypt......................................................................................... 8023 Yu, Liguo / Indiana University – South Bend, USA................................................................ 2739,7116 Yücer, Emre / Erzincan University, Turkey..................................................................................... 7831 Yuhua, Fu / CNOOC Research Institute, China.............................................................................. 4399 Yusuf, Sarina / Universiti Putra Malaysia, Malaysia............................................................. 1704,2761 Zaeri, Fahimeh / Auckland University of Technology, New Zealand................................................ 539 Zainab, Tazeem / University of Kashmir, India..................................................................... 5262,6739 Zaitsev, Dmitry A. / International Humanitarian University, Ukraine............................................ 7731 Zeinali, Ali Akbar / Universiti Sains Malaysia, Malaysia............................................................... 5136 Zhang, Xinxin / University of Alberta, Canada.............................................................................. 2369 Zhang, Yu-Jin / Tsinghua University, China................................................................. 1308,1319,1344 Zhao, Wenbing / Cleveland State University, USA.......... 1091,1238,1647,3056,4927,5876,5941,8056 Zhou, Chunfang / Aalborg University, Denmark............................................................................ 4178 Zhu, Changye / University of Chinese Academy of Sciences, China................................................. 132 Zhu, Tingshao / University of Chinese Academy of Sciences, China................................................ 132 Zhu, W. K. / The University of Hong Kong, Hong Kong.................................................................... 505 Zunino, Alejandro / ISISTAN, UNICEN-CONICET, Argentina..................................................... 6658 Zurloni, Valentino / Università di Milano-Bicocca, Italy............................................................... 6223 Zykov, Sergey V. / National Research University Higher School of Economics, Russia.................. 1396

Table of Contents by Volume

Preface................................................................................................................................................ clxii Guide to the Encyclopedia of Information Science and Technology, Fourth Edition.............. clxviii Acknowledgment............................................................................................................................... clxx About the Editor.............................................................................................................................. clxxi

Volume I (A - Bu)

Category: Accounting and Finance Applying Artificial Intelligence to Financial Investing........................................................................... 1 Hayden Wimmer, Georgia Southern University, USA Roy Rada, University of Maryland – Baltimore County, USA Distributed Parameter Systems Control and Its Applications to Financial Engineering....................... 15 Gerasimos Rigatos, Industrial Systems Institute, Greece Pierluigi Siano, University of Salerno, Italy Does Inter-Bank Investments Restraints Financing Performance of Islamic Banks?............................ 36 Mohammad Taqiuddin Mohamad, University of Malaya, Malaysia Munazza Saeed, University of Malaya, Malaysia An Extension to the Delone and Mclean Information Systems Success Model and Validation in the Internet Banking Context................................................................................................................. 49 Veeraraghavan Jagannathan, National Institute of Technology, India Senthilarasu Balasubramanian, National Institute of Technology, India Thamaraiselvan Natarajan, National Institute of Technology, India Impact of Business Groups on Payout Policy in India........................................................................... 61 Ahana Bose, Indian Institute of Management Calcutta, India





Noise Trader........................................................................................................................................... 71 Po-Keng Cheng, State University of New York, Stony Brook University, USA Safeguarding of ATM............................................................................................................................ 77 Srividhya Srinivasan, University of Madras, India Priya Krishnamoorthy, SASTRA University, India Raghuraman Koteeswaran, SASTRA University, India

Category: Artificial Intelligence Artificial Ethics...................................................................................................................................... 88 Laura L. Pană, Polytechnic University of Bucharest, Romania Artificial Intelligence............................................................................................................................. 98 Steven Walczak, University of South Florida, USA Artificial Intelligence Review.............................................................................................................. 106 Amal Kilani, University of Gabes Tunisia, Tunisia Ahmed Ben Hamida, University of Sfax, Tunisia Habib Hamam, University of Moncton, Canada Artificial Neural Networks................................................................................................................... 120 Steven Walczak, University of South Florida, USA Automatic Emotion Recognition Based on Non-Contact Gaits Information...................................... 132 Jingying Wang, University of Chinese Academy of Sciences, China Baobin Li, University of Chinese Academy of Sciences, China Changye Zhu, University of Chinese Academy of Sciences, China Shun Li, University of Chinese Academy of Sciences, China Tingshao Zhu, University of Chinese Academy of Sciences, China Board Games AI.................................................................................................................................. 144 Tad Gonsalves, Sophia University, Japan Computational Intelligence Approaches to Computational Aesthetics............................................... 156 Erandi Lakshika, University of New South Wales, Australia Michael Barlow, University of New South Wales, Australia Dotted Raster-Stereography................................................................................................................. 166 Muhammad Wasim, Usman Institute of Technology, Pakistan Fauzan Saeed, Usman Institute of Technology, Pakistan Abdul Aziz, Usman Institute of Technology, Pakistan Adnan Ahmed Siddiqui, Hamdard University, Pakistan Hybrid Computational Intelligence and the Basic Concepts and Recent Advances............................ 180 Georgios Dounias, University of the Aegean, Greece



Incremental Approach to Classification Learning............................................................................... 191 Xenia Alexandre Naidenova, Research Centre of Military Medical Academy – Saint Petersburg, Russia Machine Dreaming............................................................................................................................... 202 James Frederic Pagel, University of Colorado School of Medicine, USA Moving Object Detection and Tracking Based on the Contour Extraction and Centroid Representation...................................................................................................................................... 212 Naveenkumar M, National Institute of Technology Trichy, India Sriharsha K. V., National Institute of Technology Trichy, India Vadivel A, National Institute of Technology Trichy, India Semantic Intelligence........................................................................................................................... 220 Maria K. Koleva, Institute of Catalysis, Bulgarian Academy of Sciences, Bulgaria The Summers and Winters of Artificial Intelligence........................................................................... 229 Tad Gonsalves, Sophia University, Japan Swarm Intelligence for Multi-Objective Optimization in Engineering Design................................... 239 Janga Reddy Manne, Indian Institute of Technology Bombay, India Trust and Decision Making in Turing’s Imitation Game..................................................................... 251 Huma Shah, Coventry University, UK Kevin Warwick, Coventry University, UK

Category: Assistive Technologies Apps as Assistive Technology............................................................................................................. 266 Emily C. Bouck, Michigan State University, USA Sara M. Flanagan, University of Kentucky, USA Missy D. Cosby, Michigan State University, USA Assistive Technology and Human Capital for Workforce Diversity.................................................... 277 Ben Tran, Alliant International University, USA Assistive Technology for Supporting Communication, Occupation, and Leisure by Children With Severe to Profound Developmental Disabilities.................................................................................. 287 Fabrizio Stasolla, University of Bari, Italy Viviana Perilli, Lega del Filo d’Oro – Molfetta, Italy Adele Boccasini, Lega del Filo d’Oro – Termini Imerese, Italy Design, Manufacture, and Selection of Ankle-Foot-Orthoses............................................................. 298 Hasan Kemal Surmen, Istanbul University, Turkey Nazif Ekin Akalan, Istanbul University, Turkey Yunus Ziya Arslan, Istanbul Univesity, Turkey



A Disability-Aware Mentality to Information Systems Design and Development.............................. 314 Julius T. Nganji, University of Ottawa, Canada

Category: Big Data Adapting Big Data Ecosystem for Landscape of Real World Applications........................................ 326 Jyotsna Talreja Wassan, University of Delhi, India Big Data Analysis and Mining............................................................................................................. 338 Carson K. Leung, University of Manitoba, Canada Big Data Analytics for Tourism Destinations...................................................................................... 349 Wolfram Höpken, University of Applied Sciences Ravensburg-Weingarten, Germany Matthias Fuchs, Mid-Sweden University, Sweden Maria Lexhagen, Mid-Sweden University, Sweden Big Data Time Series Stream Data Segmentation Methods................................................................ 364 Dima Alberg, Shamoon College of Engineering (SCE), Israel Challenges for Big Data Security and Privacy.................................................................................... 373 M. Govindarajan, Annamalai University, India How Visualisation and Interaction Can Optimize the Cognitive Processes Towards Big Data.......... 381 Antonio Feraco, Fraunhofer IDM@NTU, Singapore Marius Erdt, Fraunhofer IDM@NTU, Singapore Managing and Visualizing Unstructured Big Data.............................................................................. 394 Ananda Mitra, Wake Forest University, USA Mining Big Data and Streams.............................................................................................................. 406 Hoda Ahmed Abdelhafez, Suez Canal University, Egypt

Category: Bioinformatics Bioinformatics...................................................................................................................................... 419 Mark A. Ragan, The University of Queensland, Australia Bioinspired Solutions for MEMS Tribology....................................................................................... 431 R. Arvind Singh, Kumaraguru College of Technology (KCT), India S. Jayalakshmi, Kumaraguru College of Technology (KCT), India Building Gene Networks by Analyzing Gene Expression Profiles...................................................... 440 Crescenzio Gallo, University of Foggia, Italy Concerns and Challenges of Cloud Platforms for Bioinformatics....................................................... 455 Nicoletta Dessì, Università degli Studi di Cagliari, Italy Barbara Pes, Università degli Studi di Cagliari, Italy



Category: Biology GWAS as the Detective to Find Genetic Contribution in Diseases..................................................... 466 Simanti Bhattacharya, University of Kalyani, India Amit Das, University of Kalyani, India RNA Interference Therapeutics and Human Diseases......................................................................... 477 Dolly Sharma, PEC University of Technology, India Shailendra Singh, PEC University of Technology, India Trilok Chand, PEC University of Technology, India

Category: Biomedical Engineering General Perspectives on Electromyography Signal Features and Classifiers Used for Control of Human Arm Prosthetics....................................................................................................................... 492 Faruk Ortes, Istanbul University, Turkey Derya Karabulut, Halic University, Turkey Yunus Ziya Arslan, Istanbul University, Turkey The Principle and Process of Digital Fabrication of Biomedical Objects........................................... 505 S. H. Choi, The University of Hong Kong, Hong Kong H. H. Cheung, The University of Hong Kong, Hong Kong W. K. Zhu, The University of Hong Kong, Hong Kong Reverse Engineering in Rehabilitation................................................................................................ 521 Emilia Mikołajewska, Nicolaus Copernicus University, Poland Marek Macko, Kazimierz Weilki University, Poland Zbigniew Szczepański, Kazimierz Wielki University, Poland Dariusz Mikołajewski, Kazimierz Wielki University, Poland

Category: Business and Organizational Research Acceptance of E-Reverse Auction From the Buyer Perspective.......................................................... 530 Cigdem Altin Gumussoy, Istanbul Technical University, Turkey Bilal Gumussoy, Shell and Turcas Petrol Inc., Turkey Advanced ICT Methodologies (AIM) in the Construction Industry................................................... 539 M. Reza Hosseini, Deakin University, Australia Saeed Banihashemi, University of Technology Sydney, Australia Fahimeh Zaeri, Auckland University of Technology, New Zealand Alireza Adibfar, University of Florida, USA Amplifying the Significance of Systems Thinking in Organization.................................................... 551 Mambo Governor Mupepi, Grand Valley State University, USA Sylvia C. Mupepi, Grand Valley State University, USA Jaideep Motwani, Grand Valley State University, USA



The Application of Crowdsourced Processes in a Business Environment.......................................... 563 Katarzyna Kopeć, Tischner European University, Poland Anna Szopa, Jagiellonian University, Poland Architecture as a Tool to Solve Business Planning Problems............................................................. 573 James McKee, Independent Researcher, Australia Benchmarking Performance Indicators of Indian Rail Freight by DEA Approach............................. 587 Neeraj Bhanot, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India Harwinder Singh, Guru Nanak Dev Engineering College, India Bi-Directional Business/IT Alignment................................................................................................ 601 Hashim Chunpir, German Climate Computing Centre (DKRZ), Germany Frederik Schulte, University of Hamburg, Germany Yannick Bartens, University of Hamburg, Germany Stefan D. Voß, University of Hamburg, Germany Business Sustainability Indices............................................................................................................ 609 Arunasalam Sambhanthan, Curtin University, Australia The Business Transformation Framework, Agile Project and Change Management.......................... 620 Antoine Trad, Institute of Business and Information Systems Transformation Management, Switzerland Damir Kalpić, University of Zagreb, Croatia The Business Transformation Framework and Its Business Engineering Law Support for  (e)Transactions..................................................................................................................................... 636 Antoine Trad, Institute of Business and Information Systems Transformation Management, Switzerland Damir Kalpić, University of Zagreb, Croatia Challenges of Meta Access Control Model Enforcement to an Increased Interoperability................. 651 Sérgio Luís Guerreiro, University of Lisbon, Portugal Cognitive Ergonomics in 2016............................................................................................................ 662 Ronald John Lofaro, Embry-Riddle Aeronautical University, USA Corporate Social Responsibility.......................................................................................................... 671 Ben Tran, Alliant International University, USA Digital Transformation Journeys in a Digitized Reality...................................................................... 682 Jurgen Janssens, QSpin, Belgium A Framework for Exploring IT-Led Change in Morphing Organizations........................................... 694 Sharon A. Cox, Birmingham City University, UK



How the Crowdsourcing Enhance the Co-Creation Into the Virtual Communities............................. 707 Bahri Ammari Nedra, IHEC of Carthage, USA Hyper-Sensitivity in Global Virtual Teams......................................................................................... 720 Andre Araujo, Texas A&M University, USA Lean and Six Sigma Innovation and Design........................................................................................ 729 Rick Edgeman, Utah State University, USA Motivational Factors of Telework........................................................................................................ 740 Arlene J. Nicholas, Salve Regina University, USA Organizational Transparency............................................................................................................... 754 Gustavo de Oliveira Almeida, Federal University of the State of Rio de Janeiro, Brazil Claudia Cappelli, Federal University of the State of Rio de Janeiro, Brazil Cristiano Maciel, Federal University of Mato Grosso, Brazil Social Business Process Modeling....................................................................................................... 765 Fadwa Yahya, University of Sfax, Tunisia Khouloud Boukadi, University of Sfax, Tunisia Zakaria Maamar, Zayed University, UAE Hanêne Ben-Abdallah, King Abdulaziz University, Saudi Arabia Social Issues in IT Project Teams:....................................................................................................... 777 Awie C. Leonard, University of Pretoria, South Africa D. H. Van Zyl, University of Pretoria, South Africa Viewpoints on Business Process Models............................................................................................. 788 Giorgio Bruno, Politecnico di Torino, Italy

Category: Business Education Serious Games in Entrepreneurship Education................................................................................... 800 Fernando Almeida, Polytechnic Institute of Gaya, Portugal Jorge Simões, ISPGaya, Portugal

Volume II (Bu - Cu)

Category: Business Information Systems Architectural Framework for the Implementation of Information Technology Governance in Organisations....................................................................................................................................... 810 Thami Batyashe, Cape Peninsula University of Technology, South Africa Tiko Iyamu, Cape Peninsula University of Technology, South Africa



Continuous Assurance and the Use of Technology for Business Compliance.................................... 820 Rui Pedro Figueiredo Marques, Universidade de Aveiro, Portugal Explaining and Predicting Users’ Continuance Usage Intention Toward E-Filing Utilizing Technology Continuance Theory......................................................................................................... 831 Santhanamery T., Universiti Teknologi MARA, Malaysia T. Ramayah, University Sains Malaysia, Malaysia Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence  Framework........................................................................................................................................... 847 Satyadhyan Chickerur, KLE Technological University, India Supreeth Sharma, Akamai Technologies, India Prashant M. Narayankar, KLE Technological University, India Integrated Data Architecture for Business........................................................................................... 862 Richard Kumaradjaja, Renaissance Consulting, Indonesia IT Strategy Follows Digitalization....................................................................................................... 873 Thomas Ochs, Villeroy & Boch, Germany Ute Anna Riemann, SAP SE, Germany The Main Concepts Behind the Dematerialization of Business Processes.......................................... 888 Liliana Ávila, University of Aveiro, Portugal Leonor Teixeira, University of Aveiro, Portugal Scanning for Blind Spots..................................................................................................................... 899 Barbara Jane Holland, Brooklyn Public Library, USA Strategic Information Systems Planning.............................................................................................. 912 Maria Kamariotou, University of Macedonia, Greece Fotis Kitsios, University of Macedonia, Greece Sustainable Advantages of Business Value of Information Technology............................................. 923 Jorge A. Romero, Towson University, USA Using Business Analytics in Franchise Organizations......................................................................... 930 Ye-Sho Chen, Louisiana State University, USA

Category: Business Intelligence Big Data, Knowledge, and Business Intelligence................................................................................ 943 G. Scott Erickson, Ithaca College, USA Helen N. Rothberg, Marist College, USA



Business Intelligence........................................................................................................................... 951 Richard T. Herschel, Saint Joseph’s University, USA Improving Competitiveness Through Organizational Market Intelligence.......................................... 961 George Leal Jamil, Informações em Rede, Brazil Leandro Rocha Dos Santos, In3 Inteligência de Mercado, Brazil Cecília C. Jamil, Stockholm University, Sweden

Category: Civil Engineering Digital Animation for Representing Architectural Design.................................................................. 973 Roberta Spallone, Politecnico di Torino, Italy Literature Review of Augmented Reality Application in the Architecture, Engineering, and Construction Industry With Relation to Building Information............................................................ 983 Aydin Tabrizi, University of Kansas, USA Paola Sanguinetti, University of Kansas, USA

Category: Clinical Science and Technologies A Gamification Update to the Taxonomy of Technology and Mental Health..................................... 995 Madeline R. Marks, University of Central Florida, USA Amanda C. Tan, University of Central Florida, USA Clint Bowers, University of Central Florida, USA Heart Sound Analysis for Blood Pressure Estimation....................................................................... 1006 Rui Guedes, Faculdade de Medicina da Universidade do Porto, Portugal Henrique Cyrne Carvalho, Serviço de Cardiologia, Hospital de Santo António, Centro Hospitalar do Porto, Portugal Ana Castro, Universidade do Porto, Portugal Sociological Perspectives on Improving Medical Diagnosis Emphasizing CAD.............................. 1017 Joel Fisher, Department of State, United States Government, USA

Category: Cloud Computing Cloud Computing............................................................................................................................... 1026 Eduardo Correia, Christchurch Polytechnic Institute of Technology (CPIT), New Zealand Cloud Governance at the Local Communities................................................................................... 1033 Vasileios Yfantis, Ionian University, Greece Clouds of Quantum Machines........................................................................................................... 1040 Nilo Sylvio Serpa, Universidade Paulista, Brazil



Cyberinfrastructure, Cloud Computing, Science Gateways, Visualization, and Cyberinfrastructure Ease of Use........................................................................................................................................ 1063 Craig A. Stewart, Indiana University, USA Richard Knepper, Indiana University, USA Matthew R. Link, Indiana University, USA Marlon Pierce, Indiana University, USA Eric Wernert, Indiana University, USA Nancy Wilkins-Diehr, San Diego Supercomputer Center, USA Fault Tolerant Cloud Systems............................................................................................................ 1075 Sathish Kumar, VIT University, India Balamurugan B, VIT University, India Fault Tolerant Data Management for Cloud Services........................................................................ 1091 Wenbing Zhao, Cleveland State University, USA From Information Systems Outsourcing to Cloud Computing.......................................................... 1101 Mohammad Nabil Almunawar, Universiti Brunei Darussalam, Brunei Hasan Jawwad Almunawar, P. T. Tegar Kupas Mediatama, Indonesia Improved Checkpoint Using the Effective Management of I/O in a Cloud Environment................. 1116 Bakhta Meroufel, University of Oran1, Algeria Ghalem Belalem, University of Es Senia Oran1, Algeria Service Quality and Perceived Value of Cloud Computing-Based Service Encounters.................... 1129 Eges Egedigwe, Dallas County Community College, USA Understanding Business Models on the Cloud.................................................................................. 1141 Arash Najmaei, Australian Catholic University, Australia Understanding Cloud Computing in a Higher Education Context.................................................... 1153 Lucy Self, University of Sussex, UK Petros Chamakiotis, University of Sussex, UK Vertical Integration Between Providers With Possible Cloud Migration.......................................... 1164 Aleksandra Kostic-Ljubisavljevic, University of Belgrade, Serbia Branka Mikavica, University of Belgrade, Serbia Virtualization as the Enabling Technology of Cloud Computing...................................................... 1174 Mohamed Fazil Mohamed Firdhous, University of Moratuwa, Sri Lanka

Category: Communications Theory Communication, Information, and Pragmatics.................................................................................. 1186 Adriana Braga, Pontifical Catholic University of Rio de Janeiro, Brazil Robert K. Logan, University of Toronto, Canada



Data Journalism................................................................................................................................. 1196 Andreas A. Veglis, Aristotle University of Thessaloniki, Greece Charalampos P. Bratsas, Aristotle University of Thessaloniki, Greece Investigating Diachronic Variation and Change in New Varieties of English................................... 1206 Rita Calabrese, University of Salerno, Italy Negotiating Local Norms in Online Communication........................................................................ 1217 Jonathan R. White, Högskolan Dalarna, Sweden

Category: Computer Engineering Architecture of an Open-Source Real-Time Distributed Cyber Physical System.............................. 1227 Stefano Scanzio, CNR-IEIIT, Italy Consistency Is Not Enough in Byzantine Fault Tolerance................................................................ 1238 Wenbing Zhao, Cleveland State University, USA

Category: Computer Simulation Data Visualization Strategies for Computer Simulation in Bioelectromagnetics.............................. 1249 Akram Gasmelseed, Qassim University, Saudi Arabia Ali H. Alharbi, Qassim University, Saudi Arabia Ergonomic Design of a Driver Training Simulator for Rural India................................................... 1260 Prabir Mukhopadhyay, Indian Institute of Information Technology Design and Manufacturing Jabalpur, India Vipul Vinzuda, National Institute of Design Gandhinagar, India 3D Scanning and Simulation of a Hybrid Refrigerator Using Photovoltaic Energy......................... 1277 Edith Obregón Morales, Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Mexico José de Jesús Pérez Bueno, Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Mexico Juan Carlos Moctezuma Esparza, Universidad Politécnica Metropolitana de Hidalgo, Mexico Diego Marroquín García, Universidad Tecnológica de San Juan del Río, Mexico Arturo Trejo Pérez, Universidad Tecnológica de San Juan del Río, Mexico Roberto Carlos Flores Romero, Universidad Tecnológica de San Juan del Río, Mexico Juan Manuel Olivares Ramírez, Universidad Tecnológica de San Juan del Río, Mexico Maria Luisa Mendoza López, Tecnológico Nacional de México, Instituto Tecnológico de Querétaro, Mexico Juan Carlos Solís Ulloa, Instituto Tecnológico Superior de Cintalapa, Mexico Yunny Meas Vong, Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Mexico Víctor Hugo Rodríguez Obregón, Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Mexico



Uniform Random Number Generation With Jumping Facilities....................................................... 1297 E. Jack Chen, BASF Corporation, USA

Category: Computer Vision and Image Processing A Critical Overview of Image Segmentation Techniques Based on Transition Region.................... 1308 Yu-Jin Zhang, Tsinghua University, China Development of Image Engineering in the Last 20 Years................................................................. 1319 Yu-Jin Zhang, Tsinghua University, China Particle Shape Analysis Using Digital Image Processing.................................................................. 1331 Katia Tannous, University of Campinas, Brazil Fillipe Souza Silva, University of Campinas, Brazil The Understanding of Spatial-Temporal Behaviors........................................................................... 1344 Yu-Jin Zhang, Tsinghua University, China

Category: Criminal Science and Forensics Forensic Investigations in Cloud Computing..................................................................................... 1356 Diane Barrett, Bloomsburg University of Pennsylvania, USA Internet-Facilitated Child Sexual Exploitation Crimes...................................................................... 1366 Keith F. Durkin, Ohio Northern University, USA Ronald L. DeLong, University of Dayton, USA Knowledge-Based Forensic Patterns and Engineering System......................................................... 1376 Vivek Tiwari, International Institute of Information Technology Naya Raipur, India R. S. Thakur, Maulana Azad National Institute of Technology, India Uncovering Limitations of E01 Self-Verifying Files......................................................................... 1384 Jan Krasniewicz, Birmingham City University, UK Sharon A. Cox, Birmingham City University, UK

Category: Crisis Response and Management Crisis Response and Management..................................................................................................... 1396 Sergey V. Zykov, National Research University Higher School of Economics, Russia Information Science and Technology in Crisis Response and Management..................................... 1407 Randy Basham, University of Texas at Arlington, USA



Category: Curriculum Development and Instructional Design Addressing Digital Competencies, Curriculum Development, and Instructional Design in Science Teacher Education.............................................................................................................................. 1420 Isha DeCoito, Western University, Canada Designing Engaging Instruction for the Adult Learners.................................................................... 1432 Karen Swanson, Mercer University, USA Geri Collins, Mercer University, USA Educational Ontology Development.................................................................................................. 1441 Galip Kaya, Havelsan Inc., Turkey Arif Altun, Hacettepe University, Turkey Factors Contributing to the Effectiveness of Online Students and Instructors.................................. 1451 Michelle Kilburn, Southeast Missouri State University, USA Martha Henckell, Southeast Missouri State University, USA David Starrett, Columbia College, USA Increasing Student Engagement and Participation Through Course Methodology........................... 1463 T. Ray Ruffin, University of Phoenix, USA & Grand Canyon University, USA & Ashford University, USA & North Carolina Wesleyan College, USA Donna Patterson Hawkins, University of Phoenix, USA D. Israel Lee, Southern Illinois University, USA & University of Phoenix, USA Instructional Real World Community Engagement........................................................................... 1474 Caroline M. Crawford, University of Houston – Clear Lake, USA Learner Engagement in Blended Learning........................................................................................ 1487 Kristian J. Spring, Brigham Young University, USA Charles R. Graham, Brigham Young University, USA Tarah B. Ikahihifo, Brigham Young University, USA Measuring Text Readability Using Reading Level............................................................................ 1499 James C. Brewer, Texas Tech University, USA Multimodal Literacy.......................................................................................................................... 1508 Maryann Tatum Tobin, University of Miami, USA An Open Learning Format for Lifelong Learners’ Empowerment.................................................... 1517 Sabrina Leone, Università Politecnica delle Marche, Italy Reflection as a Process From Theory to Practice............................................................................... 1529 Sonia Bharwani, Indian School of Management and Entrepreneurship, India Durgamohan Musunuri, Bhavan’s Usha and Lakshmi Mittal Institute of Management, India



Relationship Among Intelligence, Achievement Motivation, Type of School, and Academic Performance of Kenyan Urban Primary School Pupils..................................................................... 1540 Jessina Mukomunene Muthee, Kenyatta University, Kenya Catherine G. Murungi, Kenyatta University, Kenya Screencasts and Learning Styles........................................................................................................ 1548 Rui Alberto Jesus, CESPU, Instituto de Investigação e Formação Avançada em Ciências e Tecnologias da Saúde, Portugal Self-Awareness and Motivation Contrasting ESL and NEET Using the SAVE System................... 1559 Laura Vettraino, Learning Community, Italy Valentina Castello, CIOFS FP, Italy Marco Guspini, educommunity – Educational Community, Italy Eleonora Guglielman, Learning Community, Italy

Category: Customer Relationship Management Analysis of Two Phases Queue With Vacations and Breakdowns Under T-Policy........................... 1570 Khalid Alnowibet, King Saud University, Saudi Arabia Lotfi Tadj, Fairleigh Dickinson University, Canada Customer Lifetime Value................................................................................................................... 1584 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand Facilitating Customer Relationship Management in Modern Business............................................. 1594 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand Implementing a Customer Relationship Management (CRM) System.............................................. 1605 Dimitra Skoumpopoulou, Northumbria University, UK Benjamin Franklin, Northumbria University, UK

Volume III (Cu - Ed)

The Intelligence of E-CRM Applications and Approaches on Online Shopping Industry................ 1616 Bashar Shahir Ahmed, University Abdelmalek Essaadi (LEROSA Laborator), Morocco Fadi Amroush, Universidad de Salamanca, Spain Mohammed Ben Maati, University Abdelmalek Essaadi, Morocco Optimizing Cloud Computing Costs of Services for Consumers...................................................... 1627 Eli Weintraub, Afeka Tel Aviv College of Engineering, Israel Yuval Cohen, Afeka Tel Aviv College of Engineering, Israel Taxonomy for “Homo Consumens” in a 3.0 Era............................................................................... 1638 Carlos Ballesteros, Universidad Pontificia Comillas, Spain



Category: Cyber and Network Security Cyber Security Protection for Online Gaming Applications............................................................. 1647 Wenbing Zhao, Cleveland State University, USA Piracy and Intellectual Property Theft in the Internet Era................................................................. 1656 Shun-Yung Kevin Wang, University of South Florida – St. Petersburg, USA Jeremy J McDaniel, Principal Financial Group, USA Secure Group Key Sharing Protocols and Cloud System.................................................................. 1667 Vaishali Ravindra Thakare, VIT University, India John Singh K, VIT University, India Security of Internet-, Intranet-, and Computer-Based Examinations in Terms of Technical, Authentication, and Environmental, Where Are We?........................................................................ 1676 Babak Sokouti, Tabriz University of Medical Sciences, Iran Massoud Sokouti, Mashhad University of Medical Sciences, Iran A Three-Vector Approach to Blind Spots in Cybersecurity.............................................................. 1684 Mika Westerlund, Carleton University, Canada Dan Craigen, Carleton University, Canada Tony Bailetti, Carleton University, Canada Uruemu Agwae, Carleton University, Canada

Category: Cyber Crime, Cyber Bullying, and Digital Terrorism Cyber Bullying................................................................................................................................... 1695 Jo Ann Oravec, University of Wisconsin – Whitewater, USA Cyberbullying Among Malaysian Children Based on Research Evidence........................................ 1704 Sarina Yusuf, Universiti Putra Malaysia, Malaysia Md. Salleh Hj. Hassan, Universiti Putra Malaysia, Malaysia Adamkolo Mohammed Mohammed Ibrahim, Universiti Putra Malaysia, Malaysia & University of Maiduguri, Nigeria The Nature, Extent, Causes, and Consequences of Cyberbullying.................................................... 1723 Michelle F. Wright, Masaryk University, Czech Republic

Category: Data Analysis and Statistics Advanced Recommender Systems..................................................................................................... 1735 Young Park, Bradley University, USA Centrality Analysis of the United States Network Graph.................................................................. 1746 Natarajan Meghanathan, Jackson State University, USA



Context-Aware Approach for Restaurant Recommender Systems..................................................... 1757 Haoxian Feng, University of Ottawa, Canada Thomas Tran, University of Ottawa, Canada Data-Centric Benchmarking.............................................................................................................. 1772 Jérôme Darmont, Université de Lyon, Lyon 2, ERIC EA3083, France Data Linkage Discovery Applications............................................................................................... 1783 Richard S. Segall, Arkansas State University, USA Shen Lu, University of South Florida, USA Exploratory Data Analysis on Breast Cancer Prognosis.................................................................... 1794 Mohammad Mehdi Owrang O., American University, USA Yasmine M. Kanaan, Howard University, USA Robert L. Copeland Jr., Howard University,USA Melvin Gaskins, Howard University Hospital, USA Robert L. DeWitty Jr., Providence Hospital, USA In-Memory Analytics......................................................................................................................... 1806 Jorge Manjarrez Sánchez, Instituto Tecnologico Superior de Jerez, Mexico Innovative Formalism for Biological Data Analysis.......................................................................... 1814 Calin Ciufudean, Stefan cel Mare University, Romania Learning From Imbalanced Data....................................................................................................... 1825 Lincy Mathews, M. S. Ramaiah Institute of Technology, India Seetha Hari, Vellore Institute of Technology, India Neighborhood Rough-Sets-Based Spatial Data Analytics................................................................. 1835 Sharmila Banu K, VIT University, India B. K. Tripathy, VIT University, India The Ontology of Randomness........................................................................................................... 1845 Jeremy Horne, The International Institute of Informatics and Systemics, USA Order Statistics and Applications....................................................................................................... 1856 E. Jack Chen, BASF Corporation, USA Recommender Technologies and Emerging Applications................................................................. 1869 Young Park, Bradley University, USA Use of Data Analytics for Program Impact Evaluation and Enhancement of Faculty/Staff Development...................................................................................................................................... 1880 Samuel Olugbenga King, Auburn University, USA



Category: Data Mining and Databases Corporate Disclosure Measurement................................................................................................... 1896 Md. Salah Uddin Rajib, Jahangirnagar University, Bangladesh Md. Qutub Uddin Sajib, China University of Geosciences (CUG), China Data Mining and Knowledge Discovery in Databases...................................................................... 1907 Ana Azevedo, Polytechnic Institute of Porto, Portugal Data Mining and the KDD Process.................................................................................................... 1919 Ana Funes, Universidad Nacional de San Luis, Argentina Aristides Dasso, Universidad Nacional de San Luis, Argentina Data Mining to Identify Project Management Strategies in Learning Environments........................ 1934 Ana González-Marcos, Universidad de La Rioja, Spain Joaquín Ordieres-Meré, Universidad Politécnica de Madrid, Spain Fernando Alba-Elías, Universidad de La Rioja, Spain Database Techniques for New Hardware........................................................................................... 1947 Xiongpai Qin, Renmin University of China, China Yueguo Chen, Renmin University of China, China Ensemble Clustering Data Mining and Databases............................................................................. 1962 Slawomir T. Wierzchon, Polish Academy of Sciences, Poland Graph-Based Concept Discovery....................................................................................................... 1974 Alev Mutlu, Kocaeli University, Turkey Pinar Karagoz, Middle East Technical University, Turkey Yusuf Kavurucu, Turkish Naval Research Center Command, Turkey Healthcare Data Analysis in the Internet of Things Era.................................................................... 1984 George Tzanis, Aristotle University of Thessaloniki, Greece N-Tuple Algebra as a Unifying System to Process Data and Knowledge.......................................... 1995 Boris Alexandrovich Kulik, Institute of Problems in Mechanical Engineering RAS, Russia Alexander Yakovlevich Fridman, Institute for Informatics and Mathematical Modelling, Kola Science Centre of RAS, Russia A Proposed Framework for Incorporating Big-Data Technology in National Crisis Management Center................................................................................................................................................. 2006 Magdy M. Kabeil, Al-Yamamah University, Saudi Arabia Ahmad M. Kabil, University of Wisconsin – Whitewater, USA Quality Evaluation for Evolving Conceptual Database Design......................................................... 2020 Elvira Immacolata Locuratolo, CNR ISTI, Italy



Query Languages for Graph Databases............................................................................................. 2031 Kornelije Rabuzin, University of Zagreb, Croatia Schema Evolution in Conventional and Emerging Databases........................................................... 2043 Zouhaier Brahmia, University of Sfax, Tunisia Fabio Grandi, University of Bologna, Italy Barbara Oliboni, University of Verona, Italy Rafik Bouaziz, University of Sfax, Tunisia Schema Versioning in Conventional and Emerging Databases......................................................... 2054 Zouhaier Brahmia, University of Sfax, Tunisia Fabio Grandi, University of Bologna, Italy Barbara Oliboni, University of Verona, Italy Rafik Bouaziz, University of Sfax, Tunisia Twitter Data Mining for Situational Awareness................................................................................ 2064 Marco Vernier, University of Udine, Italy Manuela Farinosi, University of Udine, Italy Gian Luca Foresti, University of Udine, Italy

Category: Decision Support Systems Cognitive Process Elements of People Decision-Making.................................................................. 2076 Thais Spiegel, Rio de Janeiro State University, Brazil Comprehensible Explanation of Predictive Models........................................................................... 2085 Marko Robnik-Šikonja, University of Ljubljana, Slovenia The Concept of the Shapley Value and the Cost Allocation Between Cooperating Participants...... 2095 Alexander Kolker, GE Healthcare, USA Decision Filed Theory....................................................................................................................... 2108 Lan Shao, University of Oulu, Finland Jouni Markkula, University of Oulu, Finland Effectively Communicating With Group Decision Support Systems Using Information Theory..... 2121 Jamie S. Switzer, Colorado State University, USA Ralph V. Switzer, Colorado State University, USA Evolutionary Algorithms for Global Decision Tree Induction.......................................................... 2132 Marek Kretowski, Bialystok University of Technology, Poland Marcin Czajkowski, Bialystok University of Technology, Poland A Family Review of Parameter-Learning Models and Algorithms for Making Actionable Decisions............................................................................................................................................ 2142 Chun-Kit Ngan, The Pennsylvania State University, USA



Informed Decision Making With Enterprise Dynamic Systems Control.......................................... 2154 Sérgio Luís Guerreiro, Instituto Superior Técnico, University of Lisbon, Portugal & INESCID, Portugal Managerial Tools and Techniques for Decision Making................................................................... 2166 Davood Askarany, University of Auckland, New Zealand A Nature-Inspired Metaheuristic Approach for Generating Alternatives.......................................... 2178 Julian Scott Yeomans, York University, Canada Preferences, Utility, and Stochastic Approximation.......................................................................... 2188 Yuri P. Pavlov, Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Bulgaria Rumen D. Andreev, Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Bulgaria Rough-Set-Based Decision Model for Incomplete Information Systems.......................................... 2200 Safiye Turgay, Sakarya University, Turkey Orhan Torkul, Sakarya University, Turkey Tahsin Turgay, Sakarya University, Turkey Using Receiver Operating Characteristic (ROC) Analysis to Evaluate Information -Based Decision-Making............................................................................................................................... 2213 Nan Hu, University of Utah, USA

Category: Digital Literacy Digital Literacy.................................................................................................................................. 2225 Anirban Ray, UNC Wilmington, USA Digital Literacy for the 21st Century................................................................................................. 2235 Hiller A. Spires, North Carolina State University, USA Casey Medlock Paul, North Carolina State University, USA Shea N. Kerkhoff, North Carolina State University, USA Digital Literacy in Theory and Practice............................................................................................. 2243 Heidi Julien, State University of New York at Buffalo, USA Encouraging Digital Literacy and ICT Competency in the Information Age.................................... 2253 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand Information Needs of Users in the Tech Savvy Environment and the Influencing Factors............... 2264 Mudasir Khazer Rather, University of Kashmir, India Shabir Ahmad Ganaie, University of Kashmir, India



A Maturity Model for Digital Literacies and Sustainable Development........................................... 2280 Ravi S. Sharma, Nanyang Technological University, Singapore Lin G. Malone, Nanyang Technological University, Singapore Chong Guan, SIM University, Singapore Ambica Dattakumar, Nanyang Technological University, Singapore Teaching Media and Information Literacy in the 21st Century......................................................... 2292 Sarah Gretter, Michigan State University, USA Aman Yadav, Michigan State University, USA Nigerian Undergraduate Students’ Computer Competencies and Use of Information Technology Tools and Resources for Study Skills and Habits’ Enhancement...................................................... 2303 Adekunle Olusola Otunla, University of Ibadan, Nigeria Caleb Okoro Amuda, University of Ibadan, Nigeria The Roles of Digital Literacy in Social Life of Youth....................................................................... 2314 Dragana Martinovic, University of Windsor, Canada Viktor Freiman, Université de Moncton, Canada Chrispina S. Lekule, St. Augustine University of Tanzania, Tanzania Yuqi Yang, University of Windsor, Canada Toward a Working Definition of Digital Literacy.............................................................................. 2326 Margaret-Mary Sulentic Dowell, Louisiana State University, USA

Category: Economics ICT Investments and Recovery of Troubled Economies................................................................... 2337 Ioannis Papadopoulos, Metropolitan College Thessaloniki, Greece Apostolos Syropoulos, Greek Molecular Computing Group, Greece Uberization (or Uberification) of the Economy................................................................................. 2345 Nabyla Daidj, Telecom Ecole de Management, France

Category: Educational Technologies Adaptive Hypermedia in Education................................................................................................... 2357 Vehbi Turel, The University of Bingol, Turkey Automatic Item Generation................................................................................................................ 2369 Mark Gierl, University of Alberta, Canada Hollis Lai, University of Alberta, Canada Xinxin Zhang, University of Alberta, Canada Challenges in Developing Adaptive Educational Hypermedia Systems........................................... 2380 Eileen O’Donnell, Trinity College Dublin, Ireland Liam O’Donnell, Dublin Institute of Technology, Ireland



Computational Thinking in Innovative Computational Environments and Coding.......................... 2392 Alberto Ferrari, University of Parma, Italy Agostino Poggi, University of Parma, Italy Michele Tomaiuolo, University of Parma., Italy Computer Agent Technologies in Collaborative Learning and Assessment...................................... 2402 Yigal Rosen, Harvard University, USA Cost-Effective 3D Stereo Visualization for Creative Learning.......................................................... 2411 R. S. Kamath, Chatrapati Shahu Institute of Business Education and Research, India R. K. Kamat, Shivaji University, India

Volume IV (Ed - F)

Could Educational Technology Replace Traditional Schools in the Future?.................................... 2421 John K. Hope, University of Auckland, New Zealand Development of Communication Skills through Auditory Training Software in Special  Education........................................................................................................................................... 2431 Eduardo C. Contreras, Autonomous University of Coahuila, Mexico Isis I. Contreras, Saltillo Institute of Technology, Mexico Digital Storytelling in Language Classes........................................................................................... 2442 Mehrak Rahimi, Shahid Rajaee Teacher Training University, Iran Distance Teaching and Learning Platforms....................................................................................... 2455 Linda D. Grooms, Regent University, USA Do Usability Design Features of a Mobile Game Influence Learning?............................................. 2466 Rex Perez Bringula, University of the East, Philippines Edison Cabrera, University of the East, Philippines Princess B. Calmerin, University of the East, Philippines Eduardo A. Lao, University of the East, Philippines Christian Gerard Sembrano, University of the East, Philippines Angelita D. Guia, University of the East, Philippines Joan P. Lazaro, University of the East, Philippines Alexis John M. Rubio, University of the East, Philippines Annaliza E. Catacutan, National University, Philippines Marilou N. Jamis, National University, Philippines Lalaine P. Abad, Department of Education, Philippines Educational Technology and Intellectual Property............................................................................ 2477 Lesley S. J. Farmer, California State University – Long Beach, USA



Employing Educational Robotics for the Development of Problem-Based Learning Skills............. 2492 Nikleia Eteokleous, Frederick University Cyprus, Cyprus From Digital Exclusion to Digital Inclusion for Adult Online Learners........................................... 2503 Virginia E. Garland, University of New Hampshire, USA From Digital Natives to Student Experiences With Technology....................................................... 2512 Sue Bennett, University of Wollongong, Australia Linda Corrin, University of Melbourne, Australia ICT Eases Inclusion in Education...................................................................................................... 2521 Dražena Gašpar, University of Mostar, Bosnia and Herzegovina The Infusion of Technology Within the Classroom Facilitates Students’ Autonomy in Their Learning............................................................................................................................................. 2532 Fariel Mohan, University of Trinidad and Tobago, Trinidad and Tobago Garry Soomarah, University of Trinidad and Tobago, Trinidad and Tobago Integrated Paper-Based and Digital Learning Material for Smart Learners...................................... 2545 Sabrina Leone, Università Politecnica delle Marche, Italy Leveraging Technology-Enhanced Teaching and Learning for Future IS Security Professionals.... 2558 Ciara Heavin, University College Cork, Ireland Karen Neville, University College Cork, Ireland Sheila O’Riordan, University College Cork, Ireland Liberating Educational Technology Through the Socratic Method................................................... 2571 Frank G. Giuseffi, Lindenwood University, USA Online Academia............................................................................................................................... 2580 Magdalena Bielenia-Grajewska, University of Gdansk, Poland Online Learning Propelled by Constructivism.................................................................................. 2588 Kathaleen Reid-Martinez, Oral Roberts University, USA Linda D. Grooms, Regent University, USA Science Animation and Students’ Attitudes....................................................................................... 2599 Sivasankar Arumugam, Sri Venkateswara College of Education, India Nancy Nirmala, Christ the King Matric Higher Secondary School, India Three Cases of Unconventional Educational Uses of Digital Storytelling........................................ 2616 Emmanuel Fokides, University of the Aegean, Greece 3D Printing Applications in STEM Education.................................................................................. 2626 Norman Gwangwava, Botswana International University of Science and Technology, Botswana Catherine Hlahla, National University of Science and Technology, Zimbabwe



Tools, Pedagogical Models, and Best Practices for Digital Storytelling........................................... 2641 Jari Multisilta, Tampere University of Technology, Finland Hannele Niemi, University of Helsinki, Finland The Use of Postcasting/Vodcasting in Education.............................................................................. 2651 Athanasios T. Stavrianos, 2nd Technical Vocational School of Xanthi, Greece Apostolos Syropoulos, Greek Molecular Computing Group, Greece The Vital Importance of Faculty Presence in an Online Learning Environment.............................. 2661 Ni Chang, Indiana University – South Bend, USA

Category: Electrical Engineering Mechanisms of Electrical Conductivity in Carbon Nanotubes and Graphene.................................. 2673 Rafael Vargas-Bernal, Instituto Tecnológico Superior de Irapuato, Mexico

Category: Electronic Business E-Business and Big Data Strategy in Franchising............................................................................. 2686 Ye-Sho Chen, Louisiana State University, USA Facilitating Interaction Between Virtual Agents Through Negotiation Over Ontological Representation.................................................................................................................................... 2697 Fiona McNeill, Heriot-Watt University, UK On-Line Credit and Debit Card Processing and Fraud Prevention for E-Business........................... 2707 James Williams, University of Pittsburgh, USA

Category: Electronic Commerce Adoption and Use of Mobile Money Services in Nigeria.................................................................. 2724 Olayinka David-West, Pan-Atlantic University, Nigeria Immanuel Ovemeso Umukoro, Pan-Atlantic University, Nigeria Omotayo Muritala, Pan-Atlantic University, Nigeria E-Commerce Models, Players, and Its Future................................................................................... 2739 Liguo Yu, Indiana University – South Bend, USA Electronic Payment Frameworks....................................................................................................... 2749 Antonio Ruiz-Martínez, University of Murcia, Spain Oussama Tounekti, University of Murcia, Spain Antonio F. Skarmeta, University of Murcia, Spain Factors Determining E-Shopping Compliance by Nigerians............................................................. 2761 Adamkolo Mohammed Mohammed Ibrahim, University of Maiduguri, Nigeria Md. Salleh Hj. Hassan, Universiti Putra Malaysia, Malaysia Sarina Yusuf, Universiti Putra Malaysia, Malaysia



Enterprise Interoperability................................................................................................................. 2773 Ejub Kajan, State University of Novi Pazar, Serbia Has Bitcoin Achieved the Characteristics of Money?....................................................................... 2784 Donovan Peter Chan Wai Loon, University of Malaya, Malaysia Sameer Kumar, University of Malaya, Malaysia The Importance of Electronic Commerce in Modern Business......................................................... 2791 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand Improving Competencies for the Courier Service Industry in Malaysia........................................... 2802 Hoo Yee Hui, Universiti Tunku Abdul Rahman, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia New Advances in E-Commerce......................................................................................................... 2810 Khaled Ahmed Nagaty, The British University in Egypt, Egypt Online Mediation in E-Commerce Matters........................................................................................ 2825 Ángela Coello Pulido, University of Vigo, Spain Reputational Mechanisms in Consumer-to-Consumer Online Commerce........................................ 2833 Mikhail I. Melnik, Kennesaw State University, USA Retail Prices and E-Commerce.......................................................................................................... 2841 Jihui Chen, Illinois State University, USA Social Commerce Using Social Network and E-Commerce.............................................................. 2851 Roberto Marmo, University of Pavia, Italy An Update on Bitcoin as a Digital Currency..................................................................................... 2861 Cecilia G. Manrique, University of Wisconsin – La Crosse, USA Gabriel G. Manrique, Winona State University, USA Use of Bitcoin for Internet Trade....................................................................................................... 2869 Sadia Khalil, NUST School of Electrical Engineering and Computer Science, Pakistan Rahat Masood, NUST School of Electrical Engineering and Computer Science, Pakistan Muhammad Awais Shibli, VisionIT, USA

Category: Electronic Services Determining Impact of Demographics on Perceived Service Quality in Online Retail.................... 2882 Prateek Kalia, I. K. Gujral Punjab Technical University, India Penny Law, Regenesys Business School, South Africa Richa Arora, Regenesys Institute of Management, India



The Impact of Carbon Nanotubes and Graphene on Electronics Industry........................................ 2897 Rafael Vargas-Bernal, Instituto Tecnológico Superior de Irapuato, Mexico Gabriel Herrera-Pérez, Instituto Tecnológico Superior de Irapuato, Mexico Margarita Tecpoyotl-Torres, Universidad Autónoma del Estado de Morelos, Mexico Integrating Content Authentication Support in Media Services........................................................ 2908 Anastasia N. Katsaounidou, Aristotle University of Thessaloniki, Greece Charalampos A. Dimoulas, Aristotle University of Thessaloniki, Greece IT Service Management Architectures.............................................................................................. 2920 Torben Tambo, Aarhus University, Denmark Jacob Filtenborg, Aarhus University, Denmark

Category: Enterprise Resource Planning Business Intelligence Impacts on Design of Enterprise Systems...................................................... 2932 Saeed Rouhani, University of Tehran, Iran Dusanka Milorad Lecic, Levi9 Global Sourcing Balkan, Serbia Deployment of Enterprise Architecture From the Activity Theory Perspective............................... 2943 Tiko Iyamu, Cape Peninsula University of Technology, South Africa Irja Naambo Shaanika, Namibia University of Science and Technology – Windhoek, Namibia ERP Systems Benefit Realization and the Role of ERP-Enabled Application Integration................ 2953 Joseph K. Nwankpa, Miami University, USA From On-Premise ERP to Cloud ERP............................................................................................... 2965 Karim Mezghani, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia & University of Sfax, Tunisia Socio-Technical Change Perspective for ERP Implementation in Large Scale Organizations.......... 2975 Jessy Nair, PES University, India D. Bhanusree Reddy, VIT University, India Anand A. Samuel, VIT University, India

Category: Entrepreneurship Current Scenario of Youth Entrepreneurship in India....................................................................... 2989 Neeta Baporikar, Namibia University of Science and Technology, Namibia & University of Pune, India Entrepreneurship................................................................................................................................ 2998 Mehmet Eymen Eryılmaz, Uludağ University, Turkey



Entrepreneurship as the Vantage Point.............................................................................................. 3009 Madhu Kishore Raghunath Raghunath Kamakula, GVP College of Engineering (Autonomous), India Chandra Sekhar Patro, GVP College of Engineering (Autonomous), India Entrepreneurship Concept, Theories, and New Approaches............................................................. 3020 José Manuel Saiz-Alvarez, Tecnologico de Monterrey, Mexico Martín García-Vaquero, Nebrija University, Spain Open Data and High-Tech Startups Towards Nascent Entrepreneurship Strategies.......................... 3032 Fotis Kitsios, University of Macedonia, Greece Maria Kamariotou, University of Macedonia, Greece

Category: Environmental Science and Agriculture Carbon Capture From Natural Gas via Polymeric Membranes......................................................... 3043 Nayef Mohamed Ghasem, UAE University, UAE Nihmiya Abdul Rahim, UAE University, UAE Mohamed Al-Marzouqi, UAE University, UAE Enhancing the Resiliency of Smart Grid Monitoring and Control.................................................... 3056 Wenbing Zhao, Cleveland State University, USA E-Waste, Chemical Toxicity, and Legislation in India....................................................................... 3066 Prashant Mehta, National Law University Jodhpur, India Green IT and the Struggle for a Widespread Adoption..................................................................... 3077 Edward T. Chen, University of Massachusetts – Lowell, USA Identification of Green Procurement Drivers and Their Interrelationship Using Fuzzy TISM and MICMAC Analysis............................................................................................................................ 3086 Surajit Bag, Tega Industries South Africa Pty Ltd., South Africa Load Flow Analysis in Smart Grids................................................................................................... 3103 Osman Hasan, National University of Sciences and Technology, Pakistan Awais Mahmood, National University of Sciences and Technology, Pakistan Syed Rafay Hasan, Tennessee Technological University, USA Methodology of Climate Change Impact Assessment on Forests..................................................... 3114 Mostafa Jafari, Regional Institute of Forest and Rangelands (RIFR), Iran Model for Assessment of Environmental Responsibility in Health Care Organizations................... 3131 María Carmen Carnero, University of Castilla-La Mancha, Spain & University of Lisbon, Portugal



Potential Benefits and Current Limits in the Development of Demand Response............................ 3144 Clementina Bruno, University of Piemonte Orientale, Italy Waste Gas End-of-Pipe Treatment Techniques in Italian IPPC Chemical Plants.............................. 3156 Gaetano Battistella, ISPRA, Italy Giuseppe Di Marco, ISPRA, Italy Carlo Carlucci, ISPRA, Italy Raffaella Manuzzi, ISPRA, Italy Federica Bonaiuti, ISPRA, Italy Celine Ndong, ISPRA, Italy

Category: Ethics and Social Responsibility The Foundation of (Business) Ethics’ Evolution............................................................................... 3173 Ben Tran, Alliant International University, USA Integrating Sustainability and CSR in the Value Chain of the Information Technology Sector....... 3183 Patricia Martínez García de Leaniz, University of Cantabria, Spain María Elena García Ruiz, University of Cantabria, Spain The Morality of Reporting Safety Concerns in Aviation................................................................... 3194 Kawtar Tani, UCOL, New Zealand Science, Ethics, and Weapons Research............................................................................................ 3205 John Forge, Independent Researcher, Australia

Category: Fuzzy Logic and Soft Computing Application of Fuzzy Numbers to Assessment Processes................................................................. 3215 Michael Voskoglou, Graduate Technological Educational Institute (T.E.I.), Greece Application of Soft Set in Game Theory........................................................................................... 3226 B. K. Tripathy, VIT University, India Sooraj T. R., VIT University, India Radhakrishna N. Mohanty, VIT University, India

Volume V (G - Ho)

Category: Gaming Application of Gamification to Blended Learning in Higher Education........................................... 3238 Kamini Jaipal-Jamani, Brock University, Canada Candace Figg, Brock University, Canada



Chemistry Learning Through Designing Digital Games................................................................... 3248 Kamisah Osman, The National University of Malaysia, Malaysia Ah-Nam Lay, Institute of Teacher Education – Sultan Abdul Halim, Malaysia Clinical Use of Video Games............................................................................................................ 3260 Ben Tran, Alliant International University, USA Leveraging the Arduino Platform to Develop Information Technology Devices.............................. 3273 Diego Reforgiato Recupero, University of Cagliari, Italy Valentino Artizzu, University of Cagliari, Italy Francesca Cella, University of Cagliari, Italy Alessandro Cotza, University of Cagliari, Italy Davide Curcio, University of Cagliari, Italy Giorgio Amedeo Iengo, University of Cagliari, Italy Riccardo Macis, University of Cagliari, Italy Andrea Marras, University of Cagliari, Italy Simone Picci, University of Cagliari, Italy Michael Planu, University of Cagliari, Italy Riccardo Scasseddu, University of Cagliari, Italy Educational Serious Games Design................................................................................................... 3287 Ilias Karasavvidis, University of Thessaly, Greece Exposure to Video Games and Decision Making.............................................................................. 3296 Giuseppe Curcio, University of L’Aquila, Italy Sara Peracchia, University of L’Aquila, Italy Learning With Games and Digital Stories in Visual Programming.................................................. 3309 Wilfred W. F. Lau, The Chinese University of Hong Kong, China The Process Model of Gameplay to Understand Digital Gaming Outcomes.................................... 3317 Linda K. Kaye, Edge Hill University, UK Serious Games Advancing the Technology of Engaging Information.............................................. 3327 Peter A. Smith, University of Central Florida, USA Clint Bowers, University of Central Florida, USA Towards Modelling Effective Educational Games Using Multi-Domain Framework....................... 3337 Mifrah Ahmad, Universiti Teknologi PETRONAS, Malaysia Lukman Ab Rahim, Universiti Teknologi PETRONAS, Malaysia Kamisah Osman, Universiti Kebangsaan Malaysia, Malaysia Noreen Izza Arshad, Universiti Teknologi PETRONAS, Malaysia



Category: Gender and Diversity Computing Technologies and Science Fiction Cinema..................................................................... 3349 Rocío Carrasco-Carrasco, University of Huelva, Spain Gender Differences in Advertising Engagement Using the Case of Facebooks Ads........................ 3359 Eva Lahuerta-Otero, University of Salamanca, Spain Rebeca Cordero-Gutiérrez, University of Salamanca, Spain The Gender Dimension in Urban Air Quality.................................................................................... 3371 Theodora Slini, Aristotle University of Thessaloniki, Greece Fotini-Niovi Pavlidou, Aristotle University of Thessaloniki, Greece How Exclusive Work Climates Create Barriers for Women in IS&T................................................ 3382 Katelyn R. Reynoldson, Old Dominion University, USA Debra A. Major, Old Dominion University, USA Women and IT in Lilongwe............................................................................................................... 3393 Alice Violet Nyamundundu, Skyway University, Malawi

Category: Geographic Information Systems Application of Geospatial Mashups in Web GIS for Tourism Development.................................... 3403 Somnath Chaudhuri, Maldives National University, Maldives Nilanjan Ray, Adamas University, India Archaeological GIS for Land Use in South Etruria Urban Revolution in IX-VIII Centuries B.C..... 3419 Giuliano Pelfer, University of Florence, Italy Exploring Tourism Cluster in the Peripheral Mountain Area Based on GIS Mapping..................... 3434 Ya-Hui Hsueh, National Taichung University of Education, Taiwan Huey-Wen Chuang, National Taichung University of Education, Taiwan Wan-Chiang Hsieh, National Taichung Girls’ Senior High School, Taiwan Geographic Information System (GIS) Modeling Analysis and the Effects of Spatial Distribution and Environmental Factors on Breast Cancer Incidence................................................................... 3448 Akram Gasmelseed, University of Science and Technology, Sudan Ali H. Alharbi, Qassim University, Saudi Arabia Geographic Information Systems...................................................................................................... 3460 Paula Remoaldo, University of Minho, Portugal Vitor P. Ribeiro, University of Minho, Portugal Hélder Silva Lopes, University of Minho, Portugal Sara Catarina Gomes Silva, University of Minho, Portugal



Geospatial Influence in Science Mapping.......................................................................................... 3473 Carlos Granell-Canut, Universitat Jaume I of Castellón, Spain Estefanía Aguilar-Moreno, Universitat Jaume I of Castellón, Spain Parallel Development of Three Major Space Technology Systems and Human Side of Information Reference Services as an Essential Complementary Method............................................................ 3484 Joyce Gosata Maphanyane, University of Botswana, Botswana Use of GIS and Remote Sensing for Landslide Susceptibility Mapping........................................... 3503 Arzu Erener, Kocaeli University, Turkey Gulcan Sarp, Suleyman Demirel University, Turkey Sebnem Duzgun, Middle East Technical University, Turkey

Category: Government and Law Accessibility in E-Government.......................................................................................................... 3516 Christian Sonnenberg, Florida Institute of Technology, USA The Adoption and Transformation of Capability Maturity Models in Government.......................... 3526 Terry F. Buss, Carnegie Mellon University, Australia Bridging Between Cyber Politics and Collective Dynamics of Social Movement............................ 3538 Kazuhiko Shibuya, ROIS, Japan Community Broadband Networks and the Opportunity for E-Government Services....................... 3549 Idongesit Williams, Aalborg University, Denmark Critical Success Factors in E-Democracy Implementation............................................................... 3561 Aderonke A. Oni, Covenant University, Nigeria Adekunle O. Okunoye, Xavier University, USA E-Activism Development and Growth............................................................................................... 3569 John G. McNutt, University of Delaware, USA Lauri Goldkind, Fordham University, USA E-Government Service Adoption and the Impact of Privacy and Trust............................................ 3579 Mehree Iqbal, North South University, Bangladesh Nabila Nisha, North South University, Bangladesh Afrin Rifat, North South University, Bangladesh Mastering Electronic Government in the Digital Age....................................................................... 3591 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand



A Model for Connected E-Government in the Digital Age............................................................... 3602 Qiuyan Fan, Western Sydney University, Australia Presidential Elections Web 2.0.......................................................................................................... 3612 Ramona Sue McNeal, University of Northern Iowa, USA Lisa Dotterweich Bryan, Upper Iowa University, USA Project Management in Government................................................................................................. 3621 Shauneen Furlong, University of Ottawa, Canada & John Moores Liverpool University, UK Technology and Terror....................................................................................................................... 3637 Maximiliano Emanuel Korstanje, University of Palermo, Argentina Geoffrey Skoll, SUNY at Buffalo, USA Users Behavioral Intention Towards eGovernment in an African Developing Country................... 3654 Ayankunle A. Taiwo, Texas A&M University – Commerce, USA Young People, Civic Participation, and the Internet.......................................................................... 3667 Fadi Hirzalla, Erasmus University Rotterdam, The Netherlands Shakuntala Banaji, LSE, UK

Category: Healthcare Administration Electronic Health Record (EHR) Diffusion and an Examination of Physician Resistance............... 3678 Kristen MacIver, Northern Michigan University, USA Madison N. Ngafeeson, Northern Michigan University, USA Internet of Things Applications for Healthcare................................................................................. 3689 Ljubica Diković, Business Technical College, Serbia Maintenance Policies Optimization of Medical Equipment in a Health Care Organization............. 3698 Juan Ignacio Roig, University of Castilla-La Mancha, Spain Andrés Gómez, University of Castilla-La Mancha, Spain Isabel Romero, University of Castilla-La Mancha, Spain María Carmen Carnero, University of Castilla-La Mancha, Spain & University of Lisbon, Portugal The Optimal Workforce Staffing Solutions With Random Patient Demand in Healthcare  Settings............................................................................................................................................... 3711 Alexander Kolker, GE Healthcare, USA Using Technology to Reduce a Healthcare Disparity........................................................................ 3725 Nilmini Wickramasinghe, Epworth HealthCare, Australia & Deakin University, Australia



Category: Health Information Systems Challenges and Implications of Health Literacy in Global Health Care........................................... 3734 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand Cyber Behaviors in Seeking Health Information............................................................................... 3745 Xiaojun (Jenny) Yuan, University of Albany, USA José A. Pino, Universidad de Chile, Chile Information Systems and Technology Projects in Healthcare Organisations.................................... 3756 Jorge Gomes, ISEG, Universidade de Lisboa, Portugal Mário José Batista Romão, ISEG, Universidade de Lisboa, Portugal Peer-to-Peer Health-Related Online Support Groups........................................................................ 3767 Neil S. Coulson, University of Nottingham, UK Software Evaluation From the Perspective of Patients and Healthcare Professionals....................... 3782 Rui Pedro Charters Lopes Rijo, Polytechnic Institute of Leiria, Portugal Domingos Alves, University of São Paulo, Brazil Technology Design and Routes for Tool Appropriation in Medical Practices.................................. 3794 Manuel Santos-Trigo, Cinvestav-IPN, Mexico Ernesto Suaste, Cinvestav-IPN, Mexico Paola Figuerola, Cinvestav-IPN, Mexico Trends in Health Care Information Technology and Informatics...................................................... 3805 T. Ray Ruffin, University of Phoenix, USA & Colorado Technical University, USA & Grand Canyon University, USA & Ashford University, USA, & North Carolina Wesleyan College, USA Donna Patterson Hawkins, University of Phoenix, USA User Resistance to Health Information Technology.......................................................................... 3816 Madison N. Ngafeeson, Northern Michigan University, USA

Category: Higher Education The Effect of Innovative Communication Technologies in Higher Education.................................. 3827 Stavros Kiriakidis, University of Crete, Greece Efstathios Kefallonitis, State University of New York at Oswego, USA Androniki Kavoura, Technological Educational Institute of Athens, Greece Experiences of Implementing a Large-Scale Blended, Flipped Learning Project............................. 3839 Hazel Owen, Ethos Consultancy NZ, New Zealand Nicola Dunham, Massey University, New Zealand



A Flipped Learning Approach to University EFL Courses............................................................... 3850 Yasushige Ishikawa, Kyoto University of Foreign Studies, Japan Reiko Akahane-Yamada, Advanced Telecommunications Research Institute International (ATR), Japan Craig Smith, Kyoto University of Foreign Studies, Japan Masayuki Murakami, Kyoto University of Foreign Studies, Japan Mutsumi Kondo, Kyoto University of Foreign Studies, Japan Misato Kitamura, Advanced Telecommunications Research Institute International (ATR), Japan Yasushi Tsubota, Kyoto Institute of Technology, Japan Masatake Dantsuji, Kyoto University, Japan A Framework for Profiling Prospective Students in Higher Education............................................. 3861 Santhosh Kumar Lakkaraju, Dakota State University, USA Deb Tech, Dakota State University, USA Shuyuan Deng, Dakota State University, USA Importance of Information Literacy.................................................................................................. 3870 Lidia Sanchez-Ruiz, University of Cantabria, Spain Beatriz Blanco, University of Cantabria, Spain An Integrated Electronic IQA System for HEI.................................................................................. 3881 Teay Shawyun, King Saud University, Saudi Arabia International Students in Online Courses.......................................................................................... 3900 María Ángeles Rodriguez Manzanares, Memorial University of Newfoundland, Canada IT Solutions Supporting the Management of Higher Education Institutions in Poland.................... 3910 Elżbieta Janczyk-Strzała, Wroclaw School of Banking, Poland Knowledge Networks in Higher Education........................................................................................ 3922 Filipa M. Ribeiro, University of Porto, Portugal Quality Online Learning in Higher Education................................................................................... 3930 Deborah G. Wooldridge, Bowling Green State University, USA Sandra Poirier, Middle Tennessee State University, USA Julia M. Matuga, Bowling Green State University, USA A Study on Extensive Reading in Higher Education......................................................................... 3945 Diana Presadă, Petroleum-Gas University of Ploiesti, Romania Mihaela Badea, Petroleum-Gas University of Ploiesti, Romania Technology Policies and Practices in Higher Education................................................................... 3954 Kelly McKenna, Colorado State University, USA



The University-Industry Collaboration.............................................................................................. 3963 Marcello Fernandes Chedid, University of Aveiro, Portugal Leonor Teixeira, University of Aveiro, Portugal Using Communities of Inquiry Online to Perform Tasks of Higher Order Learning........................ 3976 Ramon Tirado-Morueta, University of Huelva, Spain Pablo Maraver-López, University of Huelva, Spain Ángel Hernando-Gómez, University of Huelva, Spain

Category: High Performance Computing Cost Evaluation of Synchronization Algorithms for Multicore Architectures.................................. 3989 Masoud Hemmatpour, Politecnico di Torino, Italy Renato Ferrero, Politecnico di Torino, Italy Filippo Gandino, Politecnico di Torino, Italy Bartolomeo Montrucchio, Politecnico di Torino, Italy Maurizio Rebaudengo, Politecnico di Torino, Italy The Future of High-Performance Computing (HPC)........................................................................ 4004 Herbert Cornelius, Intel Corporation EMEA, Germany High-Performance Reconfigurable Computing................................................................................. 4018 Mário Pereira Vestias, Instituto Politécnico de Lisboa, Portugal

Category: Hospitality, Travel, and Tourism Management Augmented Reality for Tourist Destination Image Formation.......................................................... 4031 Azizul Hassan, Cardiff Metropolitan University, UK

Volume VI (Ho - It)

Destination @-Branding of Ten European Capitals Through the Institutional Stems and Commercial Logos............................................................................................................................. 4038 Elena Bocci, Sapienza University of Rome, Italy Annamaria Silvana de Rosa, Sapienza University of Rome, Italy Laura Dryjanska, Sapienza University of Rome, Italy The Effect of Social Media Networking in the Travel Industry......................................................... 4052 Androniki Kavoura, Technological Educational Institute of Athens, Greece Efstathios Kefallonitis, State University of New York at Oswego, USA Evaluative Dimensions of Urban Tourism in Capital Cities by First-Time Visitors......................... 4064 Annamaria Silvana de Rosa, Sapienza University of Rome, Italy Laura Dryjanska, Sapienza University of Rome, Italy Elena Bocci, Sapienza University of Rome, Italy



Fifty Shades of Dark Stories.............................................................................................................. 4077 Lea Kuznik, University of Maribor, Slovenia Social Media as a Channel of Constructive Dialogue for Tourism Businesses................................. 4088 Marios D. Sotiriadis, University of South Africa (UNISA), South Africa Usability Evaluation of Tourism Icons in India................................................................................. 4099 Rajshree Tushar Akolkar, Zeal College of Engineering and Research, India Ganesh D. Bhutkar, Vishwakarma Institute of Technology, India Virtual Tourism and Its Potential for Tourism Development in Sub-Saharan Africa....................... 4113 Paul Ankomah, North Carolina A&T State University, USA Trent Larson, North Carolina A&T University, USA

Category: Human-Computer Interaction Affect-Sensitive Computer Systems.................................................................................................. 4124 Nik Thompson, Curtin University, Australia Tanya McGill, Murdoch University, Australia David Murray, Murdoch University, Australia Computer-Assisted Indian Matrimonial Services.............................................................................. 4136 Robert Leslie Fisher, Independent Researcher, USA Creative Collaborative Virtual Environments.................................................................................... 4146 Luís Eustáquio, Universidade do Porto, Portugal Catarina Carneiro de Sousa, Polytechnic Institute of Viseu, Portugal Cyberbullying.................................................................................................................................... 4157 Gilberto Marzano, Rezekne Academy of Technologies, Latvia Defining and Conceptualizing Cyberbullying................................................................................... 4168 Karin Spenser, Nottingham Trent University, UK Lucy R. Betts, Nottingham Trent University, UK Developing Creativity and Learning Design by Information and Communication Technology (ICT) in Developing Contexts............................................................................................................ 4178 Chunfang Zhou, Aalborg University, Denmark Aparna Purushothaman, Aalborg University, Denmark Existential Aspects of the Development E-Culture........................................................................... 4189 Liudmila Vladimirovna Baeva, Astrakhan State University, Russia The Fundamentals of Human-Computer Interaction......................................................................... 4199 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand



Interface Trends in Human Interaction, the Internet of Things, and Big Data.................................. 4210 William J. Gibbs, Duquesne University, USA Internet Addiction in Context............................................................................................................ 4223 Petra Vondrackova, Charles University in Prague, Czech Republic David Šmahel, Masaryk University – Brno, Czech Republic Mediated Embodiment in New Communication Technologies......................................................... 4234 Laura Aymerich-Franch, CNRS-AIST JRL (Joint Robotics Laboratory), AIST, Japan The Nature of Cyber Bullying Behaviours........................................................................................ 4245 Lucy R. Betts, Nottingham Trent University, UK Screen Culture.................................................................................................................................... 4255 Ana Melro, University of Aveiro, Portugal Lídia Oliveira, University of Aveiro, Portugal Technology Assessment of Information and Communication Technologies.................................... 4267 Armin Grunwald, Karlsruhe Institute of Technology, Germany Carsten Orwat, Karlsruhe Institute of Technology, Germany Towards an Interdisciplinary Socio-Technical Definition of Virtual Communities.......................... 4278 Umar Ruhi, University of Ottawa, Canada The Trajectivity of Virtual Worlds..................................................................................................... 4296 Christophe Duret, Université de Sherbrooke, Canada Virtual Hoarding................................................................................................................................ 4306 Jo Ann Oravec, University of Wisconsin – Whitewater, USA

Category: Human Resources Management Cyberloafing and Constructive Recreation........................................................................................ 4316 Jo Ann Oravec, University of Wisconsin – Whitewater, USA Influencing People and Technology Using Human Resource Development (HRD) Philosophy...... 4326 Claretha Hughes, University of Arkansas, USA Matthew W. Gosney, University of Colorado – Health, USA Cynthia M. Sims, Clemson University, USA Performance Appraisal....................................................................................................................... 4337 Chandra Sekhar Patro, Gayatri Vidya Parishad College of Engineering (Autonomous), India Promoting Strategic Human Resource Management, Organizational Learning, and Knowledge Management in Modern Organizations............................................................................................. 4347 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand



Technology, Learning Styles, Values, and Work Ethics of Millennials............................................. 4358 Harish C. Chandan, Argosy University, USA

Category: Industrial Engineering and Informatics Cuckoo Search Algorithm for Solving Real Industrial Multi-Objective Scheduling Problems........ 4369 Mariappan Kadarkarainadar Marichelvam, Mepco Schlenk Enginering College, India Mariappan Geetha, Kamaraj College of Engineering and Technology, India The Trends and Challenges of 3D Printing........................................................................................ 4382 Edna Ho Chu Fang, University of Malaya, Malaysia Sameer Kumar, University of Malaya, Malaysia

Category: Information Resources Management Advanced Model of Complex Information System........................................................................... 4391 Miroslav Svitek, Czech Technical University in Prague, Czech Republic Computer Information Library Clusters............................................................................................ 4399 Fu Yuhua, CNOOC Research Institute, China The Impact of the Impact of Meta-Data Mining From the SoReCom “A.S. de Rosa” @-Library.... 4404 Annamaria Silvana de Rosa, Sapienza University of Rome, Italy Laura Dryjanska, Sapienza University of Rome, Italy Elena Bocci, Sapienza University of Rome, Italy Information and Its Conceptual Perspectives..................................................................................... 4422 José Poças Rascão, Institute Polytechnic of Setúbal, Portugal Open Data Repositories in Knowledge Society................................................................................. 4436 Nadim Akhtar Khan, University of Kashmir, India Sara Sohrabzadeh, Tehran University of Medical Science, Iran Garvita Jhamb, University of Delhi, India Quantum Information Science Vis-à-Vis Information Schools......................................................... 4448 P. K. Paul, Raiganj University, India D. Chatterjee, Seacom Skills University, India A. Bhuimali, Raiganj University, India Towards a General Theory of Information........................................................................................ 4459 Laura L. Pană, Polytechnic University of Bucharest, Romania

Category: Information Retrieval Analysis and Assessment of Cross-Language Question Answering Systems................................... 4471 Juncal Gutiérrez-Artacho, University of Granada, Spain María-Dolores Olvera-Lobo, University of Granada, Spain



Challenges in Collecting Qualitative Data for Information Systems Studies.................................... 4480 Tiko Iyamu, Cape Peninsula University of Technology, South Africa Irja Naambo Shaanika, Namibia University of Science and Technology, Namibia Cognitive and Psychological Factors in Cross-Language Information Retrieval.............................. 4490 Rowena Li, Bayside High School Library, USA A Fast and Space-Economical Algorithm for the Tree Inclusion Problem....................................... 4502 Yangjun Chen, University of Winnipeg, Canada Yibin Chen, University of Winnipeg, Canada Information Seeking Models in the Digital Age................................................................................ 4515 Mudasir Khazer Rather, University of Kashmir, India Shabir Ahmad Ganaie, University of Kashmir, India An Insight Into Deep Learning Architectures.................................................................................... 4528 Nishu Garg, VIT University, India Nikhitha P, VIT University, India B. K. Tripathy, VIT University, India Online Information Retrieval Systems Trending From Evolutionary to Revolutionary  Approach............................................................................................................................................ 4535 Zahid Ashraf Wani, University of Kashmir, India Huma Shafiq, University of Kashmir, India

Category: IT Research and Theory Adaptive Networks for On-Chip Communication............................................................................. 4549 Mário Pereira Vestias, Instituto Politécnico de Lisboa, Portugal Business Model Innovation-Oriented Technology Management for Emergent Technologies.......... 4560 Sven Seidenstricker, Fraunhofer Institute for Industrial Engineering, Germany Ardilio Antonino, Fraunhofer Institute for Industrial Engineering, Germany Cognitive Mapping in Support of Intelligent Information Systems.................................................. 4570 Akbar Esfahanipour, Amirkabir University of Technology, Iran Ali Reza Montazemi, McMaster University, Canada Computer-Assisted Parallel Program Generation.............................................................................. 4583 Shigeo Kawata, Utsunomiya University, Japan Constrained Nonlinear Optimization in Information Science............................................................ 4594 William P. Fox, Naval Postgraduate School, USA Decimal Hardware Multiplier............................................................................................................ 4607 Mário Pereira Vestias, INESC-ID/ISEL/IPL, Portugal



Digital Divide.................................................................................................................................... 4619 Patrick Flanagan, St. John’s University, USA Eight Tips for the Theme, “Data and Forecasts”............................................................................... 4629 Alessio Drivet, GeoGebra Institute of Torino, Italy Exploring New Handwriting Parameters for Writer Identification.................................................... 4643 Verónica Inés Aubin, Universidad Nacional de La Matanza, Argentina Jorge Horacio Doorn, Universidad Nacional del Oeste, Argentina & Universidad Nacional de La Matanza, Argentina Haptics-Based Systems Characteristics, Classification, and Applications........................................ 4652 Abeer Bayousuf, King Saud University, Saudi Arabia Hend S. Al-Khalifa, King Saud University, Saudi Arabia Abdulmalik Al-Salman, King Saud University, Saudi Arabia The Holon/Parton Structure of the Meme, or The Unit of Culture.................................................... 4666 J. T. Velikovsky, University of Newcastle, Australia ICT Standardization........................................................................................................................... 4679 Kai Jakobs, RWTH Aachen University, Germany Immersing People in Scientific Knowledge and Technological Innovation Through Disney’s Use of Installation Art............................................................................................................................... 4692 Jonathan Lillie, Loyola University Maryland, USA Michelle Jones-Lillie, Lillie Pad Studios, USA Indicators of Information and Communication Technology.............................................................. 4704 Gulnara Abdrakhmanova, National Research University Higher School of Economics, Russia Leonid Gokhberg, National Research University Higher School of Economics, Russia Alexander Sokolov, National Research University Higher School of Economics, Russia Information Technologies and Social Change................................................................................... 4715 Muhammet Ali Köroğlu, Uşak University, Turkey Cemile Zehra Köroğlu, Uşak University, Turkey iSchools Promoting “Information Science and Technology” (IST) Domain Towards Community, Business, and Society With Contemporary Worldwide Trend and Emerging Potentialities in  India................................................................................................................................................... 4723 P. K. Paul, Raiganj University, India D. Chatterjee, Seacom Skills University, India Logic Programming for Intelligent Systems...................................................................................... 4736 James D. Jones, Liberty University, USA



Methods for Improving Alias Rejections in Comb Filters................................................................. 4746 Gordana Jovanovic Dolecek, Institute INAOE Puebla, Mexico A Paradoxical World and the Role of Technology in Thana-Capitalism........................................... 4761 Maximiliano Emanuel Korstanje, University of Palermo, Argentina Performance Measurement of Technology Ventures by Science and Technology Institutions......... 4774 Artie W. Ng, The Hong Kong Polytechnic University, Hong Kong Benny C. F. Cheung, The Hong Kong Polytechnic University, Hong Kong Peggy M. L. Ng, The Hong Kong Polytechnic University, Hong Kong The Skills of European ICT Specialists............................................................................................. 4785 Francesca Sgobbi, University of Brescia, Italy A Trust Case-Based Model Applied to Agents Collaboration........................................................... 4797 Felipe Boff, Lutheran University of Brazil (ULBRA), Brazil Fabiana Lorenzi, Lutheran University of Brazil (ULBRA), Brazil Understanding and Assessing Quality of Models and Modeling Languages.................................... 4810 John Krogstie, Norwegian University of Science and Technology, Norway Utilizing Information Science and Technology in Franchise Organizations..................................... 4822 Ye-Sho Chen, Louisiana State University, USA

Category: IT Security and Ethics Computer Fraud Challenges and Its Legal Implications.................................................................... 4837 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA

Volume VII (It - Ma)

Cost Estimation and Security Investment of Security Projects.......................................................... 4849 Yosra Miaoui, University of Carthage, Tunisia Boutheina Fessi, University of Carthage, Tunisia Noureddine Boudriga, University of Carthage, Tunisia Development of Personal Information Privacy Concerns Evaluation............................................... 4862 Anna Rohunen, University of Oulu, Finland Jouni Markkula, University of Oulu, Finland Digital Video Watermarking Using Diverse Watermarking Schemes............................................... 4872 Yash Gupta, Maulana Abul Kalam Azad University of Technology, India Shaila Agrawal, Maulana Abul Kalam Azad University of Technology, India Susmit Sengupta, Maulana Abul Kalam Azad University of Technology, India Aruna Chakraborty, Maulana Abul Kalam Azad University of Technology, India



Ethical Computing Continues From Problem to Solution................................................................. 4884 Wanbil William Lee, The Computer Ethics Society, Hong Kong & Wanbil & Associates, Hong Kong Group Signature System Using Multivariate Asymmetric Cryptography......................................... 4898 Sattar J. Aboud, University of Bedfordshire, UK Hexa-Dimension Code of Practice for Data Privacy Protection........................................................ 4909 Wanbil William Lee, Wanbil & Associates, Hong Kong Information and Communication Technology Ethics and Social Responsibility.............................. 4920 Tomas Cahlik, Charles University Prague, Czech Republic & University of Economics Prague, Czech Republic Intrusion Tolerance Techniques......................................................................................................... 4927 Wenbing Zhao, Cleveland State University, USA New Perspectives of Pattern Recognition for Automatic Credit Card Fraud Detection.................... 4937 Addisson Salazar, Universitat Politècnica de València, Spain Gonzalo Safont, Universitat Politècnica de València, Spain Alberto Rodriguez, Universidad Miguel Hernández de Elche, Spain Luis Vergara, Universitat Politècnica de València, Spain Privacy, Algorithmic Discrimination, and the Internet of Things..................................................... 4951 Jenifer Sunrise Winter, University of Hawaii at Manoa, USA The Protection Policy for Youth Online in Japan.............................................................................. 4962 Nagayuki Saito, Ochanomizu University, Japan Madoka Aragaki, Business Breakthrough University, Japan Security of Identity-Based Encryption Algorithms........................................................................... 4975 Kannan Balasubramanian, Mepco Schlenk Engineering College, India M. Rajakani, Mepco Schlenk Engineering College, India Steganography Using Biometrics...................................................................................................... 4985 Manashee Kalita, NERIST, India Swanirbhar Majumder, NERIST, India Usable Security.................................................................................................................................. 5004 Andrea Atzeni, Politecnico di Torino, Italy Shamal Faily, Bournemouth University, UK Ruggero Galloni, Square Reply S.r.l., Italy

Category: Knowledge Management Boosting the Social Development of the Majority Through the Creation of a Wireless Knowledge Society................................................................................................................................................ 5015 Danilo Piaggesi, Framericas, USA



Communities of Practice as a Source of Open Innovation................................................................ 5027 Diane-Gabrielle Tremblay, University of Quebec, Canada Indigenous Knowledge Systems........................................................................................................ 5036 Osarumwense Iguisi, University of Benin, Nigeria Osaro Rawlings Igbinomwanhia, University of Benin, Nigeria Integrating Knowledge Management and Business Processes.......................................................... 5046 John Steven Edwards, Aston University, UK Intellectual Capital Measurement...................................................................................................... 5056 Lukasz Bryl, Poznan University of Economics and Business, Poland Knowledge Acquisition on Dante Alighieri’s Works......................................................................... 5067 Elvira Immacolata Locuratolo, ISTI-CNR, Italy Valentina Bartalesi Lenzi, ISTI-CNR, Italy Knowledge Management for Development (KM4D)......................................................................... 5077 Alexander G. Flor, University of the Philippines, Philippines Knowledge Management From the Metaphorical Perspective.......................................................... 5085 Magdalena Bielenia-Grajewska, University of Gdansk, Poland Theory and Practice of Online Knowledge Sharing.......................................................................... 5093 Will W. K. Ma, Hong Kong Shue Yan University, Hong Kong Visualization as a Knowledge Transfer.............................................................................................. 5103 Anna Ursyn, University of Northern Colorado, USA

Category: Language Studies Mobile Testing System for Developing Language Skills................................................................... 5116 Svetlana Titova, Far Eastern Federal University, Russia Nominalizations in Requirements Engineering Natural Language Models....................................... 5127 Claudia S. Litvak, Universidad Nacional de La Matanza, Argentina & Universidad Nacional del Oeste, Argentina Graciela Dora Susana Hadad, Universidad Nacional del Oeste, Argentina Jorge Horacio Doorn, Universidad Nacional del Oeste, Argentina & Universidad Nacional de La Matanza, Argentina Word Formation Study in Developing Naming Guidelines in the Translation of English Medical Terms Into Persian............................................................................................................................. 5136 Ali Akbar Zeinali, Universiti Sains Malaysia, Malaysia



Category: Learning Assessment and Measurement Implicit Cognitive Vulnerability........................................................................................................ 5149 Caroline M. Crawford, University of Houston – Clear Lake, US Learning Analytics............................................................................................................................. 5158 Constanţa-Nicoleta Bodea, Bucharest University of Economic Studies, Romania Maria-Iuliana Dascalu, University Politehnica of Bucharest, Romania Radu Ioan Mogos, Bucharest University of Economic Studies, Romania Stelian Stancu, Bucharest University of Economic Studies, Romania Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine........... 5169 Sajid Umair, National University of Sciences and Technology (NUST), Pakistan Muhammad Majid Sharif, National University of Sciences and Technology (NUST), Pakistan The Relationship Between Online Formative Assessment and State Test Scores Using Multilevel Modeling............................................................................................................................................ 5183 Aryn C. Karpinski, Kent State University, USA Jerome V. D’Agostino, The Ohio State University, USA Anne-Evan K. Williams, Billings Middle School, USA Sue Ann Highland, Grand Canyon University, USA Jennifer A. Mellott, Kent State University, USA

Category: Library Science and Administration Change Leadership Styles and Behaviors in Academic Libraries..................................................... 5194 John Kennedy Lewis, Salve Regina University, USA Changing Expectations of Academic Libraries................................................................................. 5204 Jennifer Ashley Wright Joe, Western Kentucky University, USA Digital Archives for Preserving and Communicating Architectural Drawings................................. 5213 Roberta Spallone, Politecnico di Torino, Italy Francesca Paluan, Politecnico di Torino, Italy Massive Digital Libraries (MDLs).................................................................................................... 5226 Andrew Philip Weiss, California State University – Northridge, USA Mission, Tools, and Ongoing Developments in the So.Re.Com. “A.S. de Rosa” @-library............. 5237 Annamaria Silvana de Rosa, Sapienza University of Rome, Italy Social Media Applications as Effective Service Delivery Tools for Librarians................................ 5252 Ihuoma Sandra Babatope, Delta State College of Physical Education, Nigeria



Web 2.0 From Evolution to Revolutionary Impact in Library and Information Centers................... 5262 Zahid Ashraf Wani, University of Kashmir, India Tazeem Zainab, University of Kashmir, India Shabir Hussain, University of Kashmir, India

Category: Logistics and Supply Chain Management Barcodes vs. RFID and Its Continued Success in Manufacturing and Services................................ 5273 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA Becoming Smart, Innovative, and Socially Responsible in Supply Chain Collaboration................. 5285 Goknur Arzu Akyuz, University of Turkish Aeronautical Association, Turkey Guner Gursoy, Okan University, Turkey Concept and Practices of Cyber Supply Chain in Manufacturing Context........................................ 5306 Anisha Banu Dawood Gani, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia The Concept of Modularity in the Context of IS Project Outsourcing.............................................. 5317 Shahzada Benazeer, University of Antwerp, Belgium Philip Huysmans, University of Antwerp, Belgium Peter De Bruyn, University of Antwerp, Belgium Jan Verelst, University of Antwerp, Belgium Developing Global Supply Chain Manager for Business Expansion................................................ 5327 Puspita Wulansari, Telkom University, Indonesia Yudi Fernando, Universiti Malaysia Pahang, Malaysia Discrete Event Simulation in Inventory Management....................................................................... 5335 Linh Nguyen Khanh Duong, Auckland University of Technology, New Zealand Lincoln C. Wood, University of Otago, New Zealand & Curtin University, Australia E-Business Supply Chains Drivers, Metrics, and ERP Integration................................................... 5345 Jean C. Essila, Northern Michigan University, USA Ecological Performance as a New Metric to Measure Green Supply Chain Practices...................... 5357 June Poh Kim Tam, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia E-Commerce in Logistics and Supply Chain Management............................................................... 5367 Yasanur Kayikci, Turkish-German University, Turkey Exploring Drivers of Closed Loop Supply Chain in Malaysian Automotive Industry...................... 5378 Fadzlina Mohd Fadzil, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia



From Business-to-Business Software Startup to SAP’s Acquisition................................................. 5388 John Wang, Montclair State University, USA Jeffrey Hsu, Fairleigh Dickinson University, USA Sylvain Jaume, Saint Peter’s University, USA An Integrated Approach to Supply Chain Simulation....................................................................... 5398 Nenad Stefanovic, University of Kragujevac, Serbia Bozidar Radenkovic, University of Belgrade, Serbia Latest Advances on Benders Decomposition..................................................................................... 5411 Antonios Fragkogios, University of Thessaly, Greece Georgios K. D. Saharidis, University of Thessaly, Greece Lean Logistics of the Transportation of Fresh Fruit Bunches (FFB) in the Palm Oil Industry......... 5422 Cheah Cheng Teik, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia Major Techniques and Current Developments of Supply Chain Process Modelling......................... 5433 Henry Xu, The University of Queensland, Australia Renae Agrey, The University of Queensland, Australia Measuring Low Carbon Supply Chain.............................................................................................. 5446 Muhammad Shabir Shaharudin, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia Missing Part of Halal Supply Chain Management............................................................................ 5456 Ratih Hendayani, Telkom University, Indonesia Yudi Fernando, Universiti Malaysia Pahang, Malaysia Notions of Maritime Green Supply Chain Management................................................................... 5465 Fairuz Jasmi, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia Offshoring IT..................................................................................................................................... 5476 Susan Cockrell, Austin Peay State University, USA Terry Stringer Damron, Austin Peay State University, USA Amye M. Melton, Austin Peay State University, USA Alan D. Smith, Robert Morris University, USA Pricing Based on Real-Time Analysis of Forklift Utilization Using RFID in Warehouse Management....................................................................................................................................... 5490 Numan Celebi, Sakarya University, Turkey Kübra Savaş, Istanbul University, Turkey Ihsan Hakan Selvi, Sakarya University, Turkey



Profit Maximizing Network Modeling With Inventory and Capacity Considerations...................... 5503 Tan Miller, Rider University, USA Renato de Matta, University of Iowa, USA Radio Frequency Identification Systems Within a Lean Supply Chain in a Global Environment.... 5516 Alan D. Smith, Robert Morris University, USA Terry Stringer Damron, Austin Peay State University, USA Susan Cockrell, Austin Peay State University, USA Amye M. Melton, Austin Peay State University, USA Reflections of the 1Malaysia Supply Chain (1MSC)......................................................................... 5527 Munira Halili, Universiti Sains Malaysia, Malaysia Latifah Naina Mohamed, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia A Review of Advances in Supply Chain Intelligence........................................................................ 5538 Nenad Stefanovic, University of Kragujevac, Serbia Danijela Milosevic, University of Kragujevac, Serbia A Review of Supply Chain Risk Management in Agribusiness Industry.......................................... 5550 Sri Widiyanesti, Telkom University, Indonesia Yudi Fernando, Universiti Malaysia Pahang, Malaysia The Role of Emerging Information Technologies for Supporting Supply Chain Management........ 5559 Zlatko Nedelko, University of Maribor, Slovenia Vojko Potocan, University of Maribor, Slovenia Samsung Company and an Analysis of Supplier-Side Supply Chain Management and IT Applications....................................................................................................................................... 5570 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA Simulating Complex Supply Chain Relationships Using Copulas.................................................... 5583 Krishnamurty Muralidhar, University of Oklahoma, USA Rathindra Sarathy, Oklahoma State University, USA Using RFID and Barcode Technologies to Improve Operations Efficiency Within the Supply Chain................................................................................................................................................. 5595 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA

Category: Management Science The Business Transformation Framework for Managers in Transformation Projects....................... 5607 Antoine Trad, IBITSM, Switzerland Damir Kalpić, University of Zagreb, Croatia



Contemporary Leadership Development in Kazakhstan................................................................... 5626 Gainiya Tazhina, University of International Business, Kazakhstan Judith Parker, Teachers College, Columbia University, USA Arslan Ivashov, Kazakh Ablai Khan University of International Relations and World Languages, USA Empirical Verification of the Performance Measurement System..................................................... 5638 Aleksander Janeš, University of Primorska, Slovenia Lack of Characteristics Management Causing Biggest Projects Failure........................................... 5650 Loredana Arana, University of Phoenix, USA Making Sense of IS Project Stories................................................................................................... 5660 Darren Dalcher, University of Hertfordshire, UK

Volume VIII (Ma - Mu)

The Measurement and Recognition of Intellectual Capital in the Process of Accounting Convergence Trends and Patterns...................................................................................................... 5669 Ionica Oncioiu, Titu Maiorescu University, Romania Project Control Using a Bayesian Approach..................................................................................... 5679 Franco Caron, Politecnico di Milano, Italy Shaping Mega-Science Projects and Practical Steps for Success...................................................... 5690 Phil Crosby, Curtin University, Australia Staying Ahead in Business Through Innovation................................................................................ 5705 N. Raghavendra Rao, FINAIT Consultancy Services, India Sustainable Competitive Advantage With the Balanced Scorecard Approach.................................. 5714 Jorge Gomes, ISEG, Universidade de Lisboa, Portugal Mário José Batista Romão, ISEG, Universidade de Lisboa, Portugal Transformational Leadership for Academic Libraries in Nigeria...................................................... 5726 Violet E. Ikolo, Delta State University Library, Nigeria

Category: Marketing Are Social Marketing Investments Used as a Tool for Voluntary Reporting or Disclosure?............ 5737 Tugba Ucma Uysal, Mugla Sitki Kocman University, Turkey Ganite Kurt, Gazi University, Turkey Ali Naci Karabulut, Mugla Sitki Kocman University, Turkey



The Impact of Artificial Intelligence and Virtual Personal Assistants on Marketing........................ 5748 Christina L. McDowell Marinchak, University of Alaska Anchorage, USA Edward Forrest, University of Alaska Anchorage, USA Bogdan Hoanca, University of Alaska Anchorage, USA Marketing and Marketing Plan for Information Services.................................................................. 5757 Sérgio Maravilhas, Universidade Salvador, Brazil A Neuroaesthetic Approach to the Search of Beauty From the Consumer’s Perspective.................. 5767 Gemma García Ferrer, Rey Juan Carlos University, Spain Social Media Use and Customer Engagement................................................................................... 5775 Aurora Garrido-Moreno, University of Malaga, Spain Nigel Lockett, University of Lancaster, UK Víctor García-Morales, University of Granada, Spain

Category: Medical Education, Ethics, and Law Comprehensive E-Learning Appraisal System.................................................................................. 5787 Jose Luis Monroy Anton, La Ribera University Hospital, Spain Juan Vicente Izquierdo Soriano, La Ribera University Hospital, Spain Maria Isabel Asensio Martinez, La Ribera University Hospital, Spain Felix Buendia Garcia, Polythecnical University of Valencia, Spain Flipping the Medical School Classroom............................................................................................ 5800 Kristina Kaljo, Medical College of Wisconsin, USA Laura Jacques, Medical College of Wisconsin, USA Integrating Evidence-Based Practice in Athletic Training Though Online Learning........................ 5810 Brittany A. Vorndran, Seton Hall University, USA Michelle Lee D’Abundo, Seton Hall University, USA Integrating Web-Based Technologies Into the Education and Training of Health Professionals...... 5820 Michelle Lee D’Abundo, Seton Hall University USA Cara Sidman, Arizona State University, USA Interactivity in Distance Education and Computer-Aided Learning, With Medical Education Examples............................................................................................................................................ 5829 D. John Doyle, Cleveland Clinic, USA Patrick J. Fahy, Athabasca University, Canada Medical Equipment and Economic Determinants of Its Structure and Regulation in the Slovak Republic............................................................................................................................................. 5841 Beáta Gavurová, Technical University of Košice, Slovakia Viliam Kováč, Technical University of Košice, Slovakia Michal Šoltés, Technical University of Košice, Slovakia



Use of Technology in Problem-Based Learning in Health Science................................................... 5853 Indu Singh, Griffith University, Australia Avinash Reddy Kundur, Griffith University, Australia Yun-Mi Nguy, Griffith University, Australia

Category: Medical Technologies Defining and Characterising the Landscape of eHealth.................................................................... 5864 Yvonne O’Connor, University College Cork, Ireland Ciara Heavin, University College Cork, Ireland Kinect Applications in Healthcare..................................................................................................... 5876 Roanna Lun, Cleveland State University, USA Wenbing Zhao, Cleveland State University, USA Neuroscience Technology and Interfaces for Speech, Language, and Musical Communication...... 5886 Dionysios Politis, Aristotle University of Thessaloniki, Greece Miltiadis Tsalighopoulos, Aristotle University of Thessaloniki, Greece Georgios Kyriafinis, AHEPA University Hospital, Greece Personalized Medicine....................................................................................................................... 5901 Sandip Bisui, Indian Institute of Technology (IIT) Kanpur, India Subhas Chandra Misra, Indian Institute of Technology (IIT) Kanpur, India Pervasive Mobile Health.................................................................................................................... 5908 Muhammad Anshari, Universiti Brunei Darussalam, Brunei Mohammad Nabil Almunawar, Universiti Brunei Darussalam, Brunei Radio Frequency Identification Technologies and Issues in Healthcare........................................... 5918 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA Social Telerehabilitation.................................................................................................................... 5930 Gilberto Marzano, Rezekne Academy of Technologies, Latvia A Validation Study of Rehabilitation Exercise Monitoring Using Kinect......................................... 5941 Wenbing Zhao, Cleveland State University, USA Deborah D. Espy, Cleveland State University, USA Ann Reinthal, Cleveland State University, USA Virtual Reality as Distraction Technique for Pain Management in Children and Adolescents......... 5955 Barbara Atzori, University of Florence, Italy Hunter G. Hoffman, University of Washington, USA Laura Vagnoli, Meyer Children’s Hospital of Florence, Italy Andrea Messeri, Meyer Children’s Hospital of Florence, Italy Rosapia Lauro Grotto, University of Florence, Italy



Category: Mobile and Wireless Computing Biogeography-Based Optimization Applied to Wireless Communications Problems...................... 5967 Sotirios K. Goudos, Aristotle University of Thessaloniki, Greece BYOD (Bring Your Own Device), Mobile Technology Providers, and Its Impacts on Business/ Education and Workplace/Learning Applications............................................................................. 5981 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA Cell Phone Conversation and Relative Crash Risk Update................................................................ 5992 Richard A. Young, Driving Safety Consulting, LLC, USA Comb Filters Characteristics and Current Applications.................................................................... 6007 Miriam Guadalupe Cruz-Jimenez, Institute INAOE, Mexico David Ernesto Troncoso Romero, CONACYT at ESCOM-IPN, Mexico Gordana Jovanovic Dolecek, Institute INAOE, Mexico Consumer Adoption of PC-Based/Mobile-Based Electronic Word-of-Mouth.................................. 6019 Akinori Ono, Keio University, Japan Mai Kikumori, Ritsumeikan University, Japan Context-Aware Personalization for Mobile Services......................................................................... 6031 Abayomi Moradeyo Otebolaku, Liverpool John Moores University, UK Maria Teresa Andrade, University of Porto, Portugal Design of Compensators for Comb Decimation Filters..................................................................... 6043 Gordana Jovanovic Dolecek, Institute INAOE Puebla, Mexico An Empirical Study of Mobile/Handheld App Development Using Android Platforms.................. 6057 Wen-Chen Hu, University of North Dakota, USA Naima Kaabouch, University of North Dakota, USA Hung-Jen Yang, National Kaohsiung Normal University, Taiwan Enhancing the Mobile User Experience Through Colored Contrasts................................................ 6070 Jean-Éric Pelet, ESCE International Business School, France Basma Taieb, University of Cergy Pontoise, France Ethical Ambiguities in the Privacy Policies of Mobile Health and Fitness Applications................. 6083 Devjani Sen, University of Ottawa, Canada Rukhsana Ahmed, University of Ottawa, Canada Exploring the Growth of Wireless Communications Systems and Challenges Facing 4G  Networks............................................................................................................................................ 6094 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA



Flying Adhoc Networks Concept and Challenges............................................................................. 6106 Kuldeep Singh, Thapar University, India Anil Kumar Verma, Thapar University, India Health Wearables Turn to Fashion..................................................................................................... 6114 Lambert Spaanenburg, Comoray AB, Sweden Human Psychomotor Performance Under the Exposure to Mobile Phones-Like Electromagnetic Fields.................................................................................................................................................. 6124 Giuseppe Curcio, University of L’Aquila, Italy Identification of Wireless Devices From Their Physical Layer Radio-Frequency Fingerprints........ 6136 Gianmarco Baldini, European Commission – Joint Research Centre, Italy Gary Steri, European Commission – Joint Research Centre, Italy Raimondo Giuliani, European Commission – Joint Research Centre, Italy The Impact of Mobile Phones on Plastic Surgery and Burn Management........................................ 6147 Maria Giaquinto-Cilliers, Kimberley Hospital Complex, South Africa Tertius N. Potgieter, Kimberley Hospital Complex, South Africa Gert Steyn, Kimberley Hospital Complex, South Africa The Intersection of Religion and Mobile Technology....................................................................... 6161 Wendi R. Bellar, Texas A&M University, USA Kyong James Cho, Texas A&M University, USA Heidi A. Campbell, Texas A&M University, USA Methods for Simultaneous Improvement of Comb Pass Band and Folding Bands........................... 6171 Gordana Jovanovic Dolecek, Institute INAOE Puebla, Mexico Micro to Macro Social Connectedness Through Mobile Phone Engagement................................... 6184 Dominic Mentor, Columbia University, USA Mobile Applications for Automatic Object Recognition................................................................... 6195 Danilo Avola, University of Udine, Italy Gian Luca Foresti, University of Udine, Italy Claudio Piciarelli, University of Udine, Italy Marco Vernier, University of Udine, Italy Luigi Cinque, Sapienza University, Italy Mobile Apps Threats.......................................................................................................................... 6207 Donovan Peter Chan Wai Loon, University of Malaya, Malaysia Sameer Kumar, University of Malaya, Malaysia Mobile Technologies Impact on Economic Development in Sub-Saharan Africa............................ 6216 Adam Crossan, Letterkenny Institute of Technology, Ireland Nigel McKelvey, Letterkenny Institute of Technology, Ireland Kevin Curran, Ulster University, UK



Mobile Virtual Reality to Enhance Subjective Well-Being............................................................... 6223 Federica Pallavicini, Università di Milano-Bicocca, Italy Luca Morganti, Università di Milano-Bicocca, Italy Barbara Diana, Università di Milano-Bicocca, Italy Olivia Realdon, University of Milano-Bicocca, Italy Valentino Zurloni, Università di Milano-Bicocca, Italy Fabrizia Mantovani, Università di Milano-Bicocca, Italy Novel Methods to Design Low-Complexity Digital Finite Impulse Response (FIR) Filters............. 6234 David Ernesto Troncoso Romero, CONACYT at ESCOM-IPN, Mexico Gordana Jovanovic Dolecek, Institute INAOE, Mexico Open Source....................................................................................................................................... 6245 Heidi Lee Schnackenberg, SUNY Plattsburgh, USA Power Consumption in Wireless Access Networks........................................................................... 6253 Vinod Kumar Mishra, B. T. Kumaon Institute of Technology, India Pankaja Bisht, B. T. Kumaon Institute of Technology, India Resource Management for Multimedia Services in Long Term Evaluation Networks..................... 6266 Vinod Kumar Mishra, B. T. Kumaon Institute of Technology, India Tanuja Pathak, B. T. Kumaon Institute of Technology, India SMS & Civil Unrest........................................................................................................................... 6275 Innocent Chiluwa, Covenant University OTA, Nigeria A Survey of People Localization Techniques Utilizing Mobile Phones............................................ 6286 Levent Bayındır, Ataturk University, Turkey Technological Innovation and Use in the Early Days of Camera Phone Photo Messaging............... 6296 Jonathan Lillie, Loyola University, USA Viterbi Decoder in Hardware............................................................................................................. 6307 Mário Pereira Véstias, Instituto Politecnico de Lisboa, Portugal Wireless Implant Communications Using the Human Body............................................................. 6319 Assefa K. Teshome, Victoria University, Australia Behailu Kibret, Victoria University, Australia Daniel T. H. Lai, Victoria University, Australia

Category: Mobile Learning E-Collaborative Learning (e-CL)....................................................................................................... 6336 Alexandros Xafopoulos, University College London, UK



Learning With Mobile Devices.......................................................................................................... 6347 Helen Crompton, Old Dominion University, USA John Traxler, University of Wolverhampton, UK Mobile Game-Based Learning........................................................................................................... 6361 Boaventura DaCosta, Solers Research Group, USA Soonhwa Seok, Korea University, South Korea Carolyn Kinsell, Solers Research Group, USA Mobile Game-Based Learning in STEM Subjects............................................................................ 6376 Marcelo Leandro Eichler, Universidade Federal do Rio Grande do Sul, Brazil Gabriela Trindade Perry, Universidade Federal do Rio Grande do Sul, Brazil Ivana Lima Lucchesi, Universidade Federal do Rio Grande do Sul, Brazil Thiago Troina Melendez, Instituto Federal Sul-Riograndense, Brazil Mobile Learning in and out of the K-12 Classroom.......................................................................... 6388 Pena L. Bedesem, Kent State University, USA Tracy Arner, Kent State University, USA A Psychological Perspective on Mobile Learning............................................................................. 6398 Melody M. Terras, University of the West of Scotland, UK Judith Ramsay, Manchester Metropolitan University, UK The Role of Distance Education in Global Education....................................................................... 6412 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand

Category: Multimedia Technology Adaptive Hypermedia Systems.......................................................................................................... 6424 Ana Carolina Tomé Klock, Federal University of Rio Grande do Sul (UFRGS), Brazil Isabela Gasparini, Santa Catarina State University (UDESC), Brazil Marcelo Soares Pimenta, Federal University of Rio Grande do Sul (UFRGS), Brazil José Palazzo M. de Oliveira, Federal University of Rio Grande do Sul (UFRGS), Brazil Group Synchronization for Multimedia Systems............................................................................... 6435 Dimitris N. Kanellopoulos, University of Patras, Greece Metadata Standards in Digital Audio................................................................................................. 6447 Kimmy Szeto, Baruch College, City University of New York, USA Multimedia-Enabled Dot Codes as Communication Technologies................................................... 6464 Shigeru Ikuta, Otsuma Women’s University, Japan



Volume IX (Mu - So)

Semantically Enhanced Authoring of Shared Media......................................................................... 6476 Charalampos A. Dimoulas, Aristotle University of Thessaloniki, Greece Andreas A. Veglis, Aristotle University of Thessaloniki, Greece George Kalliris, Aristotle University of Thessaloniki, Greece Transmedia and Transliteracy in Nemetical Analysis........................................................................ 6488 Michael Josefowicz, Nemetics Institute Kolkata, USA Ray Gallon, The Transformation Society, France Maria Nieves Lorenzo Galés, The Transformation Society, Spain

Category: Networking and Telecommunications Autonomic Cooperative Communications......................................................................................... 6499 Michal Wodczak, Samsung Electronics, Poland Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs............................. 6507 Natarajan Meghanathan, Jackson State University, USA Distributed Methods for Multi-Sink Wireless Sensor Networks Formation..................................... 6522 Miriam A. Carlos-Mancilla, CINVESTAV Unidad Guadalajara, Mexico Ernesto Lopez-Mellado, CINVESTAV Unidad Guadalajara, Mexico Mario Siller, CINVESTAV Unidad Guadalajara, Mexico A Graph-Intersection-Based Algorithm to Determine Maximum Lifetime Communication Topologies for Cognitive Radio Ad Hoc Networks........................................................................... 6536 Natarajan Meghanathan, Jackson State University, USA Improving Quality of Business in Next Generation Telecom Networks........................................... 6546 Vesna Radonjić Đogatović, University of Belgrade, Serbia Information-Centric Networking....................................................................................................... 6556 Mohamed Fazil Mohamed Firdhous, University of Moratuwa, Sri Lanka Interoperability Frameworks for Distributed Systems....................................................................... 6566 José Carlos Martins Delgado, Universidade de Lisboa, Portugal Neural Networks and Their Accelerated Evolution From an Economic Analysis Perspective......... 6579 Stelian Stancu, Bucharest University of Economic Studies, Romania Constanţa-Nicoleta Bodea, Bucharest University of Economic Studies, Romania Oana Mădălina Popescu(Predescu), Bucharest University of Economic Studies, Romania Alina Neamţu(Idoraşi), Bucharest University of Economic Studies, Romania



Optimization of Antenna Arrays and Microwave Filters Using Differential Evolution  Algorithms......................................................................................................................................... 6595 Sotirios K. Goudos, Aristotle University of Thessaloniki, Greece QoS Architectures for the IP Network............................................................................................... 6609 Harry G. Perros, North Carolina State University, USA Throughput Dependence on SNR in IEEE802.11 WLAN Systems.................................................. 6618 Ikponmwosa Oghogho, Delta State University, Abraka-Oleh Campus, Nigeria

Category: Neural Networks Applications of Artificial Neural Networks in Economics and Finance............................................ 6631 Iva Mihaylova, University of St. Gallen, Switzerland Artificial Neural Networks and Their Applications in Business........................................................ 6642 Trevor J. Bihl, Air Force Institute of Technology, USA William A. Young, Ohio University, USA Gary R. Weckman, Ohio University, USA Recurrent Neural Networks for Predicting Mobile Device State....................................................... 6658 Juan Manuel Rodriguez, ISISTAN, UNICEN-CONICET, Argentina Alejandro Zunino, ISISTAN, UNICEN-CONICET, Argentina Antonela Tommasel, ISISTAN, UNICEN-CONICET, Argentina Cristian Mateos, ISISTAN, UNICEN-CONICET, Argentina

Category: Optical Engineering Visible Light Communication Numerous Applications..................................................................... 6672 Ala’ Fathi Khalifeh, German Jordan University, Jordan Hasan Farahneh, Ryerson University, Canada Christopher Mekhiel, Ryerson University, Canada Xavier Fernando, Ryerson University, Canada

Category: Public Sector Management Community Outreach......................................................................................................................... 6685 Loriene Roy, The University of Texas at Austin, USA Antonia Frydman, The University of Texas at Austin, USA Exploring “Hacking,” Digital Public Art, and Implication for Contemporary Governance.............. 6695 Amadu Wurie Khan, University of Edinburgh, UK Chris Speed, University of Edinburgh, UK



Political Context Elements in Public Policy of Radio Frequency Information Technology and Electromagnetic Fields....................................................................................................................... 6710 Joshua M. Steinfeld, Old Dominion University, USA Public Policies for Providing Cloud Computing Services to SMEs of Latin America...................... 6727 Mohd Nayyer Rahman, Aligarh Muslim University, India Badar Alam Iqbal, Aligarh Muslim University, India

Category: Research Methods and Scholarly Publishing Advancement and Application of Scientometric Indicators for Evaluation of Research Content..... 6739 Tazeem Zainab, University of Kashmir, India Zahid Ashraf Wani, University of Kashmir, India Electronic Theses and Dissertations (ETDs)..................................................................................... 6748 Ralph Hartsock, University of North Texas, USA Daniel G. Alemneh, University of North Texas, USA The Nature of Research Methodologies............................................................................................. 6756 Ben Tran, Alliant International University, USA Research Methodology...................................................................................................................... 6767 Swati C. Jagdale, MAEER’s Maharashtra Institute of Pharmacy, India Rahul U. Hude, MAEER’s Maharashtra Institute of Pharmacy, India Aniruddha R. Chabukswar, MAEER’s Maharashtra Institute of Pharmacy, India Scholarly Identity in an Increasingly Open and Digitally Connected World..................................... 6779 Olga Belikov, Brigham Young University, USA Royce Kimmons, Brigham Young University, USA

Category: Risk Assessment Fuzzy Logic Approach in Risk Assessment...................................................................................... 6789 Çetin Karahan, Directorate General of Civil Aviation, Turkey Esra Ayça Güzeldereli, Afyon Kocatepe University, Turkey Aslıhan Tüfekci, Gazi University, Turkey Predictive Analytics and Intelligent Risk Detection in Healthcare Contexts..................................... 6806 Nilmini Wickramasinghe, Epworth HealthCare, Australia & Deakin University, Australia Stress Testing Corporations and Municipalities and Supply Chains................................................. 6813 Frank Wolf, CSSTA L3C, USA



Category: Robotics Binary Decision Diagram Reliability for Multiple Robot Complex System..................................... 6825 Hamed Fazlollahtabar, Sharif University of Technology, Iran & National Elites Foundation, Iran Seyed Taghi Akhavan Niaki, Sharif University of Technology, Iran A Bio-Inspired, Distributed Control Approach to the Design of Autonomous Cooperative Behaviors in Multiple Mobile Robot Systems................................................................................... 6836 Gen’ichi Yasuda, Nagasaki Institute of Applied Science, Japan Improving Dependability of Robotics Systems................................................................................. 6847 Nidhal Mahmud, University of Hull, UK Robotics and Programming Integration as Cognitive-Learning Tools.............................................. 6859 Nikleia Eteokleous, Frederick University Cyprus, Cyprus State of the Art and Key Design Challenges of Telesurgical Robotics.............................................. 6872 Sajid Nisar, National University of Science and Technology, Pakistan Osman Hasan, National University of Science and Technology, Pakistan Telesurgical Robotics and a Kinematic Perspective.......................................................................... 6882 Sajid Nisar, National University of Sciences and Technology, Pakistan Osman Hasan, National University of Sciences and Technology, Pakistan Using Global Appearance Descriptors to Solve Topological Visual SLAM..................................... 6894 Lorenzo Fernández Rojo, Miguel Hernandez University, Spain Luis Paya, Miguel Hernández University, Spain Francisco Amoros, Miguel Hernandez University, Spain Oscar Reinoso, Miguel Hernandez University, Spain

Category: Small and Medium Enterprises Big Data and Simulations for the Solution of Controversies in Small Businesses............................ 6907 Milena Janakova, Silesian University in Opava, Czech Republic Financing Micro, Small, and Medium Enterprises in Indian Industry.............................................. 6916 Shromona Ganguly, Indian Institute of Management Calcutta, India & Reserve Bank of India, India Software Development Process Standards for Very Small Companies............................................. 6927 Rory V. O’Connor, Dublin City University, Ireland



Category: Social Networking and Computing Adolescents’ Food Communication in Social Media:....................................................................... 6940 Christopher Holmberg, University of Gothenburg, Sweden Agent-Based Social Networks........................................................................................................... 6950 Federico Bergenti, Università degli Studi di Parma, Italy Agostino Poggi, Università degli Studi di Parma, Italy Michele Tomaiuolo, Università degli Studi di Parma, Italy Aspects of Various Community Detection Algorithms in Social Network Analysis........................ 6961 Nicole Belinda Dillen, St. Thomas’ College of Engineering and Technology, India Aruna Chakraborty, St. Thomas’ College of Engineering and Technology, India Classification of Traffic Events Notified in Social Networks’ Texts.................................................. 6973 Ana Maria Magdalena Saldana-Perez, Instituto Politecnico Nacional, Mexico Marco Antonio Moreno-Ibarra, Instituto Politécnico Nacional, Mexico Miguel Jesus Torres-Ruiz, Instituto Politécnico Nacional, Mexico Communication Privacy Management and Mediated Communication............................................. 6985 Debra L. Worthington, Auburn University, USA Margaret Fitch-Hauser, Auburn University, USA The Dual Nature of Participatory Web and How Misinformation Seemingly Travels...................... 6993 Sameer Kumar, University of Malaya, Malaysia Effective Cultural Communication via Information and Communication Technologies and Social Media Use.......................................................................................................................................... 7002 Androniki Kavoura, Technological Educational Institute of Athens, Greece Stella Sylaiou, Aristotle University of Thessaloniki, Greece From the Psychoanalyst’s Couch to Social Networks........................................................................ 7014 Annamaria Silvana de Rosa, Sapienza University of Rome, Italy Emanuele Fino, Psychologist, Psychometrician, Italy Elena Bocci, Sapienza University of Rome, Italy The Internet Behavior of Older Adults.............................................................................................. 7026 Elizabeth Mazur, Pennsylvania State University, USA Margaret L. Signorella, Pennsylvania State University, USA Michelle Hough, Pennsylvania State University, USA Issues and Challenges in Enterprise Social Media............................................................................ 7036 Sarabjot Kaur, IIT Kanpur, India Subhas Chandra Misra, IIT Kanpur, India



Mapping the Dissemination of the Theory of Social Representations via Academic Social Networks........................................................................................................................................... 7044 Annamaria Silvana de Rosa, Sapienza University of Rome, Italy Laura Dryjanska, Sapienza University of Rome, Italy Elena Bocci, Sapienza University of Rome, Italy The NetLab Network.......................................................................................................................... 7057 Dimitrina Dimitrova, York University, Canada Barry Wellman, NetLab Network, Canada Online Dating/Dating Apps............................................................................................................... 7069 Vladimir Santiago Arias, Texas Tech University, USA Narissra Maria Punyanunt-Carter, Texas Tech University, USA Online Prosocial Behaviors................................................................................................................ 7077 Michelle F. Wright, Pennsylvania State University, USA William Stanley Pendergrass, American Public University System, USA Online Social Networking Behavior and Its Influence Towards Students’ Academic  Performance....................................................................................................................................... 7088 Maslin Masrom, Universiti Teknologi Malaysia, Malaysia Selisa Usat, Universiti Teknologi Malaysia, Malaysia Parental Mediation of Adolescent Technology Use........................................................................... 7097 J. Mitchell Vaterlaus, Montana State University, USA The Qualities and Potential of Social Media..................................................................................... 7106 Udo Richard Averweg, eThekwini Municipality, South Africa Marcus Leaning, University of Winchester, UK Short History of Social Networking and Its Far-Reaching Impact.................................................... 7116 Liguo Yu, Indiana University – South Bend, USA Social Media and Business Practices................................................................................................. 7126 Ashish Kumar Rathore, Indian Institute of Technology Delhi, India P. Vigneswara Ilavarasan, Indian Institute of Technology Delhi, India Social Media Credit Scoring.............................................................................................................. 7140 Billie Anderson, Ferris State University, USA J. Michael Hardin, Samford University, USA Social Network Analysis and the Study of University Industry Relations........................................ 7150 Fernando Cabrita Romero, University of Minho, Portugal Social Networking and Knowledge Sharing in Organizations........................................................... 7161 Sarabjot Kaur, Indian Institute of Technology Kanpur, India Subhas Chandra Misra, Indian Institute of Technology Kanpur, India



Understanding the Potentials of Social Media in Collaborative Learning......................................... 7168 Adem Karahoca, Bahcesehir University, Turkey İlker Yengin, A*STAR, Institute of High Performance Computing, Singapore Using Social Media to Increase the Recruitment of Clinical Research Participants......................... 7181 Saliha Akhtar, Seton Hall University, USA Why It Is Difficult to Disengage From Facebook.............................................................................. 7190 Sonda Bouattour Fakhfakh, University of Tunis El-Manar, Tunisia

Category: Socio-Economic Development Community Science and Technology and Its Meaning to Potential Requirement............................ 7201 P. K. Paul, Raiganj University, India A. Bhuimali, Raiganj University, India Financial Inclusion, Content, and Information Technologies in Latin America................................ 7214 Alberto Chong, Georgia State University, USA & Universidad del Pacifico, Peru Cecilia de Mendoza, Ministry of Production of Argentina, Argentina The Growing Impact of ICT on Development in Africa.................................................................... 7223 Sherif H. Kamel, The American University in Cairo, Egypt Manufacturing vs. Services and the Role of Information Technology.............................................. 7234 Arnab Adhikari, Indian Institute of Management Ranchi, India Shromona Ganguly, Indian Institute of Management Calcutta, India New Faces of Digital Divide and How to Bridge It........................................................................... 7248 Viktor Freiman, Université de Moncton, Canada Dragana Martinovic, University of Windsor, Canada Xavier Robichaud, Univesité de Moncton, Canada The Potential Role of the Software Industry in Supporting Economic Development....................... 7259 Sherif H. Kamel, The American University in Cairo, Egypt Socio-Economic Processes, User Generated Content, and Media Pluralism.................................... 7270 Androniki Kavoura, Technological Educational Institute of Athens, Greece

Volume X (So - W)

Category: Sociology Bipolar Model in Collective Choice.................................................................................................. 7282 Ayeley P. Tchangani, Université Fédérale Toulouse Midi-Pyrénées, France



Censorship in the Digital Age the World Over.................................................................................. 7292 Kari D. Weaver, University of South Carolina – Aiken, USA Information-Based Revolution in Military Affairs............................................................................ 7302 Rafal Kopec, Pedagogical University of Krakow, Poland The Networked Effect of Children and Online Digital Technologies................................................ 7312 Teresa Sofia Pereira Dias de Castro, University of Minho, Portugal António Osório, University of Minho, Portugal Emma Bond, University Campus Suffolk, UK Suggestions for Communication of Information for Multicultural Co-Existence............................. 7327 Noriko Kurata, Tokyo University of Science, Suwa, Japan Vitalizing Ancient Cultures Mythological Storytelling in Metal Music........................................... 7338 Uğur Kilinç, Ondokuz Mayıs University, Turkey

Category: Sports and Entertainment Mining Sport Activities..................................................................................................................... 7348 Iztok Fister Jr., University of Maribor, Slovenia Iztok Fister, University of Maribor, Slovenia Sport Exergames for Physical Education........................................................................................... 7358 Pooya Soltani, University of Porto, Portugal João Paulo Vilas-Boas, University of Porto, Portugal

Category: Systems and Software Engineering Adopting Open Source Software in Smartphone Manufacturers’ Open Innovation Strategy........... 7369 Mohammad Nabil Almunawar, Universiti Brunei Darussalam, Brunei Muhammad Anshari, Universiti Brunei Darussalam, Brunei Heru Susanto, Indonesian Institute of Sciences, Indonesia & Tunghai University, Taiwan Assessing Computer-Aided Design Skills......................................................................................... 7382 Yi Lin Wong, The Hong Kong Polytechnic University, Hong Kong Kin Wai Michael Siu, The Hong Kong Polytechnic University, Hong Kong The Challenges of Teaching and Learning Software Programming to Novice Students................... 7392 Seyed Reza Shahamiri, Manukau Institute of Technology, New Zealand Developing a Glossary for Software Projects.................................................................................... 7399 Tamer Abdou, Suez Canal University, Egypt Pankaj Kamthan, Concordia University, Canada Nazlie Shahmir, WestJet Airlines Limited, Canada



Displaying Hidden Information in Glossaries................................................................................... 7411 Marcela Ridao, Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina Jorge Horacio Doorn, Universidad Nacional del Oeste, Argentina & Universidad Nacional de La Matanza, Argentina Dynamic Situational Adaptation of a Requirements Engineering Process........................................ 7422 Graciela Dora Susana Hadad, Universidad Nacional del Oeste, Argentina & Universidad de Belgrano, Argentina Jorge Horacio Doorn, Universidad Nacional del Oeste, Argentina & Universidad Nacional de La Matanza, Argentina Viviana Alejandra Ledesma, Universidad Nacional del Oeste, Argentina & Universidad Nacional de La Matanza. Argentina A Formal Approach to the Distributed Software Control for Automated Multi-Axis Manufacturing Machines................................................................................................................... 7435 Gen’ichi Yasuda, Nagasaki Institute of Applied Science, Japan Model-Driven Software Modernization............................................................................................. 7447 Liliana Maria Favre, Universidad Nacional Del Centro De La Provincia De Buenos Aires, Argentina Liliana Martinez, Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina Claudia Teresa Pereira, Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina Mutation Testing Applied to Object-Oriented Languages................................................................. 7459 Pedro Delgado-Pérez, University of Cádiz, Spain Inmaculada Medina-Bulo, University of Cádiz, Spain Juan José Domínguez-Jiménez, University of Cádiz, Spain Object-Oriented Programming in Computer Science........................................................................ 7470 Rahime Yilmaz, Istanbul University, Turkey Anil Sezgin, Yildiz Technical University, Turkey Sefer Kurnaz, Istanbul Esenyurt University, Turkey Yunus Ziya Arslan, Istanbul University, Turkey The Past, Present, and Future of UML.............................................................................................. 7481 Rebecca Platt, Murdoch University, Australia Nik Thompson, Curtin University, Australia Petri Nets Identification Techniques for Automated Modelling of Discrete Event Processes........... 7488 Edelma Rodriguez-Perez, CINVESTAV Unidad Guadalajara, Mexico Ernesto Lopez-Mellado, CINVESTAV Unidad Guadalajara, Mexico



Research and Development on Software Testing Techniques and Tools........................................... 7503 Tamilarasi T, VIT University, India M. Prasanna, VIT University, India The Role of Feedback in Software Process Assessment.................................................................... 7514 Zeljko Stojanov, University of Novi Sad, Serbia Dalibor Dobrilovic, University of Novi Sad, Serbia Secure Software Development of Cyber-Physical and IoT Systems.................................................. 7525 Muthu Ramachandran, Leeds Metropolitan University, UK Software Literacy............................................................................................................................... 7539 Elaine Khoo, University of Waikato, New Zealand Craig Hight, University of Newcastle, Australia Software Process Improvement for Web-Based Projects Comparative View.................................... 7549 Thamer Al-Rousan, Isra University, Pakistan A Study of Contemporary System Performance Testing Framework................................................ 7563 Alex Ng, Federation University, Australia Shiping Chen, CSIRO Data61, Australia A Tale of Two Agile Requirements Engineering Practices............................................................... 7577 Pankaj Kamthan, Concordia University, Canada Terrill Fancott, Concordia University, Canada Towards an Understanding of Performance, Reliability, and Security.............................................. 7588 Ye Wang, Zhejiang Gongshang University, China Bo Jiang, Zhejiang Gongshang University, China Weifeng Pan, Zhejiang Gongshang University, China Understanding User Experience........................................................................................................ 7599 Camille Dickson-Deane, University of Melbourne, Australia Hsin-Liang (Oliver) Chen, University of Massachusetts Boston, USA The What, How, and When of Formal Methods................................................................................ 7609 Aristides Dasso, Universidad Nacional de San Luis, Argentina Ana Funes, Universidad Nacional de San Luis, Argentina

Category: Teacher Education Constructing Preservice Teachers’ Knowledge of Technology Integration....................................... 7623 Kathleen A. Paciga, Columbia College Chicago, USA Angela Fowler, Erikson Institute, USA Mary Quest, Erikson Institute, USA



Effectiveness of Teacher Training in Using Latest Technologies...................................................... 7635 Revathi Viswanathan, B. S. Abdur Rahman University, India Role of Educational Leaders in Supporting Beginning Teachers in Al Ain Schools in the UAE..... 7647 Salam Omar Ali, Brighton Collage Al Ain, UAE The Technological Pedagogical Content Knowledge of EFL Teachers (EFL TPACK).................... 7659 Mehrak Rahimi, Shahid Rajaee Teacher Training University, Iran Shakiba Pourshahbaz, Shahid Rajaee Teacher Training University, Iran Ubiquitous Teachers’ Training and Lessons Learned with the uProf! Model................................... 7671 Sabrina Leone, Università Politecnica delle Marche, Italy Giovanni Biancofiore, giovannibiancofiore.com, Italy Video Considerations for the World Language edTPA...................................................................... 7682 Elizabeth Goulette, Georgia State University, USA Pete Swanson, Georgia State University, USA

Category: Theoretical Computer Science Efficient Optimization Using Metaheuristics..................................................................................... 7693 Sergio Nesmachnow, Universidad de la República, Uruguay An Essay on Denotational Mathematics............................................................................................ 7704 Giuseppe Iurato, University of Palermo, Italy Quantum Computing and Quantum Communication........................................................................ 7715 Göran Pulkkis, Arcada University of Applied Sciences, Finland Kaj J. Grahn, Arcada University of Applied Sciences, Finland Sleptsov Net Computing.................................................................................................................... 7731 Dmitry A. Zaitsev, International Humanitarian University, Ukraine

Category: Ubiquitous and Pervasive Computing Energy Conservation in the Era of Ubiquitous Computing............................................................... 7745 P. P. Abdul Haleem, Farook College, India From General Services to Pervasive and Sensitive Services............................................................. 7754 Mario Vega-Barbas, KTH-Royal Institute of Technology, Sweden & ESNE-Universidad Camilo José Cela, Spain Iván Pau, Universidad Politécnica de Madrid, Spain Fernando Seoane, KI-Karolinska Institutet, Sweden & University of Borås, Sweden



Home UbiHealth................................................................................................................................ 7765 John Sarivougioukas, “G. Gennimatas” Athens General Hospital, Greece Aristides Vagelatos, CTI&P, Greece Konstantinos Parsopoulos, University of Ioannina, Greece Isaac E. Lagaris, University of Ioannina, Greece Multifaceted Applications of the Internet of Things.......................................................................... 7775 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand The Role of U-FADE in Selecting Persuasive System Features........................................................ 7785 Isaac Wiafe, Ghana Institute of Management and Public Administration, Ghana Social Computing.............................................................................................................................. 7796 Nolan Hemmatazad, University of Nebraska at Omaha, USA Ubiquitous Computing, Contactless Points, and Distributed Stores.................................................. 7805 Marco Savastano, Sapienza University of Rome, Italy Eleonora Pantano, Middlesex University London, UK Saverino Verteramo, University of Calabria, Italy

Category: Urban and Regional Development Climate Change as a Driving Force on Urban Energy Consumption Patterns.................................. 7815 Mostafa Jafari, Agricultural Research Education and Extension Organization (AREEO), Iran Pete Smith, University of Aberdeen, UK Determination of Urban Growth by the Night-Time Images............................................................. 7831 Emre Yücer, Erzincan University, Turkey Arzu Erener, Kocaeli University, Turkey Need for Rethinking Modern Urban Planning Strategies Through Integration of ICTs.................... 7843 Rounaq Basu, Indian Institute of Technology Bombay, India Arnab Jana, Indian Institute of Technology Bombay, India Reconstructive Architectural and Urban Digital Modelling.............................................................. 7856 Roberta Spallone, Politecnico di Torino, Italy Regional Development and Air Freight Service Needs for Regional Communities.......................... 7869 Tarryn Kille, Griffith University, Australia Paul Bates, University of Southern Queensland, Australia Patrick S. Murray, University of Southern Queensland, Australia



Category: Virtual Learning Environments Creating Active Learning Spaces in Virtual Worlds.......................................................................... 7880 Reneta D. Lansiquot, City University of New York, USA Tamrah D. Cunningham, New York University, USA Zianne Cuff, City University of New York, USA Developments in MOOC Technologies and Participation Since 2012.............................................. 7888 Jeremy Riel, University of Illinois at Chicago, USA Kimberly A. Lawless, University of Illinois at Chicago, USA Massive Open Online Courses and Integrating Open Source Technology and Open Access Literature Into Technology-Based Degrees....................................................................................... 7898 Maurice Dawson, University of Missouri – St. Louis, USA Sharon Burton, Grand Canyon University, USA Dustin Bessette, National Graduate School of Quality Management, USA Jorja Wright, University of Charleston, USA Open Source Software Virtual Learning Environment (OSS-VLEs) in Library Science Schools.... 7912 Rosy Jan, University of Kashmir, India Teacher Presence................................................................................................................................ 7922 Caroline M. Crawford, University of Houston – Clear Lake, USA Virtual Worlds in the Educational Context........................................................................................ 7935 Felipe Becker Nunes, Federal University of Rio Grande do Sul, Brazil Fabrício Herpich, Federal University of Rio Grande do Sul, Brazil Leo Natan Paschoal, University of Cruz Alta, Brazil

Category: Web Technologies Anger and Internet in Japan............................................................................................................... 7946 Hiroko Endo, Rissho University, Japan Kei Fuji, University of Tsukuba, Japan Determine Democracy in Web Design.............................................................................................. 7956 Rowena Li, Bayside High School Library, USA Discussion Processes in Online Forums............................................................................................ 7969 Gaowei Chen, The University of Hong Kong, Hong Kong Ming M Chiu, The Education University of Hong Kong, Hong Kong The Economics of Internetization...................................................................................................... 7980 Constantine E. Passaris, University of New Brunswick, Canada



An Efficient and Effective Index Structure for Query Evaluation in Search Engines........................ 7995 Yangjun Chen, University of Winnipeg, Canada Improving Usability of Website Design Using W3C Guidelines...................................................... 8006 G. Sreedhar, Rashtriya Sanskrit Vidyapeetha (Deemed University), India Internet Phenomenon......................................................................................................................... 8015 Lars Konzack, University of Copenhagen, Denmark An Overview of Crowdsourcing........................................................................................................ 8023 Eman Younis, Minia University, Egypt Revisiting Web 2.0............................................................................................................................. 8036 Michael Dinger, University of South Carolina Upstate, USA Varun Grover, Clemson University, USA Search Engine Optimization.............................................................................................................. 8046 Dimitrios Giomelakis, Aristotle University of Thessaloniki, Greece Andreas A. Veglis, Aristotle University of Thessaloniki, Greece Toward Trustworthy Web Services Coordination.............................................................................. 8056 Wenbing Zhao, Cleveland State University, USA Usability of CAPTCHA in Online Communities and Its Link to User Satisfaction.......................... 8066 Samar I. Swaid, Philander Smith College, USA Visual Identity Design for Responsive Web...................................................................................... 8079 Sunghyun Ryoo Kang, Iowa State University, USA Debra Satterfield, California State University – Long Beach, USA Web Site Mobilization Techniques.................................................................................................... 8087 John Christopher Sandvig, Western Washington University, USA What Accounts for the Differences in Internet Diffusion Rates Around the World?......................... 8095 Ravi Nath, Creighton University, USA Vasudeva Murthy, Creighton University, USA About the Contributors.................................................................................................................. clxxii Index................................................................................................................................................ clxxiii

Table of Contents in Alphabetical Order

Volume I, pp. 1-808 · Volume II, pp. 809-1615 · Volume III, pp. 1616-2420 · Volume IV, pp. 2421-3236 · Volume V, pp. 3237-4037; Volume VI, pp. 4038-4848 · Volume VII, pp. 4849-5668 · Volume VIII, pp. 5669-6475 · Volume IX, pp. 6476-7280 · Volume X, pp. 7281-8104

Acceptance of E-Reverse Auction From the Buyer Perspective.......................................................... 530 Cigdem Altin Gumussoy, Istanbul Technical University, Turkey Bilal Gumussoy, Shell and Turcas Petrol Inc., Turkey Accessibility in E-Government.......................................................................................................... 3516 Christian Sonnenberg, Florida Institute of Technology, USA Adapting Big Data Ecosystem for Landscape of Real World Applications........................................ 326 Jyotsna Talreja Wassan, University of Delhi, India Adaptive Hypermedia in Education................................................................................................... 2357 Vehbi Turel, The University of Bingol, Turkey Adaptive Hypermedia Systems.......................................................................................................... 6424 Ana Carolina Tomé Klock, Federal University of Rio Grande do Sul (UFRGS), Brazil Isabela Gasparini, Santa Catarina State University (UDESC), Brazil Marcelo Soares Pimenta, Federal University of Rio Grande do Sul (UFRGS), Brazil José Palazzo M. de Oliveira, Federal University of Rio Grande do Sul (UFRGS), Brazil Adaptive Networks for On-Chip Communication............................................................................. 4549 Mário Pereira Vestias, Instituto Politécnico de Lisboa, Portugal Addressing Digital Competencies, Curriculum Development, and Instructional Design in Science Teacher Education.............................................................................................................................. 1420 Isha DeCoito, Western University, Canada Adolescents’ Food Communication in Social Media:....................................................................... 6940 Christopher Holmberg, University of Gothenburg, Sweden Adopting Open Source Software in Smartphone Manufacturers’ Open Innovation Strategy........... 7369 Mohammad Nabil Almunawar, Universiti Brunei Darussalam, Brunei Muhammad Anshari, Universiti Brunei Darussalam, Brunei Heru Susanto, Indonesian Institute of Sciences, Indonesia & Tunghai University, Taiwan The Adoption and Transformation of Capability Maturity Models in Government.......................... 3526 Terry F. Buss, Carnegie Mellon University, Australia

 



Adoption and Use of Mobile Money Services in Nigeria.................................................................. 2724 Olayinka David-West, Pan-Atlantic University, Nigeria Immanuel Ovemeso Umukoro, Pan-Atlantic University, Nigeria Omotayo Muritala, Pan-Atlantic University, Nigeria Advanced ICT Methodologies (AIM) in the Construction Industry................................................... 539 M. Reza Hosseini, Deakin University, Australia Saeed Banihashemi, University of Technology Sydney, Australia Fahimeh Zaeri, Auckland University of Technology, New Zealand Alireza Adibfar, University of Florida, USA Advanced Model of Complex Information System........................................................................... 4391 Miroslav Svitek, Czech Technical University in Prague, Czech Republic Advanced Recommender Systems..................................................................................................... 1735 Young Park, Bradley University, USA Advancement and Application of Scientometric Indicators for Evaluation of Research Content..... 6739 Tazeem Zainab, University of Kashmir, India Zahid Ashraf Wani, University of Kashmir, India Affect-Sensitive Computer Systems.................................................................................................. 4124 Nik Thompson, Curtin University, Australia Tanya McGill, Murdoch University, Australia David Murray, Murdoch University, Australia Agent-Based Social Networks........................................................................................................... 6950 Federico Bergenti, Università degli Studi di Parma, Italy Agostino Poggi, Università degli Studi di Parma, Italy Michele Tomaiuolo, Università degli Studi di Parma, Italy Amplifying the Significance of Systems Thinking in Organization.................................................... 551 Mambo Governor Mupepi, Grand Valley State University, USA Sylvia C. Mupepi, Grand Valley State University, USA Jaideep Motwani, Grand Valley State University, USA Analysis and Assessment of Cross-Language Question Answering Systems................................... 4471 Juncal Gutiérrez-Artacho, University of Granada, Spain María-Dolores Olvera-Lobo, University of Granada, Spain Analysis of Two Phases Queue With Vacations and Breakdowns Under T-Policy........................... 1570 Khalid Alnowibet, King Saud University, Saudi Arabia Lotfi Tadj, Fairleigh Dickinson University, Canada Anger and Internet in Japan............................................................................................................... 7946 Hiroko Endo, Rissho University, Japan Kei Fuji, University of Tsukuba, Japan



The Application of Crowdsourced Processes in a Business Environment.......................................... 563 Katarzyna Kopeć, Tischner European University, Poland Anna Szopa, Jagiellonian University, Poland Application of Fuzzy Numbers to Assessment Processes................................................................. 3215 Michael Voskoglou, Graduate Technological Educational Institute (T.E.I.), Greece Application of Gamification to Blended Learning in Higher Education........................................... 3238 Kamini Jaipal-Jamani, Brock University, Canada Candace Figg, Brock University, Canada Application of Geospatial Mashups in Web GIS for Tourism Development.................................... 3403 Somnath Chaudhuri, Maldives National University, Maldives Nilanjan Ray, Adamas University, India Application of Soft Set in Game Theory........................................................................................... 3226 B. K. Tripathy, VIT University, India Sooraj T. R., VIT University, India Radhakrishna N. Mohanty, VIT University, India Applications of Artificial Neural Networks in Economics and Finance............................................ 6631 Iva Mihaylova, University of St. Gallen, Switzerland Applying Artificial Intelligence to Financial Investing........................................................................... 1 Hayden Wimmer, Georgia Southern University, USA Roy Rada, University of Maryland – Baltimore County, USA Apps as Assistive Technology............................................................................................................. 266 Emily C. Bouck, Michigan State University, USA Sara M. Flanagan, University of Kentucky, USA Missy D. Cosby, Michigan State University, USA Archaeological GIS for Land Use in South Etruria Urban Revolution in IX-VIII Centuries B.C..... 3419 Giuliano Pelfer, University of Florence, Italy Architectural Framework for the Implementation of Information Technology Governance in  Organisations....................................................................................................................................... 810 Thami Batyashe, Cape Peninsula University of Technology, South Africa Tiko Iyamu, Cape Peninsula University of Technology, South Africa Architecture as a Tool to Solve Business Planning Problems............................................................. 573 James McKee, Independent Researcher, Australia Architecture of an Open-Source Real-Time Distributed Cyber Physical System.............................. 1227 Stefano Scanzio, CNR-IEIIT, Italy



Are Social Marketing Investments Used as a Tool for Voluntary Reporting or Disclosure?............ 5737 Tugba Ucma Uysal, Mugla Sitki Kocman University, Turkey Ganite Kurt, Gazi University, Turkey Ali Naci Karabulut, Mugla Sitki Kocman University, Turkey Artificial Ethics...................................................................................................................................... 88 Laura L. Pană, Polytechnic University of Bucharest, Romania Artificial Intelligence............................................................................................................................. 98 Steven Walczak, University of South Florida, USA Artificial Intelligence Review.............................................................................................................. 106 Amal Kilani, University of Gabes Tunisia, Tunisia Ahmed Ben Hamida, University of Sfax, Tunisia Habib Hamam, University of Moncton, Canada Artificial Neural Networks................................................................................................................... 120 Steven Walczak, University of South Florida, USA Artificial Neural Networks and Their Applications in Business........................................................ 6642 Trevor J. Bihl, Air Force Institute of Technology, USA William A. Young, Ohio University, USA Gary R. Weckman, Ohio University, USA Aspects of Various Community Detection Algorithms in Social Network Analysis........................ 6961 Nicole Belinda Dillen, St. Thomas’ College of Engineering and Technology, India Aruna Chakraborty, St. Thomas’ College of Engineering and Technology, India Assessing Computer-Aided Design Skills......................................................................................... 7382 Yi Lin Wong, The Hong Kong Polytechnic University, Hong Kong Kin Wai Michael Siu, The Hong Kong Polytechnic University, Hong Kong Assistive Technology and Human Capital for Workforce Diversity.................................................... 277 Ben Tran, Alliant International University, USA Assistive Technology for Supporting Communication, Occupation, and Leisure by Children With Severe to Profound Developmental Disabilities.................................................................................. 287 Fabrizio Stasolla, University of Bari, Italy Viviana Perilli, Lega del Filo d’Oro – Molfetta, Italy Adele Boccasini, Lega del Filo d’Oro – Termini Imerese, Italy Augmented Reality for Tourist Destination Image Formation.......................................................... 4031 Azizul Hassan, Cardiff Metropolitan University, UK



Automatic Emotion Recognition Based on Non-Contact Gaits Information...................................... 132 Jingying Wang, University of Chinese Academy of Sciences, China Baobin Li, University of Chinese Academy of Sciences, China Changye Zhu, University of Chinese Academy of Sciences, China Shun Li, University of Chinese Academy of Sciences, China Tingshao Zhu, University of Chinese Academy of Sciences, China Automatic Item Generation................................................................................................................ 2369 Mark Gierl, University of Alberta, Canada Hollis Lai, University of Alberta, Canada Xinxin Zhang, University of Alberta, Canada Autonomic Cooperative Communications......................................................................................... 6499 Michal Wodczak, Samsung Electronics, Poland Barcodes vs. RFID and Its Continued Success in Manufacturing and Services................................ 5273 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA Becoming Smart, Innovative, and Socially Responsible in Supply Chain Collaboration................. 5285 Goknur Arzu Akyuz, University of Turkish Aeronautical Association, Turkey Guner Gursoy, Okan University, Turkey Benchmarking Performance Indicators of Indian Rail Freight by DEA Approach............................. 587 Neeraj Bhanot, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India Harwinder Singh, Guru Nanak Dev Engineering College, India Bi-Directional Business/IT Alignment................................................................................................ 601 Hashim Chunpir, German Climate Computing Centre (DKRZ), Germany Frederik Schulte, University of Hamburg, Germany Yannick Bartens, University of Hamburg, Germany Stefan D. Voß, University of Hamburg, Germany Big Data Analysis and Mining............................................................................................................. 338 Carson K. Leung, University of Manitoba, Canada Big Data Analytics for Tourism Destinations...................................................................................... 349 Wolfram Höpken, University of Applied Sciences Ravensburg-Weingarten, Germany Matthias Fuchs, Mid-Sweden University, Sweden Maria Lexhagen, Mid-Sweden University, Sweden Big Data and Simulations for the Solution of Controversies in Small Businesses............................ 6907 Milena Janakova, Silesian University in Opava, Czech Republic Big Data, Knowledge, and Business Intelligence................................................................................ 943 G. Scott Erickson, Ithaca College, USA Helen N. Rothberg, Marist College, USA



Big Data Time Series Stream Data Segmentation Methods................................................................ 364 Dima Alberg, Shamoon College of Engineering (SCE), Israel Binary Decision Diagram Reliability for Multiple Robot Complex System..................................... 6825 Hamed Fazlollahtabar, Sharif University of Technology, Iran & National Elites Foundation, Iran Seyed Taghi Akhavan Niaki, Sharif University of Technology, Iran Biogeography-Based Optimization Applied to Wireless Communications Problems...................... 5967 Sotirios K. Goudos, Aristotle University of Thessaloniki, Greece Bioinformatics...................................................................................................................................... 419 Mark A. Ragan, The University of Queensland, Australia A Bio-Inspired, Distributed Control Approach to the Design of Autonomous Cooperative Behaviors in Multiple Mobile Robot Systems................................................................................... 6836 Gen’ichi Yasuda, Nagasaki Institute of Applied Science, Japan Bioinspired Solutions for MEMS Tribology....................................................................................... 431 R. Arvind Singh, Kumaraguru College of Technology (KCT), India S. Jayalakshmi, Kumaraguru College of Technology (KCT), India Bipolar Model in Collective Choice.................................................................................................. 7282 Ayeley P. Tchangani, Université Fédérale Toulouse Midi-Pyrénées, France Board Games AI.................................................................................................................................. 144 Tad Gonsalves, Sophia University, Japan Boosting the Social Development of the Majority Through the Creation of a Wireless Knowledge Society................................................................................................................................................ 5015 Danilo Piaggesi, Framericas, USA Bridging Between Cyber Politics and Collective Dynamics of Social Movement............................ 3538 Kazuhiko Shibuya, ROIS, Japan Building Gene Networks by Analyzing Gene Expression Profiles...................................................... 440 Crescenzio Gallo, University of Foggia, Italy Business Intelligence........................................................................................................................... 951 Richard T. Herschel, Saint Joseph’s University, USA Business Intelligence Impacts on Design of Enterprise Systems...................................................... 2932 Saeed Rouhani, University of Tehran, Iran Dusanka Milorad Lecic, Levi9 Global Sourcing Balkan, Serbia Business Model Innovation-Oriented Technology Management for Emergent Technologies.......... 4560 Sven Seidenstricker, Fraunhofer Institute for Industrial Engineering, Germany Ardilio Antonino, Fraunhofer Institute for Industrial Engineering, Germany



Business Sustainability Indices............................................................................................................ 609 Arunasalam Sambhanthan, Curtin University, Australia The Business Transformation Framework and Its Business Engineering Law Support for  (e)Transactions..................................................................................................................................... 636 Antoine Trad, Institute of Business and Information Systems Transformation Management, Switzerland Damir Kalpić, University of Zagreb, Croatia The Business Transformation Framework for Managers in Transformation Projects....................... 5607 Antoine Trad, IBITSM, Switzerland Damir Kalpić, University of Zagreb, Croatia The Business Transformation Framework, Agile Project and Change Management.......................... 620 Antoine Trad, Institute of Business and Information Systems Transformation Management, Switzerland Damir Kalpić, University of Zagreb, Croatia BYOD (Bring Your Own Device), Mobile Technology Providers, and Its Impacts on Business/ Education and Workplace/Learning Applications............................................................................. 5981 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA Carbon Capture From Natural Gas via Polymeric Membranes......................................................... 3043 Nayef Mohamed Ghasem, UAE University, UAE Nihmiya Abdul Rahim, UAE University, UAE Mohamed Al-Marzouqi, UAE University, UAE Cell Phone Conversation and Relative Crash Risk Update................................................................ 5992 Richard A. Young, Driving Safety Consulting, LLC, USA Censorship in the Digital Age the World Over.................................................................................. 7292 Kari D. Weaver, University of South Carolina – Aiken, USA Centrality Analysis of the United States Network Graph.................................................................. 1746 Natarajan Meghanathan, Jackson State University, USA Challenges and Implications of Health Literacy in Global Health Care........................................... 3734 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand Challenges for Big Data Security and Privacy.................................................................................... 373 M. Govindarajan, Annamalai University, India Challenges in Collecting Qualitative Data for Information Systems Studies.................................... 4480 Tiko Iyamu, Cape Peninsula University of Technology, South Africa Irja Naambo Shaanika, Namibia University of Science and Technology, Namibia



Challenges in Developing Adaptive Educational Hypermedia Systems........................................... 2380 Eileen O’Donnell, Trinity College Dublin, Ireland Liam O’Donnell, Dublin Institute of Technology, Ireland Challenges of Meta Access Control Model Enforcement to an Increased Interoperability................. 651 Sérgio Luís Guerreiro, University of Lisbon, Portugal The Challenges of Teaching and Learning Software Programming to Novice Students................... 7392 Seyed Reza Shahamiri, Manukau Institute of Technology, New Zealand Change Leadership Styles and Behaviors in Academic Libraries..................................................... 5194 John Kennedy Lewis, Salve Regina University, USA Changing Expectations of Academic Libraries................................................................................. 5204 Jennifer Ashley Wright Joe, Western Kentucky University, USA Chemistry Learning Through Designing Digital Games................................................................... 3248 Kamisah Osman, The National University of Malaysia, Malaysia Ah-Nam Lay, Institute of Teacher Education – Sultan Abdul Halim, Malaysia Classification of Traffic Events Notified in Social Networks’ Texts.................................................. 6973 Ana Maria Magdalena Saldana-Perez, Instituto Politecnico Nacional, Mexico Marco Antonio Moreno-Ibarra, Instituto Politécnico Nacional, Mexico Miguel Jesus Torres-Ruiz, Instituto Politécnico Nacional, Mexico Climate Change as a Driving Force on Urban Energy Consumption Patterns.................................. 7815 Mostafa Jafari, Agricultural Research Education and Extension Organization (AREEO), Iran Pete Smith, University of Aberdeen, UK Clinical Use of Video Games............................................................................................................ 3260 Ben Tran, Alliant International University, USA Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs............................. 6507 Natarajan Meghanathan, Jackson State University, USA Cloud Computing............................................................................................................................... 1026 Eduardo Correia, Christchurch Polytechnic Institute of Technology (CPIT), New Zealand Cloud Governance at the Local Communities................................................................................... 1033 Vasileios Yfantis, Ionian University, Greece Clouds of Quantum Machines........................................................................................................... 1040 Nilo Sylvio Serpa, Universidade Paulista, Brazil Cognitive and Psychological Factors in Cross-Language Information Retrieval.............................. 4490 Rowena Li, Bayside High School Library, USA



Cognitive Ergonomics in 2016............................................................................................................ 662 Ronald John Lofaro, Embry-Riddle Aeronautical University, USA Cognitive Mapping in Support of Intelligent Information Systems.................................................. 4570 Akbar Esfahanipour, Amirkabir University of Technology, Iran Ali Reza Montazemi, McMaster University, Canada Cognitive Process Elements of People Decision-Making.................................................................. 2076 Thais Spiegel, Rio de Janeiro State University, Brazil Comb Filters Characteristics and Current Applications.................................................................... 6007 Miriam Guadalupe Cruz-Jimenez, Institute INAOE, Mexico David Ernesto Troncoso Romero, CONACYT at ESCOM-IPN, Mexico Gordana Jovanovic Dolecek, Institute INAOE, Mexico Communication, Information, and Pragmatics.................................................................................. 1186 Adriana Braga, Pontifical Catholic University of Rio de Janeiro, Brazil Robert K. Logan, University of Toronto, Canada Communication Privacy Management and Mediated Communication............................................. 6985 Debra L. Worthington, Auburn University, USA Margaret Fitch-Hauser, Auburn University, USA Communities of Practice as a Source of Open Innovation................................................................ 5027 Diane-Gabrielle Tremblay, University of Quebec, Canada Community Broadband Networks and the Opportunity for E-Government Services....................... 3549 Idongesit Williams, Aalborg University, Denmark Community Outreach......................................................................................................................... 6685 Loriene Roy, The University of Texas at Austin, USA Antonia Frydman, The University of Texas at Austin, USA Community Science and Technology and Its Meaning to Potential Requirement............................ 7201 P. K. Paul, Raiganj University, India A. Bhuimali, Raiganj University, India Comprehensible Explanation of Predictive Models........................................................................... 2085 Marko Robnik-Šikonja, University of Ljubljana, Slovenia Comprehensive E-Learning Appraisal System.................................................................................. 5787 Jose Luis Monroy Anton, La Ribera University Hospital, Spain Juan Vicente Izquierdo Soriano, La Ribera University Hospital, Spain Maria Isabel Asensio Martinez, La Ribera University Hospital, Spain Felix Buendia Garcia, Polythecnical University of Valencia, Spain



Computational Intelligence Approaches to Computational Aesthetics............................................... 156 Erandi Lakshika, University of New South Wales, Australia Michael Barlow, University of New South Wales, Australia Computational Thinking in Innovative Computational Environments and Coding.......................... 2392 Alberto Ferrari, University of Parma, Italy Agostino Poggi, University of Parma, Italy Michele Tomaiuolo, University of Parma., Italy Computer Agent Technologies in Collaborative Learning and Assessment...................................... 2402 Yigal Rosen, Harvard University, USA Computer-Assisted Indian Matrimonial Services.............................................................................. 4136 Robert Leslie Fisher, Independent Researcher, USA Computer-Assisted Parallel Program Generation.............................................................................. 4583 Shigeo Kawata, Utsunomiya University, Japan Computer Fraud Challenges and Its Legal Implications.................................................................... 4837 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA Computer Information Library Clusters............................................................................................ 4399 Fu Yuhua, CNOOC Research Institute, China Computing Technologies and Science Fiction Cinema..................................................................... 3349 Rocío Carrasco-Carrasco, University of Huelva, Spain Concept and Practices of Cyber Supply Chain in Manufacturing Context........................................ 5306 Anisha Banu Dawood Gani, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia The Concept of Modularity in the Context of IS Project Outsourcing.............................................. 5317 Shahzada Benazeer, University of Antwerp, Belgium Philip Huysmans, University of Antwerp, Belgium Peter De Bruyn, University of Antwerp, Belgium Jan Verelst, University of Antwerp, Belgium The Concept of the Shapley Value and the Cost Allocation Between Cooperating Participants...... 2095 Alexander Kolker, GE Healthcare, USA Concerns and Challenges of Cloud Platforms for Bioinformatics....................................................... 455 Nicoletta Dessì, Università degli Studi di Cagliari, Italy Barbara Pes, Università degli Studi di Cagliari, Italy Consistency Is Not Enough in Byzantine Fault Tolerance................................................................ 1238 Wenbing Zhao, Cleveland State University, USA



Constrained Nonlinear Optimization in Information Science............................................................ 4594 William P. Fox, Naval Postgraduate School, USA Constructing Preservice Teachers’ Knowledge of Technology Integration....................................... 7623 Kathleen A. Paciga, Columbia College Chicago, USA Angela Fowler, Erikson Institute, USA Mary Quest, Erikson Institute, USA Consumer Adoption of PC-Based/Mobile-Based Electronic Word-of-Mouth.................................. 6019 Akinori Ono, Keio University, Japan Mai Kikumori, Ritsumeikan University, Japan Contemporary Leadership Development in Kazakhstan................................................................... 5626 Gainiya Tazhina, University of International Business, Kazakhstan Judith Parker, Teachers College, Columbia University, USA Arslan Ivashov, Kazakh Ablai Khan University of International Relations and World Languages, USA Context-Aware Approach for Restaurant Recommender Systems..................................................... 1757 Haoxian Feng, University of Ottawa, Canada Thomas Tran, University of Ottawa, Canada Context-Aware Personalization for Mobile Services......................................................................... 6031 Abayomi Moradeyo Otebolaku, Liverpool John Moores University, UK Maria Teresa Andrade, University of Porto, Portugal Continuous Assurance and the Use of Technology for Business Compliance.................................... 820 Rui Pedro Figueiredo Marques, Universidade de Aveiro, Portugal Corporate Disclosure Measurement................................................................................................... 1896 Md. Salah Uddin Rajib, Jahangirnagar University, Bangladesh Md. Qutub Uddin Sajib, China University of Geosciences (CUG), China Corporate Social Responsibility.......................................................................................................... 671 Ben Tran, Alliant International University, USA Cost-Effective 3D Stereo Visualization for Creative Learning.......................................................... 2411 R. S. Kamath, Chatrapati Shahu Institute of Business Education and Research, India R. K. Kamat, Shivaji University, India Cost Estimation and Security Investment of Security Projects.......................................................... 4849 Yosra Miaoui, University of Carthage, Tunisia Boutheina Fessi, University of Carthage, Tunisia Noureddine Boudriga, University of Carthage, Tunisia



Cost Evaluation of Synchronization Algorithms for Multicore Architectures.................................. 3989 Masoud Hemmatpour, Politecnico di Torino, Italy Renato Ferrero, Politecnico di Torino, Italy Filippo Gandino, Politecnico di Torino, Italy Bartolomeo Montrucchio, Politecnico di Torino, Italy Maurizio Rebaudengo, Politecnico di Torino, Italy Could Educational Technology Replace Traditional Schools in the Future?.................................... 2421 John K. Hope, University of Auckland, New Zealand Creating Active Learning Spaces in Virtual Worlds.......................................................................... 7880 Reneta D. Lansiquot, City University of New York, USA Tamrah D. Cunningham, New York University, USA Zianne Cuff, City University of New York, USA Creative Collaborative Virtual Environments.................................................................................... 4146 Luís Eustáquio, Universidade do Porto, Portugal Catarina Carneiro de Sousa, Polytechnic Institute of Viseu, Portugal Crisis Response and Management..................................................................................................... 1396 Sergey V. Zykov, National Research University Higher School of Economics, Russia A Critical Overview of Image Segmentation Techniques Based on Transition Region.................... 1308 Yu-Jin Zhang, Tsinghua University, China Critical Success Factors in E-Democracy Implementation............................................................... 3561 Aderonke A. Oni, Covenant University, Nigeria Adekunle O. Okunoye, Xavier University, USA Cuckoo Search Algorithm for Solving Real Industrial Multi-Objective Scheduling Problems........ 4369 Mariappan Kadarkarainadar Marichelvam, Mepco Schlenk Enginering College, India Mariappan Geetha, Kamaraj College of Engineering and Technology, India Current Scenario of Youth Entrepreneurship in India....................................................................... 2989 Neeta Baporikar, Namibia University of Science and Technology, Namibia & University of Pune, India Customer Lifetime Value................................................................................................................... 1584 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand Cyber Behaviors in Seeking Health Information............................................................................... 3745 Xiaojun (Jenny) Yuan, University of Albany, USA José A. Pino, Universidad de Chile, Chile Cyber Bullying................................................................................................................................... 1695 Jo Ann Oravec, University of Wisconsin – Whitewater, USA



Cyberbullying.................................................................................................................................... 4157 Gilberto Marzano, Rezekne Academy of Technologies, Latvia Cyberbullying Among Malaysian Children Based on Research Evidence........................................ 1704 Sarina Yusuf, Universiti Putra Malaysia, Malaysia Md. Salleh Hj. Hassan, Universiti Putra Malaysia, Malaysia Adamkolo Mohammed Mohammed Ibrahim, Universiti Putra Malaysia, Malaysia & University of Maiduguri, Nigeria Cyberinfrastructure, Cloud Computing, Science Gateways, Visualization, and Cyberinfrastructure Ease of Use........................................................................................................................................ 1063 Craig A. Stewart, Indiana University, USA Richard Knepper, Indiana University, USA Matthew R. Link, Indiana University, USA Marlon Pierce, Indiana University, USA Eric Wernert, Indiana University, USA Nancy Wilkins-Diehr, San Diego Supercomputer Center, USA Cyberloafing and Constructive Recreation........................................................................................ 4316 Jo Ann Oravec, University of Wisconsin – Whitewater, USA Cyber Security Protection for Online Gaming Applications............................................................. 1647 Wenbing Zhao, Cleveland State University, USA Database Techniques for New Hardware........................................................................................... 1947 Xiongpai Qin, Renmin University of China, China Yueguo Chen, Renmin University of China, China Data-Centric Benchmarking.............................................................................................................. 1772 Jérôme Darmont, Université de Lyon, Lyon 2, ERIC EA3083, France Data Journalism................................................................................................................................. 1196 Andreas A. Veglis, Aristotle University of Thessaloniki, Greece Charalampos P. Bratsas, Aristotle University of Thessaloniki, Greece Data Linkage Discovery Applications............................................................................................... 1783 Richard S. Segall, Arkansas State University, USA Shen Lu, University of South Florida, USA Data Mining and Knowledge Discovery in Databases...................................................................... 1907 Ana Azevedo, Polytechnic Institute of Porto, Portugal Data Mining and the KDD Process.................................................................................................... 1919 Ana Funes, Universidad Nacional de San Luis, Argentina Aristides Dasso, Universidad Nacional de San Luis, Argentina



Data Mining to Identify Project Management Strategies in Learning Environments........................ 1934 Ana González-Marcos, Universidad de La Rioja, Spain Joaquín Ordieres-Meré, Universidad Politécnica de Madrid, Spain Fernando Alba-Elías, Universidad de La Rioja, Spain Data Visualization Strategies for Computer Simulation in Bioelectromagnetics.............................. 1249 Akram Gasmelseed, Qassim University, Saudi Arabia Ali H. Alharbi, Qassim University, Saudi Arabia Decimal Hardware Multiplier............................................................................................................ 4607 Mário Pereira Vestias, INESC-ID/ISEL/IPL, Portugal Decision Filed Theory....................................................................................................................... 2108 Lan Shao, University of Oulu, Finland Jouni Markkula, University of Oulu, Finland Defining and Characterising the Landscape of eHealth.................................................................... 5864 Yvonne O’Connor, University College Cork, Ireland Ciara Heavin, University College Cork, Ireland Defining and Conceptualizing Cyberbullying................................................................................... 4168 Karin Spenser, Nottingham Trent University, UK Lucy R. Betts, Nottingham Trent University, UK Deployment of Enterprise Architecture From the Activity Theory Perspective............................... 2943 Tiko Iyamu, Cape Peninsula University of Technology, South Africa Irja Naambo Shaanika, Namibia University of Science and Technology – Windhoek, Namibia Designing Engaging Instruction for the Adult Learners.................................................................... 1432 Karen Swanson, Mercer University, USA Geri Collins, Mercer University, USA Design, Manufacture, and Selection of Ankle-Foot-Orthoses............................................................. 298 Hasan Kemal Surmen, Istanbul University, Turkey Nazif Ekin Akalan, Istanbul University, Turkey Yunus Ziya Arslan, Istanbul Univesity, Turkey Design of Compensators for Comb Decimation Filters..................................................................... 6043 Gordana Jovanovic Dolecek, Institute INAOE Puebla, Mexico Destination @-Branding of Ten European Capitals Through the Institutional Stems and Commercial Logos............................................................................................................................. 4038 Elena Bocci, Sapienza University of Rome, Italy Annamaria Silvana de Rosa, Sapienza University of Rome, Italy Laura Dryjanska, Sapienza University of Rome, Italy



Determination of Urban Growth by the Night-Time Images............................................................. 7831 Emre Yücer, Erzincan University, Turkey Arzu Erener, Kocaeli University, Turkey Determine Democracy in Web Design.............................................................................................. 7956 Rowena Li, Bayside High School Library, USA Determining Impact of Demographics on Perceived Service Quality in Online Retail.................... 2882 Prateek Kalia, I. K. Gujral Punjab Technical University, India Penny Law, Regenesys Business School, South Africa Richa Arora, Regenesys Institute of Management, India Developing a Glossary for Software Projects.................................................................................... 7399 Tamer Abdou, Suez Canal University, Egypt Pankaj Kamthan, Concordia University, Canada Nazlie Shahmir, WestJet Airlines Limited, Canada Developing Creativity and Learning Design by Information and Communication Technology (ICT) in Developing Contexts............................................................................................................ 4178 Chunfang Zhou, Aalborg University, Denmark Aparna Purushothaman, Aalborg University, Denmark Developing Global Supply Chain Manager for Business Expansion................................................ 5327 Puspita Wulansari, Telkom University, Indonesia Yudi Fernando, Universiti Malaysia Pahang, Malaysia Development of Communication Skills through Auditory Training Software in Special  Education........................................................................................................................................... 2431 Eduardo C. Contreras, Autonomous University of Coahuila, Mexico Isis I. Contreras, Saltillo Institute of Technology, Mexico Development of Image Engineering in the Last 20 Years................................................................. 1319 Yu-Jin Zhang, Tsinghua University, China Development of Personal Information Privacy Concerns Evaluation............................................... 4862 Anna Rohunen, University of Oulu, Finland Jouni Markkula, University of Oulu, Finland Developments in MOOC Technologies and Participation Since 2012.............................................. 7888 Jeremy Riel, University of Illinois at Chicago, USA Kimberly A. Lawless, University of Illinois at Chicago, USA Digital Animation for Representing Architectural Design.................................................................. 973 Roberta Spallone, Politecnico di Torino, Italy



Digital Archives for Preserving and Communicating Architectural Drawings................................. 5213 Roberta Spallone, Politecnico di Torino, Italy Francesca Paluan, Politecnico di Torino, Italy Digital Divide.................................................................................................................................... 4619 Patrick Flanagan, St. John’s University, USA Digital Literacy.................................................................................................................................. 2225 Anirban Ray, UNC Wilmington, USA Digital Literacy for the 21st Century................................................................................................. 2235 Hiller A. Spires, North Carolina State University, USA Casey Medlock Paul, North Carolina State University, USA Shea N. Kerkhoff, North Carolina State University, USA Digital Literacy in Theory and Practice............................................................................................. 2243 Heidi Julien, State University of New York at Buffalo, USA Digital Storytelling in Language Classes........................................................................................... 2442 Mehrak Rahimi, Shahid Rajaee Teacher Training University, Iran Digital Transformation Journeys in a Digitized Reality...................................................................... 682 Jurgen Janssens, QSpin, Belgium Digital Video Watermarking Using Diverse Watermarking Schemes............................................... 4872 Yash Gupta, Maulana Abul Kalam Azad University of Technology, India Shaila Agrawal, Maulana Abul Kalam Azad University of Technology, India Susmit Sengupta, Maulana Abul Kalam Azad University of Technology, India Aruna Chakraborty, Maulana Abul Kalam Azad University of Technology, India A Disability-Aware Mentality to Information Systems Design and Development.............................. 314 Julius T. Nganji, University of Ottawa, Canada Discrete Event Simulation in Inventory Management....................................................................... 5335 Linh Nguyen Khanh Duong, Auckland University of Technology, New Zealand Lincoln C. Wood, University of Otago, New Zealand & Curtin University, Australia Discussion Processes in Online Forums............................................................................................ 7969 Gaowei Chen, The University of Hong Kong, Hong Kong Ming M Chiu, The Education University of Hong Kong, Hong Kong Displaying Hidden Information in Glossaries................................................................................... 7411 Marcela Ridao, Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina Jorge Horacio Doorn, Universidad Nacional del Oeste, Argentina & Universidad Nacional de La Matanza, Argentina



Distance Teaching and Learning Platforms....................................................................................... 2455 Linda D. Grooms, Regent University, USA Distributed Methods for Multi-Sink Wireless Sensor Networks Formation..................................... 6522 Miriam A. Carlos-Mancilla, CINVESTAV Unidad Guadalajara, Mexico Ernesto Lopez-Mellado, CINVESTAV Unidad Guadalajara, Mexico Mario Siller, CINVESTAV Unidad Guadalajara, Mexico Distributed Parameter Systems Control and Its Applications to Financial Engineering....................... 15 Gerasimos Rigatos, Industrial Systems Institute, Greece Pierluigi Siano, University of Salerno, Italy Does Inter-Bank Investments Restraints Financing Performance of Islamic Banks?............................ 36 Mohammad Taqiuddin Mohamad, University of Malaya, Malaysia Munazza Saeed, University of Malaya, Malaysia Dotted Raster-Stereography................................................................................................................. 166 Muhammad Wasim, Usman Institute of Technology, Pakistan Fauzan Saeed, Usman Institute of Technology, Pakistan Abdul Aziz, Usman Institute of Technology, Pakistan Adnan Ahmed Siddiqui, Hamdard University, Pakistan Do Usability Design Features of a Mobile Game Influence Learning?............................................. 2466 Rex Perez Bringula, University of the East, Philippines Edison Cabrera, University of the East, Philippines Princess B. Calmerin, University of the East, Philippines Eduardo A. Lao, University of the East, Philippines Christian Gerard Sembrano, University of the East, Philippines Angelita D. Guia, University of the East, Philippines Joan P. Lazaro, University of the East, Philippines Alexis John M. Rubio, University of the East, Philippines Annaliza E. Catacutan, National University, Philippines Marilou N. Jamis, National University, Philippines Lalaine P. Abad, Department of Education, Philippines The Dual Nature of Participatory Web and How Misinformation Seemingly Travels...................... 6993 Sameer Kumar, University of Malaya, Malaysia Dynamic Situational Adaptation of a Requirements Engineering Process........................................ 7422 Graciela Dora Susana Hadad, Universidad Nacional del Oeste, Argentina & Universidad de Belgrano, Argentina Jorge Horacio Doorn, Universidad Nacional del Oeste, Argentina & Universidad Nacional de La Matanza, Argentina Viviana Alejandra Ledesma, Universidad Nacional del Oeste, Argentina & Universidad Nacional de La Matanza. Argentina



E-Activism Development and Growth............................................................................................... 3569 John G. McNutt, University of Delaware, USA Lauri Goldkind, Fordham University, USA E-Business and Big Data Strategy in Franchising............................................................................. 2686 Ye-Sho Chen, Louisiana State University, USA E-Business Supply Chains Drivers, Metrics, and ERP Integration................................................... 5345 Jean C. Essila, Northern Michigan University, USA E-Collaborative Learning (e-CL)....................................................................................................... 6336 Alexandros Xafopoulos, University College London, UK Ecological Performance as a New Metric to Measure Green Supply Chain Practices...................... 5357 June Poh Kim Tam, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia E-Commerce in Logistics and Supply Chain Management............................................................... 5367 Yasanur Kayikci, Turkish-German University, Turkey E-Commerce Models, Players, and Its Future................................................................................... 2739 Liguo Yu, Indiana University – South Bend, USA The Economics of Internetization...................................................................................................... 7980 Constantine E. Passaris, University of New Brunswick, Canada Educational Ontology Development.................................................................................................. 1441 Galip Kaya, Havelsan Inc., Turkey Arif Altun, Hacettepe University, Turkey Educational Serious Games Design................................................................................................... 3287 Ilias Karasavvidis, University of Thessaly, Greece Educational Technology and Intellectual Property............................................................................ 2477 Lesley S. J. Farmer, California State University – Long Beach, USA Effective Cultural Communication via Information and Communication Technologies and Social Media Use.......................................................................................................................................... 7002 Androniki Kavoura, Technological Educational Institute of Athens, Greece Stella Sylaiou, Aristotle University of Thessaloniki, Greece Effectively Communicating With Group Decision Support Systems Using Information Theory..... 2121 Jamie S. Switzer, Colorado State University, USA Ralph V. Switzer, Colorado State University, USA Effectiveness of Teacher Training in Using Latest Technologies...................................................... 7635 Revathi Viswanathan, B. S. Abdur Rahman University, India



The Effect of Innovative Communication Technologies in Higher Education.................................. 3827 Stavros Kiriakidis, University of Crete, Greece Efstathios Kefallonitis, State University of New York at Oswego, USA Androniki Kavoura, Technological Educational Institute of Athens, Greece The Effect of Social Media Networking in the Travel Industry......................................................... 4052 Androniki Kavoura, Technological Educational Institute of Athens, Greece Efstathios Kefallonitis, State University of New York at Oswego, USA An Efficient and Effective Index Structure for Query Evaluation in Search Engines........................ 7995 Yangjun Chen, University of Winnipeg, Canada Efficient Optimization Using Metaheuristics..................................................................................... 7693 Sergio Nesmachnow, Universidad de la República, Uruguay E-Government Service Adoption and the Impact of Privacy and Trust............................................ 3579 Mehree Iqbal, North South University, Bangladesh Nabila Nisha, North South University, Bangladesh Afrin Rifat, North South University, Bangladesh Eight Tips for the Theme, “Data and Forecasts”............................................................................... 4629 Alessio Drivet, GeoGebra Institute of Torino, Italy Electronic Health Record (EHR) Diffusion and an Examination of Physician Resistance............... 3678 Kristen MacIver, Northern Michigan University, USA Madison N. Ngafeeson, Northern Michigan University, USA Electronic Payment Frameworks....................................................................................................... 2749 Antonio Ruiz-Martínez, University of Murcia, Spain Oussama Tounekti, University of Murcia, Spain Antonio F. Skarmeta, University of Murcia, Spain Electronic Theses and Dissertations (ETDs)..................................................................................... 6748 Ralph Hartsock, University of North Texas, USA Daniel G. Alemneh, University of North Texas, USA An Empirical Study of Mobile/Handheld App Development Using Android Platforms.................. 6057 Wen-Chen Hu, University of North Dakota, USA Naima Kaabouch, University of North Dakota, USA Hung-Jen Yang, National Kaohsiung Normal University, Taiwan Empirical Verification of the Performance Measurement System..................................................... 5638 Aleksander Janeš, University of Primorska, Slovenia Employing Educational Robotics for the Development of Problem-Based Learning Skills............. 2492 Nikleia Eteokleous, Frederick University Cyprus, Cyprus



Encouraging Digital Literacy and ICT Competency in the Information Age.................................... 2253 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand Energy Conservation in the Era of Ubiquitous Computing............................................................... 7745 P. P. Abdul Haleem, Farook College, India Enhancing the Mobile User Experience Through Colored Contrasts................................................ 6070 Jean-Éric Pelet, ESCE International Business School, France Basma Taieb, University of Cergy Pontoise, France Enhancing the Resiliency of Smart Grid Monitoring and Control.................................................... 3056 Wenbing Zhao, Cleveland State University, USA Ensemble Clustering Data Mining and Databases............................................................................. 1962 Slawomir T. Wierzchon, Polish Academy of Sciences, Poland Enterprise Interoperability................................................................................................................. 2773 Ejub Kajan, State University of Novi Pazar, Serbia Entrepreneurship................................................................................................................................ 2998 Mehmet Eymen Eryılmaz, Uludağ University, Turkey Entrepreneurship as the Vantage Point.............................................................................................. 3009 Madhu Kishore Raghunath Raghunath Kamakula, GVP College of Engineering (Autonomous), India Chandra Sekhar Patro, GVP College of Engineering (Autonomous), India Entrepreneurship Concept, Theories, and New Approaches............................................................. 3020 José Manuel Saiz-Alvarez, Tecnologico de Monterrey, Mexico Martín García-Vaquero, Nebrija University, Spain Ergonomic Design of a Driver Training Simulator for Rural India................................................... 1260 Prabir Mukhopadhyay, Indian Institute of Information Technology Design and Manufacturing Jabalpur, India Vipul Vinzuda, National Institute of Design Gandhinagar, India ERP Systems Benefit Realization and the Role of ERP-Enabled Application Integration................ 2953 Joseph K. Nwankpa, Miami University, USA An Essay on Denotational Mathematics............................................................................................ 7704 Giuseppe Iurato, University of Palermo, Italy Ethical Ambiguities in the Privacy Policies of Mobile Health and Fitness Applications................. 6083 Devjani Sen, University of Ottawa, Canada Rukhsana Ahmed, University of Ottawa, Canada



Ethical Computing Continues From Problem to Solution................................................................. 4884 Wanbil William Lee, The Computer Ethics Society, Hong Kong & Wanbil & Associates, Hong Kong Evaluative Dimensions of Urban Tourism in Capital Cities by First-Time Visitors......................... 4064 Annamaria Silvana de Rosa, Sapienza University of Rome, Italy Laura Dryjanska, Sapienza University of Rome, Italy Elena Bocci, Sapienza University of Rome, Italy Evolutionary Algorithms for Global Decision Tree Induction.......................................................... 2132 Marek Kretowski, Bialystok University of Technology, Poland Marcin Czajkowski, Bialystok University of Technology, Poland E-Waste, Chemical Toxicity, and Legislation in India....................................................................... 3066 Prashant Mehta, National Law University Jodhpur, India Existential Aspects of the Development E-Culture........................................................................... 4189 Liudmila Vladimirovna Baeva, Astrakhan State University, Russia Experiences of Implementing a Large-Scale Blended, Flipped Learning Project............................. 3839 Hazel Owen, Ethos Consultancy NZ, New Zealand Nicola Dunham, Massey University, New Zealand Explaining and Predicting Users’ Continuance Usage Intention Toward E-Filing Utilizing Technology Continuance Theory......................................................................................................... 831 Santhanamery T., Universiti Teknologi MARA, Malaysia T. Ramayah, University Sains Malaysia, Malaysia Exploratory Data Analysis on Breast Cancer Prognosis.................................................................... 1794 Mohammad Mehdi Owrang O., American University, USA Yasmine M. Kanaan, Howard University, USA Robert L. Copeland Jr., Howard University,USA Melvin Gaskins, Howard University Hospital, USA Robert L. DeWitty Jr., Providence Hospital, USA Exploring Drivers of Closed Loop Supply Chain in Malaysian Automotive Industry...................... 5378 Fadzlina Mohd Fadzil, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia Exploring “Hacking,” Digital Public Art, and Implication for Contemporary Governance.............. 6695 Amadu Wurie Khan, University of Edinburgh, UK Chris Speed, University of Edinburgh, UK Exploring New Handwriting Parameters for Writer Identification.................................................... 4643 Verónica Inés Aubin, Universidad Nacional de La Matanza, Argentina Jorge Horacio Doorn, Universidad Nacional del Oeste, Argentina & Universidad Nacional de La Matanza, Argentina



Exploring the Growth of Wireless Communications Systems and Challenges Facing 4G  Networks............................................................................................................................................ 6094 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA Exploring Tourism Cluster in the Peripheral Mountain Area Based on GIS Mapping..................... 3434 Ya-Hui Hsueh, National Taichung University of Education, Taiwan Huey-Wen Chuang, National Taichung University of Education, Taiwan Wan-Chiang Hsieh, National Taichung Girls’ Senior High School, Taiwan Exposure to Video Games and Decision Making.............................................................................. 3296 Giuseppe Curcio, University of L’Aquila, Italy Sara Peracchia, University of L’Aquila, Italy An Extension to the Delone and Mclean Information Systems Success Model and Validation in the Internet Banking Context................................................................................................................. 49 Veeraraghavan Jagannathan, National Institute of Technology, India Senthilarasu Balasubramanian, National Institute of Technology, India Thamaraiselvan Natarajan, National Institute of Technology, India Facilitating Customer Relationship Management in Modern Business............................................. 1594 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand Facilitating Interaction Between Virtual Agents Through Negotiation Over Ontological Representation.................................................................................................................................... 2697 Fiona McNeill, Heriot-Watt University, UK Factors Contributing to the Effectiveness of Online Students and Instructors.................................. 1451 Michelle Kilburn, Southeast Missouri State University, USA Martha Henckell, Southeast Missouri State University, USA David Starrett, Columbia College, USA Factors Determining E-Shopping Compliance by Nigerians............................................................. 2761 Adamkolo Mohammed Mohammed Ibrahim, University of Maiduguri, Nigeria Md. Salleh Hj. Hassan, Universiti Putra Malaysia, Malaysia Sarina Yusuf, Universiti Putra Malaysia, Malaysia A Family Review of Parameter-Learning Models and Algorithms for Making Actionable Decisions............................................................................................................................................ 2142 Chun-Kit Ngan, The Pennsylvania State University, USA A Fast and Space-Economical Algorithm for the Tree Inclusion Problem....................................... 4502 Yangjun Chen, University of Winnipeg, Canada Yibin Chen, University of Winnipeg, Canada



Fault Tolerant Cloud Systems............................................................................................................ 1075 Sathish Kumar, VIT University, India Balamurugan B, VIT University, India Fault Tolerant Data Management for Cloud Services........................................................................ 1091 Wenbing Zhao, Cleveland State University, USA Fifty Shades of Dark Stories.............................................................................................................. 4077 Lea Kuznik, University of Maribor, Slovenia Financial Inclusion, Content, and Information Technologies in Latin America................................ 7214 Alberto Chong, Georgia State University, USA & Universidad del Pacifico, Peru Cecilia de Mendoza, Ministry of Production of Argentina, Argentina Financing Micro, Small, and Medium Enterprises in Indian Industry.............................................. 6916 Shromona Ganguly, Indian Institute of Management Calcutta, India & Reserve Bank of India, India A Flipped Learning Approach to University EFL Courses............................................................... 3850 Yasushige Ishikawa, Kyoto University of Foreign Studies, Japan Reiko Akahane-Yamada, Advanced Telecommunications Research Institute International (ATR), Japan Craig Smith, Kyoto University of Foreign Studies, Japan Masayuki Murakami, Kyoto University of Foreign Studies, Japan Mutsumi Kondo, Kyoto University of Foreign Studies, Japan Misato Kitamura, Advanced Telecommunications Research Institute International (ATR), Japan Yasushi Tsubota, Kyoto Institute of Technology, Japan Masatake Dantsuji, Kyoto University, Japan Flipping the Medical School Classroom............................................................................................ 5800 Kristina Kaljo, Medical College of Wisconsin, USA Laura Jacques, Medical College of Wisconsin, USA Flying Adhoc Networks Concept and Challenges............................................................................. 6106 Kuldeep Singh, Thapar University, India Anil Kumar Verma, Thapar University, India Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence  Framework........................................................................................................................................... 847 Satyadhyan Chickerur, KLE Technological University, India Supreeth Sharma, Akamai Technologies, India Prashant M. Narayankar, KLE Technological University, India Forensic Investigations in Cloud Computing..................................................................................... 1356 Diane Barrett, Bloomsburg University of Pennsylvania, USA



A Formal Approach to the Distributed Software Control for Automated Multi-Axis Manufacturing Machines................................................................................................................... 7435 Gen’ichi Yasuda, Nagasaki Institute of Applied Science, Japan The Foundation of (Business) Ethics’ Evolution............................................................................... 3173 Ben Tran, Alliant International University, USA A Framework for Exploring IT-Led Change in Morphing Organizations........................................... 694 Sharon A. Cox, Birmingham City University, UK A Framework for Profiling Prospective Students in Higher Education............................................. 3861 Santhosh Kumar Lakkaraju, Dakota State University, USA Deb Tech, Dakota State University, USA Shuyuan Deng, Dakota State University, USA From Business-to-Business Software Startup to SAP’s Acquisition................................................. 5388 John Wang, Montclair State University, USA Jeffrey Hsu, Fairleigh Dickinson University, USA Sylvain Jaume, Saint Peter’s University, USA From Digital Exclusion to Digital Inclusion for Adult Online Learners........................................... 2503 Virginia E. Garland, University of New Hampshire, USA From Digital Natives to Student Experiences With Technology....................................................... 2512 Sue Bennett, University of Wollongong, Australia Linda Corrin, University of Melbourne, Australia From General Services to Pervasive and Sensitive Services............................................................. 7754 Mario Vega-Barbas, KTH-Royal Institute of Technology, Sweden & ESNE-Universidad Camilo José Cela, Spain Iván Pau, Universidad Politécnica de Madrid, Spain Fernando Seoane, KI-Karolinska Institutet, Sweden & University of Borås, Sweden From Information Systems Outsourcing to Cloud Computing.......................................................... 1101 Mohammad Nabil Almunawar, Universiti Brunei Darussalam, Brunei Hasan Jawwad Almunawar, P. T. Tegar Kupas Mediatama, Indonesia From On-Premise ERP to Cloud ERP............................................................................................... 2965 Karim Mezghani, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia & University of Sfax, Tunisia From the Psychoanalyst’s Couch to Social Networks........................................................................ 7014 Annamaria Silvana de Rosa, Sapienza University of Rome, Italy Emanuele Fino, Psychologist, Psychometrician, Italy Elena Bocci, Sapienza University of Rome, Italy



The Fundamentals of Human-Computer Interaction......................................................................... 4199 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand The Future of High-Performance Computing (HPC)........................................................................ 4004 Herbert Cornelius, Intel Corporation EMEA, Germany Fuzzy Logic Approach in Risk Assessment...................................................................................... 6789 Çetin Karahan, Directorate General of Civil Aviation, Turkey Esra Ayça Güzeldereli, Afyon Kocatepe University, Turkey Aslıhan Tüfekci, Gazi University, Turkey A Gamification Update to the Taxonomy of Technology and Mental Health..................................... 995 Madeline R. Marks, University of Central Florida, USA Amanda C. Tan, University of Central Florida, USA Clint Bowers, University of Central Florida, USA Gender Differences in Advertising Engagement Using the Case of Facebooks Ads........................ 3359 Eva Lahuerta-Otero, University of Salamanca, Spain Rebeca Cordero-Gutiérrez, University of Salamanca, Spain The Gender Dimension in Urban Air Quality.................................................................................... 3371 Theodora Slini, Aristotle University of Thessaloniki, Greece Fotini-Niovi Pavlidou, Aristotle University of Thessaloniki, Greece General Perspectives on Electromyography Signal Features and Classifiers Used for Control of Human Arm Prosthetics....................................................................................................................... 492 Faruk Ortes, Istanbul University, Turkey Derya Karabulut, Halic University, Turkey Yunus Ziya Arslan, Istanbul University, Turkey Geographic Information System (GIS) Modeling Analysis and the Effects of Spatial Distribution and Environmental Factors on Breast Cancer Incidence................................................................... 3448 Akram Gasmelseed, University of Science and Technology, Sudan Ali H. Alharbi, Qassim University, Saudi Arabia Geographic Information Systems...................................................................................................... 3460 Paula Remoaldo, University of Minho, Portugal Vitor P. Ribeiro, University of Minho, Portugal Hélder Silva Lopes, University of Minho, Portugal Sara Catarina Gomes Silva, University of Minho, Portugal Geospatial Influence in Science Mapping.......................................................................................... 3473 Carlos Granell-Canut, Universitat Jaume I of Castellón, Spain Estefanía Aguilar-Moreno, Universitat Jaume I of Castellón, Spain



Graph-Based Concept Discovery....................................................................................................... 1974 Alev Mutlu, Kocaeli University, Turkey Pinar Karagoz, Middle East Technical University, Turkey Yusuf Kavurucu, Turkish Naval Research Center Command, Turkey A Graph-Intersection-Based Algorithm to Determine Maximum Lifetime Communication Topologies for Cognitive Radio Ad Hoc Networks........................................................................... 6536 Natarajan Meghanathan, Jackson State University, USA Green IT and the Struggle for a Widespread Adoption..................................................................... 3077 Edward T. Chen, University of Massachusetts – Lowell, USA Group Signature System Using Multivariate Asymmetric Cryptography......................................... 4898 Sattar J. Aboud, University of Bedfordshire, UK Group Synchronization for Multimedia Systems............................................................................... 6435 Dimitris N. Kanellopoulos, University of Patras, Greece The Growing Impact of ICT on Development in Africa.................................................................... 7223 Sherif H. Kamel, The American University in Cairo, Egypt GWAS as the Detective to Find Genetic Contribution in Diseases..................................................... 466 Simanti Bhattacharya, University of Kalyani, India Amit Das, University of Kalyani, India Haptics-Based Systems Characteristics, Classification, and Applications........................................ 4652 Abeer Bayousuf, King Saud University, Saudi Arabia Hend S. Al-Khalifa, King Saud University, Saudi Arabia Abdulmalik Al-Salman, King Saud University, Saudi Arabia Has Bitcoin Achieved the Characteristics of Money?....................................................................... 2784 Donovan Peter Chan Wai Loon, University of Malaya, Malaysia Sameer Kumar, University of Malaya, Malaysia Healthcare Data Analysis in the Internet of Things Era.................................................................... 1984 George Tzanis, Aristotle University of Thessaloniki, Greece Health Wearables Turn to Fashion..................................................................................................... 6114 Lambert Spaanenburg, Comoray AB, Sweden Heart Sound Analysis for Blood Pressure Estimation....................................................................... 1006 Rui Guedes, Faculdade de Medicina da Universidade do Porto, Portugal Henrique Cyrne Carvalho, Serviço de Cardiologia, Hospital de Santo António, Centro Hospitalar do Porto, Portugal Ana Castro, Universidade do Porto, Portugal



Hexa-Dimension Code of Practice for Data Privacy Protection........................................................ 4909 Wanbil William Lee, Wanbil & Associates, Hong Kong High-Performance Reconfigurable Computing................................................................................. 4018 Mário Pereira Vestias, Instituto Politécnico de Lisboa, Portugal The Holon/Parton Structure of the Meme, or The Unit of Culture.................................................... 4666 J. T. Velikovsky, University of Newcastle, Australia Home UbiHealth................................................................................................................................ 7765 John Sarivougioukas, “G. Gennimatas” Athens General Hospital, Greece Aristides Vagelatos, CTI&P, Greece Konstantinos Parsopoulos, University of Ioannina, Greece Isaac E. Lagaris, University of Ioannina, Greece How Exclusive Work Climates Create Barriers for Women in IS&T................................................ 3382 Katelyn R. Reynoldson, Old Dominion University, USA Debra A. Major, Old Dominion University, USA How the Crowdsourcing Enhance the Co-Creation Into the Virtual Communities............................. 707 Bahri Ammari Nedra, IHEC of Carthage, USA How Visualisation and Interaction Can Optimize the Cognitive Processes Towards Big Data.......... 381 Antonio Feraco, Fraunhofer IDM@NTU, Singapore Marius Erdt, Fraunhofer IDM@NTU, Singapore Human Psychomotor Performance Under the Exposure to Mobile Phones-Like Electromagnetic Fields.................................................................................................................................................. 6124 Giuseppe Curcio, University of L’Aquila, Italy Hybrid Computational Intelligence and the Basic Concepts and Recent Advances............................ 180 Georgios Dounias, University of the Aegean, Greece Hyper-Sensitivity in Global Virtual Teams......................................................................................... 720 Andre Araujo, Texas A&M University, USA ICT Eases Inclusion in Education...................................................................................................... 2521 Dražena Gašpar, University of Mostar, Bosnia and Herzegovina ICT Investments and Recovery of Troubled Economies................................................................... 2337 Ioannis Papadopoulos, Metropolitan College Thessaloniki, Greece Apostolos Syropoulos, Greek Molecular Computing Group, Greece ICT Standardization........................................................................................................................... 4679 Kai Jakobs, RWTH Aachen University, Germany



Identification of Green Procurement Drivers and Their Interrelationship Using Fuzzy TISM and MICMAC Analysis............................................................................................................................ 3086 Surajit Bag, Tega Industries South Africa Pty Ltd., South Africa Identification of Wireless Devices from their Physical Layer Radio-Frequency Fingerprints.......... 6136 Gianmarco Baldini, European Commission – Joint Research Centre, Italy Gary Steri, European Commission – Joint Research Centre, Italy Raimondo Giuliani, European Commission – Joint Research Centre, Italy Immersing People in Scientific Knowledge and Technological Innovation Through Disney’s Use of Installation Art............................................................................................................................... 4692 Jonathan Lillie, Loyola University Maryland, USA Michelle Jones-Lillie, Lillie Pad Studios, USA The Impact of Artificial Intelligence and Virtual Personal Assistants on Marketing........................ 5748 Christina L. McDowell Marinchak, University of Alaska Anchorage, USA Edward Forrest, University of Alaska Anchorage, USA Bogdan Hoanca, University of Alaska Anchorage, USA Impact of Business Groups on Payout Policy in India........................................................................... 61 Ahana Bose, Indian Institute of Management Calcutta, India The Impact of Carbon Nanotubes and Graphene on Electronics Industry........................................ 2897 Rafael Vargas-Bernal, Instituto Tecnológico Superior de Irapuato, Mexico Gabriel Herrera-Pérez, Instituto Tecnológico Superior de Irapuato, Mexico Margarita Tecpoyotl-Torres, Universidad Autónoma del Estado de Morelos, Mexico The Impact of Mobile Phones on Plastic Surgery and Burn Management........................................ 6147 Maria Giaquinto-Cilliers, Kimberley Hospital Complex, South Africa Tertius N. Potgieter, Kimberley Hospital Complex, South Africa Gert Steyn, Kimberley Hospital Complex, South Africa The Impact of the Impact of Meta-Data Mining From the SoReCom “A.S. de Rosa”  @-Library.......................................................................................................................................... 4404 Annamaria Silvana de Rosa, Sapienza University of Rome, Italy Laura Dryjanska, Sapienza University of Rome, Italy Elena Bocci, Sapienza University of Rome, Italy Implementing a Customer Relationship Management (CRM) System.............................................. 1605 Dimitra Skoumpopoulou, Northumbria University, UK Benjamin Franklin, Northumbria University, UK Implicit Cognitive Vulnerability........................................................................................................ 5149 Caroline M. Crawford, University of Houston – Clear Lake, US The Importance of Electronic Commerce in Modern Business......................................................... 2791 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand



Importance of Information Literacy.................................................................................................. 3870 Lidia Sanchez-Ruiz, University of Cantabria, Spain Beatriz Blanco, University of Cantabria, Spain Improved Checkpoint Using the Effective Management of I/O in a Cloud Environment................. 1116 Bakhta Meroufel, University of Oran1, Algeria Ghalem Belalem, University of Es Senia Oran1, Algeria Improving Competencies for the Courier Service Industry in Malaysia........................................... 2802 Hoo Yee Hui, Universiti Tunku Abdul Rahman, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia Improving Competitiveness Through Organizational Market Intelligence.......................................... 961 George Leal Jamil, Informações em Rede, Brazil Leandro Rocha Dos Santos, In3 Inteligência de Mercado, Brazil Cecília C. Jamil, Stockholm University, Sweden Improving Dependability of Robotics Systems................................................................................. 6847 Nidhal Mahmud, University of Hull, UK Improving Quality of Business in Next Generation Telecom Networks........................................... 6546 Vesna Radonjić Đogatović, University of Belgrade, Serbia Improving Usability of Website Design Using W3C Guidelines...................................................... 8006 G. Sreedhar, Rashtriya Sanskrit Vidyapeetha (Deemed University), India Increasing Student Engagement and Participation Through Course Methodology........................... 1463 T. Ray Ruffin, University of Phoenix, USA & Grand Canyon University, USA & Ashford University, USA & North Carolina Wesleyan College, USA Donna Patterson Hawkins, University of Phoenix, USA D. Israel Lee, Southern Illinois University, USA & University of Phoenix, USA Incremental Approach to Classification Learning............................................................................... 191 Xenia Alexandre Naidenova, Research Centre of Military Medical Academy – Saint Petersburg, Russia Indicators of Information and Communication Technology.............................................................. 4704 Gulnara Abdrakhmanova, National Research University Higher School of Economics, Russia Leonid Gokhberg, National Research University Higher School of Economics, Russia Alexander Sokolov, National Research University Higher School of Economics, Russia Indigenous Knowledge Systems........................................................................................................ 5036 Osarumwense Iguisi, University of Benin, Nigeria Osaro Rawlings Igbinomwanhia, University of Benin, Nigeria



Influencing People and Technology Using Human Resource Development (HRD) Philosophy...... 4326 Claretha Hughes, University of Arkansas, USA Matthew W. Gosney, University of Colorado – Health, USA Cynthia M. Sims, Clemson University, USA Information and Communication Technology Ethics and Social Responsibility.............................. 4920 Tomas Cahlik, Charles University Prague, Czech Republic & University of Economics Prague, Czech Republic Information and Its Conceptual Perspectives..................................................................................... 4422 José Poças Rascão, Institute Polytechnic of Setúbal, Portugal Information-Based Revolution in Military Affairs............................................................................ 7302 Rafal Kopec, Pedagogical University of Krakow, Poland Information-Centric Networking....................................................................................................... 6556 Mohamed Fazil Mohamed Firdhous, University of Moratuwa, Sri Lanka Information Needs of Users in the Tech Savvy Environment and the Influencing Factors............... 2264 Mudasir Khazer Rather, University of Kashmir, India Shabir Ahmad Ganaie, University of Kashmir, India Information Science and Technology in Crisis Response and Management..................................... 1407 Randy Basham, University of Texas at Arlington, USA Information Seeking Models in the Digital Age................................................................................ 4515 Mudasir Khazer Rather, University of Kashmir, India Shabir Ahmad Ganaie, University of Kashmir, India Information Systems and Technology Projects in Healthcare Organisations.................................... 3756 Jorge Gomes, ISEG, Universidade de Lisboa, Portugal Mário José Batista Romão, ISEG, Universidade de Lisboa, Portugal Information Technologies and Social Change................................................................................... 4715 Muhammet Ali Köroğlu, Uşak University, Turkey Cemile Zehra Köroğlu, Uşak University, Turkey Informed Decision Making With Enterprise Dynamic Systems Control.......................................... 2154 Sérgio Luís Guerreiro, Instituto Superior Técnico, University of Lisbon, Portugal & INESCID, Portugal The Infusion of Technology Within the Classroom Facilitates Students’ Autonomy in Their Learning............................................................................................................................................. 2532 Fariel Mohan, University of Trinidad and Tobago, Trinidad and Tobago Garry Soomarah, University of Trinidad and Tobago, Trinidad and Tobago



In-Memory Analytics......................................................................................................................... 1806 Jorge Manjarrez Sánchez, Instituto Tecnologico Superior de Jerez, Mexico Innovative Formalism for Biological Data Analysis.......................................................................... 1814 Calin Ciufudean, Stefan cel Mare University, Romania An Insight Into Deep Learning Architectures.................................................................................... 4528 Nishu Garg, VIT University, India Nikhitha P, VIT University, India B. K. Tripathy, VIT University, India Instructional Real World Community Engagement........................................................................... 1474 Caroline M. Crawford, University of Houston – Clear Lake, USA An Integrated Approach to Supply Chain Simulation....................................................................... 5398 Nenad Stefanovic, University of Kragujevac, Serbia Bozidar Radenkovic, University of Belgrade, Serbia Integrated Data Architecture for Business........................................................................................... 862 Richard Kumaradjaja, Renaissance Consulting, Indonesia An Integrated Electronic IQA System for HEI.................................................................................. 3881 Teay Shawyun, King Saud University, Saudi Arabia Integrated Paper-Based and Digital Learning Material for Smart Learners...................................... 2545 Sabrina Leone, Università Politecnica delle Marche, Italy Integrating Content Authentication Support in Media Services........................................................ 2908 Anastasia N. Katsaounidou, Aristotle University of Thessaloniki, Greece Charalampos A. Dimoulas, Aristotle University of Thessaloniki, Greece Integrating Evidence-Based Practice in Athletic Training Though Online Learning........................ 5810 Brittany A. Vorndran, Seton Hall University, USA Michelle Lee D’Abundo, Seton Hall University, USA Integrating Knowledge Management and Business Processes.......................................................... 5046 John Steven Edwards, Aston University, UK Integrating Sustainability and CSR in the Value Chain of the Information Technology Sector....... 3183 Patricia Martínez García de Leaniz, University of Cantabria, Spain María Elena García Ruiz, University of Cantabria, Spain Integrating Web-Based Technologies Into the Education and Training of Health Professionals...... 5820 Michelle Lee D’Abundo, Seton Hall University USA Cara Sidman, Arizona State University, USA



Intellectual Capital Measurement...................................................................................................... 5056 Lukasz Bryl, Poznan University of Economics and Business, Poland The Intelligence of E-CRM Applications and Approaches on Online Shopping Industry................ 1616 Bashar Shahir Ahmed, University Abdelmalek Essaadi (LEROSA Laborator), Morocco Fadi Amroush, Universidad de Salamanca, Spain Mohammed Ben Maati, University Abdelmalek Essaadi, Morocco Interactivity in Distance Education and Computer-Aided Learning, With Medical Education  Examples............................................................................................................................................ 5829 D. John Doyle, Cleveland Clinic, USA Patrick J. Fahy, Athabasca University, Canada Interface Trends in Human Interaction, the Internet of Things, and Big Data.................................. 4210 William J. Gibbs, Duquesne University, USA International Students in Online Courses.......................................................................................... 3900 María Ángeles Rodriguez Manzanares, Memorial University of Newfoundland, Canada Internet Addiction in Context............................................................................................................ 4223 Petra Vondrackova, Charles University in Prague, Czech Republic David Šmahel, Masaryk University – Brno, Czech Republic The Internet Behavior of Older Adults.............................................................................................. 7026 Elizabeth Mazur, Pennsylvania State University, USA Margaret L. Signorella, Pennsylvania State University, USA Michelle Hough, Pennsylvania State University, USA Internet-Facilitated Child Sexual Exploitation Crimes...................................................................... 1366 Keith F. Durkin, Ohio Northern University, USA Ronald L. DeLong, University of Dayton, USA Internet of Things Applications for Healthcare................................................................................. 3689 Ljubica Diković, Business Technical College, Serbia Internet Phenomenon......................................................................................................................... 8015 Lars Konzack, University of Copenhagen, Denmark Interoperability Frameworks for Distributed Systems....................................................................... 6566 José Carlos Martins Delgado, Universidade de Lisboa, Portugal The Intersection of Religion and Mobile Technology....................................................................... 6161 Wendi R. Bellar, Texas A&M University, USA Kyong James Cho, Texas A&M University, USA Heidi A. Campbell, Texas A&M University, USA



Intrusion Tolerance Techniques......................................................................................................... 4927 Wenbing Zhao, Cleveland State University, USA Investigating Diachronic Variation and Change in New Varieties of English................................... 1206 Rita Calabrese, University of Salerno, Italy iSchools Promoting “Information Science and Technology” (IST) Domain Towards Community, Business, and Society With Contemporary Worldwide Trend and Emerging Potentialities in  India................................................................................................................................................... 4723 P. K. Paul, Raiganj University, India D. Chatterjee, Seacom Skills University, India Issues and Challenges in Enterprise Social Media............................................................................ 7036 Sarabjot Kaur, IIT Kanpur, India Subhas Chandra Misra, IIT Kanpur, India IT Service Management Architectures.............................................................................................. 2920 Torben Tambo, Aarhus University, Denmark Jacob Filtenborg, Aarhus University, Denmark IT Solutions Supporting the Management of Higher Education Institutions in Poland.................... 3910 Elżbieta Janczyk-Strzała, Wroclaw School of Banking, Poland IT Strategy Follows Digitalization....................................................................................................... 873 Thomas Ochs, Villeroy & Boch, Germany Ute Anna Riemann, SAP SE, Germany Kinect Applications in Healthcare..................................................................................................... 5876 Roanna Lun, Cleveland State University, USA Wenbing Zhao, Cleveland State University, USA Knowledge Acquisition on Dante Alighieri’s Works......................................................................... 5067 Elvira Immacolata Locuratolo, ISTI-CNR, Italy Valentina Bartalesi Lenzi, ISTI-CNR, Italy Knowledge-Based Forensic Patterns and Engineering System......................................................... 1376 Vivek Tiwari, International Institute of Information Technology Naya Raipur, India R. S. Thakur, Maulana Azad National Institute of Technology, India Knowledge Management for Development (KM4D)......................................................................... 5077 Alexander G. Flor, University of the Philippines, Philippines Knowledge Management From the Metaphorical Perspective.......................................................... 5085 Magdalena Bielenia-Grajewska, University of Gdansk, Poland



Knowledge Networks in Higher Education........................................................................................ 3922 Filipa M. Ribeiro, University of Porto, Portugal Lack of Characteristics Management Causing Biggest Projects Failure........................................... 5650 Loredana Arana, University of Phoenix, USA Latest Advances on Benders Decomposition..................................................................................... 5411 Antonios Fragkogios, University of Thessaly, Greece Georgios K. D. Saharidis, University of Thessaly, Greece Lean and Six Sigma Innovation and Design........................................................................................ 729 Rick Edgeman, Utah State University, USA Lean Logistics of the Transportation of Fresh Fruit Bunches (FFB) in the Palm Oil Industry......... 5422 Cheah Cheng Teik, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia Learner Engagement in Blended Learning........................................................................................ 1487 Kristian J. Spring, Brigham Young University, USA Charles R. Graham, Brigham Young University, USA Tarah B. Ikahihifo, Brigham Young University, USA Learning Analytics............................................................................................................................. 5158 Constanţa-Nicoleta Bodea, Bucharest University of Economic Studies, Romania Maria-Iuliana Dascalu, University Politehnica of Bucharest, Romania Radu Ioan Mogos, Bucharest University of Economic Studies, Romania Stelian Stancu, Bucharest University of Economic Studies, Romania Learning From Imbalanced Data....................................................................................................... 1825 Lincy Mathews, M. S. Ramaiah Institute of Technology, India Seetha Hari, Vellore Institute of Technology, India Learning With Games and Digital Stories in Visual Programming.................................................. 3309 Wilfred W. F. Lau, The Chinese University of Hong Kong, China Learning With Mobile Devices.......................................................................................................... 6347 Helen Crompton, Old Dominion University, USA John Traxler, University of Wolverhampton, UK Leveraging Technology-Enhanced Teaching and Learning for Future IS Security Professionals.... 2558 Ciara Heavin, University College Cork, Ireland Karen Neville, University College Cork, Ireland Sheila O’Riordan, University College Cork, Ireland



Leveraging the Arduino Platform to Develop Information Technology Devices.............................. 3273 Diego Reforgiato Recupero, University of Cagliari, Italy Valentino Artizzu, University of Cagliari, Italy Francesca Cella, University of Cagliari, Italy Alessandro Cotza, University of Cagliari, Italy Davide Curcio, University of Cagliari, Italy Giorgio Amedeo Iengo, University of Cagliari, Italy Riccardo Macis, University of Cagliari, Italy Andrea Marras, University of Cagliari, Italy Simone Picci, University of Cagliari, Italy Michael Planu, University of Cagliari, Italy Riccardo Scasseddu, University of Cagliari, Italy Liberating Educational Technology Through the Socratic Method................................................... 2571 Frank G. Giuseffi, Lindenwood University, USA Literature Review of Augmented Reality Application in the Architecture, Engineering, and Construction Industry With Relation to Building Information............................................................ 983 Aydin Tabrizi, University of Kansas, USA Paola Sanguinetti, University of Kansas, USA Load Flow Analysis in Smart Grids................................................................................................... 3103 Osman Hasan, National University of Sciences and Technology, Pakistan Awais Mahmood, National University of Sciences and Technology, Pakistan Syed Rafay Hasan, Tennessee Technological University, USA Logic Programming for Intelligent Systems...................................................................................... 4736 James D. Jones, Liberty University, USA Machine Dreaming............................................................................................................................... 202 James Frederic Pagel, University of Colorado School of Medicine, USA The Main Concepts Behind the Dematerialization of Business Processes.......................................... 888 Liliana Ávila, University of Aveiro, Portugal Leonor Teixeira, University of Aveiro, Portugal Maintenance Policies Optimization of Medical Equipment in a Health Care Organization............. 3698 Juan Ignacio Roig, University of Castilla-La Mancha, Spain Andrés Gómez, University of Castilla-La Mancha, Spain Isabel Romero, University of Castilla-La Mancha, Spain María Carmen Carnero, University of Castilla-La Mancha, Spain & University of Lisbon, Portugal Major Techniques and Current Developments of Supply Chain Process Modelling......................... 5433 Henry Xu, The University of Queensland, Australia Renae Agrey, The University of Queensland, Australia



Making Sense of IS Project Stories................................................................................................... 5660 Darren Dalcher, University of Hertfordshire, UK Managerial Tools and Techniques for Decision Making................................................................... 2166 Davood Askarany, University of Auckland, New Zealand Managing and Visualizing Unstructured Big Data.............................................................................. 394 Ananda Mitra, Wake Forest University, USA Manufacturing vs. Services and the Role of Information Technology.............................................. 7234 Arnab Adhikari, Indian Institute of Management Ranchi, India Shromona Ganguly, Indian Institute of Management Calcutta, India Mapping the Dissemination of the Theory of Social Representations via Academic Social  Networks............................................................................................................................................ 7044 Annamaria Silvana de Rosa, Sapienza University of Rome, Italy Laura Dryjanska, Sapienza University of Rome, Italy Elena Bocci, Sapienza University of Rome, Italy Marketing and Marketing Plan for Information Services.................................................................. 5757 Sérgio Maravilhas, Universidade Salvador, Brazil Massive Digital Libraries (MDLs).................................................................................................... 5226 Andrew Philip Weiss, California State University – Northridge, USA Massive Open Online Courses and Integrating Open Source Technology and Open Access Literature Into Technology-Based Degrees....................................................................................... 7898 Maurice Dawson, University of Missouri – St. Louis, USA Sharon Burton, Grand Canyon University, USA Dustin Bessette, National Graduate School of Quality Management, USA Jorja Wright, University of Charleston, USA Mastering Electronic Government in the Digital Age....................................................................... 3591 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand A Maturity Model for Digital Literacies and Sustainable Development........................................... 2280 Ravi S. Sharma, Nanyang Technological University, Singapore Lin G. Malone, Nanyang Technological University, Singapore Chong Guan, SIM University, Singapore Ambica Dattakumar, Nanyang Technological University, Singapore The Measurement and Recognition of Intellectual Capital in the Process of Accounting Convergence Trends and Patterns...................................................................................................... 5669 Ionica Oncioiu, Titu Maiorescu University, Romania



Measuring Low Carbon Supply Chain.............................................................................................. 5446 Muhammad Shabir Shaharudin, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia Measuring Text Readability Using Reading Level............................................................................ 1499 James C. Brewer, Texas Tech University, USA Mechanisms of Electrical Conductivity in Carbon Nanotubes and Graphene.................................. 2673 Rafael Vargas-Bernal, Instituto Tecnológico Superior de Irapuato, Mexico Mediated Embodiment in New Communication Technologies......................................................... 4234 Laura Aymerich-Franch, CNRS-AIST JRL (Joint Robotics Laboratory), AIST, Japan Medical Equipment and Economic Determinants of Its Structure and Regulation in the Slovak Republic............................................................................................................................................. 5841 Beáta Gavurová, Technical University of Košice, Slovakia Viliam Kováč, Technical University of Košice, Slovakia Michal Šoltés, Technical University of Košice, Slovakia Metadata Standards in Digital Audio................................................................................................. 6447 Kimmy Szeto, Baruch College, City University of New York, USA Methodology of Climate Change Impact Assessment on Forests..................................................... 3114 Mostafa Jafari, Regional Institute of Forest and Rangelands (RIFR), Iran Methods for Improving Alias Rejections in Comb Filters................................................................. 4746 Gordana Jovanovic Dolecek, Institute INAOE Puebla, Mexico Methods for Simultaneous Improvement of Comb Pass Band and Folding Bands........................... 6171 Gordana Jovanovic Dolecek, Institute INAOE Puebla, Mexico Micro to Macro Social Connectedness through Mobile Phone Engagement.................................... 6184 Dominic Mentor, Columbia University, USA Mining Big Data and Streams.............................................................................................................. 406 Hoda Ahmed Abdelhafez, Suez Canal University, Egypt Mining Sport Activities..................................................................................................................... 7348 Iztok Fister Jr., University of Maribor, Slovenia Iztok Fister, University of Maribor, Slovenia Missing Part of Halal Supply Chain Management............................................................................ 5456 Ratih Hendayani, Telkom University, Indonesia Yudi Fernando, Universiti Malaysia Pahang, Malaysia



Mission, Tools, and Ongoing Developments in the So.Re.Com. “A.S. de Rosa” @-library............. 5237 Annamaria Silvana de Rosa, Sapienza University of Rome, Italy Mobile Applications for Automatic Object Recognition................................................................... 6195 Danilo Avola, University of Udine, Italy Gian Luca Foresti, University of Udine, Italy Claudio Piciarelli, University of Udine, Italy Marco Vernier, University of Udine, Italy Luigi Cinque, Sapienza University, Italy Mobile Apps Threats.......................................................................................................................... 6207 Donovan Peter Chan Wai Loon, University of Malaya, Malaysia Sameer Kumar, University of Malaya, Malaysia Mobile Game-Based Learning........................................................................................................... 6361 Boaventura DaCosta, Solers Research Group, USA Soonhwa Seok, Korea University, South Korea Carolyn Kinsell, Solers Research Group, USA Mobile Game-Based Learning in STEM Subjects............................................................................ 6376 Marcelo Leandro Eichler, Universidade Federal do Rio Grande do Sul, Brazil Gabriela Trindade Perry, Universidade Federal do Rio Grande do Sul, Brazil Ivana Lima Lucchesi, Universidade Federal do Rio Grande do Sul, Brazil Thiago Troina Melendez, Instituto Federal Sul-Riograndense, Brazil Mobile Learning in and out of the K-12 Classroom.......................................................................... 6388 Pena L. Bedesem, Kent State University, USA Tracy Arner, Kent State University, USA Mobile Technologies Impact on Economic Development in Sub-Saharan Africa............................ 6216 Adam Crossan, Letterkenny Institute of Technology, Ireland Nigel McKelvey, Letterkenny Institute of Technology, Ireland Kevin Curran, Ulster University, UK Mobile Testing System for Developing Language Skills................................................................... 5116 Svetlana Titova, Far Eastern Federal University, Russia Mobile Virtual Reality to Enhance Subjective Well-Being............................................................... 6223 Federica Pallavicini, Università di Milano-Bicocca, Italy Luca Morganti, Università di Milano-Bicocca, Italy Barbara Diana, Università di Milano-Bicocca, Italy Olivia Realdon, University of Milano-Bicocca, Italy Valentino Zurloni, Università di Milano-Bicocca, Italy Fabrizia Mantovani, Università di Milano-Bicocca, Italy



Model for Assessment of Environmental Responsibility in Health Care Organizations................... 3131 María Carmen Carnero, University of Castilla-La Mancha, Spain & University of Lisbon, Portugal A Model for Connected E-Government in the Digital Age............................................................... 3602 Qiuyan Fan, Western Sydney University, Australia Model-Driven Software Modernization............................................................................................. 7447 Liliana Maria Favre, Universidad Nacional Del Centro De La Provincia De Buenos Aires, Argentina Liliana Martinez, Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina Claudia Teresa Pereira, Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina The Morality of Reporting Safety Concerns in Aviation................................................................... 3194 Kawtar Tani, UCOL, New Zealand Motivational Factors of Telework........................................................................................................ 740 Arlene J. Nicholas, Salve Regina University, USA Moving Object Detection and Tracking Based on the Contour Extraction and Centroid  Representation...................................................................................................................................... 212 Naveenkumar M, National Institute of Technology Trichy, India Sriharsha K. V., National Institute of Technology Trichy, India Vadivel A, National Institute of Technology Trichy, India Multifaceted Applications of the Internet of Things.......................................................................... 7775 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand Multimedia-Enabled Dot Codes as Communication Technologies................................................... 6464 Shigeru Ikuta, Otsuma Women’s University, Japan Multimodal Literacy.......................................................................................................................... 1508 Maryann Tatum Tobin, University of Miami, USA Mutation Testing Applied to Object-Oriented Languages................................................................. 7459 Pedro Delgado-Pérez, University of Cádiz, Spain Inmaculada Medina-Bulo, University of Cádiz, Spain Juan José Domínguez-Jiménez, University of Cádiz, Spain The Nature, Extent, Causes, and Consequences of Cyberbullying.................................................... 1723 Michelle F. Wright, Masaryk University, Czech Republic A Nature-Inspired Metaheuristic Approach for Generating Alternatives.......................................... 2178 Julian Scott Yeomans, York University, Canada



The Nature of Cyber Bullying Behaviours........................................................................................ 4245 Lucy R. Betts, Nottingham Trent University, UK The Nature of Research Methodologies............................................................................................. 6756 Ben Tran, Alliant International University, USA Need for Rethinking Modern Urban Planning Strategies Through Integration of ICTs.................... 7843 Rounaq Basu, Indian Institute of Technology Bombay, India Arnab Jana, Indian Institute of Technology Bombay, India Negotiating Local Norms in Online Communication........................................................................ 1217 Jonathan R. White, Högskolan Dalarna, Sweden Neighborhood Rough-Sets-Based Spatial Data Analytics................................................................. 1835 Sharmila Banu K, VIT University, India B. K. Tripathy, VIT University, India The NetLab Network.......................................................................................................................... 7057 Dimitrina Dimitrova, York University, Canada Barry Wellman, NetLab Network, Canada The Networked Effect of Children and Online Digital Technologies................................................ 7312 Teresa Sofia Pereira Dias de Castro, University of Minho, Portugal António Osório, University of Minho, Portugal Emma Bond, University Campus Suffolk, UK Neural Networks and Their Accelerated Evolution From an Economic Analysis Perspective......... 6579 Stelian Stancu, Bucharest University of Economic Studies, Romania Constanţa-Nicoleta Bodea, Bucharest University of Economic Studies, Romania Oana Mădălina Popescu(Predescu), Bucharest University of Economic Studies, Romania Alina Neamţu(Idoraşi), Bucharest University of Economic Studies, Romania A Neuroaesthetic Approach to the Search of Beauty From the Consumer’s Perspective.................. 5767 Gemma García Ferrer, Rey Juan Carlos University, Spain Neuroscience Technology and Interfaces for Speech, Language, and Musical Communication...... 5886 Dionysios Politis, Aristotle University of Thessaloniki, Greece Miltiadis Tsalighopoulos, Aristotle University of Thessaloniki, Greece Georgios Kyriafinis, AHEPA University Hospital, Greece New Advances in E-Commerce......................................................................................................... 2810 Khaled Ahmed Nagaty, The British University in Egypt, Egypt New Faces of Digital Divide and How to Bridge It........................................................................... 7248 Viktor Freiman, Université de Moncton, Canada Dragana Martinovic, University of Windsor, Canada Xavier Robichaud, Univesité de Moncton, Canada



New Perspectives of Pattern Recognition for Automatic Credit Card Fraud Detection.................... 4937 Addisson Salazar, Universitat Politècnica de València, Spain Gonzalo Safont, Universitat Politècnica de València, Spain Alberto Rodriguez, Universidad Miguel Hernández de Elche, Spain Luis Vergara, Universitat Politècnica de València, Spain Nigerian Undergraduate Students’ Computer Competencies and Use of Information Technology Tools and Resources for Study Skills and Habits’ Enhancement...................................................... 2303 Adekunle Olusola Otunla, University of Ibadan, Nigeria Caleb Okoro Amuda, University of Ibadan, Nigeria Noise Trader........................................................................................................................................... 71 Po-Keng Cheng, State University of New York, Stony Brook University, USA Nominalizations in Requirements Engineering Natural Language Models....................................... 5127 Claudia S. Litvak, Universidad Nacional de La Matanza, Argentina & Universidad Nacional del Oeste, Argentina Graciela Dora Susana Hadad, Universidad Nacional del Oeste, Argentina Jorge Horacio Doorn, Universidad Nacional del Oeste, Argentina & Universidad Nacional de La Matanza, Argentina Notions of Maritime Green Supply Chain Management................................................................... 5465 Fairuz Jasmi, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia Novel Methods to Design Low-Complexity Digital Finite Impulse Response (FIR) Filters............. 6234 David Ernesto Troncoso Romero, CONACYT at ESCOM-IPN, Mexico Gordana Jovanovic Dolecek, Institute INAOE, Mexico N-Tuple Algebra as a Unifying System to Process Data and Knowledge.......................................... 1995 Boris Alexandrovich Kulik, Institute of Problems in Mechanical Engineering RAS, Russia Alexander Yakovlevich Fridman, Institute for Informatics and Mathematical Modelling, Kola Science Centre of RAS, Russia Object-Oriented Programming in Computer Science........................................................................ 7470 Rahime Yilmaz, Istanbul University, Turkey Anil Sezgin, Yildiz Technical University, Turkey Sefer Kurnaz, Istanbul Esenyurt University, Turkey Yunus Ziya Arslan, Istanbul University, Turkey Offshoring IT..................................................................................................................................... 5476 Susan Cockrell, Austin Peay State University, USA Terry Stringer Damron, Austin Peay State University, USA Amye M. Melton, Austin Peay State University, USA Alan D. Smith, Robert Morris University, USA



Online Academia............................................................................................................................... 2580 Magdalena Bielenia-Grajewska, University of Gdansk, Poland On-Line Credit and Debit Card Processing and Fraud Prevention for E-Business........................... 2707 James Williams, University of Pittsburgh, USA Online Dating/Dating Apps............................................................................................................... 7069 Vladimir Santiago Arias, Texas Tech University, USA Narissra Maria Punyanunt-Carter, Texas Tech University, USA Online Information Retrieval Systems Trending From Evolutionary to Revolutionary  Approach............................................................................................................................................ 4535 Zahid Ashraf Wani, University of Kashmir, India Huma Shafiq, University of Kashmir, India Online Learning Propelled by Constructivism.................................................................................. 2588 Kathaleen Reid-Martinez, Oral Roberts University, USA Linda D. Grooms, Regent University, USA Online Mediation in E-Commerce Matters........................................................................................ 2825 Ángela Coello Pulido, University of Vigo, Spain Online Prosocial Behaviors................................................................................................................ 7077 Michelle F. Wright, Pennsylvania State University, USA William Stanley Pendergrass, American Public University System, USA Online Social Networking Behavior and Its Influence Towards Students’ Academic  Performance....................................................................................................................................... 7088 Maslin Masrom, Universiti Teknologi Malaysia, Malaysia Selisa Usat, Universiti Teknologi Malaysia, Malaysia The Ontology of Randomness........................................................................................................... 1845 Jeremy Horne, The International Institute of Informatics and Systemics, USA Open Data and High-Tech Startups Towards Nascent Entrepreneurship Strategies.......................... 3032 Fotis Kitsios, University of Macedonia, Greece Maria Kamariotou, University of Macedonia, Greece Open Data Repositories in Knowledge Society................................................................................. 4436 Nadim Akhtar Khan, University of Kashmir, India Sara Sohrabzadeh, Tehran University of Medical Science, Iran Garvita Jhamb, University of Delhi, India An Open Learning Format for Lifelong Learners’ Empowerment.................................................... 1517 Sabrina Leone, Università Politecnica delle Marche, Italy



Open Source....................................................................................................................................... 6245 Heidi Lee Schnackenberg, SUNY Plattsburgh, USA Open Source Software Virtual Learning Environment (OSS-VLEs) in Library Science  Schools............................................................................................................................................... 7912 Rosy Jan, University of Kashmir, India The Optimal Workforce Staffing Solutions With Random Patient Demand in Healthcare  Settings............................................................................................................................................... 3711 Alexander Kolker, GE Healthcare, USA Optimization of Antenna Arrays and Microwave Filters Using Differential Evolution  Algorithms......................................................................................................................................... 6595 Sotirios K. Goudos, Aristotle University of Thessaloniki, Greece Optimizing Cloud Computing Costs of Services for Consumers...................................................... 1627 Eli Weintraub, Afeka Tel Aviv College of Engineering, Israel Yuval Cohen, Afeka Tel Aviv College of Engineering, Israel Order Statistics and Applications....................................................................................................... 1856 E. Jack Chen, BASF Corporation, USA Organizational Transparency............................................................................................................... 754 Gustavo de Oliveira Almeida, Federal University of the State of Rio de Janeiro, Brazil Claudia Cappelli, Federal University of the State of Rio de Janeiro, Brazil Cristiano Maciel, Federal University of Mato Grosso, Brazil An Overview of Crowdsourcing........................................................................................................ 8023 Eman Younis, Minia University, Egypt A Paradoxical World and the Role of Technology in Thana-Capitalism........................................... 4761 Maximiliano Emanuel Korstanje, University of Palermo, Argentina Parallel Development of Three Major Space Technology Systems and Human Side of Information Reference Services as an Essential Complementary Method............................................................ 3484 Joyce Gosata Maphanyane, University of Botswana, Botswana Parental Mediation of Adolescent Technology Use........................................................................... 7097 J. Mitchell Vaterlaus, Montana State University, USA Particle Shape Analysis Using Digital Image Processing.................................................................. 1331 Katia Tannous, University of Campinas, Brazil Fillipe Souza Silva, University of Campinas, Brazil The Past, Present, and Future of UML.............................................................................................. 7481 Rebecca Platt, Murdoch University, Australia Nik Thompson, Curtin University, Australia



Peer-to-Peer Health-Related Online Support Groups........................................................................ 3767 Neil S. Coulson, University of Nottingham, UK Performance Appraisal....................................................................................................................... 4337 Chandra Sekhar Patro, Gayatri Vidya Parishad College of Engineering (Autonomous), India Performance Measurement of Technology Ventures by Science and Technology Institutions......... 4774 Artie W. Ng, The Hong Kong Polytechnic University, Hong Kong Benny C. F. Cheung, The Hong Kong Polytechnic University, Hong Kong Peggy M. L. Ng, The Hong Kong Polytechnic University, Hong Kong Personalized Medicine....................................................................................................................... 5901 Sandip Bisui, Indian Institute of Technology (IIT) Kanpur, India Subhas Chandra Misra, Indian Institute of Technology (IIT) Kanpur, India Pervasive Mobile Health.................................................................................................................... 5908 Muhammad Anshari, Universiti Brunei Darussalam, Brunei Mohammad Nabil Almunawar, Universiti Brunei Darussalam, Brunei Petri Nets Identification Techniques for Automated Modelling of Discrete Event Processes........... 7488 Edelma Rodriguez-Perez, CINVESTAV Unidad Guadalajara, Mexico Ernesto Lopez-Mellado, CINVESTAV Unidad Guadalajara, Mexico Piracy and Intellectual Property Theft in the Internet Era................................................................. 1656 Shun-Yung Kevin Wang, University of South Florida – St. Petersburg, USA Jeremy J McDaniel, Principal Financial Group, USA Political Context Elements in Public Policy of Radio Frequency Information Technology and Electromagnetic Fields....................................................................................................................... 6710 Joshua M. Steinfeld, Old Dominion University, USA Potential Benefits and Current Limits in the Development of Demand Response............................ 3144 Clementina Bruno, University of Piemonte Orientale, Italy The Potential Role of the Software Industry in Supporting Economic Development....................... 7259 Sherif H. Kamel, The American University in Cairo, Egypt Power Consumption in Wireless Access Networks........................................................................... 6253 Vinod Kumar Mishra, B. T. Kumaon Institute of Technology, India Pankaja Bisht, B. T. Kumaon Institute of Technology, India Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine........... 5169 Sajid Umair, National University of Sciences and Technology (NUST), Pakistan Muhammad Majid Sharif, National University of Sciences and Technology (NUST), Pakistan Predictive Analytics and Intelligent Risk Detection in Healthcare Contexts..................................... 6806 Nilmini Wickramasinghe, Epworth HealthCare, Australia & Deakin University, Australia



Preferences, Utility, and Stochastic Approximation.......................................................................... 2188 Yuri P. Pavlov, Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Bulgaria Rumen D. Andreev, Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Bulgaria Presidential Elections Web 2.0.......................................................................................................... 3612 Ramona Sue McNeal, University of Northern Iowa, USA Lisa Dotterweich Bryan, Upper Iowa University, USA Pricing Based on Real-Time Analysis of Forklift Utilization Using RFID in Warehouse Management....................................................................................................................................... 5490 Numan Celebi, Sakarya University, Turkey Kübra Savaş, Istanbul University, Turkey Ihsan Hakan Selvi, Sakarya University, Turkey The Principle and Process of Digital Fabrication of Biomedical Objects........................................... 505 S. H. Choi, The University of Hong Kong, Hong Kong H. H. Cheung, The University of Hong Kong, Hong Kong W. K. Zhu, The University of Hong Kong, Hong Kong Privacy, Algorithmic Discrimination, and the Internet of Things..................................................... 4951 Jenifer Sunrise Winter, University of Hawaii at Manoa, USA The Process Model of Gameplay to Understand Digital Gaming Outcomes.................................... 3317 Linda K. Kaye, Edge Hill University, UK Profit Maximizing Network Modeling With Inventory and Capacity Considerations...................... 5503 Tan Miller, Rider University, USA Renato de Matta, University of Iowa, USA Project Control Using a Bayesian Approach..................................................................................... 5679 Franco Caron, Politecnico di Milano, Italy Project Management in Government................................................................................................. 3621 Shauneen Furlong, University of Ottawa, Canada & John Moores Liverpool University, UK Promoting Strategic Human Resource Management, Organizational Learning, and Knowledge Management in Modern Organizations............................................................................................. 4347 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand A Proposed Framework for Incorporating Big-Data Technology in National Crisis Management Center................................................................................................................................................. 2006 Magdy M. Kabeil, Al-Yamamah University, Saudi Arabia Ahmad M. Kabil, University of Wisconsin – Whitewater, USA



The Protection Policy for Youth Online in Japan.............................................................................. 4962 Nagayuki Saito, Ochanomizu University, Japan Madoka Aragaki, Business Breakthrough University, Japan A Psychological Perspective on Mobile Learning............................................................................. 6398 Melody M. Terras, University of the West of Scotland, UK Judith Ramsay, Manchester Metropolitan University, UK Public Policies for Providing Cloud Computing Services to SMEs of Latin America...................... 6727 Mohd Nayyer Rahman, Aligarh Muslim University, India Badar Alam Iqbal, Aligarh Muslim University, India QoS Architectures for the IP Network............................................................................................... 6609 Harry G. Perros, North Carolina State University, USA The Qualities and Potential of Social Media..................................................................................... 7106 Udo Richard Averweg, eThekwini Municipality, South Africa Marcus Leaning, University of Winchester, UK Quality Evaluation for Evolving Conceptual Database Design......................................................... 2020 Elvira Immacolata Locuratolo, CNR ISTI, Italy Quality Online Learning in Higher Education................................................................................... 3930 Deborah G. Wooldridge, Bowling Green State University, USA Sandra Poirier, Middle Tennessee State University, USA Julia M. Matuga, Bowling Green State University, USA Quantum Computing and Quantum Communication........................................................................ 7715 Göran Pulkkis, Arcada University of Applied Sciences, Finland Kaj J. Grahn, Arcada University of Applied Sciences, Finland Quantum Information Science Vis-à-Vis Information Schools......................................................... 4448 P. K. Paul, Raiganj University, India D. Chatterjee, Seacom Skills University, India A. Bhuimali, Raiganj University, India Query Languages for Graph Databases............................................................................................. 2031 Kornelije Rabuzin, University of Zagreb, Croatia Radio Frequency Identification Systems Within a Lean Supply Chain in a Global Environment.... 5516 Alan D. Smith, Robert Morris University, USA Terry Stringer Damron, Austin Peay State University, USA Susan Cockrell, Austin Peay State University, USA Amye M. Melton, Austin Peay State University, USA



Radio Frequency Identification Technologies and Issues in Healthcare........................................... 5918 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA Recommender Technologies and Emerging Applications................................................................. 1869 Young Park, Bradley University, USA Reconstructive Architectural and Urban Digital Modelling.............................................................. 7856 Roberta Spallone, Politecnico di Torino, Italy Recurrent Neural Networks for Predicting Mobile Device State....................................................... 6658 Juan Manuel Rodriguez, ISISTAN, UNICEN-CONICET, Argentina Alejandro Zunino, ISISTAN, UNICEN-CONICET, Argentina Antonela Tommasel, ISISTAN, UNICEN-CONICET, Argentina Cristian Mateos, ISISTAN, UNICEN-CONICET, Argentina Reflection as a Process From Theory to Practice............................................................................... 1529 Sonia Bharwani, Indian School of Management and Entrepreneurship, India Durgamohan Musunuri, Bhavan’s Usha and Lakshmi Mittal Institute of Management, India Reflections of the 1Malaysia Supply Chain (1MSC)......................................................................... 5527 Munira Halili, Universiti Sains Malaysia, Malaysia Latifah Naina Mohamed, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Malaysia Pahang, Malaysia Regional Development and Air Freight Service Needs for Regional Communities.......................... 7869 Tarryn Kille, Griffith University, Australia Paul Bates, University of Southern Queensland, Australia Patrick S. Murray, University of Southern Queensland, Australia Relationship Among Intelligence, Achievement Motivation, Type of School, and Academic Performance of Kenyan Urban Primary School Pupils..................................................................... 1540 Jessina Mukomunene Muthee, Kenyatta University, Kenya Catherine G. Murungi, Kenyatta University, Kenya The Relationship Between Online Formative Assessment and State Test Scores Using Multilevel Modeling............................................................................................................................................ 5183 Aryn C. Karpinski, Kent State University, USA Jerome V. D’Agostino, The Ohio State University, USA Anne-Evan K. Williams, Billings Middle School, USA Sue Ann Highland, Grand Canyon University, USA Jennifer A. Mellott, Kent State University, USA Reputational Mechanisms in Consumer-to-Consumer Online Commerce........................................ 2833 Mikhail I. Melnik, Kennesaw State University, USA



Research and Development on Software Testing Techniques and Tools........................................... 7503 Tamilarasi T, VIT University, India M. Prasanna, VIT University, India Research Methodology...................................................................................................................... 6767 Swati C. Jagdale, MAEER’s Maharashtra Institute of Pharmacy, India Rahul U. Hude, MAEER’s Maharashtra Institute of Pharmacy, India Aniruddha R. Chabukswar, MAEER’s Maharashtra Institute of Pharmacy, India Resource Management for Multimedia Services in Long Term Evaluation Networks..................... 6266 Vinod Kumar Mishra, B. T. Kumaon Institute of Technology, India Tanuja Pathak, B. T. Kumaon Institute of Technology, India Retail Prices and E-Commerce.......................................................................................................... 2841 Jihui Chen, Illinois State University, USA Reverse Engineering in Rehabilitation................................................................................................ 521 Emilia Mikołajewska, Nicolaus Copernicus University, Poland Marek Macko, Kazimierz Weilki University, Poland Zbigniew Szczepański, Kazimierz Wielki University, Poland Dariusz Mikołajewski, Kazimierz Wielki University, Poland A Review of Advances in Supply Chain Intelligence........................................................................ 5538 Nenad Stefanovic, University of Kragujevac, Serbia Danijela Milosevic, University of Kragujevac, Serbia A Review of Supply Chain Risk Management in Agribusiness Industry.......................................... 5550 Sri Widiyanesti, Telkom University, Indonesia Yudi Fernando, Universiti Malaysia Pahang, Malaysia Revisiting Web 2.0............................................................................................................................. 8036 Michael Dinger, University of South Carolina Upstate, USA Varun Grover, Clemson University, USA RNA Interference Therapeutics and Human Diseases......................................................................... 477 Dolly Sharma, PEC University of Technology, India Shailendra Singh, PEC University of Technology, India Trilok Chand, PEC University of Technology, India Robotics and Programming Integration as Cognitive-Learning Tools.............................................. 6859 Nikleia Eteokleous, Frederick University Cyprus, Cyprus The Role of Distance Education in Global Education....................................................................... 6412 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand



Role of Educational Leaders in Supporting Beginning Teachers in Al Ain Schools in the UAE..... 7647 Salam Omar Ali, Brighton Collage Al Ain, UAE The Role of Emerging Information Technologies for Supporting Supply Chain Management........ 5559 Zlatko Nedelko, University of Maribor, Slovenia Vojko Potocan, University of Maribor, Slovenia The Role of Feedback in Software Process Assessment.................................................................... 7514 Zeljko Stojanov, University of Novi Sad, Serbia Dalibor Dobrilovic, University of Novi Sad, Serbia The Role of U-FADE in Selecting Persuasive System Features........................................................ 7785 Isaac Wiafe, Ghana Institute of Management and Public Administration, Ghana The Roles of Digital Literacy in Social Life of Youth....................................................................... 2314 Dragana Martinovic, University of Windsor, Canada Viktor Freiman, Université de Moncton, Canada Chrispina S. Lekule, St. Augustine University of Tanzania, Tanzania Yuqi Yang, University of Windsor, Canada Rough-Set-Based Decision Model for Incomplete Information Systems.......................................... 2200 Safiye Turgay, Sakarya University, Turkey Orhan Torkul, Sakarya University, Turkey Tahsin Turgay, Sakarya University, Turkey Safeguarding of ATM............................................................................................................................ 77 Srividhya Srinivasan, University of Madras, India Priya Krishnamoorthy, SASTRA University, India Raghuraman Koteeswaran, SASTRA University, India Samsung Company and an Analysis of Supplier-Side Supply Chain Management and IT Applications....................................................................................................................................... 5570 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA Scanning for Blind Spots..................................................................................................................... 899 Barbara Jane Holland, Brooklyn Public Library, USA Schema Evolution in Conventional and Emerging Databases........................................................... 2043 Zouhaier Brahmia, University of Sfax, Tunisia Fabio Grandi, University of Bologna, Italy Barbara Oliboni, University of Verona, Italy Rafik Bouaziz, University of Sfax, Tunisia



Schema Versioning in Conventional and Emerging Databases......................................................... 2054 Zouhaier Brahmia, University of Sfax, Tunisia Fabio Grandi, University of Bologna, Italy Barbara Oliboni, University of Verona, Italy Rafik Bouaziz, University of Sfax, Tunisia Scholarly Identity in an Increasingly Open and Digitally Connected World..................................... 6779 Olga Belikov, Brigham Young University, USA Royce Kimmons, Brigham Young University, USA Science Animation and Students’ Attitudes....................................................................................... 2599 Sivasankar Arumugam, Sri Venkateswara College of Education, India Nancy Nirmala, Christ the King Matric Higher Secondary School, India Science, Ethics, and Weapons Research............................................................................................ 3205 John Forge, Independent Researcher, Australia Screencasts and Learning Styles........................................................................................................ 1548 Rui Alberto Jesus, CESPU, Instituto de Investigação e Formação Avançada em Ciências e Tecnologias da Saúde, Portugal Screen Culture.................................................................................................................................... 4255 Ana Melro, University of Aveiro, Portugal Lídia Oliveira, University of Aveiro, Portugal Search Engine Optimization.............................................................................................................. 8046 Dimitrios Giomelakis, Aristotle University of Thessaloniki, Greece Andreas A. Veglis, Aristotle University of Thessaloniki, Greece Secure Group Key Sharing Protocols and Cloud System.................................................................. 1667 Vaishali Ravindra Thakare, VIT University, India John Singh K, VIT University, India Secure Software Development of Cyber-Physical and IoT Systems.................................................. 7525 Muthu Ramachandran, Leeds Metropolitan University, UK Security of Identity-Based Encryption Algorithms........................................................................... 4975 Kannan Balasubramanian, Mepco Schlenk Engineering College, India M. Rajakani, Mepco Schlenk Engineering College, India Security of Internet-, Intranet-, and Computer-Based Examinations in Terms of Technical, Authentication, and Environmental, Where Are We?........................................................................ 1676 Babak Sokouti, Tabriz University of Medical Sciences, Iran Massoud Sokouti, Mashhad University of Medical Sciences, Iran



Self-Awareness and Motivation Contrasting ESL and NEET Using the SAVE System................... 1559 Laura Vettraino, Learning Community, Italy Valentina Castello, CIOFS FP, Italy Marco Guspini, educommunity – Educational Community, Italy Eleonora Guglielman, Learning Community, Italy Semantically Enhanced Authoring of Shared Media......................................................................... 6476 Charalampos A. Dimoulas, Aristotle University of Thessaloniki, Greece Andreas A. Veglis, Aristotle University of Thessaloniki, Greece George Kalliris, Aristotle University of Thessaloniki, Greece Semantic Intelligence........................................................................................................................... 220 Maria K. Koleva, Institute of Catalysis, Bulgarian Academy of Sciences, Bulgaria Serious Games Advancing the Technology of Engaging Information.............................................. 3327 Peter A. Smith, University of Central Florida, USA Clint Bowers, University of Central Florida, USA Serious Games in Entrepreneurship Education................................................................................... 800 Fernando Almeida, Polytechnic Institute of Gaya, Portugal Jorge Simões, ISPGaya, Portugal Service Quality and Perceived Value of Cloud Computing-Based Service Encounters.................... 1129 Eges Egedigwe, Dallas County Community College, USA Shaping Mega-Science Projects and Practical Steps for Success...................................................... 5690 Phil Crosby, Curtin University, Australia Short History of Social Networking and Its Far-Reaching Impact.................................................... 7116 Liguo Yu, Indiana University – South Bend, USA Simulating Complex Supply Chain Relationships Using Copulas.................................................... 5583 Krishnamurty Muralidhar, University of Oklahoma, USA Rathindra Sarathy, Oklahoma State University, USA The Skills of European ICT Specialists............................................................................................. 4785 Francesca Sgobbi, University of Brescia, Italy Sleptsov Net Computing.................................................................................................................... 7731 Dmitry A. Zaitsev, International Humanitarian University, Ukraine SMS & Civil Unrest........................................................................................................................... 6275 Innocent Chiluwa, Covenant University OTA, Nigeria



Social Business Process Modeling....................................................................................................... 765 Fadwa Yahya, University of Sfax, Tunisia Khouloud Boukadi, University of Sfax, Tunisia Zakaria Maamar, Zayed University, UAE Hanêne Ben-Abdallah, King Abdulaziz University, Saudi Arabia Social Commerce Using Social Network and E-Commerce.............................................................. 2851 Roberto Marmo, University of Pavia, Italy Social Computing.............................................................................................................................. 7796 Nolan Hemmatazad, University of Nebraska at Omaha, USA Social Issues in IT Project Teams:....................................................................................................... 777 Awie C. Leonard, University of Pretoria, South Africa D. H. Van Zyl, University of Pretoria, South Africa Social Media and Business Practices................................................................................................. 7126 Ashish Kumar Rathore, Indian Institute of Technology Delhi, India P. Vigneswara Ilavarasan, Indian Institute of Technology Delhi, India Social Media Applications as Effective Service Delivery Tools for Librarians................................ 5252 Ihuoma Sandra Babatope, Delta State College of Physical Education, Nigeria Social Media as a Channel of Constructive Dialogue for Tourism Businesses................................. 4088 Marios D. Sotiriadis, University of South Africa (UNISA), South Africa Social Media Credit Scoring.............................................................................................................. 7140 Billie Anderson, Ferris State University, USA J. Michael Hardin, Samford University, USA Social Media Use and Customer Engagement................................................................................... 5775 Aurora Garrido-Moreno, University of Malaga, Spain Nigel Lockett, University of Lancaster, UK Víctor García-Morales, University of Granada, Spain Social Network Analysis and the Study of University Industry Relations........................................ 7150 Fernando Cabrita Romero, University of Minho, Portugal Social Networking and Knowledge Sharing in Organizations........................................................... 7161 Sarabjot Kaur, Indian Institute of Technology Kanpur, India Subhas Chandra Misra, Indian Institute of Technology Kanpur, India Social Telerehabilitation.................................................................................................................... 5930 Gilberto Marzano, Rezekne Academy of Technologies, Latvia Socio-Economic Processes, User Generated Content, and Media Pluralism.................................... 7270 Androniki Kavoura, Technological Educational Institute of Athens, Greece



Sociological Perspectives on Improving Medical Diagnosis Emphasizing CAD.............................. 1017 Joel Fisher, Department of State, United States Government, USA Socio-Technical Change Perspective for ERP Implementation in Large Scale Organizations.......... 2975 Jessy Nair, PES University, India D. Bhanusree Reddy, VIT University, India Anand A. Samuel, VIT University, India Software Development Process Standards for Very Small Companies............................................. 6927 Rory V. O’Connor, Dublin City University, Ireland Software Evaluation From the Perspective of Patients and Healthcare Professionals....................... 3782 Rui Pedro Charters Lopes Rijo, Polytechnic Institute of Leiria, Portugal Domingos Alves, University of São Paulo, Brazil Software Literacy............................................................................................................................... 7539 Elaine Khoo, University of Waikato, New Zealand Craig Hight, University of Newcastle, Australia Software Process Improvement for Web-Based Projects Comparative View.................................... 7549 Thamer Al-Rousan, Isra University, Pakistan Sport Exergames for Physical Education........................................................................................... 7358 Pooya Soltani, University of Porto, Portugal João Paulo Vilas-Boas, University of Porto, Portugal State of the Art and Key Design Challenges of Telesurgical Robotics.............................................. 6872 Sajid Nisar, National University of Science and Technology, Pakistan Osman Hasan, National University of Science and Technology, Pakistan Staying Ahead in Business Through Innovation................................................................................ 5705 N. Raghavendra Rao, FINAIT Consultancy Services, India Steganography Using Biometrics...................................................................................................... 4985 Manashee Kalita, NERIST, India Swanirbhar Majumder, NERIST, India Strategic Information Systems Planning.............................................................................................. 912 Maria Kamariotou, University of Macedonia, Greece Fotis Kitsios, University of Macedonia, Greece Stress Testing Corporations and Municipalities and Supply Chains................................................. 6813 Frank Wolf, CSSTA L3C, USA A Study of Contemporary System Performance Testing Framework................................................ 7563 Alex Ng, Federation University, Australia Shiping Chen, CSIRO Data61, Australia



A Study on Extensive Reading in Higher Education......................................................................... 3945 Diana Presadă, Petroleum-Gas University of Ploiesti, Romania Mihaela Badea, Petroleum-Gas University of Ploiesti, Romania Suggestions for Communication of Information for Multicultural Co-Existence............................. 7327 Noriko Kurata, Tokyo University of Science, Suwa, Japan The Summers and Winters of Artificial Intelligence........................................................................... 229 Tad Gonsalves, Sophia University, Japan A Survey of People Localization Techniques Utilizing Mobile Phones............................................ 6286 Levent Bayındır, Ataturk University, Turkey Sustainable Advantages of Business Value of Information Technology............................................. 923 Jorge A. Romero, Towson University, USA Sustainable Competitive Advantage With the Balanced Scorecard Approach.................................. 5714 Jorge Gomes, ISEG, Universidade de Lisboa, Portugal Mário José Batista Romão, ISEG, Universidade de Lisboa, Portugal Swarm Intelligence for Multi-Objective Optimization in Engineering Design................................... 239 Janga Reddy Manne, Indian Institute of Technology Bombay, India A Tale of Two Agile Requirements Engineering Practices............................................................... 7577 Pankaj Kamthan, Concordia University, Canada Terrill Fancott, Concordia University, Canada Taxonomy for “Homo Consumens” in a 3.0 Era............................................................................... 1638 Carlos Ballesteros, Universidad Pontificia Comillas, Spain Teacher Presence................................................................................................................................ 7922 Caroline M. Crawford, University of Houston – Clear Lake, USA Teaching Media and Information Literacy in the 21st Century......................................................... 2292 Sarah Gretter, Michigan State University, USA Aman Yadav, Michigan State University, USA Technological Innovation and Use in the Early Days of Camera Phone Photo Messaging............... 6296 Jonathan Lillie, Loyola University, USA The Technological Pedagogical Content Knowledge of EFL Teachers (EFL TPACK).................... 7659 Mehrak Rahimi, Shahid Rajaee Teacher Training University, Iran Shakiba Pourshahbaz, Shahid Rajaee Teacher Training University, Iran Technology and Terror....................................................................................................................... 3637 Maximiliano Emanuel Korstanje, University of Palermo, Argentina Geoffrey Skoll, SUNY at Buffalo, USA



Technology Assessment of Information and Communication Technologies.................................... 4267 Armin Grunwald, Karlsruhe Institute of Technology, Germany Carsten Orwat, Karlsruhe Institute of Technology, Germany Technology Design and Routes for Tool Appropriation in Medical Practices.................................. 3794 Manuel Santos-Trigo, Cinvestav-IPN, Mexico Ernesto Suaste, Cinvestav-IPN, Mexico Paola Figuerola, Cinvestav-IPN, Mexico Technology Policies and Practices in Higher Education................................................................... 3954 Kelly McKenna, Colorado State University, USA Technology, Learning Styles, Values, and Work Ethics of Millennials............................................. 4358 Harish C. Chandan, Argosy University, USA Telesurgical Robotics and a Kinematic Perspective.......................................................................... 6882 Sajid Nisar, National University of Sciences and Technology, Pakistan Osman Hasan, National University of Sciences and Technology, Pakistan Theory and Practice of Online Knowledge Sharing.......................................................................... 5093 Will W. K. Ma, Hong Kong Shue Yan University, Hong Kong Three Cases of Unconventional Educational Uses of Digital Storytelling........................................ 2616 Emmanuel Fokides, University of the Aegean, Greece 3D Printing Applications in STEM Education.................................................................................. 2626 Norman Gwangwava, Botswana International University of Science and Technology, Botswana Catherine Hlahla, National University of Science and Technology, Zimbabwe 3D Scanning and Simulation of a Hybrid Refrigerator Using Photovoltaic Energy......................... 1277 Edith Obregón Morales, Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Mexico José de Jesús Pérez Bueno, Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Mexico Juan Carlos Moctezuma Esparza, Universidad Politécnica Metropolitana de Hidalgo, Mexico Diego Marroquín García, Universidad Tecnológica de San Juan del Río, Mexico Arturo Trejo Pérez, Universidad Tecnológica de San Juan del Río, Mexico Roberto Carlos Flores Romero, Universidad Tecnológica de San Juan del Río, Mexico Juan Manuel Olivares Ramírez, Universidad Tecnológica de San Juan del Río, Mexico Maria Luisa Mendoza López, Tecnológico Nacional de México, Instituto Tecnológico de Querétaro, Mexico Juan Carlos Solís Ulloa, Instituto Tecnológico Superior de Cintalapa, Mexico Yunny Meas Vong, Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Mexico Víctor Hugo Rodríguez Obregón, Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Mexico



A Three-Vector Approach to Blind Spots in Cybersecurity.............................................................. 1684 Mika Westerlund, Carleton University, Canada Dan Craigen, Carleton University, Canada Tony Bailetti, Carleton University, Canada Uruemu Agwae, Carleton University, Canada Throughput Dependence on SNR in IEEE802.11 WLAN Systems.................................................. 6618 Ikponmwosa Oghogho, Delta State University, Abraka-Oleh Campus, Nigeria Tools, Pedagogical Models, and Best Practices for Digital Storytelling........................................... 2641 Jari Multisilta, Tampere University of Technology, Finland Hannele Niemi, University of Helsinki, Finland Toward a Working Definition of Digital Literacy.............................................................................. 2326 Margaret-Mary Sulentic Dowell, Louisiana State University, USA Towards a General Theory of Information........................................................................................ 4459 Laura L. Pană, Polytechnic University of Bucharest, Romania Towards an Interdisciplinary Socio-Technical Definition of Virtual Communities.......................... 4278 Umar Ruhi, University of Ottawa, Canada Towards an Understanding of Performance, Reliability, and Security.............................................. 7588 Ye Wang, Zhejiang Gongshang University, China Bo Jiang, Zhejiang Gongshang University, China Weifeng Pan, Zhejiang Gongshang University, China Towards Modelling Effective Educational Games Using Multi-Domain Framework....................... 3337 Mifrah Ahmad, Universiti Teknologi PETRONAS, Malaysia Lukman Ab Rahim, Universiti Teknologi PETRONAS, Malaysia Kamisah Osman, Universiti Kebangsaan Malaysia, Malaysia Noreen Izza Arshad, Universiti Teknologi PETRONAS, Malaysia Toward Trustworthy Web Services Coordination.............................................................................. 8056 Wenbing Zhao, Cleveland State University, USA The Trajectivity of Virtual Worlds..................................................................................................... 4296 Christophe Duret, Université de Sherbrooke, Canada Transformational Leadership for Academic Libraries in Nigeria...................................................... 5726 Violet E. Ikolo, Delta State University Library, Nigeria Transmedia and Transliteracy in Nemetical Analysis........................................................................ 6488 Michael Josefowicz, Nemetics Institute Kolkata, USA Ray Gallon, The Transformation Society, France Maria Nieves Lorenzo Galés, The Transformation Society, Spain



The Trends and Challenges of 3D Printing........................................................................................ 4382 Edna Ho Chu Fang, University of Malaya, Malaysia Sameer Kumar, University of Malaya, Malaysia Trends in Health Care Information Technology and Informatics...................................................... 3805 T. Ray Ruffin, University of Phoenix, USA & Colorado Technical University, USA & Grand Canyon University, USA & Ashford University, USA, & North Carolina Wesleyan College, USA Donna Patterson Hawkins, University of Phoenix, USA Trust and Decision Making in Turing’s Imitation Game..................................................................... 251 Huma Shah, Coventry University, UK Kevin Warwick, Coventry University, UK A Trust Case-Based Model Applied to Agents Collaboration........................................................... 4797 Felipe Boff, Lutheran University of Brazil (ULBRA), Brazil Fabiana Lorenzi, Lutheran University of Brazil (ULBRA), Brazil Twitter Data Mining for Situational Awareness................................................................................ 2064 Marco Vernier, University of Udine, Italy Manuela Farinosi, University of Udine, Italy Gian Luca Foresti, University of Udine, Italy Uberization (or Uberification) of the Economy................................................................................. 2345 Nabyla Daidj, Telecom Ecole de Management, France Ubiquitous Computing, Contactless Points, and Distributed Stores.................................................. 7805 Marco Savastano, Sapienza University of Rome, Italy Eleonora Pantano, Middlesex University London, UK Saverino Verteramo, University of Calabria, Italy Ubiquitous Teachers’ Training and Lessons Learned with the uProf! Model................................... 7671 Sabrina Leone, Università Politecnica delle Marche, Italy Giovanni Biancofiore, giovannibiancofiore.com, Italy Uncovering Limitations of E01 Self-Verifying Files......................................................................... 1384 Jan Krasniewicz, Birmingham City University, UK Sharon A. Cox, Birmingham City University, UK Understanding and Assessing Quality of Models and Modeling Languages.................................... 4810 John Krogstie, Norwegian University of Science and Technology, Norway Understanding Business Models on the Cloud.................................................................................. 1141 Arash Najmaei, Australian Catholic University, Australia



Understanding Cloud Computing in a Higher Education Context.................................................... 1153 Lucy Self, University of Sussex, UK Petros Chamakiotis, University of Sussex, UK The Understanding of Spatial-Temporal Behaviors........................................................................... 1344 Yu-Jin Zhang, Tsinghua University, China Understanding the Potentials of Social Media in Collaborative Learning......................................... 7168 Adem Karahoca, Bahcesehir University, Turkey İlker Yengin, A*STAR, Institute of High Performance Computing, Singapore Understanding User Experience........................................................................................................ 7599 Camille Dickson-Deane, University of Melbourne, Australia Hsin-Liang (Oliver) Chen, University of Massachusetts Boston, USA Uniform Random Number Generation With Jumping Facilities....................................................... 1297 E. Jack Chen, BASF Corporation, USA The University-Industry Collaboration.............................................................................................. 3963 Marcello Fernandes Chedid, University of Aveiro, Portugal Leonor Teixeira, University of Aveiro, Portugal An Update on Bitcoin as a Digital Currency..................................................................................... 2861 Cecilia G. Manrique, University of Wisconsin – La Crosse, USA Gabriel G. Manrique, Winona State University, USA Usability Evaluation of Tourism Icons in India................................................................................. 4099 Rajshree Tushar Akolkar, Zeal College of Engineering and Research, India Ganesh D. Bhutkar, Vishwakarma Institute of Technology, India Usability of CAPTCHA in Online Communities and Its Link to User Satisfaction.......................... 8066 Samar I. Swaid, Philander Smith College, USA Usable Security.................................................................................................................................. 5004 Andrea Atzeni, Politecnico di Torino, Italy Shamal Faily, Bournemouth University, UK Ruggero Galloni, Square Reply S.r.l., Italy Use of Bitcoin for Internet Trade....................................................................................................... 2869 Sadia Khalil, NUST School of Electrical Engineering and Computer Science, Pakistan Rahat Masood, NUST School of Electrical Engineering and Computer Science, Pakistan Muhammad Awais Shibli, VisionIT, USA Use of Data Analytics for Program Impact Evaluation and Enhancement of Faculty/Staff Development...................................................................................................................................... 1880 Samuel Olugbenga King, Auburn University, USA



Use of GIS and Remote Sensing for Landslide Susceptibility Mapping........................................... 3503 Arzu Erener, Kocaeli University, Turkey Gulcan Sarp, Suleyman Demirel University, Turkey Sebnem Duzgun, Middle East Technical University, Turkey The Use of Postcasting/Vodcasting in Education.............................................................................. 2651 Athanasios T. Stavrianos, 2nd Technical Vocational School of Xanthi, Greece Apostolos Syropoulos, Greek Molecular Computing Group, Greece Use of Technology in Problem-Based Learning in Health Science................................................... 5853 Indu Singh, Griffith University, Australia Avinash Reddy Kundur, Griffith University, Australia Yun-Mi Nguy, Griffith University, Australia User Resistance to Health Information Technology.......................................................................... 3816 Madison N. Ngafeeson, Northern Michigan University, USA Users Behavioral Intention Towards eGovernment in an African Developing Country................... 3654 Ayankunle A. Taiwo, Texas A&M University – Commerce, USA Using Business Analytics in Franchise Organizations......................................................................... 930 Ye-Sho Chen, Louisiana State University, USA Using Communities of Inquiry Online to Perform Tasks of Higher Order Learning........................ 3976 Ramon Tirado-Morueta, University of Huelva, Spain Pablo Maraver-López, University of Huelva, Spain Ángel Hernando-Gómez, University of Huelva, Spain Using Global Appearance Descriptors to Solve Topological Visual SLAM..................................... 6894 Lorenzo Fernández Rojo, Miguel Hernandez University, Spain Luis Paya, Miguel Hernández University, Spain Francisco Amoros, Miguel Hernandez University, Spain Oscar Reinoso, Miguel Hernandez University, Spain Using Receiver Operating Characteristic (ROC) Analysis to Evaluate Information -Based Decision-Making............................................................................................................................... 2213 Nan Hu, University of Utah, USA Using RFID and Barcode Technologies to Improve Operations Efficiency Within the Supply  Chain.................................................................................................................................................. 5595 Amber A. Smith-Ditizio, Texas Woman’s University, USA Alan D. Smith, Robert Morris University, USA Using Social Media to Increase the Recruitment of Clinical Research Participants......................... 7181 Saliha Akhtar, Seton Hall University, USA



Using Technology to Reduce a Healthcare Disparity........................................................................ 3725 Nilmini Wickramasinghe, Epworth HealthCare, Australia & Deakin University, Australia Utilizing Information Science and Technology in Franchise Organizations..................................... 4822 Ye-Sho Chen, Louisiana State University, USA A Validation Study of Rehabilitation Exercise Monitoring Using Kinect......................................... 5941 Wenbing Zhao, Cleveland State University, USA Deborah D. Espy, Cleveland State University, USA Ann Reinthal, Cleveland State University, USA Vertical Integration Between Providers With Possible Cloud Migration.......................................... 1164 Aleksandra Kostic-Ljubisavljevic, University of Belgrade, Serbia Branka Mikavica, University of Belgrade, Serbia Video Considerations for the World Language edTPA...................................................................... 7682 Elizabeth Goulette, Georgia State University, USA Pete Swanson, Georgia State University, USA Viewpoints on Business Process Models............................................................................................. 788 Giorgio Bruno, Politecnico di Torino, Italy Virtual Hoarding................................................................................................................................ 4306 Jo Ann Oravec, University of Wisconsin – Whitewater, USA Virtualization as the Enabling Technology of Cloud Computing...................................................... 1174 Mohamed Fazil Mohamed Firdhous, University of Moratuwa, Sri Lanka Virtual Reality as Distraction Technique for Pain Management in Children and Adolescents......... 5955 Barbara Atzori, University of Florence, Italy Hunter G. Hoffman, University of Washington, USA Laura Vagnoli, Meyer Children’s Hospital of Florence, Italy Andrea Messeri, Meyer Children’s Hospital of Florence, Italy Rosapia Lauro Grotto, University of Florence, Italy Virtual Tourism and Its Potential for Tourism Development in Sub-Saharan Africa....................... 4113 Paul Ankomah, North Carolina A&T State University, USA Trent Larson, North Carolina A&T University, USA Virtual Worlds in the Educational Context........................................................................................ 7935 Felipe Becker Nunes, Federal University of Rio Grande do Sul, Brazil Fabrício Herpich, Federal University of Rio Grande do Sul, Brazil Leo Natan Paschoal, University of Cruz Alta, Brazil



Visible Light Communication Numerous Applications..................................................................... 6672 Ala’ Fathi Khalifeh, German Jordan University, Jordan Hasan Farahneh, Ryerson University, Canada Christopher Mekhiel, Ryerson University, Canada Xavier Fernando, Ryerson University, Canada Visual Identity Design for Responsive Web...................................................................................... 8079 Sunghyun Ryoo Kang, Iowa State University, USA Debra Satterfield, California State University – Long Beach, USA Visualization as a Knowledge Transfer.............................................................................................. 5103 Anna Ursyn, University of Northern Colorado, USA The Vital Importance of Faculty Presence in an Online Learning Environment.............................. 2661 Ni Chang, Indiana University – South Bend, USA Vitalizing Ancient Cultures Mythological Storytelling in Metal Music........................................... 7338 Uğur Kilinç, Ondokuz Mayıs University, Turkey Viterbi Decoder in Hardware............................................................................................................. 6307 Mário Pereira Véstias, Instituto Politecnico de Lisboa, Portugal Waste Gas End-of-Pipe Treatment Techniques in Italian IPPC Chemical Plants.............................. 3156 Gaetano Battistella, ISPRA, Italy Giuseppe Di Marco, ISPRA, Italy Carlo Carlucci, ISPRA, Italy Raffaella Manuzzi, ISPRA, Italy Federica Bonaiuti, ISPRA, Italy Celine Ndong, ISPRA, Italy Web Site Mobilization Techniques.................................................................................................... 8087 John Christopher Sandvig, Western Washington University, USA Web 2.0 From Evolution to Revolutionary Impact in Library and Information Centers................... 5262 Zahid Ashraf Wani, University of Kashmir, India Tazeem Zainab, University of Kashmir, India Shabir Hussain, University of Kashmir, India What Accounts for the Differences in Internet Diffusion Rates Around the World?......................... 8095 Ravi Nath, Creighton University, USA Vasudeva Murthy, Creighton University, USA The What, How, and When of Formal Methods................................................................................ 7609 Aristides Dasso, Universidad Nacional de San Luis, Argentina Ana Funes, Universidad Nacional de San Luis, Argentina



Why It Is Difficult to Disengage From Facebook.............................................................................. 7190 Sonda Bouattour Fakhfakh, University of Tunis El-Manar, Tunisia Wireless Implant Communications Using the Human Body............................................................. 6319 Assefa K. Teshome, Victoria University, Australia Behailu Kibret, Victoria University, Australia Daniel T. H. Lai, Victoria University, Australia Women and IT in Lilongwe............................................................................................................... 3393 Alice Violet Nyamundundu, Skyway University, Malawi Word Formation Study in Developing Naming Guidelines in the Translation of English Medical Terms Into Persian............................................................................................................................. 5136 Ali Akbar Zeinali, Universiti Sains Malaysia, Malaysia Young People, Civic Participation, and the Internet.......................................................................... 3667 Fadi Hirzalla, Erasmus University Rotterdam, The Netherlands Shakuntala Banaji, LSE, UK

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Preface

Influencing every facet of business, society, and life worldwide, with speed beyond imagination, the field of information science and technology has without a doubt brought upon a revolution in the way the human population interacts, does business, and governs. As one takes into account the leaps and bounds experienced in information sharing and communication exchange over the last few decades, a truly admirable phenomenon presents itself and clearly shows that the results of this pivotal rising will monumentally impact the way the world thinks, subsists, and evolves. With a long history of expeditious evolution, the growth and expansion of information technology began during the early 1950s with the main purpose of initiating scientific computing, expanding research, and utilizing the power of computers as a means to support a mass volume of computational tasks in scientific applications and discoveries. Later, during the 1960s and ’70s, the use of computer technology was extended to business applications, mostly in accounting and financial areas that involved processing numbers and collecting data in a quantitative sense. As a result, the use of this technology was limited to those who had an expansive knowledge of these systems and had access to computer programming languages. With the evolution of computers and telecommunications in the 1980s, a new information technology was born with a strong focus on the management and dissemination of information by both information providers and users across the globe. In the early 1990s, the most noticeable advancement in the information technology revolution was the creation of the Internet. During the past two decades, Internet technologies have become the driving force in allowing people worldwide to communicate and exchange information, creating a new virtual, interactive dimension and providing a digital forum for global social connection. In recent years, through the use of Web-enabled technologies, organizations of all types and sizes around the world have managed to utilize these technologies to disseminate and process information with prospective customers, suppliers, students, and governments. Today, the ability to communicate and connect from many locations through personal computers has influenced different people in many different societies. These technologies allow everyone, regardless of their geographic location, to bring the information age to its full realization. In recent years, the science of understanding the nature of information processing and management, along with the computers and technologies that decipher, disseminate, and manage information, has become known as information science and technology. Technology has profoundly impacted science, business, and society, thus constructing an entity that improves access to the rapidly expanding body of knowledge in almost every discipline. Society fuels this knowledge creation, as it receives, manages, educates, and collects information. The volume and intensity of research in information science and technology have exceeded many other fields of science, and research discoveries have become the impetus behind many emerging tools and applications seen at every organizational level.  

Preface

In addressing this need for the representation of evolving information science and technology disciplines in academic literature, the first edition of the Encyclopedia of Information Science and Technology, released in early 2005, positioned itself as the first of its kind in reference publications, offering an invaluable source highlighting major breakthroughs, discoveries, and authoritative research results in technological advancements. In providing this compendium of references, definitions, and keywords within this field of pivotal social and organizational movement, the five-volume Encyclopedia of Information Science and Technology (First Edition) supplied researchers with a definitive one-stop reference source. In late 2008, the Encyclopedia of Information Science and Technology (Second Edition) followed the first edition with an eight-volume compendium of updated research on crucial topics previously covered in the first edition, as well as an advanced treatment of new developments, technologies, and areas of research in the field of information and communications science. With an expanded number of contributions, the second edition surpassed the first in terms of timeliness, comprehensiveness, and critical acclaim, lauded as an essential reference for any academic library. With the endeavor of continuing to exhibit the latest research innovations and advances, the third edition of the Encyclopedia of Information Science and Technology was comprised of 10 volumes of all new content and uncovered the most current research findings related to technological, organizational, and managerial issues, challenges, trends, and applications of information technologies in modern organizations. The coverage in the third edition of the encyclopedia bridged existing gaps in available references on technology and methodologies with its contribution as a valuable resource of encompassing paradigms shaping the ever-changing research, theory, and discovery of information science and technology. This brings us to the present, and three years later we are again releasing new materials providing the latest trends and research in this ever-changing field. The selected topics of this encyclopedia provide a balanced representation of concepts and issues from researchers around the world. These researchers were asked to submit proposals describing the topic and scope of their articles. All proposals were carefully reviewed for suitability by the Editor-in-Chief. Upon the receipt of full article submissions, each contribution was forwarded to at least three expert external reviewers on a double-blind peer review basis. Only submissions with strong and favorable reviews were chosen as articles for this edition of the Encyclopedia of Information Science and Technology. In some cases, submissions were sent back for several revisions prior to final acceptance. The goal was to assemble the best minds in the information science and technology field from all over the world to contribute articles to this encyclopedia and to apply the highest level of quality in selecting articles for inclusion. As a result, over 700 new articles were carefully selected for inclusion in this 10-volume encyclopedia based on their presentation of the most comprehensive, innovative, and in-depth coverage of the current concepts, issues, and emerging technologies in the field of information science and technology. The articles included in this edition of the encyclopedia are written by more than 1,400 distinguished scholars and researchers from hundreds of prominent research institutions all over the world. Over 5,000 technical and managerial terms and their definitions have been organized by the authors to enhance the articles, allowing for extensive research into core concepts and ideas. In addition, this 10-volume set offers a thorough reference section with approximately 15,000 sources of additional information for scholars, students, and researchers in the field of information science and technology. Multiple tables of content have been organized to better assist readers in navigating and identifying information. Contents are structured through alphabetical and categorical listings for easy reference.

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Preface

The topics covered in this all-encompassing publication include some of the most influential areas in the field. One such example is the area of artificial intelligence and its current application within organizational spheres: specifically, technologies such as machine learning, computational intelligence, digital ecosystems, neural networks, and adaptive systems. Content coverage such as this will supply audiences with reputable sources for identifying future trends and breakthrough technologies that will directly impact everyday aspects of life, with specific examples being the latest computing and technological advancements in smart homes, as well as the integration of intelligent diagnostics and speech recognition technology in a variety of settings. A substantial portion of this encyclopedia is dedicated to the latest advancements in business research, management, and technologies. Through the review of business organizational research and business information systems and their ever-changing state, this encyclopedia analyzes the driving force of globalization, and the effect of these systems on international trade, economics, and capital. Because business information systems are so far-reaching, from the intranet to export tracking and manufacturing intelligence, the research results presented within these articles examine a breadth of review and discussion of methodologies behind these systems and how they are implemented in the field of business. Cybercrime, cyber bullying, and digital terrorism, a growing discipline fueled by daily reminders in news outlets around the world, is covered in several articles within this encyclopedia. Special attention has been paid to how behavior across all cultures affects all participants, with good and bad effects in our digitally wired world. Continuously utilized in the evolution of technical applications, the areas of data mining and databases are comprehensively covered in a significant number of articles in this edition of the encyclopedia for both the purpose of introduction and advancement. Taking into consideration the growing use of data mining and database management in a number of activities such as Web development and engineering progress, these resources supply readers with authoritative results and a foundation for additional research. With a considerable effect on the global economy, electronic business applications are discussed in this edition of the encyclopedia and offer researchers a credible source of knowledge for understanding the current realms of mobile applications for business functions, social commerce, and virtual enterprises. Seeing the importance of these technology-based management systems, modern electronic business is analyzed as a growing phenomenon of capitalism. As the world becomes increasingly engaged in online shopping and trade, electronic commerce is fully presented in this edition of the encyclopedia as a phenomenon of progressive change. With significant growth since the dot-com explosion of the past two decades, e-commerce is now considered a discipline with significant global implications, particularly on international trade and how goods and currencies are exchanged and services are dealt. As a contemporary social motivator, educational technologies are comprehensively and extensively examined in this edition of the encyclopedia. This is an area of research with philosophical and constructivist reach into all levels of learning. In discussing blended learning, distance education, tools for online learning, advanced pedagogy, virtual learning environments, and computational support for teaching, educational technologies demand further attention in developing reliable research. As a popular area of examination, this encyclopedia lends support to emerging trends in the discipline while supplying a venue for further discussion of terms, themes, and additional implications.

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A new category on gaming makes its debut in this fourth edition, and reflects on how pervasive the gaming industry has become, no longer just for entertainment but a very real part of learning. Cognitive effects, theoretical models, game-based designs, and modeling are discussed in this section of the encyclopedia. Given the worldwide focus on the environment, this edition of the encyclopedia provides tremendous coverage of emerging technologies and applications in the areas of environmental science and agriculture. As a growing academic research area, this discipline is supported by many research papers written by prominent international researchers studying the future of maintaining environmental functions and safeguarding the planet. Global information technology has emerged as an area of study with growing importance as digital communications spread internationally. This edition of the encyclopedia supplies articles that delve into the importance of the global economy, as well as exhibiting regional adaptation, resistance, and adoption of technologies. Patient monitoring systems, e-health, and cybertherapy are three of the many topics extensively covered in this edition of the encyclopedia by providing a wealth of knowledge related to health information systems and their use and implications in modern societies. With new assistive and rehabilitative technologies, medical data storage, and issues of security and privacy, health information systems affect the lives of human beings worldwide. Considering this growth, research results prove invaluable for healthcare administrators and academic disciplines, such as nursing and health management. As technological demands extend and users multiply, high-performance computing seeks to maintain the flow of communication between servers, knowledge workers, and organizations. With much recent advancement in cloud computing and technologies to handle large amounts of data, this edition of the encyclopedia offers researchers a comprehensive convergence of all topics related to cloud computing applications and management. Considering the human element in making technology the paradigm it is today, this edition of the encyclopedia provides comprehensive coverage of human aspects of technology. With an analysis of end-user behavior, gender differences, and ubiquity of computing, readers will find an extensive amount of research results and analysis to assist in building the literature in an important area of study: humancomputer interaction. Following the advancements of the Industrial Revolution, industrial informatics is described in this encyclopedia as a bridge between technological progress and the application of manufacturing, transportation, and construction. With growing utilization in enhancing and expediting good production, building, and usage, research results examine the future trends of such technologies and how these applications will incorporate into the daily lives of individuals around the globe. Taking into account the massive growth in technological utilization for sensitive data, both in personal and organizational functions, IT security and ethics as an area of study continues to evolve, expanding securitization and streamlining for the best protection of digital information. In examining such areas as digital forensics, authentication, cryptography, cyber warfare, and trustworthy computing, analysis of IT security and ethics takes prominence as a point of key discussion in this edition of the encyclopedia. As the result of the current structures of both public and private sector administration, the field of knowledge management has been positioned as a concept of both utility and application. Considering the need for quality sources indicating best practices in the management of knowledge workers, IT governance, and information sharing, this encyclopedia supplies readers and researchers with a considerable selection of current themes, terms, and topics relating to the current and future state of knowledge management. clxv

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With many library systems implementing digital filing and cataloguing systems, the study of bibliometrics has flourished in the scientific world. With cutting-edge technologies in archiving and classification, preservation, and reformatting, digital library science and administration has developed into an intricate branch of information science, and this encyclopedia provides a tremendous coverage of the vast spectrum of this study. As issues such as open access, security, and Internet property rights take center stage, legal aspects of the digitization of information are explored extensively. Advanced medical technologies are incorporated into daily measures to secure and save lives. Medical informatics is explored in this fourth edition encyclopedia through an intricate look at the updates and improvements in this pivotal field. Medical imaging, biosensors, and new nanotechnology modernizations for surgical procedures are a few of the exciting and significant advances in medical technologies. Rehabilitation, disease detection, and mobile medical care are assisted and revamped with advances in hospital machinery and tools. Mobile devices, wireless systems, sensors, and wearable computing applications are technologies that are shaping communication and public administration. Keeping in mind the spread of the need for reliable means of information transfer, mobile and wireless computing is explored in this edition of the encyclopedia as a field of widening pervasiveness. With uses ranging from machine correspondence to business interrelationships, important research findings are effectively exposed and discussed by top researchers in the field. Whether utilized for entertainment, learning, or public policy, multimedia technology as a form of study involves behavioral and practical analysis. This edition of the encyclopedia offers readers a wealth of research coverage addressing many aspects of analysis on topics such as simulation, digital imaging, and hypermedia to best understand how the world is displaying its information for consumers and administrators. Social networking and computing describes the intersection of social behaviors and computer systems, intricately examining areas such as social networking sites, augmented realities, and online auctions, and their substantial impact on society. Tools such as blogs, Wikis, tags, and podcasts have expanded the user’s online researching experience. This edition of the encyclopedia expounds on social informatics and explores emerging technologies and applications such as instant messaging, virtual groups, mailing lists, and forums: just a few of the places that users interact with one another. For several decades, technological engineers have focused on algorithms, modeling languages, and kernel applications in designing and formatting the machines and automation that the world uses today. Innovative systems and software engineering technologies now play a crucial role in the future design and improvement of the ever-changing world of automation. This edition of the encyclopedia provides ample research coverage of the emerging technologies in this area and their vulnerabilities, specifications, quality, and architectures. As a result of the Internet explosion of the 1990s, Web technologies as a field of study has taken the stage as a discipline of ubiquitous importance. With the Web being a single source of infinite information and computing, responding and expanding at exponential rates, this encyclopedia offers researchers a tremendous coverage of emerging innovations in disciplines such as portal technologies, semantic computing, Web 2.0, and service-oriented technologies.

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In closing, the diverse and comprehensive coverage of multiple disciplines of information science and technology in this 10-volume, authoritative encyclopedia are sure to contribute to a better understanding of all topics, research, and discoveries in this evolving field. Furthermore, the contributions included in this publication will be instrumental in the expansion of knowledge in this field. This publication will inspire its readers to further contribute to the current discoveries in this immense field, creating possibilities for further research and discovery into the future of information science and technology and what lies ahead for the knowledge society. Mehdi Khosrow-Pour, D.B.A. Editor-in-Chief Encyclopedia of Information Science and Technology, Fourth Edition

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Guide to the Encyclopedia of Information Science and Technology, Fourth Edition

ORGANIZATION The Encyclopedia of Information Science and Technology, Fourth Edition is a 10-volume set comprised of over 700 articles. All articles are divided into categories relevant to their topical coverage. There are 86 different category sections, with each volume containing multiple categories. All category sections are arranged alphabetically across the 10 volumes, beginning with “A” categories and ending with “W” categories. Within each category section, the articles are also arranged in alphabetical order. As each new category is introduced, section dividers represent the transition from one category to the next. Also, in the print version of the encyclopedia, letters for the categories represented in each volume are clearly marked on the side binding of the cover. To assist with easy navigation, there are two different tables of content compiled at the beginning of each volume. The first represents the “Contents by Volume,” which displays the arrangement of the content in its respective categories, and the second represents the “Contents in Alphabetical Order,” which displays the arrangement of content from A to Z by the articles’ titles.

EACH VOLUME CONTAINS • • • • •

 

A preface and user’s guide. The Editor-in-Chief’s biography and acknowledgment. Two tables of content are compiled at the beginning of each volume: “Contents by Volume” and “Contents in Alphabetical Order.” Several authoritative, research-based articles contributed by thousands of researchers and experts from all over the world. A comprehensive index supporting the extensive system of cross-references.

Guide to the Encyclopedia of Information Science and Technology, Fourth Edition

EACH ARTICLE INCLUDES • • •

• • • • • • •

A brief introduction to the topic area describing the general perspective and objectives of the article. A background providing the broad definitions and discussions of the topic and incorporating the views of others (i.e., a literature review) into the discussion to support, refute, or demonstrate the author’s position on the topic. Various perspectives examining the issues, controversies, and problems as they relate to the theme. Also provided are arguments supporting the position as well as a comparison and contrast with regards to what has been and/or is currently being done as it relates to the article’s specific topic and the overall theme of the encyclopedia. A discussion of solutions and recommendations in dealing with the issues, controversies, or problems presented in the preceding section. Charts, graphs, tables, and formulae are included as illustrative examples whenever appropriate. A discussion of future research directions. A conclusion to discuss the overall coverage of the article and present concluding remarks. An extensive list of references so that readers can benefit from the sources cited within the text. An additional readings section consisting of sources that complement the topical coverage within the article. A key terms and definitions section providing 7-10 terms related to the topic of the article with a clear and concise definition for each term.

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Acknowledgment

Editing and completing an authoritative and comprehensive scholarly research publication such as the Encyclopedia of Information Science and Technology, Fourth Edition requires tremendous contributions, and a great deal of assistance from large groups of scholars and staff. The primary objective of this encyclopedia is to provide the most up-to-date scholarly coverage of all topics related to information science and technology as it is applied to several discipline areas, such as business, engineering, medicine, education, public administration, computer science, as well as the social sciences and humanities. I am indebted to all the authors for their excellent contributions to this edition of the encyclopedia. All submitted manuscripts to this 10-volume edition underwent a double-blind peer review process in order to achieve the highest level of quality and accuracy. I am thankful to all the reviewers of this edition for providing their expertise and their rigorous, unbiased assessment of the manuscripts assigned to them on a double-blind basis, as well as the members of the Editorial Advisory Board for their wisdom, guidance, and assistance with various decisions throughout the editorial process. I would also like to convey my deep appreciation and gratitude to Jan Travers, Director of Intellectual Property and Contracts, for all her tireless efforts assisting me with bringing a publication of this size to fruition, as well as to the Acquisitions, Development, Copy Editing, and Production Divisions of the Editorial Content Department at IGI Global for their valuable assistance in support of this project, especially Chris Shearer, Copy Editing Manager, Christina Henning, Production Editor, Courtney Tychinski, Assistant Managing Editor (Book Development), Mariah Gilbert, Assistant Managing Editor (Acquisitions), and also to Lindsay Wertman, IGI Global’s Managing Director. Additionally, I would like to thank the IGI Global Sales and Marketing Department for their endless support in promoting this invaluable reference source. Thank you to everyone who has provided me immeasurable amounts of knowledge, wisdom, and patience over the last 30 years. Mehdi Khosrow-Pour, D.B.A. Editor-in-Chief Encyclopedia of Information Science and Technology, Fourth Edition

 

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About the Editor

Mehdi Khosrow-Pour, D.B.A., received his Doctorate in Business Administration from the Nova Southeastern University (Florida, USA). Dr. Khosrow-Pour taught undergraduate and graduate information system courses at the Pennsylvania State University – Harrisburg for almost 20 years. He is currently Executive Editor at IGI Global (www.igi-global.com). He also serves as Executive Director of the Information Resources Management Association (IRMA) (www.irma-international.org) and Executive Director of the World Forgotten Children’s Foundation (www.world-forgotten-children.org). He is the author/editor of more than 100 books in information technology management. He is also currently the Editor-in-Chief of the International Journal of Green Computing, International Journal of Library and Information Services, International Journal of E-Entrepreneurship and Innovation, and International Journal of Natural Computing Research, and is also the founding Editor-in-Chief of the Information Resources Management Journal, Journal of Electronic Commerce in Organizations, Journal of Cases on Information Technology, and the Journal of Information Technology Research, and has authored more than 50 articles published in various conference proceedings and scholarly journals.



Category A

Accounting and Finance

1

Category: Accounting and Finance

Applying Artificial Intelligence to Financial Investing Hayden Wimmer Georgia Southern University, USA Roy Rada University of Maryland – Baltimore County, USA

INTRODUCTION

BACKGROUND

Artificial intelligence (AI) techniques have long been applied to financial investing scenarios to determine market inefficiencies, criteria for credit scoring, and bankruptcy prediction, to name a few. While there are many subfields to artificial intelligence this work seeks to identify the most commonly applied AI techniques to financial investing as appears in academic literature. Techniques identified in this work include fuzzy systems, swarm intelligence, case-based reasoning, hybrid systems, genetic algorithms, neural networks, and machine learning. AI techniques, such as knowledge-based, machine learning, and natural language processing, are integrated into systems that simultaneously address data identification, asset valuation, and risk management. Frequently, machine learning is applied to technical financial indicators in order to make predictions about the direction of stock prices. Financial investing requires data identification, asset valuation, and risk management. One such example of applying AI techniques to financial investing is the application of knowledge-based techniques for credit risk assessment and machine learning techniques for stock valuation. Future trends will continue to integrate hybrid artificial intelligence techniques into financial investing, portfolio optimization, and risk management. The remainder of this article summarizes key contributions of applying AI to financial investing as appears in the academic literature.

What Is Artificial Intelligence? In the early days of computing, a typical task for a computer program was a numerical computation, such as computing the trajectory of a bullet. In modern days, a typical task for a computer program may involve supporting many people in important decisions backed by a massive database across a global network. As the tasks that computers typically perform have become more complex and more closely intertwined with the daily decisions of people, the behavior of the computer programs increasingly assumes characteristics that people associate with intelligence. When exactly a program earns the label of ‘artificial intelligence’ is unclear. The classic test for whether a program is intelligent is that a person would not be able to distinguish a response from an intelligent program from the response of a person. This famous Turing Test is dependent on factors not easily standardized, such as what person is making the assessment under what conditions. A range of computer programming techniques that are currently, popularly considered artificial intelligence techniques includes (Rada, 2008): • •

Knowledge-based techniques, such as in expert systems. Machine learning techniques, such as genetic algorithms and neural networks.

DOI: 10.4018/978-1-5225-2255-3.ch001 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

A

Applying Artificial Intelligence to Financial Investing

•

Sensory or motor techniques, such as natural language processing and image processing.

These methods may apply to investing. For instance, expert systems have been used to predict whether a company will go bankrupt. Neural networks have been used to generate buy and sell decisions on stock exchange indices. Natural language processing programs have been used to analyze corporate news releases and to suggest a buy or sell signal for the corporate stock. While artificial intelligence (AI) could apply to many areas of investing, much of what happens in computer-supported investing comes from nonAI areas. For instance, computational techniques not considered primarily AI techniques include numerical analyses, operations research, and probabilistic analyses. These non-AI techniques are routinely used in investing.

Investing and Data The process of investing has 3-stages of: 1. Data Identification, 2. Asset Valuation, and 3. Risk Management. AI has been most often applied to asset valuation but is also applicable to data identification and risk management. Two, high-level types of data used in financial investing are technical data and fundamental data. The price of an asset across time is technical data and lends itself to various computations, such as the moving average or the standard deviation (volatility). Fundamental data should support causeand-effect relationships between an asset and its price. For instance, the quality of management of a company should influence the profitability of a company and thus the price of its stock. The universe of fundamental data is infinite. Many streams of data that might be relevant, such as corporate earnings or corporate debt, might also

2

be related to one another. Various non-AI tools, such as linear regression analysis and principal components analysis, might be used in identifying what sets of data are more likely to be useful than what other sets. Such non-AI, computational techniques can be combined with AI techniques in experimenting with various combinations of data and choosing what data to use in asset valuation.

ARTIFICIAL INTELLIGENCE APPLIED TO FINANCIAL INVESTING AI Trends A multi-agent architecture for an integrated system that considers data identification, asset valuation, and risk management has been proposed by researchers at Carnegie Mellon University. The system is called WARREN which refers to the first name of the famous investor Warren Buffet (Sycara, Decker, Pannu, Williamson, & Zeng, 1996). The WARREN system design includes components for collecting large amounts of realtime data, both numeric and textual. The data would be pre-processed and then fed to various asset valuation agents that would, in turn, feed their assessments to a portfolio management agent. The portfolio management agent would interact with clients of WARREN. Systems with various features of WARREN are available from commercial vendors and are developed in-house by large investing companies, but more research is needed on how to develop integrated, AI systems that support investing. Natural language processing systems may include large bodies of domain knowledge and parse free text so as to make inferences about the content of the text. However, such natural language processing systems do not seem as popular in investing applications as much simpler natural language processing techniques. The natural language processing work that has been applied to investing seems to be largely of the sort in which the distribution of word frequencies in a

Category: Accounting and Finance

document is used to characterize the document. In this word-frequency way, Thomas (2003) has shown a potential value to processing news stories to help anticipate stock price changes. As one can see cycles in the value of financial assets, one can also see cycles in the frequency of publication of articles on certain topics. In the field of artificial intelligence, one might identify, roughly speaking, three phases as follows (Rada, 2008): 1. Machine learning, in what was then called perceptron and self-organizing systems research, was popular from 1955 to 1975, 2. Knowledge-based, multi-agent, or expert systems work was popular from 1975 to 1995, and 3. Machine learning research, now called neural networks or genetic algorithms research, returned to dominate the AI research scene from 1995 to 2013. When AI research has been applied to investing, the AI technique used has tended to be the technique popular at the time. This leaves unaddressed the question of whether investing is more appropriately addressed with one AI technique or another. The recent literature is rich with neural network applications to investing, but a new trend is the combining of knowledge-based techniques with neural network and genetic algorithm techniques. For instance, Tsakonas, Dounias, Doumpos, and Zopounidis (2006) use ‘logic’ neural nets that can be directly understood by people (traditional neural nets are a ‘black box’ to humans). Genetic programming modifies the architecture of the logic neural net by adding or deleting nodes of the network in a way that preserves the meaning of the neural net to people and to the net itself. Bhattacharyya, Pictet, and Zumbach (2002) have added knowledge-rich constraints to the genetic operators in their application for investing in foreign exchange markets.

A promising research direction is to combine the earlier knowledge-based work on financial accounting with the more recent work on machine learning for stock valuation. For instance, neural logic nets could represent some of the causeeffect knowledge from a bankruptcy system and become part of a learning system for predicting stock prices. Some of the bankruptcy variables are readily available online, such as a company’s debt, cash flow, and capital assets. The financial markets are human markets that evolve over time as opportunities to make profits in this zero-sum game depend on the changing strategies of the opponent. Thus, among other things, what is important in the input may change over time. An AI system should be able to evolve its data selection, asset valuation, and portfolio management components. The future direction for AI in investing is to integrate the three major tools of AI (knowledge-based systems, machine learning, and natural language processing) into a system that simultaneously handles the three stages of investing (data collection, asset valuation, and portfolio management). Such systems will interact with humans so that humans can specify their preferences and make difficult decisions, but in some arenas, such as program trading, these sophisticated AI systems could compete with one another.

Advances in AI and Finance 2009-2013 Artificial intelligence techniques continue to be applied to areas in finance in order to extend human capabilities by surveying large and distributed data sources (Phillips-Wren, 2012). Artificial neural networks (ANNs) continue to be the most popular approach to combining artificial intelligence with finance (Bahrammirzaee, 2010). ANNs have been applied to a variety of issues in the finance domain. One such example is applying ANNs to auditor selection where the process of selecting and appointing an auditing firm is a complex

3

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Applying Artificial Intelligence to Financial Investing

process which can benefit from the ANN approach (Kirkos, Spathis, & Manolopoulos, 2010). Employing ANNs was found to outperform the standard approach of logistic regression. ANNs are suited to modeling non-linear as well as linear relationships and are well suited to applications in financial, time-series forecasting and can be tailored by customizing the input and hidden neurons of the ANN (Tarsauilya, Kant, & Kala, 2010). Currency exchange rates are another financial time-series where an ANN approach can be effective (Oyewale, 2013). In addition to ANN techniques, techniques such as nearest neighbor (kNN) have been explored. One such technique combines a kNN approach with technical analysis techniques for stock trading where the method was more profitable than the standard buy and hold technique (Teixeira & de Oliveira, 2010). Support Vector Machines (SVMs) are supervised learning models which are used for data analysis and pattern recognition. SVMs have been applied to a range of finance research with one such being financial distress prediction (Bae, 2012). Besides SVM, fuzzy approaches, or approaches which deal with approximations, have begun to emerge in portfolio selection (Magoč & Modave, 2011). Evolutionary approaches have been studied, as in (Chi & Hsu, 2012), where a genetic algorithm (GA) was employed in variable selection for determining credit scoring of business firms. The GA was used to augment a bank’s internal scoring model and a credit bureau scoring model in order to improve the performance of credit risk management of mortgage accounts. Dividend policy forecasting was also shown to benefit from the introduction of evolutionary computation methods where the introduction of a GA was able to outperform the average prediction accuracy of standard machine leaning decision tree classifiers such as CHAID, CART, and C4.5 (Won, Kim, & Bae, 2012). Genetic Algorithms applied to technical trading indicators improve the ability to predict major losses (Kaucic, 2010). Following the theme of technical analysis, Fu, Chung, and

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Chung (2013) proposed a 2 step process to technical analysis by first applying a GA to determine the optimal technical indicators and then again to weight the investments of a financial portfolio. The method lowered risk, as measured by the Sharpe ratio, and outperformed the buy and hold method over different market sectors in bullish and bearish market conditions. While artificial neural networks remain the most popular research stream for applying artificial intelligence techniques to finance (Bahrammirzaee, 2010), hybrid AI approaches are emerging as a viable, however complex, alternative. For example, decision trees have been shown to be effective when combined with logistic regression when applied to financial distress prediction (Chen, 2011). The hybrid approach trend continues by combining decision trees and fuzzy approaches. The high dimensionality and non-stationary variations in stock price make financial trend discovery difficult; therefore, an approach which uses fuzzy logic combined with a decision tree classifier has been shown to be effective when applied to the S&P 500 (Chang, Fan, & Lin, 2011). Genetic algorithms show promise when paired with fuzzy techniques and applied to portfolio selection (Bermúdez, Segura, & Vercher, 2012). While Fuzzy approaches are viable in portfolio selection, extending fuzzy approaches by incorporating swam convergence techniques provides satisfactory performance when compared with other techniques (Ladyzynski & Grzegorzewski, 2013). Compared with other independent methods, ANN approaches have demonstrated higher performance as evidenced by (Rafiei, Manzari, & Bostanian, 2011). This makes combining ANN with other AI approaches a natural extension to the research. Combining ANN with Case-Based Reasoning (CBR) was explored by (Chuang & Huang, 2011) and compared to ANN alone and shown to improve accuracy and reduce misclassification errors. Hybrid approaches that pair ANN with Fuzzy systems have also emerged as an extension to ANN alone. One such approach integrates ANN with genetic fuzzy systems and

Category: Accounting and Finance

applies the technique to stock price forecasting (Hadavandi, Shavandi, & Ghanbari, 2010). The method was applied to a subset of stocks in the IT and airline sector and was demonstrated to outperform pervious methods of stock price prediction. A similar method of combining ANN and fuzzy systems was examined in (Boyacioglu & Avci, 2010) where the authors concluded the approach of combining ANN and fuzzy systems, namely fuzzy logic, is applicable to emerging markets’ stock price prediction. In continuing with stock price prediction, K.-L. Shen (2013) combined ANN and a fuzzy approach to create a Fuzzy-ANN model which was shown to outperform two major market indices from Taiwan from 2008-2011.

LITERATURE TREND 2013-2015 In order to examine trends in the literature, a Google Scholar search on “Artificial Intelligence and Investing” and “Artificial Intelligence and Financial Investing” was performed. The search spanned January 1, 2013 until July 1, 2015. In 2013, the dominant categories of artificial intelligence and financial investing literature can be classified as fuzzy approaches, neural networks, hybrid approaches, and other methods. In 2014, hybrid methods increase in popularity. Hybrid systems are systems which utilize a combination of two or more techniques and/or methods. This increase in hybrid system interest continues into 2015 which concludes this analysis. Machine learning and neural networks are two of the most common methods found in hybrid systems thereby Table 1. Citation count from google scholar for “artificial intelligence and investing” Year

Citation Count

2013

1930

2014

1980

2015

1330

2013-2015

5230

Table 2. Citation count from google scholar for “artificial intelligence and financial investing” Year

Citation Count

2013

7

2014

12

2015

12

2013-2015

26

Table 3. Google scholar sub-field citation count AI Subfield Citation Count 2013-2015 (Oct) Topic

Citation Count

Fuzzy System and Investing

213

Swarm Intelligence and Investing

304

Case Based Reasoning and Investing

394

Hybrid System and Investing

982

Genetic Algorithm and Investing

1930

Neural Network and Investing

2760

Machine Learning and Investing

3360

explaining the high citation count. Most hybrid systems do not specifically mention the term “hybrid” in the article title thereby explaining the lower number of hybrid citations.

Artificial Intelligence and Financial Investing (2013) Fuzzy approaches were well represented. For example, Svalina, Galzina, Lujić, and Šimunović (2013) generate a prediction model based on an adaptive neuro-fuzzy inference system to detect the close price of the Croatian Zagreb Stock Exchange. Yunusoglu and Selim (2013) propose a new fuzzy rule expert system that focusses on helping investors with investment decisions. Messaoudi and Rebai (2013) develop a new fuzzy goal programming model, which takes into consideration stochastic and fuzzy uncertainty for real life decision making problems for portfolio selection. Hassan, Ramamohanarao, Kamruzzaman, Rahman, and Hossain (2013) introduce a new adaptive inference system, which generates new fuzzy rules for forecasting

5

A

Applying Artificial Intelligence to Financial Investing

Figure 1. Citation counts of “artificial intelligence and investing” google scholar search from 2013-2015

the nonlinear time series data in stock market which performs better than the standard FIS and the performance of this system indicates that it can predict a number of stock indices including Dow Jones Industrial index, and NASDAQ index. Gradojevic and Gençay (2013) address the trading uncertainty problem by employing fuzzy logic to reduce trading uncertainty. Neural networks remained a well-represented area of research. de Oliveira, Nobre, and Zárate (2013) use a neural network approach to predict the direction of change in stock price. Korol (2013) apply neural networks to bankruptcy prediction. Birău, Ehsanifar, and Mohammadi (2013) employ neural networks for predicting emerging stock prices. Devadoss and Ligori (2013) show that neural networks can be an effective tool for predicting closing stock prices. Liu, Dang, and Huang (2013) propose a recurrent neural network with convergence for real time portfolio optimization for portfolio optimization. Özkan (2013) focuses on improving the foreign exchange rate forecast accuracy by using neural networks for forecasting the foreign exchange rates. Dong, Fataliyev, and Wang (2013) utilize Neural Network techniques for forecasting the stock prices. Kumar and Murugan (2013) emphasize the importance of neural networks for stock indices and conducts experiments to analyze the forecasting accuracy. Al-Jumeily, Hussain, and Alaskar (2013) propose

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a new technique for predicting change in the large financial data sets used for forecasting the noisy time series data using a Dynamic Self-Organized Multilayer Neural Network based on the Immune algorithm. Hybrid systems continued to gain momentum as an area of interest. Sermpinis, Theofilatos, Karathanasopoulos, Georgopoulos, and Dunis (2013) develop a hybrid approach pairing neural network with particle swarm optimization to predict foreign exchange rates. Evans, Pappas, and Xhafa (2013) create a hybrid system for genetic algorithms and artificial neural networks applied to intra-day market prediction. Manahov and Hudson (2013) combine agent based systems with genetic programming to evaluate the price of stocks represented by the Dow Jones Industrial Average, General Electric, and IBM. Nhu, Nitsuwat, and Sodanil (2013) combine the Firefly Algorithm and Adaptive Neuro-Fuzzy Inference System (ANFIS) and validate the approach compared with ANFIS trained by the Hybrid Algorithm, Back Propagation and Particle Swarm Optimization. Andriosopoulos, Doumpos, Papapostolou, and Pouliasis (2013) use a genetic algorithm paired with differential evolution algorithm for portfolio optimization and for addressing the problem of index-tracking in shipping stock and freight markets. D. Wang, Liu, and Wang (2013) predict the price trends of stock futures by proposing a new DT-

Category: Accounting and Finance

SVM (Decision Tee and Support Vector Machine) based on hybrid methods which overcome the problems encountered with alternative methods such as fuzzy logic, or genetic algorithms. Chen (2013) integrates particle swarm optimization with an adaptive-network-based fuzzy inference system (ANFIS) model to predict business failure. Other approaches that are worth mentioning include machine learning, optimization, casebased reasoning, and evolutionary approaches. Benhayoun, Chairi, El Gonnouni, and Lyhyaoui (2013) employ a Support Vector Machine Model in order to predict a company’s creditworthiness by using historical data. Liang and Qu (2013) use a Multi-objective Dynamic Multi-Swarm Particle Swarm Optimizer in order to overcome the difficulties involved in classical methods previously applied to portfolio optimization problems. Chuang (2013) employs case-based reasoning to predict business failures. Xiao, Che, Wang, and Yang (2013) analyze stock price movements by studying the technical pattern of stock prices and provide a strategy for case-based reasoning approaches. Canelas, Neves, and Horta (2013) combine a symbolic Aggregate approximation (SAX) technique (for identifying the relevant patterns) with an optimization kernel based on genetic algorithms (GA) for generating investment rules in order to define less risky trading strategies.

Artificial Intelligence and Financial Investing (2014) Hybrid approaches increased in popularity with many researchers combining AI techniques. For example, Adhikari and Agrawal (2014) combine random walk with artificial neural networks to create a hybrid system that outperforms its respective parts when applied to forecasting financial data. Bagheri, Peyhani, and Akbari (2014) predict market trends using a hybrid system with swarm intelligence and a fuzzy inference system. Semaan, Harb, and Kassem (2014) demonstrate that neural network based approaches can outperform standard statistical analysis when predicting

exchange rates. Li (2014) applies a hybrid neural network and bee colony algorithm for gold price prediction. Sanz, Bernardo, Herrera, Bustince Sola, and Hagras (2014) demonstrate a compact evolutionary interval-valued fuzzy rule-based classification system, which is able to predict the real world financial data more accurately. Oreski and Oreski (2014) create a new novel classifier based on Hybrid Genetic algorithm and artificial neural network which is able to find the most relevant data and spend less time in the search space. Costea (2014) combines neural networks with genetic algorithms and fuzzy logic to evaluate the performance of non-banking financial institutions. Pulido, Melin, and Castillo (2014) create a hybrid system by integrating neural networks with particle swarm optimization and fuzzy based system to predict the Mexican stock exchange. Kristjanpoller, Fadic, and Minutolo (2014) implement a hybrid neural network model to predict financial market volatility. Reid, Malan, and Engelbrecht (2014) focus on solving realistic portfolio problems by using the Particle Swarm Optimization (PSO) and the Ant Colony Optimization (ACO) algorithms in order to overcome the problem of the uncovered interest rate parity condition. F. Wang, Yu, and Cheung (2014) propose a new performance based reward strategy that combines the moving average and the trading range breakout in a particle swarm optimization scenario to predicting the stock price trends accurately. Other approaches worth mentioning: Bertella, Pires, Feng, and Stanley (2014) use agent based techniques to support decision making in stock market evaluation. K.-Y. Shen and Tzeng (2014) use a neuro-fuzzy approach to financial performance evaluation evaluated on commercial banks in Taiwan. Aouni, Colapinto, and La Torre (2014) show that goal programming can be applied to various applications of portfolio selection problem and helps the financial decision maker improve their decision making process. Zheng, Zhou, Chen, and Ekedebe (2014) propose a new automated evaluation system, which utilizes machine learning to automatically predict risk based on the data in

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the SEC EDGAR database. Finally, Yu, Miche, Séverin, and Lendasse (2014) focus on utilizing the Leave-One-Out-Incremental Extreme Learning Machine (LOO-IELM) in order to study the bankruptcy prediction problem.

Artificial Intelligence and Financial Investing (2015) This article concludes with the first 2 quarters of the 2015 calendar year. We see hybrid systems continuing to garner interest in the academic community. Patel, Shah, Thakkar, and Kotecha (2015) use a hybrid approach consisting of support vector regression, random forest, and neural networks for stock market index prediction. Rather, Agarwal, and Sastry (2015) propose combining neural networks with linear models for stock market data prediction. Musto, Semeraro, Lops, DeGemmis, and Lekkas (2015) propose a new framework for personalized investment portfolios, which is based on a combining case based reasoning with diversification techniques. Silva, Vasconcelos, Barros, and Franca (2015) integrate case-based reasoning with neural networks for credit risk analysis. Iturriaga and Sanz (2015) propose a new hybrid model implemented with a multilayer perceptron and self-organizing map in order to identify new metrics of financial risks in US commercial banks. Finally, Two other approaches include examples where Chen and Chen (2015) propose a fuzzy system for making stock market predictions and Yang, Chen, and Huang (2015) advance agent based systems by illustrating a framework for developing agents to stock market trading which was shown to outperform current agent based approaches such as random traders.

FUTURE RESEARCH DIRECTIONS Artificial neural networks (ANN) remain the most popular form of AI technique in finance research (Bahrammirzaee, 2010). ANN approaches have been shown to outperform other techniques such as

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genetic algorithms and multivariate discriminant analysis by more than 5% and 18% respectively (Rafiei et al., 2011). Research has moved away from a purely ANN approach to hybrid approaches, such as ANN and support vector machines, ANN and evolutionary computing, as well as ANN and fuzzy approaches. Future research will benefit from refining the hybrid AI approaches as well as incorporating additional AI techniques into hybrid systems when applied to finance. As big data and data science emerge, one would expect to see more literature encompassing related techniques. Future directions would benefit from increasing the variety and mixture of hybrid techniques while exploiting data science and big data concepts such as parallel processing (map reduce) or even real-time stream mining.

CONCLUSION This article presents an overview of artificial intelligence applied to financial investing. First, the background of AI techniques and finance are discussed. Following the background, the focus of the article is identifying the direction of AI in finance from 2009 to 2013 and subsequently 2013 to the first half of 2015. A trend has been shown that single AI techniques dominated early research and progresses toward hybrid approaches. Neural networks remained one of the most popular methods throughout the literature and, as hybrid systems advanced, neural networks were one of the most common techniques included in hybrid systems.

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ADDITIONAL READING Deborah, L. M., Richard, F., James, R., & Steve, W. (2000). An Environment for Merging and Testing Large Ontologies. Morgan Kaufmann. Holland, J. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.

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KEY TERMS AND DEFINITIONS Artificial Intelligence: The ability of a computer to perform activities which are normally considered to require human intelligence. Asset Valuation: The process of determining the worth of something. Case-Based Reasoning: The process of solving new problems based on successful past solutions to similar problems. Decision Tree: A tree like structure for modeling decisions and classifying data. Evolutionary Computing: Branch of artificial intelligence which mimics biological evolution and often applied to optimization problems. Expert System: A program that uses knowledge and inferences to solve problems in a way that experts might. Fuzzy Systems: Systems which deal in approximations as opposed to exact representations (i.e. true/ false).

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Genetic Algorithm: An algorithm that mimics the genetic concepts of natural selection, combination, selection, and inheritance. Hybrid System: A system which employs a combination of techniques and methods. Investing: The act of committing money to an endeavor with the expectation of obtaining profit. Machine Learning: A method of automatically learning patterns from data in order to make future predictions. Neural Networks: Programs that simulate a network of communicating nerve cells to achieve a machine learning objective. Risk Management: The process of managing the uncertainty in investment decision-making. Support Vector Machines (SVM): Supervised learning model to analyze data and predict which possible classes form the output. Swarm Intelligence: Intelligence based on many individuals and decentralized control and self-organization.

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Distributed Parameter Systems Control and Its Applications to Financial Engineering Gerasimos Rigatos Industrial Systems Institute, Greece Pierluigi Siano University of Salerno, Italy

INTRODUCTION In several problems of financial engineering, such as options and commodities trading, forecasting of options’ values, estimation of financial distress and credit risk assessment, validation of option pricing models, etc. one comes against Partial Differential Equations (PDEs). Moreover, in problems of control of financial systems where the aim is to stabilize financial processes which are described by PDE models, one has to harness again the complex PDE dynamics through the application of an external input. In the recent years differential flatness theory has emerged as an approach to the control and stabilization of systems described by PDE dynamics (Rudolph, 2003), (Rigatos, 2015). This research work focuses on differential flatness theory for the control and stabilization of single asset and multi-asset option price dynamics, described by PDE models. It is shown how the differential flatness approach achieves, stabilization of distributed parameter financial systems (that is systems modelled by PDEs) and how it enables convergence to specific financial performance indexes (Rigatos, 2014a; Rigatos, 2014b; Rigatos, 2014c; Rigatos, 2015a; Rigatos, 2015b; Rigatos, 2015c). The Black-Scholes PDE is the principal financial model used in this study. It is demonstrated how with the use of semi-discretization and a finite differences scheme the single-asset (equivalently multi-asset) Black-Scholes PDE is transformed into a state-space model consisting of ordinary

nonlinear differential equations. For this set of differential equations it is proven that differential flatness properties hold (Rigatos, 2011; Rigatos, 2013; Rigatos, 2015). This permits to arrive at a solution for the associated control problem and to ascertain stabilization of the options’ dynamics. By proving that it is feasible to control the singleasset (equivalently multi-asset) Black-Scholes PDE it is also concluded that through a selected trading policy, the price of options can be made to converge and stabilize at specific reference values. The computational part of the considered feedback control method is as follows: For the local subsystems, into which the single-asset (equivalently multi-asset) Black-Scholes PDE is decomposed, it becomes possible to apply boundary-based feedback control. The controller design proceeds by showing that the state-space model of the single-asset (equivalently multi-asset) Black-Scholes PDE stands for a differentially flat system. Next, for each subsystem which is related to a nonlinear ODE, a virtual control input is computed, that can invert the subsystem’s dynamics and can eliminate the subsystem’s tracking error. From the last row of the state-space description, the control input (boundary condition) that is actually applied to the single-asset (equivalently multi-asset) Black-Scholes PDE system is found. This control input contains recursively all virtual control inputs which were computed for the individual ODE subsystems associated with the previous rows of the state-space equation. Thus, by tracing the rows of the state-space model

DOI: 10.4018/978-1-5225-2255-3.ch002 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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backwards, at each iteration of the control algorithm, one can finally obtain the control input that should be applied to the single-asset (equivalently multi-asset) Black-Scholes PDE system so as to assure that all its state variables will converge to the desirable setpoints. The structure of the chapter is as follows: in Section “The problem of boundary control of the single-asset Black- Scholes PDE”, an overview about the single-asset Black-Scholes PDE is given and the associated boundary control problem is formulated. In Section “Option pricing modelling with the use of the single-asset Black-Scholes PDE” the concept of option pricing for the singleasset Black-Scholes PDE is explained. In Section “Transformation of the single-asset Black-Scholes PDE into nonlinear ODEs” it is explained how the single-asset Black-Scholes PDE dynamics can be transformed to an equivalent state-space form. In Section “Computation of boundary control for the single-asset Black-Scholes PDE ” a boundary feedback control law is computed for the single-asset Black-Scholes PDE In Section “Closed loop dynamics of the single-asset BlackScholes PDE” the dynamics of the closed control loop of the single-asset Black-Scholes PDE is analysed. In Section “The problem of boundary control of the multi-asset Black-Scholes PDE ” the multi-asset Black-Scholes PDE is introduced and the associated boundary control problem is formulated. In Section “Boundary control of the multi-asset Black-Scholes PDE” it is explained how the multi-asset Black-Scholes PDE can be transformed into an equivalent state-space description. In Section “Flatness-based control of the multi-asset Black-Scholes PDE” it is analysed how a boundary feedback control input can be computed for the multi-asset Black-Scholes PDE. In Section “Simulation tests” the satisfactory performance of the control loop is confirmed through simulation experiments for both the single-asset and the multi-asset Black-Scholes PDE. Finally, in Section “Conclusions” concluding remarks are stated.

16

BACKGROUND One can note two modelling approaches for describing option price dynamics. The first one makes use of stochastic differential equations (SDEs). The underlying asset evolves according to a stochastic process that is driven by a random input. Using this representation, stochastic control methods for option price SDE diffusion models have been developed (Bensoussan, 2000), (Pascucci, 2011). Moreover, according to Kolmogorov’s theory, diffusion stochastic processes can be equivalently represented by partial differential equations (PDEs). These provide as solution the spatiotemporal distribution of the options’ value. The Black-Scholes PDE is such a relation (Platen & Heath, 2008; Sircar & Papanicoloau, 1998). Consequently, methods for PDE boundary control can be used for modifying option price dynamics. This topic will be elaborated in this article.

THE PROBLEM OF BOUNDARY CONTROL OF THE SINGLEASSET BLACK- SCHOLES PDE Control and stabilization of financial systems is a difficult problem since the associated models have spatiotemporal dynamics and are either described by partial differential equations or by stochastic differential equations (Platen & Heath, 2006; Pascucci, 2011). On the one side control approaches for financial systems have been developed with the use of stochastic differential equations (Oksendal & Sulem, 2006; Stojanovic, 2007; Yin et al., 2010). On the other side, control of financial dynamics through the use of the associated partial differential equations description remains an open problem for which efficient solutions have to be provided (Rudolph, 2003; Smyshlyaev & Krstic, 2010). To this end, in this research work a new control method is developed for the diffusion-type of the Black-Scholes PDE which describes the dynamics of options in financial markets. It is

Category: Accounting and Finance

shown that boundary control, that is control based on the boundary conditions of the PDE, can be exerted on the Black-Scholes PDE thus modifying its dynamics and leading the options’ prices to converge to specific reference values. Controlling the dynamics of financial systems with the application of exogenous inputs and feedback has been attempted in several research works (Miller & Weller, 1995; Barmish, 2011; Fliess & Join, 2012; Fliess & Join, 2010). Control of financial systems is important for minimizing default risks, maximizing profits or stabilizing the dynamics of stock market indexes (Fedorov & Mikhailov, 2001; Forsyth & Labahn, 2007; Bernhard, 2006; Windcliff et al., 2004). The present chapter treats the problem of boundary control of the nonlinear Black-Scholes PDE, which means that the boundary conditions are used as control inputs to modify this PDE dynamics. Boundary control or lumped-input control of nonlinear distributed parameter systems are problems of elevated difficulty and many remarkable results on this topic exist (Woitteneck & Mounier, 2010; Mounier et al., 2010; Fliess & Mounier, 2002; Boussaada et al., 2013). If the PDE system is decomposed into an equivalent set of ordinary nonlinear differential equations, then controlling such a set of dynamical subsystems by the boundary conditions implies underactuation. The control approach followed in this chapter is as follows: First, by implementing a procedure as in the numerical solution of the Black-Scholes PDE a set of equivalent nonlinear ordinary differential equations is obtained (Pinsky, 1991; Gerdts et al., 2008; Guo & Billings 2007; Kroner, 2011; Winkler & Lohmann, 2010; Dragonescu & Soane, 2013). Next it is shown that the system of the nonlinear ODEs is a differentially flat one. This means that all its state variables and the control inputs can be written as differential functions of one single algebraic variable which is the flat output (Sira-Ramirez & Agrawal, 2004; Lévine, 2009; Bououden et al., 2011; Fliess & Mounier, 1999; Lévine, 2011). Moreover, by examining independently each nonlinear ODE it is shown

that this stands again for a differentially flat system, for which a virtual control input can be computed as in the case of flatness-based control for the trivial system. The virtual control input is chosen such that the ODE subsystem dynamics is linearized and the tracking error is eliminated. The boundary condition that appears in the nonlinear ODE subsystem that comprises the last row of the state-space description, stands for the aggregate control input. The computation of the boundary control input uses recursively all virtual control inputs mentioned above, moving from the last ODE system to the first one. Thus, by tracing the rows of the state-space model backwards, at each iteration of the control algorithm, one can finally obtain the control input that should be applied to the Black-Scholes PDE system so as to assure that all its state vector elements will converge to the desirable setpoints. By analyzing the dynamics of the closed-loop system that results from the application of the aforementioned control method, asymptotic stability is confirmed.

OPTION PRICING MODELLING WITH THE USE OF THE SINGLEASSET BLACK-SCHOLES PDE Definition of Options A financial derivative is called a European option if it gives the right for pay-off according to a function H : [0, ∞] → R which has as principle variable the security index St , while there is also a predefined expiration date T ∈ [0, ∞) . If the payoff can take place before the expiration date then one has an American option. The value function of a European option is denoted as V (t, St ) , where t varies in the interval t ∈ [0,T ] , while the associated payoff function is defined as H and the maturity time is T ∈ [0, ∞) . The value function V : [0,T ]× [0, ∞) → R for a European option, can be differentiated with respect to time while it is also twice differentiable with respect to the security index S.

17

A

Distributed Parameter Systems Control and Its Applications to Financial Engineering

Option Price Modelling with The Use of Stochastic Differential Equations The option price model assumes that the security price S = {St , t ∈ [0,T ]} with initial value equal to S 0 follows a geometric Brownian motion dSt = at Stdt + σt StdWt

(1)

V (T , S ) = H (S )

(4)

Equation 3 and Equation 4 form the BlackScholes partial differential equation The BlackScholes PDE is a diffusion partial differential equation which describes the evolution of the option’s price distribution V (t, S ), as a function of time t and of the underlying asset (security index) S .

where a = {at , t ∈ [0,T ]} is the appreciation rate whichisapositivevariable,and σ = {σt , t ∈ [0,T ]} is the volatility parameter. Variable W denoted a Wiener process, W = {Wt , t ∈ [0,T ]} . There is also a second stochastic process in the model which is given by dBt = rt Btdt

(2)

where Bt corresponds to the domestic savings parameter B = {Bt , t ∈ [0,T ]} with initial

TRANSFORMATION OF THE SINGLE-ASSET BLACK-SCHOLES PDE INTO NONLINEAR ODES Next, the following nonlinear Black-Scholes PDE is considered: ∂V (t, S ) ∂V (t, S ) + rt S + ∂t ∂S 2 1 2 2 ∂ V (t, S ) − rV (t, S ) = 0 σS t 2 t ∂S 2

(5)

value B0 = 1 . Variable rt is the interest rate r = {rt , t ∈ [0,T ]} .. The domestic savings account is also called locally riskless asset when there is no noise term in the SDE model given in Eq. (2).

The Black-Scholes PDE A model that is equivalent to the SDE description of option price dynamics is provided by the BlackScholes PDE (Platen & Heath, 2006; Pascucci, 2011). The Black-Scholes partial differential equation is given by ∂V (t, S ) ∂V (t, S ) + rt S + ∂t ∂S 1 2 2 ∂V 2 (t, S ) − rV ( t , S ) = 0 σt S t 2 ∂S 2

linear dependence on Vss that is σt = σ(Vss )

(6)

∂2V (t, S ) and σ is a nonlinear func∂S 2 tion. A grid of N points is considered, that is {s1, s2 ,..., sN −1, sN } which are placed at equal distances on the S axis. At the points of spatial discretization, it holds where Vss =

∂V (t, Si )

(3)

For t ∈ (0,T ) and S ∈ (0, ∞) where r is the interest rate and σt is the volatility, while the associated terminal condition is

18

It is assumed that the volatility σt has a non-

= rV (t, Si ) − t ∂t ∂V (t, Si ) 1 2 2 ∂2V (t, Si ) − 2 σt Si rt Si ∂S ∂S 2

(7)

where i = 1, 2,..., N . The following state vector is defined as shown in Box 1.

Category: Accounting and Finance

Box 1.­

A

~

V = [V (t, s1 ),V (t, s2 ),V (t, s 3 ),...,V (t, sΝ−1 ),V (t, sΝ )] ¿r ~



(8)

V = [V1,V2 ,V3 ,...,VN −1,VN ]

thus one has

∂V1

V − 2V1 +V0 1 − σt2 (Vss )S12 2 ∂t ∆S 2 ∆S 2 ∂V2 V −V2 1 2 V − 2V2 +V1 − r1S 2 3 − σt (Vss )S 22 3 = rV 1 2 ∂t ∆S 2 ∆S 2 ∂V3 V − 2V3 +V2 V −V3 1 2 − σt (Vss )S 32 4 = rV − r1S 3 4 1 3 ∂t ∆S 2 ∆S 2 ................................................................................. .................................................................................. ∂VN −1 ∂t

= rV − r1S1 1 1

= rV − r1S N −1 1 N −1

∂VN −1 ∂t

= rV − r1S N 1 N

V2 −V1

V − 2VN −1 +VN −2 1 − σt2 (Vss )S N2 −1 N ∆S 2 ∆S 2 ......

VN +1 −VN ∆S

− 2VN +VN −1 V 1 − σt2 (Vss )S N2 N +1 2 ∆S 2

Vi +1 −Vi

V 1 − σt2 (Vss )Si2 i +12 + ∂t ∆S 2 ∆S V V 1 2 2 2 2 i i −1 σt (Vss )Si − σt (Vss )Si ∆S 2 2 ∆S 2 = rV − ri Si i i

(10)

Equation 32 is also written in the form 2 σt2 (Vss ) 2 1 σt (Vss ) 2 S V + Si Vi − ∂t 2 ∆S 2 i i −1 ∆S 2 2 ri Si ri Si 1 σt (Vss ) 2 [ + S ]V + [ri + ]V ∆S i ∆S 2 ∆S 2 i i +1 (11)

∂Vi

=−

Next, by denoting K1 = −σt2 (Vss )Si2 , K 2 = −[2rt Si ∆S + σ (Vss )S ] 2 t

(9)

VN −VN −1

For the i-th ODE one has at sampling point Si along the S axis one has ∂Vi



2 i

and f (Vi ) = [rt +

rt Si

]V one obtains the follow∆S i ing description for Equation 11 ∂Vi ∂t K2 ∆S 2

=

K1 2∆S

2

Vi −1 −

K1 ∆S 2

Vi +



(12)

Vi +1 + f (Vi )

For the i-th ODE where i = 1, 2,..., N , coefficients K1 and K 2 are computed at the local grid point Si . Next, the following state vector is defined for the PDE model: ~

Y = [y1,1, y1,2 ,.., y1,i ,..., y1,N −1, y1,N ] , where y1,1 = V1 , y1,2 = V2 ... y1,N −1 = VN −1 and y1,N = VN . It will be shown that the state-space description of the nonlinear PDE dynamics

19

Distributed Parameter Systems Control and Its Applications to Financial Engineering

Box 2.­ i

y 1,N =

K1

y1,N + f (y1,N ) +

K2

φN +1 +

K1

y1,N −1 (13) ∆S 2∆S 2∆S 2 i K1 K2 K1 y 1,N −1 = y + f (y1,N −1 ) + y + y1,N −2 2 N 2 1,N −1 2∆S 2∆S 2 ∆S i K1 K2 K1 y 1,N −2 = y + f (y1,N −2 ) + y + y1,N −3 2 N −1 2 1,N −2 2∆S 2∆S 2 ∆S ...................................................................................................... i K1 K2 K1 y 1,i = y + f (y1,i ) + y + y1,i −1 2 1,i +1 2 1,i ∆S 2∆S 2∆S 2 ..................................................................................... i K1 K2 K1 y 1,3 = y + f (y1,3 ) + y + y1,2 2 1,4 2 1,3 2∆S 2∆S 2 ∆S i

y 1,2 = i

y 1,1 =

2

K1 ∆S

2

y1,2 + f (y1,2 ) +

2

y1,1 + f (y1,1 ) +

K1 ∆S

2

K2 2

y1,3 +

2

y1,2 +

2∆S K2 2∆S

K1

y1,1

2∆S 2 K1 2∆S 2

φ0

It can be proven that the state-space model of the Black-Scholes PDE is a differentially flat one, considering as flat output y1,1 = V1 . This means that all state variables and the control inputs of the model can be expressed as differential functions of this flat output. Moreover, by examining the i-th row of the state-space description as an independent subsystem with output x i and virtual control input x i+1 it can be shown that this subsystem is also differentially flat with local flat output x i .

To implement boundary feedback control, the nonlinear diffusion-PDE model is rewritten as shown in Box 2. The boundary condition φN +1 is assumed to be known and remains steady. The boundary 20



(15)

(16)

(17)



(18)



(19)

condition φ0 stands for the control input (for example, it can be dependent on the interest rate). The feedback control law is designed as follows: d

i 1 [y 1,N − k p.1 (y1,N − y1d,N )] + 2 (K1 / 2∆S ) K2 − f (y1,N ) − φN +1 2∆S 2

a1 = y1*,N −1 = K1 ∆S 2

y1,N

(20)

a2 = y1*,N −2 = d

COMPUTATION OF BOUNDARY CONTROL FOR THE SINGLEASSET BLACK-SCHOLES PDE

(14)

i 1 d y [ 1,N −1 − k p .2 (y1,N −1 − y1,N −1 )] (K 1 / 2∆S 2 ) K K2 + 1 2 y1,N −1 − f (y1,N −1 ) − yN ∆S 2∆S 2 i 1 [ (y1,N −1 − a1 )] + ⇒ a2 = a 1− k p .2 (K 1 / 2∆S 2 ) K1 K2 − f (y1,N −1 ) − y yN 2 1,N −1 ∆S 2∆S 2 (21)

Category: Accounting and Finance

aN −1 = y1*,1 =

a 3 = y1*,N −3 = d

i 1 d y [ 1,N −2 − k p .3 (y1,N −2 − y1,N −2 )] + 2 (K1 / 2∆S ) K1 K2 y − f (y1,N −2 ) − yN −1 2 1,N −2 ∆S 2∆S 2 i 1 [ (y1,N −2 − a2 )] + ⇒ a3 = a 2− k p .3 (K1 / 2∆S 2 ) K1 K2 y − f (y1,N −2 ) − yN 2 1,N −2 ∆S 2∆S 2 (22)

and continuing in a similar manner

A

d

i 1 d y [ 1,2 − k p ,N −1 (y1,2 − y1,2 )] (K1 / 2∆S 2 ) K K2 + 1 2 y1,2 − f (y1,2 ) − y1,3 ∆S 2∆S 2 ⇒ aN −1 =



(25)

i 1 [ a (y − aN −2 )] N −2 − k p ,N −1 1,2 (K1 / 2∆S 2 ) K K2 + 1 2 y1,2 − f (y1,2 ) − y1,3 2∆S 2 ∆S

and finally

ai = y1*,N −i =

d

id

1 [y 1,N −i +1 − k p,i (y1,N −i +1 − y1d,N −i +1 )] + (K1 / 2∆S 2 ) K1 K2 − f (y1,N −i +1 ) − y yN −i +2 2 1,N −i +1 ∆S 2∆S 2 ⇒ ai = i 1 a [ − ai −1 )] + i −1 − k (y p .i 1,N −i +1 (K1 / 2∆S 2 ) K1 K2 y f y y1,N − ( ) − 1 2 1 2 , N , N − − 2∆S 2 ∆S 2

(23)

Following this procedure one arrives to compute the control inputs which are associated with the last two rows of the state-space model aN −2 = y1*,2 = d

i 1 d y [ 1,3 − k p ,N −2 (y1,3 − y1,3 )] 2 (K1 / 2∆S ) K K2 + 1 2 y1,3 − f (y1,3 ) − y1,4 ∆S 2∆S 2 ⇒ aN −2 = i 1 [ a (y1,3 − aN −3 )] N −3 − k p ,N −2 (K1 / 2∆S 2 ) K K2 + 1 2 y1,3 − f (y1,3 ) − y1,4 2∆S 2 ∆S



(24)

i 1 d [ aN = φ0 = y 1,1 − k p ,N (y1,1 − y1,1 )] (K1 / 2∆S 2 ) K K2 + 1 2 y1,1 − f (y1,1 ) − y1,2 ∆S 2∆S 2 i 1 [a N −1 − k p,N (y1,1 − aN −1 )] ⇒ aN = (K1 / 2∆S 2 ) K2 K y1,2 + 1 2 y1,1 − f (y1,1 ) − ∆S 2∆S 2 (26)

Consequently, the computation of the aggregate control input aN = φ0 which is exerted on the PDE model is performed by moving backwards, and by substituting recursively into φ0 the virtual control inputs aN −1, aN −2 ,.., ai ,..., a2 , a1 . It is noted that implementation of flatness-based control in cascaded loops has been studied in the case of lumped parameter systems, as for example in electric machines (Dannehl & Fuchs, 2006; Rigatos & Siano, 2002; Rigatos & Siano, 2012). The approach followed in this paper extends this concept to a problem of elevated difficulty, and with a state-space description of higher dimensionality, that is control of distributed parameter systems and in particular control of the Black-Scholes PDE.

21

Distributed Parameter Systems Control and Its Applications to Financial Engineering

CLOSED LOOP DYNAMICS OF THE SINGLE-ASSET BLACK-SCHOLES PDE

i

i

y 1,i = a i −1 − k p,i (y1,i − ai −1 ) ⇒ i

i

(y 1,i − a i −1 ) + k p,i (y1,i − ai −1 ) = 0 ⇒

(30)

i

By substituting Equation 26 into Equation 19 of the state-space model of the PDE dynamics, and using the definition y1,1 − y1d,1 = z 1 one has id

i

y 1,1 = y 1,1 − k p,1 (y1,1 − y1d,1 ) ⇒ id

i

(y 1,1 − y 1,1 ) + k p,1 (y1,1 − y1d,1 ) = 0 ⇒

z i + k p,i z i = 0 By substituting Equation 21 into Equation 14 of the state-space model of the PDE dynamics, and using the definition y1,N −1 − aN −2 = z N −1 one has

(27)

i

i

i

y 1,N −1 = a N −2 − k p,N −1 (y1,N −1 − aN −2 ) ⇒

z 1 + k p,1z 1 = 0

i

Equivalently, by substituting Equation 25 into Equation 18, and using the definition y1,2 − a1 = z 2

i

(y 1,N −1 − a N −2 ) + k p,N −1 (y1,N −1 − aN −2 ) = 0 ⇒ i

z N −1 + k p,N −1z N −1 = 0

one has i

Finally, by substituting Equation 20 into Equation 13, and using the definition y1,N − aN −1 = z N

i

y 1,2 = a 1 − k p,2 (y1,2 − a1 ) ⇒ i

i

(y 1,2 − a 1 ) + k p,2 (y1,2 − a1 ) = 0 ⇒

(28)

one obtains

i

i

z 2 + k p,2z 2 = 0

i

y 1,N = a N −1 − k p,N (y1,N − aN −1 ) ⇒ i

Similarly, continuing with the rest of the equations of the state-space model and by substituting Equation 24 into Equation 17, while also using the definition y1,3 − a2 = z 3 one has i

i

y 1,3 = a 2 − k p,3 (y1,3 − a2 ) ⇒ i

i

(y 1,3 − a 2 ) + k p,3 (y1,3 − a2 ) = 0 ⇒

i

(y 1,N − a N −1 ) + k p,N (y1,N − aN −1 ) = 0 ⇒

(32)

i

z N + k p,N z N = 0 Thus, the dynamics of the closed-loop system becomes i

z 1 + k p.1z 1 = 0

(29)

i

i

z 2 + k p .2 z 2 = 0

z 3 + k p , 3z 3 = 0

i

z 3 + k p .3 z 3 = 0

Moving backwards, and by substituting Equation 23 into Equation 16 of the state-space model of the PDE dynamics, and using the definition y1,i − ai −1 = z i one has

........................ i

z i + k p.i z i = 0 ........................ i

z N −1 + k p.N −1z N −1 = 0 i

z N + k p.N z N = 0

22

(31)



(33)

Category: Accounting and Finance

The dynamics of the closed-loop system can be also written in matrix form i ~

~

Z + KP Z = 0

(34)

where ~

Z = [z 1, z 2 , z 3 ,..., z i ,..., z N −1,z N ] , and K p = diag[k p , k p , k p ,..., k p ,..., k p 1

2

i

3

N −1

, k p ] . N

After suitable selection of the coefficients k p , i = 1, 2,..., N such that the monomials i

p(s ) = s + k p to have a negative root, it can be i

assured that lim z i (t ) = 0 and that the closed-loop t →∞

system is asymptotically stable. Moreover, to prove asymptotic stability for the closed-loop system, the following Lyapunov function can be used N 1 VL = ∑ z i2 i =1 2

(35)

The derivative of this Lyapunov function with respect to time is given by i

i

N

i

N

V L = ∑ z i z i ⇒ V L = ∑ z i (−k p z i ) ⇒ i =1

i

i =1

N

V L = −∑ −k p z < 0 i =1

i

2 i

i

(36)

Thus, it is proven again that the closed-loop system is globally asymptotically stable.

THE PROBLEM OF BOUNDARY CONTROL OF THE MULTIASSET BLACK-SCHOLES PDE

A

A multi-asset option price model is considered next. In such a case, the dynamics of the options’ value is described by the multi-asset Black-Scholes PDE (Wade et al., 2007; Moon & Kim, 2013; Lotstedt et al., 2007; Moon et al., 2006; MartinsVaquero et al., 2014). The chapter treats also the problem of feedback control and stabilization of the aforemention PDE. As already noted, methods for feedback stabilization of systems with nonlinear PDE dynamics have been a flourishing research subject in the last years (Balogh & Kristic, 2002; Bensoussan et al., 2006; Basseville & Nikiforov, 1993; Boussaada et al., 2013; Smyshlyaev & Krstic, 2010; Boskovic et al., 2002; Liu, 2003). In particular, feedback control of diffusion-type (parabolic) PDEs has been a subject of extensive research and several remarkable results have been produced (Maidi & Corriou, 2014; Zwart et al., 2011; Woitteneck & Mounier, 2010; Mounier et al., 2010). As already pointed out, for the control of diffusion PDEs, boundary and distributed control methods have been developed (Fliess & Mounier, 2002; Rigatos, 2015a; Winkler & Lohmann, 2010). By showing the feasibility of control of the multi-asset Black-Scholes PDE it is also proven that through a selected trading policy, the price of options can be made to converge and stabilize at specific reference values. By applying semidiscretization and the finite differences method, the multi-asset Black-Scholes PDE model is written in a state-space form (Olivier & Sedoglavic, 2001; Kroner, 2011). This state-space description stands for a differentially flat system which means that all its state variables and control inputs can be written as differential functions of the flat output vector (Bououden et al., 2011; Lévine, 2009; Lévine, 2011). One can note several results on the use of differential flatness theory in the

23

Distributed Parameter Systems Control and Its Applications to Financial Engineering

control of PDEs (Rudolph, 2003; Rigatos, 2013; Rigatos, 2015b). The first stage in the proposed control approach for the multi-asset Black-Scholes PDE is to decompose the state-space description of the PDE into an equivalent set of nonlinear ODEs (Guo & Billings, 2007; Pinsky, 1991; Utz et al., 2011; Laroche, 2000). Next by examining independently each nonlinear ODE it is shown that this stands again for a differentially flat system, for which a virtual control input can be computed as in the case of flatness-based control for the trivial system. The virtual control input is chosen such that the ODE subsystem dynamics is linearized and the tracking error is eliminated. The boundary condition that appears in the nonlinear ODE subsystem that comprises the last row of the state-space description, stands again for the aggregate control input. The computation of the boundary control input uses recursively all virtual control inputs mentioned above, moving from the last ODE system to the first one. This stands for implementation of flatness-based control in successive (cascading) loops. Thus, by tracing the rows of the state-space model backwards, at each iteration of the control algorithm, one can finally obtain the control input that should be applied to the multi-asset Black-Scholes PDE system so as to assure that all its state vector elements will converge to the desirable setpoints. The stability of the control loop is proven in two manners. First, convergence to zero is proven for the tracking error of all subsystems into which the PDE’s state-space model is decomposed. Next, with the use of Lyapunov analysis it is reconfirmed that this control scheme is asymptotically stable.

BOUNDARY CONTROL OF THE MULTI-ASSET BLACK-SCHOLES PDE Next, the multi-asset Black-Scholes PDE is introduced (Martins-Vaquero et al., 2014; Wade et al., 2007):

24

N N ∂V ∂2V = ∑ ∑ ρσi σ j Si S j + ∂t ∂Si S j i =1 j =1 N ∂Vi ∑ rSi ∂S − rV i =1 i

(35)

Moreover, without loss of generality the twoasset Black-Scholes PDE is considered 1 1 ∂2V ∂V ∂2V = σ12S12 2 2 + σ22S 22 2 2 + 2 2 ∂t ∂ S2 ∂ S1 2 ∂V ∂V ∂V + rS1 ρσ1σ2S1S 2 + rS 2 − rV ∂S1∂S 2 ∂S 1 ∂S 2 (36) Semi-discretization and the finite differences method is applied. To this end the partial derivatives appearing in Equation 36 are computed as follows: ∂V V (S1,i +1, S 2, j ) −V (S1,i , S 2, j ) = ∆S1 ∂S1

(37)

∂2V = ∂2S1 V (S1,i +1, S 2, j ) − 2V (S1,i , S 2, j ) + V (S1,i −1, S 2, j ) (38) ∆S12

V (S1,i , S 2, j +1 ) −V (S1,i , S 2, j ) ∂V = ∆S 2 ∂S 2

(39)

∂2V = ∂2S 2 V (S1,i , S 2, j +1 ) − 2V (S1,i , S 2, j ) + V (S1,i , S 2, j −1 ) ∆S 22

(40)

Category: Accounting and Finance

Box 3.­ V (S1,i +1, S 2, j +1 ) −V (S1, j +1, S 2, j ) −V (S1,i , S 2, j +1 ) + V (S1,i , S 2, j ) ∂V = ∆S1∆S 2 ∂S1∂S 2           Using the previous semi-discretization, for grid point (i.j ) it holds ∂V (S1,i , S 2, j ) 1 2 2 V (S1,i +1, S 2, j ) − 2V (S1,i , S 2, j ) +V (S1,i −1, S 2, j ) = σ1 S1,i [ ]+ ∂t 2 ∆S12 1 2 2 V (S1,i , S 2, j +1 ) − 2V (S1,i , S 2, j ) +V (S1,i , S 2, j −1 ) σS [ ]+ 2 1 2, j ∆S12 V (S1,i +1, S 2, j +1 ) −V (S1,i +1, S 2, j ) −V (S1,i , S 2, j +1 ) +V (S1,i , S 2, j ) ]+ ρσ1σ2S1,i S 2, j [ ∆S1∆S 2 V (S1,i , S 2, j +1 ) −V (S1,i , S 2, j ) V (S1,i +1, S 2, j ) −V (S1,i , S 2, j ) ] + rS 2, j [ ] − rV (S1,i , S 2, j ) rS1,i [ ∆S1 ∆S 2

A

2

and in Equation 41 (shown in Box 3). The boundary conditions of the PDE are taken to be Vi,0 ≠ 0 only if i = 1, V0, j ≠ 0 only if j = 1 and V (i, j ) = ct (constant) if i > N or j > N .. C o n s i d e r i n g t h a t i = 1, 2,..., N a n d j = 1, 2,..., N the option’s values at the grid points (i, j) are denoted as Vi, j . Using this notation, the semi-discretized model of the PDE takes the following form: At grid point i = 1 and j = 1 ∂V1,1

1 2 2 V2,1 − 2V1,1 +V0.1 σS [ ]+ ∂t 2 1 1,1 ∆S12 V − 2V1,1 +V1,0 1 + σ22S 22,1[ 1,2 ]+ 2 ∆S 22 V2,2 −V2,1 −V1,2 +V1,1 ρσ1σ2S1,1S 2,1[ ]+ ∆S1∆S 2 V −V  V1,2 −V1,1 1,1  +rS1,1  2,1 rS [ ] − rV1,1 +  2,1 ∆S 2  ∆S1  (43) =

∂Vi, j

(41)

(42)

1 2 2 Vi +1, j − 2Vi, j +Vi −1. j σS [ ]+ ∂t 2 1 1,i ∆S12 − 2Vi, j +Vi, j −1 V 1 + σ22S 22, j [ 1, j +1 ]+ 2 ∆S 22 −Vi +1, j −Vi, j +1 +Vi, j V ]+ ρσ1σ2S1,i S 2, j [ i +1, j +1 ∆S1∆S 2 V −Vi, j  V −Vi, j +rS1,i  i +1, j ] − rVi, j + rS 2, j [ i, j +1  ∆S 2  ∆S1  (44) =

Next, the following state vector variables are defined x (i −1)N + j = Vi, j , i = 1, 2,..., N and j = 1, 2,..., N . The system’s state vector becomes x = [x 1, x 2 ,..., x (i −1)N +( j −1), x (i −1)N + j ,..., x N 2 ]T

and comprises N 2 elements. Using this notation of state variables Equation 81 becomes

At grid point i > 1 and j > 1 it holds

25

Distributed Parameter Systems Control and Its Applications to Financial Engineering

1 2 2 x N +1 − 2x 1 σS [ ]+ 2 1 1,1 ∆S12 1 2 2 x 2 − 2x 1 σS [ ]+ 2 2 2,1 ∆S12 − x 2 − x N +1 + x 1 x ]+ +ρσ1σ2S1,1S1,2 [ N +2 ∆S1∆S 2 − x1 x x − x1 rS1,1[ N +1 ] + rS 2,1[ 2 ]+ ∆S1 ∆S 2 V V 1 1 [ σ12S12 0,12 + σ22S 22 1,02 ] 2 2 ∆S1 ∆S 2

FLATNESS-BASED CONTROL OF THE MULTI-ASSET BLACK-SCHOLES PDE

i

x1 =

First, it can be proven that the state-space description of the multi-asset Black-Scholes PDE, given in Equation 50, is a differentially flat one, with flat output y = x N 2 (Rigatos & Siano, 2016). Solving the i-th row of the state space model, where i = 1, 2,..., N 2 , with respect to x i+1 one (45)

Thus, by defining the control input associated with the boundary conditions as u = [V0,1,V1,0 ]T and one obtains a description for Equation 45 in the form i

x 1 = f1 (x ) + c1u

(51)

Using the virtual control inputs of Equation 51 in the state-space model of Equation 50 one gets i

x N 2 = fN 2 (x ) + cN 2 a1 i

i

x (i −1)N + j = f(i −1)N + j (x ) + c(i −1)N + j x (i −1)N +( j −1) (49) C o n s i d e r i n g t h a t i = 1, 2,..., N a n d j = 1, 2,..., N there are N 2 state-space equations. Thus, the dynamics of the PDE model is written as i

x N 2 = fN 2 (x ) + cN 2 x N 2 −1 i

x N 2 −1 = fN 2 −1 (x ) + cN 2 −1x N 2 −2 ................................................... i

x (i −1)N + j = f(i −1)N + j (x ) + c(i −1)N + j x (i −1)N +( j −1) ................................................... i

x 2 = f2 (x ) + c2x 1 i

26

a1 = x N 2 −1, a2 = x N 2 −2 ,..., aN 2 −(i −1)N −( j −1) = x (i −1)N +( j −1),..., aN 2 −1 = x 1

(46)

Equivalently for Equation 44 one obtains Equation 47 (shown in Box 4). Equation 48 is finally written in the form

x 1 = f1 (x ) + c1u

finds that state variables x i+1 is a differential function of the flat output y. Moreover, from the last row of Equation 50 it holds that u is a function of the flat output and its derivatives. Next, the following virtual control inputs are defined

(50)

x N 2 −1 = fN 2 −1 (x ) + cN 2 −1a2 ....... i

x (i −1)N + j = f(i −1)N + j (x ) + c(i −1)N + jaN 2 −(i −1)N −( j −1) ....... i

x 2 = f2 (x ) + c2aN 2 −1 i

x 1 = f1 (x ) + c1u

(52)

By examining independently each nonlinear ODE of the previous state-space description of Equation 52 and by defining as local flat output for the i-th ODE the state variable x i it can be shown that the i-th row of the state-space description stands again for a differentially flat system. Actually, one has now N 2 subsystems, each one of them related to a row of the state-space model and the local flat outputs for these subsystems are

Category: Accounting and Finance

Box 4.­

A

1 2 2 x iN + j − 2x (i −1)N + j + x (i −2)N + j ]+ σS [ 2 1 1,i ∆S12 x (i −1)N +( j +1) − 2x (i −1)N + j + x (i −1)N +( j −1) 1 + σ22S 22, j [ ]+ 2 ∆S 22 x iN +( j +1) − x (i −1)N +( j +1) − x iN + j + x (i −1)N + j +ρσ1σ2S1,i S 2, j [ ]+ ∆S1∆S 2 x iN + j − x (i −1)N + j x (i −1)N +( j +1) − x (i −1)N + j +rS1,i [ ] + rS 2, j [ ] − rx (i −1)N + j ∆S1 ∆S S2 i

x (i −1)N + j =

(47)

          Equation 47 can be also written in the form

1 2 2 x iN + j − 2x (i −1)N + j + x (i −2)N + j ]+ σS [ 2 1 1,i ∆S12 x (i −1)N +( j +1) − 2x (i −1)N + j 1 + σ22S 22, j [ ]+ 2 ∆S 22 x iN +( j +1) − x (i −1)N +( j +1) − x iN + j + x (i −1)N + j +ρσ1σ2S1,i S 2, j [ ]+ ∆S1∆S 2 x (i −1)N +( j +1) − x (i −1)N + j x iN + j − x (i −1)N + j ] + rS 2, j [ ] − rx (i −1)N + j + +rS1,i [ ∆S1 ∆S 2 1 1 +[ σ22S 22, j ]x (i −1)N +( j −1) 2 ∆S 22 i

x (i −1)N + j =

Y = [x 1, x 2 , , x (i −1)N + j , , x N 2 −1, x N 2 ]

(53)

From the i-th row of the state-space model it can be seen that the virtual control input αi is a differential function of the local flat output x i , which shows again that the i-th subsystem, if independently examined, is also differentially flat. The virtual control input for the i-th row of the state space model is chosen such that the ODE subsystem dynamics is linearized and the tracking error is eliminated. The boundary condition that appears in the nonlinear ODE subsystem that comprises the last rows of the state-space description, was used as the aggregate control input (Rigatos & Siano, 2016). One can find the values that the virtual control inputs should have, so as to eliminate the tracking error for each one of the subsystems that are



(48)

obtained from the per-row decomposition of Equation 52. For the first row of Equation 52 one has * 1

a =

1 cN 2

i*

[x N 2 − fN 2 (x ) − k p (x N 2 − x N* 2 )] i

(54)

with k p > 0 , while it also holds that a1* = x N* 2 −1 . 1

Continuing with the second row of Equation 52, the associated virtual control input can be obtained: a2* =

1 cN 2 −1

i*

[x N 2 −1 − fN 2 −1 (x ) − k p (x N 2 −1 − x N* 2 −1 )] i

(55)

with k p > 0 , while it also holds that a2* = x N* 2 −2 . 2

Continuing in a similar manner, for the i-th row

27

Distributed Parameter Systems Control and Its Applications to Financial Engineering

of Equation 52, the associated virtual control input is a *N 2 −(i −1)N −( j −1) =

c(i −1)N + j

−k p

N 2 −( i −1 ) N −( j −1 )

[x (i −1)N + j − f(i −1)N + j (x ) −

i*

1 c(i −1)N + j

i*

1

aN* 2 −(i −1)N −( j −1) =



[a N 2 −(i −1)N −( j −1)−1 − f(i −1)N + j (x ) −

−k p

N 2 −( i −1 ) N −( j −1 )

(x (i −1)N + j − x (*i −1)N + j )]

(x (i −1)N + j − aN* 2 −(i −1)N −( j −1)−1 )]

(61)

(56) with k p

N 2 −( i −1 ) N −( j −1 )

*

> 0 where

aN* 2 −1 =

a *N 2 −(i −1)N −( j −1) = x (*i −1)N +( j −1) By applying the same procedure, the virtual control input for the N 2 − 1 row of Equation 52 is found *

*

aN 2 −1

1 i = [x 2 − f2 (x ) − k p (x 2 − x 2* )] N 2 −1 c2

(57)

Finally, from the N 2 -th row of Equation 53 one computes the boundary control input that is really exerted on the system *

1 i u = [x 1 − f1 (x ) − k p (x 1 − x 1* )] N2 c1

(58)

where aN* 2 = u . Using the previous definitions, the virtual control inputs can be written as a1* =

* 2

a =

28

1 cN 2

1

i*

[x N 2 − fN 2 (x ) − k p (x N 2 − x N* 2 )] 1

(59)

i*

[a 1 − fN 2 −1 (x ) − k p (x N 2 −1 − a1* )] 2 cN 2 −1 (60)

1 i 2 [a N −2 − f2 (x ) − k p (x 2 − aN* 2 −2 )] N 2 −1 c2 (62)

*

1 i u = [a N 2 −1 − f1 (x ) − k p (x 1 − aN* 2 −1 )] N2 c1

(63)

Stability analysis for the case of the multiasset Black-Scholes PDE of Eq. (52) is performed as in the case of the single-asset Black-Scholes PDE. This was explained in Section “Closed Loop Dynamics of the single-asset Black-Scholes PDE”. In the multi-asset Black-Scholes PDE one demonstrates that the N 2 state-vector elements converge to the associated reference values. Moreover the associated Lyapunov function will be a sum of N 2 terms (instead of N ).

SIMULATION TESTS Simulation results about the proposed control method for distributed parameter systems and for the case of the single-asset Black-Scholes PDE (BSPDE) are depicted in Figure 1 (a). The spatial discretization of the single-asset PDE model consisted of N = 25 points. The sampling period was chosen to be Ts . The boundary condition VN +1 of the PDE was taken to be known and constant. Simulation examples for the case of the multi-asset Black-Scholes PDE are depicted in

Category: Accounting and Finance

Figure 1 (b) and in Figure 2(a) and 2(b). The spatial discretization of the PDE model consisted of N = 16 points. The sampling period was chosen to be again Ts = 0.01sec. The boundary condition V0,1 served as the control input, while the boundary condition V1,0 was set equal to zero.

The numerical simulation experiments have confirmed the theoretical findings of this chapter. It has been shown that by applying the proposed control method the single-asset and multi-asset Black-Scholes PDE dynamics can be modified so as to converge to the desirable reference profile. The control input that succeeds this, requires the recursive computation of the virtual control inputs which were defined in Section “Computation of boundary control for the single-asset Black-Scholes PDE” and in Section “Flatness-based control of the multi-asset Black-Scholes PDE” The accuracy of tracking of the reference setpoints was indeed satisfactory. It is noted that the application of the proposed Black-Scholes PDE feedback control method is not limited to the case of option pricing but can be also extended to other financial models and indexes with spatiotemporal dynamics.

FUTURE RESEARCH DIRECTIONS

A

There is a wide class of problems in finance related to PDE models (Boucekkine et al., 2013). There can be for instance, capital mobility and growth problems or commodities pricing problems (e.g. pricing of consumption goods, agricultural products, mining products, oil, electric power, services, etc.). In particular, it has been shown that commodities’ price dynamics is equivalent to option price dynamics, with the spot price and the convenience yield of the commodity to have a role equivalent to the one of the underlying assets that appear in the Black-Scholes PDE (Pirrong, 2014; Swartz, 1998). Therefore, one can also apply PDE control methods based on differential flatness theory for the stabilization of commodity prices. It is noted that the differential flatness theorybased control methods are of proven convergence and stability, whereas the convergent solution of optimal control problems in finance associated with the Hamilton-Jacobi-Bellman PDE can be assured only in specific cases.

Figure 1. (a) single-asset BSPDE: variation in time of the solution of the PDE at grid point x N (blue line) and associated reference setpoint (dashed red line) (b) multi-asset BSPDE: Tracking of reference setpoint No 1 (dashed red line) by the value of the PDE system (blue line) at the final grid point VN ,N

29

Distributed Parameter Systems Control and Its Applications to Financial Engineering

Figure 2. (a) multi-asset BSPDE: Tracking of reference setpoint No 2 (dashed red line) by the value of the PDE system (blue line) at the final grid point VN ,N (b) multi-asset BSPDE: Tracking of reference setpoint No 3 (dashed red line) by the value of the PDE system (blue line) at the final grid point VN ,N

CONCLUSION First, a boundary control method for the singleasset Black-Scholes PDE has been introduced, aiming at developing an approach for the stabilization of options dynamics. Following a semidiscretization method and a finite differences scheme, the Black-Scholes PDE model has been decomposed into an equivalent set of nonlinear ordinary differential equations (ODEs) and a states-space model has been obtained. Next, it has been proven that each one of the aforementioned ODEs stands for a differentially flat subsystem. This enables to compute for each ODE subsystem, a virtual control input which linearizes its dynamics and eliminates the associated output’s tracking error. From the state equations that constitute the last subsystem one can find the boundary condition that also stands for the control input to the single-asset Black-Scholes PDE model. Second, a feedback control and stabilization scheme has been developed for the multi-asset Black-Scholes PDE. It has been shown that by applying again semidiscretization and the finite differences method, the multi-asset Black-Scholes PDE can be written in a state-space form for which differential flatness properties hold. Next, the state-space description

30

of the system was decomposed into an equivalent set of nonlinear ODEs and a control algorithm consisting of successive (cascading) loops was developed. To compute the boundary control input of the single-asset, as well as of the multi-asset, BlackScholes PDE model one has to use recursively all virtual control inputs which are applied to the previously mentioned ODE subsystems. The control input of i-th subsystem becomes a reference setpoint for its successive (i+1-th) subsystem. Thus computation of control inputs moves progressively from the last ODE system to the first one. Consequently, by tracing the rows of the state-space model backwards, at each iteration of the control algorithm, one can finally obtain the control input that should be applied to the single-asset, as well as to the multi-asset, Black-Scholes PDE so as to assure that all its state vector elements will converge to the desirable setpoints. By analyzing the dynamics of the closed loop system that results from the application of the aforementioned control method, asymptotic stability is confirmed. Finally, numerical simulation experiments have been provided about the application of the proposed control method to the model of both the single-asset and the multi-asset Black-Scholes

Category: Accounting and Finance

PDE. These performance tests have shown the control method’s accuracy and reliability. The method can be of interest for trading of options in open electricity markets as well as for the pricing of commodities. By showing the feasibility of such a control method it is also proven that through a selected trading policy the price of the negotiated option can be made to converge and stabilize at specific reference values.

Boucekkine, R., Camacho, C., & Fabbri, G. (2013). On the optimal control of some parabolic partial differential equations arising in economics. Document de Recherche EPEE. Centre d’Etudes des politiques economiques de l’université d’ Evry.

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ADDITIONAL READING

Wade, B. A., Khaliq, A. Q. M., Yousuf, M., Vigo-Aguiar, J., & Deininger, R. (2007). On smoothing of the Crank-Nicolson scheme and higher-order schemes for pricing barrier options. Journal of Computational and Applied Mathematics, Elsevier, 204(1), 144–158. doi:10.1016/j. cam.2006.04.034

Chen, P., & Islam, S. (2005). Optimal control problems in finance: A new computational approach. Springer.

Windcliff, H., Forsyth, P. A., & Vetzal, K. R. (2006). Analysis of the stability of the linear boundary condition for the Black-Scholes equation. Journal of Computational Finance, 8(1), 65–92. doi:10.21314/JCF.2004.116 Winkler, F. J., & Lohmann, B. (2010). Design of a decoupling controller structure for first order hyperbolic PDEs with distributed control action. 2010 American Control Conference, Baltimore, MD. doi:10.1109/ACC.2010.5530561

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Bhar, R. (2010). Stochastic Filtering with applications in finance. World Scientific. doi:10.1142/7736

Cossin, D., & Aparicio Acosta, F. (2001). Optimal Control of Credit Risk. Kluwer. doi:10.1007/9781-4615-1393-3 Ma, J., & Wohar, S. (2015). Recent advances in estimating nonlinear models: with applications to economics and finance. Springer. Shadbolt, J., & Taylor, J. (2002). Neural networks and the financial markets: Predicting, combining and portfolio optimization. Springer. doi:10.1007/978-1-4471-0151-2 Zeng, Y., & Wu, S. (2013). State-space models: Applications in economics and finance. Springer. doi:10.1007/978-1-4614-7789-1

Category: Accounting and Finance

KEY TERMS AND DEFINITIONS Asset: A parameter in financial models which denotes a possession or a resource which can be evaluated and traded. Assets can take the form of financial derivatives (such as options and futures) or commodities or the form of equipment or other proprietary holdings. Black-Scholes PDE: A diffusion partial differential equation which describes the dynamics of option prices. It computes the distribution of the option price as a function of time and an underlying asset variable. It can be dependent on one single asset or on multiple assets. Boundary Control: An approach to the control of partial differential equations in which the control action is exerted to the PDE through its boundary conditions. This is different to distributed or pointwise control of PDEs, in which the control action is exerted at several points of the system’s state space. Differential Flatness Theory: A primary research direction in the area of nonlinear dynamical systems control. It considers that instead of describing the system’s dynamics through its entire state vector, one can use for this purpose specific algebraic variables which are called flat outputs and which are dependent only on certain elements of the state vector. Differential flatness theory enables to succeed global linearization for complicated nonlinear dynamics and in this manner to solve the associated control and state estimation problems. Distributed Parameter System: A dynamical system that evolves not only in time but also in space. Otherwise stated the system exhibits spatiotemporal dynamics along the time axis and along one or more spatial axes. Systems described

by partial differential equations are distributed parameter ones. Feedback Control: The action of applying an external excitation to a dynamical system which is dependent on the value of the system’s state vector and on the deviation of this state vector from a reference value that is called setpoint. Lyapunov Function: This is an energy function of the system which depends on quadratic terms of the system’s state vector error. It takes positive values apart from the equilibrium where it becomes zero. A system is stabilized when the associated Lyapunov function becomes zero. Options: Financial derivatives which generate a secondary value for traded assets (commodities, goods, services etc.). Options are related to long term agreements about the exploitation of the traded assets so they do not reflect only the spot price of the assets but they also show the ability to accomplish the contract term’s. Pricing: The procedure of defining the price of traded assets, services or commodities. Spot pricing is an on-site agreement about the price of the traded resource based on offer and demand. There can be more elaborated pricing schemes taking place in longer time intervals (trading of financial options or commodities) which apart from offer and demand are also dependent on supporting services and procedures for the trading transaction (e.g. ability to store and transport goods, availability of equipment, credibility of the trading parts etc.). Stability: A property of a dynamical system denoting that the state vector of the system converges to a specific point in the state space, which is called equilibrium or to a bounded region in the state-space which is called domain of attraction and remains there.

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Category: Accounting and Finance

Does Inter-Bank Investments Restraints Financing Performance of Islamic Banks? Mohammad Taqiuddin Mohamad University of Malaya, Malaysia Munazza Saeed University of Malaya, Malaysia

INTRODUCTION Interbank markets proved to play a crucial role in propagating the distress during the recent financial crisis. Unsecured financing determines clear links between creditors and debtors, stating explicitly the risk relation (Leur, 2016). If a debtor defaults, the lender’s risk materializes and she has to bear the losses. Since the beginning of the financial crisis, the interbank market has been carefully scrutinized by commentators and policy-makers. It is also considered as crucial stress indicators during financial crises: they reveal not only banks’ concerns regarding to the risk of financing of their counterparts, but also regarding their own liquidity needs. Accordingly, the impact of the monetary-policy measures followed by Malaysia Islamic banks, the need to look long term effect involving financing capability. Disentangling financing and liquidity effects has essential policy implications. On the one hand, if a rise in spreads reflects poor liquidity, policy measures should aim at improving funding facilities. On the other hand financing concerns should be addressed by enhancing debtors’ solvency (Buigut, 2010). This question has been of utmost importance in this industry over the last few years, where most of the interbank operations conducted by Central Bank of Malaysia were designed to reduce interbank market stress. This article seeks to examine the involvement of Malaysia Islamic banks in Islamic inter‐bank money market (IIMM) investment within a financing framework in dual banking

system in Malaysia. This paper also focuses on effects bank specifications, changes in monetary policy and economic environment on financing behaviour of Malaysia Islamic banks. The article is divided into five parts. The second section describes background and performance of financing activities in Islamic banks in Malaysia. The third section, examines some previous researches and articles related with this topic. In fourth section, model and data specification adopted used in this article. The fifth section is the finding of the research. Section five and six look at the future research direction and conclusion.

BACKGROUND Since Islamic banking was established in Malaysia in 1983, financing growth rate showed an encouraging performance. However, that performance was not consistent over the operation period of 27 years. Since there are circumstances where it is affected in economy situation. The evidence is in 1987 and 1998 when the global economy was suffering from the recession. At that time Islamic banking financing decreased from 18.36 percent to 2.68 percent. However, this situation improved and the amount of Islamic financing continued to rise for the next year since the world economy got in good condition and stable. Table 1 shows the use of the Malaysia Islamic banking funds in aggregate in the form of several types of financing. In reference to the table, the

DOI: 10.4018/978-1-5225-2255-3.ch003 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Accounting and Finance

Table 1. Financing by type/sector Type of Financing

2006

2007

2008

2009

A

2010

RM Million Overdraft

2731.0

3,278.0

3,740.1

4,203.5

4,446.7

Total

26567.7

29,208.8

33,570.7

39,164.1

44,959.0

where: Passenger car

23127.2

25,422.9

29,154.8

36,498.9

41,569.3

376.5

505.2

331.1

760.4

875.4

0.0

0.0

0.0

0.0

0.0

Connector financing

369.2

465.9

384.4

413.5

397.6

Syndicate financing

1199.1

772.2

521.8

2,504.4

2,061.1

Factoring

0.0

0.0

0.0

0.0

0.0

Personal financing

4526.4

6,001.0

8,484.0

11,727.3

15,540.2

Home financing

16403.0

17,036.6

18,940.8

22,728.3

29,792.6

Others

14027.9

17,764.8

23,882.5

34,453.2

43,181.6

Until 1 year

1255.6

2,096.6

4,289.4

4,183.1

3,878.4

Exceeding 1 year

53460.3

60,720.5

75,642.8

104,596.9

127,185.1

Financing Bill

9164.2

10,291.8

10,070.0

8,056.9

7,881.8

Trustworthy receipt

512.4

571.3

728.2

652.8

664.3

Revolve credit

2117.3

2,079.6

3,058.5

5,268.8

6,230.4

Financing in foreign currencies

327.4

841.6

2,628.2

3,132.9

3,956.9

Others

1004.6

1,050.8

1,381.6

1,907.6

2,425.0

Total

80460.5

89,867.6

107,721.8

134,973.5

162,412.6

Term financing

Hire purchase

Leasing Financing based on block

Due time which:

Source: National Bank of Malaysia (2006-2010)

Islamic banking financing flows are broken down into some type of overdraft financing: term financing, bill financing, trust receipts, revolving credit in foreign currencies, and the rest is represented by other available financing. During the five years of 2006 to 2010, the direction of financing flow of Malaysia Islamic banking increased around 11 to 25 percent each year with the latest financing in December 2010 making RM162,412.6 million. The majority of the total financing is contributed by the term financing type that covered financing such as leasing, financing by block, syndicate,

factoring, private financing, home financing and others. Meanwhile the least type of financing demand by the client is the trustworthy receipt1 which only represents around 0.4 per cent from the total financing every year (Mahmood, 1997). It should be noted, though Islamic banking offers the client interest-free financing, but the reality is the Islamic banking is still facing various form of risk s especially credit risk which involves the capability of the bank to offer more financing. When this situation occurs, the bank will strive to reduce the financing volume in the future where

37

Does Inter-Bank Investments Restraints Financing Performance of Islamic Banks?

this will directly lead to the fall in bank’s profit. Apart from credit risk, the other example of risk that could not be taken lightly is the interest risk rate which involves the existence of substitution effect where client switches to conventional bank financing due to the decrease in interest rate. This causes the financing cost in conventional bank to be much lower compared to financing cost of Islamic banking (Kader R. A., 2009).

Literature Review In this section, the authors put forth some of past research that touched directly and indirectly on the bank’s loans/financing behaviour relating to the bank’s specifications, the economic environment and also the market. The review covers both local and international researchers. Hassan (1993) studied the loan sales activities of commercial banks in the United States that refers to the risks vulnerability in the capital market. To achieve the set objectives, six bank specification variables were included which are the credit variables, interest rate and also business variables. Based on five markets of risk measurement, this study found out that specification and loan expansion have a positive connection with all forms of market risk. Hatakeda (2000) studied the banks loan in Japan under liquidity constraint from year 1975 to 1995. Researchers use estimation method of Ordinary Least Squares (OLS) towards bank sample data as well as other methods such as Augmented Dickey Fuller (ADF) unit roots test and co integration relation between variables. This study is successful in finding the empirical evidence of the existence of third regime in any bank sample that is financing under their liquidity constraint. From this regime, both land price index and bank capital have positive and great effect on bank financing. On the other hand, the call rate and economy activity (Real GDP) have a negative effect. This study also discovered how the liberation and regulation on bank capital are also influenced by bank financing behaviour.

38

Cebenoyan, A.S; Strahan, P.E (2004) studies on how domestic commercial bank in United States manages credit exposed risk through sales loan affect the bank capital structure, loan, benefits and also the risk from 1987 to 1994. This study use series of cross section estimation method to measure the usage of loan sales market for risk management. The study discovered bank samples balancing the financing portfolio exposure through loan trading, where bank use loan sales market as a platform in risk management and not for changing loan principal, having less capital from other bank. They produce riskier financing (financing business) as percentage to total asset compared to other banks. The study also discovered sample bank had a low risk and high profit compared to other bank. The study concluded that sophisticated risk management practice in banking affair increase the readiness bank credit but not fully decrease bank2 risk. Atunbas Y, Gambacorta L and Marques D (2007) studied the effect of drastic increase in security activity on banks credit offered in Europe. In this matter, the security is found to change the function of credit market by reducing fundamental liquidity role which played by financial mediator. Besides that, the variation of bank role from its original function had changed the bank capability in offering credit and bank financing channel efficiency for finance principle. This study found that the security usage protects bank financing offer from finance principle effect. In addition, bank security activity also concretes the bank capacity to offer new loan, but this capacity is dependent on business cycle condition and risk position. Hazli and Ismail (2008) studied the effect on Malaysia Islamic banking involvement in securitisation activity towards loan/financing offer and risk tolerance level. Theoretically, securitisation activities will decrease the degree of bank avoidance risk. Thus, banks are motivated to increase the percentage of assets with a concentration of the risk through the granting of loans to economy sectors. This study refers to Islamic commercial

Category: Accounting and Finance

banks in Malaysia for years 1994 to 2004 by using the panel data analysis. The findings show significant securitisation activity cut the growth of financing. This indicates that securitisation is the replacement to the Malaysia Islamic banking financing. In addition, this study supports the moral harzard hypothesis where the bank involved in the securitisation will reduce financing with least risk and could do financing on riskier one. Altunbas, Gambacorta, and Marques (2009) did an empirical research on the bank borrowing channel in accordance with European Community perspective which emphasis the role of financial innovation. This study used a dynamic model with panel data refer omg to the data of the quarter of year 1999 to the fourth quarter of 2008 in 643 banks throughout Europe3. As a result, the study found a significant relationship exists between low interest rates with risk taking by the bank, where the bank’s actions have an impact on the risk of the bank. Besides that, the study also found that monetary policy is only partially neutral on monetary policy stability. Apart from that, the study also found that the banks have different lending channel especially those referring to size and credit reaction on GDP while the bank’s capital is not involved in the shock transmission. Rahman (2009) studied the relationship between the loans structure and market risk exposure of banks in Malaysia by adapting an estimation of unbalanced panel data to the eleven banks from the year of 1994 to 2006. In this study, the influence of the loans structure is analysed using four main measurements which are the properties lending, specialization index, short term loans stability and long term stability loans. In conclusion, the study found a phase of loan structure influence the level of high market risk when the financial crisis in 1997 and the year after. Besides that, bank and investors should take the effect of additional loan structure into consideration for the effect of loan expansion and management efficiency when the bank is exposed to the risk. Sarantis and Nicholas (2009) did an empirical research on the bank loan channel for financial transmission in 8 countries of CEE (Central and

Eastern Europe), namely the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, the Republic of Slovakia and Slovenia who have joined the European Union. In this study, researchers tested whether changes in monetary policy affect bank loans which differ in the size of the bank, capital strength, liquidity and structure of ownership. To achieve the objective of the study, researchers used a method of estimating dynamic panel on bulk of the data bank panel in year 1994 to 2003. As a result, this study found that the size of the bank and liquidity has the most significant role in differentiating the reactions of banking financial policy changes. The study also looked at macroeconomic effects through bank borrowings and found evidence that linking the aggregate would lend to economic activities in CEE countries. Kader and Leong (2009) review the impact of interest rate changes on the demand for Islamic financing in the dual system of banking in Malaysia. In their analysis, the researchers using time series on political Unit Root Test, Co integration, Vector Autoregressive (VAR), Granger Causality and Impulse Response Function (IRF) on monthly data provided by Bank Negara Malaysia (BNM) from 1999 to 2007. Several variables are included such as the total number of conventional banking financing of residential properties, the number of real estate financing Islamic banking and the base lending rate (base rate was BLR). The conclusion in this study is any increase in base lending rate (BLR) will encourage users to obtain financing from Islamic banking and vice versa. Buigut (2010) evaluates the importance of the bank’s loans financial policy transmission channel in Kenya. Loan theory stated that a strengthen financial which affect demand aggregate by moving loan supply bend to the left. This reduction may be due to the stress of the demand for loans through conventional interest rate. In explaining this ambiguity, the researchers tested loan channels by using vector error correction models (VECM) to identify supply and demand curve shifts in the market for bank loans. With this method, identification of existing problems 39

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Does Inter-Bank Investments Restraints Financing Performance of Islamic Banks?

based on aggregate data are presented clearly. Impulse method responses also used to analyse the effects of financial policy shocks on the quantity of loans, loan rates and true output. The study found that banks in Kenya have to master all loan channels that exist. Oliver, María Pía; Yuan Li; Jeon, Bang Nam (2010) reviewed how competition between banks influences the monetary policy transmission through loans channel of commercial banks in 10 Asian countries and 10 countries of Latin America. This study used the procedure of two-step estimation data panel from year 1996 to 2006. The first stage of the estimation is to measure the level of competition between the banks by adopting the methodology done by previous researchers. Then the next step is to estimate the equation in loan growth and included the independent variables of the bank competitor. Estimation results provided consistent evidence that the increased competition in the banking sector will weaken financial transmission policy through bank borrowings. This is true especially for banks in Latin America and small-sized banks with low liquidity and capitalisation. Based on the above studies, researcher found a specific study related to financing institutions behaviour that it is still dominated by studies involving conventional banking and Islamic banking context where the study on these issues has yet to be done in a comprehensive manner. Existing study on Islamic banking financing done by (Kader & Leong, 2009), (Kassim & Majid, 2010) and (Rahman, 2009) are only from a different perspective and leaving literature gap that can still be filled by more research.

Model and Data Specification The present study is based mainly on secondary data. The data and information have been collected from the publications of the National Bank of Malaysia; Handbook of statistics on Malaysia Economy and Annual reports & other valuable publications of public sector, 17 Islamic

40

banks in Malaysia. The period covered under the study is from 1983 to 2014. However, the period varies according to the nature of subject dealt with and availability of data. The bank-specific determinants selected for assessing impact on financing are past financing, profit, risk, capital and size of respective banks. The monetary policy changes for examining the impact on financing are money supply, Islamic Interbank Investment and investment in government securities. While the economic environment for examining impact on Islamic banks financing growth domestic product, inflation rate and index of economic freedom. Financing model in this article adopted the approach that has been done by earlier researchers such as Hassan (1993), Hatakeda (2000), Cebenoyan, A.S and Strahan, P.E (2004), Sarantis and Nicholas (2009) by using bank annual data for viewing the response of institutional banks in offering financing. Based on the evaluation, the study proposes the following model specifications as the basis to conduct financing of Islamic banking: ∆TFit = β1 + β2∆TFit −1 + β3 profitit + β4riskit + β5capit + β6sizeit + β7 ∆M 3t +β8iibrt + β9mgst + β10∆gdpt + β11cpit +β11econfreet + εit + ui

i = 1, 2,....N (bank amount ) t = 1, 2....T (period )



where µit is the fixed effect of time, ui is fixed effect firm and εi, t is the errors term which are not serially correlate nor it correlate with all variable at time t-1. This study defines TFit as level of financing offered by Islamic banking that covers every economic sub-sector from year 1994 to 2014. Due to the lag variable (TFit-1) is independent variables in this study, the above specifications of the model developed become inconsistent. Therefore, Arellano, M; Bond, S (1991), recommended the use of estimation method of GMM which is more effective and consistent.

Category: Accounting and Finance

Table 2. Descriptive of variables statistics Variable

Mean

Std. Dev.

Skewness

Kurtosis

Jarque-Bera

∆TFit-1

13.50

2.95

-1.97

8.80

377.44*

profitit

0.01

0.01

-0.12

11.39

511.63*

riskit

0.46

0.23

-0.10

2.37

3.33

capit

41.50

364.83

9.81

101.21

76069.98*

sizeit

14.54

2.11

-0.37

4.58

24.76*

∆M3t

13.13

0.41

-0.20

2.26

8.05*

iibrt

4.06

1.91

1.19

2.97

64.81

mgst

3.78

1.53

0.98

2.43

47.25*

∆gdpt

12.83

0.40

0.14

1.95

13.34*

cpit

2.70

1.37

0.48

2.41

14.49*

econfree t

64.60

3.63

0.40

2.05

17.36*

A

Note: *Significant at 5% **Significant at 1% *Significant at 10%

FINDINGS 1. Descriptive Analysis The descriptive analysis to view statistics data and variables used in the model study was formed. Some of the statistics used in determining the statistical behaviour of variables are the mean, median, standard deviation, skewness, kurtosis and Jaque-Bera. Min refers to the average value of each variable for the whole samples, while the standard deviation shows the variation of data from the mean value. Referring to Table 2, the variable specification sample which Islamic banking has a value of capit while the largest mean has a value of profitit mean the smallest and least disperse in terms of the distribution of data. The summary of the statistics also showed that the total Islamic banking specification data sample skew to the left except capit which skew to the right. Kurtosis value shows the value exceeds its normal distribution where data distribution is leptokurtic shape and capit had the highest peak of 76069.98 whereas riskit record as the lowest value of 2.3744 that is close to normal data distribution. For monetary policy changes variables, the distribution data shows the average value of a dispersion of ∆M3t was the highest with the value

13.13 and mgsit indicate the average value of the lowest at 3.78. The distribution data for iibrit and mgsit is a skew to the right with the exception of data for mgsit that skew to the left with 0.20. Thus the value of kurtosis for ∆M3t, iibrt dan mgst all approaching the value of normal distribution respectively of 2.26, 2.97 and 2.43. The variable of economic cycles, econfreet data indicated that the highest dispersion of data distribution is 64.60 with the value of the standard deviation of 3.63. On the other hand, the data for the variable inflationit indicates the lowest data distribution that is the value of 64.600 with the value of the standard deviation of 3.63. Thus the distribution data for ∆gdpt, cpit, dan econfreet indicates the data has skewed to the right. Kurtosis value of ∆gdpt data has less than the normal distribution which is 1.95 while the data for cpit and econfreet approaching the value of normal distribution with the value of each is 2.41 and 2.05 respectively. Next test of jarque-Bera test was carried out to evaluate whether the data used are scattered normally or not. The results are that all data for variables used significantly at the significance level of 0.05 per cent but with the exception of data for the variable iibrt. These results show that almost all the data used in this study are not scattered normal. 41

Does Inter-Bank Investments Restraints Financing Performance of Islamic Banks?

Table 3. Matrix correlation ∆TFit-1

profitit

riskit

capit

∆gdpt

∆M3t

iibrt

cpit

mgsit

sizeit

∆TFit-1

1.0000

profitit

-0.1869

1.0000

riskit

0.4898

-0.0440

1.0000

capit

-0.5432

0.2131

-0.2320

1.0000

∆gdpt

0.5481

-0.1016

0.1406

-0.1168

∆M3t

0.5399

-0.1032

0.1414

-0.1070

0.9950

1.0000

iibrt

-0.4071

0.1679

0.0957

0.1016

-0.5302

-0.5427

1.0000

cpit

-0.0920

0.0200

0.0912

0.0896

0.0474

0.0010

0.5557

1.0000

mgst

-0.4197

0.1655

0.0658

0.1062

-0.5536

-0.5719

0.9905

0.5689

1.0000

sizeit

0.8859

-0.1468

0.2070

-0.2399

0.6500

0.6429

-0.4935

-0.0921

-0.5058

1.0000

econfree t

-0.1750

0.1896

0.1372

0.1344

-0.1535

-0.1670

0.7518

0.5499

0.7410

-0.1953

econfree it

1.0000

1.0000

2. Analysis of Matrix Correlation

a. Bank Specific

Table 3 above shows the result of matrix correlation between independent variables (∆TF) and other independent variables. Correlation matrix above shows the variable representing the Islamic bank specification sample which are profitit, capitt, and sizeit in relation to inverse with financing Islamic banking level, while riskit on positive relation. For the relationship between financial policy variables and Islamic banking financing level, the variables of iibrt dan mgst are inversely related and on the other hand ∆M3t variable is positively related. The economic cycle variable, namely cpiit dan econfree it has negative correlation with the dependent variables, while financing ∆gdpt relating positively to 0.548 correlations.

Result estimation shows that lag variable of financing (∆TFit-1) shows an increase in current levels of financing and this result confirms the dynamic specifications at the level of five percent. The higher level of past financing is influenced by prudent “monetary policy” which then bring an excellent repayment track record of the customers. In addition, the past financing flow have provided in many productive sectors who are capable of generating high returns to the institution which in turn promoting Islamic banking to offer more financing for the next year. The results also show an increase of one per cent risk level, increased the level of bank financing by 2.2700 percent. This situation reflects the Islamic banking in changing their financing portfolio by moving towards a more risky financing as a reaction to highly decreasing profit from low-risk financing. Islamic banking is generally involved in various forms of financing such as property, consumer financing, commercial financing, industrial, and others where all of which are involved in the profile and the level of risk a particular credit risks as well as other unique risks. Besides that, the effect of their active engagement in the securitisation activity also makes the bank focus

3. Model Estimation Table 4 shows the estimation of a dynamic model floor financing Islamic banking. The results showed that the Islamic banking financing behaviour in this study is determined by three main factors, namely, the bank specifics, monetary policy changes and economic environment.

42

Category: Accounting and Finance

Table 4. Model estimation result

GMMDifference

GMM-System

∆TFit-1

0.0871* (2.4755)

0.1046* (2.2779)

profitit

-1.1897 (-0.4768)

1.2797 (0.3381)

riskit

3.0248* (11.6600)

2.2700* (2.9838)

capit

-0.0019* (-15.9870)

-0.0020* (-15.6427)

sizeit

0.7042* (8.6663)

0.7783* (8.8067)

∆M3t

1.0876 (0.7368)

0.4277 (0.7483)

iibrt

-0.5965* (-4.4083)

-0.3869* (-2.6096)

mgst

0.6559* (3.3014)

0.4755* (2.3540)

binding or do not affect the conduct of the bank in offering financing. Rules for banks that have surplus capital, but is bound by the rules occasionally won’t increase the offer of financing but also restrict the factors each financing offered resulted in an increase in risk-weighted assets ratio. The study also shows sizeit in have a positive relation with financing with 0.7783 percent. This shows the size factor is as the main determinant of whether a bank will add financing or not. These findings are consistent with the findings of (Sarantis & Nicholas, 2009) who found the size of the bank has the most significant role in differentiating the reactions of banking monetary policy changes. (Oliver, María Pía; Yuan Li; Jeon, Bang Nam;, 2010) also found smaller banks with low liquidity and capitalisation with limited ability in offering financing/loans.

∆gdpt

-0.5927 (-0.4413)

-0.0037 (-0.0061)

b. Monetary Policies Changes

cpit

-0.0065 (-0.2271)

-0.0229 (-1.0957)

econfree t

0.0070 (0.3458)

0.0085 (1.0097)

Specification

Sargan Test

Estimation Parameter

126.3045*

3.7883*

AR(1)

-0.42

-0.11

AR(2)

-0.89

-0.77

*Significant at 5%

**Significant at 1%

*Significant at 10%

() t-value Sargan Test is referring to exceed limitation recognition

on their assets are motivated financing risky on the factors the potential high returns (Hazli & Ismail, 2008). In terms of capital structure, the study found an increase of one per cent of the capital structure of Islamic banking will limits the ability of Islamic banking in offering financing by -0.0020 per cent. This decision shows the Islamic banking financing and improved the provisions reducing the construction capital and this finding is consistent with research that has been done by Liu, Beng & Hua, Min, (2009) which linked the capital control of the bank (bank capital regulations) either a

In this section, study found only two variables, which is iibrt dan mgst shows the importance of the level of financing in Islamic banking. However there is a difference of the two variables where iibrt shown negative while positive sign from mgsit. The negative relationship between the variable iibrt with the TFit in this study showed an increase of one percent in the interbank money market investment will lead to Islamic banking reduce level of financing -0.3869 percent. These findings prove the investment activities between Islamic banks (Islamic interbank investment) has “substitution effect” and decreased their capabilty of financing because of their tendency to maintain liquidity (Othman, Ahmad, & Kechot, 1992) While mgst variables showed an increase of one percent hold significant in government securities in increasing the level of financing Islamic banking of 0.4755 per cent. This shows the retention level of high government securities held by Islamic banking and sell them when enough maturity will result in high profit return as well. This returns then are distributed in the capital formation bank for the purpose of channel it into financ-

43

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Does Inter-Bank Investments Restraints Financing Performance of Islamic Banks?

ing (ElGindi, Said, & Salevurakis, 2009). Next, growth of money supply variable (∆M3t) indicates positive sign for financing level.These results are consistent with the theory of financing policy to link money growth increase was in line with the level of financing in bank institutions very closely with monetary policy expands. Although these results are consistent with the theory of money supply, but the study found the relationship that exists is insignificant.

c. Economic Environment In this section, three variables included in this analysis did not indicate the importance for Islamic financing level even though the resulsign is in line with theories and studies carried out previously. For example the variable ∆gdpt and cpit show negative sign while positive sign for econfreeit variables. Insignificant of ∆gdpt in influencing financing decisions of Islamic banking in accordance with research acquired by (Hazli & Ismail, 2008) who found the Islamic banking more depending on the balance sheet indicators as a signal the economy condition compared with the general economic performance measurement. On the other hand, one percent increase in econfreet found to increase Islamic banking financing by 0.0085%. This condition exists when economic units, especially traders and entrepreneurs have freedom and confidence doing business. Freedom means covering the freedom of doing business, trade, fiscal, investment, finance, corruption, labor and so on. The higher the level of it freedom will encourage economic units to get financing from Islamic banking which in turn contributed to Islamic banking profits (Habibullah; Sufian, Fadzlan; Shah, Muzafar, 2010). On the other hand countries that have weak financial institutions such as the corruption and democracy will increase the financing problem (Boudriga, Taktak, & Jellouli, 2009)

44

FUTURE RESEARCH DIRECTIONS This article showed that while the level of market domination by Islamic banks in Malaysia is still small, but the level of public confidence towards financing facilities offered by Islamic banks is always high and increases from year to year since the establishment of the first Islamic bank in 1983. This achievement can be proud of, but the strength of this credit creation they had does not mean arbitrary Islamic banking can generate any amount of financing desired restore options unless it is appropriate in order to avoid the risk of instability, especially related to subprime financing. Thus, this study presents some of the future research directions seen to conduct Islamic banking investment in interbank money market instruments: 1. Selection criterions of unproductive and high risky investment in money market instruments which will generate more profit to the bank. 2. Government’s strategies through the implementation of fiscal and monetary policy as a responses of the unique features and capabilities of Islamic banking in offering financing. This research is to prevent the occurrence of shock at the industry that eventually resulted in the Islamic banking unable to react well. Failure to respond with good will ultimately upset Islamic banking into near instability in turn expose the bank to the various forms of credit risk specifically related. 3. Therefore, the future researchers can compare the patterns and performance of Islamic banking investment in interbank investment with conventional banking sector while taking other factors into account, particularly relating to fiscal developments in Malaysia.

Category: Accounting and Finance

CONCLUSION In conclusion to, this study has discovered a pattern and behavior of the Malaysia Islamic banking financing. Based on the results, the Malaysia Islamic banking in proven extend financing taking into account the interaction between the bank’s specific, changes in monetary policies and economic environment. Bank specifics involve factors of financing growth, the level of risk, vulnerability and capitalization of size. Refer to the monetary policy changes, the mechanisms of investment in government financial markets that the turned out to make an impact in determining the strength of Islamic banking credit. However, economic environment is not really a factor in influencing Islamic banking, where the Islamic banking in this study is more depended on the balance sheet indicator. It is a signal to the economic condition as compared to general measurement in performance of the economy. This study provided a limited information in relation to conduct Islamic banking in offering financing, especially in terms of samples, the use of the formulation and the variables included in the model construction research.

REFERENCES Altunbas, Gambacorta, & Marques. (2009). Financial innovation, bank capital and the bank lending channel: A European empiricist’s perspective. Conference of Monetary Policy Transmission Mechanism in the euro area in its Kertas kerja Seminar Monetary Policy Transmission Mechanism in the euro area in its first 10 years, Frankfurt, Germany. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. doi:10.2307/2297968

Atunbas, Y., Gambacorta, L., & Marques, D. (2007). Securitisation and the bank lending channel. Working Paper Series, 838. Bacha, O. I. (2008). The Islamic inter bank money and a dual banking system: The Malaysian Experiences. International Journal of Islamic and Middle Eastern Finance and Management, 210-226. Banking supervision and nonperforming loans: a cross-country analysis. (2009). Journal of Financial Economic Policy, 1(4), 286-318. Boudriga, A., Taktak, N. B., & Jellouli, S. (2009). Banking supervision and nonperforming loans: a cross-country analysis. Journal of Financial Economic Policy, 286-318. Buigut, S. (2010). Is There a Bank Lending Channel of Monetary Policy in Kenya? International Research Journal of Finance and Economics, 45, 183–192. Cebenoyan, A. S., & Strahan, P. E. (2004). Risk management, capital structure and lending at banks. Journal of Banking & Finance, 28(1), 19–43. doi:10.1016/S0378-4266(02)00391-6 Demetrio & Tovar-García. (2015). Exposure to interbank market and risk-taking by Mexican banks. Cuadernos de Economia (Santiago, Chile). ElGindi, T., Said, M., & Salevurakis, J. W. (2009). Tamer ElGindi, Mona Said, John William Salevurakis. 2009. Islamic Alternatives to purely Capitalist Modes of Finance: A study of Malaysian Banks from 1999 to 2006. The Review of Radical Political Economics, 4(4), 516–538. doi:10.1177/0486613409341453 Habibullah & Fadzlan. (2010). Does economic freedom fosters banks’ performance? Panel evidence from Malaysia. Journal of Contemporary Accounting & Economics, 6, 77-91. Hassan, M. (1993). Capital market tests of risk exposure of loan sales activities of large US commercial banks. Quarterly Journal of Business and Economic, 32(1), 27–49.

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Hatakeda, T. (2000). Bank lending behaviour under a liquidity constraint. Japan and the World Economy, 12(2), 127–141. doi:10.1016/S09221425(99)00040-7 Hazli, R., & Ismail, A. G. (2008). Does Islamic bankssecuritization involvement restrain their financing activity. Humanomics, 24(2), 95–109. doi:10.1108/08288660810876813

Naser, K., Jamal, A., & Al-Khatib, K. (1999). Islamic Banking: A study of customer satisfaction and preferences in Jordan. International Journal of Bank Marketing, 17(3), 135–150. doi:10.1108/02652329910269275 National Bank of Malaysia. (2006-2010). Financial Report. Kuala Lumpur: National Bank of Malaysia.

Heider, F., Hoerova, M., & Holthausen, C. (2015). Liquidity hoarding and interbank market rates: The role of counterparty risk. Journal of Financial Economics, 118(2), 336–354. doi:10.1016/j. jfineco.2015.07.002

Oliver, M. P., Li, Y., & Jeon, B. N. (2010). Competition in banking and the lending channel: Evidence from bank-level data in Asia and Latin America. Journal of Banking & Finance, 35(3), 560–571. doi:10.1016/j.jbankfin.2010.08.004

Hoshi, T. (2006). Creditor Rights and credit Creation by Banks in Transition Economies. Working paper, Graduate School of International Relations and Pacific Studies University of California, San Diego.

Othman, Z., Ahmad, J., & Kechot, M. (1992). Ekonomi Kewangan. Kuala Lumpur, Malaysia: Dewan Bahasa dan Pustaka.

Ismail, S. (2009). Pengurusan Bank Perdagangan di Malaysia. Dewan Bahasa dan Pustaka. Kader, R. A. (2009). The impact of Interest rate changes on Islamic Bank Financing. International Journal of Business Research Papers, 5(3), 189–201. Kader & Leong. (2009). The impact of Interest rate changes on Islamic Bank Financing. International Journal of Business Research Papers, 5(3), 189-201. Leur, M. W. (2016). Interbank loans, collateral and modern monetary policy. ECB Working Paper 1959, European Central Bank. Liu, B., & Hua, M. (2009). Islamic banking: Interest-free or interest-based? Pacific-Basin Finance Journal, 17, 124–144. Mahmood, R. (1997). Konsep Asas Perbankan. Kuala Lumpur: Dewan Bahasa dan Pustaka.

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Podder, J., & al-Mamun, A. (2004). Loan loss provisioning system in Bangladesh banking: A critical analysis. Managerial Auditing Journal, 19(6), 729–740. doi:10.1108/02686900410543859 Rahman, A. A. (2009). Lending Structure and Market Risk Exposures: The Malaysian Case. Asian Academy of Management Journal, 14(1), 1–20. Sarantis, & Nicholas. (2009). The bank lending channel and monetary transmission in Central and Eastern European countries. Journal of Comparative Economics, 37, 321–334. Toutounchian, I. (2009). Islamic Money & Banking: Integrating Money in Capital Theory. Singapore: John Wiley & Sons (Asia) Pte. Ltd.

ADDITIONAL READING Bacha, O. I. (2008). The Islamic inter bank money and a dual banking system: The Malaysian Experiences. International Journal of Islamic and Middle Eastern Finance and Management, 210-226.

Category: Accounting and Finance

Edgar Demetrio & Tovar-García. (2015). Exposure to interbank market and risk-taking by Mexican banks. Cuadernos de Economia (Santiago, Chile). Heider, F., Hoerova, M., & Holthausen, C. (2015). Liquidity hoarding and interbank market rates: The role of counterparty risk. Journal of Financial Economics, 118(2), 336–354. doi:10.1016/j. jfineco.2015.07.002

KEY TERMS AND DEFINITIONS Bank Profit (profitit): Measurement of profit before tax divided by total assets in bank. This variable indicates the number of bank profits to total assets. Bank Risk (riskit): Describe the results of risk-taking by banks in the appropriate timeliness. The dependence of these indicators suggest risk weights reflect the economic risks for different asset categories. Bank Size (sizeit): This ratio represents the ownership of assets by banks. High asset ownership enables banks to offer more financial services at low cost. Capitalization (capit): Capital and reserves, as a part of the liabilities in the balance sheet total. This includes paid-up capital, reserve funds, retained earnings and other capital funds. Capital and reserves comprise own funds or a bank’s core capital. More investment risk was so much more is needed capital. Consumer Price Index (cpit): The relationship between the consumer price index or inflation with bank performance depends on whether inflation is expected (anticipated) or unexpected (unanticipated). In the second case (ie, inflation is not expected), the bank’s actions in adjusting interest rates be the leading bank costs have increased more than the bank. This second type of inflation has a negative impact on bank profits, which in turn reduces the capital structure.

Economic Freedom Index (econfree t): A ranking of countries or states based on the number and intensity of government regulations on wealth-creating activity. Metrics that an economic freedom index evaluates include international trade restrictions, government spending relative to GDP, occupational licensing requirements, private property rights, minimum wage laws and other government-controlled factors that affect people’s ability to earn a living and keep what they earn. Such indexes are usually produced by economic think tanks. Growth Domestic Product (∆gdpt): This is a key indicator of a country’s macroeconomic management. Any changes in this indicator will change the loan/financing which in turn affects the adjustment capital ratio and bank risk observation for certain years. Islamic Interbank Investment Rate (iibrt): A short-term intermediary to provide a ready source of short-term investment outlets based on Syariah principle. Through the IIMM, the Islamic banks and banks participating in the Islamic Banking Scheme (IBS) would be able to match the funding requirements effectively and efficiently. Malaysian Government Securities (mgst): Islamic securities that shows the loan by the government from financial institutions and others. Effectively it is a loan taken by the government of the people themselves. These loans are usually required by the state to finance recurrent expenditure and development expenditure for public projects. Money Supply (∆M3t): Growth in money supply indicators show real growth potential, especially for future growth. Total Financing (TF): This ratio shows the behavior of banks in the pursuit of profit and risk-taking. This behavior is consistent with profit-sharing paradigm that allows Islamic banking offer long-term financing to the project risk profile and high returns.

47

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Does Inter-Bank Investments Restraints Financing Performance of Islamic Banks?

ENDNOTES

1

48

Trust receipt is a document signed by the importer, confirming receipt of the shipping documents from the bank and allows the importer to take action on behalf of the bank to get the imported items of shipping companies. Upon receipt of the goods, the importer will be selling it and using the revenue to pay the bank.



2



3

This study also found all of the bank specification variables are significant in relation to the risk. Capital and liquidity variables in negative relation to the risks, while credit variables in positive relation with risk. Those countries are Belgium, Denmark, German, Greece, Finland, France, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, England and United States of America.

49

Category: Accounting and Finance

An Extension to the Delone and Mclean Information Systems Success Model and Validation in the Internet Banking Context Veeraraghavan Jagannathan National Institute of Technology, India Senthilarasu Balasubramanian National Institute of Technology, India Thamaraiselvan Natarajan National Institute of Technology, India

INTRODUCTION Web-based applications in the recent years helps organizations to retain customers, and offering new services and products to them (DeLone & McLean, 1992; Tan & Teo, 2000). Internet Banking is considered as an online revolution of the traditional banking services which offers customers the greatest expediency for performing banking transactions via the Internet (Furst, Lang, & Nolle, 2000; Patnasingam, Gefen, & Pavlou, 2005). More precise definition of Internet Banking is given by Sathye (1999): With the term electronic banking we consider all the possible transactions of a bank which are performed with the use of electronic means, mainly through Internet, but also through VPNs (Virtual Private Networks), Intranet, Extranet, phone and mobile phone, and these transactions do not necessitate that the customer must visit a branch. There is a fundamental shift in banking delivery channels since mid-1990s (Pikkarainen, Pikkarainen, Karjaluoto, & Pahnila, 2004) and many banking executives perceived technology as the key solution for controlling costs (C.-P.

Lee, Mattila, & Shim, 2007). Internet Banking improves the bank’s profit levels through the reduction of both variable and infrastructure costs, provides a source of differentiation and competitive advantage, provides global reach, adds another communication and feedback channel, increases customer satisfaction through the reduction of waiting times. thus improving service performance (Harridge-March, Wong, Rexha, & Phau, 2008). Internet Banking has appeared as the trend in banking, nowadays, and emerged as one of the payment models required to enable pure e-commerce models, rather than traditional banking (Zolait, 2010). Some of the benefits to customers identified (Angelakopoulos & Mihiotis, 2011) are no time limitation, better time organization, no geographical limits, lower costs, 24 hour support, effortless accessibility for disabled people, integrated environment for Internet Banking transactions. In recent years, a large number of banks have started to adopt Internet Banking as an additional channel to reach and interact with clients. For financial institutions, Internet or Electronic banking is recognized as a tool that can significantly reduce their overhead costs as well as day-to-day expenses (Alhinai, Albadi, Alshihi, & Al-Gharbi, 2013).

DOI: 10.4018/978-1-5225-2255-3.ch004 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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An Extension to the Delone and Mclean Information Systems Success Model

BACKGROUND Despite the recent advancements in internet security technologies such as, digital signatures, certificates, encryption algorithms. authentication mechanisms, consumers are still concerned about the security of monetary transactions over the internet (C. Yoon, 2010). In a report of Internet and Mobile Association of India (IAMAI -2010-11) it was found that people are hesitant to do banking transactions through the web sites of the bank, because of: security concerns (43 percent); preference for face-to-face transactions (39 percent); lack of knowledge about online transactions (22 percent); lack of user friendliness environment (10 percent); and lack of this facility in current bank (2 percent). M.-C. Lee (2009) found that the intention to use online banking is adversely affected mainly by the security/privacy risk. Hence, for the success of Internet Banking security plays a key role in customer trust of the website and satisfaction, which ultimately contribute to the success of the Information System(IS). Moreover, studies on IS Success related to Internet Banking are very scarce in the literature (Hoehle et al, 2012). Furthermore, the studies were conducted in developed countries and there is a paucity of studies in developing countries. This study intends to address the knowledge gap with the help of the proposed model. Therefore, the following research questions are framed based on the research gap: 1. What are the factors contributing to the success of Internet Banking? 2. What are the impacts security dimension make in the IB use and IB user satisfaction? Finding the answers to the research questions can initiate improvement and enhance the performance of services provided via the electronic channel. It may also provide valuable feedback for the banks for satisfying the expectations of the bank customer, who intend to use IB in future. It is expected that this study will result in a re-evaluation of the IS Success model under new circumstances,

50

enhance understanding of consumer behaviors in correspondence with IB service, and provide suggestions for making sustainable IB usage. The next few sections form the literature survey of IB studies and relevant theories associated with the proposed framework.

LITERATURE REVIEW The seminal work of Delone and Mclean (DeLone & McLean, 1992) pawed the way for measuring IS success, which was elusive to researchers till then. Their (DeLone & McLean, 1992) paper proposed a six factor taxonomy in system quality, information quality, use, user satisfaction, individual impact, and organizational impact, using the multitude of measures existed in previous literatures. The authors also proposed temporal and causal relationship between the constructs. The IS success model presumes that system quality and information quality, individually and jointly, affect user satisfaction and use. It also posited use and user satisfaction to be reciprocally interdependent, and presumes them to be direct antecedents of individual impact. In addition, the amount of use can affect the degree of user satisfaction either positively or negatively and vice versa. According to the IS Success model Individual impact should also lead to organizational impact. DeLone and McLean (DeLone & McLean, 1992) characterize system quality as desired characteristics of the information system itself, and information quality as desired characteristics of the information product. They incorporate four scales from the Bailey-Pearson (Bailey & Pearson, 1983) instrument into system quality (convenience of access, flexibility of the system, integration of the system and response time) and nine scales into information quality: accuracy, precision, currency, timeliness, reliability, completeness, conciseness, format and relevance. Delone and Mclean (Delone & McLean, 2003) came up with an update of their model, based on the research finding from their 1992 model. The

Category: Accounting and Finance

important change was the addition of the construct ‘Service Quality’ recommended by (Pitt, Watson, & Kavan, 1995). Second major change to their initial (Pitt et al., 1995) model was grouping of the two impacts constructs ‘individual impact’ and ‘organizational impact’ in to single construct ‘net benefits’. In the context of Internet Banking, customers use the IB web site to conduct money transfer, checking the account balance and pay for online e-commerce transactions, This makes Internet Banking website a communication and IS phenomenon suits itself to the updated IS success model. DeLone and McLean (Delone & Mclean, 2004) argued that the Internet applications process fits well into their updated IS success model and the six success dimensions. The authors also encourage researchers to continue testing and challenging their model. Also Research has acknowledged the need for customized measures of IS success based on the context of the system, the work process supported, and the stakeholders considered (Delone & McLean, 2003).

RESEARCH MODEL AND HYPOTHESIS We base our work on IS Success Model (Delone & McLean, 2003). According to DeLone and McLean (Delone & McLean, 2003) ‘‘To measure the success of a single system (individual system), ‘information quality’ or ‘system quality’ may be the most important quality component. The authors argued that for measuring the overall success of the IS department, as opposed to an individual system, ‘service quality’ may become the most important variable.” Therefore, though service quality is important for Internet Banking, it was excluded from this study because this study is based on individual level of analysis. The next few sections describe the hypotheses of the proposed model.

HYPOTHESES

A

According to Chellappa & Pavlou (Chellappa & Pavlou, 2002) perceived information security is defined as the subjective probability with which consumers believe that their personal information will not be viewed, stored or manipulated during transit or compromised by inappropriate parties in a manner consistent with their confident expectations. The authors also claim that the customer perceptions of information security is influenced by encryption, protection, verification and authentication. The important empirical finding of their research is that perceived security strongly influences customer trust than financial liability of the customer. Their findings of their study support antecedents of perceived security like encryption, protection, and authentication. Kim, Tao, Shin, & Kim (C. Kim, Tao, Shin, & Kim, 2010) found positive association between users’ perceived security and their use of epayment systems. Yoon (C. Yoon, 2010) found that security in online banking strongly influences customer satisfaction. The importance of security and privacy for the acceptance of online banking has been noted in many banking studies (Chen & Barnes, 2007; Mauro C. Hernandez & Afonso Mazzon, 2007). With this, we want to test whether security features role in Internet Banking use and customer satisfaction. Hence, we propose the following hypothesis. H1a: Security of IB website positively influences IB user satisfaction. H1b: Security of IB website positively influences IB use. System quality was defined as quality manifested in a system’s overall performance and measured by individuals’ perceptions (Delone & McLean, 2003). Since customers cannot directly interact with the bank employees directly, the system quality here is the website quality. Also the

51

An Extension to the Delone and Mclean Information Systems Success Model

studies (Petter, DeLone, & McLean, 2008; Petter & McLean, 2009) found strong positive association between System quality and User Satisfaction. So we propose the following the hypotheses. H2a: System quality of IB website positively affects customer satisfaction. H2b: System quality of IB website positively affects IB use. Information quality perceived by the user has been instrumental in providing user satisfaction (Hirschheim, 2007; Venkatesh, Davis, & Morris, 2007). According to (Hirschheim, 2007; Venkatesh et al., 2007)the quality of information, as assessed by customers, influences their satisfaction. In the net banking scenario represents the content of the website. Information quality is considered as an integral part of customer satisfaction. So we propose H3a: Information quality of IB website positively affects customer satisfaction. H3b: Information quality of IB website positively affects IB use. Delone and Mclean (DeLone & McLean, 1992) claims that use of the system (here Internet Banking website) leads to customer satisfaction and net benefits to the user. Many authors tested these two relationship with different contexts and there are moderate support for these relationships found in the meta analysis analysis(Petter et al., 2008). Hence, we propose H4a: Use of the IB website leads to positive effects customer satisfaction. H4b: Use of the IB website provides net benefits to the user. We measure information systems success in the context of individual customer. Delone and Mclean (DeLone & McLean, 1992, 2003) claims that the more the user is satisfied, the net benefits is more. Here if the individual user uses more net

52

banking, he/she may get more net benefits. So the study hypothesizes H5a: Positive customer satisfaction with Internet Banking usage leads net benefits to the user.

RESEARCH METHODOLOGY Data Collection In this study, individuals who used internet, and have experienced Internet Banking services for at least six months, were selected as research participants. We selected students of a premier educational institution in Tamil Nadu, India for this study. Students were chosen for evaluating the model because they are best suited for concept development (Sekhon, Lifen Zhao, Koenig-Lewis, Hanmer-Lloyd, & Ward, 2010). Data was collected using a printed survey questionnaire. A seven point likert type scale was employed with 1 indicating ‘strongly disagree’ to 7 indicating ‘strongly agree’ where 4 being ‘neither disagree nor agree (neutral). Although we used established and validated measures in the previous studies, we developed a new dimension ‘security’ in the study. Hence, a pilot was conducted prior to that to ensure that there are no dubious questions. The questions were coined in such a way to avoid any technical jargons. For example instead of the question ‘My bank has firewall facility’, we use, ‘I perceive Internet Banking provides enough protection against hacker threats’. The participants of the survey were informed that the collected responses would be used only for research purposes. During the weeklong survey period there were 312 samples were collected by randomly visiting classrooms and obtaining permission from the respective faculty members to administer the survey. As all fields were marked mandatory and all the collected responses were verified for missing values. Students who did not answer any questions were asked to answer the same. This ensured there were no missing values.

Category: Accounting and Finance

Demographics Out of the 312 samples 184 samples were male and 128 sample were female students accounting for 58% percent and 42% respectively. The reported age groups were fallen between 18 and 24 with the average age of 20.52. Post Graduate students were 94 and Graduate students were 218. All students have Internet Banking experience ranging from 1 to 6 years where the average experience being 2.6 years. All students reported that they own a laptop.

Measurement Development There are six constructs in total viz. information quality, system quality, security features, use, user satisfaction and net benefits. Five items of Information quality were adapted from (Song, Baker, Lee, & Wetherbe, 2012). System quality is measured using eight items adapted from (Wang, Wang, & Shee, 2007). Net benefits has five items based on (Angelakopoulos & Mihiotis, 2011), with our own wordings. Three measures of satisfaction were adapted from (Roca, Chiu, & Martínez, 2006). Use is measured with five variables adapted from (Delone & Mclean, 2004; Wang et al., 2007; H. S. Yoon & Steege, 2013). Five measures of security were adapted from (Chellappa & Pavlou, 2002). Customer satisfaction was measured with five measures adapted from (Delone & Mclean, 2004; Gable, Sedera, & Chan, 2008). There are 31 items

in total representing six dimensions of our model. The proposed model is shown in Figure 1.

Exploratory Factor Analysis Since the proposed model involves latent reflective constructs, it was proposed to conduct an Exploratory Factor Analysis (EFA). The Kaiser-MeyerOlkin (KMO) Measure of Sampling Adequacy was 0.9, which was > 0.5 the recommended threshold. In addition, a chi-square value of 5437.719 and significance level of.000 and degrees of freedom 296, were obtained using Bartlett’s sphericity test. The results clearly indicates that the inter correlation matrix contains enough common variance, justified the need for factor analysis. Hence, an EFA was conducted with SPSS 20, using Principal Component Analysis (PCA) as the extraction method, with varimax rotation with Kaiser normalization to identify the underlying factors. The PCA procedure was converged in six iterations. We deleted four items from system quality, two from information quality, two items from net benefits, due to loadings 0.7) and loaded low on other factors, proving discriminant validity. In addition, each item loads on its latent factor at the significance level of 0.05, indicating

Figure 1. Theoretical model

53

A

An Extension to the Delone and Mclean Information Systems Success Model

good convergent validity. The factor loadings of the items are shown in Table 1.

TESTING THE MEASUREMENT MODEL Reliability and convergent validity of the factors were assessed by composite reliability (CR) and average variance extracted (AVE) (see Table 2). The composite reliabilities were calculated as follows: summation of the factor loadings)/ [(square of the summation of the factor loadings) + (summation of error variables)]. The composite reliabilities were above 0.8 for our measurement Table 1. Rotated pattern matrix and factor loadings Factors 1

2

3

4

5

6

USE3_1

.852

.074

-.004

.060

.176

.039

USE4_1

.814

.082

-.014

.144

.073

.107

USE1_1

.792

.141

.123

.073

.087

.279

USE5_1

.791

.011

.079

.099

.080

.292

USE2_1

.599

.239

.316

-.069

.184

.083

SEC4_1

.024

.819

-.016

.078

.144

-.115

SEC3_1

.105

.818

.092

.078

.139

.194

SEC5_1

.069

.807

.077

.106

.226

.009

SEC2_1

.158

.699

.099

.222

.000

.212

SEC1_1

.238

.555

.252

-.033

.047

.429

IQ1_1

.030

.058

.876

.109

.181

.138

IQ2_1

.125

.087

.874

.033

.217

.175

IQ3_1

.106

.128

.838

.133

.148

.141

WSQ8_1

-.018

.182

.129

.795

-.048

.081

WSQ3_1

.079

.049

-.036

.790

.104

.072

WSQ6_1

.134

-.006

.060

.772

.207

-.052

WSQ7_1

.082

.173

.120

.749

-.026

.184

NB3_1

.171

.181

.258

.136

.777

.143

NB4_1

.187

.216

.212

.072

.771

.302

NB5_1

.217

.252

.243

.077

.715

.323

US3_1

.317

.172

.247

.154

.269

.689

US4_1

.282

.155

.151

.216

.216

.688

US2_1

.226

.026

.189

.039

.352

.644

54

model (Table 2). Construct reliability of all the six constructs were tested using Cronbach’s Alpha. The final scales of all the six constructs demonstrate high internal consistency with Cronbach’s Alpha values exceeding Nunnally’s (Nunnally, Bernstein, & Berge, 1967) recommendation of at least 0.7. The reliability of each factor is as follows: System quality: 0.809; information quality: 0.902; Security: 0.841; net benefits: 0.874; user satisfaction: 0.805. The Average Variance Extracted (AVE) is greater than 0.50 for all the constructs. It implies that that more than one-half of the variances observed in the items were accounted for by their hypothesized factors. Discriminant validity was assessed, by checking whether the square root of AVE for a construct had a higher value than the variance shared between the construct and other constructs (Fornell & Larcker, 1981). The values of all diagonal elements were greater than those of off-diagonal elements (Table 3), suggesting that all of the constructs were distinct. Also the AVE for each construct is greater than the correlation between that and all other constructs. From this, it is evident that discriminant validity is proved. All the measures of reliability and validity are depicted in Table 3. Overall, the measurement model exhibited adequate reliability, convergent validity, and discriminant validity. The final measurement model achieved an acceptable fit to the data based on a range of commonly used fit indicators. As shown in Table IV, the CFI, GFI, NFI values are all above 0.9. The RMSEA of 0.051 is below the 0.05 value thus indicating a good fit according to (Hu & Bentler, 1995) Also, the ratio of the chi-square value to the degrees of freedom is 1.824 and thus within the recommended range of 1 to 3 (McIver, 1981).

RESULTS The Structural Equation Modelling (SEM) approach was adapted to validate the proposed structural model. SEM allows us to estimate the

Category: Accounting and Finance

Table 2. Reliability and validity indicators CR

AVE

NB

Use

SEC

IQ

SQ

NB

0.883

0.716

0.846

Use

0.875

0.587

0.497

0.766

SEC

0.834

0.559

0.513

0.335

0.748

IQ

0.903

0.756

0.566

0.304

0.291

0.870

SQ

0.810

0.516

0.292

0.251

0.357

0.244

0.718

US

0.815

0.597

0.745

0.666

0.465

0.561

0.382

A

US

0.772

Note: NB-Net Benefits; SEC-Security; SQ-System Quality; IQ-Information Quality; US-User Satisfaction

strength of interrelationships between the latent constructs (Gallagher, Ting, & Palmer, 2008). The data was analyzed using AMOS Version 22 with the proposed framework shown Figure 1. The Structural model was analyzed with similar fit indices as the measurement model. All the fit indices were according to the recommended level (See Table 2). Hence, we proceed with analyzing the path co-efficient of our model. Except for the hypothesis between use and net benefits (H4b where B =-0133 and p =0.177) the entire hypothesis were supported. The hypothesis with security (H1a, H1b) on use and customer satisfaction are strongly supported (b=0.318 and pNB

.968

0.117

8.726

***

Strong support

Note: ***P< 0.001

Figure 2. Path coefficients

Note ***p, 0.001, **p 0 for ψ ≠ 0 , otherwise ψ | ψ = 0; 3. 3. If a and b are complex scalars, them χ | a ψ1 + bψ2 = a χ | ψ1 + b χ | ψ2 ;

4. The space is complete in the norm ψ =

ψ|ψ .

Finally, the implementation of symmetries in generalized quantum mechanical coordinates1 may be represented by a unitary operator in the Hilbert space, so that,  †  = 1 , H,   = 0; for the groundstate of the Hamiltonian 2 2 H =− ∇ +V , 2m  ψ0 = ψ0 ; (this is not so obvious!) H ψ0 = E 0 ψ0 ;

C

ˆ

where Oˆ is an operator that defines a motion constant (thereby furnishing good quantum numbers for the states of the system) so that Oˆ† = Oˆ , and ω is the set of parameters defining the matrix S. Of course, as an effect of the macroscopic intervention, µ\ shows some classic traces inherited from the apparatus. But quantum mechanics says nothing about de world out of the experiment. Also, it is important to clarify that it is not always possible to carry out a complete and decisive experiment in this area. For instance, with respect to gravity, an approach by quantum field theory would need 1) an understandable model of gravitation accordingly some quantization algorithm applied to general relativity, which seems little bearing, and 2) an experimental frame able to reproduce the physical conditions under which the hypothetical quantum nature of gravity may come about, such as in a black hole singularity. In fact, one reason to brush aside an experimental program in this way is the difficulty of formulating quantum theory in a cosmological context in which the observers must be part of the system. Although it appears out of the blue, we may suppose there is a real messenger of gravity and imagine a “metaframe” to render gravitation in a familiar figurative language with no a priori concerns whether the messenger and its supersym-

1043

Clouds of Quantum Machines

metric partner follow Bose or Fermi statistics beneath lab apparatus. This was my proposal: a supersymmetric meta-field theory on gravity (Serpa, 2015). So, I define meta-field theory as a theory that introduces a supersymmetric metaframe to describe fields as sets of particular transformations between two types of entities, the supersymmetric partners in focus. As in the supersymmetric meta-field theory, it is possible to build a similar metaframe to describe cloud computing in its continuous process of increasing complexity. I will try, so much as possible, to refine the presentation of the formalism in order to avoid time lost with unclear notations and conventions.

THE QUANTUM BIT The quantum bit, or qubit, is the quantum tile of information and differs from the classical bit by the fact that it is generally given in a superposition of two basic states, e. g., 0 and 1 , so that the Dirac ket of the time-dependent state-function of a qubit is denoted by Ψ(t ) = c0 (t ) 0 + c1 (t ) 1 , wherec0 (t )and c1 (t ) are complex time functions. The binary assigned to the basic states are associated with discrete values assumed by physical degrees of freedom of elementary particles, such as the spin. The qubit state fulfills equation (1) in such manner that the Hamiltonian operator takes the form

c (t ) h (t ) h (t ) c (t )   01   0  . i  0  =  00 c1 (t ) h10 (t ) h11 (t ) c1 (t ) Last, normalization condition applies 2

2

c0 + c1 = 1 .

QUANTUM TELEPORTATION Quantum teleportation is a very different conception of their science fiction counterparts. Since the nineties authors have studied the subject in theoretical and experimental approaches (Deutsch & Jozsa, 1992; Braunstein, 1996; Bouwmeester, 1997; Zhang et al., 2002; Bowen, 2003). The concept of quantum teleportation and the so-called quantum entanglement form the basis of cloud computing as conceived here. The latter is one of the biggest sources of confusion in science, since quantum entanglement became a paradox in quantum theory because of its conflict with causality. Two quantum objects are said entangled if they are linked in such manner that their behaviors are bonded never minding how much distant they are from one another. I will try to explain the central idea in the cloud context with a maximum of formal consistence on the previous sections. We start with two state functions to be entangled at server A , Ψ ↑ and Ψ ↓ . The enA

A

tanglement is given by the instruction 0 ( I 0 ) of the experiment I 0 : Ψ↑

A

# Ψ↓

A

⇒ Ψ (↑↓)(↓↑)

A

.

(11)

H (t ) = h00 (t ) 0 〈0 | +h01(t ) 0 〈1 | +h10 (t ) 1 〈0 | +

+h11 (t ) 1 〈1 | . Sometimes it is useful to rewrite Schrödinger’s equation in matrix formalism as

1044

The two state functions are now non-causally correlated. After the entanglement, we apply by instruction 1 ( I 1 ) a classical procedure P1 to carry the second entangled state function on server B, that is,

Category: Cloud Computing

I 1 : P1 Ψ (↑↓)(↓↑) ≡ Ψ (↓↑)

B

°

= > Ψ (↑↓)(↓↑) A

(−) Ψ (↑↓) A

≡ A

.

Now we perform a measurement M in server A on the combined state which we gain putting Ψ ↑(↓)

A

in contact with the unknown state

function χ . Having done this interaction, server A transmits to server B, through a classical channel reachable by procedure P2 , a complete description of the results of the quantum measurement on χ Ψ ↑(↓)

A

in order to enable server B

to perform certain linear transformation δ on Ψ (↑)↓

B

; in fact, the measurement described an-

nihilates χ , but the linear transformation δ rebuilds the latter at server B from Ψ (↑)↓

B

, so

that by instruction 2 ( I 2 ) °

I 2 : P2 M χ Ψ (↑↓) = > δ Ψ (↓↑)

B

≡ χ .

This is what we call teleportation of the state χ from server A to server B. Quantum teleportation refers to the “blind” teleport of the state of a quantum system about which there was no information. The measurement does not provide any information from the state function χ . All of the quantum state information is passed by the non-causal link between the entangled states and Ψ (↓↑) . The main consequence of B the process is the annihilation of the initial quantum state at server A rebuilt at server B. It must be understood that it is the quantum states which are destroyed and recreated in the teleportation process, and not material components. Thereby, cloning is an impossible operation in quantum physics; we simply can generate an almost-perfect Ψ (↑↓)

A

replica of the original destroyed after teleportation. It is also important to remember that quantum information within a state function is available only as probabilities or, as we commonly say, expectation values.

ENTANGLEMENT: THE PICTURES ON THE POOL It was pointed that quantum processing was born from “purely philosophically motivated questions” (Walther, 2006) on non-locality and completeness of quantum mechanics fomented mainly by Einstein from his collaborative work with Podolsky and Rosen in 1935. In fact, as once observed, it was Einstein whom restored in modern science the Cartesian metaphysical sense of philosophy, turning physics into a real theory of knowledge (Charon, 1967). This important note remembers to us that philosophy will always be present in the process of creation. It is precisely its absence that determines little creativity that prevails today in all fields. Thus, to understand what is entanglement it will be necessary a reflective process of reconstruction of the conceptual foundations of physics, which will lead to a comprehensive review of the applicability of the notion of causality. The main controversies of quantum mechanics ever resided in the difficulty of the human mind to separate the physical fact from its perception or representation. Indeed we always work with our perceptions; we took from them the full potential of human development and survival offered, creating representations for all we observe. There was a time when I was a follower of a kind of fruitless and paralyzing materialism that insisted to reify the world. Later, influenced by some physicists adepts of the operationalism, I came also to sympathize with the dresser and foolish idea that the only thing that matters is the calculation and not the ultimate nature of things. Thanks to my growing interest in quantum computing, I could deepen those controversial discussions and reach my own conclusions about them. Of course, long before the

1045

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Clouds of Quantum Machines

seventies there were eloquent speeches from the great thinkers of modern physics. Weizsäcker, for instance, in the Spanish version of 1974: “El átomo no es inmediatamente perceptible para nuestros sentidos, y cualquier experimento lleva sólo una determinada propiedad del átomo al ámbito de una perceptibilidad mediata”2 (Weizsäcker, 1974). But that was still little; not just to observe a predicate and describe it by means of classical concepts. It was necessary a phenomenal texture made by the experimental apparatus from which one could then extract useful measurements (information). In this it would lie a deepening of the famous complementarity of Bohr: the ultimate hidden object and its accessible and inseparable image. Inspired by those philosophical texts from the first half of the twentieth century and early second half, I could refine my ideas and reach an understanding which I consider acceptable, although limited by the nature of human thought. Now I believe that the understanding of the quantum entanglement, one of the most intriguing phenomena of the quantum world, rises, for happiness of the philosophers, in a reflection on the edge of a pool. One summer night, I sat in a chair right in front of a lighted lamp whose flickering light was reflected in the pool. The image of the lamp stretched like a rubber with the ripples of the water and sometimes came to double or even to quintuple depending on the swings of the water. Both, the lamp and its images in water, are real, belonging to the world of mater and perceptions. But imagine that we could not see the lamp, only their images reflected in the water. We would think that two objects born of a unique (duplicate picture) would be irrevocably united, although separated; any change in one of them would “cause” an instantaneous change in the other. With respect to the quantum world is passing up something similar. We have no direct access to the ultimate reality (as the hidden lamp), only to the images produced by our experiments. What we see are the “pictures in the pool” and these are as real as the object that produced them. Clearly, these images carry information from the ultimate object, which makes them tractable to

1046

control. Instead of using the ultimate object we use them with all their informational potential. This potential is the base of the teleport process, since we teleport physical states, not matter in itself. In short, the quantum world is so light and sensible to our presence that it would be impossible to get direct benefits from their objects. All we can do is work with “pools”. As Weizsäcker said: “Todo experimento es un acto material que es simultáneamente um acto de percepción”3 (Weizsäcker, 1974). To get a slight idea of quantum entanglement we consider the process of creation and destruction of a pair of quantum bits called “pair of Einstein, Podolsky and Rosen” (EPR pair). So, let us suppose a quantum bit in a zero-state, Ψ1 = 1 0 + 0 1 = 0 . Now, let us take the Hadamard matrix h 2 1 1   h 2 =  1 −1 and u2 =

1 2

h 2.

We make Ψ 1' = u 2 Ψ 1 =

=

1 1  1 1    =    2 1 −1 0

1 1 1 1   = 0 + 1 .  1  2  2 2 Also we take another quantum bit in zero-state

Ψ2 = 1 0 + 0 1 = 0 .

Category: Cloud Computing

Performing a tensor product between Ψ 1' and Ψ 2 we gain Ψ 1' ⊗ Ψ 2 =

1 1 1    ⊗   =   2 1 0

1      1 1 1 0 00 + 0 01 + 10 + 0 11 .   = 2 2 2 1    0

It is convenient to define certain unitary transformation, called “control NOT-gate” (CNot). Using Pauli matrices 0 1 0 −i  1 0    , σ =   , σ =   , σx =  1 0 y  i 0  z 0 −1

we may write CNot =

1  0 =  0  0

0 1 0 0

1 + σz 2

0 0 0 1

CNot Ψ 1' Ψ 2

⊗ 1+

1 − σz 2

⊗ σx =

0 1 0 0

(12)

The point is that for entangled states, as expressed in the above result, decomposition does not hold, that is,   1 1 00 + 11  .  (ϕ1, ϕ2 ) / ϕ1 ⊗ ϕ2 =   2 2   The very strangeness of entanglement may be explained taking two distinct moments of experimental intervention. Just prior the CNot transformation we perform a measurement to obtain the probability of 0 . The measurement operator is 1  0 M 0 =  0  0

0 0 0 0

0 0 1 0

0  0  . 0  0

Before the CNot transformation the system is in state

0  0  , so that 1  0 1  0 =  0  0

1      1 1 1 0 = 00 + 11 .   = 2 2 2 0   1 

Ψ 1' Ψ 2 =

0 0 0 1

1  0     0 1 0    = 1 2 1     0 0

1 2

00 + 0 01 +

1 2

10 + 0 11 .

Therefore, p(0) = =

Ψ 1' Ψ 2 M 0†M 0 Ψ 1' Ψ 2 =

Ψ 1' Ψ 2 M 0 Ψ 1' Ψ 2 =

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Clouds of Quantum Machines

1  0 1 = (1, 0, 1, 0)0 2  0

 =  

0 0 0 0

1  0     0 1 0    = 0 2 1     0 0

0 0 1 0

1  0 1 = (1, 0, 0, 1)0 2  0

 1       2  1 1  0  , 0, , 0   = 1. 2 2   1     2  0 

Ψ1' Ψ 2 M 0†M 0 Ψ 1' Ψ 2

M0 Ψ3

 1       2  0     1     2  0  = = Ψ 1' Ψ 2 . 1

We see that measurement had no influence on the first quantum bit that remains in a superposition of 0 and 1 . This is not the case when we perform the same measurement just after the CNot application. Now we start from 1 2

00 +

1 2

11 .

Let us compute the probability of 0 by means of p(0) = Ψ 3 | M 0†M 0 | Ψ 3 = Ψ 3 | M 0 | Ψ 3 =

1048

1  0     0 1 0 1    = 0 2 0 2    0 0 .

1 / 2 . After measurement, the state vector of the system took the form

(13)

Ψ3 =

0 0 1 0

Thereby, probability of 0 was changed to

After measurement we get for the state of the system

M 0 Ψ 1' Ψ 2

0 0 0 0

Ψ 3 | M 0†M 0 | Ψ 3

1   1 0 =  2 0  0

0 0 0 0

0 0 1 0

0  0  × 0  0

 1     1    1         2  2  0  1 0  0 ×  =   = 0 = 00 . 0  2 0        0  1  0      2 This is somewhat astonishing. Measuring one quantum bit we modify the probabilities of the other quantum bits of the system. However, as strange as the phenomenon is, the role of science is only to describe what happens, leaving aside ontological speculations about the “why” of the things being as they are.

MAIN FOCUS Operators in quantum mechanics commonly provide representations of experimental interventions. But this does not mean that reality is determined by the observer. In fact, the experiment is just a physical intervention rationally controlled similar to the natural physical interventions occurring at random in the interactions between systems. An-

Category: Cloud Computing

other important consideration to be made is about the clear representational role that mathematical constructs which serve to quantum mechanics must have, for example, the construct “quantum gate”. Accordingly McMahon (2008), A gate can be thought of as an abstraction that represents information processing. [...] In a quantum computer, information is also processed using gates, but in this case the “gates” are unitary operations. Since quantum gates are just unitary operators, we’ll often go back and forth between the words gate and operator --- so keep in mind they mean the same thing in this context. (McMahon, 2008). Then, we may say that quantum gates are connectors that allow us to building quantum circuits. They act upon quantum bits; thus, generalizing the concept, we may think of them as formal representations of particular circumstances imposed by the environment or the observer, never minding weather they are controlled or not. They are operators of certain type acting on quantum bits to do something, that is, to produce some specific quantum configuration.

THINKING ABOUT NON-UNITARY OPERATIONS EMBEDDED IN ENTANGLED STATES: DOES IT MAKE SENSE? In its current formal representation, quantum computation deals only with quantum gate operations which are necessarily unitary, a fact that turns difficult or even impossible to solve central problems such as decoherence and feasibility of measurements in the middle of the computation. From my theoretical background, the restriction to unitary gates and pure quantum states seems very arbitrary. Quantum gates can perfectly represent general quantum operations, not exclusively unitary, providing more flexibility and facility to building algorithms. Unitary operations can,

at best, be elected to represent the evolution of a quantum system under observational control, but not necessarily to represent quantum systems free of human intervention. Many works were performed on quantum gates (Barenco et al., 1995; Raussendorf & Briegel, 2000; Wang et al., 2001), so that the reader can deepen his particular search as desired. What I want to show is that, given two entangled quantum bits, it is not possible to know whether the entanglement arose from the interference of some non-unitary action on a given quantum bit in a pure initial state. This means that quantum bits separated by large distances may carry effects of primary out-of-measurement processes that originated them. So, could we embed quantum states with these non-unitary actions, that is, these out-of-measurement transformation rules? If so, how would be the protocol for that? First of all, as already suggested, I assume that certain operators reflect environmental conditions that favor the creation of new qubits from a primary qubit. In my former works, I developed the math entity named “progenitor” as the Kronecker operator to obtain two entangled qubits. According to my considerations on Bohr’s philosophical thinking, there must be an abstract quantum physical description of the natural process that leads to entangled qubits interacting through a quantum channel. I assume that if what connects two entangled qubits far from one another is a non-classical channel, that is, no transaction between entangled qubits occurs in common spacetime, so what triggered such a connection is an imaginary “operation”. So, the transfer of information in a quantum system, based on the non-classic connection between qubits, is holding trough an imaginary “operation” like 1 1i -1i 0 0 1 0 -1i   Gˆ :=  =    , 2  0 -1i 0 1 2 0 -1i where 1i = −1 , and

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Clouds of Quantum Machines

1i -1i -1i 1i 1 0       0 -1i  0 1i = 0 1 .      The object 1 0 -1i   2 0 -1i is the progenitor, that is, an operator which acting on a qubit by a Kronecker product on the left gives one two-qubit system in a certain configuration such that, under a control gate, it outputs a pair of entangled qubits. The Kronecker product protocol (KP) comes  0     0    -1i   1 0 -1i 1 1  0     .  ⊗   = 2 0 -1i 0 2  0     0    -1i  0   

(14)

Applying the correct imaginary gate, it follows 1i 0 0 0 0 0 0 0  0  0       0 1i 0 0 0 0 0 0  0        0    0 0 1i 0 0 0 0 0 1i -1i       0 0 0 1i 0 0 0 0 1 0  -1i 0  ×    =   =  0 0 0 0 0 0 0 0 1i  2   2 0       0 0 0 0 0 0 1i 0 1i  0         0 0 0 0 0 1i 0 0 -1i 0 0 0 0 0 1i 0 0 0 0  0      

=

1050

1 2

11 +

1 2

00 .

(15)

The reader must remember that tensor product warrants the permanence of the superposition principle, i.e., there is a way to have a little of the  G -operator and a little of the original quantum state in the final state. The advantage of this approach is the presumption of a genuine quantum channel through which imaginary transformations occur, even though we do not know the dynamic essence of the non-local phenomenon; so, the imaginary operations are the logical quantum channel paths (not physical paths in spacetime).

PRESERVING QUANTUM ENTANGLEMENT The question is not so much of mathematical theorems but appropriate representations for the physical phenomena examined here. Thus, all my effort was directed to set representations that can be implemented in quantum algorithms to solve or minimize decoherence problems. The quantum world is very difficult to understand because of the lack of realistic correspondence with Euclidian world. But, if we open our minds and enlarge common geometry to imagine the geometry of an object with no extension we shall be very close to the language we need to describe quantum mechanical facts in quantum computing. For the sake of simplicity, we could call “point” any indivisible structure, not uniquely the point of geometry. Thus, two entangled particles would constitute a “point” in which refers their interactions. However, this point was called “tapestry” in my quantum language. It may sound strange to call “tapestry” an entity with no extension, but this is intentional. A tapestry is understood from the onefold coverage provided by unique progenitor tensor product on one qubit. This is like to define the element of a wool rug, the minimal knot that begins a complete Persian tapestry. Thereby, one simple knot is in fact a tapestry of one element.

Category: Cloud Computing

A fundamental issue about entanglement is that, no matter how far apart two entangled particles are from one another, what happens to one brings instantaneous response from the other. I sustain that if we use the particle image of matter, it is impossible to conceive the phenomenon of quantum entanglement, since what affects the particle can not propagate instantaneously to another particle. On the other hand, if we think about a continuous entity as a tapestry not made up by parts, then it is easy to see much more; to have no parts means to be indivisible in the space of configuration. In fact, two entangled particles constitute a physical monad as in the metaphysical Leibinizian sense. What is really missing is a rational form of expression suitable for such a phenomenon, both mathematically as literarily. We can well conceive the quantum monad as an imaginary tapestry of one element, cohesive, indivisible and intractable by common sense. Our tapestry is an imaginary quantumfold, that is, a onefold that only exists in quantum descriptions of nature. From this quantumfold we may obtain an imaginary representation of entangled states by tensor operations applied on it. As the quantumfold is not geometrically thinkable because it is not composed by parts, it is covered, as I said, by unique progenitor tensor product to one qubit. To extract entangled states from this coverage, that is, to obtain real descriptions of states not separable from superposition principle, we logically need one imaginary gate, the only to transform (that is, to Wick-rotate) representations of imaginary objects into real ones. This gate builds a bridge between the two representations. So, let us take an important definition. Definition 1: A tapestry  is a map that carries a pair progenitor-cum-qubit ξ † , ψ ± on a

(

)

column vector  with entries (0, −1i) by a Kronecker product ... , ...

 : ξ † , ψ ± →  (0,−1i ) .

, so that

This approaching is derived from Serpa’s proposal on Wick-rotations (Serpa, 2015). In some sense, tapestry is the generalized geometrical locus of qubit transformations that lead to entanglements. Definition 2: A quantum channel is a manifold built by the connected sum of two or more tapestries such that the canonical representation of each tapestry is a word as Γ1Γ2 ...Γg Λ1Λ2 ...Λh = 1, meaning that the tapestry has genus g and h holes.

CONNECTED TAPESTRIES OVER SERVERS IN CLOUD But not everything is perfect, since the phenomenon of decoherence — the loss of entanglement by environmental interferences — haunts quantum computing labs and brings puzzles to theorists. The problem of decoherence tantalizes scientists since long ago. However, it was demonstrated that the existence of quasiparticle excitations named non-Abelian anyons, neither classified as bosons nor fermions, is related to certain topological configurations that make immune to local decoherence the quantum information stored in such configurations (Sarma, et al., 2006). Also it was reported recently what would be the first experimental demonstration of a loss resilient entanglement-based protocol, probing that, in some circumstances, it’s possible to preserve properties acquired by qubits at first in entangled states (Zhang, 2013). These findings encourage theoretical investigations, including progenitor’s protocol. According to the last, when a pair of qubits is produced by progenitor’s action on a qubit, the imaginary tensor “operation” is somewhat “memorable” inside the generated pair in

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tapestry. As we know, tensor product retains a little of the progenitor and a little of the original quantum state in the final state. To understand exactly where I want to go, let us consider the concept of measurement from the point of view of quantum mechanics. Following von Neumann, we say that a consistent description of the measurement process in quantum physics must consider the interaction between the quantum system under observation and the quantum measurement apparatus (von Neumann, 1932). Thereby, a measurement is an intervention described by a unitary transformation that evolves the initial global state of the combined system. In this sense, the application of a certain controlled-gate builds a fact from what really happens. From the point of view of the model presented here, quantum entanglement precedes every physical measurement operation; two qubits are said entangled if they result of the transformation of a single qubit via progenitor, such that there must be at least one controlled-gate (a Wick-rotation matrix) capable of translating this entanglement as mathematically associated with an observable, albeit indirectly. The proposed protocol establishes the mathematical design of two entangled qubits from one qubit and one progenitor instead of two former qubits. Theoretically, from the notions of tapestry, imaginary quantum channel and progenitor it is possible to reduce the loss of the amount of entanglement. Now we take the Kronecker protocol (14), from which we have the tapestry representation of entangled states KP 10 = 0 01 + 0 01 + 0 11 + 0 11 + 1i 1i +0 00 + 0 10 − 00 − 10 . 2 2 Relating the terms of last expression with the formalism of surface topology, we can imagine an algorithm that converts this representation into a string (or word) such that in server A we read

1052

iI

A

= aaccbdef

with a := 0 01 ; c := 0 11 ; b := 0 00 ; d := 0 10 ;



e := −1i / 2 00 ; f := −1i / 2 10 . We can make bdef → b , so that canonical representation gives aaccb = 1 , which is a Klein bottle formed by two cross caps (aa, cc ) and a hole (b ) (see Figure 1). Clearly, the topology shall depend on the initial number of entangled qubits. It is obvious that cyclic order is not important to identify the topology, but once determined this latter, the cyclic order operates as a part of the signature of the entanglement itself; we may fix the total additional information about the implicit manifold, including that signature, to be transmitted by a classical channel to server B into a complete topological information-state packet coupled to the qubit, so that we may ensure high efficiency and fidelity repairing entanglement. The number

Figure 1. Topological planar model of KP 10

Category: Cloud Computing

of possible anagrams (signatures) from a word in which there is repetition is given by (q1 ,q2 ,q 3 ...)

Pn

=

5! = 30. 2!2!

Thus, there are 30 possible signatures for the Klein bottle. That information-state packet is the “memory” of entanglement and serves to preserve it from external perturbations. The connected sum (entanglement) iI # iI represents the physical A

B

connection — the quantum channel — between the qubits and is written as iI

A#B

C

n! , q1 !.q 2 !.q 3 !...

whereq1, q 2 , q 3 … are the numbers of times that repeated letters appear in the word. For the case of Klein bottle, P5(2,2) =

Figure 2. Connected sum of two tori

= a1a1c1c1b1a2a2c2c2b2 .

Then, entanglement is a quantum phenomenon in which, through the quantum channel, the two tapestries in servers A and B are connected as the two tori in Figure 2. All we have to do is to transmit the topological information-state associated to the Kronecker protocol from server A to server B . If noise removes entanglement between two qubits, the reapplication of the control imaginary gate (the reconstruction of the quantum channel between the qubits) in server B through a quantum circuit will restore, in thesis, the entanglement from that memorized information shared by the two qubits. a1b1a1−b1−a2b2a2−b2− .

ORCHESTRAL METALANGUAGE To think about quantum cloud computing is a natural consequence when we investigate the potential of quantum machines. From here, it rises a plethora of interesting subjects, including security and privacy of computation by blind quantum computing based on the transmission of individual photonic qubits (Barz et al., 2012). Thus, it is a great challenge to understand the complexity of services to be orchestrated in quantum clouds. The task of identifying and matching services is far from obvious. Potential applications of quantum mechanics in everyday life always bump into a linguistic modeling problem, a fact which greatly complicates the accurate understanding of what we want to do and what we can really do. For this reason, the structuring of an orchestral metalanguage is decisive for the correct construction of the topology of services, evidencing scalability and giving descriptive accuracy and, in the same breath, great facility of implementation of changes. In addition, as well observed by Metodi and Chong, a quantum computer of practical value must be up to storing and orchestrating a system comprising tens of millions qubits (Metodi & Chong, 2006). Therefore, that orchestral metalanguage would be an interesting tool to represent such a complex dynamics. With respect to the above introduced metalanguage and to make it clearer, it is important to consider the idea of orchestration as referring not

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Clouds of Quantum Machines

only to the service composition, but also to the topology that determines the order in which the services occur in a given process. We begin by defining a functional f as a unique service for state analysis. It informs a given composite service S about the best orchestration of elementary services to be performed depending on demand and the current state of the environment or on a collection of orchestrations identified on the same basic services to be initialized in parallel, depending on the overhead consumption. There are two ways of doing work one functional on a composite service as we shall see. The lexicon of the metalanguage, that is, its main catalogue of elementary connectors and words, is given by what we call a primitive base as follows: Now we look at the grammar of the metalanguage, that is, the rules to combine connectors and words in such manner that we may build meaningful statements (axioms, definitions, sentences, etc.): Table 1. Primitive base S

Composite service

s

Elementary service

f

Functional



Combined with (to compose services)



Call forward

≺

Call backward

≺

Parallel running



Defined as

〈f |

Right functional

f

Left functional

O1

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Orchestration in one dimension

Definition 1: It is called weak coupling the orchestration O 1 of elementary services si to which there is mutual communication among the orchestration component services. Axiom 1: For any set of elementary services si there is at least one weak coupling O 1 on si , such that O 1 on si is an application of the topology T 1 on si equivalent to the composite service S 1 :

{∀ {s } ∃O{ } / O{ } : T i

1 si

1 si

1

{s } ⇔ S

1

i

}.

Definition 2: It is called agglomerate the meeting O k of k weak couplings on the same basic services, being k = 1, 2, 3... n the size of the agglomerate and O k ⇔ S k . Axiom 2: For every set of basic servicessi , there is at least one agglomerate O k onsi , such that O k on si is an application of the topology T k on si equivalent to the composite service S k :

{∀ {s } ∃O{ } / O{ } : T i

k si

k si

k

{s } ⇔ S i

k

}.

Definition 3: It is called “left” functional action the initialization of the weak coupling O 1 which has the best performance among all identified weak couplings on the same basic services. Definition 4: It is called “right” functional action the initialization of the agglomerateO k . Corollary: All weak coupling O 1 is an agglomerate of dimension k = 1. It must be understand that both lexicon and grammar can be enlarged as the representational complexity advances. For instance, let us take an example of a functional action at “left” and at “right”,

Category: Cloud Computing

f S 1  s1  s 2  s 3  s 4  s 5  s 6

;

s1  s 2  s 3  s 4  s 5  s 6 S 4 〈f | 

s1  s2  s 3  s 4  s5  s6 s1  s2  s 3 ≺ s 4  s5  s6

,

s1  s2  s 3  s 4  s5  s6 O1 S 4 〈f | 

O2 O3

.

O4

(16)

Expression (16) is called parallelism matrix and its dimension k depends primarily on the number of elementary services and the number of available qubits. It is understood that service combinations, expressed between kets, must precede external calls. Large service chains with related functions can be represented in this way, documenting all the required topologies. Considering two servers, A and B, and taking the last column-matrix, quantum teleportation of this four-dimensional agglomerate from server A to server B, by the instruction 2 from Equation 6, would be given by I 2 : P2 M O 1O 2O 3O 4 ⇒ δ Ψ (↓↑)

1

Ψ (↑↓) 2

3

4

°

=> A

≡ OOOO .



B

Operating under metacomputation, B will perform the same task as server A could do, however, with no needs to repeat the process of topological analysis done by A. In server B, all topological possibilities can be used simultaneously to perform parts of a computation (service). Figure 3 (in appendix) outlines the SOA overlay intermediating clients and quantum machines in a certain hypothetical production environment. Servers A, B and C are supercomputers originally sharing the same quantum channel. At the right of the ESB a client requests certain complex service. By means of a protocol translation hardware (PT)

located at the left of the ESB, a query is addressed to the quantum machines B and C to know whether the requested service is already available. If the answer is negative, the request is forwarded to the quantum analyzer A. Until now, all we have done took place by means of classic channels. From now on, having defined the best orchestration, the quantum analyzer teleports the state matrix to both servers B and C; the probabilistic parallel processing begins in B and C at the same time that state matrix is destroyed at A. This diagram was inspired in a more general scheme called one-to-many teleportation. Due to entanglement, probabilities in B and C interfere with one another. The requested service results from the instantaneous “collapse” (see Figure 4 in appendix) of the copies of the state matrix into new states at PT (mathematically, this is a change of probabilistic reference class). Finally, service is really available for the client through ESB. While in machines B and C the service stays divided in probabilities, only manifesting as an effective product in daily world after protocol translation. With continuous reductions, that is, with unbroken chain of very fast reductions, availability and quickness are theoretically warranted in a level never seen before. Lastly, as pointed out by Schmidt and colleagues, the virtualized infrastructure of the bus allows it to grow or shrink according to the workload which it is supporting (Schmidt, 2005). The one-to-many teleportation has become well known since the end of the nineties (Murao et al., 2000), now considered by Ghiu (2012). Let us first establish the initial state in server A related to a half-spin particle, ψ

A

= α 0 + β 1 .

We want to broadcast the information of this state to servers B and C, so that they share the final state Ψ

BC

= α φ0 + β φ1 .

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Clouds of Quantum Machines

The general representation of a quantum channel shared by the three servers is given from 

(

=N 0

ABC

A

φ0

BC

+n 1

A

φ1

BC

(

)

(

)

ψi =  Ψ i+,A α φ0 + β φ1 + Ψ i−,A α φ0 − β φ1 +  + Ψ i+,B β φ0 + α φ1 + Ψ i−,B −β φ0 + α φ1  . 

(

),

)

(

)



DISCUSSION where N = 1 / 1 + n2 . If we take parameter n = 1 we get one-to-many teleportation. Now considering Bell-states, it follows the whole system state N  + −  Ψa α φ0 + β φ1 + Ψa α φ0 − β φ1 + 2 + Ψb+ β φ0 + α φ1 + Ψb− −β φ0 + α φ1  . 

( (

ψ  =

) )

( (

) )

Lastly, servers B and C have to make local appropriate transformations to get final state 1

ψ =

2

2

α +n β

2

(α φ

0

+ n β φ1

)=α φ

0

+ β φ1 .



Now we apply the metalanguage defined above. Each elementary service has a complete state function, so that, with Bell-basis, we may write for the whole system ψ1  ψ2  ψ3  ψ4  ψ5  ψ6 S 4 〈f | 

N 2

ψ1  ψ2  ψ3 

ψ4  ψ5  ψ6

ψ1  ψ2  ψ3 ≺ ψ4  ψ5  ψ6

,



ψ1  ψ2  ψ3  ψ4  ψ5  ψ6

(17) So we can say that the functional applied to the right in the above expression corresponds to the initialization of the quantum channel between the functions ψi that make up the orchestration S 4 , so that, for each elementary service si we have

1056

According to Definition 1 and Axiom 1, S 1 has flexible nature enough to incorporate virtually any weak coupling O 1 on si services. Based on the Definition 2 and the Axiom 2, this flexibility extends to n dimensions according to Definition 4, which states that in practice an array of identified weak couplings is executable under demand and according to the availability of resources. Definition 3 requires logistical criteria previously established in the architecture itself. It is worth remembering that both auxiliary services and application services fall within the formal framework described above. The word “agglomerate” was used rather than “cluster” precisely to avoid confusion with the concept of “cluster of machines”. Based on quantum principles, Server A arrived at the best possible solutions in four dimensions. During the short period of processing, the computer repeated the test in hundreds of different ways to make sure that there was not a better selection of ways to perform the required task. Thus, given a cloud under a SOA overlay disposing services s1 , s2 , s 3 , s 4 , s5 , s6 , this agglomerate shall be analyzed in server B according to local environmental conditions. The advantage is that the teleported matrix already contains the best selections of orchestrations on the same services for a certain global task to be replied in a remote location. Due to the uncommon nature of the qubit itself, in comparison with the classical bit, quantum computers are expected to prove in labs to be able to operate many times faster executing complex tasks of analysis and recombination. Nevertheless, as Nielsen and Chuang pointed out, “we do not understand what exactly it is that makes quantum

Category: Cloud Computing

computers powerful, or on what class of problems they can be expected to outperform classical computers” (Nielsen & Chuang, 2000). In fact, our example of orchestration was a simple one, but in reality quantum servers shall deal with a high number of services and dimensions, a situation now difficult to govern by common computers. In addition, quantum principles applied in computation should help to solve the most challenging problem in computer science: the construction of learning machines. By making computers select and analyze teleported agglomerates (as server B) based on previous experiences (server A), there is hopeless to improve artificial intelligence in clouds for complex decisions related to global scenarios of production. I think that there shall be not by way of individual processing but by metacomputation that we shall obtain the best performance gains and cyber intelligence with quantum machines.

SPONTANEOUS ENTANGLEMENT In fact, the creation of a pair of qubits from one qubit, as presented previously, is understandable as an outcome of the growth in the complexity of cybernetic autonomous devices of information interchanging and their links, but changes of complexity stay obscure; they require spontaneous entanglement. This is what keeps physicists separate from the world outside the laboratory. Quantum processors need to operate at superconductivity regime in order to make superposition happen. A viable way to achieve this is using metal niobium and lowering the temperature of the apparatus to -272.98oC, close to absolute zero. This is a physical precondition to hold quantum phenomena. It happens that spontaneous entanglement is a response to complex stimuli from the environment; the more you induce the increasing required complexity of the system, the more you rise the chances of new entanglements. As the phenomenon of mutation useful for the survival of a species, or the emergence of new synapses in the brain, allowing connections between in-

tellective processes, it is not known precisely how occurs spontaneous quantum entanglement between qubits from the incitement of the process until the conflagration of the fact itself; it is an evolutionary interval that remains confined to a black box. Spontaneous entanglements are not observed, in the same way that it is not feasible a snapshot of the natural extinction of a species.

A METAFRAME FOR CLOUDS IN HILBERT SPACE Constructions of type-cloud are more than sets of devices. Clearly, there is a succession of scales if we agree that to be a member of the larger system (cloud), the element (server) must be enrolled in some metric with tier below the tier of the metric of the first. Altaisky (2001) understood very well the problem of formal description of complex cybernetic systems, including systems of typecloud. A state function to describe the members x (servers) of an object X (cloud) would be

{Ψ (X ), Ψ (X , x )}, being Ψ (X ) the global state function and

Ψ (X , x ) the state function of the elements. Since

the Ψ (X , x ) are in Ψ (X ) , it is not valid the commutation rule  Ψ (X ), Ψ (X , x ) .  

This means that, in principle, we can not apply operators on these functions such that we can measure both simultaneously; either we observe the overall behavior of the cloud, or the isolated behavior of one of its members. In theoretical terms, in a perfectly entangled quantum cloud, to measure the state of a server blurs the overall state of the cloud and vice versa. As the objects Ψ (X , x )

and Ψ (X ) inhabit different functional spaces,

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their signatures or sets of coordinates associated with them assume a hierarchical network. Each level of the hierarchy is described by the structure  = , β  , Tβ





THE BEEHIVE EFFECT

in which

 → the signature of scale, β  → the symmetry group at scale , 

T β → some topology on β  (in other words, the coordinates at  -th level).

In Hilbert space  of these hierarchical states, Ψ 1, Ψ 2 ∈ ; α, β ∈ C ⇒ αΨ 1 + βΨ 2 ∈ , where C is the set of the complex numbers, we would have for the general state, by definition,

{ ( ),

αΨ (X ) = αψX1 τ β 

{



( )

αψXx2 τ1β 1

2

1



( )} }

,..., αψXx2 τnβ n

2

,... ,



remembering that by the re-ingoing nature of Ψ (X ) there is no commutation. The structure of  is enough to preach cybernetic systems of type “cloud”, although here I have provided only a brief formalism. This structure includes complexities such as dynamic provisioning of computing resources, dynamic balancing of the workload and performance monitoring. The application of cloud computing is a reality on the Internet (Google and Yahoo). In 2008 the total of the clouds of the five largest Internet search companies amounted to around 2 million servers. The main advantage of this computational model is the significant reduction of

1058

the time-to-market for on-demand e-business and Web 2.0 applications, that is, the gradual allocation of resources by necessity.

Quantum communication uses the informational content of entangled systems in order to obtain an extra resource. Quantum entanglement is essential to reach the exponential speed-up anticipated by some quantum algorithms. From the theoretical point of view, all the information in a state of maximum entanglement is contained in the joint properties of the systems and not in individuals separately. The so-called “beehive effect” originates from a large scattering of entangled states distributed across multiple quantum servers via broadcast channels. Aiming to make an effective distribution of information by entangled states we would have to build a linked set of transmitters like in the most stable going experiments in which we start from the distribution of entangled photons through glass fibers, since they are installed underground and thus they are less vulnerable to external disturbances. Eventually, it will be necessary the use of satellite technology. Due to the spread of entangled states over arbitrary distances, the cloud assumes a global behavior from each stimulus locally introduced. The global response is resulting not only from the entanglement, but also from the state analysis promoted by auxiliary services of SOA architecture on the entangled states. It is usual to expect that quantum computing comes to fruition in the next ten/twelve years, mainly solving problems about the transfer of large amounts of complex data by teleporting based on quantum entanglement. The evolution to an intelligent cloud of entangled quantum servers with the ability to send and receive large amounts of data analyzing and deciding what to do is a more distant win I suppose, but even so it would be risky to make estimates from the present stage of research.

Category: Cloud Computing

FUTURE RESEARCH DIRECTIONS In the future, clouds will be able to make global decisions supported by entangled states of information shared among all quantum servers, including via teleportation of entanglement itself. As well as, local decisions will be madden after the beehive analysis of the situation, a complex task which requires multiple entanglements and teleportations. Quantum cloud computing is still in its infancies, but it is very far from science fiction.

CONCLUSION Physicists agree on that we are a long way off —decades, they suppose — from employing quantum features to build quantum hardware with practical applicability (Woo, 2013). The correlation quantumness between particles is in principle given by entanglement, and entanglement is very sensible to environmental perturbations, which turns impracticable to maintain superposition of states. Researchers are now working on new ideas to quantify the disagreement (the “discord”) between quantum and classical ways of calculating the same property as a manner to solve the problem of sensibility to the environment, but it is still not clear if discord really fulfills general computing quantumness (Tyler, 2013). As Professors Hadjiivanov and Todorov said, Quantum mechanics, created during the first quarter of XX century is finding wide applications only after the invention of the transistor in 1948 and the development of the laser in the late 1950’s. The true applications of the ‘second quantum revolution’ are yet to come. (Hadjiivanov & Todorov, 2015). Currently, with the improvement of laser technology, there are several advanced experiments

applying quantum entanglement/teleportation over distances of about 89 mi. We expect that computers endowed of quantum microprocessors shall develop capabilities to perform what I call “self-entanglement”, transmitting packets of entangled states with embedded spinor-like gates to other computers in a cloud, interacting with high efficiency by teleportation of states. The “quantum cloud” will be many times faster to provide services than any conceived architecture now available. This is the beginning of an increasing intelligence, since entanglement and teleportation open doors to infinity of interactions from server to server.

ACKNOWLEDGMENT I acknowledge Doctor José Abdalla Helayël-Neto, at Brazilian Center of Physics Research, by the great support to this work.

REFERENCES Altaisky, M. (2000). What can biology bestow to quantum mechanics? arXiv:quant-ph/0007023v1 Anderson, R., & Ciruli, D. (2006). Scaling SOA with distributed computing. Retrieved June 12, 2011, from http://www.ddj.com/architect/193104809 Barenco, A., Bennett, C. H., Cleve, R., DiVincenzo, D. P., Margolus, N., Shor, P., & Weinfurter, H. et al. (1995). Elementary gates for quantum computation. Physical Review A., 52(5), 3457–3467. doi:10.1103/PhysRevA.52.3457 PMID:9912645 Barz, S., Kashefi, E., Broadbent, A., Fitzsimons, J., Zeilinger, A., & Walther, P. (2012). Demonstration of blind quantum computing. Science, 335(6066), 303–308. doi:10.1126/science.1214707 PMID:22267806

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Bowen, W., Treps, N., Buchler, B. C., Schnabel, R., Ralph, T. C., Bachor, H.-A., & Lam, P. K. et  al. (2003). Experimental investigation of continuous variable quantum teleportation. Physical Review A., 67(3), 032302. doi:10.1103/ PhysRevA.67.032302 Bouwmeester, D., Pan, J.-W., Mattle, K., Eibl, M., Weinfurter, H., & Zeilinger, A. (1997). Experimental quantum teleportation. Nature, 390(6660), 575–579. doi:10.1038/37539 Braunstein, S. (1996). Quantum teleportation without irreversible detection. Proceedings of the Royal Society of London, 53, 1900–1903. PMID:9913086 Charon, J. (1967). De la fisica al hombre. Madrid: Ediciones Guadarrama. Deutsch, D., & Jozsa, R. (1992). Rapid solution of problems by quantum computation. Proceedings of the Royal Society of London. Series A, 439(1907), 553–558. doi:10.1098/rspa.1992.0167 Erl, T. (2005). Service-oriented architecture: Concepts, technology, and design. Upper Saddle River, NJ: Prentice Hall PTR. Frenken, T., Spiess, P. & Anke, J. (2008). A flexible and extensible architecture for device-level service deployment. LNCS, 5377, 230-241. Ghiu, I. (2012). Generalized telebroadcasting of entanglement. Department of Physics, University of Bucharest. Hadjiivanov, L., & Todorov, I. (2015). Quantum Entanglement. Bulg. J. Phys., 42, 128–142. Lemos, A., Daniel, F., & Benatallah, B. (2015). Web service composition: A survey of techniques and tools. ACM Computing Surveys, 48(3), 33. doi:10.1145/2831270 Linthicum, D. (2009). Cloud computing and SOA convergence in your enterprise. New York: Addison-Wesley.

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McMahon, D. (2008). Quantum computing explained. John Wiley & Sons, Inc. Forum, M. E. F. (2015). The third network: Lifecycle service orchestration vision. Whitepaper, Retrieved May 26, 2016, from https://www.mef. net/Assets/ White_Papers/MEF_Third_Network_LSO_Vision_FINAL.pdf Metodi, T., & Chong, F. (2006). Quantum computing for computer architects. Morgan & Claypool Publishers. Murao, M., Plenio, M., & Vedral, V. (2000). Quantum information distribution via entanglement. Physical Review A., 61(3), 032311. doi:10.1103/ PhysRevA.61.032311 Nakamura, M. (2004). Implementing integrated services of networked home appliances using service oriented architecture. In M. Aiello, M. Aoyama, F. Curbera, M. Papazoglou (Eds.), Service-Oriented Computing - ICSOC 2004: Second International Conference. doi:10.1145/1035167.1035206 Natis, Y. (2007). Twelve common SOA mistakes and how to avoid them. Retrieved May 10, 2013, from http://www.gartner.com/it/content/754400/754413/twelve_common_soa_mistakes.pdf Nielsen, M., & Chuang, I. (2000). Quantum computation and quantum information. Cambridge, UK: Cambridge University Press. Raussendorf, R., & Briegel, H. (1995). Quantum computing via measurements only. arXiv:quantph/0010033 Sarma, S., Freedman, M., & Nayak, C. (2006). Topological quantum computation. Physics Today. American Institute of Physics. Serpa, N. (2015). New lectures on supergravity. Saarbrücken: Verlag/Lambert Academic Publishing.

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Shah, M. (2007). SOA reusability: Shrinking the lag between business and IT. Retrieved June 12, 2011, from http:// today.java.net/pub/a/ today/2007/ 07/24/soa-reusability-shrinking-lagtime.html Schmidt, M., Hutchison, B., Lambros, P., & Phippen, R. (2005). The Enterprise Service Bus: Making service-oriented architecture real. IBM Systems Journal, 44(4), 4. doi:10.1147/sj.444.0781 Tyler, C. (Ed.). (2013). Quantum discord: Theoretical advance toward practical quantum computing. Los Alamos Science and Technology Magazine. von Neumann, J. (1932). Mathematische grundlagen der quantenmechanik. Berlin: Springer Verlag. Zhang, T. (2002). Quantum teleportation of light beams. arXiv: quantph/0207076. Zhang, Z., Tengner, M., Zhong, T., Wong, F., & Shapiro, J. (2013). Entanglement’s benefit survives an entanglement-breaking channel. arXiv: quantph/13035343 Walther, P., & Zeilinger, A. (2006). Quantum entanglement, purification, and linear-optics quantum gates with photonic qubits. In L. Accardi, M. Ohya, & N. Watanabe (Eds.), Quantum probability and white noise analysis (Vol. 19, pp. 360–369). Singapore: World Scientific Publishing Co. Wang, X. (2001). Multi-bit gates for quantum computing. Physical Review, 86, 3907–3910. PMID:11329354

KEY TERMS AND DEFINITIONS Beehive Effect: The supposed global — and even intelligent — behavior of a cloud of servers acting under quantum principles. Cloud Computing: A model of computation by which IT resources are randomly dispersed in the network, being offered as services. Progenitor: The gate generator of a two-qubit system which under the action of a control gate creates a pair of entangled states. Quantum Bit (or Qubit): The quantum tile of information that can assume both states 0 and 1 at the same time. Quantum Entanglement: The matting of quantum states to which decomposition does not hold. Quantum Machine: A computer whose general operation follows the laws of quantum mechanics. Quantum Teleportation: The long-distance replication of a quantum state. SOA (Service Oriented Architecture): A computational architecture for the provision of services as packages of specific tasks over the network.

ENDNOTES

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von Weizsäcker, C. (1974). La imagen física del mundo. Madrid: Biblioteca de Autores Cristianos. Woo, M. (2013). How Caltech physicists are helping to bring us ever closer to our quantum future. Engineering & Science. Retrieved July 08, 2013, from http://eands.caltech.edu/articles/LXXVI1/ 2013_Spring_What_Is_a_Quantum_Computer. pdf



2



3

A symmetry in quantum mechanics is a discrete transformation or a group of continuous transformations that let invariant the Hamiltonian (or the Lagrangian) and the canonical commutation relations of the system. The atom is not immediately perceptible to our senses, and any experiment takes only a specific property of the atom to the ambit of a mediated sensibility. Every experiment is a material act which is simultaneously an act of perception.

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APPENDIX Figure 3. Operating sketch of a cloud of quantum machines, showing simple quantum cloud architecture linked to an Enterprise Service Bus through the protocol translator hardware

Figure 4. Detail of the interactions at protocol translator

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Cyberinfrastructure, Cloud Computing, Science Gateways, Visualization, and Cyberinfrastructure Ease of Use Craig A. Stewart Indiana University, USA Richard Knepper Indiana University, USA Matthew R. Link Indiana University, USA Marlon Pierce Indiana University, USA Eric Wernert Indiana University, USA Nancy Wilkins-Diehr San Diego Supercomputer Center, USA

INTRODUCTION

BACKGROUND

Computers accelerate our ability to achieve scientific breakthroughs. As technology evolves and new research needs come to light, the role for cyberinfrastructure as “knowledge” infrastructure continues to expand. In essence, cyberinfrastructure can be thought of as the integration of supercomputers, data resources, visualization, and people that extends the impact and utility of information technology. This article defines and discusses cyberinfrastructure and the related topics of science gateways and campus bridging, identifies future challenges in cyberinfrastructure, and discusses challenges and opportunities related to the evolution of cyberinfrastructure and cloud computing.

Today’s US national cyberinfrastructure ecosystem grew out from the National Science Foundation-funded supercomputer centers program of the 1980s (National Science Foundation, 2006). Four centers provided supercomputers and support for their use by the US research community. Researchers generally accessed one supercomputer at a time, sometimes logging into a front-end interface. At this time, the focus of the research computing community was centered on supercomputers – traditionally defined as computers that are among the fastest in existence. Over time there have been several different supercomputer architectures, but the key points were that supercomputers were monolithic systems that were among the fastest in the world. At present we can think of supercomputers as being a subset of the more general term high performance computer (HPC) – where HPC means that many computer processors work together, in concert, to solve large computational

DOI: 10.4018/978-1-5225-2255-3.ch092 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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challenges and where the computer processors communicate via very fast, networks internal to the HPC system. HPC focuses on computing problems where a high degree of communication is needed among the processors working together on a particular problem. HPC is a more general term than supercomputers because there are many HPC systems that are modest in total processing capacity relative to the fastest supercomputers in the world (cf. Top500.Org, 2016). In the early days of supercomputing, using multiple supercomputers in concert was not possible. In the late 1980s, the National Research and Education Network initiative created several testbeds for distributed computing, including the CASA testbed which linked geographically distributed supercomputers to solve large-scale scientific challenges (U.S. Congress Office of Technology Assessment, 1993). A turning point in distributed high performance computing was the I-WAY project – a short-term demonstration of innovative science enabled by linking multiple supercomputers with high performance networks (Korab & Brown, 1995). It demonstrated the possibilities to advance science and engineering by linking supercomputers using high-speed networks. In the late 1990s, the NASA Information Power Grid provided a production grid of multiple supercomputers connected by a high-speed network (Johnston, Gannon, & Nitzberg, 1999). Around this time began also the concept of high throughput computing (HTC) with a software system called Condor (Litzkow, Livny, & Mutka, 1988). HTC takes the approach of breaking a problem up into small pieces of work and distributing them to multiple CPUs over network connections that may be relatively slow. HTC best suits problems where relatively little communication is needed among the processors working together on a particular problem or simulation. Because HTC applications can operate relatively efficiently on processors with little communication among the processors, HTC applications have always fit naturally into a distributed computing environment (Thain,

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Tannenbaum, & Livny, 2005). Today, a popular framework for distributed storage and processing of large data sets is Apache Hadoop (The Apache Software Foundation, 2006). Over time, distributed computing evolved into ‘grids,’ with grids emerging as a commonly used term in the late 1990s. Typically, computational grids are the hardware and software infrastructure which provides access to the computational capabilities (Foster & Kesselman, 1998, 2004). Middleware is a key software component of cyberinfrastructure, enabling the disparate components of cyberinfrastructure to work together. In effect, middleware manages complex interactions between resources which allows for the development of new networked applications (National Science Foundation, 2004). Around the turn of the century, the US government funded two major grid projects – TeraGrid and the Open Science Grid. In 2001, the NSF funded an experimental computational, storage, and visualization resource called TeraGrid, which developed grid capabilities for supercomputer centers (National Science Foundation, 2006). The Open Science Grid (OSG) (Livny et al., 2006; Open Science Grid, 2015), first funded with that name in 2006, grew out of three projects that developed HTC grids for the purpose of analyzing data from physics experiments (Avery, 2007). Tying geographically distributed computing systems together into grids to create a whole greater than the sum of its parts was widespread around the turn of the century. However, the term grid computing was becoming laden with sometimes competing definitions. In addition to computing and data grids, other terms such as collaboration, semantic, and peer-to-peer grids emerged, distinguished by the characteristics of the protocols and interactions between components (Fox, 2006). The potential for confusion and competing definitions of different types of grids led Dr. Ruzena Bajcsy, then NSF assistant director of the Computer and Information Science and Engineering Directorate, to use the term cyberinfrastructure when charging a new advisory group to offer advice to

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the NSF – the “Blue Ribbon Advisory Panel on Cyberinfrastructure.” The term cyberinfrastructure had been used before, in a different sense, by Richard Clarke, then US National Coordinator for Security, Infrastructure Protection, and Counterterrorism (Clarke & Hunker, 1998). Bajcsy stated that she used the term cyberinfrastructure because she wished to “create a program … that would involve the broader computer science/information technology community” (Bajcsy, 2013). The committee report goes on to state, “the newer term cyberinfrastructure refers to infrastructure based upon distributed computer, information and communication technology. If infrastructure is required for an industrial economy, then we could say that cyberinfrastructure is required for a knowledge economy” (Atkins et al., 2003). Bajcsy’s successor at the NSF, Dr. Peter Freemen, stated that this report “led to the creation of a term for infrastructure that attempts to capture the integration of computing, communications, and information for the support of other activities (especially scientific in the case of NSF)” (Freeman, 2013). In 2007, Freemen wrote “cyberinfrastructure can have many definitions and, to some extent, the definition is in the eye of the beholder” (Freeman, 2007). To make it clearer for scientists outside of science and physics, Indiana University developed a definition identifying components and function: Cyberinfrastructure consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high performance networks to improve research productivity and enable breakthroughs not otherwise possible. (Stewart et al., 2010) The EDUCAUSE Campus Cyberinfrastructure Working Group and the Coalition for Academic Scientific Computation developed a definition which includes teaching and learning:

Cyberinfrastructure consists of computational systems, data and information management, advanced instruments, visualization environments, and people, all linked together by software and advanced networks to improve scholarly productivity and enable knowledge breakthroughs and discoveries not otherwise possible. (Dreher et al., 2009) The characteristics distinguishing cyberinfrastructure from other IT terms and concepts is the inclusion of resources like instruments and sensor networks as well as people and a focus on knowledge breakthroughs. Cyberinfrastructure may be distinguished in particular from the more European term eScience on the basis of the explicit role of people in cyberinfrastructure. eScience is defined as “the large scale science that will increasingly be carried out through distributed global collaborations enabled by the Internet. Typically, a feature of such collaborative scientific enterprises is that they will require access to very large data collections, very large scale computing resources and high performance visualization back to the individual user scientists” (National e-Science Centre, 2010).

CYBERINFRASTRUCTURE TODAY The broad use of cyberinfrastructure in science and engineering envisaged by Bajcsy is in ample evidence today. That cyberinfrastructure enables breakthroughs not otherwise possible is demonstrated by two Nobel prizes for work made possible by major cyberinfrastructure resources – the Open Science Grid and XSEDE. The Open Science Grid is an international HTC resource. Many different organizations own the computers participating in the grid (Open Science Grid, 2015). OSG’s people part of cyberinfrastructure is organized through dozens of Virtual Organizations (VOs) that use the computational resources of the OSG, each supporting its own uses and users. Analysis of data from the Large

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Hadron Collider (LHC) is the paradigmatic use case for HTC. LHC data can be broken down into large numbers of small data sets, each of which may be analyzed in isolation. The 2013 Nobel Prize for Physics was awarded to François Englert and Peter Higgs for the theoretical discovery of the particle now known as the Higgs Boson. The existence of the Higgs Boson was verified in experiments at the LHC, with the data analyses made possible by the OSG. The largest HPC-oriented cyberinfrastructure in the use is the eXtreme Science and Engineering Discovery Environment (XSEDE) (Towns et al., 2014). XSEDE is a single, virtual system which is comprised of a collection of integrated and highly-advanced digital resources and constitutes the largest HPC resource funded by the US government (Towns et al., 2014; XSEDE, 2013, 2015). The 2013 Nobel Prize in Chemistry was awarded to Martin Karplus, Michael Levitt and Arieh Warshel, for the development of multiscale computer models of complex chemical systems. Karplus used resources of the TeraGrid, the predecessor of XSEDE, and Warshel uses resources of XSEDE (XSEDE, 2015). Figure 1. The open science grid

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Cyberinfrastructure may support a particular research domain or application. Cyberinfrastructure has also been widely adopted in the private sector, particularly in advanced engineering, medicine and pharmaceuticals, mining and oil exploration, finance, and manufacturing (Tabor Griffin Communications, 1998). Cyberinfrastructure systems need not be massive to be important. A Specialized cyberinfrastructure supports NASA’s Operation IceBridge in measuring polar ice sheets in Greenland and Antarctica. Operation IceBridge uses sophisticated synthetic aperture radar (SAR) systems to study polar ice and map the bedrock base in Greenland and Antarctica (Hayden, Fox, & Gogineni, 2007; Knepper, Link, & Standish, 2015). One of the characteristics of SAR is that one doesn’t get an image out of SAR systems directly; a great deal of computation is required to generate an image. In-plane computation and data storage provide real-time analysis of multiple radar data sources (Figure 2). This cyberinfrastructure is highly specialized to deal with the rigors of fieldwork in Antarctica. The cyberinfrastructure designed to support Operation IceBridge enables real-time

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Figure 2. NASA operation icebridge field radar data processing cyberinfrastructure

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reactions to data as it is being collected in an airplane over the Antarctic ice sheets – something not possible before this system was developed.

EVOLVING COMPONENTS OF CYBERINFRASTRUCTURE INCLUDING CLOUD COMPUTING In his 2007 article, Freeman stated that the definition of cyberinfrastructure will evolve over time (Freeman, 2007). Cloud computing can thus be thought of as a particular approach to computing infrastructure and as a component of cyberinfrastructure which includes computational resources and data storage resources. According to the National Institute of Standards and Technology (NIST), Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and

released with minimal management effort or service provider interaction. (Mell & Grance, 2011) Key in cloud computing are on-demand selfservice, broad network access, resource pooling, rapid elasticity, and measured service. It can be used as a solution to applications that are “hosted in the cloud” or integrated into cyberinfrastructure. Like a high performance computer, a cloud computing solution can be monolithic or integrated into a larger cyberinfrastructure facility. For example, the NSF recently funded a cloud system that will be integrated as part of XSEDE called Jetstream (Stewart et al., 2015). It can be used in isolation as a scientific cloud system or as part of a larger integrated cyberinfrastructure facility. Data storage systems, advanced instruments and data repositories have also changed over time. Some recent changes in needs and data resources are described in the final report of the NSF Advisory Committee for Cyberinfrastructure Task Force on Data and Visualization (NSF Advisory Committee for Cyberinfrastructure Task Force on Data and Visualization, 2011) and the work

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from Hey, Tansley, and Tolle (2009). The NSF has recently funded a new data-oriented storage and analytics facility called Wrangler to add new resources for big data to the US infrastructure funded by the NSF and supported by XSEDE. Middleware has evolved significantly since the early days of cyberinfrastructure. Globus, one of the most widely used families of software in the world, now includes authentication, secure access capabilities, and data and metadata management tools (Foster, 2005; Globus Online, 2013). Other middleware includes workflow systems that coordinate the use of cyberinfrastructure and automate complex analyses; examples include Apache Airavata (Marru et al., 2011), Kepler (Ludäscher et al., 2006), and Pegasus (Deelman et al., 2005). Visualization systems — hardware (display, visualization, and interaction) and software (applications, libraries, middleware, and data format standards) — have evolved dramatically since the inception of the term cyberinfrastructure. Visualization was one of the earliest cyberinfrastructure components to promote distributed applications and high levels of interoperability, largely because of the network of homogeneous CAVE Automatic Virtual Environments (CAVEs) and smaller devices using similar software launched in the last half of the 1990s (NCSA, 2001). Us-

ers at multiple sites could synchronously interact with the same data sets and observe remote participants via virtual avatars while communicating over IP-based audio and video channels. CAVEs and similar devices introduced new capabilities for understanding complex 3D and 4D data from other cyberinfrastructure resources. However, cost and scarcity, limited their impact on day-to-day scientific investigation. The 2000s saw affordable graphics cards, projectors, and high-definition, stereoscopic displays. Consumer-level technologies spurred a range of innovative systems for stereoscopic and ultra-resolution visualization (Sherman, O’Leary, Whiting, Grover, & Wernert, 2010), democratizing advanced visualization systems and techniques. Figure 3 shows a CAVE diagram and an ultra-high resolution tiled wall assembled from commodity HD televisions. Shown in the figure at right, scholars at Indiana University’s Mathers Museum of World Cultures use an IQ-Wall to compare high-resolution images of textiles. This 3x4 wall is free-standing and was installed in a museum gallery in an afternoon. Such a display wall, which supports collaborative research, can now be created for a few tens of thousands of dollars, making them widely accessible in research environments.

Figure 3. At left is a CAVE, a room-scale visualization environment. At right is an ultra-high resolution tiled wall built in 2013 using commodity HDTV displays. Source: © 2015, Trustees of Indiana University. Used with permission

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FUTURE RESEARCH DIRECTIONS Cloud Computing, Cyberinfrastructure, Exascale Computing, and the Economics of Computing Cloud computing and more traditional HPCs (supercomputers) have complementary strengths and weaknesses. Cloud computing facilities may have internal networks of modest speed compared to supercomputers. On the other hand, cloud computing may be purchased in modest increments and are thus more accessible to a larger user community than supercomputers. Cloud computing is commonly used for “big data” applications, characterized by data volume, velocity, and variety (Laney, 2001). US President Obama’s recent executive order (Obama, 2015) sets a new agenda for the creation of exascale computing facilities (capable of 1015 mathematical operations per second) while calling for joint development of exascale and big data/cloud computing facilities. Fox and collaborators (Fox, Qui, Kamburugamuve, Jha, & Luckow, 2015) depict many of the commonalities between cloud computing and HPC and propose an alignment and set of commonalities between HPC and big data stacks that can form a foundation for the sort of joint development of both approaches called for by President Obama in his recent executive order. Cloud computing and HPC need not be an “either/or” choice. There are business cases for selecting cloud computing or local servers (Brumec & VrčEk, 2013; Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). However, choosing between cloud and HPC can be very complicated as cloud resources may not be able to support science applications that require low-latency internal networks or large amounts of memory as prerequisites, even if the cost of cloud computing seems lower than HPC per CPU hour. A locally owned HPC or HTC system can be acquired as a one-time cost where the capacity of the system limits the usage over time but remains useable for

several years. In contrast, use of cloud computing may have a smaller cost for initial use but requires ongoing payments. This suggests tradeoffs in “locally owned” versus “cloud” that will suggest solutions strongly influenced by local conditions and financial systems at any given organization. Cyberinfrastructure challenges include documenting return on investment and energy costs to operate at large scale. An analysis of return on investment in XSEDE is explored by Stewart and collaborators (Stewart et al., 2015). Energy costs and the economics of large-scale data centers help drive many activities into cloud computing. Security and data privacy are also concerns.

Science Gateways, Campus Bridging, and Cyberinfrastructure Ease of Use For years, researchers accessed cyberinfrastructure exclusively through command-line interfaces. This sort of interface made it difficult to do long complex tasks and they were not particularly user friendly. Today, access to and the utility of cyberinfrastructure has been considerably expanded through the deployment of science gateways, use of cloud computing tools, and campus bridging. In particular, science gateways provide access to cyberinfrastructure to a broad set of users by employing graphical user interfaces and sophisticated tools for orchestrating computational workflows. Science gateways make it possible to weave together a set of complicated tasks to achieve an overarching goal – like search for drug candidates or predict the path of a tornado. More formally, science gateways are defined as “a communityspecific set of tools, applications, and data collections that are integrated together via a portal or a suite of applications” that can “support a variety of capabilities including workflows, visualization as well as resource discovery and job execution services” (Wilkins-Diehr, 2007). There are now dozens of science gateways in use or in development (Lawrence et al., 2015). For example, the CiPRES portal (Cyberinfrastructure for Phylogen-

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tic Research) enabled thousands of researchers – including research students – to do sophisticated analyses of evolutionary histories from genetic information (CIPRES, 2016). Another important science gateway is SEAGrid – the Science and Engineering Applications gateway (SEAGrid, 2015). SEAGrid is geared toward chemical and mechanical analyses, and can, for example, be used to search for potential new drug candidates. The major components of SEAGrid (see Figure 4) exemplify a multi-tiered approach commonly used in science gateways. Science gateways have also had a profound impact on citizen science—the public contribution to scientific discoveries (OpenScientist, 2011). Zooniverse is a web-based front end to several science gateways supporting citizen science. Dozens of projects use citizen science in weather, archaeology, biology, and medicine, where thousands of people help analyze research data – particularly image data – that might otherwise go unanalyzed for months or years into the future (Zooniverse, 2015). Campus bridging approaches a different set of cyberinfrastructure issues. One of the significant challenges in cyberinfrastructure is integrating across scales of resources. Campus bridging focuses on integrating local, often modest scale cyberinfrastructure facilities with regional, na-

tional, and even international cyberinfrastructure resources. The goal of campus bridging is to “bridge” from the campus to the approach enables small or resource-constrained groups to operate as if the higher-level resources were close at hand (Hallock, Knepper, & Stewart, 2015; NSF Advisory Committee for Cyberinfrastructure Task Force on Campus Bridging, 2011). Some campus bridging issues are solvable simply with funding; for instance, networking becomes ever cheaper and it becomes ever more feasible to have good network connectivity from campus to national resources. Recent efforts have focused on technical interoperability among campus computing clusters and national level resources like XSEDE. The technical aspects of such interoperability include adding software to local institutional cyberinfrastructure systems that match those used on nationally-shared resources and as a result enabling training and educational materials developed for nationally shared systems to be used in support of smaller, local resources. Networking in support of bridging from campus to national resources has also been furthered by a network concept called the Science DMZ, which explicitly creates a portion of network “specifically engineered for science applications and does not include support for general-purpose use. By separating the high-performance science network

Figure 4. Science gateways such as SEAGrid uses a multi-tiered gateway architecture

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(the Science DMZ) from the general-purpose network, each can be optimized without interfering with the other” (ESnet, 2016).

CONCLUSION Cyberinfrastructure has evolved from supercomputer centers into an integrated and distributed suite of powerful and flexible resources that integrate supercomputers, data resources, visualization, and people in ways that go beyond the capabilities of any of the individual components of cyberinfrastructure. It has led to new products, medical treatments, and improved business processes that improve quality of life. In the long run we believe that cloud computing, high performance computing, and high throughput computing will be seen not as alternatives but as complementary tools used flexibly in response to the particular science and engineering needs and particular local conditions of researchers and organizations making use of cyberinfrastructure. In summary, the future offers tremendous opportunities for science and society to use cyberinfrastructure to enable new discoveries and improve the quality of life of people everywhere as new tools for visualization, science gateways, campus bridging, citizen science, and cloud computing evolve and deliver new capabilities to the public and the scientific and technical communities worldwide.

ACKNOWLEDGMENT This material is based upon work supported by the National Science Foundation under grants 0504075, 0451237, 0723054, 1062432, 0116050, 0521433, 0503697, 1053575, and ACI-1445604, and support provided by the Indiana University Pervasive Technology Institute. Any opinions, findings and conclusions or recommendations expressed herein are those of the authors and do

not necessarily reflect the views of the supporting agencies. Robert Quick of IU created the OSG map in Figure 1. Editing by Greg Moore and Winona Snapp-Childs is gratefully acknowledged; any errors are the responsibility of the senior author.

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Dreher, P., Agarwala, V., Ahalt, S. C., Almes, G., Fratkin, S., Hauser, T., … Stewart, C. A. (2009). Developing a Coherent Cyberinfrastructure from Local Campuses to National Facilities: Challenges and Strategies. EDUCAUSE. Retrieved from http:// www.educause.edu/Resources/DevelopingaCoherentCyberinfras/169441 or http://hdl.handle. net/2022/5122 ESnet. (2016). Why Science DMZ. Retrieved June 30, 2016, from https://fasterdata.es.net/sciencedmz/motivation/ Foster, I. (2005). Globus Toolkit Version 4: Software for Service-Oriented Systems. In H. Jin, D. Reed, & W. Jiang (Eds.), Network and Parallel Computing (Vol. 3779, pp. 2–13). Springer Berlin Heidelberg. http://doi.org/ doi:10.1007/11577188_2 Foster, I., & Kesselman, C. (1998). The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann.

Globus Online. (2013). Globus Toolkit Homepage. Retrieved from http://www.globus.org/toolkit/ Hallock, B., Knepper, R., & Stewart, C. A. (2015). Workshop Report: Campus Bridging: Reducing Obstacles on the Path to Big Answers, 2015. IEEE Cluster 2015. Retrieved from http://hdl.handle. net/2022/20538 Hayden, L., Fox, G., & Gogineni, P. (2007). Cyberinfrastructure for Remote Sensing of Ice Sheets. Madison, WI: TeraGrid. Retrieved from http://cerser.ecsu.edu/citeam/teragrid07.pdf Johnston, W. E., Gannon, D., & Nitzberg, B. (1999). Grids as production computing environments: the engineering aspects of NASA’s Information Power Grid. High Performance Distributed Computing, 1999. Proceedings. The Eighth International Symposium on, 197–204. http://doi.org/ doi:10.1109/HPDC.1999.805298

Foster, I., & Kesselman, C. (2004). The Grid 2, Second Edition: Blueprint for a New Computing Infarstructure. Morgan Kauffman.

Knepper, R., Link, M. R., & Standish, M. (2015). Big Data on Ice: The Forward Observer System for In-flight Synthetic Aperture Radar Processing. Procedia Computer Science, 51, 1504–1513. Retrieved from http://hdl.handle.net/2022/20471

Fox, G. (2006). Collaboration and Community Grids. In International Symposium on Collaborative Technologies and Systems (pp. 419–428). IEEE Computer Society. http://doi. org/ doi:10.1109/CTS.2006.24

Korab, H., & Brown, M. D. (1995). Virtual Environments and Distributed Computing at SC’95: GII Testbed and HPC Challenge Applications on the I-WAY. In H. Korab & M. D. Brown (Eds.), SUPERCOMPUTING ’95. New York, NY: ACM.

Fox, G. C., Qui, J., Kamburugamuve, S., Jha, S., & Luckow, A. (2015). HPC-ABDS High Performance Computing Enhanced Apache Big Data Stack. 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 1057–1066. http://doi.org/ doi:10.1109/CCGrid.2015.122

Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety. META Delta. META Group. Retrieved from http://blogs. gartner.com/doug-laney/files/2012/01/ad949-3DData-Management-Controlling-Data-VolumeVelocity-and-Variety.pdf

Freeman, P. A. (2007). Is Designing Cyberinfrastructure or, Even, Defining It Possible? First Monday, 12(6). doi:10.5210/fm.v12i6.1900

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Litzkow, M., Livny, M., & Mutka, M. (1988). Condor - A Hunter of Idle Workstations. 8th International Conference of Distributed Computing Systems, 104–111.

Category: Cloud Computing

Livny, M., Avery, P., Pordes, R., Foster, I., & Lazzarini, A. (2006). Sustaining and Extending the Open Science Grid: Science Innovation on a PetaScale Nationwide Facility. National Science Foundation. Retrieved from http://nsf.gov/awardsearch/showAward.do?AwardNumber=0621704

NSF Advisory Committee for Cyberinfrastructure Task Force on Data and Visualization. (2011). NSF Advisory Committee for Cyberinfrastructure Task Force on Data and Visualization Final Report. Retrieved from http://www.nsf.gov/od/oci/ taskforces/TaskForceReport_Data.pdf

Ludäscher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger, E., Jones, M., & Zhao, Y. et al. (2006). Scientific workflow management and the Kepler system. Concurrency and Computation, 18(10), 1039–1065. doi:10.1002/cpe.994

Obama, B. (2015). Executive Order -- Creating a National Strategic Computing Initiative. Retrieved from https://www.whitehouse.gov/ the-press-office/2015/07/29/executive-ordercreating-national-strategic-computing-initiative

Marru, S., Gunathilake, L., Herath, C., Tangchaisin, P., Pierce, M., & Mattmann, C., … Weerawarana, S. (2011). Apache airavata: a framework for distributed applications and computational workflows. In Proceedings of the 2011 ACM workshop on Gateway computing environments (pp. 21–28). Seattle, WA: ACM. http://doi.org/ doi:10.1145/2110486.2110490

Open Science Grid. (2015). About the Open Science Grid. Retrieved November 30, 2015, from http://www.opensciencegrid.org/

Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing - The business perspective. Decision Support Systems, 51(1), 176–189. doi:10.1016/j.dss.2010.12.006 Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing (SP 800-145). Recommendations of the National Institute of Standards and Technology. Retrieved from http://csrc.nist.gov/ publications/nistpubs/800-145/SP800-145.pdf National Science Foundation. (2006). Cyberinfrastructure: From Supercomputing to the TeraGrid. Retrieved from http://www.nsf.gov/news/ special_reports/cyber/fromsctotg.jsp NCSA. (2001). CAVERN Users Society. Retrieved November 30, 2015, from http://cavernus.ncsa. illinois.edu/ NSF Advisory Committee for Cyberinfrastructure Task Force on Campus Bridging. (2011). NSF Advisory Committee for Cyberinfrastructure Task Force on Campus Bridging Final Report. Retrieved from http://www.nsf.gov/od/oci/taskforces/TaskForceReport_CampusBridging.pdf or http://pti.iu.edu/campusbridging/

Sherman, W. R., O’Leary, P., Whiting, E. T., Grover, S., & Wernert, E. A. (2010). IQ-station: A low cost portable immersive environment. Lecture Notes in Computer Science, 6454, 361–372. http:// doi.org/ doi:10.1007/978-3-642-17274-8_36 Stewart, C. A., Cockerill, T. M., Foster, I., Hancock, D., Merchant, N., & Skidmore, E., … Gaffney, N. (2015). Jetstream - A self-provisioned, scalable science and engineering cloud environment. In Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure. http://doi.org/ doi:10.1145/2792745.2792774 Stewart, C. A., Roskies, R., Knepper, R., Moore, R. L., Whitt, J., & Cockerill, T. M. (2015). XSEDE Value Added, Cost Avoidance, and Return on Investment. In Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure (pp. 23:1–23:8). New York, NY: ACM. http://doi.org/ doi:10.1145/2792745.2792768 Stewart, C. A., Simms, S., Plale, B., Link, M., Hancock, D., & Fox, G. (2010). What is Cyberinfrastructure? SIGUCCS 2010. doi:10.1145/1878335.1878347

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Tabor Griffin Communications. (1998). HighPerformance Computing Contributions to Society. Tabor Griffin Communications. Retrieved from http://www.tgc.com/hpcbook Thain, D., Tannenbaum, T., & Livny, M. (2005). Distributed computing in practice: The Condor experience. Concurrency and Computation, 17(2-4), 323–356. http://doi.org/doi doi:10.1002/cpe.938 The Apache Software Foundation. (2006). Hadoop Distributed File System. Retrieved May 6, 2014, from http://hadoop.apache.org/ Top500.Org. (2016). The List. Retrieved June 30, 2016, from https://www.top500.org/ Towns, J., Cockerill, T., Dahan, M., Foster, I., Gaither, K., Grimshaw, A., … Wilkins-Diehr, N. (2014). XSEDE: Accelerating Scientific Discovery. Comput. Sci. Eng., 16, 62. doi:10.1109/ MCSE.2014.80 U.S. Congress Office of Technology Assessment. (1993). Advanced Network Technology-Background Paper. Retrieved from https://www. princeton.edu/~ota/disk1/1993/9304/9304.PDF XSEDE. (2013). 2013 Nobel Prize in Chemistry winners bring HPC to the lab. Retrieved November 30, 2015, from https://www.xsede.org/2013nobel-prize-in-chemistry XSEDE. (2015). Overview. Retrieved November 30, 2015, from https://www.xsede.org/overview

KEY TERMS AND DEFINITIONS Campus Bridging: The seamlessly integrated use of cyberinfrastructure operated with other local or remote cyberinfrastructure as if they were proximate to the user.

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Citizen Science: The work of individuals or teams of amateur, non-professional, or volunteer scientists who conduct research, gather and analyze data, perform pattern recognition, and develop technology, often in support of professional scientists. Cloud Computing: On-demand, affordable access to a distributed, shared pool of computing and storage resources, applications, and services usually via the Internet for a large number of users. Cyberinfrastructure: Computational systems, data and information management, advanced instruments, visualization environments, and people, all linked together by software and advanced networks to improve scholarly productivity and enable knowledge breakthroughs and discoveries not otherwise possible. eScience: Computationally intensive science carried out through distributed global collaborations enabled by the Internet, involving access to large data collections, very large scale computing resources and high performance visualization. High Performance Computing: Many tightly integrated computer processors that run very large scale computations and data analyses quickly where communication among the many processors is required. High Throughput Computing: A computing paradigm that focuses on the efficient execution of a large number of loosely-coupled tasks Science Gateways: Community-developed tools, applications, and data integrated via a portal or a suite of applications, usually in a graphical user interface, and customized to the needs of specific communities.

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Category: Cloud Computing

Fault Tolerant Cloud Systems Sathish Kumar VIT University, India Balamurugan B VIT University, India

INTRODUCTION Computing is a study of algorithms, automation, programming the information. Programming is a way of designing algorithms which are aimed at controlling, executing the computing devices. These devices have the basic features such as the amount of data they can store and process speed to perform in a reliable time. Traditionally in

1980’s desktop personal computers (PCs) are used to support in creating, editing and manipulating documents. Further, these PCs are connected to the devices like a scanner to scan the documents, printer to take hard copies of the documents, etc. Later these devices are connected together to form a simple network. Since PCs has more of devices and it occupies more space the devices like laptop, tablet, mobile phone came into the context.

Figure 1. Sample computing paradigm shift

DOI: 10.4018/978-1-5225-2255-3.ch093 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Fault Tolerant Cloud Systems

BACKGROUND Computing Shift from Mainframe to Cloud There are five distinct stages that cloud computing arrived. Initially one computer terminals like keyboard monitor to access the mainframes systems. In stage1, personal computers (PCs) were used to manipulate user requirements. In stage2, several PCs were connected to form a network called local Figure 2. Mainframe to cloud shift

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network and user can access the PCs from their own PCs. In stage3, several local networks were connected to a global network called the internet. From the internet, the users can remotely access the systems. In stage4, the grid computing came into the context were resources were shared distributedly. The user uses PCs to access the grid. In stage5, the user employs a computing technique called cloud computing that allows users to access the resources through the internet.

Category: Cloud Computing

COMPUTING TECHNIQUES ERA

Cloud Computing

Cluster Computing

Cloud computing is an enabling technology that provides access to computer resource on-demand. Cloud computing follows model pay-as-yougo models. The important properties of cloud computing are on-demand self service, broad network access, resource pooling, elasticity, and measured service.

A cluster computing consists of several standalone computers which are a distributed loosely or tightly connected system and performs several tasks which are viewed as a single system. The features of cluster computing are reducing cost, power; it uses improved network technology, availability, and scalability.

Service-Oriented Architecture (SOA) Service-oriented architecture is a loosely coupled distributed system that follows standard protocols to provide services over a network. The aim of service-oriented architecture is to divide the problems into separate distinguishable sections and emphasizes in a single software. The feature of the service-oriented architecture is it is independent of any product or technology.

Grid Computing Grid computing is the technology that came into the contrast of electrical power grid, which allows the resource to be shared independently between power grids. Grid computing is a loosely coupled distributed system connected over a network. Grid computers are geographically distributed and heterogeneous in nature. The feature of grid computing is it improves scalability and performance of information system.

Utility Computing Utility computing is a technology makes computing resource available as a service and provides a resource on demand and charges them depends on the usage. Utility computing used some form of virtualization like storage virtualization and hardware virtualization. The feature of utility computing is in reducing the cost of buying a resource in rental.

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TYPICAL COMPUTING RESOURCE The devices that are connected physically or virtually, internally or externally to the computer systems are called resources. Basically, computing resource can be classified into two categories namely physical resource and logical resource.

Physical Resource Physical resources are the devices that are connected physically to the system. For example, a personal computer has several components connected together like, a monitor used to display the video outputs, a keyboard to give inputs, a secondary memory disk to store the inputs and outputs and a primary memory used for processing. Henceforth we can classify physical resource as follows, central processing unit, memory, storage, workstations, network elements, sensors.

Central Processing Unit (CPU) Cpu is one of the most important units in the computer system were most of the processing were done. In the context to cloud computing utilization of cpu is considered. That is the amount of task processed by the cpu.

Memory In early days, memory is managed statically. Since cloud computing is dynamic there is a need to fill dynamic memory allocation techniques for

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cloud computing. Basically, memory is a block to store arrived data or task based on some policies. Memory can be allocated in three ways namely, schematic memory allocation technique: were memory allocation follows policies like FIFO, LRO. Static memory allocations technique: were memory block are divided into two parts and each part is partitioned using some static ratios. Dynamic memory allocation technique: were memory block are divided into two parts and each part is partitioned dynamically.

Storage Cloud is models were resources are accessed remotely; the data’s are stored in the remote database. These storage are maintained redundantly to ensure the client that they can access the data on demand. By this way, reliability is achieved in the storage of cloud and cloud maintains this storage as service.

Workstations Workstations are the CPUs with high processing speed and have good internet connectivity so that the user can attain good services all the times. Usually, the workstation is automated without human interaction so that high performance is attained.

Network Elements The components like routers, hubs, switches, bridges, etc are called network elements. To build an efficient data center to cloud switches like Top of Rack (TOR) switch, End of Rack (EOR) switch, and the virtual switch was used.

Logical Resources Logical resources are used to control the physical resources so that we can use physical resource efficiently. Some of the logical resources are operating systems, network throughput, network loads, protocols, network delays etc.

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Operating System Operating systems help in using the available resource efficiently by managing hardware’s, software’s, files, devices, performance, and fault tolerance.

Network Throughput The maximum number of bits per transferred per second is called throughput. Likewise, the maximum number of data bits transferred per second in network link is called network throughput. Higher the network throughput higher network efficiently.

Network Delay A small difference in a second or even in millisecond made a major difference in reliability, scalability, and even in performance also. For example, a delay in virtual machine setup, virtual machine migration causes a delay in availability of virtual machines. So the minimal in delay can increase the system accuracy.

Network Protocols The irregular communication between workloads leads to inability. So, some set of rules to be defined to overcome those inabilities. Protocols are kinematics of identifying the devices and connect them with each other. It also setup the rules for packing the data into a message and how to transfer these data’s.

VIRTUALIZATION METHODOLOGY Virtualization is a methodology used in cloud computing to determine an abstraction of software or hardware. Virtualization provides the resource that follows go-to technology. It helps in improving a cost effective performance by hiding the physical resource and communication between these resources and users.

Category: Cloud Computing

Table 1. Evolution of virtualization Technology

Period

C

Use

Virtual Memory

Late 1950

Developed for automatic page replacement for transistorized main frame computers.

Virtual Machine

In 1960

IBM developed a first virtual machine to run multiple operating systems on a single processor machine.

Virtual Machine with Time Sharing

Mid 1960 Mid 1970

Hardware virtualization is developed. The concept of virtual machine monitor is introduced to control this hardware virtualization. Every operating system accepted to use virtualization.

Hypervisor

In 1972

IBM developed first hypervisor VM-CP.

Evolution of Virtualization The term virtual has introduced in late 1950, in transistorized mainframe system by creating a virtual memory for page replacement. In 1960 the first virtual machines are introduced by IBM. These virtual machines allow the user to run multiple operating systems on a single processor machine. In the mid of 1960 hardware virtualiza-

tion with time sharing is introduced. The concept of virtual machine monitor is developed during this time which is used to run a virtual machine in a protected environment. In the mid of 1970, every operating system accepted to use virtualization. In 1972 IBM has developed hypervisors called VMCP which is used to control the virtual machines. VM-CP is the type1 hypervisor which runs directly on hardware to control virtual machines.

Figure 3. Basic virtualization

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Terminologies and Components of Virtualization Host Machine A host machine is a machine that includes the resources such as, CPU, memory, hard disk, network components that were used by virtual machines,

Virtual Machine (VM)

locating the resources between the host machine and VMs. It does have separate OS on it. TYPE II hypervisor so called hosted hypervisor has a separate OS called guest OS which is responsible for allocating the resources between the host machine and VMs.

TYPES OF VIRTUALIZATION

Host operating system (OS) is the OS installed in the host system and manages resources such as memory, processing speed of the host machine. Finally, it assigns these resources to the virtual machines on demand.

Basically there are two types of virtualization techniques are followed. One is software-based virtualization and another one is hardware assisted virtualization. In software-based virtualization: the VMM are maintained as software and the hardware interface is handed over to the guest OS. Now VMM has full control over Guest OS by writing dynamic binary translation code. It has two types of virtualization Full virtualization and para virtualization. In hardware-assisted virtualization: the VMM is directly installed on hardware so the dynamic binary translation is removed and the performance overhead during this dynamic binary translation is overcome.

Guest Operating System

Emulation

Guest operating system (OS) is the OS installed on the virtualized setting. This guest OS may installed in the client system, physical server etc. This guest OS in controlled by hypervisors.

Emulation is software created to replicate the hardware and process of a system. Guest OS need not be modified. The instructions from the guest OS should be interpreted by the host OS. This leads guest OS to slow down. So emulation suits for the systems where speed is not significant. The disadvantages of emulation are low performance and low density.

The virtual machine is a virtualized physical machine implemented in host machine that acts as real host machines. In the host machine, it is created as a single file or a single folder. It s controlled by a software called virtual software.

Host Operating System

Hypervisor Hypervisor or virtual machine manager (VMM) is the software that runs on the virtual machine. Each OS in the host machine has its own memory, processing speed, allocation scheme etc. The responsible for VMM is to control the allocation of resources and memory to the VMs. There are two groups of hypervisors namely: TYPE I hypervisor and TYPE II hypervisor. The TYPE1 hypervisor so called native or bare-metal hypervisor runs directly on the hardware. It is responsible for al-

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Full Virtualization/ Native Virtualization Full virtualization is a hardware virtualization uses a hypervisor to translate the instruction to the host OS. This leads to a significant impact on system performance. Guest OS is not modified to make feel that it is running as host OS.

Category: Cloud Computing

Figure 4. Full virtualization

Para Virtualization The guest OS is modified and it can be able to communicate directly with the hypervisor. This tends in the reduction of instruction translation time and operational cost. The OS available to the guest is limited that depends on the availability of the hypervisor.

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Operating System Level Virtualization (OSLV) OSLV is the method to provide multiple separate user-space instances for an operating system kernel. It allows the user-space application to run independently from other software.

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Figure 5. Para virtualization

Server Virtualization Server virtualization is a technique that shares a single physical machine into multiple virtual machines (VM). Each VM have its own virtual memory, virtual interface, virtual cpu, and any one the functions such as mail, file, internet.

Network Virtualization

Resource Virtualization The resources such as software resource (software, applications) and hardware resource (storage, network device etc.) are shared by the guest OS. Resource virtualization is classified into • • • • •

Storage Virtualization Server Virtualization Network Virtualization Desktop Virtualization Application Virtualization

Storage Virtualization Storage virtualization is a technique that shares the multiple physical storage resources into a single storage and managed centrally. Storage virtualization generally implemented in file systems, tape systems, and storage area network.

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Network virtualization is a technique that changes the physical network into software called virtual network. The networking devices such as routers, switches, port, firewall etc. A virtual network will act same as a physical network. Network virtualization is the seamless step after storage and server virtualization. The network virtualization is grouped into two namely; External network virtualization: where multiple Local Area Networks were combined to form a Virtual Local Area Network (VLAN). Internal network virtualization: where hypervisors and containers combined to control the networks.

Desktop Virtualization Desktop virtualization is a technique, on an existing desktop running OS it creates a separate OS. There are two man groups of desktop virtualization such as, Remote Desktop Virtualization also called as server-hosted virtualization where the operations are hosted on servers and accessed by the client over the network, Local desktop virtualization also called as client- hosted virtualization where local physical systems and hardware’s are virtualized and monitored by the software called hypervisor.

Application Virtualization Application virtualization is a technique that virtualizes particular application from the desktop

Category: Cloud Computing

Figure 6. Operating system level virtualization

installed locally in the container. The container controls the interaction of the applications and components on how to communicate with other systems and components.

VIRTUALIZATION TOOLS In the view of computing technology, a tool is a software use to process or to control a task or a request. Henceforth, virtualization tool is a software use to achieve virtualization for hardware

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or any other resource in computer systems. The virtualization tools are broadly classified into the following modes: full virtualization, para virtualization, native virtualization, Operating system level virtualization and emulators. The availability of these tools can be commercial, open source and freely available. In full virtualization mode, many tools are available, most commonly used virtualization tool is based on VMware used to manage a virtual infrastructure. The other tools of VMware are VMware workstation and VMware Venter converter that runs on open source OS.

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Table 2. Comparing features of virtualization Type

Advantage

Disadvantage

Virtualizations

     • Cost of hardware implementation and cost of energy is reduced.      • Backup and recovery s faster and earlier.      • More Efficient.

     • Considering software license.      • Experts in virtualization are less.      • Maintaining the resource in virtualization environment with leasing plans is difficult.

Operating system Virtualization

     • Reduce overhead.      • Increase performance.      • Increase compatibility.

     • Have one base operating system and multiple guest operating systems.      • If base OS fails the entire guest OS also fails.

Hypervisors

     • Guest OS can be modified or unmodified.      • In an unmodified OS if any one crashed it does not affect entire OS.      • Supports wide range of hardware.

     • In a modified OS if any one OS fails all others guest OS also fails.      • If base OS is rebooted all other guest OS also rebooted reduces availability of VM’s.

FAULT TOLERANCE

VMware server is freely available tool that runs on both windows and Linux platforms. Microsoft virtual PC is a commercially available tool that only works on Microsoft platforms. In para virtualization mode xen is an open source tool used for virtual machine migration.usermode Linux (UML), Vserver, Linux container(LXC) are the open source tools available for linux hardwares. LXC is a container based virtualization tool. In native virtualization mode, QEMU is an open source tool used for heterogeneous hardware’s and used as an emulator. A virtual box is a commercial tool used to control remote desktop protocols. Openvz is an open source operating system level tool used to partition the resource efficiently and it is a container based tool. Bochs is an open source emulator tool used to debug guest OS.

For a failure or fault system fault tolerance is a system that should identify the fault and recovers the failed system back without any damage. Fault: A fault is an underlying defect of a system that leads to an error. Error: An error is a faulty system state, which May leads to failure. Failure: A failure is a system error that affects its functionality of a system. Fault Detection: Fault detection is a technique used to detect a fault or error of a system. Fault Recovery: Fault recovery is a technique used to repair a fault or to repair a failed system after fault detection.

Table 3. Comparison of virtualization tools Tool

Virtualization type

Host CPU

Guest CPU

Host OS

Guest OS

Live Migration

License

Xen

Para Virtualization

X86,X64

X86,X64

Linux, Unix

Windows, Linux, Unix

Yes

Open Source

KVM

Full Virtualization

X86,X64

X86,X64

Linux

Windows, Linux, Unix

Yes

Open Source

Virtual Box

Full Virtualization

X86,X64

X86,X64

Windows, Linux, Unix

Windows, Linux, Unix

Yes

Commercial

VMWare

Full Virtualization

X86,X64

X86,X64

Proprietary Unix

Windows, Linux, Unix

Yes

Open Source

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Category: Cloud Computing

Figure 7. Virtualization tools

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FAULT TOLERANCE TECHNIQUES

Hardware Redundant Fault Tolerant

There are numerous fault tolerance techniques followed to overcome faults. Fault tolerance can be classified based on redundancy, load-balancing, and policies.

Numerous redundant modules are created to perform the function the requirement. Later the outputs were ranked and if any fault occurred, errors were removed by this ranking.

Fault Tolerance Based on Redundancy

Software Redundant Fault Tolerant

Redundancy is the method, were multiple copies are maintained so that availability of a resource attained efficiently. Based on redundancy fault tolerance is classified as, hardware redundancy, software redundancy, time redundancy.

In software redundant fault tolerant two or more solution of a problem are identified. In either way, two or more versions of software are also maintained so that failures will not occurr for large inputs.

Figure 8. System failure

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Time Redundant Fault Tolerant To achieve an effective fault tolerant, time redundant fault tolerant uses a technique such as it execute the result repeatedly in different hardware and software and increase the accuracy. It also executes the result with increased time with the same hardware and software.

Fault Tolerance Based on Load Balancing Load balancing is a technique used to balance the work load of a network in a peak traffic time. Based on load balancing it is categorized into hardware load balancing, network load balancing, and dispatcher software load balancing.

Hardware Load Balancing Fault Tolerant Hardware based load balancing controls single host IP and sends to multiple hosts using network address translation. Backup load balancers were, used to handle the failure of single host IP.

Dispatcher Load Balancing Fault Tolerant Dispatcher load balancing handle single point failure by transmitting dispatch function to another computer after failure. It maintains a separate server to control the entire incoming request.

Network Load Balancing Fault Tolerant Network load balancing is distributed software used to create redundancy host and controls the entire request for an efficient load balancing.

Fault Tolerance Based on Policies There are numerous fault tolerant techniques follows some kind of policies such as creating check points, migration of tasks, replicating the task

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or workflow, resubmission of the task, retrying the failed job to recovers etc. There is two kinds of fault tolerance techniques categorized based on policies such as proactive fault tolerance and reactive fault tolerance.

Reactive Fault Tolerance Technique When a failure occurs, reactive fault tolerance follows some kind of policies and helps to recover from failed state. The following or the policies were reactive fault tolerance follows,

Check Point/Restart The process of saving the system states periodically when a system is in the failure-free state is called checkpoint. If a failure occurred during the execution the system is restarted from the last saved check point and it is roll backed or recovered to the fail free state.

Replication Replication is the process used to store the most commonly used data to multiple locations and ensures the availability of data when needed. The data are accessed from nearest locations when a failure occurred.

Proactive Fault Tolerance Technique Proactive fault tolerance techniques used to predict the fault in prior state and either replaces the components that are to fail or transfer the component that is predicted to be failed to failure free component. Some of the proactive fault tolerance policies are, 1. Software Rejuvenation: It is used to periodically reboot the system to ensure the system is in fail free state, 2. Self-Healing: It is a technique used to automatically handle the failure state and

Category: Cloud Computing

Table 4. Tools for fault tolerant technique Fault Tolerant Policy

Tool

Reactive

HAProxy, Hadoop, Amazon E2

Replication

Reactive

HAProxy

Job migration

Reactive

SGaurd

Checkpoint/restart

Reactive

Scientific Work Flow Systems

Task Resubmission

Reactive/Proactive

Assure

Retry, Self-Healing

Reactive

Adaptive Fault Tolerance (AFTRC)

Checkpoint/restart

Reactive

Low Latency Fault Tolerance (LLFT)

Replication

Reactive

FTWS

Replication/Resubmission

Reactive

FTM

Replication

Reactive/Proactive

CANDY

Preemptive migration

Reactive

Astrolabe

Checkpoint/restart

Reactive

VisPerf

Replication

Reactive

GEMS

Checkpoint/restart

Proactive

Cloudinit.d

Software Rejuvanation

3. Preemptive Migration: Used to transfer the failed component based on feedback from clients.

DEPENDABILITY Dependability used to define the reliability of the system in offering a service. Dependability generally studies the following metrics: reliability, availability, confidentiality, integrity, security. Reliability: is defined as the probability of the system performs without any variations in the system for a specific period of time. Reliability is given by, Reliability function = n (t) / N = Failure free elements/number of elements at time zero. Reliability is also given by, Reliability R = Availability / MTBF + MTTR. MTBF: Mean time between failures.

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Technique

MTTR: Mean time to repair. Availability: is defined as the availability of a system when it is needed without any fail and able to complete the opted task. Availability of a system is given by, Availability (A) = MTBF / MTBF + MTTR. Reliability at time‘t ‘is given by the probability of time ‘T’ to failure after time‘t’. R (t) =P {T > t}.

ESTIMATING DEPENDABILITY METRICS In general to estimate dependability metrics two models are used. One is combinatorial model and another model is a state-phase model. Combinatorial model: used to study the system interaction that leads to system failure n terms of the structural relation between them. State-phase model considers the state and event of system dynamically. Following are the models used to represent both combinatorial and state-phase models,

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Table 5. Comparison of fault tolerance models Types

Fault Tolerant Properties

Fault Tolerant Techniques & Policies

Fault Tolerance Model

Fault Tolerance Type

Reliability

Performance

Response Time

LLFT

Reactive Fault Tolerance

High

High

Average

FTM

Reactive Fault Tolerance

High

Low

FTMC

Reactive Fault Tolerance

High

AFRTC

Proactive Fault Tolerance

FTWS MapReduce

SelfHealing

Preemptive Migration

Checkpoint/ Restart

Replication

Job Migration

No

No

No

Yes

No

Average

No

No

Yes

Yes

Yes

Low

Average

No

No

Yes

Yes

No

High

High

Average

No

No

Yes

Yes

Yes

Reactive Fault Tolerance

Average

Average

Average

No

No

Yes

Yes

No

Proactive Fault Tolerance

High

High

Average

Yes

Yes

No

No

No

Figure 9. Dependability models

CONCLUSION Formal definitions of computing, computing components, virtualization, fault, error, fault tolerance, fault tolerant model are discussed. Also tools for virtualization and fault tolerance techniques are discussed. A mathematical representation of fault tolerance is modeled. In this chapter, fault tolerant techniques for the virtual machine were discussed. Looking at the performance metrics and features of application

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the fault tolerant techniques discussed here helps in improving fault tolerance.

Future Trends Considering the limitations and problems, following problems are identified. 1. The efficiency of fault tolerant technique in the real cloud is to be evaluated.

Category: Cloud Computing

2. A fault tolerant technique for performance overhead in the large heterogeneous cloud is modeled. 3. A complete fault tolerant technique is developed and implemented as one the service in the cloud. 4. Based on the application type an optimum fault tolerant technique is to be developed. 5. If a number of component increases it increases the probability of failure. So an efficient fault tolerance technique is modeled. In the context of data transmission if a large amount of data is transmitted for a long distance it increases the probability of failure. Here, an efficient fault tolerance technique is modeled.

REFERENCES Bala, A., & Chana, I. (2012). Fault tolerancechallenges, techniques and implementation in cloud computing. IJCSI International Journal of Computer Science Issues, 9(1), 1694–0814. Bressoud, T. C., & Schneider, F. B. (1996). Hypervisor-based fault tolerance. ACM Transactions on Computer Systems, 14(1), 80–107. doi:10.1145/225535.225538 Chen, P. M., & Noble, B. D. (2001, May). When virtual is better than real [operating system relocation to virtual machines]. In Hot Topics in Operating Systems, 2001. Proceedings of the Eighth Workshop on (pp. 133-138). IEEE. Cheraghlou, M. N., Khadem-Zadeh, A., & Haghparast, M. (2016). A survey of fault tolerance architecture in cloud computing. Journal of Network and Computer Applications, 61, 81–92. doi:10.1016/j.jnca.2015.10.004 Chouikhi, S., El Korbi, I., Ghamri-Doudane, Y., & Saidane, L. A. (2015). A survey on fault tolerance in small and large scale wireless sensor networks. Computer Communications, 69, 22–37. doi:10.1016/j.comcom.2015.05.007

Dijkstra, E. W. (1974). Programming as a discipline of mathematical nature. The American Mathematical Monthly, 81(6), 608–612. doi:10.2307/2319209 Egwutuoha, I. P., Levy, D., Selic, B., & Chen, S. (2013). A survey of fault tolerance mechanisms and checkpoint/restart implementations for high performance computing systems. The Journal of Supercomputing, 65(3), 1302–1326. doi:10.1007/ s11227-013-0884-0 Frohlich, D., Thomas, P., Hawley, M., & Hirade, K. (1997). Inaugural issue editorial: Future personal computing. Personal Technologies, 1(1), 1–5. doi:10.1007/BF01317881 García-Valls, M., Cucinotta, T., & Lu, C. (2014). Challenges in real-time virtualization and predictable cloud computing. Journal of Systems Architecture, 60(9), 726–740. doi:10.1016/j. sysarc.2014.07.004 Lira, V., Tavares, E., Fernandes, S., & Maciel, P. (2015). Dependable virtual network mapping. Computing, 97(5), 459–481. doi:10.1007/s00607014-0431-8 Manvi, S. S., & Shyam, G. K. (2014). Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey. Journal of Network and Computer Applications, 41, 424–440. doi:10.1016/j.jnca.2013.10.004 Masood, A., Sharif, M., Yasmin, M., & Raza, M. (2015). Virtualization tools and techniques: Survey. Nepal Journal of Science and Technology, 15(2), 141–150. doi:10.3126/njst.v15i2.12131 Padhy, R. P. (2012). Virtualization techniques & technologies: state-of-the-art. Journal of Global Research in Computer Science, 2(12), 29-43. Patra, P. K., Singh, H., & Singh, G. (2013). Fault tolerance techniques and comparative implementation in cloud computing. International Journal of Computers and Applications, 64(14).

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Qi, Z., Xiang, C., Ma, R., Li, J., & Wei, D. (n.d.). ForenVisor: A Tool for Acquiring and Preserving Reliable Data in Cloud Live Forensics. Academic Press. Sahoo, J., Mohapatra, S., & Lath, R. (2010, April). Virtualization: A survey on concepts, taxonomy and associated security issues. In Computer and Network Technology (ICCNT), 2010 Second International Conference on (pp. 222-226). IEEE. doi:10.1109/ICCNT.2010.49 Sandholm, T., & Lee, D. (2014). Notes on Cloud computing principles. Journal of Cloud Computing, 3(1), 1–10. Singh, G., & Kinger, S. (2013, June). A survey on fault tolerance techniques and methods in cloud computing. International Journal of Engineering Research and Technology, 2(6). Singh, H. (n.d.). Cloud Computing Paradigms and Challenges. Academic Press. Stanković, R., Štula, M., & Maras, J. (2015). Evaluating fault tolerance approaches in multiagent systems. Autonomous Agents and MultiAgent Systems, 1–27. Sun, D., Chang, G., Miao, C., & Wang, X. (2013). Analyzing, modeling and evaluating dynamic adaptive fault tolerance strategies in cloud computing environments. The Journal of Supercomputing, 66(1), 193–228. doi:10.1007/s11227-013-0898-7 Tedre, M. (2011). Computing as a science: A survey of competing viewpoints. Minds and Machines, 21(3), 361–387. doi:10.1007/s11023011-9240-4 White, J., & Pilbeam, A. (2010). A survey of virtualization technologies with performance testing. arXiv preprint arXiv:1010.3233

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ADDITIONAL READING Benmessaoud, N., Tulloch, M., Williams, C., & Mudigonda, U. M. (2014). Microsoft System Center Network Virtualization and Cloud Computing. Pearson Education. Jaramillo, D., Furht, B., & Agarwal, A. (2014). Mobile Virtualization Case Study. In Virtualization Techniques for Mobile Systems (pp. 27–36). Springer International Publishing. doi:10.1007/978-3-319-05741-5_4 Ruest, N., & Ruest, D. (2009). Virtualization, A Beginner’s Guide. McGraw-Hill, Inc. Xing, Y., & Zhan, Y. (2012). Virtualization and cloud computing. In Future Wireless Networks and Information Systems (pp. 305–312). Springer Berlin Heidelberg. doi:10.1007/978-3-64227323-0_39

KEY TERMS AND DEFINITIONS Cloud Customer: A cloud customer is who consumes the services provided by the cloud provider. Cloud Models: Cloud is basically deployed in three models as public, private and hybrid. Cloud Provider: Cloud providers are who provides the services to the customer who needs the cloud services. Cloud Services: Cloud provides their resources as services to their customers. So the customers pay for the services and use them. Virtual Machine Migration: The process of migrating, the virtual machine from one location to another is called virtual machine migration. Work Load: The number of resources consumed and shared by the users for a period of time.

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Category: Cloud Computing

Fault Tolerant Data Management for Cloud Services Wenbing Zhao Cleveland State University, USA

INTRODUCTION The pervasiveness of cloud services has significantly increased the dependability requirement of cloud systems. Most cloud services are implemented according to a three-tier architecture where the presentation, application logic execution, and data management are separately handled by each tier (Zhao, Moser, and Melliar-Smith, 2005). The middle-tier servers implement the application logic and they are designed to be either stateless or to only maintain session state. Hence, this separation of concerns has greatly increased the scalability of such systems because the middletier servers can be easily scaled out. On the other hand, this design makes the data management tier ever more important because the availability and integrity of the services hinges on the data management tier. The data must be made highly available and protected against various hardware faults and malicious attacks. While it is relatively straightforward to ensure high availability for Web servers and application servers by simply running multiple copies according to the three-tier architecture, it is not so for a database management system, which has abundant state. The subject of highly available database systems has been studied for more than two decades and there exist many alternative solutions (Agrawal et al., 1997; Cecchet, Candea, & Ailamaki 2008; Drake et al., 2005; Garcia, Rodrigues, & Preguiça, 2011; Kemme, & Alonso, 2000; Patino-Martinez et al., 2005). In this article, we provide an overview of two most popular database high availability strategies, namely database replication and database clustering. The emphasis is given to those

that have been adopted and implemented by major database management systems (Banker 2011; Davies & Fisk, 2006; Vallath 2004).

BACKGROUND A database management system consists of a set of data and a number of processes that manage the data. These processes are often collectively referred to as database servers. The core programming model used in database management systems is called transaction processing. In this programming model, a group of read and write operations on the some data set are demarcated within a transaction. A transaction has the following ACID properties (Gray & Reuter, 1993): •

•

• •

Atomicity: All operations on the data set agree on the same outcome. Either all the operations succeed (the transaction commits), or none of them are (the transaction aborts). Consistency: If the database is consistent at the beginning of a transaction, then the database remains consistent after the transaction commits. Isolation: A transaction does not read or overwrite a data item that has been accessed by another concurrent transaction. Durability: The update to the data set becomes permanent once the transaction is committed.

An example of an atomic transaction is shown in Figure 1. This transaction involves a debit

DOI: 10.4018/978-1-5225-2255-3.ch094 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Figure 1. An example of atomic transactions

operation on a savings account and a credit operation on a checking account. If both operations are successful and the user decides to commit the transaction, the changes to both accounts are made permanent, as shown in Figure 1(a). On the other hand, if the user decides to abort the transaction, the changes to both accounts will be reversed so that the account balances of both accounts are restored to the values at the beginning of the transaction, as illustrated in Figure 1(b). To support multiple concurrent users, a database management system uses sophisticated concurrency control algorithms to ensure the isolation of different transactions even if they access some shared data concurrently (Bernstein et al., 1987). The strongest isolation can be achieved by imposing a serializable order on all conflicting read and write operations of a set of transactions so that the transactions appear to be executed sequentially. Two operations are said to be conflicting if both operations access the same data item and at least one of them is a write operation, and they belong to different transactions. Another popular isolation model is the snapshot isolation. Under the snapshot isolation model, a transaction performs its operations against a snapshot of the database taken at the start of the transaction. The transaction will be committed if the write operations do not conflict with any other transaction that has committed since the snapshot was taken. The snapshot

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isolation model can provide better concurrent execution than the serializable isolation model. A major challenge in database replication, the basic method to achieve high availability, is that it is not acceptable to reduce the concurrency levels. This is in sharp contrast to the replication requirement in some other field, which often assumes that the replicas are single-threaded and deterministic (Castro & Liskov, 2002). To achieve high availability, a database system must try to maximize the time to operate correctly without a fault and minimize the time to recover from a fault. The transaction-processing model used in database management systems has some degree of fault tolerance in that a fault normally cannot corrupt the integrity of the database. If a fault occurs, all ongoing transactions will be aborted on recovery. However, the recovery time would be too long to satisfy the high availability requirement. To effectively minimize the recovery time, redundant hardware and software must be used. Many types of hardware fault can in fact be masked. For example, power failures can be masked by using redundant power supplies, and local communication system failures can be masked by using redundant network interface cards, cables and switches. Storage medium failures can be masked by using RAID (redundant array of inexpensive disks) or similar techniques.

Category: Cloud Computing

To tolerate the failures of database servers, several server instances (instead of one) must be used so that if one fails, another instance can take over. The most common techniques are database replication and database clustering. These two techniques are not completely distinct from each other, however. Database replication is typically used to protect against total site failures. In database replication, two or more redundant database systems operate in different sites, ideally in different geographical regions, and communicate with each other using messages over a (possibly redundant) communication channel. Database clustering is used to provide high availability for a local site. There are two competing approaches in database clustering. One uses a shared-everything (also referred to as shared-disk) design, such as the Oracle Real Application Cluster (RAC) (Vallath 2004). The other follows a shared-nothing strategy, such as the MySQL Cluster (Davies & Fisk, 2006) and most of DB2 shared database systems. To achieve maximum fault tolerance and hence high availability, one can combine database replication with database clustering.

DATABASE REPLICATION Database replication means that there are two or more instances of database management systems, including server processes, data files and logs, running on different sites. Usually one of the replicas is designated as the primary and the rest of the replicas as backups. The primary accepts users’ requests and propagates the changes to the database to the backups. In some systems, the backups are allowed to accept read-only queries. It is also possible to configure all replicas to handle users’ requests directly. But doing so increases the complexity of concurrency control and the risk of more frequent transaction aborts. Depending on how and when changes to the database are propagated across the replicas, there are two different database replication styles, often referred to as eager replication and lazy replica-

tion (Gray & Reuter, 1993). In eager replication, the changes (i.e., the redo log) are transferred to the backups synchronously before the commit of a transaction. In lazy replication, the changes are transferred asynchronously from the primary to the backups after the transactions have been committed. Because of the high communication cost, eager replication is rarely used to protect site failures where the primary and the backups are usually far apart. (Eager replication has been used in some shared-nothing database clusters.)

Eager Replication To ensure strong replica consistency, the primary must propagate the changes to the backups within the boundary of a transaction. For this, a distributed commit protocol is needed to coordinate the commitment of each transaction across all replicas. The benefit for doing eager replication is that if the primary fails, a backup can take over instantly as soon as it detects the primary failure. The most popular distributed commit protocol is the two-phase commit (2PC) protocol (Gray & Reuter, 1993). The 2PC protocol guarantees the atomicity of a transaction across all replicas in two phases. In the first phase, the primary (which serves as the coordinator for the protocol) sends a prepare request to all backups. If a backup can successfully log the changes, so that it can perform the update even in the presence of a fault, it responds with a ‘’Yes’’ vote. If the primary collects ‘’Yes’’ votes from all backups, it decides to commit the transaction. If it receives even a single ‘’No’’ vote, or it times out a backup, the primary decides to abort the transaction. In the second phase, the primary notifies the backups of its decision. Each backup then either commits or aborts the transaction locally according to the primary’s decision and sends an acknowledgment to the primary. As can be seen, the 2PC protocol incurs significant communication overhead. There are also other problems such as the potential blocking if the primary fails after all backups have voted to

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commit a transaction (Skeen, 1981). Consequently, there has been extensive research on alternative eager replication techniques, e.g., the epidemic protocols (Agrawal et al., 1997; Stanoi et al., 1998), and multicast-based approaches (Kemme & Alonso, 2000; Patino-Martinez et al., 2005). However, they have not been adopted by any major commercial product due to their high overhead or complexities.

management system can be dynamically increased by adding more inexpensive nodes while keeping the old equipment. There are two alternative approaches in database clustering. In one approach, which is pioneered in Oracle RAC, adopts a shared-everything architecture. A number of other products choose to use the shared-nothing architecture. Both approaches have their challenges and advantages.

Lazy Replication

Shared Everything Cluster

Most commercial database systems support lazy replication. In lazy replication, the primary commits a transaction immediately. The redo log, which reflects the changes made for the recently committed transactions, is transferred to backups asynchronously. Usually, the backup replicas lack behind the primary by a few transactions. This means that if the primary fails, the last several committed transactions might get lost. Besides the primary/backup replication approach, some database management systems allow a multi-primary configuration where all replicas are allowed to accept update transactions. If this configuration is used with lazy replication, different replicas might make incompatible decisions, in which case, manual reconciliation is required.

In a shared-everything database cluster, all server instances share the same storage device, such as a storage area network. The cluster nodes typically connect to the shared storage device via a fiber channel switch or shared SCSI for fast disk I/O. The shared storage device must also have builtin redundancy such as mirrored disks to mask disk failures. To minimize disk I/O, all server instances share a common virtual cache space. The virtual cache space consists of local cache buffers owned by individual server instances. A number of background processes are used to maintain the consistency of the data blocks in the cache space. These processes are also responsible to synchronize the access to the cached data blocks because only one server instance is allowed to modify a data block at a time. Each server instance has its own transaction logs stored in the shared disk. If a server instance fails, another server instance takes over by performing a roll-forward recovery using the redo log of the failed server instance. This is to ensure that the changes made by committed transactions are recorded in the database and not get lost. The recovery instance also rolls back the transactions that were active at the time of the failure and releases the locks on the resources used by those transactions. The shared-everything design makes it unnecessary to repartition the data, and therefore, easies the tasks of cluster maintenance and management. However, this benefit does not come for free. The most prominent concern is the cost

DATABASE CLUSTERING In recent several years, database clustering has evolved to be the most promising technique to achieve high availability as well as high scalability (Vallath 2004; Davies & Fisk, 2006). Database clustering, as the name suggests, uses a group of computers interconnected by a high speed network. In the cluster, multiple database server instances are deployed. If one instance fails, another instance takes over very quickly so high availability is ensured. Database clustering not only brings high availability, but the scaling-out capability as well. Scaling-out means that the capacity of a database

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Category: Cloud Computing

of inter-node synchronization. Unless high-speed interconnect is used and the workload is properly distributed among the server instances, the internode synchronization might limit the scalability of the cluster. Also, the requirement for high-speed shared disk system also imposes higher financial cost than using conventional disks.

Shared Nothing Cluster In a shared-nothing database cluster, each node runs one or more server instances and has its own memory space and stable storage. Essential to the shared-nothing approach, the data must be partitioned either manually or automatically by the database system across different nodes. Each partition must be replicated in two or more nodes to keep the desired redundancy level. Concurrency control and caching are carried out in each local node, and therefore, they are more efficient than those in shared-everything clusters. However, to ensure the consistency of replicated data and fast recovery, the two-phase commit protocol is often used to ensure atomic commitment of the transactions in the cluster. Comparing with the shared-everything approach, the cost of inter-node synchronization is essentially replaced by that of distributed commit. The shared nothing approach faces the additional challenge of split-brain syndrome prevention (Birman, 2005). The split-brain syndrome may happen if the network partitions, and if each partition makes incompatible decisions on the outcome of transactions or their relative orders. To prevent this problem, typically only the main partition is allowed to survive. The minor partition must stop accepting new transaction and abort active transactions. Usually, the main partition is the one that consists of the majority of the replicas, or the one that contains a special node designated as the arbitration node (Davies & Fisk, 2006).

FUTURE TRENDS Non-Traditional Database Systems An interesting development in database systems is the popularity of NoSQL databases. NoSQL databases are designed to offer highly available storage services for large number of users by using weaker consistency models than those used in traditional relational database systems (Bartholomew 2010). Furthermore, such systems offer explicit user control regarding the placement of the replications. As such, NoSQL databases, such as Dynamo (DeCandia et al., 2007), MongoDB (Chodorow 2013), and Cassandra (Han et. al., 2011) are predominately used for cloud services (Bermbach et al., 2011). In the following, we give a brief overview of the replica placement schemes and replica consistency levels offered by Cassandra. In Cassandra, three replica placement schemes are offered to users. In the default scheme (SimpleStrategy), nodes are organized into a logical ring and nodes that are next to each other on the ring are selected as replicas. In the NetworkTopologyStrategy scheme, users can choose the number of replicas from different datacenters. In the OldNetworkTopologyStrategy scheme, one replica is chosen from one datacenter, and the remaining replicas are chosen from another datacenter. Cassandra offers a variety of replica consistency levels to its users for both read and write operations. The strongest consistency level is ALL, which means the operation (read or write) would be applied to all replicas. For a read-ALL operation, it would fail if one or more replica are unavailable. A slightly weaker level is EACH_QUORUM, where the operation is applied to a quorum of replicas in every datacenter. The next weaker level is QUORUM, where the operation is applied to a quorum of replicas in any one of the datacenters. Another consistency level is

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LOCAL_QUORUM, where a read/write operation would be applied to a quorum of replicas in the datacenter where the coordinator node resides. Furthermore, a user could choose to apply to one, two, or three replicas, as well as to use the SERIAL and LOCAL_SERIAL consistency levels to achieve linearizable consistency.

Beyond Crash Fault Tolerance Existing database systems are designed to tolerate process crash fault and hardware fault. However, considering the increased pace of security breaches, future database management systems must be designed to be intrusion tolerant, i.e., they should provide high availability against a variety of security threats, such as the unauthorized deletion and alteration of database records, the disruption of distributed commit (may cause replica inconsistency), and the exposure of confidential information. To make a database system intrusion tolerant, many fundamental protocols such as the 2PC protocol must be enhanced. There may also be a need to design special tamper-proof storage devices to protect data integrity (Strunk et al., 2000). Even though there have been intensive research in this area (Deswarte et al., 1991; Garcia, Rodrigues, & Preguiça, 2011; Mohan et al., 1983; Prez-Sorrosal et al., 2006; Zhang et al., 2012; Zhao, 2014), the results have rarely been incorporated into commercial products yet. The primary barrier is the high commutation and communication cost, the complexity, and the high degree of replication required to tolerate malicious fault.

CONCLUSION Database systems are the corner stones of today’s information systems. The availability of database systems largely determines the quality of service provided by the information systems. In this article, we provided a brief overview of the state of the art database replication and clustering techniques. For

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many, a low-cost shared-nothing database cluster that uses conventional hardware might be a good starting point towards high availability. We envisage that future generation of database management systems will be intrusion tolerant, i.e., they are capable of continuous operation against not only hardware and process crash fault, but a variety of security threats as well.

REFERENCES Agrawal, D., El Abbadi, A., & Steinke, R. C. (1997). Epidemic algorithms in replicated databases. Proceedings of the ACM Symposium on Principles of Database Systems. Tucson, AZ: ACM Press. Banker, K. (2011). MongoDB in action. Manning Publications Co. Bartholomew, D. (2010). SQL vs. NoSQL. Linux Journal, 2010(195), 4. Bermbach, D., Klems, M., Tai, S., & Menzel, M. (2011, July). Metastorage: A federated cloud storage system to manage consistency-latency tradeoffs. In Cloud Computing (CLOUD), 2011 IEEE International Conference on (pp. 452-459). IEEE. doi:10.1109/CLOUD.2011.62 Bernstein, P. A., Hadzilacos, V., & Goodman, N. (1987). Concurrency Control and Recovery in Database Systems. Reading, MA: Addison-Wesley. Birman, K. (2005). Reliable Distributed Systems: Technologies, Web Services, and Applications. Springer-Verlag. Cecchet, E., Candea, G., & Ailamaki, A. (2008, June). Middleware-based database replication: the gaps between theory and practice. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data (pp. 739-752). ACM. doi:10.1145/1376616.1376691 Chodorow, K. (2013). MongoDB: the definitive guide. O’Reilly.

Category: Cloud Computing

Davies, A. & Fisk, H. (2006). MySQL Clustering. MySQL Press. DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A.,... Vogels, W. (2007, October). Dynamo: Amazon’s highly available key-value store. In SOSP (Vol. 7, pp. 205-220). Academic Press. Deswarte, Y., Blain, L., & Fabre, J. C. (1991). Intrusion tolerance in distributed computing systems. Proceedings of the IEEE Symposium on Research in Security and Privacy. Oakland, CA: IEEE Computer Society Press. Drake, S., Hu, W., McInnis, D. M., Sköld, M., Srivastava, A., Thalmann, L., & Wolski, A. (2005). Architecture of highly available databases. In Service Availability (pp. 1–16). Springer Berlin Heidelberg. doi:10.1007/978-3-540-30225-4_1 Garcia, R., Rodrigues, R., & Preguiça, N. (2011, April). Efficient middleware for byzantine fault tolerant database replication. In Proceedings of the sixth conference on Computer systems (pp. 107-122). ACM. doi:10.1145/1966445.1966456 Gray, J., & Reuter, A. (1993). Transaction Processing: Concepts and Techniques. San Mateo, CA: Morgan Kaufmann Publishers. Han, J., Haihong, E., Le, G., & Du, J. (2011, October). Survey on NoSQL database. In Pervasive computing and applications (ICPCA), 2011 6th international conference on (pp. 363-366). IEEE. Kemme, B., & Alonso, G. (2000). A new approach to developing and implementing eager database replication protocols. ACM Transactions on Database Systems, 25(3), 333–379. doi:10.1145/363951.363955

Mohan, C., Strong, R., & Finkelstein, S. (1983). Method for distributed transaction commit and recovery using Byzantine agreement within clusters of processors. Proceedings of the ACM Symposium on Principles of Distributed Computing. Montreal, Quebec, Canada: ACM Press. doi:10.1145/800221.806712 Patino-Martinez, M., Jimenez-Peris, R., Kemme, B., & Alonso, G. (2005). Middle-R: Consistent database replication at the middleware level. ACM Transactions on Computer Systems, 23(4), 375–423. doi:10.1145/1113574.1113576 Prez-Sorrosal, F., Patino-Martinez, M., JimenezPeris, R., & Vuckovic, J. (2006). Highly available long running transactions and activities for J2EE applications. IEEE International Conference on Distributed Computing Systems. Lisboa, Portugal: IEEE Computer Society Press. doi:10.1109/ ICDCS.2006.47 Skeen, D. (1981). Nonblocking commit protocols. Proceedings of the ACM International Conference on Management of Data. Ann Arbor, MI: ACM Press. Stanoi, I., Agrawal, D., & El Abbadi, A. (1998). Using broadcast primitives in replicated databases. Proceedings of the IEEE International Conference on Distributed Computing Systems. Amsterdam, Netherlands: IEEE Computer Society Press. Strunk, D., Goodson, G., Scheinholtz, M., Soules, C., & Ganger, G. (2000). Self-securing storage: protecting data in compromised systems. Proceedings of Symposium on Operating Systems Design and Implementation. San Diego, CA: USENIX Association. Vallath, M. (2004). Oracle real application clusters. Elsevier.

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Zhang, H., Chai, H., Zhao, W., Melliar-Smith, P. M., & Moser, L. E. (2012). Trustworthy coordination of Web services atomic transactions. IEEE Transactions on Parallel and Distributed Systems, 23(8), 1551–1565. doi:10.1109/TPDS.2011.292 Zhao, W. (2014). A Novel Approach to Building Intrusion Tolerant Systems. International Journal of Performability Engineering, 10(2), 123. Zhao, W., Moser, L. E., & Melliar-Smith, P. M. (2005). Unification of transactions and replication in three-tier architectures based on CORBA. IEEE Transactions on Dependable and Secure Computing, 2(1), 20–33. doi:10.1109/TDSC.2005.14

Castro, M., & Liskov, B. (2002). Practical Byzantine fault tolerance and proactive recovery. ACM Transactions on Computer Systems, 20(4), 398–461. doi:10.1145/571637.571640 Cattell, R. (2011). Scalable SQL and NoSQL data stores. SIGMOD Record, 39(4), 12–27. doi:10.1145/1978915.1978919 Chai, H., Zhang, H., Zhao, W., Melliar-Smith, P. M., & Moser, L. E. (2013). Toward trustworthy coordination of Web services business activities. IEEE Transactions on Services Computing, 6(2), 276–288. doi:10.1109/TSC.2011.57

ADDITIONAL READING

Escriva, R., Wong, B., & Sirer, E. G. (2012). HyperDex: A distributed, searchable key-value store. Computer Communication Review, 42(4), 25–36. doi:10.1145/2377677.2377681

Abadi, D. (2012). Consistency tradeoffs in modern distributed database system design: CAP is only part of the story. Computer, 45(2), 37–42. doi:10.1109/MC.2012.33

Gray, J., Helland, P., ONeil, P., & Shasha, D. (1996, June). The dangers of replication and a solution. [). ACM.]. SIGMOD Record, 25(2), 173–182. doi:10.1145/235968.233330

Barber, R. J., Herbert, D. M., Mohan, C., Somani, A., Watts, S. J., & Zaharioudakis, M. (2001). U.S. Patent No. 6,173,292. Washington, DC: U.S. Patent and Trademark Office.

Han, J., Song, M., & Song, J. (2011, May). A Novel Solution of Distributed Memory NoSQL Database for Cloud Computing. In Computer and Information Science (ICIS), 2011 IEEE/ACIS 10th International Conference on (pp. 351-355). IEEE. doi:10.1109/ICIS.2011.61

Birman, K. P., Freedman, D. A., Huang, Q., & Dowell, P. (2012). Overcoming cap with consistent soft-state replication. Computer, 45(2), 50–58. doi:10.1109/MC.2011.387 Blundell, C., Lewis, E. C., & Martin, M. (2005, June). Deconstructing transactional semantics: The subtleties of atomicity. In Annual Workshop on Duplicating, Deconstructing, and Debunking (WDDD) (pp. 48-55). Borr, A. J. (1984, August). Robustness to Crash in a Distributed Database: A Non Shared-Memory Multi-Processor Approach. In VLDB (pp. 445453). Brewer, E. (2012). CAP twelve years later: How the. Computer, 45(2), 23–29. doi:10.1109/ MC.2012.37

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Lent, B., Swami, A., & Widom, J. (1997, April). Clustering association rules. In Data Engineering, 1997. Proceedings. 13th International Conference on (pp. 220-231). IEEE. doi:10.1109/ ICDE.1997.581756 Levandoski, J. J., Lomet, D. B., Mokbel, M. F., & Zhao, K. (2011). Deuteronomy: Transaction Support for Cloud Data. In CIDR (pp. 123-133). Lloyd, W., Freedman, M. J., Kaminsky, M., & Andersen, D. G. (2011, October). Don’t settle for eventual: scalable causal consistency for wide-area storage with COPS. In Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles (pp. 401-416). ACM. doi:10.1145/2043556.2043593

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Malkhi, D., & Reiter, M. (1997). Byzantine quorum systems. Proceedings of the ACM Symposium on Theory of Computing, El Paso, Texas: ACM Press. 569 – 578. Membrey, P., Plugge, E., & Hawkins, T. (2010). The definitive guide to MongoDB: the noSQL database for cloud and desktop computing. Apress. Padhy, R. P., Patra, M. R., & Satapathy, S. C. (2011). RDBMS to NoSQL: Reviewing Some Next-Generation Non-Relational Databases. International Journal of Advanced Engineering Science and Technologies, 11(1), 15–30. Ramakrishnan, R., & Gehrke, J. (2000). Database management systems. Osborne/McGraw-Hill. Shapiro, M., Preguiça, N., Baquero, C., & Zawirski, M. (2011). Conflict-free replicated data types. In Stabilization, Safety, and Security of Distributed Systems (pp. 386-400). Springer Berlin Heidelberg. doi:10.1007/978-3-642-24550-3_29 Stonebraker, M. (2010). Errors in Database Systems, Eventual Consistency, and the CAP Theorem. Communications of the ACM, BLOG@ ACM. Thomson, A., Diamond, T., Weng, S. C., Ren, K., Shao, P., & Abadi, D. J. (2012, May). Calvin: fast distributed transactions for partitioned database systems. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (pp. 1-12). ACM. doi:10.1145/2213836.2213838 Wada, H., Fekete, A., Zhao, L., Lee, K., & Liu, A. (2011, January). Data Consistency Properties and the Trade-offs in Commercial Cloud Storage: the Consumers’ Perspective. In CIDR (Vol. 11, pp. 134-143). Wiesmann, M., Pedone, F., Schiper, A., Kemme, B., & Alonso, G. (2000). Understanding replication in databases and distributed systems. In Distributed Computing Systems, 2000. Proceedings. 20th International Conference on (pp. 464-474). IEEE. doi:10.1109/ICDCS.2000.840959

Zhao, W. (2014). Building dependable distributed systems. John Wiley & Sons. doi:10.1002/9781118912744 Zhao, W. (2015). Optimistic Byzantine fault tolerance. International Journal of Parallel, Emergent and Distributed Systems, 1-14.

KEY TERMS AND DEFINITIONS Database Cluster (Shared-Everything, Shared-Nothing): A database management system runs on a group of computers interconnected by a high speed network. In the cluster, multiple database server instances are deployed. If one instance fails, another instance takes over very quickly to ensure high availability. In the shared-everything design, all nodes can access a shared stable storage device. In the shared-nothing design, each node has its own cache buffer and stable storage. Database Recovery (Roll-Backward, RollForward): Recovery is needed when a database instance that has failed is restarted or a surviving database instance takes over a failed one. In roll-backward recovery, the active transactions at the time of failure are aborted and the resourced allocated for those transactions are released. In roll-forward recovery, the updates recorded in the redo log are transferred to the database so that they are not lost. Database Replication (Eager, Lazy): Multiple instances of a database management system are deployed in different computers (often located in different sites). Their state is synchronized closely to ensure replica consistency. In eager replication, the updates are propagated and applied to all replicas within the transaction boundary. In lazy replication, the changes are propagated from one replica to others asynchronously. High Availability (HA): The capability of a system to operate with long uptime and to recover quickly if a failure occurs. Typically, a highly available system implies that its measured uptime is

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five nine (99.999%) or better, which corresponds to 5.25 minutes of planned and unplanned downtime per year. NoSQL Database: This is a database that adopts a data storage and retrieval mechanism that is different from that used in traditional relational databases. The actual mechanisms differ in different NoSQL databases, such as key-value stores, document store, and graph, etc. Transaction: A transaction is a group of read/ write operations on the some data set that succeed or fail atomically. More accurately, a transaction

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has the atomicity, consistency, isolation and durability properties. Two-Phase Commit (2PC) Protocol: This protocol ensures atomic commitment of a transaction that spans multiple nodes in two phases. During the first phase, the coordinator (often the primary replica) queries the prepare status of a transaction. If all participants agree to commit, the coordinator decides to commit. Otherwise, the transaction is aborted. The second phase is needed to propagate the decision to all participants.

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From Information Systems Outsourcing to Cloud Computing Mohammad Nabil Almunawar Universiti Brunei Darussalam, Brunei Hasan Jawwad Almunawar P. T. Tegar Kupas Mediatama, Indonesia

INTRODUCTION Outsourcing is a business term to describe a mechanism in which a company utilizes services provided by another company, normally through a contract, to fulfill some of its required business resources or functions. Outsourcing is commonly practiced by business organizations, as it is believed that it can cut costs and simplify management. For service providers, outsourcing gives them a long-term source of revenue. Nowadays most business organizations outsource some part of their business operations. One of the most common is information systems (IS) outsourcing. This may range from computer maintenance, website development and maintenance, e-Business to the whole IS function (Dibbern, Goles, Hirschheim, & Jayatilaka, 2004). Actually, IS outsourcing is an old story which started as early as 1963 when Frito-Lay and Blue Cross & Blue Shield outsourced their data processing jobs to Electronic Data Systems (Lacity & Hirschheim, 1993). In fact, Eastman Kodak outsourced the whole of its IS functions to IBM, DEC and Businessland in 1989, 25 years ago (Gupta & Gupta, 1992). In the early stages of IS outsourcing, the issue being addressed in business organizations was whether they should outsource. Over time, the issue was no longer on whether to outsource or not to outsource, but how much to outsource (Lee, Huynh, Kwok, & Pi, 2003). This indicates that IS outsourcing has been adopted by many business organizations.

The advancement of Internet technology, especially the Web as well as high-speed and broadband access to the Internet, enabled a new computing model, “cloud” computing. The new model allows organizations to outsource some components or whole of their IS in the cloud that can be controlled and utilized from anywhere with a web browser. With this model organizations do not need to purchase hardware and expensive software licenses and surely they do not need to worry about software and hardware maintenance, which is normally a large portion of the total ownership costs of an IS to estimate the overall cost (direct and indirect) of an IS in a given time frame. Cloud computing vendors normally offer a pay-per-use method for their services, making cloud computing services like paying utilities. Perhaps cloud computing is the realization of McCarthy’s dream of utility computing, a package of computing resources that can be rented or subscribed just like other utilities (Garfinkel, 2011). What makes the cloud computing system different from conventional computing systems? In conventional computing systems (mainframe, client-server or personal computer systems), most of the computing resources owned by an organization normally reside in the organization’s premises. The organization has to manage these resources to make sure they can be utilized to support the organization in attaining its goals. The organization incurs all costs in owning these resources, which may include investment, operation and maintenance costs. In contrast, an organization

DOI: 10.4018/978-1-5225-2255-3.ch095 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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does not need to own most of the computing resources in a cloud computing system. Instead, the organization utilizes computing resources offered by a provider and accesses the resources as needed. The organization only needs to own client devices (low cost terminals or thin clients) to utilize the computing resources through the Internet. Consequently, the organization does not need to bear the burden of all the costs mentioned previously. Of course, the organization needs to pay the provider for using the resources with a pay-per-use method of payment. The numbers of providers offering various computing resources in the cloud are growing and some big players include IBM, Amazon.com, Google and Microsoft. These companies foresee a lucrative business in cloud computing as it offers a new business model that may attract many customers. There are three types of customers: small organizations, medium and large organizations, and consumers. However, there are some adoption issues that need to be addressed properly by providers (Kim, Kim, Lee, & Lee, 2009). This chapter discusses concepts and applications of cloud computing. The history of the development as well as some related computing concepts such as grid computing will be highlighted. Advantages and disadvantages of cloud computing, including several issues, including adoption issues will be discussed. Future direction will be presented in the last part of this chapter. The next section will discuss the development of cloud computing, computing models and available services. Section 3 will focus more on core technology, business model and related issues, including some criticisms of cloud computing. Section 4 is the future direction and the last section (Section 5) is the conclusion.

BACKGROUND IS outsourcing has evolved to cloud computing where many business organizations see that it is a good option to outsource their computing re-

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sources to cloud computing providers. There are many definitions of cloud computing. A study on definitions of cloud computing (Vaquero, RoderoMerino, Caceres, & Lindner, 2009) found there are at least 20 definitions. This study summarizes three necessary components of cloud computing: a large pool of computing resources accessible through a computer network, dynamically and scalable resource allocations, and a pay-per-use method of payment. The National Institute of Standards and Technology (NIST) at the U.S. Department of Commerce provides a short definition for cloud computing: “Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.” (Mell & Grance, 2011). Figure 1 illustrates a cloud computing models where clients’ machines access computing resources offered by cloud providers. The client machines can be desktops, laptops, smart phones, terminals or thin client machines that access the computing resources (normally computer servers) offered by cloud providers through the Internet or other networks. There are many services that can be provided through cloud computing. In general, these services can be grouped into three service models: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Organizations can outsource their IS through these three models. We will discuss these services with examples in the next section. There are some pros and cons of cloud computing. However, many experts concur with McCarthy’s prediction: “Computing may someday be organized as a public utility just as the telephone system is a public utility” (Garfinkel, 2011). At the consumer level, people, especially mobile users access software and storage online as they are connected to the Internet most of the time. According to Pew Internet report on the future of cloud computing (Anderson & Rainie, 2010),

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Figure 1. The cloud computing model

a solid majority of experts and stakeholders who participate in the survey on expected future of the Internet by 2020 agree that most people will access software applications and share information through the Internet using remote servers rather than local machines. Cloud computing will become dominant and will overtake desktops in the next decade. We are witnessing smart phones overtake desktops and laptops at the consumer level. People use their smart phones to access the cloud, from sharing information through social networking sites (such as Facebook, LinkedIn, WhatsApp) to processing and sharing documents using Google Docs.

Information Systems Outsourcing and Cloud Computing As highlighted in the introduction, IS outsourcing is not new. It started in the 60’s and became a hot issue when Eastman Kodak outsourced the whole function of its IS. However, there were some successes and failures in implementing IS outsourcing and organizations need to carefully consider before outsourcing their IS to vendors (Lacity & Hirschheim, 1993).

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Outsourcing is far more complex than is generally understood and IS outsourcing is even more complex. Many pros and cons have been identified and discussed in the literature (Gupta & Gupta, 1992; Palvia, 1995; Vitharana & Dharwadkar, 2007; Weidenbaum, 2005). Gonzalez (2009) studied the reasons and risks of IS outsourcing and confirmed them through surveys on large companies in Spain. With the increasing adoption of cloud computing, rapid changes in IS outsourcing is expected, as cloud computing is its latest trend (Dhar, 2012). The development of cloud computing can be traced back to mainframe computing. In fact, virtualization – creating several virtual versions from one entity, the core technology in cloud computing, is not new as it is part of mainframe technology (Zhang, Cheng, & Boutaba, 2010). Mainframe computing relies of a powerful machine located in a specialized room. The machine is accessed via dumb terminals connected to it. The two closely related computing models with cloud computing is grid and utility computing. Grid computing is a middleware consisting of interconnected heterogeneous computer systems in a high-speed network to solve computationintensive problems. The idea was developed to

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solve scientific problems that need intensive and high power computations in a low cost virtual supercomputer as compared to a high cost supercomputer (Buyya & Venugopal, 2005; Weinhardt et al., 2009). Utility computing is a computing model where the available computing resources can be rented based on a pay-per-use payment model. Essentially cloud computing uses the clientserver computing model where the servers belong to the providers. Users can access services offered by cloud computing servers remotely over a network (normally the Internet) through their client machines (thin client machines should be enough). To create powerful computing power it also uses the grid computing model connecting many servers using a computer network. However, unlike grid computing that connects computers from many organizations for a sharing purpose, servers in cloud computing normally belong to an organization (provider) for a commercial purpose. Virtualization is a key enabler of cloud computing. Virtualization creates virtual versions of a real thing. Virtualization of a machine normally creates virtual versions of the machine, meaning several independent virtual machines for different purposes can be generated. In cloud computing, virtualization is used to generate virtual servers or virtual resources (such as storage) dynamically. Therefore, multiple virtual machines (possibly with different operating systems) can be generated and muted on-demand on a single machine, creating optimal flexibility (Buyya, Yeo, Venugopal, Broberg, & Brandic, 2009; Rosenthal et al., 2010; Zhang et al., 2010). Most of the literature in cloud computing classified services offered into three clusters (Figure 2). Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) (Badger, Grance, Patt-Corner, & Voas, 2012; Sultan, 2009; Zhang et al., 2010). IaaS providers normally possess a myriad of servers in their data centers. For example, Amazon Elastic Computer Cloud (EC2) cloud has 454,400 Linux servers (Liu, 2012) in its data centers. With

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virtualization technology, EC2 can create millions of virtual servers and one or more virtual servers can serve an organization. Besides virtual servers, related services such network-accessible storages and network infrastructure components are provided on-demand (Badger et al., 2012). PaaS providers offer the infrastructure as well as application development platform. All tools for development of applications are provided by PaaS providers, including automation in designing, deploying, testing and administering applications to simplify application development (Badger et al., 2012; Zhang et al., 2010). The most common type of cloud computing service is SaaS, accessible through the Web. Normally users access available applications ondemand using a web browser and pay the service based on the usage (per execution, per record processed, etc.). There are many SaaS vendors offering their services on the Web. Some examples are Google Apps from Google, iCloud from Apple and Adobe Creative Cloud from Adobe. We interact with cloud applications every day such as Facebook, Twitter, YouTube, Wikis, and email (such as Live, Yahoo mail or Gmail). These applications are deployed for public or public cloud. There are four deployment models of cloud computing namely public cloud, private cloud, community cloud, and hybrid cloud (Mell & Grance, 2011; Subashini & Kavitha, 2011). A public cloud as mentioned above is cloud infrastructure for general public use. The public has no control on the infrastructure, platform, or the applications. The cloud can be owned or managed by individuals, academic institutions, private or public organizations. A private cloud is cloud infrastructure exclusively owned and used by an organization to serve its intended users (such as workers or customers). The owner of the cloud (depending on the service model) has some control of the cloud. A community cloud is a cloud infrastructure for a particular community serving its members. It may be owned and managed by one or more entities in the community. A hybrid cloud is composed of two or three combinations

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Figure 2. Cloud computing services

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Source: Adapted from Yankee Group

of cloud deployment models, such as combination of private and public clouds, which serve different purposes but have same look and feel such as Google Apps.

BENEFITS, ISSUES, AND CHALLENGES OF OUTSOURCING IN THE CLOUD The IS or IT outsourcing market is growing. According to Gartner, global IT outsourcing is predicted to reach $ 288 billion in 2013, a slight increase (2.8 percent) from the previous year. Annual growth of the market is forecast to grow 5.4 percent. It was mentioned that cloud computing has taken away IS/IT outsourcing and caused downward pricing on IT services (Gartner, 2013; Overby, 2013). The growth of the public cloud computing market is much faster than IS/IT outsourcing in general. The global public cloud market is forecast to grow 18.5 percent in 2013 with total $131 billion as compared to the previous year $111 billion and IaaS segment market is predicted growing faster, 42.4 percent according to Gartner (2013). According to 451 Research, the market size of enterprise cloud computing is expanding rapidly, much faster than the expansion of IT (451 Research, 2013). The annual growth of enterprise cloud computing is predicted to be 36

percent (Columbus, 2013b). Global spending in IaaS is predicted to reach $16.5 billion in 2015. SaaS market will grow globally, from $49 billion in 2015 to $67 billion by 2018. Cloud applications have been dominating mobile data traffic globally, 81% in 2014 and reaching 90% in 2019 (Columbus, 2015). The rapid growth of the cloud computing market is the result of growing adoption of cloud computing by the public as well as business organizations properly (Kim, Kim, Lee, & Lee, 2009). There are some obvious advantages of cloud computing, which influence its adoption. The following are some of these advantages (Armbrust et al., 2009; Buyya, Yeo, Venugopal, Broberg, & Brandic, 2009; Zissis & Lekkas, 2012).

Some Benefits of Cloud Computing The first and important benefit of cloud computing is simplicity and flexibility. In a cloud computing environment, users are not required to own hardware other than terminals for connection, no software licenses needed hence users are free from the hassle of maintenance. Unlike the traditional computing model, users can scale-up and scale-down the usage as needed. Cloud computing providers normally upgrade their systems to improve services and the users should benefit from the ever up-to-date technology, both hardware and software.

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The second benefit is organizational agility. Organizational agility is the ability of an organization to adapt quickly and effectively to the changing environment, such as rapid and effective response to customers’ preferences while achieving strategic adaptation (adapting to trends and issues) in the medium term and long term shaping. As computing resources can be utilized in a very short time and portable applications or services can be developed quickly, cloud computing supports organizational agility. In addition, all services provided can be accessed any time globally with various client devices. The third benefit is reliability and availability. Each cloud has a myriad of servers at different sites. Redundancy can be implemented to increase reliability and availability. The traditional computing systems rely on one or a few servers. Problems in these servers or even upgrading the servers may interrupt services. It is common that users of traditional computing systems receive messages from their system administrators that there is an interruption of services for a few hours because of maintenance. To ensure reliability and availability, conventional computing systems use redundant data centres. However, this is costly. A cloud computing service provider will definitely ensure high reliability and availability of its services as this is at the root of its credibility. In cloud computing systems, maintenance or server upgrades will not affect the services as they are addressed by other servers seamlessly. The fourth benefit is cost reduction. Cost reduction on IT spending is highly expected as cloud computing can eliminate many conventional IT costs such as investment costs of hardware and software licenses as well as maintenance cost. As both investment costs and maintenance costs can be removed from IT spending, overal IT expenses in cloud computing is cheaper that conventional computing. The fifth benefit is scalable infrastructure. In the cloud computing system, new hardware and new nodes can be added easily with limited modification to infrastructure setup and software

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as the cloud architecture is designed to accommodate horizontal and vertical expansions. Also, the scale of the service can be expanded as requested. Hence, users can start with small and relatively inexpensive services and then expand the services as needed later. The sixth benefit is seamless upgrading and migration to new technology. Technology, especially IT, keeps changing. It is hard to keep pace with the fast changing technology. However, it is important to keep updated with the latest technology as it helps improve computing performance. Updating to the latest technology is expensive and migration to a new system is not simple. Frequently, a migration requires a shutdown for a while, followed by a testing of the new system, which can create interruption of services. Upgrading and migration to new technology in cloud computing is seamless and the interruption of services is not necessary. Cloud computing providers normally keep their system up to date and the system update will have no implication for services.

Some Issues, Challenges, And Possible Outcomes Although many advantages of cloud computing are discussed in the literature, there are many problems, issues and challenges as well such as issues on security, transparency and trust. We will discuss some issues, risks, and challenges of cloud computing shortly followed by possible solutions. Since the space is limited, we will only raise some important ones. We encourage the reader to find papers in the references or reading list for more related information.

Trust The first and obvious issue is users (customers of cloud computing) lose control of their data. This can be a big hurdle for the adoption of cloud computing by both public and private organizations. With the conventional computing systems, all data and information are stored in the organizations’

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server, policy of access on the data are established and enforced locally. Organizations make sure their information resources, especially data and information are protected from unauthorized access. Can organizations trust cloud providers to protect data on the same level of protection as if the data was stored locally? This is a very difficult question to deal with. Trust cannot be established easily, especially if there is no third party that can guarantee the security and privacy of data or information stored in the cloud. Everett (2009) discussed the issue of trust in cloud computing. One possible solution of this problem is to establish an assurance by an independent party through a certification or accreditation (such as ISO 27001 or SAS 70). An assurance from an independent third party can help establish trust between a provider and its customers.

Security The second issue, which is highly related to the first issue, is security and privacy. This issue is also the issue of conventional computing, however, since users and their data are geographically separated and the users access the data through an open network such as the Internet, the issue highly affects the users’ confidence in the cloud (Armbrust et al., 2010; Zhu, 2010). The security problems may happen in servers within the cloud, the client machines, and the network and each service model has its security issues. Subashini and Kavitha (2011) classify security issues on cloud computing into four categories: security related to third party resources, application security, data transmission security, and data storage security. The security issues, especially on information security, can be systematically addressed using three components of information security: confidentiality, integrity, and availability (CIA). Confidentiality on data and information belonging to customers (users) is to protect the privacy of the data and information. Protecting privacy of data in conventional computing system is a technical challenge and the challenge is more complicated

in the cloud computing environment, which is distributed, and the customers are not aware of the location of data and access authorization. Sharing resources, although logically separated, if not taken care properly may pose another risk related to confidentiality of data (Badger et al., 2012). There are some possible solutions on confidentiality of data. To keep the privacy of data, especially the sensitive ones, data must be stored in an encrypted form in the cloud storage. This may slow down data processing, as data need to be decrypted before processing. It is important to make sure whether the cloud provider has a strong encryption algorithm ready to be used. Otherwise, customers need to have their own encryption to protect their data. To avoid unintended loss of the logical separation of resource, customers may need to rest physical resource exclusively instead of rely on virtual machines allocated by the provider. On the integrity of data or information (accuracy and consistency of stored data or information), cloud computing poses complex challenges. In a single standalone system, data integrity can be preserved by a database system through atomicity, consistency, isolation, and durability (ACID). The distributed nature of cloud computing makes data integrity a difficult issue (Abadi, 2009; Subashini & Kavitha, 2011) and cloud computing does not have assurance of data integrity. A new protocol that can solve data integrity on the cloud is needed. Kumar & Poornima (2012) proposes encoding data and data recovery that can ensure data integrity in the cloud. Availability of data when needed is crucial. Just imagine what will happen when the data is needed for an urgent matter but the cloud is not available, neither applications nor data can be accessed. There are several possible causes of this problem; among others are attacks of denial of service (DOS), outage (including power failures, target of regulatory actions, or out of business), or simply storage problems in the cloud. Data redundancy and replication, including local replication of data can help to solve the problem.

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There are some other issues on security. A survey on cloud computing security issues can be seen in (Subashini & Kavitha, 2011) and further discussion on security issues of cloud computing can be seen in (Jansen & Grance, 2011; Roberts & Al-Hamdani, 2011).

Lock-In and Interoperability One of the business strategies of providers is to create switching cost to lock in customers so that they pay high prices to switch to another provider. Although interoperability among platforms may be supported by vendors, application program interfaces (APIs) for cloud-storage systems are proprietary and there is no standard for the APIs in the horizon (Armbrust et al., 2010; Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). According to Stallman (2008) customer lock-in on proprietary systems is a business trap since providers can change the price of the service at will. In the worst situation, the provider goes out of business, leaving customers wondering how to deal with their data stored in a proprietary format as the case of Coghead in 2009 (Marks, 2009). To avoid customer lock-in, standardization on APIs for storage systems is needed to allow interoperability among cloud providers.

Integration It is highly likely that an organization will have multiple public clouds or hybrid clouds. Each cloud has its own data and APIs and the integration of applications or processes and data is needed. In addition, the existing legacy systems need to be integrated as well. Technology for such integration exists such as federated or heterogeneous database systems and enterprise application integration. These technologies can be adapted for the integration of clouds (Kim et al., 2009). There are many works to solve this issue; in fact some major vendors such as IBM and Oracle have offer their solutions, however, the complexity of the problem is not easy to solve as it may involve

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other issues such security, scalability, management, and open platforms.

Performance The distance between client machines and servers in the cloud and the network speed highly affect the performance of computation. The possibility of data transfer bottlenecks as the intensity of data processing and transfer as well as the number of users accessing the data increase may complicate the performance and costs as data transfer consumes the communication bandwidth (Armbrust et al., 2010; Kim et al., 2009). If the slow performance cannot be tolerated, the customers need to ask the provider to increase the bandwidth of data transfer. If we assume the provider can scale-up its infrastructure, the customer only incurs the additional cost for the scale-up. However, if the provider is unable to fulfill the demand as the scale-up may be beyond their expectations, things become complicated. Disk access for data-intensive computation can be another source of performance problem as the I/O operation is much slower than CPUs and main memories (possibly shared by many virtual machines) and disks most likely shared by many users may cause unpredictable performance. Perhaps flash memory can be a solution, as the price is getting cheaper, consumes less energy and is much faster than disks (Armbrust et al., 2010).

FUTURE RESEARCH DIRECTIONS Establishing trust between cloud providers and their customers is one of key success factors of cloud computing adoption. Trusted third parties can help increase the level of trust. Cloud computing governance model needs to be developed, as this can be used as a framework to strengthen the relationship a cloud provider has with its customers. As the interest in the cloud computing is increasing and business in cloud computing is

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growing, standardization of APIs and data format need to be developed to support interoperability and integration of the clouds. It can also remove vendor lock in impression. Perhaps XML data format is a good candidate for storing data in the clouds’ storages as demonstrated by Knight (2009) in integrating Salesforce data. The large penetration of small mobile devices (SMD) such tablet computers and smartphones globally has changed the way people interact and communicate. Users of these devices are connected to the Internet and they use cloud computing for many purposes, such as social networking, emailing, blogging, gaming, and storing information in the cloud storage. Mobile cloud computing is growing very fast most public clouds will be accessed by SMD. A recent survey in mobile cloud computing is discussed in (Fernando, Loke, & Rahayu, 2013). The integration of mobile and cloud computing is an interesting research area as it allows SMD to access powerful applications in the cloud. Research on how education uses cloud computing so far and how cloud computing can support education will be very interesting, including mobile learning through the cloud and scientific research in the cloud. There are many opportunities created by cloud computing for education. Some cloud-computing vendors such as IBM and Google actively promote cloud computing as research tools and there are growing adoption of cloud computing by education institutions (Sultan, 2009). Most of the APIs in cloud computing are proprietary and this raises concern of free/open source software communities. In fact, Richard Stallman (2008), the founder of Free Software Foundation, accused that cloud computing is another lock-in trap of proprietary systems with a different model. This accusation could be true as the APIs and data formats are proprietary as discussed above. Perhaps the open source community needs to create Open Cloud Computing fully supported by open source software using a grid computing model for the hardware connections.

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Looking back at the development of IS technologies and methodology, it is apparent that the advent of cloud computing as it is today is a logical and inevitable advancement. We see that the everchanging world of technology and globalization necessitates that businesses keep their information systems up to date and remotely accessible, and those who cannot keep up risk going out of business. Hence, the turn towards outsourcing their information systems to take advantage of a system that is scalable, never obsolete, secure and costs less. The option to employ a cloud computing service, though, is not without its risks and questions. Will the data stored be secure from the likes of hackers and espionage? Can the providers be trusted with the privacy and sensitivity of the information? Can they keep their systems up to date? What if the provider itself goes out of business? Businesses must have full confidence in their providers before fully outsourcing their information systems. Nowadays with smart phones becoming more and more powerful yet affordable, cloud computing will surely soon dominate, with desktops becoming less of a necessity. With the market for IS outsourcing in the hundreds of billions of dollars and expanding, more issues become apparent. Standardization must be established with regards to the data format and API’s to prevent from customer lock-ins. This would in turn reduce the dependency of individuals and businesses on a single provider, reducing the risks associated with such a choice as outsourcing their IS. The development of cloud computing is in full swing, and there is no question that as humans become more mobile and connected, the need for robust and secure cloud computing systems is becoming more important. Its vast effect on society is apparent in the popularity of web-based applications such as e-mail and social networking. New businesses want to setup their information systems to be web-based and accessible from thin

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clients, and the developers of the web applications for these businesses want to develop their applications using computing resources in the cloud. For some, there is no choice but to employ a cloud computing solution. In fact, all must use a form of cloud computing in order to stay competitive in today’s world, and this is the leading factor why it is predicted that the dominance of cloud computing will soon overtake desktop computers.

REFERENCES 451Research. (2013, August). Market Monitor: Cloud Computing. Retrieved on November 10, 2013, from https://451research.com/marketmonitor-cloud-computing Abadi, D. J. (2009). Data Management in the Cloud: Limitations and Opportunitie s. A Quarterly Bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering, 32(1), 3–12. Anderson, J. Q., & Rainie, L. (2010). The future of cloud computing. Pew Internet. Retrieved from http://pewInternet.org/Reports/2010/The-futureof-cloud-computing.aspx Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., … Zaharia, M. (2009). Above the Clouds: A Berkeley View of Cloud Computing (No. Technical Report No. UCB/EECS-2009-28). Electrical Engineering and Computer Sciences, University of California at Berkeley. Retrieved from http://www.eecs. berkeley.edu/Pubs/TechRpts/2009/EECS-200928.html Armbrust, M., Stoica, I., Zaharia, M., Fox, A., Griffith, R., Joseph, A. D., & Rabkin, A. et  al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50. doi:10.1145/1721654.1721672

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Badger, L., Grance, T., Patt-Corner, R., & Voas, J. (2012). Cloud Computing Synopsis and Recommendations (No. Special Publication 800-146). Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), 599–616. doi:10.1016/j.future.2008.12.001 Columbus, L. (2013a, July 10). Why salesforce is winning the cloud platform war. Forbes. Retrieved November 9, 2013, from http://www.forbes.com/ sites/louiscolumbus/2013/10/07/why-salesforceis-winning-the-cloud-platform-war/ Columbus, L. (2013b, September 4). Predicting Enterprise Cloud Computing Growth. Forbes. Retrieved November 10, 2013, from http://www. forbes.com/sites/louiscolumbus/2013/09/04/ predicting-enterprise-cloud-computing-growth/ Columbus, L. (2015, November 4). Roundup of cloud computing forecasts and market estimates q3 update, 2015. Forbes. Retrieved November 4, 2015, from http://www.forbes.com/sites/ louiscolumbus/2015/09/27/roundup-of-cloudcomputing-forecasts-and-market-estimates-q3update-2015/ Dhar, S. (2012). From outsourcing to Cloud computing: Evolution of IT services. Management Research Review, 35(8), 664–675. doi:10.1108/01409171211247677 Dibbern, J., Goles, T., Hirschheim, R., & Jayatilaka, B. (2004). Information systems outsourcing: A survey and analysis of the literature. SIGMIS Database, 35(4), 6–102. doi:10.1145/1035233.1035236 Everett, C. (2009). Cloud computing – A question of trust. Computer Fraud & Security, 6, 5–7. doi:10.1016/S1361-3723(09)70071-5

Category: Cloud Computing

Fernando, N., Loke, S. W., & Rahayu, W. (2013). Mobile cloud computing: A survey. Future Generation Computer Systems, 29(1), 84–106. doi:10.1016/j.future.2012.05.023

Kumar, V. V., & Poornima, G. (2012). Ensuring Data Integrity in Cloud Computing - EICA272. Journal of Computer Applications, 5(EICA20124), 513–520.

Garfinkel, S. (2011). The Cloud Imperative. MIT Technology Review. Retrieved November 2, 2015, from http://www.technologyreview.com/ news/425623/the-cloud-imperative/

Lacity, M. C., & Hirschheim, R. (1993). The Information Systems Outsourcing Bandwagon. MIT Sloan Management Review. Retrieved on November 4, 2013, from http://sloanreview.mit. edu/article/the-information-systems-outsourcingbandwagon/

Gartner. (2013, July 17). Gartner Says Worldwide IT Outsourcing Market to Reach $288 Billion in 2013. Retrieved November 10, 2013, from http:// www.gartner.com/newsroom/id/2550615 Gupta, U. G., & Gupta, A. (1992). Outsourcing the IS function: Is It Necessary for Your Organization? Information Systems Management, 9(3), 44–47. doi:10.1080/10580539208906881 Jansen, W., & Grance, T. (2011). Guidelines on Security and Privacy in Public Cloud Computing (No. NIST SP 800-144). NIST - national Institute of Standards and Technology. Retrieved from http://csrc.nist.gov/publications/ nistpubs/800-144/SP800-144.pdf Kajko-Mattsson, M., & Gustafsson, L. (2010). Cloud Outsourcing requires a proper handover process (position paper). In 2010 6th International Conference on Advanced Information Management and Service (IMS) (pp. 142–146). Academic Press.

Lee, J.-N., Huynh, M. Q., Kwok, R. C.-W., & Pi, S.-M. (2003). IT Outsourcing evolution—: Past, present, and future. Communications of the ACM, 46(5), 84–89. doi:10.1145/769800.769807 Liu, H. (2012, March). Amazon data center size. Huan Liu’s Blog. Retrieved from http://huanliu. wordpress.com/2012/03/13/amazon-data-centersize/ Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing — The business perspective. Decision Support Systems, 51(1), 176–189. doi:10.1016/j.dss.2010.12.006 Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing (Special Publication No. 800-145). National Institute of Standards and Technology. Retrieved from http://csrc.nist.gov/ publications/nistpubs/800-145/SP800-145.pdf

Kim, W., Kim, S. D., Lee, E., & Lee, S. (2009). Adoption issues for cloud computing. In iiWAS ’09 Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services (pp. 3–6). Kuala Lumpur: ACM.

Overby, S. (2013, August 2). Gartner Predicts Limited IT Outsourcing Growth and Increased Volatility. CIO. Retrieved November 10, 2013, from http://www.cio.com/ article/737472/Gartner_Predicts_Limited_ IT_Outsourcing_Growth_and_Increased_Volatility

Knight, R. (2009, June 30). The new role of XML in cloud data integration. CT316. Retrieved November 13, 2013, from http://www.ibm.com/ developerworks/webservices/library/x-sftoeap/

Palvia, P. C. (1995). A dialectic view of information systems outsourcing: Pros and cons. Information & Management, 29(5), 265–275. doi:10.1016/0378-7206(95)00030-9

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Reyes Gonzalez, J. G. (2009). Information Systems Outsourcing Reasons and Risks: An Empirical Study. International Journal of Humanities and Social Science, 4(3), 181–192. Roberts, J. C. II, & Al-Hamdani, W. (2011). Who can you trust in the cloud?: a review of security issues within cloud computing. In Proceedings of the 2011 Information Security Curriculum Development Conference (pp. 15–19). New York, NY: ACM. doi:10.1145/2047456.2047458 Stallman, R. (2008, September 29). Cloud computing is a trap, warns GNU founder Richard Stallman. The Guardian. Retrieved from http://www.theguardian.com/ technology/2008/sep/29/cloud.computing.richard.stallman Subashini, S., & Kavitha, V. (2011). A survey on security issues in service delivery models of cloud computing. Journal of Network and Computer Applications, 34(1), 1–11. doi:10.1016/j. jnca.2010.07.006 Sultan, N. (2009). Cloud computing for education: A new dawn? International Journal of Information Management, 30(2), 109–116. doi:10.1016/j. ijinfomgt.2009.09.004 Vaquero, L. M., Rodero-Merino, L., Caceres, J., & Lindner, M. (2009). A break in the clouds: Towards a cloud definition. SIGCOMM Comput. Commun. Rev., 39(1), 50–55. doi:10.1145/1496091.1496100 Vitharana, P., & Dharwadkar, R. (2007). Information Systems Outsourcing: Linking Transaction Cost and Institutional Theories. Communications of the Association for Information Systems, 20(1). Retrieved from http://aisel.aisnet.org/cais/vol20/ iss1/23 Weidenbaum, M. (2005). Outsourcing: Pros and cons. Business Horizons, 48(4), 311–315. doi:10.1016/j.bushor.2004.11.001

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Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18. doi:10.1007/s13174010-0007-6 Zhu, J. (2010). Cloud Computing Technologies and Applications. In Handbook of Cloud Computing (pp. 21–45). Retrieved from http://content.schweitzer-online.de/ static/content/catalog/newbooks/ 978/144/196/9781441965233/ 9781441965233_Excerpt_001.pdf Zissis, D., & Lekkas, D. (2012). Addressing cloud computing security issues. Future Generation Computer Systems, 28(3), 583–592. doi:10.1016/j. future.2010.12.006

ADDITIONAL READING Abadi, D. J. (2009). Data Management in the Cloud: Limitations and Opportunitie s. A Quarterly Bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering, 32(1), 3–12. Anderson, J. Q., & Rainie, L. (2010). The future of cloud computing. PewInternet. Retrieved from http://pewInternet.org/Reports/2010/The-futureof-cloud-computing.aspx Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., & Konwinski, A. … Zaharia, M. (2009). Above the Clouds: A Berkeley View of Cloud Computing (No. Technical Report No. UCB/EECS-2009-28). Electrical Engineering and Computer Sciences, University of California at Berkeley. Retrieved from http://www.eecs. berkeley.edu/Pubs/TechRpts/2009/EECS-200928.html

Category: Cloud Computing

Armbrust, M., Stoica, I., Zaharia, M., Fox, A., Griffith, R., Joseph, A. D., & Rabkin, A. et  al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50. doi:10.1145/1721654.1721672 Badger, L., Grance, T., Patt-Corner, R., & Voas, J. (2012). Cloud Computing Synopsis and Recommendations (No. Special Publication 800-146). Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), 599–616. doi:10.1016/j.future.2008.12.001 Columbus, L. (2013, September 4). Predicting Enterprise Cloud Computing Growth. Forbes. Retrieved November 10, 2013, from http://www. forbes.com/sites/louiscolumbus/2013/09/04/ predicting-enterprise-cloud-computing-growth/ Dibbern, J., Goles, T., Hirschheim, R., & Jayatilaka, B. (2004). Information systems outsourcing: A survey and analysis of the literature. SIGMIS Database, 35(4), 6–102. doi:10.1145/1035233.1035236 Everett, C. (2009). Cloud computing – A question of trust. Computer Fraud & Security, (6): 5–7. doi:10.1016/S1361-3723(09)70071-5 Fernando, N., Loke, S. W., & Rahayu, W. (2013). Mobile cloud computing: A survey. Future Generation Computer Systems, 29(1), 84–106. doi:10.1016/j.future.2012.05.023 Gartner. (2013, July 17). Gartner Says Worldwide IT Outsourcing Market to Reach $288 Billion in 2013. Retrieved on November 10, 2013, from http://www.gartner.com/newsroom/id/2550615 Gupta, U. G., & Gupta, A. (1992). Outsourcing the IS function: Is It Necessary for Your Organization? Information Systems Management, 9(3), 44–47. doi:10.1080/10580539208906881

Jansen, W., & Grance, T. (2011). Guidelines on Security and Privacy in Public Cloud Computing (No. NIST SP 800-144). NIST - national Institute of Standards and Technology. Retrieved from http:// csrc.nist.gov/publications/nistpubs/800-144/ SP800-144.pdf Kim, W., Kim, S. D., Lee, E., & Lee, S. (2009). Adoption issues for cloud computing. In iiWAS ’09 Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services (pp. 3–6). Kuala Lumpur: ACM. Knight, R. (2009, June 30). The new role of XML in cloud data integration. CT316. Retrieved November 13, 2013, from http://www.ibm.com/ developerworks/webservices/library/x-sftoeap/ Kumar, V. V., & Poornima, G. (2012). Ensuring Data Integrity in Cloud Computing - EICA272. Journal of Computer Applications, 5(EICA20124), 513–520. Lacity, M. C., & Hirschheim, R. (1993, October 15). The Information Systems Outsourcing Bandwagon. MIT Sloan Management Review. Retrieved November 4, 2013, from http://sloanreview.mit.edu/article/the-information-systemsoutsourcing-bandwagon/ Liu, H. (2012, March). Amazon data center size. Huan Liu’s Blog. Retrieved from http://huanliu. wordpress.com/2012/03/13/amazon-data-centersize/ Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing - The business perspective. Decision Support Systems, 51(1), 176–189. doi:10.1016/j.dss.2010.12.006 Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing (Special Publication No. 800-145). United State: National Institute of Standards and Technology. Retrieved from http:// csrc.nist.gov/publications/nistpubs/800-145/ SP800-145.pdf

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Overby, S. (2013, August 2). Gartner Predicts Limited IT Outsourcing Growth and Increased Volatility. CIO. Retrieved on November 10, 2013, from http://www.cio.com/article/ 737472/Gartner_Predicts_Limited_IT_Outsourcing_Growth_and_Increased_Volatility Palvia, P. C. (1995). A dialectic view of information systems outsourcing: Pros and cons. Information & Management, 29(5), 265–275. doi:10.1016/0378-7206(95)00030-9 451. Research. (2013, August). Market Monitor: Cloud Computing. Retrieved on November 10, 2013, from https://451research.com/marketmonitor-cloud-computing Reyes Gonzalez, J. G. (2009). Information Systems Outsourcing Reasons and Risks: An Empirical Study. International Journal of Humanities and Social Science, 4(3), 181–192. Roberts, J. C. II, & Al-Hamdani, W. (2011). Who can you trust in the cloud?: a review of security issues within cloud computing. In Proceedings of the 2011 Information Security Curriculum Development Conference (pp. 15–19). New York: ACM. doi:10.1145/2047456.2047458 Stallman, R. (2008, September 29). Cloud computing is a trap, warns GNU founder Richard Stallman. The Guardian. Retrieved from http:// www.theguardian.com/technology/2008/sep/29/ cloud.computing.richard.stallman Subashini, S., & Kavitha, V. (2011). A survey on security issues in service delivery models of cloud computing. Journal of Network and Computer Applications, 34(1), 1–11. doi:10.1016/j. jnca.2010.07.006 Sultan, N. (2009). Cloud computing for education: A new dawn? International Journal of Information Management, 30(2), 109–116. doi:10.1016/j. ijinfomgt.2009.09.004

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Vaquero, L. M., Rodero-Merino, L., Caceres, J., & Lindner, M. (2009). A break in the clouds: Towards a cloud definition. SIGCOMM Comput. Commun. Rev., 39(1), 50–55. doi:10.1145/1496091.1496100 Vitharana, P., & Dharwadkar, R. (2007). Information Systems Outsourcing: Linking Transaction Cost and Institutional Theories. Communications of the Association for Information Systems, 20(1). Retrieved from http://aisel.aisnet.org/cais/vol20/ iss1/23 Weidenbaum, M. (2005). Outsourcing: Pros and cons. Business Horizons, 48(4), 311–315. doi:10.1016/j.bushor.2004.11.001 Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18. doi:10.1007/s13174010-0007-6 Zhu, J. (2010). Cloud Computing Technologies and Applications. In Handbook of Cloud Computing (pp. 21–45). Retrieved from http://content.schweitzer-online.de/ static/content/catalog/newbooks/ 978/144/196/9781441965233/ 9781441965233_Excerpt_001.pdf Zissis, D., & Lekkas, D. (2012). Addressing cloud computing security issues. Future Generation Computer Systems, 28(3), 583–592. doi:10.1016/j. future.2010.12.006

KEY TERMS AND DEFINITIONS Cloud Computing: Is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.

Category: Cloud Computing

Grid Computing: Is a middleware consisting of interconnected heterogeneous computer systems in a high-speed network to solve computationintensive problems. IaaS: Is a type of cloud computing service that provide infrastructure, especially servers or virtual servers located in data centers to customers. The servers or virtual servers are normally accessed through a computer network such the Internet. Outsourcing: Is a business term to describe a mechanism in which a company utilizes services provided by another company, normally through a contract, to fulfill some of its required business resources or functions. PaaS: Is a type of cloud computing service that offer the infrastructure as well as application development platform. PaaS providers accessible

through a computer network or the Internet provide all tools for development of applications. These tools may include automation in designing, deploying, testing, and administering applications to simplify application development. SaaS: Is a type of cloud computing service that offer various software accessible to customers through the Internet. Virtualization: Is mechanism to create virtual version of a real thing. Virtualization of a machine normally creates virtual versions of the machine, meaning several independent virtual machines for different purposes can be generated. In cloud computing, virtualization is used to generate virtual servers or virtual resources (such as storage) dynamically.

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Improved Checkpoint Using the Effective Management of I/O in a Cloud Environment Bakhta Meroufel University of Oran1, Algeria Ghalem Belalem University of Es Senia Oran1, Algeria

INTRODUCTION The emergence of cloud computing has brought a new dimension to the world of information technology. Although cloud computing offers several advantages such as virtualization, cost reduction, multi-tenancy, etc., there are risks and failures associated with it (Yang et al., 2014). A key challenge for research in cloud computing is to ensure the reliability of the system without reducing the overall system performance. Among of fault tolerance, there is the strategy of checkpointing. The major problem of checkpointing is the overhead caused by the storage time of checkpointing files in stable storage, this time is estimated at 70% of checkpointing process time caused by the storage (Ouyang et al., 2009a;Cornwell &kongmunvattana, 2011a), Figure1 shows the main phases of the checkpointing process. This process is based on three phases: i) suspend communication between processes and ensure consistent state; ii) use the checkpointing library to create and store checkpoints; iii)re-connect processes and continue execution. The aim of our work is to minimize the overhead of checkpointing by minimizing its storage time. To ensure this goal, we improve the I/O management and we propose a checkpointing strategy of three phases:

a virtual hierarchical topology; it minimizes checkpointing time and I/O time at the same time. In our system, each VM has a reactive agent responsible of the local I/O management; at the end of this phase some of these reactive agents will be activated to manage the I/O of a group of VMs of the server. In this case, the I/O will be hierarchical. 2. Creating the checkpointing files using coordinated checkpointing protocol. 3. Ensuring a lightweight and fault-tolerant storage of these files by using Collective and Selective Data Sieving input/output (CSDS I/O), which is executed by only the active agents. CSDS is an improved ROMIO I/O strategy. However, this strategy has several problems and limitations (Fu et al., 2011). Figure 1. The time of the phases of the checkpointing process

1. The construction of VRbIO topology (Virtual RbIO): RbIO proposed in (Lui et al., 2010) is DOI: 10.4018/978-1-5225-2255-3.ch096 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Cloud Computing

Our algorithm with its three phases provides solutions for most issues raised by the use of classical checkpointing with ROMIO as an I/O strategy. The rest of the chapter is organized as follows: Section 2 presents the background in the field of aggregating I/O techniques with a comparative study. ROMIO and its features are illustrated in Section 3. Section 4 presents our contribution, each service of this contribution is described in details, and all the problems cited in previous section are solved in this section. Section 5 presents some experimental results, followed by a conclusion and future research directions.

BACKGROUND An important reason for the limitations of I/O systems is that applications often send smaller queries disjoint. This access mode generates a first additional cost to the large number of applications running on various transmission channels, but more significantly increases the processing time of the latter (Sadiku et al., 2014). To deal with this problem, several “aggregation” methods have been proposed we can distinguish two types of aggregations strategies: dependent and collective. Independent I/O is a straightforward form of I/O and is widely used in parallel applications. This form of I/O can be called independently by an individual process or any subset of processes of a parallel application. The advantage of independent I/O is that users have the freedom to perform I/O for each individual process or any subset of the processes that open the file. The buffering is an Independent I/O (Cornwell & Kongmunvattana, 2011a). In conventional strategies, the write operation transfers data from the buffer to the local disk from their reception. Buffering proposes that the buffer will be used for temporary storage of I/O. The write operation includes small blocks in a buffer (of limited size). Once the buffer is completely filled, it will be forwarded to the local disk.

The “List I/O” approach (Thakur et al., 1999a) provides routines to indicate within a single call access number. A list of torque (offset, size) describes the distribution of data in memory and a similar list is used to perform matching on disk. Data sieving (Thakur et al., 1999a) is one of the techniques proposed to address this issue by aggregating small requests into large ones. Instead of accessing each small piece of data separately, data sieving accesses a large contiguous scope of data that includes the small pieces of data. The additional unrequested data are called holes. The size of holes compared to the requested data controls the efficiency of data sieving. For many parallel applications, even though each process may access several non-contiguous portions of a file, the requests of multiple processes are often interleaved and may constitute a large contiguous portion of a file together (Chen et al., 2010). In order to achieve better I/O performance, a group of processes may cooperate with each other in reading or writing data in a collective and efficient way, which is known as collective I/O. The collective I/O is a general idea that exploits the correlations among accesses from multiple processes of a parallel application and optimizes its I/O accesses. The basic idea behind this technique is to coordinate I/O accesses from different processors. The processors exchange information regarding what data each of them needs to access. This information is used to derive an efficient I/O schedule. The collective I/O can distinguished by the “physical place” where the operation group is performed: if aggregation is executed among processes, the most used method is the “Two-Phase I/O” approach; if aggregation is performed at the records, we are talking about system “DiskDirected I/O” if the approach is finally realized within a server, the method is “server-directed I/O”. “Two-Phase I/O”, this method (Del Rosario et al., 1993), as its name suggests, consists of two main phases: after a consensus between the

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Table 1. Comparative study between aggregation techniques Criteria

Data Sieving

Coll. I/O

ListI/O

ViewI/O

Buff

Useless Data Transfer

Yes

No

Yes

No

No

Network Data Transfer

Read:1 Write: 2

2

1

1

1

Responsible

All

All

All

All

Manager

Resource

Local buffer

Local Buffer

Piggy-backed Message

Local/ Coll Buffer

Local/Coll Buffer

File Distribution

Logic

Logic

Logic\Physic

Logic\Physic

Logic

processes involved, the first step is to retrieve the data, the second concerns the redistribution between each of the latter processes. To improve the I/O strategies (independent and collective I/O), the “File View” (Isaila et al., 2003) proposes to specify the access patterns and reacts according to each model. The access pattern is defined according to the knowledge of Logical/Physical distribution of files in client and memory level.

in node and system levels (Thakur et al., 1999a). ROMIO three phases is observed:

ROMIO

Figure 2 presents an example of ROMIO I/O in four processes. After phase ‘Read’ the Data sieving is applied in each process to create contiguous blocks. ROMIO does not use a common buffer and the data are distributed over the buffers

ROMIO is one of the most famous I/O implementation, it combine between Two phases Collective I/O and Data Sieving to create contiguous requests

Figure 2. Example ROMIO (Chen et al., 2010)

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• • •

Independent requests, each process run independent requests to access a contiguous region of data. Create contiguous blocks using “Data Sieving” in each process. Collective I/O: processes communicate between them to collect contiguous blocks of the same file for different processes.

Category: Cloud Computing

of different processes (number of aggregators = number of processes) (Chen et al., 2010). In this case, the process should contact each other to create more contiguous.The contiguous requests for the same file. The checkpointing using the ROMIO I/O management generally suffers from several problems such as: •

•

•

•

•

Problem 1: Expensive communication phase. The number of messages sent during the communication phase increases with the number of processes and the number of files blocks to read or write. Problem 2: Limited buffer of nodes. ROMIO is completely distributed, so the local buffers of processes are insufficient to store contiguous blocks. Problem 3: Quantity of unnecessary data can be high. ROMIO creates contiguous blocks by using Data sieving. It is possible that the holes beyond the useful data, which makes reading/writing expensive. Problem 4: The storage time is still high. Although ROMIO uses Data sieving and collective I/O to create contiguous blocks and eliminate duplicate blocks, and since ROMIO is completely distributed, each process will be blocked to achieve I/O. Problem 5: Files storing is not fault tolerant. To ensure the correctness of checkpointing, we must ensure the storage atomicity. The checkpointing with ROMIO is responsible for I/O without taking into account the atomicity.

Like other IT services, cloud services are not immune to failures. In addition in such systems the failure rate increases with the size of the system itself. In this context, the application of operational safety is an element of primary importance, so a fault tolerance mechanism is necessary to ensure certain aspects such as availability. The checkpoint is one of tolerance mechanisms most used in distributed systems failures. Our contribution is

proposed an approach for the management of I/O to minimize the time of checkpointing by reducing its storage time. In this work, we propose an adaptive checkpointing approach that solves each of these problems using a virtual hierarchic topology and a lightweight I/O strategy, we called CSDS (Collective and Selective Data Sieving) I/O and which ensures parallelism and scalability making it adequate to cloud computing.

CONTRIBUTION The checkpointing time is divided into two parts: the overhead and the latency. Overhead checkpointing is the time necessary to ensure the consistency and capturing the current state of the system (Create checkpointing files). It depends on the used checkpointing protocol (coordinated, uncoordinated, induced communication,...). We have proposed a fault tolerant checkpointing strategy that provides a hierarchical storage without losing the notion of I/O parallelism and also improves the I/O management by a smart strategy CSDS.

System Model Figure 3 shows the different services in the server. Servers (Physical Machine) are assigned to one or more virtual machines (VMs) using a hypervisor. The hypervisor is the software that provides a virtualized hardware environment to support running multiple operating systems simultaneously using a single physical server. The server contains a topology constructor service and fault tolerance service. The objectives of these services are respectively: creating VRbIO topology and providing fault tolerance using checkpointing protocol. (controlling checkpointing interval, ensuring checkpointing atomicity, ensuring a consistent state,…). Each VM can accept an application for special user when executing an application. The kernel in the virtual machine contains the communication modules and task management. We have extended

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Figure 3. Server architecture

this architecture by implementing a checkpoints module (CP module) inside each VM. CP module creates checkpoints file that saves the actual VM state. A reactive agent inside the VM is responsible of storing the checkpointing file created by the CP module. The colored boxes in each VM represent the buffers where the checkpointing files will be stored temporarily. The server is structured in virtual topology named VRbIO where the reactive agents inside VMs can be activate to manage an intelligent I/O of their server using CSDS modules (Collective and Selective Data Sieving). So CSDS module is activated only in case of active agent. We also assume that each data center contains a permanent storage memory where the checkpointing files will be stored. Only active agents have the right to write into the memory.

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Algorithm of Our Approach The algorithm consists of three main phases to ensure fault tolerance by checkpointing: Create a virtual topology VRbIO, create checkpointing files using coordinated checkpointing protocol then run CSDS I/O to store files in the stable storage server.

VRbIO Topology ROMIO is a strategy completely distributed. So, all the nodes use their buffers for temporarily storing files. Then they communicate with each other to create more contiguous blocks (Collective I/O) which increases the time of communications phase. To solve this problem (problem 1), we propose to use a hierarchical topology for managing I/O. According to the comparative study of different

Category: Cloud Computing

topologies proposed in (Lui et al., 2010), it is clear that RbIO is the best topology that ensures the scalability and the parallelism. In RbIO there are two types of nodes: Worker and Writers. The Writer is the node responsible for managing I/O of a set of Workers. The Worker is an ordinary node; it belongs to a single writer on its server. RbIO proposed in (Lui et al., 2010) is physical. However, in our work we will create a virtual fault tolerant RbIO named VRbIO in each server (Host).In our server, we suppose that each VM has a reactive agent responsible of managing the local I/O. In order to simulate the comportment of Worker and Writers of the physical RbIO, our VRbIO activates some reactive agents of the server to play the role of Writers. The other inactivated agents (reactive agents) will be Workers. The existence of reactive agents in all VMs and the activation process allow more dynamicity and flexibility in the architecture. When an active agent fails, it is sufficient to activate one reactive agent in its group, without any intervention. The activation of an agent means the activation of the CSDS service. Only the active agent can access to the stable memory to store the checkpointing files. To create the VRbIO, the topology constructor of the server i selects at first γi reactive agents to be activated. We suppose that ³ i is equal to the square root of the number of VMs created in that server i. The list of VMs in server i is sorted in descending order of speed and buffer size. Starting from the beginning of the sorted list, the topology constructor activates the first γi reactive agents (the rest agents of the list remain reactive). The reactive agents are assigned to theirs active agents in a round-robin fashion (See Formula (1)). We note AAk,i as the active agent k in server i. so ALi={AA0,i, AA1,i, …, AA(γ-1),i} is the list of active agents in the server i. The list of reactive agents is RLi={RAγ,i, RA(γ+1),i, …, RAn,i} where RAj,i is the reactive agent j in server i and n is the number of VMs in the server. We also define G(AAk,i) as the list of reactive agents associated to the active agent AAk,i (the Workers).

RAj ,i ∈ G (AAk ,i ) ⇔ k = j mod γi 0

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We note also VM(RAj,i) as the VM of reactive agent j (and VM(AAj,i) as the VM of active agent j). The VRbIO improves the checkpointing, When checkpointing interval expires, the VM(RAj,i \ RAj,i ∈ G(AAk,i)) creates a checkpoint file (temporarily stored in the local buffer) then sends this file to its active agent AAk,i. Upon the reception of checkpoints files, the AAk,i uses CSDS to improve I/O and minimize time storage of these files (See next section). The fact that each reactive agent knows exactly its active agent, the communication time is reduced dramatically (problem.1). The problem with the strategy RbIO and even in ROMIO is the buffer size which may be insufficient to store checkpoints files (problem.2). In our approach, the VM of active agent (Writer) must be able to store the files of its group of VMs with reactive agents (Workers) to optimize I/O using CSDS. To consider this problem, we used an idea proposed in BlobCR (Nicolae & Cappello, 2011) strategy. According to this strategy, after the construction of VRbIO, each VM(RAj,i) gives a part of the buffer to the VM of its active agent VM(AAj,i). The Algorithm 1 gives the detail of VRbIO construction. In Figure 3 that represents the server i, the list of active agents is: ALi = {AA0,i, AA1,i } where {VM(AA0,i) = VM1, VM(AA1,i) = VM2}. The list of reactive agent is: RLi {RA2,i, RA 3,i , RA 4,i , RA 5,i ,RA 6,i } where {VM(RA 2,i ) =VM3, VM(RA3,i) = VM4, VM(RA4,i) = VM5, VM(RA5,i)=VM6,VM(RA6,i)=VM7 } For the group affectation, we have G(AA0,i)= {RA2,i, AA4,i, AA6,i} and G(AA1,i)={RA3,i, AA5,i}

Checkpointing Creation In our work, we used coordinated checkpointing. Where all VMs synchronize their efforts to create a consistent state using control messages. In a consistent state, orphan and transit messages are

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Algorithm 1. The VRbIO construction //γi: Number of Active agents in the server i. // ListVMi:The list of VMs in the server i. // ALi: List of active agents in the server i. // RLi: The list of Reactive agents in the server i. // Initiation For j = 1 to ListVMi.size do //All the agents in the VM list are reactive in the beginning Agent(ListVMi (j)).State=Ractive; RLi.add(Agent(ListVMi (j))) End for

γi ← ListVM i .size()

;

ListVMi sorted in descending order of speed and buffer size // the activation of reactive agents For j = 1 to γi do Agent(ListVMi (j)).State=Active; Agent(ListVMi (j)).ID=i-1;

( j ) )); RL .remove(Agent( ListVM ( j ) ));

RLi.add(Agent( ListVM i i

i

End for For j = 1 to RLi.size do RLi(j).ID = (γi-1)+j; End for // The affectation of reactive agents to their active //agents For j = 1 to RLi.size≠0 do For k = 0 to ALi.size≠0 do If RLi(j).ID MOD γi=k Then G(ALi(k)).add(RLi(j)) End If End for End for

avoided by blocking the communication during the checkpointing phase and storing the susceptible transit messages (Kangarlou et al., 2012). In this case, the system stores a single recovery line in the stable memory. The coordinated checkpointing is used in several studies (Arockiam & Geo Francis, 2012), (Kangarlou et al., 2012). Fault tolerance service and topology constructor are responsible for selecting the checkpointing interval Timecp adequate to system features. If Timecp expires, the service informs its VMs by sending a request for checkpointing. Upon receiving this message, each VM creates its own checkpointing file (stored in local buffer). VRbIO topology allows us to use the technique of Soft checkpointing. In conventional strategies (Hard checkpointing) VMs store their files directly in the stable memory which increases the storage time (Chen et al., 2010).

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In our strategy, each VM(RAj,i \ RAj,i ∈G(AAk,i)) sends its checkpoints file to VM(AAk,i) and then continues immediately the execution. The AAk,i is responsible for storing the files of its reactive agent’s VMs in the stable memory. This hierarchical checkpointing storage is called Soft checkpointing and it is a solution for the problem 4. The major problem in the checkpointing is to ensure its atomicity, that is to say, we must ensure that all concerned VMs complete the creation of their checkpointing (create the file checkpointing and stored). In our approach, the active agent ensures partial checkpointing atomicity of the VMs of its group of reactive agent including its VM. If during the checkpointing, a VM(RAj,i \ RAj,i ∈G(AAk,i)) is incapable of creating its checkpoint (this is the case of a failed VM, blocked VM, busy VM, …), its reactive agent AAk,i will be informed (either by a special message, fault

Category: Cloud Computing

Figure 4. Checkpointing management

detection service or even a timeout). In this case, AAk,i cancels the checkpointing of VM in G(AAk,i) by sending a message “Delete” to them (VMs that have not yet send their checkpointing file) and to the other active agents in ALi of its server i. If a AAj,s \s≠i receives “Delete” from AAk,i, it cancels the checkpointing of its G(AAk,i). This partial atomicity technique solves the Problem 5 and makes the checkpointing itself fault tolerant. After the AAk,i collects checkpointing files of its G(AAk,i), it uses the CSDS service to optimize the I/O (See Figure 4).

CSDS for Checkpointing Storage After the AAk,i ensures checkpointing atomicity of its group, its CSDS service handles the I/O management to minimize the checkpointing latency. CSDS is similar to ROMIO. But in ROMIO, the Data Sieving and collective I/O will always be executed regardless of the size of useless data quantity caused by Data Sieving (Holes). In this case, the amount of unnecessary data can be large

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compared with the useful data, which increases the cost and the time of the I/O (problem 3). To resolve this issue, the CSDS of the active agents AAk,i executes Collective I/O for data requested by different VM(RAj,i \ RAj,i ∈G(AAk,i)), and then performs Data Sieving only if the size of useless data UDj does not exceed a certain threshold αj versus the total size of data to be written Dj by the active agent j. The threshold α is specified by SLA criteria (System Level Agreement). The βj parameter of active agent j represents the percentage of useless data written relative to the entries data (see Formula 2). The βj will be compared to αj to decide to perform a Data Sieving or not (simple buffering). βj =

(100 ×UD j ) Dj



(2)

This strategy eliminates the problem 3. The details of CSDS algorithm executed by an active agent AAk,i are illustrated in Figure 5.

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Figure 5. CSDS Algorithm

EXPERIMENTAL RESULTS Our approach noted CP_CSDS is destined to improve the checkpointing performances and it uses CSDS to manage the I/O, so we compare it with a classical coordinated checkpointing using ROMIO as I/O strategy (CP_ROMIO). We used several scenarios and parameters and we implement the

approaches in CloudSim simulator(Calheiros et al., 2011). In the first experiment, we measured the storage time of checkpoints files (checkpointing latency) in both I/O strategies using a server of 10 to 20 VMs and a checkpointing interval=10 minutes. The results are presented in Figure 6. CP_CSDS reduces the storage time of checkpoints files by a

Figure 6. Impact of number of checkpoints on storage time

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Figure 7. Impact of checkpoints sizes on storage time

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Figure 8. Impact of number of nodes involved in the checkpointing on storage time

most 30% compared to CP_ROMIO. CP_CSDS uses VRbIO to reduce the communication phase. This is due to the hierarchical checkpointing storage used by RbIO and the I/O management based on CSDS that reduces the communication phase. The second experiment is destined to measure also the storage time in case of different sizes of checkpointing files. According the results illustrated in Figure 7, the storage time increases if the size of files increases. However, our strategy CP_CSDS is better than CP_ROMIO (a gain of 20%) because the CSDS reduces the transfer of useless data during the I/O. The purpose of the third experiment is to study the scalability of our strategy in terms of number of participating nodes in checkpointing

process. From the results shown in Figure 8, increasing the number of participating nodes in the checkpointing automatically increases the storage time in the classical approach of CP_ROMIO. In CP_ROMIO, all the nodes must communicate with each other to ensure the “Collective I/O” phase. In our approach CP_CSDS, the topology VRbIO provides a load balancing and scalability. Just the active agents communicate with each other to ensure the I/O which greatly reduces the storage time. The gain in term of storage time is ≈30% compared to CP_ROMIO. The last experiment calculates the overhead caused by the checkpointing using both approaches I/O (See Figure 9). It is clear that our approach CP_CSDS reduces significantly the

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Figure 9. Impact of number of checkpoints on the Overhead

overhead of checkpointing (≈18%) because in addition to reducing the storage time, it uses the soft checkpointing.

CONCLUSION Checkpointing is very powerful fault tolerance technique in term of fault management and can be used for different objective such as migration and load balancing. The major problem of this technique is the storage time which can increase response time and minimizes the possibility to meet the deadlines specified by the user and the SLA rules. To improve the performance of checkpointing in the clouds, we have focused in this paper on minimizing the storage time of checkpointing files in stable storage server. Our approach involves three main steps: the construction of the virtual topology VRbIO, creating checkpointing

files using coordinated checkpointing protocol and finally save these files in the stable memory using a new strategy I/O that combines between the collective I/O and intelligent data sieving CSDS. In this paper we explained the various problems of using checkpointing with ROMIO as I/O strategy (CP_ROMIO). Our approach solves the majority of classical problems and makes the checkpointing appropriate for clouds environment. A brief comparison between our approach CP_CSDS and that of checkpointing with ROMEO I/O (CP_ROMIO) is presented in Table2. In this table, the parameter n presents the number of nodes (VMs) in the server.

FUTURE RESEARCH DIRECTIONS Several directions can be exploited, we can mention:

Table 2. CP_ROMIO vs CP_CSDS Topology

CP_ROMIO

Distributed

CP_CSDS

Hierarchical (VRbIO)

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Number of Writer n

n

Flexibility

Type of Checkpointing

Communication Phase

Buffer Problem

Transfert of Useless

Atomicity

No

Hard

Yes

Yes

High

No

Yes

Soft

No

No

Reduced

Yes

Category: Cloud Computing

•

•

•

Improve strategies of VRbIO topology by making the assignment of workers (reactive agents) to their writers (active agents) dynamic depending on the load and the characteristics of the system; Improve CSDS by the choice of other critical criteria to swing between the transfer block and the data Sieving as: i) data transfer time; ii) energy consumption; iii) the cost. Extend CSDS in a predictive system by pre-loading for user checkpoint file playback which minimizes recovery time in case of failures.

REFERENCES Arockiam, L. & Geo Francis E. (2012). FTM-A Middle Layer Architecture for Fault Tolerance in Cloud Computing. ICNICT, (2), 12-16. Calheiros, R. N., Ranjan, R., Beloglazov, A., DeRose, C. A. F., & Buyya, R. (2011, January). CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software, Practice & Experience, 41(1), 23–50. doi:10.1002/ spe.995 Chen, Y., Sun, X.-H., Thakur, R., Song, H., & Jin, H. (2010). Improving Parallel I/O Performance with Data Layout Awareness, Proceedings of the 2010 IEEE International Conference on Cluster Computing, 302-311. doi:10.1109/ CLUSTER.2010.35 Cornwell, J. & Kongmunvattana, A. (2011). Efficient System-Level Remote Checkpointing Technique for BLCR. Eighth International Conference on Information Technology: New Generations, 1002-1007. doi:10.1109/ITNG.2011.172

Del Rosario, J., Bordawekar, R., & Choudhary, A. (1993). Improved parallel I/O via a two-phase runtime access strategy. ACM SIGARCH Computer Architecture News, 21(5), 31-38. Fu, J., Min, M. S., Latham, R., & Carothers, C. D. (2011). Parallel I/O Performance for ApplicationLevel Checkpointing on the Blue Gene/P System. Workshop on Interfaces and Architectures for Scientific Data Storage (IASDS), 465-473. doi:10.1109/CLUSTER.2011.81 Isaila, F., & Tichy, W. F. (2003). View I/O: improving the performance of non-contiguous I/O. The Third IEEE International Conference on Cluster Computing, 336-343. Kangarlou, A., Eugster, P., & Xu, D. (2012). VNsnap: Taking Snapshots of Virtual Networked Infrastructures in the Cloud. IEEE Transactions on Services Computing, 5(4), 484–496. doi:10.1109/ TSC.2011.29 Liu, N., Fu, J., Carothers, C. D., Sahni, O. K. E., Jansen, K. E., & Shephard, M. S. (2010). Massively Parallel I/O for Partitioned Solver Systems. Parallel Processing Letters, 20(4), 377–395. doi:10.1142/S0129626410000302 Nicolae, B., & Cappello, F. (2011). BlobCR: Efficient checkpoint-restart for HPC applications on IaaS clouds using virtual disk image snapshots. Int’l Conference on High Performance Computing Networking, Storage and Analysis (SC’2011), 1-34. doi:10.1145/2063384.2063429 Ouyang, X., Gopalakrishnan, K., & Panda, D.-K. (2009a). Accelerating checkpoint operation by node-level write aggregation on multicore systems. Proceeding of International Conference on Parallel Processing (ICPP’2009), 34-41. doi:10.1109/ ICPP.2009.73 Sadiku, M. N. O., Musa, S. M., & Momoh, O. D. (2014). Cloud computing: Opportunities and challenges. Potentials IEEE Journal, 33(1), 34–36. doi:10.1109/MPOT.2013.2279684

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Thakur, R., Gropp, W., & Lusk, E. (1999a). Data Sieving and Collective I/O in ROMIO. Proceedings of the Seventh Symposium on the Frontiers of Massively Parallel Computation, 182–189. doi:10.1109/FMPC.1999.750599 Yang, B., Pang, X.-Q., Du, J.-Q., & Xie, D. (2014). Effective Error-Tolerant Keyword Search for Secure Cloud Computing. Journal of Computer Science and Technology, 29(1), 81–89. doi:10.1007/ s11390-014-1413-1

KEY TERMS AND DEFINITIONS Cloud Computing: Refers to applications and services offered over the Internet. These services are offered from data centers all over the world, which collectively are referred to as the “cloud”. Checkpointing: A technique to add fault tolerance into computing systems. It basically consists

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of saving a snapshot of the application’s state, so that it can restart from that point in case of failure. Collective I/O: In many parallel applications, despite the fact that each process may need to access several noncontiguous portions of a file, the requests of different processes are often interleaved and may together span large contiguous portions of the file. Collective I/O procedure is used to improve significantly the I/O performance by merging the requests of different processes and servicing the merged request. Data Sieving: To reduce the effect of high I/O latency in parallel applications, it is critical to make as few requests to the file system as possible. Data sieving is a technique that enables an implementation to make a few large, contiguous requests to the file system even if the user’s request consists of several small, noncontiguous accesses. ROMIO: A portable MPI-IO implementation that works on many different machines and file systems.

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Category: Cloud Computing

Service Quality and Perceived Value of Cloud ComputingBased Service Encounters Eges Egedigwe Dallas County Community College, USA

INTRODUCTION Cloud computing based technology is becoming increasingly popular as a way to deliver quality education to community colleges, universities and other organizations. At the same time, compared with other industries, colleges have been slow on implementing and sustaining cloud computing services on an institutional level because of budget constraints facing many large community colleges, in addition to other obstacles. Faced with this challenge, key stakeholders are increasingly realizing the need to focus on service quality as a measure to improve their competitive position in today’s highly competitive environment. The purpose of this article is to present a study that examined the expectations and perceptions of instructors’ usage of cloud computing based technology on overall quality of service (QoS). The article explores literature review that establishes the rationale and framework for this investigation, research methodology, data analysis, and results. A final section will include a summary of the findings, conclusions, and recommendations from the study.

BACKGROUND Cloud Computing Environments The explosive growth in computer usage by business, government, educational institutions, combined with global collaboration provided by the Internet, and competition has brought a considerable increase towards computer usage along

with the associated need to maximize the use of available resources while minimizing costs. One area of growing interest for meeting these needs is the use of cloud computing to centralize computing and information management functions for large, often geographically dispersed organizations. Users only need to pay for the services they actually use (Kim, Kim, Lee, & Lee, 2009). It offers potential benefits related to reductions of server/storage infrastructure and delivery of services (Leavitt, 2009). Some of the primary types of cloud computing services include infrastructure as a service, platform as a service, and software as a service (Leavitt, 2009; “National Institute,” 2011). Leavitt (2009) also included a general group called services, which consist of storage, middleware, collaboration, and databases provided via the Internet. These technologies and services together comprise the majority of the types of computing services available from cloud computing, ranging from hardware and software services, to entire computing environments. Cloud computing offers potential benefits related to reductions of server/storage infrastructure and delivery of services (Kim et al., 2009; Robinson, 2009). Cloud computing can be highly beneficial in educational settings. Among the possible benefits is the enhanced usefulness of the existing technology (Erenben, 2009). With its emphasis on the delivery of low-cost or free applications anywhere on the Internet, cloud computing is a promising prospect for educational institutions faced with budget restrictions and mobile student population (Denton, 2012). This study builds on the SERVQUAL model, discussed next, to analyze

DOI: 10.4018/978-1-5225-2255-3.ch097 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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the significance of expectations and perceptions of instructors’ usage of cloud computing technology in community colleges.

learning, but with support services such as information technology (Smith et al., 2007).

SERVICE QUALITY MEASUREMENT

MEASURING SERVICE QUALITY IN HIGHER EDUCATION

According to Renganathan (2011), quality is a subjective concept that has no generally agreed definition for it. The word quality means different things to people according to the context. In general it is difficult to measure and quantify service quality. The main purpose of measuring service quality is to ensure whether service is provided as per the expectations of the customers. There are several well-known tools for measuring service quality or customer satisfaction. The most eminent instrument in attempting to systematize the service quality is “The gap model” of service or SERVQUAL developed by Parasuraman et al. (1985). This conceptual framework was developed initially to measure customer perception of service quality for the financial service sectors but later extended to sectors such as hospitality, telecommunications and healthcare. The SERVQUAL’s model, which was developed by Parasuraman et al. (1988) used a survey to ask respondents for an indication of their expectations as well as their perceptions of service, and establishes the gap between the two. Other researchers, such as Cronin and Taylor (1992), held that only the perception of quality is important. The next section highlights how SERVQUAL has been used in universities to assess satisfaction not only with teaching and

Frequently, higher education institutions seek to provide high quality services in all parts of their educational curricula and administrative processes. Therefore, the importance of service quality makes its measurement and its subsequent management an issue of utmost importance (Shekarchizadeh, Rasli & Hon-Tat, 2011). The review of literature shows that some studies used the SERVQUAL model to measure service quality in higher education. Boulding, Kalra, Staelin, and Zeithaml (1993) used SERVQUAL model to study expectations and perceptions linked with the delivery of services in an educational environment. Their study used SERVQUAL to measure students’ satisfaction with overall quality of service in a higher educational setting (Al-alak & Alnaser, 2012). Table 1 below shows hypotheses’ testing results of their study. All but the sixth hypothesis was accepted. Hampton (1993) also used SERVQUAL model to measure college student satisfaction with professional service quality. In examining students’ perceptions of service delivery, he applied the gap model (the disparity between expectations and experiences). These studies support the use of SERQUAL model to measure instructors’ usage of cloud computing technology

Table 1. Hypotheses’ testing results H

Result

1

There is a significant relationship between service quality dimensions and students satisfaction.

Accepted

2

There is a significant relationship between tangibles and students satisfaction.

Accepted

3

There is a significant relationship between reliability and students satisfaction.

Accepted

4

There is a significant relationship between empathy and students satisfaction.

Accepted

5

There is a significant relationship between assurance and students satisfaction.

Accepted

6

There is a significant relationship between responsiveness and students satisfaction.

Rejected

Source: Schwantz, 2012, p. 161, Table 4

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Category: Cloud Computing

Another study that supports the use of a modified version of SERVQUAL in academia to measure student satisfaction of the service they received was done by Schwantz (1996). Schwantz modified the usage of SERVQUAL instrument to make the comparison between traditional and non-traditional students’ views of the quality of service in one higher educational institution. Students were asked to compare the quality of service (expected and received) of the support staff with that of faculty members. Based on factor analysis, the researcher identified the dimensions of the instrument where he used two dimensions instead of five, which are acknowledged by Parasuraman, Zeithaml and Berry (1990). The outcome of this study revealed no significant difference in the expectations or perceptions of traditional versus non-traditional students. There were no significant differences in students’ expectations for support staff versus faculty. However, there was a significant difference in the students’ perceptions of service quality from support staff versus service quality from faculty. Other studies have borrowed some of the dimensions of SERVQUAL model to investigate the impact of a number of service quality attributes on satisfaction and loyalty in a higher education setting. Investigating the differences in student satisfaction and identifying dimensions of overall perceived quality, a study by Ong and Nankervis (2012) revealed that students with different academic performances perceived the impact of quality attributes on satisfaction differently compared with students with lesser performances. It was also shown that differences in overall satisfaction with educational experience were found among different lines of specializations. Drawing concepts from services marketing and assessment literature, Duque and Weeks (2010) developed a conceptual model to assess student learning outcome. It was found that student perceptions of educational quality had a noticeable impact on student satisfaction. There has also been considerable research to reexamine the reliability and validity of SERVQUAL

(Asubonteng, McCleary & Swan, 1996; Brown, Churchill & Peter, 1993; Ladhari, 2008; Lam, 1997; Shahin, 2004). Ladhari (2008) suggested that industry-specific measures of service quality might be more appropriate than a single generic scale. He then encouraged researchers and scholars toward the development of an alternative industryspecific research instruments for measuring service quality. Lam (1997) found that the results are consistent with those reported in Babakus, Boller (1992), and Parasuraman et al. (1996), suggesting that both measures exhibit desirable levels of reliability and internal consistency. This view was echoed by Asubonteng et al., in their 1996 research: that SERVQUAL will predominate as a service quality measure. Summarizing, although, SERVQUAL has been proven to be a reliable instrument for measuring expectations and perceptions of service quality (Parasuraman, Zeithaml & Berry, 1990; Parasuraman, Zeithaml & Berry, 1994) and most of studies have focused on students’ satisfaction with overall quality of service and/or with professional service quality in high education. But the literature review reveals the lack of studies on instructors’ satisfaction with cloud computing technology in educational settings. This study fulfils this need. The next section will describe the methodology used for the study followed by the reliability and validity of survey instrument and data analysis.

METHODOLOGY The current study involves the use of a quantitative method to collect and analyze data received from the sample population regarding instructors’ perception of the service quality provided by cloud computing based system in large community colleges in Texas. The use of quantitative methods ensure the current study is specific and narrow, whereby the researcher can uncover measurable, observable data on the variables. Quantitative research enables the collection of data from instruments with preset questions and responses, and

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Table 2. Reliability analysis CONSTRUCTS

Number of Items

Cronbach Alpha (α)

ETN (Service Quality Expectation on Tangibles)

6

0.869

ERL (Service Quality Expectation on Reliability)

4

0.949

ERS (Service Quality Expectation on Responsiveness)

5

0.974

EAS (Service Quality Expectation on Assurance)

6

0.909

EEM (Service Quality Expectation on Empathy)

5

0.968

PTN (Service Quality Perception on Tangible)

7

0.843

PRL (Service Quality Perception on Reliability)

4

0.955

PRS (Service Quality Perception on Responsiveness)

4

0.955

PAS (Service Quality Perception on Assurance)

5

0.982

PEM (Service Quality Perception on Empathy)

5

0.972

acquire data from a large population (Creswell, 2005). The study identified one primary research question that was used to guide this investigation, which is: Do the difference between instructors’ perception and expectation significantly affect their perceived service quality of cloud computing based systems? The study collected and analyzed data based on the above research question from large community (or two year) colleges in Texas. The target population for this study comprised of instructors or faculty members (referred to as ‘participant’) with sufficient experience using cloud computing technology in two year colleges in the State of Texas. Subjects were drawn mostly from faculty members’ of three large community colleges (Dallas County Community College District (DCCCD), Houston College System (HCS), and Lone Star College System (LSC)) in Texas that provided Institutional Review Board approvals. The sample size is determined based on the size of the target population and the desired accuracy of the study. The target population is 11,395. A random sample of 470 email addresses of faculty were selected from the target population using the “Random Numbers Generator” feature of the SPSS statistical package. All the 470 instructors that were randomly selected received an online survey hosted by SurveyMonkey.com. The model for this study leverages service quality (SERVQUAL) multi-item scale developed to

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assess customer perceptions of service quality in service and retail businesses (Parasuraman et al., 1988). The scale breaks down the notion of service quality into five dimensions identified through this process and assessed using 22 item scale: Tangibles - physical facilities, equipment, staff appearance, etc.; Reliability - ability to perform service dependably and accurately; Responsiveness - willingness to help and respond to customer need; Assurance - ability of staff to inspire confidence and trust; and Empathy - the extent to which caring individualized service is given. Survey questions were designed based on the 22 questions of SERVQUAL. Some modifications to the wording were made to make them relevant to the cloud computing based environment.

RELIABILITY AND VALIDITY OF SURVEY INSTRUMENT The reliability of each of the SERVQUAL’s dimensions was assessed using Cronbach (1951)’s alpha as depicted in Table 2 - Reliability Analysis. The survey was also pre-tested for its reliability (Nunnally & Bernstein, 1994; Straub, 1989). Reliability in this context is the extent to which a measurement procedure is free from error. The estimation of alpha under the different number of items were examined using SPSS. The reliability coefficient

Category: Cloud Computing

(Cronbach’s alphas) for all the constructs ranges between 0.869 - 0.982. This supports reliability and face validity for the SERVQUAL scores for all the constructs. The survey did not require personally identifying information. Anonymity was guaranteed by instructing participants to avoid placing their name, return address, or any identifying information on the survey. Strict controls over all data collected were maintained by not sharing the responses from any participants. Once the data was collected and downloaded, a Likert-scale type result spreadsheets/database of the survey instruments was generated. A codebook was built for this study describing each independent, dependent and other variables used in the data analysis. The responses to the variables were entered into the statistical applications software package - Mplus version7.3 and IBM Statistical Package for the Social Sciences (SPSS) v22 – used for analysis.

DATA ANALYSIS AND RESULTS According to Marshall and Rossman (1995), “Data analysis is the process of bringing order, structure, and meaning to the mass of collected data. It is a messy, ambiguous, time-consuming, creative, and fascinating process” (p.111). The survey responses from 301 participants were analyzed

using a mixture of statistical approaches in an effort to provide order, structure, and meaning to the survey data collected. Data was scanned for univariate and multivariate outliers, defined as values that are greater than 3.29 standard deviations from the mean (Stevens, 2009). Three participants were removed for being multivariate outliers. Another 46 participants were removed from the data collected for not completing major sections of the survey. A random sample of 470 potential participants was selected. From those, 301 participants (64%) took part in the study. Data analysis was conducted on 252 participants (54%) after removing sixteen (16%) percent of those responses that were incomplete or unusable. The analysis of the data was reported using the research questions as a foundation. The analysis plan of hypothesis testing is shown in Table 3. The statistical data were analyzed using descriptive statistics (frequencies and percentages, mean, standard deviation, skewness, and kurtosis) and inferential statistics (correlation, shared covariance, structural equation modeling (SEM), and ANCOVAs). Descriptive research answers the questions of who, what, where, when, and how; however, it is not used to create a causal relationship (Gay, 1992). Table 4 presents the descriptive statistics for the expected, perceived, and differences scores.

Table 3. Analysis plan of hypothesis testing Number

Hypothesis

Statistical Test

RQ1: Do the difference between instructors’ perception and expectation significantly affect their perceived service quality of cloud computing based systems? H 01

The difference between expectation and perception of reliability will not significantly load onto perceived service quality of cloud computing based systems - (DRL).

SEM & z-test

H02

The difference between expectation and perception of assurance will not significantly load onto perceived service quality of cloud computing based systems - (DAS).

SEM & z-test

H03

The difference between expectation and perception of tangibles will not significantly load onto perceived service quality of cloud computing based systems - (DTN).

SEM & z-test

H04

The difference between expectation and perception of empathy will not significantly load onto perceived service quality of cloud computing based systems - (DEM).

SEM & z-test

H05

The difference between expectation and perception of responsiveness will not significantly load onto perceived service quality of cloud computing based systems (DRS).

SEM & z-test

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Service Quality and Perceived Value of Cloud Computing-Based Service Encounters

Table 4. Descriptive statistics for expected, perceived, and difference scores Score

Min

Max

M

SD

Skewness

Kurtosis

Expected (E) ETN

1.00

7.00

5.27

1.00

-1.03

1.87

ERL

1.00

7.00

5.83

1.37

-1.17

0.86

ERS

1.00

7.00

5.71

1.42

-0.98

0.17

EAS

1.00

7.00

5.71

1.27

-1.29

1.68

EEM

1.00

7.00

5.74

1.33

-1.11

1.09

PTN

1.00

7.00

5.09

1.09

-0.42

0.37

PRL

1.00

7.00

5.69

1.35

-0.89

0.32

Perceived (P)

PRS

1.00

7.00

5.37

1.47

-0.60

-0.20

PAS

1.00

7.00

5.39

1.40

-0.84

0.51

PEM

1.00

7.00

5.32

1.42

-0.49

-0.23

Difference (E – P) DTN

-2.43

2.29

0.15

0.76

0.13

1.05

DRL

-2.25

3.00

0.08

0.81

0.43

1.93

DRS

-2.00

3.40

0.33

0.95

1.37

1.96

DAS

-2.33

3.17

0.33

0.82

1.18

2.07

DEM

-3.00

3.80

0.42

1.04

0.82

1.60

Prior to assessing the research question, the model fit of the empirical model (Figure 1) was examined through structural equation modeling (SEM) for goodness-of-fit. SEM was used to determine the model fit. Perceived quality of service (PSQ) is not a measured construct, and thus regression analysis is not possible. PSQ is a first order latent variable made up of the differences between expectation and perception of reliability, assurance, tangibles, empathy, and responsiveness. To have a good model fit, the model should have a non-significant χ2 statistic. However, since the χ2 statistic can be unreliable for larger sample sizes, additional fit indices were also examined for to determine model fit (Kline, 2005). The comparative fit index (CFI) should be above 0.90. The root mean square error of approximation (RMSEA) should be below 0.10. Due to poor model fit, χ2(5) = 32.36, p 0,D2(x, y, z,α)=(-M(f /x, y, z,α)), if M(f/x, y, z,α) 0; D’(x, y, z,α) = (-D2(x, y, z,α)), M(f/x, y, z,α) Fj)            Replace j by the new solution;            End            A fraction (pa) of worse nests are abandon and            new once are built            Keep the best solutions (or nests with quality solutions);            Rank the solutions and find the current best;       End while       Post-process results and visualization; End

Cuckoos’ survival effort hopefully converges to a state that there is only one cuckoo society, all with the same profit values. During the analysis of the system, following steps are considered. • • • • • •

Determination of the information system. Definition of the distinctive Matrix. Discrimination relationship with Cuckoo algorithm. Realizing of the reduction. Forming of the decision rules. Classification of the new features.

Determination of the Information System Information system is a table in which rows consist of the units (samples) and in column includes the each unit features. S represents the information system and S=< U,A,V > consists of the U is a Units set, A symbolizes the conditional attributes of the each unit, V represents the each units attribute value. Any character of the attribute value is

∀a ∈ A

a : U →V a,

(2)

(Lee ve Vatchsevanos, 2002). Va is the a’s value set.

Definition of the Distinctive Matrix In S information system, M(A)=(mij)nxn represents the distinctive matrix for A attributes. M(A) can shown in

M (A)

 Φ  =   a ∈ A : a xi ≠a 

{

( )

(x )}



j

(3)

M(A) has distinctive symmetric matrix characteristics. Each element of the xi and different xj values compromise the M(A). Distinctive matrix shows the binary comparison result of the attributes different values.

2203

Rough-Set-Based Decision Model for Incomplete Information Systems

Applying Cuckoo Algorithm for Discrimination of Relationships In rough set discrimination relationship represents two approach which are upper and lower approaches. We can able to use the Cuckoo optimization algorithm to determination of the alternative upper and lower solutions for beginning points. Rules induced from the lower approximation concept certainly describe the rules, which are called certain. On the other hand, rules induced from the upper approximation of the concept describe the concept possibly, so these rules are called possible. Rough set approach is based on two concepts called upper and lower approach defined as follows: • •

Elements are strictly belong to related set. Elements are likely to belong to related set.

X represents the lower approach which includes the certain that the interior value and it combinates the primary cluster. AX = {x i ∈ U | [x i ]Ind (A) ⊂ X }

(4)

X upper approach, in the form of demonstrations and the intersection of the empty set’s combination of the non-primary cluster with X. AX = {x i ∈ U | x i  ∩ X ≠ 0} IND (A)

(5)

Realizing of the Reduction • • •

2204

Reduced Feature Sets: It represents the data structure in information system with minimum feature set values. Core Feature Sets: It occurs from the reduced features set’s shared elements. Similarity Matrix: Reduced feature sets used the similarity matrix with reduced feature sets elements. It is logical function and used the AND and OR functions.

Reduced feature sets and information system can defined lesser than feature. Realizing minimum feature sets represent the rule base.

Forming of the Decision Rules D = (U , A ∪ {d },V ) decision system is represented by D’s degree of the r(d). d decision features’ values equal to the counts of the set elements, which is including the degree of D r (d) are indicated with r (d), the value of d is equal to the number of elements in a set of decision attributes. d decision feature set value is Vd decision feature values set is

{V

1

∪ .... ∪V A

} . d decision

r (d ) A

feature, U units set is the

CLASS (d ) = {X A

1

}

r (d )

....X A

A

and  = X A x ∈U  k

X

k A

d (x )=V

  ' 1 ≤ k ≤ r (d )  d  (6) k

is the k th decision feature value and

decision class. Two unit values has the same decision feature value belong to the same decision class and represents with the U unit set is

{X

1 A

}.

r (d )

∪ .... ∪ X A

Validation Test of the Features Dependencies Dependency of the feature is between coefficient of the A conditional features set and d decision features that are represented with γ (A, d ) . γ (A, d ) =

POS (d ) A

U

(7)

Category: Decision Support Systems

γ (A, d ) have between 0 and 1 values.

γ (A, d ) = 1 is the decision feature value which

The standard Cuckoo Search algorithm includes the three basic rules (Yang and Deb, 2009):

depends on the A conditional feature sets. If γ (A, d ) ≤1 then d decision feature values depends

•

on the semi A conditional features set. In Listing 2, suggested algorithm of rough set based decision model is introduced. Listing 2. Algorithm of cuckoo search based rough set decision model

•

Input: Each m feature valued and C class set member’s n objects, U missing quantitative value data sets. Output: Strict and possible rough rule set Step 1: Label of class consider the discrete feature sub set and divide each other. Same C l class’s objects represent Xl .

Step 2: v (i ) represents the each Obj (i ) objects quantitative values for each Ai i=1 to nuntil j=1….m. Function values shown as follows:  p(i ) p(i ) p(ji )   j1 j2 l   + + .... +  Rj Rj   Rj1 2 l

(8)

For simplicity, this last assumption can be approximated by a fraction pa of the n nests being replaced by new nests (with new random solutions at new locations). When generating new solutions x (t+1) for, say cuckoo i, a Levy flight as a local random walk is performed x i(t+1)= x i(t) + α ⊗ L (s, λ),

(10)

where α > 0 is the step size which should be related to the scales of the problem of interest. The L function as a global random walk is  

Rjk represents the Aj ’s k th region strict value and l (=| Aj |) Aj shows the region count. (*) or (?) symbols symbolize the missing values. Step 3: Missing has unclear value with the primary cluster of qualifications. If, for this the exact possible value ‘= ‘ equity class into the possible incomplete (c) format ; For trivial value (*) imprecise into each missing class equality (u) format ; Lost value for the (?) It was the object located in the same area as unclear fuzzy missing class into the equation (l) is placed. Possible values is calculated as follows: to be precise =, and 0; PAjk =Min p(jki)

•

Each cuckoo lays one egg at a time, and dumps it in a randomly chosen nest; The best nests with high quality of eggs (solutions) will carry over to the next generations; The number of available host nests is fixed, and a host can discover an alien egg with a probability pa∈ [0, 1].

(9)

L(S,λ)=

( ) sin  2 

  1 (s >>s 0 > 0) s 1+

(11)

In most cases, we can use α = O(1). The product ⊗ means entry-wise multiplications. Levy flights essentially provide a random walk while their random steps are drawn from a Levy distribution for large steps: x i(t+1)= x i(t) + α ⊗H (pa-∈) ⊗(xj(t)-xk(t)),

(12)

which has an infinite variance with an infinite mean. Here the consecutive jumps/steps of a cuckoo essentially form a random walk process which obeys a power-law step length distribution with a heavy tail. In this case, the host bird can either throw the egg away or abandon the nest so as to build a

2205

D

Rough-Set-Based Decision Model for Incomplete Information Systems

completely new nest in a new location. For simplicity, this last assumption can be approximated by a fraction pa of the n nests being replaced by new nests (with new random solutions at new locations). For a maximization problem, the quality or fitness of a solution can simply be proportional to the objective function. Step 4: Start with q = 1. Then q determines the count of the number of the current feature with lower approach. Cuckoo lays one egg at a time, and dumps it in a randomly chosen nest with Levy approach in this step with below loops: Obtain C1,…,k group solutions in the solution space       For i=1 to K            Situation(Ci) =

∑

x ∈Ci

f (x )

solution count

      End aim =ArgMini∈{1,...,K} average(Ci) [Minimization problem] h_situation=ArgMini∈Caim f(x) [Minimization problem]       For i=1 to N            For k=1 to d                 xi,k=h_ situation k + λ*(xi,k-h_ situation k)            End       End

Step 5: Each Xl class consider the q feature in A subset value with lower approach calculate as follows: A* (x ) ={ ( Ak (Obj (i ) ), PA (Obj (i ) )) | 1 ≤ i ≤ n, k

Obj

(i )

∈ Xl ,

Akc (Obj (i ) ) ⊆ Xl ,1 ≤ k ≤ |A(Obj (i ) ) |}

2206

(13)

A feature sub set includes the Obj (i ) , which is derived the missing similarity class value that represents Akc (Obj (i ) ). It is the k th strict part. Step 6: Each missing values for Obj (i ) object calculated the following steps are followed: a. If Obj (i ) object is the A th k combinations, which is applied lower approach to the subset, then obtained the uncertain value as follows:

Obj

(r )

∑

∈Akc (Obj ( r ) )

∑

v (jr )xp(jkr )

Obj ( r ) ∈Akc (Obj ( r ) )

p(jkr )



(14)

v (jr ) is the quantity value for Aj in Obj (r ) and f jk(i ) , RBk is the membership value for v (jr ) . Thus, predicted Obj (i ) values changed in rough sets. Rough missing equation class has zero membership values (Obj (i ) ,u) or deleted (Obj (i ) ,l) changed with (Obj (i ) ,c). Rough missing approach evaluates the similarity class values again. In addition Obj (i ) function applies to same process with the lower approach. b. Obj (i ) object compares the one more than values with lower approach and postpone the prediction of the missing value until determine of the new features. Step 7: q = q + 1. Then if q> m then repeat Steps 5- 7. Step 8: Obj (i ) object has multiple missing value, which consideres the maximum numerical expression to estimate the significance of uncertain value object. For estimate and process of the data go to Step 6 (a). Step 9: Each missing a certain rules derived with the lower approach and next future data determined with the rough set approach.

Category: Decision Support Systems

Step 10: Final rule’s effectiveness measures then compares the same exact condition rules and delete the equal and smaller. Step 11: If q = 1 than do. q is used to count the number of missing upper approach. Cuckoo lays one egg at a time, and dumps it in a randomly chosen nest with Levy approach in this step with below loops: Obtain C1,…,k group solutions in the solution space       For i=1 to K            Situation(Ci) =

∑

x ∈C i

f (x )

solutioncount

      End aim =ArgMaxi∈{1,...,K} average(Ci) [Maximization problem] h_situation=ArgMaxi∈Caim f(x) [Maximization problem]       For i=1 to N            For k=1 to d                 xi,k=h_ situation k + λ*(xi,k-h_ situation k)            End       End

Step 12: Each Xl class’s q quality, each subset A missing degree calculate with upper approach as follows: A* (x )={ ( Ak (Obj (i ) ), µB (Obj (i ) )) | 1 ≤ i ≤ n, Akc k

(Obj ) ∩ Xl ≠ ϕ , A (Obj (i ) ) ⊄ Xl , 1 ≤ k ≤ |A( (i )

Obj (i ) )|}

c k

(15)

A(Obj (i ) ),Obj (i ) a set of equations derived missing subset of the class that contains Akc (Obj (i ) ), A(Obj (i ) )the k-th class defined part of the equation.

Step 13: Rough set approach in each Obj (i ) object of the missing upper method applied to imprecise (trivial or loss), the following steps are: a. Obj (i ) object with missing upper approach, is assigned the k-th sub set of A feature set RBk in the combination of the area, which represents the missing Obj (i ) uncertain value as follows:

∑

Obj ( r ) ∈Akc (Obj ( r ) )&Obj ( r ) ∈Xl

∑

v (jr )xp(jkr )

Obj ( r ) ∈Akc (Obj ( r ) )&Obj ( r ) ∈Xl

p(jkr )



(16)

b. Obj (i ) function, if one or more missing value is presented than finding of the feature process postponed until the missing information is estimated to have identified them. Step 14: q = q + 1 and if q> m then repeat Steps 11-13 otherwise Stop. Step 15: Each Xl class missing rough class is used to upper approach for suitable degree as follows:

∑

P( Akc (Obj (i ) ))=

Obj ( r ) ∈Akc (Obj ( r ) )&Obj ( r ) ∈Xl

∑

Obj ( r ) ∈Akc (Obj ( r ) )

p(jkr )

p(jkr )

(17)

Step 16: Obj function represents the missing rough low approach, multiple rough equality missing class introduces, use a maximum suitability measure for estimating the value of certain non- object. Estimates and processing to Step 13 (a) is performed as follows. Step 17: Possible rules from the rough set approach evaluate subset of missing each Class A and the estimated size of the objects are derived. In addition, the measure of effectiveness for future data determined from the possible values of equality classes. (i )

2207

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Rough-Set-Based Decision Model for Incomplete Information Systems

Step 18: Possible essential part of the rules are the same, which both the effectiveness and appropriateness of the measure value are equal to or less than clear, that specialized and other possible precise and rules determined.

FUTURE TRENDS The development of decision support one of the key issues in the uncertainties and in- completed situations. The decision strategy developed in this paper provides a way for designing rough set theory are application independent, and improves the performance of decision system with possible proper decision with uncertainty. The experiments have shown that the proposed approach is effective and promising. This study focuses on an algorithm that provides valuable quantitative inferences from incomplete data sets with cuckoo search algorithm based rough set theory for decision rules. Suggested algorithm of rough set based decision model will be used in decision support system for real life applications in various aspects. For future research, it is noted that cuckoo search algorithm based rough set based decision model involves not just modelling the data but also modelling operations on the data. Also different methodological approach in hybrid structure will be developed such as matrix based; entropy based; multi integer programming approach and feature extraction approach for alternative making decision style.

CONCLUSION This article discusses a variety of issues in adapting rough set approach in distributed decision system. This study shows how rough set approach can be introduced to different aspects of rule execution from event detection to different situations with sensitivity analysis. Our suggested model includes the determination of information system; defini-

2208

tion of the distinctive matrix; determination of the discrimination relationship; realizing of the reduction; forming of the decision rules; classification of the new features. Our next work, which considers as a rough set theory with sensitivity analysis in different factors. Our suggested cuckoo search algorithm based rough set model can be applied to distributed decision system. The conditional attribute structure, discernibility matrix is shown in detail. We discussed the distributed decision information system for uncertainty and incomplete data. In addition, this algorithm is applied to data sets containing only the missing attribute value of lost species; it is found that increasing the number of rules obtained in this way and reduces the success rate. Missing attribute the increase in the number formed by the success rate of decline in large-scale and useful information obtained due to the increase in the number of rules.

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Shu, W., & Qian, W. (2015). An incremental approach to attribute reduction from dynamic incomplete decision systems in rough set theory. Data & Knowledge Engineering, 100, 116–132. Spiric, J. V., Stankovic, S. S., Docic, M. B., & Popovic, T. D. (2014). Using the rough set theory to detect fraud committed by electricity customers. International Journal of Electrical Power & Energy Systems, 62(November), 727–734. doi:10.1016/j.ijepes.2014.05.004 Sun, L., Xu, J. C., & Tian, Y. (2012). Feature selection using rough entropy-based uncertainty measures in incomplete decision systems. Knowledge-Based Systems, 36, 206–216. doi:10.1016/j. knosys.2012.06.010 Swiniarski, R. W., & Skowron, A. (2003). Rough set methods in feature selection and recognition. Pattern Recognition Letters, 24(6), 833–849. doi:10.1016/S0167-8655(02)00196-4 Tseng, T.L., Huang, C.C., Fraser, K., & Ting, H.W. (2016). Rough set based rule induction in decision making using credible classification and preference from medical application perspective. Computer Methods and Programs in Biomedicine, 127, 273-289. Tsumoto, S. (1998). Automated extraction of medical expertsystem rules from clinical databases based on rough set theory. Information Sciences, 112(1-4), 67–84. doi:10.1016/S00200255(98)10021-X Yang, X.-S., & Deb, S. (2009). Cuckoo search via Levy flights. In Proc. of World Congresson Nature \& Biologically Inspired Computing (pp. 210–214). IEEE Publications. doi:10.1109/ NABIC.2009.5393690 Yao, Y. Y. (2001). Information granulation and rough set approximation. International Journal of Intelligent Systems, 16(1), 87–104. doi:10.1002/1098-111X(200101)16:13.0.CO;2-S

Category: Decision Support Systems

Yao, Y. Y. (2010). Three-way decisions with probabilistic rough sets. Information Sciences, 180(3), 341–353. doi:10.1016/j.ins.2009.09.021 Yao, Y. Y., & Zhao, Y. (2008). Attribute reduction in decision-theoretic rough set models. Information Sciences, 178(17), 3356–3373. doi:10.1016/j. ins.2008.05.010 Yumin, C., Duoqian, M., & Ruizhi, W. (2010). A rough set approach to feature selection based on ant colony optimization. Pattern Recognition Letters, 31(3), 226–233. doi:10.1016/j.patrec.2009.10.013 Zhang, Y., Li, T., Luo,C., Zhang, J., Chen, H. (2016). Incremental updating of rough approximation in interval-valued information systems under attribute generation. Information Sciences, 373, 461-475. Zhong, N., Dong, J. Z., & Ohsuga, S. T. Y. L. (1998). An incremental probabilistic rough set approach to rule discovery. The IEEE International Conference on Fuzzy Systems, 2. doi:10.1109/ FUZZY.1998.686243 Zhong, N., & Skowron, A. (2001). A rough setbased knowledge discovery process. International Journal of Applied Mathematics and Computer Science, 11, 603–619.

ADDITIONAL READING Hu, B. (2016). Three-way decision spaces based on partially ordered sets and three- way decisions based on hesitant fuzzy sets. KnowledgeBased Systems, 91, 16–31. doi:10.1016/j.knosys.2015.09.026 Nina, F. R. C. (2007). “ On Applications of Rough Sets Theory to Knowledge Discovery”, Doctor pf Philosophy Thesis in Computing and Information Sciences and Engineering, University of Puerto Rico.

Rissino, S. Lambert-Torres (2009). “Rough Set Theory – Fundamental Concepts, Principals, Data Extraction, and Applications”, Data Mining and Knowledge Discovery in Real Life Applications, Julio Ponce and Adem Karahoca, eds., Vienna, Austria: I-Tech, 438. Wei, D., Zhao, Y., & Zhou, X. (2006). “A Rough Set Approach to Incomplete and Fuzzy Decision Information System”, Proceedings of the 6th World Congress on Intelligent Control and Automation, June 21 - 23, 2006, Dalian, China. Yang, X. S., & Deb, S. (2013). Multi objective cuckoo search for design optimization. Computers & Operations Research, 40(6), 1616–1624. doi:10.1016/j.cor.2011.09.026

KEY TERMS AND DEFINITIONS Alternative Set Theory: It means system set theory that related the positive set theory and constructive set theory. Attribute: Refers with decision table in rough set which is divided into two disjoint groups called condition and decision attributes (action, results, outcome, etc.). Cuckoo Search Algorithm: A method of global optimization based on the behaviour of cuckoos was proposed by Yang & Deb (2009). The breeding behaviour types are, laid their eggs in the host nests; if not detected and destroyed, the eggs are hatched to chicks by the hosts. Decision Rules: It determines decision with rules under certain and uncertain conditions. Decision Support System: It is computer based information system that support decision making activities with inputs, user knowledge and expertise, outputs and decision components. Incomplete Information: Three types of incomplete data consists of attribute values which are lost values; attribute missing values; irrelevant concept data in attribute.

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Indiscernibility Relation: It is a central concept of Rough Set Theory which relates between two or more objects identical relation in subset of the attributes. Information System: It consists of objects and attributes that shown in table with rows-objects and columns-attributes. Lower Approximation: It consists of all objects which surely belong to the set. Rough Relations: Collection of such relations is closed under different binary compositions

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such as, algebraic sum, algebraic product etc. for uncertainty and incomplete data. Rough Set Theory: It is first described by Zdzislaw I. Pawlak in early 1980’s. Every object of the universe of discourse some information (data, knowledge) is associated with lower and upper approximation. Sensitivity Analysis: Analysis of the uncertainty output in system is evaluated by the different certain or uncertain input. Upper Approximation: It contains all objects which possibly belong to the set.

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Category: Decision Support Systems

Using Receiver Operating Characteristic (ROC) Analysis to Evaluate Information -Based Decision-Making Nan Hu University of Utah, USA

INTRODUCTION Business operators and stakeholders often need to make decisions such as choosing between A and B, or between yes and no. These decisions include, but are not limited to, whether to invest in project A versus project B, or whether to continue running a company. These are often made by using a classification tool or a set of decision rules. For example, banks often use credit scoring systems to classify lending companies or individuals into a high or low risk of default, thus helping to decide whether to grant a loan. One important question businesses need to answer is how accurate the information based on these classification tools can help them make a correct decision, or how correctly they can be used to discriminate between two groups of subjects. In this chapter, we address this important issue by presenting accuracy parameters for assessing classification tools such as test modalities, scoring systems, and prediction models. Specifically, we introduce the receiver operating characteristics (ROC) curve as a statistical tool to evaluate these modalities. The ROC curve is widely used in business optimization analysis, health policy making, clinical studies, and health economics (Kampfrath & Levinson, 2013). In the Background section, we give updated examples of using the ROC related methods for assessing decision-makings based on our most current literature review. In the Main Focus section of this chapter, we provide mathematical definitions of the classification accuracy parameters, and describe the procedure to obtain an ROC curve. In addition, we present recent statistical developments

in ROC curve methodologies and applications of ROC analysis in a diversity of research areas.

BACKGROUND Business classification tools include scoring systems, predictive models, and quantitative test modalities. A classification tool is useful in business analytics only if it is shown to distinguish entities with a certain condition from those without that condition. For instance, a credit scoring system is a valuable classification tool for bankers when it can accurately classify between companies with default status (cases) and without default status (controls). A perfect test modality would categorize all default companies as cases and all non-default companies as controls. However, in practice, almost none of the testing modalities can make such a perfect classification. This implies that misclassifications can always exist and the correct classification rate may vary from one test to another. Thus, assessing classification performance among different test modalities is always a necessary step in making important businessrelated decisions.

MAIN FOCUS We first define accuracy parameters of binary classification tools, and then extend the evaluation method to test modalities with continuous or discrete ordinal values. By applying accuracy parameters and ROC analysis, business analysts

DOI: 10.4018/978-1-5225-2255-3.ch192 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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can easily examine the expected downstream harms and benefits of positive and negative test results based on these test modalities, and directly link the classification accuracy to important decision making (Cornell, Mulrow & Localio, 2008).

Accuracy Parameters for Classification and Decision Making The accuracy of decision making should be measured by comparing the decision taken by a business to the choice that would be taken in order to maximize its benefit. In this section, we introduce two basic accuracy parameters, sensitivity and specificity, and two misclassification measures, the false positive rate and false negative rate. We define accuracy parameters in the context of classifying the default status of borrowers (companies that apply for a loan). Let S denote the dichotomous true default status such that S = 0 represents “no default,” and S = 1 indicates “default.” Let Y be the value of a test modality or scoring system. We suppose that Y is also binary such that Y = 1 denotes the test positive for default, and Y = 0 indicates the test negative. In reality, companies with a positive test result are often refused for a loan. The sensitivity of the binary test Y is defined as the probability of test positive among companies with default status (S = 1). Mathematically, this probability can be expressed as Sensitivity = Pr(Y = 1 | S = 1), where the symbol | denotes the statistical concept of conditioning, the definition of which can be found in introductory statistics books such as Wasserman (2004), Chap. 1. The sensitivity of a test is also known as the true positive rate (TPR). Another important accuracy parameter is the specificity of Y, which is defined as the probability of test negative when the default status is absent. This probability is given by Specificity = Pr(Y = 0 | S = 0).

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Specificity is often used interchangeably with the true negative rate (TNR) in the literature. Both sensitivity and specificity are correct classification rates of a test. Since such a test may also misclassify subjects, error rates are of interest as well. There are also two types of misclassification rates. The first is the false positive rate (FPR), which is defined as the probability of test positive when the default status is absent. Mathematically, FPR = Pr(Y = 1 | S = 0). A false positive occurs when a “refusal of loan” decision is made to companies that would never default. By examining the definitions of FPR and specificity, we note that FPR = 1- specificity. Another misclassification rate is the false negative rate (FNR), which is the probability of test negative when the default status is present. This rate can be expressed by FNR = Pr(Y = 0 | S = 1). A false negative occurs when a loan is granted to a company that later defaults on the loan. Also, we note that FNR=1-sensitivity. Table 1 summarizes the aforementioned accuracy and misclassification parameters. The rows of this two-by-two table are split by the true default status (S = 1 versus S = 0), and columns are classified by test results (Y = 1 versus Y = 0). In each of the four cells defined by S and Y, the top row displays the frequency of the cell and the bottom row lists the mathematical equation for the accuracy parameter or misclassification rate corresponding to that cell.

Comparing Test Modalities With Binary Values In the process of decision making, business analysts often have several candidate test modalities with binary values without knowing which modality has the best classification accuracy.

Category: Decision Support Systems

Table 1. Frequencies and mathematical equations of accuracy measures for a binary test (Y) Test Results

D

Positive (Y=1)

Negative (Y=0)

Total

Positive (S=1)

True positives A Sensitivity = A/(A+B)

False positives B False positive rate = B/(A+B)

A+B

Negative (S=0)

False negatives C False negative rate = C/(C+D)

True negatives D Specificity = D/(C+D)

C+D

A+C

B+D

A+B+C+D

True Status

Total

For example, banks often want to minimize the chance for granting loans to companies that could default. In another words, banks wish to obtain the test modality that leads to the highest sensitivity. In contrast, when evaluating new projects for investment, business owners prefer to reduce the chance of a poor investment to the greatest extent possible. That is, they need a test modality that produces the greatest specificity. In order to compare different test modalities, historical data based on a relatively large sample is needed. Unbiased sampling from the study population is also required. To compare two different modalities, two types of study design can be adopted: unpaired design and paired design. Under the unpaired design, the two test modalities are applied to two different groups of subjects or companies. Thus, each subject experiences only one of the two tests. Under the paired study design, every subject is tested by both modalities (Pepe, 2003). In practice, business analysts may give priority to comparing either sensitivity or specificity between tests. In order to obtain statistical significance of the comparisons, a hypothesis test on two different proportions should be used. Under the unpaired design, test for the proportions of two candidate classification modalities is asymptotically a two-sample normal test, or Z-test. Thus, when the sample size is large, we can always perform a Z-test to compare sensitivities or specificities. However, when the sample size is small, Fisher’s exact test should be used. Under the paired design, McNemar’s test for paired binary data should be used to compare candidate

modalities. Statistically, this is a Chi-squared test with one degree of freedom. The aforementioned hypothesis tests can be easily performed using commercial software such as SAS (SAS Institute, Inc., Cary, NC, USA) and STATA (Stata Inc., College Station, TX, USA), and the free statistical package R (www.r-project.org).

ROC Curves Many test modalities are measured on a continuous or discrete ordinal scale. When such tests are evaluated, we cannot define their sensitivity and specificity without dichotomizing their distributions. A standard and popular tool for evaluating accuracy of these modalities is the receiver operating characteristic (ROC) curve, which has been studied or described in detail in literature such as Swets and Pickett (1982), Swets (1988), Hanley (1989), Begg (1991), Chock, Irwig, Berry & Glasziou (1997), Zhou, Obuchowski & McClish (2002), and Pepe (2003). To dichotomize the continuous distribution, we usually need to specify a testing threshold or cut-point upon which to base our decision. In this section, we use an example of classifying the default status to illustrate features of the ROC curves. There are two dimensions of an ROC curve. The first is the gold standard (GS) of the test, also known as the reference standard (RS), which is the true status being classified. It is often assumed that the GS for each observation can be confirmed without error. In assessing a credit scoring system, the true default status for each company is the GS, which can be determined based on the bank’s guidelines

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for default. Although a GS is binary in nature, it can also take on more than two categories. For example, in personal health assessment, the true status could be “excellent,” “very good,” “good,” “fair,” or “poor.” In order to use ROC analysis to assess risk prediction models, investigators often need to dichotomize the categorical variable. Perneger & Courvoisier (2011), in their study of assessing multi-attribute health utility measures, dichotomized the true health status as “excellent” or “very good” versus “good,” “fair,” or “poor.” Classifiers are another aspect of ROC curves. They are the measurements being used for classifications. To classify the default status, the classifier could be a credit scoring system or a risk predictive score estimated from a regression model. When the single threshold value for classifiers needed to make a decision is not known a priori, an ROC curve provides a useful description of accuracies for the classifiers. The ROC curve plots a test’s sensitivity versus its FPR (or 1-specificity) at each cut-point c. As the cutpoint defining the positivity changes across all

possible values of the test scale, the ROC curve is plotted. In other words, an ROC curve tracks all points of sensitivity and FPR pairs at all possible threshold values. The number of points on the ROC curve is determined using the possible number of available thresholds. For continuous test results with a known distribution, there are an infinite number of cut-points from which we can choose to dichotomize the distributions of cases and controls. In this situation, the resultant ROC curve is smoothed with an infinite number of points. To mathematically define an ROC curve, let Y be a random variable denoting the value of a test modality with the convention that higher values of Y are correlated with a higher probability of being a case. Let S denote the GS status such that S = 1 indicates a case and S = 0 indicates a control. YD and YC represent test results for cases and controls, respectively. When a cut-point value c is used to dichotomize the distribution of Y, the sensitivity and FPR are mathematically given by:

Figure 1. Distribution of test result for cases/controls and the corresponding ROC curve

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Category: Decision Support Systems

Sensitivity = Pr(Y > c | S =1) = SD(c) = 1- FD(c), and

where FD(c) and FC(c) represent the distribution functions of YD and YC, respectively. If we denote the FPR at cut-point c as FPR(c) = SC(c) = v, then the ROC function is defined as the corresponding sensitivity at FPR = v. Mathematically, this is a composite function of SD and SC in the following form:

mation about classification accuracies. Because an ROC curve is a plot across all possible decision thresholds, it does not require the specification of threshold values to generate the curve (Zweig and Campbell, 1993). It is independent of the number of cases that occur among the whole population, and it is invariant with respect to monotone transformations that keep the rank of the test score for each individual. ROC curves are monotonic and non-decreasing. An ROC curve that is located closer to the upper and left boundary of the unit square indicates better performance of the test.

ROC(v) = SD(c) = SD(SC-1(v)),

Area Under the Curve

where SC -1(.) denotes the inverse function of SC such that SC -1(v) = c is equivalent to SC(c) = v. Figure 1 shows the process of making an ROC curve, and the relationship between the ROC curve and distributions of test modality Y. To recap, YD and YC are used to denote the test results for cases and controls, respectively. We assume that the test modality here is the predicted risk score from a regression model, cases represent companies that would default, and controls are companies that would not default. In Figure 1, the distribution for controls is located to the left of the distribution for cases because cases usually have higher estimated risk scores. There is an overlapping of the two distributions as represented by the shaded area. At a given threshold c, the sensitivity of the test can be visually represented by the area under the distribution of YD from c to the maximal value of YD. This sensitivity is shown on the y-axis of the ROC curve for the corresponding risk score (the right panel of Figure 1). In addition, the shaded area on the right side of c represents the probability that YC is greater than c, which is the FPR of the risk score and corresponds to the x-axis of the ROC curve. As the cut-points are changed over an infinite number of all possible test values, a continuous ROC curve from point (0, 0) to (1, 1) within the unit square is obtained. ROC curves are simple and straightforward graphical tools that convey comprehensive infor-

The area under the ROC curve (AUC) is an indicator of the overall accuracy of classification tools. The AUC can be interpreted in the following three ways (Zhou, Obuchowski & McClish, 2002): (1) the average sensitivity over all possible values of specificity; (2) the average specificity across all possible values of sensitivity; and (3) the overall concordance of the test values with the gold standard. Specifically, the AUC equals the probability that an underlying test value Y will assign a greater probability of being a case than that of being a control (Greenland, 2008). In this sense, the AUC is equivalent to the concordance C-statistic proposed by Harrell (2001).

FPR = Pr(Y > c | S = 0) = SC (c) = 1 - FC(c),

Choosing the Optimal Decision Threshold Test modalities with continuous values, by themselves, do not usually constitute compelling evidence that leads directly to either a positive or a negative decision (Swets, 1992). Rather, a cut-point needs to be specified in order to define a positive or negative decision for or against the condition being classified. In practice, a cut-point can be set anywhere along the range of the test scale such that values above the cut-point uniformly lead to a positive decision, and values below it result in a negative decision. Although an ROC curve is plotted across all possible cut-points of the test,

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accuracy parameters (i.e., sensitivity and specificity) cannot be determined unless a cut-point is fully specified. This implies that the resultant test accuracies are linked to the testing threshold used in the study (Zweig & Campbell, 1993). Choosing a particular decision threshold that is best for a given purpose or for a specific situation is not a trivial task, since many aspects of the classification need to be considered and balanced. In the area of health economics, for example, it has been argued that all related economics techniques and principles should be used to identify a testing threshold (Phelps & Mushlin, 1988; Zweig & Campbell, 1993; Laking, Lord & Fischer, 2006). First, sensitivity and specificity, as performance criteria, are highly correlated (Greenland, 2008; Sanghera, Orlando & Roberts, 2013). Thus, they cannot be considered separately, and the optimal threshold should have an appropriate balance between sensitivity and specificity (Zweig & Campbell, 1993). Consider the breast cancer screening test. While high sensitivity is required in such tests, a low specificity (or high FPR) usually cannot be tolerated, since otherwise a large proportion of healthy women will be referred to invasive confirmative tests such as biopsy. In addition, cut-points should be specified in the most cost-effective way. Many authors proposed to use costs associated with each classification rate as the major criteria. For example, Greenland (2008) pointed out that costs are a crucial element in the optimization process, and proposed the use of a cost-related loss function to define decision rules and specify optimal thresholds. In health economics studies, many authors have suggested that decision criteria should incorporate not only the cost of misclassifications but also information regarding cases and controls. Phelps and Mushlin (1988) proposed a criterion called “expected value of information” (VOI), which is a comprehensive cost-related parameter that combines decision accuracies and epidemiologic information about disease prevalence. The VOI method defines the net benefit of a decision related to a certain disease, which is the difference between the total utility of

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a decision and the total cost of the decision. Then, a formula is applied to ensure that the optimal net benefit of a test modality is identified for a given disease prevalence (Altman & Bland, 1994). The results are then used to create a tangent on the ROC curve, and the point where the tangent and the ROC curve meet is the optimal cut-point. This process can be extended to accommodate a range of prevalence values, sensitivities, and specificities.

Extension to TimeDependent ROC Analysis When the binary decision space is a function of time, the traditional ROC analysis introduced in previous sections will no longer be adequate. For example, in assessing the performance of credit scoring systems for detecting default risk, a company’s default status can change over time. In order to accommodate this situation and obtain appropriate evaluations, a time-dependent ROC analysis should be used to assess the accuracies of the credit scoring system in a time-varying fashion (e.g., to assess the accuracy for predicting default status three years after the loan is granted). In medical decision making, time-dependent ROC analyses can be applied to evaluate the prognostic accuracy of various biomarkers for the early detection of cancers. If a prognostic tool is clinically accurate enough, doctors could make decisions based on the prognostic modality and give special treatment priority to patients with bad prognostic results. Hu & Zhou (2010) and Hu (2013) summarized the up-to-date statistical methods in time-dependent ROC analysis, and readers who are particularly interested in this issue are referred to their paper for an overview.

Applications ROC analyses can also be used to evaluate a certain component or parameter in a credit scoring system or a prediction model. Garanin et al (2014) utilized ROC curve analysis for assessing the performance of credit decision making by testing the classifica-

Category: Decision Support Systems

tion accuracy among loan applicants. The authors used the logistic regression model to evaluate the probability of default, and used the ROC curve to test credit scoring and used logistic regression model to test the variables selection in scorecard (Garanin, Lukashevich, & Salkutsan, 2014). Vlasselaer and colleagues used the area under the ROC curve to evaluate the benefit of including certain predictor variables in the Anomaly Prevention using Advanced Transaction Exploration (APATE) model that united both intrinsic features and net-work based features (Vlasselaer et al., 2015). The goal of using the APATE method is to better detect fraudulent credit card transactions by analyzing the customer past spending history and behaviors into useful meaningful features and make comparisons of those feature with a new incoming transaction. From deriving recency, frequency and monetary (RFM) characteristicscustomer past incoming transactions and spending history, intrinsic features can be generated. The authors found that, when the model included only 9 network variable, the model reached an AUC of 0.92. However, when the model included all of the social network and RFM variables, the model reached the highest AUC scores of 0.987. They, thus, concluded that RFM variables are good predictors of fraud and there is a strong bind between intrinsic aspects and net-work based feature. ROC curve analysis is also widely used in examining the performance and utility of a credit scoring system. Byanjankar and colleagues used ROC analysis to evaluate the neural network credit scoring system for detecting the borrowers’ credit-worthiness in the peer-to-peer (P2P) lending (Byanjankar, Heikkila, & Mezei, 2015). In addition, ROC analysis is intensively used in assessing the performance on recently proposed ensemble models for predicting the loan risk. Goyal & Kaur (2016) was seeking for optimum results in credit risk by using support vector machine (SVM), the random forest network, and

Tree model for Genetic Algorithm, and combing these three models (Ensemble model) to analyze the loan risk (Goyal & Kaur, 2016). They crossvalidate the results by using 70% of their data as the training set and the rest 30% as the validation set. Accuracy of model was performed based on ROC and the AUC. The authors claimed that the accuracy rate was the highest, in terms of AUC, for the ensemble model, which optimize the result for loan risk prediction. Yao & Lian (2016) also proposed a SVM based Ensemble Model, named SVM-BRS. Their model combined the random subspace, boosting and SVM classifier. The authors also use AUC under the ROC curve as the metric to evaluate the classification accuracy of different models. The authors found that SVM-BRS has a better performance than a single model because SVM-BRS as it had the highest AUC of 77.4%. Figini and colleagues conducted the ROC analysis and use AUC as the metric to compute weights in aggregation probabilities in the weighting ensemble scheme, these authors also used the AUC for evaluating the prediction accuracy. Regis and Artes assessed credit card risks using multi-state Markov models by studying the attributes of different state transition in client-institution relationships over time, and proposed score models for different purposes (Regis & Artes, 2016). The authors compared the results of logistic regression to the results of multistate Markov models using a Brazilian financial institution database (Regis & Artes, 2016). The category of a client was classified as bad when the credit score is lower or equal to a cut-point c, and as a good when the credit score is higher than c. They used area under the ROC curve to evaluate the quality of model. They authors found that the multi-state Markov models had the better performance than logistic regression models in predicting the risk of default risk, but logistic regression models had better performance when predicting the loan cancellation risk.

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Applications of ROC Analysis in Areas Other Than Financial Decision Making In addition to applications in business analytics and optimization, ROC analysis is widely implemented in medical prognosis, diagnosis (Zhou, Obuchowski & McClish, 2002; Pepe, 2003), and psychometrics (Swets, 1986; Dolle, Schulte-Körne, O’Leary, von Hofacker, Izat & Allgaier, 2012). ROC analyses have recently been extended to several business-related fields such as the analysis of economic evaluations (Laking, Lord & Fischer, 2006; Sutton, Cooper, Goodacre & Stevenson, 2008; Sanghera, Orlando & Roberts, 2013), assessment of financial parameter-derived classifiers for deciding if cloud computing should be used in projects (Kornevs, Minkevica & Holm, 2012), analysis of health administrative data (Nachev, Hill, Barry & Stoyanov, 2010; Benchimol, Manuel, To, Griffiths, Rabeneck & Guttmann, 2011), financial distress analysis (Shams, Sheikhi & Sheikhi, 2011), and examining customer churn predictions (Ballings, Van den Poel & Verhagen, 2012), analyzing credit risk of small and medium enterprises (SMEs) in China (Chen, Wang & Wu, 2010). Particularly, with a growing focus on comparative effectiveness research and personalized medicine, ROC analysis will play a more important role in health care decision-making. Specific applications of ROC analysis in health care research include predictive model validation, biomarker diagnostics/prognostics, responder analysis in patient-reported outcomes, and comparisons of alternative treatment options (Alemayehu & Zou, 2012).

FUTURE TRENDS With the rapid increase in the number of new decision making tools and risk predictive models, the assessment of test modalities will become a key element in business analytics, policy making,

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and health economics. Benchimol, Manuel, To, Griffiths, Rabeneck & Guttmann (2011) claim that sensitivity, specificity, and the C-statistic (AUC) are among the areas of priority when designing and assessing the quality of validation studies. With the recent development of statistical methodologies in ROC regression (Pepe, 2003), AUC regression (Dodd & Pepe, 2003), and time-dependent ROC methods (Hu & Zhou, 2010), business analysts and policy makers can perform more specific analyses when assessing decision making (e.g., the covariate effect on classification accuracies and time-specific predictions).

CONCLUSION Classification modalities, including decision making rules, test scoring, and risk predictive models, are quantitative tools that can help businesses make correct decisions. Assessing accuracy is the most important procedure in validating these tools. Sensitivity and specificity are two important aspects of classification accuracy. For a test modality with continuous or discrete ordinal values, an ROC curve plots its sensitivity versus FPR (1-specificity) across all possible decision thresholds, and the area under the ROC curve (AUC) summarizes the overall performance of the modality. To find the optimal threshold value is a challenging task, and often requires the consideration of several aspects of decision making. In presenting this topic to business owners, operating analysts, and public policy makers, we suggest that ROC analysis will become a fundamental tool in making important business and policy decisions.

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Category: Decision Support Systems

Altman, D. G., & Bland, J. M. (1994). Statistics notes: Diagnostic tests. 1: Sensitivity and specificity. BMJ: British Medical Journal, 308(6943), 1552. doi:10.1136/bmj.308.6943.1552 PMID:8019315

Dodd, L. E., & Pepe, M. S. (2003). Semiparametric regression for the area under the receiver operating characteristic curve. Journal of the American Statistical Association, 98(462), 409–417. doi:10.1198/016214503000198

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Dolle, K., Schulte-Körne, G., OLeary, A. M., von Hofacker, N., Izat, Y., & Allgaier, A. K. (2012). The Beck Depression Inventory-II in adolescent mental health patients: Cut-off scores for detecting depression and rating severity. Psychiatry Research, 200(2), 843–848. doi:10.1016/j.psychres.2012.05.011 PMID:22657953

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Figini, S., Savona, R., & Vezzoli, M. (2016). Corporate Default Prediction Model Averaging: A Normative Linear Pooling Approach. Intelligent Systems in Accounting, Finance & Management, 23(1-2), 6–20. doi:10.1002/isaf.1387 Garanin, D. A., Lukashevich, N. S., & Salkutsan, S. V. (2014). The Evaluation of Credit Scoring Models Parameters Using Roc Curve Analysis. World Applied Sciences Journal, 30(8), 938–942. Goyal, A., & Kaur, R. (2016). Loan Prediction Using Ensemble Technique. International Journal of Advanced Research in Computer Communication Engineering, 5(3), 523–526. Greenland, S. (2008). The need for reorientation toward cost‐effective prediction: Comments on ‘evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Statistics in Medicine, 27(2), 199–206. doi:10.1002/sim.2995 PMID:17729377 Hanley, J. A. (1989). Receiver operating characteristic (ROC) methodology: The state of the art. Critical Reviews in Diagnostic Imaging, 29(3), 307–335. PMID:2667567 Harrell, F. E. (2001). Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis. Springer. doi:10.1007/978-1-4757-3462-1

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He, Y. H., Chen, Y. C., Jiang, G. X., Huang, H. E., Li, R., Li, X. Y., & Cheng, Q. et  al. (2012). Evaluation of anthropometric indices for metabolic syndrome in Chinese adults aged 40 years and over. European Journal of Nutrition, 51(1), 81–87. doi:10.1007/s00394-011-0195-2 PMID:21479941 Hu, N. (2013). Evaluating the over-time prognostic performance of biomarkers for cancer prognoses using time-dependent receiver operating characteristic (ROC) curve. Transl Med (Sunnyvale), 3(2), 45–46. Hu, N., & Zhou, X.-H. (2010). A review of timedependent ROC curve for evaluating the prognosis capacity of biomarkers and semiparametric regression methods. Proceeding of Joint Statistical Meeting, 3336-3347. Kampfrath, T., & Levinson, S. (2013). Brief critical review: Statistical assessment of biomarker. Clinica Chimica Acta, 419, 102–107. doi:10.1016/j.cca.2013.02.006 PMID:23428592 Kornevs, M., Minkevica, V., & Holm, M. (2012). Cloud computing evaluation based on financial metrics. Information Technology and Management Science, 15(1), 87–92. doi:10.2478/v10313-0120013-8 Laking, G., Lord, J., & Fischer, A. (2006). The economics of diagnosis. Health Economics, 15(10), 1109–1120. doi:10.1002/hec.1114 PMID:16652389 Nachev, A., Hill, S., Barry, C., & Stoyanov, B. (2010). Fuzzy, distributed, instance counting, and default artmap neural networks for financial diagnosis. International Journal of Information Technology & Decision Making, 9(06), 959–978. doi:10.1142/S0219622010004111 Pepe, M. S. (2003). The statistical evaluation of medical tests for classification and prediction. New York: Oxford University Press.

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Swets, J. A., & Pickett, R. M. (1982). Evaluation of diagnostic systems: Methods from signal detection theory. New York: Academic Press. Vlasselaer, V. V., Bravo, C., Caelen, O., EliassiRad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2015). APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75, 38–49. doi:10.1016/j.dss.2015.04.013 Wasserman, L. (2004). All of statistics: A concise course in statistical inference. Springer. doi:10.1007/978-0-387-21736-9 Yao, J., & Lian, C. (2016). A New Ensemble Model based Support Vector Machine for Credit Assessing. International Journal of Grid and Distributed Computing, 9(6), 159–168. doi:10.14257/ ijgdc.2016.9.6.16 Zhou, X.-H., Obuchowski, N. A., & McClish, D. K. (2002). Statistical methods in diagnostic medicine (Vol. 712). New York: Wiley. doi:10.1002/9780470317082 Zweig, M. H., & Campbell, G. (1993). Receiveroperating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clinical Chemistry, 39(4), 561–577. PMID:8472349

KEY TERMS AND DEFINITIONS AUC (C-Statistic): The area under an ROC curve that summarize the overall probability for correct classification. Diagnostics Test: A quantitative test modality that is used to discriminate cases of interest from non-cases (controls). False Negative Rate: The probability that a diagnostic test classify incorrectly a case as a control. False Positive Rate: The probability that a diagnostic test incorrectly classify a control as a case. Gold Standard: A standard that can specify the true status being evaluated without error (a.k.a., reference standard). ROC Curve: A curve that plots a diagnostic test’s sensitivity versus its false positive rate across all possible threshold values for defining positivity. Sensitivity: The probability that a diagnostic test can correctly identify a true case. Specificity: The probability that a diagnostic test can correctly specify a non-case.

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Category: Digital Literacy

Digital Literacy

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Anirban Ray UNC Wilmington, USA

INTRODUCTION

BACKGROUND

Decoding digital literacy is a descriptive act of interpreting, reinterpreting, and understanding the relationship between the terms digital and literacy in the expanding space of information and communication technologies (ICTs). While the idea of literacy reveals a long evolutionary past associated with the term literate, the construct of digital, as we use it today, is shaped by the use of the digits, 0 and 1, in the 1930s and 1940s to represent computer data—a practice that eventually came to be known as digital. With the emergence of the Internet and the Web as the dominant systems of information organization and knowledge creation, the concept of literacy was broadened from its original notion of skills in reading and writing to developing cultural, historical, social, and technical awareness— a shared assumption critical to and closely associated with the understanding of ICTs and their use as well. The shift has influenced the definition of literacy as “primarily a technology of which records are the end products” (Clancy, 1993, p. 20). Although contemporary discourse in digital literacy assumes a much expanded scope of understanding than a product view of technology, the deterministic tendencies are evident in instances in which digital literacy is viewed as a set of benchmark skills. Broadly speaking, digital literacy is couched in both “conceptual” as well as “standardized operational” definitions (Lankshear & Knobel, 2008, p. 2), the key distinction being the former places digital literacy within the multiplicity of frameworks and models, while the latter measures and observes skills and performances that advance the “standards” of being digitally literate.

In 1981 The Washington Post first pioneered the concept that demanded “special skills” to use and manage computers (Warschauer 111) and invented the term “computer literacy.” Later, extension of the term “literacy” included “information literacy,” “digital literacy,” and “media literacy” to broaden the idea of skills. Paul Gilster (1997) in his pioneering book, Digital Literacy, popularized digital literacy as a shorthand for understanding and using information in multiple formats “from a wide range of sources presented via computers” (p.33). He operationalized and extended the term throughout the book, postulating that “digital literacy is about mastering ideas, not keystrokes” (p.1)—a call to attention between a “special kind of mindset or thinking” and “limited technical skills” (Bawden, 2008, p.19) premised on tasks and performances on the other. According to Gilster, digital literacy is about developing a critical approach toward using digital sources and forming awareness about our “expanded ability” (p.31) to connect with people and information using these sources. Over the years, digital literacy has addressed the split through skill and knowledge perspectives. Evidently, the skill construct affirms the neutrality thesis of technologies in which technologies are understood as means or instruments that need to be learned; conversely, the knowledge model ascertains technologies as more complex systems, not free of social, cultural, and political biases. Despite these prevalent articulations, the challenges of defining digital literacy stem from a lack of consensus building among stakeholder disciplines, including education, communication studies, English, media studies, library infor-

DOI: 10.4018/978-1-5225-2255-3.ch193 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Digital Literacy

mation studies and computing. The problem is further compounded by competing interpretive frameworks and theoretical models (Boechler et al., 2014) that stake claims on the scope and application of digital literacy. Considering the value of addressing the diverse views, the scholars have framed a discourse around digital literacy to accommodate dominant perspectives. These perspectives coalesce the domain-specific views into two broad categories that are identified as conservative, sometimes called traditional, and skeptical (Aviram & Eshet-Alkalai, 2006; Boechler et al., 2014). The former is uncritical of existing literature and accepts it in face value, privileging an instrumental view of digital literacy implicated in the notion of acquiring threshold or generic set of technical skills. This perpetuates the standardized paradigm of skill acquisition, a method common in educational institutions that aligns pedagogy through traditional conceptualizations of computer literacy (Ferrari, Punie & Redecker, 2012), information literacy (Mackey and Jacobson, 2011), and network literacy (McClure, 1994). Notwithstanding the widespread adoption of the view in curricular mapping and technology developments, the assumption is challenged as a didactic model that stabilizes teaching and learning as a set of prescriptive and durable practices that have fixed unities of time and place in which the role of technology is regarded as neutral. The skeptical or functional approach, on the other hand, gained prevalence as a reaction to the conservative approach. The underlying thesis favors contextualization of digital literacy and by extension digital technologies, reframing digital literacy as a plural concept. As an alternative strategy, it recognizes that digital literacy cannot replace traditional learning but can enhance the learning environment. The thinking here is that the functional approach potentially erases the dichotomies between digital and print literacies by emphasizing the hidden aspects of “learning styles, multiple intelligences, personality types,” and capacities (Aviram & Eshet-Alkalai, 2006). The perspective coincides with the idea of meta-

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literacy and value adds a plural approach to digital literacy discourse, facilitating a strategic inclusion of multiple critical conditions such as crosscultural contexts (Thatcher, 2010), privacy and surveillance (Reilly, 2016), and situated learning within the wider conceptual framework. The skeptical formulation questions the skill paradigm but also situates literacy beyond cognitive processes of reading, writing, and information seeking. In that it underlines the social dimension of literacy, emphasizing critical exchange and application of thoughts and ideas between individuals. Reframing literacy along these lines was done by a group of scholars in the 1980s and 1990s who called it “the New Literacy Studies” (NLS); there are still others who focus “on more recently developed literacy practices which are often (but not always) associated with ‘new technologies’ like computer and the Internet” (Jones & Hafner, 2012, p.13) and define it as “new literacies.” Digital literacy functions as a type of new literacies among several others, like computer literacy, Internet literacy, network literacy or hyper-literacy, and media literacy; other analogs include, Web literacy and game literacy (Buckingham, 2008); library literacy and reproduction literacy (Koltay, 2011); ICT skills, e-Skills, and ICT literacy (Lee, 2014), which all share common conceptual assumptions. Digital literacy incorporates a strong social component reimagined through concepts like user, access, practice, consumption, interpretation, and production that gain emphasis within the contemporary literacy discourse. Importantly, there are four basic assumptions of new literacies that help to conceptualize digital literacy within a larger framework of literacy: (a) innovations in ICTs require new skills, competencies, awareness, and strategies of use; (b) new literacies develop continually as their defining technologies change (c) literacy components empower individuals as global citizens; (d) new literacies are multi-dimensional and multi-modal and their understanding positively impact our social participation (Leu et al., 2007). These assumptions underscore the critical perspective articulated by Paul Gilster (1997)

Category: Digital Literacy

and later explained by David Bawden (2008). Similarly, others have emphasized the importance of approaching digital literacy and production from feminist perspectives (see Hawisher and Selfe, 1999; Wajcman, 2004), raising questions like online equity and subject positions, whether technological innovation equals advancement of literacy or how costs of digital technologies hinder or facilitate the learning process across social, cultural, or institutional spaces. From a critical digital perspective, it is difficult to imagine digital literacy as a stand-alone term; the critical dimension includes the internal “faculties of analysis and judgement applied to the content, usage, and artefact of the technology” while the external critical element refers to the cultural and historical circumstances of the wider field of technology than computers (Hinrichsen and Coombs, 2013). Thus, the idea of literacy in digital literacy continues to be redefined by innovations of digital technologies and signals an opportunity to reconstruct a definition based on contextualized meaning and practices than checklist of skills. Bawden (2008) offers an updated version of digital literacy by suggesting that “it is not sensible to reduce it to a finite number of linear stages” (p. 28). For him, the six-stage linear model for information literacy formulated by American Library Association in 1989, which had a considerable influence on the scholarship and conceptualization of digital literacy, does not adequately characterize contemporary understanding of digital literacy. Bawden formulates a digital literacy framework in the light of the changing circumstances and awareness for rapid technological innovation. It includes (a) underpinnings as literacy per se and computer/ICT literacy, (b) background knowledge as complex of information and nature of information resources, (c) central competencies as basic skills such as reading and understanding digital and non-digital formats, knowledge assembly, evaluation of information, etc., and (d) attitudes and perspectives as underlying concept connecting modern idea of literacy with the values of older

literacy through independent learning and moral/ social responsibilities (p.29-30). Similarly, Yoram Eshet-Alkalai (2004) encapsulates a framework with five types of digital literacy: (a) photo-visual literacy to interact with the visual-graphical interface (b) reproduction literacy to reproduce meanings by repurposing and recombining preexisting information elements; (c) information literacy to assess and evaluate the credibility of online information; (d) branching literacy to navigate the non-linear Web structure; (e) socio-emotional literacy to safeguard personal interests within the expanded scope of collaboration and networking offered by the ICTs. The framework was further updated with the real-time digital skill characterized by the ability of users to manage and respond to real-time, high-speed, and quick-response digital scenarios and genres (Eshet, 2012) such as gaming and simulation. Both these models expand the scope of digital literacy from practice-orientated to (critical) knowledge-orientated conceptualizations. They index the idea of literacy as a discourse, embedding a complex understanding of the relationships between the tool, the environment, and the actor. The frameworks position digital literacy as a “survival skill” and explore the idea of social membership within the digital space. Most importantly, the shift from a tool-centered toward a critical knowledge orientated framework exemplifies learning as a social phenomenon external to tools or residing outside of tools and machines.

GLOBAL DIGITAL LITERACY PROGRAMMES AND POLICIES From a policy standpoint, the emphasis placed on digital literacy training is enormous across the global societies. Initiatives by various national and international organizations, governments, and educational institutions support the continuing relevance of digital literacy. The content, strategies, and development of training programs and materials are often structured in conjunction with

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the demands and needs of the target populations. For example, a macro perspective on literacy was adopted by UNESCO to guide the digital literacy policy framework that defined literacy as “a continuum of learning in enabling individuals to achieve their goals, to develop their knowledge and potential, and to participate fully in their community and wider society” (UNESCO, 2004). Further, UNESCO’s Information for All Programme3 (IFAP) recognizes digital literacy as “life skill,” outlining objectives squarely related to the more critical idea of digital literacy— “ICT skills, civic skills, learning to learn skills, and participation of adults in lifelong learning” (UNESCO, 2011). The objectives, such as these, are aptly coordinated with the support from national governments and agencies through centralized polices. Evidently, these policies vary across the geographical boundaries both from conceptualization and implementation standpoints. In the United States, the mainstreaming of digital literacy is guided by several plans of action formulated by governmental and nongovernmental agencies. According to the Fact Sheet: Digital Literacy report 2011, “28 percent of Americans do not use the Internet at all” (commerce.gov). Toward that end, the transformative task of building discourse communities for participating in information economy is largely facilitated by strategic interventions at K-12 levels and adult community learning centers such as the Literacy Information and Communication Systems (LINCS), a national leadership initiative of the U. S. Department of Education, Office of Career, Technical Adult Education (OCTAE), Division of Adult Education and Literacy (DAEL) (lincs.ed.gov). The compelling idea behind digital literacy education is to ensure digital inclusion by raising the quality of general awareness and critical thinking among learners—a contention echoed by the Association of American Colleges and Universities (AAC&U) in their publication, Greater Expectations that envisions college graduates in the role of “integrative thinkers who can see connections in seemingly disparate information

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and draw on a wide range of knowledge to make decisions” (aacu.org). Other western nations also emphasize the importance of digital education as part of their broader educational goals. For instance, the United Kingdom has in place the Digital Inclusion Strategy with the aim to reduce digital exclusion. The main implications of the plan are outlined in terms of skill acquisition, ICT connectivity, and equal accessibility. The government has adopted the Action 15 document with the stated purpose to “’Collaborate with partners across public, private and voluntary sectors to help people go online’” (gov.uk). On a larger scale the European Union (EU) has under taken the Digital Literacy 2.0 funded in the “Lifelong Learning Programme” to bring Web2.0 affordances within the ambit of everyday life. The salience of digital literacy is recognized as “one of the key competences to ensure social cohesion, active citizenship and personal fulfillment” (digital-literacy2020.eu). The most comprehensive plan for enhancing citizens’ digital knowledge was envisioned by the European Commission Institute for Prospective Technological Studies. The institute’s goals are premised on the notion that “participation in the digital domain is no longer a question of ‘haves’ or ‘have nots,’ but rather an issue of competence” so much so that digital literacy is accepted as one of the eight key competencies for “Lifelong learning.” Within this context, digital literacy is identified as a “transversal key competence” that enables acquisition of other key competences like language, mathematics, learning to learn, cultural awareness, etc. The European Digital Competence Framework, the primary instrument of implementation, contains 21 competences structured according to 5 areas: information, communication, content-creation, problem solving, and safety. A lot of emphases are laid for utilizing digital literacy for job searching process and improving employable qualifications (openeducationeuropa.eu). Digital literacy penetration in South America presents a very different picture altogether. Ac-

Category: Digital Literacy

cording to a World Economic Forum global technology report, the social and economic impacts resulting from ICTs are still somewhat low. Despite the efforts made by the government agencies to digitally connect its population, problems with infrastructure, political instability, regulatory policies and above all low skill base pose serious challenges (Bilbao-Osorio et al., 2013, p. 15). The ICT Development Index (IDI) 2011 of the major economies shows Argentina ranked at 56, Brazil at 60, and Mexico at 79 respectively (www.itu. int). Argentina in 2006 made a longstanding and comprehensive commitment to introduce digital literacy in educational sector especially at the secondary level. The highlights of the policy include improving computer infrastructure in classroom, training teachers to encourage adoption of digital technologies in pedagogy, developing appropriate educational content to integrate technologies, and implementing One Laptop per Child (OLPC). The Digital Literacy National Campaign, started in 2006, promotes digital literacy in conjunction with Encuentro TV channel that regularly broadcasts quality educational content to schools. Further, the creation of the national portal, www.educ.ar, facilitates teacher training, connects schools to the information superhighway, and develops and delivers digital content for education purposes (Lujambio et al., 2008, p.80-81). Compared to South American countries, Asian economies perform better when it comes to diffusion of digital literacy. A quick survey of the Association of Southeast Asian Nations (ASEAN) reveals that all eight member–nations have progressed in the direction of digitizing social and cultural life of their citizens (Bilbao-Osorio et al., 2013, p. 15). For instance, The Digital Saksharta Abhiyan (DISHA) or National Digital Literacy Mission (NDLM) envisaged by the government of India in 2015 aims to bring at least one member from the household to participate and train in digital literacy. Given the complex diversity and huge population of India, the program enables the participating families to nominate one member to undergo the certificate training. In the Indian

context, digital literacy is defined as “the ability of individuals and communities to understand and use digital technologies for meaningful actions within life situations.” The introduction of the concept proceeds through two staggered levels, (a) appreciation of digital literacy which includes orientating with and operating digital devices like mobile phones and tablets, and (b) basic digital literacy which enables active citizenship through participating in e-governance (ndlm.in). In addition to government initiatives, there are numerous private agencies and organizations involved in the idea of digital literacy for the commons. The “Hole-in-The Wall Education Project (HiWEP) started by a private individual in the late 90s has over 300 learning stations for educating poor and disadvantaged children in India and Africa. Using a principle known as “Minimally Invasive Education,” HiWEP installs networked computers or learning stations in public places like streets, market places, and playgrounds with the purpose to attract and encourage young children to learn ICTs in independent, self-organizing, and sometimes collaborative environments (Mitra & Dangwal, 2010). The project has received a lot of traction among underprivileged communities where technology access is a chronic problem. HiWEP has been praised by various international organizations including UNESCO and continues to remain a crucial instrument for spreading digital literacy. Global diffusion of digital literacy is based on the idea of strategically exposing the populations to the practical and ever changing dynamics of digital technologies. Regardless of global economic status, the digital literacy policy discourse is dominated by three fundamental optics: reach, penetration, and applicability. Reach entails closing the digital gap, penetration involves targeting to specific needs, and applicability concerns transference of competence for performing activities within social contexts. In the case of the developed nations, the quantum of applicability is very diverse and complex and is thus central to policy implementation. For most developing nations, however, the challenges are capacity

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building and task prioritization and hence reach and penetration act as vital indicators. Interestingly, the global diffusion pattern reveals distinct strategies, varying across geographic, cultural, and political scales. For instance, while the EU focuses on employability aspects, the United States is more concerned with early exposures at institutional levels from K-12 upwards. Similarly in the case of South America, the emphasis lies on infrastructure while India, being a collectivist culture, attempts to create a large “family” of digital users by targeting individual households.

ISSUES AND CHALLENGES Lack of consensus among researchers is a major hindrance to developing a theory of digital literacy that could be translated to educational contexts to serve the current generations of students. Eshet-Alkalai (2004) suggests that “indistinct use of the term causes ambiguity, and leads to misunderstanding, misconceptions, and poor communication.” In the absence of a consolidated definition, educators are faced with the challenges of curricular development and identifying proper assessment techniques consistent with students’ learning objectives; an allied problem is reorienting teachers toward embracing a philosophy of digital literacy that can ultimately address the practical and social needs of students. As the notion of literacy shifts from a text-based syntactic to a graphic and link based semantic conceptualization (Nielsen, 1993), digital literacy must be understood as a moving target because digital technologies evolve rapidly. This has led to question the assumptions whether the so called ‘digital natives’ are truly equipped to understand and use ICTs in their current iterations. The problem is more acute at the K-12 level where in absence of a unified digital literacy curriculum, the institutions are capitulating to what is referred to as the “standards movement” (Trotter, 1997 in Boechler et al., 2014) conceived by the International Society for Technology in Education (ISTE)—an

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organization dedicated to “leveraging the use of technology in K-12 education” (Boechler et al., 2014). Thus, as ‘screen becomes the dominant site of texts,’ emphases are laid on measuring concepts of digital literacy through qualitative, quantitative, normative, and formative assessment structures such as, self-reported surveys, Software Recognition Test (SRT), Educational Activities Checklist (EAC), Recreational Experience Scale (RES) (see, Boechler et al., 2014); other formulations include code breaking, text-participating, text-using, and text-analyzing (see, Hinrichsen & Coombs, 2013) and operational digital literacy, usage digital literacy, communication and interaction digital literacy, and creation digital literacy (see Lee, 2014). The strategic integration of these techniques in curricular mapping remain vital as also properly training and upskilling teachers who ultimately impart classroom knowledge. Another important aspect worth considering is digital literacy is not just an educational construct; it must also be fundamentally approached as a “social fairness issue” (Seale, 2009). The viewpoint generates awareness about digital divide and ethnocentric biases— two critical concepts surrounding the social aspect of digital literacy. In the prevailing circumstances social, political, and commercial activities are increasingly structured on ICTs and therefore for population with low digital literacy competence are at a risk of further marginalization. This divide or gap can potentially disengage individual from active citizenship, creating practical barriers for activities like accessing heath and government information, public service information, engaging through social media, learning in mediated environments such as Massive Open Online Courses (MOOCs). In this connection, digital literacy education or DLE has significant implications in addressing the issue of digital divide. The primary goal of DLE is to “support learners’ knowledge and skill construction process through education and practices to enhance their digital literacy” (Lee, 2014). DLE aims to incorporate the social learning paradigm in life situations.

Category: Digital Literacy

As indicated earlier, each society shapes and constructs definition of digital literacy according to its own social environment. However, many societies consider that other cultures must imitate their patterns of appropriation of digital technologies as “best practices” (Thatcher, 2010, p. 170)—a symptom of ethnocentric oversight. Digital literacy perceptions in global societies are influenced by numerous differentials including infrastructure, regulations, access, community participation, schools versus adult education, and individualism versus collectivism. It would therefore be a gross mistake to assume a universal approach. Consequently, effective measures can be developed to prevent ethnocentric biases in cross-cultural communications. According to Thatcher, one must be sensitized to the need to understand the rhetorical nature of the digital medium itself, to configure the characteristics of the medium to individual purpose, demands, and constraints, to assess the situation in the target culture, and finally to align to communication strategies to the expectations of target culture (p.169). This provides a functional framework that can negotiate the differences across disparate cultural configurations.

FUTURE RESEARCH DIRECTIONS Current research in digital literacy reveals a solid direction toward developing a working definition of digital literacy and creating assessment tools for educational training. While establishing some type of functional metrics is important, it is equally important to build a corpus of scholarship addressing global discourse communities. Most research currently is confined to micro aspects of digital literacy, focusing mainly on theoretical constructs sometimes discounting the fact that in an information economy the scale of operation is not just regional or national but is global as well. Therefore, given the digital literacy landscape of

shared creativity and involvement in networked activities, it is time that conversations focused on exploring differences and trends between the west and the east, between the developed and the developing nations. Additionally, studies should refine the understanding of digital divide itself since most research on the digital divide tends to make a broad generalization using “multivariate analyses of several individual properties and aggregating them to produce properties of collectives” to support explanation (van Dijk 10). Generally, most approaches lack in conceptual clarity on one hand, and the idea of localization of use on the other. For example, in the west digital divide is defined in terms of use whereas in most other places it is still a matter of access to digital technologies. They also ignore or gloss over cultural factors and focus more on the overall use of technologies and their characteristics. Thus a repurposing of outcomes are warranted to take into consideration the more nuanced elements of digital literacy studies.

CONCLUSION Digital literacy is multi-dimensional and no single context, culture or society has patent over its definition. Both conservative (skill) and skeptical (knowledge) orientations of digital literacy are still emerging, or at best tentative hypotheses and therefore extensive analyses are warranted before utilizing them as conclusive models. Knowledge paradigm enables individual agency by referencing one’s situation unlike the skill model where the user adopts a top-down task orientated structure. Digital literacy is indeed a crucial “life skill” whose salience cannot be overestimated as society transitions from essentially a linear to a hyperlinear mode of information processing. The digital literacy arc has shifted from its original focus on computers to technologies and to finally the idea of human agency.

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REFERENCES Aviram, A. & Eshet-Alklai, Y. (2006). Towards a theory of digital literacy: three scenarios for the next steps. European Journal of Open Learning, Distance and E-Learning, 1. Bawden, D. (2008). Origins and concepts of digital literacy. In Digital literacies: Concepts, policies and practices (pp. 17-33). New York: Peter Lang Publishing. Bilbao-Osorio, B., Dutta, S., & Lanvin, B. (Eds.). (2013). The Global Information Technology Report 2013 Growth and Jobs in a Hyperconnected World. World Economic Forum. Retrieved from http://www3.weforum.org/docs/WEF_GITR_Report_2013.pdf Boechler, P., Dragon, K., & Wasniewski, E. (2014). Digital Literacy Concepts and Definitions. International Journal of Digital Literacy and Digital Competence, 5(4), 1–18. doi:10.4018/ ijdldc.2014100101 Clanchy, M. T. (1993). From memory to written record: England, 1066-1307 (2nd ed.). Cambridge, MA: Blackwell.

European Commission. (2014). A common European Digital Competence Framework for Citizens. Retrieved from http://openeducationeuropa.eu/ sites/default/files/DIGCOMP%20brochure%20 2014%20.pdf Ferrari, A., Punie, Y., & Redecker, C. (2012). Understanding Digital Competence in the 21st Century: An Analysis of Current Frameworks. Lecture Notes in Computer Science 21st Century Learning for 21st Century Skills, 79-92. Gilster, P. (1997). Digital Literacy. New York: Wiley. Greater Expectations. A New Vision for Learning as a Nation Goes to College. (2002). Association of American Colleges and Universities (AAC&U). Retrieved from https://www.aacu. org/sites/default/files/files/publications/GreaterExpectations.pdf Hinrichsen, J., & Coombs, A. (2014). The five resources of critical digital literacy: A framework for curriculum integration. Research in Learning Technology, 21(0). doi:10.3402/rlt.v21.21334

Dijk, J. V. (2005). The deepening divide: Inequality in the information society. Thousand Oaks, CA: Sage Publication.

International Telecommunication Union. (2012). Measuring the Information Society. Retrieved from http://www.itu.int/en/ITUD/Statistics/Documents/publications/mis2012/MIS2012_without_Annex_4.pdf

DLit 2.0. (2015). Digital Literacy 2020. Retrieved from http://www.digital-literacy2020.eu/content/ sections/index.cfm

Jones, R. H., & Hafner, C. A. (2012). Understanding digital literacies: A practical introduction. London: Routledge.

Eshet, Y. (2012). Thinking in the Digital Era: A Revised Model for Digital Literacy. Issues in Information Science and Information Technology, 9, 267–276.

Koltay, T. (2011). The media and the literacies: Media literacy, information literacy, digital literacy. Media Culture & Society, 33(2), 211–221. doi:10.1177/0163443710393382

Eshet-Alkalai, Y. (2004). Digital literacy: A conceptual framework for survival skills in the digital era. Journal of Educational Multimedia and Hypermedia, 13(1), 93–106.

Lankshear, C., & Knobel, M. (Eds.). (2008). Digital literacies: Concepts, policies and practices. New York: Peter Lang.

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Leu, D. J., & Zawilinski, L. (2007). The new literacies of online reading comprehension. New England Reading Association Journal, 43(1). Retrieved from http://search.proquest.com.liblink. uncw.edu/docview/206029040?accountid=14606

UNESCO. (2011). Policy Brief: Digital Literacy in Education. UNESCO Institute for Information Technologies in Education. Retrieved from http://iite.unesco.org/pics/publications/en/ files/3214688.pdf

LINCS. (n.d.). Digital Literacy Initiatives. Retrieved from http://lincs.ed.gov/about-lincs

Wajcman, J. (2004). TechnoFeminism. Cambridge, UK: Polity Press.

Lujambio, D., Roveri, F., Fernández, C., Kozenitzky, I., Escobar, M. V., Fascendini, F.,... Dachesky, M. (2008). Argentina. Global Information Society Watch. Retrieved from https://www.giswatch.org/ hi/node/65

Warschauer, M. (2004). Technology and social Inclusion: Rethinking the digital divide. Cambridge, MA: MIT Press.

Mackey, T. P., & Jacobson, T. E. (2010). Reframing Information Literacy as a Metaliteracy. College & Research Libraries, 72(1), 62–78. doi:10.5860/ crl-76r1

ADDITIONAL READING

McClure, C. R. (1994). Network literacy: A role for libraries? Information Technology and Libraries, 13(2), 115–125. Mitra, S., & Dangwal, R. (2010). Limits to selforganising systems of learning-the Kalikuppam experiment. British Journal of Educational Technology, 41(5), 672–688. doi:10.1111/j.14678535.2010.01077.x National Digital Literacy Mission. (2015). Department of Electronics and Information Technology and Ministry of Communications and Information technology, Government of India. Retrieved from http://ndlm.in/overview-of-ndlm.html Nielsen, J. (1993). Usability engineering. San Diego, CA: Morgan Kaufman. Seale, J. (2009, December). Digital Inclusion BETA: A Research Briefing. The Teaching and Learning Research Programme. Retrieved from http://tel.ioe.ac.uk/inclusion/digital-inclusionresearch-briefing/ Thatcher, B. (2010). Understanding Digital Literacy across Cultures. In R. Spilka (Ed.), Digital Literacy for Technical Communication. New York: Routledge.

Amant, K. S., & Olaniran, B. A. (2011). Globalization and the digital divide. Amherst, NY: Cambria Press. Dijk, J. V., & Deursen, A. V. (2014). Digital skills: Unlocking the information society. New York, NY: Palgrave Macmillan. doi:10.1057/9781137437037 Hartley, J. (2011). The uses of digital literacy. New Brunswick, NJ: Transaction. Hawisher, G. E., Selfe, C. L., Guo, Y., & Liu, L. (2006). Globalization and agency: Designing and redesigning the literacies of cyberspace. College English, 68(6), 619–636. doi:10.2307/25472179 Lankshear, C., & Knobel, M. (2011). New literacies: Everyday practices and classroom learning. Maidenhead: Open University Press. Strover, S. (2014). The US digital divide: A call for a new philosophy. Critical Studies in Media Communication, 31(2), 114–122. doi:10.1080/1 5295036.2014.922207 Svensson, P., & Goldberg, D. (Eds.). (2015). Between Humanities and the Digital. Cambridge, Massachusetts: The MIT Press. Wiesinger, S., & Beliveau, R. (2016). Digital Literacy: A Primer on Media, Identity, and Evolution of Technology. New York, Bern, Berlin: Peter Lang. doi:10.3726/978-1-4331-2821-9

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KEY TERMS AND DEFINITIONS Digital Divide: Disparities among individuals, generations, societies, and cultures resulting from unequal access to and use of computer and Internet technologies and digital infrastructures. Besides suggesting a physical quantity of presence or absence of technologies within a context, the divide implies a difference in quality of use of digital technologies within the same context. Digital Native: A post-millennial term describing individuals who are part of the digital age from birth. Hyperliteracy: A systematic process of finding, linking, and retrieving information by developing both critical and functional knowledge of the non-sequential structure of the Web. The primary focus is on understanding the structure of the Web

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as providing freedom of organizing information by means of linking of ideas in a nonlinear way. Information Literacy: A set of competencies associated with identifying the need for information, locating appropriate information, evaluating information, and utilizing information to participate effectively in cultural and social contexts. Recognized as a lifelong process of self-directed learning, information literacy underlies the role of informed citizenship through a proper understanding and use of digital technologies for fulfilling academic, professional, and personal goals. Reproduction Literacy: Abilities to recreate and repurpose existing digital contents including text, sound, images, graphics, and videos into a new format using digital production capabilities.

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Digital Literacy for the 21st Century Hiller A. Spires North Carolina State University, USA Casey Medlock Paul North Carolina State University, USA Shea N. Kerkhoff North Carolina State University, USA

INTRODUCTION In the past few decades, technology has spanned the globe, connected people in a whole new way. As a result, citizens of all countries have not only had to learn to use new technology, but also learn how to interact with one another. Skills that comprise these abilities have been combined under the term “digital literacy.” The purpose of this chapter is to (a) define digital literacy and its changing nature, (b) discuss implications of digital literacy on contemporary schooling, (c) demonstrate the impact of digital literacy on digital citizenship, and (d) analyze the implications of digital literacy on educational equity.

BACKGROUND Almost two decades ago, Gilster (1997) defined digital literacy as the “ability to understand and use information in multiple formats from a wide range of sources when it is presented via computers” (p. 1). At this time, the Internet was in its infant stages. More than a decade later with Internet usage in full swing, Fieldhouse and Nicholas (2008) asserted that terms like literacy and fluency can be used to describe how users find and evaluate information within digital environments. Digital literacy involves any number of digital reading and writing techniques across multiple media forms, including: words, texts, visual displays,

motion graphics, audio, video, and multimodal forms. In the same way that literate individuals can negotiate print text through the processes of reading and writing, literate users of technology are able to consume and produce digital compositions. There are many cognitive processes at work, along a continuum from consumption to production when a reader is immersed with digital content. The digital context is challenging for all readers due to the fluid nature of the Web and the demand for critical judgments (Spires & Estes, 2002) as the reader makes decisions about how to locate information as well how to discern the reliability and credibility of that same information.

WHAT IS DIGITAL LITERACY? Spires and Bartlett (2012) have divided the various intellectual processes associated with digital literacy into three categories: (a) locating and consuming digital content, (b) creating digital content, and (c) communicating digital content (see Figure 1). Learners must develop evaluative dispositions as they navigate digital content. A discerning mindset is essential in order to interact with online resources with accuracy. Without critical evaluation, the learner may easily be directed by the technology rather than the learner directing the inquiry.

DOI: 10.4018/978-1-5225-2255-3.ch194 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Figure 1. Digital literacy practices involve the ability to locate and consume, create, and communicate digital content, while simultaneously employing a process of critical evaluation

Adapted from Spires & Bartlett (2012)

Locating and Consuming Digital Content It is essential to develop the skills to locate, comprehend and consume digital content on the Web. Central to being effective with the Web is strategically searching for information and evaluating its accuracy and relevancy (Leu et al., 2008). There is consensus that effective Web search skills must be developed for educational success in a digital society, and instruments such as The Teaching Internet Comprehension to Adolescents (TICA) checklist can ensure that students have the necessary prerequisite Web search skills (Leu et al., 2008). However, more challenging is how to incorporate the effective teaching and development of Web search skills in the classroom (Moraveji et al., 2011). Nevertheless, some important skills are considered necessary for locating and using digital content: domain knowledge, a working knowledge of how to use search engines, basic literacy skills, and a general knowledge of resources available on the Web (Moraveji et al., 2011). In addition to building on the ability to craft productive Web search terms, search lessons should involve direct modeling of the use of search techniques, differentiating between domain names, and querying sites for accuracy and transparency.

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Creating Content Digital content is easily created by teachers and students alike through multiple media and a variety of Web 2.0 tools. The implementation of digital content may be an important and effective method of enhancing teaching and learning (Bakkenes, Vermunt, & Wubbles, 2010), enabling teachers to embrace the 21st century skills that students are expected to master. Digital resources can also free up teachers, allowing them to spend more time facilitating student learning and less time lecturing. Allowing students to create and consume digital content in the classroom may increase engagement while also encouraging the development of skills needed for a technological society. For example, students can create video content with easy-touse video editors such as Animoto, WeVideo, and Powtoon, just to name a few. Because there is a low bar for technical expertise, students can spend more time on the quality of the content rather than learning the process of a new tool. An added benefit is that the products look polished and professional. Although the creation of digital content is becoming increasingly simple, personalization of learning will require teachers to locate and utilize a variety of digital resources to meet the needs of every learner. Personalization will also

Category: Digital Literacy

put a heavier emphasis on asking students to show mastery of learning by producing digital content. This generative process requires more time from teachers in terms of designing appropriate rubrics for performance-based learning.

Communicating Digital Content Digital content must be communicated effectively in order to be a useful educational medium. Using social networking sites like Facebook, Twitter, and Instagram requires users to understand and manipulate information in multiple formats. Web 2.0 tools are social, participatory, collaborative, easy to use, and facilitate the creation of online communities. Being able to communicate digital content using mobile devices such as cellphones and tablets provides convenience and immediacy to the communication process for teachers and students. Additionally, it provides access to an infinite set of people and digital content resources globally to enrich the learning experience. This type of communication affords the possibilities of more customization and personalization for individual learners’ interests and needs, which has the potential to increase student engagement in academic learning. A popular type of digital communication is the act of curating The capacity to curate at a sophisticated level, both in terms of content and visual appeal, is quickly becoming a necessity for educators who engage in online teaching and learning (Thompson, 2015). The word curate comes from the Latin root Curare, or “to cure,” and historically has meant “to preserve” (Mihailidis & Cohen, 2013). As students learn to be creators and curators of digital content, there is some evidence that it contributes to their ability to be critical readers of digital texts (O’Byrne, 2012). The word curate derives from the Latin root Curare, or ‘to cure.’ To curate, historically, has meant to take charge of or organize, to pull together, sift through, select for presentation, to heal and to preserve. Within digital spaces, organizing and preserving online content is the purview of the individual (Mihailidis

& Cohen, 2013). This online communication trend has created a need to understand how individuals select, sort, synthesize and display content within these spaces.

The Changing Nature of Digital Literacy and Learners Contemporary education is permeated by the millennial generation, also referred to as Generation Y and the Net Generation. This group is defined as those individuals who were born between the early to mid-1980s and the early to mid-2000s, possessing the following traits: confident, team oriented, conventional, pressured and achieving (Howe & Strauss, 2000). This generation, bigger than previous generations, is entering the workforce and contributing to a shift in our society (Winograd & Hais, 2011). This generation is immersed in a world of multimodality, or how individuals make meaning with different modes, such as print, video, speech, music, or gesture. At the heart of multimodality, is semiotics, which is the study of signs (Kress & Van Leeuwen, 1996). As society has shifted from written to visual texts in contemporary culture, more demand has been placed on teachers to learn how to make instructional changes that take these shifts into account. Leu and his colleagues (Leu et al., 2015) used the term deictic to refer to the changing nature of literacy, which is prompted by the constantly changing technologies within our society. By all accounts, these changes will continue to take place since the total number of Internet users is at over 3 billion worldwide and growing.

Digital Citizenship As technology has spread across the globe, our world has become more connected than ever. This has created a global virtual world that all technology users inhabit, and as a result, technology users have had to learn how to become “digital citizens” (Isman & Canan Gungoren, 2014). Although there are various definitions of this term,

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the definitions are similar; they express that first and foremost, a digital citizen must be able to use technology intelligently. Furthermore, one should understand cultural and societal issues as they relate to technology; as a result, digital citizens demonstrate various characteristics. For example, Isman and Canan Gungoren (2014) state: [They] practice legal and ethical behavior; advocate and practice safe, legal, and responsible use of information and technology; exhibit a positive attitude toward using technology that supports collaboration, learning and productivity; demonstrate personal responsibility for lifelong learning; and exhibit leadership for digital citizenship. (p. 73) In order to foster the development of these skills, various organizations have begun to to develop models and programs designed to assist in educating people on digital citizenship. For example, ISTE published a model listing behaviors associated with digital citizenship (Brichacek, 2014). Such behaviors include “no stealing or damaging others’ digital work, identity or property;” “using digital tools to advance learning and keeping up with changing technologies;” “protecting personal information from forces that might cause harm;” and “equal digital rights and access for all” (Searson, Hancock, Soheil, & Shepherd, 2015, p. 731). Another non-profit organization, iKeepSafe, worked with Microsoft and AT&T to develop an online questionnaire that measures digital safety skills and attitudes in six areas, known as the BEaPRO index: balancing digital usage, practicing ethical digital usage, protecting personal information, maintaining healthy and safe relationships, building a positive reputation, and achieving online security (Searson, Hancock, Soheil, & Shepherd, 2015; iKeepSafe, 2015). Still, there is much work to be done in developing global digital citizenship. The findings from iKeepSafe’s questionnaire indicated “although many individuals want to foster good digital citizenship practices, most have limited knowledge

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about how to do so” (Searson, Hancock, Soheil, & Shepherd, 2015, p. 733, emphasis in original). These authors provide a list of recommendations and actions needed to help further global digital citizenship. They suggest that both national and local leadership organizations, such as public policy agencies, law enforcement, and industry leaders, work together in order to tackle the issue. Furthermore, they recommend that educational institutions begin to provide professional development for teachers in order to educate teachers as to how they can teach their students to be digital citizens. They also maintain that stakeholders must be held accountable for privacy and safety of community members, and reported incidents should inform digital citizenship education services and policy development. Although digital citizenship is a fairly new concept, it is one that is highly important in our globalized, virtual world. It involves not only competent technology use, but also responsible and ethical use of the web. Digital citizenship is largely considered an aspect of digital literacy, and many organizations are working to understand how to include it in digital literacy education.

Digital Literacy and Educational Equity The digital divide is a gap in access to or usage of ICTs between people, demographic groups, or countries (OECD, 2001). In other words, the global digital divide is one of access to the Internet and also one of users’ competence with ICTs. Access to ICTs continues to be divided within countries as well as among countries and is often associated with socioeconomic status. As of January 2015, only 42% of the world was active Internet users with Canada holding the highest percentage of 93% and India holding the lowest percentage of Internet users at 19% (Kemp, 2015). Access and usage are related in that lack of access leads to less practice digital literacy skills, whereas more access leads to more opportunities to practice.

Category: Digital Literacy

Problems of access include cost of computers and subscriptions, broadband width of the Internet, and restrictedness of content (Tongia, 2005). Lack of access can be seen at the country-level, such as governments censoring content on the Internet and restricting what sources and what information citizens can obtain. Lack of access can also be seen at the demographic level when certain demographic groups are able to spend more time on the Internet than other groups. In the US, research has shown that students from underprivileged schools spent less time using ICTs even though the amount of computers and broadband width were similar across schools (Leu et al., 2015). One reason for this could be that digital literacy is not tested on government issued assessments tied to funding; therefore, time is spent on what is tested in order to score higher on the assessments and receive needed funding (Leu et al., 2015). This phenomenon has implications for future K-12 assessments.

SOUTIONS AND RECOMMENDATIONS Digital Literacy and the Impact on Contemporary Schooling As technology has become more integral to students’ lives, there has been an ever-increasing digital “home-school divide” (Honan, 2006, p. 41); students are using technologies outside of school that are not available in school, while educators struggle to effectively use what technology they have in their classrooms (Henderson, 2011). There is still great debate on exactly how to integrate digital literacy instruction into traditional instruction, and many studies have been and are still being conducted in an attempt to understand how best to bridge the two together (Kervin, Verenikina, Jones, & Beath, 2013; Henderson, 2011; Walsh, 2010; 2008). Nevertheless, there is little debate on the value of these skills; many countries have begun

to reform their education programs to include better digital education. Some countries even have standards and requirements for students to achieve digital literacy. In 2008, Australia began its Digital Education Revolution in order to equip schools, teachers, and students with the technology necessary to provide a quality digital education. England has Computing Programmes of Study (United Kingdom Dept. of Education, 2013) as part of its National Curriculum, with part of its stated goal that “pupils become digitally literate—able to use, and express themselves and develop their ideas through, information and communication technology—at a level suitable for the future workplace and as active participants in a digital world” (Purpose of Study section, para. 1). The International Society for Technology in Education (ISTE; 2007) has also developed standards for students, teachers, and administrators. Not only has digital literacy changed educational standards, but it has also changed the content that must be taught in schools. Although today’s students’ are often considered “digital natives” (Prensky, 2001), they are not necessarily able to use these digital tools in a knowledgeable or critical way (Jones et al., 2010). Students therefore must be taught such skills and how to use technology effectively (Leu et al., 2015), including evaluating and critically analyzing information. Students must also be taught about cyber safety, “digital footprints,” and how to be responsible online (Osborne & Connely, 2015). In fact, many educational programs are now including standards that foster the teaching of digital responsibilities, such as respecting copyright laws, using valid information, and following safe and ethical behaviors when online. (American Association of School Librarians, 2007; ISTE, 2007). Government organizations are also making sure such education is available to students. For example, Qatar’s Ministry of Information and Communications Technology, known as ictQATAR (2015), works alongside teachers and parents to teach children Internet responsibility and safety.

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Digital literacy has had—and is continuing to have—an impact on contemporary education. Information is readily available to students, and educators are working to teach adolescents how to use this information effectively, ethically, and responsibly. One organization, the Partnership for 21st Century Learning, was developed in order to help foster 21st century learning for students through collaborative partnerships. The 21st Century Learning Framework (Partnership for 21st Century Learning, 2009) has been used in the U.S. as well as other countries to support the inclusion of 21st century skills in education. Although educators are still trying to discover exactly how digital literacy fits into the classroom, it is clear that digital literacy has already greatly altered modern education.

FUTURE RESEARCH DIRECTIONS Future research should focus on clarifying best practices for teaching students how to navigate digital environments effectively. Specifically, teachers need to know how to help students locate, create and communicate digital content in productive and ethical ways. Additionally, teachers need best practices for how to integrate gamebased learning into their classrooms and support students as they navigate virtual spaces related to content learning. One emerging trend is Online Reading Comprehension Assessments (ORCA), in which students capacity to conduct effective information searches is assessed in a controlled Web environment (Leu et al., 2015). Online and offline reading require different skills, so assessments must be sensitive to the distinctions.

CONCLUSION In this chapter our aim was to provide a definition of digital literacy and how it is evolving, discuss the implications of digital literacy on contemporary schooling, demonstrate the impact of digital

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literacy on digital citizenship, and analyze the implications of digital literacy on educational equity.

REFERENCES American Association of School Librarians. (2007). Standards for the 21st Century Learner. American Library Association. Retrieved from http://www.ala.org/aasl/standards Antonio, A., & Tuffley, D. (2014, July 7). Digital literacy in the developing world: A gender gap. The Conversation. Retrieved from http://theconversation.com/digital-literacy-in-the-developingworld-a-gender-gap-28650 Bakkenes, I., Vermunt, J. D., & Wubbles, T. (2010). Teacher learning in the context of educational innovation: Learning activities and learning outcomes of experience teachers. Learning and Instruction, 20(6), 533–548. doi:10.1016/j. learninstruc.2009.09.001 Brichacek, A. (2014). Infrographic: citizenship in the digital age. Retrieved from: https://www.iste. org/explore/articleDetail?articleid=192 Bunker, B. (2010). Retrieved from http://iitp. nz/files/201001%20Digital%20Literacy%20Research%20Report.pdf Coiro, J., & Dobler, E. (2007). Exploring the online reading comprehension strategies used by sixth‐grade skilled readers to search for and locate information on the Internet. Reading Research Quarterly, 42(2), 214–257. doi:10.1598/ RRQ.42.2.2 Fieldhouse, M., & Nicholas, N. (2008). Digital literacy as information Savvy: The road to information literacy. In M. Knobel & C. Lankshear (Eds.), Digital literacies concepts, policies and practices (pp. 43–72). New York, NY: Peter Lang Publishing. Gilster, P. (1997). Digital literacy. New York: Wiley and Computer Publishing.

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Hargittai, E., & Walejko, G. (2008). The participation divide: Content creation and sharing in the digital age. Information Communication and Society, 11(2), 239–256. doi:10.1080/13691180801946150 Henderson, R. (2011). Classroom pedagogies, digital literacy and the home-school digital divide. International Journal of Pedagogies and Learning, 6(2), 152–161. doi:10.5172/ijpl.2011.152 Honan, E. (2006). Deficit discourses within the digital divide. Engineers Australia, 41(3), 36–43. Howe, N., & Strauss, W. (2000). Millennials rising: The next great generation. New York, NY: Vintage Books. ictQATAR. (2015). Digital literacy. Retrieved from http://www.ictqatar.qa/en/digital-society/ digital-literacy iKeepSafe. (2015). About BEaPRO. Retrieved from: http://ikeepsafe.org/be-a-pro/info/ International Society of Technology in Education (ISTE). (2007). ISTE Standards. Retrieved September 23, 2015, from http://www.iste.org/ standards/iste-standards/ Isman, A., & Canan Gungoren, O. (2014). Digital citizenship. TOJET: The Turkish Online Journal of Educational Technology, 13(1), 73–77. Jones, C., Ramanau, R., Cross, S., & Healing, G. (2010). Net generation or digital natives: Is there a distinct new generation entering university? Computers & Education, 54(3), 72–732. doi:10.1016/j. compedu.2009.09.022 Kemp, S. (2015, January 21). Digital social & mobile worldwide in 2015. We Are Social. Retrieved from wearesocial.net/tag/statistics Kervin, L., Verenikina, I., Jones, P., & Beath, O. (2013). Investigating synergies between literacy, technology and classroom practice. Australian Journal of Language and Literacy, 36(3), 135–147.

Kress, G., & Van Leeuwen, T. (1996). Reading images: The grammar of visual design. London: Routeledge. Leu, D. J., Coiro, J., Castek, J., Hartman, D., Henry, L. A., & Reinking, D. (2008). Research on instruction and assessment in the new literacies of online reading comprehension. In C. Collins-Block, S. Parris, & P. Afferbach (Eds.), Comprehension instruction: research based best practices (pp. 321–346). New York: Guilford Press. Leu, D. J., Forzani, E., Rhoads, C., Maykel, C., Kennedy, C., & Timbrell, N. (2015). The new literacies of online research and comprehension: Rethinking the reading achievement gap. Reading Research Quarterly, 50(1), 37–59. Mihailidis, P., & Cohen, J. N. (2013). Exploring Curation as a core competency in digital and media literacy education. Journal of Interactive Media in Education. Retrieved from: http://www-jime. open.ac.uk/articles/10.5334/2013-02/ Moraveji, N., Morris, M. R., Morris, D., Czerwinski, M., & Riche, N. (2011). ClassSearch: Facilitating the development of Web search skills through social learning. In Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems (pp. 1797-1806). New York: ACM Press. doi:10.1145/1978942.1979203 O’Byrne, I. (2012). Facilitating critical evaluation skills through content creation: Empowering adolescents as readers and writers of online information (unpublished dissertation). University of Connecticut. OECD. (2001). Understanding the digital divide. Retrieved from http://www.oecd.org/sti/1888451. pdf OECD. (2011). PISA 2009 results: Students on the line: Digital technologies and performance. Paris: OECD.

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Osborne, N., & Connelly, L. (2015). Managing Your Digital Footprint: Possible Implications for Teaching and Learning. In Proceedings of the 2nd European Conference on Social Media 2015: ECSM 2015 (p. 354). Academic Conferences Limited.

United Kingdom Department of Education. (2013). National Curriculum in England: Computing programmes of study. Retrieved from https:// www.gov.uk/government/publications/nationalcurriculum-in-england-computing-programmesof-study/

Partnership for 21st Century Learning. (2009). Framework for 21st Century Learning. Retrieved from: http://www.p21.org/our-work/p21-framework

Walsh, M. (2008). Worlds have collided and modes have merged: Classroom evidence of changed literacy practices. Literacy, 42(2), 101–108. doi:10.1111/j.1741-4369.2008.00495.x

Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5), 1–6. doi:10.1108/10748120110424816

Walsh, M. (2010). Multimodal literacy: What does it mean for classroom practice? Australian Journal of Language and Literacy, 33(3), 211–239.

Reynolds, R., & Chiu, M. M. (2015). Reducing digital divide effects through student engagement in coordinated game design, online resource use, and social computing activities in school. Journal of the Association for Information Science and Technology.

Winograd, M., & Hasi, M. D. (2011). Millennial momentum: How a new generation is remaking America. Piscataway, NJ: Rutgers University Press.

Searson, M., Hancock, M., Soheil, N., & Shepherd, G. (2015). Digital citizenship within global contexts. Education and Information Technologies, 20(4), 729–741. doi:10.1007/s10639-015-9426-0

KEY TERMS AND DEFINITIONS

Spires, H., & Bartlett, M. (2012). Digital literacies and learning: Designing a path forward. Friday Institute White Paper Series. NC State University. Spires, H., & Estes, T. (2002). Reading in webbased learning environments. In C. Collins Block & M. Pressley (Eds.), Comprehension instruction: Research-based best practices (pp. 115–125). New York: Guilford Press. Thompson, T. L. (2015). Digital doings: Curating work–learning practices and ecologies. Learning, Media and Technology, 1–21. Tongia, R. (2005). Access to ICTs for education. In B. Bracey & T. Culver (Eds.), Harnessing the potential of ICT for education: A multistakeholder approach (pp. 143–152). New York: UN ICT Task Force.

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Digital Citizenship: The capacity to conduct oneself in a responsible and ethical manner within public digital environments. Digital Content: Content that uses information and communication technologies. Digital Curation: The capacity to select, sort, synthesize and display digital content. Digital Divide: The gap and access to or usage of ICTs between people, demographic groups or countries. Digital Footprint: An individual’s profile that is depicted to others through the Web. Digital Literacy: The ability to locate, create, and communicate digital content. Online Reading Comprehension: The ability to locate reliable sources on the Internet and synthesize for multiple purposes. Web 2.0 Tools: Technology tools that allow interactivity among users and digital content.

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Digital Literacy in Theory and Practice Heidi Julien State University of New York at Buffalo, USA

INTRODUCTION The concept of digital literacy must be understood in the context of “literacies” writ broadly. Contemporary understandings of literacy have expanded the traditional definition that includes reading and writing (possibly also including numeracy and oralcy), to include interpretive and creative abilities or competencies across a range of texts, in written and other forms. Text, in its contemporary sense, would include the written word, whether rendered on paper or digitally, as well as film and multi-medias. Competencies with texts of any kind are culturally situated, and therefore to be literate is to have the ability to make meaning within particular social conditions (Hoechsmann & Poyntz, 2012). Thus, meaning-making competency for economically privileged youth in a Western urban setting will differ markedly from the meaning-making by adults in a traditional agricultural milieu half-way around the globe with little access to networked communications. In Western industrialized societies, social communication practices via digital means, including interpretation, production, and dissemination, are now commonplace; the degree to which people have the abilities required to participate in these practices can be considered “digital literacy.”

BACKGROUND Digital literacy, from a pragmatic point of view, is the set of skills, knowledge and attitudes required to access digital information effectively, efficiently, and ethically. It includes knowing how to evaluate digital information, and how to use it in decision-making. This definition is

a useful one, but it is one among many. Jaeger, Bertot, Thompson, Katz, and DeCoster (2012), for example, suggest that “digital literacy encompasses the skills and abilities necessary for access once the technology is available, including a necessary understanding of the language and component hardware and software required to successfully navigate the technology” (p. 3). For Jaeger and colleagues, digital literacy expands notions of the digital divide (a continuing challenge, even in wealthy nations), to add the ability to use technology, in addition to having access to it. They note that “digital literacy” came into its own in the 1990s, and they give credit to Gilster (1997) for moving the concept beyond the lists of information-handling skills articulated by national library associations in various countries, and for emphasizing information understanding and use. For Jaeger et al. (2012), “information literacy” is a subset of digital literacy. Another perspective is that information literacy is the broader concept, since “information” need not be digital in format. The concept of information literacy has usually emphasized the contextual nature of information seeking, as well as the importance of information quality (Koltay, 2011). For some (e.g., Hobbs, 2010), information creation is an important aspect of digital literacy; that additional aspect relates digital literacy to the term “media literacy” which is also a commonly used term. There is no doubt that conceptual confusion is evident in this area, in which ICT (Information and Communication Technologies) literacy, computer literacy, computational literacy, technological literacy, information literacy, information fluency, digital literacy, transliteracy, and media literacy overlap in their meanings, and are employed differently by different authors and

DOI: 10.4018/978-1-5225-2255-3.ch195 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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agencies. As noted above, related concepts include literacy (basic reading and writing) and visual literacy, in addition to metaliteracy (a reframing of information literacy that emphasizes participatory online environments (Mackey & Jacobson, 2011)). Bawden (2008) focuses on competencies, suggesting that digital literacy consists of competency in internet searching, hypertext navigation, knowledge assembly, and content evaluation. Koltay (2011) believes that these competencies include notions of critical thinking (a traditional conceptual foundation of information literacy), knowledge assembly (collecting quality information), as well as publishing and communicating information. A broad definition of digital literacy is offered by Martin (2006, p. 19): Digital Literacy is the awareness, attitude and ability of individuals to appropriately use digital tools and facilities to identify, access, manage, integrate, evaluate, analyse and synthesize digital resources, construct new knowledge, create media expressions, and communicate with others, in the context of specific life situations, in order to enable constructive social action; and to reflect upon this process. Bawden (2008) notes that, Digital literacy touches on and includes many things that it does not claim to own. It encompasses the presentation of information, without subsuming creative writing and visualization. It encompasses the evaluation of information, without claiming systematic reviewing and metaanalysis as its own. It includes organization of information but lays no claim to the construction and operation of terminologies, taxonomies and thesauri. (p. 26) Conceptual confusion is exacerbated since the preferred term of the European Commission (2007) is media literacy, with a particular focus on critical awareness of commercially-produced information (Koltay, 2011, p. 217). A specific

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emphasis on discerning the perspectives, intent, and quality of commercial information is not generally the focus of digital literacy discussions in the United States, for example. UNESCO uses the term “media and information literacy,” and it focuses on the need to empower citizens with essential knowledge about the functions of media and information systems in democratic societies. Digital and media literacy is viewed as contributing to sustainable human development, participatory civic societies, sustainable world peace, freedom, democracy, good governance, and fostering of intercultural knowledge and mutual understanding. Such lofty goals place considerable intellectual, political, and practical burdens on a concept such as digital literacy. From the UNESCO perspective, media and information literacy is core to freedom of expression and information, empowering citizens to understand functions of media and other information providers, to enable critical evaluation of content, and to facilitate citizens to make informed decisions as users and producers of information and media content. Karpati (2011), reflecting a UNESCO perspective, states that digital literacy includes “the use and production of digital media, information processing and retrieval, participation in social networks for creation and sharing of knowledge, and a wide range of professional computing skills” (p. 1), broadening the scope of this concept to include high-level technological competence. UNESCO is particularly focused on the relevance of digital literacy to enhance employability, and lifelong learning, with an obvious goal towards human economic and social development. For Karpati (2011), the most important aspects of digital literacy are “accessing, managing, evaluating, integrating, creating, and communicating information individually or collaboratively in a networked, computer supported, and webbased environment[s] for learning, working, or leisure” (p. 4). Karpati cites the UNESCO’s Annual World Report 2009, Information Society Policies (UNESCO, 2009), which focuses on the relevance of the digital divide, and digital literacy,

Category: Digital Literacy

in developing nations. In these contexts, digital literacy is considered critical to development of basic literacy and to lifelong learning (Karpati, 2011, p. 6). Hobbs (2010), writing in the U.S. context, also uses the term “digital and media literacy.” This term is defined very broadly to include the “full range of cognitive, emotional and social competencies that includes the use of texts, tools and technologies; the skills of critical thinking and analysis; the practice of message composition and creativity; the ability to engage in reflection and ethical thinking; as well as active participation through teamwork and collaboration” (p. 17). For Hobbs, what is particularly relevant is the capacity for digital and media literacy to empower people to critically analyze the agendas inherent in information sources, and to advocate for minority or marginalized points of view. She clearly takes a competency view of digital and media literacy, noting that the concept includes the following skills: the ability to access and share information using media and technology, the ability to critically analyze and evaluate information, the ability to create information in sophisticated ways, the ability to reflect on information and communication from an ethical perspective, and the ability to work individually or with others to share information in all contexts (personal, workplace, and at all community levels) (Hobbs, 2010, p. 19). Wohlsen (2014) reiterates the lack of a clear definition for digital literacy, but makes the point that information creation is an important element of the concept. In the United States, digital literacy tends to be contextualized in terms of the ongoing digital divide, and so it is viewed as important for digital inclusion in communities (Institute of Museum and Library Services, 2012). Digital inclusion assumes the ability to appreciate the benefits of ICTs, and means that citizens are able to use ICTs to access educational, economic, and social opportunities. This concern for expansion of opportunity echoes that expressed by UNESCO. The American Library Association Digital Literacy

Task Force (2013) defines digital literacy as “the ability to use information and communication technologies to find, understand, evaluate, create, and communicate digital information, an ability that requires both cognitive and technical skills” (Digital Literacy Task Force, 2013, p. 2). This report expands the concept to include information stewardship, communication with others, civic participation, and democratic engagement. For the American Library Association, digital literacy is conceived in global terms, and its importance is underlined by tying digital literacy to its potential role in helping the U.S. compete in global economic, educational, and intellectual contexts. The educational link is also made between digital literacy and the Common Core State Standards Initiative for U.S. schools, which actually focuses on “media”. The Digital Literacy Task Force (2013) affirms the critical role of libraries in promoting digital literacy and in supporting programs which develop digital literacy, in partnership with other organizations and institutions.

DIGITAL LITERACY TRAINING The importance of digital literacy is widely recognized internationally, and top-down efforts in many nations to encourage digital literacy were evident from the 1990s forward. For example, the New Zealand Computer Society has stated that digital literacy is “an essential life skill and right of every… citizen” (Bunker, 2010, p. 5). Further, the Society states that addressing ICT competence in the workforce could increase productivity by $1.7 billion (in time saved); that ICT competence improves employment opportunities, overcomes isolation, builds confidence and leads to further learning; and, it recommends that national governments should take leadership roles in developing digital literacy among citizens. Digital literacy training initiatives around the world fall on a continuum: basic initiatives, which are the most common, focus on developing basic computing skills applied to everyday simple

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tasks. A second level, found less often, focuses on using basic functionality of key applications (word processing, spreadsheets, presentation tools, email, web searching). At the third level, which is rarely found, the focus is on developing confident use of digital tools and facilities to identify, access, manage, integrate, evaluate, analyze, and synthesize digital resources; construct new knowledge; create media expressions; use the net for transactions; and, develop awareness of security issues. Most digital literacy initiatives are derived from centralized policies at national or regional levels, and are strategically linked with government objectives such as developing an information society, and bridging the digital divide. Therefore, centralized initiatives tend to focus on disadvantaged groups such as the elderly, disabled, or unemployed. Other digital literacy initiatives focus on related strategic goals, such as social cohesion, immigrant integration, supporting lifelong learning, and supporting optimal use of online government services. Usability and accessibility, as components of digital literacy, tend not to be emphasized. Often, digital literacy initiatives are partnerships between governments and other institutions. Non-governmental bodies partnering on such initiatives tend to be motivated by a desire to help disadvantaged groups, while private companies may well be motivated by perceived opportunities to grow their market share of products, such as broadband in rural areas, hardware or software purchase by the elderly or disabled, and improving workforce competencies (Shapiro, 2009). A recent analysis of 470 digital literacy initiatives and survey data in the EU (“Digital Literacy European Commission Working Paper”, n.d.) found that digital literacy programs have grown, especially for young people. In addition, the analysis determined that more efforts are required to develop digital literacy skills among older and disadvantaged groups, and that developing trust and confidence in digital transactions remains challenging. This report notes that investment in digital literacy programs have shown positive

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outcomes, including expanding internet access and use, encouraging non-users or less-skilled users, and providing training based on user preferences for informal learning approaches and focusing on daily life activities and interests. The report concludes that there remains a significant proportion of the population that does not use the Internet, and that potential exists to focus on differences in quality of use. In the United States, digital literacy is being supported by a number of organizations and agencies. An online portal, DigitalLiteracy.gov (http:// www.digitalliteracy.gov/), provides resources to communities and organizations to support local digital literacy initiatives. Individuals can also access training resources on that portal to develop their own digital literacy skills. Digital literacy efforts in schools have been spurred by legislation. In the U.S., the No Child Left Behind Act and the related Enhancing Education Through Technology Act require technological literacy for all children, which enhances digital literacy efforts, especially for children from families without online access (American Library Association, 2012, para. 23). Also in the education context, UNESCO has been active in discussions about technology in education (cf. UNESCO’s Grünwald Declaration on Media Education, 1982), has worked to develop international media education guidelines, and has developed a media and information literacy curriculum (American Library Association, 2012, para. 20). There is little question that digital literacy is important to education at all levels, and is especially relevant in inquiry-based learning. Many universities worldwide have incorporated digital literacy outcomes into strategic planning efforts, although the degree to which these outcomes are evident in their graduates remains uncertain. There has been a significant growth in resources to support digital literacy learning, provided by a range of public and private organizations and partnerships. Examples include the Public Library Association’s Digital Learn hub (http:// digitallearn.org/), MediaSmarts in Canada (http:// mediasmarts.ca/), the LinkAmerica’s Founda-

Category: Digital Literacy

tion Digital Literacy site (http://www.ictliteracy. info/#), Microsofts’ Digital Literacy site (https:// www.microsoft.com/en-us/digitalliteracy/overview.aspx) and Google’s Digital Literacy and Citizenship Curriculum (https://www.google. com/goodtoknow/web/curriculum/). Some sites focus on internet safety for children and youth, such as the UK Safer Internet Centre (http://www. saferinternet.org.uk/). U.S. Digital Literacy (n.d.) is an example of a professional development resource designed by teachers for teachers. The site provides definitions and a multiplicity of learning opportunities for teachers who wish to learn about this topic.

CHALLENGES Digital literacy is recognized as critical to positive health outcomes (of particular importance when so much health information is now obtained online), workforce development, and participative governance (since participatory citizenship is dependent upon relatively sophisticated information finding skills). Digital literacy goes beyond social networking, and increasingly, governments are delivering information and services online, and online only, which requires citizens to be at least minimally digitally literate in order to access that information. In addition, participation in the ‘commons’ and in ‘civil society’ depends on citizens’ ability to find and evaluate information. Digital literacy is also recognized as an essential competency for job performance, since information gathering, manipulation, and application are key work tasks. Those without good digital literacy skills will be marginalized in private and public life, including employment. A recent report (Head, 2012) suggests that employers in the U.S. are not generally pleased with the digital literacy skill set exhibited by employees newly graduated from university. These young people rely on superficial information searches, lack the skills and perseverance to conduct sophisticated and in-depth information searches, and fail to bring

information from a variety of sources together in useful ways. Of increasing concern is the growing recognition that digital literacy skills are not developed through experience alone. The key role for formal digital literacy training efforts is twofold: to ameliorate the digital divide, and to emphasize the role of critical analysis in communicative practice (Hoechsmann & Poyntz, 2012, 147). Effective and efficient information-finding skills take time and effort to learn. Information is organized in complex ways, and can be difficult to evaluate. There is growing consensus that, for most people, confidence in information skills exceeds actual skill level. Skills deficits are especially apparent for effective information finding skills and information evaluation skills. Many people do not understand the context of information—how or why it is produced, nor the purposes for which different types of information are made available. Thus, critical evaluation is difficult. Research shows that students entering post-secondary education typically are “surf savvy” but not “search savvy,” and many students, and people in general, do not understand how to evaluate the information they find (Nicholas et al., 2009). Authority is assessed within seconds by dipping and cross-checking across different sites and by relying on favored brands (e.g., Google). The speed of web searching indicates that little time is spent evaluating information for relevance, accuracy or authority. In addition, many people do not understand (or respect) ethical boundaries on using others’ ideas and writing, relying on cut and paste techniques to bring disparate information together. It seems clear that there is significant potential for learning institutions at all levels to play important roles to expand the interpretive repertoires of their learners, and to develop a “questioning and reflective approach that recognizes the social and cultural implications of the technologies, institutions, and texts” (Hoechsmann & Poyntz, 2012, 149). If many people with access to computers and networks remain digitally illiterate, how can this status quo be addressed? Presumably, digital

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literacy skills can be learned from teachers in school; however, despite curricular mandates in many jurisdictions, and the presence of certified school librarians in some schools, we know that actual skills continue to be low (Julien & Barker, 2009; Gross & Latham, 2012). Teachers are not necessarily digital literate themselves, so it may be unrealistic to expect them to impart skills they do not yet possess. Teachers’ classroom time also is limited, and digital literacy skills tend not to be tested. What is not tested is less likely to be emphasized. When parents are digitally literate, children may learn these skills, but many parents lack this knowledge to pass along. Often, opportunities to develop digital literacy skills exist at post-secondary educational institutions, but instruction is not systematic, may be very limited, and may not be done well (Julien, 2006). The same concerns apply to opportunities at public libraries and other community centers. Other challenges to developing widespread digital literacy skills include unfounded beliefs about the relative skills and understandings of so-called “digital natives,” and assumptions about the value of experience with ICTs without formal learning of skills. Competence with social media or quick Google searches does not necessarily translate into sophisticated information evaluation skills. Another challenge is assumptions about the capacity of libraries to play significant roles in developing citizens’ digital literacy skills. Libraries often face severe resource challenges, library administrators may place relatively little emphasis on client training in digital literacy, library customers may lack confidence in the potential for librarians to contribute to digital literacy training, and librarians may be poorly prepared for instructional work (Julien & Hoffman, 2008). In most jurisdictions there is also limited coordination between school teachers and librarians in public libraries and academic libraries, as well as insufficient numbers of teacher-librarians in schools; in many jurisdictions globally, librarian positions in schools have been eliminated entirely. Where potential exists for librarians across contexts to work together to develop community capacity in 2248

digital literacy, actual cooperation or coordination is rare.

FUTURE RESEARCH DIRECTIONS Research in digital literacy focuses largely on the ways in which it can be further developed among specific populations or in specific contexts, and there remains substantial work to do in this area. For instance, there are nuances related to the learning and expression of digital literacy that will differ between adults and children, and between disparate cultural, educational, and workplace settings. Certainly a focus on outcomes of digital literacy education is also warranted, and should be a significant concern for societies globally. In addition, the digital literacy landscape will evolve with changes in information technologies, and these changes will merit research attention.

CONCLUSION Despite these challenges, the list of potential benefits arising from individual and community digital literacy is lengthy, and the value of digital literacy is significant. Some perspectives and agendas focus on overtly political outcomes of widespread digital literacy, including enhancing democracy, world peace, and empowering previously marginalized groups politically and socially. Digital literacy certainly has the potential to contribute to far-reaching and important personal and societal consequences. Thus, increasing focus on development of digital literacy, however defined, should be a policy priority for all sectors.

REFERENCES American Library Association. (2012). Digital literacy – ALA Digital Literacy Task Force draft report. Retrieved November 13, 2015 from http:// connect.ala.org/files/94226/digilitreport2012_ COMMENT%20DRAFT_9%2018%2012.pdf

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Bawden, D. (2008). Origins and concepts of digital literacy. In C. Lankshear & M. Knobel (Eds.), Digital literacies: Concepts, policies and practices (pp. 17–32). New York: Peter Lang. Bunker, B. (2010). A summary of international reports, research and case studies of digital literacy. New Zealand Computer Society Inc. Retrieved November 13, 2015 from http://www.iitp. org.nz/files/201001%20Digital%20Literacy%20 Research%20Report.pdf Digital Literacy European Commission Working Paper and Recommendations from Digital Literacy High-Level Expert Group. (n.d.). Retrieved November 13, 2015 from www.ifap.ru/library/ book386.pdf Digital Literacy Task Force. Office for Information Technology Policy. American Library Association. Conclusions and Recommendations for Digital Literacy Programs and Libraries. (2013). Retrieved November 13, 2015 from http:// www.districtdispatch.org/2013/06/ala-task-forcereleases-recommendations-to-advance-digitalliteracy/ European Commission. (2007). A European approach to media literacy in the digital environment. Retrieved November 13, 2015 from http://eur-lex. europa.eu/LexUriServ/LexUriServ.do?uri=COM %3A2007%3A0833%3AFIN%3AEN%3APDF Gilster, P. (1997). Digital literacy. New York: Wiley. Gross, M., & Latham, D. (2012). Whats skill got to do with it?: Information literacy skills and self-views of ability among first-year college students. Journal of the American Society for Information Science and Technology, 63(3), 574–583. doi:10.1002/asi.21681 Head, A. (2012). Learning curve: How college graduates solve information problems once they join the workplace. Project Information Literacy Research Report, October 16, 2012. Retrieved November 13, 2015 from http://projectinfolit.org/ images/pdfs/pil_fall2012_workplacestudy_fullreport_revised.pdf

Hobbs, R. (2010). Democracy and media literacy: A plan of action. Boulder, CO: Aspen Institute. Hoechsmann, M., & Poyntz, S. R. (2012). Media literacies: A critical introduction. Chichester, UK: Blackwell Publishing. doi:10.1002/9781444344158 Institute of Museum and Library Services. University of Washington, International City/County Management Association. (2012 January). Building digital communities: A framework for action. Washington, DC: Institute of Museum and Library Services. Retrieved November 13, 2015 from http://www.imls.gov/assets/1/AssetManager/ BuildingDigitalCommunities_Framework.pdf Jaeger, P. T., Bertot, J. C., Thompson, K. M., Katz, S. M., & DeCoster, E. J. (2012). The intersection of public policy and public access: Digital divides, digital literacy, digital inclusion, and public libraries. Public Library Quarterly, 31(1), 1–20. doi:1 0.1080/01616846.2012.654728 Julien, H. (2006). A longitudinal analysis of information literacy instruction in Canadian academic libraries. Canadian Journal of Information and Library Science, 29(3), 289–313. Julien, H., & Barker, S. (2009). How high school students evaluate scientific information: A basis for information literacy skills development. Library & Information Science Research, 31(1), 12–17. doi:10.1016/j.lisr.2008.10.008 Julien, H., & Hoffman, C. (2008). Information literacy training in Canadas public libraries. The Library Quarterly, 78(1), 19–41. doi:10.1086/523908 Karpati, A. (May 2011). Policy brief: Digital literacy in education. UNESCO Institute for Information Technologies in Education. Retrieved November 13, 2015 from http://iite.unesco.org/ pics/publications/en/files/3214688.pdf Koltay, T. (2011). The media and the literacies: Media literacy, information literacy, digital literacy. Media Culture & Society, 33(2), 211–221. doi:10.1177/0163443710393382 2249

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Mackey, T. P., & Jacobson, T. E. (2011). Reframing information literacy as a metaliteracy. College & Research Libraries, 72(1), 62–78. doi:10.5860/ crl-76r1 Martin, A. (2006). Literacies for the digital age. In A. Martin & D. Madigan (Eds.), Digital literacies for learning (pp. 3-25). London: Facet. Nicholas, D., Huntington, P., Jamali, H. R., Rowlands, I., & Fieldhouse, M. (2009). Student digital information-seeking behaviour in context. The Journal of Documentation, 65(1), 106–132. doi:10.1108/00220410910926149 Shapiro, H. (2009). Supporting digital literacy public policies and stakeholder initiatives. Topic report 4. Conclusions and recommendations based on reviews and findings. Danish Technological Institute. Retrieved November 13, 2015 from https://joinup.ec.europa.eu/sites/default/ files/files_epractice/sites/Topic%20Report%20 4%20-%20Conclusions%20and%20recommndations%20based%20on%20reviews%20and%20 findings.pdf UNESCO. (1982). The Grünwald declaration on media education. Retrieved from http://www. unesco.org/education/pdf/MEDIA_E.PDF

ADDITIONAL READING Andreae, J., & Anderson, E. L. (2011). Re-conceptualizing access. Communications in Information Literacy, 5(2), 74–81. Bawden, D. (2001). Information and digital literacies: A review of concepts. The Journal of Documentation, 57(2), 218–259. doi:10.1108/ EUM0000000007083 Beagle, D. D. (2012). The emergent information commons: Philosophy, models, and 21st century learning paradigms. Journal of Library Administration, 52(6-7), 518–537. doi:10.1080/01930 826.2012.707951 Bernsmann, S., & Croll, J. (2013). Lowering the threshold to libraries with social media. The approach of Digital Literacy 2.0, a project funded in the EU Lifelong Learning Programme. Library Review, 62(1), 53–58. doi:10.1108/00242531311328168 Bradley, C. (2013). Information literacy in the programmatic university accreditation standards of select professions in Canada, the United States, the United Kingdom and Australia. Journal of Information Literacy, 7(1), 44–68. doi:10.11645/7.1.1785

UNESCO. (2009). Information society policies. Annual world report. Retrieved from http://portal. unesco.org/ci/en/files/29547/12668551003ifap_ world_report_2009.pdf/

Catts, R. (2012). Indicators of adult information literacy. Journal of Information Literacy, 6(2), 4–18. doi:10.11645/6.2.1746

U.S. Digital Literacy. (n.d.). Retrieved November 13, 2015 from http://digitalliteracy.us/

Clark, L., & Visser, M. (2011). Digital literacy takes center stage. Library Technology Reports, 47, 38.

Wohlsen, M. (2014). Digital literacy is the key to the future, but we still don’t know what it means. Wired. Retrieved November 13, 2015 from http:// www.wired.com/2014/09/digital-literacy-keyfuture-still-dont-know-means/

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Feezell, J. T., Kahne, J., & Lee, N.-j. (2012). Digital media literacy education and online civic and political participation. International journal of communication (Online), 1+.

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Ferran-Ferrer, N., Minguillón, J., & PérezMontoro, M. (2013). Key factors in the transfer of information-related competencies between academic, workplace, and daily life contexts. Journal of the American Society for Information Science and Technology, 64(6), 1112–1121. doi:10.1002/asi.22817 Ferro, E., Gil-Garcia, J. R., & Helbig, N. C. (2011). The role of IT literacy in defining digital divide policy needs. Government Information Quarterly, 28(1), 3–10. doi:10.1016/j.giq.2010.05.007 Ferro, E., Helbig, N. C., & Gil-Garcia, J. R. (2011). The role of IT literacy in defining digital divide policy needs. Government Information Quarterly, 28(1), 3–10. doi:10.1016/j.giq.2010.05.007 Gee, J. P. (2012). Digital games and libraries. Knowledge Quest, 41, 60. Gross, M., & Latham, D. (2012). Whats skill got to do with it?: Information literacy skills and self-views of ability among first-year college students. Journal of the American Society for Information Science and Technology, 63(3), 574–583. doi:10.1002/asi.21681 Gutiérrez-Martín, A. G., & Tyner, K. (2012). Media literacy in multiple contexts. Comunicare: Journal for Communication Sciences in Southern Africa, 19(38), 10–12. doi:10.3916/ C38-2012-02-00 Hamilton, B. J. (2009). Transforming information literacy for nowgen students. Knowledge Quest, 37, 48. Hicks, A. (2013). Cultural shifts. Communications in Information Literacy, 7(1), 50–65. Hobbs, R. (2011). Digital and media literacy: Connecting culture and classroom. Thousand Oaks: Sage. Jaeger, P. T., Bertot, J. C., Thompson, K. M., Katz, S. M., & DeCoster, E. J. (2012). The intersection of public policy and public access: Digital divides, digital literacy, digital inclusion, and public libraries. Public Library Quarterly, 31(1), 1–20. doi:1 0.1080/01616846.2012.654728

Jayakar, K., & Park, E.-A. (2012). Funding public computing centers: Balancing broadband availability and expected demand. Government Information Quarterly, 29(1), 50–59. doi:10.1016/j. giq.2011.02.005 Jones, R. H., & Hafner, C. A. (2012). Understanding digital literacies: An introduction. New York: Routledge. Knutsson, O., Blåsjö, M., Hållsten, S., & Karlström, P. (2012). Identifying different registers of digital literacy in virtual learning environments. The Internet and Higher Education, 15(4), 237– 246. doi:10.1016/j.iheduc.2011.11.002 Lankshear, C., & Knobel, M. (Eds.). (2008). Digital literacy: Concepts, policies and practices. New York: Peter Lang Publishing. Lowe, M. (2012). Information literacy 2011: A selection of 2011’s literature on IL. Codex (2150086X), 1(4), 46-74. Mills, K. A. (2010). A review of the digital turn in the new literacy studies. Review of Educational Research, 80(2), 246–271. doi:10.3102/0034654310364401 Saunders, L. (2012). Faculty perspectives on information literacy as a student learning outcome. Journal of Academic Librarianship, 38(4), 226–236. doi:10.1016/j.acalib.2012.06.001 Tyner, K. (2009). Media literacy and the tyranny of the narrative. Afterimage, 37, 3. Weiner, S. (2011). Information literacy and the workforce: A Review. Education Libraries, 34(2), 7–14.

KEY TERMS AND DEFINITIONS Digital Divide: Inequalities between people with access to digital technologies and those without such access. Access may include access to hardware, software, internet connections, and possessing the skill set needed to make use of these technologies. 2251

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Digital Literacy: The set of skills, knowledge and attitudes required to access, create, use, and evaluate digital information effectively, efficiently, and ethically.

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Digital Native: A person who has interacted with digital technology for most of his or her life.

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Category: Digital Literacy

Encouraging Digital Literacy and ICT Competency in the Information Age Kijpokin Kasemsap Suan Sunandha Rajabhat University, Thailand

INTRODUCTION The growing prominence of the Internet as educational tool requires research regarding learners’ digital literacy (Greene, Yu, & Copeland, 2014). Nowadays, students autonomously acquire their digital literacy and are adept at using various ICT tools to enrich their daily leisure lives (Ting, 2015). Digital literacy includes the ability to search for information and to integrate that information while monitoring progress toward achieving educational goals (Bråten, Britt, Strømsø, & Rouet, 2011). Digital natives often engage themselves in the use of ICT tools and in accessing, creating, and sharing both text and videos on the Web 2.0 (Junco, 2012). The ability of digital natives to embrace ICT suggests that they possess a certain level of digital literacy (Ng, 2012). Competency refers to the ability resulting from individual’s knowledge, skills, characteristics, and attitude in executing work to achieve success (Malinina, 2015). ICT plays a critical role in enhancing the quality of education (Vitanova, Atanasova-Pachemska, Iliev, & Pachemska, 2015). Within the context of 21st century skills, the importance of being digitally competent is reflected in the international and national policies for the educational ICT utilization (Kozma, 2008). These policies for educational ICT utilization have introduced ICT competency in the national and school curricula (Aesaert, Vanderlinde, Tondeur, & van Braak, 2013), such as the integration of ICT competences in the educational curricula. ICT competency standards practically define the achievement expectations for students (Thomas & Knezek, 2008).

This article aims to bridge the gap in the literature on the thorough literature consolidation of digital literacy and ICT competency. The extensive literatures of digital literacy and ICT competency provide a contribution to practitioners and researchers in order to maximize the impact of digital literacy and ICT competency in the information age.

BACKGROUND Technology Acceptance Model (TAM), such as Unified Theory of Acceptance Use of Technology (UTAUT), explains the degree of acceptance of the utilization of information technology (IT) toward adopting the technological infrastructure (Nchunge, Sakwa, & Mwangi, 2013). TAM helps managers and decision makers to evaluate the success of the acceptance of technology to the organization, and motivate users to accept the systems. UTAUT identifies four key factors (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions) and four moderators (i.e., age, gender, experience, and voluntariness) concerning behavioral intention toward utilizing technology in organizational contexts (Venkatesh, Thong, & Xu, 2016). Digital literacy refers to the variety of literacies associated with the use of new technologies (Mohammadyari & Singh, 2015). Digital literacy is a fundamental life skill in today’s knowledge economy and information society (Bawden, 2001). Digital literacy constitutes new practices rather than new instances of established practices (Simpson & Obdalova, 2014). Proficiency in

DOI: 10.4018/978-1-5225-2255-3.ch196 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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digital literacy refers to the ability to read and write using online sources, and includes the ability to select sources relevant to the task, synthesize information into a coherent message, and communicate the message with an audience (Bulger, Mayer, & Metzger, 2014). Appel (2012) defined digital literacy as the ability to find and analyze information by using computers. Digital literacy is a broad concept encompassing the different aspects, and its development follows a continuum from the acquisition of instrumental skills to that of strategic competence and cognitive skills (Calvani, Fini, Ranieri, & Picci, 2012). Digital literacy is the awareness, attitude and ability of individuals to appropriately utilize the digital tools to identify the digital resources, construct the new knowledge, create the media expressions, and communicate with others (Martin, 2005). Hatlevik and Christophersen (2013) used the term digital competence to describe the acquisition and processing of digital information and the ability to produce the digital information. Competency is made up of knowledge, skills, and attitude (Malinina, 2015). ICT competency is considered as the educational outcomes (Thomas & Knezek, 2008). ICT competency refers to knowledge, skills, and ability to take advantage of ICT for the purpose of gathering, processing, and presenting the information in support of activities among different groups of people (Albirini, 2006). Traditional methods of teaching ICT are not an effective way for learners to acquire ICT competencies or to gain more positive ICT perceptions (Goktas, Yildirim, & Yildirim, 2008). Instead, learners should interact with new information in ways that enable the active inquiry to promote the useful learning (Daugherty, 2005). To gain ICT competency, learners should be given opportunities to create their own meaning-making processes in order to establish their own knowledge (Goktas & Demirel, 2012).

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ADVANCED ISSUES OF DIGITAL LITERACY AND ICT COMPETENCY This section emphasizes the overview of digital literacy and ICT competency, the encouragement of digital literacy in the information age, and the encouragement of ICT competency in the information age.

Overview of Digital Literacy Twenty-first century learning skills require the ability to use the Internet technology (Greene et al., 2014). The prominent role the Internet plays in home and classroom lives demands careful attention to its link to student knowledge gains (Greene et al., 2014). The Internet, as a text, consists of multiple print, images, videos, and interactive simulations, all used to communicate with the subsequent effects upon cognition (Collins & Halverson, 2009). While it is important to consider how the Internet utilization affects the students’ different cognitive processes (Reinking, 2005), it is important to consider how different cognitive processes influence how students engage with the Internet (Strømsø & Bråten, 2010). Digital literacy is an important determinant to consider as the number of electronic learning (e-learning) tools has expanded to incorporate the Web 2.0 innovations, such as blogs, podcasts, and wikis (Mohammadyari & Singh, 2015). Digital literacy is a significant determinant of attitudes toward computer-assisted language learning (Oz, Demirezen, & Pourfeiz, 2015). Ullrich et al. (2008) stated that the rapid spread of these tools has meant that individuals often have had to train themselves in how to use these tools. Individuals with a high level of digital literacy have been better able to leverage these new tools to self-manage their training and execute their continuing education activities in an informal setting, toward reducing the interruption to their working lives (Hargittai, 2010).

Category: Digital Literacy

Besides the technical awareness, digital literacy includes the social and cognitive skills required in the digital environment (Huerta & SandovalAlmazan, 2007). Eshet-Alkalai (2004) stated that digital literacy includes five skills: photo-visual skills, reproduction skills, branching skills, information skills, and socio-emotional skills. The key motivator behind the growing use of these tools has been the ability to quickly incorporate the material about new developments in a field into the training material, which is important for the fields, which are affected by technology-related issues, such as privacy, security, and standards (Arbaugh & Duray, 2002).

Overview of ICT Competency ICT competency refers to the learning-process oriented competence used in the complex, authentic, and unpredictable situations, and is underpinned by the technical and application ICT knowledge and skills (Aesaert et al., 2013). Markauskaite (2007) indicated that ICT competency refers to the interactive use of cognitive capabilities and technical capabilities in order to accomplish the cognitive information and ICT-based tasks. Digital information processing and digital communication are recognized as ICT competencies to be measured because these are identified as two themes in the national and international ICT frameworks (Voogt & Roblin, 2012). Application of ICT initiates the new opportunities in arranging the educational environment (Malinina, 2015). Various types of professional development programs concerning ICT implementation have been organized for in-service teachers toward upgrading ICT competency among teachers and bringing the change to their teaching practices, such as integrating ICT in classroom (Borko, 2004). In order to produce the effective learning through ICT utilization, students should develop the technological competencies, and teachers should develop the teaching, learning, and technological competencies (Pineida, 2011).

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Digital literacy refers to the skills and knowledge in using computers in a hypermedia environment (Ting, 2015). Digital literacy refers to the cognitive processes that individuals partake in during the utilization of computer-based, multimodal information (Mayer, 2005). Digital literacy, as a complex frame of specific diversified capabilities, represents an actual phenomenon within the social matrix, sourced in the developing potential of the digital technology and the required information literacy (Javorský & Horváth, 2014). Institutions need to place greater value on digital literacy, and better prepare their students and their own organizational processes to thrive in an age of digital knowledge practices (Littlejohn, Beetham, & McGill, 2012). In terms of the ways in which digital literacy is acquired, adolescents, in particular, engage in a broad range of computerized activities, including doing homework, searching and gathering information on the Internet, using social media platforms to communicate with friends, watching videos on YouTube, and playing educational video games (Appel, 2012). Educational computer games can motivate students to develop the basic competencies and encourage challenging themselves to be better and learn the additional knowledge related to the important tasks (Kasemsap, 2017a). Social media tools are open to anyone, whereas reaching the traditional media often requires a lot of money and a good network of media industry contacts (Kasemsap, 2016a). The use of social media has created the highly effective communication platforms where any user, virtually anywhere in the world, can freely create the content and disseminate this information in real time to a global audience (Kasemsap, 2017b). For ICT tools, especially for those intended for entertainment purposes, students are often more skilled and adept at using them than their teachers, such as when the ICT tools are used to edit videos and upload them to Facebook or Youtube (Gu, Zhu,

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& Guo, 2013). Students autonomously learn the instrumental skills and knowledge of computers and the Internet outside formal education (Eynon & Malmberg, 2011). Because students are raised in such the networked digital environment, their patterns of thinking and communication, notions of learning, needs for control, and even their personal and social values have been shaped by this networked digital environment (Gu et al., 2013). There has been an increasing interest in the ways that young people are using the Internet and other new technologies in their everyday lives and how such use may enhance the informal and formal learning opportunities (Lim, Zhao, Tondeur, Chai, & Tsai, 2013). The advent of a knowledge-based society, where economic wealth depends on individuals’ ability to deal with the abundance of information and to adapt to an ever-changing working environment, makes digital literacy an obvious concept for examining the individual adoption of IT (Mohammadyari & Singh, 2015). Digital literacy empowers individuals to communicate with others, work more effectively, and increase the individual’s productivity, particularly with those who have the same skills and proficiency levels (Martin, 2008). Digital literacy reduces the individuals’ inclination to unfavorably regard their achievements (Eastin & LaRose, 2000), which should make them more confident about their expected performance. Self-regulated learning (Winne & Hadwin, 2008) skills, inclusive of making effective plans, and controlling these plans, as well as the strategies used to enact those plans and the learning that results (Azevedo & Jacobson, 2008), are likely to be the critical components of digital literacy. Some researchers have equated digital literacy with search literacy, or searching for information online and information literacy (Hockley, 2012). For example, cognitive overload and disorientation are two primary reasons why students struggle to effectively search the Internet (Gerjets, Scheiter, & Schuh, 2008).

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Encouragement of ICT Competency in the Information Age Regarding ICT competency encouragement, blogs can be utilized as an online learning tool to provide students with access to the significant information, to enlarge the students’ understanding of specific issues, and to direct students to investigate the additional materials (Luehmann, 2008). Using blogs in ICT courses can increase the students’ perceived technological competencies, while promoting their dynamic engagement (Goktas & Demirel, 2012). Blogs can encourage engagement in the educational activities and involve the interactive communities of learners (Goktas & Demirel, 2012). Individuals who are regarded as having ICT competency must be able to produce necessary documents, find out solutions to problems, and choose proper ICT tools for problem solving and effective work (Malinina, 2015). Creating and maintaining a weblog using the created blogging software is an easy process, and an instructor can use this learning format to publish the course materials and post the announcements, presentations, timetables, and other information on the Web toward increasing the students’ ICT competency (Wassell & Crouch, 2008).

FUTURE RESEARCH DIRECTIONS The classification of the extensive literature in the domains of digital literacy and ICT competency will provide the potential opportunities for future research. Human capital is the collective skills, knowledge, or other intangible assets of individuals that can be used to create the economic value for the individuals, their employers, or their community (Kasemsap, 2016b). Continuing professional development is the training and education that continues throughout an individual’s career in order to improve the skills and knowledge (Kasemsap, 2017c). An examination of linkages

Category: Digital Literacy

among digital literacy, ICT competency, human capital, and continuing professional development in the workplace would seem to be viable for future research efforts.

CONCLUSION This article highlighted the overview of digital literacy and ICT competency, the encouragement of digital literacy in the information age, and the encouragement of ICT competency in the information age. Ability to use technology is a critical prerequisite for understanding information communicated through that technology. Digital literacy and ICT competency are the abilities to locate, organize, understand, evaluate, and analyze information using computers and digital technology. Digital literacy and ICT competency have led to the great increases in information that can be conveniently and quickly accessed and effectively facilitate the collaboration and sharing of knowledge. While employability is an obvious driver, developing learners who can learn in the digital society is the significant role for universities and colleges. Digital literacy and ICT competency look beyond functional IT skills to describe a richer set of digital behaviors, practices, and identities. Digital literacy and ICT competency are a set of academic practices supported by diverse learning methods, such as electronic learning and gamebased learning. Encouraging discussion about supporting students with digital skills and the associated impact on the teaching staff roles helps broaden the awareness of both digital literacy and ICT competency across educational institutions. The staff and students involved do not need to be technology experts; effective communication skills, educational flexibility, and an eagerness to learn are crucial toward encouraging both digital literacy and ICT competency. Benefits of digital literacy and ICT competency include the enhanced capacity to remain abreast of technology developments and effectively

utilize technology to increase productivity and competitiveness in all sectors of the economy and to develop the innovative networks, products, and services for the rapidly growing ICT marketplace. The encouragement of digital literacy and ICT competency is essential for modern organizations that seek to serve suppliers and customers, increase business performance, strengthen competitiveness, and achieve regular prosperity in the information age. Thus, it is required for modern organizations to encourage their digital literacy and ICT competency and develop a strategic plan to regularly investigate their practical improvements toward satisfying customer requirements. Encouraging digital literacy and ICT competency has the potential to enhance organizational performance and achieve strategic goals in the information age.

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Price-Dennis, D., Holmes, K. A., & Smith, E. (2015). Exploring digital literacy practices in an inclusive classroom. The Reading Teacher, 69(2), 195–205. doi:10.1002/trtr.1398 Rambousek, V., Štípek, J., & Wildová, R. (2015). ICT competencies and their development in primary and lower-secondary schools in the Czech Republic. Procedia: Social and Behavioral Sciences, 171, 535–542. doi:10.1016/j.sbspro.2015.01.158 Scuotto, V., & Morellato, M. (2013). Entrepreneurial knowledge and digital competence: Keys for a success of student entrepreneurship. Journal of the Knowledge Economy, 4(3), 293–303. doi:10.1007/ s13132-013-0155-6 Spires, H. A. (2015). Digital game-based learning: What’s literacy got to do with it? Journal of Adolescent & Adult Literacy, 59(2), 125–130. doi:10.1002/jaal.424 van Tryon, P. J. S. (2013). The instructional designer’s role in forming university-community partnerships in digital literacy. TechTrends, 57(1), 52–58. doi:10.1007/s11528-012-0631-z Voogt, J., Erstad, O., Dede, C., & Mishra, P. (2013). Challenges to learning and schooling in the digital networked world of the 21st century. Journal of Computer Assisted Learning, 29(5), 403–413. doi:10.1111/jcal.12029 Zimmermann, A., & Ravishankar, M. N. (2014). Knowledge transfer in IT offshoring relationships: The roles of social capital, efficacy and outcome expectations. Information Systems Journal, 24(2), 167–202. doi:10.1111/isj.12027

KEY TERMS AND DEFINITIONS Blog: The website, similar to an online journal, that includes chronological entries made by individuals. Competency: The important skill that is needed to do a job.

Category: Digital Literacy

Information Technology: The set of tools, processes, and associated equipment employed to collect, process, and present the information. Internet: The worldwide computer network that provides information on very many subjects and enables users to exchange messages. Knowledge: The state of knowing about or being familiar with something.

Learning: The activity of obtaining knowledge. Literacy: The knowledge of the particular subject, or the particular type of knowledge. Technology: The use of scientific knowledge to solve practical problems, especially in industry and commerce.

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Information Needs of Users in the Tech Savvy Environment and the Influencing Factors Mudasir Khazer Rather University of Kashmir, India Shabir Ahmad Ganaie University of Kashmir, India

INTRODUCTION Information environment is enough rich, characterized by a growth in information sources as well as providers, a variety of approaches and techniques for accessing information, and a redundancy of content from multiple sources. In this “overloaded” information environment, many information users tend to experience a sense of insufficiency in locating the precise information which leads to anxiety. In this complex information environment, understanding the way individuals choose to satisfy their information needs takes on new urgency. Insight into information seeking can be gained by understanding how users seek information sources and how they locate the desired information to meet their needs (Chandra, Lynn, Lawrence & Lillie, 2007). The concept of information needs was coined by an American information scientist Robert S. Taylor in his article “The Process of Asking Questions” published in American Documentation (Now is Journal of the American Society of Information Science and Technology. There are many definitions of information need. According to Case (2012) information need is a recognition that your knowledge is inadequate to satisfy a goal that you have. He explains that “having information” is not the same as “being informed.” Information need is one of the cognitive needs of humanity. Information need determines information-seeking behavior and these concepts harmonize one an-

other. Information need is influenced by a number of factors. It is revealed from the literature that ‘information scattered in too many sources’ and that too in multi-formats is the problem often faced by users. For fulfilling the information needs, users access different sources of information Sources. Scientists, engineers and technologists in general use encyclopedias, handbooks, textbooks, periodicals, abstracts, indexes, standards, patents, etc. for their research and development works. He showed that information needs of scientists, engineers and technologists are equally based on their knowledge about those sources of information and accessibility of these information sources (Gayatri, 2006). Post Graduate students and Research Scholars mostly use journals, library books and textbooks for completing their course work (Fidzani, 1998). The information needs of teachers were found to be mostly related to guidance on administrative procedures, having lesson plans ready, mechanisms for evidencing work, etc (Williams, 2005). Further, information needs of the General people are found to be varied. The areas in which they needed information are diverse. These range from the information needs of the farmers, to that of the petty traders, artisans, blacksmiths, weavers, painters, fishermen, postmasters, labors, adult learners etc (Kadli & Kumbar, 2011). This chapter provides an overview of information needs of users, their types and also the various factors influencing the information needs of users in the digital age.

DOI: 10.4018/978-1-5225-2255-3.ch197 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Digital Literacy

BACKGROUND Developments in the present tech savvy information world have been witnessed only since when the communication of information has been speedy and rapid. Information is the fundamental unit of communication. Communication is the transmission of information in the form of signs and symbols which gave birth to the concept of data. Data is a codified and communicable symbolic representation of entities, properties and their states. They have content (representation) and form (record) that allow storage, retrieval, transfer, aggregation, and analysis. Data can turn into information if they are put into a context and given meaning. Information is a string of signs and symbols that can be interpreted as a message. It can be transmitted in the form of signals. Information is any sort of event that changes the state of a dynamic system. The meaning of this concept varies in various contexts. Information is closely correlated to notion of data, message, knowledge, wisdom, meaning understanding, perception, communication etc (Silvio, 2006). According to some authors, data are understood to be symbols that have not yet been interpreted, information is data with meaning, and knowledge is what facilitates people to allocate meaning and thereby generate information. Data have generally been taken as simple facts that can be structured to develop information. Information is the English word which is apparently derived from the Latin stem (informatio): this noun is derived from the verb “informare” (to inform) in the sense of “to give form to the mind”, “to discipline”, “instruct”, “teach”. Inform itself comes from the Latin verb informare, which means to give form, or to form an idea of. Furthermore, Latin itself already contained the word information meaning concept or idea, but the extent to which this may have influenced the development of the word information in English is not clear (Wikipedia, 2013). A number of authors have given their views and opinions regarding the concept of information. According to Cawkell, (2003), “Information is an assemblage of data in a comprehensible form

capable of communication and use”. While as Martin (1995) believes Information is that which adds to or modifies knowledge structure. Singh (2007) reveals that Information seems to be everywhere. Information is being encoded in the genes, disseminated by media of communication, exchanged in conversation [discussion], contained in all sorts of things, libraries are overflowing with it, institutions are bogged down by it, and people are overloaded with it, still no one seems to known exactly what information is. AdeotiAdekeye (1997) explains that there are three major fields of information which have traditionally been divided and separated. The first is the literature field of libraries and archives, where information has been put into recorded form. The second is the document field of information centers and record centers, where information has been collected and organized but perhaps not seriously evaluated in the same sense as in the literature field. The third information field is the data field of computers, telecommunications and automated information systems where the information is often numerical or structured.

Data vs. Information Data refers to raw, unevaluated facts, figures, symbols, objects, events, etc. Data may be a collection of facts lying in storage, like a telephone directory or census records. Information is data that have been put into a meaningful and useful context and communicated to a recipient who uses it to make decisions. Information involves the communication and reception of intelligence or knowledge. It appraises and notifies surprises and stimulates, reduces uncertainty, reveals additional alternatives or helps eliminate irrelevant or poor ones, and influences individuals and stimulates them to action. An element of data may constitute information in a specific context; for example, when you want to contact your friend, his or her telephone number is a piece of information; otherwise, it is just one element of data in the telephone directory (Babu, Singh & Sachdeva, 2013). 2265

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Knowledge is the outcome of information and has been defined in a number of ways. Al-Salti and Hackney, (2011) defines knowledge as “a product of human reflection and experience”. Moreover, it has been considered as a powerful tool to develop better decision making and innovation. Recently, many researchers have stressed that knowledge is an important factor to gain and sustain a competitive advantage. There are two main dimensions of knowledge: explicit and tacit. Explicit knowledge is transmittable knowledge that exists in symbolic or written form and stored in readily accessible media such as manuals, documentations, procedures and program codes. Tacit knowledge, on the other hand, is knowledge that resides in the minds of people but difficult, or even in some cases impossible, to be expressed in verbal, symbolic and written form such as insights, expertise and experience. Such knowledge cannot be transferred through a written document, and yet it is very important in the organization. The above discussion reveals that Knowledge is the outcome of information which itself is considered as the meaningful data.

Data, Information, Knowledge, and Wisdom Many authors and researchers around the globe have co-related the concept of data, information with knowledge and wisdom. According to Tuomi (2000), when the information is interpreted or put into context, or when meaning is added to it, it is converted into knowledge. There are a number of variations of this widely accepted theme. The general idea is that data is somewhat less than information and that information is less than that of knowledge. Furthermore, it is understood that data is necessary for the creation of information and knowledge emerges only when we have knowledge (Figure 1). Figure 1 is in accordance with what Singh, (2007) divulges that there is an established hierarchical liaison between data, information and knowledge. Data can be defined easily as “raw”

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Figure 1. Relation b/w data, information, knowledge, and wisdom

facts, which can be expressed in terms of numbers, symbols, text, images or voice, etc., representing quantities, actions and objects. But it is complicated to describe knowledge and discriminate it from information. Data becomes “information” when it is placed into some context. Information reduces ambiguity and changes one’s way of thinking. Knowledge has become the most important asset or resource in contrast to information or data as it is not easily identified, understood, classified, organized, shared, measured or to imitate. Pantzar (2000) highlights that Data and information are components of the knowledge transmitted within the information society, in such a manner that data forms the level of the elements. Data is raw, simply exists and has no implication beyond its existence. It can subsist in any form, usable or not. It does not have meaning of itself. The transformation of data into information is thus a process of reception, recognition and conversion. Information and knowledge are probably the very concepts that have been confused most in the information society debate. Knowledge is to be understood as a phenomenon that is larger than information but uses information as its building material. If knowledge is “knowing how” to do something, wisdom is “knowing why, what and how” to do something. Wisdom also extends to the application of knowledge in action. A simplistic representation of the relationship between wisdom and knowledge is captured in the following expression:

Category: Digital Literacy

Figure 2. Data to knowledge

Wisdom = Knowledge +Ethics + Action. Helina and Harmaakorpi, (2008) are of the opinion that: Data is the representation of an object. Information is the aggregation of data into something that has meaning (semantics) through interpretation by human or automated processes and Knowledge is that which is derived and inferred from assimilating information against perceived context, experience or business rules. The schema depicted in Figure 2 reflects data, information and knowledge as distinct kinds of economic goods, each possessing a specific type of utility. The utility of data resides in the fact that it can carry information about the physical world; that of information, in the fact that it can modify an expectation or a state of knowledge; finally, that of knowledge in the fact that it allows an agent to act in adaptive ways in and upon the physical world. Telephone books are paradigmatically data goods; specialized newsletters, being more selective, exemplify information goods; and brain surgery can be thought of as knowledge good (Boisot & Canals, 2003).

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Understanding the concept of data, information and knowledge is becoming increasingly important in relation to the design and development of electronic information. Information is a critical resource in the operation and management of organizations. Timely availability of relevant information is vital for effective performance of managerial functions such as planning, organizing, leading, and control. As and when the citizens of any nation become aware with the concept of information or in other words become information literate, it ensures the paramount research and development of the realm.

Features of Information The features of good information are relevance, timeliness, accuracy, cost-effectiveness, reliability, usability, exhaustiveness, and aggregation level. Information is relevant if it leads to improved decision making. It might also be relevant if it reaffirms a previous decision. If it does not have anything to do with ones problem, it is irrelevant. Timeliness refers to the currency of the information presented to the users. Currency of data or information is the time gap between the occurrence of an event in the field until its presentation to 2267

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the user (decision maker). When this amount of time is very short, we describe the information system as a real-time system. Accuracy is measured by comparing the data to actual events. The importance of accurate data varies with the type of decisions that need to be made (Babu, Singh, & Sachdeva, 2013).

Value of Information Information has a great impact on decision making, and hence its value is closely tied to the decisions that result from its use. Information does not have an absolute universal value. Its value is related to those who use it, when it is used, and in what situation it is used. In this sense, information is similar to other commodities. Information supports decisions, decisions trigger actions, and actions affect the achievements or performance of the organization (Babu, Singh, & Sachdeva, 2013).

Importance of Information Good information is essential for effective operation and decision making at all levels in academics as well as business. Every society is surely an information society and every organization is an information organization. Therefore, information is a basic resource like materials, money and personnel. Information can be considered either as an abstract concept (ideas) or as a commodity, usually in the form of letters and reports. Essentially, therefore, information has become a critical resource, just like energy, both of which are vital to the wellbeing of individuals and organizations in the modern world (Adeoti-Adekeye, 1997). Kaye (1995) mentions that most managers would agree that good information is essential to the success of an organization. If an organization is to survive and prosper, it must understand both its own internal workings and the nature of the environment to which it has to adapt and respond. Good information improves decision making, increases efficiency and provides a competitive edge to the organization which knows more than the opposition. 2268

Information Needs and Its Types Information environment is enough rich, characterized by an increase in information sources as well as information providers, a variety of approaches and methods for accessing information, and a redundancy of content from multiple sources. In this “overloaded” information environment, many information users tend to experience a sense of insufficiency in locating the precise information which leads to anxiety. In this complex information environment, understanding the way individuals choose to satisfy their information needs takes on new urgency. Insight into information seeking can be gained by understanding how users seek information sources and how they locate the desired information to meet their needs (Chandra, Lynn, Lawrence & Lillie, 2007). The concept of information needs was coined by an American information scientist Robert S. Taylor in his article “The Process of Asking Questions” published in American Documentation (Now is Journal of the American Society of Information Science and Technology (Agropedia, 2015). There are many definitions of an information need. According to Krippendorf (1990) information need is a recognition that your knowledge is inadequate to satisfy a goal that you have. He explains that “having information” is not the same as “being informed.” Therefore, the problem is not in obtaining information but, rather in understanding the information that you do have. Each individual’s need is formed by the actual situation and by the way the individual defines that situation (Snunith & Sarah, 2007). Campbell, (2000) defines an information need as “the perception of a lack of information that provokes one to then develop a need for it.” Some information researchers incorporated the study of information need into the larger concept of information behavior which also includes information giving, seeking and use (Pettigrew, Fidel & Bruce, 2001), while others took the information need and believe it to be representative of the whole information-seeking process (Westbrook, 1993).Users’ information needs have indeed changed (and are still chang-

Category: Digital Literacy

ing) as a result of the emergence and expansion of the electronic form in which information content is being made available for users’ access and use (Kebede, 2002). Numerous studies have been carried out which divulge that information needs change according to the nature of subject field and requirement of users. Some of which are: Political Respondents need information mostly while preparing for debates, speeches and questions (Alemna & Skouby, 2000). Information needs of people with Medical professionals are seen to be complex as the needs change over time; variations in the desire for information; and there are differences in how information is needed due to the physical and psychological state of different patients (Mark, Janet & Nicole 2003). It is also found from the literature that Students in universities may need information in the following types:

The information needs of teachers were found to be mostly related to guidance on administrative procedures, having lesson plans ready, mechanisms for evidencing work, etc (Williams, 2005). Further, information needs of the General people are found to be varied. The areas in which they needed information are diverse. These range from the information needs of the farmers, to that of the petty traders, artisans, blacksmiths, weavers, painters, fishermen, postmasters, labors, adult learners etc. Their needs, however, crystallized into the following major areas:

• • • • •

Use of Information Sources to Fulfill Information Needs

Educational information need, Health information needs, Employment information needs, Social needs, Political information needs (Silvio, 2006)

• • • • • •

Agriculture information needs. Health information needs. Political information needs. Educational information needs. Economic information needs. Social information needs (Momodu, 2002).

For fulfilling the information needs, users access different sources of information Sources. Scien-

Figure 3. Generation of information needs among users

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tists, engineers and technologists in general use encyclopedias, handbooks, textbooks, periodicals, abstracts, indexes, standards, patents, etc. for their research and development works. He showed that information needs of scientists, engineers and technologists are equally based on their knowledge about those sources of information and accessibility of these information sources (Gayatri, 2006). Post Graduate students and Research Scholars mostly use journals, library books and textbooks for completing their course work (Fidzani, 1998). Wilson, (2006) shows through a model (Figure 3), the mechanism that helps to know how the information needs generates among the users of an information system as:

Types of Information Needs Information age has driven every user towards a way where he needs information in various types and formats. As a result, multiple information sources have been generated to satisfy the user information needs. These needs have been categorized according to the users and their requirements into several types in different fields of knowledge. Moreover, it has been a topic of numerous research papers and information professionals from time to time. Broadly, there are three types of needs:

• • •

Expressed or Articulated Need: The need that is expressed. Unexpressed Needs: The user is aware of the needs but does not like to express it. Dormant Needs: The user is unaware about his need. However, Information service provider brings to light their needs.

However, information needs have been categorized into different types by various authors globally. Some of which are as: Shenton (2007) divulges that information needs (Figure 4) can be divided into five broad categories: 1. Needs that are known to the individual but not to the information professionals. 2. Needs that are known to both parties. 3. Needs that are known to the information professional but not the individual. 4. Needs that are misunderstood by the individual. 5. Needs that are not known to either the individual or the information professional. Khoir, Du & Koronios (2014) while discussing about the information needs of general people conclude that their need categories include: housing, schooling, health, banking and finances, driver

Figure 4. Types of information need as represented in a Johari Window

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Figure 5. Types of Information needs

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Source DOI: 10.1108/00220410610714895

licenses, government-related issues, legal issues and practical information. While as, Alemna and Skouby (2000) reveal with regard to the type of information often sought by the MPs, these were varied and interesting. A broad categorization and ranking of the information types relate to the needs of the society. However, when considered in terms of hierarchy, most of the MPs considered information on rural development, agriculture, and human rights more important than information on foreign affairs and military. Wilson has divided the information needs of a person according to the circumstance of his related environment (Work environment, Socio-cultural environment, Politico-economic environment) into three main types (Figure 5): 1. Physiological needs. 2. Affective needs. 3. Cognitive needs.

Factors Influencing Information Needs and Seeking Behavior of Users and Their Types Information need is one of the cognitive needs of humanity. Information need determines information-seeking behavior and these concepts harmonize one another. Information need and information-seeking behavior are affected by many factors. It is observed from the data that ‘information scattered in too many sources’ and that too in multi-formats is the problem often faced by users. The users sometimes face problems such as needed information is not available in library, incomplete information in sources, do not know how to use online catalogue, do not know how to use electronic resources, lack of information skills to search and internet speed is slow (Kadli & Kumber, 2011). Some early studies suggest that demographic factors such as tenure, experience,

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and education affect information seeking behavior (Keller & Holland, 1978; O’Reilly, 1982). Some of the authors are of the opinion factors influencing information seeking behavior of users are of the following types: 1. External Factors: These are the factors that include social, organizational, time, navigation, project, demographics and other external factors. 2. Internal Factors: These include the factors related to the internal ability and skills of user like the previous knowledge, information literacy, level of experience, self-precision, self- efficiency etc. Broadly speaking, most of the authors and researchers found that factors influencing the information seeking behavior of users are of two main categories (Figure 6 and Figure 7) viz: • •

Micro Macro factors Both the types are discussed as follows.

Micro Factors The literature related to the factors responsible for influencing information seeking behavior of users reveal that demographics (gender, nationality and age), discipline, level of study, type of enrolment, stage of study, pedagogy etc are the micro factors of information-seeking behavior.

Demographic Characteristics Some demographic characteristics that directly or indirectly influence the Information seeking behavior of users are as: •

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Gender: Gender is believed to play an important role in the information seeking behavior of users. Some studies show female have higher social-cultural, and psychologi-

•

•

cal adaptation scores than male. However, others reveal that women have lower competency in using computerized library resources and technologies. As most database searching requires pervasive computer use and familiarity, it could affect their ability on Information seeking behavior There-fore, males were more satisfied and confident and had fewer difficulties than females. As revealed by Al-Muomen, Morris and Maynard (2011), gender issues were also considered problematic by some focus group attendees. Many universities in the Arab world, segregate students in the classrooms according to their gender. Male and female students were separated at Kuwait University in 1996. According to the responses of one of the attendees “segregating genders on the campus had led to a big deterioration in the level of education in the country”. Another noted that since genders are now separated in campus and says “sending a mixed group of males and females as a group to work in the library on a research project has obvious consequences”. Nationality: Studies reveal that level of adjustment of students from different nationalities have a good effect on Information seeking behavior. It could be because of the cultural, language and education system that may be highly influencing the information seeking of users. Age: Age of the users can affect the information seeking behavior of users. They learn skills and become more educated with age in an academic institution. Postgraduate students are usually older than undergraduate students, they may have advanced level of library skills information literacy and may be more aware with online recourses and search engine which have been used during undergraduate degree. Meanwhile, an old person may lose his remembrance which may prove adverse to his information seeking behavior.

Category: Digital Literacy

•

Financial Problem/ Income: If students are wealthier and financially well, they can have better electronic gadgets and get more services than those that are financially weak. Chen (2010) indicated that wealthier immigrants are more adaptive to the host country. It may be because they can visit host country more and had more opportunities to participate to more ceremonies of the host country which makes their social adjustment easier and hence influences their information seeking behavior.

Cultural Aspects Cultural factors may be viewed as those aspects of culture that members of cultural groups have acquired, intentionally or unintentionally, and carry with them where ever they go. When cultural factors of one group or one individual interface with another culture, it is quite likely that some form of dissimilarity will occur. Such dissension offers the potential for misunderstanding and in the information seeking environment it frequently leads to less-than-successful learning experiences for those who are cultural outsiders and leads to frustration, loss of motivation, and decline in self-esteem and individual value. It is good to say that the role played by cultural factors is highly influencing the information seeking process of students (Chen, 2010).

Information Literacy A person must be able to recognize when information is needed and have the ability to locate, evaluate, and use effectively the needed information. To produce such skilled learners’ schools colleges and universities appreciate and integrate the concept of information literacy into their learning processes and that they play a leadership role in equipping individuals and institutions to take benefit of the opportunities inherent within the

information society. Information literate people are those who have learned how to learn. They know how to learn because they know how knowledge is organized, how to find information and how to use information in such a way that others can learn from them. They are people prepared for lifelong learning, because they can always find the information needed for any task or decision at hand (Susie, 2005). Hence, Information literacy greatly influences the information seeking behavior of users.

IT Skills The more competent the students attain their IT skills, the more highly they are able to locate suitable resources and to use the online databases. They are also more aware of the e-resources and experience fewer problems while using computers and networks for online searching purposes than students who have less IT skills.

Psychological Factor Psychology plays a pivotal role in influencing the information seeking behavior of students. Before the relevant information is retrieved the students must overcome possible barriers, which sometimes are psychological. They must experience the situation as gratifying enough and themselves as competent enough to actually take the final decision to seek information. Motivation and interest influence the way information is used and evaluated. More the student is interested in the topic, the more efficiently he seeks information about it. In the use of information systems just technical skills are not enough, positive attitude and self-confidence are needed in order to excel with the systems. Emotional aspects like feelings of dissatisfaction, annoyance, information overload, confrontation to new information and computer loathing may form barriers to the search process (As cited by Heinstrom, 2003).

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Academic Influence

Availability and Constraints to Access

Teachers also influence the information seeking behavior of students by offering guidance on how to conduct a literature review, guide with their research process, and help them on how to use information resources. Teachers can also recommend journals and papers by renowned authors and can give projects and assignments that student require in their research. Available literature also reveal that academics do influence students information seeking behavior and play an important role in encouraging students to explore a wide range of information resources. The importance of academic influence on searching behavior has also been noted by other studies as revealed by Barrett (2005) and Tenopir (2003) (As cited by Al-Muomen, Morris & Maynard, 2011).

Availability of good computer systems and internet access is one of the main influential factors related to information seeking behavior of students. Besides, slow internet access, electricity disruptions and database connection failures discourages the students in seeking precise information. Problems in obtaining usernames and passwords from a particular library to have access to its collection are other issue related to the students information seeking.

Macro Factors The Macro Factors that influence the information needs of users are discussed as under:

Information Resource Design Information resource design is a term given by Urquhart and Rowley (2007) to cover all the aspects relevant to the devise of training materials to facilitate students locate and use information resources successfully. In focus group discussions with graduate students, Urquhart and Rowley found that much training material provided by the library or staff members often lacked detail. In this study, students attending the focus groups also expressed concern about the lack of, and variability in, information resource training materials. For example, two students from Social Sciences pointed out that some academic staff used the same materials for both Bachelor and Masters’ programmes without recognizing the need for it to be pitched at different levels.

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Policies and Funding We are in an era when people cannot wait and every user needs information instantly. It calls for the Libraries to provide more services and facilities to its users and the libraries should not be just used as a quiet place of study. It is only possible when libraries possess enough financial resources to facilitate better services to its users. Lack of financial resources can become a major problem as this limited the purchase of books and access to databases. One academic solution suggested is that this could be partially overcome by having shared access to materials held in a consortium across universities

Organizational Knowledge and Culture Organizational culture is another issue that surely will affect the information seeking behavior of students and users of its information sources. The time of working hours and the facilities like Photostat and printing as well as scanning, often needed by a student, can either enhance the skill of information seeking behavior of a student or can prove otherwise. Obviously, barriers and obstacles such as these will affect what, how and when information is sought.

Category: Digital Literacy

Figure 6. Factors influencing the information needs of users

Information Learning Technology Infrastructure Efficient virtual learning environment and use of webinars also have good influence on the information behavior of users. In their research in Kuwait

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University, Urquhart and Rowley (2007) found that students praised the use of virtual learning environments for providing access to materials which enabled them to tailor their learning and to learn at their own speed. In this case, the majority of graduate students expressed concern over

Figure 7. Factors influencing the information needs of users

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the lack of sufficient virtual learning materials and reported that the low usage of electronic information resources available through their library portals and virtual systems was, in part, a consequence of this.

FUTURE RESEARCH DIRECTIONS Information needs of users have indeed changed (and are still changing) as a result of the emergence and extension of the electronic form in which information content is being made available for users’ access and use. Information needs of users help in understanding and examining their Information Seeking Behavior and prove useful to select appropriate methodologies and adopt best techniques to carry out research in more systematic manner. These help in better understanding of tasks, activities, available services, infrastructure and problems faced by users while searching their needed information thereby help administrators and higher authorities to take right decisions, acquire relevant information sources, offer best services, implement tech savvy infrastructure and satisfy the information needs of users. This study has a vast scope for future research. It can be extended to study and highlight the information needs of Professors, Lecturers, Teachers, Post Graduate & Under Graduate Students associated with teaching and learning processes in different Colleges and Universities globally. Further, research can be extended towards exploring information retrieval and information seeking models.

CONCLUSION AND SUGGESTION Information needs are highly related to the fields of study of users and shows a remarkable diversion in their nature. Further, knowing the information needs of various users in diverse fields of study will surely help to understand the information seeking behavior of users. There is great diversity

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in the information needs of users depending upon the field of study with which they are associated. In general, these needs are Physiological needs, Affective needs and Cognitive needs. Information need determines the information-seeking behavior of a user and is affected by many factors. There are two main types of factors viz Macro and Micro Factors. Macro Factors include Information resource design, Availability and constraints to access, Policies and funding etc. while as Micro Factors include demographics (gender, nationality and age), discipline, level of study, type of enrolment etc.

REFERENCES Adeoti-Adekeye. (1997). The importance of management information systems. Library Review, 4(6), 318–327. doi:10.1108/01435129510772283 Agropedia. (2015). Information need. Retrieved from: http://agropedia.iitk.ac.in/content/information-needs Al-Muomen, N., Morris, A., & Maynard, S. (2012). Modelling information-seeking behavior of graduate students at Kuwait University. The Journal of Documentation, 68(4), 430–459. doi:10.1108/00220411211239057 Al-Salti, Z., & Hackney, R. (2011, September 27). Factors impacting knowledge transfer success in information systems outsourcing. Journal of Enterprise Information Management., 24(5), 455–468. doi:10.1108/17410391111166521 Alemna, A., & Skouby, K. (2000). An investigation into the information needs and informationseeking behavior of members of Ghanas legislature. Library Management, 21(5), 235–240. doi:10.1108/01435120010324815 Babu, R. A., Singh, Y. P., & Sachdeva, R. K. (2013). Natural Resources Management and Environment Department. Retrieved from: www. fao.org/nr/index_en.htm

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Boisot, M., & Canals, A. (2004, January 01). Data, information and knowledge: Have we got it right? Journal of Evolutionary Economics, 14(1), 43–67. doi:10.1007/s00191-003-0181-9 Campbell, I. (2000). The ostensive model of developing information-needs. Glasgow University. Retrieved from: www.dcs.gla.ac.uk/∼ iain/papers/ phd.pdf Case, D. O. (2012). Looking for Information – A Survey of Research on Information Seeking, Needs and Behavior. Bingley, UK: Emerald Group Publishing. Cawkell, T. (2003). Information. In International encyclopedia of library and information Science (2nd ed.). London: Rout Ledge. Chandra, P., Lynn, S., Lawrence, O., & Lillie, R. (2007). What is enough? Satisfying information needs. The Journal of Documentation, 63(1), 74–89. doi:10.1108/00220410710723894 Chen, W. (2010). Internet-Usage Patterns of Immigrants in the Process of Intercultural Adaptation. Cyberpsychology, Behavior, and Social Networking, 13(4), 387–399. doi:10.1089/cyber.2009.0249 PMID:20712497 Fidzani, B. (1998). Information needs and information-seeking behavior of graduate students at the University of Botswana. Library Review, 47(7), 329–340. doi:10.1108/00242539810233459 Gayatri, M. (2006). Information-seeking behavior of undergraduate biology students: A comparative an analysis of first year and final year students in University College Dublin. Library Review, 54(2), 86–99. doi:10.1108/00242539810233459 Heinstrom, J. (2003). Five personality dimensions and their influence on information behavior. Information Research, 9(1). Retrieved from http:// InformationR.net/ir/9- 1/paper165.html]

Helina, M. H., & Harmaakorpi, V. (2008). Data, information and knowledge in regional innovation networks: Quality considerations and brokerage functions. European Journal of Innovation Management, 11(1), 103–124. doi:10.1108/14601060810845240 Kadli, J., & Kumbar, B. D. (2011). Faculty Information-Seeking Behaviour in the Changing ICT Environment: A Study of Commerce Colleges in Mumbai. Library Philosophy and Practice. Retrieved from http://www.webpages.uidaho. edu/~mbolin/kadli-kumbar.htm Kaye, K. (1995). The importance of information. Library Management, 16(5), 6–15. doi:10.1108/01435129510772283 Kebede, G. (2002). The changing information needs of users in electronic information environments. The Electronic Library, 20(1), 14–21. doi:10.1108/02640470210418227 Keller, R., & Holland, W. (1978). Individual characteristics of innovativeness and communication in research and development organizations. The Journal of Applied Psychology, 63(6), 672–759. doi:10.1037/0021-9010.63.6.759 Khoir, S., Du, J. T., & Koronios, A. (2014). Study of Asian Immigrants’ Information Behaviour in South Australia: Preliminary Results. In iConference 2014 Proceedings (pp. 682–689). doi:10.9776/14316 Krippendorff, K. (1990). Models and Metaphors of Communication. Media and Communication, Construction of Realities. Retrieved from http:// repository.upenn.edu/asc_papers/276 Mark, H., Janet, H., & Nicole, J. (2003). Information needs of people with multiple sclerosis and the implications for information provision based on a national UK survey. Aslib Proceedings, 55(5/6), 290–303. doi:10.1108/00012530310498860

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Martin, W. J. (1995). The global information society. Aldershot, UK: Aslib Gower. Momodu, M. (2002). Information needs and information seeking behavior of rural dwellers in Nigeria: A case study of Ekpoma in Esan West local government area of Edo State, Nigeria. Library Review, 51(8), 406–410. doi:10.1108/00242530210443145 OReilly, C. A. (1982). Variations in decision makers use of information sources: The impact of quality and accessibility of information. Academy of Management Journal, 25(4), 756–771. doi:10.2307/256097 Pantzar, E. (2000). Knowledge and wisdom in the information society. Foresight, 2(2), 230–236. doi:10.1108/14636680010802573 Pettigrew, K., Fidel, R., & Bruce, H. (2001). Conceptual frameworks in informational behavior. Annual Review of Information Science & Technology, 3(5), 43–78. Shenton, A. K. (2007). Viewing information needs through a Johari Window. RSR. Reference Services Review, 35(3), 487–496. doi:10.1108/00907320710774337 Silvio, D. H. (2006). The information needs and information seeking behavior of immigrant southern Sudanese youth in the city of London, Ontario: An exploratory study. Library Review, 55(4), 259–266. doi:10.1108/00242530610660807 Singh, S. P. (2007). What are we managing – knowledge or information? Vine, 37(2), 169–179. doi:10.1108/03055720710759946 Snunith, S., & Sarah, K. (2007). Information needs of North American immigrants to Israel. Journal of Information Communication and Ethics in Society., 5(2/3), 185–205. doi:10.1108/14779960710837641 Susie, A. (2005). From prescribed reading to the excitement or the burden of choice: Information literacy: foundation of e-learning. Aslib Proceedings, 57(2), 181–190. doi:10.1108/00012530510589146

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Tuomi, I. (2000). Data is More Than Knowledge: Implications of the Reversed Knowledge Hierarchy for Knowledge Management and Organizational Memory. Proceedings of the Hawaii International Conference on System Sciences, 3(2), 45. Retrieved from: http://www.jstor.org/ stable/40398446 Urquhart, C., & Rowley, J. (2007). Understanding student information behavior in relation to electronic information services: Lessons from longitudinal monitoring and evaluation, part 2. Journal of the American Society for Information Science and Technology, 58(8), 1188–1197. doi:10.1002/asi.20562 Westbrook L. (1993). User needs: A synthesis and analysis of current theories for the practitioner. RQ, 32(4), 541-549. Wikipedia. (2013). Establishing a management information system. Author. Williams, P. (2005). Using information and communication technology with special educational needs students: The views of frontline professionals. Aslib Proceedings, 57(6), 539–553. doi:10.1108/00012530510634262 Wilson, T. (2006). On user studies and information needs. The Journal of Documentation, 62(6), 658–670. doi:10.1108/00220410610714912

ADDITIONAL READING Al-Ansari, H. (2006). Inter net use by the faculty members of Kuwait University. The Electronic Library, 24(6), 791–80. doi:10.1108/02640470610714224 Alazemi, T. R. (2015). Users’ Information Seeking Behaviors, their Interactions and Experience with the Academic Library Web Interface. University of Salford. Retrieved from: http//usir.salford. ac.uk/36705/1/Final%20Thesis.pdf

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Caroline, F. T., & Cees, A. W. (2010). Developing scales for information-seeking behavior. The Journal of Documentation, 66(1), 46–69. doi:10.1108/00220411011016362 Ganaie, S. A., & Khazer, M. (2015). Diversity of information sources in the digital age an overview. Journal of Advancements in Library Sciences, 2(2), 53–61. Singh, S. P. (2007). What are we managing – knowledge or information? Vine, 37(2), 169–179. doi:10.1108/03055720710759946 Snunith, S., & Sarah, K. (2007). Information needs of North American immigrants to Israel. Journal of Information Communication and Ethics in Society., 5(2/3), 185–205. doi:10.1108/14779960710837641 Tanackovic, F. S., Horvatic, I. F., & Badurina, B. (2015). European Union information in an acceding country. Library Hi Tech, 33(1), 143–158. doi:10.1108/LHT-10-2014-0103

KEY TERMS AND DEFINITIONS Information: Information is an assemblage of data in a comprehensible form capable of communication and use. Information Behavior: Information behavior refers to how people approach and handle information. Information Literacy: The ability to know when there is a need for information, to be able to identify, locate, evaluate, and effectively use that information for the issue or problem at hand. Information Need: The perception of a lack of information that provokes one to develop a need for it. Information Seeking Behavior: A special case of problem solving which, includes recognizing and interpreting the information problem, establishing a plan of search, conducting the search, evaluating the results, and if necessary, iterating through the process again. Knowledge: When the information is interpreted or put into context, or when meaning is added to it, it is converted into knowledge.

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A Maturity Model for Digital Literacies and Sustainable Development Ravi S. Sharma Nanyang Technological University, Singapore Lin G. Malone Nanyang Technological University, Singapore Chong Guan SIM University, Singapore Ambica Dattakumar Nanyang Technological University, Singapore

INTRODUCTION It is a given that the world is now becoming increasingly digitialised. However the speed at which this digitisation has occurred, has led to unequal progression amongst societies. A key aspect of digitisation is the notion of “digital inclusion”; the empowerment of individuals through digital participation. Successful initiatives, supported by digital literacy, have enabled those that are isolated to gain on a social and economic front (Sharma & Mokhtar, 2006). This paper recounts the role of digital literacies in supporting participative, and therefore sustainable, development. Taking a historical development perspective, the paper concludes with a maturity model that links digital policies with socio-economic well-being. Building on the pioneering work of Gilster (1997), Belshaw (2012) offers a comprehensive definition of modern literacies in digital society: Literacies involve the mastery of simple cognitive and practical skills. To be ‘literate’ is only meaningful within a social context and involves having access to the cultural, economic and political structures of a society. In addition to providing the means and skills to deal with written texts, literacies bring about a transformation in human

thinking capacities. This intellectual empowerment happens as a result of new cognitive tools (e.g. writing) or technical instruments (e.g. digital technologies). (p.90) It has been suggested that digital inclusion and participation enables the grassroots to be engaged, bridging some of the prevailing socio-economic disadvantages (SEDs) that exist within societies, as well as across countries (Armenta et al., 2012). This is the fundamental premise of digital literacies – the set of skills and tools that will empower individuals and groups to participate fully in the increasingly digital future and hence bridge the disparities in socio-economic opportunities.

BACKGROUND The Evolution of Digital Literacy Lanham (1995) first conceptualised digital literacy as the ability to comprehend information, regardless of the medium. This definition focused on the user’s ability to navigate between the various online and offline mediums. Since this original conceptualisation, the term digital literacy has evolved along with pervasive Information and

DOI: 10.4018/978-1-5225-2255-3.ch198 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Communication Technology (ICT) in society. While Lanham created awareness of the need to comprehend the transformations brought about by the incorporation of ICTs, it was Gilster (1997) who popularised the concept of digital literacy and its emergence as a critical skill. His portrayal of digital literacy as “mastering ideas, not keystrokes” (1997, p.15), positioned the concept to focus more on cognitive ability, as opposed to competencies. This was considered a milestone, as society rapidly digitised and network effects arising from social media led to the development of social capital as a socio-economic advantage. Building on this, Eshet-Alkalai (2004) presented five survival skills for the digital era: photovisual literacy, reproduction literacy, information literacy, branching literacy and social-emotional literacy. Of these five digital literacies, four of them are largely based on specific digital skills. As the contrasting element, socio-emotional literacy is of particular interest. The definition of socio-emotionally literate users offered by Eshet-Alkalai (2004) is individuals who are able to work with others, sharing and evaluating information and knowledge, in order to construct new knowledge. This refers to the participation and communication that occurs in the digital world, as well as the opportunities offered via this medium. Where participation leads to collective intelligence, new knowledge may be developed. By situating socio-emotional literacy as a digital literacy skill, the Internet and new media present a new cultural environment, with its own unique values and practices for engagement. The socio-cultural dimension of digital literacy is further discussed by Bélisle (2006). Although her work focuses on a re-conceptualisation of literacy and not merely digital literacy, Bélisle’s research is important as it explains the changes to society as a result of the digital knowledge revolution. In fact, it could be said that Bélisle (2006) truly grasped the essence of the changes to the concept of literacy within the digital society. Bélisle (2006) examines three dimensions of literacy: Functional, Socio-cultural and Transformational. Functional

literacy refers to the basic skills required to lead a day-to-day life. In the conventional sense, this refers to the skills of reading, writing, speaking and listening. In relation to digital literacies, this includes the ability to perform operational computer skills, such as input, output and searching, but also the ability to understand when and where each skill set is relevant. This dimension of digital literacy could be read in parallel with Lanham’s original concept. Bélisle’s second dimension of literacies is the socio-cultural. Literacy ultimately serves to address a purpose; it “[gives] access to, and understanding of, the structures of power and authority through mastery of written texts and numbers” (Bélisle, 2006, p.53). Socio-cultural literacy includes knowledge of a society’s values, attitudes, practices and conventions; and an understanding of where each of these apply. This is important in relation to digital literacy, as the digital world provides new channels for participation and communication. Literacy is only meaningful when contextualised to the cultural fabric of society; the socio-cultural dimension of digital literacy enables individuals to immerse themselves within and to participate in social and economic structures of digital society. Hence, when referring specifically to digital literacy, it may be more accurate to consider the socio-cultural dimension of literacy as a “socio-economic” function. This would better capture its impact on and empowerment of users in online communities. The final dimension of literacy which Bélisle describes is the transformational dimension of digital literacy. This “brings a profound enrichment and eventually entails a transformation of human thinking capacities” (Bélisle, 2006, p.54). The individuals’ intellectual empowerment through literacy may have the power to transform society, especially where creative cognitive ability leads to the creation of new cognitive tools (Bélisle, 2006). If Bélisle’s transformative digital literacy is viewed alongside Eshet-Alkalai’s socio-emotional literacy, the online world opens up new opportunities for collaboration and creation. This ultimately

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brings new knowledge to society, transforming it and those within it. More recently, Belshaw (2012) has utilised the term digital literacies, rather than digital literacy, both to avoid reducing it to a finite outcome, as well as to address the complexity of the concept. Belshaw (2012) presents eight, non-hierarchic elements of digital literacies: Cultural, Cognitive, Constructive, Communicative, Confident, Creative, Critical and Civic. Within these elements, it can be seen that digital literacies have evolved from a mere set of skills, to encompass cognitive ability, to facilitate cultural engagement, and to enable critical analysis. Continuing the evolutionary path of digital literacies, Belshaw’s research (2012) can be seen as particularly prominent, especially where it links the element of “Cultural” to that of “Civic”. This emphasizes participation, social justice and civic responsibility; digital literacies are not simply about functional ability but also about the inclusion, participation and empowerment that result from socio-cultural interaction. Belshaw’s work draws heavily on Bélisle’s; his definition of literacies feeds directly into Bélisle’s proposed three Figure 1. Levels of digital development

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dimensions of digital literacy. Linking Belshaw’s work to Bélisle (2006), transformative digital literacy can go beyond the ability to transform an individual through self-enhancement to transforming societies through these “entitlements” gained by individuals.

MAIN FOCUS OF THE ARTICLE Issues, Controversies, Problems Levels of Digital Development The development of a digital society takes place in levels, each of which lays the foundations for the succeeding ones. In the context of the digital divide, Armenta, Serranob, Cabrera and Conte (2012) propose four levels to development; each centred on a key tension point (see Figure 1). The first level addresses the problem of access, distinguishing between those who have access to the infrastructure and those who do not. The second level focuses on usage, in terms of the social-economic indicators governing usage, while

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the third level addresses the role of participation within the digital divide and focuses on the need for human development through participation. Armenta et al. (2012) further suggest that we are currently entering the fourth level of digital development, where digital inclusion must be mediated by human values. At this level, there comes a focus on the need for community involvement and technology adoption. Harding (2016) argues that while the digital world had promised to create a level playing field by creating equal access to information, such information possesses a different cost to different individuals in accordance to their socio-economic status. This highlights the importance of digital inclusion to enable a level playing field. While each level of the developmental model seeks to discourage digital exclusion, it is the fourth level of human values which has the potential to ensure that as society progresses, the socio-economically disadvantaged (SEDs) do not get marginalised and dis-enfranchised. Thus, the links between the civic and cultural elements in Belshaw’s model draws parallels to the fourth level of “human values” in work by Armenta et al. (2012) on digital inclusion.

Further analysis of Armenta et al.’s model (2012) has shown each level to reflect different dimensions of digital literacies. This can be seen through adopting a modified lens of Bélisle’s (2006) digital literacies and applying it to Armenta at al.’s model (see Figure 2). A juxtaposition of Bélisle’s work (2006) examines three dimensions of functional, socio-cultural and transformational literacies. This provides further clarification of the four developmental levels in Figure 1, by linking digital literacy with digital development. Access (level 1 of development) may be mapped directly to the outcome of functional literacy as without the former, one cannot possibly achieve the latter. Usage and participation (levels 2 and 3) normally lead to socio-cultural (or interchangeably socio-economic) participation. The subtle difference between usage and participation is that while usage is transactional, participation also involves the production of social (emotional) capital. Finally, at the highest level of human values, individuals and society transforms into knowledge societies (again, from the foundations of information societies enabled in levels 1 and 2).

Figure 2. Model of digital development as supposed by digital literacies

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As the fundamental argument, bridging digital disparities to access and usage of technologies must be supported by individuals’ functional abilities to utilise these tools. Going further into the model, it can be seen that usage still draws upon the socio-economic function of digital literacies. Armenta et al. (2012) discusses how at the second level, applications suited to the needs of the population were incorporated to bridge an existing digital usage divide. There are ample opportunities to develop context relevant applications, opening up new ways of gaining socio-economic status. It may also be said that usage of these applications draw on the socio-economic dimension of digital literacies, as one is able to participate meaningfully within society. Mansell and Tremblay (2013) have suggested that human development is the process of improving and increasing each individual’s choices. The socio-economic dimension of digital literacies can indeed enable this by opening up opportunities for grass-root participation and community leadership through creating meaning around these activities. This increases inclusion in society, and may enable improvement of one’s economic status. Morris and Morris (2013), for example, have revealed that Internet access and usage could reduce the socio-economic gap in knowledge and participation. Beyond the socio-economic dimension, level three of participation also supports the transformative capacity of digital literacies. The digital environment holds a myriad of potential for users who are empowered to explore them; digital literacies can open up opportunities for individuals to transform themselves, their abilities and their circumstances. This final level of human values is demonstrative of transformational digital literacy beyond self-enhancement to enable societal development. As discussed by Belshaw (2012), the digital environment creates new civic responsibilities for users. Relating back to Armenta et al.’s model, the fourth level of human values must comprise an element of civic responsibility to enable society’s transformation through community involvement

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and technology adoption. By drawing on human values, this level will promote digital inclusion for all.

Digital Maturity and Sustainable Development “Knowledge societies will not really be worthy of the name unless the greatest possible number of individuals can become knowledge producers rather than mere consumers of already available knowledge.” (UNESCO, 2005, p.189). Indeed, UNESCO’s further probing (Mansell & Tremblay, 2013) highlights the need for knowledge societies to be based on inclusion to ensure their sustainability. The 2013 report underscores the importance of human values for digital inclusion and participation. The need to respect, welcome and appreciate new ideas is key to building an innovative and sustainable society. Prior work by Sharma and his associates highlight key characteristics of knowledge societies: 1) they are necessary and sufficient conditions for growth in the knowledge economy, 2) they have high knowledge absorptive capacity and complex chains of creation, production and distribution, and 3) they consist of a sustainable learning community which emphasises innovation (Sharma, Ng, Dharmawirya & Lee, 2008). Four knowledge pillars actively used by the World Bank’s Knowledge Assessment Methodology (information infrastructure, economic and institutional governance, education and human capital, and innovation system) have been effective in deriving a set of best practices in developing knowledge policies (cf Sharma et al., 2008; 2009; Chandrasekar & Sharma, 2010). However, these studies did not examine participation gaps arising from the uneven distribution of resources in developing skills and literacies throughout the world, and the lack of transparency in the way digital literacies shape perceptions of the world. At this juncture of the new participatory culture, the key question of “how we can guarantee that the rich opportunities afforded by the expanding

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digital landscape are made available to all,” still remains unanswered. Knowledge societies which are based on an institutional framework that supports collective security system and wider welfare can lead to sustainable development (Spangenberg, 2005a; 2005b). As a knowledge society is based on the need for knowledge distribution, access to information and skills to transform information into knowledge, inclusion within such societies is imperative. This may bridge the existing disparities within society and subsequently, promote sustainable development, where progress benefits all and no one gets left behind. Synthesising the discussion in the previous section with the gap in prior work identified, the relationship between knowledge societies, digital inclusion and digital literacies may be examined through a proposed Digital Literacies Maturity

Model (DLMM) (see Figure 3 below). A maturity model is an assessment tool used to evaluate an entity (e.g., an organization) or a process. The term “maturity” relates to the degree of optimization of practices. Typically, a maturity model formally defines steps and result metrics which are applied to the management of best practices and active optimization of processes (Humphrey, 1988). Our proposed DLMM combines the World Bank’s four knowledge policy pillars with the four levels of digital development and provides a framework on how to promote sustainable development and socio-economic well-being. Please note that the colouring of the cells in Figure 3 correspond to the different levels of literacy identified in Figure 2 above. Each cell in the matrix has a focus theme that characterises knowledge policy for a given pillar and level of development. For example, at the most basic level of

Figure 3. Digital literacies maturity model

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digital development (the access level), governance policies mainly focus on universal service obligations extracted by the regulator from licensed service providers so that no segment of society is excluded from access to basic digital services such as telephony, Internet, cable television and so on. The infrastructure dimension focuses on improving access to devices and networks and the training is geared towards utilitarian functions. Most innovative efforts are engaged in improving access, efficiency and effectiveness. Assuming prevalence of internet access is achieved, the next level focuses on the usage. Government policies promoting skill-based training and applications are essential to the development of an information economy. When both internet usage and access becomes common place, the next question to be asked is how to accelerate digital participation, such as grassroots participation, community leadership and knowledge exchange. Likewise at the highest level of digital development (the human values level), innovation policies must seek to capture the collective intelligence of the masses (sometimes known as the wisdom of the crowds). However, the field success of the DLMM is dependent on the research methodology chosen. In empirical research conducted on digital literacies (Bonfadelli, 2002; Hargittai & Hinnant, 2008), quantitative data analysis appears to be the preferred research methodology. However, when examining, for example, Hargittai’s (2009) weboriented survey measures to gauge digital literacies, the research may be adequate in capturing functional digital literacy, but its methodology completely overlooks the socio-economic and transformation dimensions of literacies, which is a cause of concern. As ICT becomes increasingly user-centric, functional ability becomes less of an issue as compared to the ability to utilise technology in meaningful ways. In contrast, Canada has provided a vision for 21st century citizens to possess and maintain values that enable them to effectively communicate “to flourish in groups of individuals with multiple-perspectives who care deeply about a topic and are empathetically

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responsive to each other’s perspectives” (Hoechsmann & DeWaard, 2015, p.19). The use of DLMM allows an economy to have its digital literacies and sustainable development processes assessed according to a conceptual framework with a clear set of benchmarks. Maturity is indicated by achieving a particular “Maturity Level”, which provides a consistent set of measurements for researchers. For policy makers, the DLMM allows a knowledge society to compare its maturity level with other economies, or other parts of their own economy and derive specific recommendations for improvements. As such, a recommended approach to the field usage of the DLMM would be to create narratives, which allows space for quantitative hypotheses as well as data collection (Sharma et al. 2012). The creation of narratives and the application of the DLMM to these narratives could yield more indepth and interesting results as compared to other methodologies. The details included through the choice of narrative approach thus become more applicable in relation to the levels of maturity (access, usage, participation, values) than the policies. For knowledge societies, it would be argued with choice narratives that government policies have been situated at the fourth level of human values in the digital development model. Whether this has led to sustainable development can be tested by tracking specific policy indicators of governance, infrastructure, education, and innovation.

SOLUTIONS AND RECOMMENDATIONS Implications of DLMM for Digital Policies Policies which encourage the continued evolution of knowledge societies must be constructed on the twin principles of inclusion and be supported by digital literacies to best enable the formation of sustainable knowledge societies. As the flowchart below illustrates (see Figure 4), governance can

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Figure 4. Flowchart mapping the relationship between knowledge society pillars, digital development levels, and digital literacies

bridge the access divide through their universal service obligations, ensuring that the Internet is made equally accessible to all citizens as a digital entitlement. Infrastructure, built with the intention of bridging the access divide, can create accessible devices and networks for all. Utility may be enabled where access is supported by education and human capital. Innovation systems which enable access can create an efficient and effective society. However, these access policies must still be supported by functional digital literacy for users. Access is redundant when functional ability is absent. Socio-economic literacy builds on functional literacy, to create meaning behind activity. On the second level, digital inclusion can be enabled through promoting usage. Government policies can enable usage through training, however these activities only have meaning when viewed through the lens of socio-economic literacy. Infrastructure may create applications tailored to the needs of the population, however it is with socio-economic digital literacy that users are motivated to utilise them. Skills obtained through formal education and life-long learning must be similarly motivated by the socio-economic dimension of literacy which creates meaning around the activity; the functional ability to use software and applications is only meaningful when understanding its socioeconomic uses, purposes and relevance to the workforce. Human Capital provides the opportunity to gain experience with using technology. It is

important for individuals to use these experiences appropriately in a given context. For example, an individual can progress from being a passive receiver of technology to an active information consumer with effective searching skills. At the highest level, he or she can apply computational thinking skills and use the information intelligently to create value. This, again, results from digital literacies’ socio-economic dimension. Finally, when innovation is supported by usage, it will facilitate the creation of the information economy, as the first step toward knowledge societies. Through empowering people with socioeconomic and transformational literacies, this may open up opportunities for digital citizens to improve their socio-economic status. The third level of digital inclusion focuses on the participation divide. Governments seeking to close the digital divide by bridging the participation gap may implement policies to promote grassroots participation supported by socio-economic digital literacy to create meaningful supporting participation. Infrastructure with the goal of achieving participation can incentivise human development, especially if it is supported by socio-economic literacy. Education and human capital, when directed toward bridging the participation divide, can enable community participation and create community leadership, as supported by socio-economic literacy. Finally, the key to the development of knowledge societies is innovation-supported participation. This can cre-

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ate exchange of knowledge, which is catalyzed by transformational literacy. Transformational literacies are especially key to the fourth level of human values. With transformation literacy as a goal, governance supporting human values can enable sustainable growth and development. Infrastructure with human values can allow community involvement and subsequently, social capital returns. Education and human capital supported by human values will create a sense of civic responsibility toward society; and innovation supported by human value will lead to the expression of collective intelligence.

FUTURE RESEARCH DIRECTIONS When viewing the digital maturity model alongside Bélisle’s conceptualisation of literacies (2006), it is apparent that digital literacies are necessary to support participation and subsequently, inclusion. The need to promote digital inclusion, especially through imparting digital literacies, becomes increasingly important for policy makers Figure 5. DLMM for policy making

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in light of the value placed on knowledge within the modern economy and the drive for countries to construct sustainable growth and development. Frameworks and measures conceptualised by government and policy makers have been developed with the intention of bridging the digital divide. The European Union and Canada, for instance, have developed conceptual frameworks of digital literacies and measures, with a focus on education and creating the 21st century citizen. When developing frameworks through which to build knowledge societies, it is important that governments and policy makers keep in mind that inclusion must serve as the foundation to such societies, not merely an afterthought. The Digital Literacies Maturity Model presented in this article is intended as a map to the creation of sustainable knowledge societies. In this regard, the Maturity Model may be represented as a table of questions for policy makers. This will enable them to determine whether policies under development place adequate emphasis on digital inclusion (see Figure 5).

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By examining policy-making decisions in this manner, digital development may thus be promoted. In addition, at every level, these decisions must be supported by the knowledge of what kinds of digital literacies are necessary to fulfil the requirements of that level. This will enable the formation of knowledge societies on the basis of inclusion.

CONCLUSION This article has provided a link between digital development and digital literacies. It highlights that in order to reach the level of human values, there needs to be a shift from functional literacy to transformational literacy. This means that the current and future generations need to be supported with other competencies to enable them to be part of sustainable knowledge societies. Digital literacy can promote inclusion, and reduce socio-economic disparities by promoting participation at an economic level. In order to enable widespread inclusion, knowledge societies need to develop in line with digital inclusion policies. The maturity model presented in this paper brings together these two concepts, offering policy makers the opportunity to identify key areas of development, and ensuring a digitally enabled future.

REFERENCES Armenta, A., Serrano, A., Cabrera, M., & Conte, R. (2012). The new digital divide: The confluence of broadband penetration, sustainable development, technology adoption and community participation. Information Technology for Development, 18(4), 345–353. doi:10.1080/02681102.2011.625925 Bélisle, C. (2006). Literacy and the digital knowledge revolution. In Digital literacies for learning. Facet Publishing.

Belshaw, D. (2012). What is ‘digital literacy’? A Pragmatic investigation (Doctoral dissertation). Durham University. Bonfadelli, H. (2002). The Internet and Knowledge Gaps: A Theoretical and Empirical Investigation Survey. European Journal of Communication, 17(1), 65–84. doi:10.1177/0267323102017001607 Chandrasekar, G., & Sharma, R. S. (2010). Analysing knowledge disparity and value creation: Towards a K-Gini coefficient. International Journal of Knowledge-Based Development, 1(3), 242–262. doi:10.1504/IJKBD.2010.035661 Eshet-Alkalai, Y. (2004). Digital literacy: A conceptual framework for survival skills in the digital era. Journal of Educational Multimedia and Hypermedia, 13(1), 93. Gilster, P. (1997). Digital literacy. New York: Wiley Computer Publications. Harding, C. (2016). The New Digital Divide. Readwrite. Retrieved from http://readwrite. com/2016/03/04/new-digital-divide Hargittai, E. (2009). An update on survey measures on web-oriented digital literacy. Social Science Computer Review, 27(1), 130–137. doi:10.1177/0894439308318213 Hargittai, E., & Hinnant, A. (2008). Digital Inequality: Differences in Young Adults Use of the Internet Survey. Communication Research, 35(5), 601–621. doi:10.1177/0093650208321782 Hoechsmann, M., & DeWaard, H. (2015). Mapping digital literacy policy and practice in the Canadian education landscape. Media Smarts. Retrieved from http://mediasmarts.ca/sites/mediasmarts/files/publication-report/full/mappingdigital-literacy.pdf (Accessed on 16 March 2016) Humphrey, W. S. (1988). Characterizing the software process: A maturity framework. IEEE Software, 5(2), 73–79. doi:10.1109/52.2014

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Lanham, R. A. (1995). Digital literacy. Scientific American, 273(3), 198–199. Mansell, R., & Tremblay, G. (2013). Renewing the Knowledge Societies Vision: Towards Knowledge Societies for Peace and Sustainable Development. Report prepared for the WSIS+10 Review for the Communication and Information Sector, UNESCO and for presentation in the “Knowledge Societies, Stakeholder Accountability for Sustainable Development” Panel at the UNESCO WSIS+10 Conference, Paris, France. Morris, D. S., & Morris, J. S. (2013). Digital Inequality and Participation in the Political Process: Real or Imagined. Social Science Computer Review, 1(5), 589–600. doi:10.1177/0894439313489259 Sharma, R. S., Iqbal, M. I. N. A., & Victoriano, M. M. (2012). On the use of benchmarking and good practices for knowledge management for development. Knowledge Management Research & Practice, 11, 1–15. Sharma, R. S., & Mokhtar, I. A. (2006). Bridging the digital divide in Asia – Challenges and solutions. International Journal of Technology. Knowledge in Society, 1(3), 15–30. Sharma, R. S., Ng, E. W. J., Dharmawirya, M., & Lee, C. K. (2008). Beyond the Digital Divide: A Conceptual Framework for Analyzing Knowledge Societies. Journal of Knowledge Management, 12(5), 151–164. doi:10.1108/13673270810903000 Sharma, R. S., Samuael, E. M., & Ng, E. W. J. (2009). Beyond the digital divide: Policy analysis for knowledge societies. Journal of Knowledge Management, 13(5), 373–286. doi:10.1108/13673270910988178 Spangenberg, J. H. (2005a). Economic sustainability of the economy: Concepts and indicators. International Journal of Sustainable Development, 8(1), 47–64. doi:10.1504/IJSD.2005.007374

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Spangenberg, J. H. (2005b). Will the information society be sustainable?: Towards criteria and indicators for a sustainable knowledge society. International Journal of Innovation and Sustainable Development, 1(1), 85–102. doi:10.1504/ IJISD.2005.008082 UNESCO. (2005). Toward Knowledge Societies. UNESCO Publishing. Retrieved from http://unesdoc.unesco.org/images/0014/001418/141843e. pdf

ADDITIONAL READING Coiro, J., Knobel, M., Lankshear, C., & Leu, D. J. (Eds.). (2014). Handbook of research on new literacies. Routledge. European Commission. (2016). The Digital Economy & Society Index (DESI). Retrieved 18th April, 2016, from https://ec.europa.eu/digitalsingle-market/en/desi Hsieh, J. J. P. A., Rai, A., & Keil, M. (2011). Addressing digital inequality for the socioeconomically disadvantaged through government initiatives: Forms of capital that affect ICT utilization. Information Systems Research, 22(2), 233–253. doi:10.1287/isre.1090.0256 Lankshear, C., & Knobel, M. (2008). Digital literacies: Concepts, policies and practices (Vol. 30). Peter Lang. Ramírez, R. (2007). Appreciating the contribution of broadband ICT with rural and remote communities: Stepping stones toward an alternative paradigm. The Information Society, 23(2), 85–94. doi:10.1080/01972240701224044 Van Dijk, J. A. (2005). The deepening divide: Inequality in the information society. Sage Publications.

Category: Digital Literacy

KEY TERMS AND DEFINITIONS Digital Development: The various levels of the digital divide which must be bridged to promote wide spread digital usage within a society. Digital Literacy Maturity Model: A consistent set of measurements for researchers to study digital literacy in accordance to the level of digital development within knowledge societies. Digital Literacy: Digital literacy consists of competence in the basic skills to utilize digital technologies, an understanding of how these competences may be utilised to create context to practices and subsequently to participate socially, culturally and economically, and it allows for the intellectual empowerment of individuals to transform society. Functional Digital Literacy: The basic competences or skills necessary to engage in the digital society.

Knowledge Societies: Societies which possess the necessary and sufficient conditions for growth in the knowledge economy, have high knowledge absorptive capacity and complex chains of creation, production and distribution, and consist of sustainable learning communities which emphasise innovation. Socio-Economic Digital Literacy: the ability of digital users to engage in the social and economic structures of the digital society. Sustainable Development: The process of developing while ensuring that the development does not promote existing inequalities, nor does it hinder the resources and abilities for future generations to continue to develop and progress. Transformational Digital Literacy: The empowerment of digital users, to be transformed intellectually, which ultimately changes society.

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Teaching Media and Information Literacy in the 21st Century Sarah Gretter Michigan State University, USA Aman Yadav Michigan State University, USA

INTRODUCTION Multimedia platforms such as blogs, social networks, forums, and video sharing websites have become a key component of communication in the 21st century. Ranging from flash news, popular press, and activism to trends, scandals, and advertising, these platforms have also become a repository of media and information in today’s hyper-connected society. Both individuals and media professionals often create, curate, and circulate content (i.e., user-generated content) in digital media spaces, thereby saturating media spaces with images and information that shape our digital culture (Gleason, 2013). Possessing the competencies to understand how information is conveyed through digital media is therefore an important skill to empower citizens to recognize its functions and effects on human communication. To address these objectives, the United Nations Educational, Scientific, and Cultural Organization (UNESCO) developed a media & information literacy (MIL) framework that encompasses the skills needed by 21st century citizens to critically evaluate information communicated through different media sources (UNESCO, 2013). Drawing from the UNESCO MIL framework, this chapter examines the skills needed by individuals to evaluate information presented through digital mass media, and discusses the role that educators can play in its instruction. The next section provides a brief history of MIL, followed by a look at the specific competencies that compose MIL.

Then, the chapter proceeds to discussing the role of educators in MIL instruction and concludes with the implications of MIL implementation in educational settings.

BACKGROUND What does it mean to be literate in the 21st century? While literacy has traditionally been contained to reading and writing skills, communication in the 21st century has expanded these customary views of literacy into an ever-evolving concept (Hobbs & Jensen, 2009). In today’s world, unfiltered information is available across multiple media platforms, such as television or newspapers, but more particularly on the Internet. Because media and other information providers are instrumental in shaping the perceptions, beliefs, and attitudes of individuals in today’s digital age (Guzzetti & Lesley, 2015), being literate in today’s society therefore includes being able to read, write, and communicate across a range of platforms, tools, and media. As a result, individuals need to master an array of literacy skills beyond basic reading and writing abilities (Livingstone et al., 2014). Citizens who are not aware of how media and information systems function are more likely to accept media messages as facts, while individuals who possess media and information literacy skills are able to evaluate and draw their own conclusion from the constant flow of mediated information (Potter, 2004).

DOI: 10.4018/978-1-5225-2255-3.ch199 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Digital Literacy

Media & Information Literacy (MIL) is an umbrella term that bridges information literacy with media literacy. Because the Internet is a digital platform that hosts multitudes of archives of mediatized information, navigating 21st century digital information implies the convergence of different sets of skills to address the challenges of our globalized world. Modern information systems are complex and multifaceted, and require individuals to be informed and engaged citizens in order to make sense of the mediatized information that surrounds them. MIL thus describes the 21st century skills that individuals need to critically evaluate information via various media sources and to become critical consumers of information (UNESCO, 2013). UNESCO (2013) articulated that media and information literate citizens should understand the importance of accessibility to information, know how to evaluate its veracity, and use it in ethical ways. Additionally, they should understand media functions and purposes, and engage with them for self-expression. For instance, individuals should be able to distinguish when media and information are used either for entertainment, decision making, problem solving, learning, or communicating with others. They should also understand how these purposes are related to the roles and the functions that different media play, and that based on these functions, different media adhere to different professional and ethical standards. With this understanding comes the ability to practice one’s own digital skills to engage with media and information for personal purposes, such as creating user-generated content, evaluating the credibility of a source, or communicating with others. This conceptual view of MIL is represented in figure 1. below. The social implication of being media information literate in the 21st century is informed participation in digital communication (Jenkins, 2009; Lee, 2013; UNESCO, 2013). MIL skills allow users to move from being passive consumers of digital information and media to being actively engaged in the information systems that shape their culture (Lankshear & Knobel, 2008).

For example, an informed media and information literate person would recognize and react to media biases when present, would engage in an ethical manner with online social exchanges, or would participate in a digital culture by creating content relevant to that culture. MIL skills not only foster individuals’ critical thinking and engagement with contemporary issues, but also allow them to take part in our era’s “participatory culture” (Jenkins, 2009). A participatory culture allows users to communicate through the creation of content to actively use media to engage audiences (Jenkins, 2009). Therefore, participants who are equipped with MIL skills can help shape today’s digital society and draw their own conclusions from the media and information that structures their culture, instead of simply accepting these media messages as unchangeable facts. Hence, possessing MIL skills can further the gap between those who participate in the culture, and those who do not because they have not acquired the necessary analytical and technical skills to do so (Jenkins, 2009). UNESCO has focused on issues of media literacy since the 1960’s, but acknowledged in the early 2000’s that technology was changing the role of media in society and that soon, individuals would need to possess new skills to make sense of new types of communication and ways to access information (Frau-Meigs, 2007). In knowledge societies, information includes and depends on the process of communication, and as a result, media literacy and information literacy are intrinsically connected (Lau, 2013). Livingstone et al. (2008) argued that despite their traditionally divergent disciplinary backgrounds, the object of inquiry in both media literacy and information literacy started to be united to understand “the public’s understanding of and effective engagement with media, information and communication technologies of all kinds” (p. 2). Koltay (2011) declared that one of the most salient commonalities between media literacy and information literacy was the analytical and critical thinking skills needed to interact with media and information.

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Figure 1. A conceptual model of media and information literacy

Source: Grizzle et al. (2013)

UNESCO (2013) conjoined both media literacy and information literacy using the overlap of critical and analytical thinking practices in a new media & information literacy (MIL) framework. UNESCO recognized that for some, information literacy would be considered the broader area of study with media literacy as part of it, while for others media literacy would be seen as the broader field with information literacy as part of

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it. UNESCO’s MIL framework, therefore, sought to bridge these two terms together in light of the converging platforms that constitute 21st century communication systems, such as Internet-based media and information platforms, and proposed MIL as the umbrella term encompassing all other literacies, such as visual literacy and digital literacy. MIL is a broad transliteracy concept because it is meant to adapt to the ever-evolving

Category: Digital Literacy

technologies and communication needs of a specific time or place. It can be applied to traditional media (e.g., books, print newspapers, television) as well as to contemporary media (e.g., Internet, online media), and offers the flexibility to be used in future information systems, regardless of their technology base or scope. Overall, MIL represents a set of analytical competencies that can transfer from one communication system to another and reflect the technological needs of 21st century digital communications. Given the scope of this encyclopedia, this chapter focuses specifically on digital communication and technology in the 21st century while acknowledging that MIL can be applied to and practiced in more traditional communication systems as well.

MEDIA AND INFORMATION LITERACY A 21st Century Competency Thanks to web-based technologies and digital media in the 21st century, individuals have more and more opportunities to become creators of knowledge and to take part in societal issues rather than simply being passive consumers of information (Buckingham, 2015; Lankshear & Knobel, 2008). UNESCO’s media & information literacy framework provides a set of competencies, or in other words a set of knowledge, skills, and attitudes, that aim to empower citizens to critically evaluate and understand the contents of today’s media and information systems (UNESCO, 2013). Because MIL relies on a combination of technical, analytical, and creative skills, it is a literacy that transcends media and type of information. That is, one can apply MIL skills while reading a novel or a newspaper, while watching television shows and advertisements, while surfing the Internet, or while engaging with others on social media. Because of the multi-faceted aspect of online communications in the 21st century and the increasing reliance on the multimodal transmission

of information (e.g., visual, textual, audio, etc.), this chapter emphasizes MIL competencies in the context of digital communication. It is important to note that in the context of 21st century communication, the term media corresponds to the means by which information is delivered, and can be both professionally produced content—typically labeled as “the media”—or user-generated content. Furthermore, UNESCO (2013) described information literacy as the skills required to seek, evaluate and create information for personal, social, educational, or professional needs (Wilson et al., 2013). Together, being literate in terms of media and information, therefore, involves the ability to recognize the power of media, to evaluate the content of the information they convey, and to produce user-generated content (Moeller et al., 2011; Wilson et al., 2013). UNESCO (2013) summarized these MIL components under three main competencies: access and retrieval, understanding and evaluation, and creation and sharing (UNESCO, 2013). These competencies, along with their sub-skills, are represented in figure 2. While analytical skills are central to MIL to evaluate media content, developing the retrieval and the creative aspect of MIL skills also relies on a set of technical skills that are intrinsically linked to technology and information systems in the 21st century (Davies, 2011; Jenkins, 2009; Wilson et al., 2013). Understanding how to access and retrieve information, along with knowing how to create and share personal content online depends on the mastery of technological and computing abilities. These skills are reviewed in the following section.

Media and Information Literacy in a Technology-Driven World In today’s digitally connected world, technology is pervasive from the use of social media (e.g., Facebook, Youtube, Snapchat, etc.) to the widespread presence of mobile devices (e.g., smartphones, laptops, tablets, etc.). A recent survey found that 95% of teenagers go online on

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Figure 2. MIL components

Source: UNESCO, 2013

a daily basis, 72% of all teens spend time with friends via social media, and 79% of all teens use instant messages with their friends (Lenhart, et. al, 2015). These staggering numbers highlight the need for young individuals to understand issues related to privacy and security on the Internet, as the risks they encounter in relation to anonymity, persuasion, or cyber-bullying grow exponentially (Livingstone & Brake, 2010). The key to developing individuals’ competencies in MIL involves creating awareness of how computerized communication and web-based digital media work. As young citizens become better informed about how information travels over the Internet, they will better understand its implications for privacy and security of information in digital communications. One of the prerequisites for MIL in a digital world is to form a solid foundation of technological skills—often referred to as ICT (Information & Communication Technology) skills (Wilson et al., 2013). For that reason, MIL also has a natural connection to components of computational thinking (CT), which has been sug-

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gested to be a 21st century cognitive and creative thinking skill that combines problem solving with principles of computing (Wing, 2006; DeSchryver & Yadav, 2015). For instance, one computational thinking idea that can support the development of MIL involves using “computational tools to analyze and study data and working with large data sets to analyze, visualize, and draw conclusions from trends” (College Board, p. 1). One of the key components of MIL is individuals’ ability to access and critically evaluate information, particularly how information can be manipulated to deliver specific messages. As people engage in computational thinking activities to analyze and visualize data, they also develop important MIL practices of how information can be visualized to convey specific messages. Specifically, as individuals create data visualizations or communicate their ideas using a variety of media, they learn to negotiate how to best represent information to their audience, which would be determined by the essence of the message they want to convey for a specific purpose. This process of evaluat-

Category: Digital Literacy

ing and presenting information to convey the gist of an idea allows individuals to critically assess the authenticity of the information encountered through digital media. Recently, many nations have come to recognize the need for their youth to acquire technological skills and to prepare them to successfully manage 21st century information. For instance, analyzing scientific claims online, locating relevant research, building arguments from a variety of resources, or producing appropriate media contents are examples of standards now expected from students in the United States (Governors Association Center for Best Practices, 2010; International Society for Technology in Education, 2015; Next Generation Science Standards, 2013). These standards aim to benefit students’ development of academic skills as well as personal and social skills in a digital world. A number of national and international educational organizations have also acknowledged the convergence and importance of student competencies in MIL and the need for students for being responsible users of digital media and information (Governors Association Center for Best Practices, 2010; International Society for Technology in Education, 2015; Next Generation Science Standards, 2013; Partnership for 21st century, 2014). As an example, the national computer science curriculum in the United Kingdom highlighted the imperative need for students to “understand a range of ways to use technology safely, respectfully, responsibly and securely, including protecting their online identity and privacy; recognize inappropriate content, contact and conduct, and know how to report concern” (Microsoft, 2014, p. 42). Similarly, within the United States of America, College Board has launched a new advanced placement course entitled Computer Science Principles (CSP) that focuses on six computational thinking practices and seven big ideas of computer science. The CSP framework is designed to allow students to understand how digital manipulation and storage of personal information has implications for privacy and security of that data (College Board,

2014). Specifically, the CSP framework focuses on advancing students’ understanding of how the Internet functions and grasping how computing can impact people and societies. While not labeled as MIL skills, there is considerable overlap between media & information literacy practices outlined in the UNESCO framework and the components in both the UK computing curriculum and the US computer science principles frameworks. However, while using technology effectively to access and assess information and to effectively use media for communication is now considered an essential 21st century skill for students, teaching these digital skills requires educators to exhibit the same competencies themselves (Wilson et al., 2014). For MIL to become a leading practice in the 21st century and for students to acquire these skills, there is an urgent need to prepare educators in MIL instruction. The following section addresses the qualities that educators should possess to successfully integrate MIL in their teaching.

Educators’ Role in Media and Information Literacy Instruction In today’s technology-driven and media-saturated world, educators will play a critical role in addressing the disparities that are emerging between those who can—or cannot—find, analyze, and critically evaluate information and media content to participate in their digital environments (Wilson et al., 2013). UNESCO in particular, envisioned that “initial focus on teachers is a key strategy to achieving a multiplier effect: from informationliterate teachers to their students and eventually to society at large” (Wilson et al., 2013, p. 17). Preparing teachers to teach MIL skills is an essential step in order to promote 21st century MIL competencies at a large scale. The UNESCO MIL curriculum framework for teachers describes the competencies that educators should possess and provides examples for educators on how to embed media and information literacy practices in their teaching (Wilson et al., 2013).

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According to the UNESCO, teachers should understand the role of media and information in their daily lives; understand media content and its uses; know how to access information effectively and efficiently; critically evaluate information; be familiar with both new and traditional media formats; and be able to situate the sociocultural context of media content (Wilson et al., 2013). UNESCO also identified various dimensions, including policy, curriculum and assessment, pedagogy, and professional training that need to be put into practice for the MIL framework for teachers to be successful (Wilson et al., 2013). For example, educators need to examine educational policies and national standards to better understand what role MIL might play in their own educational context. They also need to learn how to apply MIL to their teaching and understand students’ interactions with media and information in their daily lives. In addition, educators must understand how MIL can be integrated in their school curriculum and how they can assess their students’ acquisition of MIL skills. However, promoting MIL skills among students and implementing them in their own teaching can be difficult for teachers to achieve on their own even if they themselves might possess these MIL skills. Progressing from being media and information literate to knowing how to embed MIL in pedagogical practices requires support at various institutional levels, from teacher training for preservice teachers, to professional development for inservice teachers or educators in non-traditional educational settings. In order to address the growing need for teacher training in media literacy and information literacy (Martens, 2010), a UNESCO international expert group working on the MIL framework suggested that there is an important need to understand teachers’ conceptions of media and information literacy (Pérez Tornero, 2008). While a number educational reform initiatives, such as the Next Generation Science Standards in the United Sates or the computer science curriculum in the United Kingdom, have recently integrated MIL-related concepts in their student

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standards, implementing MIL in teacher education and teacher professional development may require time and institutional changes. The following section offers recommendations that educators can flow on their own as a first step towards the goal of promoting MIL to their students.

RECOMMENDATIONS FOR EDUCATORS Given the current focus on implementing MIL in educational settings, it is critical that teachers understand and adapt to 21st century media and information in order to prepare learners to use technology in their personal, academic, and future professional lives. The following set of recommendations represent active steps that educators can take in order to accomplish that goal. 1. MIL is an attitude. Encourage your students to habitually question the information they encounter in their daily lives as well as the media through which the information is conveyed. 2. MIL is a literacy that transcends types of communication (e.g., books, radio, television, social networks). Practice it with your students both with traditional and new media. 3. MIL is knowledge about how information is transmitted. Teach your students how information is passed on through different media forms. 4. MIL is an investigative activity. Show your students the necessary steps to access and retrieve information based on their needs. 5. MIL is awareness. Help your students develop an awareness of how traditional and digital media providers transform and distort information to convey specific messages. 6. MIL is an evaluative skill. Model to your students how they can assess the veracity of information transmitted through different media.

Category: Digital Literacy

7. MIL is creative. Encourage and guide students’ creation of media to participate in their digital culture of interest. In addition, organizations such as the International Society for Technology in Education (http://www.iste.org/standards/ISTE-standards/ standards-for-students), and the Partnership for 21st Century Skills (http://www.p21.org/storage/ documents/P21_framework_0515.pdf) further explicate some of the MIL competencies that both students and educators should possess to successfully evaluate, use, and create digital information and media in the 21st century. We recommend readers to look at their standards in order to obtain a complete picture of the current landscape in MIL-related skills.

SUMMARY AND FUTURE DIRECTIONS This chapter discussed the importance of media & information literacy for digital communication in the 21st century. It provided a brief history of the MIL concept, as well as a discussion of the competencies that compose MIL. We also explored the essential role that teachers play in teaching MIL skills in educational settings and presented recommendations for educators to take the first step in embedding MIL principles in their teaching. Overall, this chapter presented an adaptive set of competencies that will help individuals situate themselves within and contribute to today’s digital culture. Future directions should explore inclusion of MIL within computing concepts, which has been highlighted as an area of importance in the classroom by many scholars (Barr & Stephenson, 2011; Eisenberg et al., 2005; Felini, 2015; Hobbs & Jensen, 2009; Perkovic et al., 2010; Qualls & Sherrell, 2010; Thomas, 2004; Wing, 2006; Yadav et al., 2014). Specifically, future work in this field should examine how exposure to computational thinking constructs also develops students’ MIL

skills. As discussed previously, future research should also help to better understand teachers’ conceptions of MIL in order to identify potential challenges and barriers to its integration in teacher education and teacher professional development. Furthermore, future research should also look at the implementation and effectiveness of MIL trainings, such as the UNESCO curriculum for teachers; both at the institutional and the individual level. At the institutional level, research is needed to understand the role that the MIL curriculum could play in teacher education programs, either as a domain-general or as a domain-specific set of skills required of future teachers. At the individual level, studies should look at teachers’ engagement with MIL on a personal level in order to understand teachers’ attitudes towards embedding MIL in their teaching. These future directions will contribute to UNESCO’s overall mission to promote MIL on a global level, while supporting learners’ acquisition of these vital 21st century skills.

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Davies, R. S. (2011). Understanding technology literacy: A framework for evaluating educational technology integration. TechTrends, 55(5), 45–52. doi:10.1007/s11528-011-0527-3 DeSchryver, M. D., & Yadav, A. (2015). Creative and computational thinking in the context of new literacies: Working with teachers to scaffold complex technology-mediated approaches to teaching and learning. Journal of Technology and Teacher Education, 23(3), 411–431. Eisenberg, M. B., Lowe, C. A., & Spitzer, K. L. (2004). Information literacy: Essential skills for the information age. Westport, CT: Greenwood. Felini, D. (2015). Crossing the bridge: Literacy between school education and contemporary cultures. Research on Teaching Literacy Through the Communicative and Visual Arts, 2, 19–25. Frau-Meigs, D. (2007). Media Education. A Kit for Teachers, Students, Parents and Professionals. Retrieved from http://portal.unesco.org/ci/ en/ev.php-URL_ID=27056&URL_DO=DO_ TOPIC&URL_SECTION=201.html Gleason, B. (2013). #Occupy Wall Street: Exploring Informal Learning with Twitter. The American Behavioral Scientist, 57(7), 966–982. doi:10.1177/0002764213479372 Governors Association Center for Best Practices, Council of Chief State School. (2010). Common Core State Standards for English Language Arts. Retrieved from: http://www.corestandards.org/ ELA-Literacy/ Grizzle, A., Moore, P., Dezuanni, M., Asthana, S., Wilson, C., Banda, F., & Onumah, C. (2013). Media and information literacy: policy and strategy guidelines. UNESCO. Retrieved from: http://unesdoc.unesco.org/images/0022/002256/225606e. pdf Guzzetti, B., & Lesley, M. (2016). Handbook of Research on the Societal Impact of Digital Media. Hershey, PA: IGI Global. doi:10.4018/978-14666-8310-5

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Category: Digital Literacy

Martens, H. (2010). Evaluating media literacy education: Concepts, theories and future directions. Journal of Media Literacy Education, 2(1), 1–22.

Potter, W. J. (2004). Theory of media literacy: A cognitive approach. Thousand Oaks, CA: Sage. doi:10.4135/9781483328881

Microsoft. (2014). Computer science in the national curriculum. Retrieved from: http://www. slideshare.net/Microsofteduk/computer-sciencein-the-national-curriculum?from_action=save

Qualls, J. A., & Sherrell, L. B. (2010). Why computational thinking should be integrated into the curriculum. Journal of Computing Sciences in Colleges, 25(5), 66–71.

Moeller, S., Joseph, A., Lau, J., & Carbo, T. (2011). Towards media and information literacy indicators. Paris: UNESCO. Retrieved from: http://www.unesco.org/new/en/communication andinformation/resources/publications-andcommunicationmaterials/publications/full list/ towards-information-literacy-indicators/(3.8. 2014)

Thomas, N. P. (2004). Information literacy and information skills instruction: Applying research to practice in the school library media center. Westport, CT: Libraries Unltd Incorporated.

National Governors Association Center for Best Practices, Council of Chief State School. (2010). Common Core State Standards for English Language Arts. Retrieved from http://www.corestandards.org/ELA-Literacy/ Next Generation Science Standards (NGSS). (2013). The Next Generation Science Standards. Retrieved from: http://www.nextgenscience.org/ next-generation-science-standards Partnership for 21st Century Skills. (2014). Framework for state action on global education. Retrieved from http://www.p21.org/storage/documents/Global_Education/P21_State_Framewor _on_Global_Education.pdf Pérez Tornero, J. M., Celot, P., & Varis, T. (2007). Study on the current trends and approaches to Media Literacy in Europe. European Commission. Retrieved from: http://ec.europa.eu/culture/ library/studies/literacy-trends-report_en.pdf Perković, L., Settle, A., Hwang, S., & Jones, J. (2010). A framework for computational thinking across the curriculum. In Proceedings of the fifteenth annual conference on Innovation and technology in computer science education (pp. 123-127). Ankara, Turkey: Association for Computing Machinery. doi:10.1145/1822090.1822126

United Nations Educational, Scientific, and Cultural Organization (UNESCO). (2013). Global Media and Information Literacy (MIL) Assessment Framework: Country Readiness and Competencies. Retrieved from: http://unesdoc.unesco.org/ images/0022/002246/224655e.pdf Wilson, C., Grizzle, A., Tuazon, R., Akyempong, K., & Cheung, C. K. (2013). Media and information literacy curriculum for teachers. UNESCO. Retrieved from: http://www.unesco.org/new/ en/communication-and-information/resources/ publications-and-communication-materials/publications/full-list/media-and-information-literacycurriculum-for-teachers/ Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. doi:10.1145/1118178.1118215 Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), 1–16. doi:10.1145/2576872

ADDITIONAL READING Hobbs, R., & Jensen, A. (2009). The past, present, and future of media literacy education. The Journal of Media Literacy Education, 1(1), 1–11.

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Jenkins, H. (2009). Confronting the challenges of participatory culture: Media education for the 21st century. Boston, MA: MIT Press. Koltay, T. (2011). The media and the literacies: Media literacy, information literacy, digital literacy. Media Culture & Society, 33(2), 211–221. doi:10.1177/0163443710393382 Lankshear, C., & Knobel, M. (2008). Digital literacies: Concepts, policies and practices (Vol. 30). New York, NY: Peter Lang. Partnership for 21st Century Skills. (2014). Framework for state action on global education. Retrieved from http://www.p21.org/storage/documents/Global_Education/P21_State_Framewor _on_Global_Education.pdf United Nations Educational, Scientific, and Cultural Organization (UNESCO). (2013). Global Media and Information Literacy (MIL) Assessment Framework: Country Readiness and Competencies. Retrieved from: http://unesdoc.unesco. org/images/0022/002246/224655e.pdf Wilson, C., Grizzle, A., Tuazon, R., Akyempong, K., & Cheung, C. K. (2013). Media and information literacy curriculum for teachers. UNESCO. Retrieved from: http://www.unesco.org/new/ en/communication-and-information/resources/ publications-and-communication-materials/publications/full-list/media-and-information-literacycurriculum-for-teachers/

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Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. doi:10.1145/1118178.1118215

KEY TERMS AND DEFINITIONS Competencies: A set of knowledge, skills and attitudes. Computational Thinking: A way of thinking and solving problems based on computer science concepts. Media: The diverse body of media technologies that reach broad audiences through mass communication. Media and Information Literacy: The skills required to critically access and assess mediated information while understanding media functions in our daily lives. Information: The facts or data conveyed or represented via various media. Participatory Culture: A culture in which individuals are engaged with media instead of simply being passive consumers of information. Twenty-First Century Skills: A set of skills that individuals need to succeed in the 21st century.

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Category: Digital Literacy

Nigerian Undergraduate Students’ Computer Competencies and Use of Information Technology Tools and Resources for Study Skills and Habits’ Enhancement Adekunle Olusola Otunla University of Ibadan, Nigeria Caleb Okoro Amuda University of Ibadan, Nigeria

INTRODUCTION Many educational institutions have adopted policies favoring the implementation of modern technology prompted by Information and Communication Technology (ICT). Modern technologies like the Internet, mobile tele-communication and World Wide Web (www) has become innovative tools that could engage students in continuous learning across homes, classes, campuses, offices, e.t.c. Thus, the role of technology using the Internet, cloud computing and networked computers in teaching and learning is turning teachers from providers of information to become facilitators of learning. As a result of technology-driven learning approaches there is a transition of teaching and learning communities from teacher-focused with traditional approaches to student-centered learning which is largely Technology-driven through the use of tools and resources that surpasses any other previous technology. Consequently, Davis (2010) affirms that technology integration within and outside the classroom is modifying the learning environment from teacher-centred to learner-centred with opportunities for personalized learning experiences. Bodys (2005) also reported that ICT tools and resources allowed students to be more individually active in the learning process and become more independent in making

decisions about how and what they need to learn using electronics learning resources. Globally, different types of Computer-Based teaching and learning approaches have been developed to achieve the desired learning objectives and outcomes. Examples of computer technology applications, tools and resources for teaching and learning that cuts across all educational levels include; computer-assisted instruction (CAI), computer-assisted learning (CAL), e-learning, interactive video, multi-channel learning, virtual learning, virtual fieldtrips laboratories, virtual libraries, web conferencing, web chatting, digital story-telling, asynchronous online discussion (AOD) flip learning, e-mail communication and other forms of electronics and mobile learning among many other emerging learning technologies. Any of the listed technologies could be combined diversely into ‘Learning Management Systems’ (LMSs).

BACKGROUND ICT integration among higher institution students particularly in Nigeria has attracted attentions through various studies. For example, Odusanya and Bamgbala (2002) reported ICT uses among final year students at the University of Lagos-

DOI: 10.4018/978-1-5225-2255-3.ch200 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Nigeria; Jagboro (2003) reported on postgraduate students’ use of the Internet to search for academic materials at Obafemi Awolowo University, Ile-Ife, Nigeria. Similarly, Ajuwon (2003) reported high rate of regular use of the Internet by medical and nursing students at the University College Hospital, Ibadan, Nigeria and Bello, Arogundade, Sanusi, Ezeoma, Abioye-Kuteyi and Akinsola (2004) also reported that a lower percentage of respondents demonstrated good knowledge of computers and IT at Obafemi Awolowo University Teaching Hospital, Ile-Ife, Nigeria. Ezekoka and Nwosu (2010) also reported that ICT has been found to be very useful in the teaching and learning processes among Nigerian students because of the extensive capacity to store and manipulate information as well as its unmatched ability to serve simultaneously many individual students in different locations as supplementary to classroom instruction. While writing of ICT policy framework, Adomi and Kpangban (2010) observed that the National Policy on Education (Federal Republic of Nigeria, 2004) recognizes the prominent role of ICTs in the modern world and advocated for its integration into the Nigeria educational system especially at the post primary and higher education levels. Further, Otunla (2013) also stress that educational planners in Nigeria have seen the need for innovative instructional materials and media integration into the school system at all levels of Nigerian education. Adeoye (2013) noted higher institution students can learn through a virtual classroom environment where they can compose e-mails to conduct research via the Internet. The author further observed that the influence of technology on teaching and learning is enormous and could increase to an unimaginable and unpredictable level as new innovations are being introduced to the school system. Otunla and Baiyelo (2013) also observed that computers and Information Communication Technology (ICT) tools and resources such as the Internet, multimedia design, digital motion pictures, digital photography, web broadcasting; and digital print-

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ing, teleconferencing, video-conferencing e.t.c. have greatly influenced mode of transaction and the way people relate in the last one and a half decades. Therefore, ICT has permeated every aspect of human existence because it has become an indispensable tool for daily living and especially the educational system. Otunla and Akinyemi (2014) noted that application and integration of mobile technologies in higher education could enhance effective communication between lecturers and students within and outside the four walls of the classroom. More recently, Ezekoka (2015) conducted a survey involving a sample of sixty lecturers in a Nigerian public university on availability of ICT facilities that could promote collaborative learning to increase the level of students’ online participation. The author presented some problems encountered by the participants which hindered their optimal performance in online collaborative learning. At the continental level Uys, Nleya and Molelu (2004) based on an outcome of three case studies of technological adoption within the African continent and abroad suggested that technology needs in Africa could be implemented within a combination of strategically developed framework for technology innovation diffusion. Subsequently, Mtebe, Dachi and Raphael (2011) from their findings on use of technology to improve teaching and learning outcomes in higher education, identified two key issues which are; the need to address problem of university teachers’ readiness to accept change in their belief-system about teaching and the use of technology, as well as barriers to access required technologies in terms of hardware and software. Further, Nagunwa, and Lwoga (2012) enumerated some initial challenges faced in their university while they made an attempt to implement computer-based curricula by adopting e-Learning approach and presented the strategic re-establishment of an e-Learning approach. Therefore, it is glaring that higher institutions in Nigeria and African continent at large are taking advantage of the digital media and technologies to support and restructure their mode of teaching

Category: Digital Literacy

and instructional delivery to prepare students for the world-of-work. However, computer literacy and skills development are very crucial when it comes to integration of Information Technology (IT) tools, applications and resources in higher education. Without IT competencies and confidence in computer skills students’ learning and research opportunities are very limited. Thus, acquiring basic skills in computing and communication media technologies by students is very essential in order for them to function in their academic activities, engage in personal studies and active learning using new technologies. Mcmillan (1996) identified the following characteristics of computer literacy, which connote that the individual; knows how to use: word processing software, an e-mail and a browser for Internet navigation and is capable of; registering or downloading information on at least an auxiliary storage or some other external saving units, recuperate information and print it elsewhere. Stein, Craig and Scollary (1997) further corroborate the need for functional definition which is tailored toward behavioural ‘competencies’ the authors added a cognitive dimension which defines computer competencies as; the ability to use ICT to identify and search efficiently for specific information in order to build knowledge and develop critical and creative thinking. Another related technology skill required for knowledge deepening and knowledge creation is ‘information literacy’ (IL). The College of Dupage Library (CoDL) defines information literacy as the ability to locate, evaluate and use effectively the needed information (CoDL, 2002). Information literacy is also regarded as the set of skills needed to locate, retrieve, analyze and use information. Such that Andretta (2005) made a comparison of standards that was tagged ‘Information literacy standards: A practioner’s guide’ as proposed by various agencies, organizations and professional bodies on skills that people must master before they will be able to perform all the necessary functions to become information literate. The organizations include; American Library Association (ALA),

Association of College and Research Libraries, Society of College, National and University libraries (SCNUL) and Australian and New Zealand Institute for Information Literacy (ANIILIL). King (2007) also argues that the attributes of information literacy are multi-dimensional such that it involves employment of both traditional and modern information technology skills to retrieve, manage and present information in an ever widening array of information sources. Therefore, tertiary institution students must become information literate for them to be able to handle acquisition, management and analyzing large qualities of information and data that are widely available from different sources and more importantly from the Internet and the World Wide Web (www). Consequently, as earlier observed students and Internet users generally, need some special computer skills and competencies to be able to handle more effectively the ever-increasing digital information that could be annexed for educational purposes.

STATEMENT OF THE PROBLEM As a result of the growth in the modern communication technology, and the need for today’s higher education learners to become life-long learners through the use of IT tools and resources in preparation for the world-of-work. Moreover, IT is changing the role of tertiary institution students from that of passive-learners to activelearners within the learning environment, so as for them to keep up with current information and trends in their future professional practices. However, computer competencies and skills in the effective use of IT tools, applications and resources becomes a vital ingredient for students’ digital mastery, active participation and visibility on the World Wide Web. Further, students are required to be technological-capable in the use and application of IT tools and resources that are abundantly available via the superhighway. Therefore, this chapter, investigate the computer competences and use of information technology

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tools and resources in enhancing study skills and habits among undergraduate students in Rivers State, Nigeria.

RESEARCH QUESTIONS 1. What is the level of computer operational skills and competencies of undergraduate students in River State, Nigeria? 2. What is the level of competencies in the use of Information Technology applications and tools among undergraduate students in River State, Nigeria? 3. What is the extent of undergraduate students’ use computer tools and software applications as part of their personal learning activities? 4. What is the extent of undergraduate students’ use of Information Technology tools and resources as part of their personal learning activities? 5. What is the pattern of undergraduate students’ computer competence in Information Technology tools and resources in relation to study skills and habits enhancement?

METHODOLOGY The study adopted Ex-post facto research design of survey type: • •

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Population: The population of this study was undergraduate students in Rivers State, Nigeria. Sampling Technique and Sample: The study involved a sample of four hundred and fifty (450) undergraduate students were randomly sampled from three Universities with 150 undergraduate students each from Rivers State University of Science and Technology, Ignatius Ajuru University of Education and University of Port Harcourt all located in Rivers State, Nigeria.

•

Instrumentation: Three researcher-designed validated instruments were used for data collection. Each of the instruments contains; (A) demographic information section in addition to section (B) that contains items measuring the variables of interest i.e. computer competency, ICT tools and resource use and study habit enhancement. They are: ◦◦ Undergraduate Students’ Computer Competency Questionnaire (USCCQ): It contains a-25 item on computer competency with 3-point Likert scale ratings of ‘Highly Competent’ (3), Slightly Competent’ (2) and ‘Not Competent’ (1). ◦◦ Undergraduate Students’ Information Technology tools and resource Use Questionnaire (USITRSQ): It contains a 25-item on use of ICT tools and resources with 3-point Likert scale ratings of ‘Regularly’ (3), ‘Rarely’ (2) and ‘Never’ (1) respectively and lastly, ◦◦ Undergraduate Students’ Information Technology Study Skills and Habit Questionnaire (USITSSHQ): A 30item undergraduate students’ use of information technology tools and resources to enhance study habit with 4-point Likert scale response options, of Strongly Agree (4); Agree (3); Disagree (2); Strongly Disagree (1).

The three instruments were trial-tested for validation on a group of students who were not part of the sample for the study; using Cronbach Alpha reliability test. The following values were recorded; USCCQ - r=0.75, USITRSQ - r=70 and USITSHQ - r=062. The reliability values were considered to be valid and acceptable for the study.

Method of Data Collection and Data Analysis Technique Primary data was collected directly from the sample covered in the study at the three higher

Category: Digital Literacy

Table 1. Mean rating on level of computer operational skills and competencies of undergraduate students in Rivers State, Nigeria Mean

SD

Decision

Starting a computer

Items

2.3384

.78055

*

Launching an application software e.g. Microsoft Word, Excel, etc.

2.6237

.62224

*

Typing document using Microsoft (MS) Word,

2.6667

.67879

*

Slide preparation using MS Power Point

2.4293

.66184

*

Data input on Microsoft Excel

2.3409

.78753

*

Data analysis using Microsoft Excel

1.9798

.84170

*

Charts creation using Microsoft Excel

2.2879

.63482

*

Data input using Microsoft Power Point

2.6288

.56576

*

Power Point Slideshow for group discussion

2.2247

.75473

*

Graphic design application tools using Corel Draw

2.1616

.87678

*

Creation of objects in Corel Draw

2.6263

.66502

*

Computer shut down processes

2.3182

.70784

*

Grand Mean

2.5429

.58736

*

Competency level was set at 1.50 and above, while Incompetence was set at 1.49 and below. Thus, ‘*’ implies competent and ‘#’ implies Incompetent on the decision column.

institutions located in Rivers State, Nigeria. Mean ratings and standard deviation; as well as analysis of variance (ANOVA) and regression analysis were used to answer the research questions.

relevant information. Finding further implies that the undergraduates in Rivers State, Nigeria are compliant to the digital literacy in line with Mcmillan (1996) and Stein, Craig and Scollary (1997).

RESULTS AND DISCUSSIONS

Research Question 2

Research Question 1

What is the level of competencies in Information Technology applications and tools among undergraduate students in River State, Nigeria? The grand mean score of 2.64 in Table 2 implies that undergraduate students in Rivers State Nigeria were competent in IT applications and tools alongside accompanying resources with the exception to Log in an e-mail account that recorded the least mean rating (M=2.22, SD=.83); this may not be unconnected with the fact that many undergraduates hardly operate institutional e-mails or maintain personal active e-mail, but depends on third party such as friends, cyber cafe and business centre operators. However, finding

What is the level of computer operational skills and competencies of undergraduate students in River State, Nigeria? The grand mean score of 2.54 in Table 1 indicated that undergraduate students covered in this study were competent in using computer technology alongside IT tools, applications and resources in their knowledge formation, generation and acquisition. The finding implies that the undergraduates are active and creative in their quest to learn and acquire knowledge using digitally information systems to generate and gather

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Table 2. Mean rating on level of competencies in information technology applications and tools Items

Mean

SD

Decision

Log in an e-mail account

2.2247

.83438

*

Composing e-mail communication

2.5126

.57133

*

Composing e-mail with attachment

2.6439

.57102

*

Sending e-mail with attachment

2.3965

.56648

*

Browsing educational information and resources using Universal Resources locator (URL) e.g. http// www.

2.4470

.62010

*

Browsing Journal articles

2.4798

.61796

*

Downloading e-book on Internet

2.7045

.62121

*

Downloading e-leaning document

2.5758

.71993

*

Downloading pictures on educational concepts

2.7096

.59022

*

Undertaking online application e.g. admission form processing

2.6995

.57200

*

Undertaking online registration e.g. course registration

2.6364

.61136

*

Submission of class assignment via e-mail

2.4419

.60308

*

Submission of class project via e-mail

2.6843

.55926

*

Grand mean

2.6429

.58736

*

Competency level was set at 1.50 and above, while Incompetence was set at 1.49 and below. Thus, ‘*’ implies competent and ‘#’ implies Incompetent on the decision column.

implies that undergraduate students in Rivers State Nigeria satisfied all the competencies highlighted by Mcmillan (1996) as well as Stein, Craig and Scollary (1997) on e-mail communications, Internet browsing and Information Technology.

Research Question 3 What is the extent of undergraduate students’ use of computer tools and software applications as part of their personal learning activities? The grand mean score of 2.32 as indicated in Table 3 implies that undergraduate students in Rivers State, Nigeria regularly engage in the use of computer tools and software applications in the course of their learning activities. Finding, further confirms that undergraduates in Rivers State, Nigeria are not only computer literate in line with Mcmillan (1996) and Stein, Craig and Scollary (1997) but are actively engaging in behavioural indicators that goes with such competencies

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Research Question 4 What is the extent of undergraduate students’ use of Information Technology tools and resources as part of their personal learning activities? The grand mean score of 2.32 in Table 4 literarily implies that undergraduate students in Rivers State, Nigeria regularly use IT tools and resources as part of their personal learning activities and thereby technologically-capable. More so, they perceived themselves to possess information literacy skills needed to locate, retrieve, analyze and use information.

Research Question 5 What is the pattern of undergraduate students’ computer competence in Information Technology tools and resources in relation to study skills and habits enhancement?

Category: Digital Literacy

Table 3. Mean rating of extent of undergraduate students’ use of computer tools and software applications as part of their personal learning activities Mean

SD

Starting a computer

Items

2.1818

.65779

Decision

Launching an application software e.g. Microsoft Word, Excel, etc.

1.9571

.73307

#

Typing document using Microsoft (MS) word,

2.4066

.59437

*

Slide preparation using MS PowerPoint

2.3283

.63110

*

Data input using Microsoft Excel

2.1818

.81894

*

Data analysis using Microsoft Excel

1.9040

.74350

#

Charts creation using Microsoft Excel

2.3258

.65406

*

Data input in power point

2.2121

.81471

*

Power Point slide show for group discussion

2.3696

.78701

*

Graphic design application tools in Corel Draw

2.3813

.68514

*

Creation of objects in Corel Draw

2.4798

.72363

*

Computer shut down processes

2.3384

.78055

*

Grand mean

2.3179

.6991

High level of use was set at 1.50 and above, while low level of use was set at 1.49 and below. Thus, ‘*’ implies high level of use and ‘#’ implies low level of use on the decision column.

Table 4. Mean rating on extent of undergraduate students’ use of IT tools and resources as part of their personal learning activities Items

Mean

SD

Decision

Log in on e-mail account

2.3081

.60883

*

Composing and sending e-mail communications

2.5480

.67896

*

Composing e-mail with attachment

2.3283

.67378

*

Sending e-mail with attachment

2.4192

.63328

*

Browsing for educational information and resources using Universal Resources Locator (URL)

2.3409

.74115

*

Browsing for Journal articles

2.3561

.62601

*

Downloading e-book on Internet

2.3586

.66576

*

Downloading e-leaning document

2.4672

.67613

*

Downloading pictures on educational concepts

2.4268

.69150

*

Undertaking online application e.g. admission

2.4394

.70719

*

Undertaking program registration e.g. course registration

2.3333

.77296

*

Submission of class assignment via e-mail

2.3763

.65784

*

Submission of class project via e-mail

2.1793

.71912

*

Grand mean

2.3179

.6991

High level of use was set at 1.50 and above, while low level of use was set at 1.49 and below. Thus, ‘*’ implies high level of use and ‘#’ implies low level of use on the decision column.

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Table 5. Summary of the regression analysis on the contribution of undergraduate students’ computer and it competencies to study skills and habits enhancement Descriptive Statistics Variable

Mean

Std. Deviation

N

Study Habits and Skills

83.7854

9.28872

396

Competencies

82.5859

7.61047

396

Table 6. Model summary and ANOVA Model 1

Sum of Squares

Df

Mean Square

F

Sig.

Regression

4378.691

1

4378.691

58.084

.000

Residual

29702.064

394

75.386

Total

34080.755

395

R 0.358 R2 0.128 Adj. R2 0.126 SE 8.68251

Table 7. Relative contribution of predictor on criterion variable Model 1

(Constant) Competence

Unstandardized Coefficients B

Std. Error

47.655

4.761

.437

.057

Table 5 and Table 6 present the patterns of use of Information Technology tools and resources with the observed variance in students’ study skills and habits which accounted for approximately 12.6% of the variance of student study skills and habits (R2 =.128, Adjusted R2 =.126). The result on ANOVA further indicated that pattern of use of IT tools and resources was a significant predictor of students’ study skills and habits enhancement (F (1, 394) = 58.084, p=.000). Table 7 reveals that the Beta values for pattern of use of Information Technology tools and resources ( β =.358; t = 7.621, p=.000) was significant at.05 alpha level in terms of predicting student study skills and habits. Findings establish the fact that undergraduate students in Rivers State, Nigeria are perceived to develop skills needed to locate, retrieve, analyze

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Standardized Coefficients

t

Sig.

10.010

.000

7.621

.000

Beta .358

and use information which partially demonstrates indexes of the College of Dupage Library (CoDL, 2002) definition which stated that information literacy is directly related to the ability to gather and assess relevant information as well as problem solving abilities. The pattern of use of computers and IT tools and resources which accounted for approximately 12.6% of the variance of undergraduate students’ study skills and habits was a significant predictor that also agrees with the assertion of King (2007) on multi-dimensional attributes of information literacy, which employ traditional and modern information technology skills to retrieve, manage and present information. Findings from this study further reveal that undergraduate student’ competence and engagement in computer and Internet activities significantly contributes or enhances their

Category: Digital Literacy

personal and study habits and skills. The finding partly agrees with the submission of Bodys (2005) on the ICT tools and resources allowed students to be more individually active and become more independent in making decisions about their learning.

FUTURE RESEARCH DIRECTIONS Future research directions could be tailored towards ascertaining various information literacy skills among tertiary institution students in different parts of Nigeria which could be a large scale assessment using case studies alongside in-depth observational techniques.

CONCLUSION In conclusion this study has revealed that there is no doubt that undergraduate students perceived themselves to possess competencies and skills needed to locate, retrieve, analyze and use information and that that information literacy is directly related to the ability to gather and assess relevant information using information technology tools. The pattern of use of computers and IT tools and resources to retrieve, manage and present information shows that higher institution students in Rivers State, Nigeria are technology-capable which invariably was perceived to enhance their study skills and habits

RECOMMENDATIONS The study recommends that: •

Undergraduate students should take advantage of the opportunities provided by the Internet computing and Information and Technology for personal study and academic activities in preparation for the real world-of-work.

•

•

University teachers and faculties in higher institutions of learning should integrate into their teachings such activities that compel students to engage in computer and Internet use as part of classroom interactions. Undergraduate students should sharpen their skills Internet computing so as to become more familiar with computer platforms, open source application, IT tools and OERs that could assist them their academic life both for their studies and research.

REFERENCES Adeoye, B. F. (2013). Recent Developments in Technology Utilization in Teaching and Learning. In U. M. O. Ivowi (Ed.), Seeking Total Quality Education in Schools (pp. 273-291). Lagos: CIBN Press Limited. Adomi, E. E., & Kpangban, E. (2010). Application of ICTs in Nigeria Secondary Schools: Library Philosophy and Practice. Digital commons@ University of Nebraska-London. Retrieved June 15, 2015, from: http://digitalcommons.unl.edu/ libphilprac/345 Ajuwon, G. A. (2003). Computer and Internet use by first year clinical and nursing students in a Nigerian teaching Hospital, British Medical Companion. Medical Information and Decision Making, 3(1), 10–15. doi:10.1186/1472-6947-3-10 Andretta, S. (2005). Information Literacy: A Practioner’s guide. Oxford, UK: Candos Publishing. doi:10.1533/9781780630755 Bello, I. S., Arogundade, F. A., Sanusi, A. A., Ezoma, I. T., Abioye-Kuteyi, E. A., & Akinsola, A. (2004). Knowledge and utilization of information technology among healthcare professionals and students in Ile-Ife, Nigeria: A case study of a University teaching hospital. Journal of Medical Internet Research, 6(4), e45. doi:10.2196/ jmir.6.4.e45 PMID:15631969 2311

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Body, W. (2005). The Integration of Information and Communication Technology in Scottish School. An Interim Report by HM Inspectors of Education. Retrieved June 15, 2014 from: http:// en.wikibooks.org.wiki/ICT in Education/introduction.htm College of Dupage Library. (2002). Student Learning Outcomes for Information Literacy Instruction Programme. Retrieved June 15, 2015, from: http://www.cod.edu/library/services/faculty/ infolit.learning David, V. (2006). Concepts of Information Technology. Cape Town: ICDL Foundation Kenilworth. Davis, M. R. (2010). E-Learning seeks a custom fit. Education Week Digital Directions, 3(2), 18–19. Ezekoka, G. K. (2015). Maximizing the effects of Collaborative Learning through ICT. International Educational Technology Conference (IETC). Retrieved online 2/6/2016 at: http://www.sciencedirect.com/science/article Ezekoka, G. K., & Nwosu, S. N. (2010). Identification of Information and Communication Technology Skills of Teachers in Imo State Secondary Schools. Journal of Studies in Education, 1(1), 244–250. Federal Republic of Nigeria. (2004). National Policy on Education Lagos (5th ed.). Lagos: NERDC Press. Jagboro, K. O. (2003). A study of Internet usage in Nigerian universities: A case study of Obafemi Awolowo University, Ile-Ife, Nigeria. First Monday: Peer Reviewed Journal on the Internet. Retrieved online at: http://www.firstMonday.dk/ issues/issues8=2/Jagboro King, L. (2007). Information Literacy of Incoming Undergraduate Arts Students at the University of Western Cape: Assessment of competencies and proficiencies (Unpublished Doctoral Thesis). The University of Western Cape.

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Mcmillan, S. (1996). Literacy and computer literacy: Definitions and comparisons. Computers & Education, 27(3-4), 161–170. doi:10.1016/ S0360-1315(96)00026-7 Mtebe, J. S., Dachi, H., & Raphael, C. (2011). Integrating ICT into teaching and learning at the University of Dar es Salaam. Distance Education for Empowerment and Development, 32(3), 289-294. Nagunwa, T. P., & Lwoga, E. T. (2012). Developing an e-Learning strategy to implement medical competency-based curricular: Experience from Muhimbili University of health and Allied Sciences. International Conference on Education and e-Learning Innovations (ICEELI). Retrieved online 2/6/2016 at: http://www.researchgate.net/ publication.261524397_development_of_eLearning Odusanya, O. O., & Bamgbala, O. A. (2002). Computing and information technology skills of final year medical and dental students at the college of medicine, University of Lagos, Nigeria. Postgraduate Medical Journal, 9(4), 189–193. PMID:12690677 Otunla, A. O. (2013). Operational Processes for Computer-Mediated Learning in Management of Large Surgical Theatres, Laboratories, and Teaching Sessions in Medical and Health Sciences. In U. M. O. Ivowi (Ed.), Seeking Total Quality Education in Nigeria (pp. 313-327). Lagos: CIBN Press Limited. Otunla, A. O., & Akinyemi, J. O. (2014). Technology Integration in the Classroom: Report of an Asynchronous Online Discussion among a Group of Nigerian Graduate Students. In B. F. Adeoye & L. Tomei (Eds.), Effects of Information Capitalism and Globalization on Teaching and Learning. IGI Global. Retrieved online 22/02/2015 at: http:// www.igi-global.com

Category: Digital Literacy

Otunla, A. O., & Baiyelo, T. D. (2012). A study on the competency and utilization patterns of ICT Tools Among Medical Undergraduates Students in Three Nigerian Universities. Journal of Educational Media and Technology, 17(2), 42–57. Stein, A., Craig, A., & Scollary, A. (1997). Preparatory IT practices and skills of transition in business students. Australasian Journal of Educational Technology, 13(1), 40–53. doi:10.14742/ ajet.1918 Uys, P. N., Nleya, P., & Molelu, G. B. (2004). Technological innovations and management strategies for higher education in Africa: Harmonizing reality and idealism. Educational Media International, 41(1), 67–80. doi:10.1080/0952398032000105120

ADDITIONAL READING American Association of School Librarians. (2001) Information Literacy. Position paper Retrieved May 10, 2015, from: http://www.ala.org/ aasl/positions/psinfolit.html Andretta, S. (2005). Information Literacy: A Practioner’s guide. Oxford: Candos Publishing. doi:10.1533/9781780630755

Californian State University Commission. (2000). Learning resources and Instructional Technology, Work Group on Information Competence. Information Competence in the CSU: A report CSUN, Californian State University. Retrieved July 27, 2014 http://library.CSUN.edu/susan. curzon/factsheet.html Condie, R., & Munro, B. (2007). The impact of ICT in schools: a landscape review. British Educational Communications and Technology Agency (BECTA) Research publications. Retrieved July 20, 2015, from: www.becta.org.uk

KEY TERMS AND DEFINITIONS Information Literacy: Information Literacy is the ability to locate, evaluate and use effectively the needed information and it is also regarded as the set of skills needed to locate, retrieve, analyze and use information (The College of Dupage Library, 2000). Information Technology (IT): IT is a broad term which covers all the aspects of the uses of computer technology which includes not only hardware and software, but also communication technology; linking computer systems, software engineering, administration and supporting the infrastructure to manage and deliver information (David, 2006).

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The Roles of Digital Literacy in Social Life of Youth Dragana Martinovic University of Windsor, Canada Viktor Freiman Université de Moncton, Canada Chrispina S. Lekule St. Augustine University of Tanzania, Tanzania Yuqi Yang University of Windsor, Canada

INTRODUCTION This chapter contains updated findings related to social aspects of digital activities of youth (Martinovic, Freiman, Lekule, & Yang, 2014). Computers, mobile devices, and the Internet are increasingly used in everyday social practices of youth. Recent statistics reveal that in September 2015, there were 1.01 billion daily active users and on average 894 million mobile daily active users of Facebook (Facebook, 2015). To be successful in school, work, and in socializing, youth need to competently use digital tools and define, access, understand, create, and communicate digital information. Being able to evaluate digital information, develop perceptions of, and respect for, social norms and values for functioning in the digital world, without compromising one’s own privacy, safety, or integrity is also important. The competencies and skills that new generations require to be successful in the digital era are largely still not being taught in schools. Results of this chapter will provide the following: •

Address the social prospects of Information and Communication Technology (ICT) use among youth;

•

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Describe the online behavior of young people through the paradoxical nature of the Internet that provides opportunities for social development but introduces risks; Inform educators and youth services about which factors to consider in designing flexible, innovative, and inclusive programs for young people to enable them to successfully function in the era of the Internet, new media, and computer technologies.

BACKGROUND In the past 15 years or so, ICTs became increasingly accessible in most countries. The ICTs like personal computers, cell phones, and the Internet can be used for both in-school and out-of-school activities, and are particularly suitable for connecting individuals and communities globally (Beetham, McGill, & Littlejohn, 2009). Using these tools appropriately so that one can live, learn, and work in a digital society, is broadly defined as being digitally literate (Beetham, 2010). However, by and large, these competencies and skills are not being taught in schools (Martinovic, Freiman, & Karadag, 2011). For

DOI: 10.4018/978-1-5225-2255-3.ch201 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Digital Literacy

example, Jenkins (2006) finds that youth are not taught how to participate in social online practices (e.g., in information sharing and collaboration) despite dangers for unskilled users, whereas some authors (Martinovic & Magliaro, 2007; Noveck, 2000) emphasize importance of understanding a paradoxical nature of the Internet, where one can be confronted with limitless information, while obtaining less knowledge; where access is relatively cheap, but the environment is increasingly commercialized; and where communities do form, but atomization prevails. Livingstone (2008) describes the dichotomy of optimistic and pessimistic opinions coming from academics and media on how ICTs affect young people: •

•

Optimists emphasize the new opportunities for self-expression, sociability, community engagement, creativity, and new literacies. They envision change in social dynamics, with youth involvement in the co-creation of innovative and counter-consumerist cultures both locally and globally. Public policy makers and educators see opportunities for engagement in collaborative learning and various online government services. Xie (2014), for example found that mobile communication through social networking sites, amplifies social capital of teenagers. Pessimists associate the behavior of youth on social networking sites with loss of privacy and lack of shame. They look at social networking as time-wasting and socially isolating activities that may have far-reaching negative effects on the safety of youth. Others fear that youth growing up in the digital age may be barren of understanding emotional nuances and reading social cues, and may lack empathy (Stout, 2010). They note that cyberbullying results in changes in behavior and deep emotional problems among its victims (Mitchell, Ybarra, & Finkelhor, 2007; Ybarra, Diener-West, & Leaf, 2007), and that the effects of Internet

communication both with peers and with strangers on well-being may be particularly adverse for lonely adolescents (Valkenburg & Peter, 2007). Patton et al. (2014) warns against increase of youth violence in online space, which includes cyber-bullying, -gangs, -stalking, and -suicide. Moreover, young people recognize that using the Internet for schoolwork may encourage taking shortcuts, cheating, laziness, low school morale, and may hinder development of study skills (Ben-David Kolikant, 2010). Based on these examples, a digitally literate person should be able to navigate between opportunities and traps created by introducing different ICTs in one’s everyday social life.

SOCIAL ONLINE PRACTICES OF YOUTH Issues, Controversies, Problems Current international statistics reveal that about 87.9% of North Americans, together with 73.5% Europeans use the Internet (Internet World Stats, 2014). Extreme growth in Internet use in Africa, Middle East, and Latin America in the last 15 years is measured in thousands of percent. Young Canadians seem to follow such common worldwide trends. One Canadian study, Young Canadians in a Wired World (Media Awareness Network, 2001-2012) looked at the online behavior, attitudes, and opinions of more than 5,200 children and youth from grades 4 to 11 in French- and English-language schools in every province and territory. The report revealed that in 2005, young Canadians were almost universally connected to the Internet through the household computer, personal computer, and/or cell phone, and were active users of the ICTs. While the participants in this study described their online experiences as generally positive and socially rewarding, they also reported being exposed to inappropri-

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ate content and risky situations such as bullying and sexual harassment which may compromise their integrity and privacy. For this reason the participants highlighted the need to learn how to distinguish credible from non-credible information on the Internet. In the further text we address the issues of formation of identity, friendship, participatory culture, and political engagement in the context of the ICT use. These areas of social development of young people are described in the literature as dependent on their age.

Identity Formation Pre-School and Primary School Children At the very young age, children discover virtual world when playing. Aguiar and Taylor (2015) investigated how preschool children would differentiate the social affordances of a virtual character that simulates social behaviors and those of a stuffed animal they used to play with. It was found that the children relate to the stuffed dog as a friend, while the virtual dog, even when sophisticatedly programmed appear to be entertaining rather than the relationship partner. Between ages 7-12 which we define as the stage of emerging digital literacy, digital life experiences of children are highly dependent on what ICT are available at home and in school. At the same time, access and use are restricted by parents, educators, laws, etc. Use of Internet by 39 fourth-graders (ages 9-11) from a small New Hampshire town was studied by Henke and Fontenot (2007). The researchers found that the children were already veteran Internet users (i.e., having at least two years of experience) and that they were able to identify the persuasive intent of commercial Websites, and to distinguish between the informational and entertainment functions of non-profit and government Websites. The children were unwilling to substitute other social activities for Internet use, which was influenced by teachers, parents, and older siblings.

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Adolescents Issues of identity and privacy become even more important in the increasing socialization of youth aged 12-18. Hundley and Shyles (2010) conducted focus-group interviews with 80 West Coast and East Coast U.S. teenagers (ages 12-16) to ascertain their perceptions and awareness of digital technology. The authors learned that the Internet, and particularly social networking sites, played an important social role in the young people’s lives: Cell phones helped them to connect to one another; computer games, to have fun; and social networking sites, to stay in touch and connected. Some geographical and cultural patterns emerged: While Facebook was almost unknown in the West (mostly Hispanic) and popular in the East (mostly white), MySpace was the overwhelming favorite among all participants (with West Coast youth more active). The number of online friends varied from 6 to 1000, averaging 85-200. Favorite activities on social networking sites were “talking/ meeting with friends,” “staying in touch,” “updating profile,” “checking other profiles,” “checking messages,” “texting friends,” and “multitasking” (e.g., simultaneously using a computer, listening to the music, and watching TV). For adolescents, electronic media were powerful socializing agents, but they did not completely replace other sources for social interaction. Hundley and Shyles’s (2010) participants liked to combine face-to-face and online socializing; their online social groups mirrored their real-life communities. The participants were aware of online risk and saw the danger of cyber-attacks on their privacy, but were ready to accept the risk as they were careful not to disclose any personal information. However, some participants acknowledged they were occasionally dishonest when providing information on Websites (e.g., hiding their age), but did not see a problem with this. Valkenburg, Schouten, and Peter (2005) surveyed 600 adolescents and found that the Internet plays an important role in their identity exploration and creation. The authors identified different self-

Category: Digital Literacy

presentational strategies used by these youth. For instance, younger adolescents, girls, and extroverts significantly more often pretended to be older than did the older adolescents, boys, and introverts. Boys and introverts presented themselves more often as a macho person, whereas girls, younger adolescents, and extroverts presented themselves more often as a more beautiful person. Finally, boys presented themselves more often than girls as a real-life acquaintance or as a fantasy character. Based on the focus group interviews with 127 Italian young Internet users (i.e., middle and high schools students of age 11–20), Bosca et al. (2015) concluded that adolescents do not speak favourably about people who on the Internet post their altered photos or lie about their appearance. Harman, Hansen, Cochran, and Lindsey (2005) argued that misuse of the Internet can affect various aspects of children’s social lives. Harman et al. conducted a study of 187 predominantly European or American sixth-, seventh-, and eighth-grade students, aged 11-16. The children who reported the most deceitful behavior on the Internet (e.g., pretending to be older) had poorer social skills, lower levels of self-esteem, higher levels of social anxiety, and higher levels of aggression (i.e., inappropriate assertiveness), regardless of gender. Frequent use of the Internet, however, did not affect the children’s social skills, self-esteem, social anxiety, and aggression. However, Livingstone (2008) noted a difference between how younger and older teenagers created their online profiles. Younger teens had a playful approach and created profiles that were decorative, stylish, and elaborate. Older teens, on the other hand, created plainer profiles that emphasized their relationships with others (e.g., links to their friends’ profiles and group photos). The danger for this latter group lay in their lack of knowledge of when or how to draw a line between what to say in private and in public medium. In addition, Livingstone found that how the teenagers classified their friends (e.g., friends from school, friends from holidays, real friends, old friends, best friends, good friends, ‘hi-and-bye’ friends)

and the corresponding granulation of closeness with them were not adequately matched by the privacy features of the social networking sites (i.e., ‘public’ or ‘private’). The author suggested that teenagers struggle with online privacy because of this mismatch between their needs and the features of the sites. Moreover, inadequate, confusing, or poorly designed site settings, along with the teens’ limited Internet literacy, often left the teens unclear about their control over who could see what about them. Nonetheless, as shown by both anecdotal and research evidence, youth are very protective of their digital spaces and they make a sharp distinction between situations in which they participate in formal learning and those in which they socialize and learn informally. For example, Marwick, Murgia-Diaz, and Palfrey (2010) argued that young people may not perceive the Internet as a public space. In many ways, “online spaces like MySpace and Facebook are seen as private social spaces where young people can engage in personal talk, gossip, ‘hanging out,’ flirting, sharing secrets, and all the other social practices that they engage in with their peers offline” (p. 61). From this perspective, youth have redefined the concepts of ‘private’ and ‘public’; they want to keep some digital spaces private from the watching eyes of adults (be they parents, teachers, or marketers) so that they can freely socialize there under their own terms. This stand for also confirmed in Bosca et al.’s (2015) study, although the Italian adolescents also mentioned using Internet to connect to parents. The authors suggested that family relationships may benefit from the Internet communication between parents and children, as long as the parents recognize adolescents’ need for autonomous use of online spaces and let them demonstrate their digital skills, which can also improve the status of their children in the family. Recent study of online behavior of 11-15 year old youth conducted by Afshar et al. (2015) expresses concerns that when shifting to the online world, adolescents can develop social phobia which drives them to having a hidden identity,

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while potentially damaging their natural and real identity. Internet use by adolescents with developmental and social differences was explored by Bannon, McGlynn, McKenzie, and Quayle (2015). The authors conducted focus groups with 36 young people (aged 13–18) who were all identified with Autistic Spectrum Disorder, moderate learning difficulty, and/or social, emotional and behavioural difficulties. Bannon et al. identified aspects of development of identity, social connectedness, and competence (e.g., in specific skills as well as a sense of self-competence) through their participants’ use of the Internet. These individuals not only developed in these three areas, but purposefully used the Internet to build social ties to others and avoid sense of anxiety of offline communication. While this segment of adolescent population used the Internet in a similar way and with similar benefits as youth in general, they may need some extra measures to be put to ensure their online safety. Young Adults Mahiri (2004) described how youth nowadays use electronically mediated popular culture to produce, consume, and propagate personal/cultural meanings, individual pleasures, and desires that inform, challenge, and often counter well-established societal norms and forms. One example can be seen in Seibel-Trainor (2004), who described a case of a college-educated, single young woman, a member and a regular contributor to The Gossamer Project, who described herself as a “media junkie” (p. 127) who spent 2-5 hours a day reading and writing for The Gossamer Project. This woman, Barbara, followed the events and characters from the series, and as other fans on the Website did, wrote about them extensively. Barbara produced character studies, wrote commentaries on each episode, and predicted what would happen in the next episode. She posted her stories about main characters from the series and received her own fan mail. This was all done under the scrutiny of the television network, which tried to block

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the writing of fans like Barbara. However, this attempt of the TV network generated strong opposition from the fans, who organized a maze of connections from their personal Web pages to the Gossamer Project Website to protect Gossamer Project contributors from intellectual property lawsuits. Seibel-Trainor concluded that the Internet removes the distinction between the writer and the reader; writers like Barbara write for their cyber-friends and other fans; they do not accept simply being consumers of media. West, Lewis, and Currie (2009) interviewed 16 undergraduate students, 21-26 years old, who reported having on average 82 real-life and 200 Facebook friends. All participants had more friends on Facebook than in real life. Facebook friends consisted of real-life friends, cousins, and friends of friends, all of whom were of similar age to the respondents. The students generally did not like the idea of having parents as friends on Facebook, and some were even happy that their parents did not know how to use the Internet, thus allowing them to keep the details of their social life private from their family. The authors suggested that youth who participate in social networking sites have a different notion of what is private and what is public, a distinction that cannot be described in a clear-cut binary way. In summarizing research on new media spaces, Zemmels (2012) took a stance that nowadays, “identity is largely constructed through media engagement” (p. 17). From that perspective, the construction of identity happens through the active use of media where young people do not only consume, gather, and distribute media content, but add their personal voice in producing media. This creates ‘new social operating system’ where “networked individuals” are constituted within “networked publics,” and where ICT increasingly shape social practices (Zemmels, 2012, p.17).

Redefinition of Friendship Social networking sites provide increasingly more attractive functions to their users. A well-

Category: Digital Literacy

developed Facebook or MySpace profile provides a look into the owner’s life in a way that the person wants to be perceived by his or her peers (Maranto & Barton, 2010). Users normally search for those who share their labels and/or interests, and request to add them as ‘friends.’ Through this snowball effect, an individual can quickly become part of a network of many friends. Intensive engagement of youth in electronic communication, such as texting, instant messaging, and Internet chats, influences the development and expression of their emotions. Some authors (see Stout, 2010), caution that due to the declining of face-to-face communication within the electronic age, the younger generation may lack exposure to real-life experiences that help them develop empathy, understand emotional nuances, and read social cues like facial expressions and body language. As a result, for today’s teenagers and preteens, friendship is apparently realized through the exchange of cell phone texting and instant messaging, or on Facebook walls and MySpace bulletins. There are also some noted gender differences in use of the social networking sites—girls use such sites to reinforce preexisting friendships, whereas boys use them to flirt and make new friends (Lenhart & Madden, 2007); technology is changing the nature of young people’s friendships. However, after Mesch (2009) conducted a survey with around 1,000 adolescents in Israel, he concluded that the youth seem to maintain two separate networks of online and off-line relationships. The type of communication channel adolescents used mostly depended on the strength and origin of social relationship between them. Those who first met face-to-face, who lived in each other’s vicinity, and who felt that their relationship is strong and trustworthy, preferred to communicate directly, face-to-face and over the phone.

Participatory Culture The Internet has shattered the traditional hierarchical boundaries between producers and consumers

of information. Nowadays, millions of youth are both reading and creating material posted on Websites related to topics of their interest. For Jenkins (2006), this type of engagement presents an example of a participatory culture where young people become actively involved in constructing their own virtual spaces from which they can critically examine the real space of adults. Social networks’ participants “are also becoming active producers of new media and distributing them in global networked publics” (Zemmels, 2012, p. 17). In an attempt to find overlaps/connections between online and offline youth cultures, Wilson and Atkinson (2005) investigated how the Internet has been integrated into the everyday subculture of Canadian youth. After analyzing two cases, the ‘Rave’ and the ‘Straightedge’, the authors concluded that the Internet is used as a device for bringing people together in real time and as an enabler of subcultural resistance. For example, music has a particular strong bonding role in youth communities; it is useful in providing group identity and in promoting a distinct lifestyle. The availability of various media formats, such as MP3 or MPEG audio files placed on Websites for free download play a great role in this matter. Various social groups, formally labeled by the mainstream culture as misfits, succeed in winning a cultural space, albeit a virtual one. Wilson and Atkinson concluded that youth culture in the age of the Internet could be viewed not only as “more fragmented, diffuse, and neo-tribal than traditional subcultures...but also as more cohesive in the sense that virtual connections can enhance local relationships” (p. 305).

Political Engagement Through social networking Websites (e.g., Facebook, MySpace), youth are emerging as important factors in political and social change. In their study of 1875 middle-school students from Hong Kong, Seoul, Singapore, Taipei, and Tokyo, Lin, Cheong, Kim, and Jung (2010) investigated youth engagement with social issues. The researchers

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found that about half of the teens read news on the Internet, visited charity, environment, or government Websites, voted to reflect their opinions, and/or signed a petition online (one in five youth). While teens preferred to use these virtual spaces to discuss entertainment, sports, or shopping, social networking sites also served as “potential mobilizing forces” (p. 852). For youth who are already interested in politics and are civic-minded, the Internet may function as an amplifier of engagement, albeit in “unconventional ways” (p. 843). However, Lin et al. (2010) also referred to other researchers (e.g., Livingstone, Bober, & Helsper, 2004) who noted that the U.K. teens they studied had little interest in political engagement on the Internet because they thought it was not very likely that politicians would ever listen to them. Yet Livingstone et al. (2004), too, found that 54% of the teens had visited either charity, government, environment, and human rights sites or Websites for improving conditions at school or work. Nowadays, youth are involved in international groups where they discuss and organize events related to environmental (e.g., the Earth Hour) and political (e.g., Relay For Life) causes. In other words, youth deploy the technology to spread their political and social views, promote environmental awareness, and raise funds for their favorite causes or charities (Maranto & Barton, 2010). Still, as Wilson and Atkinson (2005) pointed out, violent confrontations between individuals and groups at public spectacles also get arranged in advance through the Internet. Also, students who are influenced by the existence of social peer in-groups in their schools (e.g., geeks, jocks, fashionistas) may take the same exclusionary attitude on the Internet and resist connecting with those who belong to groups other than their own.

SOLUTIONS AND RECOMMENDATIONS Whatever we say and write about the youth and their habits, connectivity remains the key, which has led some authors to name them the connected 2320

generation (Mason & Rennie, 2006)—that is, the generation that is involved in a wide spectrum of online activities, including buying online. While the positive attributes of connectivity are well recognized, the authors see it also as a trap, especially for youth; instant messaging could be a distraction to learning, extensive use of chats and multiuser discussions could be associated with impaired academic achievement and social isolation, as well as privacy could be lost through providing access to personal data to unintended recipients. Providing solutions and recommendations is problematic, as digital literacy phenomena are quickly changing, as the ICTs and practices that surround them change. Davies and Cranston (2008) proposed using social networking sites as tools to encourage youth to become more socialized and to learn something informally. They acknowledged, however, that “something” learned may well be undesirable, ranging from relatively benign commercial advertisements to content that does not contribute to healthy social and cognitive development. Thus, young people need supports in developing “appropriate skills and resilience to navigate online social networking risks and opportunities” and developing “shared understandings of safe and reasonable online behaviour patterns” (p. 3). This suggestion extends beyond the simple blocking of social networking sites and instead encourages working with youth as an alternative. In a recent paper, Vodanovich et al. (2015) point at the lack of holistic approach to fully grasp the impact of social networking sites on digital natives’ well-being, in particular their social competence. The authors point at an important role that ubiquitous spaces play in a complex processes of youth becoming independent from their parents, gaining social acceptance from their peers and establishing a coherent identity. Hence, the role of adults is to understand, appreciate and in turn support digital natives in making the all-important transition to adulthood within this new environment.

Category: Digital Literacy

FUTURE RESEARCH DIRECTIONS Our work has allowed us to identify issues related to youth’s understanding and respecting online social norms and values. But these issues easily become more complex and subtle when we take into consideration meta-social skills (identity formation, friendship, self-esteem, autonomy, etc.). We found that technologies do affect the development of well-rounded citizens of modern society in different ways, and that this development is not easy to capture and assess because no ready-to-use and evidence-based developmental frameworks exist. There is a conceptual and methodological gap between the need for such frameworks and reliable data available to support their development and validation across cultures, age groups, social groups, and individuals.

CONCLUSION Young people nowadays face challenges that require development of healthy social behavior with regard to online identity formation and protection of privacy. Some positive aspects are seen in the rising of a participatory culture among youth, whereby they use virtual spaces to critically examine the real space of adults. Youth subcultures emerge, and often-marginalized youth find ways to exercise creative expression and shape their own identity. Moreover, the ICTs allow young people to emerge as legitimate social and political actors. Using unconventional ways of engagement, young people surface as political beings and as individuals interested in civic and environmental issues. Some ICT-related addictions (e.g., to computer games, to Facebook, to text messaging) may disappear on their own, while for some problems, interventions may be needed. For example, adolescents may spend less time playing computer games when they start developing an interest in dating and socializing face-to-face. It seems that youth need to be

educated about the importance of maintaining a balance between their real and virtual lives. Organizing outings and clubs where youth play, compete, and socialize may counteract to some extent the allure of the technology that youth are exposed to.

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Harman, J. P., Hansen, C. E., Cochran, M. E., & Lindsey, C. R. (2005). Liar, liar: Internet faking but not frequency of use affects social skills, selfesteem, social anxiety, and aggression. Cyberpsychology & Behavior, 8(1), 1–6. doi:10.1089/ cpb.2005.8.1 PMID:15738687 Henke, L., & Fontenot, G. (2007). Children and Internet use: Perceptions of advertising, privacy, and functional displacement. Journal of Business and Economics Research, 5(11), 59–66. Hundley, H. L., & Shyles, L. (2010). US teenagers perceptions and awareness of digital technology: A focus group approach. New Media & Society, 12(3), 417–433. doi:10.1177/1461444809342558 Internet World Stats. (2012). Retrieved from http:// www.internetworldstats.com/stats2.htm Jenkins, H. (2006). Confronting the challenges of participatory culture: Media education for the 21st century. The John D. and Catherine T. MacArthur Foundation series on digital media and learning. Retrieved from digitallearning.macfound.org/.../ JENKINS_WHITE_PAPER.PDF Lenhart, A., & Madden, M. (2007). Social networking websites and teens. Pew Research Center. Retrieved from http://www.pewinternet.org/~/ media//Files/Reports/2007/PIP_SNS_Data_ Memo_Jan_2007.pdf.pdf Lin, W.-Y., Cheong, P. H., Kim, Y.-C., & Jung, J.-Y. (2010). Becoming citizens: Youths civic uses of new media in five digital cities in East Asia. Journal of Adolescent Research, 25(6), 839–857. doi:10.1177/0743558410371125 Livingstone, S. (2008). Taking risky opportunities in youthful content creation: Teenagers use of social networking sites for intimacy, privacy and self-expression. New Media & Society, 10(3), 393–411. doi:10.1177/1461444808089415 Livingstone, S., Bober, M., & Helsper, E. J. (2004). Active participation or just more information? Young people’s take up of opportunities to act and interact on the Internet. London: London School of Economics and Political Science. 2322

Mahiri, J. (2004). New literacies in new century. In J. C. Mahiri (Ed.), What they don’t learn in school: Literacies in the lives of urban youth. New York, NY: Peter Lang Publishing Inc. Maranto, G., & Barton, M. (2010). Paradox and promise: MySpace, Facebook, and the sociopolitics of social networking in the writing classroom. Computers and Composition, 27(1), 36–47. doi:10.1016/j.compcom.2009.11.003 Martinovic, D., Freiman, V., & Karadag, Z. (2011). Child and Youth Development beyond Age 6—Transitions to Digitally Literate Adults. Unpublished report, Ministry of Child and Youth Services, Ontario, Canada. Martinovic, D., Freiman, V., Lekule, C., & Yang, Y. (2014). Social Aspects of Digital Literacy. In The Third Edition of Encyclopedia of Information Science and Technology. IGI Global. Martinovic, D., & Magliaro, J. (2007). Computer networks and globalization. Brock Education Journal., 16(2), 29–37. Marwick, A. E., Murgia-Diaz, D., & Palfrey, J. (2010). Youth, privacy and reputation: Literature review. The Berkman Center for Internet & Society Research Publication Series. Retrieved from http://ssrn.com/abstract=1588163 Mason, R., & Rennie, F. (2006). Elearning: The key concepts. Key Guides. Oxon, UK: Routledge. Media Awareness Network. (2001−2012). Young Canadians in a Wired World. Retrieved from http:// mediasmarts.ca/research-policy Mesch, G. S. (2009). Social context and communication channels choice among adolescents. Computers in Human Behavior, 25(1), 244–251. doi:10.1016/j.chb.2008.09.007 Mitchell, K. J., Ybarra, M., & Finkelhor, D. (2007). The relative importance of online victimization in understanding depression, delinquency and substance use. Child Maltreatment, 12(4), 314–324. doi:10.1177/1077559507305996 PMID:17954938

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Noveck, B. S. (2000). Paradoxical partners: Electronic communication and electronic democracy. In P. Ferdinand (Ed.), The Internet Democracy and Democratization (pp. 18–36). London, UK: Frank Cass. doi:10.1080/13510340008403643

Wilson, B., & Atkinson, M. (2005). Rave and Straightedge, the virtual and the real: Exploring online and offline experiences in Canadian youth subcultures. Youth & Society, 36(3), 276–311. doi:10.1177/0044118X03260498

Patton, D. U., Hong, J. S., Ranney, M., Patel, S., Kelley, C., Eschmann, R., & Washington, T. (2014). Social media as a vector for youth violence: A review of the literature. Computers in Human Behavior, 35, 548–553. doi:10.1016/j. chb.2014.02.043

Xie, W. (2014). Social network site use, mobile personal talk and social capital among teenagers. Computers in Human Behavior, 41, 228–235. doi:10.1016/j.chb.2014.09.042

Seibel-Trainor, J. (2004). Critical cyberliteracy: Reading and writing the x-Files. In J. C. Mahiri (Ed.), What they don’t learn in school: Literacies in the lives of urban youth. New York, NY: Peter Lang Publishing Inc. Stout, H. (2010, Apr. 30). Antisocial networking? The New York Times, Fashion and Style. Retrieved from http://www.nytimes.com/2010/05/02/ fashion/02BEST.html Valkenburg, P. M., & Peter, J. (2007). Internet communication and its relation to well-being: Identifying some underlying mechanisms. Media Psychology, 9(1), 43–58. doi:10.1080/15213260709336802 Valkenburg, P. M., Schouten, A. P., & Peter, J. (2005). Adolescents identity experiments on the Internet. New Media & Society, 7(3), 383–402. doi:10.1177/1461444805052282 Vodanovich, S., Shen, K., & Sundaram, D. (2015). Social competence of digital natives: Impact of Social Networking Sites (SNS) use. In AMCIS 2015 Proceedings (pp. 1–9). Association for Information Systems. West, A., Lewis, J., & Currie, P. (2009). Students Facebook friends: Public and private spheres. Journal of Youth Studies, 12(6), 615–627. doi:10.1080/13676260902960752

Ybarra, M., Diener-West, M., & Leaf, P. (2007). Examining the overlap in Internet harassment and school bullying: Implications for school intervention. The Journal of Adolescent Health, 41(6), 42–50. doi:10.1016/j.jadohealth.2007.09.004 PMID:18047944 Zemmels, D. R. (2012). Youth and new media: Studying identity and meaning in an evolving media environment. Communication Research Trends, 31(4), 4−22. Retrieved from http:// search.proquest.com/docview/1349959575?acc ountid=14592

ADDITIONAL READING Albirini, A. (2008). The Internet in developing countries: a medium of economic, cultural and political domination. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 4(1), 49−65. American Library Association Presidential Committee on Information Literacy. (1989). Final Report. Chicago, IL: American Library Association. Aufderheide, P. (1992). Media Literacy. A Report of the National Leadership Conference on Media Literacy. Washington, DC: Aspen Institute. Retrieved from http://www.medialit.org/reading_room/article356.html

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Bannon, S., McGlynn, T., McKenzie, K., & Quayle, E. (2015). The positive role of Internet use for young people with additional support needs: Identity and connectedness. Computers in Human Behavior, 53, 504–514. doi:10.1016/j. chb.2014.11.099 Bauer, J., & Kenton, J. (2005). Toward technology integration in the schools: Why it isn’t happening. Journal of Technology and Teacher Education, 13(4), 519–546. Bawden, D. (2008), Origins and concepts of digital literacy. In C. Lankshear, and M. Knobel, M. (Eds.), Digital Literacies: Concepts, policies and practices (pp. 17−32). New York, NY: Peter Lang. Bunker, B. (2010). A Summary of international reports, research and case studies of digital literacy: Including implications for New Zealand of adopting a globally-recognised digital literacy standard. New Zealand: Computer Society Inc. Dijst, M. (2004). ICTs and accessibility: An action space perspective on the impact of new information and communication technologies. In M. Beuthe, V. Himanen, A. Reggiani, & L. Zamparini (Eds.), Transport Developments and Innovations in an Evolving World (pp. 27–46). Berlin: Springer. doi:10.1007/978-3-540-24827-9_3 Eugster, P. T., Felber, P. A., Guerraoui, R., & Kermarrec, A.-M. (2003). The many faces of publish/subscribe. ACM Computing Surveys, 35(2), 114–131. doi:10.1145/857076.857078 Huang, C., & Lin, C. (2011). Enhancing classroom interactivity and engagement: CFL Learners perceptions of the application of Web 2.0 technology. British Journal of Educational Technology, 42(6), E141–E144. doi:10.1111/j.14678535.2011.01219.x International Visual Literacy Association. (2009). What is ‘Visual Literacy? Retrieved from http:// www.ivla.org/orgwhatvislit.htm

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Knutsson, O., Blåsjö, M., Hållsten, S., & Karlström, P. (2012). Identifying different registers of digital literacy in virtual learning environments. The Internet and Higher Education, 15(4), 237– 246. doi:10.1016/j.iheduc.2011.11.002 Koltay, T. (2011). The media and the literacies: Media literacy, information literacy, digital literacy. Media Culture & Society, 33(2), 211–221. doi:10.1177/0163443710393382 Livingstone, S., & Helsper, E. J. (2007). Gradations in digital inclusion: Children, young people and the digital divide. New Media & Society, 9(4), 671–696. doi:10.1177/1461444807080335 Martin, A. (2005). DigEuLit—a European framework for digital literacy: A progress report. Journal of eLiteracy, 2, 130−136. Martin, A. (2006). Literacies for the Digital Age. In A. Martin & D. Madigan (Eds.), Digital literacies for learning (pp. 3–25). London, UK: Facet. Martin, A. (2009). Digital literacy for the Third Age: Sustaining identity in an uncertain world. eLearning Papers, 12. Retrieved from www. elearningpapers.eu Oliver, M., & Gourlay, L. (2012). Un-defining digital literacies: Students’ day-to-day engagements with technologies. EdTech Conference 2012, NUI Maynooth, 31st May 2012. Retrieved from http://www.slideshare.net/lesleygourlay/ oliver-gourlay-keynote-ed-tech-maynooth-1 Opgenhaffen, M., & d’Haenens, L. (2012). Heterogeneity within homogeneity: Impact of online skills on the use of online news media and interactive news features. Communications—the European Journal of Communication Research, 37(3), 297−316. Richtel, M. (2010, November 20). E-Mail Gets an Instant Makeover. The New York Times. Retrieved from http://www.nytimes.com/2010/12/21/ technology/21email.html?_r=0

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Schnellert, G., & Keengwe, J. (2012). Digital technology integration in American public schools. International Journal of Information and Communication Technology Education, 8(1), 36–44. doi:10.4018/jicte.2012070105 Simsek, E., & Simsek, A. (2013). New literacies for digital citizenship. Journal of Educational Technology, 4(3), 126–137. Watson, J. A., & Pecchioni, L. L. (2011). Digital natives and digital media in the college classroom: Assignment design and impacts on student learning. Educational Media International, 48(4), 307–320. doi:10.1080/09523987.2011.632278 Wilson, E. K., Wright, V. H., Inman, C. T., & Matherson, L. H. (2011). Retooling the social studies classroom for the current generation. Social Studies, 102(2), 65–72. doi:10.1080/003 77996.2010.484445 Wodzicki, K., Schwammlein, E., & Moskaliuk, J. (2012). Actually, I wanted to learn: Study-related knowledge exchange on social networking sites. The Internet and Higher Education, 15(1), 9–14. doi:10.1016/j.iheduc.2011.05.008

KEY TERMS AND DEFINITIONS Active Users of the ICTs: Individuals, groups and organizations that utilize e-mail, instant messaging, chat rooms, access and download media content, write blogs, and play online games. Developmental Stages of Digital Literacy in the Social Domain: Stages through which an individual, over time, acquires the digital literacy skills necessary to be considered competent at using digital tools and at defining, accessing, understanding, creating, and communicating digital information.

Digital Literacy (as a Complex Term): One of many constructs that involve the word “literacy”; it includes elements drawn from information literacy, media literacy, and visual literacy. Digital Literacy (as Ability): Ability to use digital technologies appropriately for learning, working, and functioning in a modern society. Information and Communication Technologies: ICTs include personal computers, laptops, tablets, PDAs, cell phones, smart phones, computer networks, and the Internet. Information Literacy: Emphasizes the need for careful retrieval and selection of information available in the workplace, at school, and in all aspects of personal decision-making, especially in the areas of citizenship and health. Media Literacy: Ability to decode, evaluate, analyze and produce both print and electronic media. Participatory Culture: A culture in which artistic expression and civic engagement are valued, oriented towards creating and sharing one’s creations. Social Capital in Online Communication: Social capital developed through online activities and participation in online social networks (e.g., text messaging, chatting, content sharing, organizing group activities, commenting, and tagging). Social Capital: Quality of group membership. Measured by for example, the frequency of faceto-face interactions with close friends, the number of offline acquaintances, sense of loneliness or lack thereof, community involvement, civic and political participation, and interpersonal trust. Visual Literacy: Ability to see, discriminate, and interpret the visible natural or artificial objects and symbols in the environment.

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Toward a Working Definition of Digital Literacy Margaret-Mary Sulentic Dowell Louisiana State University, USA

INTRODUCTION Digital literacy (Alkali & Amichai-Hamburger, 2004; Bawden, 2008; Buckingham, 2006; Gilster, 1997) is a broad, umbrella term that pertains to the use of literacy skills defined as reading, writing, listening, speaking, composing, communicating, and interacting within digital environments. For example, accessing information and sending information via the internet such as viewing and posting YouTube videos or creating, sending, and receiving e-mails is digital literacy. As well, anime, manga, blogging, fandom blogging, texting, tweeting, designing memes, sharing headcannons, and other forms of creating ideas and communicating perspectives through social media platforms such as Facebook, twitter, Tumblr, and myriad others ways to share thoughts and opinions over the internet or in cyberspace, all qualify as digital literacy (Beach, 2012; Black, 2005; Booth, 2012; Martin & Madigan, 2006; Kist, Tollafield, & Dagistan, 2014; Rodesiler, 2015). Also referred to as new literacies (Coiro, Knobel, Lankshear, & Leu, 2007; 2008; Hagood (2009), Knobel & Lankshear, 2014; Lankshear & Knobel, 2006; Street, 1998), digital literacy implies both the technical ability and emotional skill level needed to generate thought and communicate in multiple formats within digital environments (Elshet-Alkalai, 2004; Landham, 1995). In particular, both the consumption and generation of text and the practices used to create and consume them, formally and informally, both outside and within school, broadly define new literacies. According to Hagood,

New literacies consist of several characteristics: (1) multimodalities, which include linguistic as well as visual, gestural, and auditory texts, (2) situated social practices, which are culturally, linguistically, and textually based, and (3) identities, which connect text users to text uses. (2009, p. 1)

BACKGROUND: A WORKING DEFINITION OF DIGITAL LITERACY Digital literacy is a complex combination of skill sets defined as the knowledge, technical skills, use, actions, and behaviors that individuals utilize with existing myriad digital and technological devices and resultant forms of communication that have become an integral part of so many people’s daily lives. For instance, every day use of a cellphone, smartphone or perhaps a tablet involve digital literacy skill sets. Thus, digital literacy implies the mastery of both the tools and embedded use of technology in personal lives. Digital literacy is also the recognition that digital forms of literacy and the aforementioned skill sets play an essential, critical role in educational and work settings, particularly the reasoned awareness regarding the content that is created and its use in digital literacy formats. Lanham (1995) argues that the notion of being literate has extended from the ability to read and write, speak and listen, to the ability to understand information that is available and accessible in multimodal ways. Being literate in the 21st century requires being skilled at interpreting complex images and discerning the “syntactical subtleties of words” (Lanham, 1995,

DOI: 10.4018/978-1-5225-2255-3.ch202 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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p. 161). Whereas historically literacy has included the notion of both composing and comprehending language, defined as the ability to speak, listen, read, and write, literacy has evolved to include a “social practices” approach from scholars within the emerging educational field of New Literacies (Barton, Hamilton, & Ivanic, 2000; Gee, 2001; Street, 1993). Similar to Lanham, Alkali and Amichai-Hamburger (2004) provide an inclusive, comprehensive definition of digital literacy specifically referring to much more than the ability and skill needed to simply use and navigate a digital device or software, rather, digital literacy requires complex thinking skills as well as critical decision-making ability. The kind of multifaceted thinking required of digital literacy equates to the knowledge beyond how to operate digital devices, access information, or utilize software and refers to the complicated cognitive abilities, requisite motor skills, as well as the sociological and emotional maturity needed to navigate digital environments effectively, usefully, and appropriately. A conceptual model detailed by the authors proposes that digital literacy encompasses five essential digital skills: photo-visual skill – gaining understanding from graphic displays of information; reproduction skill – wherein individuals employ digital reproduction expertise to recreate or craft innovative, significant materials from pre-existing materials; branching skills – defined as building and fashioning knowledge from non-linear, hypertextual navigation); information skills – both assessing and gauging the quality and legitimacy of information), and socio-emotional skills – the mindfulness of the tacit rules that exist and are in place in cyberspace and being able to apply this awareness in online cyberspace communications (Alkali & Amichai-Hamburger, 2004).

Expansion of Digital Literacy Digital literacy has significant implications for society as a whole and for learning and teaching specifically as new forms of communicating

become the norm (Davies & Merchant, 2009; Gilsten, 1997; Hunter & Caraway, 2014; Pool, 1997). As a practical illustration of what was once a new form of communication becoming the norm, twenty years ago, most households in the United States (US) had landlines. Currently, many US homeowners have discontinued a landline and exclusively use a cell phone. In fact, the various platforms and constructs of social media for the current generation of students equates to most students using at least one form of social media as part of their daily lives (Jenkins, 2006; Lapp, Fisher, Frey, & Gonzalez, 2014; New Media Consortium, 2007). These students will matriculate to a career and the work force, taking with them the expectations of using digital literacies. As an example of the burgeoning use of digital literacy, Rideout, Foehr and Roberts, assert that, “Eight to eighteen-year-olds spend more time with media than in any other activity besides (maybe) sleeping—an average of more than 7½ hours a day, seven days a week.” (2010, p. 1). Rideout, Foehr and Roberts surveyed a nationally representative sample of 2,002 3rd–12th grade students, ages 8–18 in the US, which contained a subsample of 702 respondents who voluntarily completed week-long (seven-day) media use diaries. Conducted from October 20, 2008, through May 7, 2009, they report that 8 to 18 year olds were spending approximately 6.5 hours with media (cell phones, internet, television, gaming, music) but because these students were adept at multitasking, they were engaged in closer to 8.5 hours of media content daily. According to the authors, based on a survey sample from 2005 to 2010, young people between the ages of 8–18 increased their media consumption by an hour and 17 minutes to 7.5 hours daily, the average work day for most adults. Considering that adults tend to work 40 hour weeks within a five day span, the consumption detailed in Rideout, Foehr & Roberts’ work, represents an incredible amount of time spent with digital literacy. Clearly, the research of Black, (2008), Jenkins (2006), Lapp, Fisher, Frey, and Gonzalez (2014), the New Media

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Consortium (2007), Rideout, Foehr & Roberts (2010), and Squire 92008) highlight the pervasiveness of digital literacy practices and depth of participation within a specific segment of US population. Researchers in the United Kingdom (Martin & Madigan, 2006; Street, 1984), Canada (Gilster, 1997), the Netherlands (Kuiper & Volman, 2008), and Australia (Lankshear & Knobel, 2006; 2008; 2012; Ng, 2006; 2010; Thibaut, 2015) also investigate the meaning, use, and prevalence of digital literacy, and postulate that expanding usage will occur and rapid developments will be accessed by younger and younger consumers of digital literacy. The implications for society at large are significant.

DIGITAL LITERACY WITHIN A SOCIO-CULTURAL FRAMEWORK As an overarching framework, Russian theorist, Vygotsky’s (1978) sociocultural theory of learning and the social nature of learning coupled with the mediated aspects of human learning situate the discussion of digital learning. Vygotsky’s view of learning is rooted in the social interactions between people and resultant learning that may occur, referred to as the zone of proximal development. French theorist Baudrillard (1990), provides a compatible framework based on his notion of media and culture in the ways that technological progress affects social change. Both Vygotsky and Baudrillard focus on social interactions, providing a link to the powerful ways digital literacy is situated in socio-cultural environs. Similarly, the work of Beach (2012) and Hagood (2000) informs the perspective that learning is situated, occurs within a socio-cultural context that is meaningful to the learner(s), and not confined to classroom walls and school. Each advocate for literacy that is social, collaborative, and relational, requisites of digital literacy. Drawing upon the work of Project Tomorrow (2012), Beach addresses the outside versus inside school contexts of leaning, supporting learning

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as social activity and occurring in environments that are meaningful and constructed by learners. Beach confronts a “false binary between uses of digital tools for school and non-school social purposes, adolescents are increasingly adopting tools to support their learning.” (2012, p. 46). According to Beach, real differences between in and out of school literacy points to students engaging in reading and writing within schools to fulfill graded assignments versus reading and writing outside of school as opportunities for both self-expression and identity construction as well a social communication activity and seeking specific information that is personally meaningful. The social aspect of students’ literacy outside of school is rooted in socially-constructed communication, and essentially addresses primary constructs of writing – audience and purpose – first established by Graves (1983). In essence, Beach confronts the notion of informal learning, learning marked by where it occurs, challenging the less than perception of where learning and literacy occurs and for what purposes. Hagood (2000) argues that the literacy practices that occur outside of formal schooling are equally important as those that are practiced within formal schooling. Perceiving an evolving literacy landscape that encompasses digital literacy forms and formats, Hagood explicates how literacy is at a crossroad, acknowledging traditional views of literacy that are socio-cultural in nature but maintaining that new literacies are evolving into deep social practices that emphasize social and cultural relationships. In many educational contexts, social media is a viable means to engage students in their learning creating possibilities to collaborate and offering opportunities to connect with peers; the digital literacies afforded by social media also provides ample opportunity for informal learning (Beach, 2012; 2012; Jenkins, Clinton, Purushotma, Robnson & Weigel, 2007; Junco, Heiberger & Loken, 2011; Kilinc, Evans, & Korkmaz, 2012; Lanksher, 1987; Street, 1984; Thibaut, 2015). In fact, there is a long tradition of literacy practices

Category: Digital Literacy

wherein communicating effectively with others in meaningful ways is situated outside schools and traditional modes of learning (Heath, 1983).

NEW LITERACIES, NEW ISSUES: THE DIGITAL LITERACY IMPACT Situated within the field of education, three prominent digital literacy issues have surfaced. Those issues are: 1. The dissonance between digital natives and digital immigrants, 2. Some forms of digital literacy enjoy acceptance and legitimacy, and 3. Access to digital literacy formats.

Digital Native or a Digital Immigrant? Digital natives or the E-generation (Jones & Flannigan, 2006) and digital immigrants, oftentimes identified as baby-boomers and the generations of adults who grew up in a world of print media (Jones & Flannigan, 2006), are defined broadly as those who have acquired digital literacy skill as children versus those who have acquired digital literacy skill in adulthood (Anderson, 2002; Bennett, Maton & Kervin; 2008; Jones & Flannigan, 2006; Prensky, 2001). This is a generational issue regarding when within a lifetime literacy is acquired and is noteworthy. Prensky (2001), credited with coining the terms digital native – digital immigrant, posits that education must keep pace with the rapid evolution and expansion of digital literacy in order to adequately prepare children for careers and a workforce that will expect sophisticated forms of digital literacy knowledge and skill. Prensky illustrates the dichotomy of traditional teaching practice with the demands of digital literacy which will continue to evolve and expand, calling for a reassessment of literacy teaching and learning. Generational shifts in literacy acquisition, increasingly sophisticated information processing, and rapidly evolving forms of digital literacy

uniquely impacts teacher preparation. In essence, 21st century teacher preparation programs and teacher educators face a conundrum; these preparation programs are preparing teacher education candidates for an unknown future. And these future teachers are charged with preparing children for unknown digitally literate futures and career requirements.

What Counts as Literacy? Certain forms of digital literacy enjoy legitimacy and are both expected and accepted within school environments while other forms are not deemed suitable yet appear more prevalent outside of school (Alverman & Hagood, 2000; Beach, 2012; Black, 2005; Black 2008; Jenkins 1992; Kilinc, Evans & Korkmaz, 2012; Mills & Chandra, 2011, Squire, 2008). In the ultra-traditional model of schooling, typified by nine-month agrarian calendars, separated subjects, and the factory model of attendance wherein the most children appear to have in common and how they are grouped is a birthdate, computer generated assignments, word processed writing, and research via the Internet or web are accepted practice, within limits. Teachers still require children to generate writing on paper, some internet sites are restricted within schools, and certain digital literacy forms of expression are not accepted. Related to the claims of Prensky (2001), the aforementioned researchers recommend that what constitutes literacy and literacy practice broaden to include new and evolving forms of literacy. In essence, traditional literacy practice should expand to include new literacies such as anime blogs, gaming, memes, fan fiction, headcannons, and multimodal forms of expression. Considering how the Internet alone has shifted literacies, emerging literacies must be valued. As literacy shifts at an increasingly rapid rate, these advances must be incorporated into learning environments. Blogs are capable of replacing the traditions of journaling; memes can be accepted ways to respond to events, headcannons can serve as a viable methods of responding to literature, anime and manga can be viewed as appropriate 2329

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forms of creative expression, and gaming within classrooms can be suitable practice. As digital literacy evolves, new and yet-to-be created forms and practices of digital literacy will need to be incorporated into schooling, career expectations and the work place. Digital literacy experts propose that the current generation of teenagers, defined as digital natives of the E-Generation, are more likely to acquire or have acquired the digital competencies needed to effectively traverse multi-dimensional and quickly evolving digital environments. For many adults who grew up in a world of books, magazines, and print literacy, negotiating cyberspace can be uncomfortable. Prensky (2001) claims that individuals who lack digital literacy struggle to live in an unfamiliar world.

Access to Opportunity: The Digital Divide or Digital Literacy as a New Gatekeeper Access to digital literacy, referred to as the digital divide, has emerged as an issue (DiMaggio & Hargittai; 2001; Flood, Heath & Lapp, 2015; Norris, 2001; Servon, 2008: Warschauer, 2004). In particular, the digital divide threatens to widen existing achievement gaps and further marginalize students who lack access to digital forms of literacy. While access is disparate, especially in urban, rural, and suburban areas, the differences between countries are alarming given rhetoric about global economy and Earth as a global village. Consider that Coiro, Knobel, Lankshear and Leu report that 99% of US classrooms have access the Internet, the European Union reports 96% Internet access compared to Brazilian schools estimating 26% access and Greece and Poland having 13 to 31% Internet access, while only 5% of Mexican schools were projected to have web access; accessibility rates in sub-Saharan Africa could not be calculated (2008, p. 4). While schools exhibit one view of accessibility, homes and communities can present a differing perspective. Access is tied to economics, whether within or

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between geographical areas of a country. Infrastructures are expensive and economics impact access. Despite the promise of global connectivity and immediacy, the issue of access is a gatekeeper to the opportunities afforded by digital literacy. Coiro, Knobel, Lankshear and Leu recognize the possibilities afforded by digital literacy when they state: “A global network such as the Internet makes it possible to develop and immediately disseminate a new technology of literacy to every person who chooses to access it.” (2008, p. 5). However, choosing to access and being able to access digital literacy is closely tied to economics. In much that same way that older, traditional notions of literacy were tied to having to access books and materials with which to practice, income can impact accessibility to digital literacy opportunities, especially in poor rural and urban schools in the US.

IMPLICATIONS While digital literacy contains the promise of enhanced learning opportunity, DiMaggio and Hargittai postulate “Internet access is an important resource and inequality in Internet access is a significant public policy issue.” (2001, p. 3). As economics emerges as a gatekeeper to digital literacy access, communities and governing structures whether they be schools, school system, families, or countries, will have to grapple with how to provide access to the opportunities digital literacy affords. Digital literacy has huge implications for teaching, learning, and career opportunities, and these issues correlate to economics. Beach (2012) reiterates that educational practice in the US impacts digital literacy acquisition, calling for a redefined, flexible criteria for assessing student learning that includes myriad forms rather than relying only on criteria created for and linked with traditional print literacies. This implies a teaching force that is current with digital literacies, administration that understands what digital literacy is

Category: Digital Literacy

and how it functions within school environments, and school systems that invest in digital technologies. Lewis extends Beach’s argument, stating that teachers and digital literacy researchers, “need to know what writers of new literacies do when they write—what they think about and how they negotiate the demands of new forms and processes of writing.” (2007, p. 229). Economics, access, and education are topics impacted by digital literacy in the US. As traditional literacy, especially print literacy, was once deemed a necessity, Eshet-Alkalai (2004) reminds us that digital literacy skills are becoming basic literacy survival skills for the 21st century. Therefore, educational practices and the use and acceptance of digital literacy skills can also have an impact on work force development.

FUTURE RESEARCH DIRECTIONS Given that technology and digital literacy will continue to expand, three areas of research should be considered. Research investigating the dissonance between digital natives and digital immigrants, especially in school environments and the workforce should be explored. Research about how children at different ages use digital literacy should be continued. As well, research into why and how some forms of digital literacy enjoy acceptance and legitimacy, particularly in school environs, should be conducted. Finally, access to digital literacy formats, whether it be in schools, in communities, in geographical areas or within the borders of countries should be investigated as well as any restrictions to access that may fall along gender, ethnic/racial, and economic lines.

CONCLUSION Regarding digital literacy, change is a constant. Shifts in digital literacy within the past 20 years have greatly impacted how people communicate, how teachers teach and children learn in the US and

elsewhere, and the expectations and demands of career and work. Physical space has been removed as a construct and communication and creativity have flourished. Given the rapid evolution of digital literacy, while we do not know how it may evolve, we can know it will evolve, exponentially and in ways not yet imagined.

REFERENCES Alkali, Y., & Amichai-Hamburger, Y. (2004). Experiments in digital literacy. Cyberpsychology & Behavior, 7(4), 421–429. doi:10.1089/ cpb.2004.7.421 PMID:15331029 Alverman, D., & Hagood, M. (2000). Fandom and critical media literacy. Journal of Adolescent & Adult Literacy, 43(5), 436–446. Anderson, N. (2002). New Media and new media literacy: The horizon has become the landscape – new media are here. Report produced by Cable in the Classroom. Barton, D., Hamilton, M., & Ivanic, R. (2000). Situated literacies: Reading and writing in context. New York, NY: Routledge. Baudrillard, J. (1990). Mass media culture. In Revenge of the crystal: Selected writings on the modern object and its destiny, 1968–1983. Academic Press. Bawden, D. (2008). Origins and concepts of digital literacy. In C. Lankshear & M. Knobel (Eds.), Digital literacies: Concepts, policies and practices, (pp. 17-32). New York, NY: Peter Lang. Beach, R. (2012). Uses of digital tools and literacies in the English language arts classroom. Research in the Schools, 19(1), 45–59. Bennett, S., Maton, K., & Kervin, L. (2008). The digital natives debate: A critical review of the evidence. British Journal of Educational Technology, 39(5), 775–786. doi:10.1111/j.14678535.2007.00793.x

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Berners-Lee, T. (1989). Information Management: A Proposal. Retrieved from http://www.w3.org/ History/1989/proposal.html Black, R. W. (2005). Access and affiliation: The literacy and composition practices of Englishlanguage learners in an online fan fiction community. Journal of Adult & Adolescent Literacy, 49(2), 118–128. doi:10.1598/JAAL.49.2.4 Black, R. W. (2008). Just don’t call them cartoons: The new literacy spaces of anime, manga, and fanfiction. In J. Coiro, M. Knobel, C. Lankshear, & D. Leu (Eds.), Handbook of research on new literacies (pp. 583–610). New York, NY: Routledge. Booth, S. E. (2012). Cultivating knowledge sharing and trust in online communities for educators. Journal of Educational Computing Research, 47(1), 1–31. doi:10.2190/EC.47.1.a Buckingham, D. (2006). Defining digital literacy. Digital Kompetanse: Nordic Journal of Digital Literacy, 1(4), 263–276. Coiro, J., Knobel, M., Lankshear, C., & Leu, D. (2008). Central issues in new literacies and new literacies research. In J. Coiro, M. Knobel, C. Lankshear, & D. Leu (Eds.), Handbook of research on new literacies (pp. 1–21). New York, NY: Routledge. Davies, J., & Merchant, G. (2007). Looking from the inside out: Academic blogging as new literacies. In C. Lankshear & M. Knobel (Eds.), New literacies sampler (pp. 167–197). New York, NY: Peter Lang. Davies, J., & Merchant, G. (2009). Web 2.0 for schools: Social participation and learning. New York: Peter Lang. DiMaggio, P., & Hargittai, E. (2001). From the ‘digital divide’to ‘digital inequality’: Studying Internet use as penetration increases. Princeton, NJ: Center for Arts and Cultural Policy Studies, Woodrow Wilson School, Princeton University.

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Eshet-Alkalai, Y. (2004). Digital literacy: A conceptual framework for survival skills in the digital era. Journal of Educational Multimedia and Hypermedia, 13(1), 93. Flood, J., Heath, S. B., & Lapp, D. (2015). Handbook of Research on Teaching Literacy through the Communicative and Visual Arts, Volume II: A Project of the International Reading Association. Routledge. Gee, J. (2001). Identity as an analytic lens for research in education. In W. Secanda (Ed.), Review of research in education (Vol. 25, pp. 99–125). Washington, DC: American Educational Research Association. Gilster, P. (1997). A primer on digital literacy. Mississauga, Canada: John Wiley & Sons. Graves, D. (1983). Writing: Teachers and students at work. Portsmouth, NH: Heinemann. Hagood, M. (2000). New times, new millennium, new literacies. Reading Research and Instruction, 39(4), 311–328. doi:10.1080/19388070009558328 Heath, S. B. (1983). Ways with Words: Language, life, and work in communities and classrooms. New York: Cambridge University Press. Hunter, J., & Caraway, H. (2014). Urban Youth Use Twitter to Transform Learning and Engagement. English Journal, 103(4), 76–82. Jenkins, H., Clinton, K., Purushotma, R., Robnson, A., & Weigel, M. (2007). Confronting the challenges of participatory culture: Media education for the 21st century. Chicago, IL: The McArthur Foundation. Jenkins. (1992). Textual poachers: Television fans and popular culture. New York, NY: Routledge. Jenkins. (2006). Fans, bloggers, and gamers: Exploring anticipatory culture. New York, NY: NYU Press.

Category: Digital Literacy

Jones, B., & Flannigan, S. L. (2006). Connecting the digital dots: Literacy of the 21st century. EDUCAUSE Quarterly, 29(2), 8–10. Junco, R., Heiberger, G., & Loken, E. (2011). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27(2), 119–132. doi:10.1111/j.13652729.2010.00387.x Kilinc, E., Evans, R. T., & Korkmaz, U. (2012). Aligning Facebook and Twitter with Social Studies Curriculum. In P. Resta (Ed.), Proceedings of Society for Information Technology & Teacher Education International Conference 2012 (pp. 517-521). Chesapeake, VA: Association for the Advancement of Computing in Education (AACE). Kist, W., Tollafield, K. A., & Dagistan, M. (2014). Leading ourselves (tweets optional): An analysis of selected Twitter users. Journal of Adolescent & Adult Literacy, 58(4), 317–326. doi:10.1002/ jaal.362 Knobel, M., & Lankshear, C. (2014). Studying New Literacies. Journal of Adolescent & Adult Literacy, 58(2), 97–101. doi:10.1002/jaal.314 Kuiper, E., & Volman, M. (2008). The web as a source of information for students in K-12 education. In J. Coiro, M. Knobel, C. Lankshear, & D. Leu (Eds.), Handbook of research on new literacies (pp. 241–266). New York, NY: Routledge. Lanham, R. (1995). Digital literacy. Scientific American, 273(3), 160–161. Lankshear, C., & Knobel, M. (2006). New literacies: Everyday practices and classroom learning (2nd ed.). New York, NY: Peter Lange. Lankshear, C., & Knobel, M. (2008). Digital literacies: Concepts, policies and practices (Vol. 30). Peter Lang.

Lapp, D., Fisher, D., Frey, N., & Gonzalez, A. (2014). Students can purposefully create information, not just consume it. Journal of Adolescent & Adult Literacy, 58(3), 182–188. doi:10.1002/ jaal.353 Lewis, C. (2007). New literacies. In C. Lankshear, M. Knobel, C. Bigum, & M. Peters (Eds.), A new literacies sampler (pp. 229–237). New York, NY: Peter Lang. Lynn, J. (2010). Internet users to exceed 2 billion this year. Reuters. Retrieved from http:// www.reuters.com/article/us-telecoms-internetidUSTRE69I24720101019 Marsh, J. (2005). Popular culture, new media and digital literacy in early childhood. Psychology Press. doi:10.4324/9780203420324 Martin, A., & Madigan, D. (2006). Digital literacies for learning. London: Facet. Mills, K. A., & Chandra, V. (2011). Microblogging as a literacy practice for educational communities. Journal of Adult and Adolescent Literacy, 55(1), 35–45. New Media Consortium. (2007). The Horizon Report. Austin, TX: Author. Ng, W. (2006). Web-based technologies, technology literacy and learning. In L. W. H. Tan & R. Subramaniam (Eds.), Handbook of Research on Literacy in Technology at the K1–2 Level (pp. 94– 117). Hershey, PA: Idea Group. doi:10.4018/9781-59140-494-1.ch006 Ng, W. (2010). Empowering students to be scientifically literate through digital literacy. In S. Rodrigues (Ed.), Multiple Literacy and Science Education: ICTs in Formal and Informal Learning Environments (pp. 11–31). Hershey, PA: IGI Global Publishing. doi:10.4018/978-1-61520690-2.ch002

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Norris, P. (2001). Digital divide: Civic engagement, information poverty, and the Internet worldwide. Cambridge University Press. doi:10.1017/ CBO9781139164887 Pool, C. R. (1997). A New Digital Literacy: A Conversation with Paul Gilster. Educational Leadership, 55(3), 6–11. Prensky, M. (2001). Digital natives, digital immigrants part 1. On the horizon, 9(5), 1–6. doi:10.1108/10748120110424816 Project Tomorrow. (2012). Mapping a personalized learning journey: K-12 students and parents connect the dots with digital learning: Speak Up 2011 National Findings. Retrieved from http:// tinyurl.com/cq7lrvq Rideout, V., Foehr, U., & Roberts, D. (2010). Generation M2: Media in the lives of 8- to 18-yearolds. Menlo Park, CA: Henry J. Kaiser Family Foundation. Rodesiler, L. (2015). The Nature of Selected English Teachers Online Participation. Journal of Adult and Adolescent Literacy, 59(1), 31–40. doi:10.1002/jaal.427

Vygotsky, L. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press. Warschauer, M. (2004). Technology and social inclusion: Rethinking the digital divide. MIT Press.

ADDITIONAL READING Alvermann, D., Moon, J., & Hagood, M. (1999). Popular Culture in the Classroom: Teaching and Researching Critical Media Literacy. Literacy Studies Series. Newark, DE: International Reading Association. Baskin, C., & Anderson, N. (2008). Learning management systems and virtual learning environments: A higher-education Focus. In J. Coiro, M. Knobel, C. Lankshear, & D. Leu (Eds.), Handbook of research on new literacies (pp. 973–998). New York, NY: Routledge. Coiro, J., Knobel, M., Lankshear, C., & Leu, D. (2008). Handbook of research on new literacies. New York, NY: Routledge.

Servon, L. J. (2008). Bridging the digital divide: Technology, community and public policy. John Wiley & Sons.

Marshall, J. P. (2008). Gender in online communication. In J. Coiro, M. Knobel, C. Lankshear, & D. Leu (Eds.), Handbook of research on new literacies (pp. 491–520). New York, NY: Routledge.

Squire, K. (2008). Video-game literacy: A literacy of expertise. In J. Coiro, M. Knobel, C. Lankshear, & D. Leu (Eds.), Handbook of research on new literacies (pp. 635–670). New York, NY: Routledge.

Mortensen, T. (2008). Of a divided mind: Weblog literacy. In J. Coiro, M. Knobel, C. Lankshear, & D. Leu (Eds.), Handbook of research on new literacies (pp. 449–466). New York, NY: Routledge.

Street, B. (1984). Literacy in theory and practice. London, UK: Cambridge University. Street, B. (1993). Cross-cultural approaches to literacy. New York, NY: Cambridge University Press. Thibaut, P. (2015). Social network sites with learning purposes: Exploring new spaces for literacy and learning in the primary classroom. Australian Journal of Language and Literacy, 38(2), 83–94.

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Quellmalz, E., & Haertel, G. (2008). Assessing new literacies in science and math. In J. Coiro, M. Knobel, C. Lankshear, & D. Leu (Eds.), Handbook of research on new literacies (pp. 941–972). New York, NY: Routledge. Snyder, I., & Bulfin, S. (2008). Using media in the secondary English classroom. In J. Coiro, M. Knobel, C. Lankshear, & D. Leu (Eds.), Handbook of research on new literacies (pp. 805–838). New York, NY: Routledge.

Category: Digital Literacy

KEY TERMS AND DEFINITIONS Anime: Hand drawn Japanese computer animation categorized by colorful graphics, vibrant characters, and fanciful themes. Blog: Typically an interactive forum or site for discussion or information-sharing created by an individual or group, centered on differing genres such as politics, music, education, health, travel etc., published on the web where entries, referred to as posts allow visitors and members to leave comments about a topic of interest and create a networking community of users. Cyberspace: Virtual environment where digital communication over computer networks occurs; typical used to represent the many ways ideas, information, and communication are shared via the Internet and networking sites. Digital Environments: A virtual or cyber-generated environment accessed or created through the use of one or more digital devices such as a computer, tablet, or a cellular phone. Digital Literacy: A broad term that refers to the use of literacy skills – reading, writing, listening, to communicate and interact within digital environments and/or using devices and cyberspace to compose and comprehend thoughts. Fan Fiction: Individuals/fans who write new stories using the characters and setting of published, popular media and share via the web.

Headcanons: From the term “canon,” which refers to events and development that happen in the actual published/aired/official text of a book, television show, movie, etc.; it is something that someone wants to believe is true or enjoys speculating on in a particular fandom based on evidence from the canon or other factors, but is generated and actually exists within their own mind. Internet: The networked system of private, public, business and governmental mainframe, personal, and wireless computer networks utilizing the Internet protocol suite (TCP/IP) to connect devices globally. Manga: Japanese graphic novels created in japan and written in Japanese. Meme: Typically an imitated image such as a photograph that is used to transmit an idea by adding text via the web. New Literacies: The technical ability and emotional skill level needed to generate thought and to communicate in multiple formats in digital environments. World Wide Web: A global publicly available hyperlinked information system, accessed through the Internet, also called simply The Web or WWW credited to Berners-Lee (1989).

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Category E

Economics

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Category: Economics

ICT Investments and Recovery of Troubled Economies Ioannis Papadopoulos Metropolitan College Thessaloniki, Greece Apostolos Syropoulos Greek Molecular Computing Group, Greece

INTRODUCTION Over the last few years, many countries faced an unprecedented crisis that negatively shaped their economic and social landscape. Here, the term “crisis” means a general turbulence in economy, which is expressed through various negative events in the public and the private Sector of the economy. Common denominator is the radical increase of unemployment that leads to negative social phenomena, poverty and forced immigration. The discussion that follows is based on (Blanchard & Johnson, 2012). Indicatively, in Public sector we have reduced GDP (Gross Domestic Product - the total amount of values produced in economy in a year) that is usually translated into potential personnel reductions (or in the best case there are no new hirings). Salaries and pensions are suffering cuts, and the same holds for the budget of public investments (new roads, hospitals, schools, etc.). Additionally, the state faces difficulties in supporting the public debt [the accumulated amount of money that the state has borrowed in the past (loans) from international financial markets in order to support its everyday needs that also include paying back other older loans], constant deficits (the public spending is higher than the collected taxes) and incapability of borrowing new loans from international markets. This happens because of the insolvency of the country based on the fact that the annual debt payments due are not supported from the overall operation of the whole economy and from its fu-

ture development prospects. Accordingly, in the private sector, firms face reduced demand for their products, difficulties in borrowing money from banks, increased tax payments and other negative facts that lead them to reduce personnel, reduce their program of investments and hold money because of the uncertainty prevailing in economy. The opposite of crisis is development. The term “development” means the expansion of an economy (i.e., new jobs, increasing of salaries and pensions, new public and private investments, new prospects and other positive social and economic events). One of the most important elements of this brief analysis is the term investment. In simply words, an investment is a dedicated amount of money used to create something, with the hope that this will return in the future some profit (typically a multiply of the initial amount) and will support the economy in its expansion. It is the cornerstone of the prosperity of a society and an economy. Investments are separated in public (roads, schools, energy infrastructure, hospitals, etc.) or private ones (new buildings, new businesses, new production lines, collaboration with public sector, etc.). Development of an open economy can only be achieved through investments. In other words, investments are done so to create wealth. Investments in information and computer technology (ICT henceforward) are highly important because they facilitate transactions, reduce bureaucracy and accelerate the operational procedures of state and businesses.

DOI: 10.4018/978-1-5225-2255-3.ch203 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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The global financial crisis of 2008 triggered a chain of events that revealed many long term structural problems well established in the foundations of the economy of a series of countries such as Greece, Ireland, and Portugal. Such problems were well hidden for many years and because of the global prosperity their importance was underestimated. Because of this, they could not affect the basic economic figures. Such countries had the ability for years to receive loans from international markets (large investment banks such as Goldman Sachs or JP Morgan) and with this method they could proceed in their everyday operational function. But after the crisis of 2008, the matter of insolvency (the degree to which a country is capable of paying back their loans) turned into a highly significant factor. Based on this, the following chain was triggered: •

•

• • •

The global financial crisis (which was primarily initiated from the collapse of the 4th largest investment bank in the USA, Lehmann Brothers) led to solvency crisis. The solvency crisis led to a borrowing crisis (global markets were not willing to borrow some countries because of the doubts they had over their ability to pay back these loans). Therefore some countries were not able to receive new loans from financial markets. This situation led to a debt crisis, meaning that these countries were not able to receive new loans in order to pay back older ones. Eventually, the debt crisis led to public expenditures cuts and an increase of taxes which in turn led to an economical and social crisis.

BACKGROUND The declination of an economy can be triggered from various factors. Officially it is said that an economy declines (i.e., is getting into recession) if the GDP in two consecutive quarters has a

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negative growth. The most significant examples of recession periods over the last century was the American great recession of 1929 - 1933 and the events that took place in the Eurozone after the global financial crisis of 2008. Once the economy declines, a series of negative phenomena are taking place at an economical and social level. All such factors have been shortly mentioned in the previous section. A general reference for studying investments is the (Bodie, Kane, & Marcus, 2013). In general and indicatively we can mention the following factors that lead an economy to declination: •

•

Economic Crisis: A crisis has not the same form across countries and/or chronological periods. A very good example is the Eurozone debt crisis which was initiated after the global financial crisis of 2008. In Ireland, the crisis was initiated from private banks and then was expanded to the public sector (because government decided to assist private banks with billion of euros to avoid bankruptcy which led to increased deficits). In Greece, the crisis was initiated from the state (the Greek government could not borrow money from international markets) and then expanded to private banks. This was happened because these banks had exposure to the Greek state debt since they had lent the state by purchasing state bonds in the past. There are also other cases that underline this ambiguous approach (e.g., Cyprus and Russia are two such cases). Uncertainty Based on Political Factors: The most recent example is the situation of Brexit. Investors, firms, individuals and other economic entities are looking for a stable political and economical environment. Major decisions such as the referendum for leaving EU, create uncertainty and instability that can push major firms to change location or individuals to reduce their demand for goods and ser-

Category: Economics

•

•

•

vices. Therefore this pushes the economy to decline (meaning recession and therefore reduced incomes and increased unemployment). External or Internal Shocks from Demand or Supply Side: Sometimes specific technical reasons can create economic instability. Various turbulences from demand or supply side can take place because of major changes in economy. For example changes can occur in consumers’ preferences, shortage in raw materials, future expectations from consumers and/or firms, restrictions in exports or imports, capital controls in banks and numerous other reasons. All these, can cause instability which can lead to a crisis. Increased Public Deficits: In some cases governments spend more in public expenditures (hospitals, schools, pensions/salaries, defense budget, public investments, that is, new roads, ports etc.) than they receive from taxes. They do so based on macroeconomical planning in which they consider the long term benefits are fully covering the short term damage because of the multiplying economic and investment effects in economy. In some cases, they do so simply because their policy makers apply low quality planning which comes along with irresponsibility. If this is the case, the national budget may show public deficits which in turn place additional burdens over the historical debt of the country. This in turn may lead to borrowing problems. This last case, leads to an increase of taxes in the private sector with all the negative effects this may cause. The result is a potential decline of the economy which initially was triggered from public deficits. The Greek crisis is a typical example of this sort of crisis. Reduced Competitiveness: Innovations in industry, new methodologies, new and complex productive procedures create

intense global competitiveness and a demanding environment for businesses and states. New markets and challenges constantly appear while firms and states that cannot be adjusted in time in this totally new environment see a reduction of their market share in the global arena. If this is the case, the exports are going to decrease while imports will increase (X-M). This triggers instability in the Balance of Trade (BOT) that causes budgetary and macroeconomic problems. When this happens, the economy declines and triggers all the negative effects in society. To reverse this development, states and firms must rapidly take decisions and proceed in new visionary plans in order to create new, competitive and efficient market procedures and products that will allow them to strongly stand in global markets. While an economy can be declined due to various and some times to nonexistent technical reasons (i.e uncertainty), recovery is much more difficult to be realized. It is the same just like saying that a building needs years to be constructed but it is just a matter of seconds to be demolished. Once the declination occurred, meaning that an economy gets into recession for many semesters / years, public policy makers and firms’ owners must focus to the recovery which must be realized as soon as possible. The more it holds the greater are the negative results to citizens and to society as a whole and the harder the way is to getting out of the tunnel. Indicatively, the following fundamental methods can be used for getting an economy back to the root of growth: 1. New private investments, domestic and foreign (FDI - Foreign Direct Investments) in critical sectors of the economy that historically appear competitive advantage in the global market. What is the impact of an investment in economy? Consider the

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ICT Investments and Recovery of Troubled Economies

calm surface of a lake. Consider now a big stone being released above the lake. Once the stone touches the water, homocentric, three-dimensional wavy (outbound) formations are being triggered. Now consider not just one but many stones being released over the lake at the same time. The same multiplying effect holds in the real economy. The more investments are being created the larger the multiplying impact stands for the economy. Many new investments create jobs, wealth and supervalues which leverage the economy initially to moving out of the recession (recovery) and subsequently to further development and prosperity. For example in Greece, the country suffered the most since the global financial crisis of 2008, new investments can be realized in sectors with competitive advantage. This is the case for boutique tourism, renewable sources of energy, shipping, mediterranean foods, services (private education, private health, informatics etc.) and other. 2. Intervention in Fiscal Policy from the ministry of finance. This intervention can come through various forms but the most well known are the following ones: Firstly, with new public investments. This is in line with the so called Keynesian policies. Famous 20th century economist John Maynard Keynes, supported the idea that when an economy comes to declination the state must support the economic circuit by increasing the public expenditures focusing in critical functions of economy i.e building new roads, ports, other important infrastructure so as to accelerate the transactions in the economy and as a consequence to move the economy again. This approach assisted US economy to move out of the recession in 1933. The Hoover Dam (amongst other infrastructure investments) was constructed in this framework. Secondly, by reducing the public consumption in carefully selected public activities which appeared as counterproductive activi-

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ties with increased bureaucracy and limited efficiency. This must be executed carefully, by bypassing such activities through the rerouting of the withdrawn resources to other efficient public activities that will actively assist the economy to making reboot. Lastly, interventions in public salaries and pensions where necessary. 3. Intervention in Monetary Policy from the central bank (in EU is the ECB). This intervention basically comes in three forms: Firstly, through the basic interest rate. When the basic interest rate, determined from the central bank, is being lowered, this gives the signal to commercial banks to lower their own borrowing interest rates (which are being added on the basic rate - i.e if for example the basic rate = 1% and the required profit from a bank is 3% then the commercial rate from this bank is 1+3=4%) and making the money cheaper. In this way, banks increase the number of provided loans to individuals and firms and thus increase the circulation of money in the economy. This increased circulation can be rooted to investments and to the production of new goods and services. Secondly, by increasing the circulating monetary mass. This is the total number of printed notes and money (cash, deposits, reserves etc) circulating across the economy (in banks, individuals, firms, state and other entities). The Central bank by using some techniques (called open market actions) can increase the monetary mass and thus creating artificial demand in economy over the short time. Inflationary phenomena may be appeared but these can be absorbed at a later time over the mid-long term. Such phenomena are barely appearing when the economy suffers crisis. Lastly, through the combination of the two methods which is the most common in the modern world. 4. Cooperation between the state and private sector in healing the primary reasons that led this economy to decline and designing

Category: Economics

the future. This can be realized with interventions in various critical sectors of the economy, in institutions and in the public services that cooperate at everyday basis with the private economy. In general, needs a national general visionary plan that will be executed from both state and leading firms so as to get the problematic economy back on track of development.

ICT INVESTMENTS AND ECONOMIC RECOVERY For many years the ICT sector was, and of course it is expected to remain, one of the largest employers. In the USA there are many ICT companies that have thousands of employers while it is expected to see an increase in job offers. Naturally, similar elements can be said for other countries. Thus the ICT sector can contribute to an economy by directly creating new jobs. Every now and then, telecommunication companies introduced new forms of home and business Internet connection. Typically, new connections offer higher speeds and better connections while the price of the new technology is always a bit higher when compared to the current technology. But gradually, the price is getting lower, allowing multiplying positive effects in economy. In order to offer a new technology, communication companies need to invest money so to attract more new customers or to convince existing customers to upgrade to new packages. Thus the new technology does not only offer a better internet home or business connection but increases also the GDP, the incomes and the potential prosperity. Today all modern states offer some sort of online service for citizens to submit their tax declaration thus avoiding queues and making the whole process easier, faster, and more efficient. More generally, many public services have become available online and some of them through mobile phones. Online bank account access is an example of a service that is both online and

through mobile phones. What is more interesting is that new online or mobile phone services and applications have been appeared. All these are crucial elements that apart from the fact that make the life of citizens easier, allow foreign and domestic investments to be accelerated but also attract new ones. In order to ensure the smooth operation of these new services it is necessary to make investments in new IT infrastructure, hire people, etc., thus contributing to the growth of the economy not mentioning the millions of working hours-saved annually by avoiding bureaucratic procedures and queues. Finally, all these systems will need a regular maintenance framework which allows the further creation of new jobs and new incomes as well. Impact sourcing is a new “industry” whose purpose is to hire people that will perform digital tasks such as transcribing audio files and editing product databases. It is a typical process of business outsourcing which can boost economic development. Typically, these digital tasks are called microworks and employees complete them by using a web-based interface. This way, skillful people from poor countries can find a job and work for a big company without the need to relocate. We have such examples in India or China. This idea has been introduced by Samasource, a San Francisco based company. Today almost all companies have a web site where their products and their operations are presented. In many cases, the companies have web stores where customers can directly purchase the company’s products. For example, all publishers have web stores while most companies that produce shoes and/or clothing have web stores too. In addition, most companies have profiles in popular social media sites while they run promotional competitions through their web site or their social media profiles. Naturally, companies need either to have a dedicated ICT department or outsource these tasks to ICT companies that specialize in this area. All these create never thought before business opportunities.

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SOLUTIONS AND RECOMMENDATIONs It is next to impossible to give a specific solution that can be applied to every possible troubled economy. Each economy has its peculiarities and thus is difficult to specialize the general solution. Thus instead of giving a general solution, we will discuss how IT investments can help a specific troubled economy to recover. In our case, the trouble economy will be Greece’s economy and the specific solution suited for Greece. The country is facing a debt crisis and has no access to markets for more than seven years. We will not discuss what led Greece in this negative situation. Instead it is better to focus to the future and discuss how the country can possibly get out of this crisis. We have already outlined the benefits of investing to ICT and now will explain how such an investment can improve a troubled economy. First of all, it is rather important to present a few facts about the use of ICT in Greece. In the end of 2014 65% of Greek households had a broadband connection when the EU average was 76%. Also, on the same year only 9% of companies were selling their goods via web shops while the EU average was 15%. In addition, 84% of enterprises are using e-government services while the EU average is 88%. These figures clearly show that there is plenty room for improvement. In addition to these characteristics, there are some serious drawbacks in the current situation. First, it seems that computer literacy is high enough, nevertheless, it is not that complete (e.g., most people know next to nothing about computer hardware and internet connectivity). Also, ICT systems are poorly maintained. Moreover, there is no plan for the use of public information and data. But the most crucial problem is that in every change of ministers and/or government a new political plan is adopted. From a purely technical point of view, there was no central planning on what computer platforms to use so that different ministries use different systems and platforms. Obviously, a good solution to avoid this sort of chaos is to

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adopt a specific open source computer platform and switch all existing systems to this platform. Apart from this obvious move, one can propose the following interventions as well: • • • • •

The modernization of public administration (e.g., reduction of bureaucracy by adopting a paperless office). Reconnection of citizens with the state (e.g., e-unification of public services and systems). Adoption of a new digital personal card for all citizens that will provide them full access to all public services. Open access to public data. Adoption of cloud, etc.

These points could create new jobs, facilitate transactions, save working hours and make the various public facilities more attractive to use by citizens and firms. They will also attract new foreign investments that will boost the economy with all the positive multiplying economic effects that this may cause. In addition, it is mandatory that the government should urge enterprises to enter the e-market and people to prefer e-shopping (a side-effect of it would be a reduction of tax evasion). Also, the government should strive to have skillful citizens, which naturally means that school curricula must be adapted accordingly.

FUTURE RESEARCH DIRECTIONS We have explained what a troubled economy is and how ICT investments can help such an economy to stand on its own two feet. Since there are no general recipes on what exactly should be done in every possible case, it would quite interesting to try to devise some method (or “algorithm”) that could guide someone to choose the best solution for each particular case. Naturally, such a method should take into consideration a number of factors such as the level of training of citizens, the current technological infrastructure and achievements

Category: Economics

(i.e., one should propose solutions that make sense), the potential availability of loans that can be rooted to ICT projects, the general legal framework, the level of international collaborations from firms and state, the general investment and tax environment that can attract ICT global firms to move resources in this country etc.

CONCLUSION Economic recovery is not something that can be achieved overnight. The government of a troubled economy or the administration of an international organization that assists troubled economies to recover (i.e IMF, EU etc), needs to carefully plan the possible recovery of the economy. Clearly, an economy can recover when many medium and large scale investments are being made at a constant rate in sectors with competitive advantage. However, it seems that amongst others, ICT investments are quite viable mainly because they are not so demanding in terms of funding. For example, in order to build a shipyard it is necessary to find a good location, build the appropriate facilities, and install the required equipment. However, in the case of ICT investments, one needs a room, a few computers and some trained personnel. This core infrastructure can massively assist entire organizations and public services accelerating transactions and creating room for multiplying positive economic phenomena. Thus when a nation invests in ICT, actually invests in the future and one can expect only growth of the economic output and prosperity for its citizens.

REFERENCES Blanchard, O., & Johnson, D. R. (2012). Macroeconomics (6th ed.). Harlow, UK: Pearson. Bodie, Z., Kane, A., & Marcus, A. J. (2013). Investments (10th ed.). McGraw-Hill Education.

Kvochko, E. (2013, April 11). Five ways technology can help the economy. Retrieved July 12, 2016, from World Economic Forum: https:// www.weforum.org/agenda/2013/04/five-waystechnology-can-help-the-economy/ Staats, F. G. (2012, December). The Microwork Solution. Retrieved July 12, 2016, from Harvard Business Review: https://hbr.org/2012/12/themicrowork-solution Tsakanikas, A., Danchev, S., Giotopoulos, I., Korra, E., & Pavlou, G. (2014, December). ICT Adoption and Digital Growth in Greece. Retrieved July 13, 2016, from http://iobe.gr/press_dtl. asp?EID=59

ADDITIONAL READING Caribbean, E. C. (2010). ICT for growth and equality: renewing strategies for the information society. Retrieved September 20, 2016, from Comisión Económica para América Latina y el Caribe: http:// www.cepal.org/en/publications/3008-ict-growthand-equality-renewing-strategies-informationsociety-summary Maryska, M., Doucek, P., & Kunstova, R. (2012). The Importance of ICT Sector and ICT University Education for the Economic Development. Procedia: Social and Behavioral Sciences, 55, 1060–1068. doi:10.1016/j.sbspro.2012.09.598 Parkin, G. (2011). Digital Marketing: Strategies for Online Success. London: New Holland Publishers. Saunders, A., & Cornett, M. (2015). Financial Markets and Institutions. New York: McGrawHill Education. Stiglitz, J. E., & Rosengard, J. K. (2015). Economics of the Public Sector (4th ed.). New York: W. W. Norton & Company.

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ICT Investments and Recovery of Troubled Economies

KEY TERMS AND DEFINITIONS Economic Crisis: A situation in which the economy of a country experiences a sudden downturn brought on by a financial crisis. Economic Recovery: A period of increasing business activity signaling the end of a recession. Economy: The large set of inter-related production and consumption activities that aid in determining how scarce resources are allocated.

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ICT: An umbrella term that includes any communication device or application, computer and network hardware and software, satellite systems and so on. Investment: An asset that is purchased with the hope that it will generate income or appreciate in the future.

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Category: Economics

Uberization (or Uberification) of the Economy Nabyla Daidj Telecom Ecole de Management, France

INTRODUCTION The management of technological innovation is one of the most demanding challenges today (Dodgson et al., 2008). The external environment characterized by globalization, convergence, competitive/market uncertainty, time-to-market pressure, shortening product lifecycles is also based on knowledge, information, fast-changing technology and an innovative economy. In recent years a series of innovations and trends have changed the way people perceive technology. The global availability of the Internet, along with innovations (products, services and applications) explain certain aspects of the dynamics of the innovation process, the diffusion of technology and the development of various platforms (product and service marketplace, social networking platform, content platform). The Uber taxi-booking smartphone app, matching those who have cars with people who need rides quickly, was created in 2009 in the US and has been progressively launched all around the world. Since this pioneering app, several other start-ups operating in various activities (transportation, flower and food delivery, events, home services, legal services) have adopted a Uber-like business model (BM) and more and more companies are looking to disrupt regulated industries, such as banking or healthcare. Uber has been a driving force behind the emergence of a new kind of platform connecting consumers and providers in real time and organizing information without any ownership of the products concerned. Uber, based on trust (MacDonald, 2016), has changed the rules of the

economic game and its success has inspired the term “uberization”. The objective of this chapter is to gain a more precise understanding of uberization and in particular its theoretical scope. How would we combine uberization with other accepted and widely used concepts in economics and strategy, such as platforms, multi-sided markets, externalities and business ecosystems? Is uberization a relevant concept, an all-purpose word with a multitude of meanings or just a buzz word? This chapter attempts to answer these questions and is structured as follows. This first section introduces the topic and key concepts and presents a brief overview of the evolution of the Uber phenomenon to a larger trend named uberization or uberification. The second section discusses the conceptual and theoretical framework to analyze the development of uberization. It develops a strategic and economic perspective. In section 3, several recommendations are proposed. Section 4 suggests directions for further research. Section 5 concludes.

BACKGROUND From Uber to Uberization Uber (the mobile application) is not uberization (Table 1). In recent years, a number of studies have been performed to gain further insight into the uberization phenomenon, notably empirical publications. The particular attention given to uberization (even in non-specialized press) shows how its importance has grown year by year since

DOI: 10.4018/978-1-5225-2255-3.ch204 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Uberization (or Uberification) of the Economy

Table 1. From Uber to uberization

Uberization of Work Employment

“But of all the ways that Uber could change the world, the most far-reaching may be found closest at hand: your office. Uber, and more broadly the app-driven labor market it represents, is at the center of what could be a sea change in work, and in how people think about their jobs”. (Manjoo, 2015) “There has been a lot of debate about how online platforms have changed the nature of work. In some cases, on-demand companies have been harshly criticized for making employer like demands on workers but denying them basic benefits and protections”. (Bernard, 2016)

Uberization of Finance and Banking

“We are on the verge of the Uberization of finance, which will bring multiple new opportunities but also a range of new risks (…). Uber is a high-tech middleman that is making the intermediaries of the past obsolete. The financial world is one of the most mediated industries on the planet, and that is precisely what is about to change. (Karabell, 2015).

Uberization of Business Schools

“What could more surely come next is the appearance of low cost and/or no frills business schools, focusing on the essentials and offering options. That’s partially the idea of online business schools, with some success, but it never really diffused to brick-and-mortar or hybrid models. With the strong discussions on fees in many countries of the world, there is now room for the emergence of a new type of business schools.” (Therin, 2015).

Source: specialised web sites.

the end of the 2000s. The definitions given in the general and specialized press are generally broad or applied to a particular industry or activity. Uberization should lead to a transformation of the entire economy. Uberization is a phenomenon based on on-demand services (enabled by the Internet and smartphones) and peer-to-peer platforms combining imitable features such as dynamic pricing sometimes called “surge” pricing (Horpedahl, 2015; Smith, 2016), mobile payment, rating systems, algorithmic and data management (Hall et al., 2015).

Towards a New Kind of Capitalism or Its End? Uberization may turn out to be a concept in the future seen as a new, complex and dynamic form of liberalization (economy) and a more perfect supply and demand equilibrium (market). Some authors suggest the evolution of capitalism towards ‘postcapitalism’ (Mason, 2015) or ‘platform capitalism’ based on a physical and digital transformation of how goods and services are produced, shared and delivered and how customers (final consumers) engage directly with each other through on-demand platforms (Davis, 2016); others consider uberization “as a form of populism” (Kyrou, 2015, p. 106).

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Some experts are even predicting the end of capitalism with the development of sharing, collaborative and participatory practices. However, the rules governing these practices activities (Cusumano, 2015) should be compatible with the market economy and capitalist system. Experts already believe that the potential for growth of the sharing and on-demand economy will be significant in the future. This nascent economy will set the stage for reshaping the economic system, renewal of the market relationships between economic actors, changes in the nature and structure of work (labor considered as a commodity) and work organization and consumer behavior shifts. The uberization phenomenon calls for a multidimensional framework for understanding its development and impact including the following levels of analysis.

Country Level The impact of uberization may differ in complexity from country to country depending on the national legislation concerned. The questions raised by uberization have become more pressing as they are closely related to a wide variety of laws and regulations including labor law, antitrust and competition law, and corporate law. The uberization of the economy is widely debated

Category: Economics

all around the world and reactions differ from one country to another. In the European Union, UberPop (where the Uber service is directly challenging the taxi market), has given rise to many disputes. Consequently, “a number of regulators in Member States took actions based on existing legal frameworks that resulted in administrative and/or criminal charges against Uber drivers and management (e.g. Netherlands, Portugal, France, Spain and Germany). As a response, Uber submitted complaints to the European Commission” (Azevedo & Maciejewski, 2015, p. 1). In India, the situation is quite different where it was not so much the regulatory and legislative framework that posed problems, rather the Uber mode of payment. Finally, Uber has accepted cash payments for rides. In the United States, Uber claims “that it is preempted from all regulation because it is an information service provider under the Telecommunications Act of 1996.” (Elliott, 2016, p. 743).

Industry and Market Level There are several factors that determine the market structure of a particular industry: buyers and sellers (number, interactions between them, bargaining power), prices, production and selling processes, and product/service differentiation based on innovation. Uberization affects all sectors of the economy, encourages the emergence of new players, creates new market opportunities and changes the competitive environment and the degree of competition to a certain extent. But as mentioned by Gabel (2016), “entry into the taxi industry involves few risks. Entrants have lower costs than the incumbents, sunk costs are small, and modern technology makes it easy to hail a cab using the Internet. Despite large scale entry and low barriers to entry, monopoly power persists. The persistence of monopoly power illustrates that new technologies may not quickly eviscerate monopoly power.” (p. 527).

Company Level

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“Traditional companies now have another set of competitors to worry about – Internet startups in the “sharing economy.” These new companies are actually Web platforms that bring together individuals who have underutilized assets with people who would like to rent those assets shortterm” (Cusumano, 2015, p.32). Consequently, uberization may lead most of the incumbent large companies, more often than in the past, to review their future strategy in order to adapt to the arrival of several new players and to achieve a sustainable competitive advantage based mainly on differentiation. There is a risk today that temporary (or transient) advantage is becoming the rule rather than the exception (McGrath, 2013; Carpenter et al., 2014; Daidj & Aras, 2015).

Platform Level Uber can be considered as an ICT platform and uberization refers then to a platform-based economy reshaping firms’ and sectors’ boundaries. In recent years, the phenomenon of the platform has been observed and the concept of the platform has been thoroughly examined by several scholars (see next section) who mostly focus on specific theoretical aspects of this issue or investigate particular industries.

UBERIZATION: A REVIEW OF LITERATURE Theoretical studies on uberization are still limited even if scholars have recognized the importance of the phenomenon which could be analyzed through the lens of marketing, strategy, finance, international business, human resources management, information systems and labor law. Uberization is a complex, dynamic and multi-dimensional issue. Probably due to the many disciplines involved in uberization – spanning a diverse field from orga-

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nization and management research to computer science and information systems – a unified body of research on uberization and the resulting social, economic and strategic implications remains a matter of debate. Various points of view of different scholars in two main disciplines are presented below as interaction and interdependence between economics and strategic management have become frequent and multi-faceted. The economics literature has addressed more specifically the challenging platform issue by defining several types of platform (one-sided, two-sided and multiple sided markets) and through the concept of externalities. In the strategy literature, uberization typically revolves around innovation, firms’ strategies, business models, business ecosystems and relationships between actors shaping the emergence and the development of different types of cooperation and competition (i.e. coopetitive practices). This section is dedicated to emerging issues with regards uberization of the economy.

anything, “uberizing” probably means “trapping” a set of innovative procedures – geo-location, online payments and workforce management and distribution – into an “app-accessible service” or a “sweatshop”, according to its critics, with lower entry barriers since people monetize resources they already own.” (Aloisi, 2015, p. 10). Regarding BMs, they evolve as the technology matures. “Disrupters often build business models that are very different from those of incumbents” (Christensen et al., 2015, 49). But can Uber be considered as a disruptive innovation and/or a disruptive business model? Nevertheless the following question remains unanswered today. According to Christensen et al., (2015), “Uber is clearly transforming the taxi business in the United States. But is it disrupting the taxi business? According to the theory, the answer is no. Uber’s financial and strategic achievements do not qualify the company as genuinely disruptive—although the company is almost always described that way” (p. 47).

Uber: Innovation and Economic Impact on Business Models

Uberization, Platforms, and Multi-Sided Markets

Christensen in his book, “The Innovator’s Dilemma” (1997) split new technology into two categories: sustaining and disruptive. Sustaining technology relies on incremental improvements to an already established technology. Disruptive technology is a term used for the first time to describe a new technology that unexpectedly overturns the dominant technology in the market sector and that forces changes in industry frontiers, business processes and business models (BMs). Disruption is rarely the result of a single innovation but occurs when two or more technologies converge. But innovation is not necessarily technological. According to the Oslo manual (OECD, 2005), an innovation is the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations. “If

Since the beginning of the 2000s, the platform issue has been studied from an economic point of view by several scholars (Caillaud & Jullien, 2001; Armstrong, 2002; Evans, 2003; Ferrando et al, 2003; Rochet & Tirole, 2006; Armstrong, 2006; Cortade, 2006; Evans & Schmalensee, 2010; Weyl, 2010). Platforms are based on technologies that provide support for, and interact with, products and services of other firms. Gawer and Cusumano have highlighted the critical role played by platforms (Gawer & Cusumano, 2002; Gawer & Cusumano, 2008; Baldwin & Clark, 2000). More recently, Staykova and Damsgaard (2014) considered that one-sided, two-sided and multisided platforms should be distinguished clearly (Table 2). “[…] one-sided platforms differ from one-sided markets which function predominantly as resellers (Hagiu and Wright, 2011). One-sided platforms facilitate the communication between

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Category: Economics

Table 2. From one-sided to multi-sided platforms One-Sided Platform

Two-Sided Platform

• Danske Bank (with Mobilpay) Barclays (Pingit) • Betfair (the world’s biggest betting exchange) previous activity before allowing other bookies to offer bets in competition with one another.

• The game console sector (consoles, video games) • Credit cards • Yellow pages

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Multi-Sided Platform • Exchange platforms (peer-to-peer platforms): Airbnb, Uber • Product marketplace: Amazon, ebay • Payments platforms: Paypal • Social networks platforms: Facebook

Source: Elaborated by the author.

the users of the platform who form one distinctive group of consumers which exhibit same-side network effects and have interchangeable roles. Thus, they differ from the two-sided platforms which link two distinctive groups of users (consumers and merchants) with strong cross-side network externalities” (see below). Multi-sided platforms allow direct interactions between two or more distinct sides and each side is affiliated with the platform. Affiliation refers to the fact that users on each side have to make platformspecific investments that are necessary in order for them to be able to directly interact with each other. The investment could be “a fixed access fee, expenditure of resources or an opportunity cost” (Hagiu & Wright, 2015, p. 6). According to Hagiu and Wright (2015), Uber can be considered as a multi-sided platform (MSP) enabling independent contractors or professionals to deal directly with clients. MSPs reduce transaction, coordination and search costs between two or more distinct groups of users (Staykova & Damsgaard, 2015). Staykova and Damsgaard’s (2014) approach is also of interest because it allows a better understanding of platform evolution as it introduces changes in the platform and development dynamic within each stage as the competition conditions evolve based on evolutionary economic concepts.. A platform is defined “as one-sided since the new product or service is still developing. As the platform becomes two-sided during the next stage, the level of competition slowly rises, but the preference is given to cooperation. The competition intensifies as the platform matures and becomes multi-sided.” (Staykova & Damsgaard, 2014, p. 12).

Uberization, Network Externalities, and Externalities The platform industry is generally characterized by the existence of high network externalities. Network analysis is often related to the theory of positive network effects which describes a positive correlation between the number of users of a network for a specific good/service and the utility of this good/service. “There are many products for which the utility that a user derives from consumption of the good increases with the number of other agents consuming the good” (Katz & Shapiro, 1985, p. 424). Network externalities are a well-known phenomenon in the IT sector, though they exist elsewhere (Arthur, 1989; David, 1986; Foray, 1989, 2002). Two types of network externalities can also be distinguished: direct and indirect externalities. Direct externalities appear when the satisfaction (utility) stemming from the consumption of a product or use of a service directly derives from the number of individuals already consuming the product or using the service. It is thus an intragroup externality. The more the product is used, the more value it acquires in relation to others. Indirect externalities emerge when the demand for a product in a market depends on the offer of another product without which the first cannot function. The appearance of this type of externality is conditioned by the existence of two complementary products forming a ‘system’ product or a platform (Daidj & Isckia, 2009). In this context, two categories of agents are distinguished, and the value of a product for one category is positively

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correlated to the number of agents on the other side of the market. This is an inter-group externality. The platform thus internalizes the indirect externalities which can usually be divided into two types: membership (or affiliation) externalities and usage externalities (Rochet & Tirole, 2004). Uber success can be explained partly by its capability to benefit from network externalities, mainly in recruiting drivers. Its development strategy has aimed to reach this critical size both at local and international level. Uberization is also closely related to scalability (taking advantage of network effects) and to a specific kind of externalities, named reputation externalities. Reputation mechanisms could reduce inefficiencies in markets with asymmetric information and assure quality of service. Even if reputation effects can be extended beyond market platforms (Noskoy &Tadelisz, 2015), reputational externalities represent a key to the success of a peer-to-peer market place based on experiences made by individuals. Reputational risks should not be underestimated; a good reputation is crucial leading to the setting up of reputational feedback mechanisms and ratings. At a more general level, economists make a distinction between what are called positive and negative externalities. Externalities represent a major cause of market failure. Rahel (2016) has analyzed the economic impact of Uber on the taxi industry and the potential negative externalities (efficiency, quality, and safety) that come with this new technology.

Uberization and Business Ecosystems The 2000s have been characterized by the emergence of a “new form” of network organization: the business ecosystem (BE). This type of network spans a variety of industries and it is often associated with platforms. Moore (1996) defines the business ecosystem as a business community that brings together various inter-dependent players who belong to different sectors. BE refers to an

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undefined, non-geographic network (often based on a platform” or “on platforms”) characterised by cooperative and “open” practices (among suppliers, organizations and customers) in order to co-create value (Daidj, 2013; Daidj, 2015). Electronic platforms play a strategic role in enhancing value creation within the business ecosystem by sustaining input from various stakeholders. Even if the platform needs a leader, firms are embedded within business ecosystems, the performance of which influences the success and survival of all their member firms. Uber, through the Uber app, has been considered as an independent transportation provider creating a new kind of business ecosystem including, in particular, independent third party transportation partners (driver partners). Smith (2016) has used the expression of “uber-all ecosystem” (p. 389). More generally, the uberization of the economy has led to the development of intermediaries within each sector and to new partnerships between main players.

SOLUTIONS AND RECOMMENDATIONS Companies are moving forward with digital transformation at varying paces and experiencing varying levels of success. Since its creation, Uber has radically disrupted an entire industry (transportation) and has continued to transform many industries around the world, removing economic, social and behavioral barriers. All stakeholders are affected by Uber’s development: regulators, incumbents, services providers, employees (for some professions), consumers etc. All of them will have to face several challenges. Challenges are competitive, technological and organizational. As the previous sections have shown, multi-sided platforms and resulting peer-to-peer platforms, such as Uber, are complex phenomena and corresponding research is very diverse. Accordingly, several theoretical perspectives can be applied to investigate the related phenomena.

Category: Economics

FUTURE RESEARCH DIRECTIONS

REFERENCES

Uberization is an evolving concept and researchers should be aware that uberization issues are strongly interconnected and that multi-disciplinary approaches will be required to address them in a comprehensive manner. Uberization is at a very early stage and will impose market and technological developments and will require changes to work patterns. Further work is required to better understand the impact of uberization on performances within business ecosystems, including case studies with different types of impact assessments. Further research into the relationships between the main actors will expand the base of evidence to reflect changes that have occurred in recent years in the business landscape in a context of uberization.

Aloisi, A. (2015). Commoditized Workers. Labour Issues arising from a Case Study Research on a Set of Online Platforms in the ‘On-Demand/Gig Economy’. Retrieved February 16, 2016 from http://ssrn.com/abstract=2637485

CONCLUSION

Azevedo, F., & Maciejewski, M. (2015). Social, Economic and legal consequences for Uber and similar transportation network companies (TNCs). Briefing, PE 563.398. Brussels, Strasbourg: European Parliament.

This chapter has highlighted the main challenges related to uberization. This fast-growing phenomenon is seen today as an important feature of economic transformation. Uberization exposes both traditional groups to direct competition with new entrants (especially peer-to-peer platform operators). The regulation by the public authorities based on the principle of equal conditions of competition for all operators on the market does not want to give new entrants a free rein. Uberization also raises questions about a radical reform of capitalism and of the market economy. Much of the interest in the uberization economy focuses on these online platforms that put forward the virtues of the sharing economy. But facilitating exchanges (of resources and/or competencies) on a platform (such as Uber) does not mean that the human being is put at the center of the economic and social development. Herein lies the heart of the problem.

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Category: Economics

Foray, D. (1989). Les modèles de compétition technologique. Une revue de la littérature. Technological competition models: A literature review. Revue dEconomie Industrielle, 48(1), 16–34. doi:10.3406/rei.1989.2244 Foray, D. (2002). Complex Systems and Collective Adoption: The Role of Networks and Partnerships as an Endogenous Mechanism to Reduce Dynamic Transaction Costs in the Context of Systemic Innovations. In de la Mothe & Link (Eds.), Networks, Alliances and Partnerships in the Innovation Process (pp. 191-198). Boston, MA: Kluwer. Fréry, F., Lecocq, X., & Warnier, V. (2015). Competing With Ordinary Resources. Sloan Management Review, 3(56), 69–77. Gabel, D. (2016). Uber and the Persistence of Market Power. Journal of Economic Issues, 50(2), 527–534. doi:10.1080/00213624.2016.1179060 Gawer, A., & Cusumano, M. A. (2002). Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry Innovation. Boston: Harvard Business School Press. Gawer, A., & Cusumano, M. A. (2008). How Companies Become Platform Leaders. MIT Sloan Management Review, 49(2), 28–35. Geradin, D. (2015). Uber and the Rule of Law: Should Spontaneous Liberalization Be Applauded or Criticized? George Mason Law & Economics Research Paper N°15-53. Retrieved January 30, 2016 from http://ssrn.com/abstract=2693683 Hagiu, A., & Wright, J. (2015). Multi-Sided Platforms. Harvard Business School. Working Paper 15-037. Hall, J., Kendrick, C., & Nosko, C. (2015). The Effects of Uber’s Surge Pricing: A Case Study. Working Paper. Retrieved January 17, 2016 from http://faculty.chicagobooth.edu/chris.nosko/research/effects_of_uber’s_surge_pricing.pdf

Horpedahl, J. (2015). Ideology Uber Alles? Economics Bloggers on Uber, Lyft, and Other Transportation Network Companies. Econ Journal Watch, 12(3), 360–374. Karabell, Z. (2015, November 6). The Uberization of Money. The Wall Street Journal. Retrieved January 28, 2016 from http://www.wsj.com/articles/ the-uberization-of-finance-1446835102 Katz, M. L., & Shapiro, C. (1985). Network externalities, competition, and compatibility. The American Economic Review, 75(3), 424–440. MacDonald, C. (2016). Uber Is Built on Trust. IEEE Technology and Society Magazine, 35(2), 38–39. doi:10.1109/MTS.2016.2554440 Manjoo, F. (2015, January 28). Uber’s Business Model Could Change Your Work. Retrieved February 19, 2016 from http://www.nytimes. com/2015/01/29/technology/personaltech/ubera-rising-business-model.html?_r=0 Mason, P. (2015). Postcapitalism. A Guide to Our Future. London, UK: Penguin Books. McGrath, R. G. (2013). The End of Competitive Advantage: How to Keep Your Strategy Moving as Fast as Your Business. Boston: Harvard Business Review Press. Noskoy, C., & Tadelisz, S. (2015). The Limits of Reputation in Platform Markets: An Empirical Analysis and Field Experiment. NBER Working Paper N°20830. Retrieved September 21, 2016 from: http://faculty.haas.berkeley.edu/stadelis/ EPP.pdf OECD. (2005). Oslo Manual. Guidelines for collecting and interpreting innovation data. Paris: OECD and Eurostat. Rahel, S. (2016). Economics of the Taxi Industry: An Uber Shake-up. Honors Theses AY 15/16. Paper 28. Retrieved September 16, 2016 from http:// repository.uwyo.edu/honors_theses_15-16/28

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Rochet, J. C., & Tirole, J. (2006). TwoSided Markets: A Progress Report. The Rand Journal of Economics, 37(3), 645–666. doi:10.1111/j.1756-2171.2006.tb00036.x Smith, W. (2016). The Uber-All Economy of the Future. The Independent Review, 20(3), 383–390. Staykova, K., & Damsgaard, J. (2014). A Model of Digital Payment Infrastructure Formation and Development: The EU Regulator’s Perspective. In Proceedings of the 13th International Conference on Mobile Business, ICMB 2014 (vol. 38). Atlanta, GA: Association for Information Systems. AIS Electronic Library (AISeL).

Cramer, K., & Krueger, A. (2016). Disruptive Changes in the Taxi Business: The Case of Uber. National Bureau of Economic Research. Retrieved February 19, 2016 from http://www.nber.org/ papers/w22083 Evans, D. S., & Schmalensee, R. (2007). The Industrial Organization of Markets with Two-Sided Platforms. Competition Policy International, 3(1), 151–179. Goel, S. (2014). Capitalism Versus the Sharing Economy. (Unpublished working paper). College Writing Spring 2014. Retrieved March 2, 2016 from http://escholarship.org/uc/item/8xd4m19w

Staykova, K., & Damsgaard, J. (2015). A Typology of Multi-sided Platforms: The Core and the Periphery. In European Conference on Information Systems (ECIS) 2015 Proceedings. Atlanta, GA: Association for Information Systems. AIS Electronic Library (AISeL).

Rochet, J. C., & Tirole, J. (2003). Platform Competition in Two-Sided Market. Journal of the European Economic Association, 1(4), 990–1029. doi:10.1162/154247603322493212

Teece, D. (2010). Business Models, Business Strategy and Innovation. Long Range Planning, 43(2/3), 172–194. doi:10.1016/j.lrp.2009.07.003

KEY TERMS AND DEFINITIONS

Therin, F. (2015). Of Uberization and Business schools (and toilet paper). EMLV, Management School Paris-La Défense. Retrieved September 30, 2015 from http://www.emlv.fr/en/of-uberizationand-business-schools-and-toilet-paper Weyl, G. (2010). Price Theory of Multi-sided Platforms. American Economic Review, 100(4),16421672.

ADDITIONAL READING Cook, J. (2015). Uber’s internal charts show how its driver-rating system actually works. Business Insider. Retrieved September 5, 2016 from: http://www.businessinsider.com/leaked-chartsshowhow- ubers-driver-rating-system-works2015-2?r=UK&IR=T

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Business Ecosystem: Interorganizational network (coalition) which brings together actively involved organizations who belong to different sectors, but share the same interests, values and common goals. Disruptive Technology: Disruptive technology or innovation create major changes in industry frontiers, business processes and business models. Network Effects: They are related to correlation between the number of users of a network for a specific good/service and the utility of this good/service. On-Demand Economy: Refers more and more to uberization closely related also to sharing practices spreading across several sectors as banks, insurance, tourism, transport, food delivery, connected health or retail. Online Platform (Company): Integrated web-based platform company allowing people (consumers, “independent contractors”) who want to share assets online.

Category: Economics

Peer-to-Peer Platform: Platform whereby two individuals can communicate, interact (and sell) directly with each other, without intermediation by a third-party. Sharing Economy: known also as collaborative consumption referring to peer-to-peer-based sharing of access to goods and services.

Uber: The name of a technology company who has launched a smartphone-based app allowing car-sharing service (connecting ride-seekers and UberX drivers). Uber’s development has inspired the term ‘uberization’.

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Educational Technologies

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Category: Educational Technologies

Adaptive Hypermedia in Education Vehbi Turel The University of Bingol, Turkey

INTRODUCTION Since the definitions of the pertinent terms such as multimedia, multiple media, interactive multimedia and hypermedia are given in a previous study (Turel, 2015a, pp. 2495-96), in this article, only the definition of adaptive hypermedia (AH) is focussed on, and the role of adaptive hypermedia (AH) in education is concentrated on. The aim of this article is: (a) to give the definition of AH and state what AH means, and (b) to explore the role of AH in education at this digital age, in which the majority of learners are generally digitally fluent and competitive (Turel 2015b, Gros et al., 2012, pp. 190-210) although some claims otherwise (Bullen, Morgan & Qayyum, 2011, pp. 1-24). Pedagogically and epistemologically, educational institutions (i.e. nursery, primary, secondary and high schools, colleges, vocational schools and colleges, life-long learning centres, adult education centres, and universities) should respond to such learning demands and differences to accommodate the digital-literate, wise and efficient learning style preferences of today’s learners by providing AH learning materials for them. More frankly, educational institutions have to use and provide AH learning materials for their learners in order to be competitive in this digital age (Turel, 2014a; Türel, 2013; Duncan-Howell, 2012).

BACKGROUND When educational (computer) technologists speak of adaptive hypermedia (AH), mostly one thing comes to mind. It is the use, combination and delivery of digital video, audio/sound, text,

visuals (i.e. pictures/images/photographs, graphics, tables, figures), animations, hyperlinks, optimum combinations, instructions etc. on the same digital platform, which are totally computerised and under computer as well as learners’ control. This digital platform also enables learners / users to make preferences, record these preferences, their individual needs and learning goals, and then uses them throughout interaction with the learners in order to meet their personal needs so that they can learn better (Turel, 2015a, p. 2497; Turel 2015b). In other words, AH (a) is a digital environment where a wide range of digital elements are combined and delivered on the same environment through hyperlinks (Figures 1 and 2), (b) has a learner model where learners can make preferences, record these preferences, their individual needs and their learning goals, and (c) uses the learner model to adapt the contents of the hypermedia according to the learners’ needs (based on the data provided and the preferences made by the learners through the learner model) (Brusilovsky 2012, p. 46; Brusilovsky, 2007, Brusilovsky & Millán, 2007; Brusilovsky, Eklund & Schwarz,1998). It is because of this ‘adaptation feature’ that it is now called ‘adaptive hypermedia’ (AH). AH is relatively a new direction in the field of educational technology (Brusilovsky 2012, p. 46), consists of different models (Kahraman et al. 2013, p. 60) and can be classified according to its application areas such as Educational Adaptive Hypermedia, which is the most popular area for research (Brusilovsky, 1996). To sum up, when the combination and delivery of a wide range of digital elements on the same digital platform offers ‘personalised learning’, then such a digital platform is called AH.

DOI: 10.4018/978-1-5225-2255-3.ch205 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Figure 1. An example of hypermedia where optimum combinations (i.e. text, video, audio, visuals, control buttons, text boxes etc.) can be provided on the same digital platform Turel, 2015b, p. XXXIII.

Figure 2. An example of hypermedia as well as hyperlinks. The “word in red colour” (i.e. abide by) contains a hyperlink. When it is clicked on, the provided definition is displayed for the learners Turel, 2015b, p. XXXVIII.

ADAPTIVE HYPERMEDIA IN EDUCATION In terms of education, outstanding differences between AH and conventional materials (CMs) exist. These are: (a) the combination and instantaneous delivery of different digital elements on the same digital platform, (b) being navigational and interactive, (c) user control and ease of use

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and (d) offering ‘personalised learning’. The objective of educational AH is to design and create pedagogically sound and epistemologically flexible learning environments which (a) not only supports a wide range of learners who are diverse in terms of abilities, disabilities, levels, interests, backgrounds, and other characteristics, but also (b) enables learners to make maximum use of the available personalised interactive and

Category: Educational Technologies

simultaneous learning material (Shute & ZapataRivera, 2012; Brusilovsky, 1996), which is also cost-effective. We can now look at these as well as the other aspects of educational AH.

Adaptive Hypermedia Has More Features AH has a wide range of combined flexible elements and features.These can make (a) input comprehensible, (b) learning enjoyable (Soboleva & Tronenko 2002, p. 493; Trinder, 2002, p. 75) and (c) adapt the same learning material to the needs of a wide range of diverse learners (Shute & Zapata-Rivera, 2012; Brusilovsky, 1996). All of these can, as a whole, result in better learning (K Govindasamy, 2013; Stepp-Greany 2002, p. 172). On the other hand, CMs (i.e. traditional books, off-air materials, tape cassettes and videotapes) do not have many such elements. For example, traditional books only feature texts, pictures/graphics/figures, indexes and inter-textual citations although ‘Choose Your Own Adventures books’ series (e.g. The Cave of Time, House of Danger) are also explicitly designed as ‘hyper-textual’. In the same way, off-air radio-programs and tape cassettes feature ‘audio-only’, while off-air TV programmes and videotapes feature both sound and visuals. Those created for learning/teaching purposes such as tape cassettes and videotapes are sometimes accompanied with learner’s books. Similarly, traditional hypermedia (i.e. multimedia, interactive multimedia) has its own limitations. For example, traditional hypermedia provides all learners with the same contents and hyperlinks regardless of their wide range of profiles, needs and individual differences. Not only is learning a multi-channel phenomenon, but also learning-style preferences are a wide range of - which can be described as learners’ natural, habitual and preferred (i.e. biological, developmental and conscious or subconscious) ways of processing, acquiring and representing (new) knowledge and skills -. While CMs provide a limited learning environment, AH provides a

multidimensional, multi-sensory and interactive environment in which not only input can be presented in different ways (Herron et al., 2002, p. 37; Leffa, 1992, p. 66), but also input can be adapted to the learners’ individual characteristics and needs. Not only can this meet the needs of learners who are diverse in terms of abilities, disabilities, levels, interests, backgrounds, and other characteristics, but this can also meet the needs of different individual learners who have different learning-style preferences such as auditory, visual, tactile, kinaesthetic, group and individual (Carson & Longhini, 2002, p. 408). For instance, while the provision of audio-only meets the needs of auditory-learners, video and (supplementary) visuals can meet the needs of visual-learners (Turel, 2014b; McLoughlin, 1999, pp. 222, 229). Equally, typing a word or sentence, clicking on a choice, recording their own voice, or dragging and dropping a word, sentence or picture meets the needs of tactile- and kinaesthetic- learners. AH features the combination of diverse elements which provides interactivity. This facilitates the negotiation of meaning, and it is necessary in learning (Harrington & Levy, 2001, p. 15; Hegelheimer & Chapelle, 2000, p. 42), as learners can access hyperlinks, glossaries, feedback, subtitles, answers, and optimum flexible, customised and conditional combinations (Turel, 2015a; Türel, 2012, pp. 40-41) immediately, and find out what and why they have not understood, and the underlying assumptions. All of these efficient features of AH facilitate learning; draw learners’ attention to their weaknesses and certain aspects of input and can result in depth processing, which are necessary conditions for learning. For example, the combination of different elements can make input comprehensible. This is a requirement of the comprehension input hypothesis and theory, which state that learners acquire only when they understand, and therefore “consider intake synonymous with comprehensible input” (Krashen; 1984; p. 21; Tschirner, 2001, p. 311). It is also a requirement of the social learning theory, which “posits that

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people’s judgements about their potential ability to perform well or to cope in a situation actually affect their efforts…” (Robinson, 1991, p. 157). The person perception theory, which requires us to avoid focussing learners’ attention on their weaknesses so that they do not develop negative judgements about their ability to perform (ibid: 157), and the social-psychological theory and the socio-educational model, which focus on the role of attitudes and motivation in learning (Masgoret & Gardner, 2003, pp. 124, 127, 158-9; Gardner, 1985, p. 158) also require us to provide comprehensible input for learners. Comprehensible input can also be considered as a requirement of the cognitive load theory and working memory, as incomprehensible input can cause risk of overload on limited working memory (Kalyuga, 2000, p. 161; Sweller, 1999). The combination of optimum different elements on the same digital environment in AH can draw learners’ attention to certain salient input. This is a requirement of the noticing hypothesis and the attention theory, which suggest that paying/ drawing attention to (specific) forms in the input is necessary for learning (Nicholas, Lightbown & Spada, 2001, p. 721; Williams, 2001, p. 335). This can be implemented more effectively especially in AH environments, as the contents can be adapted to the needs of different individuals who are diverse. The combination of optimum different elements on the same digital platform in AH is also the requirement of the dual-coding theory and the generative theory of multimedia, as it provides learners with more than one concurrent mode/ element which targets to teach one thing. It is learned better than those coded only in one mode because dual-code provides more paths of recall, which can aid to build two types of recall cues in memory (Ginther, 2002, pp. 133-67; Moreno & Mayer, 2002) although this might not always be the case with all combinations (Amrhein et al., 2002, pp. 843-57; Kalyuga, 2000, pp. 162-63), as it also requires considering the requirements of the cognitive load theory and working memory.

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All Elements Are on the Same Platform While working with CMs requires learners to use additional sources (i.e. accompanying auxiliary books, answer keys, dictionaries, grammar books etc.), which might be frustrating, distracting and time-consuming for learners (Leffa 1992, pp. 7072), in AH, all elements are on the same digital platform and learners find such a combination and delivery motivating (Soboleva & Tronenko, 2002, p. 483; Ayres, 2002, pp. 247-48). As a result, AH provides considerably greater opportunities for ease of use and learner-control than have been possible with CMs. For example, Leffa’s (1992, p. 71) study revealed that “the electronic glossary… was not only more efficient but also seemed to demand no previous training…” and “…was significantly superior to the traditional dictionary”; as less time needed for the electronic mode (ibid, p. 72) and it resulted in better comprehension (ibid, p. 70). Likewise, AH is more efficient as well as more cost-effective in comparison to traditional hypermedia, as the former adapts the learning contents to the needs of different learners who are diverse in terms of abilities, disabilities, levels, interests, backgrounds, and other characteristics. Learners can instantly access different parts of AH (i.e. video, audio, optimum combinations, feedback, glossaries, available sections, syntax, lexis, subtitles etc.), which are combined and delivered on the same digital platform, all of which are good opportunities to exercise control over input (Tschimer, 2001, pp. 312-3) unlike with CMs. These positive aspects of AH can enable learners (a) to find out the difficulties, the right solutions, what the rules are; (b) to analyse the mistakes that have been made; and (c) to find out why they have made such mistakes by assessing their answers, recording and scoring them, pointing out and explaining mistakes (Mangiafico, 1996, p. 52). This capability provides more realworld learning contexts and more authentic and interactive tasks. Ashward (1996, p. 80) states that “the ability to display such a variety of resources,

Category: Educational Technologies

to link them together, and to combine all these resources with tutorial programs on the computer provide a highly sophisticated, yet potentially easyto-use and easy-to-author medium for developing education materials in any subject”. Equally, these positive aspects are highlighted by Fox (1995, p. 43), and learners also appreciate them (Brett, 1997, pp. 46-7).

Adaptive Hypermedia Gives Learners Control AH provides learners with the opportunity to learn any subject at their own pace, without fear of making mistakes in the presence of a teacher or other learners. It is a tension free environment in which learners can use AH environments individually, as no teachers and friends are present. Especially, during self-study, AH is a private and flexible workplace where learners can take risks; work in their own place (Tschirner, 2001, p. 307), in their own time, at the pace they need, and in the way they enjoy; as it gives them the control (Soboleva & Tronenko 2002, p. 493; Trinder, 2002, p. 75) and provides them with different choices, tasks and feedback. Moreover, the contents of AH are adapted to the needs of individual learners. All these positive aspects can make them feel more comfortable and might result in promoting development of self-confidence and provoking working hard. It is due to these reasons that learners do not complain about the fear of making mistakes. Conversely, they express their comfort of working with hypermedia. Ease of use - in relation to pacing and control- (Trinder, 2002, p. 75; Brett, 1997, pp. 46-7) and “‘user-friendliness” - at an operational level - (Fleta et al., 1999, p. 55) are some hypermedia features found most useful. User-control is a feature that has an impact on learning effectiveness (Peter, 1994, pp. 157-8), which is also considered an essential principle in instructional design (Hoven, 1999, p. 91) and a condition that is necessary in autonomous materials (Watts, 1997, p. 7). It is also a requirement of autonomous learning and development, the main

way of life-long learning, and the requirement of the autonomous learning theory, which “demands that learners take control of their learning” (Voller, 1997, p. 106).

Adaptive Hypermedia Motivates Learners As AH offers learners many elements on the same digital environment, it is highly motivating. When learners make mistakes, this does not even demotivate them because they have the opportunity to receive instant and meaningful feedback, and practise as many times as they want and need to. Learners find hypermedia motivating and they are engaged, active, attentive and interested in hypermedia when they work with it (Soboleva & Tronenko, 2002, p. 483; Lyall & McNamara, 2000, p. 135). This is an important factor in learning especially during autonomous study because “how to engage the interest of the learner and so sustain his motivation to learn” is considered “a related problem for the writer of self-study materials” (Frankel, 1987, p. 53). Most importantly, (1) motivation is “directly linked to achievement” according to the socioeducational model’ (Masgoret & Gardner, 2003, p. 129), which suggests that integrativeness and attitudes towards the learning situation cause learners’ motivation to learn input and motivation is responsible for achievement (ibid: 24; Gardner, 1985, p. 158). (2) Motivation is also common to all models of learning (i.e. the acculturation model, the conscious reinforcement model, the intergroup model, the interactionist model, the LMR-plus Model, the monitor model, the social context model, the social psychological model, the elaboration theory model, ibid: 25-66).

Adaptive Hypermedia Prepares, Directs, and Guides Learners Since AH (a) enables the combination and delivery of different elements on the same digital platform more effectively (Turel & Kılıc, 2014; Türel 2012,

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pp. 35, 45) such as supplying a non-linear editing facility (Tschirner, 2001, p. 307) and (b) also adapts the contents to the needs of individual learners, it enables materials writers to create effective and interactive applications directly to their customers’ specifications, their needs, interests and learning styles (Cauldwell, 1996, p. 526). AH can prepare learners more effectively and adequately for input, as it empowers materials writers (1) to provide learners with different elements such as unknown items, technical words and concepts (i.e. terminology), the objective of the topic, syntax, special features of text type/input, short audio messages, short video clips, sample sentences, graphics, animations, visuals, simplified written versions of the text; short information about the topics, the speakers of the existing audio and video clips, their roles, how they interact, the content and cultural differences (Turel, 2014c). (2) Since featuring, delivering and combining different elements on the same digital platform, AH can guide learners more effectively. For example, when learners make a mistake, they receive adapted instant and meaningful feedback. This can enable them to find out why they have made the particular mistakes, how they can overcome such difficulties in future occasions, and improve new strategies. Due to these reasons, AH is considered efficient for selfstudy use (Soboleva & Tronenko, 2002, p. 483). Additionally, if learners are instructed about which strategies they need to follow in which situations, they can be directed and guided (Debski & Gruba, 1999, pp. 219-20). Similarly, learners’ attention can be directed to metacognitive strategies such as planning, directing attention, self-monitoring, self-evaluation and similar strategies which are effective for exploitation of the facility itself. This is important because learners can be taught to “use appropriate comprehension strategies” (Goh, 2000, p. 71) and it is mostly needed during self-study, as learners are by themselves (Debski & Gruba, 1999, pp. 219-20). AH can qualify learners to overcome potential sources of difficulties in the context of learning

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any subject. For example, while learning a foreign language, unfamiliar items, proper names, syntax, fast speech, and unfamiliar accents can be taught more efficiently (Tschirner; 2001; p. 306). For instance, the meanings of unfamiliar items can be provided through hyperlinks or glossaries. These can be explained in through synonyms or antonyms. Likewise, learners’ attention can be drawn to cognates, false cognates and polysemous words, which are useful for vocabulary acquisition and helpful for understanding, and improving listening and other skills (Vidal, 2003, p. 80). Additionally, simple sentences and short paragraphs featuring unknown items can be provided. In some cases, they can be explained throughout visuals, audio, video or optimum combinations. Their equivalents in first language can be given if learners are monolingual. Unfamiliar proper names can also be given in advance so that learners will not have difficulty. Such names can be given through pictures/cartoons that have labels and instructions. Similarly, cultural differences, syntax and lexis can be illustrated through simple samples, pictures, audio or video clips (Turel, 2014c, Turel & Kılıc, 2014). Equally, while studying a different culture, religion, geography, science or any other topics, the pertinent input and relevant features such as cultural difficulties, costumes, traditions, figures, shapes and the assumptions underlying them can be presented and taught more efficiently through providing customised/adapted and combined comprehensible input and interactive tasks with instantaneous feedback.

Adaptive Hypermedia Provides Efficient and Instant Feedback In CMs, feedback is normally given in learner’s books or answer-key books. When learners have difficulty, they can access them. Although this is useful, it is very limited, as (a) it is not instant and (b) it consists of restricted elements such as text, pictures and graphics. In AH, feedback

Category: Educational Technologies

is (a) immediate, (b) can consist of different elements, which meet both learners’ visual and acoustic needs resulting in learning and (c) can be conditional/adapted. Such feedback can help learners to find out what and why they could not understand and overcome the difficulties causing them not to comprehend, which facilitates meaning negotiation (Williams, 2001, p. 337; Smith, 2003, pp. 39-40), draws attention and raises conscious/ metacognitive awareness. This can guide and lead learners to develop new and effective strategies, which is one of the targets that material writers want to, and need to, fulfil especially in autonomous materials. This also draws learners’ attention to the input, which is necessary for learning (Nicholas, Lightbown & Spada, 2001, p. 721; Williams, 2001, p. 335).

Adaptive Hypermedia Meets the Needs of Different Learners It is a known fact that all learners do not have the same background and abilities. While some learners have high abilities and know quite a lot about target input/topic, other learners may not. It is also a de facto that not only do AH enable materials writers to make use of different elements, which can make input comprehensible and create gradual and different tasks, but it also enable material writers to adapt the learning contents to the needs of different learners who are diverse in terms of abilities, disabilities, levels, interests, backgrounds, and other characteristics. As a result, different learners can find what is most appropriate for them or a way of working which is most convenient for them. For example, learners with high ability can be provided with higher-level exercises and tasks, while those with low ability can be provided with lower-level exercises and tasks. Similarly, if some learners find audio/ video clips too fast, then they can slow down the clips or alternatively they can be provided with the slow versions.

FUTURE RESEARCH DIRECTIONS In spite of the above mentioned advantages of AH, many hypermedia applications on the market are not adequate. They contain certain features that are neither pedagogically nor psychologically sophisticated (Türel 2014; Turel, 2010, p. 399; Trinder, 2002, pp. 69-84). The problems fundamentally stem from hypermedia developers, not the pertinent technology itself (Turel & McKenna, 2012, pp.188-189) although technology has its’ own limitations, as well. Therefore, AH applications need to be better developed. The pertinent findings in the field need to and ought to be fully implemented. For example, many AH applications do not feature proper selfassessment tests, although self-assessment tests are an essential element for learning especially during self-study. Similarly, AH applications need to give flexibility to learners. To overcome such shortages, we need to have more comprehensive and flexible AH evaluation criteria so that AH applications can be evaluated based on these criteria although some useful studies have been conducted to this end (Elissavet & Economides 2003; Ferney & Waller 2001). We also need to have enhanced AH development models. AH should be fully integrated into education alongside CMs. It is striking that hypermedia is still not widely used (Turel, Calik, & Doganer, 2015a, Turel, Calik, & Doganer, 2015b, Turel, 2014a). Priority should be given to young learners, as they have more positive perceptions towards such technology (Türel, 2014). AH developers should further develop fully efficient AH applications in all subjects for all-level learners. Future investigations need to develop better AH models, find out how to design each element as well as a whole more precisely, and improve efficient, comprehensive and flexible evaluation criteria. These will eventually result as a whole in creating ideal AH environments that facilitate learning.

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CONCLUSION Although AH has a vital role in learning, this is not the only issue that we need to be aware of. To ensure that AH plays its role in learning in true sense, efficient and fully adapted hypermedia environments need to be designed and created. Not only does this require the selection of the right input (i.e. in terms of age, level, contents and acquisition), but it also requires the adapted effective design of every single available element. These can only be achieved in true sense by the active and full participation of the pertinent experts (Turel & McKenna, 2013, pp. 189-190). Only such participation ensures that cost-effective AH can be created. These are very important and all have their role in the effectiveness of AH in education, the final aim of which is to enable learners to learn and acquire the necessary 21st century knowledge, skills and competencies.

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Brusilovsky, P. (2007). Adaptive Navigation Support. In P. Brusilovski, A. Kobsa, & W. Nejdl (Eds.), The Adaptive Web: Methods and Strategies of Web Personalization (Vol. 4321, pp. 263–290). Berlin: Springer-Verlag. doi:10.1007/978-3-54072079-9_8 Brusilovsky, P. (2012). Adaptive Hypermedia for Education and Training. In P. J. Durlach & A. M. Lesgold (Eds.), Adaptive Technologies for Training and Education (pp. 46–68). Cambridge, UK: CUP. doi:10.1017/CBO9781139049580.006 Brusilovsky, P., Eklund, J., & Schwarz, E. (1998). Web-based education for all: A tool for development adaptive courseware. Computer Networks and ISDN Systems, 30(1), 291–300. doi:10.1016/ S0169-7552(98)00082-8 Brusilovsky, P., & Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In P. Brusilovski, A. Kobsa, & W. Nejdl (Eds.), The Adaptive Web: Methods and Strategies of Web Personalization (Vol. 4321, pp. 3–53). Berlin: Springer-Verlag. doi:10.1007/978-3-54072079-9_1 Bullen, M., Morgan, T., & Qayyum, A. (2011). Digital learners in higher education: Generation is not the issue. Canadian Journal of Learning and Technology, 37(1), 1–24. Carson, J. G., & Longhini, A. (2002). Focussing on learning styles and strategies: A diary study in an immersion setting. Language Learning, 52(2), 401–438. doi:10.1111/0023-8333.00188 Cauldwell, R. T. (1996). Direct encounters with fast speech on CD-AUDIO to teach listening. System, 24(4), 521–528. doi:10.1016/S0346251X(96)00046-2 Debski, R., & Gruba, P. (1999). A qualitative survey of tertiary instructor attitudes towards project-based CALL. Computer Assisted Language Learning, 12(3), 219–239. doi:10.1076/ call.12.3.219.5715

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Duncan-Howell, J. (2012). Digital mismatch: Expectations and realities of digital competency amongst pre-service education students. Australasian Journal of Educational Technology, 28(5), 827–840. doi:10.14742/ajet.819

Govindasamy, K, M. (2013). Embodied agent in tutor role: Effects on field dependent and independent low achievers’ retention and perceived science self efficavcy beliefs. Journal of Educational Multimedia and Hypermedia, 22(3), 273–297.

Elissavet, G., & Economides, A. A. (2003). An Evaluation Instrument for Hypermedia Courseware. Journal of Educational Technology & Society, 6(2), 31–44.

Gros, B., Garcia, I., & Escofet, A. (2012). Beyond the Net Generation Debate: A Comparison of Digital Learners in Face-to-Face and Virtual Universities. International Review of Research in Open and Distance Learning, 13(4), 190–210. doi:10.19173/irrodl.v13i4.1305

Ferney, D., & Waller, S. (2001). Reflections on multimedia design criteria for the international language learning community. Computer Assisted Language Learning, 14(2), 145–168. doi:10.1076/ call.14.2.145.5781 Fleta, B. M., Sabater, C. P., Salom, L. G., Guillot, C. P., Monreal, C. S., & Turney, E. (1999). Evaluating multimedia programs for language learning: A case study. ReCALL, 11(3), 50–57. Fox, J. (1995). Multimedia for language learning: Some Course Design Issues. Computer Assisted Language Learning, 8(1), 31–44. doi:10.1080/0958822950080103 Fox, J., Romano-Hvid, B. C., & Sheffield, J. S. (1992). New perspectives in Modern Language Learning. Sheffield, UK: Group UK, Employment Department. Frankel, F. (1987). Self-study materials: Involving the learner. In M. Geddes & G. Sturtridge (Eds.), Individualisation (pp. 52–60). Hong Kong, China: Modern English Publications Ltd. Gardner, R. C. (1985). Social psychology and second language learning: The role of attitudes and motivation. Baltimore, MD: Edward Arnold. Ginther, A. (2002). Context and Content Visuals and Performance on Listening Comprehension Stimuli. Language Testing, 19(2), 133–167. doi:10.1191/0265532202lt225oa Goh, C. C. M. (2000). A cognitive perspective on language learners listening comprehension problems. System, 28(1), 55–75. doi:10.1016/ S0346-251X(99)00060-3

Harrington, M., & Levy, M. (2001). CALL begins with a C: Interaction in computer-mediated language learning. System, 29(1), 15–26. doi:10.1016/ S0346-251X(00)00043-9 Hegelheimer, V., & Chapelle, C. A. (2000). Methodological Issues in Research on LearnerComputer Interactions in CALL. Language Learning & Technology, 4(1), 41–59. Herron, C., Dubreil, S., Corrie, C., & Cole, S. P. (2002). A classroom Investigation: Can video improve intermediate-level French language students ability to learn about a foreign culture? Modern Language Journal, 86(1), 36–53. doi:10.1111/1540-4781.00135 Hoven, D. (1999). A model for listening and viewing comprehension in multimedia environments. Language Learning & Technology, 3(1), 88–103. Kahraman, H. T., Sagiroglu, S., & Colak, I. (2013). A novel model for web-based adaptive educational hypermedia systems: SAHM (supervised adaptive hypermedia model. Computer Applications in Engineering Education, 21(1), 60–74. doi:10.1002/ cae.20451 Kalyuga, S. (2000). When using sound with a text or picture is not beneficial for learning. Australasian Journal of Educational Technology, 16(2), 161–172. doi:10.14742/ajet.1829 Krashen, S. D. (1984). Principles and practice in second language acquisition. Oxford, UK: Pergamon.

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Leffa, V. J. (1992). Making foreign language texts comprehensible for beginners: An experiment with an electronic glossary. System, 20(1), 63–73. doi:10.1016/0346-251X(92)90008-Q Lyall, R., & McNamara, S. (2000). Learning tool or potplant stand? Students opinions of learning from a CAL program in a distance education context. Australasian Journal of Educational Technology, 16(2), 126–146. doi:10.14742/ajet.1827 Mangiafico, L. F. (1996). The relative effects of classroom demonstration and individual use of interactive multimedia on second listening comprehension (Unpublished Ph.D. Thesis). Vanderbilt University, Nashville, TN. Masgoret, A. M., & Gardner, R. C. (2003). Attitudes, motivation, and second language learning: A meta-analysis of studies conducted by Gardner and associates. Language Learning, 53(1), 123–163. doi:10.1111/1467-9922.00212 McLoughlin, C. (1999). The implications of the research literature on learning styles for the design of instructional material. Australasian Journal of Educational Technology, 15(3), 222–241. doi:10.14742/ajet.1859 Moreno, R., & Mayer, R. E. (2002). Verbal redundancy in multimedia Learning: When reading helps listening. Journal of Educational Psychology, 94(1), 156–163. doi:10.1037/00220663.94.1.156 Nicholas, H., Lightbown, P. M., & Spada, N. (2001). Recasts as feedback to language learners. Language Learning, 51(4), 719–758. doi:10.1111/0023-8333.00172 Peter, M. (1994). Investigating into the design of educational multimedia: Video, interactivity and narrative (Unpublished Ph.D. Thesis). Open University, London, UK.

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Robinson, G. L. (1991). Effective feedback strategies in CALL: Learning theory and empirical research. In P. Dunkel (Ed.), Computer-Assisted Language Learning and Testing: Research Issues and Practice (pp. 155–167). New York, NY: Newbury House. Shute, V. J., & Zapata-Rivera, D. (2012). Adaptive educational systems. In J. D. Paula & M. L. Alan (Eds.), Adaptive technologies for training and education (pp. 7–27). Cambridge, UK: Cambridge University Press. doi:10.1017/ CBO9781139049580.004 Smith, B. (2003). Computer-mediated negotiated interaction: An expanded model. Modern Language Journal, 87(1), 38–57. doi:10.1111/15404781.00177 Soboleva, O., & Tronenko, N. (2002). A Russian multimedia learning package for classroom use and self-study. Computer Assisted Language Learning, 15(5), 483–499. doi:10.1076/call.15.5.483.13470 Stenton, T. (1998). Hypermedia: a new consensus for the 1990’s. In K. Cameron (Ed.), Multimedia CALL: Theory and Practice (pp. 11–16). Exeter, Elm Bank Publications. Stepp-Greany, J. (2002). Student Perceptions on Language Learning in a Technological Environment: Implications for the New Millennium. Language Learning & Technology, 6(1), 165–180. Sweller, J. (1999). Instructional design. Melbourne, Australia: ACER. Trinder, R. (2002). Forum: Multimedia in the business English classroom: The learners point of view. Computer Assisted Language Learning, 15(1), 69–84. doi:10.1076/call.15.1.69.7291 Tschirner, E. (2001). Language acquisition in the classroom: The role of digital video. Computer Assisted Language Learning, 14(3-4), 305–319. doi:10.1076/call.14.3.305.5796

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Turel, V. (2010). Advanced Turkish. ReCALL, 22(3), 396–401. doi:10.1017/S0958344010000224 Türel, V. (2012). Design of feedback in interactive multimedia language learning environments. Linguistik Online, 54. Türel, V. (2013). The use of educational technology at tertiary level. Hacettepe University Journal of Education, 28(2), 482–496. Türel, V. (2014). Learners’ perceptions towards interactive multimedia environments. Hacettepe University Journal of Education, 29(3), 167–183. Turel, V. (2014a). Teachers computer self-efficacy and their use of educational technology. [TOJDE]. Turkish Online Journal of Distance Education, 15(4), 130–149. doi:10.17718/tojde.81990 Turel, V. (2014b). The use and design of supplementary visuals for the enhancement of listening skills in hypermedia. In C. M. Akrivopoulou & N. Garipidis (Eds.), Human rights and the impact of ICT in the public sphere: Participation, democracy, and political autonomy (pp. 268–291). Hershey, PA: IGI-Global; doi:10.4018/978-14666-6248-3.ch016 Turel, V. (2014c). Design of cultural differences in hypermedia environments. International Journal of Human Rights and Constitutional Studies, 2(2), 150–170. doi:10.1504/IJHRCS.2014.062766 Turel, V. (2015a). Hypermedia and its’ role in learning. In M. Khosrow-Pour (Ed.), Encyclopaedia of information science and technology (3rd ed.). Hershey, PA: IGI-Global. doi:10.4018/9781-4666-5888-2.ch243 Turel, V. (2015b). Listening, multimedia and optimum design. In V. Turel (Ed.), Intelligent Design of Interactive Multimedia Listening Software (pp. xx–xxxiiv). Hershey, PA: IGI-Global. doi:10.4018/978-1-4666-8499-7.ch003

Turel, V., & Kılıc, E. (2014). The Inclusion and Design of Cultural Differences in Interactive Multimedia Environments. In Human Rights and the Impact of ICT in the Public Sphere: Participation, Democracy, and Political Autonomy. Hershey, PA: IGI-Global. Turel, V., & McKenna, P. (2013). Design of Language Learning Software. In B. Zou et al. (Eds.), Computer-Assisted Foreign Language Teaching and Learning: Technological Advances (pp. 188– 209). Hershey, PA: IGI Global. doi:10.4018/9781-4666-2821-2.ch011 Vidal, K. (2003). Academic listening: A source of vocabulary acquisition? Applied Linguistics, 24(1), 56–89. doi:10.1093/applin/24.1.56 Voller, P. (1997). Does the teacher have a role in autonomous language learning? In P. Benson & P. Voller (Eds.), Autonomy & Independence in Language Learning. New York: Addison Wesley Longman Ltd. Watts, N. (1997). A learner-based design model for interactive multimedia language learning packages. System, 25(1), 1–8. doi:10.1016/S0346251X(96)00056-5 Williams, J. (2001). The effectiveness of spontaneous attention to form. System, 29(3), 325–340. doi:10.1016/S0346-251X(01)00022-7

ADDITIONAL READING Bailey, C., Hall, W., Millard, D. E., & Weal, M. J. (2007). Adaptive hypermedia through contextualized open hypermedia structures. Journal ACM Transactions on Information Systems, 25(4), 1–36. Cristea, A. (2005). Authoring of adaptive hypermedia. Journal of Educational Technology & Society, 8(3), 6–8.

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Ewais, A., & Troyer, O. D. (2013). Usability evaluation of an adaptive 3D virtual learning environment. International Journal of Virtual and Personal Learning Environments, 4(2), 16–31. doi:10.4018/jvple.2013010102 Jones, L. C. (2015). Effects of annotations on inferring meaning within a listening comprehension environment. In V. Turel (Ed.), Intelligent design of interactive multimedia listening software (pp. 1–26). Hershey, PA: Information Science Reference; doi:10.4018/978-1-4666-8499-7.ch001 Kang, T. (2015). The effectiveness of multiple media tools in L2 listening: A meta-analysis. In V. Turel (Ed.), Intelligent Design of Interactive Multimedia Listening Software (pp. 246–275). Hershey, PA: Information Science Reference; doi:10.4018/978-1-4666-8499-7.ch010 Knutov, E., De Bra, P., & Pechenizkiy, M. (2009). AH 12 years later: A comprehensive survey of adaptive hypermedia methods and techniques. New Review of Hypermedia and Multimedia, 15(1), 5–38. doi:10.1080/13614560902801608 Rada, R. (1995). Hypertext, multimedia and hypermedia. New Review of Hypermedia and Multimedia, 1(1), 1–21. doi:10.1080/13614569508914658 Retalis, R., & Papasalouros, A. (2005). Designing and generating Educational Adaptive Hypermedia Applications. Journal of Educational Technology & Society, 8(3), 26–35. Shapiro, A. M. (2008). Hypermedia design as learner scaffolding. Educational Technology Research and Development, 56(1), 29–44. doi:10.1007/s11423-007-9063-4 Turel, V., & McKenna, P. (2015). Design of multimedia listening software: Instructions, tasks, texts, and self-assessment tests. In V. Turel (Ed.), Intelligent design of interactive multimedia listening software (pp. 142–169). Hershey, PA: Information Science Reference; doi:10.4018/9781-4666-8499-7.ch006

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Wang, V. C. X., Russo, M. R., & Dennett, S. (2013). Electronic education and lifelong learning. [IJAVET]. International Journal of Adult Vocational Education and Technology, 4(1), 46–60. doi:10.4018/javet.2013010104

KEY TERMS AND DEFINITIONS Adaptive Hypermedia (AH): The combination and delivery of digital elements (i.e. text, sound/audio, visuals, animation, video, feedback, instructions, self-assessment tests etc.) on the same digital platform that adapts the learning contents to the needs of different learners who are diverse in terms of abilities, disabilities, levels, interests, backgrounds, and other characteristics. Hyperlinks: A button, image, icon etc. in a digital environment on which learners can click to navigate to another part of the environment. Hypermedia: IMM is also called hypermedia. Hypertext: Hypermedia text retrieval system that enables learners to access particular media types in certain locations or files in applications, webpages or other digital environments. Interactive Multimedia (IMM): The combination and delivery of digital elements on the same digital platform which have links between elements in the form of buttons, hotspots or hyperlinks to create an interactive application in which users can navigate. Multimedia: The combination and delivery of digital elements on the same digital platform. Multiple Media: The use of different tools such as television, the tape recorder, video, the overhead projectors, slide projector etc.

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Category: Educational Technologies

Automatic Item Generation Mark Gierl University of Alberta, Canada Hollis Lai University of Alberta, Canada Xinxin Zhang University of Alberta, Canada

INTRODUCTION As the importance of technology in society continues to increase, countries require skilled workers who can produce new ideas, make new products, and provide new services. The ability to create these ideas, products, and services will be determined by the effectiveness of our educational programs. Education provide students with the knowledge and skills required to think, reason, communicate, and collaborate in a world that is shaped by knowledge services, information, and communication technologies (e.g., Binkley, Erstad, Herman, Raizen, Ripley, Miller-Ricci, & Rumble, 2012; Darling-Hammond, 2014). Educational testing has an important role to play in helping students acquire these foundational skills. Educational tests, once developed almost exclusively to satisfy demands for accountability and outcomes-based summative testing, are now expected to provide teachers and students with timely, detailed, formative feedback to directly support teaching and learning. To meet these teaching and learning directives, formative principles are beginning to guide our educational testing practices. Formative principles can include any assessment-related activities that yield constant and specific feedback to modify teaching and improve learning, including administering tests more frequently (Black & Wiliam, 1998, 2010). But when testing occurs frequently, more test items are required. These additional test items must be

created efficiently and economically while maintaining a high standard of quality. Fortunately, this requirement for frequent and timely educational testing coincides with the dramatic changes occurring in instructional technology. Developers of local, national, and international educational tests are now implementing computerized tests at an extraordinary rate (Beller, 2013). Computerized testing offers many important benefits to support and promote key principles in formative assessment. Computers permit testing on-demand thereby allowing students to take the test at any time during instruction; items on computerized tests are scored immediately thereby providing students with instant feedback; computerized tests permit continuous administration thereby allowing students to have more choices about when they write their exams. In short, computers are helping infuse formative principles into our testing practices to support teaching and learning. Despite these important benefits, the advent of computerized testing has also raised formidable challenges, particularly in the area of test item development. Educators must have access to large numbers of diverse, high-quality test items to implement computerized testing because items are continuously administered to students. Hundreds of items are needed to develop the test item banks necessary for computerized testing. Unfortunately, test items, as they are currently created, are time consuming and expensive to develop because each individual item is written, initially, by a content

DOI: 10.4018/978-1-5225-2255-3.ch206 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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specialist and, then, reviewed, edited, and revised by groups of content specialists (Gierl & Lai, 2016a; Rudner, 2010). Hence, item development has been identified as one of the most important problems that must be solved before we can fully migrate to computerized testing because large numbers of high-quality, content-specific, test items are required (Haladyna & Rodriguez, 2013; Webb, Gibson, & Forkosh-Baruch, 2013). One promising test item development method that may be used to address this challenge is with automatic item generation (AIG) (Gierl & Haladyna, 2013; Irvine & Kyllonen, 2002). AIG is a relatively new but rapidly evolving research area where cognitive and psychometric modeling practices guide the production of tests that include items generated with the aid of computer technology. Research on AIG has adopted different perspectives, including the use of natural language processing and rule-based artificial intelligence (e.g., Gütl, Lankmayr, Weinhofer, & Höfler, 2011; Moser, Gütl, & Lui, 2012), frame-semantic representations (e.g., Cubric & Toasic, 2010; Higgins, Futagi, & Deane, 2005), schema theory (e.g., Singley & Bennett, 2002), and sematic web-rule language (Zoumpatianos, Papasalouros, & Kotis, 2011). The purpose of this chapter is to describe and illustrate the most practical method for generating test items, which is template based. By template-based AIG, we mean methods that draw on item models to guide the generative process. Gierl and Lai (2013, 2016a, 2016b) developed a three-step process for template-based AIG. In step 1, content specialists create a cognitive model for AIG. A cognitive model is a representation that highlights the knowledge, skills, and content required to generate new test items. In step 2, an item model is developed to specify where the cognitive model content is placed in each generated item. An item model is a template that highlights the variables in a test item that can be manipulated to produce new items. In step 3, computer algorithms place the cognitive content into the item model. With this process, hundreds of items can be created from a single item model.

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The purpose of this chapter is to describe how AIG can be used to generate test items using the selected-response (i.e., multiple-choice) format. To ensure our description is both concrete and practical, we illustrate template-based item generation using an example from the complex problem-solving domain of the medical health sciences. The chapter is concluded by describing two directions for future research.

BACKGROUND Gierl and Lai (2013, 2016a, 2016b) described a three-step approach for template-based AIG. In step 1, a content specialist creates a cognitive model for AIG. In step 2, an item model is developed to specify where the cognitive model content is placed in each generated item. In step 3, algorithms place the cognitive content into the item model.

Step 1: Identify Content for Item Generation To begin, the content for item generation is identified by the content specialists. This content is identified using design principles and guidelines that highlight the knowledge, skills, and abilities required to solve problems and perform tasks in a specific domain. A cognitive model for AIG is a representation that organizes the cognitive- and content-specific information into a structured representation of how the content specialist expects that examinees will think about and solve problems. Recently, Gierl and Lai (2016b) proposed the key features cognitive model for AIG. With this model, item generation is guided by the relationships among the key features specified in the cognitive model. It is used when the attributes or features of a task are systematically combined to produce meaningful outcomes across the item feature set. The use of constraint programming in step 3 of the AIG process ensures that the relationships among the features yield meaningful items.

Category: Educational Technologies

The key features cognitive model is most suitable for measuring the examinees’ ability to assemble and apply key features within a domain as well as to solve problems using these key features.

Step 2: Create Item Models In step 2, an item model is developed to specify where the content from the cognitive model must be placed to generate new items. An item model is a template that specifies the features in an item that can be manipulated to generate new items. Item models (LaDuca, Staples, Templeton, & Holzman, 1986) specify which parts of the assessment task can be manipulated for item generation. For a selected-response item type, it includes the stem, the options, and the auxiliary information. The stem is the part of an item which formulates context, content, and/or the question the examinee is required to answer. The options contain the alternative answers with one correct option and one or more incorrect options or distracters. Auxiliary information includes any additional material, in either the stem or option, required to generate an item, including digital media such as text, images, tables, diagrams, sound, and/or video. The stem and options can be divided further into elements. These elements are denoted as nonnumeric values (i.e., strings) and numeric values (i.e., integers). By systematically manipulating the elements, many items can be generated from one item model.

Step 3: Generate Test Items Using Computer Technology In step 3, computer algorithms place the cognitive model content specified in step 1 into the item model developed in step 2. Different types of software have been written to generate test items (e.g., Gütl et al., 2011; Higgins, 2007; Higgins et al., 2005; Singley & Bennett, 2002). Gierl, Zhou, and Alves (2008) introduced and demonstrated the use of the computer program IGOR (which is the acronym for Item GeneratOR) for template-

based AIG. IGOR is a JAVA-based program designed to assemble test items using the items models from all combinations of elements specified in the cognitive model. Once the content is specified in step 1 and the template is created in step 2, IGOR systematically places the content into the template to produce new items as part of step 3. IGOR has been used to generate items in the content areas of mathematics, science, social studies, architecture, logical reasoning, dentistry, nursing, medical education, business management, and non-verbal reasoning.

The Unique Problem of Generation Distractors for Selected-Response Items For the selected-response format, items must not only include a stem with a corresponding correct option, but also include a set of incorrect options or distractors. These incorrect options are typically designed from a list of plausible but incorrect alternatives linked to misconceptions or errors in thinking, reasoning, and problem solving. Using a traditional item development approach, a set of distractors are created by a content specialist that is specific to each item. But because AIG produces hundreds of items, an alternative approach is needed to create a correspondingly large number of plausible but erroneous distractors. Three different methods can be used to generate distractors. The first method for generating distractors is often employed when the elements manipulated as part of the item generation process are first specified. Recall, elements are the variables identified by content specialist and then manipulated using computer algorithms to produce new items. A distractor can be specified as an element that contains content that is related to the correct option but still yields an incorrect response. To identify the content for this element, content specialists identify a list of plausible but incorrect options that are appropriate for all possible items generated with a given item model. Then, distractors are randomly selected from

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this pool of plausible but erroneous content and added to each generated item. Hence, this method is called the distractor pool method with random selection. It is based on the assumption that a pool of plausible distractors can be created. A sample of these plausible distractors are selected at random to complete the item generation process. The strength of this method is its simplicity. This method can yield large numbers of distractors. The weakness of this method resides with the two strong assumptions required to use this approach. First, it must be assumed that all pooled distractors are equally plausible for all generated items. Equal plausibility is a strong and, in many cases, restrictive assumptions. Second, there is little reasoning to guide how distractors are paired with the correct option because pairing is achieved with random assignment. The distractor pool method with random selection is most appropriate for casespecific item generation, where a well-defined set of distractors are known to be plausible across a large number of the generated item. To improve the plausibility of the distractors, rules and rationales that yield errors or misconceptions can be used to create distractors. Distractor rationales are short description provided by the content specialist to ensure that the reasoning which underlies each option is explicitly stated. When rationales are available, distractors can be systematically generated such that each distractor matches with the rationale. Hence, this approach is called the systematic generation with rationales method. It is based on the assumption that algorithms, rules, and procedures can first be articulated by content specialists and then used to create plausible but incorrect alternatives. The strength of this method is that the distractors are much more specific and, hence, plausible and appropriate, especially when compared to the previous method. There is also a precedent for this approach as systematic generation using distractor rationale has been used in quantitative- and rule-based content areas, such as mathematics and science, to produce incorrect options (Haladyna

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& Rodriguez, 2013). This method also has some weaknesses. For instance, plausible but incorrect rationales must first be created for each option by the content specialist. This process can be time consuming. Algorithmic or rule-based manipulations must also be permissible for every incorrect option. In some content areas and in some reasoning and problem-solving situations, generic rules or robust algorithms may not be available to guide the development of distractor rationales. To overcome the weaknesses of the first two methods, a third method for distractor generation is also available. It is called systematic distractor generation (Lai, Gierl, DeChamplain, & Boulais, 2015). This method is closely linked to the cognitive modeling approach to AIG. Recall that to generate an item stem and correct option, specific information must be collected in the form of a cognitive model (i.e., step 1 in the three-step AIG process). The systematic distractor generation method also involves specifying information related to errors and misconceptions in the form of a cognitive model in order to generate plausible but incorrect options. The process of modeling incorrect options is more complex compared to modeling the correct option because the item stem is constantly changing in a generative item development system. As a result, the small number of constraints required to ensure that information presented in the stem yields a correct response must be counterbalanced with a much larger number of constraints required to ensure the information presented in the incorrect options is erroneous but plausible. Also, more than one incorrect option is required for a selected-response item. Typically, three, sometimes four, incorrect options must be produced for each generated item. Systematic distractor generation has many strengths. For example, it can be used in diverse testing situations and across many content areas because of its generality. It is also an adaptive method because distractors are selected conditionally using the associate information in the stem and the correct option. Finally, the features that lead to the correct

Category: Educational Technologies

and incorrect options are explicitly identified. This method also has weaknesses. Systematic distractor generation requires that specific information be collected for each distractor. Because selectedresponse items contain two or more incorrect options, this specific information is required for each item in order to model the distractors. Hence, it is a time-consuming method for generating the distractors because data must be collected from content specialists, these data must be structured for distractor generation, and then computer code must be written to assemble the content for each option in a selected-response item.

AN EXAMPLE FROM THE MEDICAL HEALTH SCIENCES To illustrate the three-step approach to AIG, an example from the medical health sciences is presented. This example is drawn from the content area of general surgery where the examinee is required to diagnosis problems that could arise from a serious abdominal injury. This example uses a key features cognitive model with systematic distractor generation. In step 1, a cognitive model for AIG is created. The model in this example was created inductively by asking two 3rd year surgical residents to identify and describe the information that would be used by a surgeon to solve two serious medical problems (i.e., splenic rupture and Grade 1 liver laceration) related to abdominal injury. Three types of information are specified in the cognitive model for AIG. This information is presented as separate panels in Figure 1. The top panel identifies the main concept or problem and its associated scenarios. In this example, the problem is abdominal injury. Two associated scenarios are splenic rupture (SR) and Grade 1 liver laceration (LL). The middle panel specifies the relevant sources of information presented with the problem. In this example, three sources of information were presented: History, physical examination, and laboratory results. The bottom panel highlights

the salient features, which includes the elements and constraints. The first component for a feature is the element. Elements contain content specific to each feature that can be manipulated for item generation. The second component for a feature is the constraint. Constraints serve as restrictions that must be applied during the generation process to ensure that content in the elements are combined in a meaningful way so useful items can be generated. For the Figure 1 example, five features (i.e., type of accident, hemodynamics, side, air entry, and Foley output) were identified across the three sources of information. Each feature specifies the elements and constraints. For instance, the type of accident feature has three elements, low speed, high speed, and motorcycle. These elements contain constraints. For example, splenic rupture is only related to the motorcycle accident (i.e., SR: Motorcycle) whereas the low and high speed accidents are related to the Grade 1 liver laceration (i.e., LL: Low, High). In step 2, the item model is created (see Table 1). The item model contains the template that will be used to generate test items. It also specifies the elements for the features. Five features are included. The Type of Accident feature has three elements. The remaining four features—Hemodynamics, Side, Air Entry, Foley Output—all contain two elements. Taken together Figure 1 and Table 1 describe the content combinations required to produce the correct option in the cognitive model. Splenic rupture is the correct option, for example, when the elements are: motorcycle accident at highway speeds (Type of Accident), blood pressure is 75/35 and heart rate is 140 (Hemodynamics), left side (Side), good air entry and a large distended abdomen with guarding (Air Entry), and 100cc of bloody urine (Foley Output). The same logic is also used to produce the incorrect options using the systematic distractor generation method. Distractor 3 is diaphragmatic rupture. This diagnosis shares some, but not all, of the elements with splenic rupture. For instance, a diaphragmatic rupture can be associated with the elements motor vehicle accident at highway speeds

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Figure 1. An abdominal injury cognitive model

(Type of Accident), blood pressure is 75/35 and heart rate is 140 (Hemodynamics), and 100cc of bloody urine (Foley Output). But a diaphragmatic rupture is not associated with pain on either the left or the ride side (Side) and does not necessarily display physical examination results related to air entry and abdominal pain (Air Entry), as presented in the cognitive or item models. As a

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result, diaphragmatic rupture is a plausible but erroneous option because it shares some but not all of the features related to splenic rupture. The same logic used to code the elements in diaphragmatic rupture is also used to code pneumothorax, cardiac tamponade, and aortic rupture in order to produce a cognitive model for the incorrect options. Then, an incorrect option is selected from

Category: Educational Technologies

Table 1. An item model for generating abdominal injury test items

Stem

A 25-year-old male is involved in a [Type of Accident]. Emergency Medical Services resuscitates him with 2L crystalloid and transports him to your tertiary centre. When he arrives his [Hemodynamics]. He has a Glasgow Coma Scale score of 14. He is complaining of lower-rib pain on his [Side]. On examination, he has [Air Entry]. [Foley Output]. What is the most likely diagnosis?

Elements

Type of Accident: 1: highway speed roll over 2: bicycle accident where he hits a barricade 3: motorcycle accident at highway speeds Hemodynamics: 1: blood pressure is 75/35 and his heart rate is 140 2: blood pressure is 140/90 and his heart rate is 90 Side: 1: left side 2: right side Air Entry: 1: good air entry and a large distended abdomen with guarding 2: good air entry, mildly tender abdomen, and no guarding Foley Output: 1: A foley catheter emits 100cc of bloody urine 2: A foley catheter emits 600cc of urine

Options

1: Splenic rupture 2: Grade 1 liver laceration 3: Diaphragmatic rupture 4: Pneumothorax 5: Cardiac tamponade 6: Aortic rupture 7: Epidural hematoma

Key

this distractor model for inclusion in the generated item whenever it shares some but not all of the elements with the correct option. In step 3, IGOR assembles the content specified in an item model, subject to elements and constraints articulated in the cognitive model for both the correct and incorrect options. Iterations are conducted in IGOR to assemble all possible combinations of elements and options, subject to the constraints. In the current example, IGOR generated 62 abdominal injury items. The model used in this example is comparatively small and is provided for illustrative purposes only in our chapter. Item generation in operational testing programs typically include 5-7 different features per model where each feature has 2-10 different elements thereby producing much larger numbers of items.

FUTURE RESEARCH DIRECTIONS Two areas of future research are recommended. First, methods exist for developing cognitive models for AIG (Gierl & Lai, 2016a). But no methods exist for evaluating the reliability and validity of these cognitive models. While Gierl and Lai (2016b) described two different types of cognitive models, many more cognitive models will likely be identified as researchers and practitioners begin to generate items in different domains and content areas. Hence, future research is required to develop methods for evaluating cognitive models as well as for expanding on the types of cognitive models that can be used for AIG. Second, new methods should be developed that allows feedback to be produced as part of the generative process. That is, the solution and rationale should be produced for

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the correct and incorrect options during the threestep item generation process. This outcome would require a significant modification to the current item models and IGOR software. The benefits, however, would be significant. Effective formative assessment permits teachers to identify students’ problem-solving strengths and weaknesses so they can adjust their instruction to overcome the weaknesses (Quellmalz & Pellegrino, 2009). But a test must contain a large number of items with carefully selected incorrect options related to a single concept in order to pinpoint different types of weaknesses, problems, and/or misconceptions. Item generation could be conducted so the solutions and rationales for the incorrect options are designed from a list of plausible but incorrect alternatives linked to common misconceptions or errors thereby providing teachers with this type of pinpoint precision. Therefore, additional research should be undertaken to refine the item models and the IGOR software so solutions and rationales that provide teachers with specific feedback can be produced as part of the item generation process.

CONCLUSION As the importance of technology continues to increase, countries require skilled workers who can produce new ideas, products, and services. The ability to create these outcomes will be determined by the effectiveness of our educational programs. Education provides students with the knowledge and skills required to think, reason, communicate, and collaborate in a world that is increasingly shaped by knowledge services, information, and communication technologies. Computerized testing will foster the development of this highly skilled workforce by helping students acquire these essential skills. Computers allow educators to administer tests more frequently so students can receive specific continuous feedback as they develop their skills. But when tests are given more frequently, a constant supply of test items is needed. Automatic item generation is a

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rapidly evolving research area where cognitive and psychometric theories are used to constantly produce tests that contain items that are generated with computer technology. The purpose of this chapter was to describe and illustrate a templatebased method for generating test items to address the challenging problem of rapidly and economically producing large numbers of high-quality, content-specific, test items required to support computerized testing.

REFERENCES Beller, M. (2013). Technologies in large-scale assessments: New directions, challenges, and opportunities. In M. von Davier, E. Gonzalez, I. Kirsch, & K. Yamamoto (Eds.), The Role of international large-scale assessments: Perspectives from technology, economic and educational research (pp. 25–45). New York: Springer. doi:10.1007/97894-007-4629-9_3 Binkley, M., Erstad, O., Herman, J., Raizen, S., Ripley, M., Miller-Ricci, M., & Rumble, M. (2012). Defining twenty-first century skills. In P. Griffin, B. McGaw, & E. Care (Eds.), Assessment and teaching of 21st century skills (pp. 17–66). New York: Springer. doi:10.1007/978-94-0072324-5_2 Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. doi:10.1080/0969595980050102 Black, P., & Wiliam, D. (2010). Inside the black box: Raising standards through classroom assessment. Phi Delta Kappan, 92(1), 81–90. doi:10.1177/003172171009200119 Cubric, M., & Tosic, M. (2010). Towards automatic generation of e-assessment using semantic web technologies. Paper presented at the International Computer-Assisted Assessment Conference (CAA 2010), Southampton, UK.

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Darling-Hammond, L. (2014). Next generation assessment: Moving beyond the bubble test to support 21st century learning. San Francisco, CA: Jossey-Bass. Gierl, M. J., & Haladyna, T. (2013). Automatic item generation: Theory and practice. New York: Routledge. Gierl, M. J., & Lai, H. (2013). Using automated processes to generate test items. Educational Measurement: Issues and Practice, 32(3), 36–50. doi:10.1111/emip.12018 Gierl, M. J., & Lai, H. (2016a). Automatic item generation. In S. Lane, M. Raymond, & T. Haladyna (Eds.), Handbook of test development (2nd ed.; pp. 410–429). New York: Routledge. Gierl, M. J., & Lai, H. (2016b). The role of cognitive models in automatic item generation. In A. Rupp & J. Leighton (Eds.), The Wiley handbook of cognition and assessment: Frameworks, methodologies, and applications (pp. 124–145). New York: Wiley. Gierl, M. J., Zhou, J., & Alves, C. (2008). Developing a taxonomy of item model types to promote assessment engineering. The Journal of Technology, Learning, and Assessment, 7(2). http://www. jtla.org Retrieved January 20, 2011 Gütl, C., Lankmayr, K., Weinhofer, J., & Höfler, M. (2011). Enhanced Automatic Question Creator – EAQC: Concept, development and evaluation of an automatic test item creation tool to foster modern e-education. Electronic Journal of eLearning, 9, 23-38. Haladyna, T. M., & Rodriguez, M. C. (2013). Developing and validating test items. New York: Routledge. Higgins, D. (2007). Item Distiller: Text retrieval for computer-assisted test item creation. Educational Testing Service Research Memorandum (RM-0705). Princeton, NJ: Educational Testing Service.

Higgins, D., Futagi, Y., & Deane, P. (2005). Multilingual generalization of the Model Creator software for math item generation. Educational Testing Service Research Report (RR-05-02). Princeton, NJ: Educational Testing Service. Irvine, S. H., & Kyllonen, P. C. (2002). Item generation for test development. Hillsdale, NJ: Erlbaum. LaDuca, A., Staples, W. I., Templeton, B., & Holzman, G. B. (1986). Item modeling procedures for constructing content-equivalent multiplechoice questions. Medical Education, 20(1), 53–56. doi:10.1111/j.1365-2923.1986.tb01042.x PMID:3951382 Lai, H., Gierl, M., DeChamplain, A., & Boulais, A. (2015, April). Systematically generating plausible distractors: Field-testing results and psychometric implications from item generation. Paper presented at the annual meeting of the Canadian Conference on Medical Education, Vancouver, BC. Moser, J. R., Gütl, C., & Liu, W. (2012). Refined distractor generation with LSA and stylometry for automated multiple choice question generation. Paper presented at the 25th Australasian Joint Conference-Advances in Artificial Intelligence (AL 2012), Sydney, Australia doi:10.1007/9783-642-35101-3_9 Quellmalz, E. S., & Pellegrino, J. W. (2009). Technology and testing. Science, 323(5910), 75–79. doi:10.1126/science.1168046 PMID:19119222 Rudner, L. (2010). Implementing the Graduate Management Admission Test Computerized Adaptive Test. In W. van der Linden & C. Glas (Eds.), Elements of adaptive testing (pp. 151–165). New York, NY: Springer. Singley, M. K., & Bennett, R. E. (2002). Item generation and beyond: Applications of schema theory to mathematics assessment. In S. H. Irvine & P. C. Kyllonen (Eds.), Item generation for test development (pp. 361–384). Mahwah, NJ: Erlbaum.

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Webb, M., Gibson, D., & Forkosh-Baruch, A. (2013). Challenges for information technology supporting educational assessment. Journal of Computer Assisted Learning, 29(5), 451–462. doi:10.1111/jcal.12033

Luecht, R. (2013). An introduction to assessment engineering for automatic item generation. In M. J. Gierl & T. Haladyna (Eds.), Automatic item generation: Theory and practice (pp. 59–76). New York: Routledge.

Zoumpatianos, K., Papasalouros, A., & Kotis, K. (2011). Automated transformation of SWRL rules into multiple-choice questions. Paper presented at the FLAIRS Conference 11, Palm Beach, FL.

Popp, E. C., Tuzinski, K., & Fetzer, M. (2016). Actor or avatar? Considerations in selecting appropriate formats for assessment content. In F. Drasgow (Ed.), Technology and testing: Improving educational and psychological measurement (pp. 79–103). New York: Routledge.

ADDITIONAL READING

Schmeiser, C. B., & Welch, C. J. (2006). Test development. In R. L. Brennan (Ed.), Educational measurement (4th ed., pp. 307–353). Westport, CT: National Council on Measurement in Education and American Council on Education.

Bormuth, J. (1969). On a theory of achievement test items. Chicago: University of Chicago Press. Drasgow, F., Luecht, R. M., & Bennett, R. (2006). Technology and testing. In R. L. Brennan (Ed.), Educational measurement (4th ed., pp. 471–516). Washington, DC: American Council on Education. Embretson, S. E., & Yang, X. (2007). Automatic item generation and cognitive psychology. In C. R. Rao & S. Sinharay (Eds.) Handbook of statistics: Psychometrics, Volume 26 (pp. 747-768). North Holland, UK: Elsevier. Geerlings, H., Glas, C. A. W., & van der Linden, W. J. (2011). Modeling rule-based item generation. Psychometrika, 76(2), 337–359. doi:10.1007/ s11336-011-9204-x Gierl, M. J., Lai, H., Fung, K., & Zheng, B. (2016). Using technology-enhanced processes to generate items in multiple languages. In F. Drasgow (Ed.), Technology and testing: Improving educational and psychological measurement (pp. 109–127). New York: Routledge. Lane, S. Raymond, M. & T. Haladyna (2016). Handbook of test development (2nd Ed.). New York: Routledge.

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KEY TERMS AND DEFINITIONS Automatic Item Generation: A process of using item models to generate test items with the aid of computer technology. Cognitive Model: A representation that highlights the knowledge, skills, problem-solving processes and/or content an examinee requires to answer test items. Distractor Pool Method with Random Selection: A method for creating distractors when the distractors created from a list and then the list is used to randomly select plausible but erroneous content for each generated item. Elements: Variables in the item model that can be modified to create new test items. Item Model: A template that highlights the features in an item that must be manipulated to generate new items. Key Features Cognitive Model: A model used to guide item generation based on the relationships

Category: Educational Technologies

among the key features specified in the cognitive model, which include the attributes or features of a task are systematically combined to produce meaningful outcomes across the item feature set. Systematic Distractor Generation: A method for generating distractors where specific informa-

tion related to errors and misconceptions is used to create plausible but incorrect options. Systematic Generation with Rationales Method: A method to systematically create distractors when rationales are used to produce a list of incorrect options.

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Challenges in Developing Adaptive Educational Hypermedia Systems Eileen O’Donnell Trinity College Dublin, Ireland Liam O’Donnell Dublin Institute of Technology, Ireland

INTRODUCTION Traditional educational hypermedia systems afford learners the “one size fits all” approach to learning (Brusilovsky, 2003, 2004; Chatti, Jarke, & Specht, 2010; Hsieh, Lee, & Su, 2013). In the “one size fits all” approach to learning each student in every cohort of students is given access to the same learning objects in the same way as every other student who is studying the same course. The learning objects or learning content stays static regardless of the learning requirements of different students. The objective of Adaptive Educational Hypermedia Systems (AEHS) is to afford learners the opportunity to engage with learning content which has been specifically designed to meet the learning requirements of each individual learner by adapting the content and the user interface to suit the needs of a specific user. AEHS could be used in the education of learners at all stages of their education from junior school to post graduate level. AEHS could also be used in organisations for continuous professional development or training for compliance purposes, for example, first aid or manual handling. Software engineering for AEHS commences with a thorough study of the requirements of the proposed system. AEHS, as proposed systems, are very complex systems to design as the software engineer has to design a system to enable non-technical and technical educator authors to design adaptive learning courses for use by students. Therefore, the design and development of AEHS are very complicated, time

consuming, and expensive. This article reviews a few of the challenges encountered in the design and development of these complex systems and some of the challenges encountered by educators who propose to use AEHS with their students. The background section of this article provides the reader with brief definitions and discussions on the concept of AEHS and positions AEHS in the larger research area of E-Learning or Technology Enhanced Learning. The main body of the paper outlines some of the challenges encountered in the development and use of AEHS including: the classification of different categories of learners; the sourcing of suitable educational materials or learning resources; gauging the impact that AEHS have on the learning experience of end users; and student access to open and editable user models/ profiles. Followed by sections on the following: solutions and recommendations; future research directions and the conclusion.

BACKGROUND “Adaptive hypermedia systems build a model of the goals, preferences and knowledge of each individual user, and use this model throughout the interaction with the user, in order to adapt to the needs of that user” (Brusilovsky, 2001, p. 87). AEHS build a model of each individual student, and use the information from this model to determine the adaptive learning experiences to be created for each student.

DOI: 10.4018/978-1-5225-2255-3.ch207 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Educational Technologies

The following sections provide more information on AEHS and include definitions of the terms Adaptive Education (AE), Adaptive Educational Hypermedia (AEH) and Adaptive Educational Hypermedia Systems (AEHS). E-learning and Technology Enhanced Learning (TEL) pertain to various forms of teaching and learning through the use of technology (O’Donnell & O’Donnell, 2015) and access to the Internet. TEL in the context of this article can be used synonymously with E-Learning. AEHS is a form of E-Learning which goes beyond the “one size fits all” approach to E-Learning by adapting the content to suit the learning requirements of individual learners.

Adaptive Education (AE) Adaptive Education (AE) can be defined as an educational experience that adapts to suit the learning requirements of each individual learner. The purpose of AE is to provide learners with learning resources which have been specially selected to suit their specific learning needs.

Adaptive Educational Hypermedia (AEH) AEH is electronic content which can be used in the provision of adaptive education. Software developers and educational providers are continuously

exploring how technology can be used to enhance the learning experience of students. In a study of AEH authoring tools, Gaffney, Staikopoulos, O’Keeffe, Conlan, and Wade (2014) suggest that “AEH authoring tools have however not been as successfully adopted as was initially expected” (p. 416). Further research is required to explore why AEH authoring tools have not been as successfully adopted as initially expected.

Adaptive Educational Hypermedia Systems (AEHS) Adaptive Educational Hypermedia Systems (AEHS) are designed and developed to deliver adaptive educational experiences to students. Some examples of AEHS include: GRAPPLE (De Bra et al., 2012; Glahn et al., 2011); AHA! (De Bra, Stash, Smits, Romero, & Ventura, 2007; Knutov, De Bra, & Pechenizkiy, 2009); and ELM-ART (Weber & Brusilovsky, 2001). AEHS use many different mediums of electronic content. Hypertext is a section of online text or an online paragraph of information that has been embedded with links to other content. Hypermedia is electronic content which includes links to many different mediums of content as shown in Figure 1, such as: text; images; tables; figures; graphics; audio; video; animations; simulations; or games.

Figure 1. Hypermedia can be linked to many different mediums of content

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The purpose of AEHS is to provide educators with the appropriate toolset to present learners with educational resources that have been specifically selected to suit their individual learning requirements. AEHS or Adaptive Hypermedia Systems for Education (AHSE) have the potential to facilitate personalised technology enhanced learning (Muntean & McManis, 2004). AEHS Authoring Tools (ATs) are created to enable non-technical educator authors to design and create adaptive learning resources.

CHALLENGES ENCOUNTERED IN THE DEVELOPMENT OF AEHS The development of AEHS is a very difficult process (Baig, 2014). There are many challenges to consider when designing these systems. A few of the challenges are outlined in this paper but others do exist. AEHS are very complex systems because they are designed to be used by many educators and many students who will possess various different knowledge levels of the use of Information and Communications Technology (ICT). There are numerous considerations and challenges involved in designing and developing systems for use by non-technical authors. There are also several challenges involved in developing systems for use by technically competent authors who may require the affordance of complicated functionality. In addition, the designers and developers of AEHS

have to meet the challenges of facilitating the functionality required by educators who employ various different pedagogical teaching methods (O’Donnell, Lawless, Sharp, & Wade, 2015) and learning theories (O’Donnell, Lawless, Sharp, & O’Donnell, 2015). Adding adaptive functionality to an educational system does not necessarily improve the system, sometimes the adaptive functionality can cause users to lose control of the system (Brusilovsky, 1996). Adding adaptive functionality to an existing Learning Management System (LMS) does not necessarily improve the students’ learning experiences. The students may not understand why they are getting access to different learning experiences to those of their peers. And, the educators may lose control over the adaptive functionality resulting in the students not achieving the expected learning outcomes. Instructional designers and academics require a number of skills to develop positive learning experiences (O’Rourke & Martin, 2011). As shown in Figure 2, some of these required skills may include: pedagogical knowledge (Hirumi, Appelman, Rieber, & Van Eck, 2010; Koh & Chai, 2014); critical and creative thinking (Baum & Newbill, 2010); knowledge of instructional development (Stes, Coertjens, & van Petegem, 2010); awareness of aesthetic principles (Yanchar & Gabbitas, 2011); experience in instructional design (van Rooij, 2010); competence in the use of ICT (O’Rourke & Martin, 2011); among others.

Figure 2. Some of the skills required to develop positive learning experiences

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Figure 3. Some of the student characteristics that can be used to classify different types of learners

The skill set required and the challenges encountered will markedly increase as instructional designers attempt to develop positive learning experiences that are also adaptable to each learner’s specific learning requirements. The use of AEHS to support learners can pose challenges to learners with respect to cognitive load and self-regulated learning, Moos (2014) and Kuo and Huang (2009) proposed the use of scaffolding in the development of AEHS to support learners. In the design and development of AEHS both the academic users and the student users (learners) have to be considered and facilitated. This dual requirement increases the complexity in designing and developing AEHS.

The Classification of Different Categories of Learners Classification of learners is necessary to inform adaptive E-Learning models (Musumba, Oboko, & Nyongesa, 2013). User profiles (Brusilovsky & Millan, 2007) are necessary to store the information collected on each user. Profiling the learners’ present knowledge informs academics about the gaps in knowledge and the level at which this information should be focused (Ajmal, Hamidullah, Rahman, & Khan, 2011). Classification can be based on various different student characteristics as outlined in Figure 3, including the following: level of knowledge (Musumba et al., 2013); learning styles (Akbulut & Cardak, 2012); cognitive styles (Lo, Chan, & Yeh,

2012); prior knowledge (Chen, Chen, & Sun, 2014; O’Donnell, Sharp, Wade, & O’Donnell, 2012); learning preferences (O’Donnell et al., 2012); cognitive ability (O’Donnell et al., 2012); navigational behaviour (Brusilovsky, 2007; O’Donnell et al., 2012); role playing behaviour (Peeters, Bosch, Meyer, & Neerincx, 2014); among others.

The Sourcing of Suitable Educational Materials or Learning Resources Another challenge encountered in the development of AEHS is the identification of suitable learning resources or objects to be used in the system. Learning objects would also have to be classified to enable retrieval and reuse by AEHS. While educational materials are frequently made available freely online, these resources are available in a number of different formats and may not be easily adopted for use in AEHS. The sourcing of suitable learning resources to facilitate the creation of Adaptive Hypermedia Systems is labour intensive (Levacher, Lawless, & Wade, 2014) and sometimes it is simply not possible to identify appropriate learning resources to use. The creation of learning resources for use in adaptive systems is a time consuming and complex process (Nurjanah & Davis, 2012), labour intensive and expensive. A research study conducted by O’Donnell (2008) was undertaken to establish lecturers views on the use of ICT in higher education. The study participants were lecturers from various disciplines within the Dublin Institute of Technology. 41 lecturers

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participated in this study. One of the findings was that only 15% of the lecturers surveyed agreed that they had sufficient time to create E-Learning resources (O’Donnell, 2008).

Gauging the Impact That AEHS Have on the Learning Experience of End Users It is very difficult to state the impact that adaptive systems have had on the learning experience of the end user (Mulwa, Lawless, Sharp, ArnedilloSanchez, & Wade, 2010; Mulwa, Lawless, Sharp, & Wade, 2012) and “the jury is still out as regards evidence that ICT supports learning” (Livingstone, 2012, p. 19). The findings of Griff and Matter (2013) from the evaluation of LearnSmart (an adaptive online learning system) was that it had no overall effect. Because it is very difficult to gauge the impact that adaptive systems have had on the learning experience of the end users it may be challenging to encourage more educators to engage with the time consuming task of creating courses using AEHS. There is a need through controlled evaluations of adaptive technologies to gauge the value added to improving student learning (Shute & ZapataRivera, 2012). Brusilovsky, Karagiannidis and Sampson (2004) proposed a layered evaluation approach be used when evaluating adaptive learning systems. Until AEHS are used in mainstream education the full impact of adaptive courses on the learning experience of students cannot be evaluated. Further investigations are necessary to gauge the impact that adaptive systems have on the learning experience of end users (learners, students, trainees, employees).

Student Access to Open and Editable User Models/Profiles Adaptive systems gather some of the data to be stored in the user model by tracking a user’s engagement with the adaptive system (Brusilovsky et al., 2008). Some adaptive systems automatically

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update the user model so that the user never sees the changes made to their personal user model. In addition, in some adaptive systems users are not given the opportunity to edit their own personal user model for fear it will interfere with the personalisation processes of the system. Ahn, Brusilovsky and Han (2015) state that many arguments have been presented “in favour of open and editable user models” (p. 202). Another challenge to the developers of AEHS is whether or not to provide the functionality to afford educators the opportunity to enable the users to see and edit their own user model. Some educators may be in favour of allowing their students to see and edit their own user model, while others may prefer to keep control of the user models through system generated automatic updates. In a study of self-regulated personalised learning, Steiner, Nussbaumer, and Albert (2009) found that “Up until now, the structures and algorithms underlying personalisation have been completely kept back from the user. The learner is completely unaware of the personalisation process that is taking place behind the scenes...” (p. 650). In a survey of forty academics conducted by O’Donnell, Sharp, Wade and O’Donnell (2012) in response to the question “Would you trust the decision making algorithms in an authoring tool to determine the most suitable learning activities for each individual student?” (p. 9), only 10% of the academics surveyed responded “yes” to this question. Further along in the same study, O’Donnell et al state “The fact that only 10% of academics surveyed would trust the decision making algorithms is a finding of statistical significance that requires further investigation” (O’Donnell et al., 2012, p. 16). If academics find it hard to trust the decision making algorithms there is a possibility that learners would also like more information about the structures and algorithms used to determine the personalisation process. Ashman, Brailsford, Cristea, Sheng, Stewart, Toms and Wade (2014) suggest that users should be informed about the personal information that is being collected and how this information is

Category: Educational Technologies

Figure 4. AEHS are complex systems to design and develop

used in educational and other settings. In a study conducted by Staikopoulos, O’Keeffe, Rafter, Walsh, Yousuf, Conlan and Wade (2014), 30.6% of the students surveyed wanted more control over the content selected by the personalised course. The amount of control to be allocated to the user of the personalised systems (Parra-Santander & Brusilovsky, 2015) and the right of the user to edit their own user model (Ahn et al., 2015) pose further challenges in the design and development of AEHS. The amount of control allocated to the user and the right of the user to edit their user model require further investigation to establish some guidelines for those challenged with the development of AEHS. AEHS are complex systems to design and develop and present many challenges as illustrated in Figure 4.

SOLUTIONS AND RECOMMENDATIONS Designers and developers of AEHS require further feedback from the academic community and student body to inform future developments of AEHS authoring tools. Instructional designers and developers of E-Learning courses require additional supports and encouragement to engage with AEHS authoring tools. Further evaluations of existing adaptive courses are required to gauge the

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impact that adaptive courses have on the learning experience of students to inform the designers and developers of AEHS and the educators who design and develop adaptive courses.

FUTURE RESEARCH DIRECTIONS Further research is required to explore why AEH authoring tools have not been as successfully adopted as initially expected. AEHS are not yet sufficiently developed for use by non-technical authors. Further work is required on the development of AEHS. Further investigations are necessary to gauge the impact that adaptive systems have on the learning experience of end users (learners, students, trainees and employees). Further investigations are also required into the amount of control that should be allocated to the end user, and the right of the user to see and edit their user model/profile.

CONCLUSION AEHS are very complex systems to design and develop. While the concept of AEHS is very promising, the realisation is very challenging, expensive, and time consuming. Some educators feel that they have insufficient time to engage in

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the creation of e-learning resources while others feel that they could not trust the decision making algorithms which determine the most suitable learning resources to present to each student. The challenges encountered in the development of AEHS are many and varied as outlined in this paper. Further research is necessary to investigate the true potential of AEHS.

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Chatti, M. A., Jarke, M., & Specht, M. (2010). The 3P Learning Model. Journal of Educational Technology & Society, 13(4), 74–85. Chen, J., Chen, M., & Sun, Y. (2014). A tag based learning approach to knowledge acquisition for constructing prior knowledge and enhancing student reading comprehension. Computers & Education, 70, 256–268. doi:10.1016/j.compedu.2013.09.002 De Bra, P., Smits, D., van der Sluijs, K., Cristea, A., Foss, J., Glahn, C., & Steiner, C. (2012). GRAPPLE: Learning Management Systems Meet Adaptive Learning Environments. In A. Peña-Ayala (Ed.), Intelligent and Adaptive ELS, SIST 17 (pp. 133–160). Berlin: Springer-Verlag. De Bra, P., Stash, N., Smits, D., Romero, C., & Ventura, S. (2007). Authoring and Management Tools for Adaptive Educational Hypermedia Systems: The AHA! Case Study. In Studies in Computational Intelligence (SCI) (Vol. 62, pp. 285–308). Berlin: Springer-Verlag. Gaffney, C., Staikopoulos, T., O’Keeffe, I., Conlan, O., & Wade, V. (2014). A training framework for Adaptive Educational Hypermedia authoring tools. Paper presented at the Open Learning and Teaching in Educational Communities 9th European Conference on Technology Enhanced Learning, EC-TEL 2014, Graz, Austria. doi:10.1007/9783-319-11200-8_35 Glahn, C., Steiner, C., de Bra, P., Docq, F., O’Donnell, E., Verpoorten, D.,... Stash, N. (2011). GRAPPLE (Generic Responsive Adaptive Personalized Learning Environment): Second empirical evaluation in academic settings. Retrieved from http://ebookbrowsee.net/d9-5-wp9-finalevaluation-v1-0-pdf-d633058884 Griff, E., & Matter, S. (2013). Evaluation of an adaptive online learning system. British Journal of Educational Technology, 44(1), 170–176. doi:10.1111/j.1467-8535.2012.01300.x

Hirumi, A., Appelman, B., Rieber, L., & Van Eck, R. (2010). Preparing instructional designers for game-based learning: Part 1. TechTrends, 54(3), 27–37. doi:10.1007/s11528-010-0400-9 Hsieh, T., Lee, M., & Su, C. (2013). Designing and implementing a personalized remedial learning system for enhancing the programming learning. Journal of Educational Technology & Society, 16(4), 32–46. Knutov, E., De Bra, P., & Pechenizkiy, M. (2009). AH 12 years later: A comprehensive survey of adaptive hypermedia methods and techniques. New Review of Hypermedia and Multimedia, 15(1), 5–38. doi:10.1080/13614560902801608 Koh, J., & Chai, C. (2014). Teacher clusters and their perceptions of technological pedagogical content knowledge (TPACK) development through ICT lesson design. Computers & Education, 70, 222–232. doi:10.1016/j.compedu.2013.08.017 Kuo, Y. H., & Huang, Y. M. (2009). MEAT: An authoring tool for generating adaptable learning resources. Journal of Educational Technology & Society, 12(2), 51–68. Levacher, K., Lawless, S., & Wade, V. (2014). Slicepedia: Content-agnostic slicing resource production for Adaptive Hypermedia. Computer Science and Information Systems, 11(1), 393–417. doi:10.2298/CSIS121223014L Livingstone, S. (2012). Critical reflections on the benefits of ICT in education. Oxford Review of Education, 38(1), 9–24. doi:10.1080/03054985. 2011.577938 Lo, J., Chan, Y., & Yeh, S. (2012). Designing an adaptive web-based learning system based on students cognitive styles identified online. Computers & Education, 58(1), 209–222. doi:10.1016/j. compedu.2011.08.018 Moos, D. (2014). Setting the stage for the metacognition during hypermedia learning: What motivation constructs matter? Computers & Education, 70, 128–137. doi:10.1016/j.compedu.2013.08.014

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Mulwa, C., Lawless, S., Sharp, M., ArnedilloSanchez, I., & Wade, V. (2010). Adaptive educational hypermedia systems in technology enhanced learning: a literature review. Paper presented at the ACM. doi:10.1145/1867651.1867672 Mulwa, C., Lawless, S., Sharp, M., & Wade, V. (2012). The Evaluation of Adaptive TechnologyEnhanced Learning Systems. Paper presented at the The World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education E-LEARN 2012, Montreal, Quebec, Canada. Muntean, C., & McManis, J. (2004). Adaptive elearning systems: Evaluation issues. Transactions on Automatic Control and Computer Science, 49(63), 193–198. Musumba, G., Oboko, R., & Nyongesa, H. (2013). Agent-based adaptive e-learning model for any learning management system. International Journal of Machine Learning and Applications, 2(1). doi:10.4102/ijmla.v2i1.6 Nurjanah, D., & Davis, H. (2012). Improving the workspace awareness of authors in asynchronous collaborative authoring of learning designs. Paper presented at the EdMedia 2012 - World Conference on Educational Media and Technology, Denver, CO. O’Donnell, E. (2008). Can e-learning be used to further improve the learning experience to better prepare students for work in industry (Masters in Information Systems for Managers). Dublin: Dublin City University. Retrieved from http:// arrow.dit.ie/buschmanoth/1 O’Donnell, E., Lawless, S., Sharp, M., & O’Donnell, L. (2015). Learning theories: epedagogical strategies for Massive Open Online Courses (MOOCs) in higher education. In E. McKay & J. Lenarcic (Eds.), Macro-Level Learning Through Massive Open Online Courses (MOOCS). Strategies and Predictions for the Future. doi:10.4018/978-1-4666-8324-2.ch006

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O’Donnell, E., & O’Donnell, L. (2015). Technology-Enhanced Learning: Towards providing supports for PhD students and researchers in higher education. In Curriculum Design and Classroom Management: Concepts, Methodologies, Tools, and Applications (Vols. 1–3, pp. 242–262). Hershey, PA: Information Science Reference. O’Donnell, E., Sharp, M., Wade, V., & O’Donnell, L. (2012). Academics’ views on personalised elearning in higher education. Paper presented at the International Conference on Engaging Pedagogy, Institute of Technology, Blanchardstown, Dublin, Ireland. Retrieved from http://arrow.dit. ie/cgi/viewcontent.cgi?article=1033&context= buschmancon ODonnell, E., Lawless, S., Sharp, M., & Wade, V. (2015). A review of personalised e-learning: Towards supporting learner diversity. International Journal of Distance Education Technologies, 13(1), 22–47. doi:10.4018/ijdet.2015010102 ORourke, S. L., & Martin, M. (2011). Instructional design of a distance education cultural awareness course to enhance currency and authenticity. British Journal of Educational Technology, 42(5), 875–882. doi:10.1111/j.1467-8535.2010.01103.x Parra-Santander, D., & Brusilovsky, P. (2015). User-controllable personalization: A case study with SetFusion. International Journal of HumanComputer Studies, 78, 43–67. doi:10.1016/j. ijhcs.2015.01.007 Peeters, M., Bosch, K., Meyer, J., & Neerincx, M. (2014). The design and effect of automated directions during scenario-based training. Computers & Education, 70, 173–183. doi:10.1016/j. compedu.2013.07.039 Shute, V., & Zapata-Rivera, D. (2012). Adaptive educational systems. In P. Durlach & A. Lesgold (Eds.), Adaptive Technologies for Training and Education (pp. 7–27). Cambridge University Press. doi:10.1017/CBO9781139049580.004

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Staikopoulos, A., OKeeffe, I., Rafter, R., Walsh, E., Yousuf, B., Conlan, O., & Wade, V. (2014). AMASE: A framework for supporting personalised activity-based learning on the web. Computer Science and Information Systems, 11(1), 343–367. doi:10.2298/CSIS121227012S

Al-Azawei, A., & Badii, A. (2014). State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss). International Journal of Computer Science and Information Technology, 6(3), 1–19. doi:10.5121/ijcsit.2014.6301

Steiner, C., Nussbaumer, A., & Albert, D. (2009). Supporting self-regulated personalised learning through Competence-Based Knowledge Space Theory. Policy Futures in Education, 7(6), 645– 661. doi:10.2304/pfie.2009.7.6.645

Arora, A., Raisinghani, M., Thompson, L., & Leseane, R. (2011). Personality scales and learning styles: Pedagogy for creating an adaptive webbased learning system. International Journal of Online Pedagogy and Course Design, 1(1), 29–49. doi:10.4018/ijopcd.2011010103

Stes, A., Coertjens, L., & van Petegem, P. (2010). Instructional development for teachers in higher education: Impact on teaching approach. Higher Education, 60(2), 187–204. doi:10.1007/s10734009-9294-x van Rooij, S. W. (2010). Project management in instructional design: ADDIE is not enough. British Journal of Educational Technology, 41(5), 852–864. doi:10.1111/j.1467-8535.2009.00982.x Weber, G., & Brusilovsky, P. (2001). ELM_ART: An adaptive versatile system for web-based instruction. International Journal of Artificial Intelligence in Education, 12, 351–384. Yanchar, S., & Gabbitas, B. (2011). Between eclecticism and orthodoxy in instructional design. Educational Technology Research and Development, 59(3), 383–398. doi:10.1007/s11423-010-9180-3

ADDITIONAL READING Acampora, G., Gaeta, M., & Loia, V. (2011). Combining multi-agent paradigm and memetic computing for personalized and adaptive learning experiences. Computational Intelligence, 27(2), 141–165. doi:10.1111/j.1467-8640.2010.00367.x

Bulu, S., & Pedersen, S. (2012). Supporting problem-solving performance in a hypermedia learning environment: The role of students prior knowledge and metacognitive skills. Computers in Human Behavior, 28(4), 1162–1169. doi:10.1016/j.chb.2012.01.026 Caravantes, A., & Galán, R. (2011). Generic educational knowledge representation for adaptive and cognitive systems. Journal of Educational Technology & Society, 14(3), 252. Colace, F., Santo, M. D., & Greco, L. (2014). E-Learning and personalized learning path: A proposal based on the adaptive educational hypermedia system. [iJET]. International Journal of Emerging Technologies in Learning, 9(2), 9–16. doi:10.3991/ijet.v9i2.3211 Cristea, A., & Ghali, F. (2011). Towards adaptation in e-learning 2.0. New Review of Hypermedia and Multimedia, 17(2), 199–238. doi:10.1080/13 614568.2010.541289 De Bra, P., Smits, D., van der Sluijs, K., Cristea, A., Foss, J., Glahn, C., & Steiner, C. (2012). GRAPPLE: learning management systems meet adaptive learning environments. In A. Peña-Ayala (Ed.), Intelligent and Adaptive ELS, SIST 17 (pp. 133–160). Berlin, Heidelberg: Springer-Verlag.

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Glahn, C., Steiner, C., De Bra, P., Docq, F., & O’Donnell, E. (2010). GRAPPLE (Generic Responsive Adaptive Personalized Learning Environment): Second documentation and training for GRAPPLE users. Retrieved from http:// grapple-project.org/public-files/deliverables/ D9.4-WP9-SecondTrainingReport-v1.1.pdf Kahraman, H. T., Sagiroglu, S., & Colak, İ. (2013). A novel model for web‐based adaptive educational hypermedia systems: SAHM (supervised adaptive hypermedia model). Computer Applications in Engineering Education, 21(1), 60–74. doi:10.1002/ cae.20451 Mahnane, L., Trigano, P., Tayeb, L. M., & Benmimoun, A. (2011). Designing an adaptive hypermedia system based on the use of psycho pedagogical criteria. [IJCSI]. International Journal of Computer Science Issues, 8(2), 669–677. Mampadi, F., & Mokotedi, A. P. (2012). Towards effective combination of prior knowledge and cognitive styles in adaptive educational hypermedia systems. [iJET]. International Journal of Emerging Technologies in Learning, 7(3), 11–18. doi:10.3991/ijet.v7i3.2079 Medina-Medina, N., Molina-Ortiz, F., & GarcíaCabrera, L. (2011). Adaptation and user modeling in hypermedia learning environments using the SEM-HP model and the JSEM-HP tool. Knowledge and Information Systems, 29(3), 629–656. doi:10.1007/s10115-010-0357-1 Mulwa, C., Lawless, S., Ghorab, M. R., O’Donnell, E., Sharp, M., & Wade, V. (2011). A framework for the evaluation of adaptive information retrieval systems through implicit recommendation. In S. Andrews, Polovina, S., Hill, R. and Akhgar, B. (Ed.), Proceedings of the International Workshop on Task Specific Information Retrieval, TSIR 2011, at the 19th International Conference on Conceptual Structures (Vol. 6828/2011, pp. 366-374). University of Derby, England: Springer.

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O’Donnell, E., Mulwa, C., Sharp, M., & Wade, V. (2013). Web-mediated education and training environments: a review of personalised interactive e-learning resources. In E. McKay (Ed.), ePedagogy in Online Learning: New Developments in Web Mediated Human Computer Interaction. Hershey, New York: IGI Global. doi:10.4018/9781-4666-3649-1.ch012 O’Donnell, E., Sharp, M., Wade, V., & O’Donnell, L. (2013). Challenges encountered in creating personalised learning activities to suit students learning preferences. In Y. Kats (Ed.), Learning Management Systems and Instructional Design: Best practices in online education (pp. 263–287). Hershey, Pennsylvania, USA: IGI Global. doi:10.4018/978-1-4666-3930-0.ch014 O’Donnell, E., Sharp, M., Wade, V., & O’Donnell, L. (2014). Personalised e-learning: the assessment of students’ prior knowledge in higher education. In V. Wang (Ed.), Handbook of research on education and technology in a changing society. Hershey, New York: IGI Global. doi:10.4018/9781-4666-6046-5.ch055 Somyürek, S. (2015). The new trends in adaptive educational hypermedia systems. International Review of Research in Open and Distance Learning, 16(1). doi:10.19173/irrodl.v16i1.1946 Souhaib, A., Mohamed, K., Kamal Eddine, E. K., & Ahmed, I. (2010). Adaptive hypermedia systems for e-learning. [iJET]. International Journal of Emerging Technologies in Learning, 5(SI3), 47–51. doi:10.3991/ijet.v5s3.1287 Steiner, C., Hillemann, E., Verpoorten, D., Kleinermann, F., Pekczynski, P., & O’Donnell, E. (2010). GRAPPLE (Generic Responsive Adaptive Personalized Learning Environment): Refinement and improvement of evaluation guidelines. Retrieved from http://www.grapple-project.org/ public-files/deliverables/D8.2b-WP8-EvaluationGuidelines-v1.0.pdf

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Yang, J., Huang, Z. X., Gao, Y. X., & Liu, H. T. (2014). Dynamic learning style prediction method based on a pattern recognition technique. IEEE Transactions on Learning Technologies, 7(2), 165–177. doi:10.1109/TLT.2014.2307858

KEY TERMS AND DEFINITIONS Adaptive Education (AE): The purpose of Adaptive Education (AE) is to provide learners with learning resources that have been specially selected to suit the specific learning requirements of each individual. Adaptive Educational Hypermedia (AEH): Electronic content which is used in the provision of adaptive education. Adaptive Educational Hypermedia Systems (AEHS): Systems that provide educators with

the appropriate toolset to present learners with educational resources that have been specifically selected to suit their individual learning requirements. AEHS are designed and developed to deliver adaptive educational experiences to students. E-Learning: Facilitating teaching and learning through the use of technology and access to the Internet. Hypermedia: Electronic content which includes links to many different mediums of content, such as: text; tables; figures; graphics; images; audio; video; animations; simulations; interactive games. Hypertext: A section of online text or an online paragraph of information that has been embedded with links to other content. Technology Enhanced Learning (TEL): Learning which is enhanced through the use of technology and the Internet.

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Computational Thinking in Innovative Computational Environments and Coding Alberto Ferrari University of Parma, Italy Agostino Poggi University of Parma, Italy Michele Tomaiuolo University of Parma., Italy

INTRODUCTION The concept of Computational Thinking has been discussed for several decades and in recent years has been brought to the attention of the scientific community by Jeanette Wing. Her article presents Computational Thinking as “a way of solving problems, designing systems, and understanding human behavior that draws on concepts fundamental to computer science.” (Wing, 2006). CT is a cognitive process involving logical reasoning that embraces the ability to think algorithmically using abstraction, decomposition and generalization. The importance of CT places it among the basic skills for 21st century, together with reading, writing and calculation, that every person will have to master, so it is important to teach it already in primary school. As the invention of printing facilitated the spread of the three Rs (reading, writing and arithmetic) technology must lead to the spread of CT. In our high-technological society teaching CT concepts in all level of instruction allows individuals to participate more equitably in society overcoming the differences now present in mastering these skills. Everyone should be able to apply computational strategies in each domain and understand what problems may be treated automatically.

CT is drawing fundamentally on concepts from computer science, it is not programming, but programming, meant as analysis and solution of problems, allows to highlight all CT features. Easy-to-use computational environments foster students in their first approach to coding and their use are becoming more common in school and university. This chapter will introduce the research on CT and, in particular, the works on innovative computational environments, and will describe the situation of the education to CT in high school and in academic courses.

BACKGROUND There is no single definition of CT and the following is the work of several authors who point out different aspects. A common feature is that CT represents a set of skills that are part of a cognitive process related to deal and seek solutions to problems. Abstraction, decomposition and generalization are common features of most of these works. The term “Computational Thinking” was adopted for the first time by Seymour Papert, with reference to the LOGO programming language (Papert, 1996). According to his theory of “constructionism”, programming is a valu-

DOI: 10.4018/978-1-5225-2255-3.ch208 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Educational Technologies

able educational tool that provides the cognitive artifacts necessary to the human mind to build a representation of the world with which it interacts. “Think like a computer scientist” means being able to deal with a problem, to design a system and understand human behavior using the fundamental concepts and tools of Computer Science (CS). The power of our “mental” tools is amplified by the power of our “metal” tools (Wing, 2008). There is still confusion over an acceptable definition for the term, in its essence, CT can be viewed as a re-foundation of “algorithmic thinking”. In the 1950s and 1960s, algorithmic thinking was understood as a mental attitude, enabling the description of a generic problem as a translation of some input data to output data, and the formalization of the required translation as an algorithm. CT is built on the same basis, with the inclusion of the ability to think about a complex problem at different levels of abstractions, to use mathematical methods, and to analyze data and the complexity of solutions, i.e., to study how a certain algorithm scales when the size of the problem grows. CT can be defined as the ability to solve complex problems by applying the logic of computing paradigm; it is a set of cognitive skills, concepts and techniques of computer science related to problem solving. In the 2010 workshop on “The Scope and Nature of Computational Thinking” participants developed a vivid discussion on what “CT for everyone” might mean. Wing describes CT as a bridge between science and engineering. Since it deals with thinking processes and abilities that can be applied to different disciplines, it represents a sort of meta-science. Moursund et al. (National Research Council, 2010) suggest a close relation between CT and Procedural Thinking, as developed by Seymour Papert in Mindstorms (Papert, 1980). CT revolution goes much deeper than the use of computer in everyday work; it is changing the way we think. Among the features of the CT we find the ability to formulate a problem and

represent its solution writing an algorithm and compare the solution with others in order to assess its efficiency. Another important feature is the ability to represent and organize data logically, using abstraction, generalization and modeling concepts, and identifying patterns within these. Data abstraction, identification of common features and functionality comes into play both when we have to decide what to abstract and when we have to define the level of abstraction. The process of abstraction must then be followed by a process of analysis to verify the correctness of the assumptions and the quality of the result obtained. Abstraction is probably the most significant process in CT: abstraction is used to define patterns, to generalize, to allow an object to represent many; abstraction allows you to extract common properties, to scale and then to deal with complexity; abstractions are the mental tools of computing. Decomposition, the ability to decompose a problem into sub-problems of smaller size, is another important aspect. Decomposing a problem usually leads to the recognition of patterns and generalization, and therefore it also leads to the ability to design algorithms. Pattern recognition, the ability to identify similarities or common differences, is the basis of problem solving and algorithm design. Generalization of pattern and abstraction allows to represent an idea or a process in general terms and then to be able to use this idea to solve other problems of the same nature. Algorithm design is finally the ability to develop a step-bystep solution for a given problem. Bundy (2007) focuses, among other things, on the ability to process large amounts of data, Big Data, and describe a new model of science that defines e-science. As we have seen, there are various definitions of CT and each one highlights certain features. A common factor among the various interpretations is the ability to solve problems using a systematic approach: problem solving.

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The attitudes to be developed in order to reach the proposed objectives include the confidence to manage complexity and to deal with complex problems, the ability to address open problems and handle situations of ambiguity and the practice of teamwork and the burden of negotiating with colleagues to evaluate different proposals. In the principal notes of the website “Exploring Computational Thinking” (Google, 2016a) Google asserts that CT is essential to the development of computer applications but at the same time it is an important factor to support problem solving across other scientific disciplines including math, science, and the humanities. Skills of CT allow students to identify a relationship between subjects as well as between school and life outside of the classroom. CT concepts are present in various aspects that characterize Computing as a science and as a discipline. The concepts, techniques and informatics principles lend themselves as a starting point to educate to CT. With the development of the CT trend, other views emerged. In particular, Denning (2009) argues that the term is neither unique to nor representative of the whole of CS; he underlines the necessity of distinguishing CS from CT; the latter in fact includes only a part of the larger set of skills and knowledge possessed by a computer scientist. After defining the fundamental principles and core practices of CS Denning rank CT not among the fundamental principles but in the practices, and he defines it as a lens for looking at the world and interpret it as an algorithmic transformation from input to output information. According to Denning (1989, 2003), CS is a combination of engineering, mathematics, and science. In its essence, it can be described as the science of information processes. CT can also be used to support problem solving across many disciplines, including math, science, and the humanities and is influencing research in nearly all disciplines, both in the sciences and the humanities (Bundy, 2007) The term CT has also been used in the past and already in the 1980’s many scientists agreed

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as computing was become a third leg of science in addition to theory and experiment so that the computation was essential for the development of the sciences. Many are the examples of CT application in fields such as biology, chemistry, physics, Perkovic and Settle (2010) present examples of CT in more general terms and in all academic disciplines. Today we can talk about e-science, science that operate in distributed network environments and that uses high-performance computer on big data sets. CT puts in relation the approach to the problems with the objective of proposing a solution that can be implemented on computer tools. The implementation is therefore closely linked to the use of programming languages. Coding is therefore one of the tools which are best suited to the teaching of the CT concepts.

INNOVATIVE COMPUTATIONAL ENVIRONMENTS CT is not programming, but programming languages and coding are excellent vehicles for gaining access to basic concepts of CT. Coding promotes the procedural thought and the process of decomposition, concepts that can be applied also in different contexts. Coding is only one aspect of CT but it is important as a medium to face fundamental concepts as abstraction, decomposition and the algorithmic approach to problem solving (Papert, 1980). Villani (2015) underlines the value of coding as a basic educational discipline and describes programming languages and mathematics as “languages that help man in his struggle for the understanding of the world”. Table 1 presents a list of computational environments adopted in project whose purpose is to introduce CT concepts through programming and problem solving. Already in the 60’s Papert at MIT develops LOGO, a very powerful language that allows

Category: Educational Technologies

Table 1. Computational environments Project

Features

LOGO

Easy-to-use programming language

Text

Scratch

Design and invent stories. Graphical manipulation of elements

Blocks

Snap!

Recursive blocks

Blocks

Alice

Storytelling in 3D environment

Sprites

App Inventor

Multimedia applications for Android devices

Blocks

Blockly

API. Engine for block application

Blocks

experts to create complex applications, but at the same time has a low threshold of access that allows its use even by primary school children. Mathematics, geometry and logic principles are learned easily through the turtle graphics; turtle, in the first version, was a robot moving on a surface via commands given through a computer. The spread of language and its use in education have a sharp increase in the years ‘80 thanks to versions of LOGO where the turtle was moving on the screen of a personal computer. Papert (1980), talking about his concept of constructionism, asserts that computer is an important simulation tool and that it is very important in education because it provides the fundamental cognitive artifacts for learning. According to Papert program languages should be easy to get started (“low floor”) and must give opportunities to develop complex projects (“high ceiling”). After a first period of great clamor in introducing coding concepts in education, the interest has gradually waned probably due to environments and languages too difficult to use in this context. In recent years, there has been a revitalization of interest in introducing programming concepts since the early years of school. Consequently, to facilitate teachers and students, many projects started. Scratch is developed by Michael Resnick and the Lifelong Kindergarten Group at the MIT Media Lab. Resnick (2009) presents Scratch as a tool for design, create, invent. Digital natives in most cases read but not write technology, so they need

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to learn some type of programming to become creators of technology. In Scratch, programming is done by graphical manipulation of elements and not through the classical textual syntax. Users compose scraps of code, script, selecting predefined blocks and connecting them together to form a puzzle that represents the algorithmic solution of the problem. The ease of interaction with the environment makes it also suitable for children, but its use has spread in information technology introductory courses for students in high school and university (Brennan, 2013). Scratch sprites are an example of computer objects with various kind of characteristics, not only graphics ones. Characteristics and behaviors are defined by scripts and in Scratch is also present a simple message exchange mechanism. (Maloney et al., 2010). Snap! is another programming environment that extends Scratch functionality allowing users to build new blocks (formerly his name was Build Your Own Blocks) and adding new capabilities, such as recursive blocks, which make it an environment aimed at a more advanced target such as high school students or university freshmen. Snap! is implemented in JavaScript and this is a favorable aspect because it does not require any software installation as it works within any browser (Garcia et al., 2012). Alice (Alice, 2016) is a 3D programming environment designed to be a student’s first exposure to object-oriented programming. It allows students to learn fundamental programming concepts dragging and dropping graphic tiles to create simple

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video games (Cooper et al., 2000). Bishop-Clark (2007) studied students using Alice in an introductory computing courses and found that they learn the programming concepts and gain a better appreciation of the complexity of programming compared with ones that start coding using text based languages. The examples presented, like most projects for the diffusion of CT through coding, are based on graphical programming languages that give users the ability to build programs by composing graphical blocks together, to form a puzzle that represents the algorithmic solutions of problems. This approach, with respect to text-based programming, enables users to avoid issues of syntax and focus on the critical processes of designing, creating, and inventing. The founding idea of block programming is to provide a graphical interface with blocks of diverse types. The user/programmer can combine the blocks in various ways through drag and drop operations, in such a way to form a puzzle representing the solution to a given problem. The blocks represent the basic elements of the language and they are differentiated by form and color, to easy their identification and usage. Novice programmers must fight two battles in writing their first programs: the logical battle and the syntax battle. In environments based on block programming is impossible to introduce syntactic errors and this allows users to focus entirely on the logics of assigned problems and their solutions. In fact, the composition of blocks is rigidly constrained by existing slots, which represent the syntactic constraints of the language. Most of initiatives prior described are directed specially toward primary school and they are based on ludic activities. The MIT App Inventor project (Wolber et al., 2011) is instead directed at a more advanced target. It is a block-based programming tool that enables the creation of simple multimedia application that can be executed on Android devices. The Google Blockly environment (Google, 2016b) is built along principles similar to those

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of the most widespread graphical language like Scratch (Resnick et al., 2009; Maloney et al., 2010), SNAP! (Garcia et al., 2012), MIT App Inventor (Wolber et al., 2011). Blockly is the engine running underneath many of projects that today propose block programming as the simple environment to introduce people of all ages to programming. There are now hundreds of educational projects that use Blockly library, the most important is probably code.org (Code.org, 2016): “Every student in every school should have the opportunity to learn Computer Science” is the opening sentence of this important project that involves tens of millions of young people (Wilson, 2014). Although most educational projects and experiences proposing a block programming approach are oriented to a very young audience, projects directed toward high school students, like previous presented APP Inventor, are beginning to appear; in these cases, block programming is intended to help students get over the initial learning curve so that they can use more easily text-based programming languages. Weintrop and Wilensky (2015, 2015b) compares conceptual understanding in block-based and text-based programming and through cognitive interviews and surveys to students found blocksbased programming to be easier because of the shapes and colors of the blocks, the drag-and-drop composition mechanism, and the ease of browsing the blocks library. Some points of strength and weakness are analyzed, as they are perceived by high school students which are allowed to move from a textual programming environment to a block-based one. Students identify the drag and drop composition interaction and the ease of browsing the language as contributing to the perceived ease-of-use of tools based on blocks-based programming. Being less powerful is indicated on the other end as drawback to blocks-based programming compared to the conventional textbased approach. Price and Barnes (2015) in their study claim that block programming interfaces compared with text based ones can significantly improve novice

Category: Educational Technologies

performance and understanding of fundamental programming concepts. Starting from Papert’s idea concerning programming languages, the evolution of the block programming can lead to broadening the horizon for applications of this type of language. Ferrari at al. (2016) present a project that extends this visual paradigm to Object Oriented Programming. Although, for complex applications, text-based languages will be more comfortable to use than those based on graphical syntax. Writing large programs in a visual programming environments is cumbersome, the same Google Blockly authors argue: “Please do not attempt to maintain the Linux kernel using Blockly”. After overcoming the first coding difficulties and when the programming concepts have been mastered, students should migrate to a conventional text-based language. For this reason, it is important already in the early examples to show the textual code, expressed in a conventional programming language, that correspond to the puzzle of blocks that represent the algorithmic solution of the problem. Block-based programming environments are proposed as alternative way of teaching coding for novice programmers and are becoming increasingly common in introductory programming courses. In addition to the discussion on the graphical or textual programming there is an important and unanswered question on which programming language is best for novices and what features make a language more or less accessible to beginners. Stefik and Hanenberg (2014) refuse the possibility of “One Language to Rule Them All” because of the variety of human perception and problem domains and even a perfect language, if it were created, may not be adopted. They present “the programming language wars” as a social ill causing problems in Computer Science discipline especially for new programmers.

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Today, in schools and universities, only few specific courses are related to CT concepts. These concepts are, or should, be treated in “Information Education” courses. Unfortunately for a long time, especially in high schools, CS has been considered only from the point of view of computer literacy, i.e., the ability to interact with computer systems and use specific programs. The situation of Informatics education in Europe is not ideal. The title of a recent study, edited by ACM, is quite evocative: “Informatics education: Europe cannot afford to miss the boat” (ACM, 2013). It starts from the consideration that both governments and citizens in EU are conscious of living in a so-called “Information Society”. But too often they satisfy themselves with the notion that learning to use digital media (i.e., digital literacy) is enough. Instead, digital literacy has to be considered as a very basic practical skill, and not an adequate intellectual tool for facing the new challenges. To distinguish the more basic skills from the real science behind IT, the report adopts the term “Informatics”, which is already common in continental Europe, but is acquiring the meaning of “Computer Science” also in the Anglo-Saxon world. Informatics is a scientific discipline that was born before actual computing machines, with the studies of Turing and Church in the 1930s. In the following decades, with its development into a full-fledged discipline, Informatics became an interesting mix of mathematical theory and electrical engineering, together with many seminal concepts of its own, including: algorithms; concepts of performance and complexity; data structures; concurrency, parallelism, and distribution; formal languages; abstractions.

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The report summarizes its conclusions in few main points, which essentially underline and explain the need of education in both digital literacy and Informatics. Informatics is described as “a major enabler of technology innovation, the principal resource for Europe’s drive to become an information society, and the key to the future of Europe’s economy”. While some good progress is being made in teaching digital literacy, nevertheless more emphasis should be put on ethical issues, for a proper use of information technology resources. Instead, Informatics education, is sorely lacking in most European countries, and the situation is depicted as being worse than in the 70s and 80s. Without rapid changes, Europe will never be a major developer of information technologies, but a mere consumer, thus harming both its economy and the education of its new generation of citizens. For achieving the desired objectives, the report advances some guidelines for the definition of new curricula. Those should take into account two main principles: “Informatics education must not just dwell on imparting information to students” and “it must draw attention to aspects of informatics that immediately appeal to young students, to encourage interaction, to bring abstract concepts to life through visualization and animation; a typical application of this idea is the careful use of (non-violent) games” (Overmars, 2004; Vassilev & Mutev, 2014). CS researchers and professionals supported the idea of a more widespread diffusion of Informatics as a scientific discipline, with dedicated hours at least in secondary schools. According to many reforms, elements of CS should be introduced since the first year in both technical schools and applied sciences secondary schools. In practice, however, the situation is changing at a very slow pace and possibly not improving, even with respect to Informatics. An important research report (Google & Gallup, 2015) reports that in U.S. many students, parents and also teachers do not properly distinguish between computer science activi-

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ties and general computer literacy; courses that school administrators consider to be computer science often lack programming. But another study by Horizon Media states that Americans now view computer science as a basic skill. 86% of interviewed say knowing how to use a computer is “just as important as knowing how to read and write.” The importance of introduction of computer science in high school is that students who learn computer science in high school are 6 times more likely to major in it and computing occupations are now the first source of new wages in U.S. (Bureau of Labor Statistics, 2016). For this reason, in 2016 the U.S. administration is investing 150 million dollars in tech education projects to launch innovative training and placement models to develop tech talent as a way to keep and create jobs in local economies.

FURTHER RESEARCH DIRECTIONS To overcome the ambiguity that has often led to regard computer literacy as the main objective of CT, research in this area is directed towards projects that aim at easing the learning of its core skills, while reducing the difficulties that students face in achieving these goals. Emphasis is on concepts and methodological approaches to problem solving, while coding becomes more and more a medium to achieve these objectives, not the ultimate goal. New educational development environments, as well as programming languages proposed, aim to drastically reduce the syntactic difficulty and to facilitate the first approach to programming and design (Kölling, 2008; Resnick et al., 2009). Specific courses about CT concepts will increasingly be separated by CS courses, CT will become a specific discipline even if linked to more general IT aspects. In fact, ‘Computational thinking is influencing research in nearly all disciplines, both in the sciences and the humanities’ (Bundy, 2007).

Category: Educational Technologies

CONCLUSION The main aim of Computational Thinking is not to foster and simplify the development of applications. However, the proposals for new environments, tools and languages, which ease the first approach to programming, can be a starting point after which the other fundamental aspects of Computational Thinking, including abstraction and modeling, can be introduced. Their use will be specifically relevant in educational settings where students face at first the typical syntactic difficulties related with coding, but then they also have to deal with the more advanced challenges of design and testing.

REFERENCES ACM. (2013). Informatics education: Europe cannot afford to miss the boat. Retrieved July 18, 2016, from http://europe.acm.org/iereport/ie.html Alice. (2016). Alice Software Website. Retrieved July 18, 2016, from http://www.alice.org Bishop-Clark, C., Courte, J., Evans, D., & Howard, E. (2007). A quantitative and qualitative investigation of using Alice programming to improve confidence, enjoyment, and achievement among non-majors. Journal of Educational Computing Research, 37(2), 193–207. doi:10.2190/J8W374U6-Q064-12J5 Brennan, K. (2013). Learning computing through creating and connecting. Computer, 46(9), 52–59. doi:10.1109/MC.2013.229 Bundy, A. (2007). Computational Thinking is pervasive. Journal of Scientific and Practical Computing, 1(2), 67–69. Bureau of Labor Statistics. (2016). Employment Projections. Retrieved July 18, 2016, from http:// www.bls.gov/emp/tables.htm Code.org. (2016). Code.org Website. Retrieved July 18, 2016, from http://www.code.org

Cooper, S., Dann, W., & Pausch, R. (2000). Alice: A 3-D tool for introductory programming concepts. Journal of Computing Sciences in Colleges, 15, 107–116. Denning, P. J. (2003). Great principles of computing. Communications of the ACM, 46(11), 15–20. doi:10.1145/948383.948400 Denning, P. J. (2009). The profession of IT Beyond Computational Thinking. Communications of the ACM, 52(6), 28–30. doi:10.1145/1516046.1516054 Denning, P. J., Comer, D. E., Gries, D., Mulder, M. C., Tucker, A., Turner, A. J., & Young, P. R. (1989). Computing as a discipline. Communications of the ACM, 32(1), 9–23. doi:10.1145/63238.63239 Ferrari, A., Poggi, A., & Tomaiuolo, M. (2016). Object Oriented Puzzle Programming. Mondo Digitale, 64. Retrieved July 18, 2016, from http:// mondodigitale.aicanet.net/2016-3/DidamaticaSessioni/Programmazione/paper_39.pdf Garcia, D., Segars, L., & Paley, J. (2012). Snap! (build your own blocks): Tutorial presentation. Journal of Computing Sciences in Colleges, 27(4), 120–121. Google. (2016a). Exploring Computational Thinking Website. Retrieved July 18, 2016, from http:// www.google.com/edu/computational-thinking/ Google. (2016b). Blockly Software Website. Retrieved July 18, 2016, from https://developers. google.com/blockly/ Google & Gallup. (2015). Images of Computer Science: Perceptions Among Students, Parents and Educators in the U.S. Retrieved July 18, 2016, from https://services.google.com/fh/files/misc/ images-of-computer-science-report.pdf Kölling, M. (2008). Using BlueJ to introduce programming. In Reflections on the Teaching of Programming (pp. 98–115). Berlin, Germany: Springer. doi:10.1007/978-3-540-77934-6_9

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Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The Scratch Programming Language and Environment. ACM Transactions on Computing Education, 10(4), 16. doi:10.1145/1868358.1868363

Vassilev, T. I., & Mutev, B. I. An Approach to Teaching Introductory Programming Using Games. Proceedings of International Conference on e-Learning, 246–253. doi:10.4018/ ijhcitp.2015010103

National Research Council. (2010). Report of a Workshop on the Scope and Nature of Computational Thinking. The National Academies Press.

Villani, C. (2015). Birth of a Theorem: a mathematical adventure. Macmillan.

Overmars, M. (2004). Teaching Computer Science through Game Design. Computer, 37(4), 81–83. doi:10.1109/MC.2004.1297314 Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. New York, NY: Basic Books. Papert, S. (1996). An exploration in the space of mathematics educations. International Journal of Computers for Mathematical Learning, 1(1), 95–123. doi:10.1007/BF00191473 Price, T. W., & Barnes, T. (2015). Comparing Textual and Block Interfaces in a Novice Programming Environment. In Proceedings of the eleventh annual International Conference on International Computing Education Research (pp. 91-99). ACM. doi:10.1145/2787622.2787712 Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., & Kafai, Y. et al. (2009). Scratch: Programming for All. Communications of the ACM, 52(11), 60–67. doi:10.1145/1592761.1592779 Settle, A., & Perkovic, L. (2010). Computational thinking across the curriculum: a conceptual framework. College of Computing and Digital Media, DePaul University. Retrieved July 18, 2016, from http://compthink.cs.depaul.edu/FinalFramework.pdf Stefik, A., & Hanenberg, S. (2014). The programming language wars: Questions and responsibilities for the programming language community. In Proceedings of the 2014 ACM International Symposium on New Ideas, New Paradigms, and Reflections on Programming & Software (pp. 283-299). ACM. doi:10.1145/2661136.2661156 2400

Weintrop, D., & Wilensky, U. (2015). Using commutative assessments to compare conceptual understanding in blocks-based and text-based programs. In Proceedings of the eleventh annual International Conference on International Computing Education Research (pp. 101-110). ACM. doi:10.1145/2787622.2787721 Weintrop, D., & Wilensky, U. (2015). To block or not to block, that is the question: students’ perceptions of blocks-based programming. In Proceedings of the 14th International Conference on Interaction Design and Children (pp. 199-208). ACM. doi:10.1145/2771839.2771860 Wilson, C. (2015). Hour of code---a record year for computer science. ACM Inroads, 6(1), 22–22. doi:10.1145/2723168 Wing, J. M. (2006). Computational Thinking. Communications of the ACM, 49(3), 33–35. doi:10.1145/1118178.1118215 Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical transactions of the royal society of London A: mathematical, physical and engineering sciences, 366(1881), 3717-3725. Wolber, D., Abelson, H., Spertus, E., & Looney, L. (2011). App Inventor. O’Reilly Media.

ADDITIONAL READING Kelleher, C., & Pausch, R. (2005). Lowering the Barriers to Programming: A Taxonomy of Programming Environments and Languages for Novice Programmers. ACM Computing Surveys, 37(2), 83–137. doi:10.1145/1089733.1089734

Category: Educational Technologies

Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51–61. doi:10.1016/j.chb.2014.09.012 National Research Council. (2011). Report of a Workshop on the Pedagogical Aspects of Computational Thinking. The National Academies Press. Scott, J., & Bundy, A. (2015). Creating a new generation of computational thinkers. Communications of the ACM, 58(12), 37–40. doi:10.1145/2791290 Shein, E. (2014). Should everybody learn to code? Communications of the ACM, 57(2), 16–18. doi:10.1145/2557447

KEY TERMS AND DEFINITIONS Algorithmic Thinking: A method for solving problems based on the clear definition of the steps needed.

Coding: The process of writing, testing, debugging and maintaining the source code of software applications. Computational Thinking: A method for solving problems mainly used for the development of computer applications, but that can also be used to support problem solving across all academic disciplines. Constructivist Theory: An active process in which learners construct new ideas or concepts based upon their current/past knowledge. E-Science: A computationally intensive science that either is accomplished in massive distributed network environments, or uses large data sets whose processing required distributed computing infrastructures. Graphical Programming Language: A programming language in which the source code is itself graphical in nature and does not mainly consist of text. Ii is also known as visual programming language.

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Computer Agent Technologies in Collaborative Learning and Assessment Yigal Rosen Harvard University, USA

INTRODUCTION In recognition of the importance of collaborative and problem solving skills, educators are realizing the need for effective and scalable learning and assessment solutions to promote the skillset in educational systems. In the settings of a comprehensive collaborative problem solving assessment, each student should be matched with various types of group members and must apply the skills in varied contexts and tasks. One solution to these assessment demands is to use computer-based (virtual) agents to serve as the collaborators in the interactions with students. The chapter presents the premises and challenges in the use of computer agents in the assessment of collaborative problem solving and describes how human and computer agent collaborative assessments are used in international learning and assessment project Animalia.

BACKGROUND Collaborative problem solving is recognized as a core competency for college and career readiness. Students emerging from schools into the workforce and public life will be expected to work in teams, cooperate with others, and resolve conflicts in order to solve the kinds of problems required in modern economies. They will further need to be able to use these skills flexibly with various group compositions and environments (Care, & Griffin, 2014; Griffin, Care, & McGaw, 2012; O’Neil, & Chuang, 2008; Rosen, & Rimor, 2012). Educational programs have focused to a greater

extent on the advancement of learning and the assessment of collaborative problem solving as a central construct in theoretical and technological developments in educational research (National Research Council, 2011, 2013; OECD, 2013a). Collaborative skills are included within the major practices in the 2014 U.S. National Assessment of Educational Progress (NAEP) Technology and Engineering Literacy (National Assessment Governing Board, 2013). In this assessment program, students are expected to show their ability in collaborating effectively with computer-based (virtual) peers and experts and to use appropriate information and communication technologies to collaborate with others on the creation and modification of knowledge products. Similarly, the Israeli national program of adopting the educational system to the 21st century illustrates a multi-year program with the goal of leading the implementation of innovative pedagogy and assessment in schools, including collaboration, communication, and problem solving (Israel Ministry of Education, 2011). Collaborative problem solving is one of the areas that the Organisation for Economic Co-operation and Development (OECD) emphasized for major development in the Programme for International Student Assessment (PISA) in addition to scientific, math, and reading literacy for the 2015 assessment. Collaborative problem solving refers to problem solving activities that involve collaboration among a group of individuals (O’Neil, Chuang, & Baker, 2010; Zhang, 1998). In the PISA 2015 Framework (OECD, 2013b), collaborative problem solving competency is defined as “the capacity of an individual to effectively engage in a process whereby two or more

DOI: 10.4018/978-1-5225-2255-3.ch209 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Educational Technologies

agents attempt to solve a problem by sharing the understanding and effort required to come to a solution and pooling their knowledge, skills, and efforts to reach that solution” (p. 6). This definition; treats the competency as conjoint dimension collaboration skills and the skills needed to solve a problem. For the PISA assessment, the focus is on individual capacities within collaborative situations. Thus, the effectiveness of collaborative problem solving depends on the ability of group members to collaborate and to prioritize the success of the group over individual successes. At the same time, this ability is still a trait in each of the individual members of the group. Development of a standardized computer-based assessment of collaborative problem solving skills, specifically for large-scale assessment programs, remains challenging. Unlike some other skills, collaborative problem solving typically requires using complex performance tasks, grounded in varied educational domains, with interaction among students. These factors can affect the level of control that can be applied to ensure accurate assessment of students. In this chapter, an operational definition of collaborative problem solving refers to “the capacity of an individual to effectively engage in a group process whereby two or more agents attempt to solve a problem by sharing knowledge and understanding, organizing the group work and monitoring the progress, taking actions to solve the problem, and providing constructive feedback to group members.”

COMPUTER AGENT TECHNOLOGIES Collaboration can take many forms, ranging from two individuals to large teams with predefined roles. For assessment purposes, collaboration can also be performed using simulated agents playing the role of team members, using computer or humans as team members. Thus, a critical distinction is whether all team members are human or some are computer agents. There are advantages and limita-

tions for each method, which are outlined below. The Human-to-Human (H-H) approach provides an authentic human-human interaction that is a highly familiar situation for students. Students may be more engaged and motivated to collaborate with their peers. Additionally, the H-H situation is closer to the collaborative problem solving situations students will encounter in their personal, educational, professional, and civic activities. However, because each human will act independently, the approach can be problematic because of individual differences that can significantly affect the collaborative problem solving process and its outcome. Therefore, the H-H assessment approach of collaborative problem solving may not provide sufficient opportunity to cover variations in group composition, diversity of perspectives, and different team member characteristics in a controlled manner for accurate assessment of the skills on an individual level. Also, computer agent technology can contribute to efficiency in data collection by dramatically decreasing the assessment time with strategic dialogue management and rapid immersion in the collaborative context. Simulated team members using a preprogrammed profile, actions, and communication can potentially provide the coverage of the full range of collaboration skills with sufficient control. In the Human-to-Agent (H-A) approach, collaborative problem solving skills are measured by pairing each individual student with a computer agent or agents that can be programmed to act as team members with varying characteristics relevant to different collaborative problem solving situations. Group processes are often different depending on the task and could even be competitive. Use of computer agents provides a component of non-competitiveness to the collaborative problem solving situation, as it is experienced by a student. Additionally, if the time-on-task is limited, time spent establishing common ground or discussing non-task relevant work may lower group productivity. As a result of these perceived constraints, a student collaborating in H-H mode may limit significantly the extent to which collaborative problem solving dimensions,

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such as shared understanding, are externalized through communication with the partner. The agents in H-A communication can be developed with a full range of capabilities, such as text-tospeech, facial actions, and optionally rudimentary gestures. In its minimal level, a conventional communication media, such as text via emails, chat, or a graphic organizer with lists of named agents can be used for H-A purposes. However, collaborative problem solving in H-A settings deviates from natural human communication delivery. The dynamics of H-H interaction (timing, conditional branching) cannot be perfectly captured with agents, and agents cannot adjust to idiosyncratic characteristics of humans. For example, human collaborators can propose unusual, exceptional solutions; the characteristic of such a process is that it cannot be included in a system following an algorithm, such as H-A interaction. Research shows that computer agents have been successfully used for tutoring, collaborative learning, co-construction of knowledge, and collaborative problem solving (Biswas, Jeong, Kinnebrew, Sulcer, & Roscoe, 2010; Graesser et al., 2008; Millis et al., 2011). A computer agent can be capable of generating goals, performing actions, communicating messages, sensing its environment, adapting to changing environments, and learning (Franklin & Graesser, 1996). One of the examples for computer agent use in education is a teachable agent system called Betty’s Brain (Biswas, Leelawong, Schwartz, & Vye, 2005; Leelawong, & Biswas, 2008). In this system, students teach a computer agent using a causal map, which is a visual representation of knowledge structured as a set of concepts and their relationships. Using their agent’s performance as motivation and a guide, students study the available resources so that they can remediate the agent’s knowledge and, in the process, learn the domain material themselves. Operation ARA (Cai et al., 2011; Millis et al., 2011) uses animated pedagogical agents that converse with the student in a game-based environment for helping students learn critical-thinking skills and scientific

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reasoning within scientific inquiry. The system dynamically adapts the tutorial conversations to the learner’s prior knowledge. These conversations, referred to as “trialogs” are between the human learner and two computer agents (student and teacher). The student learns vicariously by observing the agents, gets tutored by the teacher agent, and teaches the student agent. A focused study has been conducted to investigate differences in student collaborative problem solving (CPS) performance in H-A and H-H modes (Rosen, 2014; Rosen, & Foltz, 2014; Rosen, & Mosharraf, 2015). Study participants included 179 students; age 14; from the United States, Singapore, and Israel. In all, 136 students participated in the H-A group and 43 participated in the H-H group (43 additional students participated in the H-H setting, acting as ‘collaborators’ for the major H-H group). Specifically in H-H assessment mode, students were randomly assigned into pairs to work on the CPS task. Because the H-H approach required pairs of students working together in a synchronized manner, the number of pairs was limited. This is due to the characteristics of technology infrastructures in participating schools. The students were informed prior to their participation in the study whether they would collaborate with a computer agent or a classmate. In a case of H-H setting, the students were able to see the true name of their partner. Students were exposed to identical collaborative problem solving assessment tasks and were able to collaborate and communicate by using identical methods and resources. However, while in the H-A mode students collaborated with a simulated computer-driven partner, and in the H-H mode students collaborated with another student to solve a problem. The findings showed that students assessed in H-A mode outperformed their peers in H-H mode in their collaborative skills. Collaborative problem solving with a computer agent involved significantly higher levels of shared understanding, progress monitoring, and feedback. The results further showed no significant differences in other student performance measures to solve the problem

Category: Educational Technologies

with a computer agent or a human partner, although on average students in H-A mode applied more attempts to solve the problem, compared to the H-H mode. A process analysis of the chats and actions of the students showed that in H-A group the students encounter significantly more conflict situations (e.g., disagreements) than in the H-H group. However, it was found that students in H-H setting were engaged in significantly more situations in which the partner proposed different solutions for a problem.

PROJECT ANIMALIA An investigation to look further at the premises of human and computer agent technology in the context of learning and assessing collaborative problem solving skills was undertaken through collaboration between World ORT, the Ministry of Education in Israel, and researchers at Pearson. Animalia is an online mini-course designed to promote students’ collaborative problem solving skills in the context of complex ecosystems (Bakken, Bielinski, Johnson, & Rosen, 2015). Animalia is a simulated fictitious village with a serious environmental problem. The fish in a local river are dying and the cause(s) of this problem is unknown. A team of scientists from an international organization (i.e. students) must determine the root cause of the problem and recommend sound, scientifically-based solutions. The foundation of the problem-solving activity relies on understanding specific science concepts such as pH, oxygen saturation, nitrogen levels, the biological effects of pollutants, ecology, frog life cycles, algae physiology, as well as cause and effects in ecosystems. To create a more realistic scenario, team members will be given multiple and sometimes misleading, irrelevant, or contradictory pieces of information about what was happening in Animalia. It was up to the students to determine the authenticity of the data and to judge the credibility of the source. Each team member have had access to only some information about the overall

situation so that determining the root cause of the problems in Animalia require each team member to share his/her information with the others. Team members were encouraged to distill the information they receive into its essential components and to hypothesize what they think are the causes of the problem(s). Team members were also given feedback on each other’s ideas and then form a consensus opinion. Finally, each team summarized findings and recommendations in a written report and an oral presentation. The teams described the cause(s) and provided recommendations to solve the environmental problems in Animalia. Learning was assessed through short quizzes and collaborative tasks. Additionally pre- and post-test of science concepts and collaborative skills were administered at the beginning and at the end of the mini-course. Collaborative problem solving proficiency was assessed at the beginning of Animalia through computer agent task ZooQuest (Rosen, 2014; Rosen, & Mosharraf, 2015). The development team designed this minicourse to be completed in approximately 14 hours. Even though this is a collaborative task, due to the constraints of physical distance and time zone differences, students typically worked independently and communicate asynchronously (at different times) with their team members in other locations. The only two times students were online at the same time was when the teams discussed townspeople interview videos and gave their final presentations using Google Hangouts. Animalia content was provided in English, and English was spoken between partner schools. This gave students the real-world experience of collaborating with people from different cultures, with different levels of English proficiency, different levels of science knowledge, and across time zones. The content and related activities were provided online via a designated Moodle website. Each team have had its own “course” in the Moodle system which enabled team members to share information with each other, but prevented them from seeing information from other teams. Each session was available to students sequentially

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during the project. When they logged in, students saw only those sessions that have been released up to that point. Across sites, teams were asked to stick to a common schedule so that all team members are progressing through the project work at the same pace. Teachers play a key role in supporting this complex project. Teaching collaborative problem solving skills might be intimidating for teachers who typically deliver content-focused science instruction. But because these skills are fundamental to college and career success, teachers must help students improve upon these skills. Therefore, Animalia includes several professional development sessions to guide teachers on implementation of the course, as well as through the process. Because students are working in teams from different time zones, teachers need to help coordinate schedules so that students are working on the same content at the same time. It is essential that teachers support and encourage their students as they progress through the sessions. Teacher’s actions help students stay engaged and positive. There are times when the teacher will need to help your students troubleshoot any social or technical difficulties that may arise. Student interaction within each group is necessary for successful completion of the task. Each student takes on a specific scientist role, and each role will gather its own unique information that must be shared with their team members to arrive at solutions or recommendations for the Animalia problem. It is important to emphasize this with students and be sure that each one understands what tools can be used to get connected and stay engaged. Students must interact on discussion boards throughout the task. It is important that these discussions remain positive and on-task. Teachers monitor these discussions, and tell students so that they know you will be monitoring their progress. Students’ awareness of their teacher’s actions help prevent the posting of inappropriate or offensive remarks.

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The study participants included 220 students, all 14 years old, from Israel, Italy, Bulgaria, Czech Republic, and Spain. The data were collected from February to April 2015. Recruitment of participating schools was achieved through collaboration with World ORT based on the following criteria: (a) population of 14-year-old students proficient in English, and (c) sufficient technology infrastructure (e.g., computers per student, high-speed Internet). In all, 108 students participated in lowCPS condition (i.e., low-CPS proficiency of all team members, as measured by CPS pre-test), and 112 participated in mixed-CPS condition (i.e., mid.-high CPS proficiency of two team members and low CPS proficiency among the other two team members). The students in both conditions were assigned to 4-person teams, two each from two different partner schools. No significant differences were found in science concepts pre-test scores between participants in the two groups. This similarity in student science knowledge allowed comparability of student results in Animalia between the two groups. Preliminary findings indicated that students participated in a mixed-CPS condition significantly outperformed the students in low-CPS condition in their science concepts post-test, as well in the written report and oral presentation scores.

FUTURE RESEARCH DIRECTIONS Team composition plays a significant role in collaborative settings (Kreijns, Kirschner, & Jochems, 2003; Nelson, 1999; Rosen, & Rimor, 2009). Collaborative problem solving performance is compromised to the extent that the division of labour is unintelligent, subgoals are not achieved, the group goals are blocked, and there are communication breakdowns. Collaborative problem solving tasks with high interdependency are very sensitive to group composition. One team member who has low competency can dramatically decrease the performance of the entire team and

Category: Educational Technologies

force other team members to compensate in order to achieve team goals. An overly strong leader can prevent other team members from manifesting their talents. A meaningful collaborative interaction rarely emerges spontaneously, but requires careful structuring of the collaboration to promote constructive interactions. Incorporating collaborative problem solving assessment, as a tool for structuring student assignment to more optimal collaborative learning experiences such as Animalia is one example of potential use of computer agent based assessment. More research is needed to explore further these conditions and to more thoroughly understand their impact on outcomes. In this pilot, new insights were gained about collaborative problem-solving, but replications of this study will shed additional light and help to further inform the work of educators, researchers, and test developers who are motivated to help students discover and refine their own collaborative problem solving skills.

CONCLUSION Students can demonstrate collaborative problem solving in tasks where assignments are distributed among team members, progress and results are integrated and shared, and products are presented jointly. Task structuring approaches aim to create optimal conditions for collaborative problem solving assessment by designing and scripting the situation before the interaction begins. It may include varying the characteristics of the participants (e.g., the size and composition of the group, or the roles), the availability and characteristics of communication and collaboration tools (e.g., the use of a phrase-chat or graphical tools), and the organization of the task. The collaborative problem solving assessment methods described in this chapter offer one of the few examples today of an approach in assessing collaborative problem solving skills. Collaborative assessments bring new challenges and considerations for the design of effective assessment approaches because they move the field beyond standard item

design tasks. The assessment must incorporate concepts of how humans solve problems in situations where information must be shared and must incorporate considerations of how to control the collaborative environment in ways sufficient for valid measurement of individual and team skills (Rosen, 2014). The quality and practical feasibility of these measures are not yet fully documented. However, these measures can rely on the abilities of technology to engage students in interaction, to simulate others with whom students can interact, to track students’ ongoing responses, and to draw inferences from those responses. Group composition is one of the important issues in assessments of collaborative skills (Webb, 1995; Wildman et al., 2012). Overcoming possible bias of differences across groups by using computer agents or other methods becomes even more important within international large-scale assessments where cultural boundaries are crossed. New psychometric methods should be explored for reliable scoring of an individual’s contribution to collaborative processes and solutions, such as stochastic and social network analyses, hidden Markov models, and Bayesian knowledge tracing (Soller, & Stevens, 2008; von Davier, & Halpin, 2013). Current research suggests that by using computer agents in a collaborative problem solving task, students are able to show their collaborative skills at least at the level of that of their peers who collaborate with human partners. Although human-to-agent interaction might not be regarded as equal to human-to-human collaboration, the advancing technology of computer agents makes the use of avatars a viable way to simulate collaboration, and this can offer researchers more control than is available with real human collaboration (OECD, 2013b; Rosen, & Wolf, 2014). However, each approach to assessment of collaboration still involves limitations and challenges that must be considered in the design of the assessments. Further research can continue to establish comprehensive validity evidence and generalization of findings both in H-A and H-H collaborative problem solving settings.

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REFERENCES Bakken, S., Bielinski, Johnson, C., & Rosen, Y. (2015). Animalia: Collaborative science problem solving learning and assessment. In Y. Rosen, S. Ferrara, & M. Mosharraf (Eds.), Handbook of Research on Technology Tools for Real-Life Skill Development (pp. 360-384). Hershey, PA: Information Science, IGI Global. Biswas, G., Jeong, H., Kinnebrew, J. S., Sulcer, B., & Roscoe, A. R. (2010). Measuring self-regulated learning skills through social interactions in a teachable agent environment. Research and Practice in Technology-Enhanced Learning, 5(2), 123–152. doi:10.1142/ S1793206810000839 Biswas, G., Leelawong, K., Schwartz, D., & Vye, N. (2005). Learning by Teaching: A New Agent Paradigm for Educational Software. Applied Artificial Intelligence, 19(3-4), 363–392. doi:10.1080/08839510590910200 Cai, Z., Graesser, A. C., Forsyth, C., Burkett, C., Millis, K., Wallace, P., & Butler, H. et al. (2011). Trialog in ARIES: User input assessment in an intelligent tutoring system. In W. Chen, & S. Li (Eds.), Proceedings of the 3rd IEEE International Conference on Intelligent Computing and Intelligent Systems (pp.429-433). Guangzhou: IEEE Press. Care, E., & Griffin, P. (2014). An approach to assessment of collaborative problem solving. Special Issue: Assessment in Computer Supported Collaborative Learning. Research and Practice in Technology Enhanced Learning, 9(3), 367–388. Graesser, A. C., Jeon, M., & Dufty, D. (2008). Agent technologies designed to facilitate interactive knowledge construction. Discourse Processes, 45(4), 298–322. doi:10.1080/01638530802145395

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Griffin, P., Care, E., & McGaw, B. (2012). The changing role of education and schools. In P. Griffin, B. McGaw, & E. Care (Eds.), Assessment and Teaching 21st Century Skills (pp. 1–15). Heidelberg: Springer. doi:10.1007/978-94-0072324-5_1 Israel Ministry of Education. (2011). Adapting the educational system to the 21st century. Ministry of Education. Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for social interaction in Computer-Supported Collaborative Learning environments: A review of the research. Computers in Human Behavior, 19(3), 335–353. doi:10.1016/S0747-5632(02)00057-2 Leelawong, K., & Biswas, G. (2008). Designing Learning by Teaching Systems: The Betty’s Brain System. International Journal of Artificial Intelligence in Education, 18(3), 181–208. Millis, K., Forsyth, C., Butler, H., Wallace, P., Graesser, A. C., & Halpern, D. (2011). Operation ARIES! A serious game for teaching scientific inquiry. In M. Ma, A. Oikonomou, & J. Lakhmi (Eds.), Serious games and edutainment applications (pp. 169–195). London: Springer-Verlag. doi:10.1007/978-1-4471-2161-9_10 National Assessment Governing Board. (2013). Technology and engineering literacy framework for the 2014 National Assessment of Educational Progress. Washington, DC: National Assessment Governing Board. National Research Council. (2011). Assessing 21st Century Skills. Washington, DC: National Academies Press. National Research Council. (2013). New directions in assessing performance of individuals and groups: Workshop summary. Washington, DC: National Academies Press.

Category: Educational Technologies

Nelson, L. (1999). Collaborative problem-solving. In C. M. Reigeluth (Ed.), Instruction design theories and models (pp. 241–267). Mahwah, NJ: Lawrence Erlbaum Associates. O’Neil, H. F. Jr, & Chuang, S. H. (2008). Measuring collaborative problem solving in low-stakes tests. In E. L. Baker, J. Dickieson, W. Wulfeck, & H. F. O’Neil (Eds.), Assessment of problem solving using simulations (pp. 177–199). Mahwah, NJ: Lawrence Erlbaum Associates. O’Neil, H. F. Jr, Chuang, S. H., & Baker, E. L. (2010). Computer-based feedback for computerbased collaborative problem solving. In D. Ifenthaler, P. Pirnay-Dummer, & N. M. Seel (Eds.), Computer-based Diagnostics and Systematic Analysis of Knowledge (pp. 261–279). New York: Springer-Verlag. doi:10.1007/978-1-4419-56620_14 OECD. (2013a). OECD skills outlook 2013: First results from the survey of adult skills. OECD Publishing. OECD. (2013b). PISA 2015 Collaborative Problem Solving framework. OECD Publishing. Rosen, Y. (2014). Comparability of conflict opportunities in human-to-human and humanto-agent online collaborative problem solving. Technology. Knowledge and Learning, 19(1-2), 147–174. doi:10.1007/s10758-014-9229-1 Rosen, Y., & Foltz, P. (2014). Assessing collaborative problem solving through automated technologies. Research and Practice in Technology Enhanced Learning, 9(3), 389–410. Rosen, Y., & Rimor, R. (2009). Using collaborative database to enhance students’ knowledge construction. Interdisciplinary Journal of E-Learning and Learning Objects, 5, 187–195.

Rosen, Y., & Rimor, R. (2012). Teaching and assessing problem solving in online collaborative environment. In R. Hartshorne, T. Heafner, & T. Petty (Eds.), Teacher education programs and online learning tools: Innovations in teacher preparation (pp. 82-97). Hershey, PA: Information Science Reference, IGI Global. Rosen, Y., & Wolf, I. (2014). Learning and Assessing Collaborative Problem Solving Skills. Paper presented at the International Society for Technology in Education (ISTE) conference, Atlanta, GA. Soller, A., & Stevens, R. (2008). Applications of stochastic analyses for collaborative learning and cognitive assessment. In G. R. Hancock & K. M. Samuelson (Eds.), Advances in latent variable mixture models (pp. 109–111). Charlotte, NC: Information Age Publishing. Von Davier, A. A., & Halpin, P. F. (2013, December). Collaborative problem solving and the assessment of cognitive skills: Psychometric considerations. Research Report ETS RR-13-41. Educational Testing Service. Webb, N. M. (1995). Group collaboration in assessment: Multiple objectives, processes, and outcomes. Educational Evaluation and Policy Analysis, 17(2), 239–261. doi:10.3102/01623737017002239 Wildman, J. L., Shuffler, M. L., Lazzara, E. H., Fiore, S. M., Burke, C. S., Salas, E., & Garven, S. (2012). Trust development in swift starting action teams: A multilevel framework. Group & Organization Management, 37(2), 138–170. doi:10.1177/1059601111434202 Zhang, J. (1998). A distributed representation approach to group problem solving. Journal of the American Society for Information Science, 49(9), 801–809. doi:10.1002/(SICI)10974571(199807)49:93.0.CO;2-Q

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ADDITIONAL READING Avery Gomez, E., Wu, D., & Passerini, K. (2010). Computer-supported team-based learning: The impact of motivation, enjoyment and team contributions on learning outcomes. Computers & Education, 55(1), 378–390. doi:10.1016/j. compedu.2010.02.003 Baylor, A. L., & Kim, Y. (2005). Simulating instructional roles through pedagogical agents. International Journal of Artificial Intelligence in Education, 15, 95–115. Brannick, M. T., & Prince, C. (1997). An overview of team performance measurement. In M. T. Brannick, E. Salas, & C. Prince (Eds.), Team performance assessment and measurement: Theory methods and applications (pp. 3–16). Mahwah, NJ: Lawrence Erlbaum Associates. Graesser, A. C., Lu, S., Jackson, G. T., Mitchell, H., Ventura, M., Olney, A., & Louwerse, M. M. (2004). AutoTutor: A tutor with dialogue in natural language. Behavior Research Methods, Instruments, & Computers, 36(2), 180–193. doi:10.3758/BF03195563 PMID:15354683 Griffin, P., & Care, E. (Eds.). Assessment and Teaching of 21st Century Skills: Vol. 2. Methods and Approach. Dordrecht: Springer. Rosen, Y., & Mosharraf, M. (2015). Computer agent technologies in collaborative assessments. In Y. Rosen, S. Ferrara, & M. Mosharraf (Eds.). Handbook of Research on Technology Tools for Real-Life Skill Development (pp. 319-343). Hershey, PA: Information Science, IGI Global.

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Stahl, G. (2006). Group cognition: Computer support for building collaborative knowledge. Cambridge, MA: MIT Press.

KEY TERMS AND DEFINITIONS Agent: Either a human or a computersimulated participant in a collaborative problem solving group. Collaboration: Coordinated, synchronous activity that is the result of a continued attempt to construct and maintain a shared conception of a problem. Collaborative Problem Solving: The capacity of an individual to effectively engage in a group process whereby two or more agents attempt to solve a problem by sharing knowledge and understanding, organizing the group work and monitoring the progress, taking actions to solve the problem, and providing constructive feedback to group members. Computer Agent: An avatar with a preprogrammed profile, actions and communication. Computer agents can be capable of generating goals, performing actions, communicating messages, sensing environment, adapting to changing environments, and learning. Problem Solving: Cognitive processing directed at achieving a goal when no solution method is obvious to the problem solver.

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Category: Educational Technologies

Cost-Effective 3D Stereo Visualization for Creative Learning R. S. Kamath Chatrapati Shahu Institute of Business Education and Research, India R. K. Kamat Shivaji University, India

INTRODUCTION Technology-enhanced learning which has become a reality with the pervasive penetration of Information and Communication Technology (ICT) in almost all the walks of higher learning is by now not a new concept, but is still quite new in many educational institutions and settings. The pragmatic view by many researchers, first hand entails, that the chalk-and-talk environment is being both less and less relevant and effective to 21st Century digital age students, and does little or no justice to the learning of academically underprepared students (Dongale, Patil & Kamat, 2015). Especially in the domain of ‘Engineering Education’ it is clearly evident that Progress in computers and various technologies have changed traditional methods for teaching. Previously dominated by simulation and animation, now the educators are realizing that both the above said tools and techniques alone cannot substantiate true real-sense learning for users. This explores need for more advanced technologies in order to improve learning. In this context, the VR technology has found numerous applications in the field of education. The growth dynamics VR area reveals it’s market size to $407.51 million which will encompass more than 25 million users by 2018 (Marketsandmarkets.com, 2015). The main challenge of VR technology, however is the exorbitant cost due to the inherent sophisticated hardware and software which inhibits its inculcation in the education paradigm. In the backdrop

of above, we present a cost effective 3D stereo visualization system conceived, designed and developed by us for creative learning in the most cost effective manner. The chapter is structured as follows: We open up with a brief overview of technology inculcation in education, which showcases the gradual progression from simple simulation and animation techniques to more sophisticated ones like VR. We also present the very notion of VR for the benefit of the broad audience of the chapter. The focus then shifts to the VR tool we developed, its system architecture, technical features and cost effectiveness. The manuscript then actually portrays setting the experimental environment for VR based pedagogy and thereby highlights its potential role in presenting the insight in realization of experiential learning in different domains.

BACKGRAOUND: PROGRESSION OF EDUCATION TECHONOLOGY FROM SIMULATION, ANIMATION TO VIRTUAL REALITY With the embryonic digital age, there has been intense discussion all over the globe, particularly in the last decade about the use of technology for personalizing the learning environment. VR is the fascinating area in computer application research (Vafadar, 2013). In recent years, 3D technologies in modeling, printing and stereoscopic have symbolized the true cutting edge in educational

DOI: 10.4018/978-1-5225-2255-3.ch210 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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systems (Dalgarno et al., 2010). The use of 3D glasses, stereoscopic 3D content and virtual environments in all curriculum areas to improve 21st century teaching and learning has been the buzz word in all the spheres of academics (Alpaslan & Sawchuk, 2004). The 3D in the Classroom has been clearly the winner over its 2D counterpart in improving the teaching-learning in the classroom. An interesting account of all these developments at the global level has been summarized in the following paragraph. Pedagogic experts believe that merely theoretical explanation without actual implementation makes learning experience invaluable. Instead of listening only to lectures, if students get real experience in a virtual wrapper can achieve the learning outcomes. In this context VR is real winner and gained immense popularity in the education spheres. It provides a visually appealing technique for presentation of teaching material. It motivates student community by encouraging active participation rather than passivity. For example, a computer-based flight simulator in which pilots can attain flying skills in the absence of a real airplane ought to instill the right kind of skills. Many studies have been conducted on the applications and effectiveness of virtual Reality in education and training. Studies show that a virtual environment can stimulate learning because of it’s a tight coupling between illustrative and experiential information (Hamada, 2008). Yahaya incorporated immersive VR technology in creating learning environment (Yahaya, 2004). His investigations indicate that learners gets engaged in real world problems associated with VR environment and it really helps in gaining the subject understanding. Elomar has explained the use of VR technology in learning environment through the ‘experiencing of real phenomena’; new educational possibilities by the integration of education with VR (Elomar, 2012). Dalgarno et al. have explained applications of 3D immersive virtual worlds in education and its implementation in education institutions across New Zealand and Australia (Dalgarno et al., 2010). The authors of the above referred study 2412

have discussed overall research design with results from the Australian/New Zealand perspective. The main finding of this study is the variety of ways of using 3D virtual worlds by academicians. Yet another paper by Piovesan et al., explained the application of VR in education. This research presents educational software, which permits students to manipulate objects in 3D with a simple interface (Piovesan et al., 2012). The software referred in the research is based on VRML for the design of models and PHP for web publishing. Manseur has discussed the use of VR tools and its applications in science and engineering education (Manseur, 2005). In particular, the VRML for model design is presented and subsequently the development of visualization tools for education has been depicted. Researcher has explained the combination of VRML with other software tools to create interactive VR solutions to support teaching and learning. Teaching with 3D technologies is attracted by students’ community as confirmed from research from case studies around the world. Learning accompanied with 3D video, interactions and simulations in virtual environment is attaining significant increase in performance, retention, abstract concept mastery, and more. Well known VR tools developed in this context are (engagingeducation.net, 2015): •

• • •

3D Ladibug Document Camera: A dynamic document camera that can show objects and manipulate them in 3D stereoscopic by using the 3D software and hardware Presente 3D: An add-on to Microsoft PowerPoint, using which students can attach 3D stereoscopic to their presentations Kid Pix: An image processing tool that can be used to create pictures and videos in 3D anaglyph Hasbro My3D: A nifty gadget that facilitates students to get the immersive experiences sensation like walking through the Solar System

Category: Educational Technologies

•

3D Books: Facilitating students understanding by both the pictures and the content in 3D

There is increasingly good number of domains apart from education where VR has been finding its applications. Diverse fields including psychology, medicine, neuroscience, and physical and occupational therapy, the ICT MedVR group explores and evaluates areas where VR can add value over traditional assessment and intervention approaches. Areas of specialization are in using VR for mental health therapy, motor and cognitive skills rehabilitation, assessment, and clinical skills training. (Medical Virtual Reality, 2015). VR is beginning to be used extensive in built environment education. 3D virtual environment can provide a rich, interactive and engaging educational context that supports experimental learning. (Nicholos et al., 2012) VR has also its impact in various fields like entertainment, manufacturing industries, military bodies in addition to education. In the field of education 3D graphics plays major role (Kamath & Kamat, 2013). 3D models are very useful to make acquainted students with features of various shapes and objects. Many games have been designed using 3D images that the user needs to intermingle with in order to gain knowledge of a certain lesson (Bowman et al., 2006). Interaction with models increases a user’s curiosity and makes learning more excitement. Simulations and visualization of complex data can be extensively done by using VR. The VR Technology has the ability to assist in the teaching process too, enabling students to view and interact with concepts they are working in 3D immersive environment (Steinicke, 2009). The user makes use of VR hardware to move and explore within an onscreen virtual environment as if in reality moving within a place in the actual world. Though VR paves the way for enhancing the visualization ability thereby making its headway in growing number of niche domains in addition to education, still it has to overcome many challenges for its adoption as discussed in the following section.

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As presented in previous section VR leads to number of benefits, but its adoption is easier said than done. Particularly its espousal in education is challenging due to number of factors such as the customization of the technology per learner’s need, lack of apt skill set amongst the faculty who are mainly digital immigrant and catering the needs of the computationally intensive software centric applications with the equally capable hardware platform. However amongst the three main challenges, the cost barrier is the main factor inhibiting the growth of VR in education. We aimed towards the cost reduction without compromising the performance and designed a 3D visualization method, an important facet of VR for active learning (Kamath, et al., 2012).We attempted developing a cost effective technique for achieving VR based active learning environment. With a combination of inexpensive hardware and simple-to-use software, students can enjoy the excitement of eye-popping 3D visualizations by employing our software suite on their fairly low end personal computers. The visualization solution developed is capable to browse the ASCII files containing the details of objects and the corresponding 3D scenes are rendered. 3D stereo visualization is a prime part of our software suite that accounts for virtual reality. This visualization solution offers inexpensive 3D interface to learner. The details of the software suite are presented in the following sections.

STRUCTURAL DESIGN OF 3D VIRTUAL REALITY LEARNING ENVIRONMENT Traditionally the models of 3D objects are designed in modeling software. Corresponding file holds the geometric information as well as topological information of the object. This results in to very heavy file size. Since the topological details of

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object are not required for visualization, executing such files for stereo visualization mode leads to time-consuming operation on a general-purpose computer. We eliminated the computationally intensive part of the traditional systems by designing a visualization solution which parses the ASCII files generated by any modeling software and retrieves only the data sets required for visualization in order get desired display. We explain the structural design of the suite in the following section with the help of system architecture, sequence of operations and the method of exploring 3D vision in virtual learning environment (Kamath & Kamat, 2010).

Figure 1. Proposed research architecture

System Architecture The block diagram of the framework is shown in the in Figure 1. The visualization tool developed displays the model for 3D visualization by executing following modules: •

•

•

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Computer Aided Design (CAD) systems are the source of design data. The objects are designed in CAD are exported to ASCII format. Our tool uses these ASCII files such as Stereo Lithography (STL) and Virtual Reality Modeling Language (VRML) files as input. The inputted STL of VRML file is given for parsing. Parser retrieves the required dataset from the inputted file for the visualization. These dataset stored in the form of scene graph structures. 3D scene is generated by rendering scene graph structures with the help of OpenGL. Rendering, i.e. the process of displaying the objects on the screen is accomplished in this manner. The Visual C++ programming language platform is used for the development of the visualization tool. It acts as an interface for STL and VRML models in 3D visualization.

Technical Features of ASCII Files As mentioned earlier the models designed in CAD system are exported to standard ASCII file formats. Information of the respective model is stored in the form of list of triangles. Coordinates of triangle vertice s and other details associated with display are included in these ASCII files. The software suite processes the STL and VRML files for rendering the object. STL file contains facet-based representation of solid objects. The code snippet of ASCII STL file is given in Figure 2. It defines the surface of an object with set of Figure 2. Syntax of ASCII STL file

Category: Educational Technologies

triangles. VRML is a scene description language (Carey et al.). It encloses all the details required for visualization of the model. Figure 3, shows the syntax of VRML file.

Sequence of Operations A flow chart shown in Figure 4 explains the sequence of tasks implemented during the development of visualization solution. The sequence of operations is as follows: 1. Initially the objects or models which are designed in CAD are exported into ASCII file formats which are input to this visualization tool. This tool imports the corresponding datasets and processes the same for visualization. 2. Inputted ASCII file is parsed and dataset required for display are retrieved by parser module. It results in to storage of model’s details in structures for the next task. 3. Display structures are created during parsing. These structures contain set of triangles definition as well as other details related to the display of CAD model.

4. These scene-graph structures are displayed on the screen. Here OpenGL graphics tool is used for the display of objects. 5. The prime feature of the software suite is to provide a 3D stereovision system that allows user to explore datasets with cost effective way explained next to this.

Cost Effective 3D Visualization Main feature of the software suite developed is cost effectiveness. The learner can view and interact with 3D visualizations in stereo mode with the help of fairly low cost red-blue glasses on a general-purpose computer. Thus the software suite achieves passive stereo vision (Wormell et al., 2007). This technique allows the perception of depth when viewed through colored red/blue glasses. Here left and right eye images are overlays in different colors and create a stereoscopic picture. The image component sent to brain by each eye with the help of a color filter and the result in three dimensions is interpreted by the brain. This is the less-expensive way of projecting in 3D, using a single digital projector (Zelle & Figura, 2004).

Figure 3. Syntax of ASCII VRML file

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Figure 4. System development flowchart

Figure 5. Blended image for two eyes

We follow the time-parallel principle for the stereo vision solution. The principle is based on providing both eye views to the viewer at a time and for directing each view to the appropriate eye it uses optical techniques. This needs the viewer to use red-blue eyewear. Simultaneous presence of both images on a screen, thus a time-parallel method is achieved. Colored filter over each eye results in one eye image which has been rendered in red and the other in blue. We use monochrome stereo images in the software suite i.e. the superimposition of left and right eye views which are in blue and red respectively. Single image is formed by combing left and right eye images. Figure 5 shows the above referred blending of images. The three-dimensional effect is perceived by viewing this blending through eyeglasses of corresponding colors but in a reversed manner. Thus the rendering has is 2416

easy to achieved using simple image processing techniques and the same is less expensive.

EXPERIMENTAL SETUP FOR VR BASED LEARNING As seen above thus the boundaries in traditional education can be broken by VR technology to create a world of imagination (Elomar, 2012). This requires time, effort and thoroughly intricate methods to fiddle with the technology for the learning purposes. As evidenced from our development, with the use of simulation and special hardware enables learners to occupy in a virtual environment. For the construction of a virtual reality system the need of special hardware and software includes;

Category: Educational Technologies

Figure 6. VR based learning environment

Figure 7. Red blue glass for 3D stereo display

1. Virtual Reality Modeling: This includes models of real world environments created using AutoCAD, 3Dstudio etc. 2. Virtual Reality Software: VR toolkits, software to support ample of applications. 3. Virtual Environment: Using display monitor and stereo glasses, user can view and interact with entities in VR environment. Stereo Glasses, allows the user to view true 3-D stereo depth of computer generated images. Gloves, allow the user to interact with virtual environment through finger control.

Figure 8. 3D stereoscopic display of an object

Figure 6 explains the VR based learning environment in education (Kamath & Kamat, 2011). Initially models are designed using CAD software. After the design the details of the model are saved as ASCII format. VR tool reads this file and presents CAD data in virtual space. A learner can visualize the 3D model by using this tool. Since VR is a computer simulation of a natural environment, interaction with a 3D model is more natural than 2D. Left eye image drawn in blue is superimposed with right eye image which is in red. This superimposition when viewed through red-blue glasses the three-dimensional effect is perceived. Authors recommend the Red-Blue glasses the one shown in Figure 7 to perceive 3D effect. Figure 8 depicts the display of doubly rendered model in red and blue as output of the VR toolkit designed by us.

FUTURE RESEARCH DIRECTIONS The assimilation of VR, simulation and pedagogy provides a natural guide for future research. We are currently working on integration of social networking and visualization suite to imbibe creativity in e-learning scenario. Thus the future research and our further work aim at realization of social classroom in a flipped mode with 24 x 7 availability, which will exploit collaboration, teamwork and creativity amongst the learners and facilitates them to learn on their own, at the pace they prefer to grasp. Coupling of our software suite to the social networking platforms will assist us to research on the learning behavior of the students from different perspectives such as social integration, working in team as well as motivational and inspiration issues towards grasping new concepts.

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Following are the set of additional facilities that would increase the supremacy and usability of this software suite; •

• • • •

Openly support other CAD file formats such as STEP (Standard for the Exchange of Product model data), IGES (Initial Graphics Exchange Specification) etc. Implement auto stereoscopic vision that is perceiving 3D effect without the use of any external interface Supporting interface for other VR devices such as head mounted display, data gloves, simulators etc Picking and selection of desired part of the displayed model Changing the display properties for the selected parts of a rendered model

We are also working on the optimizing the software suite for a smart phone environment especially with the Android OS. This will help the learners to use this valuable learning aid on a move.

SIGNIFICANCE AND CONCLUSION Virtual Reality has a pivotal role in bringing in meaningful teaching-learning ambience in education and training. Research on educational applications of VR, shows that VR technology can significantly improve the effectiveness of teaching by allowing learners to experience theoretical knowledge. In addition, it nurtures imagination, innovation, problem-solving of the learners thereby harnessing their creativity. We presented in this chapter a cost effective 3D stereo visualization solution which has great potential in education for active learning. This tool can be executed on a fairly general purpose low end computing platform. Students have the benefit of learning in such a VR based environment since it renders the display in the most personalized manner and thereby inculcate experiential learning.

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Major advantages of using such a virtual reality tool in pedagogy are: •

• •

• • •

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VR based learning promotes a learner’s ability to conceptualize in a three-dimensional space, and provide a sense of achievement whilst doing so. It makes the learning environment easy to use, cost effective, and convenient for users. The learners can study and test theoretical details in a virtual environment. Relevant information conveyed to students more efficiently than traditional techniques with the help of depth perception. VR has the prospective to revolutionize and improve the ways in which students are trained. VR grabs and holds the attention of students. It allows extreme close-up examination of an object. Engineering students can use less expensive projection or flat screen desktop stereoscopic solutions for 3D visualization and interaction in virtual environment (Sampaio et al., 2010) Medical students can learn spatial cues and sense of depth using Z co-ordinate while viewing material (Al-khalifah et al., 2006) Stereoscopic 3D Visualization has its impact in almost every area in education (Nicholos et al., 2012)

The development of this visualization suite was mainly an investigation on the technical practicability of a Virtual Reality System. We explored CAD as a medium to support early conceptual design through rapid prototyping of mechanical models. We thus explored Virtual Reality as a potential design prototyping environment in which prototypes of designs can be constructed, communicated and visually evaluated. The designed interface serves the visualization and evaluation of CAD geometry. Through a series of experiments, we thus implemented a method of achieving an

Category: Educational Technologies

important feature of VR, stereoscopic display, in a cost-effective way for the most aspiring young learners. Interactive immersive less expensive 3D visualization of CAD object is the major outcome of this research which will no doubt yoke the creativity of the students who are now essentially digital natives. We are further working on coupling the software suite to the social networking platforms to experiment on the learning behavior of the students from different perspectives such as social integration, working in team as well as motivational and inspiration towards grasping new concepts.

Dongale, T., Patil, S., & Kamat, R. (2015). Learning by Simulations. International Journal of Quality Assurance in Engineering and Technology Education, 4(2), 13–25. doi:10.4018/ IJQAETE.2015040102

REFERENCES

Hamada, M. (2008). An example of virtual environment and web-based application in learning. The International Journal of Virtual Reality, 7(3), 1–8.

Al-khalifah, A. H., McCrindle, R. J., Sharkey, P. M., & Alexandrov, V. N. (2006). Using virtual reality for medical diagnosis, training and education, Proc. 6th Intl Conf. Disability, Virtual Reality & Assoc. Tech. doi:10.1515/IJDHD.2006.5.2.187 Alpaslan, Z. Y., & Sawchuk, A. A. (2004). Threedimensional interaction with auto stereoscopic displays. Proceedings of the Stereoscopic Displays and Virtual Reality Systems X I Symposium. Black, N. (2015). 5 Stereoscopic 3D Resources for Elementary Students. Retrieved 14 October 2015, from http://www.engagingeducation.net/ wordpress/5-stereoscopic-3d-resources-forelementary-students/ Bowman, D. A., Chen, J., & Chadwick, A. (2006). New directions in 3D user interfaces. The International Journal of Virtual Reality, 5(2), 3–14. Carey, R., & Bell, G. (n.d.). The annotated VRML 97 reference manual. Retrieved from http://accad. osu.edu/~pgerstma/class/vnv/resources/info/AnnotatedVrmlRef/appd.htm Dalgarno, Carlson, & Tynan. (2010). 3D immersive virtual worlds in higher education: An Australian and New Zealand scoping study. Proceedings ASCILITE Sydney.

Elomar. (2012). Virtual Reality Technology as a Didactical and Pedagogical Resource in Distance Education for Professional Training. INTECHOpen Science. Engagingeducation.net. (2015). Retrieved from http://www.engagingeducation.net/wordpress/5stereoscopic-3d-resources-for-elementarystudents/

Indeptheducation.com. (2015). Retrieved from http://www.indeptheducation.com/wp/infographic-stereoscopic-3d-enhances-learning/ Kamath, R.S., Dongale, T.D., & Kamat, R.K. (2012). Development of Virtual Reality Tool for Creative Learning in Architectural Education. International Journal of Quality Assurance in Engineering and Technology Education, 2(4), 16-24. Kamath, R. S., & Kamat, R. K. (2010). An Effective Stereo Visualization System Implementation for Virtual Prototyping. International Journal on Computer Science and Engineering, 2(7), 2316–2321. Kamath, R. S., & Kamat, R. K. (2011). Simple, low cost 3D Stereo Visualization Technique for mechanical engineering learners. Proceedings of the IETEC’11 Conference, 50-56. Kamath, R. S., & Kamat, R. K. (2013). Development of an Intelligent Virtual Environment for Augmenting Natural Language Processing in Virtual Reality Systems. International Journal of Emerging Trends & Technology in Computer Science, 2(3), 198–203.

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Manseur, R. (2005). Virtual reality in science and engineering education, Frontiers in Education, FIE ‘05. Proceedings 35th Annual Conference. doi:10.1109/FIE.2005.1612051

Yahaya, R. A. (2006). Assessing the Effectiveness of Virtual Reality Technology as part of an Authentic Learning Environment. IEEE Computer Society of India. doi:10.1109/ICALT.2006.1652420

Marketsandmarkets.com. (2015). Retrieved from http://www.marketsandmarkets.com/MarketReports/augmented-reality-virtual-reality-market-1185.html

Zelle, Z. M., & Figura, C. (2004). Simple, low cost stereo graphics: VR for everyone. Proceedings of the SIGCSE’04. doi:10.1145/971300.971421

Medvr.edu. (2015). Retrieved from http://medvr. ict.usc.edu/ Nicholos, A. N., & Jones, B. L. (2012). Virtual Reality Learning: Effects in College and Training Environment. 66th EDGD Mid year conference proceedings. Piovesan, S. D., Passerino, L. M., & Pereira, A. S. (2012). Virtual Reality as a Tool in the Education. IADIS International Conference on Cognition and Exploratory Learning in Digital Age. Sampaio, A. Z., Henriques, P. G., & Martins, O. P. (2010). Virtual reality technology used in civil engineering education. The Open Virtual Reality Journal, 2(1), 18–25. doi:10.2174/1875 323X01002010018 Steinicke, F., Bruder, G., Hinrichs, K., Jerald, J., Frenzy, H., & Lappey, M. (2009). Real walking through virtual environments by redirection techniques. Journal of Virtual Reality and Broadcasting, 6(2). Vafadar. (2013). Virtual Reality: Opportunities and Challenges. International Journal of Modern Engineering Research, 3(2), 139-1145. Wormell, D., Foxlin, E., & Katzman, P. (2007). Improved 3D interactive devices for passive and active stereo virtual environments. Proceedings of the 13th. Eurographics Workshop on Virtual Environments.

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ADDITIONAL READING Kamath, R. S., & Kamat, R. K. (2012). A Monogram on Design and Implementation of Spatially and Temporally Efficient Visualization Algorithms. Lap Lambert.

KEY TERMS AND DEFINITIONS OpenGL: A software interface to graphics hardware for displaying object on the screen. Parser: A module receives VRML or STL file as input, extracts the required data sets for visualization and creates display structure. Passive Stereo: An inexpensive technique uses red-blue eyewear to perceive 3D effect. Prototyping: An early model built to act as a thing to be replicated. Renderer: A computer program which makes an object appears on the screen. Scene Graph: An arrangement of logical and special representation of a graphical scene. Stereo Visualization: A computer generated scene viewed using stereo glass to get threedimensional effect. Three-Dimensional: A set of geometric three parameters height, width and depth which describes an image. Virtual Reality: A computer graphics technology that offers a simulated three-dimensional world.

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Category: Educational Technologies

Could Educational Technology Replace Traditional Schools in the Future? John K. Hope University of Auckland, New Zealand

INTRODUCTION During a recent presentation, Professor Karen Willcox, Professor of Aeronautics and Astronautics and co-Director of the Centre for Computational Engineering at the Massachusetts Institute of Technology, showed a painting of a university classroom in the Middle Ages with a teacher on a podium at the front of a room lecturing to a group of students, one of whom was asleep. Her next picture was of a twenty first century classroom with a teacher at the front lecturing to students, some of whom appeared to be uninterested. Her point was that little has changed in university classrooms for centuries, yet the affordances of e-learning suggest that university education could change more in the next fifty years than any change in the previous five hundred years.

BACKGROUND The scenario portrayed above is also visible in the compulsory schooling sector where the majority of compulsory education still occurs in classrooms with a teacher, but it could be argued that technology-induced change is already occurring. Information and communication technology has penetrated even the most remote schools in developed countries and is infiltrating schools in some of the least developed countries in the world. Futurists have predicted the demise of many brick and mortar universities resulting from the affordances of technology that allow learning to occur anytime, anywhere and in whatever format the learner desires. What of bricks and mortar

schools? The growth of virtual education is not limited to adult education, schools are already incorporating virtual education into their educational programmes and some parents are opting for home based virtual education in preference to traditional schooling. Marketing of educational technology has attracted some of the biggest names in business and the education market has become an important component of the world economy, so change is inevitable. The presence of technology in a classroom should not be seen as an indicator of change in the learning process; the early use of computers in schools was merely replacing teacher instruction with computer instruction, termed programmed learning. In both cases the instruction was top down, based on the view that the teacher or computer programmer was the font of knowledge whose task was to deliver information for the student to memorise. This instructional process can be useful for the delivery of some learning, such as presenting factual information, but the exponential growth of knowledge resulting since the computer revolution began, and the ease of access to that knowledge via technology, makes knowledge transmission possible without the need for a teacher in a classroom. It is easier to keep a computer up to date with the latest knowledge than to keep a teacher up to date. So what is driving changes to the form of schooling as it has traditionally been known?

Changes to Schooling Globalisation has changed the range of knowledge, skills and dispositions required for a satisfying and

DOI: 10.4018/978-1-5225-2255-3.ch211 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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productive life. Traditional employment areas such as book-keeping have all but disappeared, new vocations such as mainframe computer engineers were created and then outlived their usefulness, and the world economy has lurched from one crisis to another due to influences beyond the control of any one individual. Soft skills are now needed to compete in a rapidly changing world and education must adapt to meet these changes. Learners have changed too. The so-called Generation Z students are now in compulsory schooling. There is a debate about the definition of Generation Z. Geck (2006) indicated that the term is used to describe the students who born in or after 1990, Shatto and Erwin (2016) stated that Generation Z follows the Millennials, while Seemiller and Grace (2016) said Generation Z’s students were born between 1995 and 2010. According to Seemiller and Grace (2016), Generation Z students are “loyal, thoughtful, compassionate, open-minded, and responsible” (p. 8). Viewed from family perspectives, Seemiller and Grace suggest that the personality characteristics of Generation Z are unique, because Generation Z students are raised by Generation X parents with an emphasis on individual responsibility and independence. Although the dates that define Generation Z may differ, scholars have reached consensus that the world of Generation Z is shaped by the internet (Bassiouni, & Hackley, 2014). Generation Z are the first generation to spend the whole of their life in a world dominated by social media use. Viewed from social media perspectives, a Pew Research Center (2014) study indicated that millennials are “digital natives—the only generation for which these new technologies are not something they’ve had to adapt to” (para. 7). They have more reliance on social media such as Facebook than their older generation. An example of the influence of the internet can be found by viewing YouTube clips1 show Generation Z babies trying to flick pictures in paper magazines to enlarge the picture or turn the page, in marked contrast to earlier generations who knew to turn a physical page.

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As with any new technologies, there can be positive and negative effects on people. Social media links make it easier to connect with other people anywhere, anytime, but can be addictive. Social media addiction is a recently defined psychological phenomena spawned by issues with teenagers who cannot be parted from their smartphone for more than an hour and whose health and school performance can suffer as a result 2. Generation Z students are now in schools, bringing with them different learning needs to those of the generations before them. Their social lives are heavily influenced by social media and they cannot see why use of social media should not be normal within their education. Again, education must adapt to the changed needs of this different generation of learners. As the baby boomer and subsequent generations age, many become financially independent, have increased free time and can turn to education once again to enhance the quality of their lives. Adult learners are either well qualified people who want to study later in life for interest, rather than to qualify for a vocation, or they may be adults who left school before earning the qualifications needed for success in life. In both cases, adult students may look to study at secondary school level, not by physically attending school where they would not fit socially, but studying virtually. As a result, Scandinavian countries have led the world in providing pathways into education for the needs of these adult learners, including use of technology (Biagetti, & Scicchitano, 2013; Boeren, Nicaise, & Baert, 2010; Rubenson, & Elfert, 2015). Adult learners who were schooled decades ago need different approaches to meet their technology education needs (Roche, 2016) as many lack the background in technology of younger generations. As globalisation has changed the nature of work, technology has influenced the nature of learning and learners themselves have changed, there is a need for schools to change. The affordances of educational technology are likely to drive this change, particularly with new forms of virtual education (Robinson & Aronica, 2015).

Category: Educational Technologies

The Schooling of the Future No one person can predict the future, least of all this author, but the remainder of this chapter is devoted to suggestions of what some scholars think the future of compulsory schooling might be, seen through the lens of educationally focused information and communication technology development. Four areas will be examined in the text that follows. The implications of social media for education, blended/virtual learning and educational gaming are learning strategies to be examined in detail, followed by the implications for technological education leadership in schools.

FUTURE RESEARCH DIRECTIONS Social Media and Education Social media such as Facebook, WhatsApp, Twitter, LinkedIn and the like are accessed daily by most adults in developed countries. According to Duggan’s (2015) survey in the United States, 70% of Facebook users, 59% of Instagram users, 27% of Pinterest users and 22% of LinkedIn users visit these platforms daily. Social media have also gained dedicated followers in schools, firstly in secondary schools and now increasingly in primary schools, despite their use in schools often not being encouraged by school authorities. Facebook states that the age limit for users is thirteen years, yet recent research has found usage at elementary level to be common. For example, McDonald, Laxman and Hope (2015) found that most nine to 12 year old students used Facebook ‘illegally’ by mis-stating their age or sometimes supported by their parents/caregivers who allow their children to use their Facebook account, despite concerns about frequently reported issues such as cyberbullying, sexual exploitation and social media addiction. By their nature, social media software packages were designed for social use so their take-up by educators has been understandably limited. However some studies

have found that school students often used social media for education related purposes. Roblyer, McDaniel, Webb, Herman and Witty (2010) found that up to 50% of students communicated about schoolwork while online using social media, the implication being that when social media are an integral part of a student’s social life, this affordance should be utilised for formal educational purposes (Baran, 2010). Benefits suggested from social media use in education include encouraging students to engage more freely with their learning and feeling less constrained when talking about it (Tynes, 2007), sharing knowledge with other students (Mazman & Usluel, 2010) and facilitating more introverted students to speak up online where they would be reluctant to do so in a face to face group situation (Larkin, 2016). To mitigate the disadvantages of social media, including the likelihood of social distractions when using social media in class, educational social media sites are available such as Twiducate.com, Classroom 2.0, Ipernity and Ning. Being virtual, these websites work both in school and out of school, blurring both physical and time divisions between the outside world and the classroom. Future use possibilities already being explored include international social network collaborations between students to develop international understanding, acceptance of difference and building international friendships. Social media are often used at all hours of the day and night, obviating the time change difficulties and financial costs that dogged earlier synchronous attempts for students to communicate between differing time zones using telephone and television links.

Blended Learning and Virtual Learning As noted above, despite predictions of the demise of some physical higher education institutions due to the rapid growth of virtual learning, most school students still learn in a face to face school environment. No futurist found in a recent literature

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search predicted the demise of schools in the near future. While as yet unknown new technologies can suddenly arrive and change the world, as did invention of the internet, current trends suggest that the near future of schooling is most likely to be founded on increased use of blended learning, where students can receive complementary face to face to face and virtual instruction (Miron & Gulosino, 2016). One high achieving Pennsylvania school district has utilised blended learning so successfully that it is used as an exemplar in an Entrepreneurship and Technology Innovations class at Harvard Business School, this use of an education example at Harvard Business School being an indication of the close link between the future of schooling and future business. The blended model adopted in Pennsylvania facilitates a move towards the previously idealistic aim of one to one individualised instruction and has seen voluntary enrolment of 300 students in the first year climb to 1100 students in year three. Blended learning does not suit all students. Initial results of the Philadelphia project show that when compared with traditional classes, two thirds of the blended programme students improved more than the traditional peer group, but one third did not improve, or did not do as well (Davis, 2016). Another indication of the future for virtual learning is the arrival of MOOCs (Massive Open Online Courses) provided tuition free courses in almost any subject imaginable by high ranked universities in many countries. Although not necessarily providing a recognised qualification there has been massive take up of these courses by adults, enrolment numbers being in the millions for MOOCs provided by top universities such as edX MOOCs provided by Harvard and MIT. Although take up is high, completion rates can be as low as 5%, the main problem stated as being student time constraints. Research into this issue has found that adult students are beginning to adapt to MOOC style learning by using the MOOC as an initial source to be supplemented by other resources, utilising only the portion of the MOOC relevant to their needs, and finding that the ability to pick

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up small components of the course at different times helped in mitigating the time constraints of modern life (Veletsianos, Reich, & Pasquini, 2016). This research highlights the affordances of virtual education such as anywhere, anytime, affordances that are also applicable to schooling. A study of edX revealed that 39% of edX MOOC participants were teachers, either enrolling for in-service professional development or to help in their pre-service certification endeavours (Ho et al., 2015). AboutEdu Inc., a non-profit organisation listing MOOCs available for the school sector, lists 33 MOOC providing institutions available to elementary and high school students, and/or MOOCs providing professional development opportunities for teachers. AboutEdu Inc. requests for MOOC assistance came from many countries with USA, China and India being the countries most frequently requesting MOOCs, a graphic illustration of the increasing global ubiquity of virtual learning. A report by the United States National Center for Education Statistics found that virtual and blended schools numbers were growing, despite some negative reports about their impact on student learning. 447 virtual schools were listed in 201314, attended by 262,000 students, with another 26,000 students attending 87 blended schools (Miron & Gulosino, 2016). This report noted concern that the graduation rate from virtual schools was 41%, from blended schools 37%, compared with 81% for all schools. Other concerns included high teacher: student ratios (35:1 for virtual schools, 32:1 for blended schools, 16:1 traditional public schools), and virtual school rolls included smaller proportions of minority and low-income students. Three quarters of the virtual schools were private schools, a major difference from the traditional school sector, making comparisons difficult. But this report and another from Stanford University that found cyber-charter schools had an “overwhelmingly negative impact” (Center for Research on Education Outcomes, 2015, p. 32) on student achievement suggests that private sector involvement in the virtual/blended school

Category: Educational Technologies

development has not always produced positive results. The issue of the increasing trend towards for-profit schooling will be discussed further in a later section of this chapter. One other component of blended learning is making rapid and seemingly more positive inroads to schooling, particularly in the United States. Open education teaching resources are free to teachers and no longer are limited to teachers sharing their personal planning and ideas on the internet. One example is EngageNY, a collection of materials published free by the state of New York. With 45 million downloads and in excess of thirteen million users, this customizable material is only one example of many that are helping teachers to cope with high workloads and increased accountability. Users of this particular resource are not limited to the United States. Although primarily aligned with United States education standards, their listed users include tens of thousands of teachers from the Philippines, Canada, India, United Arab Emirates and the United Kingdom (New York State Education Department, n.d.). Other open education providers such as UnboundEd (www.unbounded.org) are appearing, increasing the likelihood that open education resources will become even more popular in the future.

Gamification Almost all school age students in developed and developing countries are aware of digital recreational games, and most play them at some point in their lives. The motivation effect, fun and challenge of recreational digital games such as Minecraft have universal appeal that prompts teachers to wonder if there are classroom applications of gamification that could improve learning. Ever since computers became common in classrooms more than 30 years ago, games have been used in classrooms. Beginning with very simple rote learning, drill and practice applications such as teaching basic mathematical facts with small electronic rewards, newer games are much more sophisticated and are increasing being seen as having potential for

school use. A large scale United States survey of 500,000 students, teachers and parents conducted by a non-profit organisation revealed that in 2015, 48% of teachers surveyed reported using games in the classroom compared with 23% in 2010, and the number of teachers using online videos had increased from 47% in 2010 to 68% in 2015 (Speak Up, 2015). Use does not necessarily equate to achieving improved learning, but the same survey found that 48% of teachers, 50% of high school students, 60% of elementary students and 40% of parents considered educational gaming a component of their ideal future school, suggesting that educational gaming is seen to have value in schooling. Students identified reasons why they saw value in educational online videos including the ability to watch again as needed, easier to understand, connected to the real world, fits learning style, easy to access on mobile devices and more engaging. Not all results were positive. The Speak Up survey also revealed that teachers were reluctant to use digital learning, and needed professional development to enable them to make maximum use of digital learning, such as when to incorporate them into the learning unit and how to sort out the educationally useful from the plethora of low quality games available on the net. Education administers were cautious about the use of educational gaming and few had planned appropriate professional development for teachers. Despite this caution, big-money developers are introducing game–based programmes designed to develop 21st century problem solving skills and having the potential use for more authentic assessment purposes, such as measuring skills less readily measured by traditional methods. For example, Microsoft has developed an education version of Minecraft that could be used in this way. At the 2015 American Education Research Association conference, presentations included studies that investigated the merging of physical movement with virtual understanding, understanding students’ virtual thinking, and virtual gardening, examples of emerging technologies

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that utilise digital learning to supplement more traditional learning methodology, blurring the lines between recreational play and school-based learning (Herold, 2015). Pokemon Go, an app that is higher tech version version of the traditional recreational game, is creating news headlines as it sweeps the world while this chapter is being written. The game requires users to get out into their community and connect the real world with the virtual Pokemon world. Some of the media headlines are negative, noting safety issues for students, the warring component and at higher Pokemon levels, the requirement to pay. Conversely, incredible levels of motivation are generated, students have to get out into their communities and there is a community educational component built into the game. This has also made educators think about the potential of the model for educational applications (Doran, 2016). Hard research demonstrating the affordances of digital games is sparse, but a meta-analysis of digital game research for K-16 students investigated the cognitive, intrapersonal and interpersonal learning domains (Clark, Tanner-Smith, & Killingsworth, 2014). Overall, analysis of 57 studies showed the digital game studies analysed to be more effective in all three learning domains than the non-game instruction measured. The authors of the research note the difficulty in all such meta-analyses across different studies, the results cannot necessarily be generalised to all other situations, only to the situations measured in the studies considered in the meta-analysis. Nevertheless, this study is one indicator of the potential of digital game based learning, particularly for supporting higher order learning outcomes.

For-Profit Education For-profit education was mentioned above due to its increasing move into the compulsory schooling sector. When 2015 annual global expenditure on educational technology hardware reaches US$15 billion (Molnar, 2016), big business takes notice. Similarly, when virtual education creates ac-

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cess to the learners of the world at the click of a mouse, big business takes notice again. For-profit education has proliferated far beyond the private international school model and coaching classes of paid education of the past, with millions of school students now paying for part of their education via virtual means. This growth suggests that for-profit education growth is not limited to hardware sales, it also encompasses delivery of education, but not always as effectively as claimed. Henry Levine from Teachers College Columbia sees for-profit education as a whole making more claims about effectiveness and cost efficiency than are justified (Gustke, 2010). A sample of the issues that have accompanied the growth of for-profit virtual education were documented earlier in this chapter, but there are also some examples where for-profit education has been beneficial. Microsoft’s launch of the education version of Minecraft is one example that is currently available free. For profit corporations donate money to facilitate research into the educational use of technology, or provide some of their products without charging. Corporations have the resources to front technological and organizational innovation (Carey, 2010). They may also close educational market gaps left by traditional education groups, and provide a second chance for students ignored by non-profit education (Chung, 2012).

Educational Leadership Earlier generations of school leaders could focus entirely on curriculum and assessment issues, their only contact with technology being chalk and writing equipment. Today’s school leaders still have to focus on curriculum and assessment, but ignore the influence of electronic technology on curriculum and assessment of student learning at their peril. Provision of the most appropriate hardware, selection from the plethora of software packages available and choosing virtual providers are new school leadership tasks. Parental demands for school access to the latest

Category: Educational Technologies

educational technology, internet security issues and the critical need for educational technology professional development of teachers all concern school leaders of today and are likely to take even more of leaders’ time in the future. A 2016 survey by the Education week Research Center found significantly more teachers finding tailored professional development conducted by sharing with other teachers, collaborative planning and having educational technology coaches available in their classrooms meeting their professional development needs better than traditional forms of professional development, such as read-made educational district and for-profit company provided professional development (Flanigan, 2016). One example that should concern school leaders is a summary of several American surveys by credible organisations, such as the Council of Chief State School officers, which found high stakes test score results varied dependent on the type of technology used to take the tests (Herold, 2016). Overall, there were suggestions that higher scores occurred when desktop computers were used, lower scores when laptops were used and lower again scores when tablets were used. One reason suggested was screen size, but results were not definitive. This example illustrates the difficulties facing school leaders when trying to decide what is best for their students. However technology affordances also increase the tools available to school leaders that will assist them to deal with these new challenges. One example is the John’s Hopkins university partnership with a non-profit organisation called Digital Promise to provide free access to a tool that provides objective evaluation information about the efficacy of technological offerings to schools (Cavanagh, 2015).

CONCLUSION This chapter began by quoting Professor Wilcox noting that little had changed in schooling over the past 500 years. In response, this chapter provides examples of how the world has changed due to glo-

balisation and e-learning becoming mainstream, the changing needs and aspirations of the current generation of learners and the rapid development of new technologies to assist learning. All of the examples quoted suggest that traditional face-toface schooling will change. Hauser and Koutouzos (2009) state “it is arguable that technology has had a greater impact on elementary and secondary school education during the last twenty-five years than virtually any other development in American society” (p. 245). As some schools in the present are already doing, most schools of the future will most likely need to embrace educational use of social media, blended and virtual learning and educational gaming as learning tools and may incorporate forprofit components into their offerings. Educational leadership will change, with much more focus on research justified use of educational technology and continuous professional development of teachers to enable them to cope with rapid technology instigated change. But none of the evidence reviewed here suggests that schools as such will disappear. The economic and social survival of nations depends on the quality of education of its people so some form of compulsory schooling will be needed, be it virtual or not. Perhaps Professor Wilcox’s lecture in ten years’ time will compare a medieval school with a virtual or partly virtual school, a clearly different learning environment minus a teacher at the front delivering factual knowledge.

REFERENCES Baran, B. (2010). Facebook as a formal instructional environment. Retrieved from http:// onlinelibrary.wiley.com.ezproxy.auckland.ac.nz/ doi/10.1111/j.1467-8535.2010.01115.x/pdf Bassiouni, D. H., & Hackley, C. (2014). Generation Z childrens adaptation to digital consumer culture: A critical literature review. Journal of Customer Behaviour, 13(2), 113–133. doi:10.13 62/147539214X14024779483591

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Biagetti, M., & Scicchitano, S. (2013). The determinants of lifelong learning incidence across European countries (evidence from EU-SILC dataset). Acta Oeconomica, 63(1), 77–97. doi:10.1556/ AOecon.63.2013.1.5 Boeren, E., Nicaise, I., & Baert, H. (2010). Theoretical models of participation in adult education: The need for an integrated model. International Journal of Lifelong Education, 29(1), 45–61. doi:10.1080/02601370903471270 Cabral, J. (2011). Is Generation Y addicted to social media? The Elon Journal of Undergraduate Research in Communications, 2(1), 5–14. Carey, K. (2010). Why do you think they’re called for-profit colleges. The Chronicle of Higher Education, 56(41), A88. Cavanagh, S. (2015). New tool helps districts evaluate ed-tech companies’ claims of ‘Evidence’. Retrieved from https://marketbrief.edweek.org/ marketplace-k-12/a-tool-for-judging-ed-techcompanies-product-research/ Center for Research on Education Outcomes. (2015). Online charter school study. Retrieved from https://credo.stanford.edu/pdfs/OnlineCharterStudyFinal2015.pdf Chung, A. S. (2012). Choice of for-profit college. Economics of Education Review, 31(6), 1084– 1101. doi:10.1016/j.econedurev.2012.07.004 Clark, D., Tanner-Smith, E., & Killingsworth, S. (2014). Digital games, design and learning: A systematic review and meta-analysis (executive summary). Menlo Park, CA: SRI International. Davis, M. (2016). Harvard Business School examines K-12 blended learning. Education Week, 35(35), 27. Doran, L. (2016). As Pokemon Go becomes a sensation, Ed. Experts weigh pros and cons. Retrieved from http://blogs.edweek.org/edweek/ DigitalEducation/2016/07/pokemon_go_eveolves_into_overni.html?cmp=eml-enl-dd-news1

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Duggan, M. (2015). Mobile messaging and social media 2015. Retrieved from http://www. pewinternet.org/2015/08/19/mobile-messagingand-social-media-2015/ Flanigan, R. (2016). Ed-Tech coaches becoming steadier fixtures in classrooms. Education Week, 35(35), 31–32. Geck, C. (2006). The generation Z connection: Teaching information literacy to the newest net generation. Teacher Librarian, 33(3), 19–23. Gustke, C. (2010). E-learning industry on the rise. Retrieved from http://www.edweek.org/dd/ articles/2010/10/20/01ebiz.h04.html Hauser, G. M., & Koutouzos, D. W. (2009). Technology training and professional development of school leaders in the USA: The critical need for reform. In S. Khine & I. Saleh (Eds.), Transformative leadership and educational excellence (pp. 245–266). Rotterdam, The Netherlands: Sense Publishers. Herold, B. (2015). Frontiers of digital learning probed by researchers. Retrieved from http://www. edweek.org/ew/articles/2015/05/06/fronteirs-ofdigital-learning-probed-by-researchers.html Herold, B. (2016). Digital device choices could impact common-core test results, studies finding. Retrieved from http://www.edweek.org/ew/ articles/2016/07/20/digital-device-choices-couldimpact-common-core-test.html Ho, A., Chuang, I., Reich, J., Coleman, C., Whitehill, J., & Northcutt, C. … Petersen, R. (2015). HarvardX and MITx: Two Years of Open Online Courses Fall 2012-Summer 2014. Available at SSRN: http://ssrn.com/abstract=2586847 or10.2139/ssrn.2586847 Larkin, P. (2016). Our students can’t be “quiet” on social media in school. Retrieved from http://blogs. edweek.org/edweek/reinventing_k12_learning/2016/06/our_students_cant_be_quiet_on_social_media_in_school.html?cmp=eml-enl-ddnews3

Category: Educational Technologies

Mazman, S. G., & Usluel, Y. K. (2010). Modeling educational usage of Facebook. Computers & Education, 55(2), 444–453. doi:10.1016/j. compedu.2010.02.008 McDonald, C., Laxman, K., & Hope, J. (2016). An exploration of the contexts, challenges, and competencies of pre-teenage children on the internet. International Journal of Technology Enhanced Learning, 8(1), 1-25. DOI: 10. 1504/ IJTEL.2016.075949 Miron, G., & Gulosino, C. (2016). Virtual schools report 2016: Directory and performance review. National Education Policy Center, School of Education, University of Colorado Boulder, CO: National Education Policy Center. Molnar, M. (2016). Spending on Ed-Tech hardware hits $15B worldwide, report finds. Retrieved from https://marketbrief.edweek.org/marketplacek-12/spending-on-education-hardware-up-7percent-worldwide-report-finds/?cmp=emlenl=ii-news1 Pew Research Center. (2014). Millennials in adulthood: Detached from institutions, networked with friends. Retrieved from http://www.pewsocialtrends.org/2014/03/07/millennials-in-adulthood/ Robinson, K., & Aronica, L. (2015). Creative schools: The grassroots revolution that’s transforming education. New York, NY: Viking. Roblyer, M. D., McDaniel, M., Webb, M., Herman, J., & Witty, J. V. (2010). Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites. The Internet and Higher Education, 13(3), 134–140. doi:10.1016/j. iheduc.2010.03.002 Roche, S. (2016). Building on shifting sands: The challenges of meeting the learning needs of adults in a rapidly changing world. International Review of Education, 62(3), 247–251. doi:10.1007/ s11159-016-9569-2

Rubenson, K., & Elfert, M. (2015). Adult education research: Exploring an increasingly fragmented map. European Journal for Research on the Education and Learning of Adults, 6(2), 125–138. doi:10.3384/rela.2000-7426.rela9066 Seemiller, C., & Grace, M. (2016). Generation Z goes to college. New York, US: John Wiley & Sons. Shatto, B., & Erwin, K. (2016). Moving on from Millennials: Preparing for Generation Z. Journal of Continuing Education in Nursing, 47(6), 253–254. doi:10.3928/00220124-20160518-05 PMID:27232222 Speak Up. (2015). From print to pixel: The role of videos, games, animations and simulations within K-12 education. Retrieved from: http://www. tomorrow.org/speakup/SU15AnnualReport.html Tynes, B. M. (2007). Internet safety gone wild: Sacrificing the educational and psychological benefits of online social environments. Journal of Adolescent Research, 22(6), 575–584. doi:10.1177/0743558407303979 Veletsianos, G., Reich, J., & Pasquini, L. A. (2016). The life between big data log events: Learners strategies to overcome challenges in MOOCs. AERA Open, 2(3), 1–10. doi:10.1177/2332858416657002 PMID:26942210

ADDITIONAL READING Berney, S., & Betrancourt, M. (2016). Does animation enhance learning: A meta-analysis. Computers & Education, 101(October), 150–167. doi:10.1016/j.compedu.2016.06.005 Hope, J. K. (2016). New learning for new students. In V. C. X. Wang (Ed.), Handbook of Research on Learning Outcomes and Opportunities in the Digital Age (pp. 819–837). Hershey, PA: IGI Global; doi:10.4018/978-1-4666-9577-1.ch036

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Khenissi, M., Essalmi, F., Jemni, M., Graf, S., & Chen, N. (2016). Relationship between learning styles and genres of games. Computers & Education, 101(October), 1–14. doi:10.1016/j. compedu.2016.05.005 Patrick, S., Worthen, M., Frost, D., & Gentz, S. (2016). Promising state policies for personalized learning. Vienna, VA: International Association for K-12 Online Learning (iNACOL). Schwartz, B., & Caduri, G. (2016). Novelties in the use of social networks by leading teachers in their classes. Computers & Education, 102(November), 35–51. doi:10.1016/j.compedu.2016.07.002

KEY TERMS AND DEFINITIONS Blended Learning: Traditional face to face learning combined with technology mediated instruction.

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E-Learning: Any learning conducted via electronic technology (computers, smartphones and the like). Gamification: Electronic video games designed for teaching and learning rather than purely entertainment. Virtual Education: Another name for elearning, but referring more to electronically aided learning conducted without any face-to-face components.

ENDNOTES

1



2

https://www.youtube.com/watch?v=aXVyaFmQNk See Cabral (2011) for research into social media addiction.

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Category: Educational Technologies

Development of Communication Skills through Auditory Training Software in Special Education Eduardo C. Contreras Autonomous University of Coahuila, Mexico Isis I. Contreras Saltillo Institute of Technology, Mexico

INTRODUCTION The primary function of language is communication and this is per excellence, of verbal nature; manifesting itself as the defining feature of men as a rational being capable of communicating through a system of signs (language) used by social communities. People with hearing loss do not have access to natural language, hampering their oral and/or written communication. Some of them have never heard any sounds; in others, the hearing loss appeared later, so they have the need for auditory therapy. Hearing impaired individuals who have received a cochlear implant, as well as those who need to use an electronic device to improve their hearing, should receive auditory therapy to teach the proper use of the devices. Those who never had access to a proper hearing do not recognize the meaning of the sound, and must learn to associate the sound with its concept. On the other hand, people who had access to hearing have the problem of oral communication, so it is necessary to receive auditory training therapy and must attend therapy in a special education (Merzenich, Pandya, & Tremblay, 2005). This paper presents a study in Saltillo, Mexico in 2014, which is intended to verify the development of communication for people with hearing loss using Auditory Training Software (ATS), which allows to develop listening skills in a motiva-

tional context and influencing positive behavior in training tasks. The ATS helps to develop the skills of identification of presence or absence of sound, identify and discriminate a syllable from similar pairs considering difficulty levels and distinguish individual words and sentences or phrases. This ATS has been useful in the community as an important support educational tool for students and teachers, it goes beyond teaching materials because it becomes relevant in the teaching-learning process, appropriation of linguistic symbols, communication (oral and written), personal identity (auditory phonetic), among others. However, the use of ATS should be customized according to the user profile considering their language, age, education, level of curricular and cultural competition, type and degree of hearing loss, type of disability, among others; the ATS development is complex, it is difficult to find freeware. Currently, there are several applications for Auditory Training (AT) in different languages and even some adapted to the educational needs of other countries, for example, from English to Spanish (Moore & Amitay, 2007). This paper contributes with a description to develop an educational software created by students and teachers of Computer Science, as well as a teaching tool to assess the user’s progress, useful for teachers of Special Education and an appropriate research method to the disciplines mentioned.

DOI: 10.4018/978-1-5225-2255-3.ch212 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Development of Communication Skills through Auditory Training Software in Special Education

BACKGROUND AT is necessary for the development of communication for people with hearing impairment. ATS has designed to help streamline the complexity of projects and tasks as well as facilitates team collaboration, to develop human skills (the ability to interact and motivate), understand concepts and develop ideas. Some basic concepts of the subject are mentioned below. •

• •

•

Prelocution Deafness: Hearing impairment is one that occurs before the child has acquired spoken language and for whom the development of speech and hearing may be affected in different ways. Post-Lingual Deafness: Hearing impairment that occurs after learning spoken language. Phonetics: The study of the sounds made by the human voice in speech; production by the speaker and reception / perception by the listener, (Llisterri, 1991, p.15). AT is the process to teach people to understand the meaning of sounds. During this training, auditory stimulation is provided

Figure 1. Phonetics in the communications process Source: Prepared, 2016.

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•

to people to learn, to identify, distinguish and conceptualize sounds. Auditory-verbal method is a process that uses multisensory rehabilitation methods such as Ling’s oral phonological system, Calvert’s Multisensory System and the Van Uden reflective Method.

In the AT, the application of the theory of phonetics is very important because it is the basis of our communication system. The AT process uses an experimental method consisting of three stages to achieve the goal of communication. This process is described below (see Figure 1). In the first stage it is necessary to differentiate the General Phonetics (production and perception of sounds) from Descriptive Phonetics (different known languages); it is important General Phonetics because it describes the communication process. The second stage is refers to the study of the sound production by the sender is the Articulatory Phonetic, the Acoustic Phonetics studies transmission and perception of the message. Finally, Perceptive Phonetics, studies hearing and perception of the message by the receiver also used in the synthesis and voice also recognition between human-computer. The application of

Category: Educational Technologies

the knowledge of phonics to re-educate children or adults with language disorders is known as Orthophony; the medical discipline that is related to voice disorders is the Phoniatry, lastly, the Audiology is focused on assessing whether there is hearing loss that involves lack of understanding of the message received by the receiver. Every single person, from birth, goes through this process, either naturally or with special training because of a deficiency in the process of communication. The practice of auditory training in the 21st century is considered in terms of the collaborative, interdisciplinary research of neuroscience, cognitive science, and auditory science (Kricos & McCarthy, 2007). Since the last century there have been published manuals that provide methods and material with basic lessons to train people with hearing impairments, particularly those who use cochlear implants or hearing aids (Rochette, Pescheux, & Bigand, 2008). People who use a hearing device or a cochlear implant need AT (González & Torre, 2006). It is important to understand that even though a child uses an appropriate hearing device, calibrated and in good condition, will not have the same access to auditory information as a child who hears normally does. The auditory-verbal approach used years ago was proposed based on a group of sequential and structured strategies that rely only on the sense of hearing to provide access to linguistic information. Generally, the educational intervention is performed as described by Pérez (2010), who mentions the necessary resources for training and appropriate intervention for children with various forms of hearing impairment. For this, various methods are used in the lessons proposed for training; generally, they provide material for contrasting sounds, recognizing sounds, sounds of the words, prayers and songs, and rhythmic patterns. Rochette & Bigand (2010) proposed a training program similar to the previous one, which has a variety of stimuli of auditory perception on cognitive operations in children with hearing impairment; it is based on the training of the cognitive operations in the perceptive processes.

The auditory verbal therapy has been currently provided with the support of the computer, as shown by the study conducted in Germany (Strehlow, 2006), which used computer-based methods in order to measure and train the temporary sound processing methods and the stimulation through phonemes in children with dyslexia, to develop skills for spelling and reading. In the study by Russoa, Nicola, Zeckerc, Hayesa & Krausa (2005) they observed that the influence of AT alters the neural structure of speech perception brainstem. They used perceptual AT software in nine patients clinically diagnosed with learning language-based. In another study by Stacey, Raine, O’Donoghue, Tapper, Twomey & Summerfield (2010) to evaluate the effectiveness of using an AT software in eleven adults with cochlear implants, focused to discriminate vowels and sentences, they found higher significance in discrimination of consonants in speech. Toledo (2005) proposed the use of Speech Analyzer (Summer Institute of Linguistics), software that allows recording and analyzing speech sounds, as a possible tool for studying speech therapy by testing the pronunciation of the consonants in people with speech problems. The test showed that these people could perceive the phonetic accent before applying the rules of orthographic accent. At the University of Konstanz a German prototype to improve the ability to discriminate sounds phonemes and syllables in people with mild cognitive impairment and mild Alzheimer’s disease; the effectiveness of this training program to mitigate or even reverse cognitive decline was assessed. Zhou, Sim, Tan, and Wang (2012), mentioned the effectiveness and efficacy of a game for mobile devices (MOGAT) in children with cochlear implant, to develop his musical ability was demonstrated. The discrimination of the tone in a pleasant, intuitive and motivating way was improved. It is currently offered in the market a variety of educational software for the hearing impaired, as they are: “Computer-Assisted Speech Perception Testing” and “Training at the Sentence Level”, or CASPERSent, which is a multimedia program

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whose main purpose is the sentence-level speechperception training and testing for persons with hearing loss. Another programs are similar to the previous “Tracking Computer Assisted Simulation” and “Computer Assisted Speech Training (CATS)”. “Computer-Assisted Speech Training (CAST)”, was originally designed for adults with cochlear implants, but it can be adapted for use with adult hearing aid wearers. Other commercial programs that can be used for people with hearing loss are: “Seeing and Hearing Speech”, “Sound and Way Beyond”, “Sound Scape” and “Speech Perception Assessment and Training System (SPATS)”. There are some apps that might help with Auditory Processing Disorder like: “Auditory Reasoning”, “Phonetic Birds”, “Auditory Memory Ride”, “Sound Match”, “Hear Coach”, “Auditory Processing Studio” and “Articulation Games”; these apps can also help with central auditory processing disorder (CAPD) or other related disorders (e.g., receptive language disorder or autism). The studies previously mentioned have different approaches, some of them use similar methods to the one presented in this chapter, but they do not cover the specific needs of Special Education in México and the above applications are not in the Spanish language. For this reason it became necessary to develop an application that meets the needs of the region of Northeast, México.

MAIN FOCUS OF THE ARTICLE The approach is to use an appropriate method to provide AT in special education schools for children with hearing loss who have the need to develop communication skills, as well as the use of auditory information to teach them how to use their residual hearing to build relationships in their community. In the studied problematic, two important elements have been identified: Children with hearing loss and AT educators.

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•

•

Children with Hearing Loss: Prelingual hearing loss and deafness can occur from birth or childhood; it means that they have never heard a sound in their entire lives. The Greek philosopher Aristotle said “all those who are born deaf are also silent, unable to speak and to rise to abstract and moral ideas”. Consequently, these children have problems of communication and education because this deficiency does not allow the full development of communicative language skills, this due to the lack of association of the signifier and the signified from the linguistic symbol as they are not able to connect sounds with its meaning. This problem also causes deficit in vocabulary, altered articulation of words, delayed morphosyntactic structure, attention deficit, and a poor oral and written communication. These individuals need a specific and planned intervention to help them access education. It is important to use the verbal hearing oral teaching method that allows deaf children (with hearing aids or cochlear implants) to structure their thinking and to acquire an inner language, which is the main base of educational work. AT Educators: Most educators in public special education centers in México do not use the software as a teaching resource because they do not have specialized software available. The existing software is foreign (in English), and the one provided in Spain contains vocabulary and pronunciation different from the Mexican ones. Software developers in México generally focus on the commercial, because this is not considered profitable. Generally, public institutions of higher education are the ones support the development of educational software for various public schools through projects financed by governmental institutions. Another problem identified in educators is that most of them are unaware

Category: Educational Technologies

of the proper use of the software and how to evaluate the learner with it. Generally, the educator traditionally continues using the audio-verbal method without the support from ICT´s, showing an attitude of technophobia and resistance to change. Added to this, the computer equipment in schools is scarce or obsolete.

Research Method

These elements are very important in developing the research method because they are variables that contain the hypotheses, the basis for raising objectives and the research questions. Based on the above a synthetic table is generated (table of methodological consistency), as shown in Table 1. From this it is checked whether there is correspondence between the variables that are going to related, and between the approach of the objectives and hypotheses, when correct, one proceeds to design the Consistency Matrix (see Table 2) based on the theoretical framework. This matrix will help us develop our instrument of evaluation and measurement of study in a methodical and systematic way (Rivas, 2004). One continues with the collection and statistical processing of data for the results of the study. The research is a study case of the explanatory, qualitative and quantitative type because it uses observation in the population sample.

The research method used in this project is described below; using a conceptual map the system is represented, the study elements are identified in it and with those the problem is defined as: Children with hearing loss need to receive AT according with their level of hearing, reinforced by visual stimulation and interactive tasks that increase their motivation, in order to develop their communication skills. The elements to define the method of study are identified, which are shown in the following functional relationship:

Sample Description

Communication of a children with hearing loss = f (listening skills, AT software, learning behavior).

The study was conducted at the Center for Multiple Attention “Benito Juárez TV”, where 11 hearing loss children were selected from among

Table 1. Methodological consistency table, where are shown objectives, hypothesis, research questions and research variables Particular Objective

Research Questions

Variable

Hypothesis

O1. To evaluate, through software, the development of hearing skills in verbal auditory processing method in the rehabilitation of persons with hearing impairment to enable them to acquire oral communication.

RQ1. How much can the software rehabilitate people with hearing impairment on the development and understanding of sounds, syllables and words, to achieve an acceptable level of communication?

Hearing ability

H1. The multimedia-learning environment improves the development of listening skills in individuals with hearing impairment to develop their communication.

O2. To verify that the use of ear training software is an adequate tool for verbal teaching method in oral communication.

RQ2. Do software elements; multimedia, user interaction, the tasks of the auditory-verbal method relate to communication skills?

Ear training software

H2 There is a positive relationship between software ear training and communication development.

O3. To explore the behavior of natural language learning in the individual with a disability when stimulated in the process of communication.

RQ3. With the stimulus presented in the multimedia educational software, to what extent does it influence attitudes, motivation and attention on learning to speak in the hearing impaired?

Learning Behavior

H3 If people with hearing impairments are stimulated through multimedia software; they present a positive performance in auditory learning to develop their communication skills.

Source: Prepared, 2016.

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Table 2. Methodological consistency matrix, where are shown research variables, factors, indicators and dimensions Variable Auditory skills

Learning behavior

Ear training software

Factor

Indicator

Sounds

Presence / absence Sound identification Sound discrimination

Syllables

Identifying syllable speech Discrimination syllable speech Identifies / discriminates a syllable in cognate pairs Syllable comprehension

Words

Identifies isolated words Identifies words in a sentence Word comprehension

Behavioral attitude

Enthusiasm Interest Constancy

Motivation

Active participation

Dimension 9 8 7 6 5 4 3 2 1

5 - Always 4 - Almost always 3 - Sometimes 2 - Rarely 1 - Never

Attention

Timing

Minutes

Multimedia elements

Audio Picture Video Text

User interface

Usability Different skill levels Manipulate interface objects Interface consistent

5 – Very good 4 - Good 3 - Regular 2 - Minimum 1 - None

Auditory-Verbal Method

Performs the tasks Schedules the tasks Stimulates the audition Evaluates the progress

Source: Prepared, 2016.

45 students in preschool and elementary school, aged between 4 and 12 years. The rest of them present intellectual disability. The therapy was given one hour a day, twice a week.

The Auditory-Verbal Method In most special schools in México, the audio-verbal approach for the development of oral communication is used. It aims to ensure that each auditory handicap student acquires oral communication through the stimulation of the auditory pathway, thereby promoting and achieving high levels of intellectual development and social interaction. The auditory-verbal method suggests an individual education according to the needs of each subject, along with periodic assessments of

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progress in each subject, as well as hearing aid evaluation, calibration and proper management for those who make use of it and are subject to the auditory method (Cervera & Ygual, 1998); (Estabrooks, 2006). The use of this method in the classrooms is described below: Level I: Sound Level Consciousness. The first requirement for AT is that the subject perceives the existence of sound, and to be able to differentiate between presence and absence of sound. Level II: Sound Alert. The aim is that the subject is interested in the sound, to pay attention to it and locate it. Level III: Thick auditory discrimination.

Category: Educational Technologies

The aim is that the subject responds to the language in the undifferentiated period, i.e.; by the emotional tone and not the meaning, and that differentiates through hearing the sounds, noises, voice and language all its acoustic characteristics. An auditory-verbal approach includes the use of a system of sequential and structured strategies that rely only on the sense of hearing, in order to provide access to linguistic information.

Auditory Training Software Description “AudiTS” for Developing Communication Skills The instructor tells the beginning user that the training he will receive is gradual and that it consists on four modules. Text instructions are displayed on screen and then an image menu is shown to select the desired module. If the user has received this training before, he will give continuity to it selecting the appropriate module. See Figure 2. The first three modules are presented in the following environments: animals (pet, farm, wild); transportation (land, air, sea); home; the street; nature.

In the first module the user identifies the absence or presence of sound to choose a subject of the aforementioned environments. The first two modules contain an additional menu to select an item in the group. For example, if the user selects “farm animals”, an image is displayed on the screen of the computer and AudiTS can produce a random sound corresponding to the image; the user must answer whether or not there is sound by clicking on one of two buttons (yes or no). The software validates whether or not there was sound. If the user succeeds, a visual message is displayed congratulating him, and if not, he is prompted to try again with another image; he will press a “Next” button to continue with another object. A digital counter is displayed in the bottom right of the screen that shows the ratio of hits between attempts to meet the user’s progress. In the second sound identification module, the user selects an item from the proposed environments. An image is displayed and a sound is played. For example, the image of a cat is displayed and a meow is heard, this is with the intention of associating meaning with the signifier. The training consists in knowing the common sounds at home, street and nature, as well as animals and means

Figure 2. The menu of auditory training software (AudiTS)

Source: Prepared, 2016.

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of transportation. In this section AudiTS does not validate answers because the child also interacts with the instructor. In the third module the discriminant is applied in the same environments as in the first module. A topic is selected, four images are displayed and a sound associated with an image is produced; the user must validate the significance of sound with the significant image by clicking on the proper image. The images displayed in groups of four are always random and there is correspondence only with the sound, which the user must learn. The software performs the same tasks as in the first module: validates response, it shows a success or failure message and displays user performance. The fourth module is divided into two areas: identifying and discriminating one syllable and the ability to identify and discriminate isolated words and contained words in sentences or phrases. To provide training about syllables a video showing an instructor pronouncing various syllables of cognate pairs is displayed, considering levels of difficulty and reinforcing learning with a text showing the syllable at the same time. Isolated words are presented in a video pronounced by an instructor emphasizing their joint and relating its meaning with an image. The words contained in the sentences are also shown in a video, and they are reinforced showing a text on the screen to

highlight the word in the sentence. In this module the instructor validates the child’s performance relying on the report template.

Description of the Evaluation Instrument The template to assess the child’s performance is constructed from the matrix of methodological consistency, as shown in Table 3. The instructor records the qualification that the child obtained in each session in the column for each indicator. Results obtained after about 4 months of effective use of AudiTS during a school period of eight months, excluding vacations and holidays, (see Table 2 for interpreting the results). In Listening Skill the average obtained in the sound factor is 7; for syllables, 4; for words, 3. Children learned globally 30% of the vocabulary contained in the application (30 of 90 words) obtaining better performance in sound recognition. In the Learning Behavior mode factor obtained in Behavioral Attitude is 4; motivation, 5; and the attention period was increased from 3 to 15 minutes. In the AudiTS variable, the average for the multimedia elements factor is 5; user interface is 4 and auditory- verbal method is also 4.

Table 3. Template where the therapist monitors progress of the person receiving the therapy Indicator

Qualification

Presence/absence of sound

6

7

5

6

7

5

6

Sound identification

6

5

6

7

6

7

6

Sound discrimination

5

7

6

7

6

5

6

Identifies syllables

3

4

3

2

3

4

3

Discrimination syllables

2

4

3

4

3

3

4

Identifies/ Discriminates a syllable in cognate pairs

3

4

2

3

2

3

4

Syllable comprehension

2

3

4

2

3

2

3

Identifies isolated words

3

2

3

4

3

3

2

Identifies words in a sentence

2

1

2

3

2

1

2

Words comprehension in a sentence

2

1

2

3

2

3

2

Source: Prepared, 2016.

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Category: Educational Technologies

SOLUTIONS AND RECOMMENDATIONS The solution presented modifies the traditional way of evaluating the child in therapy. The assessment tool is proposed to also consider elements, such as attitude, motivation and attention, as these are very important for a person to accept the therapy. The template also suggests how to make the performance evaluation of a commercial computer program for AT. The recommendations of the proposed solution are: •

•

•

•

The use of this software requires the supervision of a qualified person because it is a study that needs to evaluate quantitative and qualitative factors under observation. The software allows to evaluate the performance of the hearing ability of the user, but implies the need for the proposal template where the therapist monitors progress of the person receiving the therapy, which can be done manually or using a spreadsheet. The recommendation to use the software will reduce the technological gap in which these vulnerable groups currently are, and will be conducive to encourage the authorities and teachers in the use and benefit that it has IT in education and training. Another recommendation is to establish a link between the departments of computer science in public schools and special education for the development of educational software according to their specific needs.

FUTURE RESEARCH DIRECTIONS The future trend in developing countries will not only bring the TIC`s to the special education classrooms, but it will also be necessary to use information sciences mainly in the implementation and use of knowledge in educational organizations

due to the complex problems they show, as it is not easy to develop adaptive skills in them. Currently conducting research projects in information science demand multidisciplinary task as it is in this case; phonetics, special education, computer science and psychology. To offer computer science teachers and students resources for research and development of software, related to phonetics, speech recognition and aphasia. The problematic identified should be resolved by the systems theory of management to interpret organizational behavior due to the complexity of the AT. This theory helps to the managers and teachers to understand the concepts: knowledge, adaptation, relationships and environment.

CONCLUSION The element of therapy which had the greatest influence on children was the positive behavior, quickly identifying sounds and to a lesser extent the recognition of words. This is the area where more work is recommended. Several special education teachers have accepted the implementation of the proposed program of AT. The viability depends on the approval of education authorities, as it is not only an economic resource issue, but it means also to prepare teachers for the acceptance of change and the use of this technology.

REFERENCES Cervera, J. F., & Ygual, A. (1998). Entrenamiento de la percepción auditiva en niños con trastornos del lenguaje. EDETANIA Estudios y propuestas de educación, 15(1), 1-11. Recovered from http://empresa.rediris.es/pub/bscw. cgi/d304118/Entrenamiento%20de%20la%20 percepci%C3%B3n%20auditiva%20en%20 ni%C3%B1os%20con%20trastornos%20del%20 lenguaje.pdf

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Estabrooks, W. (2006). Auditory-verbal therapy and practice. Washington, DC: Alexander Graham Bell Association for the Deaf and Hard of Hearing. Galloway, J. P. (2009). Contemporary Issues in Teaching and Learning with Technology. In M. Khosrow-Pour (Ed.), Encyclopedia of Information Science and Technology. IGI Global. doi:10.4018/978-1-60566-026-4.ch119 González, I., & Torre, G. (2006). Guía de recursos de la deficiencia auditiva. APADA-Asturias, Oviedo. Kricos, P., & McCarthy, P. (2007). From ear to there: A historical perspective on auditory training. Seminars in Hearing, 28(2), 89–98. doi:10.1055/s-2007-973435 Leirer, V. M., Glockner, F., Elbert, T., & Kolassa, I. T. (2009). An auditory computer-based training for mild cognitive impairment and mild Alzheimer’s Disease - German prototype of the Brain Fitness Program. Proceedings of the 2nd International Conference on Pervasive Technologies Related to Assistive Environments. Llisterri, J. B. (1991). Introducción a la fonética: el método experimental. Barcelona: Anthropos. Mele, C., Pels, J., & Polese, F. (2010). A Brief Review of Systems Theories and Their Managerial Applications. Service Science, 2(1-2), 126-135. 10.1287/serv.2.1_2.126

Rochette, F., & Bigand, E. (2010). Auditory training in deaf children. Rethinking physical and rehabilitation medicine. Collection de L’Académie Européenne de Médecine de Réadaptation, 193201. Rochette, F., Pescheux, P., & Bigand, E. (2008). Entrainement auditif et éducation auditive des enfants sourds. Revue de Neuropsychologie, 13(2), 1–19. Russoa, N., Nicola, T., Zeckerc, S., Hayesa, E., & Krausa, N. (2005). Auditory training improves neural timing in the human brainstem. Behavioural Brain Research, 156(1), 95–103. doi:10.1016/j. bbr.2004.05.012 PMID:15474654 Stacey, P. C., Raine, C. H., ODonoghue, G. M., Tapper, L., Twomey, T., & Summerfield, A. Q. (2010). Effectiveness of computer-based auditory training for adult users of cochlear implants. International Journal of Audiology, 49(1), 347–356. doi:10.3109/14992020903397838 PMID:20380610 Strehlow, U., Haffner, J., Bischof, J., Gratzka, V., Parzer, P., & Resch, F. (2006). Does successful training of temporal processing of sound and phoneme stimuli improve reading and spelling? Germany. European Child & Adolescent Psychiatry, 15(1), 21–29. doi:10.1007/s00787-006-05004 PMID:16514506

Merzenich, M., Pandya, P., & Tremblay, K. L. (2005). Roundtable discussion. Plasticity and auditory training. Seminars in Hearing, 26(3), 144–148. doi:10.1055/s-2005-916377

Sullivan, J. R., & Thibodeau, L. M. (2010). Computer-based auditory training to improve speech recognition in noise by children with hearing impairment (Doctoral Dissertation). University of Texas at Dallas, Richardson, TX.

Moore, D. R., & Amitay, S. (2007). Auditory Training: Rules and Applications. Seminars in Hearing, 28(2), 99–109. doi:10.1055/s-2007-973436

Toledo, G. A. (2005). Uso del speech analizer para la enseñanza de la ortofonía, la fonética y la fonología españolas. Revista de filología, 293-304.

Pérez, M. (2010). La deficiencia auditiva. Revista Digital “Innovación y Experiencias Educativas”. Granada, España.

Zhou, Y., Sim, K. C., Tan, P., & Wang, Y. (2012). MOGAT: Mobile Games with Auditory Training for Children with Cochlear Implants. Proceedings of the 20th ACM international conference on Multimedia, 429-438. doi:10.1145/2393347.2393409

Rivas, L. A. (2004). Cómo hacer una tesis de maestría? Taller Abierto.

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ADDITIONAL READING

KEY TERMS AND DEFINITIONS

Chang, M., Iizuka, H., Naruse, Y., Ando, H., & Maeda, T. (2014). Proceedings of the 5th Augmented Human International. Conference, Article No. 28, ACM New York, NY, USA. doi:10.1145/2582051.2582079

Aphasia: An impairment of language, affecting the production or comprehension of speech and the ability to read or write. Center for Multiple Attention: Schools for students with disabilities to develop various skills known in México as CAM. Consistency Matrix: A Chart to verify the logical coherency in a research method and to develop a tool for data collection. Deaf: The person whose hearing is not functional for ordinary life. Hypoacustic: A person whose hearing, though poor, is functional with or without prostheses. Methodological Consistency: Research method, which consists in relating the basic elements in a table to verify the logical relationship of the study. Motivational Context: Cognitive affective factor present in every act of learning.

Horvitz, E., Kadie, C., Tim Paek, T., & Hovel, D. (2003). Models of Attention in Computing and Communication: From Principles to Applications. Communications of the ACM, 46(3), 52. doi:10.1145/636772.636798 Lopes, M., Mendes, T. C., Batista, A. P., Rademaker, P., Leite de Carvalho, F., & Tabith, A. (2013). Pediatric phoniatry outpatient ward: Clinical and epidemiological characteristics. Brazilian Journal of Otorhinolaryngology, 79(2). doi:10.5935/18088694.20130029 PMID:23670320 Lozano, A. I., & Romero, E. (2010). Adquisición de las habilidades lingüísticas y cognitivas, relevancia para el aprendizaje del lenguaje escrito. Umbral Científico. 16(1) 8-12. Recovered from http:// www.redalyc.org/articulo.oa?id=30418644002

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Digital Storytelling in Language Classes Mehrak Rahimi Shahid Rajaee Teacher Training University, Iran

INTRODUCTION Storytelling is an effective and unique way of communication that takes the listeners into an imaginary world, motivates them to think about the happenings and phenomena and sometimes even induces them to do things (Handler-Miller, 2004). Storytelling makes connections between a storyteller and the audience (Smyth, 2005) and thus lets both express and share their emotions and thoughts. Story is an old-fashioned human experience (McDrury & Alterio, 2003) and “a natural component of society and culture” (Frazel, 2011, p. 6). Stories are made, told, read, and listened to by people with different tastes and views (Jones, 2006) for transferring knowledge and/or sharing experience. The social value of storytelling and writing lies in the fact that stories can link past, present, and future generations (Chung, 2007) of micro and macro cultures and pave the way for the nation to preserve their cultural heritage. It is believed that as the food people consume makes their bodies, the stories they hear construct their minds (Wright, 2008) and form their viewpoints on the world and life. Recent studies on human’s memory show that the brain saves the events in the form of scenarios (Schank, 1990) or chains of happenings. The human’s brain is story-oriented and experiences are kept like storyboards and thus are not very easily forgotten. This feature of brain and memory has inspired practitioners and educationist to take advantage of storytelling as a technique for training, teaching, and learning (Handler-Miller, 2004). Storytelling can improve teacher quality (McDrury & Alterio, 2003) and is an effective teaching tool that helps students understand intricate and

complex experiences and concepts (Sadik, 2008). Stories prepare learners for communication; make them literate; engage them in an entertaining way (Huffaker, 2004); help them learn language forms; and expand their vocabulary stock (Wang, Li, & Dai, 2008). It is empirically verified that stories can promote students’ problem solving skills (Radbakhsh, Mohammadifar, & Kianersi, 2013); increase their attention and motivation (Mostafazadeh, 2010); lower the physiological and emotional anxiety (Zarei, et al., 2013); and increase their self-esteem (Soltani, Arian, & Angoji, 2013) while making learning more joyful and less frustrating (Rahimi & Soleymani, 2015a). Stories convey their message when they are told orally, written in words, or are drawn as images. The most primitive way of recording and transferring stories into the future was cave wall drawings. This mechanism of knowledge transferring was altered fundamentally by the invention of writing and then printing press. In the 21st century, technology and its varying forms have had a far reaching influence on the way stories are told, stored, and shared. Traditional forms of storytelling now have evolved into modern ways of storytelling called digital storytelling (Frazel, 2011), where stories are told by the combination of narration, music, images, texts, and movies. In other words, the audiences do not just listen to a story or read it, they have this opportunity to listen, read, watch, and enjoy the combination of different media in the environment of digital devices. This type of storytelling has attracted the attention of researchers especially pedagogues to scrutinize its impact on learning different subjects. Significant characteristics of digital stories such as flexibility, universality, and interactivity have made them a practical and powerful technologi-

DOI: 10.4018/978-1-5225-2255-3.ch213 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Educational Technologies

cal tool in instruction. In this way, listening to stories can grant the opportunity for students to enhance their engagement in problem solving and deep learning as well as working collaboratively in teams. Although digital storytelling has been found to be a valuable technique of teaching, language teachers have shown some resistance to use it in their teaching due to certain reasons including • • • • •

A lack of confidence in their ability to tell stories or read storybooks aloud. A feeling that the language in storybooks was too difficult. A feeling that the content of storybooks was sometimes too childish. A lack of understanding about the true value of using storybooks. A lack of understanding of how to use storybooks and of time to prepare a plan of work (Ellis & Brewster, 2014, p. 6).

One way to overcome these resistances is providing teachers with required resources, giving them technical and pedagogical support to develop appropriate teaching materials, and providing them with other teachers’ experiences (Ellis & Brewster, 2014, p. 6). To give more guidance to language teachers, this chapter deals with basic features of digital stories, the educational values of digital stories, and how they can be made and used in teaching. The chapter thus is organized in four sections dealing with theoretical framework of digital stories, the salient features of digital stories, educational values of digital storytelling, and the making of digital stories.

BACKGROUND Joe Lambert, Bernajean Porter, and Dana Atchley are the pioneers and founders of digital storytelling (Frazel, 2011). The term digital storytelling (DST) was coined by Dana Atchley, who adapted and improved her personal stories through the use

of digital media (McLellan, 2006). Later, Lambert was astonished by the way people can easily, fast, and with a little price capture their stories using this technique (Robin, 2008). In 1994, the San Francisco Digital Media Center, which later became the Center for Digital Storytelling (CDS), was founded by Joe Lambert, Dana Atchley, and Nina Mullen. Since then, this center has been responsible for developing, training and helping individuals who are interested in creating digital stories and telling personal stories (Robin, 2008). It is hard to give an exact definition of digital storytelling because it has been used in many different ways. Digital storytelling employs the art of storytelling and technological tools using graphics, images, music and sound mixed together with the creator’s own voice (Porter, 2004). It is the act of merging unmoving images with a narrated soundtrack including both music and voice (Bull & Kajder, 2004). Lambert (2009) defines digital storytelling as sharing one’s story through several medium of voice, imagery, music, text, sound, video and animation. Based on a five-part definition, digital stories should (Kittle, 2009): • • • • •

Include a compelling narration of a story; Provide a meaningful context for understanding the story being told; Use images to capture and/or expand upon emotions found in the narrative; Employ music and other sound effects to reinforce ideas; Invite thoughtful reflection from their audience(s) (p. 169).

Digital stories can be made by both students and teachers by narrating different types of stories from personal experiences to historical events or literary works. The stories can have different topics based on the creators’ interests and needs (Gyabak & Godina, 2011). Digital stories can be made very easily with different types of movie maker software programs or online platforms. The main underlying theoretical basis of digital stories is Cognitive Theory of Multimedia Learn-

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ing (CTML) (Mayer, 2005). CTML basically focuses on understanding how learning takes place through cognitive processes such as selecting (transferring some incoming data to working memory for further processing), organizing (classifying images and words into pictorial and verbal models respectively in the working memory), and integrating (connecting the pictorial, verbal, and schemata activated from the long term memory). CTML works based on the assumption that the working memory’s storage capacity is limited and the instructional design should minimize working memory load to utilize the greater capacity of long-term memory. This is possible through eliminating the unrelated information and chunking the related information in the longterm memory schema (Renkl & Atkinson, 2003). Hence, the visual information processing channel might become overloaded when students process on-screen graphics and on-screen text at the same time (Mayer, 2001). Using narration can reduce the cognitive load in the visual channel by processing words in the verbal channel (Figure 1). Another scientific theory that can be associated with digital storytelling is ‘mediation’. Based on sociocultural theory “human forms of mental activity arise in the interactions we enter into with other members of our culture and with the specific experiences we have with the artifacts produced by our ancestors and by out contemporaries”

(Lantolf, 2000, p. 79). The process of mediation in second language learning involves “1. mediation by others in social interaction, 2. mediation by self through private speech, and 3. Mediation by artifacts (for example, tasks and technology) (Ellis, 2008, p. 270). It is evident that computer-based multimedia can help students learn better than traditional classrooms (Bagui, 1998). Based on the theories mentioned, the success of multimedia in language learning can be attributed to three basic factors: •

• •

The dual coding or using more than one code during the learning process (Najjar, 1996); as two media improve learning better than one (Mayer & Anderson, 1991). Presenting the information in a non-linear format (in a hypermedia format). Interactivity of multimedia in comparison with traditional methods and its flexible learning environment (Najjar, 1996).

Educational Value of Digital Storytelling Some characteristics of digital storytelling such as flexibility, universality, and interactivity have made it a beneficial tool in learning and teaching (Park & Seo, 2009). Digital stories can engage students in problem solving (Kajder, 2004) and

Figure 1. Cognitive Theory of Multimedia Learning (CTML)

Mayer, 2005, p. 37.

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deep learning (Pounsford, 2007). Further, they can make learners technologically competent (Ware, 2006) and increase their collaboration and group working (Di Blas, 2010). Digital stories can enhance learners’ creativity (Park & Baek, 2009) and make them interested in learning (Tsou, Wang, & Tzeng, 2006). They utilize both teachers and students’ imagination (Robin & Pierson, 2005) and enable teachers to integrate technologies and stories into the teaching program (Porter, 2006) more effectively. Digital stories can be applied in the classroom to motivate students (Coiro, 2003) and notify, teach or communicate with them (Robin, 2008) owing to its discontinuity, nonlinearity, and autonomy (Signes, 2008). Digital stories provide students with authentic scenarios linked to their personal experiences and thus makes the content easier to be understood (Neo & Neo, 2010) while engaging both teachers and students actively in the class procedure (Dockter, Haug, & Lewis, 2010). Teachers have found multimedia-rich digital stories to be powerful learning tools for instruction across curricular subjects (Robin, 2008). It is believed that digital stories can develop five various skills consisting of (Robin, 2008): • • • • •

Digital Literacy: The ability to communicate with a community to discuss subjects and catch information. Global Literacy: The ability to read, respond, and interpret messages from a global view. Technology Literacy: The ability to apply computers and other technology. Visual Literacy: The ability to understand through visual images. Information Literacy: The ability to find and evaluate information.

It is also believed that using digital stories in the classroom has certain benefits such as (Frazel, 2011):

• • • • • •

Creating individualized digital stories as a class assignment. Engaging and motivating students to learn the content. Addressing the learning needs of K-12 students. Providing opportunities to apply emerging technologies as part of the students’ learning. Supporting team teaching and learning across the curriculum. Providing an active instructional format and promoting group activities in the classroom.

Additionally, twelve unique characteristics of digital stories have been suggested by Schafer (2004) to have a vital role in learning and teaching including: interactivity, coherence, concreteness, involvement, cognitive effort, continuity, control, structure, vitality, spatiality, collaboration and immersion. Digital devices are increasingly being used in education with the progress of technology. Some studies have been done on scrutinizing the value of digital stories in education in the last decade. The positive impact of using digital stories on literacy skills (Kajder, 2004), listening comprehension (Verdugo & Belmonte, 2007), understanding theoretical concepts (Coventry, 2008), learning of school subjects such as English, science, math, and social science (Sadik, 2008), creativity and cultural awareness (Benmayor, 2008), learning achievement, writing skill, verbal skills, critical thinking (Yuskel, Robin, & McNeil, 2011), learning motivation (Yang & Wu, 2012), reading comprehension and vocabulary learning (Chuang & Kue, 2013), literacy skills (Abdollahpour & Asadzadeh Maleki, 2012), listening comprehension (Soureshjani & Etemadim 2012), and listening anxiety (Rahimi & Soleymani, 2015a) is now evident.

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How to Make Digital Stories Making digital stories is not a difficult job, especially by the availability of lots of software programs made for producing movies and animation, both offline and online. Producing digital stories has two distinct parts: one part is related to language and content including the topic, difficulty level, and the length of the story; another part is related to technical and artistic aspects of making the multimedia file. Generally, seven elements have been identified by scholars in producing digital stories including (Lambert, 2010): 1. Point of view (what of the story) which shows the intention of the author of the story or the main point of it. 2. Dramatic question (why of the story) provokes the audience’s inquisitiveness, will be answered by the end of the story and keeps the audience members’ attention. 3. Emotional connection to the listener which involves the audience in the story or writing of the story. 4. Economy avoids overloading the viewer with excessive use of visuals and/or audio. 5. Pacing provides a rhythm to the story and copes with how slowly or quickly it moves. 6. The gift of voice (narration of the text) helps the audience understand the story through personalization of the narration. 7. The accompanying soundtrack (music) supports the story with appropriate music. Digital stories can be made by a variety of media editing software including Movie Maker, iMovie, Photo Story, and Corel VideoStudio Pro (Skinner & Hagood, 2008). Digital stories can differ in length; but most stories generally last between two and five minutes. It is suggested that after minute 3, attention to the multimedia and thus comprehension declines (Rahimi & Soleymani, 2015b).

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Generally speaking three stages are necessary in making digital stories: Preparation, production, and presentation. Preparation stage is the stage before creating the story, where the audience and the way of presenting the story are determined. The story map for writing the script should be prepared here as well. In addition, the creator or the storyteller determines the type of final product (video or podcast) and how it will be presented to the audience (online or face to face). In the classroom situation, the teacher with the help of students decides about the theme of the stories and the type of audience. Then, they organize preliminary materials and plan for digital storytelling project/ assignment. The teacher will decide at this point whether to have students work as individuals, in small groups, or as a whole class. As a part of preparation, the teacher may choose to create an introductory digital story about the selected topic and have students brainstorm ways to find meaning, point of views, or emotional connections they had with the narrator. In this part the language teacher pays careful attention to the content of the story by considering the difficulty level of the story and the number of new words and grammatical points it has. One way to take these into account is calculating the readability of the story based on the available indices. The difficulty level of the story should have the features of ‘the comprehensible input’ and thus be in the range of students’ language repertoire, or competence. In production stage, the digital story file is produced by the teacher or students. Once all the resources and the storyboard are ready, students begin production phase. Students should be familiar with the media editing software to create multimedia stories. However, it is possible for the students to create an audio story, or a podcast using audio production software. The teacher acts as a mentor, and aids students with activities such as putting the slides in order or timing the slides. Music and sound effects may be employed. Most

Category: Educational Technologies

of the required media can be found from different websites and programs. Enthusiastic students create much of their own media. The prepared digital stories should be reviewed and assessed by other colleagues or peers before the files are going to be shared. Based on the provided feedback the file(s) would be edited. Any problematic content including words, and grammatical structures would be modified at this stage. Presentation stage is the last but the most critical step among all in which prepared files will be used in the classroom or shared through the Internet (Frazel, 2011). For presentation, the digital story should be saved onto a file sharing website or written onto a CD or DVD. There are numerous variations of this process and many genres of digital stories and digital story making applications are available (Frazel, 2011).

Digital Storytelling Teachers In order to make digital stories the following steps should be followed by teachers:

Stage One 1. Selecting a topic for the story based on students’ interest and needs. 2. Deciding on the purpose of the story. Is it going to inform, convince, provoke, or improve language skills? 3. Deciding on the point of view of the story. 4. Writing a script for the story. 5. If the story is taken from English literature, the text should undergo linguistic filters to make the text appropriate for the students based on their language proficiency level.

Stage Two 1. Searching for image resources for the story including pictures, drawings, or photographs. 2. Finding music resources and sound effects. 3. Importing images into a media editing program (e.g. Movie Maker).

4. Modifying images and/or image order, if necessary. 5. Mixing the narration and images and creating necessary special effects and graphics using the facilities of the software. 6. Saving the digital story file onto a DVD (MP4) to be watched and listened to by technological devices (such as cell phones, tablets, or laptops).

Stage Three 1. Reviewing of the digital story by colleagues. 2. Using the received feedback to improve and revise the story. 3. Finalizing and preparing the digital story to be performed in the classroom (Frazel, 2011). Teachers can use different types of questions for developing critical thinking activities to accompany digital storytelling projects. These questions are useful for getting students to think about different parts of the story and improve both their general and language knowledge. Followings are sample questions that can be asked • • • • •

What are other possible ways to tell this story? What do we expect the audience to tell us at the end of this story? What is the role of music in this story? What additional information might the audience need to understand the story better? What happens if the music and special effects (e.g., graphics) are removed?

Language teachers can use digital stories in teaching four language skills, that is listening, speaking, reading, and writing. The teachers can use this invaluable teaching content to arise students’ interest to go through a language task or work on digital stories as independent language tasks. Different types of exercises and activities

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can be designed to be done during listening and watching the files or in the phase after it.

Digital Storytelling Students Morra (2014) has given the detailed description of how teachers and students can make digital stories together in the classroom by specifying an 8-step cycle (Figure 2).

Start with an Idea Like all stories, digital stories should begin with an idea such as student’s questions or topic of a text or lesson. When the basic ideas/themes are discussed and agreed upon, the teacher or students should convert them to concrete materials such as a draft, an outline or a concept map.

Research/Explore/Learn Students should gather information about the topic they have selected. Different resources are recommended to find more information about the topic; the Internet seems to be the more useful one. Figure 2. The 8-step cycle of making digital stories Morra, 2014.

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However, atlases, encyclopedias, and periodicals are recommended to be searched as more authentic and trustworthy resources.

Write/Script Based on the general theme and the specific information found, the students start writing the script. This is the first draft of the story. The script can be reviewed by peers and the teacher for corrections and improvement. So it is possible that different drafts are written and re-written before the final script is produced. It is noteworthy that script writing of the story should follow the process of writing including drafting, revising, editing, and producing the final product based on the feedback received. As a result digital stories are suitable language tasks for collaborative writing in language classes.

Storyboard/Plan The writing will be converted into storyboards by the help of which students prepare a graphic

Category: Educational Technologies

representation of the script with specified characters and sequences of events. Storyboards can be made on paper or using media producer software (Figure 3). As the storyboard is the basis of the story, it is recommended that students spend quality time on this stage to produce storyboards that are understandable and manageable to be made. Otherwise they may face serious problems in producing the multimedia file.

Gather and Create Images, Audio, and Video To change the storyboard into a real story, images, audios, and videos are required. Students should follow the steps of the storyboard carefully and find the necessary media to make the digital story.

Put It All Together At this stage students put everything together based on the storyboard in the technology environment. They have to use the affordances of the media producing software to make the digital story as attractive as possible. In other words, the pictures, narration, and the background music will be mixed while special digital effects are added in the environment of the software. This stage needs much of taste and artistry from the side of students.

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When the story is made, it should be shared with the audience. This can be done online using the available platforms or offline as digital video files copied on DVDs.

Reflection and Feedback At the end, the students reflect on their own work and give feedback to the works of others. Online or in-class sessions can be used to help students get feedback and opinion about their made stories (Kajder et al, 2005). In this phase students can do lots of activities including discussions and debates and writing critical paragraphs.

SOLUTIONS AND RECOMMENDATIONS This chapter aimed at making language teachers familiar with a handy technological artifact that can be easily made both by teachers and students and used in language classes. Digital stories have been reported to be valuable instructional materials in mainstream education, however, their miraculous role in promoting language learning is just being revealed.

Figure 3. A simple storyboard Bird, 2011

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Digital stories can be used in teaching and practicing almost any type of language activity including macro language skills (listening, speaking, reading, and writing) and micro skills (pronunciation, vocabulary, and grammar). As multimedia files, they can improve the capacity of working and short term memory and make things being remembered longer. As collaborative tasks, they let language learners participate in making stories and then acquire new points by cooperation and collaboration. Further, as they are symbolic artifacts, they can enhance the cognitive process and impact the way students think and process information. One possible benefit of human/technology interaction “is transfer of the partnered skill to the individual such that a particular mental operation that entails technology is mastered to a greater degree as a result of the partnership” (Lantofl, 2000, p. 93). It is noteworthy to mention that without necessary skills and knowledge base of making digital stories their effect in language classes may be spoiled, as a part of teacher Technological Pedagogical and Content Knowledge (TPACK) is being able to integrate technology in instruction based on sound pedagogical theories and practices.

Comparative quantitative and qualitative studies on the way teachers/students with different personal characteristics make and use digital stories in teaching and learning English are required to be examined. The mediating role of nationality and the context of teaching and learning can also be investigated. Other types of multimedia contents such as multimedia stories and posters are also required to be introduced to teachers and their effects be examined empirically.

CONCLUSION This chapter presents some guidelines on what digital stories are and how they can be made to be used in teaching, particularly in language classes. The chapter opens with an introduction on the key role of literature and especially stories in peoples’ life and education. It further goes on with probing into the value of digital storytelling as an innovative technique in pedagogy. The chapter ends with listing educational values of digital stories and how teachers can make and use them in their teaching.

FUTURE RESEARCH DIRECTIONS

REFERENCES

This study investigated the educational values of digital stories in mainstream education in general and teaching English as a foreign language in particular. It also familiarized language teachers with how to make and use digital stories in language classes. Language educationists and teachers are urged to do empirical studies on the theoretical guidelines this chapter or similar works offer to verify the value of digital stories in language classes. This may include studies done to compare the effects of digital story in teaching receptive and productive language skills as well as the impact of digital storytelling teachers vs. digital storytelling students.

Abdollahpour, Z., & Asadzadeh Maleki, N. (2012). The impact of exposure to digital flash stories on Iranian EFL learners’ written reproduction of short stories. Canadian Journal on Scientific and Industrial Research, 3(2), 40–53.

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Bagui, S. (1998). Reasons for increased learning using multimedia. Journal of Educational Multimedia and Hypermedia, 7(7), 3–18. Benmayor, R. (2008). Digital storytelling as signature pedagogy for the new humanities. Arts and Humanities in Higher Education, 79(2), 188–204. doi:10.1177/1474022208088648

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Bull, G., & Kajder, S. (2004). Digital storytelling in the language arts classroom. Learning and Leading with Technology, 32(4), 46–49. Chuang, W., & Kuo, F. (2013). Improving reading comprehension among Taiwanese EFL young EFL young learners using digital stories. Proceedings of 2013 Asian Literacy Conference, 134-146. Chung, S. K. (2007). Art education technology: Digital storytelling. Art Education, 60(2), 17–22. Coiro, J. (2003). Reading comprehension on the internet: Expanding our understanding of reading comprehension to encompass new literacies. The Reading Teacher, 56(5), 458–464. Di Blas, N., Paolini, P., & Sabiescu, A. (2010). Collective digital storytelling at school as a wholeclass interaction. Proceedings of the 9th international Conference on interaction Design and Children, 11-19. doi:10.1145/1810543.1810546 Dockter, J., Haug, D., & Lewis, C. (2010). Redefining rigor: Critical engagement, digital media, and the new English/Language arts. Journal of Adolescent & Adult Literacy, 53(February), 418–420. doi:10.1598/JAAL.53.5.7

Huffaker, D. (2004). Spinning yarns around the digital fire: Storytelling and dialogue among youth on the internet. Information Technology in Childhood Education Annual, 1, 63–75. Kittle, P. (2009). Student engagement and multimodality: Collaboration, schema, identity. In A. Herrington, K. Hodgson, & C. Moran (Eds.), Teaching the new writing: Technology, change, and assessment in the 21st-Century classroom (p. 169). New York: Teachers College. Lambert, J. (2009). Digital storytelling: Capturing lives, creating community. Berkeley, CA: Digital Diner Press. Lambert, J. (2010). Digital Storytelling cookbook. Available online at: http://www.storycenter.org/ storage/publications/cookbook.pdf Lantolf, J. P. (2000). Second language learning as a mediated process. Language Teaching, 33(02), 79–96. doi:10.1017/S0261444800015329 Mayer, R. E. (1997). Multimedia learning: Are we asking the right questions. Educational Psychologist, 32(1), 1–19. doi:10.1207/ s15326985ep3201_1

Ellis, G., & Brewster, J. (2014). Tell it again: The storytelling handbook for primary English language teachers. British Council.

Mayer, R. E. (2001). Multimedia learning. New York, NY: Cambridge University Press. doi:10.1017/CBO9781139164603

Ellis, R. (2008). The study of second language acquisition (2nd ed.). Oxford, UK: Oxford University Press.

Mayer, R. E. (2005). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The Cambridge Handbook of Multimedia Learning (pp. 31–48). New York: Cambridge University Press. doi:10.1017/CBO9780511816819.004

Frazel, M. (2011). Introduction to Digital Storytelling. ISTE. Gyabak, K., & Godina, H. (2011). Digital storytelling in Bhutan: A qualitative examination of new media tools used to bridge the digital divide in a rural community school. Computers & Education, 57(4), 2236–2243. doi:10.1016/j. compedu.2011.06.009 Handler-Miller, C. (2004). Digital Storytelling: A creator’s guide to interactive entertainment. Oxford, UK: Elsevier.

Mayer, R. E., & Anderson, R. B. (1991). Animations need narrations: An experimental test of dual-coding hypothesis. Journal of Educational Psychology, 83(4), 484–490. doi:10.1037/00220663.83.4.484 McDrury, J., & Alterio, M. (2003). Learning through storytelling in higher education: Using reflection and experience to improve learning. London: Kogan Page.

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McLellan, H. (2006). Digital storytelling in higher education. Journal of Computing in Higher Education, 19(1), 65–79. doi:10.1007/BF03033420 Meadows, D. (2003). Digital storytelling: Research-based practice in new media. Visual Communication, 2(2), 189–193. doi:10.1177/1470357203002002004 Morra, S. (2014). 8 steps to great digital storytelling. Available online at: http://edtechteacher. org/8-steps-to-great-digital-storytelling-fromsamantha-on-edudemic/ Mostafazadeh, F. (2010). Storytelling: A new clinical education method. [in Persian]. Research in Medical Teaching, 2(2), 53–58. Najjar, L. J. (1996). Multimedia information and learning. Journal of Educational Multimedia and Hypermedia, 5(2), 129–150. Neo, M., & Neo, T. K. (2010). Students’ perceptions in developing a multimedia project within a constructivist learning environment: A Malaysian experience. The Turkish Online Journal of Educational Technology, 9(1), 176–184. Park, E. J., & Seo, J. H. (2009). Applying digital storytelling technique to website navigation for improving emotional user experience. Proceeding of the International Association of Societies of Design Research, 4125-4128. Porter, B. (2006). Beyond words: The craftsmanship of digital products. Learning and Leading with Technology, 28–31. Pounsford, M. (2007). Using storytelling, conversation and coaching to engage: How to initiate meaningful conversations inside your organization. Strategic Communication Management, 11(3), 32–35. Radbakhsh, N., Mohammadifar, M., & Kiani-Ersi, F. (2013). The impact of games and stories on children’s creativity. [in Persian]. Innovation and Creativity in Humanities, 2(4), 177–195.

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Rahimi, M., & Soleymani, E. (2015a). The impact of digital storytelling on language learners’ listening anxiety. Quarterly of Educational Psychology, 6(1), 87–111. Rahimi, M., & Soleymani, E. (2015b). Digital Storytelling in Language Classes Discovering the Power of Corel® VideoStudio® Pro X7. Tehran: Rash Publications. Renkl, A., & Atkinson, R. (2003). Structuring the transition from example study to problem solving in cognitive skill acquisition: A cognitive load perspective. Educational Psychologist, 38(1), 15–22. doi:10.1207/S15326985EP3801_3 Robin, B., & Pierson, M. (2005). A multilevel approach to using digital storytelling in the classroom. Digital Storytelling Workshop, SITE 2005, University of Houston. Available online at: http://faculty.coe.uh.edu/brobin/homepage/ SITE2005-article.htm Robin, B. R. (2008). Digital storytelling: A powerful technology tool for the 21st century classroom. Theory into Practice, 47(3), 220–228. doi:10.1080/00405840802153916 Sadik, A. (2008). Digital storytelling: A meaningful technology-integrated approach for engaged student learning. Educational Technology Research and Development, 56(4), 487–506. doi:10.1007/s11423-008-9091-8 Schafer, L. (2004). Models for Digital Storytelling and interactive narratives. The Proceedings of the 4th International Conference on Computational Semiotics for Games and New Media, 148-155. Schank, R. (1990). Tell me a story: Narrative and intelligence. Evanston, IL: Northwestern University Press. Signes, C. G. (2008). Practical Uses of Digital Storytelling. Digital Storytelling/Relato Digital. Available online at: http://www.uv.es/gregoric/ DIGITALSTORYTELLING/DS_files/DST_15_ ene_08_final. pdf

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Smyth, J. (2005). Storytelling with young children. Watson, ACT: Early Childhood. Soltani, M., Arian, K., & Angaji, L. (2013). The impact of group storytelling on female second grade primary-school students’ self-esteem. [in Persian]. Quarterly of Educational Psychology, 29(9), 95–108. Soureshjani, K., & Etemadi, N. (2012). Listening comprehension success among EFL preschool children using internet-based materials. Journal of Social Sciences and Humanities, 7(1), 243–251. Retrieved from http://journalarticle. ukm. my/5025/ Tsou, W., Wang, W., & Tzeng, Y. (2006). Applying a multimedia storytelling website in Foreign Language Learning. Computers & Education, 47(1), 17–28. doi:10.1016/j.compedu.2004.08.013 Verdugo, D. R., & Belmonte, I. A. (2007). Using digital stories to improve listening comprehension with Spanish young learners of English. Language Learning & Technology, 11, 87–101. Wang, D., Li, J., & Dai, G. (2008). A pen and speech-based storytelling system for Chinese children. Computers in Human Behavior, 24(6), 2507–2519. doi:10.1016/j.chb.2008.03.014 Wright, A. (2008). Storytelling with children (2nd ed.). Oxford University Press. Yuksel, P., Robin, B., & McNeil, S. (2011). Educational uses of digital storytelling all around the world. In M. Koehler & P. Mishra (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 1264-1271). Chesapeake, VA: AACE. Zarei, K., Parand, Z., Seyedfatemi, N., Khoshbakht, F., Haghani, H., & Zarei, M. (2013). The impact of storytelling on physiologic anxiety, anxiety and social anxiety of school-age hospitalized patients. [In Persian]. Medical Surgical Nursing Journal, 2(3-4), 115–121.

ADDITIONAL READING Alameen, G. (2011). Learner digital stories in a Web 2.0 age. TESOL Journal, 2(3), 355–369. doi:10.5054/tj.2011.259954 Alexander, B. (2011). The new digital storytelling: Creating narratives with new media. Santa Barbara: Praeger. Alexander, B., & Levine, A. (2008). Web 2.0 storytelling: Emergence of a new genre. EDUCAUSE Review, 43(6), 40–56. Azmi, A. L., & Nuraihan, M. D. (2013). Pre-service ESL teachers’ perceptions of parody integration in digital stories. World Applied Sciences Journal, 21, 28–35. Bird, R. (2011). 3D character animation. Retrieved from https://software.intel.com/en-us/articles/3dcharacter-animation-part-1 Farmer, L. (2004). Using technology for storytelling: Tools for children. New Review of Childrens Literature and Librarianship, 10(2), 155–168. doi:10.1080/1361454042000312275 Li, L. (2007). Digital Storytelling: Bridging traditional and digital literacies. In T. Bastiaens & S. Carliner (Eds.), Proceedings of World Conference on ELearning in Corporate, Government, Healthcare, and Higher Education 2007 (pp. 6201–6206). Chesapeake, VA: AACE; Available online at http://www.editlib.org/p/26774 Pounsford, M. (2007). Using storytelling, conversation and coaching to engage: How to initiate meaningful conversations inside your organization. Strategic Communication Management, 11(3), 32–35. Tucker, G. (2006). First person singular: The power of digital storytelling. Screen Education, 42, 54–58.

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KEY TERMS AND DEFINITIONS Collaborative Storytelling: A kind of digital storytelling by which groups of students work on certain themes to produce and share their stories with others by collaborative technologies such as online story making platforms. Digital Storytelling: Sharing one’s story through several medium of voice, imagery, music, text, sound, video and animation using technological tools. Movie Makers: Software programs that let the users combine pictures, sound, music, and movie to make multimedia content. Multimedia: The combination of several media (sound, image, graphics, movies, music) to present content.

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Multimedia Cognitive Theory of Learning (MCTL): The underlying theoretical basis of digital storytelling that deals with how learning takes place through cognitive processes such as selecting, organizing, and integrating. Multimedia Learning: The kind of learning that focuses on how people learn more deeply from words and graphics than from words alone. Storytelling: Narrating sequences of events in an artistic manner for a group of audience orally or in written words. Teacher-Made Digital Stories: Digital stories that are made by teachers based on the objectives of the lesson to teach a certain point/objective.

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Distance Teaching and Learning Platforms Linda D. Grooms Regent University, USA

INTRODUCTION The knowledge explosion, the increased complexity of human life, and the ubiquitous, 24/7 nature of technology coupled with the globalization of the marketplace herald the need to embrace the most effective methods and formats of teaching and learning. Currently providing powerful educational opportunities, the science and technology of distance learning continues to multiply at unprecedented rates. Where historically traveling from village to village verbally disseminating knowledge was the only process of training those at a distance, today’s learners eagerly embrace the rapidly expanding web-based delivery systems of the 21st century, which offer a plethora of educational alternatives. So with this rapidly changing distance educational landscape, one must question, what exactly is distance teaching and learning, how has it evolved, and what is its future?

BACKGROUND In very simplistic terms, distance learning is just that--learning that occurs at a distance (Rumble & Keegan, 1982; Shale, 1990; Shale & Garrison, 1990) or that which is characterized by a separation in geographical proximity and/or time (Holmberg, 1974, 1977, 1981; Kaye, 1981, 1982, 1988; Keegan, 1980; McIsaac & Gunawardena, 1996; Moore, 1973, 1980, 1983, 1989a, 1989b, 1990; Ohler, 1991; Sewart, 1981; Wedemeyer, 1971). In his 1986 theory of transactional distance, Moore (Moore & Kearsley, 1996) defined distance not only in terms of place and time but also in terms of structure and dialogue between the learner and the instructor. In this theory, distance

becomes more pedagogical than geographical. As structure increases, so does distance. As dialogue increases, distance declines thus establishing the foundational role interaction plays in the distance learning environment. Saba (1998) furthered this concept concluding, … the dynamic and systemic study of distance education has made ‘distance’ irrelevant, and has made mediated communication and construction of knowledge the relevant issue …. So the proper question is not whether distance education is comparable to a hypothetical ‘traditional,’ or face-to-face instruction, but if there is enough interaction between the learner and the instructor for the learner to find meaning and develop new knowledge. (p. 5) To facilitate greater interaction in the geographically and/or organizationally dispersed distance environment, today the convergence or fusion of technologies enable individuals to overcome the barrier of separation, affording institutional and learner opportunity to transcend intra- and inter-organizational boundaries, time, and even culture. By definition, the paradigm of distance, online, or e-learning revolutionizes the traditional environment; however, even with this change, learning, which involves some manner of interaction with content, instructor, and/or peers, remains at the core of the educational process. Although imperative in both environments, research shows these three types of interaction to be the hub of the ongoing traditional versus distance argument. Traditionalists often fear that with anything other than face-to-face instruction, interaction somehow will decrease thus making learning less effective, when in reality, numerous

DOI: 10.4018/978-1-5225-2255-3.ch214 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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studies have revealed no significant difference in the learning outcomes between traditional and distance courses (Russell, 1999). In fact, distance courses have been found to “match conventional on-campus, face-to-face courses in both rigor and quality of outcomes” (Pittman, 1997, p. 42). Despite these findings, critics still abound. Two distinguishing characteristics of the nontraditional environment--individualized learning and flexibility--often arouse suspicion and caution among traditionalists (Grooms, 2000). Many are convinced that with any form of study outside the confines of the typical brick and mortar, “every vestige of intellectual rigor [will] disappear into oblivion.... [These skeptics interpret] individualized learning as individualized isolation, especially from faculty, and they look on flexibility as no more than a synonym for escape from regulation and responsibility” (Gould, 1972, p. 9). In contrast, with their introduction of Equivalency Theory, Simonson, Schlosser, and Hanson (1999) accentuated the concept of equivalency as “central to the widespread acceptance of distance education” (p. 72) thus supporting Keegan’s (1989) call for parity in quality, quantity, and status. Further, recognizing the need to bring integrity and prestige to the field, Shale and Garrison (1990) suggested building a framework based not on isolation but upon interdependence, which would imply that distance learning would merely become an alternative method for delivering traditional content with the context dictating the type of interaction required. So how did we get to where we are now?

MAIN FOCUS OF THE ARTICLE Distance Learning Evolution As previously mentioned, distance learning has been with us in one form or another virtually since the creation of time. For years, itinerant teachers traveled from village to village verbally disseminating information to those hungry for knowledge;

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however, the invention of Guttenberg’s printing press in 1440, made possible serious distribution of learning to larger numbers of people. Capitalizing on this broader use of print media, correspondence study became a popular form of distance education, the first record of which was in 1728 when Caleb Philipps’ advertised the introduction of shorthand (Battenberg as cited in Baath, 1980 & Holmberg, 1986). Often conjuring thoughts of isolation and autonomy, this record of instruction mirrored those images. In fact, in this account there was no mention of interaction of any type other than what was inherent with the content. Over a hundred years later in his 1833 Swedish advertisement, although not directly stated, Meuller’s offer to study composition seems to be the first to imply some form of exchange between the student and teacher. More definitively, in 1840, the most acknowledged root of distance learning explicitly employing learner-instructor interaction began in the United Kingdom. Using passages from the Bible, Isaac Pitman taught shorthand (Baath, 1980; Holmberg, 1974; Kaye, 1988; Rumble, 1986), yet this time, once learners transcribed these passages, they were returned for correspondence with the teacher via the penny post, thus some called it postal teaching (Dewal, 1988). As evidenced in these early days of pure correspondence education, any offered guidance transpired through some form of dispatched communication such as the mail (Wedemeyer, 1971) and student contact, even with the instructor, was not necessarily encouraged. This is clearly seen in Keegan’s (1980) classic article, On Defining Distance Education, where he documented that in its strictest sense, pure correspondence study advocates specified that “students enrol [sic] with them because they ‘want to be left alone’” (p. 31). Directly challenging this belief, Holmberg (1982) later advocated that “any post-graduate distance study must have a truly communicative character if more is meant than merely providing reading lists and odd comments on students’ work” (p. 259).

Category: Educational Technologies

While print remained the primary mode of distance learning until the 1920s and 30s, the introduction of radio broadcasts soon followed with television and satellite delivery systems initiating the labor pains for the birth of the current online technological revolution. Prior to the advent of the World Wide Web (WWW) in the early 1990s, interaction continued to transpire primarily between the learner and content with occasional interaction between the learner and the instructor through such means as telephone and videoconferencing. As the distance learning landscape continued to evolve, learner-instructor interaction became increasingly important, thus catapulting the first of two significant paradigm shifts. While some (Daniel & Marquis, 1979) were accentuating the importance of getting the right independenceinteraction mixture, others heralded the positive impact (e.g. Cookson, 1989; Robinson, 1981) and, even more so, the critical nature of the tension between the role of technology and type of educational process with its accompanying interaction in the distance learning environment (e.g. Bocarnea, Grooms, & Reid-Martinez, 2006; Grooms, 2000, 2003; Grooms & Reid-Martinez, 2011, 2012, 2013).

The Current State of Affairs To be embraced, any new mode or method of education such as distance learning with its multiple technological changes must do more than merely emulate the status quo of traditional education. The virtual environment of the 21st century claims to do just that. While offering flexibility from traditional geographical proximity and time constraints (Barnes & Greller, 1994; Harasim, 1990; Hiltz & Johnson, 1990; Kaye, 1989; Moore, 1983), computer-mediated communication (CMC) (Harasim, 1993; Kaye, 1989; McIsaac & Gunawardena, 1996) and other information and communication technology (ICT) serve as excellent participation equalizers or what

Szecsy (2011) refers to as “leveling devices.” These unprecedented technological advances increasingly blur the line between traditional face-to-face learning and that which occurs at a distance. While multiple studies have indicated there is no significant difference between distance and traditional learning effectiveness, the geographical dispersion of people, shifting market conditions, and rapid technological changes continue to compel transformation in the way we do business both in the marketplace and in the halls of academe. Promising to deliver increased access, quality, and efficiency of learning in an ever-growing competitive market (Benoit, Benoit, Milyo, & Hansen, 2006), as it moved online, the technology of higher education altered both teaching and learning (Kapitzke, 2000) and instructor and student roles (Stadtlander, 1998). With this in mind and regardless if its now considered online, e- or m-, learning is now mediated through synchronous (interactive/real time) or asynchronous (delayed interaction) means and the most critical consideration continues to remain in aligning the task, the delivery method, and the delivery format.

Distance Learning Delivery Methods Almost 150 years following the advent of postal teaching and the first record of any form of learnerinstructor interaction, Linda Harasim (1989), a pioneer in the online classroom, clearly differentiated three delivery methods that she believed distinguished traditional, distance, and online education: one-to-many, as in the traditional lecture method when one instructor addresses many students; one-to-one, as in the tutorial method; and many-to-many, a collaborative process with students learning from each other, with or without an instructor. In the first method, learners are mere passive recipients of knowledge and information, whereas in the latter two, they are actively involved in the learning process. A clear shift in the role of the instructor transpired: from information dispenser to one who facilitates an environment

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where knowledge “emerges from active dialogue among those who seek to understand and apply concepts and techniques” (Hiltz, 1990, p. 135). Although the traditional face-to-face environment is time-place dependent, it allows for the implementation of all three delivery methods contingent upon the instructional task. In contrast, the distance and online environments are time-place independent and mediated, facilitating flexibility and reflective response; however, in like manner they also align method with context. When appropriate, distance learning uses the oneto-one (e.g., print media, programmed instruction, adaptive educational hypermedia, or even 3D and immersive virtual worlds) or one-to-many (e.g., audio teaching, streaming video, podcasting, or

even videologging such as YouTube) methods, while online classes additionally employ the many-to-many concept, which forces the second paradigm shift: the need for peer interaction which often uses such platforms as chat rooms, instant messaging, or one of the many web conferencing options.

Distance Learning Delivery Formats Distance learning formats typically corral into four arenas: print, audio, video, or digital (see Table 1) with all serving as viable options. Apart from media or technology differences, communication within these formats can either be one-way with the learner taking a passive role or two-way with

Table 1. Distance learning delivery formats Media

Passive One-Way

Interactive Two-Way Asynchronous Delayed

Synchronous Real Time

Print

• Syllabi • Texts • Instructor notes • Study guides • Workbooks • Fax

Audio

• Radio • Audiotape • CD Rom • Voicemail

• Telephone • Audio Conferencing

Video

• Videotapes • DVD • Film • Cable, Broadcast, and Digital Television (one-way audio and video)

Satellite Videoconferencing (two-way audio and one-way video)

Digital

• CAI (computer assisted instruction) – using the computer as a self-contained teaching machine • Webcasts • Streaming Video • Podcasting & its varied forms (e.g. Vodcasting & Phonecasting) • Some forms of blogging such as moblogging, linklogging, photologging, edublogging, and videologging such as YouTube

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• CMI (computer-mediated instruction) • E-mail • Texting • Bulletin Boards (Threaded Discussions; Newsgroups) • Listservs • Wikis • Web-based instruction (e.g., Blackboard) • Cloud Technology (e.g., Google Apps) • Open Source Learning Platforms (e.g., Moodle) • Social Networking (e.g., Facebook, LinkedIn, and Google+) • Other forms of blogging and many of its variations including microblogging (e.g., Twitter, Tumblr, and SnapChat)

• Web Conferencing (e.g., Webinars, Google Hangouts, Blackboard Collaborate, and GotoMeeting) • Chat Rooms • Instant Messaging • Shared Whiteboards • Interactive Optical Sensory Technology • 3D and Immersive Virtual Worlds • Adaptive educational hypermedia

Category: Educational Technologies

the learner interacting with either the instructor or peers. Whether using syllabi, texts, instructor notes, or other forms of print, today this medium still remains a significant component in distance learning. In addition, where telephones, audio conferencing, and video technology augmented print in the latter part of the 20th century, today the exponential proliferation of the Internet has provided a plethora of independent and collaborative learning opportunities, both synchronous and asynchronous. Regardless of the format, researchers (Beetham & Sharpe, 2007; Rourke & Coleman, 2011) caution that rather than technology driving pedagogy, pedagogy should drive technology with the ultimate goal always of learning. Most recently with the plethora of multiple media at hand, many educational institutions have turned to massive open online courses (MOOCs), while others have employed a blended or hybrid learning approach combining some form of the traditional with the digital. Osguthorpe and Graham (2003) summarized the latter approach very well. Those who use blended approaches base their pedagogy on the assumption that there are inherent benefits in face-to-face interaction (both among learners and between learner and instructor) as well as understanding that there are some inherent advantages to using online methods in their teaching. Thus, the aim of those using blended learning approaches is to find a harmonious balance between online access to knowledge and face-to-face interaction. The balance … will vary for every course [based upon the] instructional goals, student characteristics, instructor background, and online resources … No two courses will be exactly the same …. [The goal] is to ensure that the blend involves the strengths of each type of learning environment and none of the weaknesses. (p. 228) Citing three typical reasons for using a blended approach--more effective pedagogy, increased convenience and access, and increased cost ef-

fectiveness (Graham, Allen, & Ure, 2005)--it is easy to see why progressively more institutions are implementing such a format. Further blurring the line between traditional and distance learning, the blending and convergence of technologies continue to increase the fluidity and ubiquity of education. Today with the tap or swipe of a finger, learners have virtually instant access to a plethora of information via the Internet, which Klinger and Coffman (2011) claim provides a transformational highway for distance learning. Given the mobility of netbooks, smartphones, and tablets, geographical proximity becomes a nonissue as learners are no longer bound by context and can learn anytime and anywhere with social media producing an ecosystem ripe for interaction. Through time, the mobility of distance learning has remained constant; however, the delivery format has opened virtual horizons far beyond traveling from village to village. Foreshadowing this technological explosion, futurists McCain and Jukes (2001) projected that “at a certain point, the boundaries between reality and virtual reality will collapse because of the increased sophistication and transparency of these powerful, fused technologies” (p. 60). With the introduction of such things as cloud computing, adaptive educational hypermedia, interactive optical sensory technology, and three-dimensional virtual and immersive learning environments, that day has already arrived. So, where do we go from here?

FUTURE RESEARCH DIRECTIONS The future of distance learning will continue to reflect the exponentially growing technological advances. In an April 1965 Electronics magazine article, Gordon Moore, cofounder and Chairman Emeritus of Intel, the world’s largest silicon chip manufacturing company, predicted that the number of transistors on an integrated circuit would double every 12-18 months. While in 1975, he amended this timeframe to roughly every 24 months, it has been accurate in excess of one half century. At

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present, what has become known as Moore’s Law shows no signs of slowing down; although, in a 2000 interview with Time magazine, Moore did acknowledge that at some point in the next two or three generations, this growth would perhaps slow down to doubling every five years. Exploring other technological alternatives, since the late 1950s some “wondered whether a better approach to miniaturization might be to ‘grow’ single molecules that functioned as electronic circuits or components. [They believed] that such molecules might be faster and smaller [than the traditional silicon chip] … and they might also be easier to make” (Kelly & Mody, 2015, 8). While this movement has waxed and waned over the years, in 2010, researchers from Japan and Michigan Technological University claimed they had succeeded in building a molecular computer that could “replicate the inner mechanisms of the human brain, repairing itself and mimicking the massive parallelism that allows our brains to process information like no siliconbased computer can” (Borghino, 2010, 1). Four years later, Sebastian (2014) unveiled that IBM scientists had created “what some have claimed is the most advanced neuromorphic (brain-like) computer chip to date” (1). Even with such remarkable advancements, we are quite a distance from reaching the apex of technological discovery, which history reveals directly affects distance teaching and learning. In reflection, these new discoveries “may very well prove the long-term solution to validate Moore’s law well into the next century” (Borghino, 2010, p.2).

CONCLUSION Whether one views distance as geographical or pedagogical, as the above suggests, the technological explosion of the 21st century provides unprecedented opportunity to render distance virtually irrelevant. If Moore’s law holds fast and technology continues to double every 18-

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24 months, the exponential growth of distance learning will continue to catapult educators into uncharted territory. The question remains, will there still be a place for the staunch traditionalist or will education continue to be so radically transformed that traditional education is hardly recognizable. Regardless of the medium (print, audio, video, or digital), method (one-to-many, one-to-one, or many-to-many), format (passive or interactive), or even device (PC, smartphone, tablet, or watch), interactive learning continues to remain at the core of the distance educational process. Negroponte (1995) poignantly prophesied, “Distance means less and less in the digital world. In fact, an Internet user is utterly oblivious to it. On the Internet, distance often seems to function in reverse” (p. 178). Supporting this prediction, science and technology have triggered physical distance to become truly irrelevant. So, what does the future hold? Where will distance learning be in 5, 10, or even 20 years from now? Some would postulate that we are limited only by our imaginations.

REFERENCES Baath, J. A. (1980). Postal two-way communication in correspondence education: An empirical investigation. Malmo, Sweden: LiberHermods. Barnes, S., & Greller, L. M. (1994). Computermediated communication in the organization. Communication Education, 43(2), 129–142. doi:10.1080/03634529409378970 Beetham, H., & Sharpe, R. (Eds.). (2007). Rethinking pedagogy for a digital age: Designing and delivering e-learning. London: Routledge. Benoit, P. J., Benoit, W. L., Milyo, J., & Hansen, G. J. (2006). The effects of traditional versus webassisted instruction on learning and satisfaction. Columbia, MO: University of Missouri Graduate School.

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Bocarnea, M. C., Grooms, L. D., & Reid-Martinez, K. (2006). Technological and pedagogical considerations in online learning. In A. Schorr & S. Seltmann (Eds.), Changing Media Markets in Europe and Abroad: New Ways of Handling Information and Entertainment Content (pp. 379392). Lengerich: Pabst Science Publishers. Borghino, D. (2010, May 10). Scientists create organic ‘molecular computer’. Gizmag [New and Emerging Technology News]. Retrieved from http://www.gizmag.com/organic-molecularcomputer/15041/ Cookson, P. S. (1989). Research on learners and learning in distance education: A review. American Journal of Distance Education, 3(2), 22–34. doi:10.1080/08923648909526661 Daniel, J. S., & Marquis, C. (1979). Interaction and independence: Getting the mixture right. Teaching at a Distance, 14, 29–44. Dewal, O. S. (1988). Pedagogical issues - distance education. Prospects, 18(1), 63–73. doi:10.1007/ BF02192959 Gould, S. B. (1972). Prologue: Prospects for nontraditional study. In S. B. Gould & K. P. Cross (Eds.), Explorations in non-traditional study (pp. 1–12). San Francisco: Jossey-Bass. Graham, C. R., Allen, S., & Ure, D. (2005). Benefits and challenges of blended learning environments. In M. Khosrow (Ed.), Encyclopedia of information science and technology (pp. 253–259). Hershey, PA: Information Science Reference. doi:10.4018/978-1-59140-553-5.ch047 Grooms, L. D. (2000). Interaction in the computermediated adult distance learning environment: Leadership development through online education. Dissertation Abstracts International, 61(12), 4692A. Grooms, L. D. (2003). Computer-mediated communication: A vehicle for learning. International Review of Research in Open and Distance Learning, 4(2). doi:10.19173/irrodl.v4i2.148

Grooms, L. D., & Reid-Martinez, K. (2011). Sustainable leadership development: A conceptual model of a cross-cultural blending learning program. International Journal of Leadership Studies, 6(3), 412–429. Grooms, L. D., & Reid-Martinez, K. (2012, November). Building a Community of Leaders through Intrapersonal, Interpersonal, and Intercultural Communication. Presented at the annual convention of the National Communication Association, Orlando, FL. Grooms, L. D., & Reid-Martinez, K. (2013, November). Cross-Cultural Learning for Leadership Resilience and Sustainability. Presented at the 15th annual International Leadership Association Conference, Montreal, Canada. Harasim, L. (1989). On-line education: A new domain. In R. Mason & A. Kaye (Eds.), Mindweave: Communication, computers and distance education (pp. 50–62). New York: Pergamon Press. Harasim, L. M. (1990). Online education: An environment for collaboration and intellectual amplification. In L. M. Harasim (Ed.), Online education: Perspectives on a new environment (pp. 39–64). New York: Praeger. Harasim, L. M. (1993). Networlds: Networks as social space. In L. M. Harasim (Ed.), Global networks: Computers and international communication (pp. 15–34). Cambridge, MA: The MIT Press. Hiltz, S. R. (1990). Evaluating the virtual classroom. In L. M. Harasim (Ed.), Online education: Perspectives on a new environment (pp. 133–169). New York: Praeger. Hiltz, S. R., & Johnson, K. (1990). User satisfaction with computer-mediated systems. Management Science, 36(6), 739–764. doi:10.1287/ mnsc.36.6.739 Holmberg, B. (1974). Distance education: A short handbook. Malmo, Sweden: Hermods.

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Holmberg, B. (1977). Distance education: A survey and bibliography. London: Kogan Page. Holmberg, B. (1981). Status and trends of distance education. London: Kogan Page. Holmberg, B. (1982). Distance study at the post-graduate level: Graduate study at a distance requires greater attention to communication with the student. In J. S. Daniel, M. A. Stroud, & J. R. Thompson (Eds.), Learning at a distance: A world perspective (pp. 258–260). Edmonton, AB, Canada: Athabasca University/International Council for Correspondence Education. Holmberg, B. (1986). Growth and structure of distance education. Wolfeboro, NH: Croom Helm. Kapitzke, C. (2000). The sociability and spatiality of online pedagogy and collaborative learning in an educational media and technology course. Journal of Educational Technology & Society, 3, 344–441. Kaye, A. (1981). Media, materials and learning methods. In A. Kaye & G. Rumble (Eds.), Distance teaching for higher and adult education (pp. 48–69). London: Croom Helm. Kaye, A. (1982). Using the media for adult basic education. In A. Kaye & K. Harry (Eds.), Using the media for adult basic education (pp. 9–29). London: Croom Helm. Kaye, A. (1988). Distance education: The state of the art. Prospects, 18(1), 43–54. doi:10.1007/ BF02192957 Kaye, A. (1989). Computer-mediated communication and distance education. In R. Mason & A. Kaye (Eds.), Mindweave: Communication, computers and distance education (pp. 3–21). New York: Pergamon Press. Keegan, D. (1989). Problems in defining the field of distance education. In M. G. Moore & G. C. Clark (Eds.), Readings in principles of distance education (pp. 8–15). University Park, PA: The Pennsylvania State University.

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Keegan, D. J. (1980). On defining distance education. Distance Education, 1(1), 13–36. doi:10.1080/0158791800010102 Kelly, K. F., & Mody, C. C. M. (2015, September 25). Whatever happened to the molecular computer. IEEE Spectrum. Retrieved from http:// spectrum.ieee.org/biomedical/devices/whateverhappened-to-the-molecular-computer Klinger, M. B., & Coffman, T. L. (2011). Emphasizing diversity through 3D multi-user virtual worlds. In G. Kurubacak & T. V. Yuzer (Eds.), Handbook of research on transformative online education and liberation: Models for social equality (pp. 86–106). Hershey, PA: Information Science Reference. doi:10.4018/978-1-60960046-4.ch005 McCain, T., & Jukes, I. (2001). Windows in the future: Education in the age of technology. Thousands Oaks, CA: Corwin Press. McIsaac, M. S., & Gunawardena, C. N. (1996). Distance education. In D. H. Jonassen (Ed.), Handbook of research for educational communications and technology (pp. 403–437). New York: Simon & Schuster Macmillan. Moore, G. (2000, June 19). Our Technology: Gordon Moore Q & A. Time, 155(25), 99. Retrieved from http://www.time.com/time/magazine/ article/0,9171,997241,00.html PMID:11765518 Moore, G. E. (1965, April 19). Cramming more components onto integrated circuits. Electronics, 38(8), 114–117. Moore, M. (1983). The individual adult learner. In M. Tight (Ed.), Adult learning and education (pp. 153–168). London: Croom Helm. Moore, M. G. (1973). Toward a theory of independent learning and teaching. The Journal of Higher Education, 44(9), 661–679. doi:10.2307/1980599 Moore, M. G. (1980). Independent study. In R. D. Boyd & J. W. Apps et al. (Eds.), Redefining the discipline of adult education (pp. 16–31). San Francisco: Jossey-Bass.

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Moore, M. G. (1989a). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1–7. doi:10.1080/08923648909526659 Moore, M. G. (1989b, May). Effects of distance learning: A summary of the literature. Paper presented for Congress of the United States Office of Technology Assessment, Washington, DC. Moore, M. G. (1990). Correspondence study. In M. W. Galbraith (Ed.), Adult learning methods: A guide for effective instruction (pp. 345–365). Malabar, FL: Robert E. Krieger Publishing. Moore, M. G., & Kearsley, G. (1996). Distance education: A systems view. Boston: Wadsworth Publishing. Negroponte, N. (1995). Being digital. New York: Alfred A. Knopf. Ohler, J. (1991). Why distance education? The Annals of the American Academy of Political and Social Science, 514(1), 22–34. doi:10.1177/0002716291514001003 Osguthorpe, R. T., & Graham, C. R. (2003). Blended learning environments: Definitions and directions. The Quarterly Review of Distance Education, 4(3), 227–233. Pittman, V. (1997). Distance education exchange. The Journal of Continuing Higher Education, 45(2), 42–43. doi:10.1080/07377366.1997.104 00321 Robinson, B. (1981). Support for student learning. In A. Kaye & G. Rumble (Eds.), Distance teaching for higher and adult education (pp. 141–161). London: Croom Helm. Rourke, A., & Coleman, K. (Eds.). (2011). Pedagogy leads technology: Online learning and teaching in higher education: New technologies, new pedagogies. Champaign, IL: Common Ground Publishing. Rumble, G. (1986). The planning and management of distance education. New York: St. Martin’s Press.

Rumble, G., & Keegan, D. (1982). Introduction. In G. Rumble & K. Harry (Eds.), The distance teaching universities (pp. 9–14). London: Croom Helm. Russell, T. L. (1999). The no significant difference phenomenon as reported in 355 research reports, summaries and papers: A comparative research annotated bibliography on technology for distance education. Raleigh, NC: North Carolina State University. Saba, F. (1998). Is distance education comparable to “traditional education”? Distance Education Report, 2(5), 5. Sebastian, A. (2014, August 7). IBM cracks open a new era of computing with brain-like chip: 4096 cores, 1 million neurons, 5.4 billion transistors. Extreme Tech: Latest Technology News. Retrieved from http://www.extremetech.com/ extreme/187612-ibm-cracks-open-a-new-era-ofcomputing-with-brain-like-chip-4096-cores-1million-neurons-5-4-billion-transistors Sewart, D. (1981). Distance teaching: A contradiction in terms? Teaching at a Distance, 19, 6–18. Shale, D. (1990). Toward a reconceptualization of distance education. In M. G. Moore, P. Cookson, J. Donaldson, & B. A. Quigley (Eds.), Contemporary issues in American distance education (pp. 333–343). New York: Pergamon Press. Shale, D., & Garrison, D. R. (1990). Education and communication. In D. R. Garrison & D. Shale (Eds.), Education at a distance: From issues to practice (pp. 23–29). Malabar, FL: Robert E. Krieger Publishing Company. Simonson, M., Schlosser, C., & Hanson, D. (1999). Theory and distance education: A new discussion. American Journal of Distance Education, 13(1), 60–75. doi:10.1080/08923649909527014 Stadtlander, L. M. (1998). Virtual instruction: Teaching an online graduate seminar. Teaching of Psychology, 25(2), 146–148. doi:10.1207/ s15328023top2502_20

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Szecsy, E. M. (2011). Building knowledge without borders: Using ICT to develop a binational education research community. In G. Kurubacak & T. V. Yuzer (Eds.), Handbook of research on transformative online education and liberation: Models for social equality (pp. 67–85). Hershey, PA: Information Science Reference. doi:10.4018/9781-60960-046-4.ch004 Wedemeyer, C. A. (1971). Independent study. In L. C. Deighton (Ed.), The encyclopedia of education (pp. 548–557). New York: Macmillan Company & The Free Press.

ADDITIONAL READING Bandyopadhyay, A., Pati, R., Sahu, S., Peper, F., & Fujita, D. (2010). Massively parallel computing on an organic molecular layer. Nature Physics, 6(5), 369–375. doi:10.1038/nphys1636 Bhagat, K. K., Wu, L. Y., & Chang, C.-Y. (2016). Development and validation of the perception of students towards online learning (POSTOL). Journal of Educational Technology & Society, 19(1), 350–359. Bozkurt, A., Akgun-Ozbek, E., Yilmazel, S., Erdogdu, E., Ucar, H., Guler, E., & Aydin, C. H. et al. (2015). Trends in distance education research: A content analysis of journals 20092013. International Review of Research in Open and Distributed Learning, 16(1), 330–363. doi:10.19173/irrodl. v16i1.1953 Burge, E., Gibson, C. C., & Gibson, T. (Eds.). (2011). Flexible pedagogy, flexible practice: Notes from the trenches of distance education. Edmonton, Alberta: Athabasca University. Ceruzzi, P. E. (2005, July). Moores law and technological determinism: Reflections on the history of technology. Technology and Culture, 46(3), 584–593. Retrieved from http://www.jstor. org/stable/40060905 doi:10.1353/tech.2005.0116

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Cheney, A., & Sanders, R. L. (Eds.). (2011). Teaching and learning in 3D immersive worlds: Pedagogical models and constructivist approaches. Hershey, PA: Information Science Reference. doi:10.4018/978-1-60960-517-9 Gedik, N., Kiraz, E., & Ozden, M. Y. (2013). Design of a blended learning environment: Considerations and implementation issues. Australasian Journal of Educational Technology, 29(1), 1–19. doi:10.14742/ajet.6 Kurubacak, G., & Yuzer, T. V. (Eds.). (2011). Handbook of research on transformative online education and liberation: Models for social equality. Hershey, PA: Information Science Reference. doi:10.4018/978-1-60960-046-4 Lê, T., & Lê, Q. (Eds.). (2012). Technologies for enhancing pedagogy, engagement and empowerment in education: Creating learning-friendly environments. Hershey, PA: Information Science Reference. doi:10.4018/978-1-61350-074-3 Lister, M. (2014). Trends in the design of elearning and online learning. Journal of Online Learning and Teaching, 10(4), 671–680. Liu, H.-C., & Yen, J.-R. (2014). Effects of distance learning on learning effectiveness. Eurasia Journal of Mathematics, Science, &. Technology Education, 10(6), 575–580. doi:10.12973/ Eurasia.2014.1218a Logofătu, B., & Vişan, A. (2015). New trends in the educational area: Case study regarding the usability of google apps tools within the department for distance learning. Elearning & Software for Education, 2, 526-531. doi: 026X 15 17110.12753/2066 Somyürek, S. (2015). The new trends on adaptive educational hypermedia systems. International Review of Research in Open and Distributed Learning, 16(1), 221–241. doi:10.19173/irrodl. v16i1.1946

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KEY TERMS AND DEFINITIONS Cloud Computing: Technologies that facilitate the sharing of digital files over the Internet. Correspondence Learning: A form of distance learning using dispatched or one-way communication. E-Learning: Learning that occurs electronically. Equivalency: Distance learning that possesses equality with learning experienced in the face-toface venue. Face-to-Face Learning: Learning that is time-place dependent.

mLearning: Learning that occurs through the use of mobile technology. Optical Sensory Technology: Technology that provides the ability to track user input within an augmented or virtual reality. Three-Dimensional Virtual Learning: A computer-based simulated environment. Time-Place Dependent: Education that transpires in the same location at the same time. Time-Place Independent: Learning that does not rely on geographical proximity or time. Traditional Study: Face-to-face learning.

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Do Usability Design Features of a Mobile Game Influence Learning? Rex Perez Bringula University of the East, Philippines Edison Cabrera University of the East, Philippines Princess B. Calmerin University of the East, Philippines Eduardo A. Lao University of the East, Philippines Christian Gerard Sembrano University of the East, Philippines Angelita D. Guia University of the East, Philippines Joan P. Lazaro University of the East, Philippines Alexis John M. Rubio University of the East, Philippines Annaliza E. Catacutan National University, Philippines Marilou N. Jamis National University, Philippines Lalaine P. Abad Department of Education, Philippines

INTRODUCTION Mobile learning allows learners to construct their own learning experience (Bandalaria, 2007). This form of learning empowers students to develop their own skills and knowledge (Sharples, Taylor & Vavoula, 2007). Students, through the use of mobile learning, may create opportunities to learn anytime and anywhere (Martin & Ertzberger,

2013; Sanchez, Mendoza, & Salinas, 2009) and can connect that learning experience to real life situations. Recently, games have been integrated in mobile platforms. Educational technology developers combined the entertaining components of games with educational contents in order to develop games for pedagogical purposes. However, the existing threads of discussion on serious game usability do not provide evidence as

DOI: 10.4018/978-1-5225-2255-3.ch215 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Educational Technologies

to which of the usability features of a mobile game can influence student learning. This chapter aims to address this issue. A serious game in disaster response was developed and utilized by first-year students. The goal of this paper is to find answers to the following questions: 1. What is the knowledge of the disaster response participants before and after playing the game? 2. What is the learning gain of the students after playing the game? 3. What are the perceptions of the participants in the usability of the game in terms of content, ease of use, usefulness, and aesthetics? 4. Is there a significant difference between the knowledge of students on disaster response before and after playing the game? 5. Do the usability factors, singly or in combination, influence the knowledge of the participants in natural disaster response?

BACKGROUND Mobile games can be utilized to channel information and learning. Games with educational content may engage people to play and learn at the same time (Muratet, Torguet, Jessel, & Viallet,2009). Studies have documented the positive results of mobile games as educational tools. Batson & Feinberg (2006) showed that students who used educational games had a positive learning experience. Mobile educational games also improve the motivation of students to learn and to have positive attitudes towards learning (Tuzun, Yilmaz-Soylu, Karakus, Inal, & Kizilkaya, 2009; Salter, Pittaway, Swabey, Capstick, & Douglas, 2012). Chow, Woodford & Maes (2011) also revealed that the use of mobile educational games improved students’ retention, critical thinking skills, and understanding of the content of an introductory statistics course (Chow et al., 2011). Moreover, it was proven that these educational materials can improve learners’ problem solving

skills and can promote collaboration among them (Sanchez & Olivares 2011). Recently, Connolly, Boyle, MacArthur, Hainey, & Boyle (2012) summarized the results of 129 studies that investigated the impacts and outcomes of serious games on learning and students’ classroom engagement. The researchers consistently found that playing serious games were associated to knowledge acquisition, content understanding, and affective and motivational outcomes. Learning of students could also be measured in terms of learning gain. Learning gain is the measurement of performance in a test as indicated by the percentage points a student can gain from the first/previous test to the second/recent test (Colt, Davoudi, Murgu, & Rohani, 2011; Steif & Dollár, 2009). Rodrigo et al. (2013) utilized this measure to show that learning gains could be influenced by the ability of the students to solve problems. Bringula, Alvarez, Evangelista, & So (in press) used this measure to determine the impact of a mobile learning software on the mathematics performance of students. They found out that after using the mobile learning software, students increased their mathematics performance by 41%. Various design models and guidelines were proposed in an attempt to guide educational game developers to develop serious games (e.g., Billi et al., 2010; Bringula, Alcid, Bandril, De Guzman, & Lopez, 2014). The goal of these models and guidelines is to balance the entertaining and educational components of the mobile game (Kreutzer, Marks, &Bowers, 2015). Usability of mobile games was explored in an attempt to warrant that the end product will be functional and playable (Olsen, Procci, & Bowers, 2011; Warren, Jons, & Lin,2011). In an educational point of view, the purpose of the exploration of serious game usability is to ensure knowledge transfer (Kreutzer, Marks, &Bowers, 2015). There is no universal definition of usability (Sindhuja & Dastidar, 2009). This is because usability is dependent on the content and nature of the systems being investigated. In mobile games

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designed for educational purposes (that is, serious games), the usual metrics of usability—efficiency, effectiveness, and user satisfaction—may not be the appropriate criteria for a usable mobile game (Thomas, Schott, &Kambouri, 2003). Researchers suggested that educational game developers must balance learning component of games while maintaining the fun in the games (Thomas et al.,2003; Ibrahim, Vela, Rodriguez, Sanchez, & Zea,2012). The content of the game can focus on the knowledge or skills that are intended to be inculcated or transferred (Thomas et al., 2003). Derakhshan (2009) showed that content was an important feature of a mobile learning environment. Kutluk and Gülmez (2014) disclosed that good organization and easy to navigate content were desirable characteristics of mobile learning environment. Shchiglik, Barnes, Scornavacca (2016) showed that the game content quality should be accurate, relevant, and easy to understand. Assuring the quality of game content can attract and motivate students to learn (Alqahtani & Mohammad, 2015). The game must educate the users while they have fun playing the game. The game must be simple and enjoyable so that it captivates the imagination of the player (Ibrahim et al., 2012). Davis (1989) defined ease of use as a system quality factor which is related to user-friendliness of application design. In other words, ease of use is equated to the effort exerted in using a technology (Bringula, 2016). Tan,Ooi,Sim,& Phusavat (2012) showed that ease of use can affect intention to use mobile learning. Hence, ease of use influences the attitudes and behaviour of people towards the technology (Abu-al-Aish & Love,2013; Alqahtani & Mohammad, 2015). In the context of mobile games, developers must ensure that the independent components of a game are easy to use (Olsen et al., 2011). In turn, learners may continue to use the game (Alqahtani & Mohammad, 2015). In a recent study, Shchiglik et al. (2016) showed that ease of use of games can be measured in terms of the following — easy to use, easy to learn and

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operate, users know what to do, easy to navigate, easy to find things in the game. The usefulness of the game is anchored to its game objective. The clarity of game objective is achieved when a player can explain the game (Bringula et al., 2014; Ibrahim et al., 2012). If learners can state the purpose of the game, they have an idea of its usefulness. The learners’ perception of the usefulness of the game can influence their intention to use the game (Tan et al., 2012). Therefore, perceived usefulness is the expectation of users towards a new technology as well as the view that it would bring satisfaction and enhanced performance. Chittaro & Sioni (2015) provided evidence that a game in disaster response must contain useful recommendations in terms of safety, evacuation, and injury reduction. Lastly, aesthetics is the most discussed factor of usability. In game design, aesthetics is the overall quality of visual design (Bringula et al., 2014). Game objects, text layout, buttons, graphics, and colors constitute the visual design of the game. Gamers are more particular in this aspect than the rest of other game design considerations. This is because the environment in mobile devices is different from the personal computers. In another study, Alqahtani & Mohammad (2015) included the adaptability of the application to the screen size of mobile devices, suitability of the design in landscape and portrait modes, and contentment with the design of the application as items of aesthetics. The findings of Shchiglik et al. (2016) are consistent to that of Alqahtani and Mohammad (2015). However, it is still unclear which usability design factors may influence student learning. Current studies focus on the usability of serious games. Absent in the literature is the evidence as to whether these game usability features could influence user learning. The main focus of this article is to determine if game usability design features could influence learning. It is hypothesized that usability factors, singly or in combination, do not influence the knowledge of the participants in natural disaster response

Category: Educational Technologies

THE EXPERIMENT AND MEASUREMENT OF LEARNING

THE USABILITY GAME DESIGN CRITERIA

A one-group pretest-posttest experimental design (Figure 1) was conducted by the researchers to address the main focus of the chapter. The study was conducted at the National University, Manila. Ninety-eight participants (34 female, 64 male, average age is 17.7 years old) have utilized a mobile android-based game named H.E.A.T. on disaster response (Figure 2). The participants used the game during their laboratory sessions for three consecutive days. The laboratory session lasted for one hour. This is the intervention period (denoted by X). A day before the intervention period, a pretest (denoted by O) was conducted to determine the prior knowledge of the participants on how to respond during a disaster. A day after the intervention period, a posttest (denoted by O) was administered. The experiment ran for five consecutive days. Each test contained 20 items.

The usability game design criteria were pretested to ensure its reliability and validity. Students from the University of the East assessed the software. It contained four dimensions which consisted of content, ease of use, usefulness, and aesthetics. Content was composed of three items. The first item refers to the completeness of the game. It measured the scope of the game in terms of different forms of natural calamities. The reliability of the game content is the third item of this dimension. Ease of use was composed of six items. It evaluated if it may be easy to use the game controls and determined if all controls are functional. Learners must also feel that the game is easy to play. They must not be confused or lost when playing the game. They can also evaluate the game in terms of how easy it is to move from one game screen to another. The last item of this criterion asked the learners to rate the overall ease of using the game. Usefulness, which is composed of four items, intended to measure the perceived educational value of the game in terms of disaster response. It

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Figure 1. One-group pretest-posttest design

Figure 2. The game utilized in the study

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determined the perceptions of the learners in terms of how much they learned from the game. Moreover, it contains an item that examined whether the game gave useful information and tips. The last criterion was aesthetics. It consisted of seven items relating to attractiveness of screen layout, comfort when playing games, appropriateness of colors, legibility of font sizes and font styles, and overall attractiveness of the game.

RESULTS It was found that the pretest score on storm had the lowest percentage at 50% while pretest score in earthquake had the highest percentage at 77%. After the intervention period, all test scores were higher. Posttest scores in earthquake, flood, and tsunami are all 81%. The overall mean also went up from 59% to 74%. Students had the most learning gain (l.g.) in terms of knowledge in flood (l.g. = 60%). Overall, the learning gain is 35%. The same sample t-test revealed that there is a significant difference on the posttest and pretest scores in fire (d = 0.66, t(97) = 4.55),

flood (d = 0.83, t(97) = 8.40), tsunami (d = 0.60, t(97) = 5.20), and volcanic eruption (d = 0.56, t(97) = 4.80). All results are significant at 0.05 level of confidence. Hence, the results are unlikely to have arisen from sampling error. On the other hand, the differences on the scores are not significant in storm (d = 0.06, t(97) = 0.46, p = 0.644) and earthquake (d = 0.16, t(97) = 1.50, p = 0.138). Usefulness had the highest mean rating of 4.18 (s.d. =0.75), while aesthetics had the lowest mean rating of 3.05 (s.d. = 0.86). Respondents agreed that the content of the game (mean = 3.92, s.d. = 0.69) is acceptable and useful (mean = 4.18, s.d. = 0.75). The standard deviations of all criteria showed that the responses are not dispersed. The responses in the open-ended questions were analyzed by taking keywords. The keywords were then grouped to determine the themes. The results are shown in Table 1. It was disclosed that the five themes emerged. Three out of the five themes are covered in the current study. Respondents commented that the game is easy to use and play. Also, the controls of the game need to be improved. In term of aesthetics, 27 respondents

Table 1. Results of open-ended questions Ease of Use      • easy to use and play (2 times mentioned)      • controls need to be improved (4 times mentioned) Usefulness      • respondents learned a lot of lessons about disasters (6 times mentioned)      • helped provide knowledge on what to do during calamities (8 times mentioned)      • gave tips on what to do during calamity      • very informative (6 times mentioned)      • good game for students (2 times mentioned)      • gave awareness about disasters      • useful (2 times mentioned) Aesthetics      • graphics needs to be improved (27 times mentioned) Fun      • very enjoyable to play (2 times mentioned)      • fun (3 times mentioned)      • happy      • boring Effectiveness      • tasks proved a bit time-limited      • walking of character is slow (14 times mentioned)      • game occupied a large memory (3 times mentioned)

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advised that the graphics of the game need to be upgraded. Twenty-six respondents agreed that the game is informative and useful. The fun component of the game is included as six participants expressed enjoyment with the game while one participant expressed otherwise. Contrary to the study of Thomas et al. (2003), effectiveness is found to be a component of serious game usability. This factor includes the time to complete the game and the memory space used in the game. The result of multiple regression of learning gain on game usability criteria showed that the p-values of Content (b = 0.01, p = 0.93), Ease of Use (beta = -0.02, p = 0.87), and Usefulness (beta = 0.08, p = 0.48) are all greater than 0.05 level of confidence. Thus, aesthetics (beta = 0.227), with an associated p-value of 0.025, was the only predictor of learning gain. Aesthetics accounts for 4% (Adj. R2) in the variability of learning gain. The result is unlikely to have arisen from sampling error (F(1,96) = 5.20, p< 0.05).

DISCUSSION The result of the experiment shows that the students have a considerable amount of knowledge on how to respond to disasters. It is interesting to note that 77% of the questions relating to earthquake response were answered correctly. This is explained by the fact that earthquake drills were conducted at school, local, and national levels at the time the study was conducted. This is in relation to the earthquake preparedness program of the government. This explains why the students have the highest percentage on earthquake items. Meanwhile, students had the lowest percentage score on typhoon preparedness. On this item, students only had a passing mark on the pretest. This finding is unexpected considering that the Philippines is frequently devastated by typhoons. According to the Philippine Statistics Authority (2014), an average of nine typhoons visited the Philippines from 1993 to 2013. This explains the reason behind this unexpected result. Filipinos

are too attuned to typhoons to the extent that they do not mind preparing for them. In fact, it was reported that the strength of Typhoon Haiyan was underestimated by the locals, resulting to poor planning (Jibiki, Kure, Kuri, &Ono, 2016). The result signals a need to conduct a study on the attitudes of Filipinos toward typhoons. This is not addressed in the current study. A game may be developed to assess the attitudes of Filipinos toward typhoons. The game may then respond depending on the assessed attitudes. It is interesting to note that all test items increased following the intervention period. The test items for storm remained passing and surfaced as having the lowest score. Students had the highest learning on flood preparation. This is a good indication since flooding can be a consequence of other natural disasters, including typhoons. The overall learning gain of 35% means that students acquired 35 percentage points out of 100 percentage points that they could have gained from the pretest to posttest. This means that students acquired more than one-third of the possible points. Moreover, a learning gain of 30% is the defining minimum value at which the educational intervention could be regarded as effective (Hake,1998; Prather, Rudolph, & Brissenden, 2009). In general, the game made it possible for students to know more about disaster preparedness. After using the game, the posttest scores proved higher than the pretest scores. These indicate that students had positive learning gains. Further statistical tests confirmed that the differences in test items in fire, flood, tsunami, and volcanic eruption are significant. This shows that students learned to be more prepared in times of fire, flood, tsunami, and volcanic eruption. Furthermore, it also implies that the software can be utilized as an instructional material for disaster preparedness and risk reduction. The evaluation of the usability design features of the game showed that students agreed that the information provided by the game is reliable. The students likewise favorably rated the software in terms of ease of use. According to the learners, it

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is easy to learn how to play the game because all controls are functional. Students also perceived that they learned a lot from the game. The game helped them to understand natural disasters and how to respond appropriately. The result of openended questions showed that 26 respondents commented that the game is informative and helpful. This confirms the numerical rating on usefulness. Meanwhile, they agreed to a lesser extent that the game is appealing. The responses of the 27 respondents on the open-ended question were in agreement that there is a need to improve the game’s graphics. The nature of the participants may explain the findings. It is worth noting that the respondents of this study are information technology students who are knowledgeable in computer games. They may compare the game to other commercially-available games with better graphics and designs. Nonetheless, the evaluation of the respondents can serve as a basis to improve it in terms of screen layout, colors, and font sizes. It was also revealed that six students enjoyed playing the game. They viewed the game as informative and helpful, as well as fun and enjoyable. The “fun” component of the game is not included in the evaluation. Future studies may include this factor in evaluating a game on disaster response. It is worth noting that students are also concerned with slow movement of the game character. The students recommended that the time to finish a task is not realistic. They were also concerned with the memory space it used when installed in a mobile device. The result of the regression analysis revealed that it is justifiable to focus on the aesthetic components of the game. It was disclosed that aesthetics is the only predictor that could influence student learning in disaster response. Bringula & De Leon (2014) said that aesthetics is a “human” side design consideration. It is an “experiential” factor where “users have to use the software in order to judge the quality of the software” (Bringula &De Leon, 2014, p. 198). In short, this is the factor that users directly experience throughout the game. Based on this finding, it is important that serious games developers consider the visual 2472

content of the game in terms of its game objects, text layout, buttons, graphics, and colors. It is recommended that game objects be made clear. The text styles and sizes should be legible. Players must instinctively know the purpose of the buttons. The screen layout must fit the screen size of mobile devices. It is essential that colors blend the overall environment of the game. These aesthetic considerations can ensure the comfort of playing a serious game.

FUTURE RESEARCH DIRECTIONS Aesthetics is found to be a significant predictor of learning in a mobile game. However, it accounts for only 4% of learning disaster response. In light of this finding, there are other usability design factors not considered in the study that might be investigated. The results of the experiment disclosed that fun and effectiveness were the two themes emerged from the open-ended questions. Future research may include these factors in the research instrument and quantitatively analyze their contributions in explaining the usability of the game. Informal interviews with students showed that the game can include more levels with different difficulties and surprises to capture the interest of the students. These design considerations will keep the students playing and will entice them to play the game again. A study on different samples is also recommended. Children, who are among the most vulnerable sector of society, can be included as participants in future studies. The game can be utilized to teach them how to respond during natural disasters.

CONCLUSION On the basis of these findings, the null hypothesis stating that the usability factors do not influence the knowledge of the participants in natural disaster response is partially rejected. It is also concluded

Category: Educational Technologies

that the game was helpful in increasing the knowledge of the students to respond during fire, flood, tsunami, and volcanic eruption. It concluded that the game was not helpful during storm and earthquake situations. The game did not significantly contribute to student knowledge on responding to storms since they are already attuned to storms. They did not achieve a significant learning gain in earthquake test items because earthquake drills were conducted in local, institutional, and national levels prior to this study.

Bringula, R. P. (2016). Factors affecting web portal information services usability: A canonical correlation analysis. International Journal of Human-Computer Interaction, 1–13. doi:10.108 0/10447318.2016.1199180

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Abu-al-Aish, A., & Love, S. (2013). Factors influencing students acceptance of m-learning: An investigation in higher education. The International Review of Research in Open and Distributed Learning, 14(5), 82–107. doi:10.19173/ irrodl.v14i5.1631 Alqahtani, M., & Mohammad, H. (2015). Mobile applications’ impact on student performance and satisfaction. The Turkish Online Journal of Educational Technology, 14(4), 102–112. Bandalaria, M. (2007). Impact of ICTs on open and distance learning in a developing country setting: The Philippine experience. International Review of Research in Open and Distance Learning, 8(1), 1–13. Retrieved from http://www.irrodl.org/index. php/irrodl/article/view/334/793 doi:10.19173/ irrodl.v8i1.334 Batson, L., & Feinberg, S. (2006). Game designs that enhance motivation and learning for teenagers. Electronic Journal for the Integration of Technology in Education, 5, 34–43. Billi, M., Burzagli, L., Catarci, T., Santucci, G., Bertini, E., Gabbanini, F., & Palchetti, E. (2010). A unified methodology for the evaluation of accessibility and usability of mobile applications. Universal Access in the Information Society, 9(4), 337–356. doi:10.1007/s10209-009-0180-1

Bringula, R. P., Alcid, A. S., Bandril, L. B. P., De Guzman, A. E., & Lopez, L. J. C. (2014). Development of game design guidelines. In A. Turnip (Ed.), 2014 2nd International Conference on Technology, Informatics, Management, Engineering & Environment (pp. 234-239). Danvers, MA: IEEE. doi:10.1109/TIME-E.2014.7011624

Bringula, R. P., & De Leon, A. N. (2014). The role of trust in web-based election system usability. In M. Indrawan-Santiago, M. Steinbauer, H.-Q. Nguyen, A. M. Tjoa, I. Khalil, & G. Anderst-Kotsis (Eds.), iiWAS 2014: The 16th International Conference on Information Integration and Web-based Applications & Services (pp. 196-199). Danvers, MA: ACM. doi:10.1145/2684200.2684317 Chittaro, L., & Sioni, R. (2015). Serious games for Serious games for emergency preparedness: Evaluation of an interactive vs. a non-interactive simulation of a terror attack. Computers in Human Behavior, 50, 508–519. doi:10.1016/j. chb.2015.03.074 Chow, A. F., Woodford, K. C., & Maes, J. (2011). Deal or no deal: Using games to improve student learning, retention and decision-making. International Journal of Mathematical Education in Science and Technology, 42(2), 259–264. doi:10 .1080/0020739X.2010.519796

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Colt, H. G., Davoudi, M., Murgu, S., & Rohani, N. Z. (2011). Measuring learning gain during a one-day introductory bronchoscopy course. Surgical Endoscopy, 25(1), 207–216. doi:10.1007/ s00464-010-1161-4 PMID:20585964

Kutluk, F. A., & Gülmez, M. (2014). Research about mobile learning perspectives of university students who have accounting lessons. Procedia: Social and Behavioral Sciences, 116, 291–297. doi:10.1016/j.sbspro.2014.01.210

Connolly, T. M., Boyle, E. A., MacArthur, E., Hainey, T., & Boyle, J. M. (2012). A systematic literature review of empirical evidence on computer games and serious games. Computers & Education, 59(2), 661–686. doi:10.1016/j. compedu.2012.03.004

Martin, F., & Ertzberger, J. (2013). Here and now mobile learning: An experimental study on the use of mobile technology. Computers & Education, 68, 76–85. doi:10.1016/j.compedu.2013.04.021

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340. doi:10.2307/249008 Derakhshan, N. (2009). Student and faculty perceptions of the features of mobile learning management systems in the context of higher education (Unpublished doctoral dissertation). Oklahoma State University. Hake, R. (1998). Interactive engagement versus traditional methods: A six-thousand student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66(1), 64–74. doi:10.1119/1.18809 Ibrahim, A., Vela, F. L. G., Rodriguez, P. P., Sanchez, J. L. G., & Zea, N. P. (2012). Playability guidelines for educational video games: A comprehensive and integrated literature review. International Journal of Game-Based Learning, 2(4), 18–40. doi:10.4018/ijgbl.2012100102 Jibiki, Y., Kure, S., Kuri, M., & Ono, Y. (2016). Analysis of early warning systems: The case of super-typhoon Haiyan. International Journal of Disaster Risk Reduction, 15, 24–28. doi:10.1016/j. ijdrr.2015.12.002 Kreutzer, C., Marks, M., & Bowers, C. (2015). A pedagogical approach to usability in serious games. In C. Stephanidis (Ed.), HCI International 2015 (pp. 43–48). Springer International Publishing; doi:10.1007/978-3-319-21380-4_8

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Muratet, M., Torguet, P., Jessel, J.-P., & Viallet, F. (2009). Towards a serious game to help students learn computer programming. International Journal of Computer Games Technology. doi:10.1155/2009/470590 Olsen, T., Procci, K., & Bowers, C. (2011). Serious games usability testing: How to ensure proper usability, playability, and effectiveness. Paper presented at First International Conference: DUXU 2011, Orlando, FL. doi:10.1007/978-3642-21708-1_70 Philippine Statistics Authority. (2014). Tropical cyclone frequency. Retrieved from http://philfsis. psa.gov.ph/index.php/id/15/matrix/J30FSTII Prather, E. E., Rudolph, A. L., & Brissenden, G. (2009). Teaching and learning astronomy in the 21st century. Physics Today, 62(10), 41–47. doi:10.1063/1.3248478 Rodrigo, M. M. T., Ong, A., Bringula, R. P., Basa, R. S., Dela Cruz, C., & Matsuda, N. (2013). Impact of prior knowledge and teaching strategies on learning by teaching. AIED Workshop on Simulated Learners: AIED 2013 Workshop Proceedings, 4, 71–84. Retrieved from http:// ceur-ws.org/Vol-1009/0408.pdf Salter, S., Pittaway, J., Swabey, K., Capstick, M., & Douglas, T. (2012). Using an online interactive game to enhance the learning outcomes for first year tertiary students. Creative Education, 3(06), 1–8. doi:10.4236/ce.2012.326114

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Sanchez, J., Mendoza, C., & Salinas, A. (2009). Mobile serious games for collaborative problem solving. In B.K. Wiederhold & G. Riva (Eds.), The Annual Review of Cybertherapy and Cybermedicine 2009 (vol. 144, pp. 193-197). Amsterdam: Studies in Health Technology and Informatics (SHTI), IOS Press. Sanchez, J., & Olivares, R. (2011). Problem solving and collaboration using mobile serious games. Computers & Education, 57(3), 1943–1952. doi:10.1016/j.compedu.2011.04.012 Sharples, M., Taylor, J., & Vavoula, G. (2007). A theory of learning for the mobile age. In R. Andrews & C. Haythornthwaite (Eds.), The Sage handbook of e-learning research (pp. 221–247). London: Sage Publications. doi:10.4135/9781848607859. n10 Shchiglik, C., Barnes, S. J., & Scornavacca, E. (2016). The development of an instrument to measure mobile game quality. Journal of Computer Information Systems, 56(2), 97–105. doi:10.108 0/08874417.2016.1117368

Tuzun, H., Yilmaz-Soylu, M., Karakus, T., Inal, Y., & Kizilkaya, G. (2009). The effects of computer games on primary school students achievement and motivation in geography learning. Computers & Education, 52(1), 68–77. doi:10.1016/j. compedu.2008.06.008 Warren, S., Jones, G., & Lin, L. (2011). Usability and play testing: The often missed assessment. In L. Annetta & S. C. Bronack (Eds.), Serious Educational Game Assessment: Practical Methods and Models for Educational Games, Simulations and Virtual Worlds (pp. 131–146). Rotterdam, The Netherlands: Sense Publishers. doi:10.1007/97894-6091-329-7_8

ADDITIONAL READING Attewell, J., & Savill-Smith, C. (2004). Learning with mobile devices: Research and development. London: Learning and Skills Development Agency.

Sindhuja, P. N., & Dastidar, S. G. (2009). Impact of the factors influencing website usability on user satisfaction. The IUP Journal of Management Research, 8(12), 54–66.

Grimus, M., & Ebner, M. (2015). Learning and teaching with mobile devices: An approach in higher secondary education in Ghana. International Journal of Mobile and Blended Learning, 7(2), 17–32. doi:10.4018/ijmbl.2015040102

Steif, P. S., & Dollár, A. (2009). Study of usage patterns and learning gains in a webbased interactive static course. The Journal of Engineering Education, 98(4), 321–333. doi:10.1002/j.2168-9830.2009.tb01030.x

Ha, K.-M. (2016). Disasters can happen to anybody: The case of Korea. Environmental Impact Assessment Review, 57, 1–9. doi:10.1016/j. eiar.2015.11.002

Tan, W.-H. G., Ooi, K.-B., Sim, J.-J., & Phusavat, K. (2012). Determinants of mobile learning adoption: An empirical analysis. Journal of Computer Information Systems, 52(3), 82–91. Thomas, S., Schott, G., & Kambouri, M. (2003). Designing for learning or designing for fun? Setting usability guidelines for mobile educational games. In J. Attewell & C. Savill-Smith (Eds.), Learning with mobile devices: Research and development (pp. 173–181). London: Learning and Skills Development Agency.

Hersh, M., & Leporini, B. (2013). An overview of accessibility and usability of educational games. In C. Gonzalez (Ed.), Student usability in educational software and games: Improving experiences (pp. 1–40). Hershey, PA: IGI Global; doi:10.4018/978-1-4666-1987-6.ch001 Hoehle, H., Aljafari, R., & Venkatesh, V. (2016). Leveraging Microsofts mobile usability guidelines: Conceptualizing and developing scales for mobile application usability. International Journal of Human-Computer Studies, 89, 35–53. doi:10.1016/j.ijhcs.2016.02.001 2475

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Schmitz, B., Klemke, R., Walhout, J., & Specht, M. (2015). Attuning a mobile simulation game for school children using a design-based research approach. Computers & Education, 81, 35–48. doi:10.1016/j.compedu.2014.09.001

KEY TERMS AND DEFINITIONS Aesthetics: A game attribute that relates to the overall visual design of a serious game. Content: A game design feature that involves completeness and reliability of information provided by the game.

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Disaster: An event that is caused by accidents or natural calamities that disrupts order and may result in damage of property or loss of life. Ease of Use: A game design feature that measures the effort of the user in playing a game. Mobile Game: A game that runs on any handheld portable device. Serious Game: A type of game that is both entertaining and educational. Usefulness: A game feature with a goal is to transfer knowledge or skills to its users.

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Category: Educational Technologies

Educational Technology and Intellectual Property Lesley S. J. Farmer California State University – Long Beach, USA

INTRODUCTION In today’s digital world, leaders and other educators can manipulate a wide variety of information for authentic projects. In the process, everyone needs to acknowledge the idea creators and their intellectual property. As technologies have expanded, and their production has become more sophisticated, the legal regulations surrounding their use have become more complex. With the advent of interactive social media and increased resource sharing, as well as growth in distance learning opportunities, complying with the legal use of information technology can be daunting. In any case, leaders and other educators should be aware of the more important aspects of technology-related copyright laws and regulations. This chapter provides an overview of copyright law and fair use for educational research purposes.

LEGAL BACKGROUND A central aspect of education is intellectual pursuit and the recognition of great minds. Yet teachers bemoan the rise in cheating, which technology facilitates. On their part, students have a more lax attitude about intellectual property. Particularly with media and crowdsourcing, which foster collaborate knowledge generation, identifying the originator of an idea can be difficult to ascertain. Furthermore, the informational content itself may be dynamic so that the authorship and copyright may change over time. Although intellectual property is sometimes used interchangeably with copyright, the former is

a broader concept. Copyright protects creative and original ideas that are recorded in tangible form. Other U. S. intellectual property laws deal with trademarks, patents, trade secretes, and licenses. Copyright laws seek solutions to give authors fair compensation for sharing their work. Begun as a way to give scientists and inventors lead time to prevent others from using their work without permission, copyright laws in the United States have become more far-ranging, both in terms of expanded formats as well as issues of authorship and access. According to current law, the copyright owner has the exclusive right, and can authorize others, to reproduce, distribute, display, publicly perform, and make derivative works based on the original work. The duration of the copyright term has lengthened over the years, starting from a length of 28 years (as established in 1790) to 70 years after the death of the author, according to the 1998 Act. Publishing has also impacted copyright over time. Reporters increasingly get personal credit and remuneration for their contributions, which historically were considered solely work for hire. Publishers create copyright agreements to cover authorship rights based on format. Multimedia copyright laws can be very specific: restricting resizing or other image manipulation, stipulating the length of music or video that can be copied legitimately. Fortunately, education falls under the umbrella of Fair Use, so restrictions are loosened up a bit in order to support personal research. The chief statute driving copyright law is the Copyright Act of 1976, which became effective in 1978. Several factors were included for the first time in this piece of legislation: a codification of

DOI: 10.4018/978-1-5225-2255-3.ch216 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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fair use, the right for an author to receive copyright for an unpublished work, and the divisibility of authors’ rights. The Act includes definitions, delineates what is copyrightable, and describes copyright rights and limitations. The Digital Millennium Copyright Act (DMCA) was added to the Act in 1998, largely to conform to international treaties (note that no international copyright law exists) that dealt with technological issues, particularly online material. DMCA limits database company liability, and addresses digital preservation. Educators and leaders also need to know about the 2002 Technology, Education, and Copyright Harmonization (TEACH) Act, which impacts copyright usage in distance education or in cases where digital information is transmitted as a supplement face-to-face instruction. Displays and performances can be disseminated only for the period of the course and only to those students who are enrolled in the course. Likewise, if teachers copy an article for a face-to-face class, then they can link to the same article online, depending on the magazine database license agreement. A better solution is for the teacher to provide the citation, and ask the students to access the article themselves from the library’s database collection. However, the teacher should not download the whole magazine issue just because it is technically possible; that action probably does not comply with copyright law. Other laws exist to support copyright law such as anti-piracy. For instance, the 1997 No Electronic Theft Act expands criminal prosecution of copyright infringements to individuals who do not benefit commercially. The Family and Entertainment Copyright Act of 2005, updated and absorbed the 1997 Act about theft, and also addressed artists’ rights. Particularly with the advent of social media in which students might produce and disseminate information publicly, copyright applications are stricter. A few examples of technology-relevant copyright practices follow.

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Images should not be resized, cropped, or changed in context without explicit permission. Photos of recognizable people require written permission if they are to be broadcast. Scanning or digitizing work is considered reproducing it, and requires prior permission if shared publicly. Other rights may be covered by patent law, such as copying code. Music and video downloads can be very problematic. It is wisest to use pre-approved sites such as iTunes. Computer program appearance, graphics and sound may be protected from copying, depending on the company’s licensing agreements. Slander and libel can occur on social networking sites such as MySpace and Facebook; students might not realize that they can be held legally responsible for their comments, and even arrested and prosecuted. Information about health and legal issues should include a disclaimer so the author is not held legally responsible in case the reader uses that information and experiences negative results. Each database aggregator and disseminator (such as video streaming) has a unique licensing agreement that covers copyright issues. A good rule of thumb is to apply the most conservative guidelines in order to avoid case-by-case decisions.

Further complicating matters, different countries have different copyright guidelines, so information accessed from around the world may be subject to conflicting laws. International aspects also impact copyright in several ways. At present, few copyright laws exist. How, then, are royalties handled when publishers are international or offer translations of works? They are done by international treaties or agreements, usually on a

Category: Educational Technologies

case-by-case basis. Right now, Disney licenses some merchandise abroad, which products are not allowed in the U.S. because of copyright restrictions; foreign entities can sell them to U.S. buyers, who cannot buy the items stateside. Such inequities need to be addressed. This situation is exacerbated in light of transnational social networking sites such as Facebook and Google, who are reluctant to comply with the most stringent regulations when other countries may have laxer laws. The International Copyright Act of 1891 was the first step in extending copyright to works of foreign nationals – of selected countries. The World Intellectual Property Organization (WIPO), established in 1967 under the auspices of UNESCO, serves as a global forum to address issues of intellectual property services, policy, information and cooperation. Currently, they are discussing the need for libraries to be able to provide all of their users with access to information, particularly to facilitate countries’ economic development (IFLA, 2014). Legal cases test the boundaries of existing copyright law. The U.S. Copyright Office’s Fair Use Index (http://www.copyright.gov/fair-use/) provides searchable summaries of major fair use decisions. For instance, Authors Guild, Inc. v. Google Inc., No. 13-4829-cv (2d Cir. Oct. 16, 2015) contested Google’s to digitally duplicate books from library collections. The court defended Google’s practice as covered under Fair Use because the process was transformative: the search function added value, and the materials were for libraries. Nor did the digitized clips jeopardize market value. Similarly, in Authors Guild, Inc. vs. HathiTrust, 755 F. 3d 87, 90-91 (2d Cir. 2014), the court upheld the practice of creating read-aloud versions of resources so they would be accessible for individuals with vision impairments; the decision was based on the Americans with Disabilities Act. On the other hand, when publishing houses sued Georgia State University for posting unlicensed works for students to access, even after using a fair use checklist;

the university was found at fault for not complying with licensing agreements as they impacted copyright (Cambridge University Press v. Mark P. Becker No. 1:08-cv-01425-ODE (N.D. Ga. March 21, 2016)). As seen above, copyright law changes over time as it tries to respond to changes in technology and societal behaviors. Every three years, the Library of Congress makes decisions about copyright waivers under DMCA; for instance, DVD snippets can be “ripped” for educational purposes, and electronic literary works may be “cracked” to enable read-aloud functionality for individuals with visual impairments (Hobbs, 2016). Other copyright issues, such as the appropriate copyright handling of orphaned works (where the author cannot be identified or contacted), remain. Crowdsourced materials also constitute a copyright headache, and one can only imagine how future formats and content interaction will impact and maybe redefine copyright. Derivative works gains another dimension in the copyright world as individuals transform existing recorded (thus copyrighted) materials (Ahmeti, 2015). The copyright holder may create derivative works such as adaptations, revisions, translations, extensive excerpts, and different formats of the same work; if others want to make a derivative work, they must ask for permission from the holder. However, if a person transforms the work, then copyright permission is not needed; transformation requires adding something new for a further or different purpose rather than a substitution of the original work; example include parodies, mash-ups, remixing. While sampling may be transformative, it should be noted that in the music world, digital sampling is very restrictive. When in doubt, users should act conservatively. On the other hand, these copyright issues can overwhelm educators, who then may be reluctant to incorporate digital sources, in particular, for fear of the “copyright police.” As a result, learners may have little access to valuable open education materials.

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FAIR USE Fortunately, educational institutions enjoy more freedom in the use of copyrighted materials because of the intent of that use: teaching and personal research without market compensation. These fair use exemptions are listed in Section 107 of 17 United States Code 106: 1. The purpose and character of the use, including whether such use is of commercial; 2. Nature or is for nonprofit educational purposes; 3. The nature of the copyrighted work; 4. Amount and substantiality of the portion used in relation to the copyrighted work as a whole; and 5. The effect of the use upon the potential market for or value of the copyrighted work. The expansion of digital resource has further complicated copyright laws and Fair Use. The ease and speed of digital duplication can tempt the most honest user. Especially when software “suites” facilitate repurposing of information (such as turning a PowerPoint into an outline word processed document), it can be difficult to explain how to use digital materials within copyright limits. How does format define information? How does multimedia incorporation change the nature and intellectual property of each information element? These issues can lead to valuable learning moments during which students can understand the nuanced nature of digital information. The National Council of Teachers of English has developed a code of best practices in fair use for media literacy education, which can serve as a useful set of guidelines (http://www.ncte.org/ positions/statements/fairusemedialiteracy). Certainly, students and teachers alike need to cite sources accurate to credit the original creators, but it is no substitute for asking permission. Basically, asking permission requires finding the author of the work, identifying the specific information to be used (full citation, including

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which section of the document), the purpose of the use, and the extent of use (e.g., how, where and frequency). Copyright clearinghouses (e.g., http://www.copyright.com) can also help users obtain permission. Fortunately, user-friendly guidelines about fair use are available. The American Library Association for Information Technology Policy has developed copyright educational tools that focus on copyright and fair use: http://www.ala. org/advocacy/pp/pub/copyright. The Copyright Advisory Office of Columbia University Libraries’ Information Services has developed a useful fair use checklist that can help the education community respect intellectual property while researching and generating ideas: https://copyright.columbia. edu/basics/fair-use/fair-use-checklist.html.

PROJECT CHECKLIST As multimedia and digital resources are incorporated into presentations, particularly as these products are broadcast online, the school community needs to follow more stringent copyright guidelines. The Washington State Library (2008) has developed a checklist of factors to consider when thinking about using and publishing information. Their first point is probably the most salient: ownership of physical and digital objects doesn’t guarantee use rights. •

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Who owns the material? Have you made a good faith effort to find the owner? Who controls access to the physical items? Who will control access to the digital items? Are there written agreements as to construction, ownership, and access to the digital collections? Determine copyright and permissions status; are the materials owned outright? Can the rights to publish the materials digitally be obtained? What percentage of the materials will need research or requests for permissions? What

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will be the added cost of devoting staff time to obtaining permissions? Is the material in the public domain or covered by copyright that your organization has legally obtained? Remember, any original student work cannot be digitized or published without a signed permission form from the student or their guardian if they are a minor. Address copyright, permission to copy on your own digital site. Post a statement to guide use of materials on your digital Web site. Provide contact information for users of your Web site.

THE CREATIVE COMMONS A growing interest in the creative “information commons” reflects a philosophy that “information yearns to be free” and that accessible information leads to expanded discourse, knowledge and progress. Public domain documents have exemplified this philosophy for over a century, and government publications carry on this tradition in the spirit of civic engagement. A welcome alternative is the Creative Commons (http://www.creativecommons.org), which enables people to upload and share data with the understanding that any use requires cited acknowledgment, and any changes to the data also need to be uploaded and cited within the Creative Commons. This proactive strategy recognizes the benefits of collaborative knowledge building. In 2005 the International Federation of Library Associations and Institutions (IFLA) called upon the World Summit on the Information Society Summit to promote a global Information Commons where all people could access and disseminate information without restrictions. Two main approaches to the creative commons have emerged: 1) contributing and modifying original information, and 2) facilitating the sharing of information.

The idea of a creative commons can be expanded across systems, disciplines, even national boundaries. Creative Commons provides an extensive directory of federated repositories (http:// wiki.creativecommons.org/Content_Directories). One of the advantages of federated collaborations is that each site typically controls copyright and licensing issues. Nevertheless, these consortia initiatives require careful, thorough planning, not only in terms of technical requirements but also in terms of fiscal and governance issues. A recent development related to the Creative Commons is the Open Textbook Initiative. In an effort to the make textbooks more affordable, the Higher Educational Opportunity Act of 2008 supported the development of open textbooks. Textbook publishers, such as Flat World Knowledge, offer free online access to textbooks under a Creative Commons license, and low-cost fees for printing copies. In addition, these textbooks are often repurposed so that instructors can essentially create customized texts for their courses. Faculty are more apt to access such materials through repositories than publishers, such as Cool4Ed, Curriki, and OpenLearn. While open textbooks certainly attracts students, some instructors are concerned that materials have not been adequately reviewed so might not be high quality. On their part, some authors are not so keen on this practice because they get little or no fiscal remuneration for the materials they create. Instead, they must be satisfied with possible increase in visibility and reputation, and derive satisfaction from contributing to the field in a timely manner. Similarly, “open source” is a growing approach to software development, which impacts intellectual property rights, mainly in the areas of patents and licensing. The term emerged with Netscape’s code, which was openly released in 2006 for others to use freely. At this point, the largest repository of open source code, SourceForge, has over than three million registered users. Open source code has been the basis for many social networking applications such as wikis and blogs.

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TECHNOLOGY-BASED INFORMATION PACKAGES Individuals and institutions routinely “package” information together as a way to synthesize the best resources for their clientele. For instance, course “readers” have been a mainstay for years. Copyright permission has always been a part of that production process. Several general copyright issues loom when dealing with technology aspects of packaged information. As libraries and other educational entities jump on the curation band wagon, especially in the digital realm, they need to be aware of copyright issues. •

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Content: Does the entity have permission to copy, download, digitize, modify, excerpt, or package information? What is the copyright for each document? What limitations exist on such practices: e.g., length of time to keep the copy, number of copies (sometimes within a specified timeframe), extent of copying (such as percentage of the entire worm), access rights, purpose limitations (such as referral vs. personal research vs. resale), format. Format: Packaging information, such as software installation instructions, for internal use, is a much different issue than scanning published cartoons for a public website. Even if the entity gets permission to copy an image, that permission might not extend to resizing or cropping the image. Copyright owners might not permit content to be reformatted or repurposed with reason, since changing context can result in different interpretations of the information. Likewise, pinning up candid student pictures in the faculty mail room requires a different level of permission than broadcasting those same pictures on the Internet, particularly if any under-age students are shown. Indeed, because of stalking and other criminal behaviors, few institutions show captioned pictures of their staff, particularly in online environments.

•

Liability: Entities need to read licensing agreements carefully to make sure that their packaging efforts comply with the legal language. Users need equitable access, proper authorization, and confidentiality; moreover, no profits can be incurred (Farb & Riggio, 2004). Once entities start to extract and synthesize information, even with permission, they stand to risk being sued. For instance, health information might be followed by some user with unpleasant results. Especially in the areas of law and health, entities need to make sure that they post a disclaimer that they are not legal or health professionals (unless so licensed). Even the software used to package information is likely to entail legal right for its use, particularly if the product is web-based and disseminated externally. The North Carolina State Library Web Portal Collection Development Policy addresses a number of these issues (http://www.digitalnc.org/about/policies/ digitization-guidelines/).

In addition, specific types of information packages have unique copyright implications, as detailed here.

Courseware Most campuses now use learning management systems or courseware as a means to access course materials or facilitate communication. Faculty may spend hundreds of hours developing and implementing their course materials: creating their own content, selecting and linking other existing resources, sequencing content, facilitating student interaction, and providing feedback. Who owns the content? Generally, their affiliated institution owns the course and its content as part of the faculty contract; it is considered work for hire. However, each institution or educational system determines intellectual property and accompanying copyright agreements. Can the faculty make money inde-

Category: Educational Technologies

pendently from that course material? Usually only if they transform the existing work. Adding an Amazon link to the instructor’s campus website so students can buy an instructor’s publications can be asking for trouble. Can another faculty member copy the course entirely, and teach with it? They need permission from the copyright holder, which is probably the campus administration; faculty should ask about the appropriate protocol ahead of time – and administration should inform all faculty about course intellectual property rights from the start. The American Association of University Professors Copyright, Distance Ed & Intellectual Property does a good job of explaining implications of technology for teaching and learning, and provides sources to help faculty protect their intellectual property (https://www.aaup.org/issues/ copyright-distance-ed-intellectual-property). What about copyright and fair use of MOOCs (Massive Open Online Courseware)? Institutions anticipate unlimited participation and open access; however, they are increasingly likely to include carefully worded licensing agreements even though they provide free access to students. In other words, one may look but not copy; fair use might not apply to non-MOOC educators. To be safe, instructors should simply link to the original material or online course rather than download or copy it (Hui-Wen & Chao-Chen, 2012).

Repositories Increasingly, institutions and organizations are developing digital repositories, either storing data or storing the documents themselves. In both cases, these services manage and disseminate digital resources. The scope of repositories can range from a single program to international consortia. The value of repositories lies in the quality of use of their content, so identifying desired kinds of documents and collecting high-quality materials are key functions. Of special interest now are repositories of data sets; U.S. federal agencies now require a data management plan as part of their grant proposals, so most academic libraries

either establish an in-house repository or join a data management consortium. Major technology management issues of a repository follow: • •

• •

Maintaining the data without damage or alteration: storage and security requirements. Providing “physical” access to the data, including extracting of it from the archive: authentification, verification, identification, metadata harvesting software. Ensuring that the user can understand and interpret the data: display/rendering software and preservation planning. Ensuring long-term stability: technology planning and data maintenance (Wheatley, 2004).

In structuring repositories, entities need to consider three layers: storage, database, and application, each of which may have copyright implications. The Academic and Research Libraries of the American Library Association developed an institutional repository SPEC toolkit to help librarians with policies and procedures (http:// www.arl.org/storage/documents/publications/ repository-services-report-jan09.pdf). One promising practice in digitizing materials is systemwide technology initiatives, usually in the form of federated repositories of archival materials. DSpace (http://www.DSpace.org) is an Open Archive Initiative that provides guidelines for digitizing, cataloging, storing, and disseminating unique digital sources. This open source solution emphasizes the need for technical expertise and planning. Nevertheless, the content within the repository still needs copyright oversight. Duranceau and Kriegsman provide guidance on open access policies for institutional repositories (http://www.ala.org/alcts/sites/ala.org.alcts/files/ content/resources/papers/ir_ch05_.pdf). Knowledge management (KM) consists of managing the knowledge of an organization, usually collecting, storing, organizing, and disseminating explicit information such as documents with the intent that others will use that

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information: an internal repository. Particularly in today’s society where in-house knowledge may be proprietary and employees may leave after a short time, making tacit knowledge explicit through the use of a knowledge management system offers an effective way to coalesce expertise. Because they are internal, such KM repositories normally do not require copyright permission, although institutions should set up policies and procedures that delineate levels of access and authorization protocols. As some of the documents may be permitted to be access externally, dealing with copyright permissions from the start is a good practice.

E-Publishing For decades, in order to inform their constituents, institutions and organizations have created documents for their own members and for the public. These might be as simple as a guide to the library or as sophisticated as a peer-reviewed journal. Be it an original document or a compilation, copyright law must be observed. For example, Stanford University Library’s HighWire Press publishes almost a thousand ejournals. The press prides itself in its efforts to incorporate multimedia, hyperlinks, interactivity, and powerful search engines. Although they publish some free online articles, the press hosts digital content on behalf of many publishers, each of which has specific terms of use. HighWire’s notice states: “Unless explicitly stated otherwise, content on HighWire’s publishing platform, including content accessible without charge, cannot be copied, re-purposed, displayed on other websites, reprinted, redistributed, entered into a database, modified, used to create derivative works or otherwise re-used without the specific permission of its publisher” (http://highwire. stanford.edu/about/terms-of-use.dtl). In a few cases, one of the impetuses is financial gain, but for the majority of e-publishers the goal is dissemination of information on topics of interest to their clientele. To this end, open access journals have become quite attractive: they are

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relatively inexpensive to create (except for labor costs of editing and layout) and disseminate, can include digital features not available in print format such as sound, and may be more timely to publish than print versions. The Scholarly Publishing and Academic Resources Coalition (SPARC) (http:// www.arl.org/sparc), founded in 1998, exemplifies a collaborative library approach to disseminating research. Internationally, the Budapest Open Access Initiative (2002) asserts that Removing access barriers to this literature will accelerate research, enrich education, share the learning of the rich with the poor and the poor with the rich, make this literature as useful as it can be, and lay the foundation for uniting humanity in a common intellectual conversation and quest for knowledge. On the other hand, copyright issues connected with maintaining a permanent collection of the e-publications may be overlooked. Furthermore, if indexing services do not receive a copy of the journal, then access to the information will become even more limited. The Association of College and Research Libraries provides a scholarly communication toolkit, which specifically supports a more open system of digital scholarship (http://acrl.ala. org/scholcomm/?page_id=42). Charles W. Bailey, Jr. maintains the most extensive overview about digital scholarship publications (http://www. digital-scholarship.org/about/overview.htm).

Linking Sources The “off-the-street” online user would like a single-stop searching tool that would link all relevant material: primary sources, secondary sources, print, web-based, audiovisual. Increasingly, libraries are using “discovery” tools that approximate this process; a first search typically results in a list of articles and a list of books and media, with a number of additional criteria (e.g., format, date, language, full-text, etc.) to filter results. The new cataloging standards are built on

Category: Educational Technologies

the Functional Requirements for Bibliographic Records (FRBR) conceptual model, which facilitates linking individual data elements of a source with other sources (Howarth, 2012). The Open Linked Data Project further extends this idea through the semantic web. While an exhaustive one-stop tool is probably not feasible in the near future (despite Google’s intention), smaller-scale reference linking has become an attractive way to add value to information, and facilitate information services. There are several ways to link sources: • • • •

Between citations (e.g., databases) Within sources (e.g., hyperlinks) Between sources and applications (e.g., course management systems) Between sources and services (e.g., e-reserves)

Usually, metadata provides the basis for these actions. Digital Object Identifiers (DOI) provide an international standards-based “system for persistent and actionable identification and interoperable exchange of managed information on digital networks” (http://www.doi.org). Links may be categorized as either static (created as a permanent link) or dynamic (created in response to user action). Normally, information packagers pursue dynamic solutions where linking can occur without full control of the resources. This approach is attractive in theory, but may threaten vendors who are less comfortable about open access. Furthermore, as entities create documents that include links, be it at the citation or source level, they need to consider how the source information is captured and authenticated, processing between links, and hosting services. All entities and related protocols need to be interoperable as well as legally compliant (Van de Sompel and Hochstenbach, 1999). Certainly, links within the same website are permissible, but sometimes links to a different website may require permission from the owner of that site; the law is not clear about that issue.

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Social media signals a new level of complexity in copyright: the interactive Internet. It fosters collaborative work, which results in collective intelligence. Determining the author and ways to recompense the use of social media works presents a real challenge legally. Furthermore, these documents are often dynamic in nature so that the content may change moment to moment. What, indeed, is the basis of copyright? At best, it would need to be based on a time-stamped version that acknowledges both the time of the creation as well as the time that someone accessed it to use it. Some people assume that all document on the Internet are free, which assumption is clearly wrong, as discussed above. In some cases, the document creators function on a creative commons agreement, or publish a statement of use. Wikipedia serves as a good example of a community-built wiki that addresses copyright issues. Wikipedia is a good example. It has what it calls a “copyleft” agreement, which states: “Wikipedia content can be copied, modified, and redistributed so long as the new version grants the same freedoms to others and acknowledges the authors of the Wikipedia article used” (http://en.wikipedia.org/wiki/ Wikipedia:Copyrights). Most of Wikipedia’s articles are co-licensed under the Creative Commons Attribution-Sharealike 3.0 Unported License and the GNU Free Documentation License. Wikipedia states that each image notes its legal usage basis. Blogs are usually the work of a person or group, and are in a recorded form, so they follow normal copyright laws by default. That argument would then also apply to comments that followers make. Of course, the blogger can provide further guidance, be it a statement about being in public domain or a explicit clause that bars anyone from reproducing any of the material in any format. Podcasts and videocasts consist of compressed audio and video files that are then broadcast by the creator or an aggregator such as iTunes. Again, these are recorded documents so are assumed to

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fall within copyright laws. ‘Casts can be easily downloaded and shared, so it is wise to contact the originator or from a reputable Internet site. Difficulties tend to arise when someone uses copyrighted sources (particularly commercial music and videos) to create a mash-up or other kind of derivative work. It can be as simple as videotaping a toddler dancing to a Bruce Springsteen song, and then uploading it to YouTube. Unless the videotaper got written permission from the publisher (or who else owned the copyright) to use that song, the broadcasting that that video constitutes illegal usage if for no other reason than that the song writer and producer should be paid royalties every time the video with the music is played. If the videotaper wants to avoid legal action, he or she should either get permission or not upload or broadcast the video; instead, that video can be recorded for home use, and friends can come over to see the bouncing baby bopper. The Podcasting legal guide (http://wiki.creativecommons.org/Podcasting_Legal_Guide) by Vogel, Marlick and the Berman Center provides current in-depth information on legal uses of these media. Social bookmarking sites, such as Delicious and LibraryThing, provide a means to store and describe (“tag”) URLs for later retrieval and use. Similar to a bibliography, a list of lists falls under copyright law. Therefore, the creator of each list has the right to determine who can use it and the conditions of its use. To some degree, that can be controlled through the settings that the originator chooses. Usually, each overarching social bookmarking site has a policy about the use of the site as a whole and the use of individual bookmark collections. Increasingly, social media websites such as StumbleUpon integrate social bookmarking, web searching, and blogging. RSS means “really simple syndication,” and an RSS feed is a means to gather information from other websites to a central location. Every single website that offers an RSS service has its own copyright policy about legal use. Of course, if the originating news is in the public domain and is copied from an open source or creative commons/

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copyleft site, then that information can be used freely. One can even broadcast that information via his or her own RSS feed; however, it is usually safer to keep one’s personal RSS feeder private and avoid copyright problems altogether. In terms of copyright law, “really simple” is not really simple. File sharing is a central feature of social networking, as evidenced by the high use of Flickr and YouTube in particular. Without explicit permission, only the owner of the files can reproduce with work, create derivative works, distribute copies of the work to the public, display or perform the work publicly (including by digital transmission). Therefore, such files cannot be integrated into a school DVD for sale or placed on an institutional website or played at a public meeting. In short, one should read each file-sharing site’s copyright guidelines before downloading documents (Talab & Butler, 2007). Fortunately, some social networking sites include as a searching filter Creative Commons/public domain so that the user can appropriate the file in good conscience. Intellectual property issues become even stickier when one joins social network sites (SNS) (Wauters, Lievens & Valcke, 2014). Often one has to register to gain access to the network’s files, which translates into providing personal information as the cost for such membership. These websites usually have terms of use contracts, but users tend not to assess the implications and consequences of sharing their own information, such as selling personal information to SNS advertisers. Users should be aware of these practices, and, at the least, check for privacy setting options. At present, SNSs offer little protection to users, and government regulators do not stringently monitor or enforce intellectual property rights. Caveat emptor. Even more fundamentally, crowdsourcing challenges the current notions of intellectual property and copyright (Almeti, 2015). If an editor solicits content, monitors and reviews each input, explains the terms of contributions clearly from the start, and produces a “fixed” work, then copyright is usually not a problem. When the work is created

Category: Educational Technologies

on a SNS, with each person able to add and edit each other, with no end date and no recorded agreement, then copyright is likely to default to the site’s creator and administrator, but the situation can be messy, especially if some kind of commercial or economic benefit is incurred. A Creative Commons license stated at the start of the enterprise is probably the best solution. Technology that can support micro-royalty payments is improving, but the logistics are still daunting. As mentioned before, clear and legally “tight” contracts stipulated from the beginning, and signed before anyone contributes, remain the best solution for the time being.

ADMINISTRATIVE ISSUES Because everyone has to comply with copyright law, it behooves everyone to be knowledgeable about it, but it can be difficult to keep current. In the final analysis, administrators are responsible for such compliance. Understanding copyright, particularly as it applies to technology, enables administrators to address the risks of technology as they optimize its benefits. The Consortium for Educational Technology for University Systems (http://www.cetus.org/intellectual-property/) suggests ways that administrators can oversee intellectual property. 1. Foster intellectual creation, its dissemination, and its use. 2. Develop, implement and enforce technology policies and procedures that optimally support the mutual rights of the academic community. 3. Facilitate the legal and ethical access, storage, and attribution of intellectual works by the entire academic community in coordination with information providers. 4. Review and revise intellectual property policies and procedures in light of technology and cultural changes that impact the academic community.

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As digital resources grow in number and percentage in institutions, the function of digital asset management has grown in complexity. How do institutions keep track of all their electronic resources as well as make sure that they are effectively stored and retrieved? Especially in today’s networked world, digital asset management has to address interactivity options and social networking features, such as push technology, RRS feeds, comment/messaging options, incorporation of faculty repositories, enterprise mash-ups, and user-customizable folksonomy “shells.” While some of these features are low-cost plug-ins, their management and incorporation into the library’s digital collection system can involve sophisticated technical support, which is usually not inexpensive. Furthermore, such customizations need to be well documented and maintained. Within that scope, the field of digital rights management (DRM) has become increasingly important. Broadly speaking, DRM deals with the exact rights that each digital content has: who holds those rights and under which circumstances, who has authority to access that content and how that can be insured as well as preventing non-authorized people access (Calhoun, 2005). Complying with intellectual property regulations is very difficult; so digital rights management technologies are being employed to control content use. While automated systems conveniently take care of authentification issues and facilitate fair royalties compensation, they can also jeopardize privacy rights and leak into discriminatory profiling practices. E-resources sometimes are not device-neutral, so rights are sometimes given for just one operating system, which belies the concept of intellectual property. Installation of DRM-protected content can be burdensome, and may malfunction, preventing authorized access. At the other end of the process, when the access key to DRM-protected content is lost or the device becomes obsolete, the content itself becomes unreadable, even if it has been paid for

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legally. DRM can also pose problems for users with special needs. Furthermore, these DRM digital tools are not standardized. In short, DRM technology may be unavoidable when purchasing some digital resources, but needs to be carefully used (Houghton-Jan, 2007). Even without considering DRM technology, digital rights management can be complicated. Libraries are usually the body that deals with commercial digital resources, and increasingly deal with institutional digital documents as part of knowledge management initiatives. Within that scope, subscription databases (including e-book aggregators) constitute the major copyright effort because each vendor has a unique set of license agreements. Farb and Riggio (2004) list basic elements of most contracts: scope, completeness of content, duration, warranties, indemnification, access, confidentiality, sharing, archiving, disability compliance, and usage statistics. Increasingly, libraries are considering “leasing with an option to buy” licenses as a way to insure access through backfile ownership; licensing a database with no copyright to the content beyond the date of the license is not attractive.

To help the educational community remain in compliance, librarians sometimes provide training about copyright because of their role as information managers. Institutions should also maintain a reviewed bibliography of copyright resources that can be consulted easily. Some good websites that explain copyright follow.

Training

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Faculty need to become aware of intellectual property issues, and their impact on education. However, with awareness sometimes comes fear and over-caution about integrating technology into their instruction. The wiser choice is to empower faculty to safely and successfully navigate copyright law waters. Trainers can share examples of copyright compliance, and have faculty discuss copyright case studies and scenarios such as downloading streaming video or examining licensing fees (Russell, 2010). Other helps include permission forms and checklists of good practices, such as: citing sources thoroughly, linking to sources rather than posting the full text, asking permission to copy sources, checking database terms of use, and using Creative Commons and public domain sources (Disclafani & Hall, 2012; Shin, 2015).

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U.S. Copyright Office (http://www.copyright.gov): Provides copyright forms and regulations, and a searchable database of copyright registrations. American Library Association Copyright (http://www.ala.org/advocacy/ copyright): Good information and links about legislation, intellectual property, and international copyright activities. Columbia University Libraries Information Services Copyright Advisory Office (http://copyright.columbia.edu): Well-respected site targeted to librarians and educators. Consortium for Educational Technology for University Systems (http://www.cetus.org/): University coalition of resources on intellectual property University of Texas (http://www.utsystem.edu/offices/general-counsel/intellectual-property): Many links on copyright issues; they have a good crash course tutorial at http://www.lib.utsystem.edu/ copyright/. Stanford University Libraries Copyright & Fair Use (http://fairuse.stanford.edu): Focuses on fair use, the public domain, and the permissions process. Copyright for Educators (http://www. koce.org/classroom/copyright.htm): Public television site designed to help educators learn about fair use. Intellectual Property Legal Center (http://www.cetus.org/fairindex.html): Provides information on several aspects of intellectual property, with a separate section on ebooks.

Category: Educational Technologies

•

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Cybercrime (http://www.cybercrime. gov): Computer crime and intellectual property section of the Criminal Division of the U. S. Department of Justice. Software & Information Industry Association (http://www.siia.net/Divisions/ IP-Protection-Services): Their Intellectual Property Division campaign tries to balance enforcement with education.

FUTURE TRENDS Copyright law tends to react to new technologies and practices, so it is unlikely that it will pro-actively change. Nevertheless, technology will continue to change and expand, so the law will need to respond to, and accommodate, such changes. In addition, technology has also impacted the interchange of information, from the local to the international level, which will affect copyright law directly and indirectly. Both technology as a whole and the phenomenon of resource sharing is already impacting the publishing model as it develops new models of production and diffusion. Not only do commercial publishers need to address digital formats and ereaders with uneven standards and proprietary operating systems, but they have to deal with unique collations of content, non-commercial competitors, all of which impact the various copyright options. Particularly as product buyers and licensees share these documents, such as libraries and organizations, determining what is a just royalty can be problematic. Micro-payments for access and downloading or printing are being incorporated, but the models for fair rights still need to be hammered out. Interactive technology corners the market in terms of complex copyright issues. As noted

above, determining the recordable version of a document when multiple users can edit it almost simultaneously can be a copyright nightmare. As mentioned above, documents and repositories are increasingly the products of collective intelligence. In the face of such dynamics, it seems almost laughable to consider that current copyright law allows for proprietary rights for 70 years after the author’s death in some cases. The core concept of a work also needs further scrutiny as works are manifested in so many different formats and appearances. Right now, an e-book might be licensed to be readable on a single device; is that fair if the content itself exists separately from that device? How different does the document have to be in order to be considered a separate work? As the content, container, and metadata elements are considered separately, will copyright elements follow? Especially as web 3.0 emphasizes the relations among documents, those relationship themselves might fall under copyright just as bibliographies and links can. As this practice expands in global environments, copyright law may need to become more regulated on the international scale so that authors can be fairly compensated. Theoretically, virtual worlds are associated with the country in which the software is based, but such boundaries may also come into question as documents are created in those virtual realities by people in other countries. This issue of global access also brings up the issue of universal access – for individuals with special needs as well as those with language and linguistic differences. For example, in 2013 the WIPO Marrakesh Treaty required countries to provide technological protection measures to control access to, and use of, digital copyrighted works by people who are print disabled. To what extent is a simpler version or a translation of a document considered a derivative work, especially if it is the result of an automated application? To what extent is automatic captioning covered by copyright? To what extent can fair use be applied?

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Particularly as digital programs automate such variations, copyright law will need to be revisited once again.

At the least, leaders and other educators need to keep abreast of copyright issues, and try to comply with them within the context of their use.

CONCLUSION

REFERENCES

How does the spirit of the U.S. Constitution “to promote the progress of science and useful arts, by securing for limited times to authors and inventors the exclusive right to their respective writings and discoveries (Article 1, Section 1, Clause 8) manifest itself in the 21st century? The countries forefathers could not have imagined the Internet and the semantic web. Nevertheless, educators and other researchers have the strength of fair use behind them so they can promote an effective learning environment, be it face-to-face or virtual. In addition, collaborative practices such as the Creative Commons promote intellectual sharing in order to advance their fields. Technology has significantly expanded the intellectual arena in terms of production and sharing of information. In response, copyright law has tried to codify intellectual property rights to balance the rights of the creator and appropriate access to information. Such laws are often format-specific, reflecting both the nature of the medium as well as the perspectives of their producers; however, the content and format are splitting apart so that each digital element may be considered separately. Dissemination models have also changed drastically, so how the information buyer uses that information constitutes another can of copyright worms. The international dimension that technology facilitates adds another layer of copyright issues. Furthermore the dynamic nature of socially constructed information insures that copyright law will always lag behind technology and social behavior. Underlying principles must be the mainstay since practices will continue to vary according to changing contexts.

Ahmeti, E. (2015). Creation of copyright derivative works through digital services. Proceedings of the Multidisciplinary Academic Conference, 1-7.

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Budapest Open Access Initiative. (2002). Retrieved October 30, 2010, from http://www.soros. org/openaccess/read.shtml Calhoun, T. (2005, March). DRM: The challenge of the decade. Campus Technology, 18-20. Disclafani, C. B., & Hall, R. (2012). Stop saying no: Start empowering copyright role models. Journal of Library & Information Services in Distance Learning, 6(3-4), 251–264. doi:10.108 0/1533290X.2012.705151 Farb, S., & Riggio, A. (2004). Medium or message? A new look at standards, structures, and schemata for managing electronic resources. Library Hi Tech, 22(2), 144–152. doi:10.1108/07378830410524576 Hobbs, R. (2016). Lessons in copyright activism: K-12 education and the DMCA 1201 exemption rulemaking process. International Journal of Information and Communication Technology Education, 12(1), 50–63. doi:10.4018/IJICTE.2016010105 Houghton-Jan, S. (2007). Imagine no restrictions. School Library Journal, (June): 53–54. Howarth, L. (2012). FRBR and linked data: Connecting FRBR and linked data. Cataloging & Classification Quarterly, 50(5-7), 763–776. doi: 10.1080/01639374.2012.680835

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Hui-Wen, H., & Chao-Chen, C. (2011). Research on copyright issues of opencourseware materials. Journal of Educational Media & Library Sciences, 48(3), 401–405. International Federation of Library Associations and Institutions. (2014). Lyon Declaration. The Hague, Netherlands: International Federation of Library Associations and Institutions. Russell, C. (2010). The best of copyright and VideoLib. Library Trends, 58(3), 349–357. doi:10.1353/lib.0.0095 Shin, S. (2015). Teaching critical, ethical and safe use of ICT in pre-service teacher education. Language Learning & Technology, 19(1), 181–197. Talab, R., & Butler, R. (2007). Shared electronic spaces in the classroom. TechTrends, 51(1), 12–14. doi:10.1007/s11528-007-0004-1 Van de Sompel, H., & Hochstenbach, P. (1999). Reference linking in a hybrid library environment. D-Lib Magazine, 5(4). doi:10.1045/april99van_de_sompel-pt1 Wauters, E., Lievens, E., & Valcke, P. (2014). Towards a better protection of social media users: A legal perspective on the terms of use of social networking sites. International Journal of Law & Information Technology, 22(3), 254–294. doi:10.1093/ijlit/eau002

KEY TERMS AND DEFINITIONS Copyright: Laws that regular the use of the work of a creator. Crowdsourcing: Practice of enlisting the help of many people in order to generate. content, particularly in online environments. Creative Commons: A nonprofit organization that seeks to make creative work available for others to build on while respective intellectual property rights. Digital Rights Management: Access control technologies used to product the copyright of electronic media. Fair Use: The legal conditions under which one can use copyrighted material without obtaining permission and paying royalties. Intellectual Property: Any intangible asset that consists of human knowledge and ideas; the ownership of ideas. License: Permission granted by authority to exercise certain rights and privileges that would otherwise constitute an illegal act. Open Access: Free availability on the public Internet. Open Source: Coding that is freely available to the public, usually applied to software development. Social Media: Interactive web; enables people to collaborate and share online.

Wheatley, P. (2004). Institutional repositories within the context of digital preservation. York, UK: Digital Preservation Coalition.

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Category: Educational Technologies

Employing Educational Robotics for the Development of ProblemBased Learning Skills Nikleia Eteokleous Frederick University Cyprus, Cyprus

INTRODUCTION The technological improvements within the robotics field and its expansion to various fields such as medicine, industry and education, calls for robotics integration within the educational practice as learning tools. Robotics in the classroom has taken a global momentum especially because of its positive contributions in the teaching of science, technology, engineering and mathematics (STEM) (Benitti, 2012). Additionally, research has shown that robotics integration in education promotes the development of various non-cognitive skills, however extremely important life skills. For example, reasoning, problem solving, tinkerning, sequencing, computational thinking, decision making, scientific investigation, collaboration, knowledge construction, critical thinking, creativity, communication (Bers, Ponte, Juelich, Viera & Schenker,, 2002; Benitti, 2012; Chambers & Carbonaro, 2003; Eteokleous, 2016; Miglino, Lund, & Cardaci, 1999; Resnick, Berg, & Eisenberg, 2000; Williams, Ma, & Prejean, 2010). Educational systems are responsible in preparing students (future citizens) for this everchanging Hi-Tech, globalized, interconnected world. Numerous 21st century skills are reported in the literature as important to be developed by future citizens as the means to address the needs and demands of the society. The 21st century skills have been outlined and described by various researchers and reports (e.g. Ananiadou & Claro, 2009; Bybee & Fuchs, 2006; Griffin & Care, 2105; Mojika, 2010; Rotherham & Willingham, 2010; Trilling & Fadel, 2009), and can be summarized

as follows: communication, collaboration, critical thinking, problem solving, knowledge construction, creativity – innovation, self-directed learning, global citizenship and digital literacy. The changes in the global competition and collaboration, the focus on service economy, as well as the information growth, constitute the development of the 21st century skills extremely important. Given the aforementioned, the workforce needs have changed, the job tasks and type of work are changing and consequently the required skills are changing. Problem solving and digital literacy is one of them and robotics and programing are becoming important elements within the educational settings. The students need to be provided with the opportunities to experience tinkering, fabrication, design and create technological artifact & interactive objects, construct their own meaningful projects, experience the scientific method of inquiry (Bers, 2008a; Bers, 2008b; Bers, Matas & Libman, 2013; Bernstein, Mutch-Jones, Cassidy, Hamner, & Cross, 2016; Eteokleous, 2016). Consequently, educators need to design the appropriate learning environments where students have the opportunity to develop the aforementioned skills.

Main Aim Robotics activities are related to addressing a problem, and usually problems in authentic, real situations. The students are given a driving question and are requested to solve a “problem”. Having noticed this connection in relation to the pressing need to develop 21st century skills, the

DOI: 10.4018/978-1-5225-2255-3.ch217 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Educational Technologies

current study evaluates the integration of robotics as an educational tool within the teaching and learning process where the problem based learning (PBL) method and the interdisciplinary approach are intertwined. Specifically, robots are used as cognitive-learning tools in order to apply the problem based learning method in early elementary grades (2nd and 3rd graders) in curricularintegrated activities (interdisciplinarity). More importantly, the study aims to examine whether the integration of robotics as cognitive-learning tools influence the development of the following PBL skills: creativity - innovation, critical thinking, and collaboration.

BACKGROUND Educational Robotics The idea of robotics integration in education has been around for more than 20 years (Miglino, Lund, & Cardaci, 1999; Papert, 1980). However, the great revolution in the field of educational robotics has been achieved throughout the last decade, where robotics escaped the laboratory and made efforts to connect to education (Chambers, & Carbonaro, 2003). The robotics materials (building blogs/ bricks, sensors and motors) are perceived as toys by the children and research revealed that regardless of age, educational background and interests, students consider working with robots to be “fun” and “interesting”. (Chambers & Carbonaro, 2003; Williams and Prejean, 2010). Numerous research studies suggest that robotics integration for educational purposes is an effective teaching method; arguing that if robotics activities are appropriately designed and implemented have great potential to significantly improve and enhance the teaching and learning process (Benitti, 2012; Bauerle, & Gallagher, 2003; Bers et al., 2002; Eteokleous, Demetriou, & Stylianou, 2013; Papert, 1993). Research has shown that robotics integration in education promotes the development of student higher-order thinking skills such as application,

synthesis, evaluation, problem solving, decision making, and scientific investigation (Bers et al. 2002; Chambers & Carbonaro, 2003; Resnick, Mojica, 2010; Berg, & Eisenberg, 2000). In order to achieve the above, robotics need to be integrated as tools and not as subject matters in the educational practice. When robotics is integrated as a subject matter, as an autonomous entity, and not within a well-designed lesson plan, there is limited educational potential and value. On the other hand, robotics integration as a learning tool, in selected teaching cases exploits its full potential; therefore it upgrades and enhances the teaching and learning process and promotes school transformation (Eteokleous, et al., 2013). The intention of this approach is not to learn how to use the robotics package, and its programming software, but to use it as a tool within a specific educational context to achieve learning objectives. In other words, robotics is employed as a tool to teach and deliver concepts within various subject matters such as Mathematics (Whitehead, 2010), Engineering (Craig, 2014), Science (Vollstedt, 2005), Physics, and even in non-technology related fields such as Biology, Psychology (Bers, Ponte, Juelich, Viera & Schenker, 2002; Eguchi, 2007, Eteokleous, et al., 2013; Craig, 2014). Robotics integration in the teaching and learning practice is defined as the use of robotics by students as a tool that enhances their learning experience and supports the achievement of specific learning goals (Ward, et al., 2012; Eteokleous, et al., 2013).

Problem-Based Learning (PBL) and Robotics Problem-based learning is an instructional method characterized by the use of “authentic” problem sets, as contexts for students to develop critical thinking and problem solving skills, and acquire the necessary course concepts. Along the same lines, problem based learning is defined as “a systematic teaching method that engages students in learning knowledge and skills through an extended inquiry process structured around complex, au-

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thentic questions and carefully designed products and tasks” (Markham, 2003,p. 4). Additionally, PBL is defined as the approach that challenges students to learn through engagement in a real problem or situation (Domin, 1999; Duch, 1995; Grant, 2009). Students are presented with realworld, multidisciplinary problems that demand critical thinking, engagement, and collaboration. Given the aforementioned, it is a challenging and demanding process, requesting students to use various types of thinking in order to realize, capture and solve the problem given. Usually robotics activities are related to addressing a problem, and sometimes problems in authentic, real situations. The students are given a driving question and are requested to program the robots in order to perform a number of activities, aiming to solve a “problem” and/ or achieve a goal (Gibbon, 2007; Varnado, 2005). There are also studies showing improvements in problem solving skills as well as self-regulation, recognition, orientation skills (Baker, Nugent, Grandgenett, & Adamchuk, 2012; Karim, Lemaignan, S., Mondada 2015). Having noticed this connection, the current study aims to bring together the PBL approach and robotics integration in the educational settings, in order to examine the role of robotics in promoting the development of specific problembased skills (creativity-innovation, critical thinking and collaboration) in early elementary grades. The learning environment were robotics are employed as educational tools includes exercises and activities where the problem-solving process in integrated. Therefore, the students are actively involved in designing, building and organizing the entire process in order to reach to a solution (to solve the problem). Additionally, in order to solve the problem, the students need to construct the appropriate robotic model as well a program it. Construction and programming provide instant feedback to students, where the students can take corrective actions through experimentation and experience the process of testing and re-testing (Alimisis, 2009; Arlegui, Menegatti, Moro, Pina, 2008).

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The Educational Robotics Package There is a great variety of educational robotics packages (BeeBots, BlueBots, Thymio, Cubelets, Roamer, LegoWeDo, Engino, Lego Mindstorms). For the purposes of this research the BeeBots robotics packages was employed. The reasons for choosing the BeeBots were due to their characteristics and ease of use given the fact that the students did not have any other experience with robotics. The BeeBot is a colorful, easy-to-operate, and friendly little robot that invite students to experience sequencing, estimation, problem-solving, and many more. It can be programmed to move forward and backward for 15 centimetres, and to turn 900 right and left. The robot can store up to 43 steps. It has also the Pause button, which is considered as a step. It can be programmed in order to perform various educational exercises using specific floor mats (i.e. alphabet mat, geometry shapes mat, treasure island mat). The floor mats are necessary since the BeeBots are programmed to perform various activities using floor mats of different concepts.

RESEARCH METHODOLOGY A descriptive case study approach was employed collecting both quantitative and qualitative data (Cohen, Manion, & Morrison, 2008; Yin, 2003). Classroom interventions were designed and implemented for two months in two classes: 2nd and 3rd grade. The population of the study was 43 primary education students: 21 2nd graders and 22 3rd graders. Two elementary school teachers and two teacher assistants closely collaborated with the Robotics Academy in designing and delivering the lessons. Specifically, The Robotics Academy at Frederick University (Department of Education) (http://akrob.frederick.ac.cy, https://www.facebook.com/AkadimiaRompotikis), is a research and educational unit that aims to promote and conduct research in the area of robotics education. The Robotics Academy had the overall responsibility

Category: Educational Technologies

Table 1. The floor mats developed by students Grade 2nd

Subject Matters Mathematics

Solid shapes

Language and Linguistics

Our school map

Arts 3

rd

Language and Linguistics

E

Floor Mats

Developed the floor mats The Alphabet Word Search Puzzle Professions of the Past

Arts

to professionally train and educationally and scientifically support the teachers and the assistants, and provide the educational robotics packages. They attended one-week intensive professional development training on educational robotics. Τhe teachers were given the flexibility to choose the disciplines (topics) that they thought it would better fit to robotics. The classroom intervention duration was 5 weeks, involved various disciplines (interdisciplinarity) and was divided in three phases. During the 1st phase the students were given an interactive presentation regarding robotics, its usefulness and value in our daily life. At the 2nd phase, during the Arts Course, the students collaborated to design and develop the floor mats to be used as educational material within other disciplines. Specifically, the 2nd graders developed two floor mats: The Solid Shapes and The School to be used in Mathematics and Health Education respectively. The 3rd graders developed four different floor mats: The Word Search Puzzle, Professions of the Future, The Similes and The Alphabet to be used in the Language and Linguistic Course and Geography (See Table 1). Finally, during the 3rd phase the teachers delivered the lessons designed where robotics employed as cognitive learning tools through various exercises. Questionnaires and focus groups with the students, as well as classroom observations conducted by the researcher’s team were the main methods of data collection. Three different instruments were used, where each instrument measures different

Developed the floor mats

problem based learning skill: 1) innovation - creativity, 2) collaboration, and 3) critical thinking. The instruments were taken from the Buck Institute of Education (www.bie.org) and were adjusted for the purposes of this study. They were translated in Greek and pilot tested. Specifically, two teachers and five students participated in the pilot study. The authors took into consideration the teachers’ and students’ comments and accordingly adjusted the instruments. The instruments were used in order to develop the three data collection methods: questionnaires, observations and focus groups. Pre- and post- questionnaires were given to students. The questionnaires were different for each grade due to the age of the students (as suggested by the Buck Institute of Education). For the 2nd graders a simple form of questionnaire was given, where a combination of smiley / sad faces and phrases (as responses) were used. However, for the 3rd graders a different form of questionnaire was used; they were required to address various statements using a 5 likert-type scale. (See see Table 2). The observation templates and the focus groups protocols were developed based on the same parameters as the questionnaires in order to record and evaluate the development of the PBL skills as well as to overall examine the teaching intervention. Observations were conducted throughout all lessons delivered. Additionally, four focus groups were conducted (two focus groups for each grade) by the completion of the classroom interventions. A total of 15 students participated

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Table 2. The PBL skills questionnaires Grade 2

3

nd

rd

Questionnaires

Questions

Question Type

Critical Thinking

6

3 point - Likert scale

Collaboration

5

Innovation – Creativity

6

Critical Thinking (4)

Collaboration (6)

Parameters

Analyzing driving question and begin inquiry

2

Gather and evaluate information

2

Use evidence and criteria

3

Justify choices

2

Takes responsibility

4

Helps the team

4

Respects others

2

Makes and follows arrangements

4

Organizes work

2

5 point - Likert scale

Works as a whole team Innovation – Creativity (4)

Define the creative challenge

1

Identify resources of information

1

Generate and select ideas

4

Present work to others

1

at the focus groups: seven 2nd grade students and eight 3rd grade students. The students were selected on a voluntarily basis. Through the focus groups the researcher’s team was also aiming to capture students’ reactions, experiences and opinions regarding the teaching intervention. The quantitative data collected from the questionnaires were used to perform descriptive (frequencies, percentages, cross tabulations) and inferential statistics (t-test, ANOVA). The SPSS package (version 19) was used. The qualitative data collected through observations and focus groups were analyzed by using the continuous comparison of data approach (Maykut & Morehouse, 1994).

MAIN FOCUS OF THE PAPER Through the activities designed, the students were involved in the extremely interesting process of programming and controlling the robots. The activities performed with the robots besides being enjoyable and creative; they promoted learning 2496

by playing, and specifically the development of some of PBL skills. Specifically, the analysis of the pre- and post-questionnaires revealed the development of the creativity-innovation and the critical thinking skills. However, the collaboration skills were not developed as much as the aforementioned skills. This is due to minor conflicts observed within the teams while trying to design and develop the floor mats as well as to program the BeeBots. Students were so excited in using the robots that the majority of them were very anxious in holding, touching and programming them. In some cases it was impossible for students to hold turns in using the BeeBots. Finally, no statistical significant differences were revealed in regards to age and gender. The observations and the focus groups complemented and further explained the results of the questionnaires in regards to the development of PBL skills. Additionally, several issues were revealed in regards to teachers’ and students’ role, difficulties encountered, student attitudes and opinions.

Category: Educational Technologies

The observations assisted also in gaining and evaluating information on issues that cannot easily and/or directly addressed through the questionnaires. The observations showed that during the lessons where the robots used; more time was needed than the allocated time for each teaching period (40 minutes). The aforementioned was observed in almost all courses (i.e. Maths, Health Education, Language and Linguistics) besides the Arts. Also, observations revealed that although students followed the directions given by the teachers and addressed the exercises and problems given, they wanted to be granted time to “play” with the BeeBot. One of them mentioned “…we want to develop the different paths for the BeeBot…”. The robots intrigued their imagination and creativity in developing by themselves the routes for the BeeBot to follow. They also reported that they wanted to develop activities for other students to solve (program the robots). The role of teachers was extremely important in helping the students, guiding, monitoring and facilitating the teaching and learning process. Additionally, the presence of a teacher assistant deemed necessary in order to provide any assistance needed to the teacher, since in some cases the students were anxious and excited, given the hands-on character of the activities and the student-centered learning environment developed. Finally, the focus groups helped the researcher looked for themes that helped her in gaining better understanding and more information on students’ experiences and opinions. The majority of the students reported that the educational robotics experience was extremely enjoyable and very interesting. Overall, the students examine various concepts (interdisciplinary approach) by programming the BeeBot, having the chance to develop various knowledge and skills through the process of programming. The students experience the multifaceted process of problem solving and decision-making, as well as cultivate collaborative and exploration skills.

FUTURE RESEARCH DIRECTIONS The results of the study provide the foundation to further investigate robotics integration in the teaching and learning process. Some more studies were designed and implemented in relation to the aforementioned end, where different educational robotics packages (Lego WeDo, Lego EV3, mBots) are used for the development of problemsolving based skills. Additionally, BeeBots and Lego WeDo are being employed within a newly designed innovative curriculum developed by Frederick University Robotics Academy (https:// www.facebook.com/AkadimiaRompotikis/) in order to examine the development of computational thinking, self-regulation and spatial skills in elementary schools students. Last but not least, the impact of robotics in developing self-confidence and self-fulfillment to special needs students is planned to be examined the upcoming semester.

CONCLUSION Overall, the study reveals the great potential of integrating robotics as a cognitive-learning tool across disciplines in order to achieve problembased learning skills. It examines the integration of robotics within a formal in-classroom setting in contrast to other studies that examine in-formal and non-formal educational settings. Additionally, it focuses on early elementary grades, whereas other studies focus on 5th and 6th graders and ever more on secondary education. The results of the study aim to promote research in the field of educational robotics in order to further examining and defining the appropriate learning pedagogies and teaching approaches to be employed when robotics is integrated as cognitive-learning tool. It also highlights the need for teachers to be professionally trained, supported and guided. Finally, the study suggests the value of adopting appropriate robotics packages, learning pedagogies, and teaching

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approaches when robotics and programming are integrated within classroom activities. The focus should be to engage teachers in using robotics and programming in their classrooms across disciplines.

Becker, H. J., & Ravitz, J. L. (2001, March). Computer Use by Teachers: Are Cuban’s Predictions Correct? Paper presented at the 2001 Annual Meeting of the American Educational Research Association, Seattle, WA.

REFERENCES

Benitti, F. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers & Education, 58(3), 978–988. doi:10.1016/j.compedu.2011.10.006

Alimisis, D. (2009). Teacher Education on Robotics-Enhanced Constructivist Pedagogical Methods. School of Pedagogical and Technological Education. Ananiadou, K., & Claro, M. (2009). 21st Century Skills and Competences for New Millennium Learners in OECD Countries. OECD Education Working Papers, No. 41. OECD Publishing. DOI:10.1787/218525261154 Arlegui, J., Menegatti, E., Moro, M., & Pina, A. (2008). Robotics, Computer Science curricula and Interdisciplinary activities. Workshop Proceedings of SIMPAR. Intl. Conf. on Simulation, Modeling and Programming for Autonomous Robots, 10-21. Barker, S. B., Nugent, G., Grandgenett, N., & Adamchuk, I. V. (2012). Robots in K-12 Education: A New Technology for Learning. Information Science Reference. IGI Global. doi:10.4018/9781-4666-0182-6 Bauerle, A., & Gallagher, M. (2003). Toying With Technology: Bridging the Gap Between Education and Engineering. In C. Crawford et al. (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference 2003 (pp. 3538-3541). Chesapeake, VA: AACE. Becker, H. J. (1993). Teaching with and about computers in secondary schools. Association for Computing Machinery, 36(5), 69–55. doi:10.1145/155049.155066

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Bernstein, D., Mutch-Jones, K., Cassidy, M., Hamner, E., & Cross, J. (2016). Robots and Romeo and Juliet: Studying Teacher Integration of Robotics into Middle School Curricula. Paper presented at the 2016 Annual Meeting of the American Educational Research Association, Washington, DC. Bers, M. U., Ponte, I., Juelich, C., Viera, A., & Schenker, J. (2002). Teachers as Designers: Integrating Robotics in Early Childhood Education. Information Technology in Childhood Education Annual, (1): 123–145. Bielaczyc, K., & Collins, A. (1999). Learning communities in classrooms: A reconceptualization of educational practice. In C. Reigeluth (Ed.), Instructional-design theories and models: A new paradigm of instructional theory (pp. 269–292). Mahwah, NJ: Erlbaum. Bybee, R. W., & Fuchs, B. (2006). Preparing the 21st century workforce: A new reform in science and technology education. Journal of Research in Science Teaching, 43(4), 349–352. doi:10.1002/ tea.20147 Chambers, J. M., & Carbonaro, M. (2003). Designing, Developing, and Implementing a Course on LEGO Robotics for Technology Teacher Education. Journal of Technology and Teacher Education, 11(2), 209–241.

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Cognition and Technology Group at Vanderbilt. (2003). Connecting learning theory and instructional practices: Leveraging some powerful affordances of technology. In H. O’Neil & P. Perez (Eds.), Technology application in Education: A learning view (pp. 173–209). Mahwah, NJ: Erlbaum.

Eteokleous, N., Demetriou, A. Y., & Stylianou, A. (2013). The Pedagogical Framework for Integrating Robotics as an Interdisciplinary Learning – Cognitive Tool. In J. Roselli & E. Gulick (Eds.), Information and Communications Technology: New Research (pp. 141–158). Nova Science Publishers, Inc.

Cohen, L., Manion, L., & Morrison, K. (2008). Research methods in education (5th ed.). London: Routledge.

Gibbon, L. W. (2007). Effects of Lego Mindstorms on convergent and divergent problem-solving and spatial abilities in fifth and sixth grades students (Doctoral dissertation). Retrieved from: ProQuest Dissertation and Theses database.

Craig, D. C. (2014). Robotics Programs: Inspiring Young Women in STEM. In J. Koch, B. Polnick, & B. Irby (Eds.), Girls and Women in STEM (pp. 153-174). Charlotte, NC: Information Age Publishing, Inc. Cuban, L., & Pea, R. (1998, February). The Pros and Cons of Te1chnology in the Classroom. Paper presented at the Funder’s Learning Community Meeting, Palo Alto, CA. Domin, D. (1999). A review of laboratory instruction styles. Journal of Chemical Education, 76(4), 543–547. doi:10.1021/ed076p543 Duch, B. J. (1995). What is problem-based learning? About Teaching: A newsletter of the Center for Teaching Effectiveness, 47. Retrieved October 7 2013, from: http://www.udel.edu/pbl/cte/jan95what.html Earle, R. S. (2002, January-February). The Integration of Instructional Technology: Promises and Challenges. ET Magazine, 42(1), 5-13.Available from: http://BooksToRead.com/etp Eguchi, A. (2007). Educational Robotics for Elementary School Classroom. In Proceedings of Society for Information Technology & Teacher Education International Conference 2007 (pp. 2542-2549). Chesapeake, VA: AACE. Eteokleous, N. (2016). Developing 21st Century Skills Through an Educational Robotics Curriculum. Paper presented at the 2016 Annual Meeting of the American Educational Research Association, Washington, DC.

Grant, M. (2009, April). Understanding projects in project-based learning: A student’s perspective. Paper presented at Annual Meeting of the American Educational Research Association, San Diego, CA. Griffin, P., & Care, E. (Eds.). (2015). Assessment and Teaching of 21st Century Skills, Methods and Approach. Dordrecht: Springer; doi:10.1007/97894-017-9395-7 Haugland, W. S. (2000). Early Childhood classrooms in the 21st century: Using Computers Maximize Learning. Young Children, 12–18. Jonassen, D. H. (1999a). Designing Constructivist Learning Environments. In C. Reigeluth (Ed.), Instructional Design Theories and Models: A new paradigm of Instructional Theory (pp. 215–239). Mahwah, NJ: Erlbaum. Jonassen, D. H. (1999b). Computer as Mindtools in Schools: Engaging Critical Thinking (2nd ed.). Columbus, OH: Prentice Hall. Karim, M., Lemaignan, S., & Mondada, F. (2015). A review: Can robots reshape K-12 STEM education? Proceedings of the 2015 IEEE - International Workshop on Advanced Robotics and its Social impacts. doi:10.1109/ARSO.2015.7428217 Larmer, J., & Mergendoller, R. J. (2010). 7 Essentials for Project-Based Learning. Educational Leadership, 68(1).

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Markham, T. (2003). Project-based learning handbook (2nded.). Novato, CA: Buck Institute for Education. Maykut, P., & Morehouse, R. (1994). Beginning Qualitative Research, A Philosophic and Practical Guide. London: The Falmer Press. Miglino, O., Lund, H. H., & Cardaci, M. (1999). Robotics as an Educational Tool. Journal of Interactive Learning Research, 10(1), 25–47. Mojica, K. D. (2010). Ordered effects of technology education units on higher-order critical thinking skills of middle school students (Doctoral dissertation). Retrieved from: ProQuest Dissertation and Theses database. Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. New York, NY: Basic Books. Papert, S. (1993). Mindstorms: children, computers, and powerful ideas (2nd ed.). New York, NY: BasicBooks. Resnick, M., Berg, R., & Eisenberg, M. (2000). Beyond black boxes: Bringing transparency and aesthetics back to scientific investigation. Journal of the Learning Sciences, 9(1), 7–30. doi:10.1207/ s15327809jls0901_3 Rotherham, J. A., & Willingham, D. T. (2010). “21st-Century” Skills: Not New, but a Worthy Challenge. American Educator, 34(1), 17–20. Salomon, G., Perkins, D. N., & Globerson, T. (1991). Partners in Cognition: Extending Human Intelligence with Intelligence Technologies. Educational Researcher, 20(3), 2–9. doi:10.3102/0013189X020003002 Varnado, T. E. (2005). The effects of a technological problem-solving activity on FIRST(TM) LEGO(TM) League participants’ problem solving style and performance (Doctoral dissertation). Retrieved from: ProQuest Dissertation and Theses database.

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Vollstedt, A-M. (2005). Using robotics to increase student knowledge and interest in science, technology, engineering and math (Master’s thesis). Retrieved from: ProQuest Dissertation and Theses database. Ward, R., Miller, J. L., Sienkiewicz, R., & Antonucci, P. (2012). ITEAMS: Increasing the selfidentification for girls and underserved youth in pursuing STEM careers. Journal of Systemics. Cybernetics & Informatics, 10(1), 95–99. Whitehead, S. H. (2010). Relationship of robotic implementation on changes in middle school students’ beliefs and interest toward science, technology, engineering and mathematics (Doctoral Dissertation). Retrieved from: ProQuest Dissertation and Theses database. Williams, D., Ma, Y., & Prejean, L. (2010). A Preliminary Study Exploring the Use of Fictional Narrative in Robotics Activities. Journal of Computers in Mathematics and Science Teaching, 29(1), 51–71. Yin, R. (2003). Case study research: Design and Methods. Thousand Oaks, CA: Sage Publications.

ADDITIONAL READING Alimisis, D., Arlegui, J., Fava, N., Frangou, S., Ionita, S., Menegatti, E., & Pina, A. et al. (2010). Introducing robotics to teachers and schools: experiences from the TERECoP project. In J. Clayson & I. Kalas (Eds.), Proceedings for Constructionism 2010 (pp. 1–13). Paris: American University of Paris. Barker. Arlegui, J., Menegatti, E., Moro, M., & Pina, A. (2008). Robotics, Computer Science curricula and Interdisciplinary activities, In Proceedings of the TERECoP Workshop “Teaching with robotics, Conference SIMPAR 2008”, Venice.

Category: Educational Technologies

Barker, B. S., & Ansorge, J. (2007). Robotics as means to increase achievement scores in an informal learning environment. Journal of Research on Technology in Education, 39(3), 229–243. do i:10.1080/15391523.2007.10782481 Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers & Education, 58(3), 978–988. doi:10.1016/j.compedu.2011.10.006 Bers, M. (2008a). Blocks to Robots: Learning with Technology in the early childhood classroom. New York: Teachers College Press. Bers, M. (2008b). Engineers and Storytellers: Using robotics manipulatives to develop technological fluency in early-childhood. In O. Saracho & B. Spodek (Eds.), Contemporary perspectives on science and technplogy in early childhood education (pp. 105–125). Charlotte, NC: Information Age. Bers, M., Matas, J., & Libman, N. (2013). Linvot ULehibanot, To Build and to Be Built: Making Robots in Kindergarten to Explore Jewish Identity. Diaspora, Indigenous, and Minority Education: Studies of Migration, Integration, Equity, and Cultural Survival, 7(2), 164–179. doi:10.1080/1 5595692.2013.787062 Bishop, A.P., Bertram, B.C., & Lunsford, K.J. & al. (2004). Supporting Community Inquiry with Digital Resources. Journal of Digital Information, 5(3). Blikstein, P. (2013). Digital fabrication and ’making’ in education: The democratization of invention. In J. WalterHerrmann & C. Bόching (eds.), FabLabs: Of Machines, Makers and Inventors (pp. 1-21). Bielefeld: Transcript Publishers. Detsikas, N., & Alimisis, D. (2011). Status and Trends in Educational Robotics Worldwide with Special Consideration of Educational Experiences from Greek Schools. Proceedings of the International Conference on Informatics in Schools: Situation, Evolution and Perspectives (ISSEP, Comenius University, Bratislava, Slovakia.

diSessa, A. A. (2000). Changing minds: computers, learning, and literacy. Cambridge, MA: MIT Press. Druin, A., & Hendler, J. (2000). Robots for kids: exploring new technologies for learning experiences. San Francisco: Morgan Kaufman/Academic Press. Lawhead. Moro, M., & Alimisis, D. (2009). From the Logo Turtle to the Tiny Robot Turtle: practical and pedagogical issues. Proceedings of the 5th PanHellenic Conference “ICT in Education, Syros (Greece).

KEY TERMS AND DEFINITIONS BeeBots: A colorful, easy-to-operate, and friendly little robot that can be used by students from the age of four to the age of ten, depending on the level of programming difficulty of each exercise. Educational Robotics: The use of robotics in the teaching and learning process (in the educational practice) as a subject matter and/or as a cognitive learning tool. Floor Mat: A paper or foam based mat (mainly located on the floor or high-table) which is separated in blocks of 15X15 centimeters. Each floor mat represents a specific theme/ concept and each block depicts different sub-theme/ sub-concept related to the overall theme/ concept. Each floor mat may have from 9 to 24 blocks (or even more!) Floor mats can be developed for various subject matters. Interdisciplinarity: It is the employment of various disciplines. The purpose is to relate/ combine two or more subject matters (or academic disciplines) into one exercise/ activity in achieving specific educational objectives. Problem Based Learning (PBL): The students are involved in a learning process where they needs to solve a specific problem using various knowledge and skills. This approach can be used within various subject matters even.

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Robotics Packages: In the education market there are various robotics packages that allow users/students to construct and program robots. These packages mainly contain bricks, wires, sensors and a visual programming software. Twenty (21st) Century Skills: The skills that considered to be important and necessary for

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student to develop in survive and succeed in the 21st century. Those are: problem solving, critical thinking, collaboration, communication, global citizenship, digital literacy, knowledge construction, creativity, innovation, self-directed learning.

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Category: Educational Technologies

From Digital Exclusion to Digital Inclusion for Adult Online Learners Virginia E. Garland University of New Hampshire, USA

INTRODUCTION In this second decade of the new millennium, it is becoming clear that nations are pressuring their citizenry to become more technologically skilled in order for them to compete globally in the quest for political, economic, military and scientific success. Educational leaders are also aware of the need to provide more “tech savvy” students who can be successful academically, socially, and professionally in the international arena (Garland & Tadeja, 2013). Adult learners are taking a greater role in that process of becoming better academic and digital citizens. For instance, “Goal 2020” of the United States Department of Education is a higher education reform policy initiative focused on the United States leading “the world in the proportion of college graduates” with “at least 8 million additional adults [who] will need to return to college and earn associate and bachelor’s degrees by the year 2020” (Gast, 2013, p.17). But the goal of digital literacy for all the world’s people is hampered by many factors. There are wide disparities in Information Science and Communication Technologies (ICT) skills between digitally excluded and digitally included online learners both nationally and internationally. There is an enormous and even widening gap between Internet users in developed versus developing nations (Pick & Azari, 2008). There are also digital exclusion issues in the United States and in other economically advanced countries. Secondary level students in the United States are increasingly taking online courses; but those public school students who live in low socio-economic areas, who speak a language other

than English, who are considered minorities, and who are disabled are more likely to lack academic technology skills (Garland, 2015). In addition, a significant number of adult learners across the globe are a new group of “digitally excluded” students. Distance education courses are increasingly being taken for academic, professional, and personal reasons by students who are over twenty-five, yet many are struggling to compete with their “digital native” peers. According to one study, “Expanding adult digital literacy is essential for confronting vulnerable adults’ issues of exclusion and marginalization that are increasingly being amplified by the digital mediation of modern social life.” (Jacobs et al, 2014, p 626) Adult learners also need full inclusion in the technological demands of online education. This chapter focuses on current trends in digital exclusion of adult learners and provides some solution strategies for ICT directors, higher education administrators, online instructors, and the older students they serve.

BACKGROUND The most recently available statistical data indicate that online degree programs have a significant impact on adult learners. Over one fourth of all higher education students are taking online courses. According to the National Center for Educational Statistics (NCES) report, “Distance Education,” 25.8% of all undergraduate and graduate students were “enrolled exclusively” (12.5%) or “enrolled in some” (13.3%) distance education courses in 2012 (NCES, Distance Education,

DOI: 10.4018/978-1-5225-2255-3.ch218 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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para 3). NCES indicated in its “Back to school statistics” report that “In 2013 there were about 12.2 million college students under age 25 and 8.2 million students 25 years old and older” (NCES, Back to school statistics, para 16). By the year 2024, these enrollments are projected to almost double, with over 23 million “total fall enrollment in degree-granting postsecondary institutions,” the majority of which, over 13 million, are expected to be women (NCES, Digest of Education Statistics, table 303.40). In addition, the number of adult students taking non-degree online programs is also increasing. McCallum (2012) states that there are over 90 million students over 25 years old who are taking post-secondary studies in the United States alone. To put a human face on these statistics, consider the case of an online adult learner named Rosa. In the fall of 2015, the author was introduced to Rosa (identifying information removed) in Texas at a national leadership conference, which focused on the accomplishments of undergraduate and graduate students. Rosa, whose primary language is Spanish, is the first in her family to go to college. Despite her academic success in being awarded a partial scholarship for her undergraduate studies, Rosa confided in me that she was struggling with financial and time management issues in trying to complete the online courses of her program. Rosa is a recently divorced, single mother of two young children. She spends most of her time working and taking care of her family. Rosa and her children have had to move back in with her parents. She does not have the resources to afford a smart phone and the latest computer upgrades. Although she is working part-time and saving money to make the tuition payments of her current degree program, Rosa feels that she might have to discontinue her undergraduate studies. It is likely that this academically talented adult student will drop out of college. Digital exclusion of adult learners is associated with technology, socio-economic and gender factors. Sadly, Rosa fits the profile of many digitally excluded online adult learners. She is a mature

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student, over the age of twenty-five, who does not have the requisite technology skills and tools to be able to easily access the Internet and to effectively use the information and communication digital tools prevalent in online courses. Rosa is also a non-traditional student, a single mother who was drawn to the flexibility in her schedule promised by the lure of distance education programs. Why do adult students like Rosa want to take online courses? They find that online courses are appealing because of their “anytime, anywhere” access. Britt states “students desire more control over their education along with a flexible schedule in the learning process which is a distinct advantage of online education” (2015, p. 400). But some mature students are “digitally excluded,” unfamiliar with and perhaps unable to afford the newer technologies that are used as instructional tools in online courses (Garland, 2009). Non-traditional students need not only technology training but also access to current digital tools in order to be successful online learners and more effective professionals. According to Gast (2013), the reasons for adult learners’ desire to take online programs include “the need for updated skills to compete in a knowledge based economy, a change in demographics due to immigration and higher retirement ages, technological advances bringing the classroom to the student, and a globalization of the higher education system. Most recently, online and for-profit institutions have been a primary beneficiary of this growing enrollment trend among adult learners” (p. 18). Indeed, online degree programs have almost doubled in a recent ten year period, “62.4% of colleges offered online degree programs at the end of 2012 which is up significantly from 32.5% in 2002” (Britt, 2015, p. 399). Although the number of online degree programs at more traditional colleges in the United States is increasing, some previously programs in distance education universities are in decline. Two distance education giants, the University of Phoenix and Corinthian Colleges, have sharply decreasing enrollments and increasing public scru-

Category: Educational Technologies

tiny. At the University of Phoenix, the largest for profit university in the United States, “enrollment numbers have been cut in half from five years ago, down from 460,000 to 213,000…[and] Corinthian Colleges, another major for-profit system, closed its doors amid legal action and accusations of financial manipulation towards students” (Jackson, 2015, para 5 & 6). One reason for enrollment declines at the University of Phoenix is competition from other distance education programs, but another reason is the attrition of adult learners in online courses.

SOCIO-ECONOMIC, GENDER, AND TECHNOLOGICAL ISSUES Despite the exponential growth of both distance education courses and the number of adult learners who are taking them, the dropout rate of students over twenty-five years of age is high (Park and Choi, 2009). Key factors that inhibit the academic success many adult learners in online courses are socio-economic, gender and technology based. These issues are helpful in understanding why certain groups of adults are especially underserved in their need for digital inclusion. They include the poor, mostly women, who have minimal literacy skills and speak English as a second language. The Literacy, Language, and Technology Research Group (LLTR) from Portland State University recently studied (Jacobs et al., 2014) the need for “adult digital literacy learning” in a technologically advanced “multiliterate world.” These researchers found that the “digital newcomer” is “economically vulnerable, under-served, and high-need adult population (i.e., low-income, low-literate, elderly, and English Speakers of Other Languages) who are new to, or have limited experience with, technology” (p. 625).

Constraints on the Adult Learner If working adult learners, many of whom are single mothers like Rosa, had more time to take

affordable online courses, they would be less likely to drop out. Based at one of the world’s largest online universities, University of Phoenix, faculty members Donnelly and Kovacich (2014), studied adult student attrition in community college based online courses in the Boston, Massachusetts area. The adult students of their study have the same characteristics of those researched by the LLTR group in Portland, Oregon. Donnelly and Kovacich found that poor, working women with children are likely to fail or drop out, stating that “Over 80% of community college students work as well as attend school, are raising children, and care for relatives… attrition rates for online courses at community colleges are 15% to 50% higher than the same courses provided in face-to-face environments” (p. 35). In a study of adult learners in Hellenic Open University, Greece’s “only university offering exclusively distance education courses,” Angelaki and Mavroidas (2013) found that women were more likely to experience “negative emotions… mainly associated to the unknown for students [sic] methodology of distance learning, the requirements of the course, the familial, professional and social responsibilities of the students as well as to the difficulties stemming from using technological tools” (p. 88). Online adult learners who determine that their online courses are a “waste of time and money” are more likely to drop out. Gast (2013) contends that fiscal and time management factors are key in understanding why adult learners fail to complete online courses. The latter researcher studied economically disadvantaged, mature students, and found that “Time and finances are cited as the most common barriers faced by adult students.” (p. 18). Even if adult students can afford their online programs, they might not have the time needed to complete the required assignments. Romero and Barbera (2011) found that there are time constraints for the older learner who has more work and family responsibilities than his or her younger peer. Similarly, Herbold (2012) states that “time” is valued more by the “nontraditional, older students…With restrictions on

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their time due to life’s other responsibilities, adult learners are better able to successfully complete a course of study if the conditions for learning are optimized” (pp. 117-118). Solutions for delivering effective online instructional strategies that make the best use of the adult learner’s limited time and financial resources are provided in the “Solutions and Recommendations” section of this chapter.

SOLUTIONS AND RECOMMENDATIONS

Technology Skills and the Adult Learner

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With access to effective, wireless technology tools that deliver a curriculum that is relevant, the adult student has a greater chance of success in online courses (Garland 2015). In her study of 69 graduate students in two online education courses, Herbold (2012) also found that “Students could access the course from any location they had Internet access, which could be anywhere with a portable smart device. The place could also be a fixed location such as a desk computer or laptop at home, work, or public places with Wi-Fi like the library, coffee shop, hotel or truck stop. These mature students can access the classroom from almost anywhere their busy and involved lives take them” (p.122). However, even if adult online learners have Internet access tools, such as the “portable smart device” described by Herbold (2012), they might still be inexperienced in “digital literacy” skills. Many people born before the “iGeneration” have smart phones, but they are more likely to use them for communication and social media purposes than as tools in the academic assignments of online courses (Garland, 2009). According to Jacobs et al (2014), “engagement with Web 2.0 involves a new ethos or way of understanding one’s relationship to knowledge construction…[The] dichotomy of digital natives and digital immigrants has been found to be overly simplistic and in need of more nuanced understandings of what it means to be able to use the computer and Internet” (p. 625). Thus, a discussion of the term “digital inclusion” must include not only access to the Internet, but also the technology skills needed for the adult learner to be fully engaged in the online course.

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These are the major intervention strategies needed to ensure success for the adult online learner: •

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Technology training and support for adult online learners in the use of the digital tools used in distance education programs Organizational and time management skill training for adult students to become more self-directed learners Curriculum design and teaching methods training for online instructors and academic coordinators Increased financial assistance opportunities for those most in need, the at risk, nontraditional student Expanded and affordable childcare programs, especially for working mothers who are also online learners

In addition to the policy initiatives at the international and national levels to make online courses more affordable and accessible to adult learners, there should be local efforts to engage the mature student. Institutions of higher education must develop and implement policies that better ensure the success of adult learners, whose enrollment projections, particularly those for women, are greater than those of their younger counterparts. Attrition is decreased if colleges and universities support the acquisition of academic and technology skills that enable the mature student to be successful. The online course curriculum should also be meaningful to the adult learner. According to Park and Choi (2009), adult learners were less likely to drop out of online courses if they had “organizational support” and that they viewed the course to be relevant to their personal and professional lives. ICT directors and university administrators should be aware that “Adult students have many commitments such as family and work in addition to school and must create the time and energy to manage them all. Schools need to understand how outside factors can hinder

Category: Educational Technologies

the adult learning process and take steps to fully understand the plight of adult learners” (Donnelly and Kovacich, 2014, p. 40). Adult student advising programs and financial assistance opportunities would benefit the mature learner. But the adult learner must also be engaged in self-directed learning. Stine (2010) found these three factors as key to the success of adult learners in distance education: high levels of student skills in technology, academics, and individual learning. One model program for preparing adult learners for online courses is the Learner Web project of Portland State University for digitally excluded, “high need” adults, “designed to give learners a self-directed learning experience…the shifts learners undergo as they gain experience and confidence with digital tools can help educators develop more robust systems for supporting vulnerable populations” (Jacobs, et al, 2014). Even non-vulnerable adult learners need to be self-directing in their online coursework. According to Jacobs’ (2012) study of online graduate students, “to address the adult learning characteristics of being autonomous, self-directing and self-responsible, students were given the latitude to select the activities they preferred and that would best meet their individual needs” (p. 122). Thus, self-directed learning is an approach that is valuable in motivating the older online learner. The adult student who takes more control of his or her own online learning would also benefit from effective time management skills. In their study of community college students who dropped online courses, Donnelly and Kovacich (2014) found that those who had higher attrition rates also had little time to focus on studying because of other tasks, such as work and family obligations. In addition to suggesting that some online adult learners study more effectively in the morning, the University of Phoenix faculty members, Donnelly and Kovacich (2014), along with Romero and Barbera (2011), recommend that more interactive course design features would also enhance student engagement in synchronous online learning activities. Adult

students who were interviewed by Donnelly and Kovacich (2014) in the community college online courses study stated that they did not drop out of online courses if they were effective in organizing their course schedules and assignment deadlines. Mature students need other motivational tools, such as having achievable goals and knowing that the instructor is meeting their needs for collaboration and evaluative feedback. Adult students are more goals oriented than their younger peers. Ekmekci (2013) found that adult students will find online courses “a waste of time” unless they are taught by knowledgeable instructors with clearly stated goals that are relevant to students’ professional lives. The goals of online courses, such as the completion of multimedia group projects, are more easily attainable with formative assessment strategies. According to Leong (2011), other instructional strategies to enhance the success of online adult learners include the instructors’ use of both collaborative activities to improve “social presence” and immediate feedback on assignments to increase “cognitive absorption” of the content being taught. Ke (2010) believes that there are three types of “presence” needed in the online course: cognitive, social and instructional. Online dialogues and prompt, effective feedback by the online instructor are viewed by Ke as ways to increase these three types of “presence.” The need for a “social presence” between instructor and student, and often between student and student, cannot be underestimated. In their study of distance education programs for adult learners in Greece, Angelaki and Mavroidis explain that “The expanded use of new tools related to Information and Communication Technologies (ICT) facilitates communication, cooperation and dialogue among participants in distance learning courses” (2013, p. 78). In addition to the value of social presence is the importance of cognitive presence. Leong’s and Ke’s findings are similar to that of Tyler-Smith (2006), who argues that “cognitive overload” is the most significant reason that the adult “eLearners” he studied in New Zealand

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dropped out within the first weeks of the online courses they were taking. The adult learner also needs to be able to afford the tuition of distance education degree programs. Despite enormous national investment in higher education, adult learners in the United States are those with the largest student debt. According to The Hechinger Report (2015), “The reason that the ‘average’ student’s debt is so high – almost $23,000 – is because the figure includes the loans of graduate students, who are permitted to borrow unlimited amounts from the federal government up to the cost of attendance. Sixty-five percent of 2012 graduates who borrowed $50,000 or more were graduate students” (para 5). In addition, Gast (2013) found that student loan debt is a significant inhibitor to adult student enrollment. The single mother who is at risk of dropping out needs not only financial aid but also improved social and family support. In a study for the Institute for Women’s Policy Research, Gault, Reichlin, and Roman (2014) recommend that federal and state student financial aid programs apportion more assistance to low-income adult learners. These researchers also advocate “more inclusive campus policies,” to meet the needs of single mothers, such as increased child care subsidies and support as well as “single-parent housing.”

FUTURE RESEARCH DIRECTIONS The attrition of adult learners in higher education online courses is an emerging field of research. Longitudinal studies of distance education programs and the mature students they serve are needed. Political science, adult motivational and women’s studies theorists can inform ICT and educational practitioners on the reasons for the alarmingly high dropout rates for adult students in distance education programs in community colleges, which have received an infusion of federal assistance in the past few years. In the United States, policy makers and politicians are making a wide variety of recommenda-

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tions to alleviate the alarming increase of student debt. One policy highly criticized is the 2010 Health Care and Education Reconciliation Act (a section of which is the Patient Protection and Affordable Care Act or “Obama Care”). Although this legislation invested two billion dollars in community colleges and “career training, student attrition and student debt have reached all time highs. Political scientists and educators need to further study the projected effectiveness of financial assistance or student debt relief programs. Adult motivation theories also shed further light on the ways to ensure the success of nontraditional students in online courses. In a study of 203 university students, Sogunro (2015) found eight factors that motivate adult learners in higher education: effective instructional methods, solid curriculum, course relevance, interactive classroom, formative and prompt assessment, student self-directedness, positive learning environment, and meaningful academic advising. All of these factors can apply to the online course; especially those focused on curriculum design, interactive technology tools and feedback methods. And, adult learners particularly need a curriculum that is relevant and advising that is supportive. More research should be conducted on the application of adult motivational factors to the design and implementation of online courses in order to reduce the high rate of adult learner attrition. Another future research direction is that of studying factors which affect the academic success rate of women, particularly single mothers, in online courses. The Institute for Women’s Policy Research has completed valuable research on the topic of college affordability for low-income adult learners. The work of this policy group should be supported and continued.

CONCLUSION In the political arena, debate continues on the affordability of higher education in the United States. For students in many other economically

Category: Educational Technologies

advanced countries, such those discussed in this chapter, New Zealand and Greece, national legislation results in the reduction of much of the cost of public higher education. But in the United States, policies on the national and institutional levels are especially needed to support the efforts of economically disadvantaged adult students who are at greatest risk of not completing their educational programs. Rising levels of adult student debt for online courses relate to the increasing attrition of mature learners in distance education programs in the United States. Although there has been recent litigation in California on the mismanagement of online student tuition and recruitment practices (Jackson, 2015), much more needs to be done to ensure that institutions of higher education which offer distance education programs are successful in meeting the academic and financial needs of all students. Colleges and universities which offer online courses should act more honestly and effectively at not only fair practices in student recruitment and in financial assistance, but also in focusing those policies on accommodating the needs of adult students. Prospective students who are over twenty-five, many of whom are working single mothers, desire to further their education through online courses because they can conveniently access them anytime, anywhere (Garland, 2015). But, these underserved adults are among the most likely to drop out of online courses (Donnelly and Kovacich, 2014). According to Gast (2013), institutions should be marketing their distance education courses with adults in mind, supporting non-traditional students with specialized services, preparing mature students for post-degree or post-certificate professional work, and making their tuition more affordable. Higher education leaders should be more proactive in developing tutoring programs, offering financial and childcare services, and providing advisors who are sensitive to the needs of prospective or enrolled adult students. Online instructors themselves can do much to design and implement online courses that are

meaningful to the adult learner. The collaborative efforts of both online teachers and their motivated, technology trained students are needed for adult learners like Rosa to transition from “digital exclusion” to “digital inclusion.”

REFERENCES Angelaki, C., & Mavroidis, I. (2013). Communication and social presence: The impact on adult learners’ emotions in distance learning. European Journal of Open Distance and E-Learning, 16(1), 78–93. Britt, M. (2015). How to better engage online students with online strategies. College Student Journal, 49(3), 399–404. Donnelly, W., & Kovacich, J. (2014). A phenomenological investigation of the problem of adult student attrition in community college online courses. Exchange, 3(1), 34–43. Ekmekci, O. (2013). Being there: Establishing instructor presence in an online learning environment. Higher Education Studies, 3(1), 29–38. doi:10.5539/hes.v3n1p29 Garland, V. E. (2009). Wireless technologies and multimedia literacies for K-12 education. In L. Hin & H. Subramaniam (Eds.), Handbook of Research on New Media Literacy at the K-12 Level. IGI Global. Garland, V. E. (2015). The digitally excluded learner: Strategies for success. In M. KhosrowPour (Ed.), Encyclopedia of Information Science and Technology (3rd ed.; pp. 2400–2408). Hershey, PA: Information Science Reference. doi:10.4018/978-1-4666-5888-2.ch233 Garland, V. E., & Tadeja, C. (2013). Educational leadership and technology: Preparing administrators for a digital age. New York: Taylor and Francis.

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Gast, A. (2013). Current trends in adult degree programs: How public universities respond to the needs of adult learners. New Directions for Adult and Continuing Education, 2013(140), 17–25. doi:10.1002/ace.20070 Gault, B., Reichlin, L., & Roman, S. (2014). College affordability for low-income adults: Improving returns on investment for families and society. Report #C412. Washington, DC: Women’s Policy Research. Herbold, K. (2012). Giving student choice in online learning environments: Addressing Adult Learner Needs. The International Journal of Technology Knowledge in Society, 7(5), 117–125. Jackson, A. (2015). It looks like this for-profit college could be in financial trouble. Business Insider. Retrieved November 11, 2015 from: http:// www.businessinsider.com/university-of-phoenixenrollment-numbers-are-down-2015-3 Jacobs, G. E., Castek, J., Pizzolato, A., Reder, S., & Pendell, K. (2014). Production and consumption: A closer look at adult digital literacy acquisition. Journal of Adolescent & Adult Literacy, 57(8), 624–627. doi:10.1002/jaal.293 Ke, F. (2010). Examining online teaching, cognitive, and social presence for adult students. Computers & Education, 55(2), 808–820. doi:10.1016/j.compedu.2010.03.013 Leong, P. (2011). Role of social presence and cognitive absorption in online learning environments. Distance Education, 32(1), 5–8. doi:10.1 080/01587919.2011.565495 McCallum, C. (2012). The perception of adult learners concerning their satisfaction of their educational experiences in a midwestern community college (Unpublished doctoral dissertation). Western Michigan University. Retrieved 1-3-16 from ProQuest Dissertations & Theses database.

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National Center for Educational Statistics. (n.d.a). Back to school statistics. Retrieved from http:// nces.ed.gov/fastfacts/display.asp?id=372 National Center for Educational Statistics. (n.d.b). Digest of educational statistics. Retrieved from https://nces.ed.gov/programs/digest/d14/tables/ dt14_303.40.asp National Center for Educational Statistics. (n.d.c). Distance learning. Retrieved from https://nces. ed.gov/fastfacts/display.asp?id=80 Park, J., & Choi, H. (2009). Factors influencing adult learners’ decision to drop out or persist in online learning. Journal of Educational Technology & Society, 12(4), 207–217. Pick, J. B., & Azari, R. (2008). Global digital divide: Influence of socioeconomic, governmental, and accessibility factors on information technology. Information Technology for Development, 14(2), 91–115. doi:10.1002/itdj.20095 Romero, M., & Barbera, E. (2011). Quality of learners time and learning performance beyond quantitative time-on-task. International Review of Research in Open and Distance Learning, 12(5), 125–137. doi:10.19173/irrodl.v12i5.999 Sogunro, O. A. (2015). Motivating factors for adult learners in higher education. International Journal of Higher Education, 4(1), 22–37. Stine, L. (2010). Basically unheard: Developmental writers and the conversation on online learning. Teaching English in the Two-Year College, 38(2), 132–148. The Hechinger Report. (2015). Heaviest Debt Burdens Fall On 3 Types of Students. Retrieved from: http://www.usnews.com/news/articles/2015/06/08/heaviest-college-debt-burdensfall-on-3-types-of-students

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Tyler-Smith, K. (2006). Early attrition among first time eLearner: A review of factors that contribute to drop-out, withdrawal and non-completion rates of adult learners undertaking eLearning programmes. MERLOT Journal of Online Learning and Teaching, 2(2), 73–85.

ADDITIONAL READING Calvin, J., & Winfrey-Freeburgh. (2010). Exploring adult learners’ perceptions of technology competence and retention in web-based courses. The Quarterly Review of Distance education, 11(2). Chang S., Shieh R., Liu E. & Yu,P. (2012). Factors influencing women’s attitudes towards computers in a computer literacy training program. Turkish Online journal of Educational Technology 11(4), 177-187.

Garland, V. E. (2010). Emerging technology trends and ethical practices for the school principal. Journal of Educational Technology Systems, 38(1), 39–50. doi:10.2190/ET.38.1.e Lee, Y., Choi, J., & Kim, T. (2012). Discriminating factors between completers of and dropouts from online learning courses. British Journal of Educational Technology, 44(2), 328–337. doi:10.1111/j.1467-8535.2012.01306.x Mok, K. H., & Leung, D. (2012). Digitalisation, educational and social development in Greater China. Globalisation, Societies and Education, 10(3), 271–294. doi:10.1080/14767724.2012.7 10118 Nartgun, S. (2011). Relationships between open education students’ economic profiles and their use of the Internet in education. Turkish Online Journal of Distance Education, 12(4), 179–200.

Cox, M. J., Niederhauser, D. S., Castillo, N., McDougall, A. B., Sakamoto, T., & Roesvik, S. (2013). Researching IT in education. Journal of Computer Assisted Learning, 29(5), 474–486. doi:10.1111/jcal.12035

Poellhuber, B., Anderson, T., & Roy, N. (2011). Distance Students readiness for social media and collaboration. International Review of Research in Open and Distance Learning, 12(6), 102–125. doi:10.19173/irrodl.v12i6.1018

Epstein, D., Nisbet, E., & Gillespie, T. (2011). Whos responsible for the digital divide? Public perceptions and policy implications. The Information Society, 27(2), 92–104. doi:10.1080/019722 43.2011.548695

Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. The Internet and Higher Education, 6(1), 1–16. doi:10.1016/S1096-7516(02)00158-6

Freeman, B. (2012). Using digital technologies to redress inequities for English language learners in the English speaking mathematics classroom. Computers & Education, 59(1), 50–62. doi:10.1016/j.compedu.2011.11.003

Yoo, S. J., Huang, W. D., & Wenhao, D. (2013). Engaging online adult learners in higher education: Motivational factors impacted by gender, age, and prior experiences. Journal of Continuing Higher Education, 61(3), 151–164. doi:10.1080/073773 63.2013.836823

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From Digital Natives to Student Experiences With Technology Sue Bennett University of Wollongong, Australia Linda Corrin University of Melbourne, Australia

INTRODUCTION The term ‘digital native’ was popularized by Prensky (2001) as a means to distinguish young people who were highly technologically literate and engaged. A ‘digital native’ can be defined as an individual who has grown up immersed in digital technology and is technologically adept and interested. The digital native is described in direct contrast to the ‘digital immigrant’, who having been exposed to digital technology later in life is fearful of it, mistrustful and lacks the skills to use technology adeptly. According to Prensky’s (2001) vision, all young people who have grown up since the widespread advent of the personal computer can be considered digital natives, and, by elimination, all older people are digital immigrants. It is argued that the existence of the digital native makes dramatic educational reforms necessary because traditional education systems do not, and can not, cater for the needs and interests of young people. As a result, outdated schools and universities and outmoded teaching simply alienate students from learning, leaving them disengaged and disenchanted by education’s alleged failure to adapt to the new digital world. By implication, education must be transformed by technology, coupled with new pedagogies. Although this argument is a familiar one to those acquainted with the broader educational technology literature, the digital native hypothesis provides a new basis

for claims for revolutionary educational change through technology integration. Recent research has revealed that the term is misapplied when used to generalized about an entire generation, and instead indicates that only a small sub-set of the population fits this characterization. This research shows significant diversity in the technology skills, knowledge and interest of young people, and suggests that there are important ‘digital divides’ which are ignored by the digital native concept. This chapter charts the development of the digital native idea and the debate that has surrounded it. It provides an account of the research and conceptual work it has stimulated, and suggests future directions research may take in the coming decades.

BACKGROUND The idea of the digital native appears to have first emerged in an essay entitled Declaration of the Independence of Cyberspace by Barlow (1995) in which he admonished parents with the charge: “You are terrified of your own children, since they are natives in a world where you will always be immigrants” (p.12). Papert (1996), in The Connected Family, similarly evokes a rift between parents and children, and teachers and students, portraying older generations as being both afraid of computers and technically incompetent. Clearly,

DOI: 10.4018/978-1-5225-2255-3.ch219 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Educational Technologies

the idea of a digital generation gap was gaining currency at this time. Regardless of its exact provenance, it has been Prensky who popularized the term ‘digital native’ in his widely cited 2001 article, Digital Natives, Digital Immigrants. Around the same time, Tapscott (1998) had put forward the similar notion of ‘the Net Generation’, while social commentators coined the term ‘Millenials’ as a generational label (Howe & Strauss, 2000). Since then a proliferation of less widely used epithets has appeared, all attempting to capture the essence of the same phenomenon (e.g., Generation C, Google Generation, Nintendo Generation, etc.). In short, the idea of the digital native captured the imaginations of teachers, parents, journalists, commentators and academics. Closer examination of Prensky’s arguments, particularly in his influential 2001 paper, reveals little in way of evidence to substantiate his claims, however. He relies on anecdotes, conjecture and speculation. Nonetheless his ideas have often been uncritically repeated and cited as if fact. Similar arguments purportedly based on evidence provide few details of the data collection methods and analysis processes, thwarting critical scrutiny of these studies (e.g., Tapscott, 1998; Palfrey & Gasser, 2008). This presents a significant challenge in assessing the quality of this research. It was a few years after Prensky’s 2001 paper before researchers began to seriously address his claims, apparently galvanized by dissatisfaction with his arguments. Since that time a significant body of international research has largely debunked the idea of a uniformly technically savvy generation. Instead it suggests that the label ‘digital native’ likely only applies to a small minority of the population. Of much greater interest is the wide diversity of technology use uncovered by this research. These differences are often thought of as ‘digital divides’ because they highlight significant gaps between the ways individuals and/ or communities engage with technology. These gaps present an ongoing challenge to those con-

cerned with equity and justice in education, and in society more broadly. More recently there have been attempts to redefine and rehabilitate the term ‘digital native’. In fact, this emerged in Dede’s (2005) argument that aptitude with technology is not necessarily related to age but to other personal characteristics. In recent years Prensky (2009) has also seemed to resile from his earlier sharp distinctions, praising rather than criticizing the role of the teacher. Nevertheless the original divisive idea remains potent. In the next section we turn to examine some of the research evidence that has emerged in response to the idea of the digital native.

RESEARCHING ‘DIGITAL NATIVES’ Researching Technology Use In the mid 2000s researchers began to investigate some of Prensky’s key claims about digital natives. The initial area of focus was on determining whether, in fact, digital technologies were as extensively used within younger generations of the population as was supposed by the digital native thesis (e.g. Kennedy, Krause, Judd, Churchward & Gray, 2006; Kvavik, Caruso & Morgan, 2004; Oliver & Goerke, 2007). These studies set about to establish the extent of access to and ownership of a wide range of technologies, and to discover the extent to which they were used for particular activities. In short, researchers wanted to know who was using what technology, how often and for what purposes. Similar research had already been conducted, for example through studies of children’s use of technology in and out of school (e.g., Downes, 2002; Kent & Facer, 2004; Kerawalla & Crook, 2002), but these studies were not specifically driven by the digital native concept. Related work was also being conducted in disciplines outside of education, such as youth studies, cultural studies and media studies, but again these did not relate to the digital native idea (e.g.,

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Livingstone & Helsper, 2007; Selwyn, 2003). These studies do, however, suggest that there was a broader appeal to research along these lines. Early ‘digital natives’ studies tended to use survey methods to collect data from large populations, often of higher education students. In this exploratory work researchers attempted to gain a broad perspective by collecting data from participants who are relatively easy to access with a focus on phenomena relatively easy to measure through self-report (e.g., Kennedy et al, 2006; Kvavik, Caruso & Morgan, 2004). While questions about access to technologies and frequency of use are common features of these studies, many have gone further to gauge skills, interests and preferences, have included multiple age ranges rather than only younger people, and in some cases incorporated qualitative methods to complement quantitative data. One of the most notable surveys has been the ECAR series in the United States, which has run since 2004 with consistently large sample sizes of college students (see Dahlstrom, Brooks, Grajek & Reeves, 2015 for the latest report). Similar studies from around the world have contributed to a developing understanding of technology use, particularly among young people (e.g., Jones, Ramanaua, Cross & Healing, 2010; Kennedy et al., 2009; Oliver & Goerke, 2007; Corrin, Lockyer & Bennett, 2010; Margaryan, Littlejohn & Vojt, 2011; Thompson, 2013; Teo, 2015; Akçayır, Dündar & Akçayır, 2016; Henderson, Finger & Selwyn, 2016; Šorgo, Bartol, Dolničar & Boh Podgornik, 2016). In sum, the main findings of these studies have been as follows: 1. There is near universal adoption of some technologies (e.g., mobile phones). 2. Some technologies have not been widely adopted, for example, RSS feeds and some forms of social media. The reasons for this are not clear, however. Perhaps, some technologies are too specialized, overly technical, or judged to be less useful.

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3. There are indicators of some differences due to age, gender, socio-economic background, year level of study, and discipline of study (at university or college), although findings are not consistent across all studies or all technologies. 4. The studies trace how some technologies are abandoned, for example, because they are superseded in favor of alternatives (e.g., the demise of MySpace and the rise of Facebook, and the shift from dial-up to broadband Internet access). 5. Skills, knowledge and interests are highly varied when comparing individuals. Findings suggest that individuals adapt their technology use to suit their needs and interests and the contexts they engage in. 6. Younger people often have lower skill and knowledge levels than what might be expected based on the digital native hypothesis. A common conclusion from these studies is that, while there appear to be some age-related factors, diversity is often higher within age groups than between them. It is also important to note that while large-scale survey studies can indicate patterns, the measures used are relatively crude and their accuracy is limited by participants’ abilities to recall and estimate their usage. There is a need for qualitative studies that are capable of exploring technology use in greater depth and with sensitivity to individuals’ contexts. There are also, to date, few studies from developing countries and of less affluent communities, making the global situation difficult to discern. In short, the research conducted thus far suggests that only a small minority of the population can be considered ‘digital natives’, even disregarding age as a factor to include technologically adept older people. People adopt technologies for a wide range of reasons and have diverse patterns and habits, and the skills they develop are often narrow and highly contextualized (i.e., fit for a particular purpose). As a result, it would be wrong

Category: Educational Technologies

to generalize about a section of a population on the basis of how they use technology, and in particular on the basis of presumed exposure to technology.

Implications for Education Prensky (2001) posed the problem for contemporary education as follows: “Our students have changed radically. Today’s students are no longer the people our educational system was designed to teach” (p. 1). This pronouncement was based on the assumption that all young people were digital natives being held back by an outdated education system. If, however, not all young people are digital natives, only some, and there is significant diversity within the population with regard to technological prowess, then the problem for education is somewhat different. The challenge of how education can cater appropriately for learners remains, but it is made more complicated by the fact that learners comprise a diverse rather than homogenous group. A further challenge for public education is that if some students are disadvantaged by virtue of their socio-economic situations, then how can an inclusive education system address that disadvantage? Concerns about a digital divide between the ‘haves’ and ‘have nots’ first emerged in relation to differences in access to technology (Warschauer, 2004). As technology became cheaper and easier for ordinary people to obtain, the focus shifted to differences in the skills and knowledge people have to make effective use of technology (Selwyn, 2004; Warschauer, 2004). And as ideas about what it means to be digitally literate have changed, this has seen a move away from a focus on developing people’s technical skills to a focus on developing their capacities to use technology responsibly, creatively and innovatively. This poses questions for education about how students can be equipped with these more sophisticated skills and understandings. The infusion of digital technologies into everyday life has also raised questions about the relation between technology in education and out,

particularly amongst those who speculate about how the high levels of motivation exhibited by young people while gaming or socializing online might be employed in learning (Prensky, 2001; Tapscott, 1999). This reflects a wider conversation about how Web 2.0 technologies might be integrated into education, and warnings that their application might not be straightforward because of fundamental differences between informal learning and formal educational contexts (Dohn, 2009). These discussions indicate that while the original digital native hypothesis is not a sound basis for recommending or planning educational change, differences in the ways technologies are used and their increasing prevalence in society continue to raise important questions for education. These are questions that need to be informed not only by empirical evidence gleaned from further research studies, but also by theories that help us to explain the phenomena and thereby better understand it.

Theoretical Perspectives Just as the original proposal of the digital native lacked empirical foundations, theoretical underpinnings were also absent. However, as the research agenda has developed, casting doubt on the general nature of the claims and in doing so revealing people’s diverse engagements with technology, researchers have begun to conceptualize both the nature of the debate itself and to propose theoretical constructs that might help to explain the phenomena and frame future investigations. The debate itself has been described as an academic form of a ‘moral panic’, a concept widely used in the social sciences (Bennett, Maton & Kervin, 2008). A moral panic, as described by Cohen (1972), occurs when a particular group is seen as a threat to societal norms. Importantly, the concern inspired exceeds the supporting evidence. Thus, the lack of evidence base and the extreme language used in arguments for the existence and importance of digital natives is consistent with a

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moral panic. This characterization is useful because it helps to explain how the idea gained such prominence on the basis of flimsy evidence. It also explains how the form for the debate stymied genuine academic discussion until the emergence of empirical research. More recently, researchers have proposed that this empirical evidence provide the stimulus for developing more sophisticated ways of thinking about and researching people’s technology use (e.g., Bennett & Maton, 2010). Drawing on a range of sociological theories, these authors argue that concepts related to social networks (Castells, 2001; Wellman, 2002), social practices (Bourdieu, 1990) and the nature of knowledge and education (Bernstein, 1999) are critical to advancing understanding in this area.

SOLUTIONS AND RECOMMENDATIONS Although research on young people’s use of technology has consistently found diversity in areas such as access, use and ability with technology, there are several ways educators can address these differences within educational environments. In acknowledging that diversity exists, educators and institutions should be encouraged to investigate the profile of students in their own context. Such understandings can inform effective design of technology-based activities and how best to articulate these to students. This includes making clear the purpose and value of using particular technologies in the context of learning activities (So, Choi, Lim & Xiong, 2012). At an institutional level, caution should be exercised when setting strategy and targets around levels of use of technology to avoid the use of technology for technology’s sake. While the digital native concept emphasizes the high levels of engagement with technology in young people’s everyday lives, research has revealed much lower levels of adaption of these technologies to support learning. The technology

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choices of young people to support their studies tend to focus on pragmatic functions like speed, convenience, and efficiency (Henderson, Selwyn, Finger & Aston, 2015; Thompson, 2013). Questions have also been raised about whether young people have the requisite pedagogical knowledge to inform innovative technology choices to support their learning (Margaryan et al., 2011). Consequently, assumptions cannot be made that young people will have the knowledge and skills to be able to adapt and use a wide range of technology in an educational context. As a result, educators have an important role to play in influencing and stimulating young people’s engagement with technology (Henderson, Finger & Selwyn, 2016). In order to do this, educators need support to develop their own digital literacy (Ng, 2012). Greater attention regarding enhancing digital literacy for young people in the context of education is required. Despite the increasing trend in young people’s exposure to and ownership of technology, ownership of technological devices alone does not directly impact levels of digital literacy (Šorgo, Bartol, Dolničar & Boh Podgornik, 2016). Recommendations from recent research consistently call for increased support for digital literacy in learning environments broadly, but also specifically within the context of particular learning activities (Buzzard et al., 2011; Toliver, 2011; Thompson, 2013). The form, timing, and extent of this support needs to be customized to the specific context, but also be flexible enough to cater for the diverse experiences and skills of the audience.

FUTURE RESEARCH DIRECTIONS Future research into people’s technology uses and choices will continue to monitor new developments, sparked by emerging technologies and changing patterns of adoption and use. In the short term, one focus will be on the impact of Web 2.0 technologies and their proposed capacity for democratizing participation in technology-based

Category: Educational Technologies

activities. More generally, the trend towards greater online connectivity through new services and devices will continue, and so pose further questions for researchers about digital divides and digital inclusion across societies. The role technology plays in supporting learning that happens in formal and informal educational contexts is another area of research that will provide insights that can inform design of learning activities and technology support. Future research will also require a commitment to developing more sophisticated understandings of technology use and choice. As noted above, in-depth qualitative research will be needed to provide insights into the diversity uncovered by recent surveys. Longitudinal studies that observe students’ technology choices over time are also needed to understand the factors that influence changes in interaction and transitions between technologies (Corrin, Bennett & Lockyer, 2013; Henderson, Finger & Selwyn, 2016). Findings from this work will enable the field to transcend simplistic labels and thereby truly account for the rich array of activities and practices with technology. These are developments that can underpin discussions about what role technology can and should play in education such that the best learning outcomes can be achieved for all students.

CONCLUSION To conclude, although misguided in its attempt to characterize a whole generation of young people, the idea of the digital native has been helpful in drawing educators’ and researchers’ attention to the under-researched area of young people’s technological experiences and preferences. It has stimulated a very productive and promising avenue for educational technology research that has the potential to lead to better informed decision-making about technology and to improved teaching and learning. The challenge for educators moving forward is to develop ways to

make effective use of technology in teaching and learning while acknowledging and addressing issues around students’ diversity in technology use and ability. This requires sophisticated thinking and careful consideration (Bennett & Maton, 2011) of the role of technology in teaching and learning that moves beyond a reliance on simplistic, generational labelling.

REFERENCES Akçayır, M., Dündar, H., & Akçayır, G. (2016). What makes you a digital native? Is it enough to be born after 1980? Computers in Human Behavior, 60, 435–440. doi:10.1016/j.chb.2016.02.089 Barlow, J. (1996). Declaration of the Independence of Cyberspace. Retrieved July 30, 2011 from http:// homes.eff.org/~barlow/Declaration-Final.html Bennett, S., & Maton, K. (2010). Beyond the digital natives debate: Towards a more nuanced understanding of students technology experiences. Journal of Computer Assisted Learning, 26(5), 321–331. doi:10.1111/j.1365-2729.2010.00360.x Bennett, S., & Maton, K. (2011). Intellectual field or faith-based religion: Moving on from the idea of ‘digital natives. In M. Thomas (Ed.), Deconstructing digital natives: young people, technology and the new literacies (pp. 169–185). New York: Routledge. Bennett, S., Maton, K., & Kervin, L. (2008). The digital natives debate: A critical review of the evidence. British Journal of Educational Technology, 39(5), 775–786. doi:10.1111/j.14678535.2007.00793.x Bernstein, B. (1999). Vertical and horizontal discourse: An essay. British Journal of Sociology of Education, 20(2), 157–173. doi:10.1080/01425699995380 Bourdieu, P. (1990). The Logic of Practice. Cambridge, UK: Polity.

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Buzzard, C., Crittenden, V. L., Crittenden, W. F., & McCarty, P. (2011). The Use of Digital Technologies in the Classroom: A Teaching and Learning Perspective. Journal of Marketing Education, 33(2), 131–139. doi:10.1177/0273475311410845 Castells, M. (2001). The Internet Galaxy. New York: Oxford University Express. doi:10.1007/9783-322-89613-1 Cohen, S. (1972). Folk Devils and Moral Panics. London: MacGibbon & Kee. Corrin, L., Bennett, S., & Lockyer, L. (2013). Digital Natives: Exploring the diversity of young people’s experience with technology. In R. Huang, Kinshuk, & J. M. Spector (Eds.), Reshaping Learning - The Frontiers of Learning Technologies in Global Context (pp. 113-138). New York: Springer-Verlag. Corrin, L., Lockyer, L., & Bennett, S. (2010). Technological diversity: An investigation of students technology use in everyday life and academic study. Learning, Media and Technology, 35(4), 387–401. doi:10.1080/17439884.2010.531024 Dahlstrom, E., Brooks, D. C., Grajek, S., & Reeves, J. (2015). ECAR Study of Students and Information Technology. Louisville, CO: ECAR. Dede, C. (2005). Planning for neomillennial learning styles: Implications for investments in faculty and technology. In D. Oblinger & J. Oblinger (Eds.), Educating the Net Generation (pp. 15.115.22). Boulder, CO: EDUCAUSE. Retrieved March 31, 2006, from http://www.educause.edu/ educatingthenetgen Dohn, N. (2009). Web 2.0: Inherent tensions and evident challenges for education. ComputerSupported Collaborative Learning, 4(3), 343–363. doi:10.1007/s11412-009-9066-8 Downes, T. (2002). Blending play, practice and performance: Children’s use of computer at home. Journal of Educational Enquiry, 3(2), 21–34.

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Henderson, M., Finger, G., & Selwyn, N. (2016). What’s used and what’s useful? Exploring digital technology use(s) among taught postgraduate students. Active Learning in Higher Education, 1–13. Henderson, M., Selwyn, N., Finger, G., & Aston, R. (2015). Students everyday engagement with digital technology in university: Exploring patterns of use and usefulness. Journal of Higher Education Policy and Management, 37(3), 308–319. doi:10 .1080/1360080X.2015.1034424 Howe, N., & Strauss, W. (2000). Millennials Rising: The Next Great Generation. New York: Vintage. Jones, C., Ramanaua, R., Cross, S., & Healing, G. (2010). Net generation or Digital Natives: Is there a distinct new generation entering university? Computers & Education, 54(3), 722–732. doi:10.1016/j.compedu.2009.09.022 Kennedy, G., Dalgarno, B., Bennett, S., Gray, K., Waycott, J., Judd, T.,... Chang, R. (2009) Educating the Net Generation - A Handbook of Findings for Practice and Policy. Australian Learning and Teaching Council. Retrieved October 19, 2009, from http://www.altc.edu.au/system/files/ resources/CG6-25_Melbourne_Kennedy_Handbook_July09.pdf Kennedy, G., Krause, K., Judd, T., Churchward, A., & Gray, K. (2006). First Year Students’ Experiences with Technology: Are They Really Digital Natives? Melbourne, Australia: University of Melbourne. Retrieved April 10, 2007, from http:// www.bmu.unimelb.edu.au/research/munatives/ natives_report2006.rtf Kent, N., & Facer, K. (2004). Different worlds? A comparison of young peoples home and school ICT use. Journal of Computer Assisted Learning, 20(6), 440–455. doi:10.1111/j.13652729.2004.00102.x Kerawalla, L., & Crook, C. (2002). Childrens computer use at home and at school: Context and continuity. British Educational Research Journal, 28(6), 751–771. doi:10.1080/0141192022000019044

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Kvavik, R. B., Caruso, J. B., & Morgan, G. (2004). ECAR Study Of Students And Information Technology 2004: Convenience, Connection, and Control. Boulder, CO: EDUCAUSE Center for Applied Research. Livingstone, S., & Helsper, E. (2007). Gradations in digital inclusion: Children, young people, and the digital divide. New Media & Society, 9(4), 671–696. doi:10.1177/1461444807080335 Margaryan, A., Littlejohn, A., & Vojt, G. (2011). Are digital natives a myth or reality? University students use of digital technologies. Computers & Education, 56(2), 429–440. doi:10.1016/j. compedu.2010.09.004 Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078. doi:10.1016/j.compedu.2012.04.016 Oliver, B., & Goerke, V. (2007). Australian undergraduates use and ownership of emerging technologies: Implications and opportunities for creating engaging learning experiences for the Net Generation. Australasian Journal of Educational Technology, 23(2), 171–186. doi:10.14742/ ajet.1263 Palfrey, J., & Gasser, U. (2008). Born digital: Understanding the first generation of digital natives. New York: Basoc Books.

Selwyn, N. (2003). ‘Doing IT for the kids’: Re-examining children, computers and the ‘information society’. Media Culture & Society, 25, 351–378. Selwyn, N. (2004). Reconsidering political and popular understandings of the digital divide. New Media & Society, 6(3), 341–362. doi:10.1177/1461444804042519 So, H., Choi, H., Lim, W. Y., & Xiong, Y. (2012). Little experience with ICT: Are they really the Net Generation student-teachers? Computers & Education, 59(4), 1234–1245. doi:10.1016/j. compedu.2012.05.008 Šorgo, A., Bartol, T., Dolničar, D., & Boh Podgornik, B. (2016). Attributes of digital natives as predictors of information literacy in higher education. British Journal of Educational Technology. Tapscott, D. (1998). Growing up Digital: The Rise of the Net Generation. New York: McGraw-Hill. Tapscott, D. (1999). Educating the Net Generation. Educational Leadership, 56(5), 6–11. Teo, T. (2015). Do digital natives differ by computer self-efficacy and experience? An empirical study. Interactive Learning Environments, 1–15. Thompson, P. (2013). The digital natives as learners: Technology use patterns and approaches to learning. Computers & Education, 65, 12–33. doi:10.1016/j.compedu.2012.12.022

Papert, S. (1996). The Connected Family: Bridging the Digital Generation Gap. Atlanta, GA: Longstreet Press.

Toliver, F. (2011). My students will Facebook me but won’t keep up with my online course: The challenges of online instruction. American Communication Journal, 13(1), 59–81.

Prenksy, M. (2001). Digital Natives, Digital Immigrants. On the Horizon, 9(5), 1–6. doi:10.1108/10748120110424816

Warschauer, M. (2004). Technology and Social Inclusion: Rethinking the Digital Divide. Cambridge, MA: MIT Press.

Prensky, M. (2009). H. sapiens digital: From digital immigrants and digital natives to digital wisdom. Innovate, 5(3). Retrieved February 4, 2009, from http://innovateonline.info/index. php?view=article&id=705

Wellman, B. (2002). Little boxes, glocalization, and networked individualism? In M. Tanabe, P. van den Besselaar, & T. Ishida (Eds.), Digital Cities II: Computational and Sociological Approaches (pp. 10–25). Berlin: Springer. doi:10.1007/3-54045636-8_2

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ADDITIONAL READING Buckingham, D. (2007). Beyond Technology: Children’s Learning in the Age of Digital Media. Cambridge: Polity Press. Centre for Education Research and Innovation. (2010). Are the new millennium learners making the grade: Technology use and educational performance in PISA. France: Organisation for Economic Co-operation and Development (OECD). Cuban, L. (2001). Oversold and underused: Computers in the classroom. Cambridge, MA: Harvard University Press. Kennedy, G., Dalgarno, B., Bennett, S., Gray, K., Judd, T., Waycott, J.,... Krause, K. (2009). Educating the Net Generation: Implications for Learning and Teaching in Australian Universities. Australia: The University of Melbourne, Charles Sturt University, Griffith University, The University of Sydney, University of Wollongong. Published under Creative Commons Attribution-NoncommercialShareAlike 2.5. Available online: http://www. altc.edu.au/system/files/resources/CG6-25_Melbourne_Kennedy_Final%20Report_July09_v2.pdf Selwyn, N. (2010). Schools and schooling in the digital age. London: Routledge. Thomas, M. (2011). Deconstructing digital natives. New York: Routledge.

KEY TERMS AND DEFINITIONS Digital Divide: Digital divides are gaps between individuals or groups due to differences in their access to digital technologies. Access

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refers to more than physical access, including also the ability to use technologies effectively. Divisions may occur due to factors such as age, gender, race/ethnicity, socio-economic status and/ or geographic location. Digital Generation Gap: The digital generation gap refers to the proposed gap between children and adults (especially parents and teachers) due to young people’s natural ability to adapt to new technologies more successfully than older generations. Digital Immigrant: A digital immigrant is a person born before the widespread adoption of computers and has had to adopt digital technology later in life. Digital immigrants are considered to be less technically able than digital natives and it is argued that they can never develop the same level of technology skills and knowledge as digital natives. Digital Inclusion: Digital inclusion refers to mindsets, strategies and initiatives that seek to ensure that all people in society have equitable access to technology regardless of their personal circumstances. It is underpinned by the belief that access to technology and the ability to use it effectively are important to citizenship and social cohesion. Digital Native: In its original sense, a digital native is a person who has grown up after the widespread introduction of the personal computer and therefore been immersed in digital technology. It is claimed that by virtue of this exposure digital natives think, behave and learn differently to older generations. More recently the term has been redefined by some to refer to a person of any age who is highly adept with technology.

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Category: Educational Technologies

ICT Eases Inclusion in Education Dražena Gašpar University of Mostar, Bosnia and Herzegovina

INTRODUCTION Access to education was defined as a fundamental human right in the framework of the Declaration on the Human Rights from 1948. However, the World Report on Disability written in 2011 by the World Health Organization and the World Bank estimates that there are between 93 and 150 million school-aged children with disabilities around the world (UNESCO, 2014). Unfortunately, the fact is that many of these children are completely excluded from educational opportunities, even primary education. Also, because of their learning difficulties, great numbers of children do not have equal access to education. The inclusion of children with special needs in regular classes creates an entire range of challenges within a given school system and requires the application of new methods and forms of work that are appropriate to each child. Information and Communication Technologies (ICT) should be increasingly involved in the educational system to improve the quality of teaching, and to provide new experiences during the teaching and learning processes. In this paper term ICT covers all the technologies used to communicate, create, store and manage information. With use of ICT it becomes possible to meet the specific needs of different groups of students, including students with special needs. For students with special needs, ICT can offer numerous ways to remove obstacles as they try to participate in the teaching and learning of the curriculum. This chapter presents brief analyses of different supportive technologies, such as hardware and software solutions, Web 2.0 technologies, virtual learning environments (VLEs), virtual

worlds, and other similar technologies. The aim of this chapter is to show the potential of ICT in education, especially when facilitating student inclusion. This chapter will also stress some open issues, including limitations in interactions, communication, and learning. ICT can provide new opportunities in inclusive education, and despite all of its potential limitations, ICT should be considered as a key tool to promote equity in educational opportunities.

BACKGROUND In this paper, the definition of inclusive education, as set out by UNESCO, is adopted: Inclusive education is a process of strengthening the capacity of the education system to reach out to all learners... As an overall principle, it should guide all education policies and practices, starting from the fact that education is a basic human right and the foundation for a more just and equal society (UNESCO, 2014, p.11). Coupled with the process of inclusion, the term “special educational needs” (SEN) is often used across Europe. This term is frequently adopted to specify learners who encounter barriers to learning, either temporarily or in the long term (EADSNE, 2013, p.6). This definition stresses that the term SEN covers not just learners with disabilities, but all learners who, for various reasons, do not make expected progress for their age. According to this concept, learners with SEN comprise a wider group of students than those with disabilities alone and there are some estimates that these individuals account for around 20% of the school-age population (EADSNE, 2013, p.6).

DOI: 10.4018/978-1-5225-2255-3.ch220 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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ICTs that are used to support children, young people, and adults with disabilities, are commonly referred to as assistive technologies (AT), although there is no one single internationally accepted definition for this term. The British Assistive Technology Association (BATA), a social enterprise that focuses on AT for inclusion in education, defines AT as any item, equipment, hardware, software, product or service which maintains, increases or improves the functional capabilities of individuals of any age, especially those with disabilities, and enables them more easily to communicate, learn, enjoy and live better, more independent lives (BATA, 2015, para.2). In this chapter, the BATA definition of AT is used. Research into assistive learning technologies has grown significantly in the last decade. Today, research into specific topics can be found in many books and journals worldwide and is the focus of many specialist publications from a variety of disciplines. As such, including all research related to ICT – or, more precisely, to AT – in this short review is in the advance lost battle. Therefore, this review should be understood simply as the author’s own choice of sources related to research on ICT in inclusive education, mostly from last five years. Recent researches have shown that ICT, in its various forms, is decreasing the gap in education and enabling the inclusion of students with special educational needs in classrooms with their classmates; furthermore, AT help students largely reach their educational goals (Brodin, 2010). Several studies point to the idea that ICT could help SEN students, particularly students with reading and/or writing disabilities, through word processors, word prediction programs, spell and grammar checks, voice recognition, text-to-speech (TTS) programs, planning and organizing tools, etc. (Anderson et al., 2009; Maor et al., 2011; Peterson-Karlan, 2011). Also, a number of studies have shown that all teachers should be familiar with the use of ICT for SEN students because students who benefit from ICT can be found in every classroom (Anderson et al., 2009; Starcic, 2010). According to the parents in Brodin’s (2010) study, ICT was 2522

used only to a limited extent, and they complained about the teacher’s lack of knowledge about ICT use and tools. In the last few years, the term “accessible technology” has been adopted to define technologies designed to allow learners to use mainstream technology without any disadvantages, as opposed to AT, which specifically support SEN learners (Barres et al., 2013; McKnight & Davies, 2012). Of course, between those two technologies, considerable overlap exists. Namely, ICT developers apply inclusive design approaches, so their technology can be used instead of AT; using methods designed to make technologies or information accessible, therefore may be counted as assistive technology approaches, as the approach is assistive, even if the technology is not necessarily so (McKnight & Davies, 2012, p.13).

ICT SUPPORT FOR INCLUSIVE EDUCATION The implementation of inclusive education within the regular school system involves a number of activities that take place throughout the entire school practice of all of its participants. The inclusion of children with special needs in regular classes requires the application of new methods and forms of work appropriate to each child (Gašpar & Vetma, 2014). This process is, by itself, very complex and its results are usually not instantly visible. ICT support could ease that process for all stakeholders and make the results more visible. When inclusive education is in question, all aspects of the use of ICT become important. ICT could provide SEN students with the following key inclusive benefits (Winter & O’Raw, 2010, p.87): • •

Better control over their own learning experience. Students can participate and contribute more fully in classroom activities, and they can also complete assignments independently.

Category: Educational Technologies

•

Students can interact, to a greater extent, with their typical peers, ultimately improving their social skills and enhancing their acceptance.

ICT Classifications There are different approaches and classifications about what inclusive ICTs in education comprise. The reason for this lies in the fact that ICTs/ATs are very diverse, numerous, and are constantly undergoing change. In one of its documents, UNESCO recognized that inclusive ICTs for education include (UNESCO, 2014, p.11): •

•

• •

•

Mainstream Technologies: Commercial products available in the market and dedicated to all individuals (computers, Web browsers, word processors, whiteboards, mobile phones and etc.). Assistive Technologies: Specific technologies that enable SEN learners to access and use mainstream technologies (medical aids, learning aids such as screen readers, alternative keyboards, augmentative and alternative communication devices, and etc.). Compatibility between AT products and mainstream technologies. Accessible media and formats, such as mainstream publication formats (MS Word, PowerPoint, and structured and tagged PDF files) or HTML5 (Hypertext Markup Language), videos with captioning, DAISY (Digital Accessible Information System) books, EPUB, etc. Accessible digital learning content and instructional delivery systems, such as those found in online learning environments, in the classroom, or in learners’ management systems.

However, there are other approaches, like the ones used at LEARN (Leading English Education and Resource Network), a non-profit organiza-

tion that primarily serves the public and private Anglophone and Aboriginal Youth and Adult education sectors of Québec, Canada (LEARN, 2013). LEARN organized learning aid in two main groups: computer and peripherals and learning and communication aid. A different approach is used by Futurelab at NFER – the National Foundation for Educational Research in the United Kingdom (FUTURELAB, 2009). Futurelab organized AT in six categories: mobile technologies, audiovisual tools, online communities, podcasts, blogs, wikis, games and learning platforms. The focus of this chapter is primarily on specific the ICT used for inclusion education (AT), rather than on the general accessibility of mainstream ICT, especially when hardware and software programs are in question. The currently developed AT classification follows the main chapter’s idea and comprises three main types of AT: • • •

AT based on hardware solutions, AT based on software solutions, and AT based on Web technologies.

Hardware AT solutions are comprised of devices that ease one’s access to a computer (PC or laptop), such as substitutes to the standard keyboard (ergonomic keyboard, keyboard with large keys, pen-touch keyboard, adjustable keyboard, touchless keyboard, left-handed/one-handed keyboard, Braille keyboard, voice-activated keyboard, illuminated keypad, etc.), a substitute to a standard screen (cursor/pointer tracking, screen reader – Braille, and speech synthesizer, etc.), or a substitute to a standard mouse (trackball, switches, mouthactivated pointing device, foot-activated pointing device, head- or eye-activated pointing device, voice-activated control device, touch screen, mouse emulator, etc.). Also, hardware AT solutions include other devices (tablet, mobile phone, digital camera, scanners, text [Optical Character Recognition, or OCR] scanner, barcode scanner with synthesized voice, devices with symbol-based communication, and TTS output, etc.) that could be used as input devices or for data capture and

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communication support to an adapted learning environment. For example, OCR is a method of converting text from paper format to an electronic version, usually by using a scanner. Accordingly, books, printed worksheets, even photographs with graphics and text can be converted to digital form and read aloud using TTS. Software AT solutions are comprised of software-enabling oral/written communication (word processors, voice recognition, TTS, word prediction, etc.), remote communication software (e-mail software with icons), software support for specific subjects (spelling/grammar check, dictionary, visual dictionary, digital calculator, digital microscope, digital encyclopedia, notation software for music, sequencer music software, synthesizer, paint or image editing software), and software support for planning and organizing (graphics organizers, electronic agenda, digital portfolio, etc). For instance, some of the built-in features in standard word processing software can support students who have difficulty with written language and processing. For example, spell check helps students with dysgraphia and other learning disabilities. Also, the autocorrect feature can be enabled or disabled depending on students’ strengths and needs. Grammar check helps students identify awkward grammatical constructions like passive sentences. TTS add-ins support auditory proofing before students submit their work, while numerous free TTS add-ins for Microsoft Word are also available (Hobgood & Ormsby, 2014). Web technologies that could be used in inclusive education are comprised of social networks (Facebook, Twitter, LinkedIn, etc.), Website creation tools (blogs, wikis, etc.), collaborative tools (virtual learning environments), threedimensional (3D) tools (virtual worlds, virtual touch, etc.), audio tools (audio creating, audio sharing, etc.), video tools (video creating, video sharing, video streaming, etc.), text-based tools (note-taking and document creating software, discussion forums, etc.), and image-based tools (image sharing, online whiteboards, drawing tools,

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etc.). Web-based systems provide opportunities for young people to access multimedia-rich resources, which can support SEN learners, and also provide a richer learning experience for all. For instance, for students who do well with written products, online text platforms like blogs and wikis can increase motivation by offering the promise of an attractive product with a “real” audience. Some blogging sites offer teachers the ability to create a classroom blog linked to individual student blogs (Hobgood & Ormsby, 2014).

Virtual Learning Environment A Virtual Learning Environment (VLE), also known as Course Management Systems (CMS) or Learning Management Systems (LMS), is a system through which learning materials are delivered to students via the Web. These systems include assessment, student tracking, collaboration, and communication tools (OXFORD ORC, 2015). These environments also offer students access wherever they are, 24 hours a day, 7 days a week, 365 days a year. Because of that, educational institutions can offer lessons not only to full-time students, but also to workers studying part time. There are different types of VLE and they fit into any one of the following three categories (OXFORD ORC, 2015, para.2): • •

•

Off-the-shelf environments, such as Blackboard or WebCT. Open source environments (often free to use and adapt, but where support is charged for), such as Moodle. Figure 1 is an example of a Moodle screen. Bespoke (developed by institutions for their own individual needs).

Virtual World A virtual world is a computer-based, online community environment that is designed and shared by individuals so that they can interact in a custombuilt, simulated world. Today, users interact in

Category: Educational Technologies

Figure 1. Example of a Moodle screen (MOODLE, 2015)

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Source: Moodle, 2015.

the virtual world using graphical models called avatars. Avatars are controlled using input devices (keyboard, mouse, and other specially designed simulation gadgets). All virtual worlds possess the qualities of persistence and interactivity. This enables the users to explore the inherent benefits of socialization and allows them to study human nature and users’ abilities (TECHOPEDIA, 2015). The educational activities that can be carried out in a virtual world are quite diverse (Dalgarno & Lee, 2012, p.241): • • •

• •

Exploration (simulation of a library, school, or church). Practicing with theoretical concepts through interaction (with other avatars or with objects in simulated locations). Carrying out activities that are difficult to perform in the real world, either for their difficult implementation or dangerousness (e.g., simulation of a medical intervention, handling chemicals). “Role playing” activities, assigning different roles to each student. Playing serious games, in which the student learns in a playful way.

Some of the popular virtual worlds are: Second Life, OpenSim, Unity, and Active Worlds. One example of the use of a virtual world in education is the “Accessibility in Virtual Worlds” project, which is aimed at blind students, whose positions are indicated by sounds, thus enabling navigation and interaction with peers, both blind and sighted (Sheehy, 2010). Another example that uses virtual worlds for inclusion is Brigadoon. It is an island in Second Life that serves as a therapeutic place for people with Asperger syndrome (autism); it facilitates these students’ social interactions by using avatars in a controlled environment (Biever, 2007). Figure 2 is an example of a Second Life screen.

SOLUTIONS AND RECOMMENDATIONS The process of implementing ICT in inclusive education is very complex, resource consuming (specifically of people, time, and material resources), and it does not always feature clearly visible and measurable results. The latest OECD PISA (Program for International Student Assessment)

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Figure 2. Example of a Second Life screen (Second Life, 2015). Source: Second Life, 2015.

report issued on September 15, 2015 says that even countries which have invested heavily in information and communication technologies (ICT) for education have seen no noticeable improvement in their performances in PISA results for reading, mathematics or science (OECD, 2015a, para.2). Conversely, although only 42% of 15-year-old students in Korea and 38% in Shanghai–China stated that they used computers at schools, students in both Korea and Shanghai–China were among the top performers in digital reading and computer-based mathematics tests in the OECD PISA in 2012. On the other hand, in countries where students demonstrated more Internet use at school for schoolwork, students’ performance in reading generally declined between 2000 and 2012 (OESCD, 2015b, p.15). The results of the OECD PISA report show that ICTs have not yet been widely adopted in formal education. But, where they are used in the classroom, their impact on student performance is mixed, at best. In fact, PISA results show no appreciable improvements in student achievement in reading, mathematics or science in the countries that had invested heavily in ICT for education (OECD, 2015b, p.15). One of the possible interpretations of these findings could be that educators need additional time and effort to learn how to use ICT in the classroom

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and, at the same time, they need to stay focused on student learning. Because of that, the PISA report results should not be interpreted in such a way that investment in and implementation of ICTs in education should be stopped. On the contrary, these results point to the need for critical thinking about how to implement and integrate ICTs in education, particularly in inclusive education. Although in most countries, gaps in computer access between advantaged and disadvantaged students has lessened between 2009 and 2012, the results from the PISA report show that once the so-called “first digital divide” (access to computers) is bridged, the remaining difference between socio-economic groups, in the ability to use ICT tools for learning is largely explained by the difference observed in more traditional academic abilities (OECD, 2015b, p.16). The conclusion is that in order to create equal opportunities in the digital world, it is better to ensure that all learners achieve a basic level of proficiency in reading and mathematics than to get expanded access to high technology (OECD, 2015b, p.16). It is obvious that relationships between learners, ICT, and the learning process are very complex and loose, so any real contributions that the ICT could make to teaching and learning have yet to be fully researched.

Category: Educational Technologies

There are a lot of open issues related to the process of ICT implementation in inclusive education, such as: •

• •

• • •

• • •

How to ensure full engagement, dedication, and participation of all relevant stakeholders (governments, teachers, [SEN] learners, parents…). How to ensure full cross-sectorial and intergovernmental agency cooperation and coordination. How to avoid harmful aspects of Internet use (information overload, plagiarism, fraud, violations of privacy, online bullying, etc.). Development, implementation, and monitoring of realistic strategies related to ICTs in inclusive education. Overestimation of digital skills among learners, teachers, and parents. Hidden resistance of teachers, learners, and parents (everyone supports innovation, but not in his/her school and not for his/her children). The benefits of “good practice” cases are mostly insufficient to mobilize support (the costs for “bad practice” are concentrated). The relatively low quality of educational software and courseware. The loose standards in developing educational software and courseware.

Some of the recommendations for coping with the listed issues are as follows: •

•

Previous experiences with the implementation of ICTs in inclusive education, whether good or bad, should be something to learn from. The government should have to ensure the framework for successful implementation of ICT in inclusive education. That framework includes appropriate policy documents (strategies, action plans, etc.) developed in coordination with all rel-

• •

•

•

•

evant stakeholders (governments, ministries, agencies, teachers, parents, etc.). Also, government responsibility includes developing adequate ICT infrastructure (especially communication infrastructure, such as providing broadband access to the Internet), additional support to educate teachers and parents about ICT in inclusive education, and enabling access to information about available ICT tools and standards (Websites). The foundation skills necessary for the digital age should be taught, and not always with the use of ICT. Teachers have to become not just leaders in the implementation of ICTs in inclusive education, but also equal partners in their design. Teachers are crucial in ensuring the successful exploitation of ICT in inclusive education, but they require education and training to equip them to succeed. Teachers should combine their efforts and experience to improve /develop teaching and learning methodologies featuring fully integrated ICT. ICT should support the teaching and learning process. The focus of teachers should be on learners, teaching, and learning methods.

ICT can be a valuable tool for SEN learners who are more vulnerable to the digital divide and to exclusion from educational opportunities. If ICT is implemented and integrated into teaching and learning processes in an appropriate way, ICT can improve students’ quality of life by increasing their participation and reducing social exclusion.

FUTURE RESEARCH DIRECTIONS Further research related to ICT in inclusive education should be focused on the following directions:

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•

•

•

• •

Forming interdisciplinary teams composed of teachers, learners, parents, ICT professionals, pedagogues, psychologists, etc., in designing and developing inclusive ICT tools. Forming interdisciplinary teams composed of teachers, learners, parents, ICT professionals, pedagogues, psychologists, etc. in developing new and improving existing teaching and learning methods. Forming interdisciplinary teams composed of teachers, learners, parents, ICT professionals, pedagogues, psychologists, etc. in developing and implementing standards for inclusive ICT. Research the use of tablet and mobile phones in inclusive education. Research new methods of interaction with ICT, which are achieved by body gestures, eye movement, and via the brain and nervous system.

CONCLUSION This chapter briefly described the types of technologies used, and the ways in which they can be used, in inclusive education. Although all aspects of the use of ICT are important, many researchers have observed the importance of how ICT is applied – meaning how it is integrated with teaching methodologies. All past experiences of ICT implementation in inclusive education have demonstrated that providing the technology itself is not enough. Teachers are essential in that process, but they require additional education and training, as well as more information on how to choose appropriate ICT tools. Namely, the use of ICT in inclusion is a lifelong requirement that extends beyond the classroom. Some learners require that information is provided in a form that they can easily understand (in an easy-to-read or symbol format), or on an accessible Website that can be read aloud using screen-reading technology. Other

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students might need a device that speaks, and thus supports communication and social interaction. By allowing users to learn at their own pace, ICT can also encourage less able students while enhancing their self-confidence. New ICT devices, like tablets and smart phones, as well as new methods of interacting with technology via body gestures, touch, eye movement, and even directly via the brain and nervous system, are providing new opportunities, as well as potential barriers, for interaction, communication, and learning, thus opening the door to new research opportunities. However, the general conclusion is that ICT should be considered as a key tool for promoting equity in educational opportunities, and that access to appropriate ICTs should be considered as an essential human right.

REFERENCES Anderson, C. L., Anderson, K. M., & Cherup, S. (2009). Investment vs. return: Outcomes of special education technology research in literacy for students with mild disabilities. Contemporary Issues in Technology & Teacher Education, 9(3), 337–355. Barres, G. D., Carrión, C. Z., & Delgado, R. (2013). Technologies for Inclusive Education: Beyond Traditional Integration Approaches. Hershey, PA: IGI Global. doi:10.4018/978-1-4666-2530-3 BATA - British Assistive Technology Association. (2015). Retrieved August, 14, 2015 from http://www.bataonline.org/further-assistivetechnology-definition Biever, C. (2007). Web removes social barriers for those with autism. New Scientist, 26–27. Brodin, J. (2010). Can ICT give children with disabilities equal opportunities in school? Improving Schools, 13(1), 99–112. doi:10.1177/1365480209353483

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Dalgarno, B., & Lee, M. J. W. (2012). Exploring the relationship between afforded learning tasks and learning benefits in 3D virtual learning environments. Proceedings of the 29th ASCILITE Conference, 236–245. EADSNE – European Agency for Development in Special Needs Education. (2013). Information and communication technology for inclusion. Odense, Denmark: European Agency for Development in Special Needs Education. FUTURELAB. (2009). Using digital technologies to promote inclusive practices in education - a Futurelab handbook. Retrieved April, 22, 2015 from http://archive.futurelab.org.uk/resources/ documents/handbooks/digital_inclusion3.pdf Gašpar, D., & Vetma, S. (2014). ICT in promoting inclusive practice in education. In Proceedings of International Conference on e-Education - ICeE (pp. 55-62). Mostar, Bosnia and Herzegovina: Faculty of Information Technologies, University “Džemal Bijedić”. Hobgood, B., Ed, D., & Ormsby, L. (2014). Inclusion in the 21st-century classroom: Differentiating with technology. Retrieved July, 12, 2015 from http://www.learnnc.org/lp/editions/ every-learner/6776 LEARN - Leading English Education and Resource Network. (2013). Types of Assistive Technology. Retrieved July, 14, 2015 from http://www. learnquebec.ca/en/content/pedagogy/insight/ intech/assistive_technology/AssisTechTypes.htm Maor, D. C. J., & Drewry, R. (2011). The effectiveness of assistive technologies for children with special needs: A review of research-based studies. European Journal of Special Needs Education, 26(3), 283–298. doi:10.1080/08856 257.2011.593821 McKnight, L., & Davies, C. (2012). Current Perspectives on Assistive Learning Technologies – 2012 review of research and challenges within the field. Oxford, UK: The Kellogg College Centre for Research into Assistive Learning Technologies.

MOODLE. (2015). Retrieved September, 9, 2015 from http://school.demo.moodle.net/mod/forum/ view.php?id=158 OECD. (2015a). New approach needed to deliver on technology’s potential in schools. Retrieved September, 22, 2015 from http://www.oecd.org/ education/new-approach-needed-to-deliver-ontechnologys-potential-in-schools.htm OECD. (2015b). Students, Computers and Learning: Making the Connection - Executive Summary. Retrieved September, 22, 2015 from http://www. keepeek.com/Digital-Asset-Management/oecd/ education/students-computers-and-learning/executive-summary_9789264239555-2-en#page1 OXFORD ORC. (2015). Learn about Virtual Learning Environment/Course Management System content. Retrieved August, 30, 2015 from http://global.oup.com/uk/orc/learnvle/ Sheehy, K. (2010). Virtual worlds, inclusive education The TEALEAF framework. In K. Sheehy, R. Ferguson, & G. Clough (Eds.), Controversial Issues in Virtual Education: Perspectives on Virtual Worlds (pp. 1–15). New York, NY: Nova Science Publishers. Starcic, A.I. (2010). Educational Technology for the Inclusive Classroom. Turkish Online Journal of Educational Technology, 9(3), 26-37. TECHOPEDIA. (2015). Definition - What does Virtual World mean? Retrieved May, 10, 2015 from https://www.techopedia.com/definition/25604/virtual-world UNESCO. (2014). Model Policy for Inclusive ICTs in Education for Persons with Disabilities. Retrieved, June, 11, 2015 from http://www.unesco. org/new/en/communication-and-information/ resources/publications-and-communicationmaterials/publications/full-list/model-policyfor-inclusive-icts-in-education-for-persons-withdisabilities/

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Winter, E., & O’Raw, P. (2010). Literature Review of the Principles and Practices relating to Inclusive Education for Children with Special Educational Needs. National Council for Special Education.

ADDITIONAL READING Beacham, N., & McIntosh, K. (2014). Student teachers attitudes and beliefs towards using ICT within inclusive education and practice. Journal of Research in Special Educational Needs, 14(3), 180–191. doi:10.1111/1471-3802.12000 Benigno, V., Bocconi, S., & Ott, M. (2007). Inclusive education: helping teachers to choose ICT resources and to use them effectively. eLearning Papers,6, 1-13. Blamire, R. (2009).ICT Impact Data at Primary School Level: the STEPS approach. In Scheuermann, F., & Pedro, F. (eds.) Assessing the effects of ICT in education, 199-211. European Union/ OECD. France Bocconi, S., Kampylis, P., & Punie, Y. (2013). Framing ICT-enabled Innovation for Learning: The case of one-to-one learning initiatives. Europe European Journal of Education, 48(1), 113–130. doi:10.1111/ejed.12021 Chantry, J., & Dunford, C. (2010). How do computer assistive technologies enhance participation in childhood occupations for children with multiple and complex disabilities? A review of the current literature. British Journal of Occupational Therapy, 73(8), 351–365. doi:10.4276/0308022 10X12813483277107 Corn, J., Tagsold, J. T., & Argueta, R. (2012). Students with special needs and 1:1 computing: a teachers perspective. Journal of Research in Special Educational Needs, 1(2), 217–223. doi:10.1111/j.1471-3802.2012.01251.x Edyburn, D. L. (2013). Critical issues in advancing the special education technology evidence base. Exceptional Children, 80(1), 7–24.

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Edyburn, D. L. (Ed.). (2015). Efficacy of Assistive Technology Interventions. Bingley, PA: Emerald Group Publishing Limited. doi:10.1108/S2056769320151 European Agency for Development in Special Needs Education. (2013). European and International Policy for Supporting ICT for Inclusion. Odense.Denmark: European Agency for Development in Special Needs Education European Commission (2013).Survey of Schools: ICT in Education – Benchmarking Access, Use and Attitudes to Technology in Europe’s Schools. EC Brussels Foley, A., & Ferri, B. A. (2012). Technology for people, not disabilities: Ensuring access and inclusion. Journal of Research in Special Educational Needs, 12(4), 192–200. doi:10.1111/j.14713802.2011.01230.x Friesen, E. L., Walker, L., Layton, N., Astbrink, G., Summers, M., & De Jonge, D. (2015). Informing the Australian government on AT policies: ARATAs experiences. Disability and Rehabilitation. Assistive Technology, 10(3), 236–239. doi:1 0.3109/17483107.2014.913711 PMID:24796214 Graham, R., & Richardson, W. (2012). Leveling the playing field: Assistive technology, special education and a Canadian perspective. American International Journal of Contemporary Research, 2(1), 6–15. Klotz, M. B., & Koch, C. (2015). Assistive Technology Advances in Education. National Association of School Psychologists. Communique, 43(8), 1–8. McKeown, S., & McGlachon, A. (2015). Brilliant Ideas for Using ICT in the Inclusive Classroom (2nd ed.). New York, PA: Routledge. Morgan, R. K., & Olivares, K. T. (Eds.). (2012). Quick Hits for Teaching with Technology: Successful Strategies by Award-Winning Teachers. Bloomington, PA: Indiana University Press.

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Murchland, S., & Parkyn, H. (2010). Using assistive technology for schoolwork: The experience of children with physical disabilities. Disability and Rehabilitation. Assistive Technology, 5(6), 438–447. doi:10.3109/17483107.2010.481773 PMID:20446900 Politis, Y., Deveril, D., Baldiris Navarro, S., Avila, C., de Lera, E., Monjo, T., & Goodman, L. (2014). Introducing the Inclusive Learning Handbook: an OER for teachers and policy makers. Valencia, PA: IATED Academy Publishing. Ravonne, A., Green, R. A., & Blair, V. (2011). Keep It Simple: A Guide to Assistive Technologies. Santa Barbara, PA: Libraries Unlimited. Reid, G., Strnadová, I., & Cumming, T. (2013). Expanding horizons for students with dyslexia in the 21 st century: Universal design and mobile technology. Journal of Research in Special Educational Needs, 13(3), 175–181. doi:10.1111/14713802.12013 Sampath, H., Sivaswamy, J., & Indurkhya, B. (2010). Assistive systems for children with dyslexia and autism. ACM Sigaccess Accessibility and Computing. SIGACCESS Newsletter, 96, 32–36. Sharma, F. R., & Wasson, S. G. (2012). Speech recognition and synthesis tool: Assistive technology for physically disabled person. International Journal of Computer Science and Telecommunications, 3(4), 86–91. Söderström, S. (2013). Digital Differentiation in Young Peoples Internet Use—Eliminating or Reproducing Disability Stereotypes. Future Internet, 5(2), 190–204. doi:10.3390/fi5020190 Steel, E. J., & de Witte, L. P. (2011). Advances in European Assistive technology service delivery and recommendations for further improvement. Technology and Disability, 28(3), 131–138.

Wastiau, P., Blamire, R., Kearney, C., Quittre, V., Van de Gaer, E., & Monseur, C. (2013). The Use of ICT in Education: A survey of schools in Europe. European Journal of Education, 48(1), 11–27. doi:10.1111/ejed.12020

KEY TERMS AND DEFINITIONS Assistive Technologies (AT): Information Communication Technologies that are used to support people with special needs. Inclusive Education: Education tailored to the needs of each learner, regardless of her/his physical, intellectual, social or other conditions. Information Communication Technologies (ICT): Term ICT covers all the technologies used to communicate, create, store and manage information, including computers, the Internet, broadcasting technologies and telephony. Students With Special Needs (SEN): Learners with difficulties in learning, temporarily or permanently, that cause that they do not make expected progress in education for their age. Virtual Learning Environments (VLEs): The specific form of educational system that includes tools for communication, collaboration, student tracking, assessment and so on, all based on Web platform. Virtual World: Computer based simulation of real world where users can create a personal avatar which explore the virtual world, participate in its activities and communicate with other avatars. Web Technologies: All technologies based on any effective computer network (Local Area Network, Wide Area Network) that make it possible for different computers to communicate and share resources.

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The Infusion of Technology Within the Classroom Facilitates Students’ Autonomy in Their Learning Fariel Mohan University of Trinidad and Tobago, Trinidad and Tobago Garry Soomarah University of Trinidad and Tobago, Trinidad and Tobago

INTRODUCTION The use of technology in education is not something new or innovative but rather an asset in assisting our students with their cognitive skills. Many people have argued that the benefit of technology infused into education is non-existent or even limited to have any significant impact to truly advocate its use in the mainstream curriculum. To have an impact, some (Niess, 2005; Shulman, 1986) suggest that in order for technology to become an integral component or tool for learning, science and mathematics pre-service teachers must develop an overarching conception of their subject matter with respect to technology and

what it means to teach with technology. Others (Hooper,1991; Rieber & Welliver, 1989) state the full potential of any educational technology can only be realized when educators progress through five steps or phases: Familiarization, Utilization, Integration, Reorientation, and Evolution, otherwise, the technology will likely be misused or discarded. It is further stated that the traditional perspective of educational technology focuses on either the technology itself or a teacher’s instruction which is only the first three phases as shown in Figure 1. The motivation behind this experiment was to investigate if technology can be used to assist students to focus on constructing his/her own

Figure 1. A model of adoption of both “idea” and “product” technologies in education

DOI: 10.4018/978-1-5225-2255-3.ch221 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Educational Technologies

Figure 2. Summary of CSEC maths pass rate (2009 – 2011)

knowledge without developing an overarching conception of their subject matter. The success of this experiment was determined by an improvement in the pass rate in teaching. Research was done in order to determine which teaching subject will be the subject of this experiment. Researchers have noted that many students entering university often fail 1st year mathematics which may be due to a poor mathematics background (Eng, Li Li, & Julaihi, 2009; Rylands, & Coady, 2009; Whannell, & Allen, 2012). In the Caribbean, there is also a general weakness in students’ mathematics background as stated by a former deputy principal of the University of the West Indies, Mona campus (Green-Evans, 2005). Students who complete high school write an official examination from Caribbean Secondary Education Certificate (CSEC) in order to get accepted into a university. Figure 2 shows a table summarising the results of all the Caribbean high school students who wrote CSEC mathematics for 3 years. This performance re-enforce the fact that students have a weakness in mathematics, especially in paper 2, the written examination. Paper 1 is multiple choice examination (Caribexams, 2004). Based on these findings, the subject chosen for the experiment was 1st year Mathematics at the University of Trinidad & Tobago (UTT)). Technology was used to build a virtual classroom (VC) to

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assist students with their cognitive skills ensuring that the subject matter in teaching mathematics did not require an overarching conception. Instead the VC and physical classroom was part of a blended approach. The VC provided an environment for students to focus on expressing their existing knowledge of mathematics, reflecting on this knowledge and thinking in order to provide prompt feedback. At the end of the experiment, students provided suggestions to enhance the VC which were considered and other VCs were designed.

BACKGROUND The relatively recent introduction of new technology into mainstream schooling was widely expected to penetrate and transform teaching and learning across the curriculum. As noted by (Kinach, 2002), teacher educators must challenge their pre-service teachers’ habitual ways of thinking about subject matter and subject matter teaching. This is especially important in the teaching of mathematics since for many years there has been increasing concerns about students failing achievement in mathematics and their negative attitudes towards mathematics, despite its importance in the ‘world today (Gresham, 2007). Some educators believe that students develop attitudes and

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emotional reactions towards mathematics from as early as 9 years old (McLeod, 1992), which are seldom ambivalent; rather, they are either positive or negative, with negative attitudes persisting well into adulthood (Brady & Bowd, 2005). Research into the factors that impact upon students’ success in, and attitudes towards mathematics point to mathematics anxiety as one of them (Shores & Shannon, 2007). The aim of this project was to investigate whether infusing technology into education can advocate use in the mainstream due to an increase in pass rate. If there was an increase of the pass rate of 1st year mathematics, it suggests this approach had a positive impact in teaching and further work is required. The VC presented teaching matter through reflective questions emphasing on background knowledge which focused on 9 years old students, e.g. fractions. The VC was designed so that it was free, accessible 24/7, fosters collaboration, anonymous and most importantly to ensure that students feel comfortable thus encouraging students to share their understanding and thoughts on a question. The students were given an incentive of 10% of final mark awarded based on the number of comments on the VC. For example, more than 150 comments was awarded 10%. Even though the students were extrinsically motivated into participating in the beginning for the marks, their attitude towards the VC changed in which they sought greater autonomy in the learning process. This drive towards autonomy was made possible because of the many benefits derived from using the VC such as the students took control of his/ her learning which was demonstrated by student’s frequent comments. This VC provided an environment where students were able to share their thoughts without being ridiculed as well as learn at his/her own pace. Thus motivating students to build on whatever existing knowledge they had which according to Maslow (Huitt, 2007) is achieved by satisfying their needs according to his hierarchal model. Based on his theory it is perceived that once ones

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basic needs are met they are able to move on to the other level of development. Maslow’s motivation theory suggest that an individual can only move up the pyramid based on the needs of the previous level being met or satisfied. Due to their needs being met, students’ level of competency proportionally increased, therefore there self-efficacy in the given study increased. A contributing factor to this was anonymity which allowed everybody using the VC to share their thoughts. This allowed students to be bold and take risks in asking questions which otherwise may not have been asked thus the VC acted as a form of scaffolding. Scaffolding acts as a stepping stone in which students build on previous knowledge before moving on to the next level. This is achieved when students are comfortable in sharing their existing level knowledge and building on it from their peers or instructor, who is an expert. According to Vygotsky (1978) scaffolding can act as a form of social and instructional support needed by students who are learning new material. After students have achieved understanding of the specific concept the scaffold is taken away so that they can move on to the next level. Vygotsky (1978) also spoke of the Zone of Proximal Development (ZPD) which he defined as the distance between the actual developmental level as determined by independent problemsolving and the level of potential development as determined through problem-solving under adult guidance, or in collaboration with more capable peers. The VC results suggest that based on the components of Vygotsky’s (ZPD), the instructor’s guided problem solving questions motivated the students to collaborate resulting in just over 2000 entries in one of the experiment. Hence they were able to move from Bloom’s (1956) cognitive taxonomy of knowledge to that of analyzing, evaluating and creating which are higher order cognitive skills in the taxonomy. The improved understanding by the students will result in more students passing thereby increasing the pass rate.

Category: Educational Technologies

DESIGN OF EXPERIMENTS The experiment was designed to help students discover his/her current knowledge and then construct new knowledge by building on his/her existing knowledge. A VC was built as a support to the traditional classroom teaching using the blended approach. The sample was 1st year UTT students and all 68 students used the VC. The objective was to improve the students’ understanding thereby improving the pass rate for UTT 1st year Mathematics for Technicians. The pass rate was 25 – 40%. All students had to use the VC and some students wanted a choice. The 2nd VC was from 100 students who chose either VC or answer the questions on paper. The objective was to determine if the improvement to the pass rate for Mathematics for Technicians Level 1 was due to technology or not. Students approached the instructor to use technology to teach without a physical classroom. This was the 3rd VC with 28 repeating students who were scheduled for classes 5:00 pm – 9:00 pm (difficulty in getting transport after 9:00 pm). The objective was to improve the pass rate for Mathematics for Technicians Level 1 regardless of the constraint of no traditional teaching. Some students reoriented the VC and a new VC was evolved through them. The 4th VC was totally the students’ idea and design. The objective was to allow students and instructor to guide the VC by the postings. Also social and entertaining postings such as videos and pictures were allowed. This VC had 100 students.

1ST VIRTUAL CLASSROOM The 1st VC was designed to incorporate technology with learning mathematics by providing a classroom outside the physical classroom which was not affected in any way. The VC, a blog from blogger, was designed using Web 2.0 technology. All 68 students had to be registered anonymously

to this one blog. The anonymity promotes honest opinions as it combats fear of judgement and embarrassment of correction, thus these barriers which exist in a physical classroom no longer were limitations. A student was allowed to register as different users to create suspense and interest. The 68 students were all the 1st year Mechanical, Chemical & Petroleum engineering students. Each discipline was taught separately but by the same instructor and had a common final examination. As an incentive to encourage the students to make comments and post questions on the VC, 10% (of final mark) was awarded based on his/her VC usage. For example, more than 150 postings was awarded the full 10%. The VC was guided by the instructor who posted questions hoping to engage the students in reflecting and making his/her comments. The comment displays the student current knowledge and can be an eye opener into background knowledge. Writing a comment will also have an additional benefit in providing an opportunity for a student to express his/her knowledge through writing thus enhancing writing skills. Some students understanding of the content lead them to provide feedback for other students’ comments thus allowing that student to take the role of the instructor. The opportunity for a student to be the instructor motivates that student to continue using the VC. This is based on the work of Steinberg and Maslow (1943) who state the importance of motivation playing an integral part in educational development of student autonomy in their learning. An incorrect comment provided an opportunity for another student to analyse and rebuild that incorrect knowledge. This dialog thus breaks down the barriors between students and instructor thereby shifting the role of instructor. An example from 2 VC users, Lifesaver and Weezy, is shown in Figure 3. Problem solving which is inherent in the study of mathematics has been demonstrated to strongly influence students’ attitudes, both positive and negative (Debellis & Goldin, 2006; Hannula, 2002). The VC strived to portray a positive outlook

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Figure 3. Dialog between students

in an attempt to connect to the individual level each student was at. Students were encouraged in the physical classroom to use the VC by assuring them prompt feedback. The instructor was registered as a very unintelligent student asking a lot of questions. This provided an avenue for students to think and answer these questions. Figure 4, shows the number of comments that a post on a given day received (Mohan, 2008). At the end of the semester, the students said the marks for VC usage motivated them to use the VC. The number of students who passed the final examination increased from 40% to 67%.

2ND VIRTUAL CLASSROOM The 2nd VC provided the 100 students with a choice, to choose to use the VC or not to use the VC. The students who choose not to use the VC, they were given all the instructor’s questions posted and had to write their answers and submit in order to

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be evaluated for the 10%. All the students were Mechanical, Chemical or Petroleum engineering students and was taught by the same instructor at the same time in the auditorium. An examination was given before using the VC to get an idea of mathematical background which was used for the initial questions posted. The VC option was chosen by 80 students while 20 students chose no VC. The idea of giving choice was to let the student take ownership of his/her own learning. This experiment aimed at investigating whether the needs of students with respect to technology as a scaffolding tool, can aid students’ performance. The VC users benefited by collaboration and learning at own pace. A student can choose to read the questions and comments, correct an incorrect comment, answer a posted question or ask a question. The anonymous student was motivated to learn at his/her pace and constructed new knowledge without the judgment of peers. This trust in ownership and autonomy in learning is highly advocated by constructivist theorist

Category: Educational Technologies

Figure 4. Graph showing usage

Jean Piaget “who articulated mechanisms by which knowledge is internalized by learners. He suggested that through processes of accommodation and assimilation, individuals construct new knowledge from their experiences.” According to Piaget, assimilation is when individuals incorporate new experiences into an already existing framework without changing that framework which can be faulty if their understanding is not clear. Whereas, accommodation “is the process of reframing one’s mental representation of the external world to fit new experiences. Accommodation can be understood as the mechanism by which failure leads to learning: when we act on the expectation that the world operates in one way and it violates our expectations, we often fail, but by accommodating this new experience and reframing our model of the way the world works, we learn from the experience of failure, or others’ failure.” This was further cemented by students being able to submit questions in their VC which promoted creative, critical and problem solving skills in the cognitive domain. While in the affective domain they developed social competencies like respect, appreciation and caring about the views of their classmates. This, in today’s society is an important

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ingredient in companies with respect to teamwork and being a team player. The VC served as a flexible, accessible and convenient community providing the ingredients necessary for collaboration. Figure 5 shows an example of a dialog from 2 VC users, Sparkle and marz. Sparkle commented and marz commented on Sparkle’s comment. This demonstrated marz reflected on sparkle’s comment and new knowledge evolved hence marz commented on sparkle’s comment (Mohan, 2011). “The learning community was like an extended family and the friends that I made here became the most important reason for me to come to class and to continue with my college education” Figure 5. Dialog among students

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Figure 6. Graph comparing performance virtual classroom vs. no virtual classroom

(Hesse and Mason, 2003). Learning communities create learning environments where students are not expected to be passive listeners, taking notes and memorizing facts, but instead are expected to work together, reading, writing, talking, and relating their learning to their daily lives. Figure 6 shows the comparison of the % of each grade. It can be seen that 0% of the students got A+ in the no-VC approach while 12% of the students got A+. The % of failure must be also noted. The results suggests the VC does cause an increase in performance and also a deeper understanding displayed by three times the A’s were achieved with a lot of A+s. The results obtained from the students who chose no VC had a pass rate increase from 40% to 48% while the students who chose VC had a pass rate increase from 40% to 69%.

3RD VIRTUAL CLASSROOM The 3rd VC focused on removing the traditional classroom for teaching. This approach was based on the university of Wisconsin-Milwaukee (Garnham & Kaleta, 2002) suggestion that students learn more in blended courses than they do in comparable traditional class sections. Blended course instruction offers more choices for content delivery and can be more effective than courses that are either fully online or fully classroombased (Singh, 2003). A group of 28 repeaters (students who failed the course) could not attend the scheduled 5:00 pm – 9:00 pm repeaters class due to transportation problem. A physical classroom was available once a week for a lunchtime

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period only and this was used for either official assessments or tutorials. The instructor used the knowledge gained by reading comments from the previous VC to address foundational problems with these students. Once these foundational problems were cleared, focus was more on the identifying the learning style of the student. Figure 7 shows the result of the repeating students. The normal pass rate at UTT for 1st year Mathematics was 40% and the result reflects an increase in pass rate to 82%.

4TH VIRTUAL CLASSROOM The 4th VC demonstrated students evolving when they decided they wanted to build the VC. They selected a social media, Socialgo, to build the VC. The students also included posting of videos and pictures in the VC. The students asked to also act as an instructor in posting reflecting and thinking questions. The identities of these students remained anonymous. Figure 8 shows the positive Figure 7. Graph showing performance using blended approach

Category: Educational Technologies

Figure 8. Graph showing performance using social virtual classroom

results in terms of improved performance. The average pass rate of the 4 disciplines showed an increase in pass rate from 40% to 62% with 2 individual disciplines above 80% pass rate. Table 1 (Mohan, 2010) show a summary of the results from the 4th VC. Even though the nonacademic parts, videos and pictures comments were both more than 100 while the academic part, blogs comments was 87, the blog postings have an average of more than 31 comments to each posting. This demonstrates that the postings stimulate thoughts from other students. The results suggest that the non-academic postings stimulated interest which lapped over to the academic posting. The students’ academic postings were more than 25% of the postings. Mohan (2013) demonstrates that when a student has ownership of his/her learning and is motivated, he/she can be very creative. For example Tom registered himself as Dr. MathsBscMscPhd, who depicted a brilliant student but Tom

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also registered himself as Big Bird, who was an extremely weak student. In a similar manner, the instructor was registered as the instructor, using the instructor’s name, and was also registered as a weak student, Black Eye-Peas. Several guidelines were followed in the VC. These guidelines and the motivation for each one are given in Table 2 (Mohan, 2013). Another important aspect of the VC was that many topics were available at the same time. A student can move at his/her individual pace of learning without interfering with another. This was especially important since time flexibility in learning a topic was essential. In Figure 9, the graph shows a summary of a portion of the 2020 comments, grouped by the time posted, timestamp (Mohan, 2011). A VC must be time convenient as shown in Figure 8. Figure 9. Graph showing usage at certain times

Table 1. Comments generated for the different type of items Created

Comments Generated

Forum

17

47

Pictures

106

12

Blogs

87

2069

Videos

105

231

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Table 2. Guidelines for the social virtual classroom Guideline

Rationale

Allow students to register as anonymous users

This guideline which ensures that all registered users are anonymous is intended for students who may have a social, cultural, or academic difference which may contribute to low esteem of themselves. It also caters for students who have a barrier due to peer pressure or other circumstance. This was used to establish collaboration.

Post stimulating fundamental questions

In order to help students extract their knowledge and realise what they know and hence what they do not know, the questions posted by the instructor were not about solving questions but rather stimulating, thinking and opinionated questions. This approach was also intended to encourage collaboration among students.

Establish that every comment is valuable

A student’s comment which is participation was encouraged. The accuracy of that comment was not considered. This approach was used in an attempt to relate to all students’ different backgrounds.

Motivate rebuilding of comments by collaboration

This guideline emphasised collaboration since strength can be obtained by variety. When a student freely shares his/her knowledge and other students contribute by making comments to that knowledge, the student can reflect on those comments which may then give him/her the opportunity to re-create their knowledge.

Provide quick feedback

The guideline of quick feedback was aimed at encouraging students that other students are important and need to be heard so that they could expect a quick response. In most cases, a comment would be answered within 2 days by either a student or an instructor.

Ensure 24/7 and absolutely free

An important consideration in this experiment was to ensure the approach was convenient and affordable. This approach must be adaptable to each student’s constraints in terms of time, place and cost. An internet-based application has the features of 24/7, anytime and anywhere. SocialGo is a free social networking application.

Attract student interest by posting a video on the social community

A student can feel a sense of satisfaction if they are the first to post a video for other students to see. This guideline is intended to help those students who are weaker academically so that they can still participate in building the social community. The feeling of belonging and creating may have positive influences on a student.

Engage students through interesting non-work postings

Since these digital native students are so visual, relating to a video or social aspect is always appealing. If the student can be engaged with the videos then the student may have a spill over and may also engage with the subject matter.

Encourage students to provide feedback to one another

The students were provided the opportunity to comment on one another’s comments thereby enabling the typical teacher-student role to be shifted since now the student can take up the role as the teacher. This approach is two-fold. The first is the virtual classroom requires a lot of time from the instructor, if only for the instructor to correct or make suggestions to comments. The confidence of a student being able to teach or identify weaknesses in the knowledge of others may boost a student’s self esteem. The second is the students getting feedback.

Enforce that comments can be questions, answers, suggestions or illustrations

The guideline assumes that a student’s knowledge is varied hence collaboration by answering or illustrating in the manner that is unique to that student can be beneficial. Any type of comment, questions, answers, suggestions or illustrations were all considered equally valuable. The idea is that the students can build on each other’s knowledge

Reward students based on the quantity of comments, not on the quality

The guideline of rewarding students (by awarding marks based on usage) is based on rewards which are given when students are playing computer games. The reward is really to motivate the student to keep trying and the more the student tries, the more points (marks) will be obtained.

This VC demonstrated that the student can posted thinking questions that led into discussions at interesting times such as 2:00 am. The student feedback was prompt and many users were correcting or improving comments. At the end of the experiment, the students were asked to fill out a survey. Each question had four responses strongly agree, agree, disagree and strongly disagree. The

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survey was divided into two categories using the VC and the community. Some of the questions from each of these two categories are shown in the Table 3 below. The names of the students were not collected in the survey. From Table 3 (Mohan, 2013), it can be seen that 80% of the students strongly agreed that being anonymous was important to them, suggesting

Category: Educational Technologies

Table 3. Student feedback Using the Social Virtual Classroom

Strongly Agree

Agree

Disagree

Strongly Disagree

Were you encouraged to think outside the classroom by using the social virtual classroom?

40

38

22

0

Did reading other users’ comments help you in your understanding?

59

30

11

0

Was being anonymous important to you?

80

17

3

0

Did the social virtual classroom help you to build more confidence in your work?

45

23

32

0

Would you prefer more courses to use this approach in teaching?

69

31

0

0

The Inclusion of the Social Community Did you believe that the social aspects of the social virtual classroom were helpful?

65

17

18

0

Was looking at other users’ videos interesting to you?

44

30

21

5

that the anonymous feature was a very important feature. 59% of the students stated that reading other users’ comments helped in understanding the subject matter. Another interesting result was that 69% of the students preferred more courses to use the VC approach in teaching. On the negative side, 32% of the students did not agree that the VC helped build more confidence and 22% of the students said that they were not encouraged to think outside the classroom by using the VC.

FUTURE RESEARCH DIRECTIONS These experiments reflected that VCs led to an increase in the pass rate of 1st year Mathematics at the University of Trinidad & Tobago. Four VC were used. The 1st VC, all students had to use and the pass rate was 67%. The 2nd VC, students were given choice and the pass rate was 69%. Future research direction is to investigate whether there is any relationship between the frequency of the VC usage and the grade obtained. Another future research would be to investigate whether anonymity is a key factor in the VC. The VC should be instructor independent hence future research should confirm this by repeating the experiment but change the traditional classroom instructor.

The 2nd VC offered choice of with or without technology and the pass rate was increased by 29% as compared to 8% respectively. A VC with technology was more than three times successful as without technology. That result is quite significant therefore further research must be to repeat this experiment to confirm technology does indeed have a positive impact in teaching. The 3rd VC was for repeating students with the constraint of no traditional classroom. Future research directions is to confirm the result is accurate by repeating a number of times. This can lead to an interesting option for teaching repeating students. The 4th VC incorporated non-academic postings eg videos and pictures. Future research direction can be to investigate if culture and social postings can contribute in further enhancing the pass rate.

CONCLUSION The experiments described in this chapter suggest that VCs do cause an increase in the pass rate of mathematics. The VCs was designed to stimulate students to think by posting stimulated opinionated questions. In the 2nd VC that the students who

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chose no VC were given the same stimulated opinionated questions on paper which resulted in an increase from 40% to 48%. The 1st and 2nd VCs got a greater increase of 67% and 69% respectively suggesting that technology assisted students with their cognitive skills. This was possible with an environment fostering students’ collaboration may motivate students to analyse and evaluate their existing knowledge. Thus enabling each student to build on their individual knowledge demonstrating self-centred learning. The success of the experiment was based on improved pass rate which was accomplished by the 4 VCs but the most impressive VC was the one 3rd VC which increased to 82%. This was the smallest group with 28 students and this was also repeating students. Another observation from the experiments was the students indicated that they were more inclined to use the VC since they were anonymous to other students. The prompt feedback encouraged the students to use the VC more frequently thus allowing them to evaluate and analyse their knowledge meta-cognitively.

REFERENCES Bloom, B. S. (1956). Taxonomy of educational objectives. In Handbook I: The cognitive domain. New York: David McKay. Brady, P., & Bowd, A. (2005). Mathematics anxiety, prior experience and confidence to teach mathematics among pre-service education students. Teachers and Teaching: Theory into Practice, 11(1), 37–46. Caribexams. (2004). Caribbean Examination Council (CXC) mathematics pass rates. Retrieved from http://www.caribexams.org/m_pass_rates Debellis, V., & Goldin, G. (2006). Affect and meta-affect in mathematical problem solving: A representational perspective. Educational Studies in Mathematics, 63(2), 131–147. doi:10.1007/ s10649-006-9026-4

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Eng, T. H., Li Li, V., & Julaihi, N. H. B. (2009). A Case Study of ‘High-Failure Rate’ Mathematics Courses and its’ Contributing Factors on UiTM Sarawak Diploma Students. Conference on Scientific & Social Research 2009, Paper 1. Garnham, C., & Kaleta, R. (2002). Introduction to Hybrid Courses. Teaching with Technology Today, 8(6). Retrieved September 22, 2009 from http://www.uwsa.edu/ttt/articles/garnham.htm Green-Evans, V. (2005). Maths failures continue at UWI, review planned. Retrieved Wednesday, November 25, 2015, Observer staff reporter, http:// www.jamaicaobserver.com/news/74399_Mathsfailures-continue-at-UWI--review-planned Gresham, G. (2007). A study of mathematics anxiety in pre-service teachers. Early Childhood Education Journal, 32(2), 181–188. doi:10.1007/ s10643-007-0174-7 Hannula, M. (2002). Attitude towards mathematics: Emotions, expectations and values. Educational Studies in Mathematics, 49(1), 25–46. doi:10.1023/A:1016048823497 Hesse, M., & Mason, M. (2005). The case for Learning Communities. Community College Journal, 76(1). Hooper, S. (1992). Cooperative Learning and Computer-Based Instruction. Educational Technology Research and Development, 40(3), 21–38. doi:10.1007/BF02296840 Huitt, W. (2007). Maslow’s hierarchy of needs. Educational Psychology Interactive. Valdosta, GA: Valdosta State University. Retrieved from, http://www.edpsycinteractive.org/topics/regsys/ maslow.html Kinach, B. (2002). Understanding and learning-toexplain by representing mathematics. Journal of Mathematics Teacher Education, 5(2), 153–186. doi:10.1023/A:1015822104536 Maslow, A. H. (1943). A Theory of Human Motivation. Psychological Review, 50(4), 370–396. doi:10.1037/h0054346

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McLeod, D. B. (1992). Research on affect in mathematics education: a reconceptualization. In D. A. Grouws (Ed.), Handbook of Research on Mathematics Learning and Teaching (pp. 575–596). New York: Macmillan. Mohan, F. (2008). yourMathsCorner: A blogbased approach to learning prerequisite mathematical knowledge at the tertiary level. International Journal of Mathematics and Computers in Simulation, 2, 95–101. Mohan, F. (2010). Using Social Networking Software to Increase Students’ Participation in a Virtual Classroom. In J. Herrington & C. Montgomerie (Eds.), Proceedings of EdMedia: World Conference on Educational Media and Technology 2010. Association for the Advancement of Computing in Education (AACE). Mohan, F. (2011). Building a cultural community classroom to connect instructors with students. ICALT 2011, IEEE International Conference on Advanced Learning Technologies, 147 - 149. doi:10.1109/ICALT.2011.49 Mohan, F. (2013). Using a Social Learning Community to Actively Engage Students’ Participation in a Virtual Classroom. In Cases on E-Learning Management: Development and Implementation (pp. 50–70). Academic Press. Niess, M. L. (2005). Preparing teachers to teach science and mathematics with technology: Developing a technology pedagogical content knowledge. Teaching and Teacher Education, 21(5), 509–523. doi:10.1016/j.tate.2005.03.006 Rieber, L., & Welliver, P. (1989). Infusing Educational Technology into Mainstream Educational Computing. International Journal of Instructional Media, 16(1), 21–32. Rylands, L., & Coady, C. (2009). Performance of students with weak mathematics in first-year Mathematics and Science. International Journal of Mathematical Education in Science and Technology, 40(6), 741–753. doi:10.1080/00207390902914130

Shores, M. L., & Shannon, D. M. (2007). The Effects of Self-Regulation, Motivation, Anxiety, and Attributions on Mathematics Achievement for Fifth and Sixth Grade Students. School Science and Mathematics, 107(6), 225–236. doi:10.1111/j.1949-8594.2007.tb18284.x Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14. doi:10.3102/0013189X015002004 Sibawu, S. (2013). The zone of proximal development in the learning of Mathematics. South African Journal of Education, 22(2), 2013. Singh, H. (2003). Building effective blended learning programs. Educational Technology, 43(6), 51–54. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press. Whannell, R., & Allen, B. (2012). First year Mathematics at a regional university: Does it cater to student diversity? The International Journal of the First Year in Higher Education, 3(2), 45–48. doi:10.5204/intjfyhe.v3i2.125

ADDITIONAL READING De Wever, B., Van Keer, H., Schellens, T., & Valcke, M. (2007). Applying multilevel modelling to content analysis data: Methodological issues in the study of role assignment in asynchronous discussion groups. Learning and Instruction, 17(4), 436–447. doi:10.1016/j.learninstruc.2007.04.001 Díaz, A. L., & Entonado, B. F. (2009). Are the Functions of Teachers in e-Learning and Face-toFace Learning Environments Really Different? Journal of Educational Technology & Society, 12(4), 331–343.

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Driscoll, A., Jicha, K., & Hunt, A., Tichavsky, & L. Thompson, G. (2012). Can Online Courses Deliver In-class Results? A Comparison of Student Performance and Satisfaction in an Online versus a Face-to-face Introductory Sociology Course. American Sociological Association, 40(4), 312–331. Dziuban, C. D., Hartman, J., Juge, F., Moskal, P. D., & Sorg, S. (2006). Blended learning enters the mainstream. In C. J. Bonk & C. Graham (Eds.), The Handbook of Blended Learning: Global Perspectives, Local Designs (pp. 195–209). San Francisco, CA: Pfeiffer Publications. Rabab’h, B. S., & Veloo, A. (2015). Prediction of Mathematics Learning Strategies on Mathematics Achievement among 8th Grade Students in Jordan. Asian Social Science, 11(2), 276–283. Rovai, A. A. (2002). A preliminary look at the structural differences of higher education classroom communities in traditional an ALN courses. Journal of Asynchronous Learning Networks, 6(1), 41–56. Simmons, G. R. (2014). Business Statistics: A comparison of Student Performance in three Learning Modes. Journal of Education for Business, 89(4), 186–195. doi:10.1080/08832323.20 13.836470 Yaratan, H., & Kasapoglu, L. (2012). Eight grade students attitude, anxiety, and achievement pertaining to mathematics lessons. Procedia: Social and Behavioral Sciences, 46, 162–171. doi:10.1016/j.sbspro.2012.05.087

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KEY TERMS AND DEFINITIONS Anonymous: Anonymous meant identity was unknown thus a student was free to share his/her understanding. It also provided a mystery within the VC. Many students registered as different user in order to get more usage. Thus developing an opportunity for a student to be creative. Autonomy: The student is given many opportunity to make decisions thus taking control of his/her own learning. The students decides if to share his/her understanding or if to reflect and then comment on another student’s comment or if to disguise as two different users. Collaboration: Collaboration in the VC is building team spirit. It is an opportunity for students to come together and share their understandings and help build one another so that everyone has the same understanding which will lead them to pass mathematics. Virtual Classroom Usage: Encouraging students to share his/her understanding so if that understanding was incorrect, it can be reconstructed and become correct. The usage is how many comments were made so the more comments the more a reward. Virtual Classroom: The VC was an online environment where students came together for learning and/or teaching. This environment facilitated individual personal needs like convenience in time and location, motivating students to share understanding by making comments and engaging students in dialog and feedback and most important providing an opportunity for students to fix his/ her background knowledge.

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Category: Educational Technologies

Integrated Paper-Based and Digital Learning Material for Smart Learners Sabrina Leone Università Politecnica delle Marche, Italy

SMART LEARNING, SMART LEARNERS, AND INCLUSIVE LEARNING Smart learners are lifelong learners (Leone, 2010) whose potential is unleashed by the seamless use of smart technologies (i.e., smartphones, tablets, tablet PCs, sensor network nodes, contact-less smart cards, RFID and QR codes) to access huge amounts of open resources and connections, anywhere anytime. Personal, and personalised, smart technologies increase a learner’s independence in a novel way, and makes the context for engaging in study more tailored and potentially self-directed (Middleton, 2015). Smart learning encompasses any teaching and learning approach that flawlessly accommodates technology and enhances practice through social uses of new inclusive spaces. Fruitful interactions are the core of a smart learning cycle and are supported by a smart learning environment (Liu, Huang & Chang, 2015). Indeed, the current digital learning environment is gradually evolving into the smart learning environment (Li, Chang, Kravcik, Popescu, Huang, Kinshuk & Chen, 2015), that is a user-friendly space that facilitates easily accessible, appealing and effective learning. A learning environment may be considered smart when it includes adaptive technologies or innovative features and capabilities that improve understanding and performance. Specifically, features of smartness are 1. Conversational support for learners, teachers and designers,

2. Dynamic updating of student profiles, resources and databases, and 3. Automatic [re-]configuration of interfaces to adjust to different learners and learning situations (Spector, 2014). The concept of smart learning includes that of ubiquitous learning (uLearning). The only difference stands in the higher power of next generation (smart) technologies that are creating disruptive learning landscapes and learners’ profiles. Today’s students expect always-on, available-anywhere information and personalised, multichannel learning. The term “classroom” is becoming more figurative than literal (IBM, 2015). In a smart learning environment the physical and virtual dimensions merge (Liu, Huang & Chang, 2015), and learning is inclusive. Inclusive education is an essential component of lifelong learning; it is concerned with an individual’s effective participation in society and with the achievement of his/her full potential. The affordances of new educational technologies can enable the development of uLearning environments and of multimodal learning contents that foster inclusion, personalisation and interaction, provided that a learner-centred and technologyenhanced approach is adopted. Internationally, inclusive education is increasingly understood more broadly as a change, in a holistic approach, that supports and welcomes diversity (in race, economic status, social class, ethnicity, language, religion, gender, sexual orientation and ability) amongst all learners (UNESCO, 2009).

DOI: 10.4018/978-1-5225-2255-3.ch222 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Since learning takes place in many contexts, formal, non-formal and informal, inclusive and quality education become synonyms and are vital for the development of more inclusive societies. Specifically, quality learning is characterised by two important components: the learner’s cognitive development, and the promotion of values and attitudes of active citizenship and/or of creative and emotional development. An inclusive curriculum is based on the four pillars of education for the 21st century – learning to know, to do, to be and to live together (Delors et al., 1996). Promoting inclusion means stimulating discussion, encouraging positive attitudes and improving educational and social frameworks. This involves changes in content, approaches, structures and strategies in order to provide all learners with flexible and personalised learning to meet individual needs, abilities and learning styles. uLearning, supported by the growing diffusion of wireless smart technologies and institutional policies, is becoming more and more a modality of flexible and participatory learning to be adopted in and out of the classroom exploiting smartphones, tablets, tablet PCs, sensor network nodes, contactless smart cards, RFID (El-Bishouty, Ogata, & Yano, 2007) and QR codes. Thanks to this technological growth, a personal learning environment could be embedded in everyday life (Ogata & Yano, 2004) and become a Computer Supported Ubiquitous Learning (CSUL) environment, characterised by permanency, accessibility, immediacy, interactivity, situatedness and adaptability (Curtis, Luchini, Bobrowsky, Quintana, & Soloway, 2002; Leone & Leo, 2011a). Learning theories for CSUL are authentic learning (Brown, Collins & Duguid, 1989), situated learning (Lave, & Wenger, 1991) and learning by doing (Schank, 1995). It is widely acknowledged that information and communication technologies (ICT) enrich the learning experience (UNESCO, 2012). Anyhow, the focus has to be placed on learning, rather than

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on technology in itself. In a technology-enhanced learning approach, the advantages arising from the integration of ICT in the learning curriculum have to be assessed within the learning experience, the usefulness of learning and its enhancements (Leone, 2008; Leone & Leo, 2011a). Pedagogical and psychological researchers have debated for decades on a common understanding of “effective learning”. According to recent literature (Bulu & Yildirim, 2008; Calvani, 2006; Ellis, 1999; Liu, Huang & Chang, 2015; Wasson, 2007), social interaction among learners is a major element of the learning process, indeed, it can decisively impact on learning outcomes (Agostinho, Lefoe, & Hedberg, 1997). Cooperation is an essential factor in the construction of an “effective learning” environment since it engages students in knowledge construction through interaction and negotiation with their peers. Cooperation enables learners to discuss, argue, agree and reflect on ideas, principles and knowledge. In the design of a suitable – situationed, real – learning environment prior attention has to be paid to knowledge construction and effective learning, that is to learning relevant for learners (Johnson & Johnson, 1994). Smart learning emphasises technologyembedded learning to enhance cooperation and interaction between learners (Bae, Shin, Kim & Choi, 2015). This work aims to illustrate the QRcode format, a framework that supports uLearning by the integration of paper-based and digital learning material through Quick Response (QR) code. The format was devised within the research project Learning4All (2009-2012) and was validated by several learning experiences of English as a foreign language (EFL) for different clusters (Leone, 2014). Subsequently, the format was selected as element of techno-pedagogical innovation in the Eureka project (2012-2014), a network of 11 schools in Apulia, Italy, for the enhancement of curriculum continuity from middle into high school.

Category: Educational Technologies

INTEGRATED PAPER-BASED AND DIGITAL LEARNING MATERIAL: LITERATURE REVIEW Paper and traditional books have been used as basic tools in developing knowledge-intensive tasks and learning (Chao & Chen, 2009). However, a paper textbook can be combined with ubiquitous technologies in a whole to deepen reading comprehension and to enrich it with audio, video and grammar, vocabulary and cultural/technical/ professional in-depth contents. Paper-based learning material has been successfully enhanced by multimedia contents in experiences on annotation conducted through digital pen (Chao & Chen, 2009; Lai, W.-C., Chao & Chen, 2007). More recently, practitioners and researchers have shown growing interest for the potential of QR code as ubiquitous learners’ tool, and several learning experiences have been conducted in different contexts: outdoor education (Lai, Chang, Wen-Shiane, Fan & Wu, 2013; Law & So, 2010) and outdoor students’ assessment specifically (Conejo, Perez de la Cruz, Barros, Galvez & Garcia-Viñas, 2013); integrated paper-based and digital learning materials to enhance listening comprehension in foreign language learning (Law & So, 2010), to enhance reading comprehension (Chen, Chia-En Teng, & Lee, 2010;), to provide support in maths homework (McCabe & Tedesco, 2012), to convey directions to English language learners and recordings for students who have difficulty in reading (Shumack, Lewis, Simmons & Carpenter, 2013), and to encourage students’ interaction during face-to-face lectures (Law, 2013). Most common uses include access to web sites with course information and study materials (Bobeva & Hopkins, 2012). Nevertheless, little literature (Leone & Leo, 2011a; Leone & Leo, 2011b; Leone, 2012; Leone, 2014) is available about the principles of instructed learning in the use of paper-based learning material integrated with digital material through QR code within a definite learning format or model.

Thanks to its two dimensions (vertical and horizontal), the QR code can store greater amounts of information and services (i.e., website addresses, text, contact details) (Ramsden, 2009; Savarani & Clayton, 2009) that learners can readily, anywhere and anytime, decode by a mobile device with an embedded camera and code reading software installed. Besides fostering flexibility of provision, the integration of QR codes with paper-based learning material also offers personalisation of learning because different learning styles and approaches to the use of ICT for learning can be accommodated.

THE QRCODE FORMAT QRcode is a technology-enhanced learning format that was devised within the research project Learning4All (2009-2012). The project investigated on how an aware adoption of ICT can contribute to improve the quality of teaching, in particular for students with special needs (UNESCO, 2009). Learning4All included seven research units: Politecnico di Milano - coordinator -, IMATI CNR di Genova and the Università di Bari, di Bologna, di Perugia, del Salento and Politecnica delle Marche (UNIVPM). The QRcode format consists in the integration of paper-based and digital learning material through QR code, aimed at facilitating personalised and flexible (anytime, anywhere) learning. Multimedia contents are coded by the teacher in QR code and are then easily accessible by a decoding software which is supplied with many mobile devices or is downloadable free (Leone & Leo, 2011b). The QRcode format is potentially adaptable to all disciplines and was implemented in several learning experiences of EFL for different clusters, with good results: in February-June 2010 in three different scenarios (adult lifelong learners, in-service teachers and secondary school students) (Leone & Leo, 2011a), in February-March 2011 with 4 classes of a secondary school and in March-

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April 2012 with 2 classes of a secondary school for surveyors (in an interdisciplinary module EFL – Building Construction) (Leone, 2014). Further, as element of techno-pedagogical innovation in the Eureka project (2012-2014), the format was implemented in the following sessions: in OctoberNovember 2012, initial training of 80 in-service teachers from the many partner institutes; in April-May 2013 and in January-February 2014, 2 interdisciplinary paths with 8 3rd-year classes of a middle school together with 8 1st-year classes of high school.

Needs, Objectives, and Competences General learning objectives of the QRcode format are: learning to learn, promoting the deepening of contents in different contexts and by different means, favour interdisciplinarity of knowledge domains, favour continuity between in-class and out-of-class activity (integration between formal and informal learning). Specific learning objectives are to: facilitate the acquisition of basic, communicative and multimedia, and digital competences; favour a better general/specific comprehension of one or more of the topics developed; support motivation, participation and interest for a discipline; promote cooperative and collaborative learning. The QRcode format can develop basic and higher cognitive and social competences (knowing and understanding the topic treated, communicating adequately in relation to the context, retrieve information from various sources and ri-elaborate it in a personal way, meet a new culture in a view of cultural pluralisme), crosscurricular (autonomous study and in-depth analysis, problem solving, relational skills), of citizenship and digital (understanding the affordances of ICT for knowledge sharing and collaborative construction) skills.

Learning Strategies The QRcode format can be developed through exercises, brainstorming (encoding in QR code 2548

of forums and wikis), collaborative and cooperative learning, learning by doing, problem solving, webquest, game based learning, simulations in immersive virtual environments. The format can be an effective inclusive learning tool since it facilitates personalised and flexible learning if a learner-centred approach is adopted, an approach attentive to each learner’s diverse needs (UNESCO, 2009). A metacognitive work that allows to enhance autonomy and responsibility could be a first step towards inclusion.

Organisation The physical places to implement the QRcode format are the classroom with a wireless connection or a hot spot, where learners operate, individually or in a group, on paper-based and digital learning material, by the various mobile devices available, and any other place by mobile devices with an Internet connection. The learning environment is, therefore, both physical (classroom and class) and virtual (web-based, with interactive activities, and/ or 3D) according to what the teacher adopts. The length of the implementation can vary in relation to the number and the extension of the uLearning modules of the designed curricular path. Necessary human resources are the teacher, that defines strategies, objectives, contents and activities, and the ICT technician, that installs/checks the QR code decoder software in the mobile devices in use, checks the Internet connection and deals with troubleshooting.

Evalutation and Assessment The QRcode format is monitored at the beginning and at the end of its implementation, in order to evaluate its effectiveness and rating from the participants’ point of view (evaluation), and in terms of achievement of learning objectives (assessment). For the evaluation, the data are collected (1) by entry and exit anonymous surveys, to record students’ digital skills, motivation and expectations on the format (entry survey), and students’ feedback (exit survey); (2) by interviews, to record

Category: Educational Technologies

teachers’ motivations for the choice of the format, expectations and context of implementation (entry interviews) and teachers’ achieved results and impressions (exit interviews). Assessment is carried out by close, semi-structured and/or open tests, and it consists in an entry test (prior knowledge and competences), formative tests (at the end of each learning unit) and a summative test (at the end of the learning path).

Recommendations for the Optimal Implementation of the Format The QRcode format is a very flexible tool. Nevertheless, thorough organisation and management of hardware and software are success key factors of the experience. Moreover, relevant elements are: a learner-centred and technology-enhanced approach, drawn on constructivisme (Jonassen & Land, 2000); an adequate familiarisation of the students with learning tools and environments; check of the accessibility of the necessary devices; modulation of space/time of class and autonomous work; collaboration with other teacher to create an interdisciplinary path; engagement of the educational institution as a whole as stakeholder of techno-pedagogical innovation.

SWOT Analysis Strengths of the QRcode format are the provision of personalised and flexible learning. Weaknesses are initial technological difficulties that can cause demotivation and disengagement. Opportunities are many: opening to a wide range of learning paperbased and digital materials; use of open software and learning materials; possible effective integration of the format in the curriculum; possible spur for interdisciplinary activities. Vice-versa, possible threats are a teacher-centred approach, that risks to void the flexibility of the format, and the limits of the participants’ mobile devices (speed of the processors, small displays, Internet connection costs), even though the technological standard of next generation (smart) devices is high.

CHARACTERISTICS AND OUTCOMES OF THE RESEARCH EXPERIENCES WITH THE FORMAT All the experiences of implementation of the QRcode format have been conducted in Italy for: 1. Nine different clusters of learners of EFL: a. In February-June 2010 in a refresher course for secondary school teachers (23 participants, 10 weekly 3-hour lectures), a language certification course for Italian secondary school students (upper classes, 16 participants, 17 3-hour lectures twice a week) and a course for Italian adult beginners (15 participants, 20 weekly 3-hour lectures). Participants were 54 in all. The author was teacher-facilitator and tutor of the research experience at the same time; b. In February-March 2011 (4 weeks, 9 hours in all for each class) with 4 classes (second- and forth-year students) of a secondary school, 85 learners in all. The author was tutor of the research experience, in support of the two curricular teachers; c. In March-April 2012, on the basis of the preceding research experiences, an implementation enriched with cooperative work, on an interdisciplinary module EFL-Building Construction (4 weeks, 3 hours a week for each class) with 2 upper classes of a secondary school for surveyors, 45 learners in all. The author was the coordinator of the research experience and English teacher, at the same time; 2. Three clusters of learners in interdisciplinary paths within the Eureka project (2012-2014) (the author was the coordinator of the technopedagogical innovation of the project, one of the teachers’ trainers and tutor in the last two experiences):

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a. In October-November 2012 in the initial training of 80 in-service teachers from the many partner institutes (2 in-presence 4-hour workshops and the ongoing distributed community of practice Comunità di Pratica Progetto Eureka, in the eLearning space www. elearnigplace.it/corsi); b. In April-May 2013 (4 weeks, 9 hours a week for each class) an interdisciplinary path on Italian, music, history, geography, French and maths with 4 3rd-year classes of a middle school together with 4 1st-year classes of high school, 193 learners in all; c. In January-February 2014 (4 weeks, 9 hours a week for each class) an interdisciplinary path on citizenship education, geography, Italian, the arts and music with 4 3rd-year classes of a middle school together with 4 1st-year classes of high school, 187 learners in all. In all the implementations the participants used their own mobile devices; in addition laptops were available at schools. All the courses were developed in uLearning on curricular in-presence and distance learning modules. Personalisation was realised by a great variety of graded learning materials (on the basis of the entry assessment

test and evaluation survey, and proposed in hard copy and pdf file, both integrated with in-depth digital contents in QR code), web-based interactive activities and self-assessment. In the interdisciplinary implementation EFLBuilding Construction of March-April 2012 (Leone, 2012) the teaching team defined strategies, objectives, contents and activities, and enriched the experience with cooperative learning, either in presence and in eLearning (PlaLE – Personal language Learning Environment, in http:// www.elearningplace.it/corsi/course/category. php?id=14), with which learners had already familiarised in EFL since the beginning of the school year. Specifically, objectives of the implementation of the QRcode format in all cooperative and interdisciplinary implementations were metacognition, the synergic enhancement of dsciplinary, technical-professional (when applicable) and language (when applicable) skills, the acknowledgement of the importance of cooperative learning in the participants’ personal growth, the acquisition of new learning tools for cooperative work, the activation of students’ participation in group work, the enhancement of peer-scaffolding. Contents included domain knowledge, microlanguage (if applicable), communicative functions (for foreign languages), inherent grammar and vocabulary, and cultural in-depth topics.

Figure 1. Integrated activities in “Talking about the past”

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Category: Educational Technologies

Figure 2. Content of Activity 2 (ex. 3), in QR code in Figure 1

Figures 1 and 2 show an extract of the activities proposed in EFL for the communicative function “Talking about the past” and for those codified in QR code, respectively. In all the 9 experiences in EFL, learners carried out an extensive range of tasks in relation to the four language skills (see details in Leone & Leo, 2011a; Leone, 2012; Leone, 2014). As a whole, results of all the experiences emphasise that learners’ motivation and active participation grew as their awareness, selfconfidence and familiarisation with the learning environment improved. In the exit interviews the teachers, too, expressed full satisfaction with the students’ participation and learning results (even learners who were usually demotivated progressed in terms of cognitive, digital and crosscurricular skills), and with their personal and professional empowerment. The main difficulties were technical-organisational ones and in a few occasions due to the teacher’s mismatching approach; in particular:

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Teenagers showed to be false digital natives; as a result, even though an extensive phase of familiarisation preceded the start of the experience, during the first and second week 40% and 10% of the time available respectively was forcedly dedicated troubleshooting on the mobile devices; Because of a recurrent insufficient Internet connection in some of the classrooms used, it was necessary to move the learners in alternative locations; In some sessions a lack of the already configured devices occurred (participants simply forgot to bring them); In some sessions learners were rather passive and disoriented because of discontinuity in the learning path (decontextualised activities) and insufficient teacher’s facilitation (guide, scaffolding and feedback during the tasks); Insufficient engagement and interest for the experience within the teachers’ institutes.

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The results obtained from the implementation of the QRcode format in the different scenarios show an important positive impact on different aspects: the uLearners’ disciplinary skills, management of ICT tools, satisfaction levels with the flexibility and personalisation of learning, as well as on the contents proposed and cross-curriculum objectives such as developing autonomy, building learning confidence, empowerment, a positive attitude and motivation towards a new way of learning, results which come to support the effectiveness of the model implemented (Leone & Leo, 2011a; Leone, 2012).

FUTURE RESEARCH DIRECTIONS In the next future, the QRcode format could be extensively used in different grades of instruction and in teachers’ continuous professional training. In addition, a pilot implementation of the format with augmented reality is starting. In order to fully exploit the potential of the format, scaffolding might be provided as an added service to tackle large margins of improvement that have emerged in the participants’ digital skills, in their approach to teaching and learning, in their approach to the adoption of ICT in the curriculum as a chance of holistic change and growth towards smart inclusive education.

CONCLUSION Personal, and personalised, smart technologies increase lifelong learners independence in a novel way, makes the context for engaging in study disruptive, more tailored and potentially self-directed, and allow for access to huge amounts of open resources and connections, anywhere anytime. The affordances of smart technologies can enable the development of uLearning environ-

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ments and of multimodal learning contents that foster inclusion, personalisation and interaction, provided that a learner-centred and technologyenhanced approach is adopted. The QRcode format has been devised with the aim of providing personalisation and empowerment to ubiquitous lifelong learners facilitating the acquisition of some of the skills necessary for the 21st century. The format is very flexible for both the teacherfacilitator and the learners, and can be enriched with interdisciplinary and cooperative learning experiences. The outcomes of all the research experiences carried out to date highlight an important positive impact on the participants’ disciplinary competences, acquisition of skills in the use of new digital tools, satisfaction for flexibility and personalisation of learning and for the contents and the crosscurricular competences, and validate the potential of the implemented framework. However, success keys of the experience are: (1) punctual organisation and management of the necessary hardware and software; (2) a suitable students’ familiarisation with the learning environment (technology, tools and learning approach); (3) a learner-centred approach; (4) a technology-enhanced, rather than technology-driven, approach.

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Middleton, A. (Ed.). (2015). Smart learning: Teaching and learning with smartphones and tablets in post compulsory education. Sheffield, UK: Media-Enhanced Learning Special Interest Group and Sheffield Hallam University. Molyneux, P., & Godinho, S. (2012). This is my thing!: Middle years students engagement and learning using digital resources. Australasian Journal of Educational Technology, 28(8), 1466–1486. doi:10.14742/ajet.782 Nalder, J. (2008). The dawn of uLearning. Master thesis. Retrieved March 10, 2010 from http:// www.scribd.com/doc/12398804/The-dawn-ofuLearning-Jonathan-Nalder-Masters-thesis Nix, J. (2005). The development of mobile learning for smartphones. Proceedings of IADIS International Conference Applied Computing, Algarve, Portugal, February 22-25, 2005. Nunan, D. (1991). Language teaching methodology. UK: Prentice Hall. Ramírez, E., Clemente, M., Cañedo, I., & Martín, J. (2012). Incorporating Internet resources into classroom practice: Secondary school teacher action plans. Australasian Journal of Educational Technology, 28(8), 1433–1450. doi:10.14742/ ajet.780 Riischoff, B. & Ritter, M. (2001). Technologyenhanced Language Learning: Construction of Knowledge and Template-based Learning in the Foreign Language Classroom. Computer Assisted Language Learning (14/3-4), 219-32. Trentin, G. (2005). From “formal” to “informal” e-Learning through knowledge management and sharing. Journal of e-Learning and Knowledge Society, 1(2), 209-217. Wexler, S., Brown, J., Metcalf, M., Rogers, D., & Wagner, E. (2008). 360°Report: Mobile learning. Santa Rosa, USA: eLearning Guild.

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KEY TERMS AND DEFINITIONS CSUL (Computer Supported Ubiquitous Learning) Environment: Everyday ubiquitous learning informed by the theories of authentic learning (Brown, Collins & Duguid, 1989), situated learning (Lave, & Wenger, 1991) and learning by doing (Schank, 1995). Formal Learning: Hierarchically structured, chronologically graded educational system running from primary through to tertiary institutions. Inclusive Learning: Learning that allows to meet not only special needs, but also diverse needs (e.g., different learning styles). Informal Learning: Unstructured learning that allows persons to acquire attitudes, values, skills and knowledge from daily experience, within the individual’s environment (i.e., family, friends, peer groups, etc.). Lifelong Learning: A holistic vision of learning in different contexts (formal, non-formal and informal) and throughout life, based on the evolution of provider-driven education toward personalised learning and aiming at improving knowledge, skills and competencies within a personal, civic, social and/or employment-related outlook. QR (Quick Response) Code: A bidimensional code (it displays information in both vertical and horizontal directions) that can hold larger amounts and different kinds of contents (e.g., website addresses, texts, numerical information, contact details) than a normal bar code (monodimensional). The information stored in a QR code can be readily decoded and accessed by a mobile device with an embedded camera and free code reading software installed. QRcode Format: A technology-enhanced learning format to be implemented in a learnercentred learning environment to offer inclusive, personalised (different learning styles and goals) and flexible (anytime, anywhere) learning by the integration of paper-based and digital learning materials through QR code.

Category: Educational Technologies

Ubiquitous Learning: Wireless learning supported by a large number of cooperative small nodes with computing and/or communication capabilities (e.g., handheld devices, sensor network

nodes, contact-less smart cards, RFID and QR codes) and characterised by high mobility and embeddedness.

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Leveraging Technology-Enhanced Teaching and Learning for Future IS Security Professionals Ciara Heavin University College Cork, Ireland Karen Neville University College Cork, Ireland Sheila O’Riordan University College Cork, Ireland

INTRODUCTION The use of social media technologies to connect with peers/colleagues is prevalent amongst students and practitioners alike. These technologies are being used to share ideas, content, resources, and experiences for both social and professional purposes. However, modern learning environments do not always implement the latest technologies and are therefore failing to support the needs and career expectations of Generation 2020. Thus, technology enhanced learning is proving invaluable in creating interactive collaborative learning environments that can address the needs of future graduates. The social business gaming platform considered in this chapter leverages the social networking concept in an academic environment. This study was undertaken in order to develop Information Systems (IS) security skillsets through the creation and facilitation of social business gaming. The online business game required students to apply what they have learned to problem situations to further develop their understanding of IS security (ISS) topics. The problems posed required learners to prove their understanding of the material being taught in the traditional lecture, and then apply what they had learned in an online environment, allowing students to both collaborate and compete against their peers in a

series of challenges. The game was utilised as a part of the continual assessment process to evaluate group interaction, role-playing, competition and learning in an ISS assignment and facilitate the students to measure their own performances of understanding. Thus, the game was not just an assessment mechanism for grades, but also a learning tool. This chapter focuses on a group of final year undergraduate students completing Bachelor of Science in IS and outlines the online ISS environment used in the study.

BACKGROUND Organisations actively use simulated environments to both test (e.g. psychometric) and train (e.g. virtual trading of stocks and case study analysis) employees. Medical and scientific educators actively promote the learning of these disciplines through simulation and modeling tools (Quellmalz & Pellegrino, 2009) but to date social gaming has not been widely applied as a learning aid for business and IS (security) graduates.This chapter endeavours to leverage social media technology to enhance and support the learning and assessment mechanisms utilised in an undergraduate final year ISS module with the objective of providing students with a practical proactive knowledge of

DOI: 10.4018/978-1-5225-2255-3.ch223 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Educational Technologies

the implementation and management of ISS in business, an increasingly important and understudied topic (White et al., 2013). The chapter is structured as follows; the subsequent section considers the area of learning, focusing on the weaknesses associated with traditional learning and highlighting how learning tools may overcome many of these. Following this, the nature of ISS education is presented and the workplace of the future is considered with particular emphasis placed on the need for business graduates with skills in social media technology. The research approach is then outlined. The case is presented and discussed and finally attention is attributed to the conclusions of the study.

Traditional Approaches in Teaching and Learning Traditional learning approaches dominate third level education, however, more recently these practices are complemented by alternative approaches to teaching and assessment. This includes the use of Web 2.0 technologies (i.e. podcasts, social network sites, media sharing platforms, etc.) as a means of active learning, to further support and engage the learner (Cao et al., 2013). Traditional learning, also known as the teacher-centered paradigm, is regarded as a learning environment that encourages passive learning (Barr & Tagg, 1995), does not develop problem-solving skills, and ignores the individual needs of the students (Hannum & Briggs, 1982). It could be argued that advances in technology, such as multimedia and virtual simulations, have left the traditional classroom trailing behind, with learners expecting more and more. Social media provides a solution to these problems by incorporating the collaborative attributes associated with Web 2.0 technologies (Schneckenberg, 2009). Instructors are redesigning learning and assessment mechanisms by leveraging the dynamic interactive capabilities that social media can provide, which ultimately helps improve the essential skills students require to become business and ISS professionals. The

widely accepted criticism of the teacher centred model is that the ‘what’ rather than the ‘how’ of the instruction is delivered (Goodlad, 1984). It is argued that problem-solving and other intellectual skills are difficult to incorporate into the traditional environment due to the very nature of the educational system. Factors such as space, the grouping of students according to grades, and the duration and size of classes all hinder the desired environment. Technology enhanced learning is not the ‘silver bullet’ solution to the problems encountered in the education system, but it can provide a necessary balance to some of the limitations experienced with the traditional approach.

Technology Enhanced Teaching and Learning Technology enhanced learning refers to the enhancement of learning using information and communication technology (ICT), with the added benefit of helping students to develop new skills with digital tools along the way (Klemke & Specht, 2013). Technology, in this instance, plays a significant role in making learning more effective, efficient, or enjoyable (Goodyear & Retalis, 2010). In the literature it is often termed e-learning and it can support the educator and learner in a number of ways. For example, differing learning styles can be catered for so that educators can reach more students in a variety of ways, this subsequently enhances the number of students able to learn the course material (Sulcic & Lesjak, 2001). It is imperative when an organization or university decides to implement an e-learning initiative that they develop an effective solution that recognizes the need for good learning practices, by incorporating good design and development guidelines (Sulcic & Lesjak, 2001) – such as the learning dimensions advocated by Reeves and Reeves (1997) for interactive learning and collaboration. Active learning approaches, such as case-based learning and problem-solving, have long been advocated as ways of fostering deeper learning (Healy & Neville, 2009; Boyce

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et al. 2001). Similarly, for many years, organisations have been using problem-solving scenarios such as business simulations to both test and train employees. Simulations enhance the learner’s logical reasoning, numeric abilities, and spatial thinking through real problem-solving scenarios. Realising the potential of such methods, however, requires active engagement from educators and learners alike (Healy & McCutcheon, 2008). For many educators, the lack of appropriate materials, learning management, assessment techniques, and guidance are often perceived as barriers to student or employee engagement. With the ‘right’ underlying pedagogical approach social media technology provides educators with the technical platform to overcome these well-cited issues. Quellmalz et al. (2013, p. 1111) suggest that “engaging students in interactive assessments may provide a better estimate of their more complex inquiry practices than active or static formats” – providing third levels educators with an impetus to deliver a more complete learning experience.

Social Media Enabled Learning Extant learning theories support the view that student learning is enhanced through opportunities to work collaboratively (Prince, 2004), and virtual learning environments actively support this form of learner collaboration (Peat, 2000). Web 2.0 has revolutionized traditional media content and the way people communicate and interact. Consequently, social media technologies have the potential to support and enhance teaching and learning in higher education (Cao & Hong, 2011; Hajli et al., 2013). In fact, social media gives learners a chance to manipulate their learning environment and to participate actively in the learning process (Hrastinski, 2009). It is through these collaborative technologies that students and knowledge workers will gain enhanced insight in the knowledge at their disposal. These tools will also enable information workers to locate and connect people with certain expertise across organizations, bringing people, systems, and data

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into alignment faster to respond to challenges and take advantage of competitive opportunities. In an educational context, social media, when properly facilitated and framed can help expand the potential for learning over time by creating more student connections (Chen & Bryer, 2012). Valjataga and Fielder (2009, p58) widely support the use of social media technology as a means of skilling students in preparation for the ‘real world’, “we need to construct opportunities for participants in higher educational settings to practice the advancement of self-directing intentional learning projects.” However, research is needed in the area of education for the use of social media applications as online learning environments, and the learning affordances they may offer (Selwyn, 2007). Currently this area is lacking rigorous and carefully conducted research (Cao & Hong, 2011). One area of the IS undergraduate curriculum that attracts considerable attention is IS security education, as it has several practical ‘real world’ implications in the form of accessibility of sensitive data (Sauls & Gudigantala, 2013). Extant research highlights the importance of ISS education in terms of developing an entry level undertanding of security from an IT infrastructure perspective as well as the complementary analytical skills and problem solving skills (Sauls & Gudigantala, 2013; White et al. 2013). The next section considers ISS as a topic which benefits from active learning approaches through the use of e-learning environments.

IS Security (ISS) in Undergraduate Education In just a few decades, the use of IS has formalised information management and streamlined the administration of organisations (Galliers & Newell, 2001; Dhillon, 2006). However, in the realms of IS security, one of the fundamental problems for an organisation is to choose the right kind of environment to function in. While many organisations have engaged in identifying security issues and as a result developed appropriate ISS policies, there is a clear mismatch between policies and

Category: Educational Technologies

what is done in practice. Therefore theories-in-use have degrees of effectiveness which are learned (Mattia & Dhillon, 2003). Espoused theory and theory-in-use are a part of the double-loop learning concept which creates a mindset that consciously seeks out security problems in order to resolve them. This results in changing the underlying governing variables, policies, and assumptions of either the individual practitioner, function or the organisation. To help address this, the literature has identified the importance of producing qualified and competent graduates to manage the security challenges facing organisations today (Sauls & Gudigantala, 2013). Hence, it is vital that IS/IT security and disaster recovery are treated as core components in business IS curriculms (Patten & Harris, 2013; White et al., 2013; Kim et.al, 2006). Considering the complexity of the subject area, it is evident that teaching the know-how and know-what of an ISS course to IS undergraduates requires a hands-on approach to adequately deal with some of the concepts and underlying principles (Ilvonen, 2013; Sauls & Gudigantala, 2013). Thus, it is necessary to go beyond the traditional learning environment where the ‘knowing what’ or declarative knowledge base is core to the learning experience (Quellmalz et al., 2013). This includes providing real-world experiences, using real industry situations, scenarios, and exercises, by training students from both a proactive and reactive viewpoint (Woodward et al., 2013). Exploring the ‘know-how’, ensures that students acquire a more complete and practical experience, better preparing them as prospective managers of tomorrow (Ilvonen, 2013).

IS Graduates of the Future The workplace is continuously evolving and adapting to support organizational and technological change (Lee & Brand, 2005). Companies and universities will experience daunting challenges as they compete for and produce the next generation of IS talent. Generation 2020s or Generation Zs are characterized by the pervasiveness and avail-

ability of technology in every aspect of their lives (Geck, 2007). Instead of educating this generation about the Internet, the focus will be providing them with the skills to leverage the value of technology (Geck, 2007). The new workplace environment expects its knowledge workers to be proactive and show initiative, collaborate smoothly with others, take responsibility for their own professional development, and be committed to high quality performance standards. Graduates will have to use IT hardware and software intensively in a richly enabling business process environment to fulfil their roles. Indeed in ‘Talent 2020’ strategy, Deloitte (2012) refer to the development of their ‘onboarding programs’, which involve the provision of technology and resources to new employees as a means of enabling their efficient and effective engagement with the workplace. Notably, college students practice poor security behaviors and fail to properly use computer security tools (Mensch & Wilkie, 2011), thus emphasising the need for IS students to gain a critical understanding of the global issues of information security and assurance from their college education (White et al., 2013). With this in mind, ‘knowing how’ through active learning has become a formative part of the IS curriculum as the nature of technologies and work practices have changed. In addition, by utilizing technology in teaching and learning, it encourages student engagement and better prepares students for the work environments already embracing such technologies (Doyle et al., 2015). Subsequently, we endeavour to investigate how potential IS graduates leverage social media technology in a manner that enables them to appreciate, first-hand, the ‘real world’ implications of ISS. Essentially moving from the teacher-centred paradigm to the learner-centred paradigm where assessment is devised based on the student’s learning needs and their motivations (Ilvonen, 2013).

Research Approach This research study outlines the adoption of a blended approach that encompasses both the

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traditional approach and the use of social media tools as part of an online game facilitated by IS Security teachers within a university setting. The case selected for this research study was an undergraduate ISS module within University College Cork (UCC), Ireland. This module was selected because of the progressive approach of the associated facilitators to develop, customize, and blend traditional learning approaches and elearning technologies. The researchers examined the development and implementation of an online game designed to allow students to leverage their classroom acquired know-what in the area of IS security in a simulated ‘real world’ environment, namely, social media technology Facebook. Table 1 provides a description of the game. The objective to the ISS Game was to establish a fictitious security company with a social media presence. Data was collected through the student groups, each group submitted a report as the continuous assessment deliverable as part of the overall module. These group reports detailed their communications (e.g. group meetings and communications), and provided an account of their Table 1. Social ISS game description

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fictious company assets, these comprised group generated content including photos, recipes and limericks, and the security attacks made by the company on other groups in the class. This data was analyzed based on the group’s ability to leverage the material they learned in class and apply it in the online game scenario, the project outcomes are outlined in Table 2. The performance of each of the groups was reflected in their project grades. Significantly, the feedback on the ISS game from the class was hugely positive, particularly in terms of making, what can be perceived as a ‘boring’ or far removed topic to undergraduate students, more interesting, engaging, and important. The authors investigated the degree to which the benefits of the game met the learning needs of the students. The analysis also expanded the on-going design of the game to provide an innovative approach to learner support that is more akin to the true essence of social learning. The next sections provide a description of the ISS module and the social game. This is followed by the results of the student’s participation and an analysis of the results.

Category: Educational Technologies

SOCIAL GAMING ENVIRONMENT This game was created to facilitate and support understanding and learning of the links between ISS and its business applications, essentially to stimulate the ‘right’ type of learning (Carless, 2007). The creation of fictitious companies and corporate espionage components presented students with the opportunity to play the role of ISS practitioners protecting and simultaneously targeting corporate boundaries in an attempt to acquire and protect assets. At the start of the teaching period 2012/2013, the class (72 students) was divided into teams of 6 members. Each group was expected to meet at least once a week. Project Teams were asked to record minutes of all meetings and day-to-day operations through Twitter and these were kept for review by the coordinators throughout the year (protected Tweets with group and coordinator access). Facebook and Twitter were used to co-ordinate the work effort; thus planning of ISS tasks and workloads well in advance formed an essential element of the overall assignment.

Objective and Outcome of the ISS Game The game was structured as part of the lecture series (24*2 hours) to gradually build knowledge of the ISS subject domains while simultaneously simulating ‘real world’ situations when the groups are asked to deliver a series of requirements to determine their level of understanding of the topics (Table 2) discussed in class. Thus following Wilson’s (2012) recommendation that if instructors choose to use an active learning environment there needs to be controls in place to guarantee that students are somewhat knowledgeable and prepared before their arrival in the classroom or in this case social media gaming environment. At the end of each assessment submission the goals for the next submission and lecture were set, based on the level of knowledge and understanding demonstrated by students up to that point. Groups submitted and

presented their deliverables at agreed deadlines. This enabled a post- mortem evaluation approach (Kasi et al., 2008). Groups were also required to select 5 security breaches and critically evaluate/ discuss the breaches in terms of the controls used before and after the breach. The business impacts were also investigated. These evaluations were then presented and discussed in class. In some instances 3 or 4 groups investigated the same case. However each had their own view regarding how the company reacted to and learned from a reported (published) incident. Case analysis and discussion was the traditional and preferred form of an in-class exercise. This component was enhanced through the use of social media to store documents (reports, articles, videos and slides) and Twitter searches using # to find the discussions which occurred in real-time. Table 2 outlines how these topics were taught, applied, and assessed through the ISS business game. Student groups submitted their secret/assets at the beginning of the game (Term 1). However while the same technologies, discussed in class and reviewed by the groups, empower ISS practitioners they also empower hackers and hacking organisations to subjugate different types of information systems. This threat became part of the game as students adopted the role of hackers and targeted the secret/asset of another groups to gain extra marks. Thus supporting extant research which suggests that teaching students to defend against IS attacks also develops their attacking skills (Ilvonen, 2013). Table 3 outlines the results of the hacking component of the game. The groups primarily tried to use man in the middle and password bypass attacks. The most successful attack was conducted by group 1. The group emailed the class using a fake email address from one of the coordinators: [email protected] requesting that students email their secrets before 5pm on the day of the agreed ‘secret’ submission. 26 students emailed their group’s secrets to the fake account. This earned group 1 two extra bonus marks due to the limit of 2 and the fact that by the time the

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Table 2. Topics taught and game outcomes

project concluded (despite being consistently targeted by other 11 groups) did not have their corporate secret stolen. This attack was also used to illustrate well published cases of man-in-themiddle attacks. That is despite the fact that this topic was covered during class (2 weeks before

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the attack occurred) to the amusement of the majority of the students, due to the simplicity of the attack, 26 students fell for it. This reinforced the importance of controls such as SETA (security, education, training and awareness). As outlined in Table 3 groups were successful in acquiring

Category: Educational Technologies

Table 3. Attack attempts (Step 7 of Table 1)

another group’s asset as well as unsuccessful in protecting their own corporate assets. In a repeated exercise, conducted with 57 students in the 2015/16 term, Table 4 outlines the number of attack attempts made by individual students and the number of assets the students were able to steal overall, based on 44 survey responses. On average, the students attempted 6 attacks and were able to steal at least one asset, though this varied across the groups. Figure 1 displays a breakdown of the companies that were attacked during the 2015/16 term exercise. It is evident that some companies were targeted more than others during the exercise. Of the students surveyed, only some of them showed an awareness of the loss of their asset (see Figure 2). To illustrate this, taking the 44 student responses in Table 4, it was claimed that 61 assets were stolen overall, however, of the 43 student responses about the awareness of this loss, 72% of the students surveyed claimed no loss of an asset and 28% recognised that they had actually lost an asset. Surveying the students enabled greater insight into the value of the exercise for learning. When asked whether their own attack attempts improved the protection of the group/company’s asset (Figure 3), 88% of students (out of 43 reponses) replied in the affirmative. Student’s had learned through attack attempts how to better improve their own security. For example, one student commented

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Table 4. Attack attempts and stolen assets (2015) Average No.

Total No.

Responses

Attack attempts

6

258

44

Stolen assets

1

61

44

“We made sure we logged off our computers in the labs at all times when left unattended after getting access to other rival company computer accounts that way”. Likewise, different strategies used by students were noted “As we composed a phishing email, it made us more aware that other groups would be doing the same so we were extra careful when receiving emails, correctly identifying them as false”.

Discussion Problem-solving skills require the use of a number of different learning strategies and types of knowledge. In this case, the students leveraged the concepts and strategies considered in the lecture in order to create a fictional company, roles (hacker and security officer), and to apply technical (bypass Facebook controls) and no- technical hacking (social engineering). Students established their secure company and attacked another company/ student group using social media. Students were exposed to a number of security threats, enabling them to protect their assets while assuming the

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Figure 1. Survey of 44 students (2015): question “what companies did you attack?)

Figure 2. Survey of 43 students (2015): question “was your asset stolen?”

role of hacker. Students became very competitive as they endeavored to steal marks as well as company assets. The learner’s own experience, internal mental models, and other ‘cognitive structures’ are necessary to ‘construct’ their own knowledge when faced with new information or different situations. This game reinforced the need to recognise that the technical side of ISS is a part of, but not the 2566

answer to, the different ISS challenges. Knowledge and expertise of the technologies are deemed both necessary and valuable in terms of alleviating ISS risks. However in order to achieve this, ISS students must be familiar with critical business processes as well as ISS business impacts. Technological changes, in both secure hardware and software, are as constant as the increase in the number of threats to corporate ISS. Forgetting the most basic

Category: Educational Technologies

Figure 3. Survey of 43 students (2015): question “did your attacking attempts improve the protection of your group/company asset?”

types of attacks and the potential for employee mistakes are common issues for organisations in general. These errors were experienced by the student groups (Table 3). The mistakes made, as well as adopting the role of hacker, reinforced the material taught in class.

FUTURE RESEARCH DIRECTIONS In their study Tay and Allen (2011, p. 153) purport that Staff (educators) saw both the necessity of including greater use of social media in teaching and, at the same time, believed that neither social media technologies themselves, nor the informal and personal cultures of use that students had developed, would necessarily mean that this innovation would – without close attention to pedagogic design – reliably improve students’ outcomes. While the use of social media technology in this study proved to positively enhance the student learning experience, it is imperative that academia continues to engage with social media technologies in order to further understand and leverage their capabilities, as well as ensuring that this convergence of the traditional and new provides students with a fulfilling learning experience ultimately preparing them for the challenges of

the workplace. Notably this was the first time the game was utilised as part of continuous assessment for an ISS module. The social gaming approach will be used in the future however the lecturers acknowledge the importance of leveraging student grades as part of the student group evaluation process in order move beyond the reported ‘novel’ and positive perceptions associated with the use of social media as part of this class. Cao et al. (2013) purport that social media is being widely used as a mechanism for learning and assessment in third level institutions. In this study, an assumption was made that students were familiar with social media technology from a social perspective, subsequently they were required to leverage this platform as a means of meaningfully engaging with ISS issues. White et al. (2012) persuade that ISS awareness and education is core to IS education and they contend that this should be supported by education in preventative education and incidence response. The social game presented in this study could be further developed to provide students with the ‘know-what’ and ‘know-how’ to devise preventative security and response strategies. Like Cao and Hong’s (2011) study of social media utilization in teaching, this research focused on the delivery and assessment of one ISS undergraduate module in one university. In order to further understand the impact of utilizing social media technology as a means of an active

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learning approach, a longitudinal study over a number of semesters would be necessary to fully investigate the strengths and weaknesses of this learning strategy (Mikropoulos and Natsis, 2011). Thus, this exercise was repeated with students during the 2015/16 term and preliminary findings were presented along with the findings from 2013/14. This time, students were surveyed after the exercise to better understand the impact of the game on their learning. Overall, students were positive about the learning impacts of the game and were able to identify multiple strategies for attacking and protecting company assets. It would also be beneficial to apply a similar social media gamed-based assessment in another undergraduate programme as a means of exploring the strength of this approach in another setting. It is only then that we can really begin to understand the true impact of the use of social media as part of the active learning approach.

CONCLUSION It is important that educators begin to place social media technology at the core of IS third level learning and assessment approaches going forward. It is through leveraging these technologies as part of the active learning approach that will better prepare our graduates for the workplace. Undoubtedly, universities and companies face daunting challenges as they compete for the best talent in 2020. They will need to attract new talent, train, retain, and create an engaged workforce. Workers will mainly be knowledge workers, utilizing the Internet, computer technology, communications technology, and knowledge processing platforms as an integral part of their work. Working in physical office spaces with colleagues will diminish, giving way to virtualized teams in distant geographical locations. Third level educators and employers will need to work together to ensure that graduate employees have access to leading edge connectivity tools and the appropriate skills to replace the lack of face-to-face communication.

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Organizational leaders are keenly aware that the workplace is changing and are already recruiting a new breed of employee. They are adapting their workplace policies and strategies to appeal to all generations. Therefore IS educators must adapt to the changing needs of industry and students in developing competitive skill-sets through the design of a curriculum that excites students, trains them [IS students] with hands-on exposure, and provides them with the necessary skills to achieve success in the IT industry (Sauls & Gudigantala, 2013). As “current university graduates will become tomorrow’s protectors of data and systems” (White et al., 2013, p. 14).

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Mattia, A., & Dhillon, G. (2003). Applying Double Learning to Interpret Implications for Information Systems Security Design. IEEE Systems, Man and Cybernetics Conference, Washington, DC. doi:10.1109/ICSMC.2003.1244262

Goodlad, J. (1984). A Place called school. New York: McGraw-Hill Book Co. Goodyear, P., & Retalis, S. (2010). Technologyenhanced learning. Sense Publishers. Hajli, M., Bugshan, H., Lin, X., & Featherman, M. (2013). From e-learning to social learning–a health care study. European Journal of Training and Development, 37(9), 851–863. doi:10.1108/ EJTD-10-2012-0062 Hannum, W., & Briggs, L. (1982). How does instructional system design differ from traditional instruction? Educational Technology, 22(1), 9–14. Healy, M., & McCutcheon, M. (2008). Engagement with Active Learning: Reflections on the Experiences of Irish Accounting Students. Irish Accounting Review, 1(15), 1–49. Healy, M., & Neville, K. (2009). A Teaching Case: Towards Bridging Disciplinary Divides in IT Education. Proceedings of the 17th European Conference on Information Systems (ECIS 2009). Ilvonen, I. (2013). Information security assessment of SMEs as coursework – learning information security management by doing. Journal of Information Systems Education, 24(1). Kim, Y., Hsu, J., & Stern, M. (2006). An update on the IS/IT skills gap. Journal of Information Systems Education, 17(4), 395. Klemke, R., & Specht, M. (2013). Technology Enhanced Learning. Proceedings of the Fourth International Conference on e-Learning (eLearning-2013).

Mikropoulos, T. A., & Natsis, A. (2011). Educational virtual environments: A ten-year review of empirical research (1999–2009). Computers & Education, 56(3), 769–780. doi:10.1016/j. compedu.2010.10.020 Patten, K. P., & Harris, M. A. (2013). The Need to Address Mobile Device Security in the Higher Education IT Curriculum. Journal of Information Systems Education, 24(1), 41. Peat, M. (2000). Towards first year biology online: A virtual learning environment. Journal of Educational Technology & Society, 3(3), 203–207. Prince, M. (2004). Does active learning work? A review of the research. The Journal of Engineering Education, 93(3), 223–231. doi:10.1002/j.2168-9830.2004.tb00809.x Quellmalz, E. S., Davenport, J. L., Timms, M. J., DeBoer, G. E., Jordan, K. A., Huang, C. W., & Buckley, B. C. (2013). Next-generation environments for assessing and promoting complex science learning. Academic Press. Quellmalz, E. S., & Pellegrino, J. W. (2009). Technology and testing. Science, 323(5910), 75–79. PubMed doi:10.1126/science.1168046 Reeves, T. C., & Reeves, P. M. (1997). Effective Dimensions of Interactive Learning on the World Wide Web. In B. H. Khan (Ed.), Web-Based Instruction (pp. 59–66). Englewood Cliffs, NJ: Educational Technology Publications, Inc.

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Sauls, J., & Gudigantala, N. (2013). Preparing Information Systems (IS) Graduates to Meet the Challenges of Global IT Security: Some Suggestions. Journal of Information Systems Education, 24(1). Schneckenberg, D. (2009). Web 2.0 and the empowerment of the knowledge worker. Journal of Knowledge Management, 6(13), 509–520. doi:10.1108/13673270910997150 Selwyn, N. (2007). Web 2.0 applications as alternative environments for informal learning - a critical review. Retrieved on May 16, 2012 from, http://www.oecd.org/dataoecd/31/37/39459090. pdf Sulcic, V., & Lesjak, D. (2001). DE in Slovenia: Where are we? Proceedings of the 9th European Conference on Information Systems (ECIS 2001): “Global Co-operation in the New Millennium”, 1087-1097. Tay, E., & Allen, M. (2011). Designing social media into university learning: Technology of collaboration or collaboration for technology? Educational Media International, 48(3), 151–163. doi:10.1080/09523987.2011.607319 Valjataga, T., & Fielder, S. (2009). Supporting students to self-direct intentional learning projects with social media. Education et Sociétés, 12(3), 58–69. White, G. L., Hewitt, B., & Kruck, S. E. (2013). Incorporating Global Information Security and Assurance in IS Education. Journal of Information Systems Education, 24(1). Wilson, L. V. (2012). An Assessment Of Learning Outcomes Based On A Comparison Of Active Learning And Traditional Lecture Pedagogical Styles In A Legal Environment Classroom. Southern Journal Of Business And Ethics, 4(101), 110.

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Woodward, B., Imboden, T., & Martin, N. L. (2013). An Undergraduate Information Security Program: More than a Curriculum. Journal of Information Systems Education, 24(1), 63.

ADDITIONAL READING Moran, M., Seaman, J., & Tinti-Kane, H. (2011). Teaching, Learning, and Sharing: How Today’s Higher Education Faculty Use Social Media. Babson Survey Research Group.

KEY TERMS AND DEFINITIONS Information System Security (ISS): The study of defending information, hardware, software resources against unwarranted attack. Knowledge Workers: A person with a specialized skillset e.g. software engineer, architect, financial analyst. Online Learning Environment: Online (web-based) channels utilised to support student learning and assessment. Social Business Gaming: The use of online social gaming as a means of measuring student performance as part of their on-going learning process. Social Media Technology: Technologies such as Facebook, LinkedIn and Twitter which facilitate user engagement and user-generated content. Student Assessment and Learning: Knowing what content and skills students have mastered. Workspace Technology: The platforms and supporting technologies utilised by Knowledge Workers in their day-to-day work routine.

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Liberating Educational Technology Through the Socratic Method Frank G. Giuseffi Lindenwood University, USA

INTRODUCTION

BACKGROUND

Digital literacy and technology are instruments for human communication and behavior (Lemke, 2010). The skills and attributes the human person needs for responsible citizenship, and workperformance, is being re-defined by, what Dede (2010) called “information and communication technologies.” In the educational world, November (2012) claimed that a revolution is happening where teachers are harnessing the uses of technology in their courses. Moreover, the influence technology is having on society is fundamentally changing the nature and functions of schools (Lever-Duffy, McDonald, & Mizell, 2010). While one should celebrate the positive results of the role technology has given students in their educational endeavors, our celebration must be tempered with caution. Recent research suggest that suggests that technology, in the form of laptops, has not raised student achievement in any significant way (Goodwin 2011; Hu 2007). If this is the reality that confronts us, we are then pressed to respond. The question for educators is what kind of response? What are the answers to this problem that seems to be growing? This chapter, per the author, suggests that a return to something used in antiquity may be the answer. This chapter explores the use of the Socratic method as a teaching technique that can give direction to the lack of pedagogical vision in the great One to One debate currently confronting schools.

In 2009, Arne Duncan, U.S. Secretary of Education exhorted public schools nationwide to implement technology in public schools (Lemke, 2010). He indicated at a national consortium that “good teachers can utilize new technology to accelerate learning and provide extended learning opportunities for students” (Lemke, 2010, p. 245). As one example of this desire to increase educational technology, schools began to invest millions of dollars in One to One laptop programs (Goodwin, 2011). However, even before this speech by Duncan, issues were raised concerning One to One laptop programs. Hu (2007) indicated that school districts in New York and elsewhere were seeing One to One laptop programs as major obstacles to student learning. As early as 2007, the United States Department of Education found that there was “no difference in academic achievement between students who used educational software programs for math and reading and those who did not” (Hu, 2007). Studies in Texas and Michigan showed mixed results in student achievement when it came to the effectiveness of laptop programs (Goodwin, 2011). One cannot also discount the influence teaching has on successful laptop programs (Stansbury, 2010). Studies published in the Journal of Technology, Learning and Assessment at Boston College’s Lynch School of Education indicated that “the most important factor of all is the teaching practices of instructors – suggesting school

DOI: 10.4018/978-1-5225-2255-3.ch224 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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laptop programs are only as effective as the teachers who apply them” (Stansbury, 2010). This is further confirmed from results of a recent study of 997 schools in the United States indicating that one of the factors that added to successful laptop programs was teacher training (Goodwin, 2011). Hence, educators must seize upon the notion that before laptops are given to students, a commitment to teacher training is needed (November, 2010). Norris and Soloway (2010) echoed this sentiment; they wrote: “To make the computer an essential tool in the classroom, and thus to realize the potential value added from technology, we need to redefine the curriculum in terms of what gets taught, and we need to redefine how it gets taught” (p. 1).Indeed, Pearlman (2010) made the bold claim that simply putting computers in the hands of students is not a solution, but actually “reinforces the old teacher-directed whole group instruction” (p. 127). The common experiences of schools that have embraced laptop programs has been to add on the technology to the same lesson assignments, instead of changing the nature of the lesson assignments. Students have been given the technology, but the lessons have not changed, resulting in the laptop becoming high-priced notebooks (November, 2010).

To fully articulate this argument, we first turn to a working definition of the Socratic method. While various definitions exist, the chapter, per the author, puts forth the following,

THE SOCRATIC METHOD Socrates continually asked insightful questions that reflected the reality that learning came from within (Cookson, 2009). This can be an important teaching technique. As Paul and Elder (2007) noted,

Believed learning came from within and that the best and most lasting way to bring latent knowledge to awareness was through the process of continual questioning and unconventional inquiry. For Socrates, answers were always steps on the way to deeper questions (P. 1).

Teachers, students, or indeed anyone interested in probing thinking at a deep level can and should construct Socratic questions and engage in Socratic dialogue. The purpose of using Socratic questioning in teaching may be to probe student thinking; to determine the extent of their knowledge on a given topic, issue, or subject; to model Socratic questioning for them; or to help them analyze a concept or line of reasoning (Pg. 36).

Socratic questions are broken down into basically three kinds: Spontaneous, Exploratory and Focused (Paul & Elder, 2007). The spontaneous question is motivated from a genuine curiosity on the teacher’s part. They are unplanned and arise from conversations that take various paths. Examples could be asking for evidence, asking for others opinions on a given topic, or examples based on a point made during the discussion.

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In the Socratic method, the classroom experience is a shared dialogue between the teacher and students in which both are responsible for pushing the dialogue forward through questioning. The “teacher” or leader of the dialogue asks probing questions in an effort to expose the values and beliefs which frame and support the thoughts and statements of the participants in the inquiry. The students ask questions as well, both of the teacher and each other (Reich, 2003) (P. 1). The intention is to advance the discussion through dialogue and to uncover, dissect and critically examine accepted positions. This is fundamentally done through thought-provoking and specific questioning. This is clearly the way of Socrates (Cookson, 2009). He [Socrates] would engage his students through questioning and examination of beliefs (Gose 2009; Morrell 2004). This kind of dialectical practice that Socrates espoused leads one to the “good life” (Yengin and Karahoca, 2012). According to Cookson (2009) ultimately, Socrates

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According to Paul and Elder (2007), this kind of question fosters a “Socratic Spirit.” One asks exploratory questions to discover what students are thinking on a particular subject or question. These questions help to broach topics or to get a sense of students’ attitudes or dispositions on a topic. The best and most effective exploratory questions, however, are carefully constructed before the dialogue begins. As Paul and Elder (2007) wrote, However, for the greatest success some preplanning or pre-thinking is helpful. For example, one could construct a list of possible questions to ask at some point in the discussion. Another preparation technique is to predict students’ likeliest responses and frame some follow-up questions. Lastly, focused questioning is often done within the context of a larger curriculum, needing to clarify or examine specific issues. The facilitator and students look at a specific topic, subject or idea. The dialogue is more structured and directed (Paul & Elder, 2007). Let us now proceed to look at the aforementioned types of questions asked based on a Socratic discussion of Aristotle’s Nicomachean Ethics in my high school History of Ideas class in the Spring of 2016. We first see spontaneous questioning occur in the following. In Book II, Chapter six, Aristotle wrote, But not every action nor every passion admits of a mean; for some have names that already imply badness, e.g. spite, shamelessness, envy and in the case of actions adultery, theft, murder; for all of these and suchlike things imply by their names that they are themselves bad, and not the excesses or deficiencies of them (Aristotle, trans. 1941, p. 959). Teacher: What do we make of this word “shamelessness?” Is shame still a common feeling in today’s society?

Student 1: The feeling of shame comes from societal pressure. This society prizes individualism. Individuals should not necessarily feel shame if it comes from society’s standards. Student 2: But society’s standards are important. Student 1: Not, however, if it takes away from you as a person. Student 2: You can become a better person from society and having feelings of shame. I was initially looking at Aristotle’s concept of the “mean,” however, my interest in “shamelessness” led him or her to ask a spontaneous question on that subject. This then led to further comments on not just shame but its relationship to society and also the importance of the individual. Staying on the philosophy of Aristotle, the following was an example of exploratory questioning. From Aristotle’s Book II, Chapter 6 of the Nicomachean Ethics, But the intermediate relatively to us is not to be taken so; if ten pounds are too much for the person who is to take it, or too little – too little for Milo, too much for the beginner in athletic exercises. The same is true of running and wrestling. Thus a master of any art avoids excess and defect, but seeks the intermediate and chooses this – the intermediate not in the object but relatively to us (Aristotle, trans. 1941, p. 958). Teacher: How does one live morally by finding the intermediate state? Student 1: They don’t go overboard or lend themselves to extremism on a moral issue. Student 2: It depends on the situation you are confronted with. Maybe some things call for extreme measures. Teacher: In what way? Student 2: Well, sometimes governments becomes so tyrannical, revolution is called for. I would say that’s a kind of extremism. Teacher: In a situation like that what would be the intermediate state? In other words, what would citizens who thought the government

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was tyrannical first seek to achieve if they were looking for Aristotle’s conception of the intermediate? Student 1: Possibly trying to find a compromise with the tyrannical government? I know that sounds a little strange, but maybe those who were feeling abused could work with the government to resolve some of the injustices. Wouldn’t that be looking for an intermediate state. I explored the issue of “the intermediate” by using moral behavior as an example. Then the discussion evolved into the possible justification, at times, of choosing the extreme. Lastly, we looked at focused questioning. Here was a discussion based on Aristotle’s Nicomachean Ethics, Book II, Chapter 7. He wrote, With regard to feelings of fear and confidence courage is the mean; of the people who exceed, he who exceeds in fearlessness has no name (many of the states have no name), while the man who exceeds in confidence is rash, and he who exceeds in fear and falls short in confidence is a coward (Aristotle, trans. 1941, p. 960). Teacher: I am intrigued by the distinctions between courage and rashness. Would coming to the aid of a friend who is being bullied by someone much stronger and larger than you be a courageous or rash act? Student 1: It depends on what you did. I would not use force on someone who is stronger and bigger than me. Student 2: But in those situations one does not worry about oneself. Student 3: Yeah but I am not going to risk my physical well-being over an issue that might be minor. I mean what really constitutes bullying anyway? Teacher: So would it be a rash act to interject in a situation that might cause you physical harm and not be very helpful to the situation?

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Student 2: I agree that rash acts often counter logic or sense. I would not excuse the scenario you gave. I think coming to the aid of someone even though you may be lacking in some quality is a courageous act. I directed the discussion on the differences between courage and rashness, citing an example in order to further illuminate in the students the nature of courage.

ONLINE SOCRATIC DIALOGUE Studies suggest increased learning in online educational environments that use the Socratic method of teaching (Tucker and Neely, 2010). Moreover, the use of the Socratic method in these same environments bolsters the facilitation of discussion and increased critical thinking skills (Jumper, 2016). Let us proceed, then, to describe how this can be done in a High School American History class that incorporates an asynchronous, One to One lap top program. In an online learning scenario with each student assigned a lap top, the instructor assigns The Gettysburg Address, one of the most well-known presidential speeches in American History; it was delivered by President Abraham Lincoln on November 19, 1863, several months after the three-day American Civil War battle in Gettysburg, Pennsylvania. The speech would be read by attaching a credible, respected History link to the school’s Learning Management System (LMS). Prior to the assignment the teacher would have: • • •

Pretested the students to ensure that previous material had been mastered Articulated the learning outcome in specific detail Ensured that technologies were available to all students

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•

Set up content modules for students to use as resources of information (Duffy, McDonald & Mizell, 2010).

Hrastinski (2008) argued that there are three necessary forms of “communications” in asynchronous education the instructor should be aware of: 1) content-related; 2) planning of tasks, and 3) social support. In content-related communication, students (e-learners) must be able to read the assigned content, ask questions, and share their thoughts. In planning of tasks, there are assigned responsibilities, collaboration and conflict resolution. Lastly, in social support, there must be teamwork, positive feedback, and technical support. In the following examples we will clearly see content-related communication. That is not to say, however, that the Socratic practitioner should not pay close attention to planning of tasks and social support (Gose, 2008). Also, whether in the traditional style of Socratic dialogue or its application in an online program, support for the students is essential. Both instructor and student must be conscious of group cohesion, respect for each other, and emotional support. This will be discussed further in the chapter. The description of the speech is then followed by three sets of Socratic questions mentioned earlier - spontaneous, exploratory and focused. The exploratory and focused questions will first be presented. These questions can be asked by the instructor for students to ponder and reflect on prior to the dialogue. In fact, having some answers to these questions will help the students develop better spontaneous questions during the dialogue. The following elucidates, through examples, the three types of Socratic questions:

Exploratory 1. What is equality? 2. What does it mean to be “conceived in liberty?”

3. What is the relationship between liberty and equality?

Focused 1. What is the “great task” Lincoln is asking the American people to continue? 2. How would you describe Lincoln’s “new birth of freedom?” 3. Do you think this speech does the men who died on that battlefield honor? Why or why not? Once the students have reflected on these questions, the teacher assigns a time and day for discussion; this is where the spontaneous questioning can take place. The teacher can first ask students about their own definitions of equality and liberty, inviting them to offer specific examples. The teacher can also investigate students’ answers on the connection between liberty and equality – challenging the notion that they are harmonious. This is where the “Socratic Spirit” is developed and a meaningful dialogue occurs. It should be noted that this educational experience would be asynchronous, where teacher and student are not communicating at the same time (Duffy, McDonald & Mizell, 2010). While one could argue that this presents a problem to Socratic dialogue, there are actually positive aspects to this as will be discussed later. Let us see what that spontaneous Socratic conversation might look like while using laptops via an online discussion board; this platform is where teachers can post information and communicate with students (Ward, 2012). In an asynchronous Socratic dialogue, it is imperative that the teacher set clear procedures and rules. In developing these rules, there will be questions that would need to be answered. For instance, what is the time frame for responding to someone’s question? Also, it will be important to know how frequently each student is expected to post on the online discussion board (Ward, 2012).

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The discussion begins with the teacher (facilitator) identified as TCH. Although not indicated on this page, the comments from each individual would include date and time of question or comment. TCH: Can somebody offer a definition of “equality?” ST#1: In my mind, equality is the idea that all people are respected before the law. ST#2: I agree, but also that all citizens of a country are protected before the law. TCH: So are both of you arguing that governments or lawmakers grant equality to people? ST#1: Yes, I would. At least a certain kind of equality is granted by governments. TCH: Let us look at the Gettysburg Address again. Lincoln says “dedicated to the proposition that all men are created equal.” Is this implying that the equality that the founders conceived was something they granted or was found in nature? ST#2: I think one can interpret from the document that they are found in nature. TCH: Whether found in nature or granted by governments, the Founders certainly believed these concepts were important to the American Founding. Is there a relationship between equality and liberty? ST#3: Yes. I was trying to come up with a definition of equality that includes liberty. For instance, a regime can promote the idea that all people are free to earn a living, worship in their own way and reside where they want to; this is a kind of equality, isn’t it? Hence, we see the progression of the lesson take this form: Figure 1.

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With this example, we see a Socratic dialogue about equality via the use of a laptop program. The nature of the Socratic dialogue stays the same as the classical one described earlier. However, unlike the classical conception of the Socratic method, the Socratic method discussion via the Forum is, as we have described earlier, asynchronous - students and instructor can take some time reflecting on a question or comment before they participate. During this “reflective time” students could have re-read the Gettysburg Address, done further research on Abraham Lincoln, or developed better questions for the dialogue. While students are doing this, instructors must be aware of the second form of communication described by Hrastinksi (2008), social support. For example, during the reflective time, the teacher could offer positive feedback to the questions and comments that were already asked, present further scholarly analyses by Lincoln scholars, or describe further information on asking insightful questions. However, it must be noted, that the teacher must be careful not to offer too much information, since he or she runs the risk of disrupting the original topic in the dialogue. Second, students who are not as confident in their speaking ability or who are shy can benefit from using the forum, via their laptops, to voice their opinions. Lastly, students who are apprehensive with their answers can take the necessary time to review and edit what they wish to articulate (Duffy, McDonald, & Mizell, 2010). In essence, we find four stages that must occur in a One to One Laptop program that uses the Socratic method of teaching and learning. First, there must be a preliminary stage where the lesson is assigned, pre-assessing and frontloading information has occurred, learning objective has been presented and laptops are available to all students. Second, there must be a clear indication of all three modes of communication – contentrelated, planning of tasks and social support. Third, Socratic questions should be delineated into three question subgroups argued by Paul & Elder (2007) – spontaneous, exploratory and fo-

Category: Educational Technologies

Figure 2.

cused. Lastly, during the reflective time, teachers must be attentive to giving social support to their students; this will help with content-mastery and achievement.

FUTURE RESEARCH DIRECTIONS The global possibilities to this educational experience are innumerable. Improvements in communication technologies caused a “death of distance” where the actual physical space between peoples from other countries is made obsolete (Zhao, 2009). Students from all over the United States and the world can participate in a Socratic discussion on the Gettysburg Address, and any other topic in American History. Therefore, it is necessary to continue research on the impact the Socratic method of teaching and learning has on One to One programs. This can be a school-wide initiative or a small sample using action research methods. Questions to consider are: •

Does the use of the Socratic method in One to One laptop programs increase student test scores?

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Does the use of the Socratic method in One to One laptop programs improve teacher pedagogy? Does the use of the Socratic Method in One to One laptop programs enhance SelfDirected Learning?

Asking these and other questions will continue the important conversation about teaching, technology and learning in the 21st century.

CONCLUSION As modern educators, we tend to quickly embrace those things we think will improve student learning. Our faith and confidence in advances in educational technologies is no different. Educators and other professionals quickly invest in the latest technologies to hopefully improve learning outcomes and teacher performance. However, the pre-thinking, and pre-planning that comes with the purchase of these technologies is often set aside or not fully addressed. We should accept that technology like medicine is constantly changing and improving. However, that acceptance is guided by a respect and recognition of those ideas

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and educational practices that have informed humankind for centuries. One of those practices has been the Socratic method. We are correct to look forward to the future of technology and the many great things it can offer the world, but we would also do well to capture and hold on to those ideas and practices that have proven beneficial to the intellectual development of humankind.

REFERENCES Aristotle,. (1941). Basic works of Aristotle (W. D. Ross, Trans.). New York, NY: Random House. Cookson, P. W. (2009). What would Socrates Say? Retrieved from http://www.ascd.org/publications/ educational-leadership/sept09/vol67/num01/ What-Would-Socrates-Say%C2%A2.aspx

Ilker, Y., & Karahoca, A. (2012). “What is Socratic method?” The analysis of Socratic method through “self determination theory” and “unified learning model”. Paper presented at the 2nd World Conference on Innovation and Computer Science, Singapore. Jumper, S. (2016). Retrieved from http://www. academia.edu/4068653/USING_SOCRATIC_ TECHNIQUES_TO_INCREASE_LEARNING_ OUTCOMES_IN_HIGHER_EDUCATION Lemke, C. (2010). Innovation through technology. In J. Bellanca & R. Brandt (Eds.), 21st century skills: Rethinking how students learn (pp. 243272). Bloomington, IN: Solution Tree Press. Lever-Duffy, J., McDonald, J. B., & Mizell, A. P. (2005). Teaching and learning with technology. Boston, MA: Pearson Education, Inc.

Dede, C. (2010). Comparing frameworks for 21st century skills. In J. Bellanca & R. Brandt (Eds.), 21st century skills: Rethinking how students learn (pp. 243-272). Bloomington, IN: Solution Tree Press.

Morris, C., & Soloway, E. (2010, May). One to one computing has failed our expectations. District Administration. Retrieved from http:// www.districtadministration.com/article/one-onecomputing-has-failed-our-expectations

Goodwin, B. (2011, February). Research says one to one laptop programs are no silver bullet. Educational Leadership, 68(5). Retrieved from http://www.ascd.org/publications/educational_ leadership/feb11/vol68/num05/One-to-One_Laptop_Programs_Are_No_Silver_Bullet.aspx

November, A. (2010). Technology rich, information poor. In J. Bellanca & R. Brandt (Eds.), 21st century skills: Rethinking how students learn (pp. 275-283). Bloomington, IN: Solution Tree Press.

Gose, M. (2009). When Socratic dialogue is flagging: Questions and strategies for engaging students. College Teaching, 57(1), 45–49. doi:10.3200/CTCH.57.1.45-50

November, A. (2012). Who owns the learning. Bloomington, IN: Solution Tree Press. Paul, R., & Elder, L. (2007). Critical thinking: The art of Socratic questioning. Journal of Developmental Education, 31(1), 32–37.

Hrastinski, S. (2008, November). Asynchronous and synchronous e-learning. Retrieved from http:// er.educause.edu/articles/2008/11/asynchronousand-synchronous-elearning

Pearlman, B. (2010). Designing new learning environments. In J. Bellanca & R. Brandt (Eds.), 21st century skills: rethinking how students learn (pp. 117-147). Bloomington, IN: Solution Tree Press.

Hu, W. (2007, May 4). Seeing no progress, some schools drop laptops. The New York Times. Retrieved from http://www.nytimes. com/2007/05/04/education/04laptop.html?_r=0

Speaking of Teaching. (2003, Fall). Stanford University Newsletter on Teaching. Retrieved from http://web.stanford.edu/dept/CTL/Newsletter/ socratic_method.pdf

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Stansbury, M. (2010). One to one computing programs only as effective as their teachers. eSchool News. Retrieved from http://www.eschoolnews. com/2010/02/16/11-programs-only-as-good-astheir-teachers/

Isenberg, S. (2007). Applying andragogical principles to internet learning. Youngstown, NY: Cambria Press.

Tucker, J. P., & Neely, P. W. (2010, June). Using Web Conferencing and the Socratic Method to Facilitate Distance Learning. International Journal of Instructional Technology and Distance Learning., 7(6), 15–22. Retrieved from http://www.itdl. org/Journal/Jun_10/article02.htm

KEY TERMS AND DEFINITIONS

Ward, D. (2012, December). Socratic method and online teaching. Retrieved from https:// www.google.com/webhp?sourceid=chromeinstant&ion=1&espv=2&ie=UTF-8#q=socrati c+method+and+online+teaching Zhao, Y. (2009). Catching up or leading the way: American education in the age of globalization. Alexandria, VA: ASCD.

ADDITIONAL READING Bassett, P. F., & Thorn, C. (Eds.). (2004). Looking ahead: independent school issues & answers. Gilsum, NH: Avocus Publishing. Chafee, J. (2012). Thinking critically. Boston, MA: Wadsworth Cengage Learning. Copeland, M. (2005). Socratic circles: Fostering critical and creative thinking in middle and high school. Portland, ME: Stenhouse Publishers.

Alan November: Current writer and theorist on the role technology has on teaching and learning. Asynchronous: Online education where students and teacher work toward a shared learning objective by communicating with each other at different times. Critical Thinking Skills: The capacity to understand and analyze ideas through a systematic form of thinking that applies logic, dialectic, selfreflection, and disciplined questioning. Digital Literacy: The interpretive understanding of technological social networks that create communication and the uses and applications of social media. Discussion Board: A social network forum that allows for communication and shared inquiry between individuals. Educational Technology: A description of any learning program and lesson assignment that incorporates technology in its learning outcome and teaching methodology. Learning Management System: Software programs that capture and documents information for educational purposes. Socratic Method: A method of teaching that applies a dialectical exchange between persons who wish to arrive at a shared understanding concerning a particular question, thought or idea.

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Online Academia Magdalena Bielenia-Grajewska University of Gdansk, Poland

INTRODUCTION The aim of this contribution is to investigate the key notions characterizing the academia of the twenty-first century, especially the sphere of online learning. To present the mentioned relations and complexities within academic learning itself, the 5S Model of Online Academia has been created and discussed by this author. The proposed model focuses the discussion about online academia on such elements as subject, situation, spirit, stakeholder, senses and subject, and their subcategories, to elaborate on the multilevel nature of universities. This framework provides an insight into the current complexities of online academia and offers an insight into its future perspectives.

BACKGROUND There are some important factors of modern academia that shape its current state. First, academia is characteristic of its dual nature, it has been an institutionalized space struggling to secure time for thought, consideration and the slower, timeconsuming and lengthy scholarly and scientific conduct deliberately detached from the faster pace of capitalist production, media, politics and their ideological apparatuses; at the same time, it has been a symbol of and an instrument of modern progress, where individual academics and scientists have formed disciplinary associations and alliances, and advocated (to various degrees, and in diverse incarnations), socio-political, economic, scientific and cultural change (Vostal, 2016: 7). Modern academia can also be discussed by looking at it as a complex adaptive system. It

can be characterized, among others, by nonlinear behavior, visible in disproportionate responses. Other important features of academia are independence, intelligence, learning and self-organization. Moreover, universities are the places of faculty disagreements and different points of control (Rouse, 2016). Academia can also be studied through the prism of its key determinants. Examining the growing role of technology in the life of universities and high schools, non-living entities such as computer systems, hardware, software and mobile technologies determine the way teaching and research are conducted. These notions are studied in the work by Davey and Tatnall (2012) who discuss the notion of technological adoption, focusing on school management software. In the past, traditional dissemination channels were used to gather and share knowledge. Nowadays there are networked scholars who use participatory technologies and online social networks in their research (Veletsianos, 2016).

ONLINE ACADEMIA- KEY ELEMENTS AND CHARACTERISTICS Modern education differs from what could be observed some years ago; teaching and learning of the twenty-first century involve not only various learners in terms of their age, gender and background, but also diversified methods of encoding and decoding knowledge. As has already been mentioned, modern academia relies on technology in all spheres of activity. Murphy, Kalbaska, Horton-Tognazzini and Cantoni (2015) discuss in their contribution four categories of online learning: resources, tutorials, courses and

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Category: Educational Technologies

MOOCs. Apart from different materials and tools used in online learning, one of the key notions is connected with online ludic corporate identity, represented in the growing popularity of ludism among the broadly understood stakeholders. It is visible in, among others, fun and relaxation that accompany shopping or telling jokes and funny stories by workers (Bielenia-Grajewska, 2016). Analysing the sphere of teaching and learning, ludic academia, responding to the needs of stakeholders, is connected with using, for instruction, the tools associated in the past exclusively with leisure activities. One example is the application of online games in teaching and learning. Since games are not associated with spending free time as it was in the past, the appearance of serious games has changed instruction in the twentyfirst century. The usage of serious games can be examined by using the 5P Model of Studying Serious Games (Bielenia-Grajewska, 2016). The first element is called Problem-solving and is connected with information processing and decision making. In the case of educational games, it can enhance the capacity of students to analyse and make decisions in virtual reality. The second notion is Personae; and is connected with elements reflecting organizational identity. In the case of educational games used for academia, verbal and non-verbal representations create the identity of a given university and show its mission and vision. The third element- People- stresses the lack of fixed hierarchy in games and interactions among people representing different levels on the real organizational ladder. Thus, serious games may be the place where students and professors interact in a more informal way. The next notion is called Proficiency; serious games are often offered in both synchronous and asynchronous ways, in a relaxed atmosphere and, consequently, their efficiency is higher than standard learning tools. The last element is Persuasion. Since the purpose of serious games is to advertise products or services or to evoke certain behaviours, they can be used

effectively in academia to attract new students or make current learners more interested in the instruction. For example, with the appearance of MOOCs and other educational offers available online, modern education is not restricted in terms of geography, prior levels of knowledge or types of accessible educational tools as it was before. Consequently, learners, teachers and instruction environments of the twenty-first century fail easy categorization. As has been mentioned earlier in this contribution, e-universities have become more and more popular nowadays. The reasons for this are mainly connected with technological development, including the growing popularity of the Internet, and the consequent needs and expectations of stakeholders towards educational services. Thus, nowadays students have the opportunity to have their courses run on educational platforms (e.g. Moodle), take part in massive online open courses (MOOCs) and participate in webinariums or chats with professors. The access to these resources is open or restricted only to the students of a given university or the participants of a given course. Although open online courses have many advantages, offering education to all interested in learning, Schulze, Leigh, Sparks & Spinello (2017) draw attention to the fact that MOOCs are characterized by low completion rates, taking into account the high number of registered users. Apart from MOOCs, gamification is used in online academia. It is defined as using the techniques and thinking characteristic of games for making people behave in a given way, promoting some learning styles and solving problems. In the sphere of education, such elements as points, virtual awards or leaderboards are used in courses to stimulate the interest of students in the learning content (Bielenia-Grajewska, 2015b). It should be mentioned, however, that apart from concentrating on education directed at many participants, online learning also focuses on individuals at the same time, offering online tutoring and mentoring (Berg, 2010).

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5S Model of Online Academia To study the complexities of offering tuition on the web, the 5S Model of Online Academia has been created and discussed by this author. The author has presented the key elements of modern academia (present in both its online and offline versions), studying their role in education by paying special attention to e-learning.

SUBJECT The first element of the studied model- Subjectincludes what constitutes the topic of learning. Subject is connected with students’ interests, needs and expectations towards the curriculum, with such frequent sub-notions as salaries, solutions, stepping stones, skills and schemes. Starting with salaries, selecting subjects is often connected with discrepancies in remuneration levels and the potential increase in salaries together with one’s higher skills or experience. Salary may also be the determinant of choosing a course for vocational training, with not the best but the cheapest option

being selected by participants. Solutions, on the other hand, encompass the ways problems and challenges can be dealt with. They stimulate a better understanding of the topic. In the case of online learning, solutions are often discussed in forums or chat rooms. Stepping stones are connected with stages of accomplishing educational goals. In the case of online courses, they often mark the level of knowledge acquired in a given stage and in a given period of time, or one necessary to embark on a new learning stage, in a way forcing students to learn systematically. Skills also constitute an important aspect of learning. In the era of online schooling, people can gain new knowledge also on the net, without leaving their houses or offices. The restrictions related to geographical barriers do not apply to the same extent as in the past, with the material ones being relevant also only to some extent since many online courses are offered free of charge. Schemes are related to consecutiveness in education. The necessity to repeat knowledge is solved in e-learning. Online courses are effective for novel and complicated materials that require repetition since students can listen to the tape or watch the video as many times as necessary.

Figure 1. 5S model of online academia (created by this author)

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SITUATION Situation is broadly understood as the learning environment. From the micro sphere, it can be understood as a course or class, whereas the more macro approach focuses on learning policies and pan-national regulations. It envelops source, saturation point (of information), season, scene and 3 Situational S-components: Storm, Silence and Struggle. The first sub-element is Source, being connected with the origin of a given situation. It encompasses the reasons why something happened. In the learning context, it can be connected with the rationale why someone wanted to start a new course or the whole curriculum. The next one is saturation point, being linked with the amount of information that has so far been available for future students; the knowledge they possess determines their interest in engaging in the offered type of schooling. In addition, an offer that in a way exceeds the mentioned saturation point and does not evoke any interest among the target audience is substituted with the more novel one. Saturation point may also be treated as the number of professionals possessing this educational profile and already present on the job market. Season is associated with determinants related to time of day, days, months and other time-related factors connected with how different notions determine organizational performance. For example, daily students usually study from Monday to Friday, with most of their courses planned for mornings and afternoons, whereas extramural students usually frequent courses during weekends or evenings. In comparison with standard forms of teaching and learning, online courses offer the possibility to study when one can and wants, taking into account time differences and one’s professional duties or household chores. Even if a webinarium is planned, students who cannot attend it synchronously can later watch the recorded video. Style reflects the way one deals with knowledge, with the way instruction is coded and decoded. In the case of education, teacher’s style may determine the way a given matter is understood. When the manner

of instruction is not favored by students, even a simple matter may be difficult to follow. It holds especially true in the case of online learning when it is difficult to observe if students are interested and pursue their studies without the control of an instructor. Simulation is connected with causing certain reactions, usually among learners. When a response is caused, then more complicated forms of interaction can be made. Traditional and online courses should stimulate participants to search for new sources of knowledge, to find answers for their queries and to pose new questions. Scene is connected with the geographical location of learning, determining when interaction takes place. In the case of online instructions, scene is not linked to one geographical location. An online course has an Internet address in order to be found in the web jungle. Learning may also be discussed through the prism of 3 Situational S-components, such as Storm, Silence and Struggle, mainly mirroring the type of situations in learning, with silence used to denote unproblematic conditions and standard ways of interactions. Storm, on the other hand, depicts crisis or changes in organizations offering tuition or among students or teachers, with struggle used to describe teachers’ or students’ engagement in protecting their workplace and place of study.

SENSES Senses encompass such notions as Speech, Scent, Sound and Sign. Speech, being the verbal representation of communication, can be subcategorized into standard communication, slang and symbolic language. Standard communication includes accepted forms of interaction, being used in everyday performance of organizations. It mainly encompasses literal forms, making it possible to express one’s point of view effectively. In addition to standard forms of communication used in teaching, students and teachers often rely on symbolic language, allowing for a more subjective type of expression. The mentioned figurative interaction encompasses metaphors, metonymies,

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and allegories. In addition to literal and non-literal dialogic representation, students and teachers rely on slang or professional dialects. Since the verbal sphere of education constitutes the most often used channel of communication, attention is paid to the way words determine modern instruction. This thought can also be used to discuss the introduction of new terms in any discipline, showing, for example, how, among different linguistic tools, metaphors play important functions in managing knowledge. Relying on well-known domains, they are effective in coding and decoding knowledge. Analyzing specialized communication, professionals relying on symbolic language communicate quickly and effectively. Since metaphors offer efficient communication, dense in terms of signs and words used, it turns out to be effective in online communication that is connected with being economical in using linguistic representation. Metaphors are also important at the organizational level, showing the profile of company activities and organizational culture (Bielenia-Grajewska, 2015a). The performance of refreshing the mind is connected with several ways the application of metaphors in modern teaching can be understood. One method is to look at symbolic language from the learners’ perspective. Metaphors make complicated and novel concepts easier to understand since they relate to well-known concepts and phenomena. In addition, metaphors make the teacher’s profession interesting; teachers searching for new metaphors in their instruction make their own job adventurous. The next sense- Scent- is associated with the olfactory dimension of communication and learning, represented in sensory marketing, with things like special scents characteristic of educational settings. This dimension may also be a part of a given lesson, such as one devoted to presenting some dishes representative of a given culture. In the case of online learning, scent has to be substituted with other forms of expression, often relying more on words and sounds to evoke similar feelings. Sounds represent the auditory sphere of educational interactions. In the case of online learning, the speed of online connection

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determines the quality of voice. Signs, although they can have different dimensions, are often used in the pictorial sphere of representation.

SPIRIT Spirit is connected with learning atmosphere and teaching rules. The first approach to learning spirit and its role for online instruction can be studied through such notions as Standards, Sanctions, Space and System. Standards are mainly understood as the set of rules and expectations that both instructors and learners have to meet in order to fulfill their roles. At the micro level, it is connected with respecting regulations connected with frequenting online courses, such as punctuality in the case of assignments, observing net etiquette on forums, etc. Sanctions, understood as an element of social control, are discussed through the prism of violating classroom codes of behavior and the consequences related to these acts of negligence. In the case of academia, they may result in suspension or relegation. Space is connected with the arrangement of learning place. In the case of academia, traditional universities are often supplemented with virtual classrooms. System can be researched through the notion of learning culture. Examining academia, it may be university culture that is recognized by both learners and instructors. There are different types of learning culture that do not foster knowledge exchange. One of them is sweep learning culture, observable in educational organizations that do not face problems and do not solve them, but rather only try to survive till the semester ends. This sort of atmosphere facilitates dissatisfaction of students and learners, being represented especially by a high rate of dropouts. Another type of learning spirit is surface learning culture that is superficial and does not rely on deep values. In this case, only basic materials are offered, with the lack of student immersion into the broader context of disciplines. The next type is stream learning culture, following the most popular model

Category: Educational Technologies

of teaching and running organizations, built on widely accepted rules and values, and likely to be met in many learning entities. In the latter one, there is limited place for own ideas and creativity.

STAKEHOLDERS Stakeholders constitute the last dimension that can be divided into inner and outer subtypes. As far as subcategorization is concerned, the inner dimension encompasses Staff (teachers, school owners) and Students, whereas the outer one can be classified as Shoppers (encompassing the ones searching for educational offers before making decisions); both groups may be viewed through the perspective of status. Status may reflect such notions as one’s position within a studied hierarchy. In the case of staff, it concerns one’s place on an educational ladder, whereas shoppers are often labelled by taking into account their social or professional position and potential interest in the offered educational products or services.

FUTURE RESEARCH DIRECTIONS Manning, Wong, and Tatnall (2010) discuss that nowadays many universities combine traditional learning with e-learning platforms, using both face-to-face interactions and distance instructions. It can be expected that in the future even more courses will be offered online, taking into account the expectations and needs of students who prefer online learning or their geographical location does not allow them to study in a traditional way. Moreover, Sancassani, Casiraghi, Corti and Trentinaglia (2014) stress the role of MOCCs in improving the quality of traditional learning as well as its cost. Taking the last money-related factor into account, it can be predicted that more educational institutions will be interested in offering instruction that is characterized by economic efficiency for both educators and students.

CONCLUSION

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The growing popularity of technology and diversified demands and expectations of academia stakeholders have led to crucial changes in the way universities perform nowadays. As has been discussed in this contribution, online academia is connected with offering its tuition and running its communication on the Internet. The proposed model offers a complex insight of how online academia can be investigated.

REFERENCES Berg, G. A. (2010). Cases on Online Tutoring, Mentoring, and Educational Services: Practices and Applications: Practices and Applications. Hershey, PA: IGI. doi:10.4018/978-1-60566876-5 Bielenia-Grajewska, M. (2015a). The role of figurative language in knowledge management. In M.Khosrow-Pour (Ed.), Encyclopedia of Encyclopedia of Information Science and Technology (pp. 4728-4736). Hershey, PA: IGI. doi:10.4018/9781-4666-5888-2.ch464 Bielenia-Grajewska, M. (2015b). Jak “rozegrać” zajęcia, czyli o roli gamifikacji w dydaktyce akademickiej. In D. Becker-Pestka & E. Kowalik (Eds.), Wyzwania współczesnej pedagogiki (pp. 245–256). Gdańsk: Wyższa Szkoła Bankowa. Bielenia-Grajewska, M. (2016). Serious games and their application in creating corporate identity. In R. Nakatsu, P. Ciancarini, & M. Rauterberg (Eds.), Handbook of Digital Games and Entertainment Technologies (pp. 1–18). Singapore: Springer. doi:10.1007/978-981-4560-52-8_53-1 Davey, B., & Tatnall, A. (2012). Using ANT to guide technological adoption: The case of School Management Software. International Journal of Actor-Network Theory and Technological Innovation, 4(4), 47–61. doi:10.4018/jantti.2012100103

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Manning, K., Wong, L., & Tatnall, A. (2010). Aspects of e-Learning in a University. International Journal of Actor-Network Theory and Technological Innovation, 2(4), 43–52. doi:10.4018/ jantti.2010100105 Murphy, J., Kalbaska, N., Horton-Tognazzini, L., & Cantoni, L. (2015). Online Learning and MOOCs: A Framework Proposal. In I. Tussyadiah & A. Inversini (Eds.), Information and Communication Technologies in Tourism 2015. Proceedings of the International (pp. 847-858). Cham: Springer. doi:10.1007/978-3-319-14343-9_61 Rouse, W. B. (2016). Universities as Complex Enterprises: How Academia Works, Why It Works These Ways, and Where the University Enterprise Is Headed. Hoboken, NJ: Wiley. doi:10.1002/9781119245872 Schulze, A. S., Leigh, D., Sparks, P., & Spinello, E. (2017). Massive Open Online Courses and Completion Rates: Are Self-Directed Adult Learners the Most Successful at MOOCs? In F. Topor (Ed.), Handbook of Research on Individualism and Identity in the Globalized Digital Age (pp. 24–49). Hershey, PA: IGI Global; doi:10.4018/978-15225-0522-8.ch002 Veletsianos, G. (2016). Social Media in Academia: Networked Scholars. New York: Routledge.

Bielenia-Grajewska, M. (2012). Linguistic aspects of informal learning in corporate online social networks. In V. P. Dennen & J. B. Myers (Eds.), Virtual Professional Development and Informal Learning via Social Networks (pp. 93–112). Hershey, PA: IGI Publishing. doi:10.4018/9781-4666-1815-2.ch006 Bielenia-Grajewska, M. (2014). Corporate Online Social Networks and Company Identity. In K. Michael & R. Abbas (Eds.), Privacy and Security Issues in Social Network. Encyclopedia of Social Network Analysis and Mining (pp. 270–275). New York: Springer. doi:10.1007/978-1-46146170-8_337 Kyei-Blankson, L., Blankson, J., Ntuli, E., & Agyeman, C. (2016). Handbook of Research on Strategic Management of Interaction, Presence, and Participation in Online Courses. Hershey, PA: IGI Publishing. doi:10.4018/978-1-4666-9582-5 Niess, M. L., & Gillow Wiles, H. (2015). Handbook of Research on Teacher Education in the Digital Age. Hershey, PA: IGI Publishing. doi:10.4018/978-1-4666-8403-4 Raisinghani, M. S. (2013). Curriculum, Learning, and Teaching Advancements in Online Education. Hershey, PA: IGI Publishing. doi:10.4018/978-14666-2949-3

Vostal, F. (2016). Accelerating Academia: The Changing Structure of Academic Time. New York: Palgrave Macmillan. doi:10.1057/9781137473608

Sancassani, S., Casiraghi, D., Corti, P. & Trentinaglia, N. (2014). MOOC, OER e l’approccio “flipped classroom”: due case study di transizione in ambito scolastico e aziendale, Form@re, Open Journal per la formazione in rete, 1/14, 49-59.

ADDITIONAL READING

Tatnall, A., & Davey, B. (2003). ICT and Training: A Proposal for an Ecological Model of Innovation. Journal of Educational Technology & Society, 6(1), 14–17.

Betts, K., Kramer, R., & Gaines, L. L. (2013). Online Faculty and Adjuncts: Strategies for Meeting Current and Future Demands of Online Education Through Online Human Touch Training and Support. Hershey, PA: IGI Publishing. doi:10.4018/978-1-4666-2949-3.ch007

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Unnithan, C., & Tatnall, A. (2014). Actor-Network Theory (ANT) Based Visualisation of SocioTechnical Facets of RFID Technology Translation: An Australian Hospital Scenario. [IJANTTI]. International Journal of Actor-Network Theory and Technological Innovation, 6(1), 31–53. doi:10.4018/ijantti.2014010103

KEY TERMS AND DEFINITIONS Academia: The academic world constituting of teachers, students and other stakeholders participating in teaching and learning. E-Learning: Learning by using tools and materials available online. Online Academia: Teaching and learning conducted by the stakeholders of the academic world on the Internet.

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Online Learning Propelled by Constructivism Kathaleen Reid-Martinez Oral Roberts University, USA Linda D. Grooms Regent University, USA

INTRODUCTION Augmenting communication in and among those in the academic, business, and military communities, the exponential advancement of science and technology has availed vast amounts of information to virtually millions of people around the globe. In conjunction with this knowledge explosion has been a growing concern for the democratization of the learning process, with constructivism driving much of the educational agenda, most particularly in online distance education. This article examines the resurgence of the constructivist approach to teaching and learning, its convergence with rapidly changing technological advances, and its relationship to future trends in online pedagogy.

BACKGROUND While the constructivist method has been highly emphasized in the recent literature for online distance education (Brown, L. 2014; Bryant & Bates, 2015; Holzweiss, Joyner, Fuller, Henderson, & Young, 2014; Lê & Lê, 2012; “Learning Theories”, 2014; Mbati & Minnaar, 2015; Symeonides & Childs, 2015; Thorne, 2013), it is not a new approach to learning. Presenting an early example, Socrates facilitated discourse with students asking directed questions to assist them in realizing the weaknesses in their logic and critical thinking. This enabled learners to share in the responsibility of their learning through active participation while

negotiating meaning in the creation of shared understanding. In contrast, medieval professors in later Western culture most often served as primary repositories of information along with the scrolls and velum texts found in the limited number of physical libraries available to educators. With the lecture serving as the quickest and easiest way to disseminate information to both small and large groups of individuals, it was both an efficient and effective delivery method in the shaping and forming of student knowledge, quickly becoming the standard for traditional education.

MAIN FOCUS OF THE ARTICLE Resurgence of Constructivism While the lecture method was the norm of information delivery for centuries in Western culture, the knowledge explosion arising from the latter part of the 20th century demanded more active learner participation. In light of this constant and rapid flux of information and knowledge, students became life-long learners compelled to use metacognitive skills to constantly evaluate and assimilate new material into their respective disciplines. As this implies, knowledge was no longer viewed as a fixed object; rather, learners constructed it as they experienced and co-created an understanding of various phenomena by collaborating and working with peers and professors as well as with the information. Now, rather than strictly acquir-

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ing information, Duffy and Cunningham (1996) explicated that “learning is an active process of constructing … knowledge and … instruction is a process of supporting that construction” (p. 171). Based on the work of Kidd (1973), Long (1983), Moore (1989), and Palmer (1993), Grooms’ (2000) Learner Interaction Model (see Figure 1) illustrates that in the constructivist culture, the learner perpetually interacts with these three components of learning--content, facilitator or professor, and peers--each mutually and non-discriminately influencing the other. Critical in this process is recognizing the shifting role of the professor who becomes the guide on the side or content facilitator and is no longer the proverbial sage on the stage or content provider. The student’s role also has changed from being a passive receiver of information to an active participant in the knowledge-making process (Weller, 1988), aligning with Bandura’s (1977, 1994) concept of the autonomous learner, an important dimension of the constructivist model. Table 1, based upon an earlier model from Reid-Martinez, Grooms, and Bocarnea (2009) and Reid-Martinez and Grooms (2015), delineates these two approaches to learning. Of special interest in the above listing is the role of community. The constructivist approach

recognizes that students do not learn strictly within the limited confines of a local educational institution, but rather within the broader international and global context of their personal lives extended through social media and multiple technologies. Consequently, the boundaries between the educational institution and the larger community become blurred creating its own unique set of opportunities and challenges. As people work collaboratively in the learning activities and new technologies, they bring multiple worldviews and experiences to each situation often creating a plethora of perspectives. During this collaborative learning process, they must negotiate and generate meaning and solutions to problems through shared understanding. Thus, education moves from a single, solitary pursuit of knowledge to a collaborative learning community that shapes and informs responses to the environment. As noted by Fuller and Söderlund (2002), this challenges the common metaphor of the university as a self-contained village.

Rapidly Changing Distance Learning Technologies Over the years, educators have experimented with and successfully employed multiple media for

Figure 1.­

2000 Grooms, L. D.

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Table 1. Approaches to Learning Traditional Professor Learner Knowledge Organization of Learning Communication Primary Resource Method

Constructivist

Sage on the Stage

Guide on the Side

Content Provider

Content Facilitator

Passive Recipient

Active Participant

Fixed Object

Fluid

Ordered & Structured

Open & Often Chaotic

Uni-directional

Multi-directional

Text & Professor

Multiple Sources

Lecture

Active Process

Media

Print

Blended

Format

Structured & Individualized

Adaptive & Collaborative

Goal-Oriented

Problem-Centered

Knowledge & Understanding

Application, Analysis, Synthesis, & Evaluation

Activities Focus of Learning Assessment

Recall

Alternative Assessment

Community

Local Educational Institution

Integrated with Life in Global Contexts

distance learning, and today, as much as in the past, they continue to stress that pedagogy must drive technology (Rourke & Coleman, 2011). As early as the 18th century, print material was used and even today still serves an important role in distance education even as it gives way to more reliance on technology and web-based resources for collaborative development of knowledge that incorporates the diversity of learners and their contexts. After the 1930s, other media became significant with audio--including radio and audiotapes--and video--including film, public broadcasting, and cable--dominating much of the 20th century. Much of this education was one-way based on a mass communication or one-to-many educational model. Basically, it was a rigid structure with information flowing in one direction, from the powerful and knowledgeable instructor reaching to the individual or even the larger group of students. It included elements of limited feedback through the use of such things as the penny post in the 19th century and the addition of the telephone and fax in the 20th century. Limited opportunities for face-to-face interaction were also incorporated

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with some programs. Thus much of distance learning during these times remained mainly non-interactive. By the 1990s, the advent of the Internet presented new opportunities in distance education. The result was the evolution of a new type of collaborative learning, in which the potential for interaction between the professor and the learner increased exponentially with wide-area networks accommodating synchronous and asynchronous communication. While exploring computer-mediated activities of the online learning environment, Santoro (1996) highlighted three broad categories: (a) computer-assisted instruction, which allows the computer to serve as “teacher” by structuring information delivered to the human user; (b) computer-based conferencing, which includes email, interactive messaging, and group conference support systems; and (c) informatics, which refers to online public access libraries and interactive remote databases. This proliferation of the Internet unlocked the door for educational institutions to reach beyond their four walls making services accessible to students around the world through online activities.

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Although the communication technologies of the 21st century--print, audio, video, digital, and the Internet--cover a broad spectrum of distance education mediums, this exponential growth in science and technology has catapulted the Internet into rapidly becoming the preferred delivery platform. Since 1995, researchers such as Cotton (1995) and others have been tracking this information along with scholars such as Bocarnea, Grooms, and Reid-Martinez (2006), Grooms and Reid-Martinez (2013), and Reid-Martinez and Grooms (2012, 2015). They continue to explore not only the trends in distance education but also the understanding of and the issues involved in aligning the environment with student needs. Typical factors include (a) the characteristics of the discipline, (b) the degree of interactivity sought in the distance learning process, (c) learner characteristics, (d) instructor traits, (e) the expansiveness of the distance education initiative, (f) the desired level of accessibility and flexibility related the delivery capacity of learning platforms and smart mobile devices as well as other methods of dissemination, and lastly (g) the availability of technical support. In addition to the global reach of the Internet, the lines among communication technologies have swiftly blurred. Today in the convergence of technologies, computers, telephones, and cameras are no longer distinct entities, but can be found bundled into one small handheld gadget through the fusion of technology (McCain & Jukes, 2001). These smaller fused devices create more mobility and simultaneously provide mixed realities through virtual immersive environments embedded within traditional spaces. Continuing advances, such as that found in interactive optical sensory technology, feed this growing world that fuses the virtual with the physical (Rolf, 2012). Through this fusion, communicating with students and colleagues has become more instantaneous, integrated and complex. While vastly expanding the means of interaction and feedback, it demands greater capacity and understanding of the multiple communication modalities.

Connected to these new technologies is the capacity to enhance adaptive individualized learning. As noted by Allen and Seaman (2013), adaptive learning helps overcome the barriers to online learning, a common concern of many academic leaders. As we see in the research of such scholars as Yang, Gamble, Hung, and Lin (2014), critical thinking can be enhanced through adaptive learning in the online environment. These new adaptive learning technologies accelerate and enhance learners’ problem solving ability. The fusion of technological capacity for adaptive learning with collaborative technology platforms results in individuals operating at a more advanced level and collectively harnessing greater learning and problem solving abilities from all participants. With such rapid technological advances, today’s educators are dropped into what Jacque Ellul (1964) described as the intersection of tension between humanity and technology. This struggle with the latent and manifest, intended and unintended consequences of technology exists as students and professors wrestle with cloud computing, three-dimensional immersive learning environments, and other rapidly expanding web opportunities. Such technology facilitates greater flexibility and customization in the learning process. Lead Learning Designer at IBM, Don Morrison (2004) demonstrated how the learning process can be established within parameters and policies that most appropriately align with the primary strengths and weaknesses of each medium. He noted that among others, cost, time constraints, delivery speed, and infrastructure help determine appropriate application. Morrison’s work also pointed to ways in which educational models can be designed to marry traditional and online means of moving from the simple to the more complex methods of learning. As the above suggests, these new electronic forms of communication have forced a paradigm shift in education. This move is most avidly seen in distance learning, where even the terminology has shifted from distance education to words such

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as online or e-learning. Such terms more clearly indicate the way in which learners can use multiple media to easily collaborate through a continuous integration of knowledge and social capital.

FUTURE RESEARCH DIRECTIONS As previously discussed, the rapid growth of technology continues to herald unprecedented opportunity for distance learning, and when wed to a constructivist approach, it presents opportunities for online pedagogy that can transcend traditional modes of education. From this marriage emerges three primary factors that define the new online pedagogy: (a) community development--the ability to build networks and communities that cross time and geographic boundaries, (b) structure--the technological ability to manage vast amounts of information, and (c) collaborative opportunities-for shared knowledge and wisdom building in response to the complexities of a global society (Reid-Martinez, 2006).

Community As Bocarnea et al. (2006) note, today’s technologies launch a new paradigm of online learning and pedagogy, which has the potential to be communal in nature. Primarily, these technologies allow for interaction between students and professors, students and peers, and the broader community in unprecedented ways. For example, students today have greater instructor and peer access through social media and e-learning platforms. Indeed the study of mobile technology for learning in environments of high action and great distance as found in the work of Black and Hawkes (2011) points to the now even more ubiquitous capacity of mobile learning (m-learning). In turn, this poses the question of expectation--whether conscious or not--regarding ubiquity of instructor presence and community development. It also enhances the ubiquity of the student in the learning process. Indeed, learning is no longer “just in time,” but

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with adaptive learning and other capacities that allow for learning that is “just for you” and “just with you” through wearable smart devices and other advancing technologies. This contemporary and developing technology now allows learning to be fluid with the learner in a simultaneous and continuous nature (Reid-Martinez, 2015). These new technological advances raise the question of boundaries in the learning process. While advancing technologies provide unprecedented opportunities for networking and building strong virtual learning communities, they do transcend geographical boundaries and normal hours of operation as well as far beyond the duration of students’ formal education. As early as 2002, Young highlighted the differences between the boundaries embedded in his traditional face-to-face class and that which he encountered online. This suggests that guidelines following best practices to manage the continuous nature of virtual learning experiences are essential to prevent online instructors and students from feeling overwhelmed by the 24-7, ubiquitous opportunity for interaction. In addition, this communal nature of the virtual learning environment provides opportunities for students to bring their local community contexts into the learning experience in direct ways as well as immediately allowing them to apply what they have learned through their study. For example, students in leadership programs can be employed full-time in leadership positions and take their learning experiences directly into their work environment through well-designed course assignments. The professor is no longer someone whom the student must wait to see in class later in the week, but rather is readily available in e-learning and m-learning platforms to serve as consultant and mentor as the student applies the principles studied that week. The professor has become the guide on the side. This triangulation of student-professor-content points to the need for well-designed learning experiences developed from a constructivist perspective to meet the challenges and needs of today’s students. Indeed, unit-

Category: Educational Technologies

ing the new technology with this approach meets the needs of contemporary students working in rapidly changing and highly demanding global environments. Additionally, rapid advancement of technology creates a moving target challenge for course developers who often find themselves reacting to the technological advances rather than proactively establishing the technology’s relationship to the learning process. While scholars such as Schweizer (1999) noted unsound pedagogy and inadequate design in early online courses, others such as Wang and Newlin (2002) reinforced the importance of incorporating the opposite as critical predictors of successful student performance. Just a short time later, others such as Beetham and Sharpe (2007) and Rourke and Coleman (2011) continued to reinforce the critical role of pedagogy in using technology. Such approaches assure that good pedagogy is the driver of learning, not new technologies. This helps resolve Bocarnea et al.’s (2006) observation and concern that theory typically “follows technology in desperate attempts to describe the impact of an already existing and rapidly fading … technological reality” (p. 385). This posits that staying focused on strategy and content design remains the dominant challenge. Online pedagogy, the science of and about online education, provides perspective to assist in focusing and maintaining the balance necessary for creating excellent online learning experiences.

Structure Heralded just over 20 years ago by Negroponte (1995), the information age is collapsing on itself as the amount of online information is becoming unbearable. After the scramble to have everything digitized, the primary challenge today is how to create meaningful knowledge from such massive amounts of data. Quality of knowledge, in contrast to quantity, drives the heart of this concern. In light of this overload, structure is essential to online knowledge development.

Related to structure, is the development of open-source initiatives. While most often understood as the software that is open for use and modification by the public, the phrase has become a recognized attribute ascribed to multiple endeavors, such as knowledge-building. The open-source nature of online initiatives pushes a new model for managing learning and knowledge-building through the communal process. It allows diverse individuals from various locations to combine information from multiple sources into distributed knowledge networks. Through this open-source structure, participants interact to share experiences and knowledge, thereby expanding their awareness of new concepts and differing approaches to problem-solving as they modify the information in the open-source environment and re-distribute it back to fellow participants (Bocarnea et al., 2006). As this suggests, through interaction, participants build complex webs of knowledge in the open-source cyberspace. The technology provides the structure to create and maintain webs of knowledge, and it also grants ease of access globally to those interested in that knowledge. In the process, knowledge is given away to others who in turn begin to use it in multiple ways while beginning the next evolution of knowledge development as they add to and transform the knowledge base they accessed through the open-source structure. With this transformation through this structural capacity is the transference of power and control that becomes less centrist and more distributed globally.

Collaborative Knowledge-Building As noted above, interaction is the key for the development of open-source knowledge-building. While scholars such as Cederbom and Paulsen (2001) posited learning as a behavioral change, others hold that it is simply when learners meet needs and establish goals for attaining knowledge (Ponton & Carr, 2000). Referring to this process as an implied contract, Keirns (1999), along with the above scholars, suggested that if online learn-

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ing is used, structure is critical to allow students to advance in their knowledge. Because “engaging the learner in reflective and collaborative thought processes … results in the most effective learning, whether the setting is a traditional classroom or an online environment” (Cox & Cox, 2008), the design of the online course becomes the principal structure to assure learners’ goals are achieved. In that course design, one way of attaining the collaborative thought processes is to incorporate structured interaction. Not only does this interaction provide for collaborative thoughts and knowledge development, it provides multiple opportunities for faculty to prompt students’ critical thinking. In keeping with this, researchers such as Pelech and Pieper (2010) went so far as to clearly lay out, among other approaches, the roles of visual literacy, bridging questioning, and kinesthetic activities in applying Bloom’s taxonomy for constructivist learning. While these dimensions of critical thinking undergird contemporary collaborative capacity in online learning, other psychological dimensions are becoming more prevalent in understanding how critical thinking and collaboration can increase. For example, Rolf (2012) surfaced the complexities of virtual collaboration in his study of contemporary technologies’ role in creating mixed realities. Another example is Mabrito’s (2011) study of vicarious interaction--that is, observing not participating in peer as well as faculty-peer online interaction--which can generate more idea awareness in a constructivist learning environment and has potential impact on how collaboration occurs. A common way to collaborate in the online environment is through interaction. Blair (2002) suggested that stronger relationships are forged through increased interaction frequency. Increased interaction relates to higher learner commitment due to the socialization the learner experiences as a participant in the knowledge-building process. Thus, learner perception of interaction plays an important role in student achievement, satisfaction, and quality of learning.

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Again, in the collaborative nature of the constructivist online culture, interaction perpetually occurs between learners and content, learners and instructors, learners and peers, and learners and external experts with each type of interaction reinforcing and fostering collaborative knowledgebuilding. Teaching disappears and “communication of information rules, where information is available to all and in abundance” (Brown, T. H., 2015, p, 228). With this in mind, online course design must include best structures to capitalize on this collaborative interaction.

CONCLUSION As the above suggests, the advent of online learning education has not just provided opportunity to disseminate information in a new medium, it has radically adjusted the distance learning paradigm in terms of distribution methods, community building, knowledge development, and learning. The use of 21st century technology is rapidly closing the gap of the communication immediacy essential in developing communities of practice for knowledge-building. With their open-source networks, these new technologies encourage and actively support constructivist pedagogy in the distance education paradigm. Most of all, distance education through its constructivist pedagogy and contemporary technologies has the technical capacity to fulfill its greatest potential, which is to reach every learner who desires to participate in the knowledge-building process. The result can be a democratization of education not previously seen, allowing for shifts in power and control throughout societies.

Category: Educational Technologies

REFERENCES Allen, I. E., & Seaman, J. (2013). Grade Change: Tracking Online Education in the United States. Babson Survey Research Group and Quahog Research Group. Retrieved from http://www.onlinelearningsurvey.com/reports/gradechange.pdf

Bryant, J., & Bates, A. J. (2015). Creating a constructivist online instructional environment. Tech Trends. Linking Research & Practice to Improve Learning, 59(2), 17–22. doi:10.1007/ s11528-015-0834-1

Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice Hall.

Cederblom, J., & Paulsen, D. W. (2001). Critical reasoning: Understanding and criticizing arguments and theories (5th ed.). Belmont, CA: Wadsworth.

Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behavior (Vol. 4, pp. 77–81). New York: Academic Press.

Cotton, C. (1995). Time-and-place independent learning: The higher education market for distance learning emerges. Syllabus, 8(5), 37–39.

Beetham, H., & Sharpe, R. (Eds.). (2007). Rethinking pedagogy for a digital age: Designing and delivering e-learning. London: Routledge.

Cox, B., & Cox, B. (2008). Developing interpersonal and group dynamics through asynchronous threaded discussions: The use of discussion board in collaborative learning. Education, 128(4), 553–565.

Black, J., & Hawkes, L. W. (2011). Making the case for mobile collaborative learning for reading comprehension on handheld computers. In A. Rourke & K. Coleman (Eds.), Pedagogy leads technology: Online learning and teaching in higher education: New technologies, new pedagogies (pp. 143–158). Champaign, IL: Common Ground Publishing.

Duffy, T. M., & Cunningham, D. J. (1996). Constructivism: Implications for the design and delivery of instruction. In D. H. Jonassen (Ed.), Handbook of research for educational communications and technology (pp. 170–198). New York: Simon Schuster Macmillan.

Blair, J. (2002). The virtual teaching life. Education Week, 21, 31–35.

Ellul, J. (1964). The technological society (J. Wilkinson, Trans.). New York: Vintage Books.

Bocarnea, M. C., Grooms, L. D., & Reid-Martinez, K. (2006). Technological and pedagogical considerations in online learning. In A. Schorr & S. Seltmann (Eds.), Changing media markets in Europe and abroad: New ways of handling information and entertainment content (pp. 379-392). Pabst Science Publishers.

Fuller, T., & Söderlund, S. (2002). Academic practices of virtual learning by interaction. Futures, 34(8), 745–760. doi:10.1016/S00163287(02)00018-6

Brown, L. (2014). Constructivist learning environments and defining the online learning community. Journal of Science Education and Technology, 9(4), 1–6. Brown, T. H. (2015). A reflection on Barber, Donnelly, and Rizvi (2013): “An avalanche is coming: Higher education and the revolution ahead”. International Review of Research in Open and Distributed Learning, 16(4), 227–234. doi:10.19173/irrodl.v16i4.1952

Grooms, L. D. (2000). Interaction in the computermediated adult distance learning environment: Leadership development through online education. Dissertation Abstracts International, 61(12), 4692A. Grooms, L. D., & Reid-Martinez, K. (2013, November). Cross-Cultural Learning for Leadership Resilience and Sustainability. Presented at the 15th annual International Leadership Association Conference, Montreal, Canada.

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Holzweiss, P. C., Joyner, S. A., Fuller, M. B., Henderson, S., & Young, R. (2014). Online graduate students perceptions of best learning experiences. Distance Education, 35(3), 311–323. doi:10.108 0/01587919.2015.955262 Keirns, J. (1999). Designs for self-instruction: Principles, processes, and issues in developing self-directed learning. Needham Heights, MA: Allyn & Bacon. Kidd, J. R. (1973). How adults learn. Chicago: Follett Publishing. Lê, Q., & Lê, M. (2012). New challenges in web-based education. In T. Lê & Q. Lê (Eds.), Technologies for enhancing pedagogy, engagement and empowerment in education: Creating learning-friendly environments (pp. 58–65). Hershey, PA: IGI Global. doi:10.4018/978-161350-074-3.ch005 Long, H. B. (1983). Adult learning: Research and practice. New York: Cambridge. Mabrito, M. (2011). Lurking and learning: A study of vicarious interaction in the online classroom. In A. Rourke & K. Coleman (Eds.), Pedagogy leads technology: Online learning and teaching in higher education: New technologies, new pedagogies (pp. 105–116). Champaign, IL: Common Ground Publishing. Mbati, L., & Minnaar, A. (2015). Guidelines towards the facilitation of interactive online learning programmes in higher education. International Review of Research in Open and Distributed Learning, 16(2), 272–287. doi:10.19173/irrodl. v16i2.2019 McCain, T., & Jukes, I. (2001). Windows in the future: Education in the age of technology. Thousand Oaks, CA: Corwin Press. Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1–7. doi:10.1080/08923648909526659

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Morrison, D. (2004). What do instructional designers design? Retrieved from http://www. morrisonco.com Negroponte, N. (1995). Being digital. New York: Alfred A. Knopf. Palmer, P. J. (1993). To know as we are known: Education as a spiritual journey. San Francisco: Harper Collins. Pelech, J., & Pieper, G. (2010). The comprehensive handbook of constructivist teaching: From theory to practice. Charlotte, NC: Information Age. Ponton, M. K., & Carr, P. B. (2000). Understanding and promoting autonomy in self-directed learning. Current Research in Social Psychology, 5(19). Retrieved from http://www.uiowa.edu/~grpproc/ crisp/crisp.5.19.htm Reid-Martinez, K. (2006). What’s that in your hand? Presented at the 2006 General Assembly, International Conference of Educators, Indianapolis, IN. Reid-Martinez, K., & Grooms, L. D. (2012, November). Building a community of leaders through communication using a constructivist approach. Presented at the annual convention of the National Communication Association, Orlando, FL. Reid-Martinez, K., & Grooms, L. D. (2015). Constructivism as the driver of 21st century online distance education. In M. Khosrow-Pour (Ed.), Encyclopedia of Information Science and Technology (3rd ed.; pp. 2229–2238). Hershey, PA: IGI Global; doi:10.4018/978-1-4666-5888-2.ch216 Reid-Martinez, K., Grooms, L. D., & Bocarnea, M. C. (2009). Constructivism in online distance education. In Encyclopedia of Information Science and Technology (2nd ed.; Vol. 2, pp. 701–707). Hershey, PA: IGI Global. doi:10.4018/978-160566-026-4.ch114

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Rolf, D. (2012). Mixed realities: Human interaction technologies. In T. Lê & Q. Lê (Eds.), Technologies for enhancing pedagogy, engagement and empowerment in education: Creating learning-friendly environments (pp. 209–216). Hershey, PA: IGI Global. doi:10.4018/978-161350-074-3.ch018 Rourke, A., & Coleman, K. (Eds.). (2011). Pedagogy leads technology: Online learning and teaching in higher education: New technologies, new pedagogies. Champaign, IL: Common Ground Publishing. Santoro, G. M. (1996). What is computer-mediated communication? In Z. L. Berge & M. P. Collins (Eds.), Computer mediated communication and the on-line classroom (Vol. 1, pp. 11–27). Cresskill, NY: Hampton Press. Schweizer, H. (1999). Designing and teaching an on-line course: Spinning your web classroom. Boston: Ally & Bacon. Symeonides, R., & Childs, C. (2015). The personal experience of online learning: An interpretive phenomenological analysis. Computers in Human Behavior, 51, 539–545. doi:10.1016/j. chb.2015.05.015 Theories, L., & Engagement, S. (2014). Article. ASHE Higher Education Report, 40(6), 15–36. doi:10.1002/aehe.20018 Thorne, J. A. (2013). Biblical online education: Contributions from constructivism. Christian Education Journal, 10(1), 99–109. Wang, A., & Newlin, M. (2002). Predictors of performance in the virtual classroom. T. H. E. Journal Online, 29(10), 21-22, 26-28. Weller, H. G. (1988). Interactivity in microcomputer-based instruction: Its essential components and how it can be enhanced. Educational Technology, 28(2), 23–27.

Yang, Y.-T. C., Gamble, J. H., Hung, Y.-W., & Lin, T.-Y. (2014). An online adaptive learning environment for critical-thinking-infused English literacy instruction. British Journal of Educational Technology, 45(4), 723–747. doi:10.1111/bjet.12080 Young, J. R. (2002). The 24-hour professor: Online teaching redefines faculty members’ schedules, duties, and relationships with students. The Chronicle of Higher Education, 48(38), A31–A33.

ADDITIONAL READING Burge, E., Gibson, C. C., & Gibson, T. (Eds.). (2011). Flexible pedagogy, flexible practice: Notes from the trenches of distance education. Edmonton, Alberta: Athabasca University. Denton, D. W. (2012, July/August). Enhancing instruction through constructivism, cooperative learning, and cloud computing. TechTrends, 56(4), 34–41. doi:10.1007/s11528-012-0585-1 Fosnot, C. (1996). Constructivism: Theory, perspectives, and practice. New York, NY: Teachers College Press. Gazi, Z., & Aksai, F. (2011). Handbook of online pedagogy. Saarbrucken, Germany: LAP LAMBERT Academic Publishing. Gülseçen, S. (2012). Digital learning environments and student centered curriculum in a university context. In T. Lê & Q. Lê (Eds.), Technologies for enhancing pedagogy, engagement and empowerment in education: Creating learning-friendly environments (pp. 209–216). Hershey, PA: IGI Global. doi:10.4018/978-1-61350-074-3.ch009 Hai-Jew, S. (2012). Constructing self-discovery learning spaces online: Scaffolding and decision making technologies. Hershey, PA: Information Science Reference. doi:10.4018/978-1-61350320-1

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McHaney, R. W. (2012). Knowledge spaced for online discovery learning. In S. Hai-Jew (Ed.), Constructing self-discovery learning spaces online: Scaffolding and decision making technologies (pp. 72–94). Hershey, PA: IGI Global. doi:10.4018/978-1-61350-320-1.ch005 Shi, Y., Fan, S., & Yue, Y. (2012). Empowering students in computer-supported education. In T. Lê & Q. Lê (Eds.), Technologies for enhancing pedagogy, engagement and empowerment in education: Creating learning-friendly environments (pp. 198–207). Hershey, PA: IGI Global. doi:10.4018/978-1-61350-074-3.ch017 Trifonas, P. P. (2012). Learning the virtual life: Public pedagogy in a digital world. New York: Routledge.

KEY TERMS AND DEFINITIONS Autonomous Learner: An individual who takes responsibility for his or her learning.

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Collaborative Learning: The process in which individuals negotiate and generate meaning and solutions to problems through shared understanding. Computer-Assisted Instruction: The computer serves as the “teacher” by structuring information delivered to the human user. Computer-Based Conferencing: E-mail, interactive messaging, and group conference support systems. Constructivism: An approach in which students share responsibility for their learning while negotiating meaning through active participation in the co-creation of shared understanding within the learning context. Distributed Knowledge: Information dispersed throughout a community of practice and not held by any one individual. Informatics: Online public access libraries and interactive remote databases. Interaction: mutual communicative exchange between individuals.

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Science Animation and Students’ Attitudes Sivasankar Arumugam Sri Venkateswara College of Education, India Nancy Nirmala Christ the King Matric Higher Secondary School, India

INTRODUCTION Teaching is a kind of social engineering that does not deal with lifeless machines and hard wares; it cultivates tender minds into brave hearts that in turn with brimming confidence is going to build the society. In this endeavour every student is important, every detail present in each topic is essential and every batch of students is precious. The digital transformations that are taking place in educational arena had opened newer avenues for the teachers, learners, administrators and researchers in the form of animation. Animation in its own virtue along with enthusiasm of digital native learners had grown leaps and bounds. The need for animation in third world countries seems to be pinning as teacher- pupil ratio is alarmingly high when comparing with many of the Western counterparts and majority of the education machinery is examination ridden. When the teacher has to run behind the content and ensure zero failure it becomes imperative to teach science with animation. And the metamorphosis that animation could take as per the projection of experts is very promising. This study makes an experimental approach with science animation in secondary school classes and its impact through achievement and attitude.

Meaning of Animation Etymologically animation has got Latin origin animatio from animare which means the condition of being alive or giving life. Rapid display of images, pictures or frames is called as animation.

The technique of capturing successive frames of pictures or positions of toys or models that create an illusion of movement while the movie is shown as a sequence gives life to animation. In other words a collection of static images joined together and shown consecutively so that they appear to move is called as animation.

Evolution of Animation Animation’s origin can be tracked right from 1824 with the wide usage of thaumatrope that was largely given credit to John Aryton of Paris. Since then the field of animation had experienced various meaningful adaptations and thereby can be classified into three wide verticals namely (i) Traditional animation, (ii) Stop frame animation and (iii) computer animation (2D and 3D). Traditional animation includes Thaumatrope, Phenakistocope, Zoetrope, Flip book, and Praxinoscope.

Thaumatrope In early 19th century, Thaumatrope was very popular. Etymologically Thaumatrope has got Greek origin Thauma meaning marvel and tropos meaning turning. Two pieces of string with two different pictures on each side, like as a tiger and a cage, is attached to a small disk. Twirling of strings quickly between the fingers seems to combine into a single image. This is due to persistence of vision, the physiological phenomenon that ensures imagery rests in the eyes and brain for a small fraction of a second even after the vision is blocked or the object is removed.

DOI: 10.4018/978-1-5225-2255-3.ch227 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Figure 1. ­

Phenakistoscope Phenakistoscope was the earliest animating device invented simultaneously by Joseph Plateau of Belgium and Simon Von Stampfer of Austria in the year 1831.In Greek Phenakezein means to deceive or to cheat. It composed of a disk with series of images pasted or drawn on radii of the disk at different distance from the centre. The Phenakistoscope would be placed in front of a mirror and rotated. While the phenakistoscope rotates, a viewer can look through the slits at the reflection of the drawings that are visible like a flash when a slot passes by the viewer’s eye and ensures the illusion of animation.

Zoetrope William George Horner in 1834 suggested the concept of Zoetrope. Etymologically Zoetrope has got Greek origin Zeo meaning life and tropos meaning turning. Zoetrope literally means “wheel of life”. The principle is same that of phenakistoscope. It is a cylindrical rotating instrument with

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many frames of images printed on a paper strip kept over the inner circumference. The observer watches through vertical slits around the sides and view the moving images on the opposite side as the cylinder rotates. As it rotates the frames between the viewing slits moves in the opposite direction of the picture on the other side and serves as a micro shutter. It does not require the use of a mirror to view the illusion, and as it has cylindrical shape it can be viewed by many at a time.

Flip Book John Barnes Linnet, introduced the first flip book in 1868 and named it as Kineograph (moving picture). It was a book which had a series of images, when flipped shows that the drawings are moving. It is the simplest way of making animation without a camera.

Praxinoscope In Greek praxein means action and scope means watcher. Praxinoscope had got two variants namely

Category: Educational Technologies

non-projective and projective. Non- projective praxinoscope is the successor to zoetrope invented in 1877 by a French science teacher, Charles-Emile Reynaud. The narrow slit in the zoetrope was replaced by an inner radius of mirrors, kept inside so that the reflections of the images seemed to be more or less stationary in position as the wheel is rotated. Non-projected praxinoscope gives images that are less distorted and brighter than the zoetrope gives. In 1889 Reynaud introduced an improvised version which could project pictures on a screen from a longer roll of images. This projected praxinoscope was used for the benefit of larger audience.

Stop Frame Animation During late nineteenth century stop frame animation had got its lime light. Stop Frame Animation is a film making technique to make a physically manipulated object appears to move on its own, this is by using individual camera shots of a different movement. Putting these shots together results in an animation impact. There are many types of stop frame animation namely clay animation, sand animation, cut-out animation, object animation, puppet animation, graphic animation and so on. Even though it is a time consuming process it leaves an impact on learning in a positive way and enhance look and feel.

Clay Animation Clay animation is one of the many forms of stop frame animation invented in the year 1897 in which each of animated piece is made of a malleable substance, usually plasticine a type of clay. In claymation (short for clay animation) each object is made of clay or pliable material usually surrounded with skeleton wire called an armature. An armature can be built to support the clay figure using wire, tin foil, Styrofoam balls and Popsicle sticks. The series of capture with malleable figures ensures the impact of animation.

Sand Animation

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Sand animation, otherwise called as sand art, in which an artist forms a series of images with sand. At first an artist applies sand to a surface like glass and then forms images by drawing lines and figures in that sand with his hands. In this sand animation overhead projector or light box is used as an aid by the performer. Then the animator moves the sand around on a back lighted or front lighted piece of glass to create each frame and on sequential grab the effect of animation is bestowed over sand images.

Cut-Out Animation Cut-out animation is one of the stop frame animation techniques using flat characters, props and background cut from materials such as paper, card, stiff fabric or even photographs. The characters are fragmented into smaller pieces and captured the pieces by giving small movements manually, at each step, to create the illusion of movement. The cut-outs may be of paper drawings, photographs or any 3D objects. The cut-out animation process is a tedious one as it needs manual intervention that is the animator has to adjust the pieces for every frame.

Object Animation The animation created with the use of any nondrawn objects like toys, blocks, dolls, etc. that are not fully malleable, such as clay or wax and not formed to look like a living or non-living character.

Puppet Animation The puppets or toys which are flexible to move their parts, so that they can be repositioned between frames to create the illusion of motion are used in this variant. When the frames are played in rapid sequence the puppets are getting life. From the ancient times puppets are used for story

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telling in ceremonies and later it came to the form of animation.

Computer Animation The animator uses software to draw models and objects. The animator can animate irrespective of physical laws like gravity, mass and force. Early digital computer animation may be the brain child of Bell Telephone laboratories during 1960’s. Lawrence Livermore of National laboratory had also indulged in developing early digital computer animation. There are two types of computer animation 2D animation and 3D animation. During 1976 the concept of 3D animation caught up and development of Computer Generated Imagery (CGI) technologies had given life to 3D games and game engines.

2D Animation The 2D animation images are created or edited using 2D bitmap graphics or 2D vector graphics. Analogue computer animation, power point animation and flash animation are some of the applications of 2D animation.

3D Animation In 1972, Frederic Parker created the first 3D human face model. In 1973, Edwin Catmull one of the founders of Pixar Animation studio created digitized hands.3D animation is digitally modelled and manipulated by an animator. 3D animation is carried out through step by step process. As in any scientific venture 3D animation also requires lots of planning. A creative artist with unquenchable imagination prepares the digital space. The 3D animation software packages like Maya and 3Ds Max exploit the use of node system. Node system is interesting, flexible and robust at the same time it is challenging when it comes to execute command. The software keeps in mind every command that is given in a linear fashion and calculates how things would look like with

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the first node and keeps in track and moves in to subsequent frames. First the digital artist creates image planes that will describe the exact objects in different axes and integrates to create the three dimensional model that is made up of wire frame structure. The Euclidean geometry model depicts X axis as horizontal, Y as vertical and Z as depth, that involves lot of mathematical equation and algorithms. In this stage in Maya the artist can view the wire frame structure by pressing 4 or he can get the other rendering options like smooth shaded, smooth shaded with hardware texturing and smooth shaded with lights. After finalising the structural part of the model the surface is textured as per the need of the animation theme. The appropriate light arrangement is provided on the objects and rendering is the part that ensures improvisation every time. Once the visuals are finalised the story board developed by the director guides the animation engineer to what extent the objects stay and move in the screen through timing schedule. The animator provides the commands to the objects to make required moments followed by rendering and editing. At last the voice over takes place with music or description addition and the 3D animation is finalised through analysis by the stake holders. Thus making 3D animation is the cyclic process that offers improvisation always.

BACKGROUND Teaching of Science Through Animation It is needless to underline the importance of Science in the digital era. The accumulation of new scientific inventions are taking place in one hand, the dynamic principles of curriculum always push forward the developments at the earliest to the curriculum on the other hand. And it is evident that many school teachers teach in schools what they studied in their college that stands testimony to the rapid developments that are taking place in the

Category: Educational Technologies

ambit of science teaching. It is the paramount task before the science teacher to explain the increasingly abstract topics in a jammy academic year full of hectic activities. This situation warrants the resetting of the compass of work ethics for science teachers. In this pressure cooker situation the search for better instructional media that will ensure greater individualisation and better illustration becomes inevitable. The present generation kids are glued to television animation and keep spending more time in animated games even. It is not pathological but has become the symptom of normalcy in the society where both the parents are employed and children left with only virtual world. Many experimental studies in this area give some meaningful findings for the usage of animation to teach science. Some topics like skeletal structure, illustration of crystal lattices, molecular labelling of chemical kinetics, internal structure and function of many internal organs like eyes, kidneys, heart and so on can be better explained with animation and aid the learners to take it to their long term memory and better achievement.

Psycho-Pedagogy Behind Animation The effective use of technology for optimum realisation of educational objectives always seems to be the task on the table for majority of educational researchers. The advances in cognitive psychology provide convincing theoretical backdrop to use multimedia and web based technologies to improve learner participation, enhance learning outcomes and to extend active retention with better memory traces. As per Mayer (1991) through multimedia learning learners indulge in three important cognitive activities like selecting, organising and integrating. By these activities the learners build connections between verbal based models and visual based models with enriched prompts and cues. It is the assumption that animation provides separate systems for processing pictorial and verbal materials. In this dual channel assumption each channel has got limited amount of material that

can be processed at a time. Meaningful learning is ensured only when these mental connections between pictorial and verbal representations are complete. The alternate school of thought which highlights the cognitive overload is diminishing day by day. As per these theorists the learners intended cognitive processing exceeds the available cognitive capacity of the learners. This argument is losing its ground as more and more scientifically designed and pedagogically superior educational animations becoming runaway success. Julie Bauer (2002) is of opinion that graphics have been used from the past to portray things that are inherently spatio-visual. Concepts that are metaphorically spatio-visual can be better explained with moving graphics than static graphics when it conforms to congruence principle of learning. For real time reorientation of time and space animation is the tool that stands taller in spatio-visual ambience. Multiple representation principle, contiguity principle, split attention principle, individual differences principle and coherence principle if applied judicially in educational animation tastier will be the fruits of education.

Need and Significance of the Study The classroom trend changes from manual to technological and therefore the conventional way of teaching is not enough for the children. Today the children need active participation in the classroom. They need their teachers to be a motivator, active participant etc., since the availability of many software make the teachers feel free and easy to design their own style. Even though it is the additional workload for the teachers the learning outcome or evaluation gives some satisfactory improvement (Sajid Musa 2015). The main intension of animation class is not only in creating curiosity but also helps to sustain their interest and attain curricular objectives. The concepts which they learnt through animation withstand the retroactive inhibition of memory which makes their studies a productive one for their future aspects.

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The animation class not only makes the children to learn, but it makes them to understand. It creates some positive attitude among the learners to pursue education with ease (Falode 2016). This study is the need of the hour to assess whether animation has got positive educational outcomes for teaching and learning communities. The effectiveness of an educational animation largely depends on the soundness of its psycho pedagogical constructs. Animation in education motivates the learner in a interesting way in which traditional method of “Chalk & Talk” is avoided and also minimises actual classroom demonstration & lecture. While using educational animations not only creativity & novelty is increased among learners monotony is also reduced. Animation creates liveliness in the classroom situation and prolongs retention, it keeps the learners attentive and thereby learners can easily understand the concepts of the subject easily when compared to traditional method of learning. Many of the complex ideas become easier while using animation and also matches the cognitive demands of a learning task. (Tversky et al.2002) Animation has the potential to attract the concentration of learners in their topics through visualization of content. It’s an enjoyable and memorable one where animation leaves a lasting impression and form a stronger relationship, making them more likely to respond. Watching a video is easier than reading a lengthy document or book; it not only saves the time but also makes sure your message get across. Present both auditory and visual stimulus to the students, allowing easier cognitive tagging and encoding of information besides triggering the imagination of learners.

Limitations The educational effectiveness of any visuals largely depends on how widely it is used by the instructor rather than how well learners learn when animation is used. So it lies with the instructor to maximize the utility of animated media to supplement the face to face instruction. Many a

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times out of vested interest or technical inertia the instructor in developing countries are miles away to make up their mind to go for animation. Mostly real life learning is lost in animation. Students are fostered with virtual ecology. The individual differences prevailing among the learners may not be effectively addressed with animations that are targeting generalised perspectives.

Review of Related Literature Review of related literature provides innumerable studies in this area, for brevity a few studies are highlighted hereunder followed by Meta synthesis. Angelos Konstantinidis and others (2013) introduced four Web 2.0 tools; Blogger, Strip Generator, Go! Animate, and Google Forms, that are free and easy to use, in an effort to motivate teachers with low technological skills in integrating them into their instruction practices. In their descriptive method they found that the aforementioned tools comprised the curriculum in a blended-learning professional development course for in-service teachers that attracted many favourable comments from the participants. Lirong Xiao (2013) made a survey through inquiry forms about animation content in education and found that in animation field, although some software companies have developed their individual production toolboxes or platforms for animation content in education, there is lack of relevant research from the perspective of animation techniques. Sederick C. Rice (2013) used interactive animations to enhance teaching, learning, and retention of respiration pathway concepts in face-toface and online high school, undergraduate, and continuing education learning environments. The study found that the content used for the research supports the development of more inquiry-based classroom and distance-learning environments that can be facilitated by teachers/instructors, it also improves retention of important respiration subject content and problem-based learning skills for students.

Category: Educational Technologies

Gokhan Aksoy (2012) analysed the impacts of animation technique on academic performance of students in the “Human and Environment” unit present in the Science and Technology course of the seventh grade in primary education with 58 students. The study revealed that animation technique is very effective when comparing with traditional teaching methods in terms of improving students’ achievement. Waqur Un Nisha Faizi and others (2012) identified the main causes of increasing attention towards animation course in different institutions of Karachi through survey method. The researchers found that majority of the respondents had a view that animation course ready to meet the need and requirement of modern times. Some respondents opined that students of animation class lead a happy and prosperous life at the end of the course and animation course grooms the ability of decision making among students. Animesh K. Mohapatra (2013) probed the extent of computer animations in teaching membrane transport to pre-service teacher trainees’ and the extent of understanding of concepts and functions in membrane transport. Many a times educators find it difficult to teach various membrane transport processes, as many of the resources are two dimensional where as membrane transport is four dimensional in nature. Much research finding support that viewing the processes in three dimensions helps better learning, and animations are perhaps effective visualization tools for learners and help to retain for long-term. It was concluded that animations with movements can provide learners with explicit dynamic information that is either implicit or unavailable in static graphics. Therefore, it was recommended to use computer animation to transform students to active mode. As it had been observed that this particular field of study has got immense potential and the available literature also has got spaces to fill with this study on “Science Animation and Students Attitude – An Analytical study” has been taken up.

METHODOLOGY

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Assumption of the Study Gaining of knowledge through learning is a day-to-day process which we experienced from our childhood. All the people don’t have an opportunity to engage in learning through schools or colleges throughout their life. Apart from the educational institution students learn a lot from their environment which are unorganized and perhaps unauthentic. The systematic method of constructing scientific and verified knowledge is received only from the schools, so one should be aware of what they learnt and it should be useful to their life. The purpose of education is to make a learner to be a learner for their life time that is they should not be filled with bookish content alone and they must know how to convert the content to their practical utility. The engaged time of the institution must be better transformed into academic learning time. The relatively small portion of time is only going to create a large impact in the cognitive structure of an individual. The tools those are useful to make better memory traces with the learner can be complete only with animation. Thus the researcher by this assumption made the following research questions. 1. Do animated lessons trigger the learners’ interest? 2. Does animation find useful place in curriculum? 3. Do animated lessons increase the span of attention? 4. Does animation make learning a joyous one? 5. Do animated lessons ensure better understanding of the content? 6. Can students achieve better with animated lessons?

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Objectives of the Study The following are the objectives 1. To measure the span of attention of the students studying through animated lessons. 2. To find out the curiosity in the learners towards animation lessons. 3. To analyse the difference in achievement between conventional class and class with animated lessons. 4. To evaluate the level of understanding of students attending the class with animated lessons. 5. To evaluate the relationship between achievement and attitude of students attending the class with animated lessons. 6. To find out the relationship between the attitude and its sub variables towards animation. 7. To evaluate the effectiveness of the class with animated lessons in science subject.

Hypotheses of the Study The following hypotheses were formulated based on the objectives of the study. 1. There is no significant difference between control and experimental group in their pretest achievement. 2. There is no significant difference between control and experimental group in their post-test achievement. 3. There is no significant correlation between experimental group post test achievement and students attitude towards animation. 4. There is no significant correlation between experimental group post test achievement and the sub variables of attitudes towards animation like narration, visual transformation, interest & attention, easy and rapid understanding and Participatory evaluation. 5. There is no significant correlation between experimental group students’ attitude and the sub variables of attitude towards animation

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like narration, visual transformation, interest & attention, easy and rapid understanding and Participatory evaluation. 6. There is no significant cross correlation between sub variables of attitude towards animation like narration, visual transformation, interest & attention, easy and rapid understanding and Participatory evaluation.

Research Design To study the effectiveness of teaching through animation, the pre-test, treatment, post-test equivalent group experimental design was adopted in the study. A sample of 43 students for each control and experimental group were selected by purposive random sampling based on their academic term achievement. The equivalence of mean achievement was taken into consideration to finalize the sample of both groups. The following tools were used in the study. Tool 1: Achievement test in science constructed & validated by the investigators. Tool 2: Scale of attitude towards animation lessons constructed & validated by the investigators.

Research Procedure The population may be school students studying science as one among their subjects, the representative sample was selected from a private school in Kumbakonam of Thanjavur District for both control & experimental groups. The students studying in eighth standard (280) were screened through their achievement in term test. Among the 280 students the investigator adopted purposive sampling technique to select the students who fall in the average category (50% - 60%) that accounted 86 students based on their term test marks. 86 students were randomized and split into 43 each to form the control and experimental groups for the study. The pre test was administrated to both the experimental and control groups and data were

Category: Educational Technologies

collected to assess the achievement of the students. The topics were selected from science textbook and were taught to the control group with conventional method. For the experimental group the same topics were taught with animated lessons for 30 days. The animated lessons are mostly 2D and 3D animations those are freely available to the users. Both control and experimental group were instructed by the same investigator. For the execution of animated science lesson the investigators had selected animated videos from selected websites that provide free feed. After the treatment period the post test was administered for the sample and data were collected for achievement test. Scale of attitude towards animation lessons was administered on experimental group after post test to assess their attitude about the treatment.

DESCRIPTION OF RESEARCH TOOLS Construction of Achievement Test The achievement test was constructed by the investigators from the topics which they planned to teach the students using animation. Investigators reviewed the Tamilnadu State Board Textbook for standard eight and finalized the topic pertaining to second term science and settled with two topics namely skeletal system and atomic structure. The syllabi and their finalization were carried out with the help of research supervisor, four experienced teacher educator and Principal of the school, Module 1 consisting animated video of human body and its movements, joints and types of joints, skeleton and movement of animals. Like wise module 2 consisting law of conservation of mass, law of definite proportion, Dalton’s atomic theory, discovery of electron, properties of cathode rays, discovery of protons and Thomson’s. atomic model.

Achievement Test

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Standardization The test items constructed were subjected to jury’s opinion consisting of research supervisor, principal of the school and four experienced teacher educators. To establish the reliability of the test, the investigator adopted split-half method. The reliability of the whole test is found to be 0.86 significant at 0.01 levels. Hence it is concluded that the test is highly reliable. To establish the validity of the test the investigator attempted to find out the correlation co-efficient between the achievement scores in the test and scores they got in their term test by product moment correlation co-efficient method by taking 25 percentage of the total sample of 86 students. The correlation co-efficient is found to be 0.831 significant at 0.01 level. Hence it is concluded that the test has high validity. The content validity of the test was ensured by the panel that acts as jury. The answer books of the students for achievement in science were scored by giving one mark each for a right response of the objective type question, thus a range of 0-30 marks can be secured by the students.

Scale of Attitude Towards Animation Lessons Attitude may be considered as the state of mind or feeling about anything or anybody. It is essential to estimate the attitude of the learners towards animation lessons and what really matters for them to have animation as the preferred mode of educational delivery. So the investigators would like to know the attitude of the students present in the experimental group towards animation lessons. The investigators after having meticulous analysis and critical review with various stake holders had extracted five important dimensions that compose the attitude of the students towards animation lessons as following

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1. 2. 3. 4. 5.

Narration Visual transformation Interest and attention Easy and rapid understanding Participatory evaluation

As much as 78 questions were developed in these dimensions. These questions were subjected to item analysis and expert regulations. The final version rested with 30 questions spread across all dimensions; the significance of each dimension is given below.

Narration Narration is the use of a selected methodology or process of giving a written or spoken commentary to unfold a story to an audience. Narration embraces a set of approaches through which the author of the story presents it. Narrative mode includes Narrative point of view: The perspective through which a story is communicated and Narrative voice: The format through which a story is communicated. In animated lessons the narration or the script board spells success to a greater extent. By narration the investigators mean the way of unfolding the scientific concepts by employing the techno pedagogical principles of learning and also child friendly elements like animated characters that may explain the lessons as video jockey with some sense of humour.

Visual Transformation The visual elements that are rich in details and better in dimension may offer a meaningful understanding of the point of discussion. By visual transformation the investigators mean the change of frames that may offer more concrete idea about abstract concepts by means of taking the learners to the quint essential part of the topic.

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Interest and Attention Interest may be defined as the feeling one may have to know or to learn more about somebody or something. It may also be defined as an activity or a subject that one may enjoy and spend the free time in doing or studying it. Attention is the act of carefully listening or thinking about something. By interest and attention the researchers mean a connection of the learner with animated lessons which affects his attitude towards it especially while he receives immense benefit and urge to attend the classes.

Easy and Rapid Understanding The knowledge that someone has about a particular situation or subject may be termed as understanding. To gain that knowledge in a given topic if the learner is going to take less cognitive effort that ensures easy understanding. If the time that is going to be consumed is less by the learner to understand that may be considered as rapid understanding. Through this dimension the researchers would like to seek whether animation lessons ensure easy and rapid understanding from the learner’s perspectives.

Participatory Evaluation Participatory evaluation is the partnership approach to evaluation in which the stakeholders actively engage in developing the evaluation and all phases of its implementation. The stakeholders are instructors, learners, decision makers and any other beneficiaries. The process includes identifying relevant questions, planning the evaluation design, selecting appropriate measures for collecting and analysing data (Ann zukoski and Mia Luluquisen).

Category: Educational Technologies

Table 1. ­

In this dimension the researcher would like to estimate the readiness of the learners to voluntarily ensure the progress by answering formative questions available in the particular topic with innate enthusiasm and self regulating their zeal to answer.

Statistical Techniques Mean, Standard deviation, ‘t’ test, Correlation, Regression, Effect size, Cohen’s d and Gain score analysis are the statistical techniques that were employed for data analysis.

ANALYSIS AND INTERPRETATION OF DATA Testing the Significance of Difference Between the Mean Scores of Pre-Test The table value of ‘t’ for the degree of freedom with 0.01 level of significance is 2.64, whereas

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the calculated ‘t’ value is 1.699. This makes it obligatory to accept the null hypothesis and concluded that there is no significant difference between the experimental group and control group students in their pre test achievement in science.

Testing the Significance of Difference Between the Mean Scores of Post-Test The significance of difference between the mean of control group and experimental group of eighth standard students in their post-test achievement in science is shown in Table 1. The calculated value of ‘t’ 21.445 is significant at 0.01 level. This makes it mandatory to reject the null hypothesis and concluded that there is significant difference between the experimental group and control group students in their achievement in science in which the experimental group has got higher scores.

Figure 2. ­

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Effect Size Analysis Effect size analysis was carried out using pooled standard deviation to find out the relative effectiveness of the animated science lessons over the conventional method. The instructional objectives were same for the pre-test and post-test. The effect size for post test achievement is 0.915 that is large effect. Likewise the value of Cohen’s d for the comparison of control and experimental group post test achievement in science is 4.543. These findings again prove the fact that students studied through animated lessons have fared better and the treatment is very effective over conventional method for the chosen topics. The average attention span observed by the investigator for experimental group was thirty five out of forty five minutes where as for the control group it was only twenty one minutes.

Gain Score Analysis The gain score analysis of control and experimental post test achievement in Science revealed that the experimental group has got mean achievement of 25.33 in contrast to 9.42 of control group that is 169% is the gain score of the experimental group Table 2. ­

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post-test achievement. This substantiates that the treatment with animated lessons seems to be very effective over conventional method of teaching.

Correlation Analysis There is significant positive correlation between experimental group post test achievement and students attitude towards animation with 0.01 level of significance. This gives way to reject the null hypothesis and concluded that achievement in Science and attitude towards animation lessons with experimental group have meaningful correlation that may be attributed to animation lessons. Testing the correlation between experimental group post test achievement and the sub variables of attitudes towards animation like narration, visual transformation, interest & attention, easy and rapid understanding and Participatory evaluation The correlation between experimental group post test achievement and the sub variables of attitudes towards animation like narration, visual transformation, interest & attention, easy and rapid understanding and Participatory evaluation is shown in Table 2.

Category: Educational Technologies

Figure 3. ­

There is significant positive correlation between experimental group post test achievement and the sub variables of attitudes towards animation like narration, visual transformation, interest & attention and Participatory evaluation except easy and rapid understanding and makes it mandatory to reject the null hypothesis except for the sub variable Easy and Rapid Understanding. Testing the correlation between experimental group students’ attitude and the sub variables of attitude towards animation like narration, visual transformation, interest & attention, easy and rapid understanding and Participatory evaluation There is significant positive correlation between experimental group students’ attitude and the sub variables of attitude towards animation like narration, visual transformation, interest & attention, easy and rapid understanding and Participatory evaluation except Easy and Rapid Understanding and thereby it is obligatory to reject the null hypothesis.

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Testing the cross correlation between sub variables of attitude towards animation like narration, visual transformation, interest & attention, easy and rapid understanding and Participatory evaluation The cross correlation between sub variables of attitude towards animation like narration, visual transformation, interest & attention, easy and rapid understanding and Participatory evaluation is shown in the Table 4. There is significant positive cross correlation between sub variables of attitude towards animation like narration, visual transformation, interest & attention, easy and rapid understanding and Participatory Evaluation. This gives way to reject the above said null hypothesis for all the sub variables except Easy and Rapid Understanding.

Regression Analysis The scores of students’ attitude towards animation and its sub variables with respect to achievement

Table 3. ­

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Table 4. ­

in science are subjected to regression analysis that yielded the following interpretation.

Predictors: (Constant)- Post Test Achievement of Experimental Group in Science The method used in this analysis is enter method. The dependent variable is achievement in science and the variables entered are attitude towards animation and its sub variables. The adjusted R square value is 0.364. This indicates that the independent variable in this model account for 36.4% variance in dependent variable that is achievement in science. Both the constant and few of the sub variables has got significance level of 0.01 – 0.05. The β coefficient is significant at 0.05 level. This indicates that the achievement in science, attitude towards animation and its sub variables are positively related except the sub variable easy and rapid understanding.

Findings 1. It is found that there is significant difference between the experimental group and control group students in their achievement in science in which the experimental group that

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learnt with animated lessons has got higher scores. 2. It is found that there is significant positive correlation between experimental group post test achievement and student’s attitude towards animated lessons which proves that instruction with animated lessons seems instrumental in building a positive attitude towards animation lessons among the majority of the students. 3. It is found that there is significant positive correlation between experimental group post test achievement and the sub variables of attitudes towards animation like narration, visual transformation, interest & attention and Participatory evaluation except easy and rapid understanding. It gives an inference that people who have positive attitude towards narration, visual transformation, interest & attention and Participatory evaluation that may be created out of animated classes had scored better in their achievement test in science. 4. It is found that there is significant positive correlation between experimental group students’ attitude and the sub variables of attitude towards animation like narration, visual transformation, interest & atten-

Category: Educational Technologies

tion, easy and rapid understanding and Participatory evaluation except Easy and Rapid Understanding that gives an insight that most of the sub variables has got right orientation with attitude towards animation and perhaps Easy and Rapid understanding had not been preferred to the extent it had been preferred that the top scorers whereas it seems the poor scorers has preferred this dimension as essential. 5. It is found that there is significant positive cross correlation between sub variables of attitude towards animation like narration, visual transformation, interest & attention and Participatory evaluation except easy and rapid understanding.

Educational Implications of the Study This study reveals that science instruction with animated lessons is very effective in improving the achievement of students in school science. The overall outcome of the study highlights the effectiveness of teaching with animated lessons with large effect size and gain score in achievement of Science. The attitude of the students towards animation classes is very positive. This study implies that teaching with animated lessons will definitely enhance the outcome of teaching learning process in the following ways, 1. Animated lessons make the learners to be attentive and focused. 2. Animated media offers in-depth details to make the students thoroughly understand the abstract elements present in a particular topic. 3. Visual transformation and Narrative paradigms that are employed in animated lessons seems to be psycho pedagogical in nature to sustain the interest of the learners and to complete the syllabus in time. 4. The effort required by teacher and learner alike can be minimised with animated classes.

5. As animated lessons are self explanatory and informational through multiple media, the impact on memory traces would be long lasting.

FURTHER RESEARCH DIRECTIONS This study entitled “Science Animation and Students Attitude-An Analytical Study” is an investigation at standard Eighth level. It is suggested that further studies may be conducted in the following areas, 1. It is suggested that teaching through animation is evaluated in other school subjects like Mathematics, Social studies, Language I and II etc. 2. It is suggested that the same study is carried out in all standards of different boards. 3. It is suggested that the same study is carried out in higher education and professional education courses. 4. It is suggested that the influence of other variables than those studied now can also be investigated. 5. It is suggested that efforts should be taken to develop animated lesson for more topics and evaluate their effectiveness.

CONCLUSION Through animated lessons science can be taught effectively. More and more new information with the support of modern electronic media can be passed to the learners easily. In the information era ocean of information cannot replace the teacher and his personal influence in the mind of the learner. In this method the teacher is having face to face interaction and gives guidance about the veracity of the content to ensure the right learning to take place through self motivated learners. Though the groups in the study are homogenous, the differences in mean score and standard deviation will stand testimony to the fact that, teaching 2613

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science through animated lessons is superior to conventional method of teaching. There seems to be no doubt that auto instructional strategies are supplementary as well as main process of learning. Teaching is replaced by learning, ultimately leading to self learning thus teaching science subject through animation fill the gaps of the conventional teaching learning process.

Gaur, A. S., & Gaur, S. S. (2009). Statistical Method for Practice and Research. New Delhi: SAGE Publications India Pvt. Ltd. Huang, H.-W., & McConnell, R. (2009). Students Experiences of Technology-Enhanced learning in Two Traditional Teacher Preparation Classrooms. Joural of Online Learning and Teaching, 5(3), 522.

REFERENCES

Konstantinidis, A. (2013). Web 2.0 Tools for Supporting Teaching. Turkish online. Journal of Distance Education, 14(4), 287–295.

Aksoy, G. (2012). The Effects of Animation Technique on the Seventh Grade Science and Technology Course. Scientific Research., 3(3), 304–308.

Mayer, R. E., & Anderson, R. B. (1991). Animations Need Narrations: An Experimental Test of Dual-coding Hypothesis. Journal of Educational Psychology, 83(4), 484-490.

Aksoy, G. (2013). Effect of Computer Animation Technique on Students Comprehension. Mevlana International Journal of Education., 3(1), 40–46. doi:10.13054/mije.13.02.3.1

Mohanasundaram, K., & Sivasankar, A. (2010). Blended Learning: A New Horizon. University News, 48(3), 1–4.

Ali, A. Z. M., & Madar, A. R. (2010). Effects of Segmentation of Instructional Animation in Facilitating Learning. Journal of Technological Education and Training., 2(2), 15–29. Animesh, K. M. (2013). Fostering Pre service Teacher Trainees’ Understanding of Membrane Transport with Interactive Computer Animation. Scientific Research, 4(10), 640–645. Everything About Animation. (n.d.). Stop motion. Retrieved from: http://animationmovies.blogspot. in/p/stop-motion.html Faizi, W. U. N., Shakil, A. F., & Kanwal, S. (2012). The Causes of Increasing Attention towards Animation Course in Different Institutes of Karachi, Pakistan. International Journal of Academic Research in Business and Social Sciences., 2(7), 444–457. Falode, O. C. et  al.. (2016). Effectiveness of Computer Animation Instructional Package on Academic Achievement of Senior Secondary School Agricultural Science Students in Animal Physiology in Minna, Nigeria. Bulgarian Journal of Science and Education Policy., 10(1), 5–8. 2614

Mohanasundaram, K., & Sivasankar, A. (2010). Effective Blends and Economical Blogs: Successful Change Agents of Higher Education to Meet New Challenges. University News, 48(28), 86–90. Reporter, B. S. (2008). Animation industry to grow at 27% CAGR: NASSCOM. Business Standard. Retrieved from: http://business-standard.com/ article/technology/animation-industry-to-growat-27-cagr-nasscom-108110600063_1.html Rice, S.C. (2013). Using Interactive Animation to Enhance Teaching Learning and Retention of Respiration Pathway Concepts in Face to Face and Online High School, Undergraduate and Continuing Education Learning Environments. Journal of Microbiology & Biology Education, 14(1), 113 – 115. Swarmy, A. M. A. (2010). Internet Awarness and Competence Among High School Students and Teachers. Edutracks, 9(7). Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animations: Can it facilitate? International Journal of Human-Computer Studies, 57(4), 247–262. doi:10.1006/ijhc.2002.1017

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Utts, J. (2003). A Study Comparing Traditional and Hybrid Internet-Based Instruction in Introductory Statistics Classes. Journal of Statistics Education, 11(3). Wikipedia. (n.d.). History of animation. Retrieved from: http://en.wikipedia.org/wiki/History_of_animation Wordpress. (n.d.). Retrieved from; http://dmizerny. files.wordpress.com201209img_0982.jpg Xiao, L. (2013). Animation Trends in Education. International Journal of Information and Education Technology, 3(3), 286 – 289. Zukoski, A., & Luluquisen, M. (2002). Community Based Public Health Policy and Practice. Academic Press.

ADDITIONAL READING

KEY TERMS AND DEFINITIONS Animation: Latin word ‘animatio’ that means the condition of being alive or giving life. Attitude: The state of mind about anything or anybody. Computer Animation: The images or objects that are created using computer software subjected to animation. Effectiveness of Animation: Remarkable achievement of learners by the impact of animation. History of Animation: The growth and development took place in the field of animation. Psycho-Pedagogy: Educating the learners through the principles of psychology. Science Animation: Animation clippings that are used to explain science concepts. Visual Transformation: Change of visual frames in any video.

Ainsworth, S. (2008). How do Animations Influence Learning? In D. H. Robinson & G. Schraw (Eds.), Current Perspectives on cognition, Learning, and Instruction: Recent Innovations in Educational Technology that Facilitate Student Learning (pp. 37–67). Charlotte, NC: Information Age Publishing.

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Three Cases of Unconventional Educational Uses of Digital Storytelling Emmanuel Fokides University of the Aegean, Greece

INTRODUCTION The narration is the art of using words and actions for the representation of the elements of a story in such a way that the listener’s imagination is stimulated (Genette, 1998). More simply put, it is the art of telling a story to an audience, in order to convey important messages. Due to the technological developments, storytelling has become digital; the oral or written story is enhanced by using multimedia and hypermedia elements (Lathem, 2005). Narrations, either digital or conventional, are useful educational tools. Since narrations cause the keen interest of students, this helps them to easily consolidate and assimilate information (Coventry, 2008). They increase the oral and written skills, strengthen critical thinking and the ability to analyze and synthesize information (Ohler, 2006). When students create their own digital stories (individually or as a group), they learn to conduct research on a topic, to ask questions, to organize ideas, to express their views and to make meaningful narratives (Robin, 2006). There is an extensive literature regarding the educational benefits when using digital storytelling (e.g., Coventry, 2008; Ohler, 2006; Robin, 2008). Disproportionally fewer studies have been conducted examining the potential of this tool in areas where the settings are not strictly instructional or the main objective is not some form of knowledge acquisition. The present study is an attempt to fill that gap, by embracing the standpoint that digital storytelling is a good method for documenting personal experiences, that it can be a form of narrative therapy and that it can help students to discover parts of their

personality (Sawyer & Willis, 2011). Three case studies are presented where digital storytelling was used in a non-mainstream, unconventional way. In all, knowledge acquisition was irrelevant or an insignificant factor; instead, the emphasis was on broader issues that students, as well as teachers, face at school.

BACKGROUND Focusing on problems that students and teachers face at school, which are not directly related to knowledge acquisition, but affect how the school functions and/or the emotional well-being of students, three areas were of interest: the poor school integration of immigrant students, young students’ adjustment to school, and bullying. In Greece, 10.35% of the total students’ population are immigrants (Hellenic Statistical Authority, 2011). Insufficient knowledge of the Greek language and, consequently, low performance in language lessons is a major problem (Retali, 2013). There is also a more important difficulty; that of poor school and/or social integration. Schools could play an important role, but the Greek educational system is not capable of assimilating immigrant students well (Skourtou, Vratsalis, & Govaris, 2004). Therefore, there is a need to help them overcome their adaptation problems. Coming to primary school for the very first time marks the beginning of a transitional period to children’s lives. Rules and routines are different from those they were accustomed in the kindergarten and their status and identity might be affected (Fabian, 2007). Problems may arise

DOI: 10.4018/978-1-5225-2255-3.ch228 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Educational Technologies

that have short and long-term educational and/ or psychological implications (Dockett & Perry, 2009). Behavioral problems are also common (Brooker, 2008). Consequently, finding ways that allow a smooth and quick transition to the school’s environment are quite important. Bullying is a phenomenon that is becoming more and more frequent in Greece’s schools. It greatly affects students’ psycho-emotional development, their school performance (Manesis & Lambropoulou, 2014) and may lead to serious psychological trauma and dysfunctional social behavior (Galanaki & Vogiatzoglou, 2015). In hypothetical situations children easily express their intentions to help the victim or to report the incident (Rigby & Johnson, 2006), but in reality, only a small percentage actually acts (Salmivalli, Lagerspetz, Bjorkqvist, Osterman, & Kaukiainen,1996). Studies, in Greece, examining interventions where students actively participated and were not just passive receivers of information are sparse (e.g., Kyriazis & Zacharias, 2015). Thus, there is a need to inform them in a more comprehensive way. The coming sections present how these issues were dealt using digital storytelling.

MAIN FOCUS OF THE ARTICLE Case 1: School Integration of an Immigrant Student In order to help a sixth-grade female immigrant student, having significant adaptation problems, a short project was planned and carried out, at a primary school in Rhodes, Greece, from late October 2014, till mid-March 2015. Twenty students (including the subject) were involved. The main idea of the project was to ask her to develop and present to her classmates an autobiographical digital story, illustrating her thoughts and feelings from her transition from one country to another. She was selected as the study’s subject because: (a) she recently came to Greece from the Domini-

can Republic. There were no other immigrants from that country; therefore, no one could help her and her family during their transitional period, (b) she did not socialize with the other students, whose attitudes toward her were “indifferent”, (c) she should be attending high-school, but because of her poor school performance she had to repeat the primary school’s last grade, and (d) her adaptation problems had worsened because of (c). From the above, it can be argued that she reflected characteristics and problems arising from the fact that she was an immigrant, therefore she constituted a critical case (Flyvbjerg, 2006). Data were gathered from multiple sources; interviews, direct observations, drawings, and the subject’s digital story. The interviews with the subject’s teacher were about her difficulties in social interaction with her classmates. The interviews with the students focused on how they view and interact with her. The interviews with the subject focused on the difficulties she was facing and the level of social interaction with her classmates. The in-classroom observations were focused on her behavior and the attitudes of the other students toward her. The development of the story lasted for a month, with a total of nine one-to-two hour sessions. It consisted of three parts: (a) “Before leaving” (seven scenes), where her thoughts, feelings, and conversations with relatives and friends were depicted, (b) “The journey” (two scenes) where her first impressions of her new home were illustrated, and (c) “In Rhodes” (two scenes) where her situation at school was portrayed. Her favorite song when she was living in her homeland accompanied the first part, while in the other two parts she used her favorite Greek song. Even though all dialogues and thoughts were written in Greek, they were “spoken” in Spanish, using her own voice. It has to be noted that during the development of the subject’s story, the researcher did not intervene in any way. This was done because guidance regarding the structure or the content of the digital story, might have resulted in the alteration of the results.

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Her detachment and loneliness were strongly portrayed at the third part of the story (e.g., all students are playing, but she sits alone and no one is talking to her), in contrast to the first, which is full of dialogues with relatives and friends. This contrast, reveals the extent of the lack of interaction and communication with others and her strong feelings of isolation and solitude. Also, the story was -in some sense- bilingual. Texts were in Greek; the narration was in Spanish. This fact, together with the Spanish song in the story’s first part, had an impact on students. Photos, language, and music, combined together, helped them in having a better understanding of the subject’s culture, as they stated in their interviews. In their initial interviews, the majority of students (16) expressed the view that they have no dissimilarities with foreigners, with the exception of language. However, their focus was on the language and communication problems and not on the immigrants’ feelings. After the presentation of the story, students stated that they gained a better understanding of the immigrants’ problems (17 cases). There was also a shift in their focus; from communication problems to the immigrants’ feelings of loneliness and isolation. From the analysis of the initial subject’s interview, the strong attachment to her country became clear. Loneliness, unhappiness, anxiety and fear about whether she will be accepted, were her strongest feelings. Significant changes were noted after the intervention; she felt more accepted and, finally, she had started socializing with other students and she was quite happy about it. The researcher’s observations and the teacher’s interview also confirmed the positive change in the subject’s behavior and socialization. A positive change to the other students’ behavior was also observed, the most important being that students acted. The communication barrier was lifted by both sides. Not only the subject was more open in joining groups of students, but also, they were more open in asking her to join them, in various activities.

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Case 2: School Adjustment In order to facilitate students understanding of the functions and rules of school and to change the attitudes and behaviors of those having adjustment problems, a short project was developed, involving the use of a technique to foster positive behaviors, combined with digital storytelling. A total of a hundred and five first-grade students participated, from five primary schools in Athens, Greece. The project lasted from mid-September (schools in Greece start in mid-September), until late October 2015. Behavioral modeling and, in specific, mimicking was utilized (Akers, 1977). In mimicking, one observes a model that expresses the desired behavior and, subsequently, he/she adopts it (Rogers, 2003). If ready-made stories were used, it would be like lecturing students. Instead, students were asked to develop their own. Since they were not yet able to read and write (at least efficiently), they acted as the “brains” and teachers acted as their “hands”. The digital stories’ developing software was projected using the classes’ video projectors, students saw what available choices they had, collectively determined what to do, and “commanded their teachers to execute their will. In reality, teachers indirectly guided students to certain key points, by constantly asking questions about the conditions and behavioral problems that prevail in their classrooms. The outline of the stories was “A day at school” and the idea was to develop two-part digital stories. On the first, “wrong” students’ behavior and dysfunctional classes were depicted. On the second, all problems were resolved and the “ideal” students’ behavior was portrayed. This stage of the project lasted for two weeks (seven two-hour sessions). Multiple sources of evidence were to be used as suggested by Yin (1994) and Paton (1990); pre-, during, and post-stage observations together with pre- and post-stage interviews with teachers and students were used for data collection purposes. This is a form of triangulation, which allows the

Category: Educational Technologies

verification of interviews while interviews allow the researcher to explore the internal aspects of the underlying behavior (Patton, 1990). The focus of the observations was incidents of poor school adjustment and behavioral problems. The teachers and the students presenting significant adjustment problems were asked about the observed episodes, in order to understand and clarify their intentions and/or interpretations of the events. During the observations prior to the development of the stories, it was noted that 14 students (9 boys and 5 girls) had considerable adjustment and behavioral problems and became the study’s focus students. Each repeatedly exhibited the following categories of problems: (a) lack of self-restraint/ discipline, (b) lack of interest or denial of participation in the lessons or in the school activities, and (c) denial to follow rules. Also, 24 students presented some of the above problems, but these were sporadic. The rest of them did not present any problems, or they were negligible. A noteworthy finding of the focus students interviews was that, though they could understand that their behavior was wrong, they could not make the connection between wrong behavior and its consequences, except for the ones that were related to them. Students found the notion that teachers were their “hands” and that they were the “brains” very interesting and fun, and they were constantly asking to add more scenes to the stories. In the first part of the stories they effortlessly illustrated the characters’ wrong behavior at school. What was not expected, but actually happened, was students to easily portray the ideal conditions. All the basic functions and rules that govern school seemed to be understood and the same applied to what was considered appropriate behavior. During post-stages’ observations, a sharp decrease in all problems was noted, but they were not totally eliminated. The results were especially interesting in the focus group, in which the majority of the focus students (12 out of 14) exhibited only minor behavioral issues. Also, teachers and students alike, quite often, referred to the digital

stories during in-classroom conversations and/ or arguments. During post-stage interviews, the matter of focus students not being able to make the connection between wrong behavior and its consequences to others was reinstated, to evaluate if any changes had occurred. Indeed, 8 students gave answers that clearly indicated that they understood that there were broader implications.

Case 3: Dealing with Bullying Since incidents of bullying are becoming more frequent in Greece’s schools, an intervention was planned and carried out in the fourth grade of a primary school in Rhodes, Greece, from late October 2015, till late November 2015 (ten two-hour sessions). Twenty-four students were involved. The goal was to inform students about bullying. The main idea was students to work in groups of four, develop bullying stories, present and discuss them to the classroom, and collectively develop one final story. The researcher offered no help or guidance and did not intervene in the process, with one exception. In the final story, he suggested three alternative endings; the victim remained silent and continued to be tormented by the bully, the victim reacted, but failed because the bully was overwhelmingly powerful, and the victim became a bully himself, harassing younger students. Following the presentation of the final story, students were asked to write a short essay, presenting their thoughts and feelings for these endings and the way they would have reacted. Research data were obtained by analyzing the digital stories and the short essays using the iterative coding process (Creswell, 2002) to identify the categories, themes, and patterns that emerged from the data. All stories were viewed once, by two individuals, to identify the main ideas. Then, they were re-viewed in more detail and the ideas were labeled with codes. This process was repeated two more times to reduce overlap and redundancy of the codes until a small set of sub-themes were identified.

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Physical together with verbal bullying were the main themes in all scenarios. All stories although simplistic, accurately illustrated what bullying is: the repeatedness of the incidents, the use of violence and bad language, the abuser’s overwhelming power, the victim’s reluctance to report the events. Emotions, like fear, depression, loneliness, and embarrassment, were also accurately presented. Bullies in all cases were boys. In all but two cases, the victim was a boy. Bystanders were included, but in most cases, there were no dialogues or thoughts accompanying these characters, so it is impossible to know which type of bystanders were portrayed. On a story, an observer helped the victim to beat the bully. The rest of the stories ended with the victim talking to an adult (two to a teacher, two to the victim’s mother, one to the headmaster). The three alternative endings of the final story were, in essence, wrong. Students’ essays were evaluated on the basis of the reasoning for rejecting them and the reasoning for selecting their own “right” course of action. The negative emotions one has when being bullied, were enough for rejecting the first two of the alternative endings. The reason for rejecting the third ending was almost totally in line with the phrase: “We do not treat others in the same way they (wrongly) treat us”. There were six cases (two boys and four girls) in which students stated that they would talk to an adult only if the bullying situation becomes intolerable, while in the rest, students stated that they would react immediately. The negative emotions of being bullied were once again the reason for seeking help if they were the victims of bullying. Finally, in all cases, talking to a parent, a teacher, the school’s headmaster, to a friend or a combination of the above, were considered the “right” reaction if students were bullied or witnesses to bullying.

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SOLUTIONS AND RECOMMENDATIONS Setting aside the satisfactory results that all had, the cases presented in the previous sections share important common features. In all, digital storytelling was used in ways that deviate from its mainstream uses, that is in a strictly instructional setting, for simply telling a story, or for acquiring literacy skills. Taking advantage of storytelling’s compatibility with young students’ mentality (Robin, 2008), they explored alternative uses that all of them tried to resolve issues that students, as well as teachers, often face at school. Indeed, bullying, immigrants’ integration problems and problems during the first days at school, are not rare and dealing with them can be quite difficult. The rationale behind the three cases was based on two concepts. The first was that students’ active participation is fundamental. Although a researcher, and/or a teacher was present, their role was limited; offering technical assistance when and if needed, not intervening at all, or covertly guiding. Social constructivism provided the theoretical foundation. By adopting the Vygotskian perspective, teachers were the ones who -indirectly- guided and supported students (Niesel & Griebel, 2007). By adopting the Piagetian perspective, students collaborated, negotiated, and came to a common consensus on how to develop their stories (Smith, 2012). By avoiding lecturing and direct manipulation, students’ stories were not the result of someone imposing his/her thoughts and views on them. Instead, students were free to: • •

Visualize their thoughts when developing the stories (Regan, 2008). Use, as raw material, pieces of previous knowledge and experiences they might have had.

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• • •

Construct their own understanding regarding important issues and socially negotiate them (Ertmer & Newby 2013). Embed their assumptions in real-world situations and determine by themselves the “right” course of actions. Become communicants of someone else’s thoughts, feelings, and problems (by viewing the digital stories) (Cane, 2010).

As a result, they formed a more comprehensive understanding regarding the issues that were discussed in the digital stories (Lenette, Cox, & Brough, 2013). Rules, instructions, good practices, behavioral patterns, and someone else’s emotions, require being deeply understood, before being accepted, applied, or become one’s own viewpoint. The study’s second notion was that digital storytelling can be a form of a narrative therapy by helping students to discover parts of their personality (Sawyer & Willis, 2011). The power of stories as therapeutic means has been recognized by psychotherapy decades ago (White and Epston, 1990) The above was considered and utilized in the case of the immigrant student. Reflection on her problems, during the development of her story, and externalizing them during the presentation of the story, held the key to overcoming her problems (Rosenthal, 2003). Reflection and discussion of an issue were also key elements in the other two cases. Literature suggests that, when it comes to digital narrations, stories have to be told directly and unfiltered in order to act as a narrative therapy but also to start a dialogue among students (Harvey & Robinson, 2012). In turn, the discussion of a narrative promotes mutual understanding between students (Caine, 2010). The digital stories offered the basis upon which the process of documenting their experiences, reflecting upon them, and discussing them, was build and facilitated. One has to be reminded that the researchers (or the teachers) avoided intervening, for purely research reasons; for not altering the results or for letting the students work by themselves. Contrary to that, in real-life situations, the teachers would

not be restricted. Taking advantage of the close affiliation between a teacher and his students, which is fundamental (Hamre & Pianta, 2006), results of such interventions are expected to be even better. That is because students will feel more comfortable in expressing themselves and the teachers, by knowing the background of each student, will be able to guide them more efficiently. Considering all the above, it can be concluded that digital storytelling offered a simple, yet effective, solution to issues that are otherwise difficult to deal with. This solution can be summarized in just three steps: (a) ask students to develop digital stories on an important issue, (b) let them free to reflect upon that issue while developing their stories, and (c) discuss with them the issue while the stories are presented.

FUTURE RESEARCH DIRECTIONS The main limitation in all the cases was the small sample size. Also, they were all conducted in Greece. Therefore, their results cannot be easily generalized. Further studies are needed with larger sample sizes and from different educational systems, in order to identify differences or similarities to the findings of the present cases and to obtain more reliable results. In addition, since the duration in all cases was short, longer-term projects can be tested, examining and comparing their results to short-term projects. It would also be interesting to conduct research using conventional, instead of digital storytelling and compare the results. By doing so, it would be possible to determine if the results can be attributed to the medium used and/or to the methods. Finally, on has to keep in mind that bullying troubles students of all ages, immigrant students face problems regardless of the level of education they study, and the transition from one level of education to another is always a sensitive period to a young student’s life. Therefore, virtually students of all ages can become target-groups of studies similar to the ones described in the previous sections.

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CONCLUSION In all the cases that were presented, the problems that were studied were dealt efficiently. Furthermore, all projects were short in duration and can be easily applied, without altering the school’s timetable. Though it is certain that long term interventions yield good results, time is a crucial factor. Short term innovative interventions are needed because results can be produced right away and problems can be dealt on the spot. In all cases, whatever results were attained, were achieved fast, probably easing the way to follow up, longer term interventions. Also, no specialized equipment was needed and software similar to the one that was used is freely available. In addition, the simplicity of the cases’ design allows similar interventions to be easily applied to kindergarten, as well as to older students. Thus, teachers, as well as policy makers, can consider using their findings when designing similar or more well-organized, long-term interventions. In conclusion, although the small sample size in all cases constitutes a considerable limitation, nonetheless, results point toward one direction; digital storytelling is a flexible and powerful instrument, an all-in-one tool, that can be used in many and diverse situations, educational or non-educational.

REFERENCES Akers, R. L. (1977). Deviant Behavior: A Social Learning Approach (2nd ed.). Belmont: Wadsworth. Brooker, L. (2008). Supporting Transitions in the Early Years. Maidenhead, UK: Open University Press. Caine, V. (2010). Visualizing community: Understanding narrative inquiry as action research. Educational Action Research, 18(4), 481–496. do i:10.1080/09650792.2010.524820

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Coventry, M. (2008). Cross-currents of pedagogy and technology: A forum on digital storytelling and cultural critique: Introduction. Arts and Humanities in Higher Education, 7(2), 165–170. doi:10.1177/1474022208088646 Creswell, J. W. (2002). Educational research: Planning, conducting, and evaluating quantitative. Upper Saddle River, NJ: Prentice Hall. Dockett, S., & Perry, B. (2009). Readiness for school: A relational construct. Australian Journal of Early Childhood, 34(1), 20–25. Ertmer, P. A., & Newby, T. J. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43–71. doi:10.1002/piq.21143 Fabian, H. (2007). Informing transitions. In A. Dunlop & H. Fabian (Eds.), Informing Transitions in the Early Years: Research, Policy and Practice (pp. 3–20). Maidenhead, UK: Open University Press. Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12(2), 219–245. doi:10.1177/1077800405284363 Galanaki, E., & Vogiatzoglou, P. (2015). Eκφοβισμός/θυματοποίηση στο σχολικό πλαίσιο και μοναξιά [Bullying/victimization in the school context and loneliness]. Παιδαγωγική, 44. Genette, G. (1998). Die ErzŠhlung (Vol. 2). Wilhelm Fink Verlag-München. Hagan, W. J. (2007). Case studies from the new entrant classroom: Children’s developing repertoires of participation. New Zealand Research in Early Childhood Education Journal, 10, 95–104. Hamre, B. K., & Pianta, R. C. (2006). Studentteacher relationships. In G. G. Bear & K. Minke (Eds.), Children’s Needs III: Development, Prevention, and Intervention (pp. 59–71). Washington, DC: National Association of School Psychologists.

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Harvey, H. B., & Robinson, K. (2012). Intercultural storytelling performance in Morocco and the United States. Storytelling, Self, Society, 8(3), 180–193. Hellenic Statistical Authority. (2011). Greek Census 2011. Retrieved from http://www.statistics.gr/home

Phinney, J. S. (1996). Understanding ethnic diversity. The American Behavioral Scientist, 40(2), 143–152. doi:10.1177/0002764296040002005 Regan, B. (2008). Why we need to teach 21th century skills and how to do it. Multimedia & Internet @ Schools, 15(4), 10-13.

Kyriazis, N., & Zacharias, P. (2015). Development of an educational digital game for learning social skills and anti-bullying behaviors in schools. Proceedings of the 4th Panhellenic Conference on ICT in Education.

Retali, K. (2013). Performance of pupils with immigrant background in Greece: Findings and challenges. In P. Angelidis & H. Hadjisotiriou (Eds.), Intercultural Dialogue in Education: Theoretical Approaches, Political Origin and Pedagogical Practices. Athens: Diadrasi.

Lathem, S. A. (2005). Learning communities and digital storytelling: New media for ancient tradition. Proceedings of Society for Information Technology & Teacher Education International Conference, 2286–2291.

Rigby, K., & Johnson, B. (2006). Expressed readiness of Australian schoolchildren to act as bystanders in support of children who are being bullied. Educational Psychology, 26(3), 425–440. doi:10.1080/01443410500342047

Lenette, C., Cox, L., & Brough, M. (2013). Digital storytelling as a social work tool: Learning from ethnographic research with women from refugee backgrounds. British Journal of Social Work, 1–18.

Robin, B. (2006). The educational uses of digital storytelling. Proceedings of Society for Information Technology & Teacher Education International Conference, 1, 709-716.

Manesis, N., & Lambropoulou, A. (2014). Σχολικός εκφοβισμός: Eνέργειες εκπαιδευτικών για την πρόληψη και την αντιμετώπισή του [School bullying: Training actions for preventing and dealing with it]. Παιδαγωγική Θεωρία & Πράξη, 7, 83-89.

Robin, B. (2008). The effective uses of digital storytelling as a teaching and learning tool. In Handbook of Research on Teaching Literacy Through the Communicative and Visual Arts. New York: Lawrence Erlbaum Associates.

Niesel, R., & Griebel, W. (2007). Enhancing the competence of transition systems through coconstruction. In A. Dunlop & H. Fabian (Eds.), Informing Transitions in the Early Years: Research, Policy and Practice (pp. 21–32). Maidenhead, UK: Open University Press. Ohler, J. (2006). The world of digital storytelling. Educational Leadership, 63, 44–47. Patton, M. Q. (1990). Qualitative evaluation and research methods (2nd ed.). Newbury Park, CA: Sage.

Rogers, B. (2003). Behaviour Recovery: Practical Programs for Challenging Behaviour and Children with Emotional Behaviour Disorders in Mainstream Schools. Australian Council for Educational Research. Rosenthal, G. (2003). The healing effects of storytelling: On the conditions of curative storytelling in the context of research and counseling. Qualitative Inquiry, 9(6), 915–933. doi:10.1177/1077800403254888

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Salmivalli, C., Lagerspetz, K., Bjorkqvist, K., Osterman, K., & Kaukiainen, A. (1996). Bullying as a group process: Participant roles and their relations to social status within the group. Aggressive Behavior, 22(1), 1–15. doi:10.1002/(SICI)10982337(1996)22:13.0.CO;2-T Sawyer, C. B., & Willis, J. M. (2011). Introducing digital storytelling to influence the behavior of children and adolescents. Journal of Creativity in Mental Health, 6(4), 274–283. doi:10.1080/1 5401383.2011.630308 Skourtou, E., Vratsalis, K., & Govaris, C. (2004). Mετανάστευση στην Eλλάδα και Eκπαίδευση: Aποτίμηση της Yπάρχουσας Kατάστασης. Προκλήσεις και Προοπτικές B [Immigration in Greece and Education: Assessment of the Existing Situation. Challenges and Prospects for Improvement]. Athens: IMEPO. Smith, P. J. (2012). Children as teachers: Creating opportunities for children to share expertise with their peers. New Zealand Journal in Early Childhood Education, 15, 84–101. White, M., & Epston, D. (1990). Narrative Means to Therapeutic Ends. New York: W. W. Norton & Company. Yin, R. K. (1994). Case study research: Design and method (2nd ed.). Thousand Oaks, CA: Sage publications.

ADDITIONAL READING Benick, G. (2012). Digital storytelling and diasporic identities in higher education. Collected Essays on Learning and Teaching, 5, 147–152. Benmayor, R. (2008). Digital storytelling as a signature pedagogy for the new humanities. Arts and Humanities in Higher Education, 7(2), 188–204. doi:10.1177/1474022208088648

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Emert, T. (2014). “Hear a story, tell a Story, teach a story”: Digital narratives and refugee middle schoolers. Voices from the Middle, 21(4), 33. Piaget, J. (1997). The Moral Judgement of the Child. Simon and Schuster. Rogoff, B. (2003). The Cultural Nature of Human Development. Oxford: Oxford University Press. Rogoff, B., Paradise, R., Arauz, M. B., Chavez, M. C., & Angelillo, C. (2003). Firsthand learning through intent participation. Annual Review of Psychology, 54(1), 175–203. doi:10.1146/annurev.psych.54.101601.145118 PMID:12499516 Trilling, B., & Fadel, C. (2009). 21st century skills: Learning for life in our times. John Wiley & Sons. Vygotsky, L. (1978). Interaction between learning and development. Readings on the Development of Children, 23(3), 34–41.

KEY TERMS AND DEFINITIONS Bullying: The repeated use of violence (verbal or physical) and/or threats, for abusing, intimidating, or dominating others. Constructivism: A learning theory which argues that humans generate knowledge and meaning from their experiences. Although not a specific pedagogy, is the underlying theme of many education reform movements. Digital Storytelling: A digital form of a story that combines a conventional story (oral or written) and multimedia and/or hypermedia elements. Mimicking: The observation of a model that expresses the desired behavior and, subsequently, adopting it. Narrative Therapy: A form of psychotherapy in which an individual, together with the therapist, co-authors a narrative about himself/herself. Through this process, the values, skills and knowledge one has are identified, so as to effectively confront whatever problems he/she faces.

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School Adjustment: The process of adapting to the role of being a student and to various aspects of the school environment.

Social Integration: The process in which all members of the society are engaged in a dialogue to achieve and maintain peaceful social relations. It does not imply or suggest forced assimilation.

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3D Printing Applications in STEM Education Norman Gwangwava Botswana International University of Science and Technology, Botswana Catherine Hlahla National University of Science and Technology, Zimbabwe

INTRODUCTION The chapter focuses on the design of educational toys for early school aged children, based on their anthropometric measurements. It also covers case study applications of 3D printing in engineering design undergraduate studies. Research on existing educational toys and different child development stages was carried out. Concepts were generated from the collected data and the best concepts selected through ranking methods. Dimensioning of the selected concepts was based on the collected anthropometry data. STL files were used to manufacture the chosen concepts by means of 3D printing. Children are active learners who use the physical environment in a direct, hands-on manner to develop different skills. Toy experts believe that educational toys play a large role in the development of children. They stimulate play, language and reading skills and help children achieve milestones in both gross and fine motor skills. The implementation of ergonomics and the consideration of children’s anthropometry dimensions in the design of toys play an important role in ensuring safety and injury risk reduction of children during play. The National University of Science and Technology (NUST) is exploring 3D printing technology in the lecture room for its BEng program; ready-to-use 3D printed gardening implements, Mass-Customization of Office Mini-Storage Products from 3D Printing and other research

projects. 3D printing enables students in science, technology, engineering and mathematics (STEM) to visualize concepts.

BACKGROUND Many of the children toys in the global market are imported from other countries, specifically China. The designers of these toys aim at achieving as good anthropometric match for as many potential customers in their country as possible. Thus the toys are custom designed to suit body dimensions of the children in that particular country yet the same toys are being exported and used by children across the world. Accidents and musculoskeletal health problems may occur due to incorrect product dimensions and sizes that do not meet the children’s dimensional requirements. Anthropometric data for children reflect general health status, dietary adequacy and growth and development over time (McDoweliet et al, 2008). Although several researchers have studied the anthropometry of children, they have most related their studies to nutritional, health and growth aspects (Khor et al, 2009). There are a few studies on the importance of child anthropometry in the design of various child products, specifically toys. Anthropometric measurements are necessary to form the data base which is required for the proper sizing of educational toys. Although the idea of considering child anthropometry in the design for child products is not new, the scarcity of avail-

DOI: 10.4018/978-1-5225-2255-3.ch229 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Educational Technologies

able sources on anthropometric data among early school aged children calls for more anthropometric research so as to customize the children toys. With respect to higher education needs, STEM education is more demanding in terms of well equipped laboratories, prototyping needs, as well as building experimental conditions that match the practical world. The pass rate at NUST has been low due to inadequate facilities for STEM enrolled students. 3D printing helps students to bridge the gap between the practical STEM world and the lecture room. Ease of recycling prototypes is also very important to keep the STEM training costs low. 3D printing, coupled with modular design concepts, was proved to be the best choice in cost effectiveness.

LITERATURE REVIEW This section covers recent, historical and empirical reviews laying the foundation for the present study. Information that is relevant for the anthropometric research for the design of educational toys is presented. The section gives an insight into anthropometry, educational toys and 3D Printing which are the main subjects used to meet the objectives of the chapter.

Anthropometry Anthropometry is the science that measures the range of body sizes in a population (Pheasant & Haslegrave, 2005). In product design, anthropometrics is the use of body measurements to determine the optimum size for products for comfortable and efficient use. Designers integrate the use of anthropometric data in their design process to optimize the usability and functioning of a product while improving comfort and safety. Advances in 3D imaging technologies have facilitated the collection of these measurements and shapes among the elderly or children (Goto, et al 2015). There are two primary types of anthropometric measurements; structural (static) and functional

(dynamic) measurement. Structural measurements are taken while the body is in a static position. These include skeletal dimensions (joint to joint measurement) and soft tissue measures in contour dimensions. Dynamic measurements are taken while the body is engaged in some kind of activity like driving a car or reaching for objects. Engineering anthropometry is concerned with the application of both types of data to the design of the products people use.

Child Development Stages Child development is the change or growth that occurs in children and the gaining of skills in all aspects of the child’s life (Frost et al, 2008). It is often divided into three main areas; physical, cognitive, and social-emotional development. Physical development fall into two main categories: 1. Gross-Motor Development: Involves improvement of skills using the large muscles in the legs and arms, such activities include running and bike riding. 2. Fine-Motor Development: The coordination of small muscles, in movements, usually involving the synchronization of hands and fingers with the eyes. Cutting, grasping, molding and writing are some of the activities that require fine-motor development. Cognitive development is about how children learn, think and develop ideas. This is one of the areas of development that is strongly influenced by the experiences a child has. For example learning the names of animals is only possible if a child has been told them. Social-emotional development is a child’s ability to understand the feelings of others, control their own feelings and behaviours and get along with peers (Hoffman, 2013). Social and emotional development involves the acquisition of a set of skills. These include the ability to:

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1. Identify and understand their own feelings 2. Accurately read and comprehend emotional states in others 3. Regulate their own behaviour 4. Develop empathy for others 5. Establish and sustain relationships

Educational Toys The early childhood years are generally regarded as the foundation upon which the rest of an individual’s life is built (Rutter, 2002). Educational toys are essential for the proper early development of growing children as they facilitate the mental and physical growth of children. Whenever a child plays with toys, they are practicing life skills. Educational toys enhance intellectual, social, emotional, and/or physical development. Toys can be divided into several groups, depending on the part of the child they help to develop. Kudrowitz (2010) presented a universal classification system to communicate and ideate new toy concepts to toy designers and students. The system consists of two graphical tools that help designers to classify and manipulate toy product concepts. Good educational construction toys promote convergent thinking while simultaneously setting the stage for creative, divergent play for children. While they follow the instructions to build the model, children gain skill with the construction pieces. Completing the instructions (convergent thinking) gives them a sense of achievement and reinforces confidence. Interlocking manipulative toys like puzzles challenge the child to improve hand-eye coordination, patience, and an understanding of spatial relationships. Modern day children are growing up in the greatest era of technological advancements, hence there is need for them to stay in touch with technology. An example of these toys is a remote controlled toy car. Children can be inspired by educational toys which touch on everything from basic STEM to the real physical world. Table 1 outlines the educational toys for the different child development stages.

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Table 1. Child development educational toys Child Development

Type of Educational Toy

Physical or Muscle Development

     • Magnetic blocks      • Writing tools and scissors      • Puzzles      • Playground equipment

Intellectual Development

     • Card games      • Sorting games      • Listening games      • Books

Creative Development

     • Clay      • Crayons      • Paints      • Paper

Sensory Development

     • Sand and water toys      • Musical instruments      • Bubbles

Make-Believe and Social Development

     • Dolls      • Puppets      • Cars and trucks

3D Printing 3D printing is a manufacturing method based on advanced technology that builds up parts, additively, in layers (Ventola, 2014). The 3D printing process starts with a 3D digital model, created using 3D software such as 3D CAD. Table 2 shows different types of 3D printing technologies.

Current and Future Applications of 3D Printing 3D Printing technology is used in the children toy industry (pre-school and primary education), secondary and higher education.

Children Toy Industry The toy industry is among the biggest beneficiaries of the technology since toys tend to be small and made out of plastic, making it easy to 3D print them. Children can customize their own unique toys by deciding on the features of the toys before they are 3D printed for them. Besides promoting creativity, 3D printing also speeds up and simplifies the making of toys, which in turn lowers the cost.

Category: Educational Technologies

Table 2. The seven 3D printing/ AM process categories (ASTM, 2014) Process Type

Brief Description

Related Technologies

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Materials

Powder Bed Fusion

Thermal energy selectively fuses regions of a powder bed

Electron beam melting (EBM), selective laser heat sintering (SLS), selective heat sintering (SHS), and direct metal laser sintering (DMLS)

Metals, polymers

Directed Energy Deposition

Focused thermal energy is used to fuse materials by melting as the material is being deposited

Laser metal deposition (LMD)

Metals

Material Extrusion

Material is selectively dispensed through a nozzle or orifice

Fused deposition modeling (FDM)

Polymers

Vat Photopolymerization

Liquid photopolymer in a vat is selectively cured by light-activated polymerization

Stereolithography (SLA), digital light processing (DLP)

Photopolymers

Binder Jetting

A liquid bonding agent is selectively deposited to join powder materials

Powder bed and inkjet head (PBIH), plaster-based 3D printing (PP)

Polymers, Foundry Sand, Metals

Material Jetting

Droplets of build material are selectively deposited

Multi-jet modeling (MJM)

Polymers, Waxes

Sheet Lamination

Sheets of material are bonded to form an object

Laminated object manufacturing (LOM), ultrasonic consolidation (UC)

Paper, Metals

Education Sector Applications The use of 3D printing in education was investigated by Slavkovsky (2012). Since physical models are important for hands-on active learning, 3D printing technology in education has been used since a while and considered for sustainable development, for secondary education in STEM projects and elementary mathematics education (Pearce et al, 2010; Berry et al, 2010; Lipson, 2007; Lacey, 2010). The best way for students to learn and retain information taught is by applying it to real-life situations. 3D Printing technology is being used in the classroom to offer a highly engaging, hands-on way to teach STEM subjects as well as to expose students to technologies of the future from an early age. Knill and Slavkovsky (2013) illustrate how 3D printing can help to visualize concepts and mathematical proofs. The new 3D printing technologies make the realization of mathematical models more accessible than ever. Visualization has always been an important ingredient for communicating mathematics (Pedersen, 2005).

Many higher education institutions have reworked all their lab activities so that they are based on a 3D printing platform. FDM 3D printers are used for most lab workbenches and are customized by installing automation hardware with display interfaces and input/output electronics. These upgrades give students valuable experience using real-world automation equipment. Schelly et al (2015) investigated the potential of open-source (OS) technologies in an educational setting, given the combination of economic constraints affecting most educational environments. The OS aspect proves to be particularly relevant in the educational setting, as it decreases cost of access and encourages active participation, innovation and improvement through real experience with design and fabrication.

SOLUTIONS AND RECOMMENDATIONS The section outlines the application of anthropometrics for early school aged children in the design of educational toys. It also covers case

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Figure 1. Graph showing the frequency of each skill

study applications of 3D printing in engineering undergraduate studies. Research on existing educational toys and different child development stages was carried out. Concepts were generated from the collected data and the best concepts selected through ranking methods.

Pre- and Primary School Toy Concepts Relevant data for the design of educational toys was gathered through the use of questionnaires, consultation and ergonomic study. The sample population used included individuals who usually spend reasonable time with children, especially mothers who have information concerning the toys that children play with. The following are some of the questions that were presented in the research instruments and their responses.

Toy property rating for a number of toy properties that are relevant in the design of a toy were outlined in the questionnaire and the respondents had to rate the relevance of each property. Figure 2 summarizes the responses. Some of the responses augment the “Let’s Play!” projects at the University of Buffalo which formulated a number of universal design guidelines for toys (Hinske et al, 2008; Hengeveld et al, 2007).

1. What are the key skills/educational values that children should acquire from playing with toys?

1. Figure 3 shows a pie chart deduced from the toy attribute survey data. This helps in comparing the value of importance and prioritizing the technical attributes. 2. Based on the various research methods outlined previously, Table 3 summaries the educational requirements of early school aged children and examples of ideal toys to meet those educational requirements. 3. An anthropometric research was carried out in order to come up with children body dimensions that are essential in the design of educational toys.

The majority of the respondents selected literacy and language, maths, science and technology, problem solving and motor skills as one of their choice. Figure 1 summarizes the responses.

Table 4 summarizes the mean and standard deviation for the various anthropometry dimensions in a typical source population composed of early school aged children between the age of 6 and 7

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Category: Educational Technologies

Figure 2. Graphical representation of the responses

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Figure 3. Pie Chart for toy attributes

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Table 3. Educational toy requirements Research Methods

Educational Values/Requirements

Educational Toy Types

Questionnaire

     • Scientific Concepts      • Maths and Problem solving      • Literacy and language

     • Electronic Toys      • Puzzles      • Computer Toys      • Dolls      • Drawing Sets

Consultation

     • Read and write      • Informal/ Formal Communication      • Mathematical calculation

     • Building Toys      • Mechanical Toys      • Make-believe Toys      • Art and craft Toys      • Word Construction Toys      • Phonic Toys

School Syllabus/ Primary school Curriculum

     • Spoken English/ Communication/ Identifying      • Addition and subtraction of number up to 20      • Division and multiplication      • Counting      • Cognitive and physical development      • Tell clock times      • Art and craft/Drawing

     • Clock toys      • Reading Cards      • Abacus      • Art Material      • Story Books

Literature Review

     • Cognitive      • Social      • Physical development

     • Construction Toys      • Puzzles      • Musical Instruments      • Pattern Making Toys      • Transportation Toys      • Jumping Ropes

Table 4. Anthropometry dimensions years. Children in this age group will be attending primary school, either in grade 1 or grade 2.

Mean, μ (cm)

Standard Deviation, SD

Standing Height

109,6

2,5

Sitting Height

78,2

1,8

Standing Shoulder Height

81,6

1,3

Sitting Shoulder Height

55,2

1,5

Standing Eye Height

98,9

2,2

Sitting Eye Height

72,1

1,3

Sitting Elbow Height

40,5

1,6

Chest Breadth

19,3

0,8

Shoulder Breadth

29,8

1,0

The dimensions of the final toy models for 3D printing were based on the anthropometric data presented in Table 4.

Elbow-Hand Length

31,3

1,5

Shoulder-Elbow Length

24,7

1,9

Palm Length

13,4

0,3

Concept for Scientific Toy

Palm Width

6,2

0,1

Thumb Width

1,5

0,1

Thumb Thickness

0,9

0,1

Minimum Holding Distance Between Thumb and Index finger

2,1

0,2

Holding Distance Between Thumb and Middle Finger

5,4

0,3

Forward Reach

47,3

1,3

Developed Toy Concepts Toy concepts for three educational values were proposed. The three educational values deduced from Table 3 are: • • •

Scientific Concepts Mathematics and Problem Solving Literacy and Language Skills.

The Balloon Boat in Figure 4 was designed to move on water by the deflation of a balloon. The child blows up the balloon, closes the balloon opening by fingers then places the boat on top of water,

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Anthropometry Dimension

Category: Educational Technologies

Figure 4. Scientific toy

obviously lead to better communication skills. The word construction tool and reading card are combined to come up with toy sown in Figure 5. •

• remove their finger and observe what happens. A rubber band is added on top to secure the balloon. •

•

Advantages: ◦◦ Incorporates fun scientific concept ◦◦ Offer opportunity to learn something new ◦◦ Active participation of child during use Disadvantages: ◦◦ Use of very light material for the boat ◦◦ Require a wide container of water ◦◦ Frequent balloon replacement

Concept for Literacy and Language Toy Reading cards and construction toys are used to acquire reading and spelling skills that will

Advantages: ◦◦ Word construction skills ◦◦ Spelling skills and reading skills ◦◦ Overall improvement of language skills Disadvantages: ◦◦ Does incorporate writing skills

Mathematics and Problem Solving Concept The concept shown in Figure 6 combines the counting frame and the mathematics calculator to come up with the mathematics and problem solving toy.

Engineering Design Course Instruction 3D printing can serve as the best platform to deliver lectures and typical design assignments to undergraduate students in engineering. Two Figure 6. Mathematics and problem solving toy

Figure 5. Literacy and language toy

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Figure 7. System architecture

projects are illustrated: online 3D printing mass customization platform and ready-to-use 3d printed gardening implements.

3D Printing Mass-Customization Design Project 3D printing offers opportunities for mass customization. An online system which can be used in customizing 3D printed products is under development at NUST. The system automatically generates the product as specified by the customer. Students and course instructors use the system to promote innovation and design thinking. System actors for the Mass Customization include the administrator, and users. The administrator administers the system, adds, modifies and deletes product design templates. Users can modify and customise product templates. The system architecture and home page are shown in Figure 7 and 8 respectively. A similar application was conducted at Griffith University (Loy, 2014). The research considered the potential of 3D printing as an eLearning tool for design education and the role of eMaking in bringing together the virtual and the physical in the design studio. 2634

Table 5 shows the product template, a cubic penholder that the customer uses to personalise the diameter and colour of the pen. It can be used for crayons, pencils and markers.

3D Printed Gardening Implements Design Project Research on possible gardening implements design was carried out as part of an undergraduate engineering design course. Having explored a variety of possible designs, the best design of the gardening implements was chosen and optimized for 3D printing. The printed models can be further subjected to the testing phase to see if they meet the design requirements and to determine their efficiency. After satisfactory performance, the digital models of the designed gardening implements were uploaded on a website. The website consists of ready to print 3D gardening implements and farmers from all over the world can access them hence eliminating the need for farmers to be experts in CAD software for designing their own implements. Three leading factors to consider during the design of 3D printed gardening implements were

Category: Educational Technologies

Figure 8. The home page

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Table 5. Output system for penholder template

found to be simplicity, portability and durability. These weights are shown in Figure 9. One chosen concept based on design attributes is illustrated in Figure 10. The design has a spinner for the sprinkler to be mounted on top of the sprinkler and will be rotating under the water

pressure distributing water evenly throughout a 3600 angle. Due to the pressure of the water there is need for a mechanism to firmly hold the two together while leaving room for free rotation of the spinner.

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Figure 9. Design attributes for gardening implements

Website for Gardening Implements Ready for 3D Printing A website which anyone could access from anywhere to upload and/or download STL CAD files ready for printing was designed as part of the engineering design course. The domain name for the website is http://anenyasha3dprints.com/. The website is shown in Figure 11.

FUTURE RESEARCH DIRECTIONS 3D printing has three major weaknesses which are time consuming, mechanically weak printed models, and limited material selection. Future research should concentrate on making the process faster as well as multi- material and colour printing. This would reduce the inventory Figure 10. Sprinkler design for 3D printing

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of 3D printers in laboratories whilst enabling students to build as many models within short time frames. Product specific research will boost the database of design for manufacture and assembly (DFMA) rules for 3D printing as well as designing ready for use 3D printed products. The wide use of smart phones and data connectivity has caused a lot of boom in the design of mobile applications (Apps) for software products that traditionally demanded more resources from desktop computers. More mobile Apps can be designed in future that focus on 3D printing. There are quite a number of softwares that can be used for non-designers to customise their products. They are both online and offline. Programmers can make use of the free libraries offered by OpenJscad, OpenCascade, PythonOCC and many others to design products that are customisable.

Category: Educational Technologies

Figure 11. Website for gardening implements ready for 3D printing

Nowadays, e-Learning systems are on the increase and more focus is being directed towards mobile platforms. Since 3D printing is highly promising in the education sector, future research should focus on integrating 3D printing Apps to e-Learning systems. A number of CAD vendors are now offering cloud based solutions to their industrial and educational software solutions. Major examples are AutoDesk and Solid Works. This helps in democratising design and CAD modelling since the approach eliminates the need for high speedy computers. Therefore, picking up lessons from the trend to design 3D printing Apps

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that can design digital models as well as actually command 3D printers from the students’ remote comfort will be a perfect integration to the boom of e-Learning systems. There is also a limited range of educational toys custom designed for children with disabilities. These children play with toys that do not suit their conditions in term of educational value, anthropometric dimensions, social and mental development. Therefore the design and customisation of educational toys for these children is a very important consideration for further work.

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CONCLUSION The chapter outlined anthropometric research for the design of educational toys which can be 3D printed for ready use. It has also shown how the technology can be used to cost-effectively allow institutions to offer their students states-of-the-art laboratory platforms, which match industry practice. 3D printing is proving to be the best platform for in-class instruction as well as for carrying out practical world design assignments. This was illustrated through two design projects carried out by engineering students in a design course; 3D printing mass-customisation for mini-office products and design of 3D printed ready to use gardening implements. Future research prospects have been highlighted, which include integration of 3D printing into the emerging mobile e-Learning platforms, as well as taking advantage of the cloud platform to offer Apps that allow digital object modeling for 3D printing.

REFERENCES Berry, R. Q., Bull, G., Browning, C., Thomas, D. D., Starkweather, K., & Aylor, J. H. (2010). Preliminary considerations regarding use of digital fabrication to incorporate engineering design principles in elementary mathematics education. Contemporary Issues in Technology & Teacher Education, 10(2), 167–172. Canessa, E., & Zennaro, M. (Eds.). (2013). Low-Cost 3D Printing for science, education and Sustainable Development. International Centre for Theoretical Physics. Kudrowitz, B. M., & Wallace, D. R. (2010). The play pyramid: a play classification and ideation tool for toy design. Academic Press. Frost, J. L., Wortham, S. C., & Reifel, S. (2008). Play and child development (3rd ed.). Upper Saddle River, NJ: Pearson.

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Goto, L., Lee, W., Song, Y., Molenbroek, J., & Goossens, R. (2015). Analysis of a 3D anthropometric data set of children for design applications. Proceedings 19th Triennial Congress of the International Ergonomics Association, 9-14. Retrieved July 5, 2016, from http://www.iea.cc/ congress/2015/1418.pdf Hengeveld, B., Voort, R., Van, B., Hummels, C., & De Moor, J. (2007). Designing for diversity: Developing complex adaptive tangible products. Proceedings of the 1st International Conference on Tangible and Embedded Interaction (TEI), 155158. Retrieved November 9, 2016, from https:// www.researchgate.net/publication/221308444_ Designing_for_diversity_developing_complex_ adaptive_tangible_products Hinske, S., Langheinrich, M., & Lampe, M. (2008). Towards guidelines for designing augmented toy environments. Retrieved November 9, 2016, from www.vs.inf.ethz.ch/publ/papers/ hinske2008dis.pdf Khor, G. L., Noor Safiza, M. N., Jamalludin, A. B., Jamaiyah, H., Geeta, A., Kee, C. C., & Ahmad, F. Y. et  al. (2009). Nutritional status of children below five years in Malaysia: Anthropometric analyses from the Third National Health and Morbidity Survey III (NHMS, 2006). Malaysia Journal of Nutrition, 15(2), 121–136. PMID:22691811 Knill, O., & Slavkovsky, E. (2013). Illustrating mathematics using 3D printers. Retrieved, July 20, 2016, from https://arxiv.org/abs/1306.5599 Lacey, G. (2010). 3D printing brings designs to life. Tech Directions, 70(2), 17–19. Retrieved from connection.ebscohost.com/c/articles/53992338/3dprinting-brings-designs-life Lipson, H. (2007). Printable 3D models for customized hands-on education. Paper presented at Mass Customization and Personalization (MCPC), Cambridge, MA.

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Loy, J. (2014). eLearning and eMaking: 3D Printing blurring the digital and the physical. Education Sciences, 4(1), 108-121. Retrieved November 8, 2016, from www.mdpi.com/2227-7102/4/1/108 McDowell, M. A., Ogden, C. L., & Flegal, K. M. (2008). Anthropometric reference Data for Children and Adults: United States, 2003–2006. National Health Statistics Reports, (10): 22. PMID:25585443 Pearce, J. M., Blair, C. M., Kaciak, K. J., Andrews, R., Nosrat, A., & Zelenika-Zovko, I. (2010). 3D printing of open source appropriate technologies for self-directed sustainable development. Journal of Sustainable Development, 3(4), 17–28. doi:10.5539/jsd.v3n4p17 Pedersen, S. A., Mancosu, P., & Jorgensen, K. F. (2005). Visualization, Explanation and Reasoning styles in Mathematics. Springer. Pheasant, S., & Haslegrave, C. M. (2005). Bodyspace: Anthropometry, ergonomics and the design of work. CRC Press. Rutter, M. (2002). The interplay of nature, nurture and developmental influences: The challenge ahead for mental health. Archives of General Psychiatry, 59(11), 996–1000. doi:10.1001/archpsyc.59.11.996 PMID:12418932 Schelly, C., Anzalone, G., Wijnen, B., & Pearce, J. M. (2015). Open-Source 3-D Printing Technologies for Education: Bringing Additive Manufacturing to the Classroom. Journal of Visual Languages and Computing, 28, 226–237. doi:10.1016/j. jvlc.2015.01.004 Slavkovsky, E. (2012). Feasability study for teaching geometry and other topics using threedimensional printers (Master’s Thesis). Harvard University. Ventola, C. L. (2014). Medical applications for 3D Printing: Current and projected uses. Pharmacy and Therapeutics. 39(10), 704-711. Retrieved July 25, 2016, from https://www.ncbi.nlm.nih. gov/pmc/articles/PMC189697/

ADDITIONAL READING Berman, B. (2012). 3-D printing: The new industrial revolution. Business Horizons, 55(2), 155–162. doi:10.1016/j.bushor.2011.11.003 Bhusnure, O. G., Gholve, V. S., Sugave, B. K., Dongre, R. C., Gore, S. A., & Giram, P. S. (2016). 3D printing and pharmaceutical manufacturing: Opportunities and challenges. International Journal of Bioassays, 5(1), 4723–4738. doi:10.21746/ ijbio.2016.01.006 Burriss, K., & Burriss, L. (2011). Outdoor play and learning: Policy and practice. International Journal of Education Policy & Leadership, 6(8), 1–12. Cheng, M., & Johnson, J. (2010). Research on childrens play: Analysis of developmental and early education journals from 2005 to 2007. Early Childhood Education Journal, 37(4), 249–259. doi:10.1007/s10643-009-0347-7 Frei, P., Su, V., Mikhak, B., & Ishii, H. (2000). curlybot: Designing a new class of computational toys. Proceedings of the Conference on Human Factors in Computing Systems, New York, 1-6, April 2000. Retrieved November 8, 2016, from http://www.cs.uml.edu/~fredm/courses/91.548spr03/papers/ Gattouffie, K., & Reisman, S. (2007). Mass customization research:Trends, directions, diffusion intensity, taxomonic and framework. International Journal of Flexible Mnufacturing System, pp. 637-666. Ibrahim, A. M. S., Jose, R. R., Rabie, A. N., Gerstle, T. L., Lee, B. T., & Lin, S. J. (2015). Three dimensional printing in developing countries. Plastic and reconstructive surgery global open 3 (7): e443. Retrieved July 21, 2016, from http:// nrs.harvard.edu/urn-3:HUL.InstRepos:21462570

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Kelly, B. (2012). The State of STEM and jobs, US News and World Report. Retrieved July 7, 2016, from. National Math and Science Initiative. Increasing the Achievement and Presence of Under-Represented Minorities in STEM Fields. Retrieved July 19, 2016, from, Priovolou, M. (2012). Let’s go to the Movies! Learning math through creativity and role playing, Proceedings of the European Conference on Games Based Learning; 2012, 378-383. Schubert, C., Van, L. M. C., & Donoso, L. A. (2014). Innovations in 3D printing: A 3D overview from optics to organs. The British Journal of Ophthalmology, 98(2), 159–161. RetrievedJuly252016 PubMed doi:10.1136/bjophthalmol-2013-304446 Soni, R. 2006. Ergonomics and Its Relationship with Anthropometry. Retrieved July 17, 2016, from http://soni2006.hubpages.comlhub/ergonomicsand-anthropometry

KEY TERMS AND DEFINITIONS 3D Printing: A radically different manufacturing method based on advanced technology that builds up parts, additively, in layers at the

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sub millimeter scale. Also known as Additive Manufacturing. Anthropometry: The science that measures the range of body sizes in a population. CAD: A software for 2D and 3D digital modelling of objects. Educational Toy: A toy that helps a child learn something good, something that will help in the future. e-Learning: Learning conducted via electronic media, typically on the Internet. Ergonomic Design: A safe and comfortable design or product achieved through the use of anthropometric data to optimize the usability and functionality. Mass Customization (MC): A business strategy that aims to provide customers with individualized products at near mass production efficiency. STEM: A curriculum based on the idea of educating students in four specific disciplines — science, technology, engineering and mathematics — in an interdisciplinary and applied approach. STL (STereoLithography): A standard file type (triangulated representation of a 3D CAD model) used by most additive manufacturing systems.

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Tools, Pedagogical Models, and Best Practices for Digital Storytelling Jari Multisilta Tampere University of Technology, Finland Hannele Niemi University of Helsinki, Finland

INTRODUCTION Sharing photos and short videos with others has become increasingly popular among youth. Using social media services, users share events and moments from their daily lives. Östman (2015) defined this phenomenon as life-publishing. Examples of life-publishing include the growing use of Snapchat and Periscope social media services among youth. According to Piwek and Joinson (2016), Snapchat users mainly share “selfies,” and they mostly use the service at home. Although sharing videos is a common activity among youth, schools are not using digital videos for learning. There is a need to study the pedagogical models that could be used in designing classroom activities involving the use of digital videos. In this chapter, digital video storytelling refers to learning activities that involve the creation and use of digital video. According to Ladeira, Marsden, and Green (2011, p. 431), “digital storytelling typically seeks to preserve and disseminate reallife, non-fiction stories.” In a learning context, digital storytelling involves the creation and distribution of content that is used in the learning process as well as the interaction between the users of the content. Digital storytelling that includes user-generated content has been used in preserving personal experiences (Ladeira, Marsden & Green, 2011), mobile collaborative live video production, such as in an event in which a Video Jockey (VJ) mixes the video feed using

the audience (Engström, Esbjörnsson & Juhlin, 2008), and in collaborative learning (Niemi, Harju, Vivitsou, Viitanen, Multisilta & Kuokkanen, 2014; Niemi & Multisilta, 2015; Tuomi & Multisilta, 2010; Wolf & Rummler, 2011). In this chapter, digital video storytelling will be discussed in the context of learning. Digital video storytelling can be seen as an approach to learning twenty-first century skills. Taking advantage of the creative potential of modern communication technologies, students can work together, explore their ideas, and become creators, producers, and active learning participants. Twenty-first century skills have become a key topic on the agendas of education systems worldwide. Educators are required to seek new forms of teaching and learning for the future. The challenge is determining how to motivate students to learn and become engaged in learning. Digital video storytelling can assist in motivating students by bringing technologies they use in their free time into the school environment. In this chapter, pedagogical models, examples, best practices, and outcomes that illustrate how students become engaged and motivated when using digital storytelling in knowledge creation in cross-cultural settings will be presented. The results are based on the empirical data and findings from several international pilots. A review of existing tools and practices for digital video storytelling will be presented. The results show that students can become highly

DOI: 10.4018/978-1-5225-2255-3.ch230 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Tools, Pedagogical Models, and Best Practices for Digital Storytelling

engaged in learning through digital storytelling. The research data indicate that engagement in digital video storytelling is a combination of a joy of learning (fun) and a commitment to hard work.

BACKGROUND The use of videos on the Internet has been expanding rapidly in the last few years. Although the most popular web video content is related to music videos and entertainment, web videos can have several educational uses. Khan Academy (www. khanacademy.com) is an example of a web video service that has a large collection of educational videos. According to Talbert (2012, para. 7), “Khan Academy is a collection of video lectures that give demonstrations of mechanical processes.” Considerable debate has taken place regarding the pedagogical model used at Khan Academy (Prensky, 2011; Talbert, 2012; Thompson, 2011). The main criticism is that Khan Academy is not supporting a constructivist learning model in which learners actively create knowledge using activities that support knowledge construction. The creation of video stories by the learners themselves is considered a more effective way of using video in learning. According to Correia et al. (2005, p. 1), “the ability to have constant access through mobile devices allows a new way of doing cinematographic narratives that can enhance the participants’ experience in a significant way.” Video stories can be interactive (Ladeira, Marsden and Green, 2011) or generated in real time with scripting (Vaucelle & Davenport, 2004). Storytelling platforms can also support automatic story creation (Multisilta & Mäenpää, 2008; Zsombori, et al., 2011). Multisilta et al. created a mobile social media service for community created videos (Multisilta & Mäenpää, 2008). MoViE, the Mobile Video Experience Platform, is a research platform for studying how people create video stories and how they share and learn with mobile social media. MoViE has been used both in primary and secondary schools (Tuomi &

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Multisilta, 2010) as well as in higher education (Kiili, Multisilta, Suominen & Ketamo, 2010). Digital stories have also been used to preserve, reflect on, and share the life experiences of people who do not have access to personal computers and who are living in rural areas in South Africa (Bidwell, Reitmaier, Marsden & Hansen, 2011). Digital story creation may also be able to provide a means of exploring self-identity through the sharing and group construction of digital video stories (Vaucelle & Davenport, 2004).

LEARNING USING DIGITAL VIDEO STORYTELLING Pedagogical Models In this subchapter, two pedagogical models for using digital videos and storytelling will be presented. The aim of the pedagogical models is to provide teachers with a sound basis for applying digital video storytelling tools in their own classrooms. The global sharing pedagogy and video inquiry learning models are both based on theoretical work that has been evaluated in empirical research projects. As a pedagogical method, digital storytelling builds on learner-centered approaches that can improve students’ learning in several ways (Kearney, 2009; Yang, 2012). According to Niemi et al. (2014), learning with digital storytelling is seen as a socially and culturally related process that takes place in the interaction between a learner and material tools, psychological tools, or other human beings (Vygotsky, 1978). In this sense, it builds on the constructivist learning model. Learners play a central role in exploring and building knowledge by using tools available in the digital learning environment. Students also interact with psychological tools when using language, brainstorming, or creating stories. Learning with others can take place when creating video stories with peers and watching stories that other students have made. When planning and making

Category: Educational Technologies

digital stories collaboratively, students can become aware of their own knowledge and experiences and reflect on and share these experiences with others. Watching other students’ stories can also create new perspectives on topics and promote the understanding of a certain phenomenon (Niemi et al., 2014). Global sharing pedagogy supports Vygotsky’s ideas of learning (Vygotsky, 1978). According to Vygotsky, learning happens in social activity and higher mental functions are mediated by tools and signs. In addition, learning happens in the zone of proximal development, that is the difference between what a learner can do without help and what he or she can do with help. The help can be provided by the teacher or by the peer learners. In addition, Vygotsky pointed out that there is a close connection between learning, thinking, and the language. The language can be seen as a tool for both social activity and thinking. In global sharing pedagogy, learning is a mediated activity with tools, signs, and social interaction. According to Niemi et al. (2014), there are four mediators of learning in the global sharing pedagogy: 1) learner-driven knowledge and skills creation, 2) collaboration, 3) networking, and 4) digital media competencies and literacies. The mediators contribute to the learners’ engagement in the learning activity. Niemi et al. (2014) illustrated that engagement in the digital video storytelling activity could be divided into two components: joy of learning and hard work. With the use of digital tools, learning can happen both in social context and through individual learning. Inquiry learning is defined as “an approach to learning that involves a process of exploring the natural or material world and that leads to asking questions, making discoveries, and rigorously testing those discoveries in the search for new understanding” (de Jong & van Joolingen, 2008, p. 458). In an earlier study, the researchers found that students find it difficult to argue and reflect on what they see in the video clips and video stories they and their peers have produced; however, argumentation, reflection, and commenting are important 21st century skills (Niemi et al., 2014).

In video inquiry learning, the learners and teachers capture mobile video recordings of events and phenomena that prompt questions from students and that serve as a basis for inquiries and collaborative learning in the Science, Technology, Engineering, and Mathematics (STEM) disciplines. Video inquiry learning is closely related to higher order thinking skills. According to Lewis and Smith (1993), higher order thinking consists of problem solving, critical thinking, creative thinking, and decision making. They explained that “higher order thinking occurs when a person takes new information and information stored in memory and interrelates and/or rearranges and extends this information to achieve a purpose or find possible answers in perplexing situations” (Lewis & Smith, 1993, p. 136). The definition clearly describes the process of inquiry video learning in which learners create video clips or video stories and argue, reflect, and comment on the content of the videos they and their peers have recorded and shared through social media.

Tools There are different types of learning tools that can be used in digital video storytelling: 1) Learning Management Systems (LMS), 2) stand-alone video editors, and 3) video sharing sites. Several learning environments and LMS include an option to upload video files and share files with other users; however, such systems are not designed for digital video storytelling because they do not have editing or remixing features and their collaboration features are limited. Instead, their purpose is to manage class activities, deliver content, provide a platform for submitting exercises, and assess student activities. The stand-alone video editor software do not support sharing and collaboration. They provide a wide range of effects and tools for adding music, titles, and subtitles to video. In this subchapter, five video sharing sites are compared based on a set of requirements that are typical for classroom use. The video sharing sites were selected based on the discussions with teachers. YouTube (www.youtube.com), Vimeo (www. 2643

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vimeo.com), and WeVideo (www.wevideo.com) are popular video sharing services; TeacherTube (www.teachertube.com) is one of the oldest video sharing sites for teachers and MoViE (ciceromovie.edu.helsinki.fi) is one of the more recent video sharing sites targeted for learning. The following features were reviewed: • • • • • • • • • •

Access (how to sign on to the service). Content moderation. Editing capabilities. Possibility to do annotations to the content. Possibility to use analytical data from the user activities. Privacy settings. Existence of content grouping and channels. Limitation of what kind of content that can be uploaded to the site. Pricing model. Pedagogical support.

It is important to have an authentication system, but there are different regulations in different countries, such as how old children should be to have an Internet identity. For classroom use, it is important that the teacher is able to moderate the content to prevent the use of inappropriate content. Digital video storytelling often requires the remixing and editing of videos. In many cases, the learners create their stories by recording several short video clips that they edit and remix later. This can be done using a stand-alone video editor or video editing features provided by the video sharing service. Annotations allow for adding small comments over the video. These comments could be used as questions, or they could include important information related to the learning content. For example, a teacher could add an annotation to the video to remind students to pay attention to a specific event in the video. Statistical data provide information to the users regarding the use of the content and could potentially be very useful for teachers, but, so far, the video sharing sites do not have effective learning analytics tools.

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Privacy, content, and cost are also important features for video sharing sites that are used in schools. All the reviewed systems have privacy settings for individual videos and groups. It is important to be able to create working areas for a class or a group of students, so that they have their own space where they can collaborate and share the videos. When schools and teachers are considering which video sharing site to use, it may be important to investigate what type of content is used in the service. Although a closed working area would be created on the site for students, the existence of entertainment content in the same system decreases the credibility of the system. Many systems are free or have a free trial option; however, the free access may include. Most video sharing sites allow registered users to upload videos to the service and to set privacy limitations for the videos they have uploaded. Moreover, most video sharing sites are not designed for learning. Many video sharing sites allow users to watch videos, but uploading your own content requires registration either with the service or using a Facebook or Google identity. For schools, this may be a problem because in many countries 13 years is the minimum age that is required to sign up for Facebook or Google (Table 1). YouTube, WeVideo, and MoViE have a video editor that can be used to do simple trims (YouTube), remixing (MoViE), or full video edits with sound and effects. YouTube and MoViE have a useful feature for annotating the videos. By using annotations, students can add subtitles that appear and disappear at certain times over the video. By using annotations, students could ask questions and make comments on the video. Some video sharing sites provide analytics tools for the owner of the video clip. Typically, the owner can see how many times the video has been watched and, in some cases, from where the watchers are geographically. Tools that could provide learning analytics to teachers would be very useful, but the systems do not have this feature yet. Moderation is also an important characteristic for teachers,

Category: Educational Technologies

Table 1. Access and moderation of the content Access YouTube

E

Moderation

Google UID

No

Vimeo

Vimeo UID, Facebook UID

Channels can have moderators.

WeVideo

Facebook UID

No

MoViE

MoViE UID

Yes

TeacherTube

TeacherTube UID

Content moderated by the service staff; content flags.

since it helps in preventing inappropriate content from being shared on the site. For TeacherTube, the service providers moderate the content. In MoViE and Vimeo, the teacher or channel owner can moderate the content. TeacherTube also has a red flag system in which users can flag inappropriate content for review by the service providers (Table 2). All the reviewed video sharing sites have a method to create a specific working area, which is called a channel, album, project, group, or collection, depending on the system. The basic idea of the working area is to separate access for a certain group of people, who can share and access materials within. Only MoViE and TeacherTube have pedagogical concepts included in the service. TeacherTube includes features from LMS (such as a class management), but it is effective in utilizing the creation and delivery of video content (Table 3).

Best Practices A digital video story can be created on almost any topic using several different methods. Table 4

shows examples of the video storytelling projects that the researchers have been implementing. In total, over 2000 video clips were created during the interventions. Some of the topics were performed similarly to a drama, that was scripted with roles and actors selected and camera angles planned well ahead. Other topics mimicked documentaries and included interviews of the subjects. In some classes, it was not possible for the students to appear in the video clips. In these cases, students created animations using self-drawn paper dolls, or they created and presented a series of slides with narration. As a result, in all of the videos the students’ voice was strong. The teachers and the researchers helped the students complete the manuscript (Penttilä, Kallunki, Niemi & Multisilta, 2016). In addition, the students needed help in selecting the shooting locations so that the sounds from the surrounding environment did not significantly affect the final outcome. Many videos were shaky because they were recorded using a mobile device that was not attached to a tripod. The students showed creativity, such as in adding a background soundtrack from a music player to the video clips while recording.

Table 2. Edit, annotations, analytics, and privacy Edit/Remix

Annotations

Analytics

Privacy

YouTube

YouTube editor

Yes

Google analytics

Videos and channels can be private.

Vimeo

No

No

Simple statistics

Videos can be private.

WeVideo

Editor

No

No

Yes

MoViE

Limited editing

Yes

Google analytics available for research purposes.

Yes

TeacherTube

No

No

No

Videos can be private.

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Table 3. Grouping, content, price, and pedagogical concepts Groups

Content

Price

Pedagogical Concepts

YouTube

Channels

Everything/not school content only.

Free

No

Vimeo

Channels, albums

Everything/not school content only; channel owner can set the content for all or mature audiences.

Basic is free.

No

WeVideo

Projects

Everything/not school content only.

Basic is free.

No

MoViE

Groups

School-related content.

Free

GSP, VIL, questionnaires.

TeacheTube

Groups, channels, collections.

School-related content.

Free, advertisements

Lessons, questionnaires, classroom management, badges.

Table 4. Examples of digital video stories Topic

Exercise

Example

Local history

Create a video story of a historical event in your own school, neighborhood, or city.

• A drama of Greek mythology. • An interview of a local Nobel prize winner. • A paper doll animation of the immigration of Europeans to the USA in 1820-1930

Influencing opinion

Create a video story of an issue or cause for which you would like to raise awareness.

• A drama of homelessness. • The use of animals in testing cosmetics.

Chemistry lab

Document the chemical experiment you are doing.

Close-up video clips of a chemical process (for example, how to create a superball using polymerization).

Physics

Create video clips of a physical phenomena.

Video clips of circular motion (the clips can be recorded outside the classroom).

Mathematics

Do your own Khan Academy video.

Create a video of a solution of a mathematical exercise using a pen and paper.

Biology

Study the circulation of water.

A narrated PowerPoint presentation with video clips recorded at the local lake or river.

SOLUTIONS AND RECOMMENDATIONS Niemi and Multisilta (2015) studied the methods of engagement in collaborative video work for learning. Based on these authors’ findings, students can become highly engaged in a learning activity through digital storytelling. In addition, engagement can be defined by two components: an emotional aspect (joy of learning or fun) and a commitment to hard work. Using digital video storytelling, students learn several 21st century skills when producing learner-driven content. In addition, working in groups is important for student motivation and enthusiasm. The digital environment was shown to be the best predictor of

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both engagement and learning outcomes (Niemi & Multisilta, 2015). The findings provide evidence that students enjoy creating digital videos. They work actively, seeking new knowledge and constructing their videos using different information sources. A general observation is that they also self-evaluate their work. It was observed that commenting on others’ work was difficult for students. Giving and receiving feedback is not self-evident. Although students find it interesting to watch others’ videos, making active comments is still fairly uncommon. For the Video Inquiry Project (VIP), designbased research and development was conducted to establish a broadly scalable approach for K-12 learners and teachers to capture mobile video

Category: Educational Technologies

recordings of events and phenomena that prompt questions that can serve as a basis for inquiries and collaborative learning in the STEM disciplines (Multisilta et al., 2014). During the VIP project, it was found that digital media, including video, can provide an important nexus attracting joint attention of peer learners or learners-and-a-teacher, catalyzing learning conversations in-the-moment. Video can also play an important bridging function, connecting and spawning learning events across settings (including school and home) and generating discussions about math, science, and engineering. These conversations provide opportunities to ask questions, express confusion, share perspectives, and provide explanations. Learnergenerated videos also provide a sense of agency and ownership that is engaging. There are several kinds of tools that can be used for digital video storytelling. Some tools can be used only for editing the video content while others have capabilities to share the video to others and collaborate with other learners. Most of the tools are available on the web, but have not been designed to be used for learning. In addition, there has been a wide discussion on if schools should block the access to YouTube and similar sites (Storm, 2012) because of the availability of distracting content. It should be on the teacher’s responsibility and right to select a pedagogical model and tools and apply them in the classroom in a meaningful way. So far, global sharing pedagogy and video inquiry learning models have been illustrated to provide engagement for STEM learning.

FUTURE RESEARCH DIRECTIONS There is a growing need to seek new methods to understand the learning process in digital learning environments. Digital learning environments can be designed so that they collect various data from users while they use the environment and interact with other leaners; however, these data are often either not available or not very useful for teach-

ers. Learning-related user data could be utilized so that they provide support in understanding the learning process in a much deeper way. In order to do so, learning data should be collected and analyzed in relation to the content. The data should be visualized so that teachers could immediately understand their meaning in relation to the class and the lesson. New methods and tools for collecting, analyzing, and visualizing learning analytics data in inquiry-video-based learning are needed. General analytics tools, such as Google Analytics, can be used to evaluate the use of a digital video storytelling service, but it only provides largely superficial information regarding the learning process. Generic data collected by the existing analytics systems include web navigation data, such as page hits, the number of visitors to a page, and the time spent on the page. By utilizing data from the content and from the learning activities, teachers could understand the learning process in a more meaningful way.

CONCLUSION By using digital video storytelling, students can engage in a variety of learning activities that could also be done outside the classroom, at home, at a sports field, or at a museum. Students already use videos often in their free time, but schools are not utilizing the expertise students have in publishing their lives in a digital space. By utilizing digital video storytelling as a tool in a classroom, students learn twenty-first century skills, such as creativity, problem solving, and collaboration. Students need teachers’ guidance in creating the manuscript and planning the video shooting locations. The stories can be dramas, plays, animations, narrated presentations, documentaries, and interviews. In the stories, the student’s own voice is strong. In the chapter, five video sharing sites were compared based on a set of requirements that are typical for classroom use. All the tools were available on the Internet. None of the tools reviewed

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will support all classroom requirements. In the end, it is up to the teacher to decide the best tool for the particular case in the classroom.

REFERENCES Bidwell, N. J., Reitmaier, T., Marsden, G., & Hansen, S. (2010). Designing with mobile digital storytelling in rural Africa. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1593–1602). New York, NY: ACM. doi:10.1145/1753326.1753564 Correia, N., Alves, L., Correia, H., Romero, L., Morgado, C., Soares, L.,... Jorge, J. A. (2005, May). InStory: a system for mobile information access, storytelling and gaming activities in physical spaces. In Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology (pp. 102-109). New York, NY: ACM. doi:10.1145/1178477.1178491 de Jong, T., & van Joolingen, W. R. (2008). Model-facilitated learning. In J. M. Spector, M. D. Merrill, J. Van Merriënboer, & M. P. Driscoll (Eds.), Handbook of Research on Educational Communications and Technology (pp. 457–468). New York, NY: Lawrence Erlbaum Associates. Engström, A., Esbjörnsson, M., & Juhlin, O. (2008, September). Mobile collaborative live video mixing. In Proceedings of the 10th international conference on Human computer interaction with mobile devices and services (pp. 157-166). New York, NY: ACM. Kearney, M. (2011). A learning design for student‐generated digital storytelling. Learning, Media and Technology, 36(2), 169–188. doi:10. 1080/17439884.2011.553623 Kiili, K., Multisilta, J., Suominen, M., & Ketamo, H. (2010). Learning experiences of mobile social media. Int. J. Mobile Learning and Organisation, 4(4), 346–359. doi:10.1504/IJMLO.2010.037533

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Ladeira, I., Marsden, G., & Green, L. (2011). Designing interactive storytelling: a virtual environment for personal experience narratives. In Human-Computer Interaction–INTERACT 2011 (pp. 430–437). Berlin: Springer. doi:10.1007/9783-642-23771-3_32 Lewis, A., & Smith, D. (1993). Defining higher order thinking. Theory into Practice, 32(3), 131–137. doi:10.1080/00405849309543588 Multisilta, J., Kallunki, V., Ojalainen, J., Penttilä, J., Liu, A., Eduard, K., & Pea, R. (2014). Engagement in Inquiry-Based Learning with Mobile Devices. Paper presented at the Nordic Research Symposium on Science Education NFSUN 2014, Helsinki, Finland. Multisilta, J., & Mäenpää, M. (2008). Mobile video stories. In Proceedings of the 3rd International Conference on Digital interactive Media in Entertainment and Arts, DIMEA ‘08 (pp. 401–406). New York, NY: ACM. Retrieved March, 16, 2016, from http://doi.acm.org/10.1145/1413634.1413705 Niemi, H., Harju, V., Vivitsou, M., Viitanen, K., Multisilta, J., & Kuokkanen, A. (2014). Digital Storytelling for 21st-Century Skills in Virtual Learning Environments. Creative Education, 5(09), 657–671. doi:10.4236/ce.2014.59078 Niemi, H., & Multisilta, J. (2014). Global is becoming everywhere: global sharing pedagogy. In H. Niemi, J. Multisilta, L. Lipponen, & M. Vivitsou (Eds.), Finnish Innovations and Technologies in Schools: Towards New Ecosystems of Learning (pp. 35–48). Rotterdam: Sense Publishers. doi:10.1007/978-94-6209-749-0_3 Niemi, H., & Multisilta, J. (2015). Digital story telling promoting 21st century skills and students’ engagement. Technology, Pedagogy and Education. doi:10.1080/1475939X.2015.1074610 Östman, S. (2015). “Millaisen päivityksen tästä sais?” Elämänjulkaisijuuden kulttuurin omaksuminen. Jyväskylä: Nykykulttuurin tutkimuskeskus. (In Finnish)

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Penttilä, J. S. M., Kallunki, V. A. J., Niemi, H. M. & Multisilta, J. A. (2016) A Structured Inquiry into a Digital Story: Primary School Students Report the Making of a Superball. International journal of mobile and blended learning (IJMBL), 8(3), pp. 19-34 Piwek, L., & Joinson, A. (2016). What do they snapchat about? Patterns of use in time-limited instant messaging service. Computers in Human Behavior, 54, 358–367. doi:10.1016/j. chb.2015.08.026 Prensky, M. (2011, Aug-Sep). Khan Academy. Educational Technology. Retrieved March, 16, 2016, from http://www.marcprensky.com/writing/ Prensky-Khan_Academy-EdTech-Jul-Aug2011. pdf Storm, S. (2012). YouTube Subtracts Racy and Raucous to Add a Teaching Tool. The New York Times. Retrieved March, 16, 2016, from http://www.nytimes.com/2012/03/10/education/ youtube-finds-a-way-off-schools-banned-list. html?_r=0 Talbert, R. (2012). The trouble with Khan Academy. The Chronicle. Retrieved March, 16, 2016, from http://chronicle.com/blognetwork/ castingoutnines/2012/07/03/the-trouble-withkhan-academy/ Thompson, C. (2011, July). How Khan Academy is changing the rules of education. Wired Digital. Retrieved March, 16, 2016, from http://www. wired.com/2011/07/ff_khan/ Tuomi, P., & Multisilta, J. (2010). MoViE: Experiences and attitudes—Learning with a mobile social video application. Digital Culture & Education, 2(2), 165–189. Vaucelle, C., & Davenport, G. (2004). A system to compose movies for cross-cultural storytelling: textable movie. In Technologies for Interactive Digital Storytelling and Entertainment (pp. 126–131). Berlin: Springer. doi:10.1007/978-3540-27797-2_17

Vygotsky, L. (1978). Mind in society (M. Cole & V. John-Steiner, Eds.). Cambridge, MA: The MIT Press. Wolf, K. D., & Rummler, K. (2011). Mobile learning with videos in online communities: the example of draufhaber.tv. MedienPädagogik, 19, 1-13. Retrieved March, 3, 2016, from http://www. medienpaed.com/19/#wolf1105 Yang, Y. F. D. (2012). Multimodal composing in digital storytelling. Computers and Composition, 29(3), 221–238. doi:10.1016/j.compcom.2012.07.001 Zsombori, V., Frantzis, M., Guimaraes, R. L., Ursu, M. F., Cesar, P., Kegel, I., & Bulterman, D. C. et al. (2011, June). Automatic generation of video narratives from shared UGC. In Proceedings of the 22nd ACM conference on Hypertext and hypermedia (pp. 325-334). New York, NY: ACM. doi:10.1145/1995966.1996009

ADDITIONAL READING Banchi, H., & Bell, R. (2008). The many levels of inquiry. Science and Children, 46(2), 26–29. Frazel, M. (2010). Digital storytelling guide for educators. Eugene, Oregon: International Society for Technology in Education. Hakkarainen, P. (2011). Promoting meaningful learning through video production-supported PBL. Interdisciplinary Journal of ProblemBased Learning, 5(1), 34–53. doi:10.7771/15415015.1217 Harju, V., Viitanen, K., & Vivitsou, M. (2014). Digital storytelling in Finnish schools. In H. Niemi, J. Multisilta, L. Lipponen, & M. Vivitsou (Eds.), Finnish innovations and technologies in schools: Towards new ecosystems of learning (pp. 49–56). Rotterdam: Sense Publishers.

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Kearney, M. (2011). A learning design for student‐generated digital storytelling. Learning, Media and Technology, 36(2), 169–188. doi:10. 1080/17439884.2011.553623 Lambert, J. (2013). Digital storytelling: Capturing lives, creating community. New York, NY: Routledge. McGee, P. (2015). The instructional value of digital storytelling: Higher education, professional, and adult learning settings. New York, NY: Routledge. Ohler, J. B. (2013). Digital storytelling in the classroom. New media pathways to literacy, learning and creativity. Thousand Oaks, CA: Corwin. doi:10.4135/9781452277479

KEY TERMS AND DEFINITIONS Constructivist Learning Model: Learners actively create knowledge using activities that support knowledge construction. Digital Video Storytelling: Learning activities that involve the creation and the use of digital video.

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Global Sharing Pedagogy: Learning is seen as a mediated activity with tools, signs, and social interaction. Global sharing pedagogy has four mediators of learning: 1) learner-driven knowledge and skills creation, 2) collaboration, 3) networking, and 4) digital media competencies and literacies. The mediators contribute to the learners’ engagement in a learning activity. Inquiry Learning: Learning activities where learners search and construct new knowledge by exploring the world or the phenomena, by asking questions, making hypothesis, and testing the hypothesis. Life Publishing: The use of social media services in which the users are sharing events and moments from their daily lives. Twenty-First Century Skills: A set of knowledge and skills that are or will be important to succeed in the future world. Examples of twenty-first century skills include creativity, communication, collaboration, digital literacy, and problem-solving skills. Video Inquiry Learning: Learning is based on the investigation of questions, scenarios, or problems using videos that students create and share using digital video storytelling tools. The videos prompt questions and serve as a basis for inquiries and collaborative learning.

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Category: Educational Technologies

The Use of Postcasting/ Vodcasting in Education Athanasios T. Stavrianos 2nd Technical Vocational School of Xanthi, Greece Apostolos Syropoulos Greek Molecular Computing Group, Greece

INTRODUCTION The terms podcasting and vodcasting refer to automatically downloadable audio and video files. Typically a podcast is an MP3 file whereas a vodcast can be any popular compressed video file. In 2004 the term podcasting was first mentioned in an article in the newspaper The Guardian (Hammersley, 2004). The term podcasting derives form iPod, the device that was first used to download and play podcasts.The inventors of the technology are Dave Winer and Adam Curry (Brown & Green, 2007). At that time, Winer was a software developer and an RSS evangelist while Curry was an MTV vj. Rich Site Summary, or just RSS, is a format for delivering regularly changing web content. Initially, podcasting was used for personal entertainment or information but soon it became clear that it could be used in education. Since its introduction the technology became very popular and this can be seen by the number of downloads. In April of 2006 ten million podcasts were downloaded while in November of the same year 17 million podcasts were downloaded. The terms podcasting and vodcasting (the vod part comes from Video On Demand) refer to a process. In particular, when there is an event, one has to capture the song, the interview, etc. The result can be either an audio file or a video file. Today’s video capturing devices can produce high definition video that is stored using a compressed codec so there is no need to re-encode the video, something that was quite common in the early

days. Then, one had to post this audio or video file to a web site or a blog and using an RSS (Rich Site Summary) envelope (RSS is a format for delivering regularly changing web content). Obviously, the author had to inform people who might be interested in the new content. This is done automatically if people subscribe to an RSS feed. Nowadays one can use her smartphone to read the RSS feed and then to download the new content. Today one can use a smartphone to record a video file or an audio file. Also, one can use a digital camera to record a video file. When one prepares a podcast, then it is recommended to use the “Audacity” open source software for audio editing. On the other hand, the open source project “pitivi” can be used to edit video files.

BACKGROUND Podcasting was primary used in tertiary education to make lectures available to students in order to clarify difficult parts and emphasize important ones. Later on, podcasts were replaced by vodcasts. Currently, there are four kinds of vodcasts that are used in education: lecture-based, enhanced, supplementary, and worked examples (Kay, 2012). A lecture-based or “substitutional” vodcast is a recording of an entire lecture. Thus students can experience what happened in the lecture hall without actually being physically present. An enhanced vodcast is video footage of a slideshow (e.g., Powerpoint or Beamer presentations) that is

DOI: 10.4018/978-1-5225-2255-3.ch231 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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The Use of Postcasting/Vodcasting in Education

presented with an audio explanation. Supplementary vodcasts are designed to augment the teaching and learning of some courses and may include administrative support, real-world demonstrations, summaries of lectures or textbook chapters, or additional material designed to broaden or deepen student understanding. There are also other ways to classify vodcasts. For example, depending on whether a vodcast is offered in segments or not, one can talk about segmented vodcasts or non-segmented vodcasts, respectively. In addition, the pedagogical strategy can be used to categorize vodcasts. In particular, there are three different teaching approaches. The first is called receptive viewing and includes vodcasts to be viewed by students in a passive manner (i.e., like watching a movie). There are problem-solving vodcasts that are designed to explain and help students in learning how to solve problems and exercises related to their courses. Naturally, such vodcasts are useful for people who study science, mathematics, or engineering. A third category includes vocasts that are created by students for students. Although vodcasts seem to be quite popular today, there are a few questions related to their use in education. The first question is whether students are ready for this technology and the second question is whether this technology is actually useful. The first question has been tackled by (Walls, Kucsera, Walker, Acee, McVaugh, & Robinson, 2010) among others. First we need to note that today most if not all students own laptop or desktop computers and smartphones, which can be used to listen to music, to watch videos, take pictures, shoot videos, and so on. Thus many of the devices of the past (e.g., iPods, mp3 players, pocket digital cameras, and so on) have been replaced by smart-phones. This simply means that all students have the potential to create, download, and watch vodcasts. It has been suggested that vodcasting can improve student learning outcomes. This suggestion is largely based on Mayer’s cognitive theory of multimedia learning (Mayer, 2001). According to this theory

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“an individual’s information processing system includes separate cognitive channels to process visual/pictorial and auditory/verbal stimuli; in this respect, learning is obtained by integrating information between such channels” (Walls, Kucsera, Walker, Acee, McVaugh, & Robinson, 2010). In different words, there is a limit to what a learner can achieve. Thus when a learner is presented with a large amount of pictures, images, talks, sounds, etc., hence she might fail to comprehend most of this information. Not so surprisingly, vodcasts can solve this problem! How? Each student will have the opportunity to watch vodcasts in the comfort of their room or in any other place that may suit a student. More importantly, students can control the speed and pace by which they will watch specific content. Of course vodcasting is not a panacea and so it has certain limitations. For example, when students are already using a variety of resources in their studies (e.g., text-books, lectures notes, etc.), then adding one more kind or resource may create some sort of cognitive overload. Although students own smart-phones and other related technological “gadgets”, still they use them for entertainment and not for their studies. Therefore, it is not obvious that students would consider using their smartphones in their studies, particularly in institutions that have not used vodcasts in their educational resources. It has also been argued that the use of vodacsting may provide a justification and excuse for students to skip classes. However, some studies have revealed that students do not consider a vodcast as a substitute for attending a lecture. After all, one cannot ask a vodcast questions! According to findings provided by (Walls, Kucsera, Walker, Acee, McVaugh, & Robinson, 2010) students do not find vodacsts particularly useful in their studies. They think that supplementary vodcasts contribute something in their learning. Also, students utilize vodcasts in rather different ways and in rather different circumstances. According to this study, they might utilize vodcasts during trips, while eating or exercising, and while study-

Category: Educational Technologies

ing. Based on these findings one could conclude that students are not particularly ready. However, students become ready when they realize that a particular technology can be really beneficial in their studies. A study that is documented by (Fernandez, Simo, & Sallan, 2009) examined the usefulness of vodcasting (the authors use the term video podcasting which has been substituted by vodcasting). Based on a number of principles of good practices aiming to improve students’ learning process in undergraduate studies, the authors examined if these are achieved with the use of vodcasting. Briefly, these principles are: 1. Active learning is more effective that passing learning; 2. Learning demands students to be focused and aware of the importance of what they study; 3. Students learn more effectively when they have reasonable and positive goals that coincide with the goals of the instructor; 4. New knowledge must be connected to previous knowledge; 5. Information must be organized in personally meaningful ways; 6. Students need feedback to check their learning progress; 7. Unlearning what has been learned is more difficult than learning new things; 8. The way students are assessed affects the way they study; 9. Mastering a skill or a body of knowledge takes a lot of time and energy; 10. Learning to apply known ideas to new contexts is quite difficult; 11. High expectations may lead to great achievements; 12. Teachers have to balance levels of intellectual challenge and instructional support; 13. Motivation to learn is never steady; and 14. Teachers and students must always interact.

Students that participated in this study hoped that vodcasts would render attending classes useless. However, this was not the case as vodcasts cannot replace a textbook, which is something these students learned soon after the experiment started. Of course, all students found out that vodcasts can be used in any place and at in any time, something that is particularly convenient. Most students used vodcasts in order to get prepared for new lectures. This way they knew the difficult parts of the lecture so they could prepare questions in order to clarify things. In general, this experiment showed that vodcasts are particularly useful. So far we discussed the use of vodcasts in tertiary education. However, their usefulness in secondary education has also been studied (Coutinho & Rocha, 2010). In this study the authors present the “Geomcasting” project whose goal was the creation of vodcasts and screencasts (i.e., video made of screenshots that is accompanied by narration) by students so to help other students to pass the descriptive geometry class. The students involved created content for different parts of the class and the resulting content was uploaded to a blog site for future use. The authors of the study noticed that students were quite happy with their participation in the project and also those that used the content in their own studies found it particularly useful.

THE EZCAST CASE So far we have given a general presentation of the technology and its use in education. In passing we mentioned how one can create content and make it available but we did not say anything about modern platforms that really facilitate the creation and sharing processes of vodcasts. In October of 2015, the first named author attended a presentation that took place in an auditorium of the Université libre de Bruxelles (ULB).1 The presentation was about innovative educational technologies that the

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teaching staff of the university developed and is actually using. In particular, the attendees were introduced to the EZcast (pronounced Easy Cast) web application that allows lecturers or teachers to share media contents with an audience. In addition, it was demonstrated how university students are using and interacting with the platform. Also, they had the chance to watch sample videos and learn about the hardware infrastructure used to record, manage, and upload multimedia content. Naturally, the presentation did not give the full details of every aspect of the project, nevertheless, it was enough for most participants to start studying all relevant details of the platform.

The Development Process EZcast (EZcast wiki, 2014) along with its frontoffice component EZplayer (Roland, 2013), was officially launched by the Université libre de Bruxelles at the beginning of the academic year 2013-2014 and was made available to all students. The project evolves rapidly and this means that new releases will be available soon. The implementation principles of the EZcast platform are a mixture of a design-based research approach (Collective, 2003) and a systemic usercentric approach (Roland, 2013). Design-based research aims at improving the educational practices using methods such as iterative cycles of analysis, design, enactment and redesign, constant collaboration between researchers and practitioners (Wang & Hannafin, 2005) leading to the production of a device (in this case the EZcast platform) and at the same time the introduction of new principles and theories. Based on this theoretical framework, ULB conducted a survey for three years collecting quantitative (5399 students answered questionnaires) and qualitative data (52 students were interviewed). The survey measured the way students use a vodcasting tool in an academic course and investigated the tool’s degree of acceptance. The survey showed that vodcasting was not just a supporting tool for taking notes but also a medium of study giving options

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to interact with the delivered knowledge (Roland, 2013). These results led to the formation of a team of people consisting of teachers, educational designers, developers, researchers, and teachers that acted as the α-testers of the first version of EZplayer that was developed in early 2013. 500 students acted as β-testers and their feedback was used to produce the second bug-free, responsivedesign, and more interactive version of EZplayer.

A Deeper Look Into EZcast EZcast is an open source web platform that allows teachers and lecturers to record lectures. Then these recordings can be made available to students via a web media player. Students have access to high quality educational multimedia content consisting not only of recorded audio and video but also by audio-video-synced slide presentations. On the other hand, EZcast allows educational material producers to easily create, edit and distribute it to targeted audience through their web browser. Each user of the platform is assigned a role. The two main roles supported by the platform (i.e., students and instructors) have access to EZplayer, an “enriched web audiovisual player” (Roland, 2013). This player has two main features: a set of audio visual handling tools that facilitate the easy transition from an AV stream to an “audioslideshow” stream and the ability to accelerate or to decelerate a stream, sharing a link in a specific time, etc. In addition, students can add temporal bookmarks to specific frames of a video, thus they are able to tag them with a title, a description, and add keyword fields so to annotate or summarize the lecture that is recorded. Teachers have also the ability to add their own tags but students can modify these teacher-tags. In this way, every student can create lists of “official” and personal tags, which they can be used when performing searches so to retrieve educational material on demand. Moreover, since February 2015, an innovative discussion function has been added to the EZplayer. Users can now pinpoint specific framesets of the video thus creating a discussion

Category: Educational Technologies

Figure 1. A general view of the EZcast Infrastructure

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(Jansens & Wijns, 2015)

thread on which everyone can contribute with a comment and then vote for the best replies. Although it is not our intention to fully describe all the features of each component of the main platform, still it is beneficial for the reader to have a general idea of the platform’s infrastructure. Complete documentation, user tutorials, and installation guides concerning EZcast can be found at http://ezcast.ulb.ac.be/

Infrastructure EZcast is a logical application framework composed of five distinct entities: EZrecorder, EZadmin, EZmanager, EZrenderer, and EZplayer (EZcast wiki, 2014). Different users can have access to four web interfaces: EZrecorder, EZadmin, EZmanager, EZplayer. As seen in Figure 1, the five entities interact with each other using 2655

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various protocols and links, but main user roles (i.e., instructors and students) mostly interact with EZplayer (i.e., watching, sharing and commenting audiovisual material), and EZrecorder (instructors recording and uploading their videos). The applications EZadmin, EZmanager, and EZplayer actively communicate with each other so they must be installed on the same server, which Figure 2. Auditorium Hardware Infrastructure (Jansens & Wijns, 2015)

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is supposed to run some flavor of UNIX or Linux. EZrecorder allows a user to record videos and publish them to a specific audience by directly communicating with EZmanager. The EZcast framework is freely available as an open source project under LGPL v.3 from 2 git repositories (EZcast2 and EZrecorder3).

Category: Educational Technologies

An Overview of the Five Entities EZrecorder allows users to monitor their recordings automatically and autonomously. After a secure authentication, lecturers can choose the album they want to record, the title of the course, and available streaming formats: Audio-Video, audio-slideshow, or audio-video slideshow. The interface provides full recording management options by giving access to camera controls and predefined scenes. At the end of recording, the sequence can be published directly on the Internet or placed in a private album. Media files sent to EZrecorder typically have been created in existing auditoriums of the institution equipped with the required hardware infrastructure. As seen in Figure 2, sound captured by a microphone is sent to an amplifier and then to a computer in order to synchronize it with the video. A permanently installed camera sends the captured video to a device that converts it from an analog to a digital format before sending it to the same computer (Mac mini Video). A VGA switch is used so to split the produced slideshows from a lecturer’s computer. The output signal is transmitted simultaneously to the auditorium projector and a VGA-to-USB converter before it is fed to another computer (Mac mini Slide). EZmanager Provides the ability to manage recorded albums, recordings from EzRecorder and submit videos from a user’s computer. EzManager offers four modes of content publishing: EZplayer, RSS feeds, direct download, or embed code. EZplayer This is an online audiovisual player allowing users (teachers and students) to interact directly with educational content. The available options are shown in Figure 2. In particular, transition through various types of streaming, video handlers, visual bookmarks, discussion threads at specific moments of the video, advanced search. EZrenderer Processes the recordings and video submissions. Users can choose a jingle and titling to be incrusted before the video, and decide on the output aspect ratio and video quality.

EZadmin This is the web interface for users and course administration. For istance, it allows administrators to create new users and courses, add new renderers, enable or disable recorders in classrooms.

SOLUTIONS AND RECOMMENDATIONS EZcast is a standalone application that must be installed on a server managed by the institution that will eventually run it. Also, it should only be available to targeted audience that belongs to the university in the broad sense of the word. On the other hand, there are ready-to-go educational web tools available to anyone, offering similar player features but with less recording and administrative options. Some of them are as follows: • • • •

TEACHEM (http://www.teachem.com/) TEACHERS TUBE (http://www.teachertube.com) KHANACADEMY (https://www.khanacademy.org/) TED (http://www.ted.com/), etc.

The tools mentioned above belong to a wide class of tools that are particularly useful for distributing educational content. In addition, most of them offer class management options, student evaluation methods (quizzes, tests, and so on), communication with parents, etc. Even though they are not categorized as vodcasting applications, multimedia content (video, images, slideshows) can be embed or uploaded from educators and learners to the community. In most cases, users passively watch videos through a player with some basic commenting options available. Content-user interactivity features such as commenting on specific moments of the video, quiz questions on the video, and bookmarking framesets for future use, are currently being introduced to some of these applications (Teachem, Khnacademy).

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Figure 3. Basic features of EZplayer (Jansens & Wijns, 2015)

FUTURE RESEARCH DIRECTIONS We have presented the idea of vodcasting, its general use in education, and a particular tool that can be used to create and distribute vodcasts. The next big thing in video is truly interactive video. Currently, some blu-ray movies provide some kind of interactivity. Thus, generating interactive educational videos may be the big thing in educational technology. Naturally, this would demand the creation of video-capturing, editing,

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and uploading tools. One more related idea, would be to consider 3D videos, especially videos that describe lab activities.

CONCLUSION We have presented scientific evidence to support that vodcasting can be beneficial to learners. In particular, they supplement their teaching material nicely. Students become easily quite familiar

Category: Educational Technologies

with vodcasting tools and seem to give a vote of confidence on their ease of use in addition to their usefulness in their studies. By introducing to vodcasts certain features that are very common to Web 2.0 tools (e.g., some form of interactivity, the ability to comment certain framesets, bookmarking abilities, alteration between vodcasts and podcasts) has turned them into a valuable learning tool. So vodcasting has become the new interactive, note-taking, knowledge acquisition, on demand tool. EZcast is a new and very promising platform that allows users to create, edit, and upload vodcasts. This tool and other similar tools should be used daily in the educational process.

REFERENCES Brown, A., & Green, T. (2007). Video Podcasting in Perspective: The History, Technology, Aesthetics, and Instructional Uses of a New Medium. Journal of Educational Technology Systems September, 36(1), 3-17. Collective, T. D.-B. (2003). Design-Based Research: An Emerging Paradigm for Educational Inquiry. Educational Researcher, 32(1), 5–8. doi:10.3102/0013189X032001005 Coutinho, C. P., & Rocha, A. M. (2010). Screencast and Vodcast: An Experience in Secondary Education. In D. Gibson & B. Dodge (Eds.), SITE 2010-Society for Information Technology & Teacher Education International Conference (pp. 1043-1050). Academic Press.

Hammersley, B. (2004, February 12). Audible revolution. Retrieved July 15, 2016, from The Guardian: https://www.theguardian.com/media/2004/feb/12/broadcasting.digitalmedia Jansens, M., & Wijns, A. (2015). EZcast infrastructures. Retrieved July 12, 2016, from http://ezcast. ulb.ac.be/files/EZcast_Infrastructure_doc.pdf Kay, R. H. (2012). Exploring the use of video podcasts in education: A comprehensive review of the literature. Computers in Human Behavior, 28(3), 820–831. doi:10.1016/j.chb.2012.01.011 Lonn, S., & Teasley, S. D. (2009). Podcasting in higher education: What are the implications for teaching and learning? The Internet and Higher Education, 12(2), 88–92. doi:10.1016/j. iheduc.2009.06.002 Mayer, R. E. (2001). Multimedia Learning. Cambridge, UK: Cambridge University Press. doi:10.1017/CBO9781139164603 Roland, N. (2013). La recherche scientifique au service de l’expérience utilisateur: retour sur le processus de conception d’EZplayer. Retrieved from http://uptv.univ-poitiers.fr/ program/campus-europeen-d-ete-2013etnbspux-designetnbsp-l-experience-utilisateur-auservice-des-apprentissagesetnbsp/video/3882/ la-recherche-scientifique-au-service-de-l-experience-utilisateur-retour-sur-le-processus-de-conce

EZcast wiki. (2014). Retrieved July 12, 2016, from https://github.com/ulbpodcast/ezcast/wiki

Walls, S. M., Kucsera, J. V., Walker, J. D., Acee, T. W., McVaugh, N. K., & Robinson, D. H. (2010). Podcasting in education: Are students as ready and eager as we think they are? Computers & Education, 54(2), 371–378. doi:10.1016/j. compedu.2009.08.018

Fernandez, V., Simo, P., & Sallan, J. M. (2009). Podcasting: A new technological tool to facilitate good practice in higher education. Computers & Education, 53(2), 385–392. doi:10.1016/j. compedu.2009.02.014

Wang, F., & Hannafin, M. J. (2005). Designbased research and technology-enhanced learning environments. Educational Technology Research and Development, 53(4), 5–23. doi:10.1007/ BF02504682

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ADDITIONAL READING Braun, L. W. (2007). Listen Up! Podcasting for Schools and Libraries. Medford, NJ: Information Today. Hubackova, S., & Golkova, D. (2014). Podcasting in Foreign Language Teaching. Procedia: Social and Behavioral Sciences, 143, 143–146. doi:10.1016/j.sbspro.2014.07.376 Morris, T., Tomasi, C., & Terra, E. (2008). Podcasting For Dummies (2nd ed.). Hobonek, NJ: Wiley Publishing. Ng, W. (2015). New Digital Technology in Education: Conceptualizing Professional Learning. Cham, Switzerland: Springer International Publishing. doi:10.1007/978-3-319-05822-1 Zheng, L. (2016). Reflection on Design-Based Research: Challenges and Future Direction. In Y. Li, M. Chang, M. Kravcik, E. Popescu, & R. Huang (Eds.), Kinshuk, et al., State-of-the-Art and Future Directions of Smart Learning (pp. 293–296). Singapore: Springer. doi:10.1007/978-981-287-868-7_35

LGPL: The GNU Lesser General Public License and details how open source software can be freely copied, distributed and modified. Podcast: An audio file that is posted to some site and which is automatically downloaded by subscribers of the site. Responsive Design: A web design approach aimed at allowing web content to be viewed properly on all devices, using CSS3 and HTML. Screencast: A kind of vodcast that consists of screenshots and narrated text. Secure Authentication: The process of identifying an individual, usually based on a username and password. Video File Format: A type of file format for storing digital video data on a computer system. Vodcast: A video file that is posted to some site and which is automatically downloaded by subscribers of the site.

ENDNOTES

1

KEY TERMS AND DEFINITIONS Educational Technology: Computer software and hardware that is used for educational purposes. Embed Code: A block of HTML code that is embed in a target page pointing back to the source page and creating relevant content.

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The visit in the university was part of a short term exchange of pupils in Belgium. This was part of an EU funded Erasmus+ Project between 7 secondary schools from Austria, Belgium, Greece, Portugal, Romania, Slovenia, and Turkey. https://github.com/ulbpodcast/ezcast https://github.com/ulbpodcast/ezrecorder

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The Vital Importance of Faculty Presence in an Online Learning Environment Ni Chang Indiana University – South Bend, USA

INTRODUCTION One of the instructional methods, which is clamored by students and which could arguably provide high quality educational opportunities, is faculty presence, as it makes possible the interaction between instructor and students and between students and students in a virtual learning environment (O’Reilly, 2009). Online instructors and academic administrators in higher education cannot simply hold an assumption that quality online courses or student learning could largely depend upon good Internet connectivity, high quality equipment, solid content knowledge of instructors (Welch & Napoleon, 2015), and beautifully designed online courses. The presence of an online instructor cannot ever be neglected or marginalized in online students’ learning success. Therefore, highly significant is to address roles that instructors play in an online learning environment in order to underscore the crucial importance of faculty presence in the success of student learning.

BACKGROUND The Paradigm Shift An ever increasing number of colleges and universities are transferring courses from face-to-face (F2F) classroom meetings to online learning environments, as students seek out different sources for their educational experience (Welch & Napoleon, 2015). More than 6.7 million students

reportedly took at least one online course during the fall semester of 2011, 570,000 more students enrolled themselves in distance education than those in the previous year (Welch & Napoleon, 2015). Recently, more than 60% of administrators primarily in charge of academics at more than 2,800 colleges and universities in the United States made clear that shifting courses from F2F meetings to online was critical to their long-term strategies (Allen & Seaman, 2013). With the ever expanding online education and given that online instruction differs distinctively from the traditional F2F instruction (Roman, Kelsey, & Lin, 2010), roles that an online instructor play in a virtual learning environment deserve a great deal of attention, as they underline the necessity of teacher presence in an online learning environment. Hernández et al.’s (2010) study focused on the roles an instructor played in both e- and traditional learning environments. The researchers performed a comparative analysis of students’ perceptions with 33 participants involved in a F2F traditional teaching while 23 students engaged in an online environment. Both of the groups taught by the same instructor. Hernández et al found students’ perceptions varied regarding the roles that the instructor played in the F2F and online contexts. Generally, F2F group valued the instructor’s role in the learning process more highly than the online group. The findings suggest online instructors ought to make additional efforts to better student learning. reported that students of distance education classes performed poorly and some even were not able to complete online courses. Furthermore,

DOI: 10.4018/978-1-5225-2255-3.ch232 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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there seems to have higher dropout rates within online courses than F2F settings, which might be due to a lack of support from instructor and peers and which might be due to students feeling emotionally isolated (Artino & Jones, 2012; Dabbagh & Kitsantas, 2012). The reported findings offer a strong indication that online instructors need to meet the needs of students (Orso & Doolittle, 2012; Welch, Napoleon, Hill, & Rommell, 2014) by playing a variety of roles in online learning classrooms. Hence, in the paradigm shift, a crucial need is to understand roles that online instructors play in a virtual learning environment (Dennen, Darabi, & Smith, 2007) in order to highlight the paramount importance of faculty presence in an online learning environment.

Faculty Presence in Online Learning Environment Faculty presence originates from. These researchers termed teacher presence as “teaching presence” and explained, “The concept of teaching presence is constitutively defined as having three categories – design and organization, facilitating discourse, and direct instruction” (p. 1). This explanation makes it clear that the presence of an online instructor embraces more than just answering emails and making announcements. With the presence of an online instructor, students would not feel like that they are situated in a “ghost town” (Online Learning Insight, 2012). According to (O’Reilly (2009)), there are five interaction points, through which online instructors could connect or interact with students. These five interaction points are announcements, email, discussion forums, feedback summaries, and chat sessions. Roles online instructors play through these avenues could also reflect the online instructors’ professional teaching dispositions, comprised of instructors’ beliefs, values, and attitudes (Welch & Napoleon, 2015). noted, “[D]ispositions have also been discussed as affective qualities including empathy,” which means that online instructors should make an effort

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to know students and to motivate them to learn. Motivation is closely related to students’ affect for learning, comprised of students’ attitudes, beliefs, and values toward learning (McCroskey, Richmond, & McCroskey, 2006). Students’ affective learning is inseparable from teacher presence governed by instructors’ beliefs, values, and attitudes.

Students’ Affective Learning Instructors’ beliefs, values, and attitudes also affect their verbal and non-verbal immediacy. Verbal immediacy is primarily concerned with ways instructors talk and facilitate student learning in the traditional classroom (Chang, 2011a), whereas nonverbal immediacy involves behaviors that are only observable to receivers or communicators, such as smile, “eye contact, body position, physical proximity, body movement” (Richmond, Gorham, & McCroskey, 1987 in Velez & Cano, 2008, p. 77). Instructor immediacy behaviors communicate positive relational affect (Velez & Cano, 2008): when there exist instructor immediacy behaviors, students feel close to their instructor (Christophel, 1990) and feel motivated to learn (Christophel, 1990; Velez & Cano, 2008). Students have a propensity to take satisfaction responding to questions and actively conceptualize and internalize knowledge (Krathwohl, Bloom, & Masia, 1964). However, when instructor verbal and nonverbal immediacy are only shown through video lectures, motivation may not be as powerful as when there is an interaction between instructor and student. In a text-based teaching and learning environment, such as discussion asynchronous forums, non-verbal immediacy is non-existent, so is verbal. Then, if instructors are “hiding” from students, a sense of insecurity and overwhelming feeling arises, which could be detrimental to otherwise enthusiastic learning desires. It is apparent that in an online learning environment, an instructor should make an effort to increase students’ affect for learning (McCroskey et al., 2006). noted that learning not only is emo-

Category: Educational Technologies

tionally oriented, but also cultivated by interacting with other people, in particular, with a course instructor. The presence of an instructor could make happen interactive communication between an instructor and students (Anderson et al., 2001). Therefore, addressing roles that instructors play is extremely necessary, as it could highlight the vital importance of teacher presence in an online learning environment.

MAIN FOCUS: ONLINE INSTRUCTORS’ ROLES Roles instructors play as facilitators for social instruction are critical in creating positive online learning environments and in promoting students’ academic engagement (Cho & Cho, 2014) and are vitally important to students’ learning satisfaction (Fedynich, Bradley, & Bradley, 2015). In the following text, the roles of an e-instructor are characterized horizontally by two categories: Pedagogical Efficacy, which chiefly focuses on

the promotion of students’ cognition (8 roles in total) and Affective Promotion, which largely focuses on the promotion of students’ affective learning (19 roles in total). In the category of Pedagogical Efficacy, there are two subtitles, namely, Knowledge Building (5 roles in total) and Instructional Preparation (2 roles in total). In the category of Affective Promotion, there are three subtitles, namely, Purposeful Commitment (9 roles in total), Purposeful Organization (4 roles in total) and Meaningful Management (6 roles in total). These roles are also set apart vertically across the two categories of Pedagogical Efficacy and Affective Promotion by three distinct stages, namely, Course Development (7 roles in total), Course Delivery (18 roles in total), and Course Completion (2 roles in total) (see Table 1) address these roles one by one, the author will present them in the form of stages in order of Course Development, Course Delivery, and Course Completion. When each of the online instructor’s roles is addressed in the following text, one of the subtitles along with the name of that particular role is placed

Table 1. Responsibilities and accountabilities of an E-instructor Stage

Course Development

Pedagogical Efficacy

Affective Promotion

Knowledge building

Instructional Preparation

Purposeful Commitment

Purposeful Organization

Inquiry

Decision Making

Course Conversion

Practice

Instructional Planning

Constructing

Meaningful Management

Redesigning and Revising Course Delivery

Course Completion

Learning Lecturing

Ongoing Assessment Ownership

Individualized Instruction

Encouraging

Open Mindedness

Monitoring Soliciting

Flexibility

Reminding

Announcement

Modifying

Acknowledging

Clarifying

Patience

Informed Decision

Receiving

Assisting

Reflective Practice

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in parentheses for easy reading. For example, (Knowledge Building/Inquiry) is referred to the role of inquiry an online instructor plays, which appears under Knowledge Building in the category of Pedagogical Efficacy.

COURSE DEVELOPMENT With respect to teaching presence, according to Anderson et al. (2001), this stage cannot be omitted. During the Course Development stage, in the category of Pedagogical Efficacy, the instructor assumes four roles, ranging from those of gaining technological skills to those of getting the course ready for teaching. That is, the instructor is responsible for acquiring necessary and useful technological skills (Seaton & Schwier, 2014) (Knowledge Building /Inquiry) and familiarizes the learned skills through practice (Knowledge Building/Practice). During this time, the instructor also needs to engage in research to decide the content of a course plan (Instructional Preparation/Decision Making). According to, the role an online instructor plays is to select and filter information for student learning. Carefully determining what information needs to be offered to online students is supported by, who found from interviewing eight e-instructors that the information covered in virtual learning environments was not equivalent to that in F2F settings. One of the interviewees noted, “I went from about 13 individual classes or modules to about six modules” (Wilson et al., 2003). Followed by the decision making, the instructor needs to lay out a course plan appropriate for the students’ learning needs (Chang, 2009b, 2014; Yang & Cornelius, 2005) (Instructional Preparation/Instructional Planning). Online instructors need to facilitate well-considered discussion, which ought to be meaningful to students and which can maintain students’ desire for an in-depth study of concepts (Yang & Cornelius, 2005).

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During this course preparation stage, students’ affective learning cannot be neglected. An einstructor plays three roles in this process (Affective Promotion). The online instructor needs to keep in mind that designing an online course is by no means “curriculum conversion”(Roman et al., 2010), because this practice is insufficient to guide students in their acquisition of knowledge in a self-controlled manner (Chang, 2009c) (Purposeful Organization/Course Conversion). Learning through a virtual classroom seems intimidating to some and confusing to others. To minimize the degree of apprehension and anxiety, useful are an analysis of the learning environment and offering details of requirements and expectations (Chang, 2011a, 2011b). Such practices are intended to avoid the phenomenon that “an instructional design project may produce a theoretically sound but practically unable product” (Tessmer, 1990, p. 56) (Purposeful Organization/Constructing). The unable product could be viewed as a fabulously built online course, but in the end the course might make students feel isolated, frustrated, and intimidated due to a lack of timely and useful assistance (Chang, 2009b, 2014). O’Reilly (2009) reported that teacher presence reflected through interactive communication between an instructor and students could lead to remove some feelings of disconnect by online students. While instructors are present and interactive, students tend to be encouraged to learn. When an online instructor is present, monitoring students’ learning, autonomy-supportive teaching takes place, which could, in turn, promote students’ motivation and inspire independent and critical thinking (Reeve, 2009). With teacher presence, if a course is repeated in a following semester, an instructor is able to easily engage in modifying redesigning and revising the course (Purposeful Organization) (Chang, 2009b, 2014) in hopes to further positive affect student learning. According to, nearly all cognitive oriented teaching and learning is involved with an affective component.

Category: Educational Technologies

COURSE DELIVERY It is through the stage of Course Delivery that interactions between an instructor and students vastly transpire. Anderson et al. (2001) found this stage could not be omitted. In other words, this stage should see much interaction between an instructor and students rather than the amount of interaction is minimized, as a great deal of apps and software, such as online video lectures, podcast, etc., is utilized to dominate course instruction. Specifically, in this stage, the category of Pedagogical Efficacy, an instructor undertakes four roles and works as an academic guide to render needed and useful help to students (Chang, 2011a, 2011b). Along with interacting with students, the instructor needs to play a role of a learner, who continuously and consistently acquires knowledge relating to content areas and to technological skills (Knowledge Building/Learning). At the very beginning of this stage, lecturing (Knowledge Building/Lecturing) students to prepare them for online learning is of essence (Chang, 2009b, 2014; Yang & Cornelius, 2005). During the lecture, the instructor needs to be cautious not to drive students away by making a poor presentation, as some students may feel unsettled by the idea of independent learning (O’Reilly, 2009; Online Learning Insight, 2012). Therefore, needed is a clear elaboration of how to engage in independent study, how to manage one’s time, and how to self-regulate one’s learning, and how to navigate through an online course as the introduction of a course at the beginning of course delivery. In the course delivery stage, the online instructor cannot “rest” and let the online course run by itself (Chang, 2011a). Nor can the instructor only conceptualize that his or her responsibility is to respond emails about logistics and/or students’ questions. This stage should see teaching presence and social presence, which are expected to bring about cognitive presence (Anderson et al., 2001) and affective learning (Chang, 2011a, 2011b). Communication between an instructor and students plays an important role in shaping students’

views and their approach to learning (Armstrong, 2010). Individualized instruction (Knowledge Building) transpires as each communication is tailored to specific needs (Deutsch, 2013). Such practice could work like a fertile ground for individualized instruction, where an instructor could provide students with targeted informational and explanatory feedback (Chang, 2011a, 2011b, 2014; Rhode, 2008), encouraging them to think deeper. This process can provoke students’ thinking and provide students with opportunities to advance their learning, by revising and relearning concepts (Chang, 2014; Nicol & Macfarlane-Dick, 2006). The researchers furthered that feedback that was informational and explanatory was low-stakes assessment, helping students see what deficiencies existed in relation to criteria. If receiving useful and meaningful feedback regularly, students can become less uncertain and anxious about their learning than without (O’Reilly, 2009). The provision of feedback on individual assignments can, in turn, simultaneously provide useful lens for an instructor to know how to further improve the course design and teaching strategies (Chang, 2011a). This kind of interaction with students is ongoing assessment (Purposeful Commitment under the category of Affective Promotion), which is essential to high quality teaching and learning. Ongoing assessment allows for providing scaffolding in order to motivate students to construct knowledge. Pivotal is the instructor’s appropriate guidance and support, which helps develop students’ skills of self-responsibility (Yang & Cornelius, 2005), self-motivation, and a sense of autonomy (Reeve, 2009). These components are all crucial to students’ affective learning (Chang, 2011a). Students’ affect for learning during the course delivery stage apparently plays an essential role in the success of their learning. It is thus not surprised to see that an online instructor plays 15 roles in this aspect, which are three times more than those in the Course Development. To cultivate students’ affective learning, an e-instructor should be purposefully committed to employing various

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strategies to encourage learners to be owners of their own learning (Reeve, 2009) (Purposeful Commitment/Ownership and Purposeful Commitment/Encouraging). Thus, students may want to actively participate in online discussions and activities (Roberson, 2013). Monitoring (Purposeful Commitment) students’ learning and then appropriately assisting (Purposeful Commitment) them in their learning would help them ease the transition and boost their self-confidence in online learning (Chang et al., 2012). Furthermore, to gradually help students transit from the familiar to the unfamiliar learning environment, the instructor needs to frequently send out email, reminding (Purposeful Commitment) students of matters requiring their attention (Chang, 2009b, 2014). In the reciprocal interaction with students, it is fundamental for the instructor to understand that one student’s question may be representative of others’ and that emerging problems in the process of teaching and learning may become a potent opportunity for the instructor to reexamine the course design and instructional strategies. These can be the basis for the instructor to clarify (Purposeful Commitment/Clarifying) topics under discussion and to modify course content as well as ongoing approaches to teaching (Chang, 2009b, 2014) (Purposeful Commitment/Modifying). To further set up an emotionally supportive learning environment, assessments of the effect of course design and delivery are continuous throughout each semester. The instructor purposefully organizes a course by evaluating it based on unexpected email messages and communications with students in various forms such as online discussions, dialogues, and chats. For example, in receiving email messages sent by students (Purposeful Organization), an instructor needs to read them carefully, as he or she may be able to learn which assignments are most or least helpful to students. In communicating with students, an instructor could also take advantage of every available opportunity to not only provoke students’ thinking, but also solicit students’ reactions toward online course delivery or arrangement

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(Purposeful Organization/Soliciting). Information gained through such a process is conducive to the improvement of instruction. In interacting with students, it is beneficial for an online instructor to let students know that assistance is readily available to them via email or phone calls (Meaningful Management/ Assisting). In a geographically isolated learning setting, such an act of an instructor would make e-students feel comfortable, as it demonstrates that the instructor sincerely cares about their online learning (Chang, 2009b, 2014). To support and sustain students’ affective learning, the instructor must also maintain an open mind in order to learn about and understand students’ needs and learning levels (Meaningful Management/Open Mindedness). During the Course Delivery stage, many unexpected events may occur. It requires the instructor to remain flexible and creative (Meaningful Management/Flexible and Creative) to appropriately deal with emergent matters and to promote student learning. In addition, the instructor also needs to utilize emails or Announcement in a course management system (CMS) to inform e-students of class-related information and business that is unrelated to course content (Meaningful Management/Announcement). Giving appropriate and relevant praise could positively encourage and further motivate students to learn (Rhode, 2008). Therefore, the instructor needs to acknowledge the efforts made by students, as the acknowledgement can encourage their continuous endeavors and promote their intrinsic motivation (Meaningful Management/ acknowledging) (Chang, 2009c). Patience plays an important role in online teaching as well (Meaningful Management). Sometimes, an explanation of an assignment needs to be repeated. Sometimes, one concept may require several elaborations. If students fail to understand points embedded in readings, the instructor should ask them to re-read required class materials and/or offer guidance, if needed (Chang, 2009b, 2014).

Category: Educational Technologies

COURSE COMPLETION During this stage, an instructor assumes two roles. One is concerned with decision making, which is in the category of Pedagogical Efficacy while the other is self-reflection, which is included in the category of Affective Promotion. Although Anderson et al. (2001) did not include this stage, I believe they are not only essential, but also crucial in the success of online instruction. The first role is that an online instructor needs to discern and analyze the data collected through the entire semester so that he or she can make decisions about next semester’s teaching and learning (Instructional Preparation/Informed Decision). The collected data may include students’ email, students’ feedback about a course, students’ survey results, and informal conversations between an instructor and students. These resources may enable the instructor to seek answers to questions of course improvement. Some of these questions may be: What has been achieved in the past semester? What has been viewed as a failure or failures? What lessons should be drawn to improve future online instruction? The instructor answers these questions through reflective practice (Purposeful Commitment) to showcase his or her purposeful examination of performance in order to know how to improve instruction.

FUTURE RESEARCH DIRECTIONS Technology has developed rapidly, so is distance education or e-learning (Margalina, De-PablosHeredero, & Botella, 2014). However, alarming is a higher drop-out rate of online learners than that of traditional learners (Rauscher & Cronje, 2005). In order to increase retention rates and to prevent from an increase in online learners’ dropout rate, noted technology should not be the target of blame for poor students’ learning. How a course is arranged and designed and how frequently communication takes place between an instructor and students matter more than what technology can

do. Clark (1994) made an analogy: “... media are mere vehicles that deliver instruction, but do not influence learner achievement any more than the truck that delivers our groceries causes changes in our nutrition’ (in Rauscher & Cronje, 2005, p. 107). In bringing about students’ learning in good quality, online instructors should pay more attention to how and why technology is used than to what technology needs to be included in teaching and learning. That is, online instructors should be fully aware of how “to be there” for students while technology delivers a course, as what instructors do or do not do could indeed have an effect on the success or failure of student learning. One aspect that determines students’ learning success or failure is students’ motivation to learn. Yet, in a virtual learning environment, the level of students’ motivation to learn ought be higher than that in a F2F setting (. Unfortunately, it does not seem the case. ascribed the high dropout rates of e-learners to a lack of motivation. As such, technology alone cannot possibly turn around the situation. It definitely require the presence of an online instructor in a virtual learning environment to employ various strategies to increase students’ motivation to learn, which poses pedagogical challenges to online instructors in transitioning from F2F to online settings (Park, Johnson, Vath, Kubiskey, & Fishman, 2013). It is always a challenge for instructors to know how to interact with online learners to build positive relationships with them (Roman et al., 2010). Therefore, it is necessary for colleges and universities to methodically devise a comprehensive plan that can aptly offer quality training and support (Deutsch, 2013; Park et al., 2013) for would-be-e-instructors and online instructors to enhance quality learning (Chang, 2011a; Roman et al., 2010). Good quality training enables online instructors not only to understand online instructional tools, but also develop knowledge as to what they could do to be present and how to attend to students’ affective learning. Such training should be a professional development and a prerequisite for success in online teaching (Margalina et al., 2014; Roman et al., 2010). In this

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way, faculty can develop knowledge and skills to seriously and carefully attend to students’ affective learning (Chang, 2011a). Teaching and learning are not simply about delivering information and/or completing assigned tasks, but they are concerned about students’ state of emotion in learning, which leads to their motivation to learn.

CONCLUSION Online learning isolates learners and the instructor, leaving the community members feeling lonely and, sometimes, frustrated, which could vastly impede teaching and learning. To reduce the level of discomfort, the instructor must be “visible” (Chang, 2009b, 2014) by assuming various roles. Therefore, not only could an instructor promote students’ cognition, but also attends to their affective learning. This article addresses the vital importance of faculty present in a virtual learning environment by focusing on various roles that an online instructor should play in an entire process of teaching and learning. There are eight roles total in the category of Pedagogical Efficacy, which are intended to promote student learning. These roles range from assisting students in gaining technological skills to teaching them academic content. This category also demonstrates that the instructor is responsible for acquiring technological skills and becomes familiar with newly learned skills through practice. There are 19 roles in the category of Affective Promotion—twice as many roles as in Pedagogical Efficacy. Affective learning has been recognized as more potent and influential than academic efforts in student learning (Rauscher & Cronje, 2005), as students’ affective learning is just as critical a dimension of learning as cognition. The roles that an online instructor assumes in this category include interactive communication between an instructor and students, encouragement, acknowledgement, and other logistics. Attending to students’ affective learning via every possible

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avenue is likely to motivate students to learn and support their cognition, as students’ emotions do affect their quality learning (Rauscher & Cronje, 2005); Instructors’ scaffolding in a virtual learning environment is also essential to students’ success of learning. In short, all these roles require an instructor to be present and visible in an online learning environment, as technology alone cannot promote students affect for learning.

REFERENCES Allen, I. E., & Seaman. (2013). Changing course: Ten years of tracking online education in the United States. Babson Survey Research Group. Anderson, T., Rourke, L., Garrison, D. R., & Archer, W. (2001). Assessing Teaching presence in a Computer Conference Environment. Journal of Asynchronous Learning Networks, 5(2), 1–17. Armstrong, D. (2010). A qualitative study of undergraduate students’ approaches, perceptions, and use of online tools. Academic Press. Artino, A. R., & Jones, K. D. (2012). Exploring the complex relations between achievement emotions and self-regulated learning behaviors in online learning. Internet and Higher Education, 15, 170-175. Chang, N. (2009b). Facilitating roles an instructor undertakes in a virtual learning environment (2nd ed.). Hershey, PA: Information Science Reference. Chang, N. (2009c). Can students improve their learning with the use of an instructor’s extensive feedback assessment process? International Journal of Instructional Technology and Distance Learning, 6(5), 32. Chang, N. (2011a). Formative assessment and feedback with teacher immediacy behaviors in an e-text-based context. Hershey, PA: IGI Global. doi:10.4018/978-1-61520-983-5.ch015

Category: Educational Technologies

Chang, N. (2011b). Pre-service teachers’ views: How did e-feedback through assessment facilitate their learning? Journal of Scholarship of Teaching and Learning, 11(2), 16–33.

Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1964). Taxonomy of educational objectives: Handbook II: Affective domain. New York: David McKay Co.

Chang, N. (2014). Roles of online instructors apt for students’ cognitive and affective learning (3rd ed.). Hershey, PA: Information Science Reference.

Margalina, V. M., De-Pablos-Heredero, C., & Botella, J. L. M. (2014). Achieving job satisfaction for instructors in e-learning: The relational coordination role. International Journal of Human Capital and Information Technology Professionals, 6(4), 64–79. doi:10.4018/IJHCITP.2015100104

Cho, M. H., & Cho, Y. (2014). Instructor scaffolding for interaction and students’ academic engagement in online learning: Mediating role of perceived online class goal structures. Internet and Higher Education, 21, 25-30. Christophel, D. M. (1990). The relationships among teacher immediacy behavior, and student motivation, and learning. Communication Education, 39(4), 321–340. doi:10.1080/03634529009378813 Dabbagh, N., & Kitsantas, A. (2012). Personal learning environments, social media, and selfregulated learning: A natural formula for connecting formal and informal learning. Internet and Higher Education, 15, 3-8. Dennen, V. P., Darabi, A., & Smith, L. J. (2007). Instructor-learner interaction in online courses: The relative perceived importance of particular instructor actions on performance and satisfaction. Distance Education, 28(1), 65–79. doi:10.1080/01587910701305319 Deutsch, N. M. (2013). Skills teaching online vs. face-to-face classes. Retrieved from http://blog. wiziq.com/online-teaching-skills Fedynich, L., Bradley, K. S., & Bradley, J. (2015). Graduate students’ perceptions of online learning. Research in Higher Education Journal, 27, 1-13. Hernández, A. B., Gorjup, M. T., & Cascón, R. (2010). The role of the instructor in business games: A comparison of face-to-face and online instruction. International Journal of Training and Development, 14(3), 169–179. doi:10.1111/ j.1468-2419.2010.00350.x

McCroskey, J. C., Richmond, V. P., & McCroskey, L. L. (2006). The role of communication in instruction: The first three decades. Mahwah, NJ: Lawrence Erlbaum. Morrison, D. (2012). Strategies for online instructors: Understanding the needs of the online learner. Retrieved from http://onlinelearninginsights.wordpress.com/2012/08/20/strategies-foronline-instructors-understanding-the-needs-ofthe-online-learner Nicol, D., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. doi:10.1080/03075070600572090 OReilly, K. (2009). Faculty presence promotes quality of education in the online asynchrounous classroom. Contemporary Issues In Education Research, 2(3), 53–58. doi:10.19030/cier.v2i3.1087 Orso, D., & Doolittle, J. (2012). Instructor characteristics that affect online student success. Faculty Focus. Retrieved from http://www.facultyfocus. com/articles/online-education/instructorcharacteristics-that-affect-online-student-success/ Park, G., Johnson, H., Vath, R., Kubiskey, B., & Fishman, B. (2013). Examining the roles of the facilitator in online and face-to-face professional development contexts. Journal of Technology and Teacher Education, 2(2), 225–245.

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Rauscher, W. J., & Cronje, J. C. (2005). Online with Krathwohl: Affective aspects of learning in an online environment. SAJHE, 19(3), 104–118. Reeve, J. (2009). Why teachers adopt a controlling motivating style toward students and how they can become more autonomy supportive. Educational Psychologist, 44(3), 159–175. doi:10.1080/00461520903028990 Rhode, J. (2008). Roles and responsibilities of the online instructor. Retrieved from http://www. slideshare.net/jrhode/roles-and-responsibilitiesof-the-online-instructor Rippe, C. (2009). Using rubrics to improve teaching, learning, and retention in distance education. Faculty Focus. Retrieved from http://facltyfocu. com/wp-content/uploads/images/facultydev-indistanceed.pdf Roberson, K. (2013). Teaching techniques that establish relevance, promote autonomy. Faculty Focus. Retrieved from http://www.facultyfocus. com/articles/effective-teaching-strategies/motivating-students-with-teaching-techniques-thatestablish-relevance-promote-autonomy/ Roman, T., Kelsey, K., & Lin, H. (2010). Enhancing online education through instructor skill development in higher education. Online Journal of Distance Learning Administration, 8(4). Seaton, J. X., & Schwier, R. (2014). An exploratory case study of online instructors: Factors associated with instructor engagement. International Journal of E-Learning & Distance Education, 29(1), 1–16. Tessmer, M. (1990). Environment analysis: A neglected stage of instructional design. Educational Technology Research and Development, 38(1), 55–64. doi:10.1007/BF02298248 Velez, J. J., & Cano, J. (2008). The relationship between teacher immediacy and student motivation. Journal of Agricultural Education, 49(3), 76–86. doi:10.5032/jae.2008.03076

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Welch, A., & Napoleon, L. (2015). Professional teaching dispositions of online instructors: Why they matter. Procedia: Social and Behavioral Sciences, 171, 584–589. doi:10.1016/j.sbspro.2015.01.164 Welch, A., Napoleon, L., Hill, B., & Rommell, E. (2014). Virtual teaching dispositions scale© (vtds): A multidimensional instrument to assess teaching dispositions in virtual classrooms. Journal of Online Learning and Teaching, 10(3). Wilson, D., Varnhagen, S., Krupa, E., Kasprzak, S., Hunting, V., & Taylor, A. (2003). Instructors’ adaptation to online graduate education in health promotion: A qualitative study. Journal of Distance Education, 18(2), 1–15. Yang, Y., & Cornelius, L. F. (2005). Preparing instructors for quality online instruction. Online Journal of Distance Learning Administration, 8(3).

ADDITIONAL READING Alvarez, I., Espasa, A., & Guasch, T. (2012). The value of feedback in improving collaborative writing in an online learning environment. Studies in Higher Education, 37(4), 387–400. doi:10.1080 /03075079.2010.510182 Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36–53. doi:10.1016/j. compedu.2015.09.005 Hung, W. C., & Jeng, I. (2013). Factors influencing future educational technologists intentions to participate in online teaching. British Journal of Educational Technology, 44(2), 255–272. doi:10.1111/j.1467-8535.2012.01294.x

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Krause, U., & Stark, R. (2010). Reflection in example- and problem-based learning: Effects of reflection prompts, feedback and cooperative learning. Evaluation and Research in Education, 23(4), 255–272. doi:10.1080/09500790.2010.5 19024

KEY TERMS AND DEFINITIONS Affective Promotion: Encompasses endeavors and strategies made by an e-instructor in fostering students’ emotional involvement in e-learning and in setting up an emotionally supportive learning environment to facilitate student learning. Instructional Preparation: Is related to avenues in which an e-instructor is engaged to make decisions based on information at hand as well as collected through previous experiences of working with students in order to help plan instruction suited to learners’ needs. Knowledge Building: Refers to an e-instructor, who keeps professionally up-to-date through self-development and learning alongside students and who attains technological knowledge and skills by attending relevant workshops and the frequent interaction with a computer. Meaningful Management: Refers to an einstructor who manages a course in ways that may help ease students’ unnecessary frustration resulting from their being situated in a novel learning environment. This type of course management

aims to promote students’ affective learning in the virtual classroom. Pedagogical Efficacy: Refers to the growth and development of both faculty and students concerning academics-oriented knowledge and skills ranging from content-specific areas to technological skills through efforts exerted by an e-instructor. Purposeful Commitment: Refers to an einstructor who is committed to helping students become owners of their own learning by the instructor becoming visible through various means in the shared virtual classroom in order to support learning. Purposeful Organization: Refers to an einstructor who is committed to helping students become owners of their own learning, achieved when the instructor becomes visible through various means in the virtual classroom. Reflective Practice: Refers to an e-instructor’s consistent behaviors in assessing the course by an ongoing, even daily, basis as well as at the end of a semester in order to motivate learners to succeed in learning. Teacher Presence: Refers to an instructor, who learns new technology, acts as an instructional developer, designs and organizes an online course prior to its commencement, who is there for students, scaffolds and facilities learning, and promotes students’ affect for learning during course delivery, and who evaluates and reflects on his or her performance to modify it for improvement.

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Electrical Engineering

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Category: Electrical Engineering

Mechanisms of Electrical Conductivity in Carbon Nanotubes and Graphene

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Rafael Vargas-Bernal Instituto Tecnológico Superior de Irapuato, Mexico

INTRODUCTION In the search for alternative materials to semiconductor materials used commonly in electronics such as silicon, germanium, gallium arsenide, gallium phosphide, etc., researchers around the world have been developing carbon-based materials with ideal electrical properties to operate with high efficiency in nanoelectronics. Carbon nanotubes (CNTs) and graphene represent two technological options for these innovative materials, which can be used either individually, or in composite or hybrid materials as electrical filler. They offer electrical properties such as high electrical conductivity and high dielectric permittivity, which can be tuned by synthesis, doping, functionalization, etc. These qualities can be exploited in applications such as interconnects, electronic devices such as field-effect transistors, batteries, fuel cells, supercapacitors (Yusoff, 2015), electrodes for touch screens (Zheng, 2015), flexible transparent memory circuits, materials for electrostatic discharge (ESD) and electromagnetic interference (EMI) shielding (Vargas-Bernal, 2015c), etc. This chapter will review the most important electrical transport mechanisms associated with the electrical conductivity of carbon nanotubes and graphene, since these can be used in individual way or within composite or hybrid materials, with the aim of discovering the origin of their extraordinary electrical properties than have been used, are being used, and will be used in diverse technological applications. The effect of a set of technical variables related with electrical behavior of carbon nanotubes and graphene, and associated with the electrical conductivity such as band gap,

intrinsic mobility, percolation threshold, electrical conductivity, and dielectric permittivity, are also discussed.

BACKGROUND Electrical conduction can be defined as the movement of electrical carriers through a transmission medium. A transmission medium is a material substance that transmits or guides through of itself electromagnetic waves. This movement of carriers generates an electrical current in response to an electrical field. Moreover, in each type of material, different mechanisms of electrical conduction are presented. For example, electrons are electrical carriers in metals, and the Ohm’s law is the mathematical relationship used to determine the mathematical expression between the electrical current (I) and the applied potential difference (V) between a pair of ends of the material (Bird, 2014): I=

V = VG, R

(1)

where R and G are electrical resistance and electrical conductance, respectively. Thus, one or more electrons from each atom can move freely within the metal, since they are loosely bound to the atom in the higher level of the valence band. These electrons are incorporated to the conduction band as electrical carriers due to the potential difference, and therefore, an electrical current is generated. An electrical current is a flow of electrical charge carried out regularly by moving electrons through a medium.

DOI: 10.4018/978-1-5225-2255-3.ch233 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Mechanisms of Electrical Conductivity in Carbon Nanotubes and Graphene

Electrical conductivity (σ) also called specific conductance can be defined as the ability of a material for conducting an electrical current. In threedimensional conductor materials, the electrical conductance can be mathematically expressed as: G=

A Wt Wt σ = = , ρL ρL L

(2)

where A is the cross-sectional area, L is the length, W is the width, t is the thickness, and, ρ and σ are electrical resistivity and electrical conductivity of the material, respectively. Two different types of electrical conductivities can be found in materials: surface conductivity and bulk conductivity. Surface conductivity or sheet conductance quantifies the electrical conductance of thin films with uniform thickness nominally. This represents the rate between the electrical conductivity of the material, and the thickness of the thin film. Therefore, it is mathematically expressed as: Gs =

t = σt, ρ

(3)

whose units are square per Ohm or Siemen square or denoted by sq/Ω or □/Ω or S·sq or S·□, which is dimensionally equal to an Siemen. Bulk conductance, specific electrical conductance, or volume conductivity (σ) is expressed in units of Siemens per meter (S/m). Materials can be electrically classified in accordance with their conductivities as conductive, static conductive, or static dissipative (Grady, 2011). The surface conductivity regimes for each are approximately greater than 10-4 sq/Ω, 10-6−10-4 sq/Ω, and 10-12-10-6 sq/Ω, respectively. Moreover, the corresponding volume conductivity regimes are > 0.1, 10-3−10-1, and 10-9−10-3 S/m. Two applications can be identified in accordance with the value of conductivity: electromagnetic interference (EMI) shielding and electrostatic dissipation (ESD). EMI shielding uses materials with

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high conductivity, while ESD requires materials with low conductivity. In materials such as insulators and semiconductors, there is an energy range called forbidden band or band gap, where electron energy states cannot exist between the top of the valence band and the bottom of the conducting band.

MAIN FOCUS OF THE ARTICLE Carbon nanotubes and graphene has a structure of conjugated system, where a system of connected p-orbitals with delocalized electrons in atoms, presents alternating single and multiple bonds, which in general may lower the overall energy of the system and increase stability. Two p-orbitals form a π-bond. A π-bond is a covalent chemical bond, where two lobes of one atomic orbital are overlapped to other two lobes of the other atomic orbital involved. Therefore, the π-electrons do not belong to a single bond or atom, but rather to a group of atoms (Jug, 2001). The conjugation can be viewed as the overlap of one p-orbital with another across a sigma bond. A simple model of the energy levels can be considered as a quantum mechanical problem of a one-dimensional particle, representing the movement of a π-electron along a long conjugated chain of carbon atoms as found in carbon nanotubes and/or graphene. In this model, the lowest possible absorption energy corresponds to the energy difference between the highest occupied molecular orbital (HOMO), and the lowest unoccupied molecular orbital (LUMO). Almost all electronic transitions in conjugated π-systems are carried from a bonding molecular orbital (MO) to an antibonding MO (π to π*), but electrons from non-bonding Lone pairs (pair of valence electrons that are not shared with another atom) can also be promoted to a π-system MO (n to π*) in charge-transfer complexes. A HOMO to LUMO transition is carried out by an electron if it is allowed by the selection rules for electromagnetic transitions. Thus, the electrical conductivity is guaranteed at using carbon nanotubes and/or

Category: Electrical Engineering

graphene in electronic devices. Carbon nanotubes can achieve electrical conductivities between 100 and 200E03 S/m, while graphene can achieve an electrical conductivity between 1,738 S/m and 100E06 S/m. The hexagonal lattice, found in the carbon nanomaterials, possesses the longest mean free path of any known material, in the order of microns. Therefore, a ballistic transport is presented in these materials, since the distance that an electron can travel freely without bumping into anything, or having its path disrupted by scattering is large, which reduces their electrical resistance even at room temperature. Graphene is an allotrope of carbon, formed by a simple sheet of graphite, which is one-atom thick; with carbon atoms arranged in a regular hexagonal pattern (see Figure 1). It has an extremely low weight, since, a graphene sheet with an area of one square meter; this material only weights 0.77 grams. As a semi-metallic material, this material is a semiconductor, and also it has high electron mobility at room temperature. Graphene has no bandgap, since its conduction and valence bands fulfill the Dirac points. It is possible to induce a

small bandgap in graphene by doping. A material without band gap can convert all wavelengths of light to electrons, with energy levels that even did not found in semiconductor materials, and thus, graphene is a wonderful candidate for use in photovoltaic cells. Graphene has high energy density and/or highest current density (a million times that of copper), that favors the storage capacity and rate of charging/discharging, which make it suitable for batteries and electrical supercapacitors. It has the highest intrinsic mobility (100 times more than silicon) between 15,000 cm2·V−1·s−1 and 200,000 cm2·V−1·s−1 nearly unaffected by the temperature (10K and 100K), a fact that hints at defect scattering, being the dominant scattering mechanism (Tanaka, 2014). Graphene has a unique combination of properties that is ideal for next-generation electronics, including mechanical flexibility, high electrical conductivity, and chemical stability. The graphene presents a very high electrical conductivity, thanks to its zero-overlap semimetallic behavior, with holes and electrons as charge carriers. The electrons in carbon atoms are distributed in the following way: 2 in the inner shell and 4 in the outer shell. Three outer electrons

Figure 1. Different types of graphene sheets in accordance with their chiral indices (n,m) and chiral angle (ψ)

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are connected to other 3 carbon atoms, and the fourth electron is left free to be exploited in the electrical conductivity. These free electrons are called pi (π) electrons and they are located in both sides of the graphene sheet. Moreover, these electrons overlap, and improve the carbon-to-carbon bonds in graphene, leading to electronic properties established by the bonding and anti-bonding, of the orbitals associated with them. Three different types of sheets of graphene can be identified: armchair, zigzag and chiral, in accordance with the chiral indices, as is illustrated in Figure 1 (Vargas-Bernal, 2015b). Zigzag graphene nanoribbons have a metallic behavior, while armchair graphene nanoribbons can have behavior either metallic or semiconducting. This last depends of the number (N) of atoms across the width where k is the number of nanoribbons presented by the material, and therefore, when N=3k-1 a metallic behavior is obtained, if N=3k a semiconducting behavior is presented, and finally, if N=3k+1, a moderate semiconducting behavior is achieved (see Figure 2). Another important parameter which defines the electrical properties of graphene is its bandgap. Graphene has no bandgap, since its conduction and

valence bands fulfill the Dirac points. The Dirac points are six locations in momentum space, on the edge of the Brillouin zone, divided into two non-equivalent sets of three points. The two sets are labeled as K and K’, as is illustrated in Figure 3. Electrons through the graphene propagate according to the tight-binding model (electronic band structure), where they are energetically dispersed as a traveling wave within the medium. The mathematical relationship for determining 2D energy dispersion of the electrons in π bands of graphene is given as (Das, 2015):  3k a   k a   k a     x   cos  y  + 4cos 2  y  ε2D = ±γ 0 1 + 4cos   2   2   2   

(4) where γ0 = 3.033 eV is the nearest-neighbor overlap energy. The ε-k (energy versus wave vector) relationship of the electrical carriers is linear for lowest energies near the six corners of the 2D hexagonal Brillouin zone, which leads to zero effective mass for electrons and holes, as shown in Figure 3 (Shuai, 2012).

Figure 2. Electrical behavior of the graphene related with the number of atoms across the width of the nanoribbon of graphene and the wave vector k

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Figure 3. Band structure of graphene (ε-k relationship of graphene nanoribbons)

A carbon nanotube is an allotrope of carbon that can be conceptualized as a layer of graphite with one-atom-thick called graphene, which is wrapped as a cylinder. One, two or more layers of graphene can be concentrically wrapped giving place to a single-wall nanotubes (SWNTs), doublewall nanotubes (DWNTs) or multi-wall nanotubes (MWNTs). The wrapping of a graphene sheet can be identified by means of the indices (n, m), where n and m denote integer numbers representing the number of unit vectors along directions in the honeycomb crystal lattice. When m = 0, the carbon nanotubes are called zigzag nanotubes, if n = m they are called armchair nanotubes, and in otherwise, they are called chiral nanotubes, as depicted in Figure 4. The diameter of an ideal carbon nanotube is determined from its (n, m) chiral indices as: d=

3aCC π

n 2 + nm + m 2

E

Figure 4. Different types of carbon nanotubes according with chiral indices

(5)

where aCC = 0.142 nm is the carbon-carbon bond length, as is illustrated in Figure 5. The electrical properties of the carbon nanotubes are directly proportional to the value of the diameter, that is, great values of diameter imply high conductivity and vice versa. The chiral angle is defined as:

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Figure 5. Estimation of the diameter of carbon nanotubes according to the chiral indices

  2n + m   ψ = cos −1   2 n 2 + nm + m 2 

(6)

It allows distinguishing three classes of carbon nanotubes with different electrical properties: armchair (n=m, ψ = 30°) with a metallic behavior, zigzag (m=0, n> 0, ψ=0°) with a semiconducting behavior, and chiral (0Z…’). As the revenue goes to the miner of longer chain, selfish miners win over the honest miners and get the incentive (Arvind Narayan, 2013). The revenue that the selfish miners get increases super-linearly with the size of the group. So, the colluding miners usually try to add more miners into their group to increase their cumulative computational power. This snowball scenario

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helps them to avoid the situation in which the honest miners find the block before the selfish miners would find the second block, due to enhanced computational resources.

Anonymity Initially, when the Bitcoin protocol was designed, it was claimed that the protocol provides complete anonymity of both the sender and the receiver and they cannot be traced back. But recently, the researchers have found ways to identify the sender and the receiver by using some extra computational power. The Bitcoin community itself states that the current implementation is not very anonymous (Anonymity, n.d.). The Bitcoin protocol is now considered to be pseudo-anonymous. Each Bitcoin transaction has a list of inputs and outputs. The input contains previous transactions so that the miners would verify that the bitcoins are not already spent (to avoid double spending). The transactions usually have two outputs; one output belongs to the receiver while the other output contains address of sender, to return the extra bitcoins. Using the reference to previous transactions from the input, transaction graphs can be built. These graphs can help in tracking the sender (Moser, 2013). People usually have to provide their personal information such as, copy of National ID card etc., to bitcoin exchanges, in order to buy bitcoins. Also, the transactions are stored publically in the block chain, so anyone can analyze these transactions to find the links between the previous owners of bitcoins.

Malware Attacks The cyber-criminals use malicious software to attack systems that have installed the Bitcoin wallets. In most cases, the purpose is to steal critical information. They try to expand the attack by targeting more and more machines, creating a botnet. The Bitcoin wallets are the digital place to store the private keys used to access bitcoins. If the keys are compromised, all of the bitcoins are lost. To attack the bitcoin wallets, the attacker 2876

writes some malicious code and spreads it to the botnet. The malware steals data from the wallet. dat file which contains sensitive information related to the keys and user preferences. Therefore, it is a common security practice to encrypt this file. However, it has been identified that some of the recent malware detected have key logging capability which lets them steal the password to decrypt the required file. Khelios and IRC are some known malwares that do this job for the attackers (Blasco, 2013). MtGox, allegedly got bankrupt due to this attack. It lost 850,000 bitcoins, out of which, 750,000 bitcoins belonged to customers (arXiv, 2014). Apart from attacking the Bitcoin wallets, some malware are designed to attack the computational resources of different machines. The incentive driven mining process motivates the miners to increase their computational power in order to get more and more reward. The attackers have now found a way to win the competition. They write and install Bitcoin daemons on victims’ machine and connect it to the mining pool. The attacker then uses victims’ computational power to mine bitcoins. As, the computational power is enhanced, the chances of solving the PoW increase, resulting in increase in miners’ incentive. Such malware is spread through fake emails, Skype, and phishing websites. Some of the known malware are Zeus, Dorkbot and Ufasoft (Blasco, 2013).

COMPARISON WITH OTHER CRYPTO-CURRENCIES Bitcoin became the pioneer crypto-currency by introducing the concept of decentralized peer-topeer cryptography based digital currency. The idea of Bitcoin has paved the way for the creation of many other crypto-currencies which have been collectively termed as the ‘altcoins’. The altcoins are considered to be modified and improvised versions of Bitcoin but still lag behind in terms of acceptance, market capitalization and liquidity. Table 1 highlights distinguishing features of Bitcoin along with top three altcoins with respect to market capitalization.

Category: Electronic Commerce

Table 1. Table showing salient features of Bitcoin and other crypto-currencies CryptoCurrency

Features Hashing Algorithm

Mining Process

Current Mining Reward

Advantages

E

Disadvantages

Maximum Supply

Bitcoin (https:// en.bitcoin.it/ wiki/Main_ Page, n.d.)

SHA 256

Proof-ofwork

25 coins (halves after every 210,000 coins)

21million

• Greatest market capitalization among crypto-currencies. • Widely accepted by businesses.

• Requires extensive computational resources (ASICs) for mining (in comparison with Litecoin, CPU mining is not possible in Bitcoin) • Prone to various attacks. (51% attack, selfish mining, compromising anonymity).

Litecoin (LitecoinOpen Source P2P Digital Currency, n.d.)

SCRYPT*

Proof-ofwork

50 coins (halves after every 840,000 coins)

84million

• Greatest market capitalization among altcoins. • Faster transaction confirmation. • Higher transaction volume in the blockchain. • Market entry costs are very low which allow anyone with a computer and internet to mine litecoins.

• Lesser market acceptance. • Exposed to similar attacks as of bitcoin (51% attack, selfish mining, and compromising anonymity).

Peercoin (http:// peercoin.net)

SHA 256

Proof-ofwork + Proof-ofStake **

25 coins (halves after 16 times increase in the network) but as the reward decreases, reward will be proportional to the miner’s stake in the currency.

No limit (designed to reach an annual 1% inflation rate)

• No limit on maximum supply. • Lesser chances of 51% attack/monopoly or due to proof-of-stake system (as new coins are generated based on the holding of the individual.) • Energy efficiency (lesser power consumption required for proof-of-stake as compared to proofof-work that involves resource intensive hashing functions).

• Lesser market acceptance. • Rich get richer. • People get rewarded for hoarding peercoins.

Namecoin (http://www. econinfosec. org)

SHA 256

Proof-ofwork

25 coins (halves after every 210,000 coins)

21million

• Same implementation as Bitcoin. • Can be used for money transfer as well as for storing information (DNS or identification/ authorization) in the blockchain. • Provides decentralized DNS to prevent internet censorship.

• Lesser market acceptance. • Users have to pay for network fees along with transaction fees. • Prone to attacks like 51% attack and compromising anonymity.

* SCRYPT is slightly simpler algorithm as compared to SHA 256 which is less susceptible to ASICs (Application Specific Integration Circuits) designed for Bitcoin mining. (http://citeseerx.ist.psu.edu). It is designed to make mining accessible to everyone without the requirement of computational resources. (Scrypt.CC, n.d.) ** A hybrid of proof-of-work and proof-of-stake is used by Peerecoin in the mining process. According to the design, proof-of-work is used only in the initial generation of coins, but in the long term proof-of-stake would be used. Proof-of-stake reduces the chances of 51% attack by allowing coin generation based on miner’s holding in the network. (http://peercoin.net)

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FUTURE DIRECTIONS

Based on the features of each of the crypto-currencies, table 2 illustrates a list of crypto-currencies along with the possibilities of different attacks associated with them. The tick mark ✓ indicates the existence of a particular attack, whereas cross mark  indicates its absence. As discussed in section IV, Bitcoin is prone to attacks like double spending, selfish mining, compromising anonymity and malware attacks etc. Altcoins are designed either to provide additional functionalities on the top of the Bitcoin protocol or to fix the issues found in the protocol. Litecoin transactions have faster block generation rate and provide greater resilience to double spending attacks. As a security measure against double spending attacks, it is a standard practice for a merchant to wait for a sufficient number of blocks/confirmations to be added to its transaction’s block. Litecoins transactions can have more confirmations during the same time that is used with Bitcoin, therefore the probability of double spending attacks is lesser. Transactions in Litecoin, Peercoin and Namecoin are all pseudo-anonymous like Bitcoin. Malware attacks can be observed in all crypto-currencies and can be avoided by adopting efficient end user security practices. Peercoin prevents 51% attacks through proof-of-stake scheme which allows miners to generate coins based on their share in the network. A successful double spending attack would require an attacker to possess a very large number of coins, which is practically infeasible. Peercoin prevents selfish mining by providing a fair distribution of coins scheme through proofof-stake.

Digital currencies like Bitcoin are of growing interest to economists, cyber security experts, speculators and media. Never in history have people experienced a decentralized money transfer system that offered anonymous and unregulated transactions to be carried out with very less transaction fees. World’s unbanked population finds no choice other than Bitcoin for sending money to other people in the world. On a larger scale, the worldwide national adoption of decentralized and virtual currencies will result in economic neutrality and political transparency. It cannot be said with surety that Bitcoin or any other crypto-currency will be able to replace the real currency but there is a great desire for the digital currencies. Human beings are considered to be the weakest link in the security chain, therefore there is a dire need of a decentralized and trustless system as it is better to trust on mathematical operations than humans. A large number of credible researchers and entrepreneurs are investing their time and money in this domain. The advantages that digital currency systems provide over physical monetary systems cannot be overlooked. However, certain economic and security challenges act as a hurdle in world-wide adoption of virtual currencies. In order to ensure successful and world-wide adoption in future, security weaknesses in the protocol need to be dealt with. Moreover, price fluctuations are causing inconvenience for businesses accepting bitcoins as they have to adjust their prices accordingly.

Table 2. Crypto-currencies and their possible attacks Crypto-Currency

Attacks Double Spending

Selfish Mining

Compromising Anonymity

Malware Attacks

Bitcoin

✓

✓

✓

✓

Litecoin



✓

✓

✓

Peercoin





✓

✓

Namecoin

✓

✓

✓

✓

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In short, Bitcoin is evolving with progress in economic and technological conditions and its future is quite vague. If the Bitcoin community puts its efforts in resolving the inherent security issues in its protocol, then Bitcoin surely has the capability of being a strong competitor of real currency.

CONCLUSION In this chapter, we have studied the Bitcoin protocol from a critical perspective and have highlighted various vulnerabilities that need to be addressed. We have presented a holistic survey of attacks that can take place during the Bitcoin transactions. We also discuss a comparative analysis of Bitcoin and other crypto-currencies by highlighting their specific features, advantages and disadvantages and possible attacks. Based on our analysis, it is suggested that changes need to be made to the Bitcoin protocol by the consensus of the open source community in order to overcome the weaknesses.

REFERENCES Ahamad, Nair, & Varghese. (2013). A Survey on Crypto Currencies. Retrieved November 11, 2015, from http://citeseerx.ist.psu.edu/viewdoc/downlo ad?doi=10.1.1.428.7952&rep=rep1&type=pdf Anonymity. (n.d.). Retrieved October 4, 2015, from https://en.bitcoin.it/wiki/Anonymity Back, A. (2002). Hash Cash-A Denial of Service Countermeasure. Retrieved June 24, 2015, from http://www.hashcash.org/papers/hashcash.pdf Bitcoin ATM News. (n.d.) Retrieved October 4, 2015, from http://www.coindesk.com/technology/ bitcoin-atm/ Certicom Research. (2000). SEC 2: Recommended Elliptic Curve Domain Parameters. Retrieved January 28,2016, from http://www.secg.org/ SEC2-Ver-1.0.pdf

Christin, T. M. (2013). Beware the middleman: Empirical analysis of bitcoin-exchange risk. In Financial Cryptography and Data Security (pp. 25–33). Springer. Controlled Supply. (n.d.). Retrieved 4 June 2015, from https://en.bitcoin.it/wiki/Controlled_supply Crypto-Currencies Market Capitalizations. (n.d.) Retrieved January 28, 2016, from http://coinmarketcap.com/ Drainville, D. (2012). An analysis of the bitcoin electronic cash system. Waterloo, Canada: University of Waterloo. Emerging Technology from the arXiv. (2014). The Troubling Holes in MtGox’s Account of How It Lost $600 Million in Bitcoins. Retrieved October 4, 2015, from http://www.technologyreview. com/view/526161/the-troublingholes-in-mtgoxsaccount-of-how-it-lost-600-million-in-bitcoins/ Eyal, I., & Sirer, E. G. (2014). Majority is not enough: Bitcoin mining is vulnerable. In Financial Cryptography and Data Security (pp. 436–454). Springer Berlin Heidelberg. doi:10.1007/978-3662-45472-5_28 Jamie Blasco. (2013) How cybercriminals are exploiting Bitcoin and other virtual currencies. Retrieved October 4, 2015, from http://www. alienvault.com/open-threatexchange/blog/howcybercriminals-are-exploiting-bitcoin-andothervirtual-currencies Kalodner, H., Carlsten, M., Ellenbogen, P., Bonneau, J., & Narayanan, A. (n.d.). An empirical study of Namecoin and lessons for decentralized namespace design. Retrieved November 11, 2015, from http://www.econinfosec.org/archive/ weis2015/papers/WEIS_2015_kalodner.pdf King, S., & Nadal, S. (2012). PPCoin: Peerto-Peer Crypto-Currency with Proof-of-Stake. Retrieved November 11, 2015, from http:// peercoin.net: http://peercoin.net/assets/paper/ peercoin-paper.pdf

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Litecoin. (n.d.). Retrieved November 11, 2015, from https://litecoin.org/ Love, D. (n.d.). $500 Million Worth of Bitcoin Has Been Stolen Since 2010. Retrieved October 4, 2015, from http://www.businessinsider.com/ how-many-bitcoins-have-been-stolen-2014-3 Moser, M. (2013). Anonymity of bitcoin transactions. Paper presented at M¨unster Bitcoin conference, M¨unster,Germany. Mt. Gox. (n.d.). Retrieved 4 June, 2015, from https://en.bitcoin.it/wiki/Mt. Gox

Moore, T. (2013). The promise and perils of digital currencies. Androulaki, E., Karame, G. O., Roeschlin, M., Scherer, T., & Capkun, S. (2013). Evaluating user privacy in bitcoin. In Financial Cryptography and Data Security (pp. 34–51). Springer Berlin Heidelberg. Poelstra, A. (2014). A Treatise on Altcoins.

KEY TERMS AND DEFINITIONS

Yelowitz, A., & Wilson, M. (2015). Characteristics of Bitcoin users: An analysis of Google search data. Applied Economics Letters, 22(13), 1030–1036. doi:10.1080/13504851.2014.995359

Altcoins: All the crypto-currencies other than the Bitcoin. Crypto-Currencies: The type of digital currencies that use cryptography for their creation, security and transference. Digital Currencies: The currencies that use internet for their transference. Digital Signature: A mathematical scheme for ensuring the authenticity of a message. The sender encrypts the message using its private key in order to create a digital signature. By doing this, the sender adds his identity to the message since the private key is only owned by that particular sender. Proof-of-Work: A defense mechanism against Denial of Service attacks in which the service requester has to perform some mathematical task before accessing the services. Public Key Cryptography: An encryption technique in which each entity possesses a key pair (public/private key). In comparison with symmetric encryption in which single key is shared among the sender and the receiver, the public key cryptography involves two keys without the need of sharing them.

ADDITIONAL READING

ENDNOTE

Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Retrieved from http:// www.cryptovest.co.uk/resources/Bitcoin%20 paper%20Original.pdf Narayan, A. (2013). Why the Cornell Paper on Bitcoin Mining is Important. Retrieved June 10, 2015, from https://freedom-to-tinker.com/blog/ randomwalker/whythe-cornell-paper-on-bitcoinmining-is-important/ Scalability. (n.d.). Retrieved October 4, 2015, from https://en.bitcoin.it/wiki/Scalability Scrypt.CC. (n.d.). Retrieved November 11, 2015 from https://scrypt.cc/ Stevenson, J. (2013). Bitcoins, litecoins, what coins? A global phenomenon. Google Commerce Ltd.

Barber, S., Boyen, X., Shi, E., & Uzun, E. (2012). Bitter to better—how to make bitcoin a better currency. In Financial cryptography and data security (pp. 399–414). Springer Berlin Heidelberg. doi:10.1007/978-3-642-32946-3_29

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The term bitcoin will be used in two contexts throughout the paper. Bitcoin (with ‘B ’in capitals) refers to the protocol suggested by Satoshi Nakamoto whereas bitcoin refers to the currency or the amount (often abbreviated as BTCs).

Category E

Electronic Services

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Determining Impact of Demographics on Perceived Service Quality in Online Retail Prateek Kalia I. K. Gujral Punjab Technical University, India Penny Law Regenesys Business School, South Africa Richa Arora Regenesys Institute of Management, India

INTRODUCTION Throughout the World Internet users, buyers and businesses are growing at an exuberant speed. Prevalence of computers and internet as one of the most influential technologies and its integration with business has made sale of goods and services through websites a profitable and low cost affair (Doostar, Akbari, & Abbasi, 2013). Businesses have realized that they can use internet as a powerful tool to increase overall service offerings (Griffith & Palmer, 1999). On the other hand, customers can avail benefit like convenience, availability of wide variety of product/ service, competitive prices, extensive information, comparing alternatives etc. However, most of the retailers are selling similar products and gaining competitive advantages solely based on a cost leadership strategy is difficult (Jun, Yang, & Kim, 2004; Shankar, Smith, & Rangaswamy, 2003). In this scenario, researchers have pointed out that superior service quality can be critical for Internet retailers’ long-term success (Fassnacht & Koese, 2006; Zeithaml, Parasuraman, & Malhotra, 2002). However, perception of the service quality can significantly differ between different customers, leading to difference in their satisfaction and future behavior (Sánchez-Pérez, Sánchez-Fernández, Marín-Carrillo, & Gázquez-Abad, 2007). Many

researchers have highlighted how demographic factors can influence customers’ preference of online store visit (Phang, Kankanhalli, Ramakrishnan, & Raman, 2010), information search behavior (Kalia, Singh, & Kaur, 2016), consumer’s online buying behavior (Li, Kuo, & Rusell, 1999), differentiation of web-shoppers and non-shoppers (Karayanni, 2003) and evaluation of the e-service quality (Barrera, García, & Moreno, 2014). Ganesan-Lim, Russell-Bennett, & Dagger (2008) also mentioned in their literature review that individual consumers perceive service differently therefore quality perceptions may vary from one segment of the population to another. Acknowledging the fact that demographic information is essential for segmentation and targeting (McCarty & Shrum, 1993) or relevant in formulation of marketing or product strategy by internet retailers (Chang & Samuel, 2006), this study tries to understand whether significant difference in perceived service quality (PSQ) exist within demographic characteristics of online shoppers, such as education, age, gender, monthly income, occupation and marital status. This article is organized as follows: a literature review relevant to service quality and demographic effects on service quality perceptions is done to develop hypotheses. Then methodology and results are discussed. At the end conclusion and managerial implications are drawn.

DOI: 10.4018/978-1-5225-2255-3.ch252 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Electronic Services

BACKGROUND Online Service Quality To measure customer perceptions of service quality in service and retailing organizations Parasuraman, Zeithaml, & Berry (1988) developed a 22-item survey research instrument called SERVQUAL. Later, through focus group research with online shoppers, Zeithaml, Parasuraman, & Malhotra (2000) developed a framework for consumer evaluation of electronic service quality, known as e-SERVQUAL. They defined e-service quality (e-SQ) as, “the extent to which a website facilitates efficient and effective shopping, purchasing, and delivery” (Zeithaml et al., 2000). Their framework considered 11 dimensions of e-SQ i.e. access, ease of navigation, efficiency, flexibility, reliability, personalization, security/ privacy, responsiveness, assurance/trust, site aesthetics, and price knowledge. On the basis of SERVQUAL number of scales for measuring online service quality were developed in subsequent years; for example, WebQual 1.0 (S. Barnes & Vidgen, 2000), PIRQUAL (J. Francis & White, 2002), WebQual 4.0 (Barnes & Vidgen, 2003), E-S-QUAL and e-RecS-Qual (Parasuraman, Zeithaml, & Malhotra, 2005), E-A-S-QUAL (M. Kim, Kim, & Lennon, 2006), eTransQual (Bauer, Falk, & Hammerschmidt, 2006) and eSELFQUAL (Ding, Hu, & Sheng, 2011). There was origin of some independent scales like SITEQUAL (Yoo & Donthu, 2001), WebQual (Loiacono, Watson, & Goodhue, 2002), IRSQ (Janda, Trocchia, & Gwinner, 2002),.comQ (Wolfinbarger & Gilly, 2002) and eTailQ (Wolfinbarger & Gilly, 2003). Number of researchers have highlighted why consumer perception of online service quality is important (Cai & Jun, 2003; Cheng, Wang, Lin, Chen, & Huang, 2008; J. E. Francis & White, 2002; Gounaris, Dimitriadis, & Stathakopoulos, 2005; Janda et al., 2002; Jun et al., 2004; Lee & Lin, 2005; Yang & Jun, 2002; Yoo & Donthu, 2001) and how service quality can significantly affect attributes like, loyalty (Dai, Haried, & Salam, 2011; Ding et al., 2011; J. Kim, Jin, & Swin-

ney, 2009; Srinivasan, Anderson, & Ponnavolu, 2002; Swaid & Wigand, 2009; Wolfinbarger & Gilly, 2003; Zehir, Sehitoglu, Narcikara, & Zehir, 2014), satisfaction (Bauer et al., 2006; Cho & Park, 2001; Ding et al., 2011; J. Kim et al., 2009; S. Kim & Stoel, 2004; Szymanski & Hise, 2000; Wolfinbarger & Gilly, 2003; Yang, Peterson, & Cai, 2003), customer retention (Wolfinbarger & Gilly, 2003), perceived value (Bauer et al., 2006; Zehir et al., 2014), attitude toward the website (Wolfinbarger & Gilly, 2003), behavioral intentions (Collier & Bienstock, 2006), and service enjoyment and commitment (Dai et al., 2011).

Demographics and Service Quality Different motivations drive shoppers to react differentially to diverse marketing messages (Moe, 2003). For instance, shoppers may search for product information actively or casually browse a webstore. This difference can be ascribed with customer’s demographics and these variables offer valuable insights into ‘who consumers are’ and ‘what they need’ (Kalia, 2016a; Phang et al., 2010). For segmentation and targeting, researchers consider demographic information fundamentally necessary (McCarty & Shrum, 1993) and suggested to understand the affect of demographics like age, income and gender with respect to customer perceptions of quality (Lim et al., 2008). This section reviews prior research on the impact of demographics on service quality perceptions. As compared to previous studies, where two, three or maximum four demographic variables are undertaken; this study has comprehensively considered six demographic variables i.e. education, age, income, occupation, marital status and gender.

Education Researchers observed that education level influence the evaluation of service quality (Min & Khoon, 2013). Vrechopoulos et al. (2001) discovered that Internet shoppers are mostly University graduates and postgraduates, simi2883

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larly Kalia (2016) reviewed that online shoppers are well educated and less resistant to change, more open-minded, venturesome, cosmopolitan in outlook, socially mobile, self-confident and mature. Barrera et al. (2014) highlighted that people without a university degree score perceived quality service higher than people with a university degree. Kumbhar (2011) argued that on the basis of education level perceived service quality, perceived value from e-banking service and overall satisfaction in e-banking vary among customers. Collectively, the preceding discussion leads to the following hypothesis. H1: Perceived service quality in online retail differ according to education.

Age Consumer age affects service quality perceptions (Lim et al., 2008). Reason to shop online vary in Internet shoppers according to their age, for example middle-aged customers (24-44) primarily shop online for ‘convenience’ than ‘price’ and ‘product selection’ (Chang & Samuel, 2006). On the other hand, older shoppers are more likely to prefer search/deliberation over hedonic browsing than younger shoppers (Phang et al., 2010). Pretorius and Smit (2010) observed difference in overall website satisfaction across the age groups and concluded that younger individuals, on average, are more satisfied with websites. Similarly, Barrera et al. (2014) found that people under 24 years perceive a greater service quality than those over 24 years. Few other studies also confirmed that internet shoppers are young and fall within the age bracket of 21-30 years (Kalia, 2016a) or 25-44 years (Vrechopoulos et al., 2001). Khare (2011) observed that Indian customers’ perceptions towards the service quality of multinational banks differ across age categories, Kumbhar (2011) noticed that (Khare, 2011) perceived service quality, perceived value from e-banking service and overall satisfaction in e-banking differ with age group of the customer and Butler et

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al. (1996) observed that age adequately predict one component of perceived hospital quality. In sum, the above discussion leads to the following hypothesis. H2: Perceived service quality in online retail differ according to age.

Income Internet shoppers who are motivated to purchase online vary in income. Middle-incomes (Aus$40,000 to $69,999) tend to be primarily influenced to purchase online for the reason of ‘convenience’ relative to’ ‘price’ and ‘product selection’ (Chang & Samuel, 2006). Kumbhar (2011) observed difference in perceived service quality, perceived value from e-banking service and overall satisfaction in e-banking due to varying income level of the customers. At large, online shoppers have higher or above average household/ disposable income incomes (Kalia, 2016a). Vrechopoulos et al. (2001) also found that internet shoppers have average monthly household income of 300000-2000000 GRD. Above discussion lead to following hypothesis. H3: Perceived service quality in online retail differ according to income

Occupation Past studies have affirmed that perceived service quality, perceived value from e-banking service and overall satisfaction in e-banking differ by profession of the customers (Kumbhar, 2011). Vrechopoulos et al. (2001) discovered that Internet shoppers are largely scientists, private employees or freelancers. Kalia (2016) also reviewed that majority of online shoppers are wealthy or dual-career families with small children. Following hypothesis is proposed in the light of above discussion. H4: Perceived service quality in online retail differ according to occupation

Category: Electronic Services

Marital Status Marital status has been found to moderate the relationship between usefulness, enjoyment, external characteristics and reliability (Doostar et al., 2013) and Internet shoppers are mostly single (Vrechopoulos et al., 2001). Bhatnagar, Misra, & Rao (2000) concluded that marital status had no significant effect, except hardware category where marital status did have a significant effect. Majority of studies supported the fact that: H5: Perceived service quality in online retail differ according to marital status.

H6: Perceived service quality in online retail differ according to gender.

METHODOLOGY Under methodology, research goal, sample and data collection are talked about. Subsequently, results of Kruskal-Wallis (H Test) to check significant difference in perceived service quality in online retail with respect to different demographic factors have been discussed. At the end results of post hoc test have been shared.

Gender

RESEARCH GOAL

Researchers have found that gender is influential in the evaluation of service quality (Min & Khoon, 2013) and Internet shoppers who are motivated to purchase online vary in gender or gender adequately predict components of perceived quality (Butler et al., 1996). Certain studies observed that women have a higher valuation of the service quality of Web sites than men (Barrera et al., 2014) and primarily, female are influenced to purchase online for the reason of ‘convenience’ relative to’ ‘price’ and ‘product selection’ (Chang & Samuel, 2006). Females also express greater overall website satisfaction (Pretorius & Smit, 2010). There have been studies which confirmed that gender moderates the relationship between usefulness, enjoyment, external characteristics and reliability (Doostar et al., 2013). Certain studies found that Internet buyers tend to be male than females, because women perceive a higher level of risk and engage in high exploratory behavior while buying online than men (Kalia, 2016a; Vrechopoulos et al., 2001). Khare (2011) found that Indian customers’ perceptions towards the service quality of multinational banks differ between the two genders. On the basis of above literature review following hypothesis is proposed.

There have been previous studies which have partially discussed demographics and service quality but there is shortage of studies which comprehensively and directly discuss the impact of demographics on perceived service quality in online retail. Further, this study is conducted in India, which is one of the most promising e-commerce market in Asia-Pacific region (eMarketer, 2015). India’s online retail industry is estimated to grow to healthy 50-55 percent CAGR of Rs 504 billion by 2015-16; this indicates that Indian eretail market has huge potential for future growth. However, due to sudden boom, competition in e-commerce market in India is getting intense (Kalia, 2016c); so much that e-retailers in India are adapting their business model to sustain and win (Kalia, 2015). Under such conditions, this research tries to determining the impact of demographics (education, age, income, occupation, marital status and gender) on perceived service quality in online retail.

SAMPLE AND DATA COLLECTION An online survey was developed to test the hypothesis. Mail containing link to questionnaire was sent to respondents in three capital cities of

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Northern India i.e. Chandigarh, Delhi/National Capital Region and Jaipur. Selection of these cities was based on eBay.in Census 2012 which identified these cities among top ten e-commerce hubs (Ebay.in, 2012). Hence, it was appropriate to collect data from respondents from these cities, who have made atleast one online purchase in past six months from popular e-retailers of India and gone through real experience of an online purchase. Snowball sampling (Malhotra, 2007) was deployed i.e. an initial group of respondents were selected randomly and subsequent respondents were selected based on the referrals. Considering 300 cases as good sample size (Comrey & Lee, 1992; Tabacnik & Fidell, 1996; Vanvoorhis & Morgan, 2007), total 308 responses have been used for data analysis. E-retailers in this research denotes business-to-consumer e-commerce companies selling products to consumers in categories like clothing & accessories, books & magazines, mobile phones & accessories, auto accessories & parts, memory cards, pen drives & HDD, watches, laptops & computer peripherals, shoes & other footwear, movies & music (CD/DVD) and home appliances. Scale developed by Jun et al. (2004), consisting twenty-one items to measure perceived online retail service quality has been used. Demographic profile of the respondents has been depicted in Table 1.

SCALE RELIABILITY AND CONSTRUCT VALIDITY Cronbach’s Alpha value of 0.928 confirmed the reliability of 21 item scale. KMO score of 0.926 (Kaiser & Rice, 1974) and Bartlett’s Test score under 0.001 (significant) indicated that data is suitable for factor analysis. Items were checked for their communality scores and all the items scored greater than permissible limit i.e. MEA> AMP>DEA> regardless of operating conditions adopted (Figure 6). The aqueous amine-based solutions are usu-

Category: Environmental Science and Agriculture

ally adopted CO2 absorption liquids and MEA is the most commonly used amongst them. The structures of alkanolamines include primary, secondary, ternary amines containing at least one OH and amine group such as MEA, DEA and MDEA. The reactivity of amines to CO2 follows the order primary, secondary and ternary amines. So as obtained MEA has greater abortion performance than the DEA. In addition to these amines, the steric hindrance amines such as AMP also proposed by several researchers. This is because that the steric character reduces the stability of the formed carbamate and easy regenerate the solution. The absorption performance of AMP is better than the DEA as shown in figures. Amino acid salts (AAS) aqueous solutions attract great research interest in recent years as CO2 absorbent liquid in GLMC because of its prominent characters such as their better affinities towards CO2 than alkanolamines and their high surface tension because of its ionic nature. Hence mostly used PG absorption performance was compared with other absorbent liquids and its absorption performance was greater than the MEA aqueous solutions. As well even after 8 hours long run the PG did not wet the membrane. Whereas the amine solutions cause membrane wetting averagely after five six

hours of continuous running, this is due to its low surface tension. Moreover from the absorption experiments it was observed the gas flow rate have a significant effect on CO2 absorption performance in GLMC. Although as shown in Figure 6 the CO2 absorption flux increased with gas flow rate the CO2 removal efficiency decreased with increased gas flow rate. For instance for PG the removal efficiency reduced from 99% to 94% with the increased of gas flow rate from 10 ml/min to 100 ml/min. This can be attributed to decrease in contact time and increase in driving force for mass transfer. Increasing the gas flow rate decreases the residence time of the gas phase in the membrane contactor and hence contact time of the gas phase with liquid. On the other hand the increase in the gas velocity results in the reduction of the boundary layer and the improvement of the total mass-transfer rate. Further it was observed for any absorption liquid the CO2 absorption performance in GLMC could be enhanced by high liquid flow rates. This enhancement in absorption performance was significant for the absorption liquids which have poor absorption performance than the absorption liquids which have good absorption performance. For PG the removal efficiency increased only by

Figure 6. CO2 absorption performance of various absortion liquids in GLMC (0.5M aqeuous solutions at room temperature flow in lumen side at10 ml/min; gas mixture 9%CO2/91%CH4 flows in shell side)

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3.3%, whereas for DEA the removal efficiency increased by 17% with increase of liquid flow rate from 10 ml/min to 50 ml/min. This is attributed to higher liquid velocity leads to a lower CO2 concentration in the liquid phase, which in turn results in a higher CO2 concentration gradient between gas and liquid phase phases. Also increasing the liquid velocity turns the fluid flow form laminar to turbulent this reduces the liquid phase boundary layer. The effect of temperature on CO2 absorption performance in GLMC is attributed to collective effect of solubility (physical absorption), chemical reaction (chemical absorption), diffusion and evaporation of absorbent. Further increase in temperature would decrease the viscosity of the solution which is favorable character of GLMC absorbent liquid. It is well known the favored chemical reaction rate and diffusion rate with temperature would enhance the absorption performance. While decrease in CO2 solubility and an increase in evaporation of absorbent (wetting) with temperature would reduce the absorption performance. Experimentally it was observed MEA and shows no any significant effect of temperature on absorption performance contrary to the results obtained with AMP and DEA. This may be due to for MEA and PG the favored chemical reaction and diffusion with temperature were compensated by the reduced solubility. Also these absorbent have good reactivity even at room temperature (around 95% removal), so the enrichment in absorption performance with temperature was not highlighted. Whereas for AMP and DEA the favored effect of temperature on chemical reaction and diffusion were higher than the reduced effect temperature on CO2 solubility and so the enrichment in absorption performance with temperature was significant. However all solvents shows some flattening off at elevated temperatures. Because the wetting caused by elevated temperature cannot be neglected in long term operation. Further the thermal degradation of membrane material also should be considered. So, solvent temperature is

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a crucial factor to be controlled very carefully for long term operation performance.

CO2 Stripping Performance of GLMC CO2 stripping performance of PVDF hollow fiber GLMC was compared between primary (MEA), secondary (DEA), hindered (AMP) amines and amine salts (PG). The liquid solutions were manually preloaded with CO2 for saturation level in order to obtain higher CO2 flux for comparing purposes In terms of regeneration AMP and PG perform well as absorbent liquid comparing to MEA and DEA. The results in Figure 7 show the effect of different aqueous solution on stripping flux as function of temperature. The regeneration energy of the absorbents was directly related with the heat of reaction and showed higher values when the binding force between amines and CO2 molecules was larger. Therefore, absorbents that formed carbamate required larger quantities of heat, and the absorption heats of the MEA and DEA were higher than those of AMP and PG. So AMP and PG shows better stripping performance compare to MEA and DEA. Regardless of type of solvent the CO2 stripping flux and efficiency increases with temperature rapidly. Because as the reaction rate increases with temperature, the formation of carbamate, which is the final product of CO2 reaction, become unstable and the energy consumed in regeneration become smaller. The studies on sweep gas flow rates exposed the gas flow rate have no any significant effect on stripping flux and efficiency regardless of type of solvent. However, when operating at high rich solution temperature, the low sweep gas flow rate, allow the vapor molecules to easily enter through the pores and wet the membrane. This reduces the effective long term operation of the membrane contactor module. So, moderate sweep gas flow rate gives better performance comparing to low sweep gas flow rate. Liquid flow rate shows some notable criteria on CO2 stripping flux and efficiency. At low temperatures the increase in

Category: Environmental Science and Agriculture

Figure 7. Effect of liquid phase temperature on CO2 stripping flux for four different solvents (saturation initial CO2 loading, vl= 50ml/min, vg = 600 ml/min and Pl −Pg = 0.5×105 Pa)

liquid flow rate reduces the stripping efficiency. In contrast at high temperature the increase in liquid flow rate increases the stripping efficiency. Two phenomenon govern the stripping efficiency. The residence time (contact time between gas and liquid phase) and liquid phase boundary layer thickness. At low temperature contact time overtaken by the boundary layer thickness. Lower liquid velocity tend to higher residence time, which lets dissolved CO2 to shift to gas–liquid interface and results in increasing the driving force of mass transfer. In contrast at high temperature due to low solubility more CO2 end to be released. So rather than residence time boundary layer thickness is important. Because the liquid flow rate reduces the boundary layer thickness and increase the mass transfer coefficient, at high temperature high flow rates gives the high removal efficiency. The liquid pressure also has great influence in the stripping performance. The considerable increase in CO2 flux with liquid pressure can be attributed to the increase in driving force for desorption as a result of an increase in CO2 concentration. Many researchers proved the CO2 absorption flux increase drastically with gas phase pressure. So vice versa by increasing the liquid side

pressure the CO2 desorption flux can be increased. But, even though, hydrophobic PVDF hollow fiber membrane can resist wetting, applying higher pressure in the liquid side can gradually cause wetting. It is well known that partial wetting of membrane can increase mass transfer resistance significantly. So the liquid side pressure should be maintained below the break through pressure. The effect of absorption liquid pH on CO2 absorption/stripping performance in gas liquid membrane contactor module was investigated by using asymmetric solutions of PG (i.e. solutions containing different molar amounts of amino acid (glycine) and base (potassium hydroxide)). At lower pH the CO2 reaction equilibrium shifts towards the release of CO2 enhancing the partial pressure of CO2. So higher molar ratios of amino acid shows better stripping and reduced absorption performance (Figure 8). Subsequently in continuous operation of absorption and followed up by stripping, the stripping performance was controlled by reduced absorption performance because of resultant low initial CO2 concentrations. Hence for the net effect the molar ratio has to be optimized to achieve both good absorption and subsequent stripping performance.

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Figure 8. Effect of different asymmetric solutions on overall efficiency

FUTURE RESEARCH DIRECTIONS GLMC due to attested several advantages has become the promising alternative for conventional CO2 absorption/stripping process. Although the GLMC for CO2 separation has been extensively studied there is still a long way ahead before this technique to completely replace the existing CO2 separation technology. The main hitch is long term stability of GLMC absorption process due to membrane wetting. The key is employing surface modified super hydrophobic membranes and high surface tension absorption liquids in GLMC absorption/stripping processes. Currently, various techniques such as surface grafting, bore filling grafting, coating/interfacial polymerization and in-situ polymerization are being investigated to improve the surface hydrophobicity. Some works are going on using ionic liquid membranes in GLMC applications. Further as a consequences of seek out for new absorption liquids that not only have high surface tension but also can be regenerable in efficient way, AAS and ionic liquids have attracted great research interest. Most importantly AASs have favorable biodegradation properties,

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which make the disposal of these solvents easier and with lower environmental impacts. In spite of all these work still the membrane wetting at elevated temperatures is arguing. Moreover, despite the fact that solvent regeneration is responsible for the major cost component in gas separation processes due to energy consumption, the studies on CO2 stripping using GLMC have started recently and there are only few reports documented in the open literature. Hence future research directions should focus on super hydrophobic membranes, absorption liquids that can be regenerated in an energy efficient way and eventually suitable membrane-absorbent combination, therefore, GLMC will thrive as a perfectly energy efficient and effective CO2 absorption/stripping technology, which can replace the current CO2 separation technology completely.

CONCLUSION The experimental results proved GLMC is promising energy efficient and effective alternative for conventional CO2 absorption/stripping technology

Category: Environmental Science and Agriculture

and the type of absorption liquid and operating parameters plays a vital role in eventual absorption/stripping performance given away by GLMC. Studied AAS (PG) shows both good absorption and stripping performance. The gas flow rate has a significant effect on CO2 absorption performance where as it has no effect in stripping performance. Further the CO2 absorption performance in GLMC could be enhanced by high liquid flow rates. This enhancement was significant for the absorption liquids which have poor absorption performance than the absorption liquids which have good absorption performance. In contrast, the stripping performance enhancement with liquid flow rate depends on liquid temperature. Because the gas– liquid contact time was a key factor to enhance the stripping flux at low temperature while liquid phase boundary layer thickness and associated mass transfer resistance is important at elevated temperature. So by controlling the liquid phase velocity and the length of module at low temperature better stripping performance can be achieved. The effect of liquid temperature on absorption performance in GLMC is not straightforward. Since the liquid temperature cooperatively influence the several factors which determines the absorption performance positively and negatively, it should be handled in care depending on the system used. However by increasing the rich solution temperature the stripping performance can be improved preferably. To improve the stripping performance enhanced CO2 desorption (based on pH-shift) was studied. In this study the pH values were changed by using asymmetric solutions of PG. Lower pH shifts the reaction equilibrium towards the release of CO2 and shows better stripping and reduced absorption performance. Thus for the net effect the molar ratio has to be optimized to achieve both good absorption and subsequent stripping performance.

ACKNOWLEDGMENT

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The authors would like to acknowledge the financial support provided by the research and graduate study of the United Arab Emirates University (UAEU) and National Research Fund (NRF). Grant number 31N168-UPAR (9) 2013.

REFERENCES Ghasem, N., Al-Marzouqi, M. H., & Rahim, N. A. (2012a). Effect of polymer extrusion temperature on poly (vinylidene fluoride) hollow fiber membranes: Properties and performance used as gas–liquid membrane contactor for CO2 absorption. Separation and Purification Technology, 99, 91–103. doi:10.1016/j.seppur.2012.07.021 Ghasem, N., Al-Marzouqi, M. H., & Rahim, N. A. (2014). Absorption of CO2 Form Natural Gas via Gas-liquid PVDF Hollow Fiber Membrane Contactor and Potassium Glycinate as Solvent. Jurnal Teknologi, 69(9), 121–126. doi:10.11113/ jt.v69.3409 Ghasem, N. M., Al-Marzouqi, M., & Zhu, L. P. (2012b). Preparation and properties of polyethersulfone hollow fiber membranes with o-xylene as a additive used in membrane contactors for CO2 absorption. Separation and Purification Technology, 92, 1–10. doi:10.1016/j.seppur.2012.03.005 Ghasem, N. M., Al-Marzouqi, M. H., & Duaidar, A. (2011). Effect of quenching temperature on the performance of poly (vinylidene fluoride) microporous hollow fiber membranes fabricated via thermally induced phase separation technique on the removal of CO2 from CO2 gas mixture. International Journal of Greenhouse Gas Control, 5(6), 1550–1558. doi:10.1016/j.ijggc.2011.08.012

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Ghasem, N. M., Al-Marzouqi, M. H., & Rahim, N. A. (2013). Modeling of CO2 absorption in a membrane contactor considering solvent evaporation. Separation and Purification Technology, 110, 1–10. doi:10.1016/j.seppur.2013.03.008 Naim, R., & Ismail, A. F. (2013). Effect of polymer concentration on the structure and performance of PEI hollow fiber membrane contactor for CO2 stripping. Journal of Hazardous Materials, 250, 354–361. doi:10.1016/j.jhazmat.2013.01.083 PMID:23474409 Rahim, N. A., Ghasem, N., & Al-Marzouqi, M. H. (2014). Stripping of CO2 from different aqueous solvents using PVDF hollow fiber membrane contacting process. Journal of Natural Gas Science and Engineering, 21, 886–893. doi:10.1016/j. jngse.2014.10.016 Rahim, N. A., Ghasem, N., & Al-Marzouqi, M. H. (2015). Absorption of CO2 from natural gas using different amino acid salt solutions and regeneration using hollow fiber membrane contactors. Journal of Natural Gas Science and Engineering, 26, 108–117. doi:10.1016/j.jngse.2015.06.010 Seo, D.-J., & Hong, W.-H. (1996). Solubilities of carbon dioxide in aqueous mixtures of diethanolamine and 2-amino-2-methyl-1-propanol. Journal of Chemical & Engineering Data, 41(2), 258–260. doi:10.1021/je950197o Ze, Z., & Sx, J. (2014). Hollow fiber membrane contactor absorption of CO2 from the flue gas: Review and perspective. Global NEST Journal, 16, 355–374.

ADDITIONAL READING Drioli, E., Criscuoli, A., & Curcio, E. (2011). Membrane Contactors: Fundamentals, Applications and Potentialities: Fundamentals, Applications and Potentialities (Vol. 11). Elsevier.

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Ghasem, N. M., & Al-Marzouqi, M. (2014). Modeling and Experimental Study of Gas-Liquid Membrane Contactor. Encyclopedia of Information Science and Technology, Third Edition (10 Volumes) July, 2014. Chapter 537, pages 54425453. Doi:10.4018/978-1-4666-5888-2 Li, J.-L., & Chen, B.-H. (2005). Review of CO2 absorption using chemical solvents in hollow fiber membrane contactors. Separation and Purification Technology, 41(2), 109–122. doi:10.1016/j. seppur.2004.09.008 Mansourizadeh, A., & Ismail, A. F. (2009). Hollow fiber gas–liquid membrane contactors for acid gas capture: A review. Journal of Hazardous Materials, 171(1-3), 38–53. doi:10.1016/j. jhazmat.2009.06.026 PMID:19616376 Mansourizadeh, A., & Ismail, A. F. (2011). CO2 stripping from water through porous PVDF hollow fiber membrane contactor. Desalination, 273(2-3), 386–390. doi:10.1016/j.desal.2011.01.055 Mulder, M. (1997). Basic Principles of Membrane Technology. Kluwer Academic Publishers. Naim, R., Ismail, A. F., Cheer, N. B., & Abdullah, M. S. (2014). Polyvinylidene fluoride and polyetherimide hollow fiber membranes for CO2 stripping in membrane contactor. Chemical Engineering Research & Design, 92(7), 1391–1398. doi:10.1016/j.cherd.2013.12.001 Rahbari-Sisakht, M., Ismail, A. F., Rana, D., & Matsuura, T. (2013). Carbon dioxide stripping from diethanolamine solution through porous surface modified PVDF hollow fiber membrane contactor. Journal of Membrane Science, 427, 270–275. doi:10.1016/j.memsci.2012.09.060 Simioni, M., Kentish, S. E., & Stevens, G. W. (2011). Membrane stripping: Desorption of carbon dioxide from alkali solvents. Journal of Membrane Science, 378(1-2), 18–27. doi:10.1016/j. memsci.2010.12.046

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Yan, S., He, Q., Zhao, S., Zhai, H., Cao, M., & Ai, P. (2015). CO2 removal from biogas by using green amino acid salts: Performance evaluation. Fuel Processing Technology, 129, 203–212. doi:10.1016/j.fuproc.2014.09.019

KEY TERMS AND DEFINITIONS AASS: Amino acid salt solution. Chemical solvent derived by mixing amino acid and alkaline hydroxides. CO2 Absorption: Operation used in removing acid gas from other gases using liquid solvent.

CO2 Stripping: Operation used in removing absorbed acid gas from absorbent liquid. GLMC: Gas Liquid membrane contactor. A device that have bundle of fibers enclosed on a shell in which gas and liquid flow without dispersing with each other. Hydrophobic: The not-affinity to water. Natural Gas: Gas consists with more than seventy percent methane. PVDF: Polyvinylidene fluoride, polymer used in fabrication. Wetting: Membrane pores filled with liquid instead of gas.

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Enhancing the Resiliency of Smart Grid Monitoring and Control Wenbing Zhao Cleveland State University, USA

INTRODUCTION Smart grid is one of the hottest research areas in recent years. The development of smart grid is partially driven by the fact that the traditional data communication infrastructure for electric power grid can no longer meet the needs of new developments (Wang, Xu, & Khanna, 2011): •

•

•

The recent deregulation would allow many independent parties to enter the utility industry by offering alternative channels for electric power generation, distribution, and trade. This inevitably demands timely, reliable and secure information exchanges among these parties (Bose, 2005). The current data communication infrastructure lacks the support for large-scale real-time coordination among different electric power grid health monitoring and control systems, which could have prevented the 2003 massive blackout incident in North America (Birman et al., 2005). The use of modern computer networking technology could also revolutionize the everyday electric power grid operations, as shown by the huge benefits of substation automation and the use of Phasor Measurement Units (PMUs) for electric power grid health monitoring (Melliopoulos, 2007).

However, the openness and the ease of information sharing and cooperation brought by smart grid also increased the likelihood of cyber attacks on the electric power grid, as demonstrated

recently by an experiment conducted by the US Department of Energy’s Idaho Lab (CNN, 2007). To address such vulnerability, intrusion detection and intrusion tolerance techniques must be used to enhance the current and future data communication infrastructure for the electric power grid. Byzantine fault tolerance is a fundamental technique to achieve the objective (Castro & Liskov, 2002; Zhao, 2014a). In this chapter, we focus our discussions on the security and reliability of smart grid health monitoring and control. We elaborate in detail the need for Byzantine fault tolerance and the challenges of applying Byzantine fault tolerance into this problem domain. In particular, we investigate experimentally the feasibility of using such sophisticated technology to meet potentially very stringent real-time requirement for the health monitoring and control of smart grid, while ensuring high degree of reliability and security of the system.

BACKGROUND A Byzantine faulty process may behave arbitrarily. In particular, it may disseminate conflicting information to different components of a system, which constitutes a serious threat to the integrity of a system (Lamport, Shostak, & Pease, 1982). Because a Byzantine faulty process may also choose not to send a message, or refuse to respond to requests, it can exhibit crash fault behavior as well. Consider the scenario that multiple PMUs periodically report their measurement results to a controller for electric power grid health monitor-

DOI: 10.4018/978-1-5225-2255-3.ch267 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Environmental Science and Agriculture

ing. When it detects an abnormally, the controller may wish to issue specific control instructions to the actuating devices, such as Intelligent Electronic Devices (IEDs) (Hossenlopp, 2007) located at the same substation as those PMUs to alleviate the problem. Due to the critical role played by the controller, it must be replicated to ensure high availability. Otherwise, the controller would become a single-point of failure. The main components and their interactions are illustrated in Figure 1. However, the controller replicas, the PMUs, and the IEDs, might be compromised under cyber attacks. Consider the following two scenarios: •

A Byzantine faulty PMU could potentially send inconsistent data to different controller replicas. Without proper coordination among the controller replicas, the state of the replicas might diverge in the former case, which would lead to inconsistent decisions among the replicas.

•

A compromised controller replica could send conflicting commands to different IEDs. Without a sound mechanism at each IED, a malicious command might be executed in the latter case, which could lead to the destruction of a generator or a transmission line, as reported by CNN (2007).

Byzantine fault tolerance (BFT) refers to the capability of a system to tolerate Byzantine faults (Lamport, Shostak, & Pease, 1982). If BFT is used, the cyber attacks illustrated above could be defeated provided that the number of compromised controller replicas, f, is below a threshold, and the number of non-faulty PMUs and IEDs are sufficient for the normal operation of the substation. For the client-server system shown in Figure 1, BFT can be achieved by using 3f + 1 replicas to tolerate up to f faulty replicas and by ensuring all non-faulty replicas to execute the same set of requests in the same order. The latter means that the server replicas must reach an agreement on the set of requests and their relative ordering

Figure 1. The interaction of substation devices (PUMs and IEDs) and the controller replicas

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despite the presence of Byzantine faulty replicas and clients. Such an agreement is often referred to as a Byzantine agreement (Lamport, Shostak, & Pease, 1982). The Byzantine agreement among the replicas ensures that a faulty client (i.e., a PMU) cannot cause the divergence of the state of non-faulty controller replicas. Furthermore, a Byzantine agreement must be reached among all non-faulty replicas on each command issued by the controller for reasons to be explained in the next section. Before an IED can accept the command, it must wait until it has collected at least f + 1 identical command from different replicas. We should note that Byzantine fault tolerance has been a hot research area in many other areas, such as Web services (Merideth et al., 2005; Zhao, 2009) and data storage systems (Rhea et al., 2003). Even though the works are in a different context, many insights are useful for BFT controls in electric power grid applications. In particular, the mechanisms designed to cope with the interaction of a replicated object and the un-replicated external entities reported in (Merideth et al., 2005) have been partially incorporated in this work.

BYZANTINE FAULT TOLERANT MONITORING AND CONTROL MECHANISMS In this work, we choose to use PBFT, a well-known Byzantine agreement algorithm developed by Castro and Liskov (2002). The PBFT algorithm is designed to support client-server applications running in an asynchronous distributed environment with the Byzantine fault model. The implementation of the algorithm contains two parts. At the client side, a lightweight library is in charge of sending the client’s request to the primary replica, retransmitting the request to all server replicas on the expiration of a retransmission timer (to cope with the primary faults and network faults), and collecting and voting on the corresponding replies. The main PBFT algorithm is executed at the server side by a set of 3f + 1 replicas to toler-

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ate up to f faulty replicas. One of the replicas is designated as the primary while the remaining are backups. Furthermore, all messages are protected by a digital signature, or an authenticator (Castro & Liskov, 2002) so that a faulty replica or client cannot tamper with the messages and cannot impersonate as another non-faulty replica or client. The normal operation of the (server-side) PBFT algorithm involves three phases: pre-prepare, prepare, and commit. In the beginning of the preprepare phase, the primary multicasts a pre-prepare message containing the client’s request, the current view number and a sequence number assigned to the request to all backups. A backup verifies the request message and the ordering information. If the backup accepts the message, it multicasts to all other replicas a prepare message containing the ordering information and the digest of the request being ordered. This starts the prepare phase. A replica waits until it has collected 2f prepare messages from different replicas (including the message it has sent if it is a backup) that match the pre-prepare message before it multicasts a commit message to other replicas, which starts the commit phase. The commit phase ends when a replica has received 2f matching commit messages from other replicas. At this point, the request message has been totally ordered and it is ready to be delivered to the server application if all previous requests have already been delivered. If the primary or the client is faulty, the Byzantine agreement on the ordering of a request might not be reached, in which case, a new view is initiated, triggered by a timeout on the current message being ordered. A different primary is designated in a round-robin fashion for each new view installed. For electric power grid health monitoring and control, however, the above BFT algorithm cannot be used directly, because normally the controller replicas collect input from the PMUs and the control commands are issued to IEDs. Furthermore, the updates from PMUs are one-way messages in that the PMUs normally do not wait for an explicit response from the controller. On the other hand, IEDs are acting as the server role

Category: Environmental Science and Agriculture

when it receives the control commands from the controller replicas. Table 1 provides a summary of the actions taken by the controller replicas in response to receiving a report from a PMU and to sending of a command to an IED. On collecting PMU data, the controller replicas engage in a Byzantine agreement for each input message as usual, as shown in Figure 2. However, the message delivery procedure must be modified. When a replica reaches a Byzantine agreement on the message, and it has delivered all previously ordered messages, it invokes the callback function provided by the controller to deliver this message. On the return of the up-call, no message is sent back to the PMU. Upon issuing a control command, a controller replica does not directly send the command to the target IED. Instead, a round of Byzantine agreement on the command message is conducted, as shown in Figure 3. The procedure is very similar to that of PMU input message ordering, except that the pre-prepare message is triggered by the issuing of a control command rather than the receiving of a client’s request, and the command is sent to the target IED when the Byzantine agreement is reached, instead of delivering a request. As mentioned in the previous Section, the target IED

must not accept a control command immediately because the command might have been sent by a faulty controller replica. By waiting until it has received f + 1 identical command from different controller replicas, it can guarantee that at least one of them is sent by a non-faulty replica, because at most f replicas can be faulty according to our assumption. If the replicas operate completely deterministically and in lock-step, the round of Byzantine agreement for the commands to the IEDs is not necessary. However, it is virtually impossible to guarantee lock-stepped execution of the replicas across a network. If the control command contains information such as the time to execute the command, the commands issued by different replicas would contain different timestamps, which would make it impossible for the IEDs to authenticate and compare the commands for acceptance. Therefore, in general, it is necessary for the replicas to reach an agreement on the command to be issued to the IEDs. Here, we assume that a backup replica is able to verify if the command proposed by the primary is valid. A backup would initiate a view change if it deems that the command from the primary is invalid. If a backup replica cannot verify the command issued by the primary,

Figure 2. Normal operation of the BFT algorithm for PMU report handling

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Figure 3. Byzantine agreement (normal operation) on the control command at the controller replicas and the acceptance of the command at an IED

Table 1. A summary of the actions taken by the controller replicas in response to receiving a report from a PMU and to sending of a command to an IED Event

Actions Taken

On receiving a report from PMU

Byzantine agreement on the report No message is sent back to PMU

On sending of a command to IED

Byzantine agreement on the command prior to sending IED must collect f+1 consistent commands from different replicas

more sophisticated mechanisms must be used, as reported in (Zhang et al., 2011).

EXPERIMENTAL ASSESSMENT The implementation of our Byzantine fault tolerance framework is based on the PBFT library developed by Castro and Liskov (2002). We incorporated the changes necessary for electric power grid monitoring and control as mentioned in the previous Section. The test-bed consists of 12 PCs in a local-area network (LAN) connected by a 100Mbps Ethernet. Four of the PCs in the LAN are equipped

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with Pentium IV 2.8GHz processors and 256MB memory, and the remaining PCs in the LAN each has a single Pentium III 1GHz processor. All PCs on the LAN run the Red Hat 8.0 Linux. The remote PC has one Pentium IV 3.2GHz processor running CentOS 4.5 Linux. The main objective of the performance evaluation is to assess whether or not the Byzantine fault tolerance mechanisms are efficient enough to meet the real-time communication requirement for power grid health monitoring and control. Consequently, we characterize the response time and jitter of the Byzantine fault tolerant system. The test application simulates the electric power grid health monitoring and control scenario

Category: Environmental Science and Agriculture

as shown in Figure 1. The controller is replicated in the 4 Pentium IV PCs (one replica per PC) and the PMUs and IEDs are run on the remaining 8 Pentium III PCs (a pair of PMU and IED on each PC). During the experiments, up to 8 concurrent PMU-IED pairs are used. A PMU (as the client) periodically reports its measured data to the replicated controller according to the IEEE 1344 standard. Upon each PMU message received, the controller replicas generate a command and send it to the corresponding IED (collocated on the same node as the reporting PMU). Note that this is done purely for the purpose of performance characterization and might not match the practical usage scenarios. The payload of each PMU report is 14 bytes long. The payload of each control command is set to 128 bytes long. Furthermore, the PCs in our test-bed are not equipped with high-resolution GPS devices, preventing us from directly measuring one-way latencies for PMU reports and control command notifications. Instead, we measure the round-trip time from the sending of a PMU report to the receiving of a command in response to the report at a collocated IED. To gain insight on the jitters of networking and Byzantine agreement processing delays, we

measure the intervals between two consecutive sending of PMU reports at each PMU and the intervals of consecutive deliveries of the PMU reports at each controller replica, and compare the probability density functions (PDFs) of the sending intervals and the delivery intervals. The PDFs provide a much more detailed and accurate picture on the predictability of the arrival rate of the PMU reports at the controller replicas than using the mean values and standard deviations. For similar reasons, the PDF is used to capture and present the round-trip times. In each run, 10,000 samples are taken. Figure 4 shows the experimental results under the normal operation condition. The number of concurrent PMU-IED pairs varies from 1 to 8. The PDFs for the sending intervals measured at the PMUs are shown in Figure 4(a). The interval between two consecutive sending is controlled by the nanosleep() API provided by Linux. Even though the target interval is 10 milliseconds, the actual intervals vary slightly (with peak value of about 11.6 milliseconds). If there is no jitter in networking and Byzantine agreement processing, the PDFs of the delivery intervals measured at the controller replicas should be identical to those of the sending intervals. The PDFs of the

Figure 4. The measured PDFs with various number of PMU-IED pairs. (a) The report-sending interval at the PMU. (b) The delivery interval at the controller replicas. (c) The round-trip time from sending a PMU report and the receiving of a control command

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delivery intervals shown in Figure 4(b) indicate that there is noticeable jitter. However, the jitter is small enough to sustain a 60Hz PMU sampling rate for all scenarios tested (up to 8 concurrent PMU-IED pairs), which is often regarded as the most demanding SCADA requirement (Johnston, 2005). Furthermore, Figure 4(c) shows that the round-trip time is in the sub- millisecond range, again for all scenarios measured, which is more than adequate to ensure urgent sensing data delivered and control command acted upon. When the primary controller replica is faulty, it may take asignificant amount of time (e.g., 2 seconds) for a view change to complete. During this period of time, the controller is basically out-of-service. To address this issue, the controller should periodically send contingency control commands to the IEDs. If an IED does not receive a control command in time, it should resort to the contingency command. We emphasize that this situation, even though not desirable, is far better than the IED executing a command sent by a malicious controller, which can lead to the destruction of critical components of the power grid.

FUTURE RESEARCH DIRECTIONS There is a large body of research work on how to restructure the current data communication infrastructure for electric power grid, such as (Zhang, Wang, & Xiang, 2015; Birman et al., 2005; Bose, 2005; Hossenlopp, 2007; Melliopoulos, 2007). The SCADA security issues have also attracted worldwide attention (http://sandia.gov/scada/). However, the work that targets both the security and reliability aspects of the infrastructure is rarely seen. Our work appears to be the first to assess if it is possible to apply the Byzantine fault tolerance technology for electric power grid health monitoring and control. We plan to carry out more in-depth investigations. In particular, the performance of the BFT technology in the widearea network environment should be carefully evaluated. Furthermore, we also plan to explore

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context-ware and adaptive fault tolerance (Buys et al., 2011) to improve the performance and robustness of our framework. Another research direction is to improve the performance of the proposed framework by adapting our replication protocol specific for SACADA interactions. We call this line of research application-aware Byzantine fault tolerance (Zhao, 2014a; Zhao, 2014b). The importance of stable sampling rate for networked sensing and control is addressed in (Liberatore, 2006). In (Liberatore, 2006), Liberatore proposed a playback-based method to increase the predictability of the sampling rate. This method seems to be a good candidate to be integrated with the proposed framework to address the potential jittering problem in the wide-area networks.

CONCLUSION In this chapter, we presented the justification and a feasibility study of applying the Byzantine fault tolerance technology to electric power grid health monitoring. We proposed and implemented the BFT mechanisms needed to handle the PMU data reporting and control commands issuing to the IEDs. We carried out an empirical study to assess the feasibility of using the BFT technology for reliable and secure electric power grid health monitoring and control. We show that under the LAN environment, the overhead and jitter introduced by the BFT mechanisms are negligible, and consequently, Byzantine fault tolerance could readily be used to improve the security and reliability of electric power grid monitoring and control while meeting the stringent real-time communication requirement for SCADA operations. While the brief out-of-service time (typically in 1-2 seconds) during a view change can be a concern for electric power grid health monitoring and control, additional mechanism, such as the playback scheme proposed in (Liberatore, 2006) could be used to alleviate the problem. In any case, the BFT sensing and control ensures that a PMU report from a compromised PMU

Category: Environmental Science and Agriculture

cannot cause the state divergence of the correct controller replica, and a control command from a compromised control replica is never accepted by a correct actuating device such as an IED.

Lamport, L., Shostak, R., & Pease, M. (1982). The Byzantine gen- erals problem. ACM Transactions on Programming Languages and Systems, 4(3), 382–401. doi:10.1145/357172.357176

REFERENCES

Liberatore, V. (2006). Integrated playback, sensing and network control. Proceedings of the 25th IEEE International Conference on Computer Communications, 1–12.

Birman, K. P., Chen, J., Hopkinson, E. M., Thomas, R. J., Thorp, J. S., Renesse, R. V., & Vogels, W. (2005). Overcoming communications challenges in software for monitoring and controlling power systems. Proceedings of the IEEE, 93(5), 1028–1041. doi:10.1109/JPROC.2005.846339

Melliopoulos, A. P. S. (2007). Substation automation: Are we there yet? IEEE Power and Energy Magazine, 5(3), 28–30. doi:10.1109/ MPAE.2007.365815

Bose, A. (2005). Improved communication/computation infrastructure for better monitoring and control. In Proceedings of the IEEE Power Engineering Society General Meeting (pp. 2715–2716). doi:10.1109/PES.2005.1489755

Merideth, M., Iyengar, A., Mikalsen, T., Tai, S., Rouvellou, I., & Narasimhan, P. (2005). Thema: Byzantine-fault-tolerant middleware for web services applications. Proceedings of the IEEE Symposium on Reliable Distributed Systems, 131–142. doi:10.1109/RELDIS.2005.28

Buys, J., De Florio, V., & Blondia, C. (2011) Towards context-aware adaptive fault tolerance in SOA applications. In Proceedings of the 5th ACM international conference on Distributed event-based system, (pp. 63-74). doi:10.1145/2002259.2002271

Rhea, S., Eaton, P., Geels, D., Weatherspoon, H., Zhao, B., & Kubiatowicz, J. (2003). Pond: the oceanstore prototype. Proceedings of the 2nd USENIX Conference on File and Storage Technologies (Vol. 3, pp. 1-14).

Castro, M., & Liskov, B. (2002). Practical Byzantine fault tolerance and proactive recovery. ACM Transactions on Computer Systems, 20(4), 398–461. doi:10.1145/571637.571640 CNN. (2007). Staged cyber attack reveals vulnerability in power grid. Retrieved from http://www. cnn.com/2007/US/09/26/power.at.risk/index.html Hossenlopp, L. (2007). Engineering perspectives on iec 61850. IEEE Power and Energy Magazine, 5(3), 45–50. doi:10.1109/MPAE.2007.365819 Johnston, R. (2005). Obtaining high performance phasor measurements in a geographically distributed status dissemination network (Master’s thesis). Washington State University.

Wang, W., Xu, Y., & Khanna, M. (2011). A survey on the communication architectures in smart grid. Computer Networks, 55(15), 3604–3629. doi:10.1016/j.comnet.2011.07.010 Zhang, H., Zhao, W., Moser, L. E., & MelliarSmith, P. M. (2011). Design and implementation of a Byzantine fault tolerance framework for nondeterministic applications. IET Software, 5(3), 342–356. doi:10.1049/iet-sen.2010.0013 Zhang, Y., Wang, L., & Xiang, Y. (2015). Power System Reliability Analysis With Intrusion Tolerance in SCADA Systems. IEEE Transactions on Smart Grid. Zhao, W. (2009). Design and implementation of a Byzantine fault tolerance framework for Web services. Journal of Systems and Software, 82(6), 1004–1015. doi:10.1016/j.jss.2008.12.037

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ADDITIONAL READING Aiyer, A. S., Alvisi, L., Clement, A., Dahlin, M., Martin, J. P., & Porth, C. (2005). BAR fault tolerance for cooperative services. Operating Systems Review, 39(5), 45–58. doi:10.1145/1095809.1095816 Amir, Y., Coan, B., Kirsch, J., & Lane, J. (2008, June). Byzantine replication under attack. In Dependable Systems and Networks With FTCS and DCC, 2008. DSN 2008. IEEE International Conference on (pp. 197-206). IEEE. doi:10.1109/ DSN.2008.4630088 Amir, Y., Danilov, C., Dolev, D., Kirsch, J., Lane, J., Nita-Rotaru, C., & Zage, D. et al. (2008). Steward: Scaling Byzantine Fault-Tolerant Replication to Wide Area Networks. IEEE Transactions on Dependable and Secure Computing, 80–93. Bessani, A. N., Alchieri, E. P., Correia, M., & Fraga, J. S. (2008, April). DepSpace: A Byzantine fault-tolerant coordination service. Operating Systems Review, 42(4), 163–176. doi:10.1145/1357010.1352610 Casey, P. R., Jaber, N., & Tepe, K. E. (2011, October). Design and implementation of a crossplatform sensor network for smart grid transmission line monitoring. In Proceedings of the IEEE International Conference on Smart Grid Communications (pp. 285-290). IEEE. doi:10.1109/ SmartGridComm.2011.6102334 Castro, M., & Liskov, B. (2000, October). Proactive recovery in a Byzantine-fault-tolerant system. In Proceedings of the 4th conference on Symposium on Operating System Design & Implementation-Volume 4 (pp. 19-19). USENIX Association. Castro, M., & Liskov, B. (2001, July). Byzantine fault tolerance can be fast. In Dependable Systems and Networks, 2001. DSN 2001. International Conference on (pp. 513-518). IEEE. doi:10.1109/ DSN.2001.941437

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Chai, H., Melliar-Smith, P. M., Moser, L. E., Zhang, H., & Zhao, W. (2013). Toward Trustworthy Coordination of Web Services Business Activities. IEEE Transactions on Services Computing, 6(2), 276–288. doi:10.1109/TSC.2011.57 Chun, B. G., Maniatis, P., & Shenker, S. (2008, June). Diverse Replication for Single-Machine Byzantine-Fault Tolerance. In USENIX Annual Technical Conference (pp. 287-292). Clement, A., Wong, E. L., Alvisi, L., Dahlin, M., & Marchetti, M. (2009, April). Vol. 9, pp. 153–168). Making Byzantine Fault Tolerant Systems Tolerate Byzantine Faults. In NSDI. Cowling, J., Myers, D., Liskov, B., Rodrigues, R., & Shrira, L. (2006, November). HQ replication: A hybrid quorum protocol for Byzantine fault tolerance. In Proceedings of the 7th symposium on Operating systems design and implementation (pp. 177-190). USENIX Association. Distler, T., & Kapitza, R. (2011, April). Increasing performance in byzantine faulttolerant systems with on-demand replica consistency. In Proceedings of the sixth conference on Computer systems (pp. 91-106). ACM. doi:10.1145/1966445.1966455 Driscoll, K., Hall, B., Sivencrona, H., & Zumsteg, P. (2003). Byzantine fault tolerance, from theory to reality. In Computer Safety, Reliability, and Security (pp. 235-248). Springer Berlin Heidelberg. doi:10.1007/978-3-540-39878-3_19 Garcia, R., Rodrigues, R., & Preguiça, N. (2011, April). Efficient middleware for byzantine fault tolerant database replication. In Proceedings of the sixth conference on Computer systems (pp. 107-122). ACM. doi:10.1145/1966445.1966456 Hendricks, J., Ganger, G. R., & Reiter, M. K. (2007). Low-overhead byzantine fault-tolerant storage. Operating Systems Review, 41(6), 73–86. doi:10.1145/1323293.1294269

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Kapitza, R., Behl, J., Cachin, C., Distler, T., Kuhnle, S., Mohammadi, S. V., & Stengel, K. et al. (2012, April). CheapBFT: resource-efficient byzantine fault tolerance. In Proceedings of the 7th ACM European conference on Computer Systems (pp. 295-308). ACM. Kotla, R., & Dahlin, M. (2004, June). High throughput Byzantine fault tolerance. In Proceedings of the International Conference on Dependable Systems and Networks (pp. 575-584). IEEE. Veronese, G. S., Correia, M., Bessani, A. N., & Lung, L. C. (2009, September). Spin One’s Wheels? Byzantine Fault Tolerance with a Spinning Primary. In Proceedings of the 28th IEEE International Symposium on Reliable Distributed Systems (pp. 135-144). IEEE. doi:10.1109/ SRDS.2009.36 Yin, J., Martin, J. P., Venkataramani, A., Alvisi, L., & Dahlin, M. (2002, June). Byzantine faulttolerant confidentiality. In Proceedings of the International Workshop on Future Directions in Distributed Computing (pp. 12-15). Zhang, H., Chai, H., Zhao, W., Melliar-Smith, P. M., & Moser, L. E. (2012). Trustworthy coordination of Web services atomic transactions. IEEE Transactions on Parallel and Distributed Systems, 23(8), 1551–1565. doi:10.1109/TPDS.2011.292 Zhang, Y., Zheng, Z., & Lyu, M. R. (2011, July). BFTCloud: A byzantine fault tolerance framework for voluntary-resource cloud computing. In Proceedings of the IEEE International Conference on Cloud Computing, (pp. 444-451). IEEE. doi:10.1109/CLOUD.2011.16 Zhao, W. (2009). Design and implementation of a Byzantine fault tolerance framework for Web services. Journal of Systems and Software, 82(6), 1004–1015. doi:10.1016/j.jss.2008.12.037 Zhao, W. (2014a). Building dependable distributed systems. John Wiley & Sons. doi:10.1002/9781118912744

Zhao, W. (2014b). Application-Aware Byzantine Fault Tolerance. In Proceedings of the 12th IEEE International Conference on Dependable, Autonomic and Secure Computing (pp. 45-50). IEEE. doi:10.1109/DASC.2014.17 Zhao, W. (2015). Optimistic Byzantine fault tolerance. International Journal of Parallel, Emergent and Distributed Systems, 1-14 (preprint). Zhao, W., & Zhang, H. (2009). Proactive service migration for long-running Byzantine faulttolerant systems. IET software, 3(2), 154-164.

KEY TERMS AND DEFINITIONS Byzantine Fault Tolerance: It refers to the capability of a system to tolerate Byzantine faults. Intelligent Electronic Device (IED): It is an actuating device that is capable of receiving commands from a controller. Example IEDs include protective relaying devices, and voltage regulators. Jitter: It refers to the deviation from the periodicity of a sequence of events or signals. Normal Operation: It refers to the operation of an algorithm during a period that either there is no fault, or the faults do not disrupt its operation. For example, when a backup replica crashes, the PBFT algorithm would still operate as normal. Phasor Measurement Unit (PMU): It is a device that measures the electrical waves in an electric power grid. The measurements must be synchronized with a global clock, such as a GPS. Sampling Rate: It is defined as the number of samples taken per unit of time. SCADA: It is short for Supervisory Control and Data Acquisition. It is a type of industrial control system that monitor and control industrial processes that exist physically. View Change: It refers to the configuration change of the group of replicas that engage in Byzantine fault tolerance. When the primary is suspected of being faulty, a new view is initiated so that a different replica is elected as the primary for the new view.

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E-Waste, Chemical Toxicity, and Legislation in India Prashant Mehta National Law University Jodhpur, India

INTRODUCTION The industrial revolution was a period of dynamic change and dramatic innovation in the history of human society (Ayers, 1999). Across the world, societies are constantly reinventing to manage revolutionary changes that have radically transformed the lifestyle of people. Some of these changes are subtle and barely noticeable, while other changes are blatant and abrupt, like advances in Information and Communication Technology (ICT) and widespread use of Electrical and Electronics Equipment (EEE), which has made human civilization to grow in a more efficient manner. Following economic liberalization in 1991, the Indian ICT industry has been one of the major drivers of economic progress both in terms of volume and applications. It has assumed the role of providing a forceful leverage to the socio, economic, and technological growth of a developing society (Joseph, 2007). However consumption and production processes of these complex electronic devices are unsustainable, pose a serious challenge to environment and human health, making e-waste one of the largest growing waste streams (Lundgren, 2012a). With waste market getting increasingly global, such waste is illegally exported to crude e-waste recycling hotspots in Asian countries, such as China, India, and Pakistan, and in some African countries, like Ghana and Nigeria (Castillo, 2011). Such illegal trade in e-waste is camouflaged and conducted under the pretext of obtaining ‘reusable’ equipment or ‘donations’ from developed nations. E-waste comprises of ICT and EEE products that are not fit for their originally intended use. It includes computers, its accessories (monitors,

printers, keyboards, and central processing units), typewriters, mobile phones and chargers, remotes, compact discs, headphones, batteries, LCD/Plasma TVs, air conditioners, refrigerators, and other household appliances (Lalchandani, 2010). The increasing ‘market penetration’ in the developing countries, ‘replacement market’ in the developed countries (Borthakur & Sinha, 2013), coupled with rapid developments, innovation, miniaturization, and replacement resulted into higher rate of obsolescence of electronics products. It is estimated that in 2014 world-wide 41.8 million metric tonnes (Mt) e-waste was generated and most of it was not collected and treated in environmentally sound manner (Baldé, Wang, Kuehr, & Huisman, 2015). Most of this either end up with municipal waste in landfills or unauthorized recycling yard (Greenpeace Press Report, 2008). As noted by UNEP in 2005, “Every year, 20 to 50 million tonnes of e-waste is generated world-wide, which could bring serious risks to human health and the environment” (Schwarzer, Giuliani, Kluser, & Peduzzi, 2005). Even though there is no clear data on the quantity of e-waste generated and disposed of each year in India, it is estimated 70 percent of e-waste handled in India is imported. It also estimates that between the years of 2007-2020, domestic television e-waste will double, computer e-waste is expected to increase five-fold, while cell phones will increase eighteen times (Disabled World, 2015). Thus knowledge society of 21st century is creating its own toxic footprint which is most debated issue amongst the environmentalists, environment regulators, worldwide environment forums, governmental, and non-governmental agencies, and policy makers.

DOI: 10.4018/978-1-5225-2255-3.ch268 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Environmental Science and Agriculture

BACKGROUND OF STUDY Solid waste management, which is already a mammoth task in India, is becoming more complicated by the invasion of e-waste, which has complex characteristics as it differs chemically and physically from urban or industrial waste. Each wave of technology creates a set of waste previously unknown by humans (Sikdar & Vaniya, 2014) making e-waste management a big issue in both developed and developing countries. The current practices of e-waste management in developing countries suffer from a many drawbacks such as informal recycling, inadequate legislation, low public awareness of the hazardous nature of e-waste, use of obsolete methods, and inadequate emphasis on the employee’s protection (Cobbing, 2008), is jeopardizing people’s health and environment (Smith, Sonnenfeld, & Naguib Pellow, 2006a). Having reviewed literature from various other studies conducted in India and abroad, and understanding the magnitude of this problem, it is time for India to critically review its management of e-waste, to work towards a strategy to create the necessary infrastructure, and mechanisms to support sustainable and environmentally friendly e-waste management besides sensitizing consumers, waste recyclers, and future decision makers on issues like e-waste characteristics, its trans-boundary movement recycling technology, social, and environmental considerations, and toxic effect on health.

DEFINITION OF E-WASTE Even though there is no standard definition for ewaste, some of the reported definitions of e-waste in literature are mentioned below: According to the Basel Convention, “Wastes are substances or objects, which are disposed of or are intended to be disposed of, or are required to be disposed of by the provisions of national laws” (Text of Basel Convention, 2014).

According to Basel Action Network (BAN), “E-waste includes a wide and developing range of electronic appliances ranging from large household appliances, such as refrigerators, airconditioners, cell phones, stereo systems, and consumable electronic items to computers discarded by their users” (Puckett, Byster, Westervelt, Gutierrez, Davis, Hussain, & Dutta, 2002), (Gaidajis, Angelakoglou, & Aktsoglou, 2010). As per European Directive 2002/96/EC, “Waste electrical and electronic equipment (WEEE), including all components, subassemblies, and consumables, which are part of the product at the time of discarding” (European Parliament, 2003, January 27), (Jain, 2008), (European Parliament, 2012, July 4). As per European Directive 75/442/EEC, Article I(a), “Any substance or object which the holder discards or is required to discard in compliance with the national legislative provisions”. Further it includes all components, subassemblies, and consumables which are part of the product at the time of discarding (Borthakur & Singh, 2012). According to Organisation for Economic Co-operation and Development (OECD), “Any household appliance consuming electricity and reaching its life cycle end”, also referred as composite waste (OECD, 2007). These differences in definitions, of what constitutes e-waste, have the potential to create disparities in both the quantification of e-waste generation and the identification of e-waste flows across nations. The lack of a precise definition of e-waste is one of the key issues that need to be addressed on an international level (Lundgren, 2012b).

COMPONENTS OF E-WASTE E-waste is classified as hazardous waste (Tsydenova & Bengtsson, 2011a), and it imposes many challenges on the recycling industry (Smith, Sonnenfeld, & Naguib Pellow, 2006b). Modern electronics can contain up to 60 different elements;

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many are valuable, some are hazardous (Third World Network, 1991) and some are both. Several rare elements are also used (Frazzoli, Orisakwe, Dragone, & Mantovani, 2010). The types and amounts of metals used in electronics products vary with evolution of technology. The most complex mix of hazardous substances is usually present in the printed wiring boards (PWBs) that contain valuable metals like copper, silver, gold, palladium, and platinum, brominated flame retardants used in connectors, cathode ray tubes and LCD contain heavy metals like lead and barium, switches and flat screens contain mercury, older capacitors and transformers contain poly chlorinated biphenyl’s (PCB’s), poly vinyl chloride (PVC) coated copper cables and casing, plastics from computer hardware that release highly toxic dioxins and furans (Sum, 1991). The fraction including iron, copper, aluminum, gold, and other metals in e-waste is over 60 percent, plastics account for about 30 percent, and the hazardous pollutants comprise only about 2.70 percent of waste (Widmer, Oswald-Krapf, Sinha-Khetriwal, Schnellmann, & Bo¨ni, 2005) besides alloys that mostly decreases metal’s recyclability.

TOXIC ELEMENTS IN E-WASTE E-waste contains thousands of components made of deadly chemicals, heavy metals, flame retardants, and potentially hazardous substances whose main routes of human exposure are through inhalation, dust ingestion, dermal exposure, and oral intake. Metal toxicity causes breathing difficulties, respiratory irritation, coughing, choking, pneumonitis, tremors, neuropsychiatric problems, convulsions, coma and even death (Yu, Welford, & Hills, 2006 a). Some toxic chemicals found in e-waste are analyzed below. 1. Beryllium (Be) is used as copper-beryllium alloys in computer motherboards, relays, and connectors (Taylor, Ding, Ehler, Foreman, Kaszuba, & Sauer, 2003). Beryllium refining

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produces fumes, dust, and oxides which are both acutely and chronically toxic to humans. If inhaled in large concentration, it causes acute lung disease, breathing discomfort, coughing, rapid heartbeat, and death in extreme cases. Its compounds are carcinogenic in nature (IARC, 1993b) and studies have shown that people can still develop beryllium disease (beryllicosis) after many years of last exposure. 2. Cadmium (Cd) is a toxic heavy metal found naturally in very low concentration (Salomons & forstner, 1984). It is used in switches, rechargeable (Ni-Cd) batteries, stabilizers, resistors, and corrosion-resistant alloys. It is released into the environment as powdered dust during crushing and milling of plastics, CRTs, and circuit boards. It is potentially a long-term cumulative poison associated with deficits in cognition, learning, behavior, and neuromotor skills in children, causes severe pain in the joints and spine (Itai-itai disease), affects kidneys and softens bones (osteomalacia and osteoporosis) in humans. There is evidence of the role of cadmium and beryllium in carcinogenicity (Strickland, & Kensler, 1995), (Pruss-Ustun & Corvalan, 2006). 3. Hexavalent Chromium (Cr-VI) is used to protect metal housings in a computer from corrosion. It is very reactive and soluble in water, making it more mobile in environment (Mukherjee, 1998). Its corrosive nature cause skin allergies (dermatitis), damage DNA, liver, kidneys, pulmonary congestion, edema, bronchial maladies including asthmatic bronchitis, and lung cancer (IARC, 1990a). 4. Lead (Pb) as lead oxide comes from breaking of CRT, lead powder is released while removing solder form microchips, and lead fumes comes from high temperature smelting processes exposing the workers (Schutz, Olsson, Jensen, Gerhardsson, Borjesson, Mattsson, & Skerfving, 2005). In unlined

Category: Environmental Science and Agriculture

landfills, lead would dissolve in leachate or mix with ground water leading to contamination. It is neurotoxin that exerts toxic effects on the central nervous system (organic affective syndrome), peripheral nervous systems (motor neuropathy), the hemopoietic system (anemia), the genitourinary system (capable of causing damage to all parts of nephron), and the male and female reproductive systems (Harrington, Aw, & Baker, 2003). It affects mental development in children with impaired cognitive function, behavioral disturbances, attention deficits, hyperactivity, conduct problems, and lower IQ. 5. Mercury (Hg) is used as lighting device which illuminates most flat panel monitors. Workers can inhale of mercury vapour or dust which released while breaking and burning of circuit boards and switches. It affects kidneys, immune system, damage to the genitourinary system (tubular dysfunction), central and peripheral nervous systems, reduced fertility, and impairs fetus growth. When inorganic mercury spreads out in the water, it is transformed into toxic methylated mercury by microbial activity, which bio-accumulates, biomagnifies in living organisms, and concentrates through the food chain, particularly by fish (Hu, & Speizer, 2001), (WHO, 1989). 6. Brominated Flame Retardants (BFRs) like Polybrominated biphenyl (PBB), Polybrominated diphenyl ether (PBDE), and Tetrabromobisphenol-A (TBBPA) are chemically persistent organic pollutants (POP) along with toxic antimony trioxide which is used as flame retardants in electronic devices. They release carcinogenic brominated dioxins and furans as gases during fire. PBDE used in transformers and capacitors is bioaccumulative, impair brain function, and can cause liver and malfunctioning of endocrine system (thyroid damage). TBBPA used in printed circuit boards contains bro-

mine that can leach into landfills. Dust on computer cabinets contains BFRs. 7. Polyvinyl Chloride (PVC) is found in circuit boards, cabinets, and insulation on cables. It is hazardous because contains upto 56 percent chlorine which are precursors to polychlorinated di-benzo-p-dioxins and di-benzo-furans (classified as POP under Stockholm Convention) during incineration along with large quantities of hydrogen chloride gas, which when inhaled may leads to cancer, respiratory problems, affect reproductive, and immune system. 8. Polycyclic Aromatic Hydrocarbons (PAH) is generated from e-waste recycling activities and have potential impacts on soil, vegetation, and human health include breathing difficulties, respiratory irritation, coughing, choking, pneumonitis, tremors, neuropsychiatric problems, convulsions, coma and even death (Yu, Welford, & Hills, 2006 b). Epidemiological studies in the past on occupational exposure to PAH, provides sufficient evidence of the role of PAH in the induction of skin and lung cancers (Stewart & Kliehues, 2003). 9. Cobalt (Co) is extensively used in integrated circuits, semi-conductors, magnetic recording media, thin metallic films, and rechargeable batteries. It is mainly absorbed from the pulmonary and the gastrointestinal tracts and cause allergic dermatitis, rhinitis, vomiting, thyroid damage, and impaired vision. Cobalt dust may cause an asthma-like disease with symptoms ranging from cough, shortness of breath, and dyspnea to decreased pulmonary function, nodular fibrosis, permanent disability, and death. In addition to the hazardous materials e-waste also contain a large number of valuable precious metals like Gold (Au), Silver (Ag), Platinum (Pt), and Palladium (Pd) in concentrations 40 to 50 times richer than there naturally occurring

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deposits. These precious metals are extracted using hydrometallurgical processes, where valuable metals first leached into acid or alkali solutions and then they are concentrated by using various methods like precipitation, cementation, solvent extraction etc.

E- WASTE DISPOSAL AND RECYCLING PRACTICES IN INDIA E-wastes disposal is a big problem faced by many countries including India. It is estimated that, by 2020, India could see nearly 500 percent rise in the number of old computers being dumped (Schluepa, et.al., 2009). E-waste disposal process work in two ways: 1. By removing the hazardous items; 2. By separating recyclable materials. The three main groups of substances that may be released during recycling and material recovery, and may pose significant human and environmental health risks are: 1. Original constituents of EEE like lead and mercury; 2. Added substance during recovery processes like cyanide; and 3. Hazardous byproducts formed by incineration of e-waste like PAH. In India, e-waste collection, transportation, segregation, dismantling, recycling, and disposal is done by unorganized small enterprise that is difficult to regulate. They employ untrained labours (more so children and women) who work in poorly-ventilated or enclosed areas without appropriate equipment and technical expertise. The process includes manual disassembly, melting, acid extraction of metals from complex mixtures, and extruding plastics. Further incineration of printed circuit boards for desoldering and removal of chips exposes

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workers to fumes of metals, particularly those in solder (often lead and tin), and other hazardous substances that can be potentially released (Tsydenova, & Bengtsson, 2011b) which pollutes the surrounding air. Inhalation and dust ingestion impose a range of potential occupational hazards including silicosis (Lepawsky, & McNabb, 2010). It exposes them to dangerous slow-poisoning chemicals on a regular basis impacting their health. Computer wastes that are land-filled produces contaminated leachate which eventually pollutes the groundwater, whereas acids and sludge obtained from melting computer chips, if disposed on the ground causes acidification of soil and irreversible damage to environment.

LEGAL FRAMEWORK IN INDIA The environmentally sound management of used EEE imports, recycling, and their disposal has become the subject of serious discussion, debate, and significant challenge among the government, organizations, environmental groups, and component manufacturers in India. The department related Parliamentary Standing Committee on Science & Technology, under Ministry of Environment & Forests (MoEF) which is also responsible for environmental legislation, in its 192nd report on the ‘Functioning of the Central Pollution Control Board (CPCB), which plays important role in drafting guidelines, has concluded that e-waste is going to be a big problem in the future due to modern life style, increase in the living standards of people, and augmentation of economic growth. The rules and regulations for waste control in India are primarily listed under the aegis of Environmental Protection Act 1986. Despite a wide range of environmental legislation in India there are no specific laws or guidelines for electronic or computer waste (Devi, Shobha, & Kamble 2004). Electronic waste is included under List-A and ListB of Schedule-3 of the Hazardous Wastes (Management & Handling Rules), 1989, as amended in 2000 and 2003. In 2007, separate guidelines

Category: Environmental Science and Agriculture

on e-waste management were implemented, but they were voluntary and had limited impact. These guidelines include details such as e-waste composition; recycle, re-use, and recovery potential of items of economic value, identification of possible hazardous contents in e-waste, treatment, disposal options, and the environmentally sound e-waste treatment technologies (Rajya Sabha Unstarred Question No. 1887, 2009). However these rules primarily dealt with industrial waste and lack elements to deal with complex nature of e-waste. Subsequently in 2008, these rules were amended to include toxic content and made registration mandatory for recyclers. The provision of environmental protection is delegated among various states in India. Following Supreme Court directions (Writ Petition (Civil) No. 657, 1995), the states have notified a set of hazardous waste laws and built a number of hazardous waste disposal facilities in the last ten years. However, the Controller and Auditor General (CAG) report found that over 75 percent of state bodies were not implementing these laws (Writ Petition (Civil) No. 10, 1995), giving rise to sloppy enforcement of e-waste related legislation. India is also a signatory to the Basal Convention (under UNEP) on the control of Transboundary Movement of hazardous wastes and their disposal but officially opposes enforcement of BAN Amendment (Basel Action Network, 2011). The regulations banning the importation of hazardous waste for disposal are weak and an imported consignment of electronic scrap still comes into the country, as they are not properly classified as plastic scrap or mixed waste. In 2014, India generated 17 lakh tonnes of e-waste increasing growing at rate of 4-5 percent per year. With such exponential growth, the Indian government finally woke up and responded by framing the E-waste (Management and Handling Rules), 2016 with the aim to make re-cycling of e-wastes environmentally friendly. To begin with, the rules put India along with a select club of nations like the United States and many European nations to have legislation to regulate and manage e-waste. These rules recognized the producer’s liability

for recycling and reducing e-waste in the country. It will apply to every producer, consumer, and bulk consumer involved in manufacture, sale, purchase, and processing of electronic equipment or components. It also brought disposal of CFL lights under its preview. While the rules seem impressive on paper, environmentalist argues that there is total oversight of the ground situation. These rules ignore the unorganized sectors where 90 percent of the e-waste is generated. Also there is lack of a safe e-waste recycling infrastructure in the organized sector where only a fraction of the e-waste (estimated 10 percent) finds its way to recyclers due to absence of an efficient take back scheme for consumers. The ministry is giving the producers of EEE a breathing period of one year to set up their collection centers (Kumar & Shah, 2014) and develop technical guidelines for the environmentally sound management of e-wastes. Thus computer, mobile handset, and consumer goods manufacturers, will be required to come up with e-waste collection centers or introduce ‘take back’ systems. Therefore over reliance on the capacities of the informal sector poses severe risks to the environment and human health.

STRATEGIC INTERVENTION A smart e-waste management system have to access current e-waste situation, recognize that e-wastes is complex mixture of hazardous substances, reduce the generation of e-waste through smart manufacturing and maintenance, reuse till functioning of EEE, and finally recycle those components that cannot be repaired or refurbished. Recycling and reuse of materials are the next level of potential options to reduce e-waste (Ramachandra & Saira, 2004). Based on current situational analysis in India, following strategic intervention is proposed: 1. Extended Producers Responsibility be introduced that involves collection and disposal of e-waste in environmentally sound manner 3071

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2.

3.

4. 5. 6.

7.

8.

or else stringent financial penalties must be imposed by state governments. Update the legal and institutional framework for e-waste management including effective enforcement of laws, regulations, and standards. Introduce strict liability clause in proposed rules for effectiveness. Defining responsibilities of prime stake holders at the level of government, supply chain, consumers, and develop a comprehensive policy that address all issues ranging from production, trade to final disposal, including technology transfers for the recycling of electronic waste. Raise public awareness, advocate for e-waste management across all stakeholders through public-private-partnerships linkages. Introduce a concept of ‘e-waste exchange’ as an independent market instrument offering services for sale and purchase of e-waste. Tighten import norms and custom procedure at port of entry. Maintain statistical records of imported of EEE goods for further analysis including their final disposal. Create a facilitative environment for investment in e-waste handling and disposal infrastructure by creating modern e-waste recycling facility and provide tax incentives to make it more effective. Introduce advance recycling fees. Also setup and operationalise an e-waste fund to benefit those working within this industry.

FUTURE RESEARCH DIRECTIONS Developing national approach to handle e-waste, strengthening regulatory environment, designing new methods to increase waste collection, integrated modelling concepts to build waste recycling capacities, and building awareness among people can be carried out on larger scale across India. Also study of impact on toxic heavy metals individually as well as their cumulative effect on ecosystem needs to be further explored. Further quantities of waste material that moves between countries, 3072

waste flows within a country and between countries and hazardous substance emissions associated with manual recycling process, social, and its health impact on children and women can also be further investigated. A comparative study of various EEE product categories can made.

CONCLUSION From the discussion above, it is aptly clear that India faces an enormous task of handling and disposing growing piles of e-waste and its impact on human health (more so of women and children) and natural environment has increased manifold. Therefore policy level interventions should include strong e-waste regulation, tight control on import and export of e-wastes, and facilitation in development of recycling infrastructure. Lack of strict enforcement of legislation is also worrisome situation. It requires building of public awareness, establishment of institutional infrastructures (including e-waste collection, transportation, treatment, storage, recovery, and disposal) at national and regional levels. Furthermore product end-of-life management should be made a priority during design of new electronic products using green materials, innovation in product technology, life cycle analysis, public outreach, and so on. Beyond conservation of raw materials and energy, there are additional environmental benefits of recycling, such as reduced land disturbance, water use, air emissions, and waste generation which in turn improves efficiency and environment. In conclusion it is time for us to look deep and ahead today as tomorrow it will be too late to act.

REFERENCES Ayers, S. (1999). The cultural impact of computer technology. Yale-New Haven Teachers Institute. Retrieved February 29, 2016 from http://www.yale.edu/ynhti/curriculum/ units/1999/7/99.07.07.x.html

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Baldé, C. P., Wang, F., Kuehr, R., & Huisman, J. (2015). The global e-waste monitor - 2014. United Nations University, IAS-SCYCLE. Basel Action Network (BAN). (2011). Toxic trade news: “Cochin Port a safe conduit for imported e-waste”. “Most aspects of e-waste not regulated in U.S., Va.”; “Research identifies U.S electronic wastes as likely source of toxic jewellery imports from China”; “178 countries agree to allow the ban on exports of toxic wastes to developing countries to become law”. Retrieved March 1, 2016 from http://www.ban.org/ Borthakur, A., & Singh, P. (2012). Electronic waste in India: Problem and Policies. International Journal of Environment Science, 3, 354–362. Borthakur, A., & Sinha, K. (2013). Generation of electronic waste in India: Current scenario, dilemmas and stakeholders. African Journal of Environmental Science and Technology, 7(9), 899–910. doi:10.5897/AJEST2013.1505 Castillo, M. (2011, January 14). Electronic Waste: Where does it go and what happens to it? Retrieved March 1, 2016 from http://techland.time. com/2011/01/14/electronic-waste-where-does-itgo-and-what-happens-to-it/ Cobbing, M. (2008). Toxic Tech: Not in our backyard. Uncovering the hidden flows of e-waste. Report from Greenpeace International, Amsterdam. Retrieved March 1, 2016 from http://www. greenpeace.org/raw/content/belgium/fr/press/ reports/toxic-tech.pdf Devi, B. S., Shobha, S. V., & Kamble, R. K. (2004). E-Waste: The Hidden harm of Technological Revolution. Journal IAEM, 31, 196–205. Disabled World. (2015). Electronic waste: Medical & health issues. Retrieved March 3, 2016 from http://www.disabled-world.com/health/ ewaste.php Disposal of e-waste. (2009). Rajya Sabha Unstarred Question No. 1887.

European Parliament. (2003, January 27). Directive 2002/96/EC of the European Parliament and of the Council on waste electrical and electronic equipment (WEEE). Official Journal of the European Union, L, 37, 24–38. European Parliament. (2012, July 4). Directive 2012/19/EU of the European Parliament and of the Council on waste electrical and electronic equipment (WEEE). Official Journal of the European Union, L, 197, 38–71. Frazzoli, C., Orisakwe, O. E., Dragone, R., & Mantovani, A. (2010). Diagnostic health risk assessment of electronic waste on the general population in developing countries scenarios. Environmental Impact Assessment Review, 30(6), 388–399. doi:10.1016/j.eiar.2009.12.004 Gaidajis, G., Angelakoglou, K., & Aktsoglou, D. (2010). E-waste Environmental Problem and Current Management. Journal of Engineering Science and Technology, Review, 3(1), 193–199. Greenpeace. (2008). Take Back Blues, An assessment of e-waste takes back in India. Retrieved February 29, 2016 from www.greenpeace.org/ india/press/reports/recycling-ofelectronic-waste Guidance Manual for the Implementation of the Organisation for Economic Co-operation and Development (OECD). (2007). Recommendation C(2004)100 on Environmentally Sound Management (ESM) of Waste, Section 5.2, 12. Retrieved March 2, 2016 from http://www.oecd.org/env/ waste/39559085.pdf Harrington, J. M., Aw, T. C., & Baker, E. L. (2003). Occupational and environmental health and safety. In A. W. David, M. C. Timothy, D. F. John, & J. B. Edward (Eds.), Oxford Textbook of Medicine (4th ed.; Vol. 1, pp. 956–960). New York: Oxford University Press. Hu, H., & Speizer, F. E. (2001). Specific environmental and occupational hazards. In Harrison’s Principles of Internal Medicine (15th ed.; vol. 2, pp. 2591-2592). McGraw-Hill Inc.

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International Agency for Research on Cancer (IARC). (1990). Chromium. IACR Monograph, 49, 677. International Agency for Research on Cancer (IARC). (1993). Beryllium and Beryllium Compounds. International Agency for Research on Cancer (IARC) Monograph, 58, 444. Jain, A. (Ed.). (2008). E-waste: Implications, regulations, and management in India and current global best practices. New Delhi: Teri Press. Joseph, K. (2007). Electronic waste management in India - Issues and Strategies. Proceedings of the Eleventh International Waste Management and Landfill Symposium. Kumar, R., & Shah, J. D. (2014). Current Status of Recycling Waste Printed Circuit Boards in India. Journal of Environmental Protection, 9-16. Lalchandani, N. (2010, April 24). E-scare. The Times of India. Lepawsky, J., & McNabb, C. (2010). Mapping international flows of electronic waste. Canadian Geographer, 54(2), 177–195. doi:10.1111/j.15410064.2009.00279.x Lundgren, K. (2012a). The Global Impact of ewaste: Addressing the Challenge. International Labour Office, Programme on Safety and Health at Work and the Environment (Safe Work), Sectoral Activities Department (Sector). Geneva: ILO. Lundgren, K. (2012b). The Global Impact of ewaste: Addressing the Challenge. International Labour Office, Programme on Safety and Health at Work and the Environment (Safe Work), and Sectoral Activities Department (SECTOR). Geneva: ILO, ISBN 978-92-2-126897-0 (print) Mukherjee, A. B. (1998). Chromium in the environment of Finland. The Science of the Total Environment, 217(1-2), 9–19. doi:10.1016/S00489697(98)00163-6 PMID:9695169

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Pruss-Ustun, A., & Corvalan, C. (2006). Preventing disease through healthy environments: Towards an estimate of environmental burden of disease. WHO Publication. Puckett, J., Byster, L., Westervelt, S., Gutierrez, R., Davis, S., Hussain, A., & Dutta, M. (2002). Exporting Harm - The High-Tech Trashing of Asia; The Basel Action Network (BAN) Silicon Valley Toxics Coalition. Seattle, WA: SVTC. Ramachandra, T. V., & Saira, V. K. (2004, March). Environmentally sound options for waste management, Envis Journal of Human Settlements. Retrieved March 2, 2016 from http://www.ces. iisc.ernet.in/energy/paper/ewaste/ewaste.html Salomons, W., & Frostner, U. (1984). Metals in hydrocycle. Springer-Verlag. doi:10.1007/9783-642-69325-0 Schluepa, M., Hageluekenb, C., Kuehrc, R., Magalinic, F., Maurerc, C., Meskersb, C.,... Wang, F. (2009). Recycling: From e-waste to resources, Sustainable Innovation and Technology Transfer Industrial Sector Studies (Nairobi and Bonn, UNEP and STeP). Retrieved March 2, 2016 from http://www.unep.org/pdf/Recycling_From_ewaste_to_resources.pdf Schutz, A., Olsson, M., Jensen, A., Gerhardsson, L., Borjesson, J., Mattsson, S., & Skerfving, S. (2005). Lead in finger bone, whole blood, plasma and urine in lead-smelter workers: Extended exposure range. International Archives of Occupational and Environmental Health, 78(1), 35–43. doi:10.1007/s00420-004-0559-5 PMID:15750821 Schwarzer, S., Giuliani, A., De Bono, G., Kluser, S., & Peduzzi, P. (2005). “E-waste: the hidden side of IT equipment’s manufacturing and use”. Early Warnings on Emerging Environmental Threats, No. 5, United Nations Environment Programme (UNEP). GRID Europe.

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Sikdar, M., & Vaniya, S. (2014). The New Millennium and Emerging Concerns. International Journal of Scientific and Research Publications, 4(2). Smith, T., Sonnenfeld, D. A., & Naguib Pellow, D. (Eds.). (2006). Challenging the chip: Labor rights and environmental justice in the global electronics industry. Philadelphia, PA: Temple University Press. Stewart, B. W., & Kliehues, P. (2003). World Cancer Report. Lyon: IARC Press. Strickland, P. T., & Kensler, T. W. (1995). Chemical and physical agents in our environment. In Clinical Oncology. Churchill Livingston Inc. Sum, E. Y. L. (1991). The recovery of metals from electronic scrap. Journal of Metallurgy, 43, 53–61. Taylor, T. P., Ding, M., Ehler, D. D., Foreman, T. M., Kaszuba, J. P., & Sauer, N. N. (2003). Beryllium in the environment: A Review. Journal of Environmental Science and Health. Part A, Toxic/Hazardous Substances & Environmental Engineering, 38(2), 439–469. doi:10.1081/ESE120016906 PMID:12638707 Text of the Basel Convention. (2014). The Control of Transboundary Movements of Hazardous Wastes and Their Disposal. United Nations Environment Programme (UNEP). Retrieved March 1, 2016 from http://www.basel.int/portals/4/basel%20 convention/docs/text/baselconventiontext-e.pdf Third World Network. (1991). Toxic Terror: Dumping of Hazardous Wastes in the Third World, Penang, Malaysia. Author. Tsydenova, O., & Bengtsson, M. (2011a). Chemical hazards associated with treatment of waste electrical and electronic equipment. Waste Management (New York, N.Y.), 31(1), 45–58. doi:10.1016/j.wasman.2010.08.014 PMID:20869229

Tsydenova, O., & Bengtsson, M. (2011b). Chemical hazards associated with treatment of waste electrical and electronic equipment. Waste Management (New York, N.Y.), 31(1), 45–58. doi:10.1016/j.wasman.2010.08.014 PMID:20869229 Widmer, R., Oswald-Krapf, H., Sinha-Khetriwal, D., Schnellmann, M., & Bo¨ni, H. (2005). Global perspectives on e-waste. Environmental Impact Assessment Review, 25(5), 436–458. doi:10.1016/j.eiar.2005.04.001 World Health Organization (WHO). (1989). Mercury, Environmental Health Criteria, 86. WHO. Yu, J., Welford, R., & Hills, P. (2006 a). Industry responses to EU WEEE and ROHS Directives: Perspectives from China. Corporate Social Responsibility and Environmental Management, 13(5), 286–299. doi:10.1002/csr.131 Yu, J., Welford, R., & Hills, P. (2006 b). Industry responses to EU WEEE and ROHS Directives: Perspectives from China. Corporate Social Responsibility and Environmental Management, 13(5), 286–299. doi:10.1002/csr.131

ADDITIONAL READING Brigden, K., Labunska, I., Santillo, D., & Allsopp, M. (2005). Recycling of electronic wastes in China & India: Workplace & environmental contamination. Greenpeace International. Bruke, M. (2007). The Gadget Scrap, Chemistry World, 44-48. Retrieved April 1, 2016 from www. chemistryworld.org Fornalczyk, A., Willner, J., Francuz, K., & Cebulski, J. (2013). E-waste as a source of valuable metals. Archives of Materials Science and Engineering, 63(2), 87–92.

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Gupta, V., & Kumar, A. (2014). E-waste status and management in India. Journal of Information Engineering and Applications, 4(9), 41–48. Khetriwal, D. S., Luepschen, C., & Kuehr, R. (Eds.). (2013). Solving the e-waste problem: An interdisciplinary compilation of international ewaste research. United Nations University. Krishna, R., & Saha, S. (2015). Study paper on e-waste management. Retrieved March 30, 2016 from http://tec.gov.in/pdf/Studypaper/e%20 waste%20management_11.08.pdf Namias, J. (2013). The future of electronic waste recycling in the United States: Obstacles and domestic solutions. Retrieved March 31, 2016 from http://www.seas.columbia.edu/earth/wtert/sofos/ Namias_Thesis_07-08-13.pdf

KEY TERMS AND DEFINITIONS Dioxins and Furan: These are persistent environmental pollutants (POPs), formed as unintentional by-product during e-waste incineration to recover valuable metals. Electronic Waste (E-Waste): It refers to EEE waste, including all components, subassemblies, and consumables which are part of the product destined for reuse, resale, salvage, recycling or disposal at the time of discarding.

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Environmental Hazard: A substance, state or event which has the potential to threaten the surrounding natural environment that adversely affects people’s health (Pollution and Natural disasters). Environmental Sustainability: It is defined as could be defined as a condition of balance, resilience, and interconnectedness that allows human society to satisfy its needs while neither exceeding the capacity of its supporting ecosystems to continue to regenerate the services necessary to meet those needs nor by our actions diminishing biological diversity. Metal Poisoning: Toxic metals in certain form and dose sometimes imitate the action of an essential element in the body; interfere with the metabolic processes that cause illness (metal poisoning). Occupational Hazard: It is risk accepted as a consequence of a particular occupation and they encompass chemical, biological, psychosocial, and physical hazards. Recycling: It is the process of converting waste materials into reusable objects; dismantling, separating fractions, and recovering material in order to reduce the consumption of fresh raw materials, energy usage, air pollution (from incineration) and water pollution (from land filling) from e-waste after the lifespan of the equipment.

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Green IT and the Struggle for a Widespread Adoption Edward T. Chen University of Massachusetts – Lowell, USA

INTRODUCTION Since the inception of computers, both for business and personal purposes, there have been multiple environmental issues that resulted from this technology. The complex electronics require significant electricity to operate them, large amounts of energy to keep them cool for prolonged usage, and various chemicals and resources to construct them. Notably, within the last decade, there has been a movement building for the ecologically responsible construction, use and disposal of computer systems and their components, including monitors, batteries and printer cartridges. This initiative is commonly known as Green Information Technology (IT), or Green IT. As both consumable and enterprise level computing products grows, a need for sustainability arises. A balance between the energy consumption and the provided services is required to ensure the environment can survive the influx of billions and billions of devices. Concepts like the Internet of Things, Big Data, smart devices and phones, and complex business analytics for corporations all drive the need for more connected devices. These devices consume more electricity than ever before and data runs the planet (Murugesan & Gangadharan, 2012; Subburaj, Kulkarni, & Jia, 2014). The Green IT (green information technology) is the practice of environmentally sustainable computing (McLaughlin, 2013). The lack of regulations, standardizations, and standard operating procedures has left this notion out of the mainstream and under the radar of many organizations’ information technology (IT) implementations. Several ideas at different levels have

been proposed over the years. Its current adoption rate is not enough for sustainability. G-Readiness framework combines properties, processes, and components that are well defined and measurable to ensure success in the greening of IT (Molla, Cooper, Corbitt, Deng, Peszynski, Pittayachawan, & Teoh, 2008). Large technology companies have designed, patented, and implemented as a way to offer a differentiated service and a competitive advantage through green IT. Some of their innovations have the potential to be replicated for further successes (Murugesan & Gangadharan, 2012).

BACKGROUND Though there is not a general consensus on the exact definition of Green IT (also referred to as green computing, green information and communication technologies (ICT), or ICT sustainability), the most commonly accepted definition was coined by San Murugesan, an outspoken university professor, in his 2008 article entitled “Harnessing Green IT: Principles and Practices”. Murugesan defined green IT as “the study and practice of designing, manufacturing, using and disposing of computers, servers, and associated subsystems… efficiently and effectively with minimal or no impact on the environment” (Murugesan, 2008). Multiple efforts can be made, both from individual home users as well as those of entire businesses, to reduce the negative impact on the environment from the technology they are using. The hardware, software, and components that make up technology are always changing and evolving. Some components like computer

DOI: 10.4018/978-1-5225-2255-3.ch269 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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processors, are gaining the ability to process information faster while the integrated circuits is getting smaller. Other devices gain new features with each new release and make the older model seem outdated or no longer usable. This perception is particularly accurate with personal technology such as laptops, phones, and tablets. Unused excess hardware accumulates in staggering quantities. In the corporate world, technology is advancing faster than the needs of many businesses. Data centers are filled with high-powered servers and storage devices, which run 24/7/365 in a production environment. Attractive and enticing price points combined with clever marketing presentations convince companies that the deployment of these systems is necessary to solve their IT and IS (information systems) problems (Nguyen, Cheriet, Lemay, Reijs, Mackarel, & Pastrama, 2012). According to Gartner Research, there are 2 billion computers in use today. They predict that the number of devices and things, items such as thermostats, refrigerators, cars, and other nontraditional computing hardware and sensors, on the Internet could surpass 40 billion by the year 2020 (Akhgar, Pattinson, & Dastbaz, 2015). Greenpeace estimates that if the Internet were a country, it would fall between Japan and Russia, or 5th place, in overall electricity consumption in the world (Cook & Pomerantz, 2015). 50% of the world’s population owns a cellular telephone. This number is only going to go up as emerging countries begin to rely on the same technology as First World countries. Tablets are expected to outpace computers in sales and use before the end of this year (Akhgar, Pattinson, & Dastbaz, 2015). The amount of technology in use and the amount of technology that has been cast aside present two challenges for the concept of green IT: reducing energy consumptions of current hardware and finding ways to safely recycle previous hardware that is no longer in use. Stated in a different way, it is solving the two problems of how to reduce CO2 emissions and how to lower e-waste (Ahmad & Ranka, 2016; Elliot, 2007).

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A study was conducted in 2009 to investigate why the lack of growth with implementing and supporting green IT initiatives and standards. It surveyed Chief Information Officers (CIOs) and other IS professionals to find the “barriers” that keep green IT from being implemented. The results of the survey show no surprises, citing a lack of business leadership; the unknown costs versus cost savings for green IT solutions, and the absence of value by turning to green alternatives (Dedrick, 2010). Also uncovered through the same survey was the importance of government incentives or regulations mandating converting to green IT. Without formal direction or instructions to do so, companies are not eager to start the perceived arduous process to switch.

INFORMATION TECHNOLOGY’S GREEN PROBLEM Energy consumption is a major aspect of IT, with the methods to produce electricity still largely powered by the depletion of fossil fuels such as coal and oil. It is estimated that the carbon dioxide (CO2) produced by a single desktop computer over its lifetime is 1,096 kilograms (Thomson & van Belle, 2015). This consumption and pollution is amplified by the increasing occurrence of non-efficient software and coding, requiring computers to take longer periods to process finite tasks. A 2009 disputed study found that the average search using the popular search engine Google produced approximately 7 grams of CO2 and required roughly half of the amount of energy needed to boil a kettle of water (Swaine, 2009; Warman, 2009). A portion of the total electricity consumed by IT is to power the computers and their components, while an even larger portion (30%) is used to cool the computers and their related hardware, particularly in the data centers associated with these information systems (IS) that house the computers and their related components, such as servers and power supplies (Murugesan, 2008; Nguyen, 2012).

Category: Environmental Science and Agriculture

The majority of businesses routinely require that their data centers remain cooled at all times at temperatures less than 70 degrees Fahrenheit (21 degrees Celsius) and one study found that only 7% of data centers in the world run at or above a temperature of 75 degrees Fahrenheit (23.8 degrees Celsius) (Mitchell, 2013). The majority of energy spent to cool these facilities utilizes air cooling as opposed to other more efficient methods of cooling and require continuous operation to maintain the set temperature, regardless of whether the IT is even in use. It is estimated that many processors sit idle between 85 to 95% of the day, requiring nearly the same amount of electricity as when active (Shah, 2012). Energy consumption utilized by IT is continually increasing as IT becomes more prevalent in society, with electricity usage by data centers in the United States rising 74% between 2000 and 2010 (van Bussel, Smitter, & Vandepas, 2015). Computer production requires extensive resources and chemicals, with their associated production facilities consuming large amounts of energy for daily operations as well as climate control. Materials such as lead, mercury, cadmium and hexavalent chromium, combined with large amounts of water, aluminum and plastic, are required to make these machines and their intricate components. With today’s pressing need for IT, this technology is more necessary than ever before to ensure each company and household remains capable of performing all manners of tasks and communications both within their community and throughout the world. Despite these high production levels of state-of-the-art equipment, many networks built to last 33 years are seeing themselves disposed of after only 3 to 5 years of use, mainly due to a knee-jerk reaction by their owners to consistently replace and update their ‘obsolete’ systems for the supposedly required next generations of technology (Ahmad & Ranka, 2016; Ogden, 2013). These discarded systems are then largely disposed of inappropriately due to minimal disposal regulations and recycling options for IT, resulting

in approximately 80% of the systems being dumped in nearby landfills or exported to developing countries such as China and Pakistan where the disposal regulations are less stringent. In 2008, it was estimated that two-thirds of the estimated 870 million personal computers that will be made within the next five years will end up discarded in landfills (Murugesan, 2008). In 2006, the global production of IT-related waste was estimated at 20 to 50 million tons per year (van Bussel et al., 2015). These inefficient practices regarding IT are contributing towards global warming, the expedited depletion of natural resources, and increasing the waste in landfills, leading to the pollution of both the land as well as water sources in the vicinity.

SOLUTIONS AND RECOMMENDATIONS Despite this dire situation, many options exist to implement green IT and make computing less of a hazard on the environment. The first practice the majority of companies and households can perform is power management, ranging from simply turning off their computers when not in use to providing a power management features to the IT to automatically reduce its electrical load following a period of inactivity. This feature, known as sleep mode, can reduce costs by 60 to 70% and now comes standard on most new computer systems after lengthy pressure was exerted on the manufacturing industry to standardize powersaving features in their products (Murugesan, 2008). Another prevalent shift in the IT industry is the development of ecological hardware that requires much lower levels of electricity to operate as well as attempts to increase the lifespan of the equipment. An example of this innovative new technology is the Atom series of energy-efficient processors designed by Intel. They are ultra-thin and lightweight and their use in IT reduces the space and energy required on the IS. In addition, the fit-PC by Compulab is a series of fanless per-

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sonal computers that are smaller and require less power than traditional IT, often able to function for long periods of time on batteries and requiring lower-power processors such as the Intel Atom. Monitors that display the IT images have improved significantly in technology and availability, greatly reducing energy costs. Light-emitting diode (LED) displays are now very comparable in cost and beneficial due to their use of significantly less electricity than liquid crystal display (LCD) monitors as well as their predecessor, the cathode ray tube (CRT) display. In 2014, the German ion research company GSI Helmholtz had created the world’s most energy-efficient supercomputer, the L-CSC, in Frankfurt as evidenced by the ‘Green 500’ report released in November of 2014. Despite being incredibly energy-efficient, the computer was also rated the fourth fastest computer in Germany upon its release (Phys.org, 2014), demonstrating that green computers can be just as powerful and effective as standard models. In addition, efficient coding of software and other applications can significantly reduce the time required for computing processes by increasing the software processing speed on each computer, reducing the electricity consumption as well as energy required to cool the IS. The increasing development and availability of green IT hardware and software are helping provide efficient alternatives to traditional IT equipment used throughout the past 30 years. There is a wide variety of cooling system methods that can be used to maintain a requisite temperature in the spaces that house computer systems. In addition to traditional air conditioning, alternative methods such as air-side economizers for facilities and liquid cooling systems for PC’s are showing themselves to be viable options for keeping equipment cool and, in many cases, requiring a fraction of the cost of air conditioning while being several hundred times more efficient. Despite these benefits, it is understood that a massive overhaul of a company’s cooling systems, as well as the required investment that would accompany it, are not simple fixes that can be implemented

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overnight. Despite these hurdles, the primary solution to high cooling costs that is immediate and is being recommended by leading green companies (such as FedEx, Raytheon and Northrop Grumman) as well as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) is to simply raise the temperature in data centers. Rather than require a temperature of no warmer than 70 degrees Fahrenheit (F), multiple experts state that all companies should be safely able to raise the temperature of its data centers to 75 degrees F and possibly even as high as 80.6 degrees F to maintain the climate required to protect the IS while simultaneously reducing the energy demands of the facility cooling systems (Mitchell, 2013). Finally, the most effective solution to reducing energy, hardware, and software costs for all entities utilizing IT seems to be virtualizing equipment. Virtualization is the practice of consolidating servers, desktops, and any other types of equipment to fewer pieces of equipment that can still handle the load requirements. Server virtualization is common in which many virtual servers are hosted on a smaller number of more powerful servers. Instead of having 20 servers throughout the country, entities can invest in three larger server facilities that can still provide full network coverage throughout the company while significantly reducing facility operating, cooling and maintenance costs (Pandi & Somasundaram, 2016). Northrop Grumman is an example of this type of virtualization, eliminating 4,000 physical servers and combining 19 data centers and 81 smaller server rooms into only three facilities (Mitchell, 2013). Desktop virtualization works in many ways by the same principles. Thin clients are stateless and fan-less computer desktop terminals with no hard drives. They simply link in to the data center to access all desktop capabilities while only drawing one-fifth of the electricity of a traditional desktop computer (Murugesan, 2008; Nguyen, 2012). Even virtual chillers are becoming a frequently-utilized concept, providing climate controls to multiple

Category: Environmental Science and Agriculture

facilities within the area at a fraction of the cost each facility would require to cool through individual systems. These concentrated efforts may take considerable planning and expense to arrange in the majority of circumstances, but the initiatives pay off for years in the future with exponentially lower energy and facilities costs to the companies (Pandi & Somasundaram, 2016).

LIMITATIONS Despite the obvious benefits and wide array of alternatives for green IT that are available to companies now more than ever before, the main deterrent is that there are very minimal laws of compliance and regulation regarding green IT. Instead of strict legislation requiring specific changes to be implemented, the majority of initiatives and alternative options available within the United States and beyond are solely advisory. The United States Environmental Protection Agency (EPA) founded the Energy Star program in 1992, giving it a significant upgrade in 2006 to include computers and other IT, but it is still mainly an optional program and not a requirement for all appliances and equipment to have the green star logo depicting environment-conscious operation. As of 2008, only 26 of the 50 United States had established a statewide recycling program for the proper disposal of older computers. The Green Electronics Council created the Electronic Product Environmental Assessment Tool (EPEAT) as a method to assist prospective IT buyers through the ranking of available for purchase equipment by more than 50 criteria topics, assigning scores based on how green the technology is (Ahmad & Ranka, 2016). In 2007, President George W. Bush signed Executive Order number 13423, which required all federal agencies to utilize EPEAT when purchasing new computer systems and requiring all vendors contracted by the Federal government to utilize EPEAT in their system purchases. President Obama recently modified this requirement in March of 2015 with Executive Order 13693, stating

that all government procurements of electronics should “meet or exceed specifications, standards, or labels recommended by (the) EPA” (Moodie, 2015). This legislation is expected to reduce the prominence of the EPEAT ratings system by instead requiring companies wishing to do business with the federal government to follow one of the many recommended ratings systems, rather than specifically the most well-known, EPEAT, but at least the directive to procure green equipment remains a priority. The Restriction of Hazardous Substances (ROHS) in Electrical and Electronic Equipment Directive makes significant restrictions on the European Union market for equipment containing specific amounts of certain hazardous substances (Murugesan, 2008). Since August of 2006, Greenpeace International has produced their quarterly Guide to Greener Electronics, ranking several companies on their overall use (or lack thereof) of green IT. Then in 2007, several manufacturers of computer equipment (including Dell, HP, Microsoft, and Intel) formed the Green Grid, a consortium dedicated to making data centers and information systems more ecologically efficient. Four general drivers for a company to choose to utilize green IT are economic, regulatory, market opportunity, and finally influence from social, cultural and political pressures (Thomson & van Belle, 2015). Despite the wide variety of laws and regulations aimed to transform companies into becoming more environmentally-conscious, the majority remain overwhelmingly optional for companies to follow. Thus, the primary motivator for companies to go green is economic, strongly appealing primarily to their own self-interests (Bohas & Poussing, 2016). Implementing green methods reduces energy costs. Green practices can extend the life of the IT and decreases the costs of disposal of obsolete equipment. In addition, following green practices will set a positive example for other companies to follow and can greatly increase the public image and reputation of the firm, known as “Green corporate image” (Bohas & Poussing, 2016).

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However, many businesses look at green IT through a purely economic mindset. Despite the benefits to the business and the environment, fiscal incentives or government subsidies will have to be involved to aid the businesses in the costly transition towards greener IT (Mitchell, 2013). Through the use of several of the advisory organizations listed above, there is a growing voice for green practices with increasing pressures being placed on both the companies as well as the governments of the world to enact stricter regulations to enable change. There is also a small but growing market opportunity for companies that specialize in green IT consulting to help companies shift to cost effective and ecological green practices. This will likely help increase the prevalence of green computing in the years to come. Another major benefit of green IT is that for the majority of the process, managerial practices should not have to change significantly in the business settings. The process of shifting systems to greener alternatives will require close coordination and decision-making between the management of each business and the IS personnel involved. Following these shifts, the systems will largely function in a way that does not change the day-today aspects of each normal business process. The employees will likely just see a greater emphasis on turning off their computers at the end of the day and other similar green initiatives that are both understandable and significant practices that businesses can implement to help their environment. Other than the higher costs that are still regularly associated with newer technology, it does not appear to be any subsequent technological problems or complications that will arise as a result of green IT. Alternatively, green methods are designed and implemented specifically to reduce the existing energy problems that have been prevalent for decades. They are consistently being seen as reliable solutions to the disadvantages of outdated technology.

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FUTURE RESEARCH DIRECTIONS The Internet of Things (IoT) promises a lot of innovation through sensors operating and interoperating together. It has already solved problems that previously having no operational solutions. IoT brings with it immense computing capabilities and endless possibilities that combine science fiction with an improved quality of life. The need for a seeming endless amount of storage to keep the big data collected from over 40 billion IoT devices. This alone facilitates the immediate need for green IT. However, this technology and this level of capability can also drive green IT initiatives. As Big Data becomes a term that continues to grow in usage, the amount of data is also growing. The more data that needs to be stored, the more natural resources it will take to power the servers, keep them cool, and house them in large facilities. One innovative team decided to see if solving green IT can be accomplished by reducing the abundant amount of data that is currently burning up more energy than necessary. While this study still needs further development, it helps to further the cause here of helping to define how to make green IT more accessible to the general IT public. It categorizes green IT into six core components. The first component is longevity of the hardware. How well was the product produced? What is the expected life span? The second component is software optimization, allowing for the hardware to consume less resource to compile and run the software. Third one is power management in finding the right formula and standard to shut off computer resources when not in use. The fourth component is the recycling of materials and not just recycling computers but finding alternative uses for the hardware. Fifth component on the list is telecommuting. An indirect benefit from fewer cars on the road will reduce carbon dioxide emissions. And finally, the sixth component is energy-efficient computing or low-power IT (van Bussel, Smitter, & van de Pas, 2015).

Category: Environmental Science and Agriculture

Current and previous research has been focused mainly on trying to better understand how green IT can be codified for implementation. Longitudinal studies have looked at ways that Green IT could be standardized, means to quantify form over function, and even look at the individuals who will need to support it and their impressions from a psychological approach. If green IT is to surpass its current adoption rate, stronger research needs to be conducted by pairing technology with green philosophies. For example, implementing power-saving functionality at the firmware level of a network switch can save energy for the entire network. Sensors abound from the Internet of Things revolution should make collected data meaningful for experiments and publish new findings and standards easier to reach target audiences (Kaushik & Vidyarthi, 2016).

CONCLUSION Through a series of missteps or misinterpretations, green IT is still struggling to find its place within the technology field. Studies have been conducted; outcomes have been published; and successes have been documented; but something remains missing in this vital area that blends innovations with ecological awareness and respect. The G-readiness framework is probably the most concrete way for a common business to assess the potential of green IT for them based on the factors most important to keeping the business operational. And while the framework might seem daunting at first, there is a structure to it that forces a common sense and logical approach to reduce energy and look at IT in a smarter way. Companies have adopted the virtualization of servers. Virtualization is not considered a win for green IT, nor did it help to mainstream the concept. Instead, IT professionals everywhere have no idea that when they learn how to run multiple services virtually, they reduce their carbon footprints. Those same IT professionals will be needed to collaborate with their business

counterparts to ensure they can provide innovative solutions to problems as they arise. This will be the cornerstone to the implementation of green IT within a business. The green IT community is still small and many of the same people have been working together on the different studies and experiments trying to garner a stronger and larger following. Social media should be exploited to ensure the next generation of IT professionals and operators know how to think green right from the beginning. It’s the Millennials that will be faced with the green IT challenge, a challenge to sustain a healthy planet through the Internet of Things and the 50 billion devices and zettabytes worth of data that comes with it. Green IT must become the new normal before that time. It is no longer an option, and it is no longer something for someone else to worry about. Currently, there are not enough green IT regulations implemented through the governments of the world, including that of the United States. They would require at least a small percentage of its implementation by the business sector to kick start the unanimous shift towards fully green computing. Further, until commercial entities are required to comply with green IT practices, the personal-use market will also not attain compliance and yet another decade could likely pass resulting in very minimal improvements. Until the requirements are enacted, the majority of businesses will simply not switch over. Before they are able to devote the requisite time and energy to make this essential shift, they want to see that green IT practices can benefit them significantly. The concept of Green IT is steadily increasing in prominence and the benefits such as reduced energy costs are making green IT practices very attractive to all businesses. They desire a sense of longevity and a cost-effective IS infrastructure. The omnipresence and convenience of IT in all venues of life factored in with the environmental benefits from computing. The reduced greenhouse gas emissions evidenced from telecommuting, online education enterprises and video telecon-

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ferencing, will keep the IT industry strong and a permanent fixture in today’s society. Green IT will eventually become the standard to follow, but the implementation will be much slower if major elements do not become mandatory through the use of effective legislation and increasing numbers of entities willing to set the correct industry examples.

REFERENCES Ahmad, I., & Ranka, S. (2016). Handbook of energy-aware and green computing. CRC Press. Akhgar, B., Pattinson, C., & Dastbaz, M. (2015). Green Information Technology: A Sustainable Approach. Waltham, MA: Elsevier, Inc. Bohas, A., & Poussing, N. (2016). An empirical exploration of the role of strategic and responsive corporate social responsibility in the adoption of different green IT strategies. Journal of Cleaner Production, 122, 240–251. doi:10.1016/j. jclepro.2016.02.029 Cook, G., & Pomerantz, D. (2015). Clicking Clean: A Guide to Building the Green Internet. Washington, DC: Greenpeace, Inc. Dedrick, J. (2010). Green IS: Concepts and issues for information systems research. Communications of the Association for Information Systems, 27(11), 173–183. Elliot, S. (2007) Environmentally sustainable ICT: a critical topic for IS research? Pacific Asia Conference on Information Systems, 114, 100-112. Kaushik, A., & Vidyarthi, D. P. (2016). A green energy model for resource allocation in computational grid using dynamic threshold and GA. Sustainable Computing: Informatics and Systems, 9, 42–56. McLaughlin, E. (2013). Definition of green IT (green information technology). Retrieved March 30, 2016 from http://searchcio.techtarget.com/definition/green-IT-green-information-technology

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Mitchell, R. (2013). IT gets its green on. Computerworld, 26–29. Molla, A. (2009). Organizational motivations for Green IT: exploring Green IT matrix and motivation models. Pacific Asia Conference on Information Systems. Molla, A., Cooper, V., Corbitt, B., Deng, H., Peszynski, K., Pittayachawan, S., & Teoh, S. Y. (2008). E-readiness to G-readiness: developing a green information technology readiness framework. Australasian Conference on Information Systems, 35, 669-678. Moodie, A. (2015). Is the Obama Administration lowering the bar on green electronics? Retrieved March 20, 2016 from http://www.theguardian. com/sustainable-business/2015/apr/23/obamaepeat-epa-technology-green-electronics-council Murugesan, S. (2008). Harnessing Green IT: Principles and practices. IT Professional, 10(1), 24–33. doi:10.1109/MITP.2008.10 Murugesan, S., & Gangadharan, G. (2012). Harnessing green IT: principles and practices. Chichester, UK: John Wiley and Sons. doi:10.1002/9781118305393 Nguyen, K. K., Cheriet, M., Lemay, M., Reijs, V., Mackarel, A., & Pastrama, A. (2012). Environmental-aware virtual data center network. Computer Networks, 56(10), 2538–2550. doi:10.1016/j. comnet.2012.03.008 Ogden, J. (2013). Blog: Green IT – a key to long-term savings. CIO Asia. Retrieved June 20, 2016 from http://www.cio-asia.com/tech/ data-center/blog-green-it--a-key-for-long-termsavings/?page=1 Pandi, K. M., & Somasundaram, K. (2016). Energy efficient in virtual infrastructure and green cloud computing: A review. Indian Journal of Science & Technology, 9(11), 1–8. doi:10.17485/ijst/2016/ v9i11/89399

Category: Environmental Science and Agriculture

Phys.org. (2014). German Supercomputer is a world champion in saving energy. Retrieved March 10, 2016 from http://phys.org/news/201411-german-supercomputer-world-championengergy.html Shah, R. (2012). Eco-friendly IT: Greener approach to IT. Journal of Management and Business Research, 2(2), 4–25. Subburaj, S., Kulkarni, S., & Jia, L. (2014). Green IT: Sustainability by aligning business requirements with IT resource utilization. International Journal of Communication Networks and Distributed Systems, 12(1), 30–46. doi:10.1504/ IJCNDS.2014.057986 Swaine, J. (2009). Two Google Searches ‘Produce Same CO2 as Boiling a Kettle. Retrieved March 30, 2016 from http://www.telegraph.co.uk/technology/google/4217055/Two-Google-searchesproduce-same-CO2-as-boiling-a-kettle.html Thomson, S., & van Belle, J. (2015). Antecedents of Green IT adoption in South African higher education institutions. The Electronic Journal Information Systems Evaluation, 18(2), 172–186. Van Bussel, G., Smitter, N., & Vandepas, J. (2015). Digital archiving, Green IT and environment. Deleting data to manage critical effects of the data deluge. The Electronic Journal Information Systems Evaluation, 18(2), 187–198. Warman, M. (2009). Green I.T.: How many Google searches does it take to boil a kettle? Retrieved March 30, 2016 from http://www.telegraph.co.uk/ technology/google/4241791/Green-I.T.-howmany-Google-searches-does-it-take-to-boil-akettle.html

Bohas, A., & Poussing, N. (2016). An Empirical Exploration of the Role of Strategic and Responsive Corporate Social Responsibility in the Adoption of Different Green IT Strategies. Journal of Cleaner Production, 122, 240–251. doi:10.1016/j. jclepro.2016.02.029 Kaushik, A., & Vidyarthi, D. P. (2016). A green energy model for resource allocation in computational grid using dynamic threshold and GA. Sustainable Computing: Informatics and Systems, 9, 42–56. Pandi, K. M., & Somasundaram, K. (2016). Energy efficient in virtual infrastructure and green cloud computing: A review. Indian Journal of Science & Technology, 9(11), 1–8. doi:10.17485/ijst/2016/ v9i11/89399

KEY TERMS AND DEFINITIONS Carbon Footprint: The total amount of greenhouse gas emissions usually expressed in tons of carbon dioxide (CO2). Data Center: A physical or virtual infrastructure used to house a large group of networked computer servers for the remote storage, processing, or distribution of large amounts of data. Energy Consumption: Amount of energy consumed in a process or system, or by an individual, organization or country. e-Waste: Any old, end-of-life or discarded electronic or electrical devices or their components. Green IT: Green information technology is the practice of environmentally sustainable and responsible use of computers and related resources.

ADDITIONAL READING Ahmad, I., & Ranka, S. (2016). Handbook of energy-aware and green computing. Chapman & Hall/CRC Computer and Information Science Series. CRC Press.

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Identification of Green Procurement Drivers and Their Interrelationship Using Fuzzy TISM and MICMAC Analysis Surajit Bag Tega Industries South Africa Pty Ltd., South Africa

INTRODUCTION Green procurement is sourcing products and services that cause minimal adverse environmental impacts. It incorporates human health, social and environmental concerns into the search for high quality products and services at competitive prices. Green procurement is generally considered a mammoth task by the procurement managers. Recently focus has been given by regulatory bodies to apply pressure on firms for implementing green programs. Environment protection bodies are regularly organizing seminars and conferences to educate and train managers in such greening initiatives. In some countries the government has developed green specifications for items and mandatory part of tender requirement for public procurement. However, the green procurement programs are still under nascent phase in most developing countries. The present research is motivated based on the study of Azevedo et al., (2011) where they have pointed potential future research area in exploring the enablers and barriers influencing companies in taking green procurement decisions. Secondly, Appolloni et al., (2014) conducted a review on green procurement considering the time frame between 1996 and 2013 but does not highlight the inter-relationships between the drivers of green procurement practices and they have also kept it under one of future research directions. They have also mentioned the need for strong qualitative and quantitative research to support the progress of green procurement.

The objective of the current study is to identify the leading drivers that influence green procurement programs and determine the interactions among the identified drivers. This chapter is structured into four additional sections. The next section presents the background of the study which helps to identify the green procurement drivers. The third section introduces Fuzzy TISM. Finally, conclusions, limitations and directions of future research are presented.

BACKGROUND In this section an attempt has been taken to briefly explain the key drivers of green procurement.

Government Policy and Regulations Governments are among the largest consumers in an economy. The public sector on average spends 45%-65% of their budgets on procurement. Given this substantial purchasing power, governments have enormous leverage to stimulate and drive markets for sustainable production and consumption when they make a determined effort to purchase ‘green’ products and services. Adopting such an approach is a smart form of procuring goods and service – it not only improves the efficiency of public procurement but also uses the public market power to bring about significant environmental and socioeconomic benefits. Supply chain management operates within a regulatory framework set by National Government and

DOI: 10.4018/978-1-5225-2255-3.ch270 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Environmental Science and Agriculture

extended by provinces and local governments to specific policies, legislation and regulations. In South Africa for instance important legislation influencing this function includes the Public Finance Management Act (1999), Preferential Procurement Policy Framework Act (2000), Preferential Procurement Framework Regulations (2001) and National Treasury Regulations (2005). The Municipal Finance Management Act (MFMA) of 2003 governs the financial and supply chain management functions of Local Government. In developing green procurement policies, local government would need to ensure that these policies: are aligned with their existing Supply chain management regulatory frameworks; avoid a clash between the Preferential Procurement regulations and environmental principles or criteria in the policy; incorporate green procurement in all dimensions of the supply chain management cycle; and institutionalize green procurement within the existing structures set out by the regulatory framework. Government policy and regulations positively influences green procurement (Min & Galle 1997; Diabat & Govindan 2011; Hassini et al., 2012; Bag., 2014; Appolloni et al., 2014)

Total Quality Environmental Management Firms with successful TQEM programs will have more formal mechanisms for interacting with suppliers. Business units with successful TQEM programs exhibit a greater degree of competitive focus and strategic sourcing. In every step of the manufacturing process there will be quality check to avoid rejections and wastage and this will assist in saving natural resources. TQEM positively influences green procurement (Khidir et al., 2010; Diabat & Govindan 2011; Ageron et al., 2012; Dubey et al., 2013; Bag., 2014; Bag & Anand., 2014; Dubey et al., 2014).

Management Support Management support is important in success of any projects and specially for green procurement

programs where strategic decisions are mainly involved. Management support have positively influenced green procurement (Min & Galle 1997; Zhu et al., 2008; Arslan 2010; Bag., 2014).

Management Review Management review periodically is necessary to check the progress of green procurement programs and see that timelines are met. The review will capture the bottlenecks, critical paths and develop strategies to find out ways to complete the activities at economical cost within the timeline. As per expert opinion management review positively influence green procurement.

Continuous Education of Employees Organizations practicing green procurement must have the transformation and diversity manager to carry out the necessary trainings of employees. Training will assist employees in gaining knowledge and deeper understanding of green procurement and its importance in supply chain management. Moreover, the training budget must be utilized carefully in proper training and must be aligned with the company mission and vision so that organization ultimately benefits in the long run. As per expert opinion continuous education of employees positively influences green procurement.

Cross Functional Team Building In a manufacturing firm there are people from planning, procurement, production, quality assurance, logistics and other functions. Since green procurement involves close coordination with all related supply chain functions therefore it is imperative that organization form a green procurement committee comprising people from all functions, i.e. a cross-functional team to drive the green procurement project. This committee will be responsible for generating weekly progress reports, communicating to internal and external environment, maintain records of consumption 3087

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of resources, reduction in energy usage and associated cost savings. As per expert opinion cross functional team building positively influences green procurement.

Organization Culture Empowering junior managers and creating innovation culture among employees will facilitate green procurement practices. Ultimately the organisational culture, structure and process must support the green procurement practices. As per expert opinion organizational culture positively influences green procurement.

Green Process and Technology Introducing ‘green’ tendering criteria can influence the marketplace and result in new entrants in the field of environmental technologies and products - potentially resulting in increased competition and reduced prices. Firms must remove the obsolete machinery and convert to green process and technology (renewable source of energy, reducing utility costs, environmental innovation and source reduction) to drive the green procurement programs. Green process and technology positively influences green procurement (Green et al., 2000; Hassini et al., 2012; Bag., 2014).

Information System Infrastructure IT Eco-Efficiency and IT Eco-Innovation is important dimension of information system infrastructure required to drive organization wide green programs. Additionally, SAP, ERP, RFID, and data analytics are important tools to derive the MIS reports for assessing the progress of green procurement projects. IT infrastructure positively influence green procurement (Green et al., 1998; Hervani et al., 2005; Bag 2014).

Green Design Driving the green procurement project starts in the design stage to ensure that products comply 3088

with restrictions on specified chemical substances in parts and materials, while complying with obligations for labeling, information provision and energy-saving standards for finished products. The green design should comply with environmental norms and regulations such as WEEE, RoHS, REACH, EU Directive on Packaging and Packaging Waste. Green Design positively influence green procurement (Mavi et al., 2013).

Re-Use, Re-Engineering, and Recycling of Products and Materials The 3 Rs’ forms the basis of any green procurement programs and enhance cost savings and savings of natural resources. 3Rs’ positively influence green procurement (Mavi et al., 2013).

Supplier Flexibility It is critical for suppliers to adopt flexibility and supply alternate material as per revised bill of material in green procurement projects. The supply risk must be minimized by supplying the alternate material in time at economical costs. Supplier flexibility positively influence green procurement (Mavi et al., 2013; Bag, 2016b)

Suppliers’ Capability to Innovate It is imperative that supplier has the capability of innovation by demonstrating knowledge and willingness by coming with new eco-products at cheaper costs. It positively influences green procurement (Min & Galle., 2001; Chiou et al., 2011; Ageron et al., 2012).

Supply Risk Management Green procurement adopt some of the best practices which automatically minimizes the supply risks associated with traditional supply chain management. It positively influences green procurement (Ageron et al., 2012; Bag 2014; Bag & Anand., 2014; Dubey et al., 2014).

Category: Environmental Science and Agriculture

Trust Building in Suppliers Trust is a soft factor associated with green procurement. It positively influences green procurement (Ageron et al., 2012; Ji et al., 2014).

systems and compressor, energy for utility and paper for printing will lead to significant savings of natural resources. As per expert opinion savings of natural resources positively influences green procurement.

Low Supplier Lead Time

Procurement Excellence

Green procurement prerequisites best operational practices such as vendor managed inventory, just in time approach and lean manufacturing to optimize costs. Automatically the supplier lead time becomes low in such cases and suppliers are able to deliver material as per delivery schedule. This involves proper strategy building and careful monitoring. Therefore, supplier lead time influences green procurement (Ageron et al., 2012; Bag. 2014; Bag & Anand., 2014; Dubey et al., 2014).

Adopting best world class sustainability practices such as green procurement leads to procurement excellence. Therefore, procurement excellence is the outcome and considered as a variable for measuring success of green procurement (Bag, 2016a).

Customer Satisfaction Customer satisfaction is the outcome of good green procurement practices and measured as a success parameter in any green programs. It positively influences green procurement (Diabat & Govindan, 2011; Ageron et al., 2012; Bag., 2014; Bag & Anand., 2014; Dubey et al., 2014).

Annual Savings From Green Procurement Practices Organizations derive both tangible and intangible benefits from green procurement practices. The direct savings from green procurement practices are significant and highly motivates the procurement managers. As per expert opinion annual savings from green purchasing positively influences green procurement.

Annual Saving of Natural Resources The annual savings of natural resources motivates managers in driving green procurement projects. The key areas where reduction can be achieved are: consumption of coal used to fire boiler, diesel oil in forklifts, steam for running hydraulic

DATA ANALYSIS The current study intends to develop green procurement theory based on fuzzy total interpretive structural modeling (Fuzzy TISM) approach. Fuzzy TISM is an advanced method and designed in a manner to capture both the statements of respondents as well as logic and interpretation. TISM has greater explanatory power than other established inductive approaches.

Fuzzy Total Interpretive Structural Modeling (FUZZY TISM) The steps of Fuzzy TISM have been followed as per Khatwani et al., (2015). The data analysis is segregated into following sub-sections:

Start the Decision Making Process In the current study, the responses of five experts from manufacturing sector have been gathered. The input has been utilized to refine the drivers and further used in modeling.

Selection of Criteria and Sub Criteria of Green Procurement This is the second step in Fuzzy TISM modeling approach. For the purpose of this study, twenty3089

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Identification of Green Procurement Drivers and Their Interrelationship

five drivers were identified from literature which influences green procurement practices and finally refined through experts’ opinion. The final twenty sub criteria are presented in the previous section. In the next step the process of gathering responses for fuzzy TISM has been explained in details.

Gathering Responses and Calculation of Aggregated SSIM For the purpose of study, the responses of five procurement managers from manufacturing sector has been collected for evaluation of interrelationship among selected drivers. These experts are well experienced with the green procurement practices. A joint meeting was organized with these five experts and the research problem and methodology was presented and explained to them in 30 minutes’ duration. Further they were requested to fill up the VAXO matrix. The entire process of filling up the VAXO matrix separately by these five experts took almost one hour. From the submitted five separate VAXO matrices, finally the aggregated SSIM matrix was calculated by applying mode. The aggregated SSIM matrix is shown in Table 1 (Appendix). Figure 1. MICMAC analysis based on crisp values

3090

Calculation of Fuzzy Reachability Matrix The fuzzy reachability matrix is generated from aggregated fuzzy SSIM matrix which was presented in the above stage. The fuzzy reachability matrix is shown in Table 2 (Appendix).

Calculation of Final Fuzzy Reachability Matrix The final fuzzy reachability matrix is derived from step above and presented in Table 3 (Appendix). From the final fuzzy reachability matrix, the dependence power (X) and driving power (Y) is calculated based on summing of columns and rows and calculating the crisp value. The crisp value of each element is used as an input to develop fuzzy MICMAC analysis.

Driving Power and Dependence Power Matrix (MICMAC) Based on Fuzzy Reachability Matrix From Table 3 See Figure 1.

Category: Environmental Science and Agriculture

Discussions Based on MICMAC Analysis Based on Fuzzy Reachability Matrix

Defuzzified Reachability Matrix and Transitivity Check

Cluster 1 - Autonomous Variables: These variables have a weak drive power and weak dependence power. In this cluster we have six variables. Total Quality Environmental management (E2), Management Support (E3), Management Review (E4), Cross functional team building (E6), Organization Culture (E7) and Information System Infrastructure (E9). Cluster 2 - Dependence Variables: These variables have a weak drive power but strong dependence power. In this cluster we have twelve variables. Green process and Technology (E8), Green Design (E10), Re-use, Reengineering and Recycling of products and materials (E11), Supplier Flexibility (E12), Suppliers’ capability to Innovate (E13), Supply risk management (E14), Trust building in suppliers (E15), Low supplier lead time (E16), Customer Satisfaction (E17), Annual Savings from green procurement practices (E18), Annual Saving of natural resources (E19) and Procurement Excellence (E20). Cluster 3 - Linkage Variables: These variables have a strong drive power as well as strong dependence power. Linkage variables are very sensitive and unstable. Any action on these variables will trigger an effect on other variables and also a feedback on themselves. In this cluster we have no variables. Cluster 4 - Driving Variables: These variables have a strong drive power but weak dependence power. In this cluster we have two variables. Government policy and Regulations (E1) and Continuous education of employees (E5).

The paired comparisons are translated in the form of reachability Matrix. The matrix has been also checked for transitivity rule. A final reachability matrix, post transitivity check is presented in Table 5 (Appendix).

Defuzzified Reachability Matrix The Defuzzified reachability matrix with fuzzy linguistic terms VH, H, as 1 and rest as 0 is shown in Table 4 (Appendix).

E

Driving Power and Dependence Power Matrix (MICMAC) Based on Defuzzified Reachability Matrix from Table 5 See Figure 2. Discussion on MICMAC Analysis Based on Defuzzified Reachability Matrix Cluster 1 - Autonomous Variables: These variables have a weak drive power and weak dependence power. In this cluster we have no variables. Cluster 2 - Dependent Variables: These variables have a weak drive power but strong dependence power. In this cluster we have nine variables. Information System Infrastructure (E9), Suppliers’ capability to Innovate (E13), Supply risk management (E14), Trust building in suppliers (E15), Low supplier lead time (E16), Customer Satisfaction (E17), Annual Savings from green procurement practices (E18), Annual Saving of natural resources (E19), Procurement Excellence (E20). Cluster 3 - Linkage Variables: These variables have a strong drive power as well as strong dependence power. Linkage variables are very sensitive and unstable. Any action on these variables will trigger an effect on other variables and also a feedback on themselves. In this cluster we have three variables. Green Design (E10), Re-use, Re-engineering and Recycling of products and materials (E11) and Supplier Flexibility (E12). Cluster 4 - Driving Variables: These variables have a strong drive power but weak de-

3091

Identification of Green Procurement Drivers and Their Interrelationship

Figure 2. MICMAC analysis based on Table 6

pendence power. In this cluster we have eight variables. Government policy and Regulations (E1), Total Quality Environmental management (E2), Management Support (E3), Management Review (E4), Continuous education of employees (E5), Cross functional team building (E6), Organization Culture (E7), and Green process and Technology (E8).

Level Partition on Reachability Matrix The final reachability matrix obtained in Table 5 is now partitioned into different levels. After the first iteration, the driver classified to level 1 is discarded and the partitioning procedure is repeated on the remaining drivers to determine the level 2. These iterations are continued until the level of each driver has been determined and presented in Table 6 (Appendix).

TISM Diagraph The connective and interpretive information contained in the interpretive direct interaction 3092

matrix and diagraph is used to derive the TISM. The nodes in the diagraph are replaced by the interpretation of elements placed in boxes. The interpretation of the cells of interpretive direct interaction matrix is depicted by the side of the respective links in the structural model. The final TISM model after removing the transitive links is presented in Figure 3.

CONCLUSION In the current study author proposes Fuzzy TISM method for identifying the interrelationships among elements influencing green procurement. Due to incorporation of fuzziness in TISM the decision makers have the flexibility in assigning the level of influence of pair wise elements. Apart from wider flexibility Fuzzy TISM also enhance the quality of decision making. The findings from the TISM model show that Customer Satisfaction is in level 1, Annual Savings from green procurement practices, Annual Saving of natural resources and Procurement Excellence are in level 2, Supply risk management is in level 3, Information System Infrastructure and Low supplier lead time is

Category: Environmental Science and Agriculture

Figure 3. TISM diagraph

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Identification of Green Procurement Drivers and Their Interrelationship

in level 4, Suppliers’ capability to Innovate and Trust building in suppliers is in level 5, Supplier Flexibility is in level 6, Re-use, Re-engineering and Recycling of products and materials is in level 7, Green Design is in level 8, Green process and Technology is in level 9, Continuous education of employees, Cross functional team building and Organization Culture is in level 10, Total Quality Environmental management and Management Review is in level 11 and Government policy and Regulations and Management Support are in the bottom level. The driving factors which emerged from Fuzzy TISM and MICMAC analyses are Government policy and Regulations (E1), Total Quality Environmental management (E2), Management Support (E3), Management Review (E4), Continuous education of employees (E5), Cross functional team building (E6), Organization Culture (E7), and Green process and Technology (E8). These elements are the key players which must be considered while planning green procurement programs.

Appolloni, A., Sun, H., Jia, F., & Li, X. (2014). Green Procurement in the private sector: A state of the art review between 1996 and 2013. Journal of Cleaner Production, 85, 122–133. doi:10.1016/j. jclepro.2014.08.106

LIMITATIONS AND DIRECTIONS OF FUTURE RESEARCH

Bag, S. (2016). Green strategy, supplier relationship building and supply chain performance: Total interpretive structural modelling approach. International Journal of Procurement Management, 9(4), 398–426. doi:10.1504/IJPM.2016.077702

The present research has certain limitations. The model is developed purely based on interview with five procurement management experts from the manufacturing sector. In future studies the author proposes to statistically validate the model using big sample size and further extend theory of green procurement.

REFERENCES Ageron, B., Gunasekaran, A., & Spalanzani, A. (2012). Sustainable supply management: An empirical study. International Journal of Production Economics, 140(1), 168–182. doi:10.1016/j. ijpe.2011.04.007

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Arslan, T., Yilmaz, V., & Aksoy, H. K. (2010). Structural Equation Model for Environmentally Conscious Purchasing Behavior. International Journal of Environmental of Research, 6(1), 323–334. Azevedo, S. G., Carvalho, H., & Machado, V. C. (2011). The influence of green practices on supply chain performance: A case study approach. Transportation Research Part E, Logistics and Transportation Review, 47(6), 850–871. doi:10.1016/j. tre.2011.05.017 Bag, S. (2014). Developing a GSCM model for the Indian rubber goods manufacturing sector (Unpublished Doctoral Dissertation). University of Petroleum & Energy Studies. Available at http://ils.ddn.upes.ac.in:8001/cgi-bin/koha/opacauthoritiesdetail.pl?authid=13645

Bag, S. (2016b). Flexible procurement systems is key to supply chain sustainability. Journal of Transport and Supply Chain Management, 10(1), 1–9. doi:10.4102/jtscm.v10i1.213 Bag, S., & Anand, N. (2014). Modeling Green Supply Chain Management framework using ISM and MICMAC analysis. African Journal of Business Management, 8(22), 1053. Chiou, T. Y., Chan, H. K., Lettice, F., & Chung, S. H. (2011). The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan. Transportation Research Part E, Logistics and Transportation Review, 47(6), 822–836. doi:10.1016/j.tre.2011.05.016

Category: Environmental Science and Agriculture

Diabat, A., & Govindan, K. (2011). An analysis of the drivers affecting the implementation of green supply chain management. Resources, Conservation and Recycling, 55(6), 659–667. doi:10.1016/j. resconrec.2010.12.002

Khatwani, G., Singh, S. P., Trivedi, A., & Chauhan, A. (2015). Fuzzy-TISM: A fuzzy extension of TISM for group decision making. Global Journal of Flexible Systems Management, 16(1), 97–112. doi:10.1007/s40171-014-0087-4

Dubey, R., & Ali, S. (2014). Identification of flexible manufacturing system dimensions and their interrelationship using total interpretive structural modelling and fuzzy MICMAC analysis. Global Journal of Flexible Systems Management, 15(2), 131–143. doi:10.1007/s40171-014-0058-9

Khidir ElTayeb, T., Zailani, S., & Jayaraman, K. (2010). The examination on the drivers for green purchasing adoption among EMS 14001 certified companies in Malaysia. Journal of Manufacturing Technology Management, 21(2), 206–225. doi:10.1108/17410381011014378

Dubey, R., Bag, S., Ali, S., & Venkatesh, V. (2013). Green purchasing is key to superior performance: An empirical study. International Journal of Procurement Management, 6(2), 187–210. doi:10.1504/IJPM.2013.052469

Mavi, R. K., Kazemi, S., Najafabadi, A. F., & Mousaabadi, H. B. (2013). Identification and assessment of logistical factors to evaluate a green supplier using the fuzzy logic DEMATEL method. Polish Journal of Environmental Studies, 22(2), 445–455.

Green, K., Morton, B., & New, S. (1998). Green purchasing and supply policies: Do they improve companies environmental performance? Supply Chain Management: An International Journal, 3(2), 89–95. doi:10.1108/13598549810215405 Green, K., Morton, B., & New, S. (2000). Greening organizations purchasing, consumption, and innovation. Organization & Environment, 13(2), 206–225. doi:10.1177/1086026600132003 Hassini, E., Surti, C., & Searcy, C. (2012). A literature review and a case study of sustainable supply chains with a focus on metrics. International Journal of Production Economics, 140(1), 69–82. doi:10.1016/j.ijpe.2012.01.042 Hervani, A. A., Helms, M. M., & Sarkis, J. (2005). Performance measurement for green supply chain management. Benchmarking. International Journal (Toronto, Ont.), 12(4), 330–353. Ji, P., Ma, X., & Li, G. (2014). Developing green purchasing relationships for the manufacturing industry: An evolutionary game theory perspective. International Journal of Production Economics.

Min, H., & Galle, W. P. (1997). Green purchasing strategies: Trends and implications. International Journal of Purchasing and Materials Management, 33(2), 10–17. doi:10.1111/j.1745493X.1997.tb00026.x Min, H., & Galle, W. P. (2001). Green purchasing practices of US firms. International Journal of Operations & Production Management, 21(9), 1222–1238. doi:10.1108/EUM0000000005923 Zhu, Q., Sarkis, J., Cordeiro, J. J., & Lai, K. H. (2008). Firm-level correlates of emergent green supply chain management practices in the Chinese context. Omega, 36(4), 577–591. doi:10.1016/j. omega.2006.11.009

ADDITIONAL READING Berry, C. (2011). The sustainable procurement guide: procuring sustainably using BS 8903. BSi. Theron, C., & Dowden, M. (2014). Strategic Sustainable Procurement: Law and Best Practice for the Public and Private Sectors. Do Sustainability.

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KEY TERMS AND DEFINITIONS Green Procurement: Green Procurement is procuring of products and services that cause minimal adverse environmental impacts. It incorporates human health, social and environmental concerns into the search for high quality products and services at competitive prices. Total Interpretive Structural Modeling (TISM): Fuzzy TISM is an extension of Interpre-

3096

tive structural modeling (ISM) technique. ISM methodology, transforms unclear, poorly articulated models of systems into clear, well-defined models. ISM uses experts to judge the variables, and the relations among the variables are interpreted. ISM depends on the experts’ knowledge and familiarity with the firm, its operations, and its industry. ISM generates deep knowledge of the subject and is greatly helpful for practitioners.

V(VH)

V(H)

V(VH)

V(VH)

V(VH)

E 11

E 12

E 13

E 14

E 15

X(VH)

A(H)

V(L)

V(H)

O(No)

V(VH)

V(H)

V(VH)

V(VH)

V(VH)

V(VH)

V(H)

V(VH)

V(VH)

E 18

A(H)

V(L)

V(H)

O(No)

V(VH)

V(L)

V(VH)

V(VH)

V(L)

V(VH)

V(VH)

V(VH)

V(H)

V(VH)

V(VH)

V(VH)

V(VH)

Source: Author own compilation

E 20

X(VH)

V(H)

X(VH)

V(H)

E9

E 10

E 19

V(VH)

E8

E 18

V(VH)

E7

V(H)

V(VH)

E6

A(VH)

V(VH)

E5

E 16

V(L)

V(VH)

E4

E 17

V(VH)

V(VH)

E3

V(VH)

V(VH)

E2

E 19

V(VH)

O(No)

E1

E 20

V(L)

O(No)

V(H)

V(H)

O(No)

V(VH)

V(VH)

V(L)

V(VH)

V(VH)

V(L)

V(L)

V(H)

V(H)

V(VH)

O(No)

E 17

Table 1. Aggregated SSIM matrix E 16

V(VH)

A(VH)

V(H)

V(VH)

V(H)

V(H)

V(L)

V(VH)

V(L)

V(L)

O(No)

V(H)

V(L)

V(L)

O(No)

E 15

A(VH)

V(L)

V(H)

V(H)

V(H)

O(No)

V(VH)

V(VH)

V(L)

O(No)

V(L)

V(H)

V(H)

O(No)

E 14

V(VH)

V(VH)

V(H)

V(H)

V(H)

V(VH)

V(VH)

V(H)

O(No)

V(H)

V(H)

V(VH)

O(No)

E 13

V(VH)

V(H)

V(H)

O(No)

V(H)

V(VH)

V(L)

O(No)

V(L)

V(H)

V(H)

O(No)

E 12

V(H)

V(H)

O(No)

V(H)

V(VH)

V(H)

O(No)

V(L)

V(H)

V(H)

O(No)

E 11

V(VH)

V(L)

V(VH)

V(VH)

V(VH)

V(H)

V(H)

V(VH)

V(VH)

V(VH)

E 10

V(L)

V(VH)

V(VH)

V(VH)

V(H)

V(H)

V(VH)

V(VH)

V(VH)

E9

V(H)

V(VH)

V(L)

O(No)

V(L)

V(VH)

V(H)

V(L)

E8

V(VH)

V(VH)

V(H)

V(H)

V(VH)

V(VH)

V(VH)

E7

O(No)

O(No)

V(L)

V(H)

O(No)

O(No)

E6

V(L)

V(H)

V(VH)

V(VH)

O(No)

E5

V(H)

V(VH)

V(H)

O(No)

E4

V(H)

O(No)

O(No)

E3

O(No)

O(No)

E2 V(VH)

E1

Category: Environmental Science and Agriculture

APPENDIX

E

3097

Identification of Green Procurement Drivers and Their Interrelationship

Table 2. Fuzzy reachability matrix E1

E2

E3

E4

E5

E6

E7

E8

E9

E10

E11

E12

E13

E14

E15

E16

E17

E18

E19

E20

E1

1

VH

No

No

No

No

No

VH

L

VH

VH

No

No

No

No

No

No

VH

VH

No

E2

No

1

No

No

H

VH

No

VH

H

VH

VH

H

H

VH

H

L

VH

VH

VH

VH

E3

No

No

1

H

VH

VH

H

VH

VH

VH

VH

H

H

H

H

L

H

VH

VH

VH

E4

No

No

No

1

H

H

L

H

L

H

H

L

L

H

L

H

H

VH

VH

VH

E5

No

No

No

No

1

L

No

H

No

H

H

No

No

No

No

No

L

H

H

VH

E6

No

No

No

No

No

1

No

VH

L

VH

VH

H

L

H

L

L

L

VH

VH

VH

E7

No

No

No

No

No

No

1

VH

VH

VH

VH

VH

VH

VH

VH

L

VH

VH

VH

VH

E8

No

No

No

No

No

No

No

1

H

VH

VH

H

H

VH

VH

VH

VH

VH

VH

VH

E9

No

No

No

No

No

No

No

No

1

L

L

No

No

H

No

L

L

L

L

H

E 10

No

No

No

No

No

No

No

No

No

1

VH

H

H

H

H

H

VH

VH

VH

H

E 11

No

No

No

No

No

No

No

No

No

No

1

H

H

H

H

H

VH

VH

VH

VH

E 12

No

No

No

No

No

No

No

No

No

No

No

1

VH

VH

H

VH

No

L

H

H

E 13

No

No

No

No

No

No

No

No

No

No

No

No

1

VH

L

H

H

VH

VH

VH

E 14

No

No

No

No

No

No

No

No

No

No

No

No

No

1

No

No

H

No

No

VH

E 15

No

No

No

No

No

No

No

No

No

No

No

No

No

VH

1

VH

No

H

H

VH

E 16

No

No

No

No

No

No

No

No

No

No

No

No

No

VH

No

1

L

L

L

H

E 17

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

1

No

No

No

E 18

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

H

1

VH

VH

E 19

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

H

VH

1

VH

E 20

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

VH

VH

VH

1

Source: Author own compilation

3098

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,5.75)

0.03

E2

E3

E4

E5

E6

E7

E8

E9

E 10

E 11

E 12

E 13

E 14

E 15

E 16

E 17

E 18

E 19

E 20

Dependence Power

Crisp Value

E2

0.07

(1.75,2,6.5)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,1)

(0.75,1.0,1.0)

Source: Author own compilation

(1,1,1)

E1

E1

0.03

(1,1,5.75)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,1)

(0,0,0.25)

(0,0,0.25)

E3

Table 3a. Final fuzzy reachability matrix

0.07

(1.5,1.75,6.50)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,1)

(0.5,0.75,1.0)

(0,0,0.25)

(0,0,0.25)

E4

0.17

(2.75,3.50,8)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,1)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0,0,0.25)

E5

0.20

(3.25,4.25,8.5)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,1)

(0.25,0.5,0.75)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0,0,0.25)

E6

0.09

(1.75,2.25,7)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,1)

(0,0,0.25)

(0,0,0.25)

(0.25,0.5,0.75)

(0.5,0.75,1.0)

(0,0,0.25)

(0,0,0.25)

E7

0.38

(5.75,7.5,11)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,1)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

E8

0.29

(4.25,6,10.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,1)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.25,0.5,0.75)

(0,0,0.25)

(0.25,0.5,0.75)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0.25,0.5,0.75)

E9

0.44

(6.75,9,12.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,1)

(0.25,0.5,0.75)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

E10

0.50

(7.5,10,13)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,1)

(0.75,1.0,1.0)

(0.25,0.5,0.75)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

E11

Category: Environmental Science and Agriculture

E

3099

3100

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(5,7,11.50)

0.36

E 16

E 17

E 18

E 19

E 20

Dependence Power

Crisp Value

E 11

E 15

(0.5,0.75,1.0)

E 10

E 14

(0.5,0.75,1.0)

E9

(1,1,1)

(0,0,0.25)

E8

(0,0,0.25)

(0.5,0.75,1.0)

E7

E 13

(0.75,1.0,1.0)

E6

E 12

(0,0,0.25)

(0.5,0.75,1.0)

E5

(0.5,0.75,1.0)

(0.25,0.5,0.75)

E4

E2

E3

(0,0,0.25)

(0.5,0.75,1.0)

E1

E12

0.40

(5.5,7.75,12)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,1)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0,0,0.25)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.25,0.5,0.75)

(0,0,0.25)

(0.25,0.5,0.75)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0,0,0.25)

E13

0.63

(9.25,12.5,15.50)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(1,1,1)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0,0,0.25)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0,0,0.25)

E14

Table 3b. Final fuzzy reachability matrix

0.43

(6.25,8.25,12.50)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,1)

(0,0,0.25)

(0.25,0.5,0.75)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0,0,0.25)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.25,0.5,0.75)

(0,0,0.25)

(0.25,0.5,0.75)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0,0,0.25)

E15

0.48

(6.5,9.5,13.50)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(0,0,0.25)

(1,1,1)

(0.75,1.0,1.0)

(0,0,0.25)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0.25,0.5,0.75)

(0.75,1.0,1.0)

(0.25,0.5,0.75)

(0.25,0.5,0.75)

(0,0,0.25)

(0.5,0.75,1.0)

(0.25,0.5,0.75)

(0.25,0.5,0.75)

(0,0,0.25)

E16

0.67

(9.5,13.5,16.75)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(1,1,1)

(0.25,0.5,0.75)

(0,0,0.25)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0,0,0.25)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.25,0.5,0.75)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.25,0.5,0.75)

(0.25,0.5,0.75)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0,0,0.25)

E17

0.79

(11.75,16,17.75)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(1,1,1)

(0,0,0.25)

(0.25,0.5,0.75)

(0.5,0.75,1.0)

(0,0,0.25)

(0.75,1.0,1.0)

(0.25,0.5,0.75)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.25,0.5,0.75)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

E18

0.82

(12,16.25,18)

(0.75,1.0,1.0)

(1,1,1)

(0.75,1.0,1.0)

(0,0,0.25)

(0.25,0.5,0.75)

(0.5,0.75,1.0)

(0,0,0.25)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.25,0.5,0.75)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

E19

0.85

(12.75,17,18.5)

(1,1,1)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0,0,0.25)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.5,0.75,1.0)

(0.5,0.75,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0.75,1.0,1.0)

(0,0,0.25)

E20

(3.25,4,8)

(3,3.75,8)

(3,3.75,8)

(1,1,5.75)

(3,4.25,8.75)

(4.25,5.5,9.5)

(2.25,2.75,7.25)

(5.25,7,10.75)

(5,6.75,12.50)

(6.50,8.75,12.50)

(7,9.50,13.25)

(3.50,5.50,9.50)

(9.25,12.25,14.75)

(10.25,13.5,15.25)

(7.75,11,14.25)

(4.75,6.75,11.25)

(8.50,12.50,16.50)

(11.5,15.75,19.25)

(10.50,14.25,16.75)

(5.75,7.50,10.75)

Driving Power

0.18

0.18

0.18

0.20

0.19

0.26

0.11

0.33

0.35

0.43

0.46

0.26

0.59

0.63

0.53

0.92

0.69

0.76

0.70

1.71

Crisp Value

Identification of Green Procurement Drivers and Their Interrelationship

Category: Environmental Science and Agriculture

Table 4. Defuzzified reachability matrix E1

E2

E3

E4

E5

E6

E7

E8

E9

E10

E11

E12

E13

E14

E15

E16

E17

E18

E19

E20

E1

1

1

0

0

0

0

0

1

0

1

1

0

0

0

0

0

0

1

1

0

E2

0

1

0

0

1

1

0

1

1

1

1

1

1

1

1

0

1

1

1

1

E3

0

0

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

1

1

1

E4

0

0

0

1

1

1

0

1

0

1

1

0

0

1

0

1

1

1

1

1

E5

0

0

0

0

1

0

0

1

0

1

1

0

0

0

0

0

0

1

1

1

E6

0

0

0

0

0

1

0

1

0

1

1

1

0

1

0

0

0

1

1

1

E7

0

0

0

0

0

0

1

1

1

1

1

1

1

1

1

0

1

1

1

1

E8

0

0

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

1

1

1

E9

0

0

0

0

0

0

0

0

1

0

0

0

0

1

0

0

0

0

0

1

E 10

0

0

0

0

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

1

E 11

0

0

0

0

0

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

E 12

0

0

0

0

0

0

0

0

0

0

0

1

1

1

1

1

0

0

1

1

E 13

0

0

0

0

0

0

0

0

0

0

0

0

1

1

0

1

1

1

1

1

E 14

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

1

0

0

1

E 15

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

1

0

1

1

1

E 16

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

1

1

0

0

1

E 17

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

E 18

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

1

1

E 19

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

1

1

E 20

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

1

1

Source: Author own compilation

Table 5. Defuzzified reachability matrix and transitivity check E1

E2

E3

E4

E5

E6

E7

E8

E9

E10

E11

E12

E13

E14

E15

E16

E17

E18

E19

E20

E1

1

1

0

0

1*

1*

0

1

1*

1

1

1*

1*

1*

1*

1*

1*

1

1

1*

E2

0

1

0

0

1

1

1*

1

1

1

1

1

1

1

1

1*

1

1

1

1

E3

0

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1*

1

1

1

1

E4

0

0

0

1

1

1

0

1

1*

1

1

1*

1*

1

1*

1

1

1

1

1

E5

0

0

0

0

1

0

0

1

1*

1

1

1*

1*

1*

1*

1*

1*

1

1

1

E6

0

0

0

0

0

1

0

1

1*

1

1

1

1*

1

1*

1*

1*

1

1

1

E7

0

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1*

1

1

1

1

E8

0

0

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

1

1

1

E9

0

0

0

0

0

0

0

0

1

0

0

0

0

1

0

0

1*

1*

1*

1

E10

0

0

0

0

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

1

E11

0

0

0

0

0

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

E12

0

0

0

0

0

0

0

0

0

0

0

1

1

1

1

1

1*

1*

1

1

E13

0

0

0

0

0

0

0

0

0

0

0

0

1

1

0

1

1

1

1

1

E14

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

1

0

0

1

E15

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

1

1*

1

1

1

E16

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

1

1

1*

1*

1

E17

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

E18

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

1

1

E19

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

1

1

E20

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

1

1

Source: Author own compilation

3101

E

Identification of Green Procurement Drivers and Their Interrelationship

Table 6. Level partitioning Elements

Reachability Set

Antecedent Set

Intersection Set

Level

E1

1,2,5,6,8,9,10,11,12,13,14,15,16,17,18,19,20

1

1

XII

E2

2,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20

1,2

2

XI

E3

3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20

3

3

XII

E4

4,5,6,8,9,10,11,12,13,14,15,16,17,18,19,20

3,4

4

XI

E5

5,8,9,10,11,12,13,14,15,16,17,18,19,20

1,2,3,4,5

5

X

E6

6,8,9,10,11,12,13,14,15,16,17,18,19,20

1,2,3,4,6

6

X

E7

7,8,9,10,11,12,13,14,15,16,17,18,19,20

2,3,7

7

X

E8

8,9,10,11,12,13,14,15,16,17,18,19,20

1,2,3,4,5,6,7,8

8

IX

E9

9,14,17,18,19,20

1,2,3,4,5,6,7,8,9

9

IV

E10

10,11,12,13,14,15,16,17,18,19,20

1,2,3,4,5,6,7,8,10

10

VIII

E11

11,12,13,14,15,16,17,18,19,20

1,2,3,4,5,6,7,8,10,11

11

VII

E12

12,13,14,15,16,17,18,19,20

1,2,3,4,5,6,7,8,10,11,12

12

VI

E13

13,14,16,17,18,19,20

1,2,3,4,5,6,7,8,10,11,12,13

13

V

E14

14,17,20

14

III

1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16

E15

14,15,16,17,18,19,20

1,2,3,4,5,6,7,8,10,11,12,15

15

V

E16

14,16,17,18,19,20

1,2,3,4,5,6,7,8,10,11,12,13,15,16

16

IV

E17

17

1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20

17

I

E18

17,18,19,20

1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,18,19,20

18,19,20

II

E19

17,18,19,20

1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,18,19,20

18,19,20

II

E20

17,18,19,20

1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19,20

18,19,20

II

Source: Author own compilation

3102

3103

Category: Environmental Science and Agriculture

Load Flow Analysis in Smart Grids Osman Hasan National University of Sciences and Technology, Pakistan Awais Mahmood National University of Sciences and Technology, Pakistan Syed Rafay Hasan Tennessee Technological University, USA

INTRODUCTION With 19320 TW-hr/yr consumption of electrical energy in the entire world nowadays, the traditional unidirectional power transmission grids are struggling to survive as the number of fluctuations, blackouts and outages is tremendously growing since the last decade (Gao et al., 2012). More reliable and safe distribution networks have become a dire requirement due to the safety and financial-critical nature of electricity these days. For example, a blackout per minute across Silicon Valley costs 75 million and 1 million dollars for Sun Microsystems alone. There are numerous environmental concerns with the present-age power generation methods as well since these methods are largely dependent on fossil fuels, which result in global warming and carbon-dioxide emissions. For example, the United States power system alone is responsible for 40 percent of carbon emission nationwide (Hledik, 2009). Thus renewable energy resources, like solar and wind based solutions, are extensively being advocated throughout the world but the traditional grid does not facilitate their integration in the national grids. Moreover, the traditional power grids are not very efficient in terms of distribution loss management as well. For example, about 17 percent of electrical energy generated in the year 2011 by Pakistan was wasted in distribution systems. Similarly, the problem of electricity theft is also a growing concern in traditional grids.

Smart grids can overcome the above mentioned shortcomings by providing an alternative electric power transmission framework that comprises of Intelligence based Electronic Devices (IED) (Momoh, 2012) for detecting and correcting faults, and advanced metering infrastructure (AMI), to facilitate the integration of multiple renewable energy sources. Some of the distinguishing characteristics of smart girds compared to traditional power grids include: •

•

•

Safety and Reliability: Smart grids can predict unforeseen situations and autonomously react accordingly to prevent them (e.g., isolating the faulty component of the grid from the entire system (Farhangi, 2010)) and hence improve the safety and reliability (Moslehi and Kumar, 2010) of power distribution and save millions of dollars. Cost-Effectiveness: Smart grids provide real-time tariff information to the consumers so that they can manage their loads to save energy and costs (Li et al., 2010). Efficiency: Smart grids allow optional usage of the assets to maximize the efficiency of the grid and thus can have a major performance impact. For example, according to the US Department of Energy (DOE), just a 5% increase in grid efficiency can have the same impact as if fuel and greenhouse gas emissions are eliminated from 53 million cars.

DOI: 10.4018/978-1-5225-2255-3.ch271 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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•

•

Security: Smart grids allow more secure electrical networks, by using tools like smart meters, and thus electricity theft can be minimized (Khurana et al., 2010, Metke and Ekl, 2010). Environmental Friendliness: Smart grid allows the integration of environmental friendly generation methods and is inline with the recent advancements in renewable energy research (RER) (Ipakchi and Albuyeh, 2009).

Based on above-mentioned capabilities, the National Academy of Engineering listed “electrification as made possible by the grid” as the most significant engineering achievement of the 20th Century. Due to the inherent randomness of smart grids, including variable loads, peak consumption times and renewable energy sources with generation capacity depending on varying weather conditions, there is a lot of interest in rigorously analyzing the voltages and load profiles for resilient and effective power delivery to the users. Besides providing means for effectively managing the energy distribution, these profiles can be used by the consumers to change their loads by a smart device from anywhere as per their requirements. Load flow analysis (Van Benthem and Doets, 2001) fulfills the above-mentioned requirements and allows us to find the magnitude and phase angle of the voltage and the real and reactive power flowing in each bus of the smart grid and the optimal parameters for various components, like inductors, conductors, transformers, and shunt capacitors. It also provides statistics about the behavior of the system during on-peak and off-peak loads in order to identify and plan the contingencies. Moreover, load flow studies help us in conducting short-circuit fault analysis and in finding the stability and the steady-state operating state of an electric power system by calculating the voltage drop on each feeder, the power flow in all branches and feeder circuits, X/R ratio in line impedances and the voltage at each bus. Finally, load flow studies can determine if system volt3104

ages remain within the given specifications and if any of the expensive equipment of the grid is overloaded. The results of load flow analysis are used to make key decisions and ensure a safe and reliable power distribution. There are various uncertain and random elements associated with the load consumption in smart grids. For example, the usage of consumer appliances depends on weather conditions and the time of the day. The distributed generation and usage of storage cells also plays a key role in varying the electrical demand. Some of the key factors that influence the loads in smart grids and must be taken into consideration for load flow analysis of smart grids include weather conditions, time-of-day, arbitrary disturbances, electricity prices, demand response, storage cells and electric vehicles.

BACKGROUND There are various uncertain and random elements associated with the load consumption in smart grids. For example, the usage of consumer appliances depends on weather conditions and the time of the day. The distributed generation and usage of storage cells also plays a key role in varying the electrical demand. The smart grid components may fail randomly and either self-repair or need manual repair to restore their operation. Some components may also have back-up protection. Similarly, the influence of electricity prices on the energy demand cannot be neglected as higher prices usually result in the reduction of energy consumption. Moreover, in smart grids, the consumers are more cautious about costs since they can get the real-time tariffs using smart meters. Time-of-Use (TOU) pricing scheme, which offers low off peak rates, encourages consumers to shift their loads to off peak hours. Moreover, electric vehicles (EVs) also greatly influence load profiles since their charging consumes a significant amount of energy and thus is recommended to be done in the off peak times.

Category: Environmental Science and Agriculture

The load flow analysis mainly involves studying the behavior of node voltages and the power entering and leaving the nodes in a smart grid while considering the above-mentioned elements of randomness and uncertainty. In this section, we describe some of the key techniques that have been used for load flow analysis.

Numerical Methods Numerical methods are one of most widely used analysis methods for load flow. Primarily, these methods are used to solve the following nodal equation: I = V.Ybus

(1)

where I is the N vector of current in each bus, V is the N vector bus voltages and Ybus is the admittance bus matrix. Iterative numerical methods can solve the Equation (1), however, they generate approximate results mainly because the precision of the results is directly proportional to the number of iterations. Some of the frequently used numerical methods for load flow analysis are described in detail below:

Gauss-Seidel Method The Gauss-Seidel method (Chapra Steven and Canale Raymond, 2008) is a widely used iterative approach for solving linear equations based on an initial guess. In load flow analysis, we develop an admittance matrix using the Kirchhoffs current law and then an iterative method is used to solve the scheduled reactive and real power at each bus, respectively. The final node voltage is determined in an iterative manner by using intermediate values of node voltages obtained after each iteration. This process converges linearly if the initial estimation is close to the unknown value. A variant of modified Gauss-Seidel method has been used for power flow calculations in (Teng, 2002). Gauss-Seidel method shows slower rates of convergence but its main strength is that it does not need to solve a

complex matrix system. Gauss-Seidel method is not suitable for radial distribution systems where there are branch connections between a large set of surrounding buses (Momoh, 2012).

Newton-Raphson Method The Newton-Raphson (NR) method is another iterative method to solve non-linear load flow equations. The sensitivity matrix is determined from inversing the Jacobian matrix, which consists of the injected power equations. The NR method has been used to solve three phase power flow equations in (Le Nguyen, 1997). This method is very useful for large systems. But it is computationally inefficient because it does not take advantage of the radial distribution systems (Momoh, 2012). Moreover, the method also fails if the Jacobian matrix is singular. Finally, in the case of a low X/R ratio value, the NR method becomes ill conditioned (Momoh, 2012). On the other hand, it offers a fast convergence rate.

Fast Decouple Method The Fast Decouple method is one of the most effective techniques used in power system analysis and design. Just like the Newton-Raphson method, it also utilizes the Jacobian matrix. On contrary, in this technique small angle approximation is used to calculate the relatively smaller elements of the Jacobian matrix. However, this method shows poor convergence for low values of the X/R ratio (Iwamoto and Tamura, 1981). This method has been successfully used for three phase power flow studies in (Zimmerman and Chiang, 1995). Besides using the above mentioned mainstream numerical methods for load flow analysis, the other significant contributions in this direction include using the interval arithmetic (Wang and Alvarado, 1992), the holomorphic embedded load flow method (Trias, 2012) and an optimal multiplier method for ill conditioned systems (Iwamoto and Tamura, 1981). Other significant methods include Zero-mismatch method, Ward and Hale method,

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Glimm and Stagg method and Secondary adjustment method for load flow analysis and more details about them are discussed in (Stott, 1974).

Simulation Methods The main idea behind simulation is to construct a computer-based model of the given system and then analyze the desired load flow properties by observing the behavior of the model under different test cases. A number of dedicated simulations packages for analyzing power distribution systems are available and have been used to analyze the following key aspects (Glover et al., 2011): • • • • • • • • • • •

Arc flash hazard and fault analysis, Circuit breaker duty, Demand management, Distribution reliability evaluation, Power factor correction, Power loss computations, Components sizing, Voltages/VAR optimization, Power quality and reliability, Harmonic analysis, and Fault detection.

Simulation is a very user-friendly analysis approach since the analysis requires test pattern generation only. On the down side, simulation cannot guarantee absolute correctness of analysis because there is always a possibility that a corner case is missed in test patterns used for analysis. Some of the common load flow software are explained below:

Electrical Transient Analyzer Program The Electrical Transient Analyzer Program (ETAP) simulator, developed by Operation Technology Inc., is one of the most widely used load flow analysis software. It allows the user to utilize the built-in templates to quickly construct a model of the entire electrical network. The ETAP simulator can be used

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to analyze integrated power systems (Khan et al., 2009) and has an ability to track up to 10 million load items. In (Sedighizadeh and Rezazadeh, 2008), a genetic algorithm is used for optimal allocation of distributed generation for improved voltage profile and the correctness of results is evaluated by ETAP. The power flow analysis package in ETAP provides both Newton-Raphson and Accelerated Gauss-Seidel method to solve power flow equations. ETAP offers two methods to calculate the X/R ratio. The first method finds the equivalent resistance and reactance of the entire system, to get a single value X/R ratio for a given location. In the second method, individual branch current contributions (each with a separate X/R ratio) are combined into a single X/R ratio. ETAP has also been used to analyze different distribution system models to minimize their power losses (Ramesh et al., 2009). The developers of ETAP claim that ETAP can also serve as a complete smart grid analysis tool.

GridLAB-D GridLAB-D is a recently developed open-source power system modeling and simulation tool by the Pacific Northwest National Laboratory (PNNL) of the US DOE. It offers distributed energy resource modeling, integration of transmission and distribution systems, SCADA and metering models. An interesting feature is its external links to MySQL, MATLAB, MS-EXCEL and MS-ACCESS (Schneider et al., 2009). This tool offers timing models ranging from a few seconds to decades. GridLAB-D gives a simulation environment that can be incorporated with a variety of data management and analysis tools. It divides the power flow problem in two parts, i.e., 1) transmission and 2) distribution. It uses Gauss-Seidel iterative method to solve power flows at the transmission side while Forward and Backward Sweep (FBS) method to solve power flow problems at the distribution side (Schneider et al., 2009). Instead of the FBS method, the newer versions of GridLAB-D utilize the Gauss Seidel Three Phase Current Injection

Category: Environmental Science and Agriculture

method due to the inability of FBS to handle networked distributions (Schneider et al., 2009).

Use of Matlab for Load Flow Simulations Although MATLAB, which is a high level language for doing intense numerical computations and programming, is not developed particularly for load flow analysis but it can be used for that purpose. Given the general-purpose nature of MATLAB, it provides a very flexible environment for load flow analysis. For example, the NR method is programmed to solve a 5-bus system on MATLAB (Mallick and Hota, 2015). The Power System Analysis Toolbox (PSAT), which is an open source MATLAB toolbox, has been used for power flow calculations (Milano, 2005).

Power World Simulator The Power World simulator is a commercial grade power system analysis and simulation package developed by the Power World Corporation. It is designed to simulate high voltage power system operations on a time frame ranging from several minutes to several days. The software can solve up to 100,000 buses with high efficiency. It extensively uses state-of-the-art graphics for better and easy understanding. The simulator is mainly based on the Newton-Raphson method for iteratively solving non-linear equations for power flows. Load flow analysis by fast decouple method is also an option available in this simulator. It uses three nested loops to solve power flows. Simulation and testing are the state-of-the-art analysis techniques; however, as we have seen that they also use the numerical methods for their computations and thus cannot guarantee accuracy. Moreover, the main idea behind simulation and testing methods is to approximate a solution to a query by observing a subset of each probable run. Hence, it is possible that a system bug may not be detected during the simulation-based analysis. Moreover, system models, used in simulation

cannot capture the true random behaviors, such as frequent changes in renewable energy generation, variations in network configurations and the peak loads, which are very frequently encountered in smart grids. In most cases, simulation based methods rely on pseudo random number generation methods for modeling these elements of randomness and the reliability of the analysis is dependent on the quality of these random number generators.

Computational Intelligence Computational Intelligence (CI) methods provide a very efficient alternative for verifying and analyzing complex systems that exhibit random behavior. Thus, the grid can be controlled more reliably and more rapidly than humans by multi-variable nonlinear optimal controller based on CI, without requiring a mathematical model of the grid. CI techniques primarily consist of Artificial Neural Networks (ANNs) (Haykin, 1994), evolutionary computation (Back et al., 1997), non-linear programming (Sasson, 1969) and fuzzy logic systems (Takagi and Sugeno, 1985). ANN based load forecaster is one of the most successful applications of CI for predicting load flows in an uncertain environment. Evolutionary computational techniques tend to solve combinatorial optimization techniques by learning and adapting to new situations and are primarily based on Genetic Algorithm (GA), particle swarm optimization (PSO) and ant colony optimization methods (Vlachogiannis et al., 2005). GAs have been used to analyze the Reactive power (VAR) with real-time operation that contains randomness and uncertainty (Bakirtzis et al., 2002). PSO (del Valle et al., 2008) uses simple mechanism that mimics social behavior of bird flocking and fish schooling to guide the particles search for globally optimal solution. In (Miranda and Saraiva, 1991), fuzzy modeling is used for optimal load flow. Fuzzy logic is also used for developing a unified power flow controller for damping the power system oscillations (Eldamaty et al., 2005). Linear, nonlinear, dynamic and integer program-

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ming methods have also been used for load flow analysis (Momoh, 2012). The above-mentioned CI based techniques have also been used to analyze various power systems (Saxena et al., 2010) including maintenance scheduling, long-term system expansion and planning and load forecasting. A very comprehensive overview about CI techniques and the advantages and disadvantages of Genetic Algorithm (GA), Simulated Annealing (SA), Artificial Neural Networks (ANNs), Expert Systems (ES) and few other techniques for analyzing power distribution systems is presented in (Saxena et al., 2010). CI techniques of linear programming, nonlinear programming, quadratic programming and Newton based techniques (Sun et al., 1984) have also been successfully used to solve various Optimal Power Flow (OPF) problems (Habibollahzadeh et al., 1989, Aoki et al., 1987). These techniques have certain drawbacks as mentioned in (Abido, 2002), like, non-linear programming has convergence problems and it is complex, quadratic programming techniques have problems with cost approximation. Newton based techniques may have convergence failure due to inappropriate initial conditions, hence, they are sensitive to initial conditions. In (Abido, 2002), a particle swarm optimization technique is proposed to do OPF.

Probabilistic Load Flow Given the large number of uncertainties involved in the load flow analysis of smart grids, probabilistic analysis methods have also been used in this domain. Probabilistic Load Flow (PLF) is primarily done by using Monte Carlo (MC) simulations and the convolution method through Fast Fourier Transforms (FFT). These MC simulations are very time consuming since a large number of samples are usually used. An extended form of PLF that supports non-linear load flow equations is presented in (Allan et al., 1976) based on numerical methods. Another technique, known as Stochastic Load Flow (SLF) (Vorsic et al., 1991), is based on the assumption that the states of the system

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and power flow outputs are normally distributed. SLF deals with short time uncertainties and is only effective for analyzing system operation and thus the reliability of this method has been questioned (Anders, 1989). PLF has been used in (Allan et al., 1974) for analyzing power flows where the nodal loads and generation are defined as random variables and power flow is computed as a probability density function. Multi-linear simulation algorithm has demonstrated better results than the MC simulation based methods (Da Silva and Arienti, 1990). Similarly, the method of combined cumulants and Gram-Charlier expansion for probabilistic load flow computation (Zhang and Lee, 2004) has reported a significantly improved performance. With this method, the probability density function of the transmission line flows is obtained. It determines the effect of prolonged uncertainty of transmission network. It also provides a new way of calculating probability density function which requires reduced storage, is applicable to larger systems and is faster than Monte Carlo simulations. It is useful in system expansion planning. Moreover, it also ensures better approximation of the cumulative distribution function curve. This method was practically demonstrated on a WSCC (Western Systems Coordinating Council) test system, which consists of 179 buses and 263 lines. The probability of any overloaded line can be easily computed through this method. The probabilistic load flow analysis using an algorithm based on the point estimate method is given in (Su, 2005). It is based on an assumption that the uncertainties of line parameters and injections in the bus can be measured. The method allows any deterministic load-flow program to be used. In order to calculate the statistical moments of load flow solution distribution for a system having m uncertain parameters, 2m calculations of load flow are used and the value of the solution is weighted at 2m locations. These moments are then used in probability density fittings. This method has been tested on several IEEE test systems and it is verified that performance of point estimate method

Category: Environmental Science and Agriculture

is better than Monte Carlo simulation and it also requires less number of computations. A detailed comparison between the cumulants method and the point estimate method is presented in (Li and Zhang, 2009) with respect to the load flow analysis. Analyzing random branch outages in load flow is another area where probabilistic load flow is considerably used since conventional load flow methods cannot cater such problems (Hu and Wang, 2006). PLF allows us to solve discrete distribution part of each state and output variable. The effect of branch outage is greater on system state than uncertainties caused by nodal power injections. For simplifying the convolution of random variables, moments and cumulants are used. Branch outages are simulated by injecting the virtual power to the related nodes. The resulting distribution is found by convolving continuous and discrete distributions. The Dynamic Stochastic Optimal Power Flow (DSOPF) (Momoh, 2009), which is based on Adaptive Dynamic Programming (ADP) technique, is another stochastic method for PLF. ADP tends to cope with the complex power system problems, which can be predicted under uncertainty conditions, and it is useful where there is not enough prior knowledge. These tools usually ensure robustness, scalability, stochasticity, predictivity, adaptability and acquisition of instantaneous data.

FUTURE RESEARCH DIRECTIONS All the above-mentioned techniques are found to be quite scalable and user- friendly but cannot guarantee the accuracy of the analysis results. The main reasons behind the inaccuracies in the result include the usage of computer-arithmetic based models, which contain round-off errors, and the sampling based nature of the analysis, i.e., the models are analyzed for a subset of all possible scenarios due to limited computational resources. Given the safety-critical nature of smart-grids, the accuracy of load flow analysis results is the most desirable feature, since an undetected fault

in the smart grid system can have major impact. For example, the analysis inaccuracy limitations have been reported as the main causes behind the 2003 Northeast blackout in the United States and Canada (Poulsen, 2004a),(Poulsen, 2004b) which approximately affected 55 Million people. As a complementary approach, formal methods (Abrial, 2009) which are computer based mathematical analysis tools, can be used to overcome the inaccuracy limitations in the domain of load flow analysis. However, to the best of our knowledge, no prior work regarding the formal load flow analysis exists so far. In order to fill this gap, we recommend to use probabilistic model checking (J. Rutten and Parker, 2004), which is a widely used formal method for analyzing Markovian models, to ensure accurate results of load flow analysis of smart grids.

CONCLUSION Load flow analysis plays a vital role in safe and effective working of the smart grid system and a number of analysis methods have been used in this domain. This paper presents a brief overview about smart grids and the main factors affecting the loads in smart grids. This information can be utilized to under- stand the random and uncertain components in smart grids and thus model them appropriately in their load flow analysis, which can be conducted using Numerical methods, Simulation methods, Computational intelligence and probabilistic load flow methods. Each of these analysis methods have their own advantages and disadvantages and they have been highlighted in this chapter.

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Iwamoto, S., & Tamura, Y. (1981). A load flow calculation method for ill-conditioned power systems. Power Apparatus and Systems, (4), 1736–1743. Khan, R. A. J., Junaid, M., & Asgher, M. M. (2009). Analyses and monitoring of 132 kv grid using etap software. In Electrical and Electronics Engineering (p. I–113). IEEE. Khurana, H., Hadley, M., Lu, N., & Frincke, D. A. (2010). Smart-grid security issues. Security & Privacy, IEEE, 8(1), 81–85. doi:10.1109/ MSP.2010.49 Le Nguyen, H. (1997). Newton-raphson method in complex form power system load flow analysis. Power Systems, 12(3), 1355–1359. doi:10.1109/59.630481 Li, F., Qiao, W., Sun, H., Wan, H., Wang, J., Xia, Y., & Zhang, P. et al. (2010). Smart transmission grid: Vision and framework. Smart Grid, 1(2), 168–177. doi:10.1109/TSG.2010.2053726 Li, G., & Zhang, X.-P. (2009). Comparison between two probabilistic load flow methods for reliability assessment. In Power & Energy Society General Meeting, (pp. 1-7). IEEE. doi:10.1109/ PES.2009.5275534 Mallick, A. K., & Hota, B. S. (2015). Load flow study in power system. Retrieved from http:// ethesis.nitrkl.ac.in/2464/1/load flow in power system.pdf Metke, A. R., & Ekl, R. L. (2010). Security technology for smart grid networks. Smart Grid, 1(1), 99–107. doi:10.1109/TSG.2010.2046347 Milano, F. (2005). An open source power system analysis toolbox. Power Systems, 20(3), 1199–1206. doi:10.1109/TPWRS.2005.851911 Miranda, V., & Saraiva, J. (1991). Fuzzy modelling of power system optimal load flow. In Power Industry Computer Application (pp. 386–392). IEEE; doi:10.1109/PICA.1991.160606

Momoh, J. (2012). Smart grid: fundamentals of design and analysis (Vol. 63). Wiley; doi:10.1002/9781118156117 Momoh, J. A. (2009). Smart grid design for efficient and flexible power networks operation and control. In Power Systems Conference and Exposition (pp. 1–8). IEEE; doi:10.1109/ PSCE.2009.4840074 Moslehi, K., & Kumar, R. (2010). A reliability perspective of the smart grid. Smart Grid, 1(1), 57–64. doi:10.1109/TSG.2010.2046346 Poulsen, K. (2004a). Software bug contributed to blackout. Retrieved from http://www.securityfocus.com/news/8016 Poulsen, K. (2004b). Tracking the blackout bug. Retrieved from http://www.securityfocus.com/ news/8412 Ramesh, L., Chowdhury, S., Chowdhury, S., Natarajan, A., & Gaunt, C. (2009). Minimization of power loss in distribution networks by different techniques. Energy and Power Engineering, 2(1). Rutten, J., Kwiatkowska, M. G. N., & Parker, D. (2004). Mathematical techniques for analyzing concurrent and probabilistic systems. American Mathematical Society. Sasson, A. M. (1969). Nonlinear programming solutions for load-flow, minimum-loss, and economic dispatching problems. Power Apparatus and Systems, (4), 399–409. Saxena, D., Singh, S., & Verma, K. (2010). Application of computational intelligence in emerging power systems. Engineering. Science and Technology, 2(3), 1–7. Schneider, K. P., Chassin, D., Chen, Y., & Fuller, J. C. (2009). Distribution power flow for smart grid technologies. In Power Systems Conference and Exposition, 2009. PSCE’09. IEEE/PES, (pp. 1-7). IEEE. doi:10.1109/PSCE.2009.4840078

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Sedighizadeh, M., & Rezazadeh, A. (2008). Using genetic algorithm for distributed generation allocation to reduce losses and improve voltage profile. World Academy of Science. Engineering and Technology, 37, 251–256. Stott, B. (1974). Review of load-flow calculation methods. Proceedings of the IEEE, 62(7), 916–929. doi:10.1109/PROC.1974.9544 Su, C.-L. (2005). Probabilistic load-flow computation using point estimate method. Power Systems, 20(4), 1843–1851. doi:10.1109/TPWRS.2005.857921 Sun, D. I., Ashley, B., Brewer, B., Hughes, A., & Tinney, W. F. (1984). Optimal power flow by newton approach. Power Apparatus and Systems, (10), 2864–2880. Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. Systems, Man and Cybernetics, (1), 116–132. Teng, J.-H. (2002). A modified Gauss–Seidel algorithm of three-phase power flow analysis in distribution networks. Electrical Power & Energy Systems, 24(2), 97–102. Trias, A. (2012). The holomorphic embedding load flow method. In Power and Energy Society General Meeting, (pp. 1–8). IEEE. Van Benthem, J., & Doets, K. (2001). Higherorder-logic. In Handbook of Philosophical Logic (pp. 189–243). Springer. Vlachogiannis, J. G., Hatziargyriou, N. D., & Lee, K. Y. (2005). Ant colony system-based algorithm for constrained load flow problem. Power Systems, 20(3), 1241–1249. doi:10.1109/ TPWRS.2005.851969

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Vorsic, J., Muzek, V., & Skerbinek, G. (1991). Stochastic load flow analysis. In Electrotechnical Conference, (pp. 1445–1448). IEEE. Wang, Z., & Alvarado, F. L. (1992). Interval arithmetic in power flow analysis. Power Systems, 7(3), 1341–1349. doi:10.1109/59.207353 Zhang, P., & Lee, S. T. (2004). Probabilistic load flow computation using the method of combined cumulants and gramcharlier expansion. Power Systems, 19(1), 676–682. doi:10.1109/ TPWRS.2003.818743 Zimmerman, R. D., & Chiang, H.-D. (1995). Fast decoupled power flow for unbalanced radial distribution systems. Power Systems, 10(4), 2045–2052. doi:10.1109/59.476074

ADDITIONAL READING Endrenyi, J., Anders, G., & Leite da Silva, A. (1998). Probabilistic evaluation of the effect of maintenance on reliability. an application. Power Systems, 13(2), 576–583. doi:10.1109/59.667385 Fan, J., & Borlase, S. (2009). The evolution of distribution. Power and Energy Magazine, IEEE, 7(2), 63–68. doi:10.1109/MPE.2008.931392 Miceli, R. (2013). Energy management and smart grids. Energies, 6(4), 2262–2290. doi:10.3390/ en6042262 Schneider, J., Gaul, A. J., Neumann, C., Hogräfer, J., Wellßow, W., Schwan, M., & Schnettler, A. (2006). Asset management techniques. Electrical Power & Energy Systems, 28(9), 643–654. doi:10.1016/j.ijepes.2006.03.007

Category: Environmental Science and Agriculture

KEY TERMS AND DEFINITIONS Asset Management Systems: Asset management of smart grids is a core requirement due to the huge investments involved. For example, according to the US Department Of Energy (DOE), around 1.5 trillion dollars have been invested in the US electricity infrastructure so far. Asset management applies to both tangible and intangible assets. Distribution Management System: The Distribution Management System (DMS) may be regarded as the control center of the smart grid. The DMS mainly uses the fault location, Geographic Information Systems (GIS) and Outage Management System (OMS) to improve the reliability of the smart grid by reducing outages and sustaining the frequency and voltage levels. The most important role of DMS is to check the faults and isolate the faulty part out of the system. The “intelligent nodes” of the DMS can communicate with one-another periodically and if a fault occurs then they work together to reconfigure the system.

Energy Management System: The Energy Management System (EMS) is used for monitoring and controlling the performance of the generation and transmission system. It allows getting real-time updates from power plants about their conditions and generation parameters. The monitor and control functions are implemented through Supervisory Control and Data Acquisition (SCADA). Renewable Energy Integration: Integrating various renewable energy sources is the most desirable feature of smart grids. However, this component faces various challenges. Advanced energy storage at the transmission, distribution, and residential levels, Static VAR compensators and synchro-phasors within the transmission grid, dynamic pricing demand response, micro grids, virtual power plants, and smart wind and solar technologies are some of the tools for Renewable Energy Integration (REI).

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Methodology of Climate Change Impact Assessment on Forests Mostafa Jafari Regional Institute of Forest and Rangelands (RIFR), Iran

INTRODUCTION Climate change is one of the main challenging issues in various countries (Jafari, 2013b) in current century. Climate change and climate variability and Global Warming and its’ effects on natural resources, plants, animal and in general on human life are among subjects that received attention of scientists and politicians in recent years. Climate change challenges need to be considered in various dimensions (Jafari, 2013c). To both understand the present climate and to predict future climate change, it is necessary to have both theory and empirical observation. Any study of climate change involves the construction (or reconstruction) of time series of climate data. How these climate data vary across time provides a measure (either quantitative or qualitative) of climate change. Types of climate data include temperature, precipitation (rainfall), wind, humidity, evapotranspiration, pressure and solar irradiance (aric, 2008). Climate change assessments and evaluation should be done by using recorded observation data as well as prepared and provided proxy data (Jafari, 2010). Plant ecophysiological study has very important role to recognize climate changes (Jafari, 2007). Trees and also woods can be used as archive of past events. Climate change will strongly affect water resources, plant communities and wildlife in the arid and semi-arid regions (FAO, 2009). Water, environment humidity and temperature are main factors of plant growth. Majority of plant and forest ecosystems on the earth are formed under these two main factors. Whatever amount of humidity and required water are available and also favorable temperature for plant growth cause

plant community reach higher plants and trees and forest ecosystems would develop. In fact plants are important climate indicators. Trees are not an exception. Plants, especially, trees are sensitive to their environmental changes, and tree-ring width is one of the reliable proxies of ambient environmental conditions. Climate and environmental changes affect natural ecosystems as well as planted forests (Kiaee and Jafari, 2014). Investigation of quantity and quality of these growths could help to consider past climatic conditions. Measuring and recording tree rings’ widths and its’ densities of early woods and late woods can provide valuable data resources to produce time series and consider its correlation with climate factors in the same time periods (Figure 1). Seasonal changes in temperate climatic region effect on tree rings widths periodically. In spring and summer time plants grow better than unpleasant seasons like fall and winter. The outermost layer of a tree is composed of bark. Bark itself is composed of two tissues: an innermost layer of live phloem, and an outer layer of periderm (the bark ‘proper’), which has an outermost layer of waterproofing cork (phellum) which protects the wood to some degree from insects, etc (Figure 2). The cork has its own cambium (phellogen) between the phloem and cork layer. Only the outermost layer of a tree is alive (essentially only the phellogen, phloem, cambium, and maturing xylem of the current year’s growth). Consequently, the majority of the trunk does not require gaseous exchange. The bark is punctuated by lenticels, a sort of giant stoma, which allows the thin outermost living layers of the trunk to ‘breathe’ (Anonymous, 2008a),

DOI: 10.4018/978-1-5225-2255-3.ch272 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Environmental Science and Agriculture

Figure 1. Tree ring width and densities, Fagus orientalis (beech tree), Mazandaran province mid-elevation forest (MA II F3)

(Author, 2010)

Growths of the vascular cambium tissue produce wood as secondary xylem production. Sapwood is xylem that conveys water and dissolved minerals from the roots to the rest of the tree. The darker heartwood is older xylem that has been infiltrated by gums and resins and has lost

its ability to conduct water. Each growth layer is distinguished by early wood (springwood), composed of large thin-walled cells produced during the spring when water is usually abundant, and the denser latewood (summerwood), and composed of small cells with thick walls. Growth rings vary in

Figure 2. Anatomy of a tree trunk (Encyclopedia Britannica, 2000)

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width as a result of differing climatic conditions; in temperate climates, a ring is equivalent to one year’s growth. Certain conducting cells form rays that carry water and dissolved substances radially across the xylem. Bark comprises the tissues outside the vascular cambium, including secondary phloem (which transports food made in the leaves to the rest of the tree), cork-producing cells (cork cambium), and cork cells. The outer bark, composed of dead tissue, protects the inner region from injury, disease, and desiccation (Encyclopedia Britannica, 2006). A big trunk of a harvested tree can be use as an archive of data and may provide its life long time series (Figure 3)(Jafari, 2010). Main objectives of dendrochronology are: a) Put the present in perspective of the past, b) Better understand current environmental processes and conditions, and c) Improve understanding of possible environmental issues of the future. To meet these objectives, the exact year of formation of each growth ring must be known: a) Merely counting rings doesn’t ensure accurate dating, and b) Crossdating, also known as pattern matching, ensures accurate dating (Sheppard, 2013).

BACKGROUND Dendrochronology is an accepted and reliable method in considering climate change impact on forest ecosystem through study tree ring widths (Jafari, 2015). The cambium of the trees growing in temperate zones becomes dormant in the falls and reactivates each spring. This leads to annual rings and the vessels produced in the spring are often larger than in the fall; the large vessels allow for rapid sap movement in the spring, whereas the narrow vessels minimize the risk of cavitations under dry conditions in late summer. This leads to a ring-porous pattern in the wood as opposed to the diffuse porous pattern where vessels are more even in size. As trees age the vessels in the center of the stem become air-filled and cease to carry water; they still function for support and storage of waste products, some of which are colored; this is the heartwood in contrast to the sapwood which carries water and is confined to the outer few annual rings (The Ohio State University, 2013). Recording temperature and using thermometers have only been widely used since around 1850. Thus, the instrumental record for earlier

Figure 3. Hyrcanian forest research site, an old trunk of Fagus orientalis L. (Asalem, Guilan province forest)

(Jafari, 2010)

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times is quite poor and full of gaps. Essentially nothing is available in the way of quantitative measurements of weather conditions for the time before 1800 A.D. To reconstruct climate change, therefore, we need to use indirect indicators. One source of information is historical records: logs, dairies, lists on when the wine harvest began, reports on when the ice first broke up in a northern river, or when the cherry trees first blossomed. In some cases, such reports go back hundreds of years, although rarely in unbroken sequence. Logs and dairies are treasured finds; they do not exist for most regions of the planet (Anonymous, 2008b). Climate factors data can be measured by direct observation in different meteorological station (like climatology and synoptic stations) or can be recorded by different instrument in different locations and with different time intervals. This information is more or less confidential for judgments on the past events, and good tools for the future projections.

Applications of Dendrochronology As definition point of view, the word dendrochronology is compose of: dendro (using trees, or more specifically the growth rings of trees), chrono (time, or more specifically events in past time), and logy (the study of). Applications in dendrochronology include: ecology (insect outbreaks, forest stand structure, past fires) (Jafari, 2012a),

climatology (past droughts or cold periods), geology (past earthquakes, volcanic eruptions), and anthropology (past construction, habitation, and abandonment of societies) (Sheppard, 2013). Also some new terms provided (Jafari, 2013a) for related applications such as, dendro-productivity (Jafari & Khoranke, 2013), dendro-genetic (Jafari et al., 2012b), dendro-medical (Jafari, 2014a). In region where the seasons provide clear seasonal climate difference, trees develop annual rings of different properties depending on weather, rain, temperature, soil pH, plant nutrition, CO2 concentration, etc. in different years. These variations are used in dendroclimatology to infer past climate variations. Annual rings width of old trees wood sample is valuable data source as a live archive document for past climate changes, if year of growth and cross dating be well recognized. In case of sample from standing live trees, growth year is identifiable. But in case of wood samples, which could be found in archeological sites, recognizing growth year by producing skeleton graph and cross dating is necessary. Wood samples could show the years of past events like fire, drought or flood in growing site of sample tree. Fire-scarred ponderosa pine (Pinus ponderosa) from Ashenfelder Basin, Laramie Peak, Wyoming, (Figure 4). Low to moderate intensity fires that burned through a forest may injure or scar surviving trees, leaving a clear record of their passage. (Swetnam and Baisan, 2002)

Figure 4. Fires may injure or scar surviving trees

(Swetnam and Baisan, 2002)

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Proxy Data and Climate Change Climatologists who study past – or paleo – climates (Paleo-climatologists) use the term “proxy” to describe a way that climate change is recorded in nature, within geological materials such as ocean or lake sediments, tree-rings, coral growth-bands, ice-cores, and cave deposits. For a proxy to be useful, it must first be established that the proxy (i.e. tree-ring width, stable isotope composition of ice, sediment composition) is in fact sensitive to changes in temperature (or some other environmental parameter). This phase of research is known as calibration of the proxy. Perhaps the most frequently used temperature proxy is the relative abundance of microfossils in sediments. That microfossils bear witness to temperature was recognized early in the history of oceanography. Measuring and recording of tree-ring width is another reliable source of proxy of ambient environmental conditions. When a tree grows at high elevation, near the tree limit, its growth is limited by temperature, and the thickness of its growth rings contains clues about whether the growing season was warm or cold. An equation can then be written relating the changes in ring width to temperature change. Similarly, if the growth is limited by water (say, in a warm semi-arid setting) the ring width can be used to calculate changes in rainfall. Climate proxies have been utilized to provide a semi-quantitative record of average temperature in the Northern Hemisphere back to 1000 A.D (Anonymous, 2008b). To provide paleo proxy data paleo- climatologists gather proxy data from natural recorders of climate variability such as tree rings, ice cores, fossil pollen, ocean sediments, corals and historical data. By analyzing records taken from these and other proxy sources, scientists can extend our understanding of climate far beyond the 100+ year instrumental record. Principle sources of the major types of proxy climatic data for palaeoclimatic reconstructions can be categorized as following groups (Jafari,

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2010): Glaciological (Ice Cores), Oxygen isotopes, Physical properties, Trace element & microparticle concentrations •

•

• •

Geological: A. Sediments, 1. Marine (ocean sediment cores), i) Organic sediments (plank-tonic & benthic fossils), Oxygen isotopes, Faunal & floral abundances, Morphological variations, ii) Inorganic sediments: Mineralogical composition & surface texture, Distribution of terrigenous material (provided by river erosion), Ice-rafted debris. Geochemistry: 2. Terrestrial, Periglacial features, Glacial deposits & erosion features, Glacio-eustatic features (shorelines and sea level changes), Aeolian deposits (sand dunes), Lacustrine deposits/varves (related to the lakes), B. Sedimentary Rocks, Facies analysis, Fossil/microfossil analysis, Mineral analysis Isotope geochemistry. Biological: Tree rings (width, density, isotope analysis), Pollen (species, abundances), Insects. Historical: Meteorological records, Parameteorological records (environmental indicators), Phenological records (biological indicators).

Proxy material differs according to its: a) its spatial coverage; b) the period to which it pertains; and c) its ability to resolve events accurately in time (Bradley, 1985). Some proxy records, for example ocean floor sediments, reveal information about long periods of climatic change and evolution, with a low-frequency resolution. Others, such as tree rings are useful only during the last 10,000 years at most, but offer a high frequency (annual) resolution. The choice of proxy record (as with the choice of instrumental record) very much depends on what physical mechanism is under review. As noted, climate responds to different forcing mechanisms over different time scales, and proxy materials will contain necessary cli-

Category: Environmental Science and Agriculture

matic information on these to a greater or lesser extent, depending on the three factors mentioned (aric, 2008).

Natural Archives Growth conditions can be recorded in tree rings. A wide ring could be define as plenty of warm days and sufficient water, a narrow ring means nasty conditions, either a short growing seasons because summer was late in coming (up on the mountain), or a severe water shortage (in the foothills, in areas where water is limiting). The mixture of conditions recorded (time of snow-melt, intensity of winter rain, temperature in June, etc.) depends on what a given tree cares about in terms of growth. Hence, a tree is a “reporter,” and the same is true for all other organisms recording climate change. What a scientist can extract from tree rings depends on how many properties of a ring can be measured (width, density of early wood, density and width of wood grown late in the season), how clever the statistical methods are, and how well the items of interest (say, spring temperature or annual rainfall) are correlated with the properties measured. For instance, special measurements can be made on the isotope chemistry of the wood. This kind of information can yield insights on the composition of the rainfall (from oxygen isotopes) and on the rate of photosynthesis (from carbon isotopes) (Anonymous, 2008b). While tree growth is influenced by climatic conditions, patterns in tree-ring widths, density, and isotopic composition reflect variations in climate. In temperate regions where there is a distinct growing season, trees generally produce one ring a year, and thus record the climatic conditions of each year. Trees can grow to be hundreds to thousands of years old and can contain annually-resolved records of climate for centuries to millennia.

Tree Rings Measurement Instruments

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Outcome from dendrochronological research studies played an important role in the early days of radiocarbon dating. Tree rings provided truly known-age material needed to check the accuracy of the carbon 14 dating method. During the late 1950s, several scientists (notably the Dutchman Hessel de Vries) were able to confirm the discrepancy between radiocarbon ages and calendar ages through results gathered from carbon dating rings of trees. The tree rings were dated through dendrochronology. Even now, tree rings are still used to calibrate radiocarbon determinations. Libraries of tree rings of different calendar ages are now available to provide records extending back over the last 11,000 years. The trees often used as references are the bristlecone pine (Pinus aristata) found in the USA and waterlogged Oak (Quercus sp.) in Ireland and Germany. Radiocarbon dating laboratories have been known to use data from other species of trees (BETA, 2013). Borer core samples submitted to the laboratory are registered, followed by preparation of optimal surfaces for analysis across several growth radii of the tree. Subsequently, tree-ring series are measured manually (Figure 5) and registered and or using specially designed measuring devices (Lintab and Aniol) connected to computers (Figure 6), screens and printers. The soft wares used for data storage, cross-correlation and statistical analyses are CATRAS and ITRDBLIB. Samples subject to wood anatomical determination are analyzed with light microscopy and compared against the laboratory’s extensive reference collection of European woody plants (Hammarlund, 2013). Major Equipment in the Biogeography/ Dendrochronology Laboratory currently houses one Velmex Measuring Machine connected to a

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Stereozoom Microscope on a boom stand and a microcomputer analysis system. The lab equipment also includes: - incremento borers, - stereozoom microscopes, - belt sanders (4X24”), - desktop and labtop computers, - GPS units, Stihl Chain saw Stihl 046 with a 24” bar, Hand saws, - Hood, - cruiser packs, - Soil Sampling probes, - Munsell Charts, - Measuring tapes, - map tubes, - Paper straws, Poplar core mounts, skeleton plot paper (Indiana State University, 2013).

Discs or Borer Core Samples The stem cross section is the best way to have a good surface on which to observe tree ring series. These discs can be sometimes obtain in co-operation with foresters when timbering is programmed. In most of the cases, cross dating and then measurements of ring-width as well as densitometry analysis are performed with small cores 4mm in diameter extracted from the tree by an increment borer. In order to avoid dissymmetry in radial tree-growth measurement 2 or 3

cores are extracted on each tree. Consequently, on each sampling site 20 to 45 cores are collected and brought back to the laboratory. Precise cares have to be taken in coring, particularly when densitometry analysis will be performed. The most important is the position of the borer on the trunk. In order to obtain lately an observation surface the most perfectly perpendicular to the long axis of tracheids and fibers, the borer has to be also positioned perpendicularly to trunk axis (TGTC, 2008). In case which disc of harvested trunk is not available (Figure 7) or it is not possible to take disc, core sampling is an alternative. There are different types of drill instrument for this purpose (Figure 8). Increment borers are instruments that take a small cylindrical core from trees and allow determination if radial growth or age of the tree (Figure 8c). The borer consists of three parts (Figure 8): the case or handle, the borer bit, and the extractor. Increment borers come in various sizes, from 4 inches (101.6 mm) to 30 inches (762 mm) length or more. The ones which are in usual

Figure 5. Measuring disc sample tree ring widths in the Golestan research centre lab. using manual measurements

(Author, 2010)

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Figure 6. Measuring core sample tree ring widths in the dendrochronology lab. using computer equipped machine (Author, 2012)

Figure 7. Big trunk disc sample taken during harvesting process (Author 2010)

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Figure 8. Wood sample by increment borer: a) Using borer in the trunk, b) Extracting core sample from borer, c) sample in wooden holder (Eng Khoshnevis, photos by the author, 2012)

use are in the 8-18 inch range (457.2 -203.2 mm). Smaller borers are used for small trees or where only recent growth is needed from larger trees (Figure 8a, b, and c, Figure 9). In the field sampling experiment, when the cores remove from the borer, for the safety, it is needed to lay them into an increment core holder that has been pre-glued. Tightly wind the glued cores with cotton string (Figure 10) so as to apply pressure during the drying process (McCarthy, 2008). Immediate observation of rings on cores extracted from the borer is rarely possible, and moreover rings width measurement quite impossible. Good observation and measurement need a perfect transverse section. After a correct reorientation of the core as the piece of wood was in the trunk, such a section is obtained either by refreshing with a razor blade or polishing the surface selected in order to obtain a plane surface allowing to access to the cell structure (TGTC, 2008).

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Measured Data Analysis A great number of laboratories in different part of the world have been established to study on wood and climate changes. Palaeo-climate changes are on the target of the most of these institutes. The International Tree-Ring Data Bank is maintained by the NOAA Paleoclimatology Program and World Data Center for Paleoclimatology. The Data Bank includes raw ring width or wood density measurements, and site chronologies (growth indices for a site). Tree-ring measurement series from other parameters are welcome as well. Reconstructed climate parameters, including North American Drought, are also available for some areas. Over 2000 sites on six continents are included (WDC, 2008). The objective of the measuring ring widths would be to develop tree-ring records of climate over the past several centuries, to understand interannual to century scale variability in climate. This study will improve the capability of understanding environmental variability and key features of the

Category: Environmental Science and Agriculture

Figure 9. Borer samples in wooden holder

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(Author, 2014)

Figure 10. Borer sample and sample holder in the field (Author, 2012)

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regional environment, e.g. persistence of drought, reliability of stream flow (Hughes & Touchan, 1997). Analyzing data by using software by accuracy of 0.01 mm (Robinson & Evans 1980) or more accurate up to 0.001 mm.

Statistical Analysis of the Climate Factors Tree- rings can provide continuous yearly paleoclimatic records for regions or periods of time with no instrumental climate data. However, different species respond to different climate parameters with, for example, some sensitive to moisture and others to temperature. For example four common species which grow in Northern Ireland and their suitability for climate reconstruction are beech, oak, ash and Scots pine. Beech and ash are the most sensitive to climate, with tree-ring widths more strongly Influenced by precipitation and soil moisture in early summer than by temperature or sunshine. Oak is also sensitive to summer rainfall, where as Scots pine is sensitive to maximum temperature and the soil temperature. The moisture-related parameters, rainfall and the Palmer Drought Severity Index (PDSI), and to a lesser extent, maximum and mean temperatures, can be reconstructed. Reconstructions of climate parameters with tree-rings as proxies may be relatively stable for some seasons such as May–July. The combinations of species are more successful in reconstructing climate than single species (GarcÍa-Suárez et al., 2009). The development of dendrochronological time series in order to analyze climate-growth relationships usually involves first a rigorous selection of trees and then the computation of the mean tree-growth measurement series. A change in the perspective, passing from an analysis of climate-growth relationships that typically focuses on the mean response of a species to investigating the whole range of individual responses among sample trees (Carrer, 2011).

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Crossdating Primary faze of crossdating work, under a good dissecting microscope, begins by counting backwards from the first known year behind the bark. Using a fine mechanical pencil, place a single dot on each decadal ring (e.g., 2010, 2000, 1990, etc.), place two dots on each 50-year ring (2010, 1960, 1910, etc.), and three dots on each century ring (2010, 1910, 1810, etc.). At this stage, these marks are just temporary year assignments. The actual years will be confirmed after skeleton plotting (McCarthy, 2008). Using a mm graph paper is first step to draft skeleton plots.The decades are labeled on the x-axis and a vertical line is drawn on a y-scale composed of 10 units. Any ring that is smaller than its neighbor rings (± 3 on either side) gets a line drawn on the paper. If the ring is very small, the line may be 10 units. If the ring is half as small as its predecessor you might code it as a 5, etc. (rings that are coded less than a 5 are rarely useful in crossdating). This is a bit counterintuitive because the longer the line, the smaller the ring. According to the provided skeleton graphs, cross-dating among different samples comparing with control sample would be possible. It is also possible to recognize different years of various samples from different sources for cross-dating. A more precise method of dating volcanic deposits of recent age is to identify anomalous growth patterns among the annual rings of trees growing at the time the deposits were emplaced. Trees that were injured but not killed by tephra or lahars may show a sequence of narrow rings beginning at the time of impact. “Cross-dating,” the matching of ring-width variation patterns in one tree with corresponding ring patterns in another, should be used to ensure that dating errors are not introduced by missing rings. Missing rings can often identified by drawing narrow rings in cross-dating progress. Control tree shows 23 annual rings between the 1472 and

Category: Environmental Science and Agriculture

1495 narrow rings (Figure 11), while the tree in the We (Mount St. Helens) tephra-fall zone shows only 20 rings. The series of missing and narrow rings starting with the 1482 ring were caused by tree injury during fallout of layer we. Because of possible missing rings, dates of past volcanic events cannot be determined unequivocally by counting back to a series of narrow rings (Brantley et al., 1986). Since, the same set of environmental factors influence tree growth throughout a region, the patterns of ring characteristics, such as ring widths, are often common from tree to tree. These patterns can be matched between trees in a process called crossdating (Figure 12), which is used to assign exact calendar year dates to each individual ring (The University of Arizona, 2013). The chronology provides two main types of information: 1. The chronology can be used as a tool for dating events that caused tree death or a

marked change in the appearance of a ring or set of rings. The death date can be used to date the tree cutting involved in the construction of wooden dwellings. Scars can record the timing of events such as fire, flood, avalanche, or other geomorphologic events, while sequences of suppressed or larger rings record events such as insect infestation, effects of pollution, or changes in forest dynamics. 2. The chronology is an average of coherent variations in growth from a number of trees. It enhances the common pattern of variation or “signal” -usually related to climate- while the non-common variance or “noise” is dampened. Chronologies from trees that are sensitive to climate can be used to reconstruct past variations in seasonal temperature, precipitation, drought, stream flow, and other climate-related variables (The University of Arizona, 2013).

Figure 11. Tree-ring cross-dating (Brantley et al., 1986)

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Figure 12. Cross-dating according to the annual ring growth of different samples from live standing trees to old or new wood samples, Illustration showing an example of time series crossdating

(Adapted from Dendrologisches Labor Hamburg, 2015, http://www.scinexx.de/dossier-detail-186-7.html)

The techniques of dendrochronological study were used to date a spruce coffin board from Pukatawagan Bay, Manitoba received from Manitoba Historical Resources. The sample contained 74 annual rings, although the outermost 16 rings were rotten and not measured. The ring width series from the coffin board was matched against records from living spruce growing near South Indian Lake. Crossdating shows that the coffin board was cut in 1878. The board also contains a ring containing poorly developed latewood (a ‘light’ ring) in 1817 that is found commonly in spruce records across northern Manitoba (Figure 12)(Scott & Nielsen, 2002). Using different wood materials of different periods, by cross-dating of the samples comparing with control one, it would be possible to record

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and estimate changes from present time back to the ancient area (Figure 13). Dr. Andrew E. Douglass, an astronomer, developed dendrochronology about 1913. Douglass used a bridging method to create his chronology. First he studied recently cut trees whose dates he knew. This initial step was critical because by knowing the cut date, Douglass knew when each tree added its last growth ring. This, in turn, let him determine the year each tree started growing. The calculation was straightforward: count the dark rings inward and subtract that number from the year the tree was cut. As Douglass matched and recorded ring patterns from trees of different ages, he confirmed that their patterns overlapped during the years the trees simultaneously lived (The University of North Carolina, 2013).

Category: Environmental Science and Agriculture

Figure 13. The bridging method, cross-dating of annual rings from present live tree to the past wood samples, for establishing a tree-ring sequence

(Kuniholm, 2001)

FUTURE RESEARCH DIRECTIONS

CONCLUSION

Enhance and improvement of measuring methods on climate change issues in different sector is a crucial and important needs. Dendrochronology study method has several different applications. It is crucial to used dendrochronological method in medical field. Some question need to be considered and if possible answered. How medical needs could link with dendrochronological experiences? What kind of element may be detected on tree rings? How people could benefit of the results? What are the best analysis methods? By using this method we will be able to extend our work in medicine science areas and speed up medical approach with lower cost and economical saving (Jafari, 2014a).

The AFOLU sector (AR5, IPCC, WGIII, Chapter 11) is responsible for just under a quarter (~10–12 GtCO2eq/yr) of anthropogenic GHG emissions mainly from deforestation and agricultural emissions from livestock, soil and nutrient management (robust evidence; high agreement) (Smith et. al., 2014). Climate change impact will cause changes in biomass production in natural ecosystems. It is a need to consider the vulnerability of Net Primary production (NPP) in forest ecosystem (Jafari, 2014b). Climate change issue is an important subject in current century. In all possible ways we need to cope with this phenomenon to enhance our understanding knowledge. Dendrochronology as an able and certified study method could be implying in a wide range of applications.

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REFERENCES Anonymous. (2008a). Plant Growth, in Science. Retrieved from http://www.steve.gb.com/images/ science/wood_layers.jpg Anonymous. (2008b). Climate Change, Past and Future, Calspace Courses. Retrieved from http://earthguide.ucsd.edu/virtualmuseum/climatechange1/cc1syllabus.shtml aric. (2008). Global Climate Change Student Guide. Author. BETA. (2013). Beta Analytic, Radiocarbon Dating. Retrieved from http://www.radiocarbon.com/ tree-ring-calibration.htm Bradley, R. S. (1985). Quaternary Palaeoclimatology: Methods of Palaeoclimatic Reconstruction. London: Unwin Hyman. Brantley, S., Yamaguchi, D., Cameron, K., & Pringle, P. (1986). Tree-Ring Dating of Volcanic Deposits. Earthquakes and Volcanoes, 18(5), 184–194. Carrer, M. (2011). Individualistic and TimeVarying Tree-Ring Growth to Climate Sensitivity. PLoS ONE, 6(7), e22813. doi:10.1371/journal. pone.0022813 PMID:21829523 FAO. (2009). Guidelines for good forestry and range practices in arid and semi-arid zones of the Near East. Cairo: FAO Regional Office for the Near East. García-Suárez, A.M., Butler, C.J., & Baillie, M.G.L. (2009). Climate signal in tree-ring chronologies in a temperate climate: A multi-species approach. Dendrochronologia, 27, 183–198. doi:10.1016/j.dendro.2009.05.003 Hammarlund, D. (2013). Equipment and sample handling. Retrieved from http://www.geol.lu.se/ dendro/en/equipment.htm Hughes, M., & Touchan, R. (1997). Preparation for Collaborative Work on Past Climate in Jordan. Science Storm, A world of wonder. Retrieved from http://www.sciencestorm.com/ 3128

Indiana State University. (2013). Dendrochronology Lab, Indiana State University. Retrieved from http://dendrolab.indstate.edu/equip_fac.htm Jafari, M. (2007). Review on needfulness for plant ecophysiological study and investigation on climate change’s effects on forest, rangeland and desert ecosystems. Presented at Workshop, Climate Change in South-Eastern European Countries: Causes, Impacts, Solutions, Orangerie, Burggarten, Graz, Austria. Retrieved from http://www.intecol.net/pages/002_personal. php?mode=list&table=blog&tb_kind=Researc h&id=mjafari&vvt=aa&bidx=27531 Jafari, M. (2010). Climate Change Impacts on Iranian Ecosystems with review on Climate Change Study Methods (Pub. No. 421). RIFR. Jafari, M. (2012a). A new approach to dendroecological studies: Climate change impact on forest’ wood production in Astara (Gilan). Iranian Journal of Wood and Paper Science Research, 27(4), 690–706. Jafari, M. (2013a). New method on dendrochronology study in forest ecosystems; two new approaches: Dendro-productivity and Dendrogenetic. Paper ID: 1076-ADA2013. Presented in The 3rd International Conference of Asian Dendrochronology Association (ADA2013), Climate Change and tree Rings, Tehran, Iran. Retrieved from http://ada2013.ut.ac.ir/paper?manu=7148 Jafari, M. (2013b). Integration of forestry and rangeland management institutions in the Islamic Republic of Iran. In Challenges in adopting an integrated approach to managing forest and rangelands in the Near East Region, Part II, Country studies (pp. 37–67). Cairo: FAO. Jafari, M. (2013c, July-December). Challenges in Climate Change and Environmental Crisis: Impacts of Aviation Industry on Human. Urban and Natural Environments in International Journal of Space Technology Management and Innovation, 3(2), 24–46.

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Jafari, M. (2014a). Dendro-Medical as New Approach on Dendrochronological Study method. Presented in the 9th International Conference on Dendrochronology, as Poster No. PO086, Ref. 1590, ThemeZ01, Melbourne, Australia. Jafari, M. (2014b). Change and vulnerability of Net Primary Production (NPP) in Iranian forest, rangeland and desert ecosystems impacted by climate change. Iranian Journal of Range and Desert Research, 21(1), 139-153. Jafari, M. (2015). Dendrochronology and Climate Change. In Encyclopedia of Information Science and Technology (3rd ed., pp. 2917–2930). IGI Global. Jafari, M. & Khorankeh S. (2013). Impact of climate and environmental changes on forest ecosystem’s productivity (case study: Galugah). Iranian Journal of Forest and Poplar Research, 21(1), 166-183. Jafari, M., Maghsudloo, M. K., Khanjani Shiraz, B., Karimidoost, A., & Hemmati, A. (2012b). New approach in dendro-genetic method for identification of beech tree variability. Iranian Journal of Rangelands and Forests Plant Breeding and Genetic Research, 20(2), 284–294. Kiaee, M., & Jafari, M. (2014). Investigation and consideration of forest tree reaction to climate and environmental changes (Case study: Lavizan forest park). Journal of Plant Research, 27(1), 130–141. Kuniholm, P. I. (2001). Dendrochronology and Other Applications of Tree-ring Studies in Archaeology. In The Handbook of Archaeological Sciences. London: John Wiley & Sons, Ltd. McCarthy, B. C. (2008). Introduction to Dendrochronology, Lab procedures. Retrieved from http://www.plantbio.ohiou.edu/dendro/ RMTRR. (2008). Rocky Mountain Tree-Ring Research (RMTRR). Retrieved from http://www. rmtrr.org/gallery.html

Robinson, W. J., & Evans, R. (1980). A microcomputer-based tree-ring measuring system. Tree-Ring Bulletin, 40, 59–64. Schweingruber, F. H. (1988). Tree Rings: Basics and Applications of Dendrochronology. Dordrecht: Reidel; doi:10.1007/978-94-009-1273-1 Scott, St. G., & Nielsen, E. (2002). Dendrochronological analysis of a coffin board recovered from Pukatawagan, Bay, Manitoba. Terrain Sciences Division, Geological Survey of Canada and Manitoba Geological Survey, Manitoba Industry, Trade and Mines. Sheppard, P. R. (2013). Crossdating Tree Rings. Laboratory of Tree-Ring Research, The University of Arizona. Retrieved from http://www.ltrr. arizona.edu/skeletonplot/applications.htm Smith, P., Bustamante, M., Ahammad, H., Clark, H., Dong, H., Elsiddig, E. A., & Tubiello, F. et al. (2014). Agriculture, Forestry and Other Land Use (AFOLU). In Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press. Swetnam Thomas, W., & Christopher, H. B. (2002). Fire Regimes in Sierran Mixed-Conifer Forests. In Sierra Nature Notes, (vol. 2). Laboratory of Tree-Ring Research, The University of Arizona. TGTC. (2008). Tree growth and the tree-site complex. TGTC. The Ohio State University. (2013). Woody plants. Department of Horticulture & Crop Science, The Ohio State University. Retrieved from http://www. hcs.ohio-state.edu/hcs300/anat2.htm, http://hcs. osu.edu/ The University of Arizona. (2013). Paleo Slide Set: Tree Rings: Ancient Chronicles of Environmental Change. Laboratory of Tree-Ring Research, The University of Arizona.

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The University of North Carolina. (2013). TreeRing Dating, Lesson 2.4. Research Laboratories of Archeology. The University of North Carolina at Chapel Hill. Retrieved from http://rla.unc.edu/ lessons/Lesson/L204/L204.htm WDC. (2008). The World Data Center for Paleoclimatology Mirror Site (WDC). Retrieved from http://wdc.obs-mip.fr/treering.html

ADDITIONAL READING Barnola, J. M., Raynaud, D., Korotkevich, Y. S., & Lorius, C. (1987). Vostok ice core provides 160,000 year record of atmospheric CO2. Nature, 329(6138), 408–414. doi:10.1038/329408a0 Bergthorsson, P. (1969). An estimate of drift ice and temperature in Iceland in 1000 years. Jökull, 19, 94–101. Briffa, K. R., & Schweingruber, F. H. (1992). Recent dendroclimatic evidence of northern and central European summer temperatures. In: Climate Since A.D.1500. Routledge, London, pp. 366-392. COHMAP Members, 1988. Climatic change of the last 18,000 years: Observations and model simulations. Science, 241, 1043–1052. Davis, M. B. (1963). On the theory of pollen analysis. American Journal of Science, 261(10), 899–912. doi:10.2475/ajs.261.10.897 Emmanuel, W. R., Shugart, H. H., & Stevenson, M. P. (1985). Climate change and the broad-scale distribution of terrestrial ecosystem complexes. Climatic Change, 7(1), 29–43. doi:10.1007/ BF00139439 Fritts, H. C. (1971). Dendroclimatology and dendroecology. Quaternary Research, 1(4), 419–449. doi:10.1016/0033-5894(71)90057-3 Fritts, H. C. (1976). Tree rings and climate (pp. 80–96). London: Academic Press. Schweingruber, F. H., Fritts, H. C., Bräker, O. U., Drew, L. G., & Schär, E. (1978). The X-ray technique as applied to dendroclimatology. Tree Ring Bull., 38, 61–91. 3130

KEY TERMS AND DEFINITIONS AFOLU: Agriculture, Forestry and Other Land Use (AFOLU), main players on emission reduction and mitigation aspect of climate change, chapter 11, WGIII, IPCC AR5 2014. Climate Change: Global Warming, climate change and climate variability are a definition of deviation of climatic factors from its normal trends mainly impacted by human activities. Global Warming and its’ effects on natural resources, plants, animal and in general on human life are among subjects that received attention of scientists and politicians in recent years. Dendrochronology: Dendrochronology was developed about 1913, and is a (climate change) method to study tree ring widths in terms of time. The word dendrochronology is compose of: dendro (using trees, or more specifically the growth rings of trees), chrono (time, or more specifically events in past time), and logy (the study of). Dendrochronology as an able and certified study method could be implying in a wide range of applications. Proxy Data: Climate change assessments and evaluation should be done by using recorded observation data as well as prepared and provided proxy data. Paleoclimatologists (climatologists who study past – or paleo – climates) use the term “proxy” to describe a way that climate change is recorded in nature, within geological materials such as ocean or lake sediments, tree-rings, coral growth-bands, ice-cores, and cave deposits. Tree Rings: Trees growing in temperate climatic region are under seasonal changes. In spring and summer time plants grow better than unpleasant seasons like fall and winter. Each growth layer is distinguished by early wood (springwood), composed of large thin-walled cells produced during the spring when water is usually abundant, and the denser latewood (summerwood), and composed of small cells with thick walls.

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Category: Environmental Science and Agriculture

Model for Assessment of Environmental Responsibility in Health Care Organizations María Carmen Carnero University of Castilla-La Mancha, Spain & University of Lisbon, Portugal

INTRODUCTION Sustainability is considered a paradigm for businesses in the 21st Century (Garcia et al., 2016). Despite this, the existing tools for helping to introduce strategies and manage activities to promote sustainable business are few (Garcia et al., 2016). These deficiencies become more important in Health Care Organizations owing to its particular conditions of resource consumption and waste production. Health Care Organizations are the only type of company which can generate all the classes of waste, from waste without risk to waste that is potentially infectious, carcinogenic, mutagenic, teratogenic or radioactive. The risk to people and to the environment from this waste is much greater if it is not correctly segregated. It is also vital to carry out action to reduce the consumption of limited natural resources such as water and energy, while increasing the protection and conservation of the environment, including reducing the emission of pollutant gases, protecting biodiversity or considering the role of suppliers in action to prevent or reduce waste. It is, therefore, essential to have objective tools to assist in monitoring environmental sustainability in this type of organization, taking into account a number of factors. That is, by assessing how improvement actions, within a process of continuous improvement, are contributing to improvements in sustainability. However, it is clear that there is little linkage between sustainability reporting and management control systems (Cintra & Carter, 2012).

Nonetheless, despite its importance, the literature on the development of systems for environmental assessment in Health Care Organizations is very limited. This Chapter therefore sets out a multicriteria assessment system constructed by extension to a fuzzy environment of the Technique for Order Preference by Similarity to Ideal Situation (TOPSIS), to assess the environmental responsibility of a Health Care Organization. This model allows joint evaluation of a significant number of decision criteria, which include any event that may cause adverse effects on water, ground, seas and rivers, wild species or their habitats; it also considers the existence of possible measures to be carried out in Health Care Organizations to minimize the probability of an event, or to eliminate all risk. However, it should be noted that this model is not intended to perform an environmental audit in the field of health care, as it would need to include economic, technical, legal and other criteria, or a system of environmental impact that would require the assessment of a variety of risks and consequences. The aim is to provide a hospital with a model which is easy to apply, with criteria specific to health care, and which allows its responsibility with regard to the environment to be monitored over time. Following the methodology laid down in Carnero (2015), criteria were used that were assessed depending on the number of admissions or annual services provided, making it possible to compare results over time for a single organization, or between organizations. The model has been used in a Public Hospital.

DOI: 10.4018/978-1-5225-2255-3.ch273 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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BACKGROUND The literature includes a large number of contributions on environmental questions (AragonesBeltran et al., 2009; Higgs et al., 2008; Hsu & Hu, 2008; Kang et al., 2007; Lamelas et al., 2008; Liang et al. 2006; Madu, Kuei & Madu, 2002; Pilavachi, Chatzipanagi, & Spyropoulou, 2009; Tzeng & Lin, 2005; Tseng, Lin, & Chiu, 2009; Van Calker et al., 2006). However, these are invariably related to manufacturing, transport or energy companies. In the case of service companies and, in particular, in Health Care Organizations, the contributions are practically non-existent (Carnero, 2015). Health Care Organizations, places dedicated to the improvement and development of preventive measures in health care, with respect to their users, those who live in the area and their workers, should be involved in minimizing their own environmental impact, as there is a strong correlation between the two (Comunidad de Madrid, 2005). In order to improve environmental sustainability of a Health Care Organization, however, it is vital to monitor sustainability over time for decision making and management of activities that constitute an organization’s system processes (Salvado et al., 2015). A system of environmental assessment should combine many factors, which may be technical, social, political, economic and environmental, which often conflict with one another (Lahdelma et al., 2000); it may also be necessary to include a number of individuals or decision groups, with different perspectives or responsibilities within the Health Care Organization; as well as the need to incorporate a great deal of information, quantitative but in many cases qualitative, relating to uncertainties, scenarios, goals, etc. (Munda, 2005). These characteristics make the use of Multi-Criteria Decision Analysis (MCDA) methods highly suitable for supporting decisions about sustainability (Santoyo-Castelazo & Azapagic, 2014). The fact that the model produced, based on mathematical techniques, is objective, also helps to guarantee public acceptance of the solution or

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result obtained (Huang, Keisler, & Linkov, 2011). MCDA methods then, although not suited to all environmental problems, are very convenient in environmental impact assessment, as they give information in a structured fashion, which can be easily interpreted by the decision makers (Neste & Karjalainen, 2013). All this has led to an increase in the literature applying MCDA in the environmental field over the last two decades (Carnero, 2014). Because of the pressure brought to bear on companies by different stakeholders and by society to address ecological and social sustainability (Garcia et al., 2016) a variety of research has been carried out applying MCDA methods in this area. Gumus (2009) presents a methodology for selection of the most suitable hazardous waste transportation firms using a fuzzy Analytical Hierarchy Process (AHP). A similar technique is used by Heo et al. (2010) to get the weightings of the criteria to establish ex-ante and ex-post stages of renewable energy dissemination programmes in Korea. Reza et al. (2011) combine morphological analysis and AHP to choose new sustainable products from the earliest stages of conception. The study of Chan et al. (2012) is along similar lines, but using fuzzy AHP. Boran et al. (2012) use intuitionistic fuzzy TOPSIS to assess renewable energy technologies for electricity generation, such as photovoltaic, hydro, wind, and geothermal energy in Turkey. Wang et al. (2012) produce a model for selecting of green initiatives in the fashion industry. Vinodh et al. (2014) describe an assessment model to determine the best method for recycling plastics. Pourebrahim et al. (2014) made a selection of criteria and alternatives for conservation development in a coastal zone. Galvez et al. (2015) propose a model combining Mixed Integer Linear Programming optimization and AHP to assess possible scenarios for the implementation of an anaerobic co-digestion logistics network used to create sustainable energy production processes from biogas. Al Garni et al. (2016) use AHP to assess renewable power generation sources including solar photovoltaic,

Category: Environmental Science and Agriculture

concentrated solar power, wind energy, biomass, and geothermal, finding that in the case of Saudi Arabia the photovoltaic, followed by concentrated solar power are the first-placed technologies. The literature which uses MCDA methods for the assessment of environmental sustainability and environmental impact includes the following contributions. Hermann et al. (2007) describe the tool COMPLIMENT, which allows the overall environmental impact of an organization to be found; they combine life cycle assessment, AHP and Environmental Performance Indicators (EPIs). Kaya and Kahraman (2011) use fuzzy AHP to build an environmental impact assessment methodology for urban industrial planning. Viaggi et al. (2011) develop and apply a multicriteria methodology to estimate the environmental effectiveness of European Union agri-environment schemes in Ireland and Emilia-Romagna (Italy). Larimian et al. (2013) use fuzzy AHP to assess environmental sustainability from the point of view of security in different areas in a region of Tehran. Egilmez et al. (2015) assess environmental sustainability in 27 Canadian and US cities using fuzzy multicriteria decision-making models. The research of Zhang et al. (2016) is also related to City sustainability evaluation via MCDA methods, in this case, of 13 cities in China. Khalili and Duecker (2013) describe a methodology for designing a sustainable environmental management system built using ELECTRE. This system serves as a back-up to monitor the efforts made by companies in the area of sustainability, for example through product design, operational development or the modelling of the supply chain. Salvado et al. (2015) use AHP to calculate a sustainability index which allows companies and their supply chains to get information about their own level of economic, social and environmental sustainability. A further review of the literature on environmental questions analysed via multicriteria techniques can be seen in Huang et al. (2011) and Neste and Karjalainen (2013). However, in the field of environmental assessment of Health Care Organizations, the only contribution is Carnero (2014) which describes

a model using a fuzzy AHP together with utility functions. This model is applied to a recently opened public hospital, giving a utility of 0.764 out of 1, and showing how this type of model can be very positive in the process for certification to standard ISO 14001. Carnero (2015) shows another, more advanced, model also using fuzzy AHP, but assessing new criteria, and most are assessed based on number of care services provided annually, which allows results to be compared over time for one Health Care Organization, or between Health Care Organizations.

MODEL FOR ASSESSMENT OF ENVIRONMENTAL RESPONSIBILITY The development of new information technologies, together with the development and application of new concepts in environmental sustainability, require a constant updating of the assessment models in this area. The choice of criteria and subcriteria takes account of Rodríguez et al. (2005), García et al. 2010, Mata et al. (2011), Tejedor (2012), Bon-García (2012), Yanguas (2012), Galdakao-Usansolo Hospital (2012), Carnero (2014) and Carnero (2015). The criteria and subcriteria used in the multicriteria model are: • •

•

Annual water consumption (C1). Annual energy consumption. Two subcriteria are considered: ◦◦ Annual consumption (MW/h) by the Hospital of electricity, refrigerating energy, thermal energy and natural gas (C2). ◦◦ Consumption of renewable energies (C3). Environmental accidents and incidents (C4). Accidents have potentially serious environmental implications, such as uncontrolled spillages into the water supply, fire, x-ray emissions, spillage of dangerous substances on the ground, leaks or

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•

•

•

•

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spillages of natural gas or diesel, spillage of acetylene or refrigerating gas, leaks of ethylene oxide, etc. Incidents are matters that give rise to internal non-compliance with standard ISO 14001 and require an analysis of the cause and corrective action. Although they are not as serious as environmental accidents, they should be analysed and their causes eliminated as quickly as possible. For example, unsuitable storage of waste or chemicals, complaints from patients or neighbours about noise, etc. Biodiversity (C5). An assessment is made of the capacity of the organization to adapt to the rural and forest environment, to take care of endangered species, and also of action taken to continuously improve the environmental impact of the organization. Activities to promote and spread the environmental message (C6). This includes annual planning of congresses, celebration, exhibitions, promotion of activities related to environmental prevention and protection, spreading of awareness of environmental aims of the Health Care Organization, etc. Training and cooperation on environmental matters (C7). The organization is assessed with respect to training programmes among care and non-care staff in the Health Care Organization, and planning of groups to analyse problems and improvements. This also considers the existence of surveys and systems for gathering complaints and suggestions, and for dealing with them efficiently. Noise inside and outside. This criterion is made up of the subcriteria: ◦◦ Noise inside the Hospital (C8). This should not exceed 30dBA, and optimum noise is between 15 and 25 dBA. ◦◦ Noise near the Hospital (C9). This should not exceed 55dBA, and optimum noise is between 35 and 45dBA.

•

Waste production. Assess annual waste production. It is divided into the subcriteria: ◦◦ Group I waste (C10). This is general waste, with no risk, such as edible oils, plastics, paper and cardboard, clothes, glass, etc. ◦◦ Group II waste (C11). Sanitary waste that may be treated as urban waste. ◦◦ Group III waste (C12). Dangerous waste products, including industrial oil, batteries, non-halogenated solvents, chemical waste, radiology liquid, out-of-date or retired medicines, anatomical remains with formaldehyde, cytostatic waste, etc.

Other waste, in other groups, including radioactive waste, is subject to special legislation which must be complied with, and so is not included in the assessment system. •

Green purchasing (C13). Inclusion in conditions for purchasing products and contracting services, of guarantees of respectful treatment of the environment. Assessment of suppliers with regard to certification with standard ISO 14001. Minimizing consumption of paper, cardboard and plastic in transactions and packages, etc.

All the criteria and/or subcriteria are assessed with respect to the number of annual admissions or services provided by the Health Care Organization. This means that the results can be compared over time for a single organization, and comparisons can also be made between Health Care Organizations (Carnero, 2015). Although different multicriteria techniques can be used to build the model, in this case, applying fuzzy TOPSIS allows simultaneous valuation of a significant number of criteria. A fuzzy MCDA will be used, as this allows the uncertainty, ambiguous situations or vagueness of the judgements of the decision makers to be

Category: Environmental Science and Agriculture

included (SeongKon et al., 2011). Furthermore, decision makers usually feel more confident in giving interval judgements rather than fixed value judgments (Isaai et al., 2011). A triangular fuzzy number a = (l,m,u ) is defined by the membership function µa (x ) which

satisfies the conditions of normality and convexity. l , m and u are real numbers which satisfy ≤m ≤ u . The membership function is defined in Equation (1) (Chang, 1996).  x l − x ∈ l,m    m − l m − l x u  µa (x ) =  − x ∈ m,u   m − u m − u 0otherwise   

(

1

1

1

2

2

2

(1)

+ l2 ,m1 + m2 , u1 + u2 )

1

(2)

(l ,m , u )  (l ,m , u ) = (l 1

1

1

2

2

2

− u2 ,m1 − m2 , u1 − l2 )

1

(3)

(l ,m , u ) ⊗ (l ,m , u ) ≈ (l l ,m m , u u ) 1

1

1

2

2

2

12

1

2

1 2

(

1

1

1

2

−1

(l ,m , u ) 1

1

1

2

2

1

/ u2 ,m1 / m2 , u1 / l2 )

(5)

≈ (1 / u1,1 / m1, 1 / l1 ) forl ,m,u > 0

(6)

λ ⊗ (l1,m1, u1 ) ≈ (λl1,λm1, λu1 ), » > 0, » ∈ R +

(7)

Fuzzy TOPSIS (FTOPSIS) was proposed by Chen (2000). In a decision problem with criteria (C 1,C 2 , ...,C n ) andalternatives (A1,A2 , ..., Am ) , the best alternative in FTOPSIS is such that should

(8)

)

xijk = aijk ,bijk , cijk

(9)

where i = 1, 2,…,m and j = 1, 2,…,n . The aggregate fuzzy weights wij of each cri-

terion given by K decision makers are calculated using Equation (10). w j =

1 ⊗ w 1j ⊕w j2 ⊕ … ⊕ w kj K

(

)

(10)

While to calculate the aggregate ratings of the alternatives Equation (11) is used (Ouma, Opudo, & Nyambenya, 2015).

(4)

(l ,m , u )  (l ,m , u ) ≈ (l

)

w kj = w kj 1,w kj 2 , w kj 3

Kaufmann and Gupta (1988) give the main algebraic operations of triangular fuzzy numbers  = (l,m,u ) and B = (l,m,u ) : A

(l ,m , u ) ⊕ (l ,m , u ) = (l

have the shortest distance to a fuzzy positive ideal solution (FPIS) and the farthest distance from a fuzzy negative ideal solution (FNIS). The FPIS is calculated using the best performance values for each criterion and the FNIS looks at the worst performance values. In FTOPSIS the decision makers use linguistic variables to obtain the weightings of the criteria and the ratings of the alternatives. If there is a decision group made up of K individuals, the fuzzy weight and rating of the kth decision maker with respect to the ith alternative in the jth criterion are respectivamente:

xij =

1 ⊗ xij1 ⊕xij2 ⊕ … ⊕ xijk K

(

)

(11)

A fuzzy multicriteria group decision-making problem which can be expressed in matrix format is shown in Equation (12) (Chen, 2000).  x   11 x12 … x1n   x   21 x22 … x21   . . . .   D=  . . .   .   . . .   .   xm 1 xm 2 … xmn   

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Model for Assessment of Environmental Responsibility in Health Care Organizations

(

)

W = w1 ,w2 , …,wn

(12)

where w j and xij are linguistic variables which can be described by triangular fuzzy numbers. The weightings of the criteria can be calculated by assigning directly the linguistic variables shown in Table 1. The ratings of the alternatives are found using the linguistic variables of Table 2 (Chen, 2000). The linear scale transformation is used to transform the various criteria scales into a comparable scale. And thus we obtain the normalized fuzzy decision matrix R (Rodrigues, Osiro, & Ribeiro, 2014).  = r   i = 1, 2,…,m; j = 1,2,…,n. . R  ij  mxn

(13)

where  l m u   rij =  ij+ , +ij , ij+  and u j+ = max i uij in the case  u u u   j j j  of benefit criteria type  l − l − l −   rij =  j , j , j  and l j− = max ilij in the case  u m l  ij ij   ij of cost criteria type Next, the weighted normalized decision matrix, V is calculated, by multiplying the weightings of the criteria w j , by the elements rij of the normalized fuzzy decision matrix.

Table 2. Linguistic variables for the ratings Linguistic Variables for the Ratings Very Poor (VP)

(0,0,1)

Poor (P)

(0,1,3)

Medium Poor (MP)

(1,3,5)

Fair (F)

(3,5,7)

Medium Good (MG)

(5,7,9)

Good (G)

(7,9,10)

Very Good (VG)

(9,10,10)

(Chen, 2000)

V = vij  mxn donde vij = xij ⊗w j  

Very Low (VL) Low (L)

Fuzzy Number (0, 0, 0.1) (0, 0.1, 0.3)

Medium Low (ML)

(0.1, 0.3, 0.5)

Medium (M)

(0.3, 0.5, 0.7)

Medium High (MH)

(0.5, 0.7, 0.9)

High (H)

(0.7, 0.9, 1.0)

Very High (VH)

(0.9, 1.0, 1.0)

(Chen, 2000)

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(14)

The distances di+ and di− of each weighted alternative from the FPIS and FNIS are calculated using Equations (15) and (16). n

(

)

(15)

(

)

(16)

d = ∑dν νij , νij+ + i

j =1

n

di− = ∑dν νij , νij− j =1

( )

where dν a, b is the distance measured between the fuzzy numbers a and b . This distance is calculated from Equation (17) (Rodrigues, Osiro, & Ribeiro, 2014).

( )

dν a, b =

Table 1. Linguistic variables for the weights Linguistic Variables for the Weights

Fuzzy Number

2 2 2 1 (la − lb ) + (ma − mb ) + (ua − ub )   3 

(17)

Finally, the closeness coefficient, CC i , is calculated for each alternative i using Equation (18). This parameter allows the degree of fuzzy satisfaction to be evaluated for each Health Care Organization. CC i =

di− di− + di+



(18)

Category: Environmental Science and Agriculture

Table 3. Fuzzy weights

SOLUTIONS AND RECOMMENDATIONS

Criteria/Subcriteria

The model was applied to a Health Care Organization whose mission is working to improve the health of the people it serves, with quality, safety and sustainability. It has 30 medical specialities, 386 beds and 1,744 staff, and provides medical cover to an area with 300,000 inhabitants. An expert in environmental matters was used to obtain a pairwise comparison matrix for the decision criteria. The decision maker was asked to evaluate the importance of the criteria or subcriteria that could be assessed by applying the fuzzy scale set out in Table 1. The resulting weightings are shown in Table 3. Next, the procedure is applied to evaluate each alternative (different years to be assessed in the Health Care Organization) via the linguistic variables from Table 2. This gives the fuzzy weighted normalized decision matrix shown in Table 4. The distances di+ and di− . of each weighted alternative from the FPIS and FNIS are calculated using Equations (15) and (16), giving the results shown in Table 5. The closeness coefficient of each alternative is set out in Table 6. From CC the ranking of the three alternatives is Hospital year 3, year 2 and year 1. The best result is obtained by the hospital in the third year assessed. This shows the process of continuous improvement undertaken by the Health Care Organization and allows a global follow-up to be carried out annually.

E

Weightings

C1

(0.900, 1.000, 1.000)

C2

(0.900, 1.000, 1.000)

C3

(0.300, 0.500, 0.700)

C4

(0.900, 1.000, 1.000)

C5

(0.700, 0.900, 1.000)

C6

(0.100, 0.300, 0.500)

C7

(0.300, 0.500, 0.700)

C8

(0.300, 0.500, 0.700)

C9

(0.100, 0.300, 0.500)

C10

(0.100, 0.300, 0.500)

C11

(0.300, 0.500, 0.700)

C12

(0.900, 1.000, 1.000)

C13

(0.300, 0.500, 0.700)

(Created by the author)

FUTURE RESEARCH DIRECTIONS AHP and fuzzy AHP are the most widely-used multicriteria techniques in the literature in general, and specifically in relation to the environment. However, this Chapter uses the fuzzy TOPSIS technique, due to the ease with which it can assess a large number of criteria and alternatives, but it would be appropriate to validate the environmental assessment models using other techniques successfully applied in a significant number of real cases, such as the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH).

Table 4. The fuzzy weighted normalized decision matrix C1

C2

C3

C4

C5

C6

C7

C8

C9

C10

C11

C12

C13

Hospital year 1

(0.630, 0.900, 1.000)

(0.630, 0.900, 1.000)

(0.210, 0.450, 0.700)

(0.300, 0.556, 0.778)

(0.350, 0.630, 0.900)

(0.050, 0.210, 0.450)

(0.090, 0.250, 0.490)

(0.129, 0.357, 0.700)

(0.043, 0.214, 0.500)

(0.090, 0.300, 0.500)

(0.270, 0.500, 0.700)

(0.630, 0.900, 1.000)

(0.150, 0.350, 0.630)

Hospital year 2

(0.810, 1.000, 1.000)

(0.810, 1.000, 1.000)

(0.270, 0.500, 0.700)

(0.300, 0.556, 0.778)

(0.490, 0.810, 1.000)

(0.070, 0.270, 0.500)

(0.270, 0.500, 0.700)

(0.129, 0.357, 0.700)

(0.043, 0.214, 0.500)

(0.090, 0.300, 0.500)

(0.270, 0.500, 0.700)

(0.810, 1.000, 1.000)

(0.210, 0.450, 0.700)

Hospital year 3

(0.810, 1.000, 1.000)

(0.810, 1.000, 1.000)

(0.270, 0.500, 0.700)

(0.500, 0.778, 1.000)

(0.350, 0.630, 0.900

(0.090, 0.300, 0.500)

(0.270, 0.500, 0.700)

(0.129, 0.357, 0.700)

(0.043, 0.214, 0.500)

(0.090, 0.300, 0.500)

(0.270, 0.500, 0.700)

(0.810, 1.000, 1.000)

(0.270, 0.500, 0.700)

(Created by the author)

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Table 6. Closeness coefficient of each year assessed

Table 5. The distance measurement d i+

d i−

Hospital year 1

7.035

6.984

Hospital year 1

0.498

Hospital year 2

6.223

7.700

Hospital year 2

0.553

Hospital year 3

6.104

7.813

Hospital year 3

0.561

Alternatives

(Created by the author)

Due to the constant evolution of information communication systems and the development of new technologies, with very dramatic effects on improvements in energy efficiency and consumption of natural resources, this model should be periodically reviewed to include new criteria or to up-date their definitions.

CONCLUSION Health Care Organizations have an essential responsibility to the environment, being significant consumers of natural resources, as well as producers of large quantities of waste, some of which can cause serious risk to people and the environment unless they are properly handled. The monitoring of a number of environmental matters is key to assessing continuous improvement in the actions undertaken. However, this monitoring requires objective tools which consider a series of criteria adapted to each organization. This Chapter therefore presents a multicriteria model for assessing environmental responsibility in Health Care Organizations. The intention is that the model, although based on mathematical tools, should be easy to apply, and should take into account the uncertainties and ambiguities which characterize the real-life decision process. The fuzzy TOPSIS technique was thus used, as it allows a large number of alternatives to be assessed in a simple manner, which means the results obtained can be compared over time.

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Alternatives

CC

(Created by the author)

To test the utility of the model, it was applied in a public Health Care Organization over three consecutive years, showing how the improvement actions undertaken increase the overall utility of the result.

ACKNOWLEDGMENT This research was supported by the Junta de Comunidades de Castilla-La Mancha and the European Regional Development Fund under Grant number PPII-2014-013-P.

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KEY TERMS AND DEFINITIONS Multi-Criteria Decision Analysis: It is a part of operations research that use multiple criteria in decision-making processes providing acceptable compromise solutions when criteria are in conflict. There is a relevant quantity of tools belonging to this category, for example Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH), ELimination Et Choix Traduisant la REalité (ELECTRE), Multi-Attribute Utility Theory (MAUT), The Preference Ranking Organization METHod for

Enrichment of Evaluations (PROMETHEE), etc. The MCDA techniques allow to construct objective models to improve understanding of underlying decision processes in the systemic processes. TOPSIS: The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multicriteria decision-making approach created by Hwang and Yoon in 1981. It is a compensatory aggregation method based on the concept that the best alternative should have the shortest geometric distance to a positive ideal solution (PIS) and the geometric farthest distance from a negative ideal solution (NIS).

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Potential Benefits and Current Limits in the Development of Demand Response Clementina Bruno University of Piemonte Orientale, Italy

INTRODUCTION Once upon a time, for many families, electricity was a somehow magic and mysterious stuff allowing houses lighting and appliances operation, whose secrets began just behind the switch or the socket. Other, more informed, users knew that it came from generation plants and “travelled” along a grid towards houses or firms. Nowadays, the role of end users has changed a lot. They have a broader knowledge of the electric system, and a certain awareness of being part of it, in some cases not simply as consumption units. Distributed generation (such as residential photovoltaic production) and demand response mechanisms have transformed (residential, industrial or commercial) users in an active part of the electricity supply chain, so that they are often defined as “prosumer” (Crispim et al., 2014). In particular, Demand Response (DR) is attracting increasing attention from regulators, policy makers and system operators due to its large potential in supporting and, in some cases, substituting generation in providing flexibility to the system. This corresponds, on the academic side, to an exponential growth of scientific production, with focus on the technical or on the socio-economic features of the issue. This chapter will provide a review of some recent contributions on this topic. Far from being exhaustive of the extremely wide related literature, the aim of this chapter is to provide a general presentation of the issue, briefly discussing the main benefits related to DR, as well as the most relevant regulatory, technological and

socio-economic challenges that can slow down or hinder its development. Therefore, this work will provide an analysis of the impact and issues related to DR from a socio-economic perspective. Moreover, it will also briefly consider the role of technology (especially information and communication technology) in supporting DR implementation and, more in general, the evolution towards “smart” systems. The rest of the chapter is organized as follows. The next section defines DR and illustrates the most relevant benefits of its development. Subsequently, challenges to DR development are discussed and some recommendations are provided. Future research directions and conclusions close the work.

BACKGROUND The literature provides a wide set of definitions of DR. Quite common across these definitions is the focus on end-users and on the modification in their electricity utilization patterns (see, for instance, the list provided in Eid et al., 2016). For example, in the FERC (Federal Energy Regulatory Commission) website1 DR is defined as Changes in electric usage by demand-side resources from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.

DOI: 10.4018/978-1-5225-2255-3.ch274 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Environmental Science and Agriculture

There are several typologies of DR mechanisms, which can be classified following different criteria (Vardakas et al., 2015). Here we propose the most common classification.

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Common forms of incentive-based mechanisms, instead, are:

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Time-based retail rates (Cappers et al., 2012), also called rate-based or price-based programs (Siano, 2014), or implicit DR (SEDC - Smart Energy Demand Coalition, 2015), provide incentive to end-users to modify their consumption as response to price variations. Price fluctuations are designed to reflect the dynamics of the wholesale market price or the grid tariff, and ultimately, of the cost of the electric service. Prices can be predetermined but different for given time periods or move dynamically depending on the system and market contingencies. Incentive-based retail programs (Cappers et. al, 2012), also defined as event-based programs (Siano, 2014), reliability-based (Shen et al., 2014) or explicit DR schemes (SEDC, 2015) reward consumers through a payment or a bill credit for a reduction in their consumption. Such mechanisms are activated by the entity managing DR services (users can contract directly with the utility or with an aggregator) in response to particular events affecting the electric system, e.g. network congestion2. Examples of price-based DR programs are:

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Time of Use tariffs, where prices are different but fixed for given time periods (e.g. times of the day or days of the week). Critical Peak Pricing, that applies particularly high prices for a limited period (few hours) in response to critical technical or economic/market events. Critical Peak Rebate, where consumers are recognized a bill rebate if they reduce their consumption below a pre-specified baseline during critical hours.

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Real Time Pricing, where prices vary dynamically (e.g. every hour) following the wholesale market price and/or the actual generation costs.

Direct Load Control, where the utility has the opportunity to manage directly some consumer’s equipment (e.g. air conditioning or heating). Interruptible/Curtailable programs, where (usually large) users accept that (a part of) their load can be disconnected, in some cases even without notification. Emergency DR programs, which provide end-users a compensation to reduce their loads when the system reliability is endangered. Ancillary Service Programs, where consumers provide “reserves” by committing to reduce their load in case of necessity.

For further examples or deeper descriptions of DR programs, see, among the others, Cappers et al. (2012), Darby and McKenna (2012), Shen et al. (2014), Siano (2014), Hu et al. (2015), Vardakas et al. (2015). Figure 1 reports some examples of DR programs. While some mechanisms are well suited even in “traditional” electric systems (e.g. Interruptible/Curtailable programs), other ones present important technological requirements, and can develop their full potential when implemented in smart grid contexts (e.g. Real Time Pricing). Following Siano (2014; p.462), a smart grid (SG) is “an electric grid able to deliver electricity in a controlled, smart way from points of generation to consumers that are considered as an integral part of the SG since they can modify their purchasing patterns and behavior according to the received information, incentives and disincentives”. This definition highlights some relevant peculiarities of SGs with respect to tra-

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Figure 1. Examples of DR programs

ditional grid structures. First, energy is delivered from generation “points”, rather than generation “plants” (thus the definition encompasses also distributed generation). Second, consumers are active subjects, because they provide services to the electric system, by modifying their behaviour. Third, such modifications are driven by (system or economic) information that consumers receive; however, we must notice that also the information flow to the utility is crucial, since it allows the application of the correct incentive or price schemes. This information exchange relies on the implementation of advanced technologies such as those embedded in smart meters, and is not possible with traditional equipment. Therefore, in SGs, both information and energy can flow from the utility to the users and vice versa, as represented in Figure 2. Finally, the delivery of power is “controlled”

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and “smart”, suggesting continuous adjustments of demand and supply to ensure high efficiency in the whole service provision. DR (especially incentive-based programs) has led to the emergence of a new subject in the electricity value chain, i.e. the aggregator. The aggregator is a market intermediary that “aggregates” the DR capacity from consumers that do not have sufficient dimension, information, knowledge, technology, reliability or availability to participate directly in electricity markets. Usually, the aggregator is an electricity supplier or an independent intermediary (SEDC, 2015; Eid et al., 2015). The potential positive effects of DR on the electric system and, more generally, on users and on the community as a whole are broadly recognised at the political level. For instance, in the European

Category: Environmental Science and Agriculture

Figure 2. Energy and information flows in smart grids

context, the Electricity Directive (2009) and the Energy Efficiency Directive (2012) recognise the opportunities related to DR development and encourage Member States to foster this form of flexibility and to remove potential barriers (e.g. regulatory barriers) to the effective participation of users in the electricity market. The advantages deriving from the implementation of DR programs are numerous. Firstly, DR provides system flexibility that can be effectively employed to contrast the intermittency and limited predictability of some Renewable Energy Sources (RES), such as wind and solar. This is especially the case of Real Time Pricing, within the price-based mechanisms, and of several incentive-based programs, such as Direct Load Control (Cappers et al., 2012). Secondly, DR improves the general system reliability, e.g. by reducing demand in case of outages, thus supporting the system recovery, as highlighted by Siano (2014)3.Thirdly, DR programs help flattening consumers’ demand, for instance by leading them to reduce consumption or to shift it to off-peak periods. In the short term, this reduces the global generation costs, by limiting the use of expensive peak generation technologies. Moreover, in the long term, this reduces or defers new investments in generation or grid capacity. These cost savings can turn into benefits for consumers. In fact, users participating to DR programs may enjoy lower bills or other compensations for their flexibility. Lower tariffs induced by lower wholesale price, however, will benefit all users, even non-participant ones. Fourthly, non-activation of peak units reduces the related emissions. Fifthly, the “distributed” nature of DR resources is also able to reduce the

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network losses associated to transmission and distribution. Finally, a more elastic demand limits the possibility of generators to exert market power. (Batlle and Rodilla, 2009; Darby and McKenna, 2012; Siano, 2014; Shen et al., 2014; Gils, 2014; Hu et al., 2015; Eid et al., 2016). Several scientific works support the desirability of DR programs, from different perspectives. For instance, Feuerriegel and Neumann (2016), relying on a mathematical model and German data, find that, when DR is employed for load shifting, it can generate savings in the order of 2.83%. Brouwer et al. (2016) employ a simulation tool to evaluate the economic impact of RES penetration for the year 2050. The results show that system costs would increase with RES penetration, due to investments and RES intermittency, but this effect could be counteracted by some options, including DR development. Dupont et al. (2014) run a simulation based on Belgian data and find that DR reduces system costs and emissions while improving reliability. Schleich et al. (2013) analyse econometrically the results of a trial in Austria and show that receiving feedback on consumption can induce households to reduce electricity use by 4.5% on average. Conversely, Torriti (2012) finds that Time-of-Use tariffs in Northern Italy generate lower bills, but higher average consumption. DR effectiveness for load shifting is supported, but limited to morning peaks. Finally, Bradley et al. (2013) rely on an extensive review of the literature and find support to the economic sustainability of DR in UK markets. Notwithstanding the broad evidence in favour of DR, its development is just in a starting phase and proceeds slowly, with limited consumers’

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involvement (Hu et al., 2015). This likely depends on the complexity of adopting and managing DR programs, related to a set of existing challenges hindering DR development. In the following section, some relevant challenges will be discussed.

REGULATORY, TECHNOLOGICAL, AND SOCIO-ECONOMIC CHALLENGES TO DR DEVELOPMENT Regulatory Challenges: DR Potential and Current Regulation in Europe In order to understand the potential limits to DR development, we provide an example referred to the European case. SEDC (2015) reports an evaluation of the regulatory framework supporting explicit DR in 20154 for 16 European Countries. Regulation is evaluated with respect to four main “areas”: “consumer access and aggregation”, “program requirements”, “measurement and verification”, “finance and penalties”. Each country receives a score for each area and an overall score resulting from their sum. Higher values correspond to environments more favourable to DR development. Results show that some states have reached an advanced level in the regulation aimed at promoting DR and consumers empowerment, whereas other ones still present relevant inconsistencies between this target and their actual regulatory framework. These differences across countries are highlighted by the diversified grades reported in the last column of table 1, which range from 16 for France and Switzerland (a star indicates existing standardized arrangements involving aggregators) to 3 or 2 for Italy and Spain, respectively. It is interesting to compare this evaluation with the results reported in Gils (2014). The author provides an assessment of the theoretical DR potential in Europe and some North-African Countries (“theoretical” indicates that the potential includes all equipment available for DR purposes, independently from possible constraints

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due to technological, economic, legal issues, etc.). Results are detailed by consumer type and by country, and demand flexibility potential is evaluated either in terms of load reduction (due to load shedding or shifting by delaying consumption) and load increase (related to load shifting by anticipating consumption). Focussing only on the 16 Countries analysed in SEDC (2015), we provide, in the second and third columns of table 1, rankings constructed on the basis of the potential load increase or reduction (originally expressed in MW by Gils, 2014). We can see that these two rankings are quite similar, and their comparison with the last column allows some reasoning. France and UK, that present very high DR potentials and favourable regulatory frameworks, seem to be isolated cases. Several other countries with noticeable potential (Germany, Italy, Spain) present very low SEDC grades. Conversely, countries like Switzerland or Ireland, whose regulatory frameworks are supportive to DR, can exploit only limited potential. Keeping in mind the limitations related to the fact that SECD (2015) focuses only on incentive-based DR, nevertheless we can conclude that there is an inconsistency between the regulatory evolution and the amount of DR resources that could be exploited. In fact, several states are neglecting the opportunity offered by DR, or, at least, are accumulating an important delay in fostering its development. This happens especially for countries where DR benefits could be large. In SEDC (2015), the main regulatory limits in promoting DR seem to be related to the area “Consumers participation and aggregation”, for several reasons. For instance, in a number of states, DR resources continue to be excluded from some markets, and generation is still the favourite source of flexibility. In some cases, markets are open to DR, but not to aggregated load, thus limiting the participation of medium or small users. Moreover, the possibility of end-users of freely choosing their DR services provider is often limited. Other limitations appear also with respect to other “areas”. For example, excessively high

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Table 1. DR potential and actual regulation in Europe (our elaboration from SEDC, 2015, and Gils, 2014) Ranking Based on Potential Load Reduction (Gils, 2014)

Ranking Based on Potential Load Increase (Gils, 2014)

SEDC (2015) Grade

Austria

13

12

10

Belgium

10

11

12

Denmark

14

14

8

Finland

9

10

12

France

2

1

*16

Germany

1

2

6

Ireland

15

15

12

Italy

3

4

3

Country

Netherlands

8

9

10

Norway

11

8

10

Poland

6

6

4

Slovenia

16

16

6

Spain

4

5

2

Sweden

7

7

10

Switzerland

12

13

*16

UK

5

3

12

capacity thresholds to access the market, or other unnecessarily binding requirements (probably a result of older market rules designed for large generators only) in terms of DR events duration or frequency can limit consumers’ participation. The fairness and the transparency of the methodologies for measuring the actual consumption reduction constitute another issue, together with the procedures to determine the standard consumption “baseline” (i.e. the starting point for evaluating reductions). Moreover, in some cases, participation to DR programs is subject to the provision of relevant bank guarantees, which can constitute a further barrier. Some of these points are highlighted also in ENTSO-E (European Network of Transmission System Operators for Electricity, 2015), that also underlines, in a discussion including price- and incentive-based programs, the “lack of effective-

ness of price signals” (p. 5), as well as the limited price variability in some retail markets, especially for medium-small users. This limits both the ability of prices to reflect actual energy costs and the related consumers’ responsiveness. Moreover, to ensure small consumers’ participation to DR programs, it is necessary that “price signals remains understandable and manageable” (p. 6).

Technological Challenges: Technology Requirements for Effectively Exploiting DR Potential In order to be effective in providing DR resources, consumers need to interact actively with suppliers or DR services providers. Moreover, they need to be able to react promptly to price or event-driven signals requiring DR activation. This is often not feasible through manual intervention, especially when signals are frequent or provided with very short notice (Cappers et al., 2012). To deal with this issue, an extended deployment of smart technologies appear necessary, as well as adequate communication and standardization. Below, these concepts are briefly introduced (see Siano, 2014; Shen et al., 2014; ENTSO-E, 2015; Hu et al., 2015; Vardakas et al., 2015). Among smart technologies, a key role is played by Advance Metering Infrastructures (AMI), systems able to measure electricity usage, to save and analyse the related data, to receive information from devices and to exchange information with utilities. Important components of AMI are smart meters, electronic devices able to measure users’ consumption at fixed time intervals (e.g. one hour) and communicate data to suppliers. In addition, automation technology allows consumers to program “smart” appliances (such as specifically designed washing machines or refrigerators) or control devices (e.g. “smart thermostats”) to automatically respond to event or price signals. For instance, “smart thermostats” can adjust the temperature of rooms in response to electricity price variations. Home Area Networks (HAN) can be employed for connecting

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all these home devices among each other and with the utilities. Finally, it is relevant to underline the key role of communication. For an effective use of the communication infrastructure, integration among networks should be promoted, and this would require the adoption of open communication standards. Concerning communication systems (Siano, 2014), they can be one-way (information and DR signals flow from the utility to the endusers), or more expensive but more effective twoway systems, that, additionally, allow utilities to receive feedback in relation to consumers response (e.g. smart meters). They can be wireless (e.g. relying on cellular networks) or wired. The latter can rely, for instance, on power line communications (PLC, either broadband or narrow band), suitable at local level and not requiring new infrastructure building, or on optic fiber communications, for longer distances. The key role of communication highlights the importance of security (Vardakas et al., 2015), either in terms of protection of consumers’ sensitive data and privacy, or in terms of preservation of system and market information from external inference or attacks that can create damages or inconvenience.

Economic and Social Challenges From the purely economic perspective, two major issues, somehow interrelated, concern the provision of sufficient economic incentives to participate to DR programs (ENTSO-E, 2015) and the distribution of investment costs related to enabling technologies. With respect to the latter point, Eid et al. (2016) underlines the problem of correctly splitting DR cost and benefits along the whole electricity supply chain (e.g. among network operators, retailer and consumers), in such a way to create positive business cases for all the involved parties. Failing in providing proper mechanisms to reach this goal generates the risk that none of the parties would chose to make the “first move” (p. 22). The authors report an example related to investments in smart meters. Moreover,

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they state that, as the (environmental) benefits of such programs would affect the whole society, this should be the case also for costs. Also Shen et al. (2014), with a similar line of reasoning, suggest that incentives to DR participation and investments could effectively receive public support, in consideration of their positive impact on the society. Hu et al. (2015) states that utilities do not have sufficient incentives to support DR in absence of subsidies. ENTSO-E (2015), however, stresses that general subsidization of DR programs must be minimal and avoid market distortions, since DR “must achieve its full economic potential in fair competition with other sources” (p.8). Turning, now, to the former point, as highlighted by Cappers et al. (2012), “the opportunities created by the DR service providers must generate sufficient value to customers or else they will eschew these offerings” (p.426). In this perspective, with specific regards to residential users, Darby and McKenna (2012) underlines the key role of rates. Cappers et al. (2012) stress also that a sufficient remuneration is necessary for DR service providers as well, which will be in charge of marketing and managing customers’ involvement in DR programs. These points highlight the crucial role played by the design of DR mechanisms: advanced optimization methods provide relevant support in this sense. Vardakas et al. (2015) report a broad survey and discussion of optimization models for DR, highlighting the plurality of available options. For instance, the objective function(s) of the optimization problem may target the minimization of electricity costs or of total consumption, or the maximization of social welfare, or combine two of these objectives. Moreover, based on the form of the objective functions or of the constraints, the optimization problem can be linear or non-linear, while the variable characteristics can lead to integer or mixed-integer optimization problems; each type of problem requires an appropriate technique to be solved. Different sources of complexity can arise in dealing with such models, e.g. problems with no feasible solutions or excessive computational

Category: Environmental Science and Agriculture

times. Additionally, game theory provides valuable theoretical support to model the interactions among electric system actors (users or utilities). For a deep discussion and examples of optimization algorithms in the context of DR and SGs, see Vardakas et al. (2015) and the references therein. Moreover, there are some social and cognitive issues potentially affecting users’ willingness to be involved in DR, and this holds especially for residential users. In fact, apart from the gratification that can derive from the idea of having contributed to improve the system reliability (Siano, 2014), users could be affected by some kinds of inconvenience. First, they will need to change some consumption habits: while some activities can be more easily shifted to a different moment in time, other activities, such as cooking (and eating) are less likely to be suitable for variations (Darby and McKenna, 2012). In addition, some consumers could have a limited willingness to accept high levels of automation and external control of their activities by a “Big Brother” (Cappers et al., 2012, p.425). Darby and McKenna (2012) suggest that home automation could effectively involve heating and cooling, as well as electric vehicles, while consumption related to other activities can be managed manually. Moreover, the same contribution highlights that consumers used to flat rates are not likely to switch easily to variable tariffs, and that a gradual passage to Time of Use pricing and subsequently to the more effective, but more complex, Real Time Pricing could represent a feasible path. Many of the mentioned authors, however, agree on the key role of consumers’ education and information in order to overcome most of the social issues described above, as long as the DR services providers will be able to achieve a high level of trust from customers and to provide an adequate protection of their rights and privacy.

SOLUTION AND RECOMMENDATIONS

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Throughout this chapter, we have drawn a picture of DR as a promising resource developing in the electric system that, however, still presents many unknown features, which make difficult evaluating its potential, thus slowing down or limiting its development. As in any other case where something “new” is developing on a broad (in this case global) scale, also with respect to DR it is important that all the involved actors (Governments, regulators, utilities, users, etc.) can make their choices on an informed basis. Therefore, as highlighted in Hu et al. (2015), it is crucial that further studies are promoted and developed. In my opinion, such studies could focus on several fields and adopt different methodologies (examples can range from game-theoretical studies aimed at identifying the best market design to field trials evaluating the practical consequences of specific projects). A multi-disciplinary approach involving different entities, professional figures and expertise (e.g. regulators and utilities, practitioners and academics) seems the most promising strategy, given the multiplicity of perspectives that need to be considered to effectively deal with such a broad issue.

FUTURE RESEARCH DIRECTIONS Future research focused on DR can take manifold directions. With reference to the economic and social sciences fields, some (not exhaustive) examples could be the following. •

Direct evaluations of consumers’ preferences and willingness to accept contracts

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•

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including different kinds of DR programs could be performed by adopting surveys approaches relying on contingent valuation or conjoint analysis. These approaches can be implemented describing hypothetical scenarios to generic consumers, or directed to subjects involved in actual trials or pilot studies. These methods can be designed also to infer the correct level of monetary incentive to be provided. DR affects system operations. Efficiency analysis techniques (parametric and nonparametric) can be effectively employed to evaluate the impact of DR on the performance of firms operating in different phases of the supply chain (generation or network management). A more general approach based on costbenefit analysis, adopting a macro-economic perspective, appears crucial for evaluating specific programs before their implementation. Such an approach should be able to incorporate also non-monetary elements, such as environmental benefits related to reduced emissions.

CONCLUSION Interest in DR as a source of flexibility is increasing from several direction (policy makers, regulators, utilities, academics and users). The potential benefits related to DR development are numerous and extremely interesting. Nevertheless, the implementation of such programs is still in a starting phase and is advancing in a relatively slow way. This is due to the complexity of the management of this new resource. This chapter has analysed the main regulatory, technological and socio-economic challenges to DR development. As a conclusion, the level of knowledge of the issue needs to be expanded in order to support informed decision-making, and multi-

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disciplinary approaches can constitute, as this article has discussed, an effective strategy given the multiplicity of perspectives to be considered in dealing with DR issues.

ACKNOWLEDGMENT This research is supported by the Associazione per lo sviluppo dell’Università nel territorio novarese.

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ADDITIONAL READING Aalami, H. A., Moghaddam, M. P., & Yousefi, G. R. (2010). Demand response modelling considering interruptible/curtailable loads and capacity market programs. Applied Energy, 87(1), 243–250. doi:10.1016/j.apenergy.2009.05.041 Agrell, P. J., Bogetoft, P., & Mikkers, M. (2013). Smart-grid investments, regulation and organization. Energy Policy, 52, 656–666. doi:10.1016/j. enpol.2012.10.026 Albadi, M. H., & El-Saadany, E. F. (2008). A summary of demand response in electricity markets. Electric Power Systems Research, 78(11), 1898–1996. doi:10.1016/j.epsr.2008.04.002 Brouwer, A. S., Van den Broek, M., Seebregts, A., & Faaij, A. (2015). Operational flexibility and economics of power plants in future low-carbon power systems. Applied Energy, 156, 107–128. doi:10.1016/j.apenergy.2015.06.065 Earle, R., Kahn, E. P., & Macan, E. (2009). Measuring the capacity impacts of demand response. The Electricity Journal, 22(6), 47–58. doi:10.1016/j. tej.2009.05.014 Feuerriegel, S., & Neumann, D. (2014). Measuring the financial impact of demand response for electricity retailers. Energy Policy, 65, 359–368. doi:10.1016/j.enpol.2013.10.012 Katz, J. (2014). Linking meters and markets: Roles and incentives to support a flexible demand side. Utilities Policy, 31, 74–84. doi:10.1016/j. jup.2014.08.003

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Kondziella, H., & Bruckner, T. (2016). Flexibility requirements of renewable energy based electricity systems – a review of research results and methodologies. Renewable & Sustainable Energy Reviews, 53, 10–22. doi:10.1016/j.rser.2015.07.199 Lang, C., & Okwelum, E. (2015). The mitigating effect of strategic behavior on the net benefits of a direct load control program. Energy Economics, 49, 141–148. doi:10.1016/j.eneco.2015.01.025 Rochlin, C. (2009). The alchemy of demand response: Turning demand into supply. The Electricity Journal, 22(9), 10–25. doi:10.1016/j. tej.2009.09.004 Rodrigues, R., & Linares, P. (2014). Electricity load level detail in computational general equilibrium – Part I – Data and calibration. Energy Economics, 46, 258–266. doi:10.1016/j. eneco.2014.09.016 Rodrigues, R., & Linares, P. (2015). Electricity load level detail in computational general equilibrium – Part II – welfare impacts of a demand response program. Energy Economics, 47, 52–67. doi:10.1016/j.eneco.2014.10.015 Ruester, S., Schwenen, S., Batlle, C., & PérezArriaga, I. (2014). From distribution networks to smart distribution systems: Rethinking the regulation of European electricity DSOs. Utilities Policy, 31, 229–237. doi:10.1016/j.jup.2014.03.007 Wang, Q., Zhang, C., Ding, Y., Xydis, G., Wang, J., & Østergaard, J. (2015). Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response. Applied Energy, 138, 695–706. doi:10.1016/j. apenergy.2014.10.048

Category: Environmental Science and Agriculture

KEY TERMS AND DEFINITIONS

ENDNOTES

Demand Response (DR): Source of flexibility for the electricity system relying on modifications in users’ consumption. Enabling Technologies: With respect to DR, this term indicates those technologies (e.g. equipment, appliances) that allow an effective implementation of DR programs. Examples could be smart meters, allowing communication between users and utilities, or home appliances that automatically react to price or non-price signals by modifying electricity consumption. Incentive-Based DR: DR mechanisms that rely on consumption modifications induced by non-price signals, for which the involved users receive compensations. Off-Peak: Points of lower demand (with respect to the peak-load) in a certain time period. Peak-Load: Within the electric system, it is the point of highest demand in a certain time period. We can have daily peaks, seasonal peaks, etc. Peak-Shifting: Refers to the possibility of moving demand from peak to off-peak times. This is one of the desirable effects of DR. Price-Based DR: DR mechanisms that rely on consumption modifications induced by price variations.

1



2



3



4

http://www.ferc.gov/industries/electric/ indus-act/demand-response/dem-res-advmetering.asp Siano (2014) identifies as a separated category demand reduction bids, where participant send their bids (offer a demand reductions and request a price) to the utility or the aggregator. The authors refers to DR benefits in a SG context, but most of the proposed lines of reasoning are likely to apply in a more general framework. As mentioned in the report, explicit DR has limited requirements in terms of public investment in technology, therefore regulation plays the more critical role in making DR programs available and suitable for end-users.

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Waste Gas End-of-Pipe Treatment Techniques in Italian IPPC Chemical Plants Gaetano Battistella ISPRA, Italy Giuseppe Di Marco ISPRA, Italy Carlo Carlucci ISPRA, Italy Raffaella Manuzzi ISPRA, Italy Federica Bonaiuti ISPRA, Italy Celine Ndong ISPRA, Italy

INTRODUCTION Due to more stringent emission regulations, very efficient new advanced emission control technologies are required adopting National IPPC (Integrated Pollution Prevent and Control) Permits (below AIA) based on Best Available Technologies (below BAT) Conclusions. Some of these techniques are operating inside Chemical Plants and Refineries based in Italy, such as Oxidation, Adsorption and Absorption devices. Other techniques (i.e. the ones that are new advanced technologies still in research or in demonstration state), are not subject of this Paper, based on describing running situation inside operating IPPC Chemical Plants and Refineries licensed in Italy at National Level. This paper includes, but are not limited to, the results of a screening of Italian Chemical IPPC Industries and Refineries up to day, trying

to highlight operating conditions and possible already existing improvements for removal of: • • • • •

VOC and other cancer causing and toxic substances; Dust, Mercury and heavy metals; NOx and Nitrogen compounds; SOx and Sulphur compounds; Chlorides and Fluoride compounds.

The abatement techniques analyzed in this work operate mainly on VOC content reduction, through the use of Oxidizing devices or on inorganic compounds abatement (in addition to VOC), through the use of Absorption or Adsorption devices. Superior Environmental Protection and Research Institute (below ISPRA) experience, mainly developed as Technical Support to Italian Minister of Environment, Land and Sea (below IMELS),

DOI: 10.4018/978-1-5225-2255-3.ch275 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Environmental Science and Agriculture

has allowed to analyze different operative conditions, related with abatement techniques and their application in IPPC permitted plants. Many pollutants emitted from IPPC plants (according to Environmental Permits limit values) have been identified and charted a profile of possible application for abatement techniques in these plants in their different IPPC categories. The results of this analysis allow to suggest a possible reconsideration and, also, new assessment for some end-of-pipe devices, in order to find other better defined operational contexts, different from actually Italian provisions and, also, an evaluation of current operational performances of the devices, in order to improve their environmental conditions, consistently with BAT application.

BACKGROUND In Italy, IPPC Permit is an authorization released for environmental protection purposes, in order to prevent and control pollution ‘at the source’ by means of an integrated authorization, allowing operation of IPPC industrial activities with specified production’s characteristics and dimensions, at both national and regional levels (Battistella, 2013). The list of the categories of these specific industrial activities is regulated by the Italian Legislative Decree n. 59/2005 and s.m.i. (Italian Legislative Decree n. 152/2006 and s.m.i.) that adopts and endorses the Directive n. 96/61/EC and s.m.i. (Directive 2008/1/EC and s.m.i.) concerning integrated pollution prevention and control (actually recast in the Directive 2010/75/EU). IPPC permits – by law definition - plan and perform an integrated prevention and control set in the exact point of pollution (‘a la source’), e.g. pollutants are identified, declared, controlled, detected and monitored in the admission/emission points of the IPPC industrial activities, as well as during all activities of industrial plants’ operation (Battistella & Di Marco, 2013a; Battistella & Di Marco, 2013b).

This means authorization of plants’ operation controlling natural resources’ usage, as well as emissions and discharges in the environment inside predefined limit values with prescriptions, adoption of predefined monitoring framework, as self-controls on selected parameters, frequencies and methodologies, with a periodic reporting and planned inspections based on the effective environmental risk (Battistella & Di Marco, 2013a; Battistella & Di Marco, 2013b). In Italy, AIAs are released by the Competent Authority, as • •

By IMELS for national strategic plants; By other Authority designed by Region or autonomous Province for others.

In order to accomplish IPPC permits operative performances in terms of assigned limit values, among other provisions, also waste gas end-ofpipe treatment devices are adopted - as well as installed and operated - in order to abate or at least decrease pollutants’ contents (even often dangerous substances and compounds) in waste-gases before their emission into open air. Adopted techniques must be considered equal to, or committed to become under IPPC permits period of duration (more or less 10 years), as Best Available Techniques and their operating performances are described in details into Reference Documents on Best Available Techniques (BRefs1).

BAT CONCLUSIONS APPLICATION IN ITALIAN IPPC PERMITTED PLANTS The Italian National Environmental Regulatory Framework for IPPC Installations As regards to Italian Regulations, Attachment X to Part II of Legislative Decree n. 152/06 [9] defines the list of main pollutants to be monitored,

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if pertinent, with reference to air emissions for IPPC Plants under operational licensee. Attachments to Part V of Legislative Decree n. 152/06 list minimum emissions values (relevance thresholds) and maximum emissions values (limit values) and related prescriptions (continuous monitoring, process parameters to monitor, measurements’ norms, continuous monitoring control procedures, conformity criteria for measurements, etc.) for pollutant substances emitted in atmosphere - as conveyed emissions - at the stacks and specific indications for some substances (as SOx, NOx, CO, Dusts, VOC, etc.) and for some types of plants.

It is relevant to highlight that Legislative Decree n. 152/06 does not establish specific limit values for emissions of pollutants from end of pipe treatment devices, as well as does not even describe specific abatement techniques. This is why applied emission limit values are strictly connected with specific pollutants and plant units from where they are generated, as mentioned in Suppl. Annex I at V Part of Italian Legislative Decree n.152/06. Table 1 shows main pollutants related with operative experience in Italian IPPC plants. In III Part of mentioned Annex I, emission levels for some specific type of production such

Table 1. Main pollutants related with operative experience in Italian IPPC plants Pollutant

Class Identity (Annex I, V Part of Italian Legislative Decree n.152/06)

Relevance Threshold (g/h)

Limit Value (mg/Nm3)

Point 1.1. Cancer-causing, reproduction toxic or mutagen compounds (Table A1) Benzene

class III

25

5

PHA (summation)

class I

0,5

0,1

class III

25

5

Organic chlorated compounds

Point 1.2. Compounds that can be especially toxic and be accumulated (Table A2) Dioxins and Furans

class I

0,02

0,01

PCB

class II

0,5

0,5

Point 2. Inorganic compounds that mainly come up in dust form (Table B) Hg

class I

1

0,2

Point 3. Inorganic compounds that mainly come up in vapour or gas form (Table C) SOx

class V

5000

500

NOx

class V

5000

500

Chlorine

class II

50

5

Chlorine inorganic compounds (as HCl)

class III

300

30

Fluorine and its compound (as HF)

class II

50

5

H2S

class II

50

5

NH3

class IV

2.000

250

Point 4. Organic Compounds that come up in dust, vapour or gas form (Table D) Organic compounds (included Chlor Aromatics, Halogenic hydrocarbons and Nitro aromatics compounds)

It depends on the specific compound

5. Total dust Total Dust

3158

-

> 5000

50

>1000 98 Odour 80-95

VOC 50-99 Inorganic compounds 90-99 SO2 80-99

VOC 80-95 Odour 80-95 H2S 80-95

Achievable emission levels [mg/Nm3]

TOC 1-4

-

HF 0.001

Battery

0.005

0.719

meant that the screen would be most likely off. This was probably a result of a lack of information about user incentive for activating the screen, such as application notifications or user location.

FUTURE RESEARCH DIRECTIONS tures (“External Energy Supply”, “Screen on/off” and “WiFi”) was performed, as Figure 6 reports. The best results were obtained when predicting “External Energy Supply”. Although the TP and TN decrease as more states into the future are predicted, they are never below 80% and 95%, respectively. The ANN also achieved good performance when predicting the “WiFi” feature with a TP rate of 70% and a TN rate of 80%. However, predicting the “Screen on/off” feature resulted in low TP and TN rates. Although the performance when predicting this feature was relatively good when considering a 1-state-in-the-future prediction, it rapidly decreased as more predictions into the future were performed. For example, predicting that the screen would be on in the second future state provided no real information about whether the screen would be on or off, as the TP rate was about 50%. Furthermore, after three states into the future, predicting that the screen would be on Figure 6. True-Positive/True-negative analysis

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Since this paper defines mobile device states using only a small subset of all the possible features, future studies will consider additional features, such as location, Bluetooth connection, incoming/ outgoing calls, cellular connection, or 3G signal strength. The main limitation of the current approach is the high number of parameters. Future research should evaluate other ANN configurations with fewer parameters. Future works will further validate the proposed approach using a larger dataset, such as the Cambridge Device Analyser dataset (Wagner et al., 2014). Additionally, the possibility of using just one ANN for modelling a number of users could be studied. This could be useful in domains in which group behaviour is more important than individual behaviour. Another line of work is comparing ANN based state prediction with other machine learnings techniques. Finally, more complex

Category: Neural Networks

ANN models should be evaluated in the future. Such models would not only include other type of ANN layers, but also expand the definition of the mobile device state by adding other features, such as location, CPU usage, and day of the week.

Liao, Z.-X., Lei, P.-R., Shen, T.-J., Li, S.-C., & Peng, W.-C. (2012). Mining temporal profiles of mobile applications for usage prediction. Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on, 890–893. doi:10.1109/ ICDMW.2012.11

CONCLUSION

Musolesi, M., Piraccini, M., Fodor, K., Corradi, A., & Campbell, A. T. (2010). Pervasive Computing: 8th International Conference, Pervasive 2010, Helsinki, Finland, May 17-20, 2010. Proceedings. Springer Berlin Heidelberg.

This study aimed at evaluating the suitability of ANN for predicting mobile device future states. The obtained evidence suggests that RNN have potential for predicting future states of mobile devices. However, the results were mixed, as the ANN was able to effectively predict with low error rates the state of features, such as “Battery” or “External Energy Supply”, while it was ineffective for predicting other features, such as “Screen on/ off” or “Time between events”.

REFERENCES Alsharif, O., Ouyang, T., Beaufays, F., Zhai, S., Breuel, T., & Schalkwyk, J. (2015). Long short-term memory neural network for keyboard gesture decoding. Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, 2076–2080. doi:10.1109/ ICASSP.2015.7178336 Do, T. M. T., & Gatica-Perez, D. (2014). Where and what: Using smartphones to predict next locations and applications in daily life. Pervasive and Mobile Computing, 12, 79–91. doi:10.1016/j. pmcj.2013.03.006 Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. doi:10.1162/neco.1997.9.8.1735 PMID:9377276

Niroshinie, F., Seng, W. L., & Wenny, R. (2013). Mobile cloud computing: A survey. Future Generation Computer Systems, 29(1), 84–106. doi:10.1016/j.future.2012.05.023 Pejovic, V. and Musolesi, M. (2015). Anticipatory mobile computing: A survey of the state of the art and research challenges. ACM Comput. Surv., 47(3), 47:1–47:29. Ravi, N., Scott, J., Han, L., & Iftode, L. (2008). Context-aware battery management for mobile phones. In Pervasive Computing and Communications, 2008. PerCom 2008. Sixth Annual IEEE International Conference on, (pp. 224–233). IEEE. doi:10.1109/PERCOM.2008.108 Rios, C., Rodriguez, J. M., Godoy, D., Schiaffino, S., & Zunino, A. (2014). Usage pattern mining for smartphone use personalization. In Proceedings of the 13th Brazilian Symposium on Human Factors in Computing Systems, (pp. 377–380). Porto Alegre, Brazil: Sociedade Brasileira de Computação. Shin, C., Hong, J.-H., & Dey, A. K. (2012). Understanding and prediction of mobile application usage for smart phones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, (pp. 173–182). New York, NY: ACM. doi:10.1145/2370216.2370243

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Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Proc. NIPS. Wagner, D. T., Rice, A., & Beresford, A. R. (2014). Device analyzer: Large scale mobile data collection. SIGMETRICS Perform. Eval. Rev., 41(4), 53–56. doi:10.1145/2627534.2627553 Wen, Y., Wolski, R., & Krintz, C. (2003). Online prediction of battery lifetime for embedded and mobile devices. Lecture Notes in Computer Science, 3164, 57–72. Williams, R. J., & Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1(2), 270–280. doi:10.1162/neco.1989.1.2.270 Zeiler, M. D. (2012). ADADELTA: An adaptive learning rate method. CoRR, abs/1212.5701

ADDITIONAL READING Burbey, I., & Martin, T. L. (2012). A survey on predicting personal mobility. International Journal of Pervasive Computing and Communications, 8(1), 5–22. doi:10.1108/17427371211221063 Campbell, A., & Choudhury, T. (2012). From smart to cognitive phones. IEEE Pervasive Computing / IEEE Computer Society [and] IEEE Communications Society, 11(3), 7–11. doi:10.1109/ MPRV.2012.41 LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. doi:10.1038/nature14539 PMID:26017442

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Rosa, F. D., Malizia, A., & Mecella, M. (2005). Disconnection prediction in mobile ad hoc networks for supporting cooperative work. IEEE Pervasive Computing / IEEE Computer Society [and] IEEE Communications Society, 4(3), 62–70. doi:10.1109/MPRV.2005.55 Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. doi:10.1016/j.neunet.2014.09.003 PMID:25462637 Zambonelli, F. (2015). Engineering self-organizing urban superorganisms. Engineering Applications of Artificial Intelligence, 41, 325–332. doi:10.1016/j.engappai.2014.10.004

KEY TERMS AND DEFINITIONS Artificial Neural Network: A kind of machine learning algorithms loosely based on how biological neural networks work. Epoch: A single training pass through the entire training set. Error Function: A function used for assessing how well a machine learning method performs. Gradient Descent: Technique for fitting parameters of a function. Mobile Device: A computational device that people carries, such as smartphones or tables. Mobile Device State: A set of variables that describe the mobile device’s current conditions. Recurrent Neural Network: ANN that uses previous states for making new predictions.

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Optical Engineering

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Category: Optical Engineering

Visible Light Communication Numerous Applications Ala’ Fathi Khalifeh German Jordan University, Jordan Hasan Farahneh Ryerson University, Canada Christopher Mekhiel Ryerson University, Canada Xavier Fernando Ryerson University, Canada

INTRODUCTION The initial theory of Visible Light Communication (VLC) was founded in the 1880s when Alexander Graham Bell invented the photo-phone which was used to transmit a voice signal using the modulated sunlight. Since the time of Graham Bell, optical communication research has attracted the interest of scholars around the world and has evolved into a new IEEE standard namely the P802.15.7 - Standard for Short-Range Wireless Optical Communication (standard, 802.15.7 (2015)). In 2003 at the Nakagawa Laboratory in Keio University, Japan, they have proposed using the Light Emitting Diodes (LEDs) for data transmission. A major factor that contributes to the evolution of VLC technology is the existing infrastructure. Hence, previously installed facilities, such as LED traffic lights or LED sign boards are readily used. Since the transmitters for VLC are light sources, they function for lighting purposes and illuminate the surrounding environment, hence the radiation power and signal-to-noise ratio (SNR) is high; paving the way for a stable communication link (T. Yamazato, I. H. (2014)). With respect to the emergence of green communication, VLC is highly energy efficient as it utilizes LEDs. The United States Department of

Energy further corroborated the importance of LED technology, as shown in Table 1. There is superiority in terms of power consumption and operating lifetime in LED technology as compared to traditional lighting technology, such as incandescent and fluorescent lighting. This clearly shows the potential of the LED lighting technology to replace all the conventional illumination tools as well as serve as a reliable transmitter for a VLC link (Chung, Y.-Y. T.-Y. (2014)). Radio Frequency (RF) wireless connectivity has been used for several decades as it allows for indoor and short distance links to be established without any physical connection. However, these solutions remain relatively expensive and have low to medium data rates. RF wireless links require that spectrum licensing fees are paid to federal regulatory bodies and are required to be contained within strict spectral zones. These frequency allocations are determined by local authorities and may vary from country to country, making a standard interface difficult. Since the visible light spectrum is not in the licensed band (400 to 790 THz), licensing fees can be avoided which effectively reduces system cost. In addition, the broadcast nature of the RF link is beneficial for mobile connectivity but this may result in interference between devices located within close prox-

DOI: 10.4018/978-1-5225-2255-3.ch578 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Optical Engineering

Table 1. Performance of the conventional and LED lighting technology Lamp Type

Watts

Lumens

Operating Lifetime

Incandescent

60

900

1000

Compact florescent lamp

15

900

8500

LED (2011)

12.5

800

25000

LED – future(2015)

5.8

800

40000

imity. Due to the RF wavelength, it is difficult to contain within boundaries and can impede system performance (Hranilovic, S. (2005)). Optical radiation in the infrared or visible range is easily contained by opaque boundaries. As a result, interference between adjacent devices can be minimized easily and economically. Additionally, inexpensive LEDs and photodiodes are able to interchangeably work between baseband and transmission frequencies where as high-frequency RF circuit design techniques are required in the RF domain. Free-space optical (FSO) links with an inherent low probability of intercept and antijamming characteristics is among the most secure of all wide-area connectivity solutions (Hranilovic, S. (2005)). Unlike many RF systems that radiate signals in all directions, thus making the signal available to all within the receiving range, FSO transceivers use a highly directional and cone-shaped beam with a dominant line-of-sight (LOS) propagation path. Therefore, interception is extraordinarily difficult and anyone tapping into the systems can easily be detected as the intercept equipment must be placed within the very narrow optical foot print (Ghassemlooy. Z., P. W. (2012)). Although this contributes to the security of wireless optical links and reduces interference it also greatly impacts the high mobility of such devices. The aim of this book chapter is to introduce the concept of VLC as an emerging technology from a system and hardware design point of view, and shed the light on the rich features and potential of this technology that make it a viable substitution for other wireless technologies. The importance of

O

this technology will be demonstrated by covering various applications and scenarios.

BACKGROUND VLC is the process of transmitting digital data by using the visible light spectrum. This can be achieved by modulating the data using a light source such as LEDs that can be switched fast enough to avoid observable flickering or light dimming, another possible way is to change the light intensity in a way that is not observable to the human eye but can be detected using an appropriate sensor such as a photo diode (PD). Along with its prime function of lighting, LEDs can also serve to transmit data as long as there are suitable receivers. Additionally, image sensor pixels can be used as an effective VLC receiver. The ability to spatially separate multiple sources of image sensors provide an attractive feature to VLC. Image sensor pixels can sense LED transmission sources and discards other pixels which detects ambient noise. More specifically, outdoor usage of VLC is possible by discarding pixels associated with noise sources such as the sunlight or streetlights. Hence, image sensor based VLC is an attractive solution for outdoor mobile applications (T. Yamazato, I. H. (2014)). With regards to short-range VLC applications, the SNR of a direct detection receiver is proportional to the square of the received optical power. Therefore, VLC links can tolerate only a comparatively limited amount of signal path loss. Figure 1 shows a general block diagram for a simple VLC transceiver. As shown in the figure,

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the upper part represents the transmitter, where binary data is first passed to a coder such as a Reed-Solomon coder (Khalifeh, A. Y. (2010)) where error correction codes are added. After that, binary data is modulated using a digital modulation scheme such as binary On-Off Keying (OOK) where data is modulated by passing it to an amplification circuit that drives an LED(s), which converts the data into light over the wireless communication channel. One can notice from Figure 2 that either a single or several LEDs can be used to transmit data. The same applies to the receiving circuit, thus four different configurations can be used to increase transmission rates, as well as improve the received SNR; (a) Single-Input Single-Output (SISO), where only one LED transmitter and one photodiode receiver is used, (b) Single-Input Multiple-Output (SIMO), where one LED transmitter and multiple photodiode receivers are used, (c) Multiple-Input Single-Output(MISO) where multiple LED transmitters and one photodiode receiver is used, and finally, (d) Multiple-Input Multiple-Output (MIMO) where multiple LED transmitter and multiple photodiode receivers are used. The data path is then transmitted to the receiver circuit, where the light pulses are detected by a photodiode(s). The output signal is then passed to an amplifier, pulse shaping and re-generation Figure 1. VLC transceiver block diagram

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blocks, a channel decoding module to correct the introduced errors, and a demodulator circuit that will detect and convert the signal back into binary data. Without any ambient noise or distortion, the transmitted bit stream should reach the destination without any errors. However, due to the internal and external noise sources, some binary data will be falsely detected which causes bit errors. In addition to the typical additive white Gaussian noise that is locally generated by the receiver electronic circuit, VLC receivers are prone to the ambient surrounding and background lights that can cause errors in the detection process. However, in order to mitigate the effect of these external sources, various techniques are used such as the one proposed in (Ya´ nez, V. T. (2009)) where the channel characteristic is estimated and the received signal is equalized based on the channel estimation model. Another technique presented in (Ya´ nez, V. T. (2009)) uses the wavelength filtering to reduce the unwanted inference signal.

VLC APPLICATIONS This section discusses in details the major applications where VLC has a great potential. Applications are divided into two main categories; indoor and outdoor. VLC can be used in various indoor

Category: Optical Engineering

Figure 2. Several transmitter-receiver configurations

applications and scenarios, namely, in high-density indoor areas such as classrooms, conference halls, convention centers and other assembly spaces where light can be used to broadcast relevant information to the audience. Entertainment applications can also utilize VLC for video and audio streaming and broadcasting. Indoor navigation and localization is another important application of VLC, and finally, VLC can play an important role in data transmission in sensitive areas where radio wireless communication is not preferable, some examples are in hospitals and inside the airplanes. A closer look for these potential applications will be described next. VLC can also be used in various environments where it is not safe or recommended to use the radio spectrum, since it may interfere with other critical electronic equipment. The other set of applications that can efficiently utilize this technology is some of the outdoor-based applications. Research has been done on the field of vehicle-to-vehicle (V2V) communications where cars can communicate with each other to exchange critical and general messages. Additionally, it can be easily used for Intelligent Transportation Systems (ITS) and vehicle-to-infrastructure and infrastructure-tovehicle (V2I and I2V) communication.

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Indoor Applications VLC has many potential indoor applications. This is due to the fact that an indoor channel changes slowly when compared to outdoor channels, where the channel may vary more rapidly. Three main applications will be discussed: Indoor localization and positioning, medical services, and VLC in-flight communication systems.

Indoor Localization and Positioning Services One important application of VLC is indoor localization and positioning. This is a vibrant research area that is gaining much attention recently. Positioning applications cover a wide area where the technology can be included into various consumer electronics. Indoor positioning technology can be used to guide users in large areas. Moreover, positioning systems using VLC can also detect products inside large warehouses, automating some record management processes. As known in literature, radio signals coming from Global Positioning System (GPS) satellites cannot penetrate well through walls of large buildings, consequently, fast and accurate indoor posi-

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tioning is difficult to achieve by GPS. To overcome this problem, there are two possible alternatives; RF and VLC-based techniques. Examples of RF based techniques are: wireless local area network, radio-frequency identification (RFID), cellular, ultra-wide band and Bluetooth. These technologies will have positioning accuracies from tens of centimeters to several meters. But unfortunately, this amount of accuracy is not sufficient for many indoor applications. Hence, the techniques based on VLC are gaining greater attention. LEDs are currently being installed in many large buildings such as museums and shopping malls, because they have the advantage of much longer life and lower operating cost. For these reasons, indoor positioning techniques based on VLC and LEDs are the desirable and preferable options (LVX Minnesota lightning. (2015)).

Medical Services Another major concern these days are regarding the compatibility of medical devices in healthcare centers with the incorporation of wireless technology (S.S. Muhlen, D. D. (2008)). RF wireless technology has always been associated with the emission of electromagnetic interference (EMI) (H. Hong, Y. R. (2008)). Intrusion of the EMI jeopardize the quality of medical monitoring, as the accuracy and efficiency of data transmission is crucial for the medical staff to provide corresponding measures or treatment, based on the real–time information received (K.S. Tan, I. H. (2001), Chung, Y.-Y. T.-Y. (2014)). VLC can be applied to indoor medical applications by transmitting patient data as well as management data. Healthcare information such as electrocardiography (ECG), photoplethysmogram (PPG) signals and text information can be transmitted simultaneously, using a single channel VLC. This allows for a more precise and accurate monitoring and diagnosis.

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VLC In-Flight Communication Systems The use of VLC technologies for offering data networking for passengers during flight is another promising indoor application. Internet access, Wi-Fi is traditionally used to connect the passenger devices to an access point that will send their request to the internet either via a satellite connection or via plane-to-earth data link. This Wi-Fi scheme shares the radio spectrum among many passengers, which may cause congestion and reduce the speed of communication. In addition, Wi-Fi may affect the airplane navigation systems especially at take-off and landing causing EMI. A better solution can be implemented by utilizing a hybrid radio and optical system, where passengers may use the radio spectrum for uploading their requests, which usually require less bandwidth than the downlink. The downlink can be provided to the passengers via the personal LED lights available on top of their seats. This scheme will increase the internet speed due to abundant bandwidth available in the optical domain and will reduce the interference to the airplane navigation system. Brighter overhead lighting will result in higher SNR and greater connectivity.

Outdoor Applications In this section, potential outdoor applications for VLC will be discussed. Despite the more challenging problems such as fast time varying channels and users’ high mobility, VLC, if designed properly, can be used in several outdoor applications such as automotive communication, intelligent transportation systems, and underwater communications.

Visible Light Communication in Automotive Industry Due to VLC being optimal for short range communication, it has significant potential in the

Category: Optical Engineering

automotive industry such as car-to-car (C2C) and Intelligent transportation systems (ITS). Additionally, existing infrastructure is in place to take advantage of VLC such as headlights and streetlights. In the next few years, the implementation of LED taillights, brake lights, will all be more than 50% (Cui, K. C. (2012)). All these conditions make it desirable to consider VLC in the automotive industry. Vehicular Visible Light Communications (V2LC) offers a dependable solution to put into operation vehicle-to-vehicle (V2V) communications and requires minimal installation cost. V2LC is challenged by dynamic ambient noise, high mobility, and moderately low transmitter heights on communicating systems such as vehicles and roadside units. These limitations make the V2LC channel modelling a challenging task. V2LC utilizes either a photodiode detector or a camera installed on the vehicle to be used as a receiving element. It was found that V2LC systems perform better in high volume vehicular traffic; this is because of the fact that visible light can mainly propagate within proximity and LOS to reduce interference and increase link scalability (CALM. (2011)).

C2C Communication System A traditional C2C -VLC scenario is shown in Fig. 3, where the car on the left side communicates with the car on the right side using its headlamp. As shown, the projected beam pattern is single, and the received light consist of the LOS and reflected or Non-Line of Sight (NLOS) components. It can

be noticed that a multi-path interference will occur due to the reflected rays from the road surface, which depends on many factors such as the pavement material, the angle of incidence as well as the weather condition (rain, snow, fog, etc.). There are many regulations issued by Economic Commission of Europe and Federal Motor Vehicle Safety Standards in United State of America related to headlamps to insure that vehicles will provide good road illumination. At the same time, the light emitted from headlamps should not visually disturb other road users. Hence, the lamp, its reflective devices, and other associated equipment must achieve specific requirements. Therefore, the Lambertian model which has been used widely in indoor VLC LED modelling, cannot be used to model the vehicle’s light pattern (Kumar, N., et al. (2012)). In C2C VLC links, usually the LOS and the NLOS components form the received optical power. For FSO, there are additional noise sources, such as the background solar radiation, streetlights, vehicles, and secondary reflections which can be treated as artificial light (Ghassemlooy. Z., P. W. (2012)). The background solar radiation is mostly the dominant noise source as it is composed of direct and scattered radiation (Cui, K. C. (2012)). As such, good noise cancellation mechanisms are required to account for the nature of these noise sources. In addition, appropriate signal processing algorithms are needed to equalize the received signal and improve its condition. Especially, the channel coherence time will be very short in an outdoor vehicular environment due to increased

Figure 3. C2C VLC and projected LOS and NLOS beam patterns

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mobility of fast moving vehicles. Hence, any channel estimation and equalization has to be achieved seamlessly.

ITS Traffic Light to Car Communication Improvements to safety, fuel consumption, and transportation time are the key objectives of an intelligent transportation system. ITS refers to the potential of adding information and smart communication technologies to the transportation infrastructure and vehicles in order to enhance transportation safety, mobility, and support productivity through the use of advanced communications technologies. VLC can play an important role in ITS, such as broadcasting important traffic information. Here several configurations, such as V2V using LED-based rear panel lights, and bidirectional infrastructure-tovehicle are possible. Traffic information system using LED-based traffic lights were investigated by many authors. They touched upon topics such as analyzing the communication performance and defining a service area, in which communications using a particular data rate together with intensity modulation OOK can be maintained. VLC is suitable for both a broadcast system in I2V communication systems and effective in V2V as well. In a V2V scenario, a vehicle in front of a traffic light can receive the information sent by the traffic light, and then it can relay this information using the brake lights to the vehicle running behind. This can be extended to establish a vehicle ad-hoc network. With respect to I2V broadcasting, there is no need for extra power to broadcast traffic safety related information, which can be continuously broadcasted to support smooth traffic flow as well as reducing accidents and fatalities (CALM. (2011)). LED-based traffic lights are also considered to be a suitable choice for traffic light Road Side Units (RSUs) in the ITS architecture without the need for additional infrastructure. One of the most

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possible approaches of communication between an LED-based RSU and a vehicle is presented in Fig. 4. The first stage is collecting and passing the detected information to a central control unit, which consists of many parts such as data processing, geographical distribution of the data, and information broadcasting. After the data collection stage, traffic control actions are made. Traditional ITS systems are normally based on wireless radio solutions and short range infrared systems. In order to deliver information from the traffic control infrastructure, which is represented by traffic lights to vehicles, VLC can be effectively integrated into this scenario. This technique offers many advantages, like cost effectiveness, alleviation of additional wireless communication support, and energy efficiency. The most important benefit is that it does not require additional power to support future communication systems. Fig. 4 shows that the ITS use control technology and access networks to achieve communication facilities. In this case, it becomes important for an ITS architecture design to be flexible enough to accommodate integration of new systems (Kumar, N. L. (2012)).

Underwater Communication Providing an efficient and reliable data transmission for underwater communication is a challenging task, this is due to the nature of water, which absorbs the energy of the transmitted signal and refracts it. Data transmission underwater suffers from high-energy consumption, long propagation delay, and limited link bandwidth. RF-based solutions are not efficient due to the high attenuation and due to the multipath signal propagation (Gabriel, C. K. (2011)). As an alternative, acoustic waves are in use for underwater communications. Table 2 compares acoustic and optical communication methods for underwater use. As shown in the table, both acoustic and optical mediums suffer from power loss. The acoustic waves suffer

Category: Optical Engineering

Figure 4. VLC integration with ITS architecture depicting the vehicle Field-of-View (FOV) and the Variable Message System (VMS)

from more than 0.1 dB per meter per Hz, so the higher the frequency and distance, the higher the attenuation and power loss. For optical transmission, the amount of power loss depends on the turbidity, which is a measure of the water purines and its clarity. The higher the turbidity, the higher the power loss. In addition, the signal strength is inversely proportional with the distance (Lanbo, L. S.-H. (2008)). Although acoustic transmission has a greater range, its transmission rate is very limited. While the optical communication link has higher data rates for low distance, it becomes detrimental over a long distance. The propagation delay for optical communication is much lower when compared

to an acoustic link, since light speed inside water is significantly faster than the speed of acoustic transmission. Moreover, optical transceivers are relatively cheap and less complicated when compared with acoustic transceivers. It is important to note that the performance metrics in Table 2 are based on a seawater environment, so it may vary for other underwater environments.

DISCUSSION Ever since the growing demand and use of data, there has been a need to reliably transmit significant amounts of data in real time. Current

Table 2. Comparison between underwater communication using acoustic and optical communication methods, assuming a seawater environment Property

Acoustic

Optical

Require alignment

No

Yes

Receiver complexity

Higher

Lower

Recommended communication range

Up to 5 km

Up to 100m

Data rate

Up to 100 Kbps

Up to 1 Gbps

Power loss

> 0.1 dB/m/Hz

proportional to water turbidity

Propagation delay /speed

large/slow (1500-2000 m/s)

low/ high (2.255 x 108)

Frequency band

∼ kHz

1015 Hz

Sources: Lanbo, L. S.-H. (2008), Yang S., K.,. (2011), Lurton, X. (2010)

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technology, as well as a growing number of users limit the communication capacity. Hence, there is a great need for high bandwidth, energy efficiency, cost, and reliability for a communication link. Hence, VLC offers an excellent alternative to traditional technology. However, there exists some challenges that were investigated to make this a viable communication technology. These challenges are dealing with the LOS requirement of VLC, inability to communicate through opaque obstacles, high mobility, ambient light interference, limited LED photon emission rate, required DC biasing, and limited modulation bandwidth as well as the non-linearity nature of LEDs (Dong-Fang Zhang, Y.-J. Z.-Y. (2013)). As previously discussed, there are several approaches which can be taken to alleviate the main challenges with VLC. To name a few, implementing a MIMO or LED array can be used to increase the SNR and improve diversity in impeding scenarios, channel modelling and equalization at the receiver, camera image sensors used to eliminate ambient light-treated as noise, and finally advanced modulation techniques to overcome limited modulation bandwidth.

FUTURE RESEARCH DIRECTIONS Since VLC has several challenges as previously mentioned, there has been a fair amount of lab research toward improving its performance as well as improving its distance range. In order to mitigate the channel effect on a VLC system, several techniques have been proposed, such as the usage of Orthogonal Frequency Division Multiplexing (OFDM), and the use of MIMO (Mohammed S. A. Mossaad, S. H. (2015)). With respect to the limited LED modulation bandwidth, polarization multiplexing methods can be combined with other multiplexing methods which can further increase the communication rate. Cvijetic et al. studied Bit Error Rate (BER)

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performance of a polarization multiplexed optical wireless transmission (Chiharu Mukai, K. O. (2012)). Additionally, advanced modulation techniques have been carried out to achieve high data rates up to 100Tbit/s by implementing orbital angular momentum (OAM) with other multiplexing domains and present a free-space data link that uniquely combines OAM-, polarization-, and wavelength-division multiplexing by using three-dimensional multiplexing. (Hao Huang, X. (2014)). In addition, researchers have looked at combining the Power Line Communication (PLC) technology with VLC, such that the power lines become the data source of these lights (Ding W., Y. F. (2015), Hao M., L. L. (2013)).

CONCLUSION In this chapter, we have discussed several indoor and outdoor potential applications that VLC can utilize in the upcoming years. Despite its performance challenges, VLC showed significant potential in several indoor and outdoor applications, such as: indoor localization and positions, in-mall advertising and user behavior tracking, and in-plane multimedia broadcasting. For outdoor applications, it shows a promising potential in the automotive industry as well as underwater communications.

REFERENCES CALM. (2015, Nov 1). Communications in Cooperative Intelligent Transport Systems - CALM for C-ITS. Retrieved from Introduction to Communications Access for Land Mobiles: http://calm. itsstandards. EU/ Chiharu Mukai, K. O. (2012). BER Performance of Multiplexed CSK using Linear/Circular Polarization in Optical Wireless Communications. ISITA.

Category: Optical Engineering

Chung, Y.-Y. T.-Y. (2014). Mobile health-monitoring system through visible light communication. Bio-Medical Materials and Engineering, 608–737.

Hong, H., Y. R. (2008). Information illuminating system for healthcare institution. Proceedings of International Conference on Bioinformatics and Biomedical Engineering, 801-804.

Cui, K. C., Chen, G., Xu, Z., & Roberts, R. D. (2012). Traffic light to vehicle visible light communication channel characterization. Applied Optics, 51(27), 6594–6605. doi:10.1364/ AO.51.006594 PMID:23033030

Hranilovic, S. (2005). Wireless Optical Communication Systems. Springer.

Ding, W. Y. F., Yang, F., Yang, H., Wang, J., Wang, X., Zhang, X., & Song, J. (2015). A hybrid power line and visible light communication system for indoor hospital applications. Computers in Industry, 68, 170–178. doi:10.1016/j.compind.2015.01.006 Dong-Fang Zhang, Y.-J. Z.-Y. (2013). Multi-LED Phase-Shifted OOK Modulation Based. IEEE Photonics Technology Letters. Retrieved from https://www.eldoled.com Gabriel, C. K. (2011). Channel modeling for underwater optical communication. Proceeding in IEEE GLOBECOM Workshops. GC Wkshps. Ghassemlooy, Z. P. W. (2012). Optical wireless communications: system and channel modeling with Matlab. CRC Press. Grantham, C. (1998). Visible Light Communication for Audio Systems. IEEE Transactions on Consumer Electronics, 1–11. Hao, M. L. L. (2013). Integration of indoor visible light and power line communication systems. 17th IEEE International Symposium in Power Line Communications and Its Applications (ISPLC). doi:10.1109/ISPLC.2013.6525866 Hao Huang, X. (2014). 100 Tbit/s free-space data link enabled by three-dimensional multiplexing of orbital angular momentum, polarization, and wavelength. Optics Letters, 39(2), 197–200. doi:10.1364/OL.39.000197 PMID:24562105

Khalifeh, A. Y., & Yousefizadeh, H. (2010). Optimal Audio Transmission Over Error-Prone Wireless Links. IEEE Transactions on Multimedia, 12(3), 204–214. doi:10.1109/TMM.2010.2041096 Kumar, N. L. (2012). Visible Light Communications in Intelligent Transportation Systems. Intelligent Vehicles Symposium Alcalá de Henares, 748-753. doi:10.1109/IVS.2012.6232282 Lanbo, L. S.-H. (2008). Prospects and problems of wireless communication for underwater sensor networks. Wirel. Commun. Mob. Comput. Lurton, X. (2010). An Introduction to Underwater Acoustics Principles and Applications. SpringerVerlag Berlin Heidelberg. doi:10.1007/978-3642-13835-5 LVX Minnesota Lightning. (2015, Nov 1). Retrieved from http://purelifi.com Masayuki Kinoshita, T. Y. (2014). Motion Modeling of Mobile Transmitter for Image Sensor Based I2V-VLC, V2I-VLC, and V2V-VLC. Globecom Workshop-Optical Wireless Communications. Mohammed, S. A., & Mossaad, S. H. (2015). Visible Light Communications Using OFDM and Multiple LEDs. IEEE Transactions on Communications, 4304–4313. Muhlen, S. S. D. D. (2008). New challenges in controlling EMI in hospitals. Proceedings of International Federation for Medical & Biological Engineering, 834-837. Pengfei, L., e. a. (2014). Fundamental Analysis of a Car to Car Visible Light Communication System. 9th International Symposium on Communication Systems, Networks & Digital Sign (CSNDSP).

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Perez-Jimenez, R. R.-H. (2011). Visible Light Communication Systems for Passenger In-Flight Data Networking. IEEE International Conference on Consumer Electronics (ICCE), 445-446. doi:10.1109/ICCE.2011.5722675 Popoola, W. O., & Ghassemlooy, Z. (2009). BPSK subcarrier intensity modulated free-space. Journal of Lightwave Technology, 27(8), 967–973. doi:10.1109/JLT.2008.2004950 Ramirez-Iniguez, R. S. M. (2008). Optical Wireless Communications: IR for Wireless Connectivity. Boca Raton, FL: CRC Press.

Zahmati & Fernando. (2011). Emerging Wireless Applications in Aerospace: Benefits and Challenges. Exponent, the Honeywell Technical Journal.

ADDITIONAL READING Gallico, P. V. (2007). Optics research trends. New York: Nova Science. Haas, H. (2015). Visible Light Communication. Optical Fiber Communication Conference.

Standard 802.15.7. (2015, Nov 1). Retrieved from https://standards.ieee.org/develop/ project/802.15.7.html

Haas, H., & Chen, C. (2015). What is LiFi? 2015 European Conference on Optical Communication (ECOC). doi:10.1109/ECOC.2015.7341879

Takaya Yamazato, I. T. (2014). Image-SensorBased Visible Light Communication for Automotive Applications. IEEE Communications Magazine, 10.

Haruyama, S. (2012). Advances in Visible Light Communication Technologies. European Conference and Exhibition on Optical Communication. doi:10.1364/ECEOC.2012.We.3.B.5

Tan, K. S. (2001). Electromagnetic interference in medical devices: Health Canada’s past current perspectives and activities. IEEE International Symposium on Electromagnetic Compatibility, 1283-1284.

Koteles, E. S., & Willner, A. E. (1995). Emerging components and technologies for all-optical networks: Conference: Papers. SPIE.

U.S. Department of Energy. (2013). Part 1: Review of Life-Cycle Energy Consumption of Incadescent, Compact Fluorescent, and LED Lamps. Author. Yamazato, T., Takai, I., Okada, H., Fujii, T., Yendo, T., Arai, S., & Kawahito, S. et al. (2014). Image sensor based visible light communication for automotive. IEEE Communications Magazine, 52(7), 88–97. doi:10.1109/MCOM.2014.6852088 Ya´nez, V. T. (2009). Illumination interference reduction system for VLC Communications. Proc. WSEAS Int. Conf. Math. Methods, Computer. Tech. Intel. Yang, S. K. (2011). Reduction of optical interference by wavelength filtering in RGB-LED based indoor VLC system. Proceeding of Opto-Electronics and Communications Conference (OECC).

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Whyte, B. (1999). Networked futures: Trends for communication systems development. Chichester, England: J. Wiley. doi:10.1002/0470841850

KEY TERMS AND DEFINITIONS CDMA: A channel access method, which allows several users to share a band of frequencies. Intelligent Transportation Systems (ITS): The potential of adding information and smart communications technologies to the transportation infrastructure and vehicles, in order to enhance transportation safety, mobility, and support productivity. Light-Emitting Diode: A semiconductor device that emits visible light at a single wavelength when an electric current passes through it. The output from an LED can range from red (at

Category: Optical Engineering

a wavelength of approximately 700 nanometers) to blue-violet (about 400 nanometers). OFDM: A method of encoding digital data on multiple carrier frequencies. Signal to Noise Ratio: A ratio of desired signal to undesired signal (noise) in the average power level of a transmission. Smart Grid: An electrical grid which includes a variety of operational and energy measures in-

cluding smart meters, smart appliances, renewable energy resources, and energy efficiency resources. Visible Light Communication (VLC): Data communications medium which uses visible light between 400 and 800 THz (780–375 nm). Wireless Communication: Transfer of information between two or more points that are not connected by an electrical conductor.

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Community Outreach

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Loriene Roy The University of Texas at Austin, USA Antonia Frydman The University of Texas at Austin, USA

INTRODUCTION Community outreach is the extension of library services outside of the physical library. In public libraries, this calls on the library to connect with actual and potential library users. Once primarily associated with serving disadvantaged potential library users, community outreach now refers to any activities libraries develop that deliver services to new constituencies. Community outreach covers the following process: identifying targeted communities, assessing a need, considering responses to this need, prioritizing options, targeting a response to the need, developing the initiative, marketing the response, delivering the program, and conducting continuous and end-ofservice evaluation. Of those actions, the process of marketing has received the most attention over recent years. The concept of marketing or ‘selling’ the library and its services has been interpreted as a form of advocacy; not only do libraries inform the public about an individual service, but individual librarians are called on to advocate for their work setting and for the professional at large. The American Library Association (ALA) now considers “Advocacy for Libraries and the Profession” as one of its eight key action areas (American Library Association, 2013). ALA has developed tools for library workers, grouped in a section of its website called “AdvocacyU.”

BACKGROUND As Heim (now, McCook) pointed out, outreach is much more than opening the doors of an inDOI: 10.4018/978-1-5225-2255-3.ch579

stitution; it extends across a spectrum of activities and actions in what Heim referred to as the Stimulation Continuum (Heim, 1984). Outreach involves reaching out to the underserved, and even intervening during times of need. Early outreach services were designed with education in mind. Early public libraries in the mid to late nineteenth century were founded to further the education of an American public that had little access to other forms of schooling. Libraries, including state library agencies, coordinated broad educational efforts under offices of library extension. These efforts were followed by programs designed to acclimate new citizens. During that time the Immigrant Publication Society issued small pamphlets to assist librarians in responding to the needs of Jews from Eastern Europe, Russian Jews, and Poles (Carr, 1919). These publications were issued with support from the ALA Committee on Work with the Foreign Born. This committee existed for thirty years, from 1918 to 1948. Over time, these library efforts have been scrutinized more closely and considered by some to be expressions of acculturation. Heim noted that after 1950, outreach services returned to the educational motif, focusing on adults’ learning needs. And since the 1950s, public libraries provided both general educational services and targeted services for specific clientele. ALA reflected this interest by establishing the Office of Library Service to the Disadvantaged (OLSD) in 1970. In the 1980s, targeted services addressed the needs of latchkey children, business people, and the homeless. The OLSD was renamed the Office of Literacy and Outreach Services (OLOS).

Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Community Outreach

OLOS’s mission describes the communities often associated with community outreach: OLOS focuses attention on services that are inclusive of traditionally underserved populations, including new and non-readers, people geographically isolated, people with disabilities, rural and urban poor people, and people generally discriminated against based on ethnicity, sexual orientation, age, language, and social class. (American Library Association, Office of Literacy and Outreach Services, 2013) OLOS’s involvement with outreach currently includes planning and delivering nationally visible professional events. This includes the annual Dr. Martin Luther King Jr. Sunrise Breakfast Celebration held during each ALA Midwinter meeting, the National Bookmobile day which is celebrated during National Library Week, and events scheduled for the ALA Annual Conference, including the Diversity and Outreach Fair and the Jean Coleman Library Outreach Lecture. In addition, the OLOS website maintains ‘toolkits’ to assist library workers in their outreach missions. These toolkits include practical advice on serving audiences including the homeless, older adults, and tribal libraries. For Heim, outreach necessitates creating a climate that supports many activities (Heim, 1982). In 2004, Osborne grouped cases of outreach into six classes: services outside library walls, outreach inside the library, outreach using information technology, technical services, advocacy, and staff development (Osborne, 2004). In her 2010 book entitled Librarians as Community Partners: An Outreach Handbook, Smallwood organized 66 examples of library outreach into ten broad categories (Smallwood, 2010). Some categories illustrate that outreach can be designed for specific clientele such as seniors, youth, culturally and ethnically diverse patron groups, and individuals residing in correctional care facilities. Approaches may include classroom outreach, book festivals, or special collections. Outreach can also be grouped

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with approaches that share specific techniques such as those that employ local media and those whose success results from collaborations with other agencies, institutions, or companies including arts centers, day cares, restaurants, book stores, and historical societies. Outreach is not limited to the public library setting or within the library’s physical facility. Academic libraries increasingly provide services to their wider community of parents, alumni, and potential library supporters. The services that libraries develop might include instruction, exhibits, lectures, events, and other public programs. Across the globe, outreach services have included various conveyance means to deliver resources and circulating materials, as well as services. Bookmobiles travel to remote areas in the United Kingdom and Scandinavia, while donkeys travel with a cart of books to children to Ethiopia, or use side packs to bring books to children in Columbia (Smallwood, 2010). Alternatively, within library facilities, the redesign of space addresses outreach missions, as patrons have access to learning commons, information commons, and/or social commons. Outreach has additionally become associated with social justice. One example of this is The People’s Library, which was founded by members of the Occupy Wall Street movement (Gray, 2012). Those involved in outreach are recognized for their innovative work, and outreach efforts are often tied to support for intellectual freedom. For example, the Robert B. Downs Award, awarded by the Graduate School of Library and Information Science at the University of Illinois at Urbana-Champaign “acknowledges individuals or groups who have furthered the cause of intellectual freedom, particularly as it impacts libraries and information centers and the dissemination of ideas” (The University of Illinois at UrbanaChampaign, 2013).

Controversies and Problems Extension of library services beyond traditional roles of collection-building and support for the

Category: Public Sector Management

reader is controversial to some library workers, and surprising to some members of the library’s public. Individual ALA members and even members of the ALA Council (the policy setting body of the association) sometimes express disagreement over whether involvement in social justice issues is supportive of the profession’s mission. Those in the profession may question whether outreach aligns with the original missions of education, information, and recreation, and whether extending services is in some way disadvantages the traditional library audience of people seeking a good book to read. This tension is more pronounced during economic downturns, when finances may be limited to improving routine services by developing and implementing new physical and virtual services. Those supportive of outreach reply that literacy programming is an active expression of the service continuum. When library workers develop services to support both existing readers and patrons seeking the skills to become readers, they are affirming not only the library itself, but also the library’s commitment to outreach (Heim, 1982). Libraries have long wrestled with measuring their impact. Starting in the 1930s, national standards were released for public libraries (McCook, 2011). Until the 1960s, these standards were revised each decade, and measured a public library’s achievement in numbers of recommended books or hours of service. This approach to evaluation changed in the early 1980s with the publication of the first of a series of planning documents for public librarians. These planning documents provided local libraries with assistance in identifying their roles for a limited period of time, usually five years, and instruction on how to measure whether they had served their community well during that time. Achievement was then measured in ratios called output measures, such as turnover rate or circulation divided by number of volumes held. Public libraries continue to use a suite of planning documents to assess their services. Academic libraries were typically ranked by the number of volumes held. New measures have

been incorporated over time, including those that measure user perceptions of library services such as the LibQUAL+ project (Snyder, 2002). A number of studies have estimated the economic impact of a library. One such study found that for every one dollar invested in public libraries in the state of Texas, a community receives $4.42 in benefits (The University of Texas at Austin, 2012). These measurements, however, do not specifically assess the impact of community outreach. Another less-mentioned challenge regarding community outreach is that it requires special interpersonal skills on the part of the library workers. Librarians involved in community outreach efforts should be aware of the skillsets and competencies required to fulfill a specific service, and attitudes required to serve a specific community. (Montiel Overall, 2009) Being a ‘well meaning person’ is not the sole predictor of success in working with communities of color (Roy, 2011). In developing outreach services, information professionals must examine their potential role as outsider and the advantages and disadvantages associated with their presence (Roy, 2009). Knowledge about a community, and how to work best within that community is a process that takes time. It involves sensitivity to protocols, good etiquette that guides interpersonal communication within a community.

Solutions and Recommendations What was once considered outreach is now linked to the concept of community engagement. Responses and solutions to the above challenges are discernable in a myriad of cases of community outreach. Successful community engagement activities include the following key features: belonging, commitment, communication, flexibility, genuineness, relevance, sustainability, and accountability (Sung & Hepworth, 2013). In the first decades of the 21st century, libraries are responding through crisis informatics: assisting those facing disasters or challenges such as job loss. Newer audiences are created out of need. Many libraries reacted to the worldwide

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economic recession by developing programs for job seekers (Roy, Brzozowski, Arnold-Garza, & Beauchemin, 2011). In America, libraries contributed to the launch of the Affordable Health Care Act (referred to familiarly in the United States as Obamacare) by offering information sessions and direct assistance to patrons who want to explore, understand, and enroll in healthcare coverage options (Goldberg, 2013). Contemporary work on community outreach includes studies of communities of need, the preparation of information professionals to serve communities of need, crisis informatics, and work on digital equity. Selected library services have demonstrated flexibility in the area of community outreach. For instance, reference services were once only patron-initiated and took place through face-toface contact. With the emergence of new communication technologies, reference services can now be conducted on the telephone, and through an array of virtual deliveries. Whereas a librarian might once have had difficulty finding anything related to a patron’s request, now the difficulty lies in narrowing down the search to find the most relevant selections within a vast number of results. Reference library staff have responded to shifting patron needs by incorporating efficiency, instruction, point-of-need services, in addition to more traditional reader advisory services. Some examples of incorporating outreach into elemental library services are tiered reference, roving reference, and embedded librarianship. Library efficiency is demonstrated through tiered reference: a reference model that provides a pyramid of service contacts in which library support staff answer uncomplicated questions and subject specialist librarians answer higher level questions. Roving reference—in which reference staff seek out patrons in their facilities to ensure that their questions are answered— is one approach for the internal outreach of reference services. This concept has been expanded in the performance and philosophy of embedded librarianship. Em-

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bedded librarianship is the extension of the triad of public services—reference, instruction, and reader’s advisory—into the community (Carlson & Kneale, 2011).

Serving Communities of Need As demonstrated by Gratia Countryman, Director of the Minneapolis (Minnesota, USA) Public Library in the early twentieth century, community outreach found a home in libraries (Benidt, 1984). In this case, libraries reached out to community members directly where they lived and worked in locations as varied as factories, hospitals, flophouses, and homeless shelters. State library agencies and national libraries focused on library development, bringing traveling libraries to remote and/or small communities and establishing services for often overlooked community members such as the blind and visually impaired. Recognizing that not all potential library patrons frequented the physical library, public libraries moved outside of their buildings. Over time this was seen in bookmobile services, services in storefronts, and in portable buildings. Librarians have a lengthy history of serving the reader, and traditional services have been defined by offering print resources and e-resources. Many studies attempt to redefine patron needs by first examining how people seek information in everyday life. Subjects of these studies include people searching for information about adoptions, immigrants living in urban settings, families coping with chronic illness, and survivors of intimate partner violence. While outreach has had its historical foundation in the public library settings, academic libraries are also reinterpreting their missions through community outreach. Leong summarized outreach activities in academic libraries in three countries: Canada, China, and the United States. He found that outreach was expressed through (1) extending library access to the public; (2) creating information literacy services; (3) entering into

Category: Public Sector Management

exchanges and other cooperative arrangements; and (4) hosting events such as exhibitions and lecture series (Leong, 2013).

Preparing Information Professionals to Serve Communities of Need Professional organizations have developed a range of documents that illustrate the skills and competencies expected of information professionals. These include competencies for reference librarians, librarians serving children and youth, and those working in the subject specific areas such as music librarianship and art librarianship. Some national competencies exist, such as the Professional Registration program developed by the Library Association of New Zealand Aotearoa (LIANZA) with the National Library of New Zealand and Te Ropu Whakahau, the national association for Maori in libraries and information management (Library Association of New Zealand Aotearoa, 2013). Whether explicitly or implicitly, graduates of programs preparing entry-level professionals to work in libraries, archives, and museums are expected to demonstrate competency in the area of community outreach. Three sample competencies are identified, as follows: 1. The Association of Library Service to Children, a division of the American Library Association, issued a competency document that outlines what is expected of public librarians serving patrons ages 0 to 14 years. Such information professionals are expected to provide “library outreach programs which meet community needs and library goals and objectives” (American Library Association. Association of Library Services to Children, 2009). 2. Librarians working in special collections are expected to be “skilled in planning and implementing programs and publications that promote and interpret the collections, such as exhibits, conferences, guest lectures,

public speaking, and other active forms of outreach” (American Library Association. Association of College & Research Libraries, 2008). 3. Standard four of the diversity standards for academic librarians asks that “Librarians and library staff shall develop collections and provide programs and services that are inclusive of the needs of all persons in the community the library serves” (American Library Association. Association of College & Research Libraries, 2012). Specific academic programs have provided opportunities within their curricula for students to participate in community outreach, oftentimes through service learning activities. These can be operated as individual initiatives such as “If I Can Read, I Can Do Anything,” a national reading club for American Indian children and youth that is managed by students and their advising faculty member in the School of Information at the University of Texas at Austin. Students in the School of Library and Information Studies at the University of Alabama (USA) provided computer training with community members with disabilities living in west Alabama. Students can focus on specific civic engagement through independent studies, internships, Capstone or graduation projects, or through opportunities embedded within a formal course. Several graduate programs accredited by the American Library Association have provided special opportunities for students to work directly with communities of need and/or to prepare librarians in these communities. The School of Library and Information Studies in the University of Alabama USA’s College of Communication and Information Studies offered Project ALFA, Accessible Libraries For All. AFLA focused on gathering data and helping students acquire skills in serving library patrons with disabilities. Simmons College in Boston, USA, collaborated with Harvard by means of a grant from the National Endowment for the Humanities to assist staff at

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Iraqi libraries. The School of Information and Library Science at the University of North Carolina USA hosted ELIME-21, Educating Librarians in the Middle East: Building Bridges for the 21st Century. Students at the University of North Texas’s Department of Library and Information Sciences collaborated to help address the needs of those living in rural areas in Jamaica. The Graduate School of Library and Information Science recognized the needs of communities and students by forming the Community Informatics Research Laboratory.

Crisis Informatics Emergency responses may replace traditional library services, especially when a natural disaster strikes. Faculty at the University of Toronto (Canada), Florida State University (USA), the University of South Carolina, and University of Washington (USA) have studied what information is needed during the early warning phases of emergencies, technology used during the 2010 Chilean earthquake, use of information in reducing HIV/AIDS in Uganda, and preparing for and responding to hurricanes.

Digital Equity Efforts toward reducing the digital disparity involve bringing technology and training to audiences that might not otherwise have access to equipment and learning opportunities. The Bill & Melinda Gates Foundation has demonstrated how issues of technological equity can be addressed nationally through its American libraries initiatives. Over a period of five years, the Foundation brought hardware, software, and training to public libraries in all fifty U. S. states, starting in the states with the highest percentage of children qualifying and receiving free lunches in the public schools. Before this initiative launched, only about one in four of American public libraries offered free public access computing. At the end of the program,

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99 percent of the libraries provided these services (Gordon, Gordon, & Moore, 2001). The Foundation extended the U.S. library program to tribal communities in the states of Utah, Colorado, New Mexico, and Arizona, with the Native American Access to Technology Program. After this program wrapped up, the Foundation supported the development and services of a web portal called WebJunction. WebJunction has built an online community that offers distance delivery of continuing education to library workers, as well as targeted services, including technology training in rural libraries, and among Spanish speaking clientele and job seekers (Mason, 2009). At the international level, the Gates Foundation has annually recognized innovation in providing free public access to the Internet through the Access to Learning Award. Past recipients have provided Internet access at rural markets in Guatemala, to children and their families in rural schools in China, to urban centers in Columbia, and to farmers in Africa. Recipients are recognized at the International Federation of Library Associations and Institutions (IFLA)’s World Library and Information Congress along with an award of $1 million USD.

FUTURE RESEARCH DIRECTIONS Those teaching and conducting research in schools of information might be involved in a wide range of investigations that address community outreach. Today, these areas include community informatics, medical informatics, global information justice, service learning, action research, evidence placed practice, information seeking, and information coping. While an individual or organization can make a great impact on addressing community needs, the wants are unending and vast. Connectivity is still a challenge. Broadband access is practically inconceivable in many areas around the world. In Alaska, USA, for example, telephone confer-

Category: Public Sector Management

ence calls are still more reliable than web based teleconferencing. In rural Native Alaskan villages, Internet access may be limited to one user and one computer at a time (American Library Association, 2011). Along with connectivity, a major challenge to digital equity is sustainability. Even with the help of major funders, initiatives usually have limited financial support. At the same time, there is a constant demand for retooling the skills of information workers, refreshing equipment, and meeting the needs of wave after wave of new library patrons. There is a call for libraries and information settings to continually document their impact. Those working in and supporting information settings are encouraged to look beyond input measures such as resources added or outputs such as circulation of materials and attendance, in order to consider new ways to measure not only cost effectiveness but return on investment. In 2007, the Urban Libraries Council identified outreach to caregivers and parents as a key strategy for supporting early literacy. Outreach initiatives such as this one would create a network of partnerships committed to helping prepare the youngest citizens for school readiness through early literacy support (Urban, 2007).

CONCLUSION Community outreach has existed as an essential reflection of the vision and mission of libraries since the beginnings of the public library movement. The elements of outreach have involved serving all members of the library’s geographic boundary and beyond. These expressions have included supporting and advancing literacy as well as assisting patrons in meeting the needs, challenges, opportunities, and tragedies of their everyday lives. Such services are challenged by budgetary constraints, problematic motivations among some service providers and patrons, and varying access to technology. Still, community outreach is the local, national, and international

stage that showcases what libraries can do best: providing an equitable playing field for access to the world of knowledge.

REFERENCES American Library Association. Association of College & Research Libraries. Rare Books and Manuscripts Section. (2008). Guidelines: Competencies for special collections professionals. Retrieved March 3, 2015, from http://www.ala. org/acrl/standards/comp4specollect American Library Association. Association of Library Services to Children. (2009). Competencies for librarians serving children in public libraries. Retrieved March 3, 2015, from http://www.ala. org/alsc/edcareeers/alsccorecomps American Library Association. Office for Information Technology Policy. (2011). Libraries vital in FCC’s efforts to build out broadband to Native nations. Retrieved March 3, 2015, from http://www.ala.org/offices/oitp/publications/officialfilings/officialfilings American Library Association. Association of College & Research Libraries. Racial and Ethnic Diversity Committee. (2012). Diversity standards: Cultural competencies for academic libraries. Retrieved March 3, 2015, from http://www.ala. org/acrl/standards/diversity American Library Association. (2013). Key action areas. Retrieved March 3, 2015, from http://www.ala.org/aboutala/missionpriorities/ keyactionareas/ American Library Association. Office of Literacy and Outreach Services. (2013). Mission. Retrieved March 3, 2015, from http://www.ala. org/offices/olos Benidt, B. W. (1984). The library book: Centennial history of the Minneapolis Public Library. Minneapolis, MN: Minneapolis Public Library and Information Center.

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Carlson, J., & Kneale, R. (2011). Embedded librarianship in the research context; Navigating new waters. College & Research Libraries News, 72(3), 167–170. Carr, J. F. (1919). Library work with the foreign born. Exploring a neighborhood: Our Jewish people from Eastern Europe and the Orient. New York: Immigrant Publication Society. Goldberg, B. (2013). Libraries stress neutrality as they prep for queries on health care law. American Libraries, 44(9/10), 13. Gordon, M. T., Gordon, A. C., & Moore, E. (2001). New computers bring new patrons. Library Journal, 126(9), 134–138. Gray, S. W. (2012, August). Reference librarianship on the fly: Taking the librarian out of the library. Paper presented at the World Library and Information Congress, Helsinki, Finland. Heim, K. M. (1982). Stimulation. In G. A. Schlachter (Ed.), The Service imperative for libraries: Essays in honor of Margaret E. Monroe (pp. 120–154). Littleton, CO: Libraries Unlimited. Leong, J. H. T. (2013). Community engagement – building bridges between university and community by academic libraries in the 21st century. Libri, 63(3), 220–231. Library and Information Association of New Zealand Aotearoa. (2013). Professional registration. Retrieved January 7, 2013, from http://www. lianza.org.nz/registration Mason, M. G. (2009). WebJunction: A community for library staff. Journal of Library Administration, 49(7), 701–705. McCook, K. P. (2011). Introduction to public librarianship. New York: Neal-Schuman. Montiel Overall, P. (2009). Cultural competence: A conceptual framework for library and information science professionals. Library Trends, 79(2), 175–204. doi:10.1086/597080

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Osborne, R. (2004). From outreach to equity: Innovative models of library policy and practice. Chicago: American Library Association. Roy, L. (2009). Service learning connecting diverse communities and LIS students and faculty. In L. Roy, K. Jensen, & A. H. Myers (Eds.), Service learning: Linking library education and practice (pp. 73–82). Chicago: American Library Association. Roy, L. (2011). Weaving partnerships with the American Indian peoples in your community to develop cultural programming. In L. Roy, A. Bhasin, & S. K. Arriaga (Eds.), Tribal libraries, archives, and museums: Preserving our language, memory, and lifeways (pp. 141–156). Lanham, MD: Scarecrow Press. Roy, L., Brzozowski, B., Arnold-Garza, S., & Beauchemin, K. (n.d.). ‘Are you searching for a new job?’: Texas public libraries provide services for job seekers. Texas Library Journal, 87(1), 30-33. Smallwood, C. (2010). Librarians as community partners: An outreach handbook. Chicago: American Library Association. Snyder, C. A. W. (2002). Measuring library service quality with a focus on the LibQUAL+ project: An interview with Fred Heath. Library Administration & Management, 16(1), 4–7. Sung, H., & Hepworth, M. (2013). Modelling community engagement in public libraries. Malaysian Journal of Library & Information Science, 18(1), 1–13. The University of Illinois at Urbana-Champaign. Graduate School of Library and Information Science. (2013). Robert B. Downs Intellectual Freedom Award. Retrieved March 3, 2015, from http://www.lis.illinois.edu/about-gslis/awards/ downs-award

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The University of Texas at Austin. Bureau of Business Research. (2012). Texas public libraries: Economic benefits and return on investment. Retrieved March 3, 2015, from https://www.tsl. state.tx.us/roi

Boff, C., Singer, C., & Stearns, B. (2006). Reaching out to the underserved: More than thirty years of outreach job ads. Journal of Academic Librarianship, 32(2), 137–147. doi:10.1016/j. acalib.2005.12.007

Urban Libraries Council. (2007). Making cities stronger: Public library contributions to local economic development. Chicago: Urban Libraries Council.

Courtney, N. (2008). Academic library outreach: Beyond the campus walls. Westport, CT: Libraries Unlimited.

Welburn, W. C., Welburn, J., & McNeil, B. (2010). Advocacy, outreach and the nation’s academic libraries: A call for action. Chicago: American Library Association.

ADDITIONAL READING American Library Association. Office for Literacy and Outreach Services. (2008). Handbook for mobile services staff. Retrieved March 3, 2015, from http://www.ala.org/offices/olos/bookmobiles/mobileservices American Library Association. Office for Literacy and Outreach Services. (2010). Keys to engaging older adults @ your library. Retrieved March 3, 2015, from http://www.ala.org/offices/olos/ toolkits/olderadults American Library Association. Office for Literacy and Outreach Services. (2012). Extending our reach: Reducing homelessness through library engagement. Retrieved March 3, 2015, from http:// www.ala.org/offices/extending-our-reach-reducing-homelessness-through-library-engagement Armour, G. (2010). Communities communicating with formal and informal systems: Being more resilient in times of need. Bulletin of the American Society for Information Science and Technology, 36(5), 34–38. doi:10.1002/bult.2010.1720360510 Blake, B., Martin, R. S., & Du, Y. (2011). Successful community outreach: A how-to-manual for librarians. Chicago: American Library Association.

Davis, D. (2009). Outreach to non-English speakers in U. S. public libraries: Summary of a 2007 study. Public Libraries, 48(4), 13–19. DelPo, A., Colletti, M., Hallock, E., & Lewis, N. (2011). Free to Learn: Best practices for serving former prisoners in public libraries. Colorado Libraries, 36(1), 1–5. Diaz, R. (2005). Developing library outreach programs for migrant farm workers. Florida Libraries, 47(1), 12–14. Featherstone, R. M. (2012). The disaster information specialist: An emerging role for health librarians. Journal of Library Administration, 52(8), 731–753. doi:10.1080/01930826.2012.746875 Ferguson, S. (2012). Are public libraries developers of social capital? A review of their contribution and attempts to demonstrate it. The Australian Library Journal, 61(1), 22–33. doi:10.1080/000 49670.2012.10722299 Fontenot, M. (2013). Five “typical” years as an outreach librarian and five things I have learned. College & Research Libraries News, 74(8), 431–432. Grace, D., & Sen, B. (2013). Community resilience and the role of the public library. Library Trends, 61(3), 513–541. doi:10.1353/lib.2013.0008 Hawk, M. (2008). Bookmobile services in rural America: Past, present, and future. Bookmobile & Outreach Services, 11(2), 57–78. Jensen, B. (2002). Service to day laborers: A job libraries have left undone. Reference and User Services Quarterly, 41(3), 223–233.

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Jones, P. A. Jr. (2004). Still struggling for equality. Westport, CT: Libraries Unlimited. LeRoux, C. J. B. (2010). Social informatics vs. community informatics: A brief overview of their origins and current status. Mousaion, 28(1), 34–44. Lewis, J. (2013). Information equality for individuals with disabilities: Does it exist? The Library Quarterly, 83(3), 229–235. doi:10.1086/670697 Marco, G. A. (2012). The American public library handbook. Santa Barbara, CA: Libraries Unlimited. Mars, A. (2012). Library service to the homeless. Public Libraries, 51(2), 32–35. Pointon, S. E. (2009). Library outreach is the future! Public Libraries, 48(3), 2–5. Rudin, P. (2008). No fixed address: The evolution of outreach library services on university campuses. The Reference Librarian, 49(1), 55–75. doi:10.1080/02763870802103761 Singer, D., & Agosto, D. (2013). Reaching senior patrons in the digitized library. Public Libraries, 52(6), 38–42. University of North Texas. PEARL project: Community outreach plans. Retrieved March 3, 2015, from http://pearl.unt.edu/communityoutreach-plans

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KEY TERMS AND DEFINITIONS Advocacy: Informing the public of the library offerings, the library as work setting, and the library profession. Community Informatics: Designing services for targeted members of the library’s user community. Crisis Informatics: Designing services that assist library patrons who are impacted by, or might be impacted by, traumatic events. Digital Equity: Promoting and designing services that provide all members of the community with access to online information and the skills and tools to access, use, and evaluate online content. Information Coping: The ability to handle news, alerts, messages, data, music or other information formats in a way that results in learning and understanding with minimal anxiety. Information Seeking: The habits and patterns that humans undertake in locating answers to questions, satisfying needs, and fulfilling desires for learning and entertainment. Service Learning: Addressing community needs through course content and personal reflection.

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Category: Public Sector Management

Exploring “Hacking,” Digital Public Art, and Implication for Contemporary Governance Amadu Wurie Khan University of Edinburgh, UK Chris Speed University of Edinburgh, UK

INTRODUCTION This article presents the application of the online (Internet) ‘hacking’ concept to community life and processes from two hypothetical contexts: First, it was hypothesised that technology could be ‘hacked’ into by disadvantaged communities to create a digital public art. Second, the community-generated digital art platform could in turn be used to ‘hack’ into images and memories to facilitate the sharing of conversations and identity, social engagement, and digital inclusion among residents. The article therefore presents how these contexts of the characteristics and practicality of online ‘hacking’ inspired the design and functionality of a community digital artwork in a disadvantage urban estate in Edinburgh, UK. In addition, the article considers the implication of the ‘hacking’ practices by and among disadvantaged communities for realizing the social action, social engagement and networked society goals of the UK Government’s ‘Big Society’ policy. This is significant because the ‘Big Society’ agenda promotes an interactive networked culture that has transformative potential to connect citizens, build knowledge and continuous learning and regenerate communities at at time of economic austerity in the UK (Mayo & Steinberg, 2007; Speed, Khan & Phillips 2016). The article is presented as follows. The following section is the conceptual path-clearing.

It traces the etymology and usage of the concept of ‘hacking’ from the techno-scientific domain. Section three makes an attempt at disambiguating the kinds of ‘hacking’ practices that are relevant to issues of community relations and processes. Section four presents the practical application of the ‘hacking’ concept that culminated in the social design of a physical digital public art, the ‘totem pole’. In section five, the implications of ‘hacking’ for the ‘Big Society’ policy is considered. Section five provides suggestions for a research agenda that could generate future design interventions that are inspired by concepts associated with digital media culture and for realizing forms of contemporary governance such as the ‘Big society’.

CONCEPTUAL BACKGROUND: THE “HACKING” FOLKLORE In tracing the etymology of ‘hacking’, folklore rather than the history of the concept should be prioritised. This is because there are many narratives to explain the emergence of the concept and its incorporation into contemporary public discourse (Devitt, 2001). History could be subjective, but mainly expected to be a precise and accurate record. Folklore, although rooted in historical narrative and passed down across generations, is not expected to carry the kind of accuracy as history should. This is because its mainly verbal

DOI: 10.4018/978-1-5225-2255-3.ch580 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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form of transmission and its inherent performance element makes folklore vulnerable to variability and manipulation (Khan, 2009). A word of caution though! History could be handed down verbally too, and like folklore could be accurate, written down and passed through generations. Nonetheless, folklore is associated with traditional stories, gossip, myths and legends, all of which are traditional art forms that are characterised by dubiety, as might be the case with history. A look at the many romanticised accounts of the origin of ‘hacking’, lends weight to prioritising the folklore around the origin of the concept over historical accounts. Against this background, folklore has it that ‘hacking’ originated from the realm of technology as student slang at the Massachusetts Institute of Technology (MIT) between 1950 and 1960 (see Levy, 2002; Devitt, 2001). MIT is said to have been amongst the first institution to offer courses in computer programming and computer science, and that it was on such a course that group students taking a class on artificial intelligence came to coining the word ‘hacker’. Students used the term to refer to their ability to manipulate a computer to perform actions not intended for that program. It has also been suggested that the term was used to convey a sense of performing a “practical joke and feeling of excitement because the team member would ‘hack away’ at the keyboard hours at a time.” (Moore, 2006). Examples of ‘hacking’ folklore associated with MIT include: in 1964, MIT students (hacks) placing a convincing replica of a campus police car on top of the Institute’s Great Dome as a form of practical joke, manipulating electric trains to make it perform faster and more efficiently (see Levy, 2002; Moore, 2006). Ward (BBC 27 Oct 2000), argued that ‘hacking’ originally meant “an elegant, witty or inspired way of doing almost anything”. Since then, the practice of ‘hacking’ has been evident in or applied to other fields and scholarly or technical communities, and not just limited to technology. The next section briefly explores these folkloric dimensions of

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‘hacking’, namely ‘phreaking’ & ‘cracking’, and ‘hobby-ism’ & ‘prosumerism’.

“Phreaking” and “Cracking” In the 1970s, ‘phreaking’ or phone ‘hacking’ emerged by which ‘hacks’ manipulated telephones to make free calls. A legendary figure in this respect was, a ‘phreaker’ John Draper, who discovered that a whistle included as a free gift in boxes of Captain Crunch cereal emitted a 2,600 hertz pitch which was the frequency used to indicate operator calls to phone exchanges (Burnham, 2009; Lapsley, 2011). Blowing the whistle into the mouthpiece of the phone meant that the call was seen to come from an operator and hence no charges were levied on the call. Not only did John Draper become known in the hacking world as Captain Crunch, but his work is seen to have inspired Steve Wozniak and Steve Jobs who went on to found Apple Computers (Burnham, 2009). By the 1980s, ‘phreaking’ was evident in computers in the form of Bulletin Board Systems (BBS), which is believed to be the precursor to the yahoo groups of today (Levy, 2002; Lapsley, 2011). In addition to enabling individuals to post messages of any kind of topics, the BBS specialized in disseminating information on how to break into computers, how to use stolen credit card numbers and share stolen computer passwords (see Levy, 2002). Known as ‘cracking’, the practice entails the circumventing computer security and unauthorized remote computer break-ins via a communication networks such as the Internet. From the above practices, two distinct but interconnected senses of the term ‘hacker’ and ‘hacking’ in the domain of technological science are discernible: the modification of use and the breaking of codes/security (Raymond, 2001). This often came together in particular ways to establish particular but interconnected areas in the domain of technological science: computer programming and computer security. In the former sense of the term ‘hacker’, emphasis is put on modifying computer programmes/technologies

Category: Public Sector Management

so that they can perform new uses, while in the latter (code-breaking) the emphasis is placed on breaking security features, either as an end in itself or as a way so that the technology can be used in ways other than originally intended. It has been suggested that the terms ‘hacker’/’hacking’ might be restricted to the first sense, whilst the terms ‘cracker’/’cracking’ might be used to refer to the second set of practices (Cramer, 2003). According to Raymond (2001), ‘hackers’ from the field of programming usually work openly and use their real name, while computer security hackers (pr crackers) prefer secretive groups and identity-concealing aliases. Also, their activities in practice are largely distinct. The former focus on creating new and improving existing infrastructure (especially the software environment they work with), while the latter primarily and strongly emphasize the general act of circumvention of security measures, with the effective use of the knowledge (which can be to report and help fixing the security bugs, or exploitation for criminal purpose) being only rather secondary.

“Hobby-Ism” and “Prosumerism” There is another sense in which ‘hacking’ and ‘hackers’ include ‘hobby-ism’, in which case individuals show commitment and passion in being creative in a given field in ways that are similar to professionals (Burnham, 2009). An example often cited is that of ‘circuit bending’, a practice associated with the techno-music sub-culture. By this way, ‘hobbyists’ customized the circuits within electronic devices such as low voltage, battery-powered guitar effects and small digital synthesizers to create new musical sound (Burnham, 2009; Early, 2007; Levy, 2002). This results in the creation of the strange, dis-harmonic digital tones that became part of the techno-music style. ‘Hobbyists’ are in the main customers of products who rather than just accept the manufacturer’s design of a product, will try to adapt it to their tastes in order to get a desired aesthetic and utility

value. It has been observed that such activities have been tapped into by manufacturers in an attempt to widened customer base and profit margins. This is by the appropriation of ‘hacker’/’hobbyist’ generated designs or specifications into new products that are sold into the market. The ‘hacker’ or ‘hobbyist’ therefore transforms from a consumer to what Toffler (1980) referred to a ‘prosumer’. In this situation (prosumerism), the role of producer/ professional and consumer merges to participate in the design and production requirements of products through mass customisation. Manufacturers adapt or create their products to the specific requirements of consumers in order to keep the latter satisfied, while generating profit. Others argued that ‘prosumers’ include consumers who rather than just accept or purchase what is on sale would actively search the market and seeks to influence the market (Crakburn, 2003). ‘Prosumers’ therefore do not only influence or dictate design, aesthetic and utility values, but also influence the markets. In this sense, ‘prosumerism’ arguably makes consumers part of the creative and marketing or pricing process, as they impart their individual and or collective preferences. The above folkloric account illustrates the nebulous origins of ‘hacking’ and its manifestations (phreaking,cracking, hobby-ism, and prosumerism) within science and technology: mechanics, computers (both programming and security), and telephonics, as well as in manufacturing. These manifestations of ‘hacking’ continue to be shrouded by accounts that have been subjected to manipulation and variability, and therefore continues to be the terrain of folklore. This, in turn, makes ‘hacking’ a romanticized construct, and a contested concept (Devitt, 2002; Thomas, 2002). ‘Hacking’ has therefore become a metaphor for the practices and actions that exploit (or explore) weaknesses or deficiencies in a system to behave or function in a certain way (Kulikauskas, 2004). The next section takes a look at some key social characteristics of ‘hacking’.

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“HACKING”: AND ITS SOCIAL CONTEXT Arguably, the actions by ‘phreakers’, ‘crackers’, ‘hobbyists’ and ‘prosumers’ constitute a creative process that is performed in a social environment (see Kulikauskas, 2004). The socially creative process involves risk-taking or the circumvention of limitations, and entails self-reliance, and learning through the sharing of skills and resources (Fitch, 2003). The creativity is motivated by intellectual, functional and aesthetic challenges (see Cramer, 2003). It is also a conscious technical act or practice that individuals undertake towards a preconceived outcome that was not intended by the original creators of the ‘thing’ that is ‘hacked’ (Levy, 2002; Dan, 2011). It therefore has the following social characteristics, namely reciprocity, resilience and moral ambiguity (Fitch, 2003; Lemos, 2002).

“Reciprocity” and “Resilience” ‘Hacking’ depends on reciprocity. This is better exemplified in computer gaming. As Crabtree (2003) explains, when people are stuck in a computer game, they will go to a gaming community online, and ask others for advice. Other gamers will help on the principle of reciprocity; they will sometime in future expect someone else (including those they have helped) to help them out with their gaming problems. Reciprocity is therefore a form of harnessing social capital among society’s members. Through reciprocity people turn to each other when they encounter everyday problems (civic, social, political, economic etc.) to access knowledge, skills, and advice from others who have expressed similar problems in a bid to solve or help overcome them. ‘Hacking’, in this sense, becomes a social, or in Crabtree’s conception a ‘civic’, phenomenon (Crabtree, 2003). It thrives on symmetrical and synchronous relationship. as well as on individual and collective resilience to cope with emerging problems and demands that citizens (non-citizens too) encounter in their everyday lives in the community (see Crabtree, 2003).

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This is not to say that ‘hacking’ is always motivated by reciprocity. It might be that there are altruistic and communitarian reasons behind an individual’s sharing of skills and resources to facilitate ‘hacking’. For example, helping others may be a source of ‘feel-good’ factor as well as perceived as a civic and social responsibility that an individual owes to others. It might also be that some individuals might not give back or reciprocate either in the same way or in kind as other participants. They simply just take away, consume or benefit from what others have contributed without returning such favours; neither would they expect similar favours from others who benefit from their contribution. In this case, the relationship is asymmetrical and asynchronous by which an individual may not reciprocate magnitude and content or information as required. It is also possible that in cases of reciprocal response, this is not spontaneous, but deferred to a later date (Hrastinki, 2008).

The “Moral Ambiguity” From the foregoing discussion, it could be suggested that hackers’ socially creative practices could be perceived to be ‘legal’ or ‘illegal’ (Fitch, 2003, Lemos, 2002). For want of clarity, we suggest that, the term ‘hacking’ is reserved or applied to ‘legal’ activities, while ‘cracking’ is for the ‘illegal’ or transgressive ones as Figure 1 shows. Yet, we know that these conceptualisations are fraught with ‘moral ambiguity’. By ‘moral ambiguity’ is meant that individuals’ perceptions and interpretations of the legitimacy of a ‘hacking’ practice is subjective and influenced by their moral values and beliefs. This begs a three-dimensions to conceptualizing ‘hacking’ as Figure 1 shows. At the extreme left and moving towards the right of the continuum [up to point zero (0)] of Figure 1, are forms of positive ‘hacking’ that are normally perceived as good and legitimate or legal. Examples of this kind of ‘legal’ activity encompasses ‘hobbyists’ and ‘prosumers’ as indicated in Figure 1. As ‘hobbyists’, they are not necessarily profes-

Category: Public Sector Management

Figure 1. The “hacking”/“cracking” continuum

sionals, but amateur enthusiasts seeking to adapt and influence the aesthetic and utility value of a product. ‘Hackers’ are ‘prosumers’ because they openly and ‘legally’ participate in the designing and production chain from the outset as well as influencing market forces. The activities are, in the main, facilitated and supported by manufacturers. ‘Cracking’ is diametrically opposite to this practice, widely perceived as illegitimate or illegal and indicated on the extreme right of the continuum [moving from point zero (0) to the right]. Examples normally perceived as illegitimate and transgressive actions include code-breaking, ‘phreaking’ and phone tapping. However, ‘code-breaking’ can be perceived as ‘legal’ and or ‘transgressive’ depending on an individual’s moral standpoint. There is therefore the possibility of a continuum of ‘hacking’ and ‘cracking’ practices, which could be designated as ‘morally ambiguous’, which is around ‘point zero’ (0) or in the middle of the continuum. It should be envisaged that there will exist a plethora of on-line and off-line practices that could be located in the continuum, albeit it underpinned by the moral persuasion of individuals. More importantly, what constitute ‘legal’ or ‘’illegal’ and their boundaries of demarcation are not only based on an individual’s ‘subjectivities’, but also constructed by legislation and policies of states, supra-national institutions and industry. Nonetheless, the illustration is intended to serve a heuristic purpose that is informed by the perception of individuals in ascribing ‘good’/’bad’ or ‘legal’/’illegal’ to ‘hacking’. The illustration also demonstrates that both ‘hacking’ and ‘cracking’ practices are underpinned by imaginativeness, intentionality and functionality (see Lemos, 2002; Fitch, 2003).

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The above characteristics and application of ‘hacking’ provided the inspiration for a community-generated digital art, known as the digital ‘totem pole’ (see Chris et al. 2016). The key hypothesis was that technology could be ‘hacked’ into to create a digital public art, which have implications for the realization of UK’s ‘Big Society’ policy among disadvantage communities. The rest of the article presents how online ‘hacking’ inspired the social design of the ‘totem pole’ and its utility for community life and processes.

FROM THEORY TO DESIGN APPLICATION This section is divided into two parts. The first describes the design locale and process of community engagement that culminated in the construction of the public digital artwork. This is followed by a discussion of the practicality and functionality of the digital ‘totem pole’ for the ‘Big Society’ as a form of contemporary governance.

Wester Hailes and the Design Process Wester Hailes is a huge housing estate constructed in the 1970s on the Western outskirts of Edinburgh, the capital city of Scotland, UK. It has been afflicted with high levels of social and economic deprivation, crime and unemployment. The social and economic challenges of the area have provided the impulse for local residents to organise community development and service delivery initiatives. In addition, Wester Hailes has historically deployed community art towards

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achieving community development, regeneration and empowerment. Community art has also been central to the desire to project a positive image of the community to contrast a mainly negative representation of its residents. Two local service providers, Prospect Housing Association (aka Prospect) and Whale Arts expressed an interest in using social media as a platform to share ideas, photos and memories. Prospect set up a Facebook page and began posting images of the area that were originally published in the community newspaper; the Wester Hailes Sentinel; latterly the West Edinburgh Times. The page quickly became popular and with photographs attracting many comments about who, when and where they were taken (http://on.fb. me/mOPPwp). This ‘write back’ facility began to enable residents to recover memories of the past and drew out many connections beyond the image itself. By the summer of 2010, the research team and local residents agreed on a design method that encapsulated ‘hacking’. This involved the development of platforms that facilitated the public ‘writing back’ on to the photos and images of Wester Hailes. To enable this process, community members chose the construction of Figure 2. Residents learning to scan

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a large wooden digital ‘totem pole’ to be located within the neighbourhood. WHALE Arts coordinated the production of the ‘totem pole’ (see Chris, Khan & Phillips, 2016). A steering group of community members and project partners was created to ensure that clear targets are set and achieved. The steering group also facilitated networking, engagement and capacity building that underpins the five participatory workshops within the community (Figure 2). The workshops were held during local civic events including the Tenth anniversary celebration of the Union Canal, at the AGM of service providers including the Prospects Housing Association and WHALE community arts project, and at the annual community Road Show in the Westside Plaza shopping centre. Participants to the workshops include local residents, staff of services and community activists. Project staff used the workshops to explain the rationale, process and outcomes with a view to generate interest and recruit local residents to participate in its development and delivery. A central element of the workshop was the display of a portable banner with embedded quick

Category: Public Sector Management

response (QR) codes (as proof of concept) and historical photographs depicting people, events and places of Wester Hailes on the outer wall of a portable shed (see Chris, Khan, & Phillips, 2016). Both the portable banner and photographs were used in combination to introduce local residents with different levels of technological expertise and interest to the technology used in the project. Participants were asked to look at the photographs to trigger their memories or any other associations that they might have with the Figure 3. The installed digital “totem pole”

people, event or place depicted. They were then encouraged to share their story/memories about these, which were captured through a voice and film recorder. Participants were also encouraged to scan the QR codes embedded on the photographs and to record their stories and upload them to the Tales of Things website (and the Wester Hailes social history archive). They were then asked to scan and listen to the replay. This exercise was to give them experience of the opportunities that the technology would offer, which is the ability to read and write into the codes. The workshops were also opportunities for participants to ask questions and provide any suggestions or views about the project and how best to improve it and to get their sustained involvement in its future development (see Chris, Khan & Phillips, 2016). The overall ethos of the workshops was one that promoted engagement with local residents and their exposure to new developments in web-based technology in a way that was empowering, collaborative, non-threatening and meaningful. The workshops were therefore central to community participation in all aspects of the totem pole project including the design, time-scales and location of the ‘totem pole’ (see Chris, Khan, & Phillips, 2016). During the carving of the pole and the ensuing deliberations, both residents and service providers realised that the pole had a potential for service delivery in the locality. It was therefore decided that, in addition to sharing stories and memories of the area, the ‘totem pole’ should include 5 QR codes that give access to a variety of services. The final product was a carved wooden digital ‘totem pole’, which was situated within Wester Hailes (Figure 3 above). Its QR codes provided a physical platform for ‘hacking’ images (through the ability of people being able to comment and create new meanings for the images) and sharing conversations about the area. The QR barcodes were also gateways to cloud based material relevant to the location of the pole. Local residents could scan one of the labelled tags to access and contribute to historical photographs, stories, video and audio clips (Figures 4 and 5). By so doing, the pole act

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Figure 4. Local residents scanning the QR codes

Figure 5. Use of historical photos to evoke memories of past community activates. Residents could “write” memories on to QR codes and attach them to the photograph.

as a social resource to help build connections between the people and the place, as well as drawing upon online resources (see Marggets,2011; Chris, Khan & Phillips, 2016). What then can be concluded from the design and practicality of the ‘totem pole’ that is relevant to developing an understanding of the intersection of the digital media culture of ‘hacking’, public art, and the ‘Big Society’ policy? To answer this question, a brief account of the main features and aims of the policy would be imperative. 6702

“Hacking” and Implications for the “Big Society” The ‘Big Society’ is a policy mainly championed by the governing Conservative Party in the UK (Szieter & Ishkanian, 2012). It is aimed at encouraging citizens (and non-citizens) to participate collectively and collaboratively in local initiatives. It is intended to make policy-makers transparent and accountable to the recipients of public services. By so doing, the policy is a bottom-up

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process that transforms policy-making and service delivery from a centralised bureaucratic control system to one that is grass roots-led. It’s proponents argued that this would make service provision decentralised and localised. Proponents also argued that this ‘localism’ enable services to be tailor-made to the needs, tastes and specifications of local people, and at the same time delivered in a better improved quality (see Lesley2010). In this sense, service users (citizens) are constructed as not only consumers, but ‘prosumers’ as they become stakeholders in the design of services from the outset of their conception and formulation. As ‘prosumers’, citizens depend on each other to pull skills and resources together for mutual benefit. Grass-roots involvement in the formulation, delivery and making choices on services, access information and knowledge, and hold public officials to account make them become empowered social actors (see Chadwick, 2009). The ‘Big Society’s’ originator, British Prime Minister David Cameron claimed that the policy will enable people powerful enough to help themselves and is the “biggest, most dramatic redistribution of power from elites to the man in the street” (Green Paper No. 14). The policy could enable a fluid and evolving response by individuals to challenging circumstances, eradicate structural inequalities, and facilitate civic engagement, social connectivity, networking, and innovation (see Szieter & Ishkanian, 2012). Given these goals of the policy, the ‘totem pole’ became relevant to the ‘Big Society’ agenda among the research team, and residents. It serves as a networked museum or repository for audio, written and visual recordings of current and past memories, images, narratives, works and ideas of local residents and others in the Diaspora. While these benefits of community digital art have been observed elsewhere (see for instance, the ‘Talking Poles’ in Moulder et al., 2011), the totem pole moved beyond its aesthetic and archival value to have a symbolic relevance to Wester Hailes as a community with historic problems of marginalisation. The general feeling among

residents was that the ‘totem pole’ served a symbolic function – that of community resilience and regeneration. It symbolises the community’s resilience to contest negative depictions of their community as afflicted with social delinquencies and deprivation. The process of designing and delivering the totem pole depended on the input of residents. As others have observed, most public artwork that incorporate digital technology and produced by non-professional artists tended to exclude people from the final stages of the creative process (Moulder et al., 2011, p.2). In contrast, the pole’s social design approach facilitated community participation at every stage - from the design process of the pole and the QR codes, the content and themes therein contained to the installation of the finished product. The pole’s design therefore reflected residents’ design preferences, aspirations and expectations of their locality (see Chris, Khan & Phillips, 2016). It made residents to be co-creators of the artwork and for them to have equal ownership of the whole project. The design process also contributed to the skills of locals for it to be a successful participatory community art project (Moulder et al., 2011, p.8; Ackoff, 1974). The lack of digital technology skills including social media, therefore, did not preclude anyone from participating. As explained earlier, the workshops built such skills among those that lacked them. Overall, the process of design nurtured an engagement between professional and non-professional artists, academics and non-academics, and digital media technologists and non-technologists. The social connectedness and networked communities was only possible by the in-built ‘read-write’ component of the ‘totem pole’. In this way, the project generated significant social capital by engaging with the diverse ages, backgrounds and interests present in the Wester Hailes community to explore and capture their memories of the area. It is also to articulate a collective future ambition for the community. In summary therefore, as a ‘hacking’ inspired design intervention, the collaborative production of the ‘totem pole’ became tangible to the ideals

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of the ‘Big Society’ agenda. Firstly, it was an embodiment of social action, civic engagement, and networking. This is because it was co-produced artwork between residents, service providers and the academic/research team to realised the community’s aspirations. Secondly, it generated a sense of community, empowerment, and identity with the estate among residents (Tonnies, 1957). This sense of community moved across geographies of locality, Diaspora, shared interest and generations. By so doing, it generates a feeling of identity with Wester Hailes among these disparate geographies. Thirdly, the sharing of images and resources constitute resilience, resourcefulness and reciprocity. For instance, the renewed sense of identity, participation in the design and use of the pole demonstrates the resilience of residents to regenerate their community at a time of decline in services such as local newspapers and economic austerity. The sharing of information, memories and images largely depend on the community pulling together its resources in a way that is reciprocal (see Chris, Khan & Phillips, 2016). These positive social processes are akin to ‘civic/ social hacking’. However, it was the view of some residents, service providers and the research team that the ‘pole’ might be used by the public in deviant or transgressive ways. For instance, there were concerns that its ‘read-write’ facility might attract unfair, impolite and gratuitous comments against residents, service providers or politicians. If these were to occur, they would constitute ‘cracking’, a phenomenon associated with the ‘hacking’ culture as enunciated earlier. To address this unwanted and transgressive use of this public art technology, an editorial policy was put in place to moderate and monitor public comments before being posted on to the ‘pole’. Although this was done in consultation with residents, it presents a moral and ethical dilemma that would confront any digital project that is community-led as would be with the ‘Big Society’ policy. For instance, how would the powers that be (meaning researchers,

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services, policy-makers and politicians) respond to activities of communities that are outside the stipulations or rules of engagement of a public art or service? Would the transgressive behaviour by individuals and communities, albeit it in their own interest and benefit, be tolerated? If tolerated, then it would make citizens to act like ‘prosumers’ who are involved in deciding on the type of service and how it is used or its usage. The extent to which such individual and collective interventions are acceptable or not acceptable, justified or not justified and whether or not they constitute social deviant behaviour are bound to be debatable. In addition, the participation and reciprocal relationship among stakeholders (meaning researchers, services and residents) in the production and functionality or workings of the pole could be asynchronous, rather than synchronous. For example, in a socially disenfranchised community like Wester Hailes, not every one had access to the Internet or interactive mobile phones (see Chris, Khan & Phillips, 2016). Many individuals could not afford the financial resources and skills required to use this technology, despite the training provided as explained earlier. These individuals are not likely to benefit from this ‘hacking’ activities that the functionality of the ‘pole’ depended on. This could further widened the gap between the empowered and the marginalized as level of participation based on their abilities and resources at their disposal are uneven. It is a potential risk to achieving the ’Big Society’ agenda: not all individuals and communities will participate either in all services or activities or in equal measure. The project claimed to promote digital inclusion in so far as many residents who had no experienced of interactive media were provided with the skills to use the ‘pole’. Nonetheless, the structural inequalities in society are bound to affect, in different ways, individual and community participation in the technology. Another moral dilemma was that, Internetfacilitated ‘hacking’ has an uneasy relationship with the goals of the ‘Big Society’. Firstly, this

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type of ‘hacking’ is mainly virtual (internet), and confronts the purpose of face-to-face social contact, social interaction and social engagement among citizens. Secondly, the ‘hacking’ through everyday social practices and sharing is conducted on a voluntary basis, albeit with the expectation of reciprocity. In the context of the ‘Big Society’, it is likely that such commitment and voluntarism will vary among society’s members. The received wisdom is that volunteering is the terrain of the middle class, and so deploying ‘hacking’ as a mechanism for pursuing the ‘Big Society’ agenda might exclude disenfranchised communities (see Chris, Khan & Phillips, 2016).

FUTURE RESEARCH DIRECTIONS The exploration of the’ hacking’ concept and its application in social design and policy interventions is heuristic. Research and design energies should therefore be devoted towards understanding the intersections between online practices (virtual communities) and offline practices, or the daily experiences of individuals in the policy context of contemporary governance. The nature of the investigation should aim at offering radical insights into how disadvantaged communities will develop any means possible to overcome the challenges they face especially at a time of financial cuts that are likely to impact upon them. These design and research interventions could be understood through the use of cross-disciplinary research: social science, arts and humanities and industrial models of co-design with communities that are impacted by policies. In addition, it has been highlighted that ‘hacking’ is a practice that is fraught with moral ambiguity: that of whether it is acceptable or non-acceptable to practice ‘hacking’ (Kulikaskas, 2004). The pejorative conception of the term is compounded by contemporary media representation of ‘hacking’ as criminal and social delinquency. ‘Hacking’ in this sense is associated with criminal acts such as identity theft, credit card

fraud and other computer-related crimes (see also Moore, 2006). The ‘phone hacking’ saga in the UK culminating in the judicial ‘Leveson Inquiry’ in 2011/2012 is a reminder that, in addition to legislative and policy considerations of the state and industry, moral ambiguity play a role in the public determination of what constitute legitimate or illegitimate ‘hacking’ (Leveson, 2012). The criminal connotation of the term is believed to have caused a segment of the computer community to ascribe the term ‘hacker’ to those engaging in legal activities. As already explained, they ascribe ‘cracker’ to those engaging in nefarious ‘hacking’ activities including those performing computer break-ins. Nonetheless, the term continues to be used by actors in the techno-scientific field for two reasons: firstly, the intended meaning can be based on the context of usage in order to clarify which meaning is intended (pejorative or complimentary); secondly, the practice describes a set of skills which are used for various reasons including nefarious criminal activity (Moore, 2006). Researchers should therefore engage in empirical investigation to gather evidence to inform this debate about what is morally legal or illegal, and who has the right or moral authority to decide these activities as indiscretions. Research should also investigate (empirically and or conceptually) the utility in employing the terms ‘social or civic hacking’ and ‘social or civic cracking’ for depicting forms of ‘hacking’. Questions that could be empirically explored include: Should the term ‘social/civic cracking’ be applied to hacking activity among communities that are ‘illegal’, transgressive or contravenes social norms, policy and legislation? Conversely, should the perceived community ‘legal’ hacking practices be ascribed as ‘social/civic hacking’? Exploring these areas would develop our understanding on how communities use digital technology for empowerment, resilience and resourcefulness beyond that which the state is currently willing or able to provide. Finally, all of the aforementioned research areas could also focus on affluent or middle class com-

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munities. This would enable an understanding of the motivations, aspirations and resourcefulness of this segment of society in harnessing technology for transgressive or legal pursuits to cope with the challenges they face. Such research should generate insights that would enable a comparative understanding of the use of technology along socio-economic demographics in the polity. It will also counterbalance stereotypes of ‘hacking’ as a practice undertaken by a certain section of society.

CONCLUSION This article has explored how the concept of ‘hacking’ provided the inspiration for a community-based digital artwork that facilitated social engagement, connections, and shared identity in a disadvantaged neighbourhood in an urban setting. This example highlights that digital media culture have utility for addressing everyday life situations, and the capacity of individuals and communities to develop or organize innovative social solutions, whether transgressive or conformist to established protocols (norms, laws) for the improvement of their lives and neighbourhoods. The chapter also considered the moral dimension to evaluating ‘hacking’ and the distinction between ‘hacking’ and ‘cracking’. Nonetheless, it has been explored that, whether or not social processes are ‘hacking’ or ‘cracking’, they have the characteristics of a community (virtual or actual). This is because they are a manifestation of empowerment, resilience and resourcefulness by individuals to access or generate benefit beyond that which the state is currently willing or able to provide. The chapter also provides suggestions for a research agenda that could generate future design interventions to develop an understanding of how digital media culture could inform forms of contemporary governance such as the ‘Big society’.

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REFERENCES Ackoff, R. L. (1974). Redesigning the Future. New York: Wiley. Andreyev, J. (2010). HIVE. Retrieved from http:// julieandreyev.wordpress.com/ Bowie, F., & Fels, S. (2009). Flow. Retrieved from http://fionabowie.org/ Chadwick, A. (2009). Web2.0: New Challenges for the Study of E-Democracy in an Era of Information Exuberance. I.S: A Journal of Law and Policy for the Information Society, 10. Retrieved from http://static1.1.sqspcdn.com/static /f/127762/17971662/1335910108743/ Crabtree, J. (2003). Civic hacking: a new agenda for e-democracy. OpenDemocracy. Retrieved from http://www.mafhoum.com/press4/136C35.htm Cramer, F. (2003). Social Hacking, Revisited. Retrieved from http://cramer.pleintekst.nl:70/all/ social_hacking_revisited_sollfrank/social_hacking_revisited_sollfrank.pdf Dan, A. (2011). Social Hacking. Retrieved from http://danariely.com/2011/01/20/social-hacking/ Devitt, M. (2001). A brief history of Computer Hacking. Dynamic Chiropractic, 17. Retrieved from http://www.dynamicchiropractic.com/ mpacms/dc/article.php?id=18078 Devitt, M. (2001). A Brief History of Computer Hacking. Retrieved from http://www.chiroweb. com/archives/19/13/04.html Earley, S. (2007). Folksonomy Versus Taxonomy. Retrieved from http://sethearley.wordpress. com/2007/02/15/folksonomy-versus-taxonomy/ Fitch, C. (2003). Crime and Punishment: The Psychology of Hacking in the New Millennium. Retrieved from http//www.giac.org/paper/gsec/3560

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Hudson, A., & Ciesla, M. (2011). Do you own the gadgets you buy?. Retrieved from http://news.bbc. co.uk/1/hi/programmes/click_online/9406690. stm Kulikauskas, A. (2004). Social Hacking: The Need for an Ethics. Retrieved from http://www.freeebay. net/site/index.php?option=com_content&task=v iew&id=289&Itemid=99999999 Lapsley, P. (2011). Phone Phreakers. Retrieved from http://voicegal.wordpress.com/2011/04/10/ phone-phreakers/ Lemos, R. (2002). New Laws Making Hacking a Black and White Choice. CNET News. Retrieved from http://news.com.com/2009-1001_3-958129. html Lesley, C. (2010). Localism & the Coalition – a neglected aspiration. Retrieved from http://idea. gov.uk/Idk/core/page.Id=24346328 Leveson, B. (2012). An Inquiry Into the Culture, Practices and Ethics of the Press (vol. 1). Retrieved from http://www.gov.uk/government/ publications/leveson-inquiry-report-into-theculture-practices-and-ethics-of-the-press Levey, S. (2002). Hackers: Heroes of the Computer Revolution. Penguin. Margettss, H. (2011). The Internet and Political Science: Re-examining Collective Action, Governance and Citizen-Government Interactions in the Digital Era. Retrieved from http://www.oii. ox.ac.uk/research/projects/?id=71 Mayo, E., & Steinberg, T. (2007). The Power of Information: An Independent Review. Retrieved from http://opsi.gov.uk/advice/poi/power-ofinformation-reviewpdf Moore, R. (2006). Cybercrime: Investigating High-Technology Computer Crime (1st ed.). Cincinnati, OH: Anderson Publishing. Moulder, V., Boschman, L. R., & Wakkary, R. (2011). The talking Poles – Public Art based in Social Design. CHI 2011, Vancouver, Canada.

Raymond, E. S. (2001). How To Become A Hacker. Retrieved from http://www.catb.org/~esr/faqs/ hacker-howto.html Speed, C., Khan, A., & Phillips, M. (2015). Contemporary Governance Discourse and Digital Media: Convergences, Prospects & Problems for the ‘Big Society’ Agenda. In D. O’Brien & P. Matthews (Eds.), After urban regeneration: Communities, policy and place (p. 147). Bristol: Policy Press. Szieter, S., & Ishkanian, A. (2012). Introduction: What is Big Society? Contemporary social policy in a historical and comparative perspective. In S. Szieter & A. Ishkanian (Eds.), The Big Society Debate (pp. 1–26). London: Edward Elgar Publishing Ltd. doi:10.4337/9781781002087.00007 Tacchi, J., Slater, D., & Hearn, G. (2003). Ethnographic action research: A user’s handbook developed to innovate and research ICT applications for poverty eradication. New Delhi: UNESCO. Thomas, D. (2002). Hacker Culture. Minneapolis, MN: University of Minnesota Press. Toffler, A. (1980). The Third Wave. New York: Morrow. Tonnies, F. (1957). Community and Society (C. P. Looms, Trans.). East Lansing, MI: Michigan State University Press. Wal, V. T. (2004). ‘Folksonomy’: Coinage and Definition. Retrieved from http://www.vanderwal. net/folksonomy.html

ADDITIONAL READING Albrow, M. (2012). ‘Big Society’ as a rhetorical intervention. The Big Society debate: A new agenda for social welfare, 105-115. Borko, F. (2011). Handbook of Augmented Reality. Springer.

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Bulley, D., & Sokhi-Bulley, B. (2014). Big society as big government: Cameron’s governmentality agenda. British Journal of Politics and International Relations, 16(3), 452–470.

Paltrinieri, R., & Esposti, P. D. (2013). Processes of Inclusion and Exclusion in the Sphere of Prosumerism. Future Internet, 5(1), 21–33. doi:10.3390/fi5010021

Buzzetto-More, N. A. (2013). Social media and prosumerism. Issues in Informing Science and Information Technology, 10, 67–80.

Spafford, E. H. (1990). Is a computer break-in ever ethical? Information Technology Quarterly, IX(2), 9–14.

Chandler, A. (1996). The changing definition and image of hackers in popular discourse. International Journal of the Sociology of Law, 24(2), 229–251. doi:10.1006/ijsl.1996.0015 Gitrell, J. F. (2002). Relationships between Service Providers and Their Impact on Customers Journal of Service Research May 2002, vol. 4, no. 4, 299311 doi:10.1177/1094670502004004007 Jørgensen, K., Sandqvist, U., & Sotamaa, O. (2015). From hobbyists to entrepreneurs On the formation of the Nordic game industry. Convergence (London). Limón, J. E., & Young, M. J. (1986). Frontiers, settlements, and development in folklore studies, 19721985. Annual Review of Anthropology, 15(1), 437–460. doi:10.1146/annurev. an.15.100186.002253 Lin, Y. (2007). Hacker culture and the FLOSS innovation. Handbook of research on open source software: technological, economic, and social perspectives, 34-46. MacKinnon, D., & Derickson, K. D. (2013). From resilience to resourcefulness A critique of resilience policy and activism. Progress in Human Geography, 37(2), 253–270. doi:10.1177/0309132512454775 O’Brien, D., & Matthews, P. (Eds.). (2015). After Urban Regeneration: Communities, Policy and Place. Policy Press. doi:10.1332/policypress/9781447324157.001.0001

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KEY TERMS AND DEFINITIONS Folklore: Generally refers to forms of cultural expressions, which people use to convey and share their history and identity. It incorporates narratives, jokes, beliefs, proverbs, legends, myths, music, songs, dances, costumes, food, and festivals. Hacking: Describes the practices and actions by which individuals and communities’ exploit (or explore) weaknesses of a system and product, which could be technological or social, to get by. It encapsulates both transgressive or ‘illegal’ and resourceful or ‘legal’ practices that individuals undertake to circumvent or respond to technological and social challenge or circumstance. Hobby-ism: This refers to a form of ‘hacking’ process and practice by which an individual customize or adapt a product to one’s preferences or tastes to get a desired utility value. People who indulge in this practice are called ‘hobbyists’, and by extension are those customers of products who rather than just accept the manufacturer’s design of a product, will try to adapt it to their specification. Prosumerism: This is a form of ‘hacking’ process by which the role of producer or professional or manufacturer and consumer merges to participate in the design and production requirements of products through mass customisation. The end product is a situation where manufacturers adapt or create their products to the specific requirements of consumers to satisfy the customers and generate profit.

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QR Code: An abbreviation of Quick Response Code. It is a barcode with a machine-readable optical label of different black squares arranged in a large square or rectangular grid. When scanned, the bar codes respond quickly to allow access to its embedded information about the item to which it is attached.

Service Providers: A term used to refer to a whole range of agencies, organisations and institutions that provider services to the public (individuals and communities).

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Political Context Elements in Public Policy of Radio Frequency Information Technology and Electromagnetic Fields Joshua M. Steinfeld Old Dominion University, USA

INTRODUCTION A pinnacle in e-governance was reached with the development of information systems that utilize radio frequency technologies. For example, Radio Frequency (RF) towers’ capabilities as communication and monitoring devices enable efficiency maximization and real-time solutions. Also, Advanced Imaging Technology (AIT) allows for quick and reliable information processing for purposes of tracking and surveillance. There are several advantages to government’s use of Radio Frequency Information Technology (RADFIT) such as the ability to quickly communicate across a wide range of global positioning systems, management of communication portals, and survey of visitors entering secure environments in the case of millimeter wave scanning. The main issue with the presence of radio frequency electromagnetic fields (RFEMF) is balancing the benefits provided from supporting the RADFIT systems with the environmental effects of electromagnetism. Regulation of technologies is controversial as agencies and stakeholders struggle to weigh benefits and costs. Hood et al. (2001) presents a framework for understanding regulatory policy domains by classifying benefits and costs of Information Technology (IT) sciences according to competing political systems: interest group, entrepreneurial, client, and majoritarian. The interaction of these political context elements influences the corresponding regulatory regimes, which are ideographs that describe the track of reasoning of RADFIT systems consumers, producers, and regulatory bodies. By examining

regulatory regimes of the RADFIT sphere, public policy implications and future research directions emerge that may improve participatory confidence and informational effectiveness while mitigating threats to communities. The main purpose of the manuscript is to discuss political context elements and their impact on the public policy arena surrounding RFEMF issues, in addition to touch upon organizational initiatives and public management alternatives. Political context elements of regulatory regimes are presented first. According to the interest group political system, incrementalism and the status quo are introduced as encumbrances to policy change. Lack of organization in the public policy arena limits viable alternatives and contributes to government lethargy. The entrepreneurial system, indicative of rational choice and new public management, is subsequently discussed as the prompt and elicitor of RADFIT solutions. Modernism and progression serve as societal themes that steer entrepreneurialism in IT and public sector activities in general. Next, the client system, which involves administrative responsibility, is highlighted as the regime offering the most potential for bureaucratic discretion and inquiry. The opportunities for interaction between regulatory agencies and resident stakeholders, creates inconsistencies and marginalization of particular societal participants. Then, the majoritarian system, serving as the basis for democratic forms of governance, is detailed so as to review the unresolved paradoxes involved in representative decision rules such as voting. Subsequently, organizational and community leadership initiatives, despite the obstacles posed

DOI: 10.4018/978-1-5225-2255-3.ch581 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Public Sector Management

by political context elements, are illustrated to show the current state of organized opposition to RADFIT proposals. Recommendations and areas for further research follow in an attempt to consolidate diffuse community efforts. There are several objectives of this entry. The controversy over RADFIT solutions is examined to explain how political context elements dictate regulatory regimes of the RFEMF sphere. This entry aims to provide an overview of the bureaucratic considerations underlying RADFIT guidelines and public policy as well as the response by communities. Public administration theory and recommendations for future action serve to provide frameworks for additional policy analysis. The goal is not to provide a comprehensive review of the RFEMF regulatory arena but instead to illuminate indicators that create the onset and resonance of various regulatory regimes, or ideographs, that dictate the decision-making of public policy participants of RADFIT applications including RFEMF, along with implications for communities.

BACKGROUND There are millions of RF tower base sites in the world. The United States alone has more than 301,779 radio frequency tower and transmission base sites (Cellular Telecommunications Industry Association, 2013). In many cases the towers stand from 50 feet to 200 feet tall. RF base sites also exist in the form of small individual devices less than 2 feet by 1 feet in size that may be mounted on building roofs or siding. Broadly, the United States Department of Commerce identifies several objectives for the use of the radio spectrum and RFEMF to carry out national policies and achieve national goals such as national security, safeguarding of life and property, support crime prevention and law, foster conservation of natural resources, provide for dissemination of information and entertainment, promote research and exploration, stimulate social and economic

progress, and generally improve the well-being of man (National Telecommunications and Information Administration, 2014: Chapter 2). More specifically, several areas of strategic interest are identified according to these objectives including agriculture, consumer expenditures and saving, education, health, oceanography, public safety, outer space, social welfare, transportation, and urbanization (National Telecommunications and Information Administration, 2014: Ch. 2). At the local level, by 2011, automatic licenseplate readers were utilized by about three-quarters of police departments surveyed (American Civil Liberties Union, 2013), with civilian uses of license-plate readers emerging (Hardy, 2014). Commercially, retailers employ companies that track shoppers through cell phones in order to identify return shoppers and learn about other shopping habits or patterns while in the store for the purposes of improving store layouts, marketing, and overall profitability (Clifford, 2013a; Clifford, 2013b). In turn, government enjoys larger tax receipts from the increases in purchases that result. The RADFIT data collected is transmitted, stored, processed, and analyzed at data centers. These large warehouse-type buildings house servers and hard-disk drives that sit on concrete slabs as large as several football fields and are typically located in rural areas where power is less expensive (McLellan, 2013). There are over 6000 data centers in the world with more than half in the U.S.; each data center consumes the amount of power equivalent to a city with 20,000-40,000 residents (President’s Council of Advisors on Science and Technology, 2014, p. 30). The environmental effects of RF tower bases have been of concern to community residents and researchers alike. For example, RF tower installation at public schools has been strongly opposed by various community voluntary initiatives and political activist groups (Steinfeld, 2013). Similar to the RF towers that utilize RFEMF technology, AIT scanners are commonly used to scan subjects entering secure areas. In May 2013, the Transpor-

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tation Security Administration (TSA) disposed of approximately 800 Rapiscan full body scanners, called “puffer” machines, due to concerns regarding irradiation of subjects (Jansen, 2013). Millimeter wave imaging systems replaced the puffer machines and function by emitting radio frequency electromagnetic waves (RFEMW) in the millimeter spectrum (30-300 gigahertz) to render images that look like photographic negatives (Elias, 2012). As of September 2012, TSA deployed about 700 AIT scanners, or Whole Body Imagers (WBI), with plans to have a total of 1800 in use by 2014 (Elias, 2012). One major issue with the systemic use of RADFIT and the immersive presence of EMF in society has to do with health concerns that have been cited in scientific studies, concluding that RFEMF can be dangerous to human health. Golbach et al. (2015) conduct a study of immune cells, in which it was concluded that exposure to LF EMF enhanced neutrophil extracellular formation, which leads to increased antimicrobial properties and damage to surrounding cells, essentially damaging the immune system. Additionally, Pall (2015) reviews 18 epidemiological studies in concluding that exposure to microwave and radio frequency EMF from cell/mobile phone bases trigger widespread neuropsychiatric effects such as depression, sleep disturbance, headache, fatigue, concentration dysfunction, memory changes, irritability, restlessness, and loss of body weight. Manzella et al. (2015) also determine that EMF affects the human biological clock. Pall (2015) reviews 6 studies that conclude radio and television antenna may have the same effects. Differently, Shi et al. (2014) use in-vitro isolation techniques to test the effects of intermediate wireless magnetic fields and report no genotoxic effects from exposure. Furthermore, Shirai et al. (2014) recorded no significant negative impacts on the brain function of three generations of rats that were exposed to cell phone radiation for 20 hours per day. Yet, Du et al. (2014) report that

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obese mice lose weight and fat when treated with direct current electromagnetic fields. Laramee et al. (2014) detected an elevation of heat shock gene expression as a result of static magnetic field exposure, indicating that RFEMF heat-up or “cook” its subjects. Lerchl et al. (2015) conclude that the number of tumors in the lungs and livers of animals were higher, in addition to elevated numbers of lymphomas, than in the sham-exposed group. The tumor producing effects are attributed to metabolic changes due to EMF exposure (Lerchl et al., 2015). Without the hindsight of the recent scholarly literature and experimental studies on RFEMF, on January 3, 1996, the Federal Communications Commission (FCC) passed the Telecommunications Act “to promote competition and reduce regulation in order to secure lower prices and higher quality services for American consumers and encourage the rapid deployment of new telecommunications technologies” (Telecommunications Act, 1996). According to Section 332(c)(7)(B)(iv), “No State or local government or instrumentality thereof may regulate the placement, construction, and modification of personal wireless service facilities on the basis of the environmental effects of radio frequency emissions to the extent that such facilities comply with the Commission’s regulations concerning such emissions.” In conjunction with this limitation, Section 214(e)(1)(a) grants telecommunications carriers eligibility to provide their own telecommunications facilities in areas designated as “universal service,” even if a competitor is already providing wireless service coverage for the area (O’Neill, 1999). Section 253(a) reinforces carriers’ rights in citing “No state or local statute or regulation, or other state or local legal requirement, may prohibit or have the effect of prohibiting the ability of any entity to provide any interstate or intrastate telecommunications service.” Incidentally, the Telecommunications Act may be in violation of fifth and tenth amendment Constitutional protections.

Category: Public Sector Management

The fifth amendment, states that persons cannot “be deprived of life, liberty, or property, without due process of law,” and the tenth amendment declares that “The powers not delegated to the United States by the Constitution, nor prohibited by it to the states, are reserved to the states respectively, or to the people” (U.S. Constitution, 1789). For AIT, Sections 101 and 109 in the Aviation and Transportation Security Act of 2001 provide for federal government’s authority to utilize RADFIT. Section 101(f)(8) makes it lawful for TSA to “identify and undertake research and development activities necessary to enhance transportation security” and Section 109(a)(5) provides for the lawful use of wireless technologies to aid in passenger screening (Aviation and Transportation Security Act, 2001). However, privacy issues related to AIT surfaced and thus Automated Target Recognition (ATR) technology was developed as an alternative means of security scanning (Elias, 2012). Essentially, WBI were transmitting and recording images of users, creating privacy issues since RFEMF pass right through clothing. Privacy-enhancing morphological edge and gradient detection software algorithmic technology was subsequently developed for millimeter wave scanners to combat the issue of privacy through distortion of facial and other physical features (Cavoukian, 2009), effectively silencing AIT opposition founded upon the privacy issue. The lack of concrete experimental data on health effects of RADFIT and shortage of innovative remedies to combat health concerns has brought into question the impact of invisible risks posed by RFEMF. Meanwhile, the Telecommunications Act has authority over local and state powers but may in fact be unconstitutional. Consequently, public policies governing RADFIT are being inconsistently applied and the regulatory environment remains ambivalent. With the failure of public management to achieve universal clarity and resident support on issues such as rights-ofway, regulatory stances continue to be conceded by political elements.

POLITICAL CONTEXT ELEMENTS AND VOLUNTARY INITIATIVES Interest Group Politics The confrontation between advocacy coalitions representing competing interest groups leads to incrementalism and the status quo. As a result, it becomes unlikely that current trends in RF tower base proliferation or use of RADFIT solutions for communicative and security applications will be curtailed. There is a status quo and changing it requires special justification (Baumgartner et al., 2009). The main issues such as whether or not to allow RF towers to be erected near schools and residential neighborhoods, or if AIT systems should be required for entry into secure areas have been at deadlock since the major benefits of RADFIT were discovered and capabilities implemented. Additionally, the interest group narratives have largely remained unchanged. For example, the American Cancer Association (ACA), a federal funding beneficiary, continues to maintain that scientific evidence has not conclusively indicated that electromagnetic fields created by RF towers and other base sites are hazardous to human health due to the longer wavelength of RF and its lower energy level as compared to gamma rays or x-rays (American Cancer Society, 2013). Meanwhile, opponents to RFEMF have advocated a not-in-my-backyard approach to RF towers and employed bounded rationality when dealing with RADFIT security apparatuses. Bounded rationality emphasizes the cognitive limitations of decision-makers and is the arbitrary underpinning of the advocacy coalition approach (True et al., 2007). Health hazards from RFEMF absorption, an invisible risk, remain disputed. From consumers and producers’ perspectives, the idea of losing mobile connectivity from absence of RF towers or being susceptible to security breaches and inefficiencies by avoiding utilization of AIT systems is tangible. Jones (1994) argued that decision changes do not arise from changes

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in preferences or irrationality; they emerge from shifts in attention called “serial shifts.” The senses process information equally but attention is serially committed to one idea at a time (Simon, 1983). The dominant, or tangible concerns, as opposed to the invisible health risk, occupy the psyche. Typically, only one primary aspect of the choice situation serves as the critical decision factor (Simon, 1957, 1985; Zaller, 1992). As a result, both individual and tactical initiatives seeking to oppose RADFIT applications are lulled by even the supporters who share the utmost concern and consciousness over the issue. The status quo is maintained and incrementalism persists through steady growth of RADFIT applications as new technologies that create efficiencies.

Entrepreneurial Politics New public management and undertones of progressivism continue to guide bureaucratic conduct in lieu of rational choice logic. While rational choice theory has been founded upon the notion that market mechanisms should be used to settle collective choice problems (Hill, 2009), as in the case of new public management (Hood, 1995), a core competency of any sound public policy involves the conception that public value is being created for society (Moore, 1995). In the case of RADFIT applications, the benefits of utilization are real and the potential costs associated with health risks are hypothetical. Meanwhile, remnants of Wilson’s (1887) progressive movement continue to trigger the cogitation of clever solutions to microcosmic issues that could have been left unresolved, such as in the case of AIT as a solution to laborious manual screening. Rational choice, in the form of economic individualism, has become a decision rule under the new public management accordance in bureaucracy, enticing public leaders and the public at large to look to the private sector for solutions and then to the government and nonmarket institutions upon market failure (Bozeman, 2007). For example, public agencies purchase AIT ap-

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paratuses in place of hiring trained workers and corporations are placated by political authorities with permissive jurisdiction that enable RF tower producers to enjoy productive economies of scale with RF tower construction and maintenance. Without major indication of widespread epidemic from RFEMF exposure and documented incurrence of costs, the entrepreneurial policy domain continues to be dictated by technocratic principles of automating bureaucratic processes. Consumers of RADFIT applications are similarly limited in scope by rational choice decision-making as it relates to social exchange and public choice theory. Under the auspices of social exchange theory, people choose courses of action that yield the least amount of blame and will behave differently based on the number of people present (Latane & Darley, 1970; Heath, 1976). Users of RFEMF are unlikely to dissent if numerous bystanders are present in the case of AIT screening or a large number of people stand to lose through loss of wireless services. Instead, users exercise public choice and abide by laws of collective action. Policy change advocates have a common interest in benefits but are unwilling to incur the costs of providing the collective good (Olson, 1965). Miller (1994) attempts to cast doubt on social exchange and public choice theory through witness of periodic altruistic behavior. Considerately, policy entrepreneurs tend to thrive from outside their formal positions of government or participative roles (Roberts & King, 1991); there is no opportunity, or policy stream (Kingdon, 2008), for advocates of regulatory change to act extemporaneously or institutionalize concerted efforts, respectively.

Client Politics While dangers to human health and wellbeing may be natural and inevitable, the exacerbation or mitigation of risks, and how costs are disbursed, are not (Minow, 2012). Public administrators are oftentimes in tension with the citizenship role, creating potential for recurring conflict between

Category: Public Sector Management

the two identities (Cooper, 2012). The extent to which public administrators are responsive to challengers or remain responsible to the bureaucracy is determined by client politics. Bureaucrats with knowledge-in-the-process (Lasswell, 1970), those administrators involved in RADFIT solicitation and implementation, are bound by characteristics of bureaucracy. For example, government or contracted operators and repair personnel of potentially hazardous RF hardware fail to express health and wellbeing concerns because current tasks are thought to be temporary given hopes of ascending in rank or being delegated task variation, even though the position continues to be in existence and held by another incumbent nonetheless (Weber, 1946). In this case, the public administrator or contractor is a client of the bureaucracy, furloughed by self-interest (see Downs, 1967; Dunleavy, 1991). Oppositely, Weber (1947) and Fayol (1949) posit bureaucracy as client-oriented and administrators as bureaucratic servants that subordinate individual interest to general interest and create a division of labor with clearly defined responsibilities. According to this rigid structure, RF tower and AIT technicians and operators employed by the government are unlikely to ever converge with clients that are external to the bureaucracy, such as concerned subjects of RADFIT applications. In accordance with Weberian traditionalism, public administrators cling to old ways despite individuals’ tendency to be progressive in other spheres of life (Manheim, 1936). Public administrators and technical experts become mesmerized with current innovative capacities instead of embracing advancement in thought, thereby neglecting ideological evolution (Burke, 1790). Soon thereafter, the bureaucrat is unable to retrospectively imagine society without a propagation of RF towers or reliance on AIT machines.

Majoritarian Politics The calculus supporting denunciation of majoritarian politics has been aptly applied over the course of policy domains (Arrow, 1951; Buchanan &

Tullock, 1962; Tullock & Wagner, 1978). From a regulatory standpoint, the economic-type limits to control, de minimis non curat fiscus, parallel downfalls of majoritarian decision rules (Hood, 1976). For instance, bureaucrats may simply lack the authority to impact implementation and utilization aspects of RADFIT components and systems, a control that could rest with a few policy makers and loitering advocacy coalitions. In other cases, colossal consumer markets welcome RADFIT solutions that promise to create even more wealth for government and societal members. The economic and ideological grandeur of RFEMF applications circumscribes the eclipsing echoes citing dangers to RF absorption. Other modes of majoritarian politics take shape through consumer federalism (Wildavsky, 1998). When one AIT model is found to be ineffective or believed to be exceptionally hazardous to users’ health, as in the case of “puffer” security scanning machines, then it is possible for consumer federalists to play off one producer against another in order to pursue agency procurement ambitions. Governmental consumers and RFEMF regulatory bodies achieve cohesion as a spectrum emanates for the devices market through not only determining the lawfulness of the technology but also devising a range of acceptable exposure restrictions. Davis, Dempster, & Wildavsky (1966) identify these decision rules as the deviant case and shift point. The deviant case involves the underlying controversy over RADFIT solutions whereas the shift point rests along the spectrum of technological capacity. These decision rules are non-incremental and therefore typically exist in a state of stasis, left to coagulate solely according to further advancements.

Voluntary Initiatives Despite the unfavorable political context elements that present obstacles to effective regulation of RADFIT, numerous initiatives have surfaced that contribute to regulation of RADFIT and EMF. The European Union, on November 14, 2011, was the first organization to ban use of “backscatter” ion6715

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ized radiation technology in favor of millimeter wave scanners (DiSalvo, 2011; Jaslow, 2011). Additionally, the International Association of Fire Fighters officially opposes the idea of mounting RFEMF applications on firehouses until further research (International Association of Firefighters, 2014). In Australia, community groups have succeeded in organizing at the national level through a group called No Towers Near Schools that provides support and information on current disputes around the country (No Towers Near Schools, 2014). Elsewhere, community leadership efforts have spanned international borders. On December 1, 2013, more than 100 people in Tsawwassen, Canada and 200 people in Point Roberts, U.S. protested near the border, on 1st avenue and 56th street, vowing to stop the installation of five towers that would affect residents on both sides (Hager, 2013). In the U.S., there are dozens of communities in California currently organizing to reject RF towers such as in San Diego (see Verdin, 2014), Los Angeles (see City of Huntington Beach, 2013; West San Pedro Neighborhood Alliance, 2014; Northwest San Pedro Neighborhood Council, 2009), Carmel (see Arcega-Dunn, 2014), Hillsboro (see Lee, 2013), and San Rafael (see Upshaw, 2009). The presence of EMF base sites has also garnered substantial attention in New York, as an example, where the Department of State released the publication entitled Planning and Design Manual for the Review of Applications for Wireless Telecommunications Facilities. Governor Andrew Cuomo believes that municipal regulation of cellular phone towers and antennas is one of the most controversial and heavily debated current local government land use issues (Legal Memorandum LU01, 2014). Similarly, the North Carolina League of Municipalities released guidance on the current tensions between wireless service providers’ desire for an efficient, streamlined, predictable, and expedited regulatory process as opposed to citizens’ desire for transparency and interest in maintaining rights inherent to democratic opposition (Hibbard, 2013).

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SOLUTIONS AND RECOMMENDATIONS New approaches to policy change are required if regulatory regimes in the RFEMF sphere are going to be expanded or added to. Currently, there is rapid promulgation and application of RADFIT solutions given the apparent stalemate within political context elements. Unchallenged RFEMF base sites vastly outnumber community leadership initiatives. Baumgartner & Jones (1993) found that policymaking leaps and periods of stasis share a tendency toward punctuated equilibrium, or unforeseen impulse; an unfavorable regulatory outcome whether it results in health epidemic, extreme loss of connectivity, or security breach. Solutions to static regulatory environments can be found by emulating the postmodern movement. Stivers (1994) emphasized bureaucratic listening as a way to compensate for spatial differences between stakeholders in regulatory settings. Regulatory agencies have neglected the wide body of research conducted that implicates RFEMF technology as hazardous. However, it may not be critical for the policy change initiatives to rest with empirical conclusions where validity and reliability remain controversial. Miller (2013) builds on postmodern approaches to public policy and administration through examination of ideographs. The narratives that produce symbolic images of policy domains have the potential to bypass the empirical struggle plaguing the certainty of RFEMF research. Scientific reference may be replaced by ideographs that portray RF towers as nuisances, eyesores, or institutional threats to privacy. The very notion of empirical uncertainty may emerge as a lasting narrative that paints ideographic imagery similar to that of the questioned invisible risk such as viable slogans that could include “you can’t regulate what you can’t see” and “go easy with radiofrequency.” According to new public management, a counterpart to postmodernism, narratives of efficiency, cost-effectiveness, capacity building, and innovation serve as underpinnings for policy implementa-

Category: Public Sector Management

tion, yet creativity remains through mobilization such as outsourcing. The Telecommunications Act of 1996 aimed to expedite telephone carriers’ efforts and conserve their vital resources while striving to provide resident, business, and government consumers with affordable and “universal service.” In Reinventing Government (1998), Osborne and Gaebler argue that government “steers rather than rows,” which is exactly what the federal government did by contracting out telecommunications infrastructure, services, and maintenance to private companies while at the same time providing authoritative means with regards to competition, site placement, and procurement (Telecommunications Act, 1996). Instead, government should “serve rather than

steer,” whereby public servants help citizens articulate and meet their shared interests rather than steering society to new directions (Denhardt & Denhardt, 2000). Local and state governments may help communities to consolidate their efforts so that advocacy can be directed to federal government action. An amendment to the Telecommunications Act is required to consider health effects and unsightliness of RADFIT. Under the revision, Title 47 of the Code of Federal Regulations needs to be specifically added to Section 332(c)(7)(B)(iv) of the Act (Table 1). Federal limits for Maximum Permissible Exposure (MPE) to radiofrequency radiation clearly state that exposure by the general public is not to exceed 30 minutes.

Table 1. Title 47: U.S. code of federal rules Code of Federal Rules Title 47: Telecommunication Volume: 1 Date: 2011-10-01 Title: Section 1.1310 - Radiofrequency radiation exposure limits. Context: Title 47-Telecommunication. Chapter 1-Federal Communications Commission. Subchapter A- Part 1. Practice And Procedure. Subpart I-Procedures Implementing the National Environmental Policy Act of 1969. Frequency (MHz)

Electric Field (V/m)

Magnetic Field (A/m)

Power (mW/cm2)

Time (mins)

Limits for Occupational/Controlled Exposures 0.3-3.0

614

1.63

*100

6

3.0-30

1842/f

4.89/f

*900/f2

6

30-300

61.4

0.163

1

6

f/300

6

5

6

300-1500 1500-100,000 Limits for General Population/Uncontrolled Exposure 0.3-1.34

614

1.63

*100

30

1.34-30

824/f

2.19/f

*180/f2

30

30-300

27.5

0.073

0.2

30

f/1500

30

1

30

300-1500 1500-100,000 Occupational/controlled limits apply in situations that persons are exposed from employment. General population/uncontrolled exposures apply in situations that the general public is exposed. f = frequency in MHz * = Plane-wave equivalent power density

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Therefore, RF towers that are mounted on or adjacent to schools, buildings, parks, neighborhoods, playing fields, or other sites may technically be rejected due to excess general population/uncontrolled exposure beyond 30 minutes. However, a strong lobby exists that demands connectivity, and connectivity cannot occur without exposure; it is the presence of RFEMF that enables RADFIT. It is unlikely that community voluntary initiatives will have the impact of freezing RADFIT expansion or destructing current base sites. However, base sites that are opposed by communities should be rejected or removed if already in place and a federal mandate is necessary to achieve equality on this matter across all stakeholder communities.

FUTURE RESEARCH DIRECTIONS Hood and Margetts (2007) see the future as an extrapolation of the past; the tools of the digital age may create heightened levels of nodality such as cyber detection and group targeting. While rapid installation of RF towers and continued usage of AIT devices will increase the number of systems users and the overall outreach of RADFIT application, it is unlikely that growth alone in the number of RF towers or AIT devices would increase nodality; advancements in RFEMF capabilities would likely be required. Furthermore, RADFIT enhancements may be met with incremental policy challenges as regulatory regimes gradually shift according to new research developments. Unfortunately, consumers and producers are forced to continually exercise a responsive-type of policy influence, as new technologies can typically be more quickly implemented than empirically deemed to be unsafe. Meanwhile, there is immense competition between researchers seeking to conduct progressive research on RFEMF utility and those experimenters aiming to uncover any negative externalities associated with RADFIT systems. RFEMF research has been conducted on rats and human organelles. Research on rats is largely

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conclusive in determining toxic effects of exposure. Yet, rats are much smaller mammals than humans and are not viewed to be similar by the general public. On the other hand, research on humans that could involve immense danger to human subjects is not feasible and there would be significant challenges to securing a control group in any case, given the numerous factors that can affect human health over the course of a study, especially if long-term effects of RF absorption are the foci of study. Nonetheless, the intrusive placement of many existent RF towers provides an opportunity for surveying human subjects, especially if the RF towers are not going to be removed. Furthermore, expansive environmental impact studies need to be conducted to ascertain the broad impact of RADFIT. On September 14, 2000, the U.S. Department of Interior, on behalf of the Fish and Wildlife Service, issued a cautionary statement of RF towers’ impact on migratory birds. A Communication Tower Working Group consisting of government agencies, industry, academic researchers, and non-governmental organizations was formed by the Service to reduce the estimated 4 to 5 million bird deaths per year that occur from colliding with communication towers (Clark, 2000). It may be possible that RFEMF has physiological effects on birds as well. In either case, birds’ migration is being obstructed which could influence other animals. Impact studies of communication tower base sites and isolated RFEMF on living organisms within complex ecosystems would advance the field’s body of knowledge. For now, the precautionary principle is suggested for dealing with the regulatory sphere of RADFIT and EMF. Previously, the precautionary principle has been advocated for application to regulatory decision-making regarding sustainable development including the Rio Convention and Kyoto Protocol (Steele, 2006), and it is now being recommended when it comes to government’s regulation for the use and power of electromagnetic fields (Portier, 2015). The precautionary principle involves employing alternatives in situations when

Category: Public Sector Management

irreparable health or environmental risk may result from a particular action while the research is inconclusive or not yet realized in terms of the risks. Considering the set of feasible alternatives determines whether the actions of independent agents need to be restricted given certain levels of risk to the environment and human health (Tickner, 2003).

CONCLUSION Expansion and application of RADFIT solutions continues to progress at a rapid pace. Empirical research regarding health effects from RFEMF absorption are surrounded by controversy and lag behind innovation. Currently, the four major political context elements and respective bureaucratic limitations of the regulatory environment results in public policy stasis. Interest group politics have contributed to incrementalism and the status quo while at the same time entrepreneurial modes of political will are defeated by deficiencies inherent to economic individualism and social exchange. In addition, client politics posit the public administrator as a participant nullified by dual roles as bureaucrat and resident client, just as majoritarian approaches to decision rules are malignantly constructed along a spectrum that serves to favor strategic public procurement of RADFIT devices. Modernism and progression have dominated the narrative platform to date. Bureaucratic listening and postmodernism in the form of ideographs serve as communicative devices that can be used by community organizations to minimize the stakeholder gap between users, consumers, and producers of RADFIT systems. Reform of the Telecommunications Act and consolidation of community leadership initiatives are concrete solutions. Given the propagation of RFEMF infrastructure, empirical studies using survey data of residents living near RF towers or frequently exposed to other AIT devices is logical and does not contribute additional threat to subjects already experiencing continual RF absorption. Irrespective

of health concerns related to RFEMF and RADFIT solutions, sociological constraints remain that are detrimental to the contributions of individual stakeholder and regulatory participants.

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Clifford, S. (2013b, June 19). Using Data to Stage-Manage Paths to the Prescription Center. The New York Times. Retrieved from http://bits. blogs.nytimes. com/2013/06/19/using‐data‐to‐ stage‐manage‐paths‐to‐the‐prescription‐ counter/ Constitution, U. S. (1789). Fifth and Tenth Amendments of the United States Constitution. Retrieved from http://www.law.cornell.edu/constitution/ tenth_amendment Cooper, T. (2012). The Responsible Administrator: An Approach to Ethics for the Administrative Role (6th ed.). San Francisco, CA: Jossey-Bass. Covoukian, A. (2009). Whole Body Imaging in Airport Scanners: Building in Privacy by Design. Information and Privacy Commissioner of Ontario. Retrieved from http://www.ipc.on.ca/ images/resources/ wholebodyimaging.pdf Davis, O., Dempster, M., & Wildavsky, A. (1966). A Theory of the Budgetary Process. The American Political Science Review, 60(3), 529–547. doi:10.2307/1952969 Denhardt, R., & Denhardt, J. (2000). The New Public Service: Serving Rather Than Steering. Public Administration Review, 60(6), 549–559. doi:10.1111/0033-3352.00117 Disalvo, D. (2011). Europe Bans Airport Body Scanners For “Health and Safety” Concerns. Forbes. Retrieved from http://www.forbes.com/ sites/ daviddisalvo/2011/11/15/europe-bansairport-body-scanners-over-health-and-safetyconcerns/ Downs, A. (1967). Inside Bureaucracy. Boston, MA: Little, Brown, and Company. Dunleavy, P. (1991). Democracy, Bureaucracy, and Public Choice: Economic Explanations in Political Science. London: Prentice Hall.

Category: Public Sector Management

Elias, B. (2012). Airport Body Scanners: The Role of Advanced Imaging Technology in Airline Passenger Screening. Congressional Research Service. Retrieved from http://www.fas.org/sgp/ crs/homesec/R42750.pdf Fayol, H. (1949). General and Industrial Administration. New York, NY: Pitman. Golbach, L. A., Scheer, M. H., Cuppen, J. J. M., Savelkoul, H., & Verburg-van Kemenade, B. M. L. (2015). Low-Frequency Electromagnetic Field Exposure Enhances Extracellular Trap Formation by Human Neutrophils through the NADPH Pathway. Journal of Innate Immunity, 7(5), 459–465. doi:10.1159/000380764 PMID:25871408 Hager, M. (2013, December). Tsawwassen and Point Roberts Residents Continue to Fight Radio Towers. Vancouver Sun, 1. Retrieved from http:// www.vancouversun.com/news/Tsawwassen+ Point+Roberts+residents+continue+ fight+ radio+towers/9234438/story.html Hardy, Q. (2014, April 19). How Urban Anonymity Disappears When All Data Is Tracked. The New York Times. Retrieved from https://bits.blogs. nytimes.com/2014/04/19/how-urban-anonymitydisappears-when-all-data-is-tracked/?_r=0 Heath, A. (1976). Rational Choice and Social Exchange: A Critique of Exchange Theory. Cambridge, UK: Cambridge University Press. Hibbard, K. (2013). City Authority to Regulate Wireless Telecommunications. Retrieved from http://www.nclm.org/SiteCollection Documents/ Legislative/2013%20--% 20Cell%20TowerWireless% 20Telecommunications%20 Regulatory%20 Authority.pdf Hill, M. (2009). The Public Policy Process (5th ed.). Harlow, UK: Pearson Longman. Hood, C. (1976). The Limits of Administration. London: John Wiley & Sons.

Hood, C. (1995). Contemporary Public Management: A New Global Paradigm? Public Policy and Administration, 10(2), 104–117. doi:10.1177/095207679501000208 Hood, C., & Margetts, H. (2007). The Tools of Government in the Digital Age. Hampshire, UK: Palgrave Macmillan. doi:10.1007/978-1-13706154-6 Hood, C., Rothstein, H., & Baldwin, R. (2001). The Government of Risk: Understanding Risk Regulation Regimes. Oxford, UK: Oxford University Press. doi:10.1093/0199243638.001.0001 Hook, G., Zhang, P., Lagroye, I., Li, L., Higashikubo, R., Moros, E., & Roti, J. et al. (2004). Measurement of DNA Damage and Apoptosis in Molt-4 Cells after In Vitro Exposure to Radiofrequency Radiation. Radiation Research, 161(2), 193–200. doi:10.1667/RR3127 PMID:14731070 Huang, T., Lee, M., Oh, E., Zhang, B., Seo, J., & Park, W. (2008). Molecular Responses of Jurkat T-cells to 1763 MHz Radiofrequency Radiation. International Journal of Radiation Biology, 84(9), 734–741. doi:10.1080/09553000802317760 PMID:18821387 International Association of Fire Fighters. (2014). Division of Occupational Health, Safety, and Medicine. Retrieved from https://www.iaff.org/ hs/Facts/CellTowerFinal.asp Jansen, B. (2013). TSA Dumps Near-Naked Rapiscan Body Scanners. USA Today. Retrieved from http://www.usatoday.com/story/ travel/flights/2013/01/18/naked-airport-scanners/1845851/ Jaslow, R. (2011). Europe Bans Airport Scanners Over Cancer Fears: How About U.S.? CBS News. Retrieved from http://www.cbsnews.com/news/ europe-bans-airport-scanners-over-cancer-fearshow-about-us/

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Jones, B. (1994). Reconceiving Decision-Making in Democratic Politics: Attention, Choice, and Public Policy. Chicago, IL: University of Chicago Press. Kingdon, J. (2003). Agendas, Alternatives, and Public Policies (2nd ed.). New York, NY: Longman. Laramee, C., Frisch, P., McLeod, K., & Li, G. (2014). Elevation of Heat Shock Gene Expression From Static Magnetic Field Exposure in Vitro. Bioelectromagnetics, 35(6), 406–413. doi:10.1002/ bem.21857 PMID:24839179 Lasswell, H. (1970). The Emerging Conception of the Policy Sciences. Policy Sciences, 1(1), 3–14. doi:10.1007/BF00145189 Latane, B., & Darley, J. (1970). Social Determinants of Bystander Intervention in Emergencies. In J. Macaulay & L. Berkowitz (Eds.), Altruism and Helping Behavior (pp. 13–27). New York, NY: Academic Press. Lee, V. (2013). Hillsboro Residents Oppose New Cell Towers. ABC News. Retrieved from http:// abclocal.go.com/kgo/story?section= news/local/ peninsula&id=9029667 Legal Memorandum LU01. (2014). Municipal Regulation of Cellular Telephone Towers and Antennas. Department of State, Office of General Counsel. Retrieved from http://www.dos.ny.gov/ cnsl/lu01.htm Lerchl, A., Klose, M., Grote, K., Wilhelm, A. F. X., Spathmann, O., Fiedler, T., & Clemens, M. et al. (2015). Tumor Promotion by Exposure to Radiofrequency Electromagnetic Fields Below Exposure Limits for Humans. Biochemical and Biophysical Research Communications, 459(4), 585–590. doi:10.1016/j.bbrc.2015.02.151 PMID:25749340

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Manheim, K. (1936). Conservatism: A Contribution to the Sociology of Knowledge (Collected Works Volume 11). New York, NY: Routledge. Manzella, N., Bracci, M., Ciarapica, V., Staffolani, S., Strafella, E., Rapisarda, V., & Santarelli, L. et al. (2015). Circadian Gene Expression and Extremely Low-Frequency Magnetic Fields: An In Vitro Study. Journal of Bioelectromagnetics, 36(4), 294–301. doi:10.1002/bem.21915 PMID:25808738 McLellan, C. (2013). The 21st Century Data Center: An Overview. ZDNet. Retrieved from http:// www.zdnet.com/the‐21stcentury‐data‐center‐an‐ overview‐ 7000012996/ Miller, H. (1994). Post-Progressive Public Administration: Lessons from Policy Networks. Public Administration Review, 54(4), 378–386. doi:10.2307/977386 Miller, H. (2013). Governing Narratives: Symbolic Politics and Policy Change. Tuscaloosa, AL: The University of Alabama Press. Minow, M. (2012). Seeing, Bearing, and Sharing Risk. In J. Hacker & A. O’Leary (Eds.), Shared Responsibility, Shared Risk: Government, Markets, and Social Policy in the Twenty-First Century (pp. 253–269). Oxford, UK: Oxford University Press. doi:10.1093/acprof:oso bl/9780199781911.003.0013 Moore, M. (1995). Creating Public Value: Strategic Management in Government. Cambridge, MA: Harvard University Press. No Towers Near Schools. (2014). Community Battles Over Siting of Mobile Phone Facilities Around Australia. Retrieved from http://www. notowersnearschools.com

Category: Public Sector Management

Northwest San Pedro Neighborhood Council. (2009). Board and Stakeholder Meeting Minutes. Retrieved from http://Nwsanpedro.org/ wp-content/uploads/2012/10/November-9-2009minutes.pdf O’Neill, K. (2009). Wireless Facilities Are a Towering Problem: How Can Local Zoning Boards Make the Call Without Violating Section 704 of the Telecommunications Act of 1996? William and Mary Law Review, 40(3), 975–1018. Olson, M. (1965). The Logic Of Collective Action: Public Goods and The Theory of Groups. Cambridge, MA: Harvard University Press. Osborne, D., & Gaebler, T. (1992). Reinventing Government: How The Entrepreneurial Spirit Is Transforming The Public Sector. Middlesex, UK: Penguin Books. Phillips, J., Ivaschuk, O., Ishida-Jones, T., Jones, R., Campbell-Beachler, M., & Haggren, W. (1998). DNA Damage in Molt-4 T-lymphoblastoid Cells Exposed to Cellular Telephone Radiofrequency Fields in Vitro. Bioelectrochemistry and Bioenergetics, 45(1), 103–110. doi:10.1016/S03024598(98)00074-9 Portier, C. (2015). The Precautionary Principle Should Be Invoked for Radio Frequency Electromagnetic Fields. Keynote Speech. Annual Meeting of the Bioelectromagnetics Society. President’s Council of Advisors on Science and Technology. (2014). Big Data and Privacy: A Technological Perspective. Report to the President, May 2014. Executive Office of the President. Roberts, N., & King, P. (1991). Policy Entrepreneurs: Their Activity Structure and Function in the Policy Process. Journal of Public Administration Research and Theory, 1(2), 147–175.

Sannino, A., Di Costanzo, G., Brescia, F., Sarti, M., Zeni, O., Juutilainen, J., & Scarfi, M. (2009). Human Fibroblasts and 900 MHz Radiofrequency Radiation: Evaluation of DNA Damage After Exposure and Co-Exposure to 3-chloro4-(dichloromethyl)-5-hydroxy-2(5 h)-furanone (MX). Radiation Research, 171(6), 743–751. doi:10.1667/RR1642.1 PMID:19580481 Shi, D., Zhu, C., Lu, R., Mao, S., & Qi, Y. (2014). Intermediate Frequency Magnetic Field Generated by a Wireless Power Transmission Device Does Cause Genotoxicity in Vitro. Bioelectromagnetics, 35(7), 512–518. doi:10.1002/bem.21872 PMID:25196478 Shirai, T., Imai, N., Wang, J., Takahashi, S., Kawabe, M., Wake, K., & Fujiwara, O. et  al. (2014). Multigenerational Effects of Whole Body Exposure to 2.14 GHz W-CDMA Cellular Phone Signals on Brain Function in Rats. Bioelectromagnetics, 35(7), 497–511. doi:10.1002/bem.21871 PMID:25196377 Simon, H. (1957). Models of Man. New York: Wiley. Simon, H. (1983). Reason in Human Affairs. Stanford, CA: Stanford University Press. Simon, H. (1985). Human Nature in Politics: The Dialogue of Psychology with Political Science. The American Political Science Review, 79(02), 293–304. doi:10.2307/1956650 Steele, K. (2006). The Precautionary Principle: A New Approach to Public Decision-Making? Law Probability and Risk, 5(1), 19–31. doi:10.1093/ lpr/mgl010 Steinfeld, J. (2013). Radio Frequency Towers: Public School Placement. In S. Jorgensen (Ed.), Encyclopedia of Environmental Management (pp. 2224–2233). New York: Taylor & Francis.

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Stivers, C. (1994). The Listening Bureaucrat: Responsiveness in Public Administration. Public Administration Review, 54(4), 364–369. doi:10.2307/977384 Telecommunications Act. (1996). Preamble. Retrieved from http://transition.fcc. gov/reports/ tcom1996.pdf Tickner, J. A. (2003). Precautionary Assessment: A Framework for Integrating Science, Uncertainty, and Preventive Public Policy. In J. A. Tickner (Ed.), Precaution: Environmental Science and Preventive Public Policy (pp. 265- 278). Washington, DC: Island Press. True, J., Jones, B., & Baumgartner, F. (2007). Punctuated-Equilibrium Theory: Explaining Stability and Change in Public Policymaking. In P. Sabatier (Ed.), The Theories of the Policy Process (pp. 155–187). Boulder, CO: Westview Press. Tullock, G., & Wagner, R. (1978). Policy Analysis and Deductive Reasoning. Lexington, MA: D.C. Heath and Company. Upshaw, J. (2009). Marinwood Residents Worried About Talk of Cell Tower. Marin Independent Journal. Retrieved from http://www.marinij. com/ marinnews/ci_12820360 Verdin, A. (2014). Residents Oppose Cell Tower Project; Fallbrook Community Planning Group Denies AT&T Project Amidst Concerns. Fallbrook Bonsall Village News. Retrieved from http://www. thevillagenews.com/story/76500/ Weber, M. (1946). Bureaucracy. In H. Gerth & C. Mills (Eds.), From Max Weber: Essays in Sociology (pp. 196–264). New York, NY: Oxford University Press. Weber, M. (1947). The Theory of Social and Economic Organizations (A. Henderson & T. Parsons, Eds.). New York, NY: Free Press. West San Pedro Neighborhood Alliance. (2014). Fight T-Mobile Cell Towers In Residential R1 Neighborhoods. Retrieved from http://www. westsanpedro.org/index.html

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Wildavsky, A. (1998). Federalism & Political Culture. New Brunswick, NJ: Transaction Publishers. Wilson, W. (1887). The Study of Administration. Political Science Quarterly, 2(2), 197–222. doi:10.2307/2139277 PMID:4591257 Zaller, J. (1992). The Nature and Origins of Mass Opinion. New York: Cambridge University Press. doi:10.1017/CBO9780511818691

ADDITIONAL READING Bell, D. (1960). The End of Ideology: On the Exhaustion of Political Ideas in the Fifties. Cambridge, MA: Harvard University Press. Bloor, D. (1976). Knowledge And Social Imagery (2nd ed.). Chicago, IL: The University of Chicago Press. Cooper, P. (2009). The War Against Regulation: From Jimmy Carter to George W. Bush. Lawrence, KS: University of Kansas. Dahl, R., & Stinebrickner, B. (2003). Modern Political Analysis (6th ed.). Upper Saddle River, NJ: Prentice Hall. Dunleavy, D., Margetts, H., Bastow, S., & Tinker, J. (2006). Digital Era Governance: IT Corporations, The State, And E-Government. Oxford, NY: Oxford University Press. doi:10.1093/acprof:o so/9780199296194.001.0001 Dunleavy, P., & Carrera, L. (2013). Growing The Productivity Of Government Services. Northampton, MA: Edward Elgar Publishing, Inc. doi:10.4337/9780857934994 Emmett, S., & Wright, P. (2011). Excellence in Public Sector Procurement: How To Control Cost And Add Value. Cambridge, England. Cambridge Academic. Fesler, J. (1980). Public Administration: Theory And Practice. Englewood Cliffs, NJ: Prentice Hall Inc.

Category: Public Sector Management

Fiorino, D. (2006). The New Environmental Regulation. Cambridge, MA: MIT Press.

Parsons, T. (1937). The Structure Of Social Action. New York, NY: The Free Press.

Hill, M., & Hupe, P. (2009). Implementing Public Policy (2nd ed.). Thousand Oaks, CA: Sage Publications Inc.

Perrow, C. (1970). Organizational Analysis: A Sociological View. London, England: Tavistock Publications, Ltd.

Hindmoor, A. (2006). Rational Choice. New York, NY: Palagrave Macamillan. doi:10.1007/978-0230-20997-8

Shadish, W., Cook, T., & Campbell, D. (2002). Experimental and Quasi Experimental Designs: For The Generalized Causal Inference. Belmont, CA: Wadsworth Cengage Learning.

Hood, C., & Lodge, M. (2006). The Politics of Public Service Bargains: Reward Competence, Loyalty And Blame. Oxford, NY: Oxford University Press. doi:10.1093/019926967X.001.0001 Kerwin, C., & Furlong, S. (2011). Rulemaking: How Government Agencies Write Law And Make Policy (4th ed.). Washington, DC: CQ Press. Mahoney, J., & Thelen, K. (2010). Explaining Institutional Change: Ambiguity, Agency, And Power. New York, NY: Cambridge University Press. Manin, B. (1997). The Principles of Representative Government. New York, NY: Cambridge University Press. doi:10.1017/CBO9780511659935 Miller, H. (2002). Postmodern Public Policy. Albany, NY: State University of New York Press.

Stone, D. (2002). Policy Paradox: The Art Of Political Decision Making. New York, NY: Norton & Company. Teppati, V., Ferrero, A., & Sayed, M. (2013). Modern RF And Microwave Measurement Techniques. New York, NY: Cambridge University Press. doi:10.1017/CBO9781139567626 Ulaby, F., Michielssen, E., & Ravaioli, U. (2010). Fundamentals of Applied Electromagnetics (6th ed.). Upper Saddle River, NJ: Pearson Prentice Hall. Weidenbaum, M. (1969). The Modern Public Sector: New Ways of Doing The Government’s Business. New York, NY: Basic Books, Inc.

Moore, M. (2013). Recognizing Public Value. Cambridge, MA: Harvard University Press.

KEY TERMS AND DEFINITIONS

Morgan, G. (2006). Images of Organization. Thousand Oaks, CA: Sage Publications Inc.

Advocacy Coalition: A group of like-minded policy participants and stakeholders that seek policy change through lobbying and communication with policy makers. Bounded Rationality: The cognitive limitations of decision-makers and the arbitrary underpinning of the advocacy coalition approach. New Public Management: An emphasis on privatization and outsourcing in order to maximize profitability and optimize resource allocation. Postmodernism: A new consideration in public administration that tends to ignore productivity and profitability constraints in favor of more humanistic indicators of preference.

National Telecommunications and Information Administration. (2014). Manual of Regulations & Procedures for Federal Radio Frequency Management (Redbook). United States Department of Commerce. 14, May, 2014. Pall, M. (2015). Microwave Frequency Electromagnetic Fields (EMFs) Produce Widespread Neuropsychiatric Effects Including Depression. Journal of Chemical Neuroanatomy.. 2015.08.001.10.1016/j.jchemneu

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Public Choice Theory: A theoretical proposition that explains policy participant and stakeholder hesitancy despite shared common interests. Rational Choice: Policy analysts make decisions that maximize public value while minimizing the cost of such benefits. Risk Regulation Regime: Ideological and narrative underpinnings of the regulatory framework governing specific public policy issues.

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Social Exchange: The notion that stakeholder behaviors change according to the situational context such as the number of bystanders present. Stasis: Public policy issues reach a stalemate where the political context elements underlying risk regulation regimes are unable to influence effective policy change.

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Category: Public Sector Management

Public Policies for Providing Cloud Computing Services to SMEs of Latin America Mohd Nayyer Rahman Aligarh Muslim University, India Badar Alam Iqbal Aligarh Muslim University, India

INTRODUCTION SMEs are an integral part of the Latin American Economic Region as they represent 99% of all the businesses and account for around 67% employment (LACFORUM, 2013). Thus, SMEs are the backbone of the Latin American economic region: a region characterised by high aspirations of the people towards trade and development. Since times immemorial governments are trying to boost the performance of SMEs due to their power of generating domestic employment. In the present world of technologically advanced operations, there is huge potential in SMEs to contribute towards the development of the region. Challenges are posed by the MNCs in the same region or the ones entering the region. MNCs are characterised by the use and employment of technologically sophisticated method of manufacturing and operations. Apart from this MNCs are also interested in R & D activities and widely invest in R & D department for improving efficiency in order to gain economies of scale. They benefit from the use of IT enabled services to increase the efficiency of the business. For managing knowledge, MNCs widely use cloud computing services provided by specialised companies. Cloud computing has been used as a tool by which ITES can be utilised by firms and it can help in better economical and operational decision making. SMEs of the Latin American economic region too aim for

increasing efficiency and optimising the use of ITES. Cloud computing will empower the SMEs of Latin America to compete with the MNCs. Cloud computing as a modern concept under felicitation of business by adding value to the business and operations achieving cost efficiency in the business. If SMEs of the Latin American economic region are provided cloud computing services through policy initiatives, it would not only add value to the SMEs businesses but also will increase the sustainability of the economic region. Cloud Computing is an opportunity to utilise shared resources for optimising business operations in the technologically driven global economic environment. Typically, cloud computing services include access to databases for the businesses, access to software that is important for decision making and contribution to knowledge and information sharing. Cloud Computing aims to reduce the amount of complexity, minimise costs, and enhance organisational agility (Ghaffari et al, 2014). Cloud Computing decreases the obstacles to conducting information process intensive activities. Indeed, people do not need to maintain their own technology infrastructure as they transfer the burden of system management and data protection to the cloud computing service provider (Jeager et al, 2008). Thus, the study will focus on the issues related to the formulation of public policies for providing cloud computing services for SMEs of Latin America.

DOI: 10.4018/978-1-5225-2255-3.ch582 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Public Policies for Providing Cloud Computing Services to SMEs of Latin America

BACKGROUND/CONCEPTUAL FRAMEWORK Cloud Computing It involves the use of appropriate hardware and software along with networks that allow centralised data storage and online access to the same. It also includes free or restricted (depending on the political and economic environment of a country) access to computer services and resources. Its aim is to achieve economies of scale and coherence by sharing knowledge resources. The term cloud is used as a metaphor for a setup both tangible and intangible that is a collection of tools and resources related to IT-enabled services. With cloud computing services, a business can optimise both its operations and decisions as more easy and fast sharing of knowledge is possible. Cloud Computing is a set of services that provide infrastructure resources using internet media and data storage on a third party server. Cloud Computing comprises of three services: 1. Software-as-a-Service (SaaS): Under this, particular service software is provided online for the consumption of the end user. It stands in opposition to the purchase of the software and then regular updates by the client businesses. The prominent software under this category are applications like Word Processing, CRM (Customer Relationship Figure 1. Service model of cloud computing Source: Prepared by author

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Management), ERP (Enterprise Resource Planning) etc. This is a matured model and through it, businesses can achieve economies of scale. Commercial vendors include Yahoo mail, Gmail, Hotmail, TurboTax Online, Facebook, Twitter, Microsoft Office Live, Google Apps, Cisco WebEx conferencing etc. 2. Platform-as-a-Service (PaaS): Under this service, software development kits and tools are provided on platforms. The tools include Java,NET, Python, Ruby on Rails. Prominent commercial vendors include Microsoft Azure Services, Amazon Web Services (AWS), Salesforce, Google App Engine Platform, IBM Cloudburst, Amazon’s relational database services, Rackspace Cloud Sites etc. 3. Infrastructure-as-a-Service (IaaS): This refers to devices such as storage devices, servers, virtual computers, network transfers etc. which are physically located in one central place which is known as data centre but they can be accessed and used over the internet from anywhere using the login authentication systems. Within organisations there are different cloud deployment models such as: 1. Public Cloud: Easily and economically available from a third party provider via

Category: Public Sector Management

Table 1. SME definitions used by multilateral institutions Institution

Max. of Employees

Max. Revenues/Turnover ($)

P

Max. Assets ($)

World Bank

300

15000000

15000000

MIF-IADB

100

3000000

None

African Development Bank

50

None

None

Asian Development Bank

No official definition/ Individual national definition used

No official definition/ Individual national definition used

No official definition/ Individual national definition used

UNDP

200

None

none

Source: Gibson &Vaart, 2008, Brookings Global Economy and Development

internet for deploying IT solutions. The famous example is Google Apps. 2. Private Cloud: It is suitable for large organisations and remains within organisations. The US government cloud product (Federal Information Security Management Act) FISMA certifies such clouds and is being handled by a third party provider e.g. Google Private Clouds. 3. Community Cloud: It is used and controlled by a group of enterprises that have shared interests in the cloud computing services e.g. US Federal government is using cloud community such as forms.gov, file.gov, cars. gov, USA.gov, Apps.gov. 4. Hybrid Cloud: It is a combination of the public and private cloud.

Public Policy Public policy refers to the administrative steps taken by executive branches of states towards a specific or a class of issues in a way that is consistent with the legal framework of the state. The aim of the strong public policy is to solve problems efficiently and effectively, serve justice, support governmental institutions and encourage active citizenship. In other words, the term public policy always refers to the actions of government and the intentions that determine those actions. The policy is a series of actions coordinated to achieve a goal. Three qualifications are necessary for public policy:

1. The idea of the intentional course of action stating not to take a certain action. 2. The requirement that official actions be sanctioned by law. 3. Laws should not be mistaken for the whole realm of the policy.

Small and Medium Enterprise (SMEs) SMEs are defined differently by different organisations and countries. Their definition may be based on the size of the firm, revenue/sales/turnover, number of employees, access to international markets etc. Different definitions of SMEs as given by multilateral institutions are presented in Table 1. Among Latin American countries there are at least two different definitions of an SME. One is based on the number of employees in the firm and the other uses sales revenue to determine the economic size of production units. The first definition ignores what was usually major differences among sectors (and among branches within each sector), often resulting in SMEs contribution to the economy being overestimated (LAEO, 2013). In Argentina and Chile, the two criterions have been used to estimate the difference between the official figures and the real figures. The results found that the official figure for the contribution of SMEs to the total number of the job was 7 percentage points too high in Argentina and more than 20 points too high in Chile (Stumpo, 2007). However, this is the criterion used in national

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Public Policies for Providing Cloud Computing Services to SMEs of Latin America

statistics departments, which often provide the data available in these countries, while the policy making bodies define company sizes based on the sales revenue criteria.

REVIEW OF LITERATURE Several studies highlight the importance and need of cloud computing in general and few others in particular about the SMEs improvement through cloud computing. From a policy perspective very few studies are available on cloud computing but no such study in reference to Latin American SMEs is available. Authors have identified the paradigm shift in businesses due to cloud computing and have equated the transformation as analogous to the replacement of individual generators by the centralised electricity grid (Etro, 2011; Li & Wang, 2011). The development of cloud computing was captured as an opportunity by large enterprises throughout the world by adopting cloud computing bandwagon (Klie, 2011; Li et al, 2011; Walsh et al, 2011). But the decision has remained tough for the SMEs to decide whether to take cloud computing services or not and if yes up to what extent (Gupta, Seetharaman& Raj, 2013). Studies have highlighted that SMEs are a potential market for cloud computing and companies are ready to offer cloud computing services at fewer prices as compared to the same service provided to large corporations. The prime need of SMEs is software and other services that help them to manage emails, licensing to software’s, other assets etc (Ferguson, 2008). King, 2008 has identified several points in relation to small businesses moving to cloud computing services such as IT infrastructure availability, minimal upfront investment, disaster recovery, software upgrades etc. Through this SMEs can reduce the cost of knowledge operations, potential avoidance of natural disaster mishaps but the fear is for reliability in using cloud computing services by the SMEs. In the study, King concluded that

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SMEs are showing positive inclination towards cloud computing. The important cloud computing parameters that are looked by SMEs includes trust in cloud providers, incremental cost, and reliability. The benefits identified for SMEs using cloud computing services includes security, reliability, trust, cost reduction, online collaboration etc. The success of the cloud computing adoption by SMEs was discussed and it was concluded that SMEs can explore cloud computing with relatively little risk (Clark, 2009). It was observed and empirically searched that security and privacy are top concerns of 51% small and medium businesses. Other factors considered by SMEs includes availability versus sudden downturn and migration across cloud services. The importance of moving to cloud step by step was recommended using a couple of tips for SMEs and thus moving to cloud computing was strongly emphasised (Martin, 2010). The variables for adoption of cloud computing services by SMEs includes cost reduction (in terms of data storage, subscription, low upfront cost, cost control via scalability, elasticity of resources), convenience (easy usage in terms of accessibility and availability from anywhere and anytime), reliability (indicates dependability to use it whenever needed), sharing and collaboration; and security and privacy (Gupta, Seetharaman& Raj, 2013). Large number of authors have classified three services under cloud computing: Software-as-a-Service, Platform-as-a-Service and Infrastructure-as-a-Service (Mahesh et al, 2011; Sultan, 2011; TruongandDustdar, 2011; Ojala&Tyrvainen, 2011; Creeger, 2009; Liet al, 2011; Durkee, 2010; Marston, Li, Bandyopadhyay, Zhang,andGhalsasi, 2011; Karadsheh, 2012; Rath, 2012; Neves, Marta,Correia, and de Castro, 2011; McAfee, 2011). There has been no common standard or definition for cloud computing seems to exist (Grossman, 2009; Voas& Zhang, 2009). In the history of IT, the manner in cloud computing is providing elasticity of using resources without having to pay a premium for large scale investment is unprecedented in the history of IT (Armbrust et

Category: Public Sector Management

Table 2. Cloud revenue (in billion dollars) Region/ Country

2011

2014 (Estimate)

2016 (Estimate)

CAGR in %

Argentina

0.2

0.4

0.6

28.5

Brazil

1.4

2.7

4.4

25

Mexico

0.6

1.1

1.8

26

Other

0.2

0.5

0.8

26

Latin America

2.4

4.7

7.6

26.4

Source: Cloud Computing in Latin America, ECLAC-Projects Documents Collection, 2014

al, 2009). A research conducted by Easynet Connect showed that UK SMEs are increasingly eager to adopt cloud computing with 47% planning to do so within the next 5 yrs. Of those which indicated their preparedness to move to cloud computing, 35% of them cited cost savings as the key driver (Stening, 2009). European Network and Information Security (ENSIA) in its survey found that 68% of the SME responses it received indicated that avoiding capital expenditure in hardware, software, IT support and information security is behind their possible engagement in cloud computing (ENISA, 2009). Sultan, 2011 concluded in its study that cloud computing is likely to be an attractive option for many SMEs due to its flexible cost structure and scalability. Weintraub and Cohen (2015). Attempted in their study to minimize the cost of Cloud Computing services provided to consumers. They came up with three models such as hierarchical, simple pricing model and the complete pricing model. In order for the price models to be effective, they also identified three pre-conditions and these will exteriorize in the long run. In another study, same authors reached to a conclusion that consumers of cloud computing services are attempting to maximize overall utility while encountering open tariffs. Bundling of Cloud computing services blocks market competition in cloud computing market (Weintraub & Cohen, 2015).

OPPORTUNITIES FOR PROVIDING CLOUD COMPUTING SERVICES TO SMEs OF LATIN AMERICA It is of paramount importance to highlight the concept of cloud computing services in Latin America as one unit and then move on to build the linkages with the SMEs of Latin America. While building this discussion it must be kept in mind that no specific study is available on the current status of SMEs of Latin America with respect to cloud computing services. The cloud computing services usage is indicated by the revenue generated by the public cloud. The data for revenue is also scarce and is available only in few reports that too of one or two specific years. Thus, it remains difficult for the researchers to highlight the increasing use of cloud computing services. In the coming lines, that would be attempted with the help of data available. Above mentioned Table 2 highlights the cloud revenue generated in Latin America as a sign of the increase in the use of cloud computing services. The figures for 2011 are real and for other years its estimated data. Argentina shows an increasing trend in the revenue collected through cloud computing services with a CAGR of 28.5%. On the other hand, Brazil and Mexico show CAGR of 25% and 26%, respectively. Both the countries also indicate an increase in the use of cloud computing services. Other countries (apart from Argentina, Brazil, and Mexico in the Latin American Region) also shows an increasing trend in the cloud computing revenues. Overall the Latin American region shows a CAGR of 26.4% which indicates potential opportunity in the future and is a clear indication that the users of cloud computing will increase. It would be justified to state that SMEs of the Latin American region can take benefit of this opportunity and can use cloud computing services. Figure 2 presents the data of Table 2 to highlight the present opportunities in Latin America.

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Figure 2. Cloud revenue

Source: Prepared by author from Table 2 data

Figure 2 clearly shows that Compounded Annual Growth rate (CAGR in %) is promising for all individual countries as well as for the Latin American region. This may be extended towards the opportunities for SMEs for using the cloud computing services. Further, Table 3highlights the important variables relevant to the cloud computing services in Latin America. Elucidation of the details of Table 3 includes variables such as Cloud Traffic Growth, Data Centre Workload, Cloud Workload and Traditional Data Centre Workload. For the period 2013-2018, cloud traffic shows an increase of 35% as Com-

pounded Annual Growth Rate. The rate is high and shows that use of cloud computing services is increasing. The second variable, that is, Data Centre Workload is also increasing with a CAGR of 21%, again highlighting the increase in the use of cloud computing. The third variable, that is, Cloud Workload shows a whooping growth of 34% CAGR and aligns itself with the findings of above data. The final variable given in the table states that traditional data is showing a very slow rate due to the slow transformation in the devices used. New devices are now preferred by the users instead of old ones.

Table 3. Latin America cloud computing data Variables

2013

2014

2015

2016

2017

2018

CAGR %

Cloud Traffic Growth (exabytes)

89

130

180

240

312

394

35

Data Center Workloads (millions)

2.6

3.2

3.9

4.7

5.7

6.9

21

Cloud Workloads (millions)

1.2

1.7

2.3

3.1

4.1

5.2

34

Traditional Data Center Workloads (millions)

1.4

1.5

1.6

1.6

1.7

1.7

3

Source: CISCO Visual Networking Index: Forecast and Methodology, 2013-2018

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Table 4. Regional consumer cloud storage users by 2018 in Latin America Consumer internet users in millions (% of population)

and 128900 new employees under SMEs due to cloud computing services in Latin America. In Brazil, in the next 10 years, the figures would be 402650 new SMEs and 945000 new jobs. This shows that cloud computing has the potential to boost SMEs in Latin America over the next decade. This opportunity must be tapped by the respective countries with the help of public policies.

355 (55%)

Average number of devices per consumer internet user

3.87

Consumer Cloud Storage users in millions (% of internet users)

109 (31%)

Source: CISCO Visual Networking Index: Forecast and Methodology, 2013-2018

PUBLIC POLICIES FOR CLOUD COMPUTING SERVICES

Table 4 highlights the estimate for 2018 about key variables that shows the use of cloud computing services. Consumer internet and cloud storage users, both will increase in the years to come. According to the estimate, 55% of the population will be using consumer internet services by 2018 in Latin America and 31% would be using cloud storage services. The average number of the device per consumer will also increase to 3.87. Remember that consumer here does not refer to individuals but the connections (where each connection is taken as a consumer). Respective data related to SMEs would be worth discussing. Table 5highlights that data. Table 5 highlights the impact of cloud computing services on SMEs growth and job creation by SMEs in Latin America. As estimated by ECLAC, beginning from 2013, in next 5 years (i.e 2014 – 2019) SMEs in Argentina can create 117300 jobs and number of SMEs will rise by 24700 due to the presence of cloud computing services. With respect to Brazil, the figures are 861000 jobs and 202100 new SMEs in the next 5 years. The estimated figures for the next 10 years are also promising and shows the strength of cloud computing towards SMEs in Latin America. In next 10 years, Argentina will have 30300 more firms

This section will be a formulation platform for policies that will boost the SMEs involvement in cloud computing services. Following policies are suggested: •

Improving Access to Cloud Computing Service: The primary objective of the administrative and executive set up in Latin America should focus on improving access to cloud computing services to SMEs. While corporations are working strategically for this, the administrative initiative is important which can be in the form of another ICT revolution or a new phase of ICT revolution in the region. For this ECLAC must take strong initiatives that should include collaborating with the cloud computing service providers. As it has been stated earlier that corporations are ready to provide cloud computing services to SMEs at low cost as compared to large corporations, they must be taken into confidence for doing so at an accelerated pace. Regulatory bodies of the respective country under Latin American economic

Table 5. Cloud computing impact on no. of SMEs and job creation Firms (in 5 yrs)

Employment (in 5 yrs)

Firms (in 10 yrs)

Employment (in 10 yrs)

Argentina

24700

117300

30300

128900

Brazil

202100

861000

402650

945000

Source: Etro & Colciago, Cloud Computing, structural change and job creation in SMEs, ECLAC, 2013

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•

•

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region should assure SMEs of the benefits they may tap in future. Improving Quality of Broadband Internet: Many countries in Latin America have low fixed or mobile broadband penetration such as Honduras, Nicaragua, Paraguay, Guatemala, Bolivia etc. By improving the quality of broadband, cost efficiency can be achieved by the SMEs. In this regard, policies should be framed that target to increase the quality of broadband. The quality of broadband has a direct relation with devices used by the firms. The cost of installation of devices must be reduced by state sponsoring or making a purchase of cloud computing devices under the category of tax deductible expense. SMEs heavily relying on the use of cloud computing services must be considered as key players and their quality of broadband be accessed regularly. Strong Legal and Regulatory Frameworks: The reality is that, in many countries within Latin America, there is no strict legal or regulatory framework. At administrative level laws must be more stringent so as to provide safety and security to the cloud computing devices may not be misused. For this fiscal norms must also be adhered to. This will boost the confidence of SMEs towards cloud computing and they will also take benefits of cloud computing. SMEs, in general, are not aware of existing legal and regulatory framework related to cloud computing. The government of the respective country should take the initiative in training SMEs about the existing laws and regulatory framework. Changes in existing framework that may benefit SMEs must be considered by policymakers on a serious note. Step by step strategies should be adopted to universalise the legal framework in the Latin American economic region.

•

•

Service Level Agreements for the Private Provider: The main concern for SMEs is about security and portability. Due to this SMEs are reluctant to fully migrate to cloud services. Private providers must be asked under regulations and cloud guidelines to follow service level agreements with SMEs and it should cover service adaptability, system security, and latency, service reliability, data security etc. This will act as a motivation for the SMEs to benefit from the cloud computing services. Strict regulation must be made for penalising the service providers in case of breach of the agreement. Presently, there is no clause of penalising the service providers. This will minimise any possible loss to the operations of SMEs and will not affect their working capital or operations negatively. Ease of Use and Convenience: SMEs must be motivated through policy to outsource their accounting and finance work to the cloud which will leave more time for SME executives to spend on strategic work and initiatives. Their move from PCbased accounting packages to cloud-based ones would be strategically beneficial as well as a source of cost reduction in operations. This will also allow avoidance of continuous hardware updates by SMEs thereby eliminating maintenance woes for utilising different machines. The cloud approach will thus eliminate administrative overhead and will permit access from any geographical location, any device, and from any organisation.

FUTURE RESEARCH DIRECTIONS The data for cloud computing services is available only in a haphazard manner. No time series data or panel data is available for cloud computing

Category: Public Sector Management

services across countries. Due to this reason, the present study has used only descriptive methodology. In future, the researchers should work on primary data and should come up with sharing of the cloud computing data so that statistical analysis is possible. On the availability of the panel or time series data, specific causality between cloud computing services and other macroeconomic variables may be identified.

Creeger, M. (2009, August). CTO roundtable: Cloud computing. Communications of the ACM, 52(8), 50–56. doi:10.1145/1536616.1536633

CONCLUSION

ENISA. (2009). An SME perspective on cloud computing. Retrieved from http://www.enisa. europa.eu/act/rm/files/deliverables/cloud-comp utingsmesurvey/?searchterm=survey

In the end, it would be justified to conclude that SMEs play an important role in the Latin American economic region and cloud computing services will improve the operations of SMEs thus benefiting the whole region. The future of cloud computing services in Latin America, particularly for SMEs is promising and will increase both number of SMEs and employment opportunities. There is a need for policies in favour of SMEs to reap the benefit of cloud computing services and for the same policies must target the issues such as access to cloud computing services, quality of broadband, legal and regulatory frameworks, service level agreements and ease and use of convenience.

REFERENCES Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., & Konwinski, A. (2009). Above the clouds: A Berkeley view of cloud computing. Electrical Engineering and Computer Sciences, the University of California at Berkeley. Retrieved from http://www.eecs.berkeley.edu/Pubs/ TechRpts/2009/EECS-2009-28.pdf Clark, L. (2009). SMEs can benefit most from the cloud. Computer Weekly, 19.

Durkee, D. (2010, May). Why cloud computing will never be free. Communications of the ACM, 53(5), 62–69. doi:10.1145/1735223.1735242 ECLAC. (2014). Cloud Computing in Latin America Current situation and policy proposals. ECLAC Project Documents Collection.

Etro, F. (2011, May). The economics of cloud computing. IUP Journal of Managerial Economics, 9(2), 7–22. Ferguson, S. (2008). Rising above the din. Education Week, 25(17), 29. Ghaffari, K., Delgosha, S. M., & Abdolvand, N. (2014). Towards Cloud Computing: A SWOT Analysis on its adoption in SMEs. International Journal of Information Technology Convergence and Services, 4(2). Grossman, R. (2009). The case for cloud computing. IT Professional, 11(2), 23–27. doi:10.1109/ MITP.2009.40 Gupta, P., Seetharaman, A., & Raj, J. R. (2013). The usage and adoption of cloud computing by small and medium businesses. International Journal of Information Management, 33(5), 861–874. doi:10.1016/j.ijinfomgt.2013.07.001 International Economic Forum Latin America and the Caribbean. (2013). Retrieved from http:// www.oecd.org/site/lacforum/LACForum_summary_ENG.pdf

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Jaeger, P.T., Lin, J., & Grimes, J.M. (2008). Cloud computing and information policy: Computing in a policy cloud?. Journal of Information Technology & Politics. Karadsheh, L. (2012, May). Applying security policies and service level agreement to IaaS service model to enhance security and transition. Computers & Security, 31(3), 315–326. doi:10.1016/j. cose.2012.01.003 King, R. (2008). Cloud computing: Small companies take flight. BusinessWeek Online, 4. Klie, L. (2011, December). SMB hosted CRM market set to triple by 2015. CRM Magazine, 15(12), 16. Latin American Economic Outlook. (2013). Retrieved from http://www.eclac.org/publicaciones/ xml/5/48385/LEO2013_ing.pdf

Neves, F. T., Marta, F. C., Correia, A. M. R., & de Castro, N. (2011). The adoption of cloud computing by SMEs: Identifying and coping with external factors. Paper presented at 11aConferência da Associac¸ ão Portuguesa de Sistemas de Informac¸ ão(CAPSI 2011) – A Gestão de Informac¸ ãona era da Cloud Computing, Lisboa, Portugal. Ojala, A., & Tyrvainen, P. (2011, July). Developing cloud business models: A case study on cloud gaming. IEEE Software, 28(4), 42–47. Rath, A. (2012). Cloud computing: Facing the reality. Bhubaneswar, India: Batoi. Stening, C. (2009). Every cloud has a Silver Lining. Retrieved from http://www. easynetconnect.net/portals/0/downloadfiles/industryinsight/ industrynews/Cloud-computing-website-articlefinal.pdf Stumpo, G. (2007). Políticas de apoyo a laspequeñas y medianasempresas en América Latina: situación actual y desafíos. Buenos Aires: SEPYME.

Li, Q., Wang, C., Wu, J., Li, J., & Wang, Z.-Y. (2011, November). Towards the business-information technology alignment in cloud computing environment: An approach based on collaboration points and agents. International Journal of Computer Integrated Manufacturing, 24(11), 1038–1057. doi:10.1080/0951192X.2011.592994

Sultan, N. A. (2011, June). Reaching for the cloud: How SMEs can manage. International Journal of Information Management, 31(3), 272–278. doi:10.1016/j.ijinfomgt.2010.08.001

Mahesh, S., Landry, B. J. L., Sridhar, T., & Walsh, K. R. (2011, July–September). A decision table for the cloud computing decision in small business. Information Resources Management Journal, 24(3), 9–25. doi:10.4018/irmj.2011070102

Truong, H.-L., & Dustdar, S. (2011). Cloud computing for small research groups in computational science and engineering: Current status and outlook. Computing, 91(1), 75–91. doi:10.1007/ s00607-010-0120-1

Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011, April). Cloud computing—The business perspective. Decision Support Systems, 51(1), 176–189. doi:10.1016/j. dss.2010.12.006

Voas, J., & Zhang, J. (2009). Cloud computing: New wine or just a new bottle? IT Professional, 11(2), 15–17. doi:10.1109/MITP.2009.23

Martin, J. A. (2010, April). Should You move your business to the cloud? PC World, 28(4), 29–30.

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Weintraub, E., & Cohen, Y. (2015). Cost Optimization of Cloud Computing Services in a Networked Environment. International Journal of Advanced Computer Science and Applications, 6(4), 148– 157. doi:10.14569/IJACSA.2015.060420

Category: Public Sector Management

Weintraub, E., & Cohen, Y. (2015). Optimizing User’s Utility from Cloud Computing Services in a Networked Environment. International Journal of Advanced Computer Science & Applications, 1(6), 153–163.

ADDITIONAL READING Azodolmolky, S., Wieder, P., & Yahyapour, R. (2013). Cloud computing networking: Challenges and opportunities for innovations. IEEE Communications Magazine, 51(7), 54–62. doi:10.1109/ MCOM.2013.6553678 Berry, R., & Reisman, M. (2012). Policy challenges of cross-border cloud computing. Journal of International Commerce and Economics, 4(2), 1–38. Cordeiro, T. D., Damalio, D. B., Pereira, N. C. V. N., Endo, P. T., de Almeida Palhares, A. V., Goncalves, G. E., & Mångs, J. E. et  al. (2010, November). Open source cloud computing platforms. In 2010 Ninth International Conference on Grid and Cloud Computing (pp. 366-371). IEEE. doi:10.1109/GCC.2010.77 Furht, B., & Escalante, A. (2010). Handbook of cloud computing (Vol. 3). New York: Springer. doi:10.1007/978-1-4419-6524-0 Gupta, P., Seetharaman, A., & Raj, J. R. (2013). The usage and adoption of cloud computing by small and medium businesses. International Journal of Information Management, 33(5), 861–874. doi:10.1016/j.ijinfomgt.2013.07.001

Knorr, E., & Gruman, G. (2008). What cloud computing really means. InfoWorld, 7. Kshetri, N. (2010). Cloud computing in developing economies. ieee. Computer, 43(10), 47–55. doi:10.1109/MC.2010.212 Rittinghouse, J. W., & Ransome, J. F. (2016). Cloud computing: implementation, management, and security. CRC Press.

KEY TERMS AND DEFINITIONS Cloud Computing Services: Services which provide the facility of data storage both offline and online. It is data storage on a virtual world platform. Community Cloud: It is used and controlled by a group of enterprises which have shared interests in the cloud computing services. Hybrid Cloud: It is a combination of the public and private cloud. Private Cloud: It is suitable for large organisations and remains within organisations. Public Cloud: Easily and economically available from a third party provider via internet for deploying IT solutions. Public Policy: It refers to the administrative steps taken by executive branches of states towards a specific or a class of issues in a way that is consistent with the legal framework of the state. Small and Medium Enterprises: Enterprises whose scale of production is relatively small in the economy.

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Category: Research Methods and Scholarly Publishing

Advancement and Application of Scientometric Indicators for Evaluation of Research Content Tazeem Zainab University of Kashmir, India Zahid Ashraf Wani University of Kashmir, India

INTRODUCTION The 20th century may be designated as the century of the growth and development of metric sciences (Kumar et al., 2009). There has been a tremendous development of metric based fields like Bibliometrics, Scientometrics, Librametrics, Technometrics, Sociometrics, Econometrics, Biometrics, Cybermetrics or Webometrics in this epoch. Scientometrics can be considered as an analogous conception to bibliometrics. Scientometrics is a novel scientific field joining science and technology with information science and expending numerous mathematical, statistical, data mining techniques and procedures to measure and quantify scientific information. It can be perceived as a discipline of science that encompasses bibliometrics, informetrics, webometrics, librametrics and other metric sciences. As matter of fact, today when the phase of scientific and technological revolution is massive but funding resources are trifling, the measurement of eminence of research productivity is essential and acknowledged by many. The basic requirement is to endorse the progression of science as competently as possible, i.e. to in the best way upsurge the power/ prize degree from the financing science (Fiala, 2013). The term scientometrics was introduced by Vassily V. Nalimov & Z. M. Mulchenko in 1969 as Naukometriya in Russian, meaning the study of the evolution of science through the measure-

ment of scientific information (Glanzel, n.d.). As per Tague-Sutcliffe (1992) Scientometrics is concerned with the quantitative facets of science. The focus of scientometrics as a discipline is the literature of science and technology. Price (1961, 1963) defines Scientometrics is a science about science. It offers numerous perceptions, representations, and practices to researchers that when functional in an academic field helps to understand its fundamentals, position, knowledgeable core, and probable forthcoming progress. Wilson (1999) indicated that everything that encompasses quantitative features of science of science, science communiqué and science policy are in the content of scientometrics. While defining the term Van Raan (1997) also accentuated the quantitative learning of science and technology. Vinkler (2010) defined that Scientometrics cannot be circumscribed within the circle of a scientific discipline. He widened the description as quantitative study of people, sets, materials and phenomena in science and their relationships. Further, he adds that scientometrics also covers various other aspects like practices of researchers, socio-organizational arrangements, administration, procedures, national economy. He also specified that Scientometrics could be foundation of statistics and also can indicate the policy in science like performance checking, research precedence assortment, science-society or science-economics relative scholarships. Wilson (1999) considers scientometrics as an organized

DOI: 10.4018/978-1-5225-2255-3.ch583 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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method to assess the past, present, and future progression of science as he believes that its origin is from the interest of trivial group of scholars in the subtleties of science.

BACKGROUND The term “Scientometrics” was not noticed in Western scientific circles until it was translated into English. The roots of Scientometrics lie in the survey piloted by Galton in 1874 among 180 prominent scientists in Britain in order to measure, comprehend, and define the eminence of significant scholars and their potentials (Godin, 2006). Then in 1900’s James McKeen Cattell, the Psychologist and Editor of Science, enthused by Galton measured scientific growth by observation and classification. He offered two important facets of scientific output: quality (i.e. worth as adjudicated by peers) and quantity (i.e. production). After that, in 1926 Lotka’s mathematical model to estimate the frequency of author’s publication in a field,in 1934, Bradford’s distribution law for articles through a set of journals.. Further the outline put forth by Price (1961, 1963) regarding the historical evolution of science and Bernal’s (1939) theory of the social function of science, idea of sociology of science by and Merton (1968, 1973) provided a back bone to the development of the field. Finally in 1969 the field was given a comprehensive name “Scientometrics” by the Russian mathematician Vasiliy Nalimov. In 1977, this area took a great leap when a maiden issue of Scientometrics journal by T. Braun was available, and the term received an academic acknowledgement. And today, there are numerous journals dedicated to scientometrics and its related fields like, Research Policy, Journal of Informetrics, Social Studies of Science, Journal of the American Society for Information Science and Technology, etc.

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SCIENTOMETRIC INDICATORS Bibliometric indicators aka Scientometric indicators (when referred in the field of Science) provide mathematical measures that are envisioned to quantitatively define the worth of scientific research and the scholarly publication in a particular domain. Scientometrics indicators are valuable assets for determining the capacity in terms of quality and quantity of scientific research of researchers, establishments or nations. El-Maamiry and Gauri (2013) define bibliometric indicator as a maneuver founded on bibliographic facts and figures employed to quantify and evaluate scientific scholarly output of an individual, institution, nation and so on. According to Vinkler (2001) Scientometric indicator is a scientometric measure which is accredited to scientometric organizations. To illustrate he has put forth the examples like Garfield Factor for a journal in a given year; Publication Productivity of an institution. Durieux and Gevenois (2010) emphasize that scientometric indicators are specifically essential for investigators and research institutions as these dimensions are frequently used in research funding, activities, and performance of researchers. They further elaborate that with increasing advances in scientific developments novel research is continuously asserted by new researchers thus making bibliometric indicators more significant. Scientometrics does not exist without the practice of quantitative information and indicators. According to him indicators are the spirit of scientometrics as they illustrate the communication process in science. Scientometric indicators are key parameters for the purpose of assessment and evaluation of scientific research output as they allow us to understand the prospective competitive aptitude of scientific research output and provide arithmetic measures to quantitatively define the worth of scholarly and scientific research, the impact and degree of scientific works

Category: Research Methods and Scholarly Publishing

-as an auxiliary for the total scientific research output, as being reliable measures for evaluating and assessing the scientific research output and most importantly policy-making.

TYPES OF SCIENTOMETRIC INDICATORS El-Maamiry and Gauri (2013) in their study have given three categories of bibliometric indicators. Quantity indicators, Quality indicators and Structural Indicators. Quantity indicators measure the total production of a researcher. Quality indicators measure the prominence and performance of researchers and Structural indicators signify the correlation between publications, researchers and research fields. A similar categorization of scientometric indicators has also been given by Durieux and Gevenois (2010). According to them, there are three types of bibliometric indicators: Quantity indicators measure the productivity of a particular researcher or research group. Performance indicators measure the quality of a journal, researcher, or research group. Structural indicators measure connections between publications, authors, or research fields. In this chapter, the scientometric indicators will be discussed under three classes, Quantity indicators, Quality indicators and Structural indicators. As per Durieux and Gevenois (2010) there are three types of bibliometric indicators: Quantity indicators measure the productivity of a particular researcher or research group. Performance indicators measure the quality of a journal, researcher, or research group. Structural indicators measure connections between publications, authors, or research fields.

PROMINENT SCIENTOMETRIC INDICATORS Various scientometric indicators have been derived so far to measure the performance of research

production of institutions, researchers, research groups, journals and so on. Most of the indicators used to evaluate the scientific research eminence are citation based, which traditionally were derived from the Citation databases like SCI, SSCI, JCR etc. Later in 21st century, web-based databases like Web of Science, Scopus and citation search engines like Microsoft Academic Search, Google Scholar, CiteSeerX began to be used to derive indicators that can be used to evaluate research. These indices help in analyzing and assessing the quality, quantity and also the co-authorship networks, collaborations between researcher, groups, institutes or countries. Some of the prominent scientometric indicators that have been evolved so far are discussed here:

1. Hirsch-Index Hirsch-index or h-index was proposed by a Physicist J.E. Hirsch in 2005. According to him a researcher has index h if h of his/her Np papers have at least h citations each and the other papers have no more than h citations each (Hirsch, 2005). H-index is considered to be the appraisal of the influence of a researchers’ aggregate research contribution. Today it is highly applied to enumerate the prominence, rank, worth, and comprehensive impression of researchers, research groups or research institutions. Some of the advantages of h-index have been put forth by Hirsh (2005) himself. The advantages pointed out are: • •

•

It pools a measure of quantity in terms of publications and quality by indicating citations received by a research. It permits us to illustrate the research production of a scientist with objectivity helping in decision making regarding upgrades, scholarships and awards. It is better executed than various other indicators used to evaluate research output like impact factor, citation per paper, number of papers, etc.

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•

It is easy to understand and can be accessed simply via Thomson ISI Web of Science, Google Scholar.

Though h-index has received a great accolade in terms of research evaluation worldwide but it has come under the scanner for its various critical points. Bornmannn and Marx (2003), BAR-ILAN (2008), Bornmann, Mutz, and Daniel (2008), Ball (2012), and Schreiber (2013) give following disadvantages of h-index: • • • • •

•

• •

More citations in h-core is of no value. H-index is field-dependent like most of the pure citation measures. Self-citations can easily influence the calculations of h-index. There is a delinquency in finding of reference standards. Data collection for h-index calculation is quite hectic. A complete list of publications with their citations of a researcher is required for its calculation. A meticulous problem of h-index is that a complete name of a researcher is required to distinguish between researchers with same name. More specifically the index handicaps newcomers as their publications and citation rates are very low. It sanctions researchers to respite on their glories as citations increase over-time even if no new publication is added.

Due to a number of limitations of h-index, a number of variants of h-index like g-index, a-index, hg-index, m-index and so on have been introduced to overcome its deficiencies.

2. Impact Factor Citations are an important factor in depicting the quality of scientific liter ature. The impact factor is based upon the count of these citations to determine the value of research. According to Saha,

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Saint and Christakis (2003) Impact factor is the citations received by a journal article in scientific literature. It is given by the number of citations in year n3 to a published work in a journal in year n1 and n2 divided by the total number of articles published in year n1 and n2 in that journal. This index as a journal quality index is based on the conception of citation frequency. Many advantages of Impact factor have been put forth by experts. It is considered to be quite beneficial for knowledge administrators and researchers; information professionals and also the librarians. Some of the advantages of Impact factor given by Honekopp and Kleber, 2008, Khan and Hedge (2009), Garfield and Pudovkin (2015) are: • • •

•

•

It enables experts to draw a comparative relation between different journals and research groups. It’s easily available to use and understand. Since Impact factor is fundamentally based on the citation patterns in a subject arear it is considered to be an objective measure of quality of research thus having a wider acceptance worldwide. Impact factors reveal the actual status of a journal within a research field, as the number of citations increases or declines in a particular timespan. Impact factor is specifically used for the prediction of the forthcoming citations of an article. Further than this, there certainly are some intrinsic snags in journal Impact factor (IF). The disadvantages have been discussed below.

Disadavantages Researchers Honekopp and Kleber (2008), Khan and Hedge (2009), Nisonger (2004), and Seglen (1997) have stated various problems that are associated with IF. Some of them are: •

Impact factor does not relate with definite citations of specific articles and hence can-

Category: Research Methods and Scholarly Publishing

• • • • • • • • •

not be considered to be statistically representing individual journal articles. Inclusion of self-citations creates a mess. Citations to “noncitable” stuff are speciously encompassed. High citations to review articles leads to inflation of the impact factor of journals. Length of articles matters a lot. Lengthy articles receive more than short length papers. Citations within the same journals inflates IF. Books are not included as a source of citation in various databases which drops the IF. IF varies from year to year as citation number keeps changing. Prejudiced towards English language, hence leading American publications. IF depends upon expansion of research area and also the relations between various research fields.

3. G-Index G-index is considered to be a well-known index to calculate the scientific output at a broader level (Woeginger, 2008). It was given by Leo Egghe in 2006 as the improved variant of h-index which defines the performance in terms of citations of an article at the global level. Egghe (2006) defines it as the f number of papers which receive total or more than g2 citations. He further elaborates that g>=h and also the g-score is high when citations are high in the g-core.

Advantages • • •

G-index provides information regarding both the size and the impact of the g-core. More comprehensive than h-index. Rather than using many indexes like h-index, r-index separately for measuring output and eminence of research, combined gindex can be employed (Schreiber, 2009).

Disadvantages

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Schreiber (2008, 2009, 2010) proposes various cons of g-index. According to him, following points should be reconsidered to improve the efficacy of g-index. • • •

Self-citations are ignored and excluded. The publication count are fractionalized. The collaboration index or the number of co-authors should be considered while calculating g-index.

4. M-Quotient The m-quotient proposes to measure the academic term and length of a researcher despite his period of scientific career. It is given by: h-index/k where k designates the number of years since a researcher’s first publication.

Advantages • •

Career length is an important factor is calculation of m-quotient. Based on citation metrics.

Disadvantages • • •

The m-quotient of an author is not properly established till initial years of his/her career. Minor changes in h-index of an author can alter the m-quotient altogether. As it is based on h-index, it nevertheless furnishes it anomalies in terms of quality and quantity measurement.

5. Shanghai Ranking Shanghai Ranking or Academic Ranking of World Universities (ARWU) is a yearly publication of

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university rankings by Shanghai Ranking Consultancy. It is based on four criteria namely quality of education, quality of faculty, research output and Per Capita Performance. Quality of faculty represents highly cited researchers, Nobel Laureates and Field Medalists. The research output is measured on the basis of indicators like papers in NATURE and SCIENCE and also papers indexed by SCI (Zhang et al., 2015).

requires careful efforts in use. Despite criticism at various levels, most professionals believe in it and is one of the widely used indicator categories. Though there is abundant literature emphasizing critics’ limits and partialities but academicians and researchers have confidence in it consider it the eminent method and agree that it is the only way to assess and evaluate the scientific research.

Advantages

FUTURE RESEARCH DIRECTIONS

•

The arena of research in scientometric evaluation is evolving with the development of more specific variants of scientometric indicators. These indicators are widely being employed in evaluation of different parameters of research including the impact of Journal publications, articles and author performance. The field is continuously evolving with the incorporation of nascent indicators each day. However there is a dearth of most comprehensible indicators that can be employed to evaluate and analyse each aspect of the scientific productivity. Therefore further research needs to be carried out to study different dimensions associated with Scientometric indicators so as to be more precise and appropriate in revealing qualitative aspects of research.

•

Its widely accepted Institutional Ranking System because of its transparency, methodology used and purpose. It is based on Scientific indicators like citations, research output, etc.

Disadvantages • • •

It is dependent on award factors decreasing the importance of instruction and humanities. It is widely criticized for the criteria it uses for ranking universities (Van Raan, 2005). Florian (2007) consider that any kind of ranking and numbering universities cannot be accurate in any kind of ranking system.

CONCLUSION

REFERENCES

The evaluation of qualitative research is being prioritized throughout the world for grading of universities and research institutions which in turn affects the research funding of such institutions by different funding agencies. Therefore analyzing research in different areas has always been an uphill task for evaluating bodies that resulted in the development of different evaluation indicators for analyzing the qualitative research. The scientometric indicators particularly are of great use in the measurement and evaluation of the scientific research output but at the same time it

Bernal, J. D. (1939). The social function of science. London: George Routledge & Sons.

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Durieux, V., & Gevenois, P. A. (2010). Bibliometric indicators: Quality measurements of scientific publication. Radiology, 255(2), 342–351. doi:10.1148/radiol.09090626 PMID:20413749 El-Maamiry, A. A., & Ghauri, M. A. (2013). Measuring information quality: Concerns on the use of Bibliometric studies. International Journal of Information Dissemination and Technology, 3(4), 274–278.

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Fiala, D. (2013). Suborganizations of institutions in library and information science journals. Information, 4(4), 351–366. doi:10.3390/info4040351

Merton, R. K. (1973). The sociology of science: Theoretical and empirical investigations. Chicago: University of Chicago Press.

Garfield, E., & Pudovkin, A. (2015). Journal Impact Factor Strongly Correlates with the Citedness of the Median Journal Paper. Collnet Journal of Scientometrics and Information Management, 9(1), 5–14. doi:10.1080/09737766.2015.1027099

Nisonger, T. E. (2004). The benefits and drawbacks of impact factor for journal collection management in libraries. The Serials Librarian, 47(1-2), 57–75. doi:10.1300/J123v47n01_04

Glanzel, W. (n.d.). A Concise Introduction to Bibliometrics & its History. Retrieved from https:// www.ecoom.be/en/research/bibliometrics Godin, B. (2006). On the origins of bibliometrics. Scientometrics, 68(1), 109–133. doi:10.1007/ s11192-006-0086-0 Hinze, S., & Glänzel, W. (2012). Scientometric indicators in use: An over-view. Presentation at the European summer school for scientometrics. Retrieved from http://www.scientometrics-school. eu/images/21_13Hinze.pdf Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National academy of Sciences of the United States of America, 102(46), 16569-16572. doi:10.1073/pnas.0507655102 Hood, W., & Wilson, C. (2001). The literature of bibliometrics, scientometrics, and informetrics. Scientometrics, 52(2), 291–314. doi:10.1023/A:1017919924342 Khan, K. M., & Hegde, P. (2009). Is impact factor true evaluation for ranking quality measure? DESIDOC Journal of Library & Information Technology, 29(3), 55-58. Retrieved from http:// www.publications.drdo.gov.in/ojs/index.php/ djlit/article/viewFile/253/163 Kumara, A., Prakasan, E. R., Mohan, L., Kademani, B. S., & Kumar, V. (2009). Bibliometric and Scientometric Studies in Physics and Engineering: Recent Ten Years Analysis. Putting Knowledge to Work: Best Practices in Librarianship, Mumbai, India. Retrieved from http://eprints.rclis. org/14829/1/BIBLIOMETRIC_AND_SCIENTOMETRIC_BOSLA-CDAC_Conf__2009-2.pdf

Price, D. D. (1961). Science since Babylon. Yale Univ. Press. Price, D. D. (1963). Little Science, Big Science. Columbia Univ. Press. Saha, S., Saint, S., & Christakis, D. A. (2003). Impact factor: A valid measure of journal quality? Journal of the Medical Library Association: JMLA, 91(1), 42. Retrieved from http://www. ncbi.nlm.nih.gov/pmc/articles/PMC141186/ PMID:12572533 Schreiber, M. (2008). The influence of self-citation corrections on Egghes g index. Scientometrics, 76(1), 187–200. doi:10.1007/s11192-007-1886-6 Schreiber, M. (2009). Revisiting the g-index: The average number of citations in the g-core. Journal of the American Society for Information Science and Technology, 61(1), 169–174. doi:10.1002/ asi.21218 Schreiber, M. (2010). How to modify the gindex for multi-authored manuscripts. Journal of Informetrics, 4(1), 42–54. doi:10.1016/j. joi.2009.06.003 Schreiber, M. (2013). How to derive an advantage from the arbitrariness of the g-index. Journal of Informetrics, 7(2), 555–561. doi:10.1016/j. joi.2013.02.003 Seglen, P. O. (1997). Why the impact factor of journals should not be used for evaluating research. BMJ (Clinical Research Ed.), 314(7079), 497. doi:10.1136/bmj.314.7079.497 PMID:9056804 Tague-Sutcliffe, J. (1994). Quantitative methods in documentation. Fifty Years of Information Progress: a Journal of Documentation Review, 147-188. 6745

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Van Raan, A. F. J. (1997). Scientometrics: Stateof-the-Art. Scientometrics, 38(1), 205–218. doi:10.1007/BF02461131 Van Raan, A. F. J. (2005). Fatal attraction: Conceptual and methodological problems in the ranking of universities by bibliometric methods. Scientometrics, 62(1), 133–143. Vinkler, P. (2010). Indicators are the essence of scientometrics and bibliometrics: Comments to the book entitled Bibliometrics and Citation Analysis, From the Science Citation Index to Cybermetrics from Nicola De Bellis. Scientometrics, 85(3), 861–866. doi:10.1007/s11192-010-0159-y Wilson, C.S. (1999). Informetrics. Annual Review of Information Science and Technology, 34, 107-247. Woeginger, G. J. (2008). An axiomatic analysis of Egghes g-index. Journal of Informetrics, 2(4), 364–368. doi:10.1016/j.joi.2008.05.002 Zhang, Y., Eckelmann, B., Andrews, T., Cheng, Y., Vught, F., & Morse, R. (2015). International Ranking & Institutional Research. Retrieved from https://manoa.hawaii.edu/miro/wp-content/ uploads/2015/06/International-rankings-handouts_final_06092015.pdf Zitt, M., & Bassecoulard, E. (2008). Challenges for scientometric indicators: Data demining, knowledge-flow measurements and diversity issues. Ethics in Science and Environmental Politics, 8(1), 49–60. doi:10.3354/esep00092

ADDITIONAL READING Andrés, A. (2009). Measuring academic research: How to undertake a bibliometric study. Oxford: Chandos Publishing. doi:10.1533/9781780630182

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Braun, T., Glanzel, W., & Schubert, A. (1985). Scientometric Indicators: A 32-Century Comparative Evaluation of Publishing Performance and Citation Impact. Singapore: World Scientific. doi:10.1142/0106 Foundation for Research Development (South Africa). (1990). SA science and technology indicators. Pretoria: Scientometric Advisory Centre. Foundation for Research Development (South Africa). (1991). South African science and technology indicators 1990. Pretoria: Foundation for Research Development, Scientometric Advisory Centre. Frankel, M. S., & Cave, J. (1997). Evaluating science and scientists: An east-west dialogue on research evaluation in post-communist Europe. Budapest: Central European University Press. Ingwersen, P. (2012). Scientometric indicators and webometrics -- and the polyrepresentation principle information retrieval. New Delhi: Ess Ess Publications. Južnič, P., Pečlin, S., Žaucer, M., Mandelj, T., Pušnik, M., & Demšar, F. (2010, January 01). Scientometric indicators: Peer-review, bibliometric methods and conflict of interests. Scientometrics, 85(2), 2. doi:10.1007/s11192-010-0230-8 Karamourzov, R. (2012, January 01). The development trends of science in the CIS countries on the basis of some scientometric indicators. Scientometrics, 91(1), 1–14. doi:10.1007/s11192011-0592-6 Kumari, G. L. (2009, September 01). Synthetic Organic Chemistry research: Analysis by scientometric indicators. Scientometrics: an International Journal for All Quantitative Aspects of the Science of Science. Communication in Science and Science Policy, 80(3), 559–570.

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Mendez, A., & Salvador, P. (1992, January 01). The application of scientometric indicators to the spanish scientific research council. Scientometrics, 24(1), 61–78. doi:10.1007/BF02026473 Raan, A. F. J. (1988). Handbook of quantitative studies of science and technology. Amsterdam: North-Holland. Ravichandra, R. I. K. (2010). Growth of literature and measures of scientific productivity: Scientometric models. New Delhi: Ess Ess Publications. Suriya, M. (2011). Gender studies in informatics: An application of scientometric indicators and mapping techniques. New Delhi: Ess Ess Publications. Tijssen, R. J. W. (1992). Cartography of science: Scientometric mapping with multidemensional scaling methods. Leiden: DSWO Press, University of Leiden.

KEY TERMS AND DEFINITIONS H-Index: H-index is considered to be the appraisal of the influence of a researchers’ aggregate research contribution. Impact Factor: Impact factor is the citations received by a journal article in scientific literature. Performance Indicators: These measure the quality of a journal, researcher, or research group. Scientometric Indicators: Metrics founded on bibliographic facts and figures employed to quantify and evaluate scientific scholarly output of an individual, institution, nation and so on. Scientometrics: The field within bibliometrics that quantifies and encompasses evaluation and assessment of scholarly content within the field of science.

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Electronic Theses and Dissertations (ETDs) Ralph Hartsock University of North Texas, USA Daniel G. Alemneh University of North Texas, USA

INTRODUCTION Academic libraries around the world are seeking to take advantage of the powerful forces that transform higher education, including new and rapidly changing technologies, an abundance of digital (mostly open access) resources in a myriad of formats, and changing practices in how scholars communicate and disseminate their research and creative work. Theses and dissertations, the monographlength essays required for graduate degrees from institutions of higher education, have evolved with the technology. Electronic Theses and Dissertations (ETDs) constitute the primary contributions to a community of research (Ramirez et al., 2014). The term “Electronic Theses and Dissertations” (ETD) is used primarily to differentiate between analog theses and dissertations (paper, microfilm) and their digital counterparts (digital objects). Since 1998, academic institutions increasingly publish theses and dissertations that are born digital.

BACKGROUND As forms of scholarship evolve, so do users’ and creators’ expectations. Theses and dissertations represent part of the historical record of graduate education at the institution. Those produced prior to the advent of the photocopier were created by the use of carbon paper. However, often in the domain of music, the need for accompanying material required the student to attach a separate

sheet with the musical notation to each copy. Students attached photographs and illustrations, predominantly black and white, in much the same manner. The first electronic theses and dissertations (ETD) project was launched in 1987 by a business company and a long-term vendor of theses and dissertations for academic libraries, University Microfilms International (UMI), by converting its large collection of dissertations on microfiches and microfilms going back to 1939 into electronic form. The first non-profit ETD hosted by a university was launched ten years later, in 1997, at Virginia Tech, which made electronic submission of theses and dissertations through its ETD system a requirement for the university’s graduating students (Ramirez et al., 2014). Virginia Tech University, along with representatives from UMI and the American Council of Graduate Schools, was one of the founders of the Coalition for Networked Information’s joint project, with the goal to collaboratively develop collections of ETDs. In 1995 this resulted in creation of the Networked Digital Library of Theses and Dissertations (Fox et al., 1997). Since the late 1990s, an increasing number of academic institutions have mandated the electronic submission of theses and dissertations. Today, textual dissertations need only be in a word processing file and converted to a more permanent and unchangeable file format to become Electronic Theses and Dissertations (ETDs). The current digital submissions of ETDs experienced significant increased usage of graphics or multimedia contents.

DOI: 10.4018/978-1-5225-2255-3.ch584 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Research Methods and Scholarly Publishing

During the analog age examples of handwritten music had to be glued into the dissertation, with the typescript below it; this included attaching the original music on the carbon copies. With the introduction of musical software (Finale, Sibelius) or imaging software, writers could place these materials inline inside the dissertation. A move to an all-digital means of providing electronic theses and dissertations is accelerating their discovery and facilitating their use, value, and impact in research.

Accompanying Materials Rebecca Lubas (2009) and Cedar C. Middleton, Jason W. Dean, and Mary A. Gilbertson (2015) present adequate processes for the cataloging and metadata creation of homogenous textual dissertations. However, dissertations increasingly have accompanying materials, most prevalent in music and the performing arts: these have included audio tapes, compact discs, or video recordings of recitals, concerts, and lectures. Traditionally, these audio tapes, either in reel-to-reel or audio cassette format, or videocassettes, in various configurations, were difficult to preserve. Equipment also went out of date, as certain formats became dominant. Beta and U-Matic declined into more limited use as VHS became the standard for videocassettes. Discs, either CDs or DVDs, became the norm during the 1990s. Since the introduction of ETDs, illustrations have become predominantly color, particularly in the arts and sciences. In addition to increased usage of graphics, those in biological and chemical fields include video demonstrations of their experiments, or may draw the elements and the design of molecules. Today, these are all submitted as streaming audio files or audio visual files and integrated seamlessly with the original ETDs.

Copyright Issues While increased availability of interoperable Open Access content helps to integrate and enhance ac-

cess to diverse digital resources, they also bring about great challenges for traditional policies. There have been some concerns, questions, and misconceptions about various issues, ranging from intellectual property to quality issues. Two of the primary concerns about ETDs and their accompanying materials are copyright and fair use. Musical and artistic works created after 1923 are held in the creator’s copyright until 75 years after that creator’s death, due to the Berne Convention Implementation Act of 1988. Musical scores may be either brief examples that support the author’s thesis, or comprise the primary contents of the thesis, as is done in new editions of music. Additionally, this affects open access to performances (audio or video) of these compositions, as it affects public performance rights (Dougan, 2011). Due to this, many institutions limit access to these recordings to those enrolled in classes or patrons on the campus. Open access has also been a concern for emerging authors wishing to publish their research in peer reviewed publications. Although some scholars consider this issue to be contentious, Ramirez et al. (2014) has alleviated this concern, stating that the thesis as a non-peer reviewed paper does not constitute double publishing.

Access and Cataloging As forms of digital scholarship evolve, so do users’ and creators’ roles and expectations. The Open Access (OA) movement has become increasingly important in shaping the ways that academic libraries provide services to support the creation, organization, management, and use of digital contents. Making ETDs Open or the removals of barriers (pricing, technical and legal hurdles) facilitates successful management of ETDs across the entire life-cycle to ensure their preservation and continuous availability in a manner that current and future users expect. Even though some ETDs are restricted to their specific institutions’ users, consulting the metadata description may convey sufficient information, and can be adequate for

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most external users’ needs. Making the metadata accessible to providers of search and discovery services via Open Archive Initiative Protocol for Metadata Harvesting (OAI-PMH) facilitates usability. Successful retrieval of materials that are useful to a user relies on the quality of the information representation. In an ideal world, digital content would also be given the same consideration as other library materials, when conducting collection development, organization and cataloging of works, etc. In information science, an ontology is a formal naming and definition of the types, properties, and interrelationships of the entities that fundamentally exist for a particular domain of discourse. It is thus a practical application of philosophical ontology, with a taxonomy. Historically, ontologies arise out of the branch of philosophy known as metaphysics, which deals with the nature of reality – of what exists. An ontology compartmentalizes the variables needed for some set of computations and establishes the relationships between them (Pawletta et al., 2014). This necessity of relationships is accentuated in music, due to the relationships between the composer, the work, and those who perform recordings of these works. Ludwig van Beethoven composed nine symphonies, each published by different organizations, and recorded by numerous conductors, from Arturo Toscanini to Leonard Bernstein. The generation of accurate indexing terms is critical to the discovery and use of specific manifestations. However, in practice it is difficult to comprehensively represent every item with appropriate descriptions and index terms. According to Alemneh and Hartsock (2014a), because ETDs usually constitute original research, each is unique to the bibliographic world; as a result, catalogers need to provide original cataloging (i.e., creation of metadata record from scratch) as opposed to copy cataloging (i.e., use of pre-existing metadata records with or without augmentations) to describe each ETD. When catalogers employ traditional controlled vocabularies, such as the Library of

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Congress Subject Headings (LCSH), creating access to these original theoretical researches becomes challenging and labor intensive. (Lubas, 2009) Considering the multi-disciplinary and interdisciplinary characteristics, several subjects and terms need to be supplied frequently to adequately represent ETDs for efficient access. Middleton et al. (2015) notes that it is uncommon to provide full level cataloging to theses and dissertations because of this increased labor. But a research status of a certain number of institutions affects accessibility and expectations of graduate students. Thus, these metadata specialists may provide full level cataloging in MARC (Machine-Readable Cataloging) and more universal descriptive standards like RDA (Resource Description and Access) to their dissertations. MARC was developed during the 1960s at the Library of Congress as a model for data input and retrieval (Seikel et al., 2011). It employed three character numerical codes, which utilized 8 bit memory as opposed to natural language labels. A majority of library catalogs online use MARC data, with public viewable interfaces installed by local system vendors. This translates the numerical code into readable labels. The new RDA standard provides a comprehensive set of guidelines and instructions for the creation of descriptions and access, covering all types of digital contents and media. (American Library Association, 2010). Furthermore, in some disciplines, the subject can also be an aspect of study. Recently introduced are genres and forms, through seven projects of the Library of Congress Genre/Form Terms (lcgft). Several professional associations provided guidance in the formulation of these terms. Beginning in 2007, the Policy and Standards Division (PSD) of the Library of Congress formulated thesauri of genre/form terms for moving images, cartography, law, literature, religion, music, and non-musical sound recordings (predominantly radio programs) (Young et al., 2013) Many genres and forms are combined into a single term. Genre indicates a category of works that is characterized by a similar plot, theme, setting, situation, or characters (such

Category: Research Methods and Scholarly Publishing

as Horror, Thriller, Western, Country). Form denotes a category of works with a particular format and/or purpose (for example, Film, Sonata, Symphony, Opera, etc.). Catalogers may add another Genre/Form for textual dissertations, the lcgft of “Academic theses.” A dissertation author may produce an edition of music, or create an original composition; the cataloger then adds the genre of the music in the ETD, for example, Anthems, Madrigals, Folk songs, or Symphonies. Within the context of radio, genre/form terms denote categories of programs. They describe a program according to its content (Westerns, Biographies), style (Audience Participation Programs, Call-in Shows), topic (Crime or Mystery Programs), structure (Magazines, Anthologies), intended audience (Children’s Programs), method of transmission (Shortwave Broadcasts) or combinations of these. A genre/form category suggests a common theme, motif, setting, situation or characterization that is easily recognizable. That means, in addition to denoting “Aboutness,” certain music and other subjects will have “Isness” as well, referred to as content in RDA (McKnight, 2012). To illustrate this, current practice prescribes that Sonata, Symphony, or Opera represent a subject in the LCSH when singular, indicating resources that discuss the form itself. For a genre/form term, the plural indicates that this is a manifestation of the genre: a score or a recorded performance. In music, the author achieves originality by analyzing a specific composer or composition(s), or applying a unique approach or analysis to a specific composition. This aspect may be applied to several works by various composers. For example, multiple dissertation writers may analyze extended techniques that employ specific instruments (trumpet, vibraphone) in relation to specific compositions (Meredith, 2008; Smith, 2008). During the analog age, from the invention of the printing press until the 1980s, the carrier determined the content of its material. Thus, books are textual and printed, audiocassettes are sound recordings on tape 1/8 inch wide, while VHS is

the standard for 1/2 inch wide videocassettes. The digital age has not only made texts and images interactive, but releases content types from single carriers. Today, a DVD may be audio or video, contain moving images, or still images, sound, textual files, and even games. In certain disciplines, institutions require the student to perform a recital (vocal or instrumental), and to present a lecture about a specific aspect of a composition, to be performed at the LectureRecital. The dissertation is a textual representation of this lecture. To provide access to all facets of ETDs, metadata departments must integrate the cataloging of all the formats involved, not just the text. In the transitory period between the late analog and the early digital age, recitals were submitted on audiocassettes, or if visual, on videocassettes. These videos could be in a number of formats, but VHS predominated. Later, students submitted their audio recordings on compact discs. The digital reformation makes possible the submission of these aural and visual data via streaming mechanisms. The electronic manifestation of the ETD becomes the record of choice, and the cataloger may provide full level access, including description, subjects from a controlled vocabulary, classification numbers, and functioning links, not only for the textual dissertation (Middleton et al., 2015), but to all accompanying materials via multiple linking fields, such as the MARC (Machine-Readable Cataloging) 856. While the subjectivity and objectivity of the process and the need to distinguish functional representation from mere descriptions of a topic still exist, recording these data has evolved from the earlier cataloging methods using Anglo-American Cataloging Rules (AACR), and Anglo-American Cataloging Rules, 2nd edition (AACR2), to the current practice of Resource Description and Access (RDA). Catalogers provide Library of Congress Subject Headings (LCSH) for the topics of each dissertation. In addition, those of musical editions contain a Medium of Performance field, located in the MARC format as the 382 tag. This

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presents a granular statement of the personnel necessary for an execution of the work, and uses a controlled vocabulary developed by the Library of Congress, with assistance from the Music Library Association. For Gregory Alan Schneider’s I never saw Another Butterfly: A Composition for SATB Choir and Chamber Orchestra (Schneider, 1997), the LCSH may be represented by a MARC 650 tag: Choruses, Secular (Mixed voices) with chamber orchestra. The Library of Congress Medium of Performance Thesaurus statement (LCMPT) more granularly exhibits which voices are represented in this chorus. This would display to the cataloger in MARC as: 382 01 mixed chorus $v SATB $a orchestra $2 lcmpt The public would see this as: Performance medium: mixed chorus (SATB), orchestra.

FUTURE RESEARCH DIRECTIONS Theses and dissertations represent a wealth of scholarly and artistic content created by graduate students in masters and doctoral programs in the degree-seeking process. The digital environment has now introduced new resource types into the current information landscape, accompanied by new user expectations. Advancing knowledge requires not only enhancing our capacity to generate more knowledge, but also cultivating our ability to provide seamless discovery processes to the vast quantities of knowledge we continue to generate. As the starting points for new researches are increasingly digital, catalogers and metadata specialists need to provide adequate descriptions of digital contents sufficient to ensure successful information retrieval. Arguably, ETDs have experienced a continuous evolution in format and structure that require dif-

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ferent lifecycle management practices to facilitate access, use, and reuse. Accompanying materials in the ETD landscape can be diverse, ranging from simple visual forms of maps or diagrams to complex and high resolution images of art objects or videos of lab observations. As demonstrated by Alemneh et al. (2014), by employing appropriate metadata elements that link various characteristics and relationships, ETDs’ associated contents can be integrated. In light of such diverse contents and formats, institutions integrate and contextualize all parts of ETDs to add value and enhance access and use. Poole (2015) argued that harnessing the principles and practices of digital curation (ranging from managing quality metadata, provenance and authenticity to ensuring long-term access and trust) facilitate the sharing, access, and reuse of scientific data. Doing so also allows researchers and other stakeholders to address new imperatives in scholarly research.

CONCLUSION There has been a shift in the way users search, access, and use ETDs. In principle, cataloging or metadata descriptions still serve the same function that they always have: to link creators with users. In other words, by describing information resources adequately, whenever users express their information need, using the best possible descriptor or search term, the results obtained match their information needs. However, in the increasingly self-structured Web 2.0 environment, it is clear that traditional user experience and access methods of a simple ranked list of search results will be of limited utility. Various emerging Web applications – driven by semantic web technologies such as the resource description framework (RDF), semantic Web rules language (SWRL), and other members of the World Wide Web Consortium (W3C) family of specifications – offer powerful data organization, combination, and query capabilities. All these have serious im-

Category: Research Methods and Scholarly Publishing

plications for how ETDs are curated. Consequently catalogers have changed their methodology and procedures significantly over the past few decades. Deployment of new cataloging standards including RDA and the exposure of RDA-based data in the linked data cloud have already transformed the traditional practices. Effective metadata descriptions, taxonomies, and ontologies add value and amplify the mostly interdisciplinary ETDs. Complex retrieval systems allow visualization of the information space, among other things–allowing ETD users to explore and delve deeper in multi-dimensional ways.

REFERENCES

Dougan, K. (2011). Dissertations in the Electronic Age: Tapping into Emerging Musicology Research. Music Reference Services Quarterly, 14(3), 109–130. doi:10.1080/10588167.2011.596094 Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199–220. doi:10.1006/knac.1993.1008 Joint Steering Committee for Revision of AACR. (1982). Anglo-American Cataloguing Rules (2nd ed.). Chicago: American Library Association. Krueger, J. M. (2013). Cases on Electronic Records and Resource Management Implementation in Diverse Environments. Hershey, PA: IGI Global. Retrieved January 29, 2016 from http://site.ebrary. com/id/10755871

Alemneh, D. G., et al. (2014). Guidance Documents for Lifecycle Management of ETDs. Educopia Institute. Retrieved January 29, 2016 from https://educopia.org/publications/gdlmetd

Lubas, R. L. (2009) Defining Best Practices in Electronic Thesis and dissertation Metadata. Journal of Library Metadata, 9(3-4), 252-263. DOI:10.1080/19386380903405165

Alemneh, D. G., et al. (2015). Knowledge Representation and Subject Access in ETDs: Analysis of Creators’ and Users’ Assumptions and Expectations. In T. Watanabe & K. Seta (Eds.), Proceedings of the 11th International Conference on Knowledge Management. Retrieved January 29, 2016 from http://ickm.kis.osakafu-u.ac.jp/ program/accepted-papers/

McKnight, M. (2012). Are We There Yet? Toward a Workable Vocabulary for Music. Fontes Artis Musicae, 59(3), 286–292.

Alemneh, D. G., & Hartsock, R. (2014a). Theses and Dissertations from print to ETD: The Nuances of Preserving and Accessing those in Music. In J. M. Krueger (Ed.), Cases on Electronic Records and Resource Management Implementation in Diverse Environments (pp. 41–60). Hershey, PA: IGI Global.

Middleton, C. C., Dean, J. W., & Gilbertson, M. A. (2015). A Process for the Original Cataloging of Theses and Dissertations. Cataloging & Classification Quarterly, 53(2), 234–246. doi:10.108 0/01639374.2014.971997

American Library Association. (2010). About RDA. Retrieved February 17, 2016 from http:// www.rdatoolkit.org/about

Meredith, S. (2008). Extended techniques in Stanley Friedman’s Solus for unaccompanied trumpet. Retrieved February 15, 2016, from http://digital. library.unt.edu/ark%3A/67531/metadc6075/

Park, E. G., & Richard, M. (2011). Metadata assessment in e‐theses and dissertations of Canadian institutional repositories. The Electronic Library, 29(3), 394–407. doi:10.1108/02640471111141124

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Pawletta, T., Durak, U. & Breitenecker, F. (2014). Ontologies in Modelling and Simulation. SNE (Simulation Notes Europe), 24(2). DOI:10.11128/ sne.24.2.1024

Young, J. L., & Mandelstam, Y. (2013). It Takes a Village: Developing Library of Congress Genre/ Form Terms. Cataloging & Classification Quarterly, 51(1-3), 6–24. doi:10.1080/01639374.201 2.715117

Poole, A. H. (2015). How has your science data grown? Digital curation and the human factor: A critical literature review. Archival Science, 15(2), 101–139. doi:10.1007/s10502-014-9236-y

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Ramirez, M. L., McMillan, G., Dalton, J. T., Hanlon, A., Smith, H. S., & Kern, C. (2014). Do Open Access Electronic Theses and Dissertations Diminish Publishing Opportunities in the Sciences? College & Research Libraries, 75(6), 808–821. doi:10.5860/crl.75.6.808

Alemneh, D. G. (2014). Metadata for ETD Lifecycle Management. In M. Schultz and K. Skinner (Eds.) Guidance Documents for Lifecycle Management of ETDs. Educopia Institute. Retrieved November 23, 2016, from http://digital.library. unt.edu/ark:/67531/metadc279711/

RDA Toolkit. (2012). Resource Description and Access. Retrieved February 4, 2016 from http:// access.rdatoolkit.org/

Chandrasekaran, B., Josephson, J. R., & Benjamins, R. (1999). What are ontologies, and why do we need them? IEEE Intelligent Systems, 14(1), 20–26. https://www.csee.umbc.edu/courses/771/ papers/chandrasekaranetal99.pdf RetrievedNovember232016 doi:10.1109/5254.747902

Schneider, G. A. (1997). I never saw Another Butterfly: A Composition for SATB Choir and Chamber Orchestra. Retrieved February 4, 2016, from http://digital.library.unt.edu/ark%3A/67531/ metadc278223/ Seikel, M., & Steele, T. (2011). How MARC Has Changed: The History of the Format and Its Forthcoming Relationship to RDA. Technical Services Quarterly, 28(3), 322–334. doi:10.108 0/07317131.2011.574519 Smith, J. D. (2008). Extended performance techniques and compositional style in the solo concert vibraphone music of Christopher Deane. Retrieved February 15, 2016, from http://digital.library.unt. edu/ark%3A/67531/metadc9030/ Sowa, J. F. (1995). Top-level ontological categories. International Journal of Human-Computer Studies, 43(5-6), 669–685. doi:10.1006/ ijhc.1995.1068 Spalding, C. S. (1967). Anglo-American Cataloging Rules. Chicago: American Library Association.

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Hartsock, R., & Alemneh, D. G. (2016). Beyond Text: Best Practices for Cataloging Music-ETDs and Associated Audio-Visual. Materials. Retrieved November 23, 2016, from http://digital.library. unt.edu/ark:/67531/metadc848622/ Library of Congress. Cataloging and Acquisitions Home: Home page of the Acquisitions and Bibliographic Access Directorate. November 23, 2016, from https://www.loc.gov/aba/ Library of Congress. Genre/Form Headings at the Library of Congress. Retrieved November 23, 2016, from http://www.loc.gov/catdir/cpso/ genreformgeneral.html Potvin, S.,.... (2015). Texas Digital Library Descriptive Metadata Guidelines for Electronic Theses and Dissertations, Version 2.0. Retrieved November 23, 2016, from https://tdl-ir.tdl.org/ tdl-ir/handle/2249.1/68437

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KEY TERMS AND DEFINITIONS Catalog: Textual representation of bibliographic information for materials, either in analog (i.e., card catalog) or digital (i.e., online catalog) forms; online catalogs employ natural language labels, each derived from MARC (Machine Readable Cataloging) numerical data. Digital Curation: The active management, preservation, and enrichment of digital resources. ETD: The term “Electronic Theses and Dissertations” (ETD) is used primarily to differentiate between analog theses and dissertations (paper, microfilm) and their digital counterparts (digital objects). However, “ETDs” may also be digitized or born digital. Intellectual Property Rights: Refers to creations of the mind: inventions; literary and artistic works and symbols, names, images, and designs used in commerce. LCGFT: Library of Congress Genre Form Thesaurus (LCGFT) is a controlled vocabulary of genres and forms maintained by the United States Library of Congress, for use in bibliographic and authority records. LCMPT: Library of Congress Medium of Performance Thesaurus (LCMPT) is a controlled vocabulary maintained by the United States Library of Congress, for use in bibliographic

and authority records. Terms provide maximum granularity of the medium of performance. LCSH: Library of Congress Subject Headings (LCSH) is a controlled vocabulary maintained by the United States Library of Congress, for use in bibliographic records. MARC: Machine-Readable Cataloging (MARC), a data retrieval system developed during the 1960s at the Library of Congress. It employed three character numerical codes, which utilized 8 bit memory as opposed to natural language labels. OA: Open Access (OA) refers to online research outputs freely accessible, without restrictions on costs (e.g. access tolls) or use (e.g., copyright, performance rights, and licensing). OAI-PMH: Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) is a mechanism developed for harvesting metadata descriptions of records in an archive. RDA: Resource Description and Access (RDA) is a standard for descriptive cataloging initially released in 2010, providing instructions and guidelines on formulating bibliographic data. RDA divides bibliographic entities into works, expressions, manifestations, and items. RDF: Resource Description Framework (RDF) is a standard model for data interchange on the Web, facilitates data merging across several schemas.

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The Nature of Research Methodologies Ben Tran Alliant International University, USA

INTRODUCTION In the research community, according to Tran (2015), the process of research, is generally defined as the procedures used in research that involve introducing a problem, narrowing the research problem into purpose statements, research questions, and hypotheses, collecting and analyzing data to address these questions and hypotheses, and using a writing structure that best fits the problem and the methods. Hence, terms such as investigator (often associated with quantitative research) and inquirer (often associated with qualitative research) are used interchangeably. Thus, a methodology refers to the philosophical framework and the fundamental assumptions of research (van Manen, 1990), research design refers to the plan of action that links the philosophical assumptions to specific methods (Creswell, 2003; Crotty, 1998), and methods are techniques of data collection and analysis (Creswell, 2003; van Manen, 1990). Some mixed methods writers, like Tashakkori and Teddlie (1998), consider this form of research a methodology and focus on the philosophical assumptions. To call a process a methodology introduces a complexity to the process of research. Other mixed methods writers, like Creswell, Plano-Clark, Guttmann, and Hanson (2003), Greene, Caraceli, and Graham (1989), and Onwuegbuzies and Teddlie (2003), emphasize the techniques or methods of collecting and analyzing data. To call mixed methods research a method is clean and concise and resonates with many researchers (Elliott, 2005). The purpose of this chapter is to cover the three types (trends) of research methodologies: the traditional (quantitative, qualitative), the universal (mixed-methods),

and the trends (blogs, webinars, virtual intercepts, and virtual reality). This chapter will also cover a brief history of research methods and the usage of research methodologies.

BRIEF HISTORY OF RESEARCH METHODOLOGIES Before the advent of mixed methods, many studies used multiple methods to achieve the benefits of triangulation (Galton & Wilcocks, 1983) without restricting themselves to any paradigmatic membership or methodological category (Tashakkori & Teddlie, 2003). Thus, during the last 50 years, writers have used different names, making it difficult to locate articles that might relate to mixed methods research. Mixed methods has been called multitrait/multimethod research (Campbell & Fiske, 1959), integrated or combined (Johnson & Onwuegbuzie, 2004, p. 17; Steckler, McLeroy, Goodman, Bird, & McCormick, 1992), and quantitative and qualitative methods (Fielding & Fielding, 1986). It has been called hybrids (Ragin, Nagel, & White, 2004), methodological triangulation (Morse, 1991a), combined research (Creswell, 1994), and mixed methodology (Tashakkori & Teddlie, 1998). It has also been called the third methodological movement (Tashakkori &Teddlie, 2002, p. 5), the third research paradigm (Johnson & Onwuegbuzie, 2004, p. 15), and a new star in social science sky (Mayring, 2007, p. 1). Nevertheless, the beginning of mixed methods is cited by some (Creswell & Plano-Clark, 2007, p. 5; Johnson, Onwuegbuzie, & Turner, 2007) to Campbell and Fiske (1959) as multitrait of multimethod research, a concept later formalized by

DOI: 10.4018/978-1-5225-2255-3.ch585 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Research Methods and Scholarly Publishing

Webb, Campbell, Schwartz, and Sechrest (1966) as triangulation (Greene, Caracelli, & Graham, 1989), and is often cited as having methodological superiority over single methods (Johnson et al., 2007; Tran, 2014a). For the first 60 years or so of the 20th century, mixed research can be seen in the work of cultural anthropologists and, especially, the fieldwork sociologists (Gans, 1963; Hollongshead, 1949; Jahoda, Lazarsfeld, & Zeisel, 1931/2003; Lynd & Lynd, 1929/1959). In social science methodological literature, Campbell and Fiske’s (1959) article introduced the idea of triangulation, referring to multiple operationalism (Bouchard, 1976). Today, the most frequently used name is mixed methods research, a name associated with the Handbook of Mixed Methods in Social and Behavioral Research (Tashakkori & Teddlie, 2003). Furthermore, early researchers’ idea of multiple operationalism follows more closely what today is called multimethod research, in contrast to what currently is called mixed methods research. However, Campbell and Fisk (1959) are rightfully credited as being the first to show explicitly how to use multiple research methods for validation purposes, and were extended further by Webb, Campbell, Schwartz, and Sechrest (1966). Thus, Webb et al. are credited with being the first to coin the term triangulation. With that said, Cook (1985) is credited for coining the term critical multiplism (also see Houts, Cook, & Shadish, 1986).

THE USAGE OF RESEARCH METHODOLOGIES In the usage of research methodologies, it was Denzin (1978, p. 291) who first outlined how to triangulate methods. Denzin outlined the following four types of triangulation: (1) data triangulation, (2) investigator triangulation, (3) theory triangulation, and (4) methodological triangulation (Tran, 2014a). According to Tran (2014a), Denzin also distinguished within-methods triangulation, from

between-methods triangulation (Tran, 2014a). According to Denzin, three outcomes arise from triangulation: convergence, inconsistency, and contradiction. Furthermore, Jick (1979) noted the following advantages of triangulation: (1) it allows researchers to be more confident of their results, (2) it stimulates the development of creative ways of collecting data, (3) it can lead to thicker, richer data, (4) it can lead to the synthesis or integration of theories, (5) it can uncover contradictions, and (6) by virtue of its comprehensiveness, it may serve as the litmus test for competing theories (Tran, 2014a). Morse (1991b) on the other hand, outlined two types of methodological triangulation: simultaneous or sequential. Sieber (1973) provided a list of reasons to combine quantitative and qualitative research data that can play a role in providing baseline information and helping to avoid elite bias. Rossman and Wilson (1985) identified three reasons for combining quantitative and qualitative research: (1) used to enable confirmation or corroboration of each other through triangulation, (2) used to enable or develop analysis in order to provide richer data, and (3) used to initiate new modes of thinking by attending to paradoxes that emerge from the two data sources. By examining published research, Greene, Caracelli, and Graham (1989) inductively identified the following five broad purposes of rationales of mixed methodological studies: (1) triangulation, (2) complementarity, (3) development, (4) initiation, and (5) expansion. Sechrest and Sidana (1995) listed four reasons for methodological pluralism: (1) for verification purposes, (2) to provide some basis for estimating possible error in the underlying measures, (3) to facilitate the monitoring of data collected, and (4) to probe a data set to determine its meaning. Also, Dzurec and Abraham (1993) identified the following six pursuits that link qualitative and quantitative research: (1) the pursuit of mastery over self and the world, (2) the pursuit of understanding through recomposition, (3) the pursuit of complexity reduction to enhance understanding,

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(4) the pursuit of innovation, (5) the pursuit of meaningfulness, and (6) the pursuit of truthfulness (pp. 76-77). Furthermore, Collins, Onwuegbuzie, and Sutton (2006) identified four rationales for conducting mixed research: participant enrichment, instrument fidelity, treatment integrity, and significance enhancement. In sum, the 20th century, according to Tran (2015), started with some use of what later came to be called mixed research, but social and psychological research quickly became primarily quantitative and coalescing into a qualitative research paradigm in the 1980s and 1990s (Guba, 1990). In reaction to the polarization between quantitative and qualitative research, another intellectual movement occurred, and it has come to be called mixed methods research, all thriving and coexisting. In contrast to Thomas Kuhn’s (1962) expectation for single paradigms characterizing normal science, the research community suggest that a three-paradigm methodological world might be healthy because each approach has its strengths and weaknesses and times and places of need.

Research Methodologies: Traditional (Quantitative and Qualitative) Quantitative research options have been predetermined and a large number of respondents are involved. By definition, measurement must be objective, quantitative and statistically valid. Simply put, it is about numbers, and objective hard data. The sample size for a survey is calculated by statisticians using formulas to determine how large a sample size will be needed from a given population in order to achieve findings with an acceptable degree of accuracy. Generally, researchers seek sample sizes which yield findings with at least a 95% confidence interval, plus/minus a margin error of 5% points. Hence, quantitative data are divided into two main categories, descriptive and continuous (Tran, 2014a, 2015). According to Tran (2014a, 2015), qualitative research is collecting, analyzing, and interpreting data by observing what people do and say. Where-

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as, quantitative research refers to counts and measures of things, qualitative research refers to the meanings, concepts, definitions, characteristics, metaphors, symbols, and descriptions of things. Qualitative research is much more subjective than quantitative research and uses very different methods of collecting information, mainly individual, in-depth interviews, and focus groups. The nature of this type of research is exploratory and openended. Qualitative data consists of open-ended information that the researcher gathers through interviews with participants. Hence, qualitative data are divided into two main categories, nominal and ordinal.

Research Methodologies: Universal (Mixed Methods) Mixed methods research has been established as a third methodological movement over the past twenty years, complementing the existing traditions of quantitative and qualitative movements (Tashakkori & Teddlie, 2003; Teddlie & Tashakkori, 2009). The term mixed methods has come to be used to refer to the use of two or more methods in a research project yielding both qualitative and quantitative data (Creswell & Palno-Clark, 2007; Greene, 2007; Teddlie & Tashakkori, 2009). Multimethods do not have the same paradigmatic problem as do mixed methods since they can adopt the paradigm appropriate to the single type of data being collected. The paradigm problem for mixed methods arises because of the so called paradigm wars of the 1970s and 1980s where the positivist paradigm of quantitative research came under attack from social scientists supporting qualitative research and proposing constructivism, or variants thereof, as an alternative paradigm (Guba & Lincoln, 1994; Reichhardt & Rallis, 1994). To deal with this problem a range of alternative approaches have been developed (Creswell & Plano-Clark, 2007; Tashakkori & Teddlie, 2003). These approaches, according to Hall (2013), can be classified into three basic categories: a-paradigm stance, multiple paradigm

Category: Research Methods and Scholarly Publishing

approach, and the single paradigm approach. The first of these simply ignores paradigmatic issues altogether, the second asserts that alternative paradigms are not incompatible and can be used in the one research project, and the third claims that both quantitative and qualitative research can be accommodated under a single paradigm. A paradigm may be viewed as a set of basic beliefs that deals with ultimate or first principles. It represents a worldview that defines, for its holder, the nature of the world, the individual’s place in it, and the range of possible relationships to that world and its parts (Guba & Lincoln, 1994).

Mixed Methods Approaches Research studies are becoming increasingly diverse and inclusive of both quantitative and qualitative methods—that is, they are mixing methods to address specific objectives (Tran, 2014a, 2015). The basic premise behind using a mixed methods research design is that the combined of both approaches provides a better understanding of a research problem than either approach could alone. Creswell and Plano-Clark (2011) argue that integrating methodological approaches strengths the overall research design, as the strengths of one approach offset the weaknesses of the other, and can provide more comprehensive and convincing evidence than mono-method studies. Another more practical benefit is that mixed method research can encourage interdisciplinary collaboration and the use of multiple paradigms. There are more than a dozen mixed methods research typologies in the literature, each emphasizing different angles. For the most part, however, typologies include at the very least two basic dimensions—timing of data integration and purpose of integration (Guest et al., 2012). The two most commonly used terms in this regard are sequential and concurrent designs (Creswell & Plano-Clark, 2007; Morgan, 1998; Morse, 1991b).

Mixing the Data: Quantitative and Qualitative

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The mixing of data is a unique aspect of the research community when it comes to definition. By mixing the datasets, the researcher provides a better understanding of the problem than if either dataset had been used alone. There are three ways in which mixing occurs: merging or converging the two datasets by actually bringing them together, connecting the two datasets by having one build on the other, or embedding one dataset within the other so that one type of data provides a supportive role for the other dataset. Thus, it is not enough to simply collect and analyze quantitative and qualitative data, they need to be mixed in some way so that together they form a more complete picture of the problem then they so when standing alone (Tran, 2015).

FUTURE RESEARCH DIRECTIONS: RESEARCH METHODOLOGIES: TRENDS (BLOGS, WEBINARS, VIRTUAL INTERCEPTS, AND VIRTUAL REALITY) Design decisions depend on the purposes of the study, the nature of the problem, and the alternatives appropriate for its investigation. Once the purposes have been specified, the study should have explicit scope and direction, and attention can be focused on a delimited target area. Design alternatives can be organized into nine functional categories based on these problem characteristics (Isaac & Michael, 1995, pp. 45-47): historical, descriptive, developmental, case or field, correlational, causal-comparative, true experimental, quasi-experimental, and action. Traditional qualitative research methods, like study groups, polls and observational studies, are going digital and expanding the ability for researchers and busi-

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nesses to target participants and collect information. The following are four types of qualitative trends: virtual intercepts, virtual reality (Bryson, N. Y.), blogs, and webinars. •

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Blogs: A blog is a personal internal journal, a discussion, or an informational site published on the World Wide Web and consisting of discrete entries typically displayed in reverse chronological order. Webinars: Web conferencing may be used as an umbrella term for various types of online collaborative services including web seminars (or webinars), webcasts, and peer-level web meetings. It may also be used in a more narrow sense to refer only to the peer-level web meeting context, in an attempt to disambiguate it from the other types of collaborative sessions. Virtual Intercepts: A web intercept (survey) is a survey which is used by websites to get feedback from users and visitors about the website. Such surveys are used to collect information regarding the website and how helpful it is for the users. A web intercept survey is an easy and quick way to intercept a website’s performance consisting of a few main questions which are related to the topics directly and are usually objective in nature. Researchers can intercept respondents online fro:m social media or surveys and route them into a one-on-one interview. Virtual Reality A technology-based Tavistock Method (Tran, 2014b) for research that harnesses virtual and augmented reality to enhance the research experience—for participant and client alike. Virtual reality captures new data, creates new experiences, and gives new insight by creating amazing reproductions of the physical world. With virtual reality technology, “where” becomes “anywhere”. Researchers can bring more people together—in virtual environments—than ever be-

fore. Researchers can bring people together from all across the country, or the world, and capture fresh data and experience it all “through the eyes” of the participants.

CONCLUSION According to Tran (2015), debates about singular or universal truths or approaches to viewing the world (Socrates, Plato), versus multiple or relative truths (the Sophists such as Protagoras and Gorgias), versus balances or mixtures of the extremes (Aristotle’s “golden mean” or principle of balance, moderate skepticism, Cicero, Sextus Empiricus), go back, at least, to ancient Western philosophy, and the spirit of these debates lives today in the different views of the three major approaches to social research. This debate continues to affect how we view knowledge, what we look for, what we expect to find, and how we believe we are to go about finding and justifying knowledge. Mixed research is between the extremes Plato (quantitative research) and the Sophists (qualitative research), with mixed research attempting to achieve a workable middle solution for many (research) problems of interest. Hence, research paradigms address the philosophical dimensions of social sciences (Tran, 2015). A research paradigm is a set of fundamental assumptions and beliefs as to how the world is perceived which then serves as a thinking framework that guides the behavior of the researcher (Jonker & Pennink, 2010). Some writers (e.g. Berry & Otley, 2004; Creswell, 2009; Neuman, 2011; Saunders, Lewis, & Thornhill, 2009) emphases that it is important to initially question the research paradigm to be applied in conducting research because it substantially influences how one undertake a social study from the way of framing and understanding social phenomena. According to Arbnor and Bjerke (2008), Guba (1990), Guba and Lincoln (1994), and Wahyuni (2012), the two main philosophical dimensions to distinguish existing research paradigms are ontology and epistemology (Kalof,

Category: Research Methods and Scholarly Publishing

Dan, & Dietz, 2008; Laughlin, 1995; Saunders, Lewis, & Thornhill, 2009), whereas only Arbnor and Bjerke (2008), Guba (1990), and Guba and Lincoln (1994) claim that is a third paradigm: methodology. As such, quantitative research and quantitative research data are static through time, compared to qualitative and qualitative data, but still have functional uses and are relevant in the digital world. According to Bryson (N. Y.), deliberative and participative research methods are gaining popularity due to their increased ability to discover information on a larger scale. For instance, the largest growth is found in social media and mobile market research, and such technologies ranges from high-definition video conferencing and instant communication around the world to the ability to reach participants on their mobile devices and access to demographics that are traditionally hard to reach, the Internet is providing technology based research methods like blogs, webinars, virtual intercepts, and virtual reality. According to Bryson (N. Y.), three of the five trends gaining traction in digital qualitative research are: (1) market research online communities, (2) social media and qualitative research, and (3) mobile ethnography.

REFERENCES Arbnor, I., & Bjerke, B. (2008). Methodology for creating business knowledge. London: Sage Publications. Berry, A. J., & Otley, D. T. (2004). Case-based research in accounting. In C. Humprey & B. Lee (Eds.), The real life guide to accounting research: A behind-the-scenes view of using qualitative research methods (pp. 231–256). Oxford, UK: Elsevier. doi:10.1016/B978-008043972-3/50016-5 Bryson, J. (n.d.). Current and emerging trends in qualitative market research. Retrieved on March 4, 2015. Available at http://qualblog. com/current-and-emerging-trends-in-qualitativemarket-research/

Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105. doi:10.1037/h0046016 PMID:13634291 Cook, T. D. (1985). Postpositivist critical multiplism. In L. Shotland & M. M. Mark (Eds.), Social science and social policy (pp. 21–62). Beverly Hills, CA: Sage Publications. Creswell, J. W. (1994). Research design: Qualitative and quantitative approaches. Thousand Oaks, CA: Sage Publications. Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed methods approaches (2nd ed.). Thousand Oaks, CA: Sage Publications. Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). Thousand Oaks, CA: Sage Publications. Creswell, J. W., & Plano-Clark, V. L. (2007). Designing and conducting mixed methods research. Thousand Oaks, California, London: Sage Publications. Creswell, J. W., & Plano-Clark, V. L. (2011). Designing and conducting mixed methods research. Thousand Oaks, CA: Sage Publications. Creswell, J. W., Plano-Clark, V. L., Guttmann, M. L., & Hanson, E. E. (2003). Advanced mixed methods research design. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 209–240). Thousand Oaks, CA: Sage Publications. Crotty, M. (1998). The foundations of social research. Thousand Oaks, CA: Sage Publications. Denzin, N. K. (1978). The research act: A theoretical introduction to sociological methods. New York: Praeger.

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Dzurec, L. C., & Abraham, J. L. (1993). The nature of inquiry: Linking quantitative and qualitative research. ANS. Advances in Nursing Science, 16(1), 73–79. doi:10.1097/00012272-199309000-00009 PMID:8311427 Elliott, J. (2005). Using narrative in social research: Qualitative and quantitative approaches. Thousand Oaks, CA: Sage Publications. doi:10.4135/9780857020246 Fielding, N. G., & Fielding, J. L. (1986). Linking data: Sage university paper series on qualitative research methods. Beverly Hills, CA: Sage Publications. doi:10.4135/9781412984775 Galton, M., & Wilcocks, J. (1983). Moving from the primary school. London: Routledge and Kegan Paul. Gans, H. J. (1963). Urban villagers: Group life and class in the life of Italian-Americans. New York: Free Press. Green, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational Evaluation and Policy Analysis, 11(3), 255–274. doi:10.3102/01623737011003255 Greene, J. C. (2007). Mixed methods in social inquiry. San Francisco, CA: John Wiley & Sons. Guba, E. G. (1990). The paradigm dialog. Newbury Park, CA: Sage Publications. Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research. In N. K. Dezin & Y. S. Lincoln (Eds.), Handbook of qualitative research (pp. 105–117). Thousand Oaks, CA: Sage Publications. Guest, G., MacQueen, K., & Namey, E. (2012). Applied thematic analysis. Thousand Oaks, CA: Sage Publications. doi:10.4135/9781483384436

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Hall, R. F. (2013). Mixed methods: In search of a paradigm. In T. Le & Q. Le (Eds.), Conducting research in a changing and challenging world (pp. 71–78). Nova Science Publishers, Inc. Hollongshead, A. B. (1949). Elmtown’s youth. New York: John Wiley. Houts, A. C., Cook, T. D., & Shadish, W. R. (1986). The person-situation debate: A critical multiplist perspective. Journal of Personality, 54(1), 52–105. doi:10.1111/j.1467-6494.1986.tb00390.x Isaac, S., & Michael, W. B. (1995). Handbook in research and evaluation: A collection of principles, methods, and strategies useful in the planning, design, and evaluation of studies in education and the behavioral sciences (3rd ed.). San Diego, CA: Educational and Industrial Testing Services. Jahoda, M., Lazarsfeld, P. F., & Zeisel, H. (2003). Marienthal: The sociography of an unemployed community. New Brunswick, NJ: Transaction Publishers. (Original work published 1931) Jick, T. D. (1979). Mixing qualitative and quantitative methods: Triangulation in action. Administrative Science Quarterly, 24(4), 602–611. doi:10.2307/2392366 Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26. doi:10.3102/0013189X033007014 Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. (2007). Toward a definition of mixed methods research. Journal of Mixed Methods Research, 1(2), 112–133. doi:10.1177/1558689806298224 Jonker, J., & Pennink, B. (2010). The essence of research methodology: A consice guide for master and PhD students in management science. Heidelberg, Germany: Springers. Kalof, L., Dan, A., & Dietz, T. (2008). Essential of social research. New York: McGraw-Hill.

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Kuhn, T. S. (1962). The structure of scientific revolutions. Chicago: University of Chicago Press. Laughlin, R. (1995). Empirical research in accounting: Alternative approaches and a case for middle-range thinking. Accounting, Auditing & Accountability Journal, 8(1), 63–87. doi:10.1108/09513579510146707 Lynd, R. S., & Lynd, H. M. (1929/1959). Middletown: A study in modern American culture. Orlando, FL: Harcourt Brace. Mayring, P. (2007). Introduction: Arguments for mixed methodology. In P. Mayring, G. L. Huber, L. Gurtler, & M. Kiegelmann (Eds.), Mixed methodology in psychological research (pp. 1–4). Rotterdam: Sense Publishers. Morgan, D. L. (1998). Practical strategies for combining qualitative and quantitative methods: Applications to health research. Qualitative Health Research, 8(3), 362– 376. doi:10.1177/104973239800800307 PMID:10558337 Morse, J. M. (1991a). Strategies for sampling. In J. M. Morse (Ed.), Qualitative nursing research: A contemporary dialogue (pp. 127– 145). Newbury Park, CA: Sage Publications. doi:10.4135/9781483349015.n16 Morse, J. M. (1991b). Approaches to qualitativequantitative methodological triangulation. Nursing Research, 40(2), 120–123. doi:10.1097/00006199199103000-00014 PMID:2003072 Neuman, W. L. (2011). Social research methods: Qualitative and quantitative approaches (7th ed.). Boston, MA: Pearson/Allyn and Bacon. Onwuegbuzie, A. J., & Teddlie, C. (2003). A framework for analyzing data in mixed methods research. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 351–383). Thousand Oaks, CA: Sage Publications.

Ragin, C., Nagel, J., & White, P. (2004). Sociology program methodology, measurement & statistics program directorate for social, behavioral & economic sciences. Workshop on Scientific Foundations of Qualitative Research, Arlington, VA: National Science Foundation. Reichhardt, C. S., & Rallis, S. F. (1994). Qualitative and quantitative inquiries are not incompatible: A call for a new partnership. In C. S. Reichhardt & S. F. Rallies (Eds.), The qualitative-quantitative debate: New perspectives. San Francisco, CA: Jossey Bass. doi:10.1002/ev.1670 Rossman, G. B., & Wilson, B. L. (1985). Numbers and words: Combining quantitative and qualitative methods in a single large-scale evaluation study. Evaluation Review, 9(5), 627–643. doi:10.1177/0193841X8500900505 Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students. London: Pearson Education. Sechrest, L., & Sidana, S. (1995). Quantitative and qualitative methods: Is there an alternative? Evaluation and Program Planning, 18(1), 77–87. doi:10.1016/0149-7189(94)00051-X Sieber, S. D. (1973). The integration of fieldwork and survey methods. American Journal of Sociology, 78(6), 1335–1359. doi:10.1086/225467 Steckler, A., McLeroy, K. R., Goodman, R. M., Bird, S. T., & McCormick, L. (1992). Toward integrating qualitative and quantitative methods: An introduction. Health Education Quarterly, 19(1), 1–8. doi:10.1177/109019819201900101 PMID:1568869 Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and quantitative approaches. Thousand Oaks, CA: Sage Publications. Tashakkori, A., & Teddlie, C. (2002). Handbook of mixed methods in social & behavioral research. Sage Publications, Inc.

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Tashakkori, A., & Teddlie, C. (2003). Handbook of mixed methods in social and behavioral research. Thousand Oaks, CA: Sage Publications. Teddlie, C., & Tashakkori, A. (2009). Foundations of mixed methods research. Thousand Oaks, CA: Sage Publications. Tran, B. (2014a). Triangulation in organizational research: Validating knowledge in human competence at work. In A. Takhar & A. Ghorbani (Eds.), Market research methodologies: Multi-method and qualitative approaches (pp. 93–117). Hershey, PA: Premier Reference Source/IGI Global. Tran, B. (2014b). Rhetoric of play: Utilizing the gamer factor in selecting and training employees. In T. M. Connolly, L. Boyle, T. Hainey, G. Baxter, & P. Moreno-Ger (Eds.), Psychology, pedagogy and assessment in serious games (pp. 175-203). Hershey, PA: Premier Reference Source: Information Science Reference/IGI Global. Tran, B. (2015). The nature of research methodologies: Terms and usage within quantitative, qualitative, and mixed methods. In J. E. Jones & L. B. Mette (Eds.), Mixed methods research for improved scientific study. Hershey, PA: IGI Global. van Manen, M. (1990). Research lived experience: Human science for an action sensitive pedagogy. State University of New York Press. Wahyuni, D. (2012). The research design maze: Understanding paradigms, cases, methods and methodologies. Journal of Applied Management Accounting Research, 10(1), 69–80. Webb, E. J., Campbell, D. T., Schwartz, R. D., & Sechrest, L. (1966). Unobtrusive measures. Chicago: Rand McNally.

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ADDITIONAL READING Bloor, M. A., & Wood, F. A. (2006). Keywords in qualitative methods: A vocabulary of research concepts. Limited. London: Sage Publications. doi:10.4135/9781849209403 Creswell, J. W. (2008). Research design: Qualitative, quantitative, and mixed methods approaches. Thousand Oaks, CA: Sage Publications. Davidson, J., & di Gregorio, S. (2011). Qualitative research and technology: In the midst of a revolution. In N. K. Denzin and S. Lincoln (4th Ed.), Handbook of qualitative research (pp. 627-643). Thousand Oaks, CA: Sage Publications. Deshpande, R. (1983). Paradigms lost: On theory and method in research in marketing. Journal of Marketing, 47(4), 101–110. doi:10.2307/1251403 Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. Flick, U. (2007). Managing quality in qualitative research. London: Sage Publications. doi:10.4135/9781849209441 Flick, U. (2014). An introduction to qualitative research (5th ed.). London: Sage Publications. Flick, U., Garms-Homolová, V., Herrmann, W. J., Kuck, J., & Röhnsch, G. (2012). I cant prescribe something just because someone asks for it …: Using mixed methods in the framework of triangulation. Journal of Mixed Methods Research, 6(2), 97–110. doi:10.1177/1558689812437183 Given, L. M. (2008). The sage encyclopedia of qualitative research method: Volumes 1 & 2. Newbury Park, CA: Sage Publications. doi:10.4135/9781412963909

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Gobo, G. (2012). Glocalizing methodology?: The encounter between local methodologies. International Journal of Social Research Methodology, 14(6), 417–437. doi:10.1080/1364557 9.2011.611379 Hanson, D., & Grimmer, M. (2007). The mix of qualitative and quantitative research in major marketing journals, 19932002. European Journal of Marketing, 41(1/2), 58–70. doi:10.1108/03090560710718111 Hennink, M. M., Hutter, I., & Bailey, A. (2011). Qualitative research methods. London: Sage Publications. Hsiung, P. C. (2012). The globalization of qualitative research: Challenging anglo-american domination and local hegemonic discourse [27 paragraphs]. Forum Qualitative Sozialforschung/Forum: Qualitative. Social Research, 13(1), 21. Available at http://nbn-resolving.de/ urn:nbn:de0114-fqs1201216 Retrieved on September 20, 2014 Layder, D. (1994). Understanding social theory. London, England: Sage Publications. Layder, D. (1998). Sociological practice: Linking theory and social research. London, England: Sage Publications. doi:10.4135/9781849209946 Marsden, D., & Littler, D. (1996). Evaluating alternative research paradigms: A marketoriented framework. Journal of Marketing Management, 12(7), 645–655. doi:10.1080/026725 7X.1996.9964442 Maxwell, J. A., & Loomis, D. M. (2003). Mixed methods design: An alternative approach. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 241–272). Thousand Oaks, CA: Sage Publications. Maxwell, S. E., & Delaney, H. D. (1994). Designing experiments and analyzing data. Mahwah, NJ: Lawrence Erlbaum.

McMullen, L. M. (2011). A discursive analysis of Teresa’s protocol: Enhancing oneself, diminishing others. In F. J. Wertz, K. Charmaz, L. M. McMullen, R. Josselson, R. Anderson, & E. McSpadden (Eds.), Five ways on doing qualitative analysis: Phenomenological psychology, grounded theory, discourse analysis, narrative research, and intuitive inquiry (pp. 205–223). New York: Guilford. Nuttall, P., Shankar, A., Beverland, M. B., & Hooper, C. S. (2011). Mapping the unarticulated potential of qualitative research. Journal of Advertising Research, 51(1), 153–163. doi:10.2501/ JAR-51-1-153-166 Onwuegbuzie, A. J., & Leech, N. L. (2010). Generalization practices in qualitative research: A mixed methods case study. Quality & Quantity, 44(5), 881–892. doi:10.1007/s11135-009-9241-z Ryan, A., & Gobo, G. (2011). Managing the decline of globalized methodology. Special Issue on Perspectives on Decolonizing Methodologies. International Journal of Social Research Methodology, 14(6), 411–415. doi:10.1080/1364557 9.2011.611378 Schnettler, B., & Rebstein, B. (2012). International perspective on the future of qualitative research in Europe. Qualitative Sociology Review, 8(2), 6–11.

KEY TERMS AND DEFINITIONS Between-Methods Triangulation: Involves the use of both quantitative and qualitative approaches. Mixed-Methods Research: Is an approach to knowledge that attempts to consider multiple viewpoints, perspectives, positions, and standpoints. Mixing Data: There are three ways in which mixing occurs: merging or converging the two datasets, connecting the two datasets by having one build on the other, or embedding one dataset within the other.

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Paradigm (Research): Is a common body of beliefs, assumptions, and rules that govern research. Qualitative Data: Consists of open-ended information that the researcher gathers through interviews with participants. Qualitative Research: Is much more subjective than quantitative research and uses very different methods of collecting information, mainly individual, in-depth interviews, and focus groups. Quantitative Data: Quantitative data includes closed-ended information such as that found on attitude, behavior, or performance instruments.

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Quantitative Research: Refers to the meanings, concepts, definitions, characteristics, metaphors, symbols, and descriptions of things. Triangulation: Is the combination of methodologies in the study of the same phenomenon. The four types of triangulation: (1) data triangulation, (2) investigator triangulation, (3) theory triangulation, and (4) methodological triangulation. Within-Methods Triangulation: Refers to the use of either multiple quantitative or multiple qualitative approaches.

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Research Methodology

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Swati C. Jagdale MAEER’s Maharashtra Institute of Pharmacy, India Rahul U. Hude MAEER’s Maharashtra Institute of Pharmacy, India Aniruddha R. Chabukswar MAEER’s Maharashtra Institute of Pharmacy, India

INTRODUCTION Research is the process to find solution to a problem through the planned and systematic collection of data, analysis, verification and interpretation of data. Research is the very important process for accessing knowledge for promoting progress and to enable people to relate more effectively to his environment to accomplish his aim and to resolve his conflicts. The research is primarily carried out to discover new facts, to verify and test important facts, to analyze an event or process or phenomenon, to identify the cause and effect relationship, to develop new scientific tools and techniques, concepts and theories, to solve and understand scientific and nonscientific problems (Rajasekar, Philominathan and Chinnathambi, 2006). In daily life new problems, events, phenomena and processes occur every day. Practically, implementable solutions and suggestions are required for tackling new problems that arise. Scientists have to undertake research on them and find their causes, solutions, explanations and applications (Gogoi and Goowalla, 2015). The term ‘Research’ consists of two words; Re and Search. ‘Re’ means again and again and ‘Search’ means to find out something (Pandey & Pandey, 2015). According to Clifford Woody of the University of Michigan, “Research is a carefully inquiry or examination in seeking facts or principles; a diligent investigation to ascertain something.”

According to C. Francies Rummel, “Research is an endeavour to discover, develop and verify knowledge. It is an intellectual process that has developed over hundreds of years, ever changing in purpose and form and always searching for truth” (Pandey & Pandey, 2015; Kothari, 2004; Singh, 2006).

BACKGROUND In the 1600s the origin of modern scientific method occurred in Europe. • • • • • • •

Copernicus: A scientific model that could be verified and checked by observation. Tycho Brahe: Accurate instrumental observations to confirm the model. Johannes Kepler: Theoretical examination of experimental data. Galileo Galilei: Scientific laws developed from experiment. Rene Descartes: Mathematics to quantitatively show theoretical ideas. Isaac Newton: Theoretical derivation of an experimentally confirmable model. Karl Popper: Scientific theory should make predictions and can be tested and verified (Frederick, 2011; https://en.wikipedia. org/wiki/History_of_scientific_method).

DOI: 10.4018/978-1-5225-2255-3.ch586 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Research Methodology

OBJECTIVE OF RESEARCH 1. To gain knowledge with a phenomenon or to achieve new perceptions into it. 2. To draw accurately the characteristics of a particular situation, individual or a group. 3. To determine the time frame with which something occurs or with which it is associated with something else. 4. To test a theory of a causal relationship between variables that is to analyses process or phenomenon. 5. To discover new facts; verify and test important facts. 6. To develop new concepts, theories and scientific tools to solve and understand the problems. 7. To find answers to scientific, nonscientific and social problems and to overcome the problems occurring in everyday life (Gogoi and Goowalla, 2015; Bhawna, and Gobind 2015).

CHARACTERISTICS OF RESEARCH 1. It is directed toward the solution of a problem. 2. It gives special importance to the development of generalizations, principles, or theories that will be helpful in predicting future events. Figure 1. Classification of research

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3. It requires expertise. 4. Research involves collecting new data or information from primary or first-hand sources or using existing information for a new motive. 5. It based upon observable experience or empirical proofs. 6. Research demands accurate systematic observation, description and accurate investigation. 7. It is characterized by carefully designed methods or plan that applies careful analysis. 8. It achieves to organize data in quantitative terms. 9. It generally requires inexpensive informational data. 10. It is based on mutually depends upon causes and effect. 11. It sometimes requires courage (Pandey and Pandey, 2015; Kumar, 2015).

TYPES OF RESEARCH The research is broadly classified into two main categories as Fundamental or Basic and Action or Applied. Various ways through which research may classify is summarizes in Figure 1 and Table 1.

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Table 1. Classification of research On the Basis Levels

Types of Research

R

Explanations

Basic level

Basic level as basic research. It does not necessarily produce results of immediate practical value and is designed to add an organized framework of scientific knowledge.

Applied level

Helps to solve an immediate practical problem and the goal of adding scientific knowledge is secondary.

Objectives of Research

Fundamental

These research are applicable to all cases.

Action

Applicable for certain conditions so it’s utility is limited.

Approach of Research

Longitudinal

Case study, historical research, genetic comes under this type of research.

Cross sectional

Survey and experimental research are the examples of cross sectional research.

Precision in Research Findings

Experimental

Such research are precise.

Non experimental

Non-experimental research is not precise.

Nature of Findings

Explanatory

Such researches explain more concerned laws, principles and theories.

Descriptive

These research are more concerned with the facts.

Basic

Those researches which accept origin or unique inspection for the advancement of knowledge.

National Science Foundation

Another Classification

Applied

It may be characterized as the usefulness in practice

Development

It uses scientific knowledge for the production of useful devices, systems, methods, materials for processes excluding design and production engineering.

Adhoc

This research is the class of inquiry used for a purpose alone and special.

Empirical

Depends upon the observation or experience of phenomena and incident.

Explained

Explained research is based on a theory.

Boarder line research

In boarder line research involves main two branches or areas of science. For example study of public school finance.

1. Basic Research Investigation or research studies of some natural phenomenon or relating to pure science are called as basic research or theoretical research. It is a finding solution on basic principles and reasons for occurrence of a particular process or phenomenon. Such researches some time may not lead to immediate application or use; not concerned with finding solution of any practical problems of immediate interest. Many applied research uses base that comes from outcomes of basic research. Research concerning some natural phenomenon or process or relating to pure mathematics, research studies deals with human behavior studied with a view to make generalizations about human behavior and elementary particles results in identification

of new particles are the examples of fundamental research. Basic research is an attempt to find answers to the following questions. • • •

Why materials are look like that? How does sodium melt? Why is sound produced when liquid is heated? (Pandey and Pandey, 2015; Patil and Mankar, 2016; http://www.mech.hku. hk/bse/bbse3002/Research_Methodology. pdf).

2. Applied Research An applied research involves solving certain problems or issues by employing well known and accepted theories and principles. Most of

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the case studies, experimental study, and interdisciplinary research are mainly applied research. Aim of Applied research is finding a solution or conclusion for an immediate problem facing a society or organization. For example, research having immediate potential applications such as increasing efficiency of a production machine, increasing gain factor of production of a material, pollution control and preparing vaccination for a disease etc. (Pandey and Pandey, 2015; Patil and Mankar, 2016; http://www.mech.hku.hk/bse/bbse3002/ Research_Methodology.pdf).

3. Experimental Development Experimental development is systematic approach, knowledge gained from research and empirical experience, which is directed to producing new products, services and devices; to installing new systems, procedures, processes, and services; or to improving significantly those already produced or installed. For examples, study of a polymerization reactions of given class under various reaction conditions; includes experimental development as scaling up process which has been optimized at the laboratory level and investigating and evaluating possible reaction methods for producing the polymer (Frascati, 2002).

4. Quantitative and Qualitative Methods The applied and basic researches can be quantitative or qualitative or even both. •

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Quantitative Research/Method: Is mainly based on the quantification or measurement of quantity or amount. Quantitative research is numerical, non-descriptive and applies statistics or mathematics. The result are conclusive often presented in tables and graphs. It also investigates the what, where and when of decision making.

•

It finds applications not only in mathematics and physical sciences but also in social sciences, economics and biology. Qualitative Research/Method: Is deals with qualitative phenomenon involving or relating to quality. Qualitative research are descriptive, non numerical, applies reasoning and uses words; data cannot be graphed. It aims to get the meaning and describe the situation. It is exploratory and investigates the why and how of decision making. Investigation of the reasons for human behavior for example why people think or do certain things? To find why certain data are random then it is a qualitative research. If the aim is to investigate how random the data is, what is the mean, mode, variance and distribution function then it becomes quantitative. Explaining how absorption of drug takes place in our body is a qualitative description. It does not involve any numbers, quantities or data. While at how much or percentage drug is absorbed to blood; involves measurement of quantity hence it becomes quantitative Rajasekar, Philominathan and Chinnathambi, 2006; Kothari, 2004; Patil and Mankar, 2016; Borrego, Douglas and Amelink, 2009; Bernhard and Baillie, 2013; Choy, 2014).

RESEARCH APPROACHES The generation of data in quantitative form which can be subjected carefully to quantitative analysis in a formal and fixed fashion is a quantitative approach. This approach can be further sub categories into inferential, experimental and simulation approaches to research. Inferential approach leads to research that forms a data base from which to infer characteristics or relationships of population. This usually survey type research where a sample of population is studied (questioned or observed) to determine its features, and then to conclude that the population has the same features or charac-

Category: Research Methods and Scholarly Publishing

teristics. Qualitative approach is concerned with subjective evaluation of opinions, attitudes and behavior. Research in such a situation is a function of researcher’s perception and ideas or feeling. This generates results either in non quantitative form or in the form which are not subjected to accurate quantitative analysis. Experimental approach is characterized by much greater control over the research variables and in this case some variables are manipulated to observe their effect and relation on other variables. Simulation approach involves the setting up of an artificial environment within which relevant information or facts and data can be generated. This allows an observation of the dynamic behavior of a system under supervise state or controlled situations. (Kothari, 2004; Bhawna and Gobind, 2015).

RESEARCH PROCESS Research process consists of series of steps as1. Formulation of Research Problem At the very beginning, the researcher must decide the general area or field of interest or aspect of a subject matter that he would like to investigate or inquire into and then research problem should be formulated or prepared. 2. Extensive Literature Survey For this purpose, the abstracting and indexing journals and published or unpublished bibliographies, academic journals, conference proceedings, government reports, books and internet search engine etc. must be utilized depending on the nature of the problem. 3. Development of Working Hypothesis Working hypothesis is tentative postulations made in order to draw out and test its logical or empirical results.

4. Preparing the Research Design

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After preparing hypothesis, research design or model (conceptual structure within which research would be conducted) is prepared. The framing of such a model helps to research to be as efficient as possible yielding maximal informative data. Research design provides collective relevant proof with ideal effort, time and expenditure. 5. Determining Sample Design A designed sample plan is decided before any data or information is actually collected for obtaining a sample from a given population. Survey involves a great deal of time, money and energy so it is impossible in practice under many situations. 6. Collecting the Data Primary data can be collected either through experiment work or through detail survey. In case of survey, data can be collected by any one or more ways like by observation, through personal interview, through telephonic interviews, by mailing of questionnaires or through schedules. 7. Execution of the Project If the execution of the project proceeds on correct proper lines, the data to be collected would be sufficient and dependable. 8. Analysis of Data The analysis of data has carried by a number of closely related operations such as establishment of categories coding, tabulation (based on the mathematical calculation of various percentages, standard deviations, coefficients etc.) and then drawing statistical inference. 9. Hypothesis Testing After analyzing the data, the researcher should go to test the hypothesis, if any, he had prepared 6771

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earlier. Do the facts that support the hypothesis or they happen to be contrary? This is to be answered by applying various tests like ‘t’ test, ’F’ test, ANOVA etc. Testing will result in either acceptance or in rejection. If the researcher had no hypothesis or theory to start with, generalizations started on the basis of identified data. 10. Generalizations and Interpretation If a hypothesis is tested and confirm several times, it may be possible for the researcher to reach at generalization that is to prepare a theory. As a matter of fact, the real value of research lies in its ability to reach at certain generalizations. If the researcher had no hypothesis to begin with, he might seek to explain his findings on the basis of some theory or facts. It is known as interpretation. 11. Preparation of the Report or the Thesis Finally, the researcher has to prepare systematic report of what has been done by him (Rajasekar, Philominathan and Chinnathambi, 2006; Kothari, 2004; Gogoi and Goowalla, 2015; Pandey and Pandey, 2015; Kothari, 2004; Patil and Mankar, 2016).

IMPORTANCE OF RESEARCH 1. Research introduces scientific and rational new thinking, promotes the development of logical habits of thinking also governs the decisions of the policy maker. 2. Research has its special importance in solving various operational problems of business, society and industry. 3. Research helps for nearly all government policies in our economic system. 4. It helps in determination of motivations underlying the consumer (market) behavior. 5. It is important for social scientists in studying social relationships and in finding and solving various problems.

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6. Research provides the intellectual satisfaction of knowing a few things just for the sake of knowledge; also practical use for the social peoples to know for the purpose of being able to do something better or in a more efficient manner than present. 7. To professionals research may mean a source of livelihood for philosophers and thinkers, research helps to outlet for new ideas, logics and insights. 8. Research is technique for the development of new styles and creative work makes it delightful and glorious. 9. Research is the base of knowledge to gain knowledge and an important source and guidelines for solving different types of problems in social, business and daily life (Rajasekar, Philominathan and Chinnathambi, 2006; Kothari, 2004).

RESEARCH METHODS AND RESEARCH METHODOLOGY Research method includes all those methods or techniques that are used for conduction of research shown in Table 2 (Kothari, 2004). Research methods are essentially designed, scientifically formulated and value neutral. They include theoretical procedures, principles, hypothesis, experimental studies, numerical schemes and statistical approaches etc. Research methods help us collect samples, data and investigate a solution to an unsolved problem. Particularly, scientific research methods require explanations which are based on collected facts, information, experiments, measurements, observations and verification and not on reasoning alone. They accept only those explanations which can be confirmed by experiments. Research methodology is a science of studying how research is to be carried out and is a systematic way to solve a problem. Various steps that are normally followed by a researcher in studying his/ her research problem along with the scientific

Category: Research Methods and Scholarly Publishing

Table 2. Method and techniques used to perform various researches (Kothari, 2004) Types Library Research Field Research

Laboratory Research

Methods

R

Techniques

1. Analysis of historical records

Recording of notes, Content analysis, Tape and Film listening analysis.

2. Analysis of documents

Statistical compilations and manipulations, reference and abstract guides.

1. Non-participant direct observation

Observational behavioral scales, use of score cards.

2. Participant observation

Interactional recording, photo graphic techniques.

3. Mass observation

Recording mass behavior, interview using independent observers.

4. Mail questionnaire

Identification of social and economic background of respondents.

5. Opinionnaire

Use of attitude scales, projective techniques, use of sociometric scales.

6. Personal interview

Interviewer uses a detailed schedule with open and closed questions.

7. Focused interview

Interviewer focuses attention upon a given experience and its effects.

8. Group interview

Small groups of respondents are interviewed simultaneously.

9. Telephone survey

Used as a survey technique for information and for discerning opinion; may also be used as a follow up of questionnaire.

10. Case study and life history

Cross sectional collection of data for intensive analysis, longitudinal collection of data of intensive character.

Small group study of random behavior, play and role analysis

Use of audio-visual recording devices, use of observers, etc.

thought behind them. Thus it necessary for the researcher to know not only about the research methods/techniques but also the methodology. Researchers need to aware how to develop certain tests or indices, how to apply particular research techniques as well as they requires to know which of these methods or techniques are applicable and which are not. They should aware the criteria by which they can decide that certain methods, techniques and procedures will be applicable to solve certain problems and others will not. All this conclude that it is necessary for the researcher to design methodology for his/ her problem as the same may differ from problem to problem. (Rajasekar, Philominathan and Chinnathambi, 2006; Kothari, 2004; Gogoi and Goowalla, 2015; Kothari, 2004; Choy, 2014; http://www.ais.utm. my/researchportal/files/2015/02/Example3-ResDesign.pdf; Agbaje and Alarape, 2010; Khairul Baharein Mohd Noor, 2008).

CRITERIA OF GOOD RESEARCH 1. The aim should be clearly defined and common general concepts be used. 2. The procedure used should be described in sufficient detail to allow another researcher to repeat the research for further development, keeping the continuity of what has already been attained. 3. The designed procedure of the research should be carefully planned and thoroughly studied to obtain results that are as goal as possible. 4. The researcher should report with complete frankness, mark in procedural design and estimate their effects upon the investigating. 5. The analysis of data should be sufficiently satisfactory to show its importance and the methods of analysis used should be proper in the circumstances. The validity and reliability of the data should be checked and verified carefully.

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6. Conclusions should be confined in area to those justified and explained by the data of the research and limited to those for which the data provide an adequate basis. 7. Greater confidence in research is justify if the researcher is experienced, has a good reputation in research and is a person of integrity. 8. In other words, we can express the good research should have characteristics such as systematic, logical, empirical and replicable (Pandey and Pandey, 2015; Kothari, 2004). PROBLEMS ENCOUNTERED BY RESEARCHERS 1. The lack of a scientific training in the methodology of research is a great obstacle for researchers. There are small quantities of competent researchers. Research is mostly a scissor and paste job without any awareness shed on the collected materials. 2. There is insufficient or lack of interaction between the university research departments on one side and business establishments, government departments and research institutions on the other side, between researcher having same research interest. 3. Most of the business units do not have the confidence that the material supplied by them to researchers will not be misused and as such they are often resistant in supplying the needed information and data to researchers. 4. Research studies overlapping one another are undertaken completely often for want of sufficient information. This results in duplication and waste away resources. 5. There does not exist a code of conduct for researchers and inter university and interdepartmental competitions are also quite common. 6. Difficulty of sufficient and timely secretarial assistance, including computerial support. This leads to unnecessary delays in the completion and finalization of research studies. 6774

7. Basic library management and functioning is not adequate at many places and much of the time and energy of researchers are spent in tracing out the books, journals, reports, and references etc. (Kothari, 2004).

FUTURE RESEARCH DIRECTIONS Current Scenario of Research and Global Funding Forecast The process of creating new products, processes and technologies that can be used and marketed for mankind’s benefit in the future is termed as research and development (R&D). Asian countries continue to grow faster than other parts of the world. Because of this, combined Asian R&D investments are growing at a faster rate and their global R&D shares continue to increase at nearly 1% per year, while R&D shares of American and European decrease, even though their absolute R&D investments also continue to increase, but not at as fast rate as they do in Asia. Total global R&D share spending regional wise are shown in Table 3 (Global R&D funding forecast A supplement to R&D magazine, 2016). The U.S. is largest single country in R&D investments with slightly more than a quarter of all global R&D spending. These U.S. R&D programs are supported and run by means of industrial (66%), federal government (25%) and academic/ non-profit (7%) investments as shown in Figure 2 (Global R&D funding forecast A supplement to R&D magazine, 2016). The global Life Science industry is one of the top large global high tech industries. It covers pharmaceuticals, biotechnology, medical devices and instruments, bioscience, veterinary, agricultural and commercial research and testing. However, most of the activities of these industries are operated in the biopharmaceutical sector by R&D, which reports for about 85% of the industries total R&D spending. Prediction shows a modest 1.8% increase in R&D spending for the global Life Science R&D marketplace in 2016 and a weak

Category: Research Methods and Scholarly Publishing

Table 3. Share of total global R&D spending (Global R&D Funding Forecast A Supplement to R&D Magazine, 2016) Region

Year 2014

2015

2016

North America

29.1%

28.5%

28.4%

U.S.

26.9%

26.4%

26.4%

Caribbean

0.1%

0.1%

0.1%

All North America

29.2%

28.5%

28.5%

Asia

40.2%

41.2%

41.8%

China

19.1%

19.8%

20.4%

Europe

21.5%

21.3%

21.0%

Russia

3.1%

2.9%

2.8%

South America

2.8%

2.6%

2.6%

Middle East

2.2%

2.3%

2.3%

Africa

1.0%

1.1%

1.1%

Total

100.0%

100.0%

100.0%

Figure 2. Country-wise share of R&D spending

Source: 2016 Global R&D funding forecast A supplement to R&D magazine

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0.6% increase in U.S. R&D spending for 2016. (Global R&D funding forecast A supplement to R&D magazine, 2016). Research on human being is very important for improvement of health and discovering new medicine and technology to treat diseases effectively. Current scenario of clinical trials reveled that there is a continuous increase in number of clinical research studies worldwide (about in 183 countries). Figure 3 shows registered numbers of clinical trials from the year 2007 to march 2015 on website www.clinicaltrials.gov.in. (Global R&D funding forecast A supplement to R&D magazine, 2016; Bangera, 2015). World health organization (WHO), in U.S. National institute of health (NIH), National cancer institute (NCI) and Department of defense (DOD); in India Department of science & technology (DBT), Department of defense (DOD) and University of grant commission (UGC) etc are the research funding agency. Information and Communication Technology (ICT) R&D are the backbone of the global digital economy and constitute a key driver of productivity growth in a knowledge-based economy and citizen’s quality of life. It is clear fact that the ICT industry and ICT R&D innovation in non ICT industries and services make an important presence

to the economic growth of advanced economies. In the EU, USA and Japan; the ICT field is the largest R&D investing sector of the economy. The EU ICT sector is a significant contributor to achieving the target of investing 3% of GDP in R&D. When comparing ICT spending over GDP, the USA, Japan, Taiwan and Korea are investing significantly more in ICT R&D than the EU. Supporting the speedy pace of revolution in ICT requires high levels of R&D. Actually, in terms of R&D expenditures budget, patents, and venture capital investments; the ICT sector exceeds all other industries by a large margin. For stronger development in ICT a new strategy was started, primarily to foster the commercialization of research such as policy governance, science base, business R&D and innovation, knowledge flows and commercialization, human resources, green innovation, clusters and regional policies and globalization (Giuditta De Prato, Daniel and Juraj, 2011; Stephen and Scott 2010; OECD, 2014).

CONCLUSION Research is a systematic process of gaining new knowledge. Main aim of research is to find out the truth which hidden or not yet discovered.

Figure 3. Number of clinical trial studies registered worldwide Source: 2016 Global R&D funding forecast A supplement to R&D magazine

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Research mainly classified into to two groups as basic and applied research. Research process starts from formulating research problem to final report through systematic and accurate investigation. All research Approaches investigates and explore the different assert to knowledge and are designed to label a specific type of research problem. The methods and procedures used by a researcher during a research study are the research methods. Research methodology is a science and systematic way of studying how research is to be carried out. It mainly provide plan of work for research. Good research should be systematic, logical, empirical and replicable. In some part researcher are facing problems during research because of lack of scientific knowledge and interaction and funding facility. Thus research introduces scientific and rational new ideas and it promotes the development of logical habits of thinking. Research has its special importance in solving various operational and planning problems of business, society and industry.

REFERENCES Agbaje, A., & Alarape, A. I. (2010). Introductory lectures on research methodology. Retrieved June 10, 2016, from http://www.ndc.gov.ng/Lectures/ Research-Methodology.pdf Bangera, S. (2015). Current Clinical Research Scenario. Retrieved June 10, 2016, from http:// cdsaindia.in/sites/default/files/00_Current%20 Clinical%20Research%20Scenario_Dr.%20Sudhakar.pdf Bernhard, J., & Baillie, C. (2013). Standards for quality of research in engineering education. Proceeding of the Research in Engineering Education Symposium. Bhawna, & Gobind. (2015). Research methodology and approaches. IOSR Journal of Research & Method in Education, 5(3), 48-51.

Borrego, M., Douglas, E., & Amelink, C. (2009). Quantitative, qualitative, and mixed research methods in engineering education. The Journal of Engineering Education, 98(1), 53–66. doi:10.1002/j.2168-9830.2009.tb01005.x Choy, L. T. (2014). The strengths and weaknesses of research methodology: Comparison and complimentary between qualitative and quantitative approaches. IOSR Journal of Humanities And Social Science, 19(4), 99–104. doi:10.9790/0837194399104 Frascati Manual Proposed standard practice for surveys on research and experimental development. (2002). OECD Publications Service. Frederick, B. (2011). Managing Science Methodology and Organization of Research, Innovation, Technology, and Knowledge Management. Springer Science+Business Media, LLC. Gogoi, L., & Goowalla, H. (2015). A study on the impact of research methodology in Ph. D course: An overview. International Journal of Development Research, 5(11), 6065–6067. History of scientific method. (n.d.). Retrieved on August 26, 2016, from https://en.wikipedia.org/ wiki/History_of_scientific_method Khairul, B. M. N. (2008). Case study: A strategic research methodology. American Journal of Applied Sciences, 5(11), 1602–1604. doi:10.3844/ ajassp.2008.1602.1604 Kothari, C. R. (2004). Research methodology methods and techniques (2nd ed.). New Age International (p) Limited. Kumar, R. (2011). A step by step guide for beginners (3rd ed.). London: SAGE Publications Ltd. OECD. (2014). OECD Science, Technology and Industry Outlook, 2014. OECD Publishing. doi:10.1787/sti_outlook-2014-en Pandey, P., & Pandey, M. M. (2015). Research methodology: Tools and techniques. Bridge Center.

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Patil, S., & Mankar, A. (2016). Research methodology: For beginners. International Research Journal of Multidisciplinary Studies, 2(1), 1–6.

Neuman, W. L. (2011). Social research methods – Qualitative and quantitative approaches (7th ed.). Allyn and Bacon Publication.

Rajasekar, S., Philominathan, P., & Chinnathambi, V. (2006). Research methodology. Retrieved June 10, 2016, from http://citeseerx.ist.psu.edu/ viewdoc/download?doi=10.1.1.262.3449&rep= rep1&type=pdf

Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77. doi:10.2753/MIS0742-1222240302

Research design and methodology. (n.d.). Retrieved June 10, 2016, from http://www.ais.utm. my/researchportal/files/2015/02/Example3-ResDesign.pdf

Whitesides, G. M. (2004). Whitesides Group: Writing a paper. Advanced Materials, 16(15), 1375–1377. doi:10.1002/adma.200400767

Research methodology. (n.d.). Retrieved June 10, 2016, from http://www.mech.hku.hk/bse/ bbse3002/Research_Methodology.pdf Singh, Y. K. (2006). Fundamental of research methodology and statistics. New Age International (p) Limited. Stephen, E., & Scott, A. (2010). ICT R&D policies an international perspective. IEEE Computer Society.

ADDITIONAL READING Ashley, P., & Boyd, W. E. (2006). Quantitative and qualitative approaches to research in environmental management. Australasian Journal of Environmental Management, 13(2), 70–78. doi:1 0.1080/14486563.2006.10648674

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KEY TERMS AND DEFINITIONS Applied Research: It deals with finding a solution or conclusion for an immediate problem by using well known theories or principles. Basic Research: It deals with the finding the truth behind natural process or pure sciences problems. Research: Finding the truth or solving the problem on the basis of scientific base is called as research. Research Approaches: It define as the dealing of research by a particular manner or process. Research Method: It includes all the procedure used to carry out research. Research Methodology: It is way or path which shows how research is carried out in systematic manner. Research Problems: It means that problems encountered during the research.

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Scholarly Identity in an Increasingly Open and Digitally Connected World Olga Belikov Brigham Young University, USA Royce Kimmons Brigham Young University, USA

INTRODUCTION The perceived role of the scholar has undergone rethinking in recent years as scholarly professionals have expanded conversations about the process of knowledge development and the role of the scholar in society. Boyer (1990) argues that “what we urgently need today is a more inclusive view of what it means to be a scholar – a recognition that knowledge is acquired through research, through synthesis, through practice, and through teaching” (p. 24), and he proposes that scholarship includes the four practices of discovery, integration, application, and teaching. In each of these aspects of scholarship, technology plays a role in defining possibilities, identifying priorities, and shaping practice, and advances in information technology over the past few decades have yielded significant technological artifacts (such as ubiquitous computing devices, data collection and storage systems, the internet, and social media) that influence what it means to be a scholar on an ongoing basis. This chapter explores the intertwined relationship between technological advances and scholarly practice, and draws attention to emergent forms of scholarship described in the literature. The chapter will then highlight commonalities and differences between these emergent forms and discuss implications of the practices, especially those that affect the identity of the scholar as

they participate in these forms of scholarship. Throughout this conversation, technology will be used as an anchor for connecting scholarly practices to advances and social shifts of our time and will be treated as a co-evolutionary artifact with scholarship rather than as a change agent (cf. Veletsianos & Kimmons, 2012b).

BACKGROUND As the historical centers of scholarly work for many centuries, universities have gradually developed and evolved in response to a variety of factors and are currently being reshaped in response to “globalization, mass expansion, and economic uncertainty, overlaid by new technologies connecting learners and content” and researchers “in new ways” (Siemens & Matheos, 2010, para. 17). Shifts in social norms and values and advances in technology have always impacted scholarship and the university, or institutionalized scholarship, in ways that reflect the needs and habits of the era (McNeely & Wolverton, 2008). Thus, when we consider emergent forms of scholarship connected to technology innovations, we must recognize that technology, society, and scholarship are all ever-evolving artifacts throughout all eras that influence and impact one another in complex and negotiated ways (Veletsianos & Kimmons, 2012b).

DOI: 10.4018/978-1-5225-2255-3.ch587 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Some specific technologies that have historically impacted the creation and evolution of universities include the printing press, radio, television, microphotography/microfilm, mass publishing, microcomputers, the internet, and social media (Binkley, 1935; Tate, 1947; Siemens & Matheos, 2010; Veletsianos & Kimmons, 2012b). Each of these technologies bring with them different affordances, limitations, assumptions, and challenges that impact how scholars work in each of Boyer’s areas of discovery, integration, application, and teaching. Discovery or the process of developing new knowledge through research is impacted as technologies improve efficiencies of data collection and analysis and allow for new methods of inquiry (e.g., big data, computational modeling). Integration is impacted as data and findings may be shared across distant locations and between experts within disciplines in a timelier manner. Application is influenced as scholars can more effectively report, serve, and collaborate with their communities, the public, and diverse colleagues from various disciplines. Teaching is impacted as scholars can teach students across geographic distances and employ new pedagogies and media to deliver instruction, assess student learning, and support student knowledge construction. Identity is impacted as scholars navigate their use of social medias and their offline and online identities converge while their sense of identities are impacted by their participation in these networks.

for those practices) in contradistinction to previous norms in the following ways:

Forms of Emergent TechnologyInfluenced Scholarship

•

Many of the emerging scholarly practices that respond to recent technological advances associated with the internet and social media have been categorized into at least five general forms: digital scholarship, social scholarship, open scholarship, public scholarship and networked participatory scholarship (Kimmons, 2015). Each of these identified forms seeks to draw attention to a set of scholarly practices (or in some cases to advocate

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•

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Digital Scholarship: Emphasizes the power of the internet and digital media for making data sharing and collaboration cheaper, faster, and easier (Andersen, 2003; Borgman, 2007; Pearce, Weller, Scanlon, & Kinsley, 2010; Russell, Weinberger, & Stone, 1999). Social Scholarship: Highlights the importance of social interaction between scholars to generate quality work and to induct new scholars into academe by utilizing computer-mediated mechanisms like discussion groups (Berge & Collins, 1995), blogs (Chong, 2010), and social networking sites (Greenhow, 2009) to support scholarship that is conversational and less formal than the traditional publication cycle (Oblinger, 2010). Open Scholarship: Emphasizes the importance of utilizing technologies and practices for teaching and research that espouse openness and sharing for the purpose of broadening access to knowledge, reducing costs, enhancing scholarly impact, and supporting transparent and equitable practices (Furlough, 2010; Wiley & Green, 2012; Eysenbach, 2006; Norris, Oppenheim, & Rowland, 2008; Veletsianos & Kimmons, 2012a, para. 3). Public Scholarship: Articulates the importance of public participation by scholars as an obligation to the community and a desire to stay relevant in their respective fields through civic engagement (BrownDean, 2015). Networked Participatory Scholarship: Builds off of the three aforementioned forms of scholarship and attempts to bring them together into a unified vision of scholars using digital and social technologies to “share, reflect upon, critique, improve,

Category: Research Methods and Scholarly Publishing

validate, and further their scholarship” in ways that fundamentally transform existing institutional structures (Veletsianos & Kimmons, 2012b).

COMMONALITIES AND DIFFERENCES These emergent forms of scholarship are not mutually exclusive nor clearly delineated but rather represent a collection of connected emerging practices evolving with a variety of technologies, and at least three of the terms have been proposed to only understand or to promote a subset of emergent practices. If we consider all five categorizations and look for common themes across them, we discover some commonalities surrounding the ideas of digitization, sharing, democratization, social networking, and transparency. These themes are interconnected, and each deserves focused attention for us to recognize the richness of emergent forms of scholarly practice. •

Digitization: Refers to the transfer of scholarly artifacts and processes from previous media formats (e.g., print journals, letters, catalogues) to digital formats (e.g., electronic media, online journals, emails, databases). The process of digitization provides many opportunities for improved efficiency and access to scholarly work both for dissemination and collaboration. For example, database technologies allow for the collection, storage, manipulation, analysis, and retrieval of large datasets for research, which has given rise to a wide array of new interdisciplinary fields and profitable research lines. Through the use of shared databases and other digital technologies, the speed and scale at which research may be conducted has increased. All five identified categorizations rely upon digitization and are empowered by it, and without digital media and artifacts that

•

improve connections, sharing, and efficiencies, we would not be able to explore new forms of scholarship that rely upon information processing, social connections at a distance, and inexpensive publishing and sharing mechanisms. Sharing: Of data has improved through digitization as well and represents a common theme from all five categorizations. Large datasets can be collaboratively constructed and analyzed by geographically dispersed researchers and shared with scholars from various fields via online portals and other mechanisms. This sharing allows for improved collaboration and interdisciplinary scholarship, as traditional institutional and departmental boundaries are breached or bypassed. Sometimes referred to as primary source sharing, improved access to data sources allows for new types of questions to be asked and research lines to be explored as scholars can combine datasets from and easily bring in experts from a variety of fields to collect and analyze data.

This sharing extends to improved dissemination of findings, research outcomes, reviews, and scholarly opinion, or secondary sources. By publishing findings in online journals and less formal online venues (e.g., discussion fora, blogs), researchers allow their work to be indexed by online catalogs and search engines and to be accessible by researchers throughout the world, thereby circumventing many persistent problems of access to academic material. This improved access gives researchers greater facility to stay abreast of new findings, review peers’ work, and to provide responses in a timely manner. Due to the nature of digital media, which can be disseminated and stored at relatively low costs, new sharing possibilities are also available through the innovation of open access journals, and scholars can engage in a variety of practices that are open, social, and networked.

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These approaches to collaborating, sharing findings, and reducing or removing traditional barriers to dissemination may have the potential to democratize some aspects of the scholarly enterprise by allowing a greater number and diversity of scholars to contribute to research endeavors and to gain access to findings and discussion. Whereas it may be that the establishment of the university at the center of scholarly work occurred in large part due to mass publication and the inability of individuals and smaller institutions to keep pace with large entities’ collection and storage of scholarly artifacts (Binkley, 1935), the reduction of these barriers may lead to a rethinking of the role of universities in society and scholars’ relationships to them. As physical barriers are removed through the replacement of many traditional libraries with online resources and monetary barriers are removed through the growth of open access journals and other scholarly portals, collaboration and access of scholarly work will likely increasingly occur in virtual spaces rather than geographic locales, which has the impact of increasing access and participation across traditional geographic barriers but also calls into question the usefulness or necessity of many geographically bound sites of scholarly endeavor. •

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Social Networking: Also influences sharing and dissemination by connecting the networked lives of scholars with their work. Emphasized in social scholarship, public scholarship, and networked participatory scholarship, each scholar has social connections that reflect personal and professional relationships, and scholars’ literacies to traverse these connections, develop new relationships, and present meaningful online personas can influence their abilities to conduct meaningful work, share findings, and find and utilize available resources. By using an informal sharing space like a blog, for instance, scholars can share their work with colleagues and the public through all stages of the scholarly

•

process and elicit feedback or solicit partnerships to improve their work. Similarly, scholars can nurture online relationships through a social networking platform and quickly gain access to a wealth of suggestions regarding research questions, data sources, previous work, funding and job opportunities, and so forth and can quickly gain insight from a variety of people even outside of their field, thereby potentially improving interdisciplinary work. These social networking sites, especially Twitter, have become increasingly prevalent in professional settings as scholars use Twitter as a conference backchannel and a professional networking platform (Kimmons & Veletsianos, under review; Veletsianos, 2012). The wide variety of social networking sites allows scholars to contribute meaningfully to the public sphere both formally and informally. Transparency: Plays a role in these practices as well, as information is diffused freely and scholars’ relationships, thoughts, opinions, and interactions take an increasingly public stage, thereby opening more aspects of their lives to both scrutiny and celebration (Tufekci, 2008). Open scholarship emphasizes the importance of removing barriers between learners and content and between learners and teachers, and for scholars to effectively connect with others socially online requires for them to be transparent in what they share (Dalsgaard & Paulsen, 2009; Mazer, Murphy, & Simonds, 2007). As scholars begin to remove these barriers, they participate in public scholarship and are able to engage in public spheres of their expertise. Such transparency suggests fundamental shifts in the nature of academic institutions and the roles that scholars play in society and may suggest shifts in thinking of scholars less like laboratory-dwelling recluses to micro-celebrities (Marwick & boyd, 2011).

Category: Research Methods and Scholarly Publishing

Throughout these discussions, some scholars approach these issues with an interest in utility and focus on technologies’ affordances to improve efficiency and scale. However, other scholars, as with open scholarship, take a social advocacy stance and argue that there are moral considerations to supporting forms of technology-enhanced scholarship that are democratized or transparent. Such fundamental shifts in thinking and emphases upon advocacy bring with them certain difficulties and implications that require attention. Democratic participation in the scholarly process, as with the political process, requires certain literacies, skills, and attitudes and an allotment of time on the part of participants. Thus, though barriers to entry may be reduced in terms of geography and overt monetary constraints, other factors may influence an individual’s or institution’s ability to effectively participate in these new forms of scholarship, suggesting that barriers may not be altogether removed but rather changed to favor new groups of scholars (Veletsianos & Kimmons, 2012a). As has been pointed out in discussions of the public domain, the openness of any domain (e.g., the internet) favors those with the resources and skills necessary to gain advantage from it (Chander & Sunder, 2004). In terms of resources, those individuals and institutions with the infrastructure necessary to support sustained access and sharing (e.g., networks, data sources) and those who have developed the skills necessary to develop and utilize social networks and to traverse massive amounts of information will gain greater advantage from these technologies. This conversely means that scholars who lack valuable social connections in the real world may have difficulty creating and sustaining new social relationships online and that traditional problems of inequity may be replicated in online spaces (cf. Thelwall, 2009, and the problem of homophily). Democratic participation also requires participants to be well-informed and to have access to dissenting views.

DIFFICULTIES AND IMPLICATIONS However, two problems have arisen in conjunction with the advent of the social and semantic webs, which pose problems for emerging scholarly practice. The social web, or Web 2.0, allows participants to connect with like-minded individuals via social media and social networks, to create and remix content, and to share content with the world. Because users of social media readily connect only with those that they agree or have some other social connection with, this creates a situation in which online echo chambers emerge and scholars naturally isolate themselves from dissenting opinion (Gilbert, Bergstrom, & Karahalios, 2009). Consider a blog that a scholar uses to share work in progress to solicit feedback. If this blog is followed by like-minded scholars, this can lead to authors only interacting with those who already share their opinions and may have the effect of minimizing or ignoring dissenting views. Similarly, the semantic web, or Web 3.0, relies upon machine algorithms to present information to users that is relevant to their interests and requirements. As scholars utilize search engines and other mechanisms for navigating web content, filter bubbles can emerge in which a scholar’s traversal of online resources is effectively managed to only deliver supporting viewpoints and similarities in thinking (Pariser, 2011). This means that if scholars are looking for evidence to support their work, they can more easily find it and not have to sift through dissenting or irrelevant evidence in the process. Both of these examples have deep implications for scholarly practice, because though critique and controversy have traditionally been valuable hallmarks of scholarship, emerging practices may lead to scholars operating within new forms of insulation and isolation. Whereas scholars have traditionally been viewed as largely operating within the “ivory tower” of an academic institution that may be disconnected from the problems and

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realities of the world at large, social technologies can both supplant these barriers by increasing communicative efficacy with those outside the institution but simultaneously instantiate new barriers based upon the social relationships, beliefs, and values of the scholar. In these new forms of scholarship, then, it behooves scholars to consider the value of dissenting viewpoints and to utilize new technologies in a manner that allows them to meaningfully grow, develop, and work within a diverse global community. There is also the prevailing issue of identity fragmentation that is affected by all of these forms of scholarly participation. Identity can be understood as a constellation of interconnected fragments of social participation (Kimmons & Veletsianos, 2014). As scholars seek to be participatory and interact with these emergent technologies, they encounter incongruencies between their values and opinions and those of the general public. When it comes to sharing, the scholar may be inclined to share something that is of value to them but may be perceived by institutions, fellow scholars, or students as inappropriate material for a public forum. These blurred lines between the personal and professional sharing done by scholars is largely in part due to the pluralistic use of blogs, social medias and other forms of sharing. With greater potential for interaction, issues of privacy are raised (Lin, Hoffman, & Borengasser, 2013). Social networking sites are used to support educational endeavors, but there is little precedence for scholars to adhere to when it comes to use of these sites. The identity of the scholar participating in social networking sites is rarely an entirely authentic or complete representation of the scholar’s identity due to issues of implicit and explicit appropriate uses of these media. The ideal of democratization through networked participatory scholarship may be turned upside down as scholars begin to be wary of participating in the public sphere for fear of scrutiny. Scholars

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may then be inclined to turn inwards and remove themselves from the democratic ideals that public and open scholarship promote. Although using these networking tools as scholars can be valuable and can nurture beneficial scholarly relationships, the effects of the convergence between offline and online personas may influence the representational identity of the scholar to the point that it may become fragmented and disjointed from the true identity of the scholar (Kimmons & Veletsianos, 2014). This fragmented and incomplete identity may hinder the scholar from using these social networking sites as a means of truly meaningful social participation.

FUTURE RESEARCH DIRECTIONS There is a great deal of ambiguity surrounding what appropriate and meaningful participatory scholarship looks like. Without a deeper understanding of how scholars can participate in the public sphere in the most impactful way possible while still preserving their values and those of their institution, they will not be able to contribute meaningfully. It would be valuable to conceive a flexible and plural framework for what networked participatory scholarship should look like. While some of these more specific forms of scholarship such as open scholarship have some prescriptions for optimal participation, the dependence that networked participatory scholarship has upon the value system of the scholar leaves room for discussion regarding guidelines for this type of participation. More specifically there is also the need to analyze what prescriptions institutions are making for the scholars and whether they are held to the standards of explicit or implicit assumptions of acceptable behavior. The framework of the acceptable identity fragment as it applies in higher education is another area that needs to be looked at more closely.

Category: Research Methods and Scholarly Publishing

While the idea of identity fragmentation has been valuable in discussing networked participation across educators of younger ages, it is important to inspect this framework to understand whether this identity fragment truly applies in higher education. The way in which scholars engage in the public sphere is very unique in comparison to other educators and understanding the way in which scholars interact with identity fragmentation needs to be further explored and understood. Along with other educators, there is also a significant amount of teachers using these forms of public participation and social networking in their classroom (Piotrowski 2015). It is crucial that as these medias become widespread as teaching tools, that we recognize the implications that come with classroom use of these tools. A deeper analysis of identity, power relationships, and community perception is vital and as some of these aspects of public participation are better understood, there may be hope for a guide of how scholars approach networked participation in both formal and informal settings.

REFERENCES

CONCLUSION

Chander, A., & Sunder, M. (2004). The romance of the public domain. California Law Review, 92(5), 1331. doi:10.2307/3481419

In this chapter, we see that emerging scholarly practices extend existing practice but also stand to continually reshape it, generating new efficiencies, possibilities, and challenges. Clear identification of all emerging practices is difficult, but the literature suggests at least five general emergent forms that include a wide range of technologyinfluenced scholarly practices, namely: digital scholarship, social scholarship, open scholarship, public scholarship, and networked participatory scholarship. Scholarly practice has historically evolved with technology and other cultural artifacts, and these categorizations of emerging scholarly practice serve to draw attention to a set of interconnected themes permeating the literature that may help guide us to meaningfully direct the ever-morphing forms that scholarship continues to take in our time.

Andersen, D. L. (2003). Digital scholarship in the tenure, promotion, and review process. M.E. Sharpe. Berge, Z., & Collins, M. (1995). Computermediated scholarly discussion groups. Computers & Education, 24(3), 183–189. doi:10.1016/03601315(95)00010-J Binkley, R. C. (1935). New tools for men of letters. The Yale Review, 24. Borgman, C. L. (2007). Scholarship in the digital age: Information, infrastructure, and the Internet. Cambridge, MA: MIT Press. Boyer, E. (1990). Scholarship reconsidered: Priorities for the professoriate. Princeton, NJ: The Carnegie Foundation for the Advancement of Teaching. Brown-Dean, K. L. (2015). Emphasizing the scholar in public scholarship. Political Science and Politics, 48(1), 55–57. doi:10.1017/ S1049096515000426

Chong, E. K. M. (2010). Using blogging to enhance the initiation of students into academic research. Computers & Education, 55(2), 798–807. doi:10.1016/j.compedu.2010.03.012 Dalsgaard, C., & Paulsen, M. (2009). Transparency in cooperative online education. International Review of Research in Open and Distance Learning, 10(3). doi:10.19173/irrodl.v10i3.671 Eysenbach, G. (2006). Citation advantage of open access articles. PLoS Biology, 4(5), e157. doi:10.1371/journal.pbio.0040157 PMID:16683865 Furlough, M. (2010). Open access, education research, and discovery. Teachers College Record, 112(10), 2623–2648.

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Gilbert, E., Bergstrom, T., & Karahalios, K. (2009). Blogs are echo chambers: Blogs are echo chambers. System Sciences, 2009. HICSS’09. 42nd Hawaii International Conference on System Sciences, 1–10. Greenhow, C. (2009). Social scholarship: Applying social networking technologies to research practices. Knowledge Quest, 37(4), 42–47. Kimmons, R., & Veletsianos, G. (2014). The fragmented educator 2.0: Social networking sites. Acceptable identity fragments, and the identity constellation. Computers & Education, 72, 292–301. doi:10.1016/j.compedu.2013.12.001 Lin, M. G., Hoffman, E. S., & Borengasser, C. (2013). Is social media too social for class? A case study of Twitter use. TechTrends, 57(2), 39–45. doi:10.1007/s11528-013-0644-2 Marwick, A., & boyd,. (2011). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media & Society, 13(1), 114–133. doi:10.1177/1461444810365313 Mazer, J., Murphy, R., & Simonds, C. (2007). Ill see you on Facebook: The effects of computermediated teacher self-disclosure on student motivation, affective learning, and classroom climate. Communication Education, 56(1), 1–17. doi:10.1080/03634520601009710 McNeely, I. F., & Wolverton, L. (2008). Reinventing knowledge. New York, NY: W.W. Norton & Co. Norris, M., Oppenheim, C., & Rowland, F. (2008). The citation advantage of open-access articles. Journal of the American Society for Information Science and Technology, 59(12), 1963–1972. doi:10.1002/asi.20898 Oblinger, D. G. (2010). From the campus to the future. EDUCAUSE Review, 45(1), 42–52. Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin UK.

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Pearce, N., Weller, M., Scanlon, E., & Ashleigh, M. (2010). Digital scholarship considered: How new technologies could transform academic work. In Education, 16(1). Piotrowski, C. (2015). Emerging research on social media use in education: A study of dissertations. Research in Higher Education, 27. Russell, K., Weinberger, E., & Stone, A. (1999). Preserving digital scholarship: The future is now. Learned Publishing, 12(4), 271–280. doi:10.1087/09531519950145670 Siemens, G., & Matheos, K. (2010). Systemic changes in higher education. Technology and Social Media, 16(1). Tate, V. D. (1947). From Binkley to Bush. The American Archivist, 10(3), 249–257. doi:10.17723/aarc.10.3.yr86jr3w0598m881 Thelwall, M. (2009). Homophily in MySpace. Journal of the American Society for Information Science and Technology, 60(2), 219–231. doi:10.1002/asi.20978 Tufekci, Z. (2008). Grooming, gossip, Facebook, and MySpace. Information Communication and Society, 11(4), 544–564. doi:10.1080/13691180801999050 UNESCO. (2002). Forum on the impact of open courseware for higher education in developing countries: Final report. Retrieved from http://unesdoc.unesco.org/images/0012/001285/128515e. pdf Veletsianos, G. (2012). Higher education scholars participation and practices on Twitter. Journal of Computer Assisted Learning, 28(4), 336–349. doi:10.1111/j.1365-2729.2011.00449.x Veletsianos, G., & Kimmons, R. (2012a). Assumptions and challenges of open scholarship. International Review of Research in Open and Distance Learning, 13(4), 166–189. doi:10.19173/ irrodl.v13i4.1313

Category: Research Methods and Scholarly Publishing

Veletsianos, G., & Kimmons, R. (2012b). Networked participatory scholarship: Emergent techno-cultural pressures toward open and digital scholarship in online networks. Computers & Education, 58(2), 766–774. doi:10.1016/j. compedu.2011.10.001 Wiley, D. (2003). A modest history of OpenCourseWare. Autounfocus blog. Retrieved from http://www.reusability.org/blogs/david/ archives/000044.html Wiley, D. (2010). Open education. In M. K. Barbour & M. Orey (Eds.), The foundations of instructional technology. Retrieved from http:// projects.coe.uga.edu/ITFoundations/index. php?title=Open_Education Wiley, D., & Green, C. (2012). Why openness in education? In D. Oblinger (Ed.), Game changers: Education and information technologies (pp. 81–89). Educause.

ADDITIONAL READING Binkley, R. C. (1935). New tools for men of letters. The Yale Review, 24. Kimmons, R., & Veletsianos, G. (2014). The fragmented educator 2.0: Social networking sites. Acceptable identity fragments, and the identity constellation. Computers & Education, 72, 292–301. doi:10.1016/j.compedu.2013.12.001

Siemens, G., & Matheos, K. (2010). Systemic changes in higher education. Technology and Social Media, 16(1). Veletsianos, G., & Kimmons, R. (2012b). Networked participatory scholarship: Emergent techno-cultural pressures toward open and digital scholarship in online networks. Computers & Education, 58(2), 766–774. doi:10.1016/j. compedu.2011.10.001

KEY TERMS AND DEFINITIONS Digital Scholarship: An emergent form of scholarship that emphasizes the use of digital technologies to support efficiency. Networked Participatory Scholarship: An emergent form of scholarship that emphasizes the role that social technologies play in reshaping educational institutions and the role of the scholar. Open Scholarship: An emergent form of scholarship that emphasizes openness, sharing, and democratization of educational resources. Public Scholarship: An emergent form of scholarship that emphasizes responsibility of the scholar to engage in the public sphere. Social Scholarship: An emergent form of scholarship that emphasizes the importance of collaboration and mentoring in the scholarly process.

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Fuzzy Logic Approach in Risk Assessment Çetin Karahan Directorate General of Civil Aviation, Turkey Esra Ayça Güzeldereli Afyon Kocatepe University, Turkey Aslıhan Tüfekci Gazi University, Turkey

INTRODUCTION As an intuitively subjective and ambiguous notion, risk requires a detailed and attentive study, though. Since risk involves the events likely to occur in the future, risk assessment is an area where uncertainty is prevalent. Therefore, making use of experience, previous statistics and prediction ability is crucial in risk studies. The field of risk management is enriched with new techniques and methodologies, which serve the purposes of discovering more data, reducing subjectivity through more quantitative models and building flexible systems conducive to be updated with the obtained data. One of these new tools is fuzzy logic representing the uncertainty and to study with imprecise and uncertain knowledge. This paper discusses the application of fuzzy logic to risk assessment process as an alternative to the traditional models due to its similarity to human reasoning and its accuracy in interpreting uncertainty. A fuzzy logic-based algorithm is developed for the purposes of enhancing risk assessment accuracy. Impact and likelihood factors, which are fundamental elements of risk, measured by the fuzzy logic-based approach. Beyond the impact and likelihood values, the factors directly effecting impact and likelihood also considered in this study and these factors included in fuzzy operations, in order to reduce subjectivity and increase precision.

In this study, an approach is explained for risk assessment. The aim of this approach is providing insight as a powerful alternative to traditional methods. A comparison between the risk values measured by the new model and those measured by the classical model supports the view that using fuzzy logic in risk assessment helps to produce more effective outcomes.

BACKGROUND The concept of fuzzy logic was first introduced in 1965 by Prof. Lotfi A. Zadeh who developed Lukasiewicz’s multivalued logic to set theory and created what he called fuzzy sets – sets whose elements belong to it in different degrees. At the start, fuzzy logic was a theoretical concept with little practical application. In the 1970’s, Prof. Edrahim Mamdani of Queen Mary College, London, built the first fuzzy system, a steam-engine controller, and he later designed the first fuzzy traffic lights. His work led to an extensive development of fuzzy control applications and products (Cirstea, Dinu, McCormick & Khor, 2002, pp. 113-114). Bellman and Zadeh (1970) developed an initial general theory on decision making in fuzzy environment which include three basic concepts as fuzzy goals, fuzzy constraints and fuzzy decisions. It is concluded that the proposed theory is generally has advantages according to the traditional probability theory.

DOI: 10.4018/978-1-5225-2255-3.ch588 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Tah and Carr (1999) claimed that the current risk management techniques mostly based on the operational research techniques developed in 1960s and usually had failed to meet the needs of project managers. They introduced a fuzzy risk analysis model for a construction project to eliminate the past studies’ concentration on particular risks and proposed a model which have a generic and generally practicable representation. The development of fuzzy set theory to fuzzy technology during the first half of the 1990s has been very fast. More than 16,000 publications have appeared since 1965. Most of them have advanced the theory in many areas. Quite a number of these publications describe, however, applications of fuzzy set theory to existing methodology or to real problems. In addition, the transition from fuzzy set theory to fuzzy technology has been achieved by providing numerous software and hardware tools that considerably improve the design of fuzzy systems and make them more applicable in practice (Zimmerman, 2001, p. xxi). Hajiha, Roodposhti and Askary (2009) provided a risk assessment approach conducted on the basis of fuzzy logic for audit risk, inherent risk and control risk. The results are compared to a real case and the accuracy level of the results is discovered to be relatively higher. Keropyan and Gil-Lafuente (2011) place the emphasis on the importance of the ability of making right decisions and provide examples of use of fuzzy logic in selection of the decision-making styles within the scope of strategic management. A. Pesic, D. Pesic and Tepavcevic (2012) proposed fuzzy logic as an innovative strategic management instrument to identify internal risks and to eliminate some of the restrictions imposed by the classical methods. Nunes and Marques (2012) developed a fuzzy logic-based model for using in risk assessment of work accidents and occupational diseases and discussed the superiority of the fuzzy logic over the classical methods arguing that it allows a more comprehensive evaluation of risks and combination of both subjective and objective criteria.

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Dainiene and Dagiliene (2013) calculated the sustainability of the business by using fuzzy logic and the competence of the business was shown experimentally. In the study, the operational value of the business is calculated on the basis of the financial and non-financial data. Shang and Hossen (2013) provided evidence for the possible use of fuzzy logic as a decisionmaking system. The study supports the view that fuzzy logic may also be used in complex risk systems along with the other risk models such as decision trees and artificial neural network. All of the studies that have been investigated support the view that the use of fuzzy logic as a decision-making system in strategic management has a number of advantages particularly in situations where there is a high level of unclarity.

USING FUZZY LOGIC TO IDENTIFY THE LEVEL OF RISK General Information on Fuzzy Logic The concept in question is that of a fuzzy set, that is, a “class” with a continuum of grades of membership. As will be seen in the sequel, the notion of a fuzzy set provides a convenient point of departure for the construction of a conceptual framework which parallels in many respects the framework used in the case of ordinary sets, but is more general than the latter and, potentially, may prove to have much wider scope of aplicability, particularly in the fields of pattern classification and information processing. Essentially, such a framework provides a natural way of dealing with problems in which the source of imprecision is the absence of sharply defined criteria of class membership rather than the presence of random variables (Zadeh, 1965, p. 339). In short, fuzzy set is a set where the members of the universal set have appointed values between the closed range of 0-1 and their membership status and membership degrees can be identified by these appointed values. The following should be taken into consideration while forming fuzzy sets:

Category: Risk Assessment

• •

•

•

Fuzzy sets which are marked in the universal set need to be distributed symmetrically. The number of fuzzy sets defined for each variable should be odd number (typically 3, 5 or 7). Thus, some fuzzy sets may remain in the middle. Here, as an example, for temperature value it can be defined 3 fuzzy sets with cold, warm and hot values. For providing each value being defined, fuzzy sets should be overlapped to eachother with a certain percentage. This will ensure the execution of more than one rule in the process of determining the output. Triangle or trapezoidal membership functions had better to be selected, which take less time to compute.

Kulkarni (2001) identifies Fuzzy Inference Systems (FIS) as a tool using if-then rules to state the relation between the input and output fuzzy sets. Fuzzy relations indicate the degree of existence or non-existence of the links and the interactions between the members of one or several sets. While a fuzzy system is designed, system inputs and outputs are identified and fuzzy sets are built for each input and each output. Relationship between these sets of input and output are built through logical processes. Fuzzy logic system converts these verbal statements into numerical statements and generates a single value at the end.

Risk and Risk Management Risk is the combination of the likelihood and the consequence of a specified hazard being realized. The type of risk analysis used should be appropriate for the available data and to the exposure, frequency and severity of potential loss. Quantitative risk analysis incorporates numerical estimates of frequency or probability and consequence. In practice a sophisticated analysis of risk requires extensive data which are expensive to acquire or often unavailable (Pokoradi, 2002, p. 64). Because it facilitates the exploration of the effects of the decisions made, actions of prioritization, mitigation and measurement, risk assessment

is a mechanism which helps a great deal with organizational stability. Risk management is an iterative and cyclic process whose main aim is to eliminate or at least to reduce the risks according to the ALARP (as low as reasonably practicable) principle. (Nunes & Marques, 2012, p. 22). Traditionally, probability theory was one of the methodologies used most in risk assessment. Probabilistic risk assessment (PRA) is the general term for risk assessment that uses theory and models to represent the likelihood of different risk levels in a population (i.e. variability) or to characterize the uncertainty in risk estimates. Among various probabilistic techniques to quantify uncertainties, the most widely used approach has been Monte-Carlo analysis (MCA) (Darbra, Eljarrat & Barcelo, 2008, p. 379). One of the most frequently conducted methods is the matrix method. Risk values are obtained by multiplying likelihood of occurrence value by impact value. Risk values are classified as low, moderate and high and the value range for each classification is determined according to the preferred scale. Below is an example of impactlikelihood matrix. Fine-Kinney method and fault-tree analysis also popularly used tools for risk assessment. Fuzzy logic approach, has recently come forward as a big contributor to the studies on risk assessment. A superior approach to model situations where there is a substantial amount of uncertainty, fuzzy logic is capable of incorporating human reasoning and judgment into the system.

Designing Fuzzy System The factors directly effecting the likelihood and impact of an event are selected to use their scores in fuzzy calculations to generate an impact and a likelihood output value. These two output values of impact and likelihood are then become the inputs of the final FIS which generates a risk score. In this sense, fuzzy logic operates on three different systems rather than only one as illustrated in Figure 2. 6791

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Figure 1. Example of impact- likelihood matrix used in risk assessment

Figure 2. Fuzzy Inference System for risk assessment

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The design of fuzzy system involves manually created fuzzy sets, functions and if-then rules as well as Fuzzy Logic component of MATLAB 7.0 program. Hence, the system can be converted to a software with any programming language easily by using these manually created and expressed fuzzy system components. MATLAB 7.0 is used to verify the calculations that are done manually. Fundamental properties of the designed FIS: Type : Mamdani “and” Method : min Defuzzification Method : centroid Implication Method : min Aggregation Method : max Impact Likelihood Risk Input : [1x2] [1x2] [1x2]

Output : [1x1] [1x1] [1x1] Rule : [1x9] [1x9] [1x25]

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A numerical example also embedded in the FIS explanations steps. The example includes impact and likelihood factors’ scores which supposed to be generated by expert opinions. In order to testify the flexibility and accuracy of the fuzzy logic system, the example uses decimal values, which would otherwise be integer1. The numerical inputs for the example is as follows: Impact Factors’ Scores Are: Financial = 7,5 and Reputation = 6,4 Likelihood Factors’ Scores Are: Complexity = 7 and Workload = 6

Table 1. Scoring criteria for the risk-related impact factor Impact Factor Financial loss (likely to occur)

Score

Expression

Damage (USD)

1

Too low

< 3.000

2

Too low

3.000 – 10.000

3

Low

10.000 – 17.000

4

Low

17.000 – 34.000

5

Moderate

34.000 – 67.000

6

Moderate

67.000 – 117.000

7

High

117.000 – 167.000

8

High

167.000 – 250.000

9

Too high

250.000 – 333.000

10

Too high

> 333.000 Damage expression

Reputational damage (likely to occur)

1

Too low

only at the level of the concerned stakeholders

2

Too low

only at the level of the stakeholders involved

3

Low

being in the news in local printed media

4

Low

being in the news in local printed and visual media

5

Moderate

being in the news in local and regional printed media

6

Moderate

being in the news in local and regional printed and visual media

7

High

being in the news in national printed media

8

High

being in the news in national printed and visual media

9

Too high

being in the news in international printed media

10

Too high

being in the news in international printed and visual media

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Design of Impact and Likelihood FISs Step 1: Identifying Input Data Set and Linguistic Variables In case a risk occurred, there might be a number of subsequent social, economic, reputation and legal impacts. In this study only two of the factors were considered as inputs (financial and reputation impact). Input values were constructed on the basis of certain criteria, which are shown in Table 1. The likelihood of a risk occurring depend on several factors such as workload and complexity of the process, the extent to which information technologies are used and the quality of the human resources. This study is concerned with only two factors as input: complexity and workload. The criteria determined for these values are shown in Table 2. The criteria specified in above tables are configured to allow for the expert opinion to be included in the basis of fuzzy logic processes.

Step 2: Generating Membership Functions Below are the membership functions built for the impact and likelihood fuzzy systems.

Table 3. Financial Impact membership functions Condition

Membership Degree

Fuzzy Set

0 < fI ≤ 2

fI md = 1

Low

2 < fI ≤ 4

fI md 4 < fI ≤ 6

6 < fI ≤ 8

fI md = 1 fI − 6 2 8 − fI = 2

fI md = fI md

8 < fI ≤ 10

4 − fI 2 fI − 2 = 2

fI md =

fI md = 1

Moderate

High Moderate

High

where fI md indicates the membership degree of financial impact.

By referencing Figure 3, the membership functions for “financial impact ( fI )” factor are generated as follows (see Table 3). Since the functions for the financial and reputation impact are the same, the graphs and the formula for the reputation impact are not provided separately. As can be seen from the Figure 4,

Figure 3. Graph showing the membership functions for financial and reputation impact

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Low Moderate

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Table 2. Scoring criteria for the risk-related likelihood factor Complexity (Assesses whether the complexity of the operations or the relevant legislation impedes the exercise of the controls, thereby increasing the likelihood of faults.) 1

Operation/legislation isn’t complex, fault likelihood is TOO LOW.

2

Operation/legislation isn’t complex, fault likelihood is TOO LOW.

3

Operation/legislation isn’t quite complex, fault likelihood is LOW.

4

Operation/legislation isn’t quite complex, fault likelihood is LOW.

5

Operation/legislation is somewhat complex, fault likelihood is MODERATE.

6

Operation/legislation is somewhat complex, fault likelihood is MODERATE.

7

Operation/legislation is complex, fault likelihood is HIGH.

8

Operation/legislation is complex, fault likelihood is HIGH.

9

Operation/legislation is excessively complex, fault likelihood is TOO HIGH.

10

Operation/legislation is excessively complex, fault likelihood is TOO HIGH.

1

The number of staff is adequate to perform the workload.

2

The number of staff is adequate yet the staff is inexperienced to perform the workload.

3

The number of staff is hardly adequate to perform the workload.

4

The number of staff is only adequate to perform the workload with extra effort and time.

5

The number of staff is seldomly inadequate to perform the workload.

6

The number of staff is sometimes not adequate to perform the workload.

7

The workload is high for the number of staff. Work is completed with short delay.

8

The workload is high for the number of staff. Work is completed with substantial delay.

9

The workload is too high for the number of staff. Some work is incomplete.

10

The workload is too high for the number of staff. Substantial amount of work is incomplete.

Workload (Assesses whether the understaff in face of the workload increases the likelihood of faults)

Figure 4. Graph showing the membership functions for the complexity and workload likelihood factors

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Table 4. If-then rules for “Impact” Financial Impact

Reputation Impact

Impact

1

Low

Low

Too low

2

Low

Moderate

Low

3

Low

High

Moderate

4

Moderate

Low

Low

5

IF

Moderate

&

Moderate

THEN

Moderate

6

Moderate

High

High

7

High

Low

Moderate

8

High

Moderate

High

9

High

High

Too high

the membership functions for likelihood factors (complexity and workload) would be the same with Table 3 entries. In this regard, the financial impact ( fI ) value of 7,5 coincides with the range of 6-8 in the membership degree functions provided. Therefore;

rI md = (6,4 – 6)/2 = 0,2 (high) - 20% “high” set member, = (8 – 6,4)/2 = 0,8 (moderate) - 80% “moderate” set member.

fI md = (7,5 - 6)/2 = 0,75 (high) - 75% “high” set member,

The complexity likelihood (cL ) value of 7 coincides in 6-8 interval in the membership degree functions provided. Therefore;

= (8 - 7,5)/2 = 0,25 (moderate) - 25% “moderate” set member.

cLmd = (7 - 6)/2 = 0,5 (high) -50% “high” set member,

In a similar way, as the reputation impact ( rI ) value of 6,4 is in 6-8 interval:

= (8 – 7)/2 = 0,5 (moderate) -50% “moderate” set member.

Table 5. If-then rules for “Likelihood” Complexity

Workload

Likelihood

1

Low

Low

Too low

2

Moderate

Low

Low

3

High

Low

Moderate

4 5

Low IF

Moderate

Moderate &

Moderate

Low THEN

Moderate

6

High

Moderate

Too high

7

Low

High

Moderate

8

Moderate

High

High

9

High

High

Too high

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Table 6. Fuzzy rule strength for the numerical example with the nonzero value Rule Num.

fI md rI md

Rule Strength (min)

Impact

5

0,25

0,8

0,25

Moderate

6

0,25

0,2

0,2

High

8

0,75

0,8

0,75

High

9

0,75

0,2

0,2

Too high

fuzzy system and then if-then rules are determined. If-then rules, which allow association between the inputs and the outputs for the impact and likelihood factors are provided below.

Step 4: Application of Fuzzy Operators

As the workload likelihood ( wL ) value of 6 coincides in 4-6 interval wLmd = 1 (Moderate) - 100% “moderate” set member.

Step 3: Rule Base Once the inputs were constructed, output membership functions (Figure 5) are developed in the

As both impact factors are the members of the 2 fuzzy sets (“high” and “moderate”) there would be 2x2=4 rules with nonzero value (see 5, 6, 8 and 9th lines in Table 4). Following the selection of the rules, strength of each rule is calculated. The strength of a rule implies its lowest or weakest value (minimum). Table 6 indicates the rule strengths for each nonzero valued rules. As one of the likelihood factors is a member of “1” set and the other is “2” sets, there appear 2x1=2 rules which are nonzero (see 5 and 6th lines in Table 5). The fuzzy rule strength for the example with the nonzero value is shown in Table 7.

Table 7. Fuzzy rule strengths for the numerical example with the nonzero value Rule Num.

cLmd

wLmd

Rule Strength (min)

5

0,50

1

0,50

Moderate

6

0,50

1

0,50

Too High

Likelihood

Figure 5. Output membership functions for the impact and likelihood factors

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Table 8. Impact/likelihood output fuzzy sets’ area formulas and centroid values Fuzzy set

Area

Too Low

Low

Moderate

Too High where

x

Centroid

(4x − x 2 ) 2

0,75

(6x − 3x 2 ) 2

2,5

4x − x

High

Centroid (x;y)

2

(x + x + ... + x ) (y + y + ... + y )  1 2 n 2 n  ; 1   n n  

5

(6x − 3x 2 ) 2

7,5

(4x − x 2 ) 2

9,25

indicates a single value generated by FIS.

Step 5: Output Membership Functions for Impact and Likelihood Factors (Defuzzification) Output membership functions, given in Figure 5, are constructed in the same way as the input membership functions. In this study, the outputs for the impact and likelihood factors are expressed as “too Low, Low, Moderate, High and too High”. Shown below are the area formula for and the centroid2 value of the impact and likelihood output membership functions given in the figure above (see Table 8). These formulas and values will eventually be used during defuzzification, which is the final process. In this stage, using the acceptable rules (those with values other than 0), aggregation process conducted. As in Table 6, more than one rule set may share the same result or system output. Where this is the case, the output which is “the most real” or Table 9. Fuzzy set threshold values for the numerical example (impact)

“the strongest” (with the maximum value) will get appointed. Among the rules producing the same results, the ones with the maximum strength are selected from Table 6 to form Table 9. In the same manner, the values received from Table 7 is as follows (see Table 10). These threshold values will be cited for the area calculation using the system output graph. Below is the table showing the area values calculated for each system output using the values provided above (see Table 11). As a consequence of the defuzzification process, the impact and likelihood scores are calculated to be 6,86 and 6,38 respectively. In order to verify this manual calculation, the same input values are also executed by the MATLAB program. The impact and likelihood values produced by MATLAB stands at 6,86 and 6,41 respectively as well.

Table 10. Fuzzy set threshold values for the numerical example (likelihood)

Too Low

Low

Moderate

High

Too High

Too Low

Low

Moderate

High

Too High

-

-

0,25

0,75

0,2

-

-

0,50

-

0,50

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Table 11. Fuzzy membership functions area values for the numerical example Fuzzy Set

Area

Impact

R

Likelihood

x

Area

Centroid

(Area x Centroid)

x

Area

Centroid

(Area x Centroid)

Too Low

(4x − x 2 ) 2

0

0

-

0

0

0

-

0

Low

(6x − 3x 2 ) 2

0

0

-

0

0

0

-

0

Moderate

4x − x 2

0,25

0,9375

5

4,6875

0,50

1,75

5

8,75

High

(6x − 3x 2 ) 2

0,75

1,40625

7,5

10,54688

0

0

-

0

Too High

(4x − x 2 ) 2

0,2

0,38

9,05

3,439

0,50

0,875

9,125

7,984375

SUM

2,72375

∑ ((centroid ) x (area ) ) ∑ (area ) i

i

18,67338

6,855761

2,625

16,73438

6,375

i

Design of Risk FIS The third fuzzy system is modelled to obtain a risk value after the impact and likelihood factors for an identified risk assessed and the outputs are generated.

By referencing this graphic, the membership functions for “impact ( I )” factor are generated (see Table 12). The impact value of 6,86 and the likelihood value of 6,38 both coincides with the range of 6

Figure 6. Input parameters membership functions for the risk value

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Table 12. Impact membership functions Condition

Membership Degree

Fuzzy Set

0 .

(1)

a,b are complex numbers and =(a*,b*)·(a,b)T=a*·a+b*·b=|a|2+|b|2=1. (2) {a*,b*} are complex conjugates of {a,b}. {|a| ,|b|2} are measurement probabilities of |0>,|1>. 2

Multiple Qubits A 2 qubit quantum state is a 22 component column vector. For |ψ1>=a·|0>+b·|1>, |ψ2>=c·|0>+d·|1>

(3)

the state is |ψ1ψ2>=|ψ1>⊗|ψ1>=a·c·|00>+a·d·|01>+b·c·|10 >+b·d·|11>. (4) ⊗ is the tensor product. |00>=|0>⊗|0>=(1,0,0,0) T , |01>=|0>⊗|1>=(0,1,0,0) T , |10>=|1>⊗|0>=(0,0,1,0) T , and |11>=|1>⊗|1>=(0,0,0,1)T are the base vectors.

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(1 / 2) ·(|00>+|11>), since one of {a,d} must be 0.

Physical Implementation of Qubits Qubits have been implemented by photon states, ion traps, cavity quantum electrodynamics (QED), nuclear magnetic resonance (NMR), quantum dots, superconducting circuits, and in silicon (Nielsen & Chuang, 2002; Devoret & Schoelkopf, 2013; Veldhorst et al., 2015). A physical qubit property is decoherence time, during which a qubit state is maintained before collaps caused by interaction with the physical environment. A qubit implementation in quantum communication is a photon polarization state consisting of all propagation planes of the electromagnetic wave of the photon. A random polarization is a superposition of an orthogonal state pair. Orthogonal state pair examples: • •

Horisontal and vertical polarization. +45° and -45° diagonal polarization. A photon polarization state is thus a qubit

|ψ>=a·|horis>+b·|vert>=c·|+45°>+d·|-45°> (5) a,b,c,d are complex numbers and |a|2+|b|2=|c|2+|d|2=1. One state in a chosen pair must be interpreted as |0>, the other as |1>. Pho-

Category: Theoretical Computer Science

ton polarization is measured with a filter. After measurement, only the component allowed by the filter proceeds. The No Cloning qubit property prevents copying unknown polarization which, however, can be teleported between photons.

Quantum Gates and Circuits

QUANTUM COMPUTING

Quantum gates change qubit states. Single qubit gates:

This chapter outlines quantum computation models, quantum algorithms, quantum programming, and quantum computation realizations.

Quantum Computation Models Proposed models: •

• •

•

Quantum Circuit: Computations are sequences of quantum gate operations, which are reversible transformations on multiple bit quantum registers. Adiabatic Quantum Computer: Computing by quantum annealing (Das & Chakrabarti, 2008). One-Way Quantum Computer: An entangled resource state is prepared and thereafter qubits are measured (Raussendorf & Briegel, 2001). Topological Quantum Computer: Computation is decomposed into braiding of anyons in a 2D lattice (Sarma, Freedman, & Nayak, 2006).

This subchapter presents the quantum circuit and the adiabatic quantum computation models, which are shown to be equivalent (Aharonov et al., 2004).

• • • •

Identity I, I·|ψ>=|ψ> Negation X (Pauli x), X·|0>=|1>, X·|1>=|0> Phase Shift Z (Pauli z), Z·|0>=|0>, Z·|1>=(-1)·|1> Hadamard H, H·|0>=2-½·(|0>+|1>), -½ H·|1>=2 ·(|0>-|1>) 2 qubit gates:

• •

Identity I, I·|ψ1ψ2>=|ψ1ψ2> Controlled-NOT Cnot, Cnot·|0ψ>=|0ψ>, Cnot·|1ψ>=|1>⊗X·|ψ>

N≥2 qubit gates are 2N×2N matrices G and G·G*=I. G* is the complex conjugate matrix of G. Quantum circuit: an interconnection of quantum gates. Figure 1: a teleportation circuit. The upper qubit is the control in both Cnot gates. m1,m2 are measured qubit values. Xm2=I,X for m2=0,1. Zm1=I,Z for m1=0,1. No Cloning occurs, since measurement m1 changes |ψ>.

Figure 1. Quantum circuit teleporting the unknown qubit state |ψ>=a·|0>+b·|1> to |θ>

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Adiabatic Quantum Computation (AQC) AQC is based on Schrödinger equation i∙ħ∙

d | Ψ (t) › dt

=H|Ψ(t)›

(6)

and the adiabatic theorem for calculations. i is the imaginary unit, ħ is the reduced Planck constant, |Ψ(t)› is the quantum system state at time t, and H is the Hamiltonian operator characterizing the total quantum system energy. In an n qubit system H is a 2n×2n hermitian matrix. The eigenvalues {Ei}, i=0,1,2,…,n-1 of H represent energies. Eigenvector(s) corresponding to the lowest eigenvalue represent ground state(s). In an n qubit system, the unknown ground state a Hamiltonian Hfinal represents a problem solution. The system is initialized to the ground state of a chosen Hamiltonian Hinit, which is adiabatically evolved to Hfinal by applying H(t) = A(t)∙Hinit +B(t)∙Hfinal

(7)

during the time t, 0≤t≤T. The adiabatic theorem: the system remains in the ground state, if T is sufficiently long and the difference between the two smallest eigenvalues of H(t/T) is strictly >0 when 0≤t≤T. The terminal system state represents the problem solution. (Farhi, Goldstone, Gutmann, & Sipser, 2000)

Quantum Algorithms A quantum circuit computation algorithm: qubit state changes in quantum gates and qubit state measurements. Any classical algorithm can be realized in a quantum circuit based computer. Some quantum algorithms have lower computational complexity than corresponding classical algorithms. Best known classical algorithm to disclose a private RSA key has superpolynomial complexity. Shor’s factorization quantum algo-

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rithm (Shor, 1994) discloses a n bit private RSA key with polynomial (cubic) complexity in a 1.5×n qubit quantum computer. Best classical algorithms for unsorted database search and solving linear equation sets have linear complexity. In a quantum computer with sufficiently many qubits, unsorted size n search has O( n ) complexity (Grover, 1996) and solving linear equation sets has logarithmic complexity (Cai et al., 2013). Quantum annealing is an AQC algorithm for minimizing a given object function represented by a Hamiltonian, whose ground state represents the minimum. Such object function: N

E ( s ) = −∑hi ⋅ si + ∑K ij ⋅ si ⋅ s j i =1

(8)

i1, A(T)/B(T))). The functions A(t), B(t) must guarantee adiabatic evolution from a state very close to the ground state of HI to a state very close to the ground state of HF. (Harris et al., 2009)

Quantum Programming Several imperative and functional programming languages, usually based on the Quantum Random Access Machine where a classical device controls quantum device computations, and also some different approaches have been proposed for quantum circuit based computers (Sofge, 2008; Miszczak, 2011). Among the first imperative languages is QCL (Quantum Computing Language) with a syntax resembling C language: The basic data type qureg (quantum register) defines qubit arrays. The standard library provides quantum gate operators. The measure statement measures qubit states. Higher abstraction level algorithms are defined like C language functions. Other imperative languages are LanQ resembling QCL and Q (Bettelli, Calarco, & Serafini, 2003) based on C++. (Miszczak, 2011) Lambda calculus and Haskell based functional quantum programming languages have been proposed. The first proposal, lambda-q calculus (Maymin, 1997) which expresses any quantum algorithm and efficiently solves problems with NP-hard complexity, is unfortunately

Physical Realization of Quantum Computers Properties restricting quantum circuit computation realizations (see Table 1): Quantum Operation Time: Qubit state change time in a basic quantum gate. Decoherence Time Scalability: Maximal qubit set in a quantum circuit.

• • •

The maximal number of quantum program operations is (decoherence time)/(quantum operation time). Currently only small scale quantum circuit computation realizations exist. A basic building block of a quantum computer, a two qubit quantum gate, has been implemented in silicon (Veldhorst et al., 2015). Shor’s quantum factorization has been executed in a 7 qubit NMR implemented

Table 1. Quantum circuit implementations properties Technology

Quantum Operation Time

Decoherence Time

# Quantum Operations

Scalability (# Qubits)

Ion traps

10-7 s

10-1 s

106

50

10

s

10 s

10

2-5

NMR

10 s

10 s

7

10

10-50

Quantum Dots

10-9 s

10-6 s

103

1000

Cavity QED

-14 -3

-5 4

9

Source: Brown, 2001

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computer (Vandersypen et al., 2001). Monz et al., (2011) describe an implemented 14 qubit register. AQC technology is magnetically coupled Josephson junction rf-SQUID (Radio Frequency Superconducting Quantum Interference Device) flux qubits (Devoret & Schoelkopf, 2013; Harris et al., 2009). Boixo et al. (2013) report experimental quantum annealing with 108 qubits in a 128 qubit adiabatic quantum computer. A 512 qubit adiabatic quantum computer has been delivered for artificial intelligence research (Jones, 2013). Also a 1000 qubit adiabatic quantum computer, D-Wave 2X (D-Wave, 2015), in available.

QUANTUM COMMUNICATION Quantum communication is qubit transfer and use of entanglement and teleportation to transfer qubit states between physical locations. Qubit transfer is photon transfer, for example polarized photons, in optical fibers or along free sight paths. Current transmission range is limited by photon state decoherence to about 300 km in optical fibers (Korzh et al., 2015) and about 150 km in free sight atmosphere paths (SchmittManderbach et al., 2007). The range in free sight space is considerably longer because of negligible decoherence. Secure quantum code transmission between two ground stations via satellite has been demonstrated (Physicists, 2013). Current quantum communication applications are Quantum Key Distribution (QKD), Quantum Key Agreement, Quantum Secret Sharing (QSS), and Quantum Signatures.

Quantum Repeaters Quantum networks over long distances are confronted with loss and operation errors, which heralded entanglement generation, purification, and deterministic quantum error correction can suppress. Used correction methods classify quantum repeaters into three generations (Munro, 2015). The first generation uses entanglement generation

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and purification. Communication rate overhead is reduced from exponential to polynomial scaling with distance. The second generation replaces entanglement purification with quantum error correction, which further reduces communication rate overhead from polynomial to poly-logarithmical scaling with distance. A third generation will eliminate two-way communication with both loss and operation error correction. Then only local quantum gate speed limits communication rate. Current optical communication technology transfers polarized photons with reasonable error rates up to about 70…100 km (Stucki, Gisin, Guinnard, Ribordy, & Zbinden, 2002; MagiQ, 2007). In long-distance quantum communication, quantum repeaters are needed on the network’s physical layer. Repeaters combine entanglement swapping and quantum memories. Transmission distance is divided into segments, entanglement is distributed independently within each segment, and entanglement swapping extends entanglement distance. Segments are mutually independent. Entanglement distribution is heralded, i.e. a signal indicates successful entanglement. Entanglement is stored in quantum memories for avoiding concurrent entanglement within each segment. (de Riedmatten, 2015) Research efforts to implement quantum repeaters are reported in (Curcic et al., 2004; QuReP, 2014).

Quantum Key Distribution (QKD) This subchapter surveys QKD protocols and their integration in the TCP/IP network protocol stack, presents current QKD technology and applications, and describes security threats.

QKD Protocols BB84 (Bennet & Brassard, 1984): 1. Alice sends over an optical channel to Bob a sequence of photons randomly polarized to a state in {|horis>, |vert>, |+45°>, |-45°>}.

Category: Theoretical Computer Science

Bob guesses the polarization base (horisontal/vertical or diagonal) for each photon and measures polarization. 2. Bob sends over a classical channel the guess sequence to Alice. 3. Alice replies about guess correctness. Bob’s measurements for correct guesses represent a shared secret key. See Figure 2. If eavesdropper Eve measures photon polarization in Alice’s sequence in a guessed base and forwards measured photons to Bob, then a random choice of |0>, |1> is forwarded for each incorrect guess. Thus successful key creation proves absence of eavesdropping (Yanofsky & Mannucci, 2008). BB84 is actually a “Sifting Phase” creating raw keys excluding not received photons. In a practical BB84 the shared secret contains the raw key bits remaining after ”Key Distillation” including ”Error Correction” to remove bits different in both raw keys and ”Privacy Amplification” to drop correct bits known by a possible eavesdropper. (Elliott et al., 2003) BB84 is a 4-state protocol as it uses four different polarization states. However, a perfectly secret QKD protocol needs only 2 non-orthogonal polarization states (Yanofsky & Mannucci, 2008). Also perfectly secret 6-state protocols have been proposed and analysed (Bruss, 1998). EPR, proposed by Einstein, Podolsk, and Rosen, is a completely different QKD protocol type based on Bell’s inequality (Yanofsky & Mannucci, 2008).

QKD Based Session Keys

T

Elliott & al. (2003) describe QKD integration in IPSec. QKD integration in TLS/SSL requires new cipher suites defining key exchange. New ServerKeyExchange and ClientKeyExchange messages are needed.

Attacks Against QKD Practical QKD security depends on environment noise, imperfect photon detection, and light sources properties. Some attacks are possible in ideal environments with single photon generators and 100% efficient detectors (Aggarwal, Sharma, & Gupta, 2011), while others stem from unsecure implementations (Gisin, Ribordy, Tittel, & Zbinden, 2002). Khan and Xu (2011) divide QKD communication into secret key generation, secret key storage and management, and encryption and transmission. Attack types are physical, quantum, and classical. Quantum attacks on QKD are intercept-resend, Trojan horse, and photon number splitting (PNS) (Kahn & Xu, 2011). In intercept-resend, Eve guesses polarization base, measures the quantum states sent by Alice, and sends replacement states to Bob. In a Trojan horse attack, Eve occupies a part of the quantum channel (i.e. the spatial, temporal, and frequency modes) to probe Alice’s apparatus. An auxiliary source is used and a detector analyses the backscattered signal. If Alice

Figure 2. The BB84 QKD protocol

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transmits a multiple photon laser pulse, then Eve could split off photons with PNS. A QKD protocol, SARG04, using the same polarization states as BB84 with a different information encoding is less vulnerable to PNS than BB84 (Scarani, Acín, Ribordy, & Gisin, 2004). Optical loophole attacks are strong light pulses (Bethune & Risk, 2000), high-power destruction of optical components, utilization of light emission from avalanche photodiodes, and faked states.

QKD Technology and Applications A QKD channel is a free sight path or an optical fiber. Free sight transmission is faster. In free sight atmosphere paths distances exceed 140 km, but exchange rate is only 12 bit/s (SchmittManderbach et al., 2007). Current QKD systems exchange 1 Mbit/s in 20 km fiber and 10 kbit/s when distance>100 km (Dixon, Yuan, Dynes, Sharpe, & Shields, 2008). Modest exchange rates depend on limited single photon detection rate. The first commercial quantum cryptography products, i.e. single photon detectors and random number generators, appeared in 2002. Encoding used photon phase. The Swiss company ID Quantique offers for the QKD protocols BB84 and SARG04 • •

A QKD research platform (Clavis, 2014) A server combining QKD with AES (Advanced Encryption Standard), securely bridging two Fast Ethernet networks, and supporting refresh rates 1 key/minute for up to 12 encryptors and 100 km transmission distance in optical fibre (Cerberis, 2012).

QPNTM Security Gateway (MagiQ, 2007) available from the US company MagiQ Technologies, Inc. adds VPN security layers and classical data encryption to QKD. BB84, IPSec, and 256 bit AES are implemented. A refresh rate up to 100 keys/second, a transmission distance of 100 km,

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and full-duplex 10/100 Ethernet ports are supported. Available is also a single photon BB84 based system (Q-BOX, 2003) for QKD research. A QKD module with key transfer 10 kbit/s for 20 km and 100 bit/s for 80 km is available for QKD research from the French company SecCureNet (Cygnus, 2013). Experimental QKD networks have been realized in USA, Europe, Japan, China, and Korea. The limited QKD range caused by photon state decoherence is extended by multi-hop networking with trusted nodes and QKD combined with the absolutely secure OTP (One Time Pad) encryption (Bellovin, 2011). China has in autumn 2015 almost completed installation of the world’s hitherto largest quantum communications network, 2000 kilometers from Beijing to Shanghai. (Elliot, 2004; Poppe et al., 2008; Gisin, 2015; Fadilpašić, 2015)

Quantum Key Agreement Subramaniam and Parakh (2014) propose a quantum Diffie-Hellman protocol. Two parties, Alice and Bob, agree on a basis set to use, the number m of qubits to exchange, and the initial qubit state |ψ>= |0>. Every basis consists of two rotation transformations, R for bit value 0 and R+90º for value 1, where R=0º or 30º or 42º etc. Then Alice and Bob independently and randomly choose m bases from the agreed set, generate a sequence of m random bits, and send a m qubit sequence to each other. Each sent qubit is a rotation of |ψ> determined by the chosen basis and the generated bit. Alice and Bob then: • • •

Apply to each received qubit a rotation determined by the chosen basis and the generated bit, Apply the rotation 90º-(previous rotation angle), Record the bit from measuring the rotated qubit in the chosen basis.

Category: Theoretical Computer Science

Both Alice and Bob thus obtain an m bit sequence from received qubits and can now announce their chosen basis sequences to each other over a public channel. A bit in the obtained sequence is discarded, if Alice and Bob use a different basis. The resulting shared secret key is a joint random selection from the remaining bits. Third party interference in qubit transmission causes bit errors.

Quantum Secret Sharing (QSS)

(2005) propose another quantum one-way function based scheme for signing general quantum states. Quantum message authentication can be based on quantum signature schemes (Barnum, Crepeau, Gottesman, Smith, & Tapp, 2002).

QUANTUM ERROR CORRECTION

The first QSS used the GHZ entanglement state (Hillery, Buzek, & Berthaume, 1999). QSS protocols have two types defined by the entanglement and product states. Zhang and Man (2005) propose a protocol based on entanglement swapping and local unitary operation. Tan, Feng, Jiang, and Fang (2013) present a verifiable quantum secret sharing protocol based on entanglement swapping. External eavesdroppers, internal cheaters, and cheating by two cooperating dishonest participants are prevented.

Quantum state decoherence, quantum noise, faults in quantum gates and qubit memories, and faulty qubit state measurements cause errors. Quantum error correction methods, codes, and algorithms are therefore required. (Lidar & Brun, 2013) Nielsen and Chuang (2002) describe quantum circuits for single qubit flip error correction. Reed et al. (2012) present superconducting circuits for three-qubit quantum error correction. A superconducting quantum computing chip, a 4 qubit square lattice, has been designed and successfully used to detect arbitrary single qubit errors (Córcoles et al., 2015).

Information-Flow Security of Quantum Systems

FUTURE RESEARCH DIRECTIONS

Entanglement can cause Information leakage. A Trojan horse may exploit entanglement between itself and a user with sensitive information as a covert channel (Ying, Feng, & Yu, 2013). The noninterference formalism is a general framework for specifying and analyzing information-flow security.

Quantum Digital Signature Classical digital signatures can be created with any one-way function (Lamport, 1979). A secure quantum one-way function based scheme for signing classical bit strings is proposed in (Gottesman & Chuang, 2001), patented in USA (Chuang & Gottesmann, 2002), and experimentally demonstrated in (Clarke et al., 2012). Lu and Feng

Research aiming to build a large scale quantum circuit based computer will continue. Possible solutions are a multiple of two qubit gates in silicon (Veldhorst et al., 2015) and superconducting qubits in a two-dimensional grid (Córcoles et al., 2015; Simonite, 2015). Future quantum computers will probably combine a classical and a quantum device. Computations are decomposed into a controlled set of subtasks. The quantum device executes subtasks, for which efficient quantum algorithms can be programmed, and some control, the classical device executes other subtasks and control. (Nielsen & Chuang, 2002) Additional polynomial quantum algorithms are being investigated to solve so called hidden subgroup problems, to which integer factoriza-

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tion belongs. New and more powerful quantum programming languages, mainly functional, will be developed. (Sofge, 2008) A new technique using twisted light will increase the efficiency of quantum communication. Polarization encodes only one bit per photon but the new technique, Orbital Angular Momentum, encodes 2.05 bits per photon and may still be extended. (Mirhosseini et al., 2015) Lack of fast photon detectors limits QKD performance. New detectors based on cryogenic niobium nitride superconductors may be a solution (Quellette, 2004). Research on ideal singlephoton sources continues, since multiple photon emissions are vulnerable. Expected future QKD advances (Hogben, 2009): • • • •

Increased QKD distance with quantum repeaters Increased transmission rate Decreased QKD costs Perfectly random OTP keys from quantum state measurements in Quantum Random Number Generation.

CONCLUSION Future quantum computing can erode security of current public key cryptography. The vulnerability of RSA based cryptography to quantum integer factorization is evident. Also current Elliptic Curve Cryptography is vulnerable to quantum algorithm attacks (Maslov, Mathew, Cheung, & Pradhan, 2009). Quantum resistant public key cryptography is thus a future need. Proposed candidates are Lattice Based Public-Key Cryptography, Multivariate Public-Key Cryptography, Code‐ Based Public‐Key Cryptography, Hash‐Based Signatures, McEliece Cryptosystems and a revised Elliptic Curve Cryptography (Buchmann, 2015; Zhang & Xu, 2014; Perlner & Cooper, 2009; Jao & de Feo, 2011). Also a quantum resistant SSL/ TLS protocol has been proposed (Chang, Chen, Wu, & Yang, 2014).

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Future quantum computing also affects symmetric cryptosystems. Grover’s quantum algorithm halves brute force search bit length. For example, quantum attack resistance of 256 bit AES is 128 bit. Quantum cryptography is an emerging information security technology, which is presently already available but still neither fully integrated in current network technology nor standardized. Technological limitations regarding quantum transmission distance and quantum device technology need to be removed.

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Maslov, D., Mathew, J., Cheung, D., & Pradhan, D. (2009). An O(m2)-Depth Quantum Algorithm for the Elliptic Curve Discrete Logarithm Problem over GF(2m). Journal of Quantum Information & Computation, 9(7), 610–627.

Jones, N. (2013, May 16). Google and NASA snap up quantum computer D-Wave machine to work on artificial-intelligence problems. Nature. Retrieved October 23, 2015, from http://www. nature.com/news/google-and-nasa-snap-upquantum-computer-1.12999 Kahn, M. M., & Xu, L. (2011). A Taxonomy of Attacks on Quantum Key Distribution. International Journal of Latest Trends in Computing, 2(3), 358–363. Korzh, B., Lim, C. C. W., Houlmann, R., Gisin, N., Li, M. J., Nolan, D., & Zbinden, H. et al. (2015). Provably secure and practical QKD over 307 km of optical fibre. Nature Photonics, 9, 163–168. doi:10.1038/nphoton.2014.327 Lamport, L. (1979 October). Constructing digital signatures from a one-way function. Technical Report CSL-98, SRI International. Lidar, D. A., & Brun, T. A. (Eds.). (2013). Quantum Error Correction. Cambridge University Press. doi:10.1017/CBO9781139034807 Lu, X., & Feng, D. (2005). Quantum digital signatures based on quantum one-way functions. Proceedings of the International Conference on Advanced Communication Technology ICACT, 7, 514-517.

mathQI Research group on mathematics and quantum information. (2015). Facultad de Ciencias Matemáticas, Universidad Complutense de Madrid. Retrieved November 2, 2015, from http:// www.mathqi.es/ Maymin, P. (1997). The Lambda-q Calculus Can Efficiently Simulate Quantum Computers. Retrieved October 23, 2015, from http://arxiv. org/pdf/quant-ph/9702057v1.pdf Mirhosseini, M., Magaña-Loaiza, O., Sullivan, M., Rodenburg, B., Malik, M., Lavery, M., & Boyd, R. et al. (2015). High-dimensional quantum cryptography with twisted light. New Journal of Physics, 17. Miszczak, J. A. (2011). Model of Quantum Computation and Quantum Programming Languages. Bulletin of the Polish Academy of Sciences Technical Sciences, 59(3), 305–324. doi:10.2478/ v10175-011-0039-5 Monz, T., Schindler, P., Barreiro, J. T., Chwalla, M., Nigg, D., William A. Coish, W. A., & Blatt, R. et al. (2011). 14-Qubit Entanglement: Creation and Coherence. Physical Review Letters, 106(13), 130506. doi:10.1103/PhysRevLett.106.130506 PMID:21517367 Munro, B. (2015). Quantum Repeaters: From the first generation to the third? Retrieved November 10, 2015, from http://wqrn.pratt.duke.edu/ presentations

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Nielsen, M. A., & Chuang, I. L. (2002). Quantum Computation and Quantum Information. Cambridge University Press. Perlner, R. A., & Cooper, D. A. (2009). Quantum Resistant Public Key Cryptography: A Survey. Proceedings of 8th Symposium on Identity and Trust on the Internet IDtrust, 8, 85-93. doi:10.1145/1527017.1527028 Physicists Successfully Transmit Quantum Code Through the Atmosphere. (2013). SciTechDaily. Retrieved October 23, 2015, from http://scitechdaily.com/physicists-successfully-transmitquantum-code-through-the-atmosphere Poppe, A., Momtchil, P., & Maurhart, O. (2008 April). Outline of the SECOQC Quantum-KeyDistribution Network in Vienna. International Journal of Quantum Information, 209-218. Q-BOX WORKBENCH Uncompromising QKD Research. (2003). MagicQ Technologies, Inc. Retrieved October 23, 2015, from http://www. magiqtech.com/Products_files/QBox%20Datasheet-2011.pdf Quantum Computing Group. (2015a). The Laboratory for Physical Sciences, University of Maryland. Retrieved November 2, 2015, from http://www. lps.umd.edu/Quantum%20Computing%20Group/ QuantumComputingIndex.html Quantum Computing Primer. (2014). D-Wave Systems Inc. Retrieved October 23, 2015 from http://www.dwavesys.com/tutorials/backgroundreading-series/quantum-computing-primer Quantum Information Laboratory. (2015b). M.V.Lomonosov Moscow State University. Retrieved October 23, 2015, from http://qilab.phys. msu.ru/ qis.mit.edu quantum information science @ mit. (2013). Massachusetts Institute of Technology. Retrieved October 23, 2015, from http://qis.mit.edu/ Quellette, J. (2004 December). Quantum Key Distribution. The Industrial Physicist, 22-25.

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QuReP Quantum Repeaters for Long Distance Fibre-Based Quantum Communication. (2014). Cordis Community Research and Development Information Service, European Commission. Retrieved October 23, 2015, from http://cordis. europa.eu/project/rcn/93257_en.html Raussendorf, R., & Briegel, H. J. (2001). A One-Way Quantum Computer. Physical Review Letters, 86(22), 5188–5191. doi:10.1103/PhysRevLett.86.5188 PMID:11384453 Reed, M. D., DiCarlo, L., Nigg, S. E., Sun, L., Frunzio, L., Girvin, S. M., & Schoelkopf, R. J. (2012). Realization of Three-Qubit Quantum Error Correction with Superconducting Circuits. Nature, 482(7385), 382–385. doi:10.1038/nature10786 PMID:22297844 Sarma, S. D., Freedman, M., & Nayak, C. (2006). Topological Quantum Computation. Physics Today, 59(July), 32–38. doi:10.1063/1.2337825 Scarani, V., Acín, A., Ribordy, G., & Gisin, N. (2004). Quantum Cryptography Protocols Robust against Photon Number Splitting Attacks for Weak Laser Pulse Implementations. Physical Review Letters, 92(5), 057901. doi:10.1103/PhysRevLett.92.057901 PMID:14995344 Schmitt-Manderbach, T., Weier, H., Fürst, M., Ursin, R., Tiefenbacher, F., Scheidl, T., & Weinfurter, H. et al. (2007). Experimental demonstration of free-space decoy-state quantum key distribution over 144 km. Physical Review Letters, 98(1), 010504. doi:10.1103/PhysRevLett.98.010504 PMID:17358463 Shor, P. W. (1994). Algorithms for quantum computation: Discrete log and factoring. Proceedings of the 35th Annual Symposium on Foundations of Computer Science, 124–134. doi:10.1109/ SFCS.1994.365700 Simonite, T. (2015, April 29). IBM Shows off a Quantum Computing Chip. MIT Technology Review.

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Sofge, D. A. (2008) A Survey of Quantum Programming Languages: History, Methods, and Tools. In Proceedings of the Second International Conference on Quantum, Nano, and Micro Technologies ICQNM 2008 (pp. 66-71). IEEE Press. doi:10.1109/ICQNM.2008.15

Ying, M., Feng, Y., & Yu, N. (2015). Quantum Information-Flow Security: Noninterference and Access Control. Retrieved October 23, 2015, from http://arxiv.org/pdf/1301.6804v1.pdf

Stucki, D., Gisin, N., Guinnard, O., Ribordy, G., & Zbinden, H. (2002). Quantum Key Distribution over 67 km with a PlugPlay System. New Journal of Physics, 4(41), 1–8.

Zhang, X., & Xu, C. (2014). Efficient Identitybased Public Auditing Scheme for Cloud Storage from Lattice Assumption. In Proceedings of the 17th International Conference on Computational Science and Engineering (pp. 1819-1826). IEEE Press. doi:10.1109/CSE.2014.334

Subramaniam, P., & Parakh, A. (2014). A Quantum Diffie-Hellman Protocol. In Proceedings of the 11th International Conference on Mobile Ad Hoc and Sensor Systems (pp. 523-524). IEEE Press.

Zhang, Z.-J., & Man, Z.-X. (2005). Multiparty quantum secret sharing of classical messages based on entanglement swapping. Physical Review A., 72(2), 022303. doi:10.1103/PhysRevA.72.022303

Tan, X., Feng, Z., Jiang, L., & Fang, A. (2013). Verifiable Quantum Secret Sharing Protocol. Proceedings of Fourth International Conference on Emerging Intelligent Data and Web Technologies (EIDWT). doi:10.1109/EIDWT.2013.44

ADDITIONAL READING

Timeline of Quantum Computing. (2015). Wikipedia. Retrieved October 23, 2015, from http://en.wikipedia.org/wiki/Timeline_of_quantum_computing van Tonder, A. (2004). A lambda calculus for quantum computation. SIAM Journal on Computing, 33(5), 1109–1135. doi:10.1137/ S0097539703432165 Vandersypen, L., Steffen, M., Breyta, G., Yannoni, C., Sherwood, M. H., & Chuang, I. L. (2001). Experimental realization of Shors quantum factoring algorithm using magnetic resonance. Nature, 414(6866), 883–887. doi:10.1038/414883a PMID:11780055 Veldhorst, M., Yang, C. H., Hwang, J. C. C., Huang, W., Dehollain, J. P., Muhonen, J. T., & Dzurak, A. S. et al. A. S. (2015). A two-qubit logic gate in silicon. Nature. doi:10.1038/nature15263 Yanofsky, N. S., & Mannucci, M. A. (2008). Quantum Computing for Computer Scientists. Cambridge University Press. doi:10.1017/ CBO9780511813887

Bergou, J. A., & Hillery, M. (2013). Introduction to the Theory of Quantum Information Processing. eBook. Germany: Springer. doi:10.1007/978-14614-7092-2 de Falco, D., & Tamascelli, D. (2011). An Introduction to Quantum Annealing. RAIRO – Theoretical Informatics and Applications, 45(1), 99-116. Gay, S. J. (2006). Quantum programming languages: Survey and bibliography. Journal of Mathematical Structures in Computer Science, 16(4), 581–600. doi:10.1017/S0960129506005378 Lidar, D. A., & Brun, T. A. (Eds.). (2013). Quantum Error Correction. eBook. United Kingdom: Cambridge University Press. doi:10.1017/ CBO9781139034807 Perlner, R. A., & Cooper, D. A. (2009). Quantum Resistant Public Key Cryptography: A Survey. In Proceedings of 8th Symposium on Identity and Trust on the Internet IDtrust (vol. 8, pp. 85-93). Gaithersburg, MD. doi:10.1145/1527017.1527028 Quantum Information Science. (2015). Wikipedia. Retrieved October 26, 2015, from http:// en.wikipedia.org/wiki/Quantum_information_ science 7729

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van Assche, G. (2006). Quantum Cryptography and Secret-Key Distillation. England: Cambridge University Press. doi:10.1017/ CBO9780511617744 Yanofsky, N. S., & Mannucci, M. A. (2008). Quantum Computing for Computer Scientists. England: Cambridge University Press. doi:10.1017/ CBO9780511813887

KEY TERMS AND DEFINITIONS AQC: Adiabatic quantum computation, calculation by quantum annealing. Avalanche Photodiode: The semiconductor analog to a photomultiplier.

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Decoherence Time: The time a qubit state prevails before collapse. Enlanglement: A multiple qubits state which cannot be created by combining single qubit states. Photon: A discrete packet of electromagnetic energy. Polarization: The propagation plane of an electromagnetic wave. Quantum GATE: A device changing the state of one or multiple qubits. Qubit: A two-state quantum-mechanical system which can be a concurrent superposition of both states. Teleportation: Transfer of a qubit state.

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Sleptsov Net Computing Dmitry A. Zaitsev International Humanitarian University, Ukraine

INTRODUCTION Recently many researchers introduce new models of hyper-computations, such as quantum computations, computations on cell membranes, spiking P neurons and DNA (Cook & Neary, 2013), capable breaking through the obstacle of intractable tasks. Petri nets have been known for years as a model of concurrent systems (Murata, 1989) but their computationally universal extensions are exponentially slow comparing Turing machines, especially when implementing arithmetic operations. A Sleptsov net concept, suggested quarter a century ago, recently acquired its second birth (Zaitsev, 2016) due to its ability of fast implementation of basic arithmetic operations. Firing a transition in a few instances at a step leads to universal constructs which run in polynomial time (Zaitsev, 2017). In Sleptsov net computing (Zaitsev, 2014a; Zaitsev & Jürjens, 2016), a program, written in Sleptsov net language preserving concurrency of an application area, runs on Sleptsov net processor which implements concurrent firing of transitions in multiple instances providing computations having ultra-performance.

BACKGROUND A concept of algorithm was formalized for the first time by Alan Turing in 1936 in the form of an abstract machine which is traditionally called a Turing machine. Universal Turing machine which runs a given Turing machine is considered a prototype of a traditional computer. Besides Turing machines, other computationally universal systems appeared: recursive functions of Kleene, normal

algorithms of Markov, tag rewriting systems of Post, register machines of Minsky. Variety of models is explained by controversial requirements of manifold application areas. Recent models employ facilities of massively parallel computations even in such simple constructs as elementary cellular automata which universality was proven in 2004 by Mathew Cook. Besides, smallest universal Turing machines were constructed in 2008 by Turlough Neary and Damien Woods which run in polynomial time. However, the way of programming cellular automata after Mathew Cook does not reveal their ability for massively parallel computing. Sleptsov net concept (Zaitsev, 2016) mends the flaw of Petri nets (Murata, 2013), consisting in incremental character of computations, which makes Sleptsov net computing (Zaitsev, 2014a; Zaitsev & Jürjens, 2016) a prospective approach for ultra-performance concurrent computing. In Zaitsev (2016), an overview of works, which refer to Sleptsov nets (Petri nets with multichannel transitions or multiple firing strategy), is presented.

MAIN FOCUS OF THE ARTICLE Issues, Controversies, Problems Definition of Sleptsov Net A Sleptsov net (SN) is a bipartite directed multigraph supplied with a dynamic process (Zaitsev, 2016). An SN is denoted as N=(P,T,W,μ0), where P and T are disjoint sets of vertices called places and transitions respectively, the mapping F specifies arcs between vertices, and μ0 represents the initial state (marking).

DOI: 10.4018/978-1-5225-2255-3.ch672 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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The mapping W: (P×T)→N∪{-1}, (T×P)→N defines arcs, their types and multiplicities, where a zero value corresponds to the arc absence, a positive value – to the regular arc with indicated multiplicity, and a minus unit – to the inhibitor arc which checks a place on zero marking. N denotes the set of natural numbers. To avoid nested indices we denote w −j ,i = w(p j , ti ) and

•

= w(ti , p j ) . The mapping μ: P→N specifies the place marking. In graphical form, places are drawn as circles and transitions as rectangles. An inhibitor arc is represented by a small hollow circle at its end, and a small solid circle represents the abbreviation of a loop. Regular arc’s multiplicity, greater than unit, is inscribed on it and place’s marking, greater than zero, is written inside it. Examples of SNs computing basic arithmetic and logic operations are shown in Figure 6. To estimate multiplicity of firability conditions on each incoming arc of a transition, the following auxiliary operation is defined

•

w

+ i, j

x / y, if y > 0  x  y = 0, if y = −1, x > 0,  if y = −1, x = 0. ∞,  To avoid inconsistency with infinite number of instances, here we prohibit transitions without input regular arcs. The behavior (dynamics) of a SN could be described by the corresponding state equation similarly to (Zaitsev, 2012). The present work considers the behavior as result of applying the following transition firing rule: •

The number of instances of transition ti firable at the current step is equal to

vi = v(ti ) = min(µj  w −j ,i ), 1 ≤ j ≤ m, w −j ,i ≠ 0 j

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When transition ti , vi > 0 fires, for ui ≤ vi it

◦◦

extracts ui ⋅ w −j ,i tokens from each its input place pj for regular arcs w −j ,i > 0 ;

◦◦

puts ui ⋅ wi+,k tokens into each its out-

put place pk, wi+,k > 0 ; The net halts if firable transitions are absent.

When a transition, having a single regular incoming arc with multiplicity a from place p and a single regular outgoing arc with multiplicity b to place p ′ , fires with ui = vi , it implements the following computations μ(p)=μ(p)mod a; µ(p ′) = µ(p ′) + b ⋅ (µ(p) div a ) . Namely, it implements division by a with a remainder and multiplication by b. Choosing either a or b equal to unit we obtain either pure multiplication or pure division. In Petri net, only one transition fires at a step while in Sawicki net (Burhard, 1981) a maximal set of firable transitions fires at a step. However in both Petri and Sawicki nets, only one instance of a transition fires at a step. Transitions could be thought of as virtual actions. The number of really started actions depends on the amount of available resources represented by transitions’ input places. Why we should restrict the number of transitions’ instances to unit and fire them in sequence, as in Petri net, when available resources allow firing them simultaneously? Anyway, classical sequential order of transitions’ firing could be obtained as a special case attaching a place to each transition connected with read arc and having marking equal to unit. Various extensions of Petri nets are known such as priority, inhibitor, timed, loaded (colored), hierarchical, and nested Petri nets (Murata, 1989). Sometimes they sophisticate the basic model considerably. Our goal consists in obtaining hyper-performance at the cost of minimal modification which consist in the multiple firing

Category: Theoretical Computer Science

strategy. As for other concepts concurrent to Petri nets, multiset rewriting systems (Verlan, 2011) should be mentioned as well as vector addition and replacement systems. Moreover, for studying protocols, the concept of Hoare’s communication sequential processes is close to Petri nets and mutual transformations are possible. An essential advantage of Petri (and Sleptsov) nets is graphical presentation of systems’ structure and behavior.

Universal Sleptsov Net as a Processor When programming in Sleptsov (Petri, Salwicki) net language, we use a concept of dedicated contact places. Before a net starts, we upload input data into its contact places, and when a net halts, we download output data from its contact places. Sometimes deeper specialization is more convenient when we distinguish separate subsets of input and output places. Note that data of various data types are encoded as nonnegative integer numbers to be processed by a net. Turing-completeness of extended PNs means that a universal net, which runs a given net, exists. Recently a series of small universal Sleptsov nets was constructed (Zaitsev, 2017, 2014b) in which each net of the series can be considered a prototype of a processor in a Sleptsov net based computer (Zaitsev, 2014a). A program of such a computer, composed in the form of a (inhibitor) Sleptsov net and encoded, is given as an input to

a processor which represents a hardware implementation of a universal Sleptsov net; Figure 1 illustrates the approach. The main obstacle for wide implementation of the Petri net paradigm of computation (Zaitsev, 2014a) has been the fact that all known small universal nets run in exponential time. In (Zaitsev, 2017), a small universal Sleptsov net was constructed which runs in polynomial time; the net contains 15 places and 29 transitions. Technique of a universal SN composition (Zaitsev, 2017) using reverse control flow and subnets for multiplication and division by a constant is handy for developing the technology of programming in SNs. The considerable complexity difference between universal PNs and universal SNs is easily explained: when processing the TM tape code, which is an exponent on the working zone size, a universal PN takes a single token at a step while a universal SN takes all of the tokens; eventually, we come to arithmetic operations, namely multiplication and division employed for encoding/ decoding input PN. In (Zaitsev, 2014a) we offer a computing memory concept for massively parallel asynchronous implementation of SN processors that promises ultra-performance when processing programs written in SN language. Apart from the problem of efficient hardware implementation of a SN computer, an issue arises regarding the technology of programming in SNs and preserving natural parallelism (concurrency) of an application area.

Figure 1. Universal net as a processor

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Universal Sleptsov Net Runs in Polynomial Time Similar to (Zaitsev, 2014b, 2017), we construct a universal SN via simulation of weakly-universal Turing machine of Neary and Woods with 2 states and 4 symbols denoted as WUTM(2,4). As far as Turing machine behavior is deterministic, we use deterministic SN where transitions are enumerated and the firable transition with the least index fires in the maximal number of instances. For visual reflection of the transitions order (priority), we use arcs connecting transitions; if there is an arc from transition t to transition t ′ , it means that index of transition t should be less than index of transition t ′ (lesser index means higher priority here). The peculiarity of programming in SN is application of reverse control flow represented with moving zero marking; thus the initial marking of control flow places is equal to 1. We check zero marking via an inhibitor arc which does not restrict the multiplicity of firability conditions. The source information for simulation is the transition function of WUTM(2,4) and the encoding of its states and tape symbols (Zaitsev, 2017) given by Table 1. In WUTM(2,4), an infinite repetition of definite blank words is written on /// / / to the left and br = 010001 its tape: bl = 0001 to the right of the working zone. According to the function s(x k −1x k −2 ...x 0 ) = ∑ s(x i ) ⋅ r i for the i =0

tape words’ encoding, the codes of the left and right blank words are: s(bl)=167 and s(br)=13596. The constructed universal Sleptsov net is denoted as USN(15,29) with respect to the total number of its places and transitions, respectively.

The general scheme of USN(15,29) is shown in Figure 2a. Subnets are depicted as rectangles with double line border. Some vertices have mnemonic names besides their numbers. Used subnets FS, MA5LR, and MD5LR are represented in Figure 2b, Figure 2c, and Figure 2d, respectively. Places with the same name (number) are considered as the same place and should be merged all over the components. The final assembly of USN(15,29) drawn in (Zaitsev, 2017) looks rather tangled. Place U contains encoded TM state s(u), place X contains encoded current cell symbol s(x), and places L and R contain encoded left and right parts of the tape working zone respectively regarding the current cell. At the beginning of each computation step, place STEP launches subnet FS, which simulates WUTM(2,4) transition function. Subnet FS produces the encoding s(u ′) of the new state and the encoding s(x ′) of the new symbol in places U and X respectively to simulate the Turing machine instruction. Subnet FS also puts a token into either place RIGHT or place LEFT to indicate the control head right or left moves correspondingly. A token is extracted from place MOVE after subnet FS has finished that launches the sequence of subnets MA5LR, MD5LR, which simulates the control head moves. At the end of a simulated computation step, a token is put into place STEP that allows the simulation of the next instruction to begin. Moreover, places LEFT and RIGHT are cleared. The TM tape is represented by the places L, X, and R containing the encoded left part of the working zone, the current cell symbol and the encoded right part of the working zone correspondingly. The moves of the tape head on the tape are

Table 1. Weakly-universal Turing machine WUTM(2,4) and its encoding

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Figure 2. Component-wise representation of USN(15,29)

simulated by the two connected subnets MA5LR and MD5LR shown in Figure 2c and Figure 2d, respectively. The meaning of subnets’ names is the following: MA5 means multiplication and addition with radix 5 (S:=S∙5+X); MD5 means modulo and division with radix 5 (S:=S div 5, X:=S mod 5); LR means choice of places either L or R, where codes of the left and right parts of the tape, regarding the current cell symbol code X, are stored, depending on the marking of places LEFT and RIGHT. Two transitions lb and rb simulate peculiarities of weakly universal TM work, they add the blank word codes s(bl) and s(br) to the codes L and R of the left and right parts of the tape working zone correspondingly when its value is equal to zero. Thus, the sequence of subnets MA5LR, MD5LR implements the following operations: • •

To simulate a left move (when place RIGHT=0): R:=R∙5+X, L:=L div 5, X:=L mod 5; To simulate a right move (when place LEFT=0): L:= L∙5+X, R:= R div 5, X:=R mod 5.

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In (Zaitsev, 2017), it was proven that USN(15,29) simulates WUTM(2,4) in time O(14∙k) and space O(k), where k is the number of WUTM(2,4) steps. Taking into consideration polynomial complexity of running a TM on WUTM(2,4) and polynomial complexity of running an SN on TM, we obtain the final polynomial complexity of the constructed universal Sleptsov net.

Principles of Programming in Sleptsov Nets Programming in SNs (Zaitsev & Jürjens, 2016) represents a composition of data and control flows supplied with transition substitution to describe hierarchical structure of routines (nets). We suppose that to combine control flow with a subnet which substitutes a transition, a special pair of places is used: Start – to launch the subnet and Finish – to indicate its completion. Control flows are represented in an inverse form with a moving zero marking because a zero marking is easily checked via an inhibitor arc that does not restrict the number of fireable instances 7735

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of a transition. Thus, initially, all control flow places should have marking equal to one. To simulate standard control flows of classic programming languages, we offer the patterns represented in Figure 3 for a sequence a), branching b), cycle c), and parallel execution d). Moreover, an arbitrary subnet with markings belonging to {0,1}m and with a pair of start/finish places could be considered as a control flow. Each of transitions in Figure 3 could be substituted by a subnet using its input place to start it and its output place to indicate its completion. Moreover, additional incidental places could be attached which represent required input and output variables. In the patterns with alternative transitions (Figure 3 b, c), we suppose either an external control via conditional variables to make it deterministic or nondeterministic choice. For computation of expressions, a data flow approach seems more efficient when a corresponding subnet is started and finished with an external control flow. It is a question of granulation whether to use strict composition of subnets launched via Figure 3. Standard control flow patterns

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control flow or to write anew a closely connected tangle of control and data flows where each of them is not even clearly distinguished. A program written in Sleptsov nets is obtained via composition of control flows with data flows using a modular approach. Substitution of a transition by a module is defined by the indication of the module name and connection (mapping) of its input and output places with places of a net which either represent variables or control flow. It is also supposed that before a module starts, all of the input data is copied into its input places while after a module finishes and before its next start, all the output data is moved from its output places. For these purposes dashed arcs were introduced (Zaitsev, 2012) to designate a brief and convenient way of working with data. A dashed input arc of a module denotes a subnet COPY for copying an input variable value into the corresponding input place of the module before launching its work via the place Start. A dashed output arc of a module denotes a sequence of subnets CLEAN, MOVE for replacing a variable

Category: Theoretical Computer Science

value with the result obtained in the corresponding output place of module. When a module has a few input (output) places, the subnets COPY (CLEAN, MOVE) are inserted using the parallel execution pattern (Figure 6). An example is shown in Figure 4 a,b where subnet a) having dashed arcs is expanded into the corresponding low-level subnet b). Introduced for reduction in auxiliary subnet size, dotted arcs denote MOVE for both input and output variables; MOVE subnet is more concise and provides cleaning of its input variable. Thus, the value of the input variable is not preserved and the value of the output variable is incremented. When the same variable is both input and output, a dotted two-direction arc is used which is expanded as MOVE before launching a module and MOVE after finishing the module. The corresponding example is shown in Figure 4 c, d where subnet c) having a dotted arc is expanded into the corresponding low-level subnet d). Thus, a module could be considered as a programming language procedure whose contact places correspond to its formal parameters. Copying variables with dashed and dotted arcs

correspond to the substitution of the actual input parameters and extracting its output parameters. The question remains open regarding the way a module call can be implemented either using an inline fashion via cloning entire modules or a call-return fashion via having a single copy of a module and switching control flows. The difference between the two mentioned ways is illustrated in Figure 5 for two references to the addition module ADD (without considering data). While the call-return fashion is more concise because of having a single copy of a module, the inline fashion is more attractive from the concurrent execution point of view (Zaitsev, 2014a). A program composed of modules is finally compiled into a plain inhibitor priority Sleptsov net that is loaded for execution on a SN computer. The best performance is achieved when all the transitions are fired independently based on their fireability conditions; to resolve conflicts a special arbiter is provided which blocks incidental places of a firing transition. This way of execution also requires writing programs which are invariant to the transitions’ firing order.

Figure 4. Examples of dashed arcs expansion

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Figure 5. Example of transitions’ substitution implementation for z1=ADD(x1,y2); z2=ADD(x2,y2)

Efficient Implementation of Arithmetic and Logic Operations To describe the transition substitution operation in a formal way, regarding an external control flow and input/output variables, a concept of module is introduced (Zaitsev, 2016). A module represents a subnet with contact (input and output) places which could be either the only connection with the outside world for closed modules or combined with the global variables usage for open modules. The module work is controlled via a special pair of contact places: place Start (s) starts work, and place Finish (f) indicates the completion of a module’s work. Extracting a token from the first control flow place Start launches the moving zero marking till it arrives to the last control flow place Finish. We put the following restrictions on the control flow graphs. It is supposed that all actions of a module are controlled by its control flows and inhibitor arcs are used to fire a transition. Thus, when all the control flow places contain a token, the module transitions are disabled. Therefore, before extracting a token from the place Start as well as after the arrival of zero to the place Finish, the module stands still.

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To provide the re-enterability of a module, it is supposed that: when it starts, all its data places except input places have zero marking, and when it finishes, all its data places except for output places have zero marking. In Figure 6, nets are represented which implement basic copying, arithmetic and logic operations. Compared to (Zaitsev, 2012), subnets are reduced by one place and one transition under the assumption that we can use the input place Start to control transitions inside a subnet, but the place Finish is not used for the control inside a subnet. Using the technique of analyzing all the permitted fireable transition, employed in (Zaitsev, 2014b), we can prove that the subnets represented in Figure 6 implement the corresponding operations. For classic Petri nets, operations of multiplication and division are the most complex from the computational point of view; the corresponding subnets were studied in (Zaitsev, 2012). Namely their complexity influences the exponential slowness of computations on Petri nets. Considered in (Zaitsev, 2017), fragments of the universal Sleptsov net efficiently implement multiplication and division by a constant (equal to 5). Now we study multiplication and division of arbitrarily

Category: Theoretical Computer Science

given nonnegative integers using multiplication and division by a constant (equal to 2). We choose simple standard algorithms of long multiplication and division. The best known efficient algorithms of multiplication and division for natural numbers could be encoded in SN as well. The multiplication algorithm represents the usual “long multiplication”. To find the current digit of the multiplier, the reminder of the division by two d=y%2 is used; then the multiplier is recalculated via y/=2 for working with the next digit on the next pass of the main loop. The multiplicand is multiplied by two x*=2 which represents its shift to the left. When the current digit d of the multiplier is equal to 1, the shifted multiplicand is added to the result. The algorithm could be optimized to avoid recalculating x*=2 when the new value of y is equal to zero but it leads to a more sophisticated SN. The algorithm was encoded as a SN and is represented in Figure 7. For Sleptsov nets shown in Figures 6 and 7, it has been proven (Zaitsev, 2016) that: •

CLEAN, MOVE, COPY, NOT, OR, AND, ADD, SUB, GT0, GT implement corresponding operations with time and space complexity equal to a constant;

•

•

MUL implements multiplication of nonnegative integer numbers x and y with time complexity O(11∙log2y+3) and constant space complexity (equal to 15); DIV implements division of nonzero integer numbers x by y with time complexity 39∙(log2x–log2y)+19 and constant space complexity (equal to 48).

MUL(x,y): z:=x∙y, x:=0, y:=0; DIV(x,y): z:=x/y, r:=x%y, x:=0, y:=0. As an example, to calculate 3∙2=6, the number 3 is loaded into place X (p1) and the number 2 is loaded into place Y (p2). To start computations, a token is removed from place S (p3). Then the only enabled firing sequence t1t5t6t8t103t11t12t13t143t16t1t6t7t8t96t11t13t146t15t2t312t4 leads to obtaining the result 6 in place Z (p4) which is indicated by the removal of a token from place F (p5). Note that, the multiplication SN module shown in Figure 7 multiplies given natural numbers X and Y, and uses for this purpose multiplication

Figure 6. Modules which implement basic operations: copying, logic, and arithmetic

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T

Sleptsov Net Computing

Figure 7. Modules which implement multiplication and division

and division by the constant 2 implemented with a single SN transition; multiplication and division by constant 2 are usually efficiently implemented as the left and right shift of binary code correspondingly. The division algorithm represents the usual “long division”. An initial dividend x is split into two parts. Namely, x which represents its head comparable to y and its tail tx. The first loop while(y0)

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moves the separation one digit back when possible guaranteeing that x is the minimal possible head that is greater than y. In a real-life implementation, either hardware or software, of SN as well as PN, we should consider the complexity of the above mentioned procedures. Supposition of a sequential computation device (Zaitsev, 2014b) yields the multiplier O(|P|∙|T|∙log2l) for PNs, where l is the maximal number of steps. For SNs, an extra complexity of multiplication and division by a constant equal to a power of 2 is also estimated as O(log2l); for an arbitrary constant the complexity is sublinear. If we suppose some independent computing memory device implements each transition (Zaitsev, 2014a),

Category: Theoretical Computer Science

we get rid of the multiplier |P|∙|T| and obtain an ultra-parallel processor of Sleptsov nets with step complexity O(log2l). The considered estimations are too meticulous for usual comparisons of arithmetic algorithms. Supposing that x and y contain about n binary digits x≈y Not Present 4

3

2

1

0

Symmetric Layout (Homepage Only) The webpage is 100% symmetric

yes

The webpage is 75% symmetric

yes

The webpage is 50% symmetric

yes

The webpage is 25% symmetric

yes

The webpage is not symmetric

yes Monumental Building

On the homepage as the focus?

yes

in the background?

yes

On a 2nd level webpage as the focus?

yes

in the background?

yes

On the 3rd level webpage/pages or beyond

yes

On none of the webpages

yes Symbol of Nationalism or Religion

on homepage and beyond

yes

on homepage only

yes

on the 2nd level page/pages

yes

on the 3rd level webpage/pages or beyond

yes

on none of the webpages

yes Link to Information about the Leaders

on every webpage

yes

on homepage and 2nd level webpages

yes

on homepage only

yes

on the 2nd level webpage/pages and beyond

yes

no such link is found

yes

Information Arranged According to the Management Hierarchy. Home Link Is Not Counted. On navigation bar, is the Administration link the first link or in the first group of links?

yes

the 2nd link or in the second group of links?

yes

the 3rd link or in the third group of links?

yes

the 4th link or in the fourth group of links?

yes

no such link found

yes

Authority figure More than one on the homepage

yes

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Determine Democracy in Web Design

Code

Present ------> Not Present 4

3

One on the homepage

yes

More than one on the 2nd level webpage/pages

yes

One on a 2nd level webpage

2

1

0

yes

On the 3rd level webpage(s) and/or beyond

yes

on none of the webpages

yes

Special Title Conferred on Members (Religious or Political, like Rev., Majesty, Honorable) More than one on the homepage

yes

One on the homepage

yes

More than one on the 2nd level webpage(s)

yes

One on the 2nd level webpage(s)

yes

More than one on the 3rd level webpage(s)

yes

One on the 3rd level webpage and/or beyond on none of the webpages

7968

yes yes

Category: Web Technologies

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Discussion Processes in Online Forums Gaowei Chen The University of Hong Kong, Hong Kong Ming M Chiu The Education University of Hong Kong, Hong Kong

INTRODUCTION Using online discussions to facilitate learning is a major issue in information science and technology, especially with the increasing number of massively open, online courses (MOOCs). Online discussions involve a group of participants exchanging ideas by posting messages on an electronic medium (e.g., discussion forum, knowledge building environment). Due to its information transparency, communication flexibility and opportunities for reflection, online discussions in both independent forums and forums linked to school courses offer students additional opportunities for information processing, higher order thinking and learning (Chen, Chiu, & Wang, 2012a, 2012b; Gillani & Eynon, 2014; Qiu & McDougall, 2013). However, an online discussion forum does not necessarily guarantee engagement, effective interactions or substantial learning (Hew & Cheung, 2014). For instance, despite the widespread use of MOOC forums, often only a small proportion of the students are active participants (Onah, Sinclair, & Boyatt, 2014). This article discusses the advantages and disadvantages that online discussions offer compared to face-to-face discussions. Specifically, individual characteristics and message attributes can influence participants’ thinking and social relationships (Chen et al., 2012a, 2012b). By understanding the discussion processes through which students create new ideas and develop social relationships in online forums, designers can improve online forum interfaces. Likewise, educators can capitalize on this information to

help students participate, cooperate and learn in online forums more effectively.

BACKGROUND While online discussions have several advantages over face-to-face discussions, they also have some drawbacks. Online discussions’ advantages include information transparency, communication flexibility and reflection opportunities. As online messages are explicit, relatively permanent and organized, they are more transparent than faceto-face talk. Online messages are written explicitly and stored, so group members and teachers/ facilitators can access them later. Furthermore, authors can organize online discussion messages to highlight their relationships to other messages by responding along a specific thread or via quotes of previous messages (Chiu & Chen, 2013). The interface designs of some online discussion forums constrain each message to respond to a single previous message, which helps establish clear connections and avoid ambiguous relationships among messages. Readers who heed these explicit relationships can read the related messages in the authors’ preferred sequence, which can facilitate their understanding of the messages’ content. As a result of their greater permanence, online discussions offer greater communication flexibility across time and space compared to face-to-face discussions. Face-to-face discussants must be in the same place at the same time to engage in a shared conversation. In contrast, synchronous online discussants can communicated with one an-

DOI: 10.4018/978-1-5225-2255-3.ch693 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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other from any location. In asynchronous forums, participants can review the relevant information or post messages at any time from any location. Moreover, the greater permanence of online discussions also allows participants to take more time to reflect before responding, in comparison to face-to-face discussions, especially during asynchronous discussions (Hew, Cheung, & Ng, 2010). During face-to-face discussions, people respond in real time to one another, so they are less likely to spend much time editing their responses. In contrast, posting asynchronous, online discussion messages on a permanent online forum provides convenient access to participants, so they can spend minutes, hours, even days gathering more information from other sources, contemplating their relationships, and evaluating competing claims and justifications before writing a suitable response. Online discussions also have some disadvantages compared to face-to-face discussions. For example, face-to-face discussion participants can use nonverbal facial expressions and social cues to clarify and reinforce their meaning. In contrast, online discussion participants cannot use them, which can lead to misunderstandings among participants (Walther, Loh, & Granka, 2005). Also, while multi-threaded discussions allow greater time flexibility, their demands are also less immediate. As these students do not need to respond immediately, they are more likely to ignore the messages and not respond at all (Hewitt, 2005; Thomas, 2002). Instead, they may initiate off-topic discussions (Wu & Hou, 2015). As participants can respond later, the group often requires more time to make group decisions, which can reduce their efficiency (Baltes, Dickson, Sherman, Bauer, & LaGanke, 2002). Online discussion forums can be independent or linked to school courses. An independent academic forum is a bulletin board on a specific subject (e.g., high school geometry) but not related to any class or school. In such forums, peers communicate with one another as they wish, without instructor moderation or inference (Chen, 2004;

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Chen & Chiu, 2008). In a course-related forum (e.g., a MOOC) however, an instructor may structure, scaffold, or moderate the discussions (Coll, Rochera, & de Gispert, 2014; Park et al., 2015).

ONLINE DISCUSSION PROCESSES Like face-to-face discussions, online discussions include both problem content and social relations (Chiu, 2008). This section explicates the processes by which online discussants create correct, new ideas (micro-creativity) and develop social relationships. First, a theoretical framework characterizes online discussions at the message level, including a message’s content and author. Then, it shows how specific message attributes and individual characteristics influence participants’ micro-creativity and use of social cues during online discussions. By understanding students’ micro-creativity and uses of social cues during online discussions, educators can help students engage in beneficial discussion processes that improve cognitive outcomes and positive social relationships.

Characterizing Online Discussion Messages An online discussion message can be characterized along four dimensions: knowledge content, social metacognition, social cues, and individual characteristics (See Table 1; Chen & Chiu, 2008; Chiu & Chen, 2013; Hara, Bonk, & Angeli, 2000; Wong, Pursel, Divinsky, & Jansen, 2015). As acquiring useful information is often a key discussion goal, the knowledge content dimension characterizes the information displayed regarding the focal topic: new ideas, old repetitions, and null content (Chiu, 2000). The validity of an idea is clear in some contexts (e.g., arithmetic), but not others (e.g., poetry). A justification provides evidence, an explanation or citation of an authority to support the validity of an idea (e.g., Neuman, Leibowitz, & Schwarz, 2000). Online discussants

Category: Web Technologies

Table 1. A framework characterizing online discussion messages Dimension Categories

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Description Knowledge Content

Correct, new idea (Micro-creativity)

An idea that is both correct (consistent with both subject content and problem constraints) and new relative to the discussion

Wrong, new idea

A new idea that is inconsistent with at least the subject content or problem constraint

Justification

An action that supports a new idea by linking it to data, using a warrant, or backing (Toulmin, 2003)

Repetition

An idea that has been mentioned earlier in the topic being discussed

No academic content

A message without any problem-related information (e.g., simple evaluations [“No”], simple questions [“What?”], or off-topic information [“Where are you from?”]) Social Metacognition

Agree

Agree with a previous message

Disagree

Disagree with a previous message

Question

A query that expects a response

Command

A directive to demand/stop an action Social Cues

Positive

Words, symbol, or emoticon expressing a positive attitude or affective state toward others

Negative

Words, symbol, or emoticon expressing a negative attitude or affective state toward others Individual Characteristics

Masculine

A masculine participant, as indicated by nickname, photo, personal statement, etc.

Feminine

A feminine participant, as indicated by nickname, photo, personal statement, etc.

Past forum experience

The number of past messages posted by a participant

Topic initiator

A discussant who initiates the current topic

can monitor and try to control one another’s actions (social metacognition, Chiu & Kuo, 2009). For example, online discussants can use evaluations (e.g., agree, disagree) to assess one another’s ideas and identify flaws. Also, they can invite audience participation to varying degrees with an invitational form (e.g., question, command). Discussions can contain positive or negative social cues. These include verbal or nonverbal actions in face-to-face discussions and words, symbols, or emoticons in written messages in online discussions (Derks, Bos, & von Grumbkow, 2007). Igniting emotional responses, social cues can help build up or tear down social relationships, along with the collaborative fabric of their discussion (Sproull & Kiesler, 1986; Swan & Shih, 2005). Lastly, individual characteristics denote a message author’s displayed identity. During online discussions, participants usually do not know one

another’s actual individual characteristics. Instead, they often portray a public image by displaying information such as online gender or past forum experience. By initiating a topic or responding to one, they also announce their role.

Micro-Creativity Processes During Online Discussions Micro-creativity is an expressed idea that is both correct (consistent with the problem situation and the subject content) and new relative to the topic discussion. Like face-to-face discussions, online discussions with greater micro-creativity are more likely to yield correct solutions to problems (e.g., Chiu, 2008). Hence, understanding micro-creativity processes can help educators improve online students’ understanding, group problem solving and learning. Specifically, new

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ideas and justifications (knowledge content), disagreements and questions (social metacognition) in recent messages and authors’ individual characteristics can facilitate or hinder participants’ micro-creativity (Chen et al., 2012a).

New Ideas and Justifications As online discussants are often geographically and culturally diverse, they are more likely than face-to-face discussants to have diverse views and sources of knowledge (Swann, Kwan, Polzer, & Milton, 2003). Capitalizing on this diversity, heterogeneous participants can both generate diverse ideas and build on one another’s micro-creativity through processes such as sparked ideas and jigsaw pieces (Paulus & Brown, 2003). One person’ micro-creativity (e.g., a key word) might spark another person’s micro-creativity by activating related concepts in his or her semantic network (Nijstad, Diehl, & Stroebe, 2003). For example, when responding to a micro-creative act, “5 × 9 = 5 × (10 − 1)”, a participant might continue the thread and add another micro-creative act: “5 × (10 − 1) = 5 × 10 − 5 × 1 = 50 − 5 = 55.” Like fitting jigsaw pieces together, participants also can combine micro-creative acts into a new micro-creative act (Milliken, Bartel, & Kurtzberg, 2003). Hence, recent micro-creativity can increase subsequent micro-creativity. A justification suggests that an idea is reasonable and hence, often enhances its perceived validity (e.g., Neuman et al., 2000). A justification can support an idea’s validity by linking it to data, using a warrant, or supporting a warrant with backing (Toulmin, 2003). Furthermore, online discussants justify new ideas in explicit writing during online discussions, so they are more likely to recognize errors and specify relationships among ideas more precisely than during face-to-face discussions (Jonassen & Kwon, 2001). Hence, justifications can support the validity component of new ideas, identify errors, and clarify relationships to aid micro-creativity.

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Disagreements and Questions Unlike agreements, a disagreement tries to alter the discussion trajectory by identifying obstacles/ flaws of the previous action or by developing alternatives. A respondent who recognizes a flaw or has a conflicting view of the previous message’s understanding of terms, concepts, or schemas is likely to disagree with the previous message (Gunawardena, Lowe, & Anderson, 1997). To support his or her disagreement, the respondent might add a new idea. Or, other participants might address the disagreement with subsequent microcreativity, according to socio-cognitive conflict theory (Buchs, Butera, Mugny, & Darnon, 2004; Piaget, 1985). In both cases, disagreements can aid micro-creativity, either immediately or in subsequent actions. When a discussant has both supportive and conflicting information regarding a previous message, online discussants are more likely than face-to-face discussants to disagree. During a face-to-face discussion, participants often seek to enhance their relationship with other group members by agreeing while withholding the conflicting information (Chiu & Khoo, 2003). During an online discussion however, concerns about one’s public self-image (face) are less salient (politeness theory; Brown & Levinson, 1987), so online discussants disagree more often than faceto-face discussants do (Lu, Chiu, & Law, 2011). As noted above, this disagreement can facilitate micro-creativity, either immediately or in subsequent messages (according to socio-cognitive conflict theory). As in face-to-face discussions, questions during online discussions can facilitate discussions by inviting new ideas. When discussants recognize problematic ideas, they can ask questions to identify these knowledge gaps and elicit more information (e.g., “Could you explain the steps further?”). These questions can invite further elaborations, justifications, and micro-creativity. Furthermore, online discussants have a permanent

Category: Web Technologies

record of their discussion, no conversation turn structure, more time to find related information and more time for reflection, so online discussants are more likely than face-to-face discussants to reflect on a question, which can increase microcreativity (Hara et al., 2000). Without a record to support their limited working memory, faceto-face discussants who cannot recall relevant information often ask other group members to review old information rather than create new information, resulting in less micro-creativity (Chiu, 2008). As face-to-face discussants wait their turn to respond to a question, they might forget their ideas or decline to share them if the topic has changed. In contrast, online discussants can write their new ideas at any time and attach their message to the relevant prior message.

Individual Characteristics Gender differences in micro-creativity might be smaller in online discussions than in face-to-face discussions. As online discussants are less certain of one another’s sex, their gender identities are less salient (Palomares, 2004). Also, as the anonymity of online discussions obscures individual differences, online discussants are more likely to perceive the group as an entity, thereby enhancing the salience of the shared social identity (social identity model of deindividuation effects; Postmes, Spears, & Lea, 1998). As a result, behaviors of males vs. females are less likely to differ in online discussions than face-to-face discussions. Online discussants’ past activities in online forums might facilitate their micro-creativity (Wong et al., 2015). Experienced participants with many past posts are more likely to carefully consider others’ messages, use time to reflect, and search for more information, all of which might aid micro-creativity. Furthermore, experienced participants are less likely to be frustrated by technological barriers of online discussions, thereby asking fewer technology-related questions (e.g., “How do I type mathematical equations in this forum?”). This in turn allows them to invest more

time and effort in content-focused discussions, which can yield more micro-creativity. In the forums where discussants respond to an initiator’s questions, initiators often ignite discussion topics because they have knowledge gaps in the subject matter and seek to fill the gap by interacting with others (e.g., “Can you explain how to solve this problem?”). Hence, an initiator often shows less micro-creativity than the respondents during discussions.

The Processes of Using Social Cues During Online Discussions Online discussants’ use of social cues can reflect their social relationships. A social cue is a participant’s expressed emotion or attitude toward others during a discussion. Specifically, positive social cues refer to a discussant’s expression of positive affective states (e.g., “Oh, I get it now”) or positive attitudes toward others (e.g., “Thanks for your explanation”). In contrast, negative social cues express negative affective states (e.g., “☹”) or negative attitudes toward others (e.g., “No, I’m not, YOU are wrong!!!”; McKee, 2002). While positive social cues signal creation and maintenance of a mutually respectful, supportive, and encouraging climate that promotes positive social relationships and collaboration in the problem content space (Garrison, Anderson, & Archer, 2000), negative social cues often create a discouraging, aggressive or hostile environment that harms participants’ social relationships and collaborative learning (Paulus & Roberts, 2006; Sproull & Kiesler, 1986); ritual insults are a notable exception (Progovac & Locke, 2009).

Agreements and Disagreements When agreeing with a previous idea, online discussants who feel strongly about it might add affective words (e.g., “Aha, I agree with you”) or emoticons (e.g., “Yes ☺”), which parallel smiling agreements in face-to-face discussions. In contrast, participants who agree but do not feel

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strongly are less likely to post a message at all. By expressing an agreement with positive social cues, the respondent support the previous participant’s face, enhance their common ground, and make the discussion more enjoyable, all of which promote positive social relationships (Vinagre, 2008). When face-to-face discussants disagree with a previous idea, they often disagree politely with positive, verbal and non-verbal social cues to mitigate the threat to the speaker’s face and show shared positioning (Brown & Levinson, 1987; Chiu & Khoo, 2003). The benefits of face-to-face polite disagreements are so strong that it is the accepted norm among peers; lack of redress during a face-to-face disagreement is noticeably rude and unacceptable (Holtgraves, 1997). During online discussions however, anonymity and reduced face concerns allow participants to disagree with one another more freely. Thus, online discussants are less likely to use positive social cues to soften or redress disagreements. Moreover, they often add negative social cues to disagree rudely due to the reduced normative constraints of online discussions (e.g., adding exclamation marks, “You made it too complicated!!!!!”).

New Ideas and Justifications Online discussants are less likely to use social cues when providing new ideas and justifications. Unlike face-to-face discussions, online discussions lack nonverbal channels’ rich interpersonal information (Derks et al., 2007). Furthermore, online discussants usually do not know each other, have weaker social commitments, and are separated by greater psychological distance (Chen & Chiu, 2008; Hiltz & Goldman, 2005). Due to narrowed social cue channels, reduced immediacy and weaker past relationships, online discussants are less likely to attend to others’ social presence when sharing new ideas (Sproull & Kiesler, 1991). If participants focus more on problem content and less on social relationships, they are less likely to use social cues when contributing

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new ideas. Furthermore, justifications facilitate calm reason-based communication (Chiu, 2008). When justifying new ideas, online discussants often focus on the problem content rather than on their social relationships, so social cues are less likely to accompany justifications.

Earlier Social Cues While the effects of earlier social cues on subsequent social cues are similar in face-to-face and online discussions, their intensities differ. People tend to reciprocate positive social cues. According to social information processing theory, online discussants often spontaneously post positive social cues to develop positive and meaningful relationships (Vinagre, 2008). Likewise, negative social cues harming social relationships might elicit more negative social cues. For example, a negative social cue, “Can you be more WRONG?!” can threaten the face of the previous participant (Brown & Levinson, 1987). The previous participant can retaliate, with another rude, emotionally-loaded response, “Can you be more STUPID?!” to protect his or her face and attack the previous participant’s face (Gottman & Krokoff, 1989). With less social presence in the online discussion environment, negative social cues occur more often there than in face-to-face discussions (cues-filtered-out perspective, Sproull & Kiesler, 1986).

Individual Characteristics As with micro-creativity, social cue use differs less across gender in online discussions than face-toface discussions. Initiating an online discussion topic often indicates both a knowledge gap in the subject and motivation to fill that gap by asking others. By using positive social cues and being polite, an initiator is more likely to receive satisfactory responses from others, especially in online discussions in which many messages receive no responses (Hewitt, 2005; Thomas, 2002).

Category: Web Technologies

RECOMMENDATIONS FOR TEACHERS AND FORUM DESIGNERS This chapter highlights how new ideas, justifications, disagreements, and questions can affect micro-creativity and social cues during online discussions. To aid students’ micro-creativity and social relationships, teachers can encourage them to prepare their messages carefully, brainstorm new ideas, justify them, and evaluate their ideas carefully (Chen et al., 2012a, 2012b). Meanwhile, forum designers can design the interactive environments that hinder the use of negative social cues (Chen et al., 2012b). Unlike real-time face-to-face discussions, asynchronous online forums allow delayed responses, so teachers can encourage students to spend more time thinking about their messages, gathering more information, and polishing their messages, which can foster their micro-creativity. Students can brainstorm many ideas before writing out their justifications to choose the best ideas. As micro-creativity can foster subsequent micro-creativity, these micro-creativity chain reactions might be more likely to yield a solution. Meanwhile, students can consider whether they can justify their ideas, which would increase their likelihood of being correct. New ideas and justifications during online discussions reduce the likelihood of social cues, suggesting that when students are reasoning about the content, they can devote less effort to social cues. Hence, when students contribute new ideas and justifications, teachers need not be concerned about the absence of social cues and need not to try to promote them. Negative social cues during disagreements can tear apart a group’s cohesiveness, so teachers often worry about them. As negative social cues and disagreements often occur together in online forums, teachers can encourage students to evaluate one another’s ideas slowly and carefully, thereby reducing impulsive, false disagreements that might yield negative social cues and face attacks. Furthermore, the prevalence of such dis-

agreements during online discussions suggests an important role for a teacher: encouraging positive social cues during disagreements to help save face and discouraging face attacks, especially with negative social cues (Savvidou, 2013). As online discussants often use negative social cues when disagreeing, forum designers can create online discussion environments that hinder their use. For example, forum designers can offer fewer ready-to-use negative emoticons (e.g., emoticons expressing anger or attack) on the toolbar. Also, they can consider adding an automatic detection function to the forums, which would remind the participants to remove inappropriate emoticons/ words before they post their messages. By doing so, online discussants might be less likely to use negative emoticons/words to express their impulsive negative feelings when disagreeing.

FUTURE RESEARCH DIRECTIONS In addition to testing the validity of the above discussion processes, future research can investigate the impact of teacher messages on student messages in course-related forums and compare how students’ cognitive and affective styles affect their responses to teachers’ messages (Park et al., 2015; Wu & Hou, 2015). Future research can also explore how techniques such as visual analytics and intelligent support can be designed and used by teachers and students to facilitate their online discussion processes (Hsiao & Awasthi, 2015; Kumar & Kim, 2014). Addressing these questions can improve the understanding of students’ discussion processes in online forums.

CONCLUSION This article discusses the processes that influence online discussants’ micro-creativity and use of social cues during their discussions. Specifically, new ideas and justifications, disagreements and questions in recent messages facilitate micro-

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creativity during online discussions. New ideas and justifications are likely to reduce both positive and negative social cues during online discussions. While agreements facilitate positive social cues, disagreements facilitate negative social cues. Meanwhile, more experienced participants (who have more past posts) are likely to show micro-creativity. Topic initiators are less likely to show micro-creativity but more likely to display positive social cues. Together, these mechanisms show how messages in online discussions create a local context that influences participants’ correct outcomes and social relationships.

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Chen, G., Chiu, M. M., & Wang, Z. (2012b). Predicting social cues during online discussions. Computers in Human Behavior, 28(4), 1497–1509. doi:10.1016/j.chb.2012.03.017 Chiu, M. M. (2000). Group problem solving processes. Journal for the Theory of Social Behaviour, 30(1), 27–50. doi:10.1111/1468-5914.00118 Chiu, M. M. (2008). Flowing toward correct contributions during group problem solving. Journal of the Learning Sciences, 17(3), 415–463. doi:10.1080/10508400802224830 Chiu, M. M., & Chen, G. (2013). Statistical discourse analysis. In H. L. Lim & F. Sudweeks (Eds.), Innovative methods and technologies for electronic discourse analysis (pp. 285–303). Hershey, PA, USA: IGI Global. Chiu, M. M., & Khoo, L. (2003). Rudeness and status effects during group problem solving. Journal of Educational Psychology, 95(3), 506–523. doi:10.1037/0022-0663.95.3.506

Brown, P., & Levinson, S. C. (1987). Politeness. New York: Cambridge University Press.

Chiu, M. M., & Kuo, S. W. (2009). From metacognition to social metacognition. The Journal of Educational Research, 3(4), 1–19.

Buchs, C., Butera, F., Mugny, G., & Darnon, C. (2004). Conflict elaboration and cognitive outcomes. Theory into Practice, 43(1), 23–30. doi:10.1207/s15430421tip4301_4

Coll, C., Rochera, M. J., & de Gispert, I. (2014). Supporting online collaborative learning in small groups. Computers & Education, 75, 53–64. doi:10.1016/j.compedu.2014.01.015

Chen, G. (2004). Online discussion in an independent academic BBS forum. Educational Research Journal, 19(2), 281–305.

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Chen, G., & Chiu, M. M. (2008). Online discussion processes. Computers & Education, 50(3), 678–692. doi:10.1016/j.compedu.2006.07.007 Chen, G., Chiu, M. M., & Wang, Z. (2012a). Social metacognition and the creation of correct, new ideas: A statistical discourse analysis. Computers in Human Behavior, 28(3), 868–880. doi:10.1016/j.chb.2011.12.006

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Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment. The Internet and Higher Education, 2(2-3), 87–105. doi:10.1016/S1096-7516(00)00016-6 Gillani, N., & Eynon, R. (2014). Communication patterns in massively open online courses. The Internet and Higher Education, 23, 18–26. doi:10.1016/j.iheduc.2014.05.004

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Jonassen, D. H., & Kwon, H. I. II. (2001). Communication patterns in computer mediated and face-to-face group problem solving. Educational Technology Research and Development, 49(1), 35–51. doi:10.1007/BF02504505

Gunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). Analysis of a global online debate and the development of an interaction analysis model for examining social construction of knowledge in computer conferencing. Journal of Educational Computing Research, 17(4), 395–429. doi:10.2190/7MQV-X9UJ-C7Q3-NRAG

Kumar, R., & Kim, J. (2014). Special issue on intelligent support for learning in groups. International Journal of Artificial Intelligence in Education, 24(1), 1–7. doi:10.1007/s40593-013-0013-5

Hara, N., Bonk, C. J., & Angeli, C. (2000). Content analysis of online discussion in an applied educational psychology course. Instructional Science, 28(2), 115–152. doi:10.1023/A:1003764722829 Hew, K. F., & Cheung, W. S. (2014). Students and instructors use of massive open online courses. Educational Research Review, 12, 45–58. doi:10.1016/j.edurev.2014.05.001 Hew, K. F., Cheung, W. S., & Ng, C. S. L. (2010). Student contribution in asynchronous online discussion. Instructional Science, 38(6), 571–606. doi:10.1007/s11251-008-9087-0 Hewitt, J. (2005). Toward an understanding of how threads die in asynchronous computer conferences. Journal of the Learning Sciences, 14(4), 567–589. doi:10.1207/s15327809jls1404_4 Hiltz, S. R., & Goldman, R. (2005). Learning together online. Mahwah, NJ: Lawrence Erlbaum. Holtgraves, T. (1997). YES, BUT … Positive politeness in conversation arguments. Journal of Language and Social Psychology, 16(2), 222–239. doi:10.1177/0261927X970162006 Hsiao, I. H., & Awasthi, P. (2015). Topic facet modeling. Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 231-235). ACM.

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KEY TERMS AND DEFINITIONS Micro-Creativity: An idea that is both correct (consistent with both subject content and problem constraints) and new relative to a discussion. Multi-Threaded Discussion: A discussion in which messages proceed along multiple threads.

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Negative Social Cue: A discussant’s expressed negative affective state or negative attitude toward others. Online Discussion Message: The content that an online discussant posts at a time. Online Discussion Topic: A topic or problem that initiates a discussion. Online Discussion: A discussion in which a group of participants exchanging ideas by posting

messages on an electronic medium (e.g., online forum). Positive Social Cue: A discussant’s expressed positive affective state or positive attitude toward others. Social Relationship: The relationship between two or more participants during an online discussion, as indicated by their use of social cues. Topic Initiator: A discussant who initiates the current online discussion topic.

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The Economics of Internetization Constantine E. Passaris University of New Brunswick, Canada

INTRODUCTION Internetization is a new word and concept that has gained currency with the advent of the new global economy of the 21st century. Prior to the ascendance of Internetization economists had embraced the term globalization to describe the operational parameters of the new economy. The problem with the word globalization is that it is neither a new concept nor is it an appropriate descriptive for the contemporary transformational change precipitated by the spectacular technological inventions on the international economic landscape. Globalization does not portray the enabling powers of contemporary technology. This electronic capacity has empowered the information technology revolution which is a defining feature of the new global economy. Globalization is a throwback to a previous age prior to electronic connectivity and with more limited means of information accessibility and rapid communication. The new word, Internetization, describes more succinctly the transformative powers of the worldwide-web and the electronic information high way on the evolving dynamics of interconnectivity for the new global economy of the 21st century. Indeed, Internetization captures the pervasive influence of technological change and electronic innovations on the global economy and all aspects of human endeavour for our civil society in the 21st century.

BACKGROUND Globalization is not a modern concept or a new theoretical construct. Indeed, it has been a constant feature of international economic outreach since

time immemorial. Globalization has evolved and mutated over the centuries to reflect the priorities and ambitions of different generations. The global outreach of nations for geopolitical, economic, military and trade benefits has transgressed the centuries and embraced almost every country in the world (Erlichman, 2013). From time immemorial, the process of globalization has taken different forms and proceeded in different directions. Through the discovery and exploitation of new found lands, through the military conquest and annexation of adjacent territories and through the signing of contemporary multilateral free trade agreements, the process of globalization has been an uninterrupted continuum in the evolving history of humankind. Examples of globalization in ancient history include the seafaring voyages of Odysseus recorded by Homer in The Odyssey. The Babylonian Empire that stretched over Mesopotamia in western Asia between the rivers Tigris and Euphrates from 1894 BC to 1595 BC, and again from 625 BC to 539 BC when its grasp reached as far as Palestine. The conquests of Alexander the Great (356 BC to 323 BC) forged an empire that included parts of Europe, Africa, and the Asian continent as far as India. In the late 3rd century BC, the Romans began their conquest of the Balkan Peninsula in search of iron, copper, precious metals, agricultural crops and slaves. This marked the beginning of the Roman Empire, which lasted from 27 BC until 476 AD, and blended unity and diversity across Sicily, Spain, Macedonia, Greece, Egypt, North Africa, Syria, parts of Asia Minor, Gaul and Britain. The Byzantine Empire lasted from 395 AD to 1453 AD and spanned the Middle East, North Africa and Spain. The British Empire from 1583 AD to 1931 AD included such a large collection

DOI: 10.4018/978-1-5225-2255-3.ch694 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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of countries around the world that it sparked the familiar phrase “the sun never sets on the British Empire” (Passaris, 2006a). This short and selective geographical survey of the history of globalization attests to the permanence of humankind’s international ambitions. The steady progression of globalization has found expression in the geopolitical and economic ambitions of military, economic and political superpowers. Their globalization ambitions have been achieved by means of wars, mercantilism, colonization, political and economic supremacy, and more recently, through international economic liaisons and multilateral trade agreements.

NEW ECONOMY The new global economy of the 21st century has transformed the economic, social, educational and political landscape in a profound and indelible manner. Never before in human history has the pace of structural change been more pervasive, rapid and global in its context. The new economy has become a catalyst for geopolitical symbiosis, economic integration and financial interconnectedness. The new economy is composed of a trilogy of interactive forces that include globalization, trade liberalization and the information technology and communications revolution. Globalization has melted national borders and redefined economic policy. Free trade has enhanced economic integration and extended the economic architecture. The information and communications revolution has made geography and time irrelevant and enhanced the reach of economic parameters (Passaris, 2015). The advent of the new economy has resulted in the fundamental restructuring of economic society. Electronic interconnectedness is the glue that holds the contemporary global economy together. The new economy is built on a culture of innovation and an emphasis on creativity. Indeed, the signature mark of the new global economy is new ideas, new technologies and new initiatives.

ECONOMIC GLOBALIZATION

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A working definition of economic globalization in its contemporary form can be summarized as the global integration of economies through trade and investment flows as well as the internationalization of the production of goods and services. The economic profile of globalization includes the development of global corporations and global networks. It also includes the widespread internationalization of all forms of economic activity in production, marketing, consumption, capital, standards and tastes. Economic governance has resulted in a rapid growth in intra-firm and intranetwork trade of components and sub-assemblies as well as finished products leading to a much higher level of specialization (Rodrik, 2008). Globalization has become the catalyst for the reorientation of large-scale production in high wage economies from economies of scale to economies of scope. It has contributed to the shortening of product cycles, placing a high premium on innovation, product quality and niche marketing. Globalization has also witnessed the rapid growth and diffusion of service and knowledge-intensive activities, for both products and processes (Huwart & Verdier, 2013). The process of globalization has been driven by technological change and financial liberalization and sustained by an appreciation among policy makers that an open and rules-based international trading and financial system is essential to global economic progress (Stiglitz, 2008). The new economy has become truly global in scope and substance. The free flow of capital, labour, goods and services within free trade regions, the development of new financial instruments and institutions, instantaneous access to information and communication through the new digital networks, have created a fully integrated global economic system of tremendous scope and opportunity. Economic globalization has achieved a higher level of international economic interdependence and linkages than ever before. For example, the speed and rapidity of economic and financial

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contamination across national borders during the 2008 financial crisis is a case in point.

TRADE LIBERALIZATION The second axiom of the new economy is trade liberalization. The prevailing philosophy in favour of trade liberalization is based on the export led growth model which espouses the economic benefits of exports to the national economy in the form of employment creation, income generation and as a contributor to economic growth (Wacziarg & Welch, 2008). In the contemporary context, most countries around the world have endorsed the principle and signed on to the potential economic rewards from global trade liberalization. The contemporary vision of the new global economy embraces the promotion of a free trade environment that encourages trade across national borders of goods and services, the legal transfer of intellectual property and the unregulated flow of financial capital. The economic benefits of this modern paradigm along with improvements in the international transportation network have resulted in the expansion of the range of tradable products and services and empowered the global integration of the domestic resources and the production capacity of national economies (Michaely, 2009). One of the most striking differences between the new economy and the one that preceded it is found in the magnitude and rapid movement of international capital flows. Capital account liberalization, the development of new financial instruments and the emergence of new digital technologies have created a fully integrated capital market of tremendous scope and composition. Indeed, a major force driving the growth of international trade and investment has been the liberalization of capital controls, exchange regulations and global financial transactions (Passaris, 2011a). In short, both deregulation and technological innovation are driving the modern globalization process by tearing down the barriers that

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have separated economic markets and reducing transportation and production costs. This has contributed to a greater willingness and ease to trade and invest in foreign countries.

INFORMATION TECHNOLOGY Information and communication technologies play a central role in the new global economy of the 21st century. The information technology revolution has profoundly altered the structural parameters and the modus operandi of most national economies. More specifically, information and communications technologies have altered the production function, enhanced productivity growth, facilitated the collection of data, spearheaded the transmission of ideas and extended the reach of economic and social interactions. Scientific advances and technological breakthroughs have contributed to the success of the information and communication technology in effectively shrinking the time and distance that separate economic regions and international markets around the world. The role of information and communications technology in the new economy has been pivotal. This is particularly true of the changing structure of international production. In this context, firms are integrating the production and marketing of goods and services across national borders. International economic transactions that were formerly conducted between independent entities are now being internalized within a single firm or multinational corporation. The new technological infrastructure has empowered services to be delinked from production and traded or performed remotely. In this contemporary venue the market for a growing number of internationally integrated but geographically dispersed business enterprises is global, rather than national or regional (Passaris, 2006a). Indeed, information and communications technologies have displaced the physical market with the virtual market of the Internet for business to business and business to consumer transactions.

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At the very heart of the information and communications revolution is the vital process of the commercialization of scientific discoveries and new inventions. There is no denying that the road well-travelled from invention to innovation is long and fraught with many obstacles (Jones, 2009). It is not unusual for many inventions to be left behind because of obstacles in securing the necessary financial capital or adapting an invention to the economic realities of mass production. Indeed, an invention that is the product of a new idea, extensive research and a successful laboratory controlled experiment does not guarantee that it will result in the launch of an innovation. An idea for a new product, a better product or a new process that meets all its specifications as a blueprint and results in a successful invention in a controlled environment may turn out to be an unprofitable undertaking in the world of mass production and global competition. Furthermore, in this modern era individual inventors like Graham Bell, Thomas Edison and Guglielmo Marconi who endowed us with path breaking inventions practically singlehanded, are few and far between. Inventions today are more likely to be the product of a team effort and a concerted research and development initiative of some government laboratory, academic institution or a major corporation. Economists are divided into two schools of thought regarding the process that leads to inventions. The first school subscribes to the notion that inventions are an incremental and marginal process. The second school of thought argues that some inventions are the catalyst for abrupt structural change that permeates the economic landscape in a tidal wave of production realignments and technological clustering. Regardless of what school one subscribes to, there is no denying that the great inventions that took place during the industrial revolution between 1860 and 1900 had a profound impact on economic productivity and personal lifestyle. These inventions included electricity, the internal combustion engine, radio, the telephone, phonograph, motion pictures, the chemical and pharmaceutical indus-

tries, advances in entertainment, communications, urban sanitation and travel in the form of air and motor transportation. The information revolution has also triggered a new spurt of inventions with expansive structural changes and a cluster of innovations with a far reaching economic and social impact. The list of inventions ascribed to the information revolution is still in its infancy but already it includes such significant inventions as computers, the Internet and wireless telecommunications devices. The role of innovation as a catalyst that drives the engine of economic growth is a fundamental postulate of the new global economy. Contemporary economies have recognized the important role of innovation in nation building and have devoted their resources and public policy focus towards supporting and enhancing the advancement of innovation. Accelerating the process of innovation is vital to ensuring that national economies create and sustain wealth in a swiftly changing global economy.

INTERNETIZATION The ancient Greek philosopher Heraclitus observed that there is nothing permanent except change. In this regard, recent structural changes on the economic landscape and the collateral requirements for rapid economic recalibration are becoming essential prerequisites for successfully navigating the new global economy. The shortcomings and limitations of the term globalization requires the introduction of a new word and a new conceptual framework. A new word that is an appropriate descriptor for the operational parameters of the new global economy of the 21st century. Indeed, the word globalization does not embrace the intricacies and nuances of the new world economic order. Furthermore, globalization does not project the extensive digitalization and the electronic connectivity that permeates the new global economy. The word Internetization is new to the economic

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lexicon. It was coined for the singular purpose of replacing the word globalization. Internetization captures the importance of the electronic and digital transformation that has defined the new global economy and the pervasive influence of the World Wide Web and the information super highway on all aspects of human endeavour for our society in the 21st century. The transformation of globalization into Internetization was facilitated by the creation of a digital infrastructure that enabled global outreach through electronic connectivity. The process of Internetization is not static. It is constantly evolving, mutating and transforming. The capacity for Internetization took a giant leap forward with the transformation of wired electronic technology into wireless devices. New technological frontiers have been reached through nanotechnology, Internet of Things, cloud computing and virtual networks. The word Intenetization has gained currency and visibility since it was first introduced into the economic lexicon during the first decade of the 21st century. It has become widely adopted by the academic community and has appeared in scholarly publications (Passaris, 2001; Passaris, 2006b; Kwan, 2011; Tan, 2013; Ankit, Khandelwal, Sinha and Alex, 2014; Iakobidze & Turashvili, 2014). Internetization has been used as a Twitter hashtag since 2009. Internetization has precipitated a global communications network that connects billions of people to data, machines, computers and each other. The Internetization process has extended social contact, facilitated economic liaisons, enhanced the transmission of services, disseminated knowledge and ideas and truly made the world a global village. In essence, Internetization refers to how people, businesses and cultures have increased their capacity to interact on multiple levels through revolutionary advances in digital technology. On the social front, Internetization has broken the ties of neighbourhood based on geographical proximity and replaced them with electronic communities. Similarly, family ties based on blood

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relatives have been extended through the medium of the Internet and social media. This domain now includes a broader social community of likeminded people with similar interests who socialize on Facebook, Skype. LinkedIn, Instagram and Twitter. In consequence creating a new code of civil values and communications etiquette. Internetization has had a significant influence on the scope and magnitude of the new global economy of the 21st century. Indeed, Internetization is the catalyst that binds and connects the three pillars of the new global economy and empowers the synergies that are the signature mark of the modern economic landscape. Internetization is a process that is empowered by the information and communications technology revolution in a borderless world with a tremendous capacity for virtual connectivity. In short, Internetization combines the modern version of globalization with the economic empowerment of the Internet. All in all, Internetization covers more ground, has more contemporary descriptive features and is a more comprehensive concept than globalization for the new economic landscape. This is especially the case as an ever growing number of people from around the world use the Internet as a daily means of accessing new information resources, conducting more convenient financial transactions, experiencing new means of social interaction and indulging in instantaneous means of communication. Hardly a day goes by when our individual and collective lives are not touched by some aspect of the information technology and communications revolution. From the way we shop, eat, dress, invest, travel, entertain ourselves, communicate with each other, access health care, or pay our bills. These are just a few of our routine daily functions that have been profoundly influenced by the process of Internetization. We shop on-line, we access government services on-line, we book our travel itinerary on-line, attend church services on-line, we pay our bills on-line, we read our newspapers on-line and we do our banking on-line. The electronic prefix that is appearing before an

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increasing number of our daily interactions such as e-commerce, e-mail, e-learning, e-banking and e-government is a tangible expression of the pervasive influence of the information technology revolution and the Internet (Passaris, 2006b). Governance and government services have experienced a foundational transformation as a result of Internetization. Public policy in particular has been subjected to a higher degree of transparency and accountability through the digitalization of government documents. Open government has become the new standard for modern governance (Passaris, 2011b). Furthermore, Internetization has facilitated the digital interface between the public and government services. This interface covers a broad range of services including accessing, completing and submitting forms and applications online, bidding for government contracts, acquiring motor vehicle licenses, filing income tax returns, the issuing of birth and death certificates, marriage licenses, applications for retirement benefits, filing copyright documents, applying for veterans benefits and a host of other public services. The process of Internetization describes the role of modern technological enablers and the capacity of the Internet in empowering the new global economy of the 21st century. This has been achieved through the advent of computerization, the technology of instant communication, by extending the capacity of economic outreach and by enhancing the speed for completing financial transactions. In short, the word Internetization combines a more comprehensive dimension of the modern era of global outreach and the economic empowerment of the Internet. Internetization has had a significant influence on the scope and magnitude of the new global economy of the 21st century. Electronic interconnectedness is the glue that holds the contemporary global economy together. The new economy is built on a culture of innovation and an emphasis on creativity. In effect, Internetization is a more comprehensive and contemporary descriptor of the new economic landscape. Particularly since

the public and private sectors, small, medium and large businesses, non-profit organizations and community associations rely on the Internet. The information technology revolution has profoundly altered the structural parameters and modus operandi of most national economies. Internetization has become the catalyst and the engine that drives the information and communication revolution in the context of the new economy. This takes the form of the digitilization of information and the creation of the information super highway. Furthermore, the industrial age created territorial communities whereas the information age is creating electronic communities. Internetization is multifaceted and multidimensional. This has become abundantly clear in the contemporary knowledge driven economy. At the very heart of the information technology applications for the knowledge based sector is the widespread use of computers and robotics. A collateral benefit of this transformation has been the extraordinary scale of research and development in the quest for new applications for the advances in information and communications technology. This has triggered the phenomenal growth of the software industry and related business services. Indeed, the scale of investment in computerized equipment and in the telecommunications infrastructure is unprecedented. In addition, the rapid growth of niche markets for satellite and peripheral industries supplying information and communications technology products and specialized components and services have catapulted the knowledge based sector into the leading sector of the new economy of the 21st century. The word global has taken on a new meaning since the emergence of the Internet more than three decades ago. The Internet has opened up countless opportunities for businesses and individuals worldwide. It has eliminated restrictions and borders regarding communication and interaction. It has archived an immense amount of information at our fingertips. Internetization has triggered an age of individual and collective empowerment that is unprecedented in our history

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of civilization. It provides individuals, businesses and governments with a global influence and outreach. In short, Internetization has made time and geography irrelevant. The ascendance of Internetization was propelled by the prominent role of human capital as a foundational characteristic of the new economy. The new global economy of the 21st century has given birth to a new and vibrant knowledge sector. More specifically, the knowledge based economy is fuelled by technology, human capital and research and development. This synergistic approach contributes to accelerating levels of productivity and economic performance. In short, the fuel of the new economy is technology and its currency is human capital. The product of the new economy is knowledge and its market is the virtual marketplace facilitated by the Internet.

FINANCIAL INTERNETIZATION The most pervasive impact of Intenetization on the economic landscape has been in the empowerment of electronic financing. Internetization is responsible for spearheading new economic, financial and commercial transactions. This takes the form of e-commerce, e-banking, electronic business transactions, instantaneous credit card purchases, debit cards, Internet bill payments, mobile phone financial transactions, electronic cash and the electronic transfer of capital. There is no denying that Internetization has facilitated the instantaneous convenience of digital financial transactions. In most advanced industrialized countries, the use of cash and paper currency as a medium of exchange and a means for payment has declined dramatically. All of this as a direct result of the advent of Internetization in the form of electronic connectivity and capacity. There has also been a marked decline in the public demand for cash services along with a deliberate switching of financial transactions from paper cash to digital currency.

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The economic impact of Internetization in terms of financial transactions is most discernible in Sweden. In this regard, Sweden has embraced and is acknowledged as a leader in the transformational process for switching from conventional currency into the digital medium. Sweden has become the role model for the effective application of Internetization in regard to conducting the modern era of financial transactions. Swedish banks have been particularly enthusiastic about this transformation. They have eliminated ATM cash dispensing machines and promoted the use of digital currency. More specifically, Sweden’s largest banks such as, SEB, Swedbank and Nordea Bank, have stopped manual cash-handling services in a large number of their local branches. This action is a consequence of their assessment that Swedes rely on credit cards, the Internet and mobile phones to make a significant portion of all their payments. The Swedish Bankers Association has noted that “over the past few decades, the use of paperbased payments such as giro forms, cheques and cash payments have rapidly been replaced by electronic payments of various types. As an example, the use of different kinds of cards has increased from 100 million transactions in the middle of the 1990s to 1,956 million transactions in 2011. During the same period, the use of cheques has in practice ceased. While the share of electronic giro payments, mainly online, has increased, the share of paper-based giro transactions has decreased. The share of Swedes older than 15 years, who pay their bills through an Internet bank, has increased from 9 percent in 1999 to 79 percent in 2012. In the younger age groups (aged up to 34 years), up to 96 percent pay their bills through the Internet. During the same period, the use of paper based payments, such as giro forms, diminished from almost 79 percent in 1999 to 12 percent in 2012.” (Swedish Bankers Association, 2013, p. 13). In consequence, the end of cash is a natural next step in an evolutionary process that has already led to the extinction of chequebooks.

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It is worth noting that the transformational impact of Internetization on the scope and substance of financial transactions has exposed several vulnerabilities and fault lines on the contemporary digital landscape. The element of financial risk takes the form of identity theft, digital financial fraud, unauthorized electronic currency transfers, misappropriation of payments in the course of electronic consumer to business transactions and electronic credit card theft.

CYBER SECURITY The rapid pace of technological innovation that has been the hallmark of the Internetization process has exposed the digital super highway to distinctive vulnerabilities. These take the form of hacking, malware infection, identity theft, electronic espionage, cyber terrorism and financial misappropriation. It is becoming increasingly clear that the contemporary digital safeguards, firewalls and electronic locks do not offer a high level of personal privacy and cyber protection. Indeed, cybersecurity has become one of the foremost threats and negative side effects of Internetization. In particular, lapses in cybersecurity have exposed the inherent systemic risk of Internetization in the conduct e-commerce, e-banking, e-entertainment and e-communication. On the contemporary landscape, advances in technological innovations are moving faster than the adoption of security protocols to police, control and protect digital connectivity. It is becoming increasingly prevalent on the contemporary digital network that security vulnerability in electronic devices has become systemic. This vulnerability may take the form of a successful cyber-attack that results in an invasion of privacy, access to personal emails, the remote high jacking of personal e-banking functions and the resetting of one’s passwords. Ultimately all of this results in stealing a person’s electronic identity.

The frequency of cyberespionage between countries has increased recently as a result of the availability of spy software and cyberespionage products. FinFisher, one of the best-known purveyors of spyware and cyberespionage training courses, has a clientele that includes countries and government agencies around the world. Modern cyberespionage products have the capacity to infect their targets’ computers and phones, copy messages, record conversations and even activate webcams. The international media make headlines when they report on countries using cyberespionage to gain illicit electronic access and eves drop on their national neighbors, competitors, enemies and even members of their own political alliances. The capacity of Internetization has been a great benefactor to the social media. Indeed, the impact of the social media on personal connectivity, business promotion, enhanced NGO’s networks and government transparency have been massive. In some cases it has also created a big electronic headache. For example, the emergence of WikiLeaks, an international non-profit journalistic organization, whose mission is to expose secret information, news leaks and classified government documents has created considerable public embarrassment for some governments. Electronic vulnerabilities and exposure are found in devices that do not offer protection from phishing emails, allow a backdoor access, can be remotely monitored and permit the tampering of an electronic smart security system. In consequence, there is an urgent need for a more effective partnership between government and manufacturers of electronic devices to enhance the security features of their devices in order to prevent the sophisticated hacking and the remote high jacking of those devices. The next generation of cybersecurity will require an elevated level of personal identification, safer digital locks and the creation of impenetrable electronic firewalls. It has become abundantly clear that a PIN, username and password are simply insufficient and inadequate. The new generation

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of cybersecurity will probably take the form of codified electronic locks, voice identification, finger print matching and eye retina protocols. All of this for the purpose of adopting fail safe cybersecurity systems that will protect individual identity, national security and corporate secrets in the digital age.

INTELLECTUAL PROPERTY A cornerstone of the new economy has been the emergence of spectacular inventions, mass consumption innovations and digital connectivity. A consequence of this global electronic outreach has been the vulnerability of Internetization in regard to the protection of intellectual property. Indeed, the enforcement of intellectual property rights has become increasingly porous and elusive in a virtual and electronically borderless world. The operational definition of intellectual property rights is the awarding of an economic monopoly to the inventor of an intellectual product or service. Intellectual property rights are foundational for the knowledge economy of the 21st century. In this regard, the conferring of intellectual property rights has legal stature and protection within most national jurisdictions. Examples of intellectual property rights are copyrights, patents, industrial designs and trademarks. The areas that are directly affected by intellectual property rights are music, literature, movies, different forms of artistic expression as well as mechanical, electronic and scientific inventions. Intellectual property law became increasingly more prevalent in countries around the world in the latter half of the 20th century. Intellectual property rights have two foremost objectives. First, to reward inventors for their intellectual discoveries. Second, to provide an incentive for future inventors who will push the boundaries of intellectual achievement and in turn will propel economic growth and development.

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The World Intellectual Property Organization (WIPO) was established in 1967 by treaty as an agency of the United Nations. It is worth noting that the WIPO Intellectual Property Handbook articulates the raison d’etre of property laws in this manner: “One is to give statutory expression to the moral and economic rights of creators in their creations and the rights of the public access to those creations. The second, is to promote, as a deliberate act of Government policy, creativity and the dissemination and application of its results and to encourage fair trading which would contribute to economic and social development.” (WIPO, 2015). While the protection and enforcement of intellectual property rights prevails in most national jurisdictions, its enforceability across national borders is more problematic. There is no denying that Internetization has diluted the protection and enforcement of intellectual property rights. This is especially the case where the advancement of scientific knowledge has spawned new industries in the fields of biotechnology and nanotechnology and the originators of these new technologies have sought intellectual property protection. On the contemporary digital landscape, electronic search engines like Kazaa and Gnutella represent a challenge for copyright policy. The Recording Industry Association of America has been at the forefront of the battle against electronic piracy and the enforcement of copyright laws. Modern legislation such as the Digital Millennium Copyright Act in the USA is an example of national attempts to thwart the consequences of malicious software and to enforce digital rights. Despite the contemporary legal protection and enforcement of intellectual rights, in 2011, trade in counterfeit copyrighted and trademarked products was a $600 billion industry on an international scale and accounted for up to 7% of global trade (Bitton, 2012).

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REDISCOVERING SCHUMPETER The ascendance of the new economy requires a rediscovery of Schumpeterian theories. Schumpeterian theories have the potential of linking microeconomic derivatives to the macroeconomic postulates for economic growth and development. Indeed, Schumpeter’s intellectual and theoretical legacy on the pivotal role of entrepreneurship and innovation is a vibrant analysis and laudable framework for determining the causal factors that promote economic prosperity and contribute to the wealth of nations in the 21st century (Passaris, 2001). Schumpeter emphasized the predominance of sectoral economic analysis and the paramount importance of the entrepreneur as a catalyst for innovation and as the engine that drives economic growth and development. In this microeconomic scenario, innovation in the Schumpeterian model consisted of new products, new processes, new qualities of products, new sources of supply and new forms of business and industry organization. Essentially, it is a “process of industrial mutation.... that incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one” (Schumpeter, 1942, p. 79). It is in this context that he gives birth to his most famous phrase- “creative destruction” by which he means the replacement of old products, old enterprises and old organizational forms by new ones. The process of Internetization and its consequential impact on the new economy is a stellar example of the modern relevance of Schumpeterian theories. Economic growth can only be sustained by finding new and better ways to utilize our limited and finite resources. In this respect, technological progress has always been an ingrained feature of national economies. Schumpeter’s explanatory model for economic growth is focused primarily on the role of technological innovation. He proposed a model that postulated growth through the interaction of bursts of technological development

and competition between firms. Schumpeter saw capitalism as moving in long waves; every 50 years or so, technological revolutions would cause “gales of creative destruction” in which old industries would be swept away and replaced by new ones. Each wave of technology would fuel an upsurge in investment and create an impressive amount of job opportunities in new industries (Schumpeter, 1935). It is worth noting that the duration of the innovation cycle appears to be contracting over time from a 50 to 60 year duration to a shorter 30 to 40 year period. What has changed in recent times is the more rapid pace of technological innovation. The role of the entrepreneur in the progression of the technological cycles was paramount. Schumpeter explains that “the function of entrepreneurs is to reform or revolutionize the pattern of production by exploiting an invention or, more generally an untried technological possibility for producing a new commodity or producing an old one in a new way, by opening up a new source of supply of materials or a new outlet for products, by reorganizing an industry and so on.” (Schumpeter, 1942, p. 132). Internetization is a vivid example of what Schumpeter describes as the process of creative destruction. In this context, Internetization has replaced old technologies with new and improved technologies. This is the process by which wealth and economic growth are achieved. It is also the process by which entrepreneurship creates its most important economic impact in the form of new investment, job creation and economic prosperity. In short, Internetization is the 21st century wave of Schumpeter’s innovation cycles and provides contemporary confirmation that the technology life cycle is contracting over time. The extrapolation of Schumpeter’s microeconomic theories has the potential to form the theoretical construct for linking microeconomics with macroeconomics in the context of the new global economy of the 21st century. In this context, innovation and technological progress are an inherently microeconomic phenomenon, which are in turn a

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consequence of optimum resource allocation and the profit oriented pursuits of economic activity. However, special mention should be made of the fact that they also result in the Schumpeterian waves of innovation and technological progress that have long term macroeconomic impact and consequences (Passaris, 2003). In its microeconomic constructs, Schumpeter’s concept of creative destruction embraces a multitude of features. These include innovation and obsolescence, the rise and fall of products and processes, entrepreneurial vision, risk and uncertainty, short term market advantage and abnormal profits. All of these microeconomic antecedents resonate with the process of Internetization and the creation of avant garde innovations. They also lead to positive long term macroeconomic consequences in the form of economic growth and development.

CONCLUSION The advent of the new global economy of the 21st century has been empowered by momentous advances in technological innovation and the unprecedented outreach through digital capacity. In consequence, a new word, Internetization, is a more appropriate portrayal of the contemporary economic system with its virtual connectivity. Internetization is the catalyst for transformative change on the modern economic landscape. It has precipitated unprecedented structural changes and redefined economic linkages on a global scale. Indeed, Internetization is playing a central role in the new global economy of the 21st century. The economic profile of the new global economy is driven by technology, fueled by innovation and based on new ideas, new perspectives and new business strategies. Internetization is a process that is empowered by the information and communications technology revolution in a borderless world with a tremendous capacity for virtual connectivity.

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FUTURE RESEARCH DIRECTIONS The economic landscape of the 21st century is changing rapidly. Transformational change is the signature mark of the dynamic internetization process. Indeed, the impact of Internetization is not static; it is evolving and pervasive. Technological innovations are being introduced at record speed. The foundational structural changes that Internetization has precipitated have also triggered several intellectual fault lines that require focused research efforts in the near future. Foremost among future research endeavors is the causal relationship between Internetization and the contemporary face of structural unemployment. Workers are being replaced as a result of technological innovations. Furthermore, they are having difficulty re-entering the workforce and transitioning to new employment opportunities. During the Industrial Revolution workers who were replaced by machines found work in the factories that made the machines. In the contemporary phase, chronic structural unemployment appears to have become a negative side effect of the Internetization process. The reason for the stubborn resistance of worker re-deployment during the Information Age is that the new jobs have raised the bar with respect to educational requirements, technical expertise and modern workplace competencies. This effectively renders the technologically unemployed ineligible for workplace re-integration. Future research should also be directed towards ascertaining the scope of the evolving and pervasive nature of Internetization. The impact of technological advances and the consequences of electronic connectivity are both diverse and diffused. Indeed, the interactive nature of governance, economy and society in the context of a virtually interconnected world is a worthwhile research undertaking. This research endeavor will require an interdisciplinary and multidisciplinary approach. For example, the impact of Internetization has not been limited to the economic landscape, but has

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also affected the political and governance process, the social values and communications etiquette of the new Information Age. Another area for future research is in the domain of economic development. The hypothesis that requires analytical examination is the extent to which developing countries have benefited from Internetization. This research endeavor should explore whether Internetization is the great economic equalizer that has created a level playing field for economic development for both developed and developing countries. Alternatively, this research project will expose the extent to which developing countries have been adversely affected as a result of the lack of digital infrastructure and the cost of electronic connectivity. In consequence, this research endeavor will answer the question if Internetization has widened the gap between developed and developing countries or bridged the divide between developing and developed countries in regard to economic growth and individual prosperity.

REFERENCES Ankit, A. P., Khandelwal, A., Sinha, A., & Alex, S. A. (2014). Epidemic Analysis (Web Application for Epidemic Analysis and Prediction). International Journal of Scientific & Engineering Research, 5(4), 900–904. Retrieved from http://www.ijser.org/paper/Epidemic-AnalysisWeb-Application-for-Epidemic-Analysis-andPrediction.html Bitton, M. (2012). Rethinking the Anti-Counterfeiting Trade Agreement’s Criminal Copyright Enforcement Measures. The Journal of Criminal Law & Criminology, 102(1), 67–117. Erlichman, H. J. (2013). Conquest, Tribute and Trade: The Quest for Precious Metals and the Birth of Globalization. Amherst, N.Y.: Prometheus Books.

Huwart. J-Y. & Verdier, L. (2013). Economic Globalization: Origins and Consequences. Paris: OECD. Retrieved from http:// www.oecd-ilibrary.org/economics/economicglobalisation_9789264111905-en Iakobidze, T., & Turshvili, T. (2014). Internet Freedom in Georgia, Tbilisi,Georgia: Institute for Development of Freedom of Information. Retrieved November 7, 2015 from https://idfi.ge/ public/upload/pdf/Internet-Freedom-Report-3-4. pdf Jones, B. F. (2009). The Burden of Knowledge and the Death of the Renaissance Man: Is Innovation Getting Harder? The Review of Economic Studies, 76(1), 283–317. doi:10.1111/j.1467937X.2008.00531.x Kwan, L. H. (2011). Competing Globally with Cost Accounting. University of Tennessee Honors Thesis Projects. Retrieved from http://trace.tennessee.edu/cgi/viewcontent. cgi?article=2417&context=utk_chanhonoproj Michaely, M. (2009). Trade Liberalization and Trade Preferences. Hackensack, N.J.: World Scientific. doi:10.1142/6918 Passaris, C. E. (2001). Schumpeter’s Legacy of Technological Innovation in the Context of the Twenty-First Century. In S. B. Dahiya (Ed.), Economic Theory in the Light of Schumpeter’s Scientific Heritage (pp. 349–361). Rohtak: Spellbound Publications. Passaris, C. E. (2003). Schumpeter and Globalization: Innovation and Entrepreneurship in the New Economy. Italy: International Institute of Advanced Economic and Social Studies. Passaris, C. E. (2006a). The Business of Globalization and the Globalization of Business. Journal of Comparative International Management, 9(1), 3–18.

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Passaris, C. E. (2006b). Immigration and Digital Government. In A.-V. Anttiroiko & M. Malkia (Eds.), The Encyclopaedia of Digital Government (pp. 988–993). London: Idea Group Reference; doi:10.4018/978-1-59140-789-8.ch148 Passaris, C. E. (2011a). Redesigning Financial Governance for the New Global Economy of the 21st Century. Journal of Comparative International Management, 14(1), 1–15. Passaris, C. E. (2011b). Economic Governance and Full Employment. In M. Ugur & D. Sutherland (Eds.), Does Economic Governance Matter (pp. 168–183). Cheltenham, U.K & Northampton, Mass: Edward Elgar Publishing. doi:10.4337/9780857931771.00014 Passaris, C. E. (2015). A New Economic Governance Model for Greece in the 21st Century. Journal of Heterodox Economics, 1(3), 163–190. Rodrik, D. (2008). One Economics, Many Recipes: Globalization, Institutions, and Economic Growth. Princeton, N.J: Princeton University Press. Schumpeter, J. A. (1935). The Analysis of Economic Change. The Review of Economics and Statistics, 17(4), 2–10. doi:10.2307/1927845 Schumpeter, J. A. (1942). Capitalism, Socialism and Democracy. London: Allen and Unwin. Stighitz, J. E. (2008). Making Globalization Work. The Economic and Social Review, 39(3), 171–190. Swedish Bankers Association. (2013). Banks in Sweden. Retrieved from http://www.swedishbankers. se/web/bfmm.nsf/lupGraphics/1302Banker%20 i%20Sverige-ENG. pdf/$file/1302Banker%20 i%20Sverige-ENG.pdf Tan, S. G. (2013). Internet Economics: A Regression Analysis on the Factors that Affect the Growth Rate of Internet Usage Around the World. Retrieved from http://www.academia.edu/5626689/ ECONMET_Paper

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Wacziarg, R., & Welch, K. H. (2008). Trade Liberalization and Growth: New Evidence. The World Bank Economic Review, 22(2), 187–231. doi:10.1093/wber/lhn007 WIPO. (2015). Intellectual Property Handbook: Policy, Law and Use. Retrieved from http://www. wipo.int/about-ip/en/iprm/

ADDITIONAL READING Abouzeedan, A., & Busler, M. (2007). Internetization Management: The Way to Run the Strategic Alliances in the E-globalization Age. Global Business Review, 8(2), 303–321. doi:10.1177/097215090700800208 Acocella, N. (2005). Economic Policy in the Age of Globalisation. Cambridge: Cambridge University Press. doi:10.1017/CBO9780511753947 Bhagwati, J. (2008). Termites in the Trading System: How Preferential Agreements Undermine Free Trade. Oxford: Oxford University Press; doi:10.1093/acprof:o so/9780195331653.001.0001 Canada. (2014). Canada’s Cybersecurity Strategy. Ottawa: Public safety Canada, Government of Canada. Retrieved from http://www.publicsafety. gc.ca/cnt/rsrcs/pblctns/cbr-scrt-strtgy/index-eng. aspx Castells, M. (2010). The Rise of the Network Society. Malden, MA, USA: Wiley-Blackwell Publishers. Chesbrough, H. W. (2006). Open Business Models: How to thrive in the New Innovation Landscape. Boston, USA: Harvard Business School Press. Etemad, H., Wilkinson, I., & Dana, L. P. (2010). Internetization as the Necessary Condition for Internationalization in the Newly Emerging Economy. Journal of International Entrepreneurship, 8(4), 319–342. doi:10.1007/s10843-010-0062-x

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Farah, P. D., & Tremolada, R. (2014). Intellectual Property Rights, Human Rights and Intangible Cultural Heritage. Journal of Intellectual Property Law, 2(I), 21–47. Fortunati, L. (2005). Mediatization of the Net and Internetization of the Mass Media. International Communication Gazette, 67(1), 27–44. doi:10.1177/0016549205049177 Freeman, C., & Louca, F. (2001). As Time Goes by: The Information Revolution and the Industrial Revolutions in Historical Perspective. Oxford: Oxford University Press.

Sassen, S. (2000). Cities in a World Economy. Los Angeles, USA: SAGE Publications. Schechter, R. E., & Thomas, J. R. (2003). Intellectual Property: The Law of Copyrights, Patents and Trademarks. New York: West/Wadsworth. Smith, C. (2010). International Trade and Globalisation. Stocksfield: Anforme. Stiglitz, J. E. (2002). Globalization and Its Discontents. New York: W.W. Norton. Trott, P. (2008). Innovation Management and New Product Development. London: Financial Times Prentice Hall.

Grossman, G. M., & Helpman, E. (2015). Globalization and Growth. The American Economic Review, 105(5), 100–104. doi:10.1257/ aer.p20151068

Wilson, E. J. (2004). The Information Revolution and Developing Countries. Massachusetts, USA: Massachusetts Institute of Technology.

Hamel, G. (2002). Leading the Revolution: How to Thrive in Turbulent Times by Making Innovation a Way of Life. New York, USA: Plume Books.

KEY TERMS AND DEFINITIONS

Irwin, D. A. (1996). Against the Tide: An Intellectual History of Free Trade. Princeton, NJ: Princeton University Press. Irwin, D. A. (2009). Free Trade Under Fire. Princeton, NJ: Princeton University Press. Jones, A. (2010). Globalization: Key Thinkers. Cambridge: Polity Press, John Wiley & Sons. LeClair, M. S. (2002). Fighting the Tide: Alternative Trade Organizations in the Era of Global Free Trade. World Development, 30(6), 949–958. doi:10.1016/S0305-750X(02)00017-7 Osle, R. D. (2010). The New Global Law. Cambridge: Cambridge University Press. Osterhammel, J., & Niels, P. P. (2005). Globalization: A Short History. Princeton, NJ, USA: Princeton University Press. Potrafke, N. (2015). The Evidence on Globalisation. World Economy, 38(3), 509–552. doi:10.1111/twec.12174

Economic Globalization: The global integration of economies through trade and investment flows as well as the internationalization of the production of goods and services. Financial Internetization: Internetization is responsible for spearheading new economic, financial and commercial transactions such as ebanking, e-commerce, instantaneous credit card purchases, debit cards, Internet bill payments, mobile phone financial transactions, digital cash and the electronic transfer of capital. Globalization: A traditional concept that embraces international outreach and global linkages. Internetization: A new word and concept that describes the empowerment of electronic connectivity on the new global economy. IT Revolution: The information technology revolution has served as a catalyst for electronic connectivity, altered the production function, enhanced productivity growth, facilitated the collection of data, spearheaded the transmission of ideas and extended the reach of economic and social interactions.

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Joseph A. Schumpeter: A 20th century economist who postulated the importance of entrepreneurship and innovation as the foundations for wealth creation. New Global Economy: Describes the 21st century economy that is composed of a trilogy of interactive features that include globalization,

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trade liberalization and the information technology and communication revolution. Trade Liberalization: Also known as free trade encourages trade across national borders of goods and services, the legal transfer of intellectual property and the unregulated flow of financial capital.

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An Efficient and Effective Index Structure for Query Evaluation in Search Engines Yangjun Chen University of Winnipeg, Canada

INTRODUCTION Indexing the Web for fast keyword search is among the most challenging applications for scalable data management. In the past several decades, different indexing methods have been developed to speed up text search, such as inverted files, signature files and signature trees for indexing texts (Anh and Moffat, 2005; Chen et al., 2004; Chen et al. 2006; Faloutsos, 1985; Faloutsos et al., 1988); and suffix trees and tries (Knuth, 1975) for string matching. Especially, different variants of inverted files have been used by the Web search engines to find pages satisfying a query (Arasu, 2001; Lemple et al., 2003). A text database can be roughly viewed as a collection of documents and each document is stored as a list of words. Over the documents, there are two kinds of Boolean queries, that is, queries that can be constructed from query terms by conjunction (∧) or disjunction (∨). A document D is an answer to a conjunctive query w1 ∧ w2 ∧ … ∧ wk if it contains every wi for 1 ≤ i ≤ k while D is an answer to a disjunctive query w1 ∨ w2 ∨ … ∨ wl if it contains any wi for 1 ≤ i ≤ l. Conjunction and disjunction can be nested to arbitrary depth, but can always be transformed to a conjunctive normal form: (w11. … ∨ w1l .) … ∧ (wk1. … ∨ wkl .) 1

k

In this chapter, we discuss a new method to evaluate both conjunctive and disjunctive queries by decomposing an inverted list into a collection of disjoint sub-lists. The decomposition is based

on the construction of a trie structure T over documents and then associating each document word with an interval sequence generated by labeling T by using a kind of tree encoding. With this method, we can improve the efficiency of traditional methods by an order of magnitude or more.

BACKGROUND In order to efficiently evaluate such queries, indexes need to be established. It is well known that English texts typically contain many different variants of basic words, by using variant word endings such as ‘ing’, ‘ed’, ‘ses’, and ‘ation’. All the variants of a word should be regarded as a match and therefore it is efficient for an index only include these basic words, or say, stems. Different algorithms have been developed to extract stems from documents. Among them, the algorithm proposed by Lovins (1968) is widely used. By the signature file, a word is hashed to a bit string (called a signature) and all the words’ signatures of a document are superimposed (bitwise OR operation) into a document signature. When a query arrives, its signature will be created using the same hash function and the document signatures are scanned and many nonqualifying documents are discarded. The rest are either checked (so that the ‘false drops’ are removed) or they are returned to the user as they are. The main disadvantage of this method is the false drop (Kitagawa et al., 1997), which needs extra time to check. The signature file is greatly improved by the so-called signature tree (Chen et al. 2006),

DOI: 10.4018/978-1-5225-2255-3.ch695 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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by which a set of signatures is organized into a binary tree structure and a sequential search of signatures is replaced with a search of binary trees. However, signature-based methods can be used only for evaluating conjunctive queries. For disjunctive queries, they are not efficient. As pointed out by many researchers (Anh et al., 2005; Ao et al., 2011; Zobel et al., 2006), the inverted file is a more competitive indexing method than signature-based approaches. It is extensively used by different web search engines due to its efficiency and simplicity. Structurally, it contains two parts: a search structure or vocabulary, containing all the distinct words to be indexed, and a set of inverted lists with each constructed for a distinct word w, storing the identifiers of all those documents containing w. Queries are evaluated by fetching the inverted lists for the query terms, and then intersecting them for conjunctive queries, or merging them (by a set union operation) for disjunctive queries. According to (Zobel et al., 1998), the inverted file is superior to the signature file in almost every respect, including functionality, query time, and space overhead. Since it was first proposed in mid-1960s, the inverted file has been adopted in information retrieval, database systems, distributed systems (Büttcher et al., 2005; Camel et al., 2001), and different search engines. Also, much effort has been spent on the improvement of its performance by using integer coding (Golomb, 1966), bitmap compression (Apaydin et al., 2006; Bjørklund et al., 2009), caching (Saraiva et al., 2001), and parallelism (Ao et al., 2011). For a static environment, the bitmap compression is most efficient. However, it is obviously not suitable for a dynamical environment like the Web. So, different set intersection algorithms have been proposed to directly manipulate sorted arrays (Barbay et al., 2009), with caching (Barbay et al., 2009; Saraiva et al., 2001), parallelism (Ao et al., 2011), interpolation (Demaine et al., 2004), and even hardware characteristics (Tsirogiannis et al., 2009) being utilized to enhance performance. The method discussed in (Ding et al., 2011) is an in-

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memory algorithm for set intersection, by which an inverted list is partitioned into a collection of sublists. Then, generate a hash image for each of them, by which a number in an inverted list is mapped to a single bit in an image. In this way, a set intersection is done by a series of hash value intersections.

INDEX STRUCTURES In this section, we mainly discuss our index structure, by which each word with high frequency will be assigned an interval sequence. We will then associate intervals, instead of words, with inverted sub-lists. To clarify this mechanism, we will first discuss interval sequences for words in Subsection 1. Then, in Subsection 2, how to associate inverted lists with intervals will be addressed.

1. Interval Sequences Assigned to Words Let D = {D1,..., Dn} be a set of documents. Let Wi = {wi1, …, wij }i = 1, …, n) be all of the words i

appearing in D i , to be indexed. Denote n

W = ∪i =1Wi , called the vocabulary. We define the word appearance frequency by the following formula: f(w) =

num. of documents containing w num. of documents

, (w

∈ W). We then define a frequency threshold ζ. For any word w with f(w) < ζ, we will associate it with an inverted list in a normal way, denoted as δ(w), exactly as in the method of inverted files. However, for all those with f(w) ≥ ζ, we will create a new index. For this, we will represent each Di as a sequence containing all those words w with f(w) ≥ ζ, decreasingly sorted by f(w). That is, in such a sequence, a word w precedes another w′ if w is more frequent than w′ in all documents.

Category: Web Technologies

Table 1. Documents and word sequences DocID

Words

Sorted Word Sequence

1

c, a, f, m, p

c, f, a, m, p

2

c, f, b, a

c, f, a, b

3

b, a, c, d

c, a, d, b

4

f, d, p, m

f, d, m, p

In addition, for any subset of words that have the same appearance frequency a global ordering is defined so that in each sorted word sequence this global ordering is followed. In addition, we maintain a hash table H that maps each word w to its inverted list δ(w) or to its new index. Example 1: In Table 1, we show a set of four documents, their words w with f(w) ≥ ζ = 0.4, and the corresponding sorted word sequences, where we use a character to represent a word for simplicity. Notice that the global order on {f, a, c} (with f(w) = 0.75) is set to be c → f → a while the global order on {m, b, p, d} (with f(w) = 0.5) is d → b → m → p. In practice, however, we can adjust ζ to include more words in the new index; and in the extreme case, all words can be included. For each document Di (i = 1, …, n), we will use si to represent its sorted word sequence. Over all such sequences S = {s1, …, sn}, we will construct a digit tree, called a trie, as follows. Assume that W = {w1, …, wm}. If |S| = 0, the trie is, of course, empty. For |S| = 1, trie(S) is a single node. If |S| > 1, S is split into m (possibly empty) subsets S1, S2, …, Sm so that a string is in Sj if its first word is wj (1 ≤ j ≤ m). The tries trie(S1), trie(S2), …, trie(Sn) are constructed in the same way except that at the kth step, the splitting of sets is based on the kth words in the sequences. They are then connected from their respective roots to a single node to create trie(S). In Figure 1, we show a trie T constructed over the sorted word sequences in Table 1.

In the trie, v0 is a virtual root, labeled with an empty word ε while any other node is labeled with a real word. Therefore, all the words on a path from the root to a leaf spell a sorted word sequence for a certain document. For instance, the path from v0 to v13 corresponds to the sequence: c, f, a, m, p. Then, to check whether two words w1 and w2 are in the same document, we need only to check whether there exist two nodes v1 and v2 such that v1 is labeled with w1, v2 with w2, and v1 and v2 are on the same path. This shows that the reachability needs to be checked for this task, by which we ask whether a node v can reach another node u through a path. If it is the case, we denote it as v ⇒u; otherwise, we denote it as v ⇏u. The reachability problem on tries can be solved very efficiently by using a kind of tree encoding, which labels each node v in a trie with an interval Iv = [αv, βv], where βv denotes the rank of v in a post-order traversal of the trie. Here the ranks are assumed to begin with 1, and all the children of a node are assumed to be ordered and fixed during the traversal. Furthermore, αv denotes the lowest rank for any node u in T[v] (the subtree rooted at v, including v). Thus, for any node u in T[v], we have Iu ⊆ Iv since the post-order traversal enters a node before all of its children, and leaves after having visited all of its children. In Figure 1, we also show such a tree encoding on the trie, assuming that the children are ordered from left to right. It is easy to see that by interval containment we can check whether two nodes are on a same path. For example, v3 ⇒v10, since I v 3

= [1, 5], I v = [3, 3], and [3, 3] ⊂ [1, 6]; but v2 10

⇏v9, since I v = [10, 13], I v = [1, 2], and [1, 2] 2

9

⊄ [10, 13]. Let I = [α, β] be an interval. We will refer to α and β as I[1] and I[2], respectively. Lemma 1: For any two intervals I and I′ generated for two nodes in a trie, one of four relations holds: I ⊂ I′, I′ ⊂ I, I[2] < I′[1], or I′[2] < I[1]. Proof: The lemma can be directly derived from the definition of intervals.

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Figure 1. A trie

However, more than one node may be labeled with the same word, such as nodes v9, and v8 in Figure 1. Both are labeled with word m. Therefore, a word may be associated with more than one node (or say, more than one node’s interval). Thus, to know whether two words are in the same document, multiple checking may be needed. For example, to check whether p and d are in the same document, we need to check v13 and v12 each against both v7 and v5, by using the node’s intervals. In order to minimize such checks, we associate each word w with a word sequence of the form: Lw = I w1 , I w2 , …, I wk , where k is the number of all

those nodes labeled with w and each I wi = [ I wi [1],

I wi [2]] (1 ≤ i ≤ k) is an interval associated with a certain node labeled with w. In addition, we can sort Lw by the interval’s first value such that for 1 ≤ i < j ≤ k we have Liw [1] < Lwj [1], which will greatly reduce the time for the reachability checking. We illustrate this in Figure 2, in which each word in Table 1 is associated with an interval sequence. From this figure, we can see that for any two intervals I and I′ in Lw we must have I ⊄ I′, and I′ ⊄ I since in any trie no two nodes on a path are labeled with the same word.

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Finally, we point out that constructing a trie as above can be used as a general method for document classification. To see this, let’s have a look at an example shown in Figure 3, which is part of an animal classification. 1, 9   f : 1, 5 10, 13 a : 1, 4 6, 8 d : 6, 7  10, 12 b : 3, 3 6, 6 m : 1, 2 10, 11 p : 1, 1 10, 10 c:

However, it is also a trie established using our method by checking the first several words in the corresponding document word sequences, where animal has the highest frequency while mammalia, birds, fish, and amphibians are less frequent.

2. Assignment of DocIDs to Intervals Another important component of our index is to assign document identifiers to intervals. An interval I can be considered as a representative of

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Figure 2. Sorted interval sequences

some words, i.e., all those words appearing on a prefix in the trie, which is a path P from the root to a certain node that is labeled with I. Then, the document identifiers assigned to I should be those containing all the words on P. For example, the words appearing on the prefix: v1 → v3 → v6 in the trie shown in Figure 1 are words c, f, and a, represented by the interval [1, 4] associated with v6. So, the document identifiers assigned to [1, 4] should be {1, 2}, indicating that both documents D1 and D2 contain those three words. See the trie shown in Figure 4 for illustration, in which each node v is assigned a set of document identifiers that is also considered to be the set assigned to the interval associated with v. Let v be the ending node of a prefix P, labeled with I. We will use δ(I), interchangeably δ(v), to

represent the set of document identifiers containing the words appearing on P. Thus, we have δ(v6) = δ([1, 4]) = {1, 2}. Lemma 2: Let u and v be two nodes in a trie T. If u and v are not on the same path in T, then δ(u) and δ(v) are disjoint, i.e., δ(u) ∩ δ(v) = Φ. Proof: It is easy to prove. Proposition 1: Assume that v1, v2, …, vj be all the nodes labeled with the same word w in T. Then, δ(w), the inverted list of w (i.e., the list of all the documents identifiers containing w) is equal to δ(v1) ⊎δ(v2) ⊎… ⊎δ(vj), where ⊎represents disjoint union over disjoint sets that have no elements in common. Proof: Obviously, δ(w) is equal to δ(v1) ∪ δ(v2) ∪ … ∪ δ(vj). Since v1, v2, …, vj are labeled with the same word, they definitely appear on different paths as no nodes on a path are labeled with the same word. According to Lemma 2, δ(v1) ∪ δ(v2) ∪ … ∪ δ(vj) is equal to δ(v1) ⊎δ(v2) ⊎… ⊎δ(vj). As an example, see the nodes v2 and v3 in Fig. 5. Both are labeled with word f. So the inverted list of f is δ(v2) ⊎δ(v3) = {4} ⊎{1, 2} = {1, 2, 4}.

QUERY EVALUATION Based on the new index structure, we design our algorithms. First, in Subsection 1, we introduce

Figure 3. Illustration for document classification

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Figure 4. Illustration for assignment of document identifiers

the concept of word topological order, which is important to our query evaluation approach. Then, we present two algorithms to evaluate conjunctive and disjunctive queries in Subsections 2 and 3, respectively.

1. Word Topological Sequence As shown below, using such interval sequences, the checking of whether two words are in the same document can be done in a very efficient way. Definition 1: (Word topological order) Let S = {s1, s2, …, sn} be a set of n sorted word sequences. A word topological order over S is a sequence ϑ = w1, w2, …, wm, which contains all the words appearing in S such that for any two words w and w′ if w appears before w′ in some sj (1 ≤ j ≤ n) then w appears before w′ in ϑ, denoted as w ≺ w′. In Figure 2, the words are also listed (from top to bottom) in a word topological order with respect to the sorted word sequences given in Table 1. To find a word topological order over S = {s1, s2, …, sn} with W = {w1, …, wm}, we will transform the corresponding trie T to an acyclic directed graph

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(DAG) G by splitting the node set of T (except for the virtual root) into m groups such that all the nodes in a group are labeled with the same word, and then collapsing each group g to a single node u. There is an edge in G from u (standing for a group g) to u’ (for another group g’) if T contains (x, y) with x ∈ g and y ∈ g’. For example, the trie shown in Figure 1 will be transformed to a DAG shown in Figure 5(a). Using a hash function H on the words in W, the transformation can be done in O(|W|) time, by which all those nodes labeled with the same word w will be mapped to a single node identified by H(w). Let G(V, E) be such a DAG. It is well known that only O(|V| + |E|) time is required to find a topological order of G, which is a linear ordering of all its nodes such that if u → v ∈ E, then u appears before v in the ordering. Replacing each node in the ordering with the corresponding word, we will obtain a word topological sequence, as illustrated in Figure 5(b).

2. Evaluation of Conjunctive Queries We first consider a query containing only two words w ∧ w′ with w ≺ w′. It is easy to see that

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Figure 5. A transformed DAG

any interval in Lw cannot be contained in any interval in Lw′. Thus, to check whether w and w′ are in the same document, we need only to check whether there exist I ∈ Lw and I′ ∈ Lw′ such that I ⊃ I′. Therefore, such a query can be evaluated by running a process, denoted as conj(Lw, Lw′), to find all those intervals in Lw′ with each being contained in some interval in Lw, stored in a new sequence L. • •

Let Lw = I 21 , I w2 , …, I wk . Let Lw′ = I w1 ' , I w2 ' , …, I wk '' . L ← ϕ. Step through Lw and Lw′ from left to right. Let I wp and I wq ' be the intervals currently encountered. We will do one of the following checking:

W

◦◦

If I wp ⊃ I wq ' , append I wq ' to then end of L. Move to I wq+1 if q < k′ (then, in ' a next step, we will check I wp against I wq+1 ). '

◦◦ ◦◦

If I wp [1] > I wq ' [2], move to I wq+1 if q < ' k′. If q = k′, stop. If I wp [2] < I wq ' [1], move to I wp+1 if p < k (then, in a next step, we will check I wp+1 against I wq ' ). If p = k, stop.

Assume that L = I1, I2, …, Il (l ≤ k′). Then, for each 1 ≤ Ij ≤ l, there exists an interval I ∈ Lw such that Ij ⊂ I, and we can return δ(I1) ⊎… ⊎δ(Ik) as the result. In Figure 6, we illustrate the working process on Ld and Lm shown in Figure 2.

Figure 6. Illustration of two-word checking

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Figure 7. A merging process

Since in this process, each interval in both Lw and Lw′ is accessed only once, the time complication of this process is bounded by O(|Lw| + |Lw′|). The above approach can be easily extended to evaluate general queries of form Q = w1 ∧ w2 ∧ … ∧ wl with w1 ≺ w2 ≺ … ≺ wl and l ≥ 1 based on the transitivity of intervals: I ⊇ I′ ⊇ I′′ → I ⊇ I′′. What we need to do is to repeatedly apply conj() to the corresponding interval sequences associated with the query words one by one. Algorithm 1 is a formal description of this process. It is easy to see that the time complexity of   the algorithm is bounded by O ∑ | Lw | , but  w ∈Q can be further improved by using the binary, or galloping search, as well as the interpolation probing. Example 2: Continued with Example 1. Let Q = f ∧ m ∧ p. Then, the execution of containment*() Algorithm 1. Con-evaluation(Q) begin  1.      Q[1] ≺ 2.      3.      4.      5.      6.      end

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    let |Q| = l; assume that Q[2] ≺ … ≺ Q[l];     L:= Q[1];     for (j = 2 to l) do     { L:= conj(L, LQ[i]); }     let L = I1, …, Ik;     return δ(I1) ⊎… ⊎δ(Ik).

will find two containment sequences: I1 = [1, 5], [1, 2], [1, 1] and I2 = [10, 13], [10, 11], [10, 10]. The results is then R = δ([1, 1]) ⊎δ([10, 10]) = {1} ⊎{4} = {1, 4}.

3. Evaluation of Disjunctive Queries Similarly, we first consider a query containing only two words w ∨ w′ with w ≺ w′. For this, we define another process, denoted as disj(Lw, Lw′), to merge Lw into Lw′, by which all those intervals I from Lw will be inserted into Lw′ if there is no interval in Lw′ is contained by I. The operation can be efficiently conducted as follows. Again, we will step through Lw and Lw′ from left to right. Let I wp and I wq ' be the intervals currently encountered. We will do the following checks: if q < k′. If q = 1. If I wp ⊃ I wq ' , move to I wq+1 ' k′, stop. 2. If I wp [2] < I wq ' [1], insert I wp into Lw′ just before I wq ' . If p < k, move to I wp+1 ; otherwise (p = k), stop 3. If I wq ' [2] < I wp [1], move to I wp+1 if q < k′. If q = k′, append I wp , …, I wk to the end of Lw′ and then stop. Example 3: Continued with Example 1. Let Q = d ∨ m. We have d ≺ m. By using the above procedure to merge Lm = [1, 2][10, 11] into Ld = [6, 7][10, 12], we will get a new sequence: [1, 2][6, 7][10, 11]. So, the result is

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δ([1, 2]) ⊎δ([6, 7]) ⊎δ([10, 12]) = {1} ⊎{3} ⊎{4} = {1, 3, 4}. In the first step, we compare I d1 = [6, 7] and I m1 = [1, 2]. Since I d1 [1] = 6 > I m1 [2] = 2, I m1 will be inserted into Ld just before I d1 . Then, in the second step,

we will compare I d1 and I m2 . Since I d1 [2] = 7 < I m2 [1] = 10, we will move to I d2 . Next, in the third step, we compare I d2 and I m2 ,

and find I d2 ⊃ I m2 . Since I m2 is the last interval in Lm, we terminate the merging process and return the result. Figure 7 shows the entire merging process. By using the operation disj(Lw, Lw′), the algorithm to evaluate disjunctive queries of the form Q = w1 ∨ w2 ∨ … ∨ wl with w1 ≺ w2 ≺ … ≺ wl and l ≥ 1 can also be efficiently implemented. In Algorithm 2, we use disj() to merge LQ[i] for i = l - 1, …, 1 into LQ[l] one by one. The running time is obviously bounded by O(l⋅r), where r is the largest number of intervals in all LQ[i]’s which are not contained in each other. Again, the time requirement can be improved by using the binary search, the galloping search, and the interpolation probing.

CONCLUSION In this chapter, a new index structure is discussed. It associates each word w with a sequence of intervals, which partition the inverted list δ(w) into a set of disjoint subsets. In this way, both the intersection (for conjunctives queries) and union (for disjunctive queries) of inverted lists can be done by checking the containment of intervals. This is much more efficient than the traditional inverted file method. Also, how to maintain such an index is described in great detail. Although the index is of a more complicated structure, the cost of maintaining it in the cases of addition and deletion of documents is (theoretically) comparable to the inverted file. Extensive experiments have

Algorithm 2. Dis-evaluation(Q) begin  1.      Q[1] ≺ 2.      3.      do {  4.      5.      6.      end

    let Q[2] ≺     L:=     for

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|Q| = l; assume that … ≺ Q[l]; LQ[l]; (i = l - 1 downto 1)

    { L:= disj(LQ[i]), L) };     let L = I1, …, Ik;     return δ(I1) ⊎…⊎δ(Ik);

been conducted, which show that our method outperforms the inverted file and the signature tree by an order of magnitude or more.

FUTURE WORK As the future work, two issues need to be addressed: the binary search of interval sequences, and the use of SIMD (single instruction/multiple data) (Inoue et al., 2015) instructions. By the former, we will not scan an interval sequence linearly, but by the binary search. Especially, by using the lowest common ancestors (LCAs) of intervals (in a trie) in a binary search, the running time can be dramatically improved (Chen and Shen, 2015). By the latter, we will study how to arrange multiple processors to do the containment checking over multiple intervals simultaneously. That is, how to use SIMD instructions to do parallel checks of containments. In this way, the running time can also be significantly reduced.

REFERENCES Anh, V. N., & Moffat, A. (2005). Inverted index compression using word-aligned binary codes, Kluwer Int. Journal of Information Retrieval, 8(1), 151–166. doi:10.1023/ B:INRT.0000048490.99518.5c

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Ao, N., Zhang, F., Wu, D., Stones, D., Wang, G., Liu, X., …, Lin, S. (2011). Efficient Parallel Lists Intersection and Index Compression Algorithms using Graphics Processing Units. Proceedings of the PVLDB ‘11, Seattle, USA. Apaydin, T., Canahuate, G., Ferhatosmanoglu, H., & Saman, T. A. (2006). Approximate Encoding for Direct Access and Query Processing over Compressed Bitmaps. Proceedings of VLDB ‘06 (pp. 846–857). Arasu, A., Cho, J., Garcia-Molina, H., Paepcke, A., & Raghavan, S. (2001, August). Searching the Web. ACM Transactions on Internet Technology, 1(1), 2–43. doi:10.1145/383034.383035 Bjørklund, T. A., Grimsmo, N., Gehrke, J., & Torbjørnsen, Ø. (2009). Inverted indexes vs. bitmap indexes in decision support systems. CIKM, 2009, 1509–1512. doi:10.1145/1645953.1646158 Büttcher, S., & Clarke, C. L. A. (2005). Indexing time vs. query time trade-offs in dynamic information retrieval systems. Proc. Intl. Conf. on Information and Knowledge Management, Bremen, Germany (pp. 317-318). Carmel, D., Cohen, D., Fagin, R., Farchi, E., Hercovici, M., Maarek, Y. S., & Soffer, A. (2001). Static index pruning for information retrieval systems. Proc. 24th Annual Intl. Conf. on Research and Development information Retrieval, New Orleans, LA, USA (pp. 43-50). doi:10.1145/383952.383958 Chen, Y. (2004). Building Signature Trees into OODBs. Journal of Information Science and Engineering, 20, 275–304. Chen, Y., & Chen, Y. B. (2006, September). On the Signature Tree Construction and Analysis. IEEE TKDE, 18(9), 1207–1224. Chen, Y., & Shen, W. (2015, July 27-30) On the Set Intersection. Proceedings of the International Conference on Foundation of Computer Science, Las Vegas, Nevada, USA (pp. 1007 – 1013).

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Demaine, K. D., LÓpez-Ortiz, A. and Munro, J. I. (2000). Adaptive set intersections, unions, and differences. Proc. 11th ACM-SIAM Symposium on Discrete Algorithms, Philadelphia (pp. 743-752). Ding, B., & König, A. C. (2011). Fast set intersection in memory. Proc. of the VLDB Endowment, 4(4), 255-266. Faloutsos, C. (1985). Access Methods for Text. ACM Computing Surveys, 17(1), 49–74. doi:10.1145/4078.4080 Faloutsos, C., & Chan, R. (1988). Fast text access methods for optical and large magnetic disks: designs and performance comparison. Proc. 14th Int’l Conf. Very Large Data Bases (pp. 280-293). Golomb, S. W. (1966). Run-length encodings. IEEE Trans. Inform. Theory, 3(July), 399-401. Inoue, H., Ohara, M., & Taura, K. (2015). Faster set intersection with SIMD instructions by reducing branch mispredictions. Proc. of VLDB (pp. 293 – 304). Kitagawa, H., & Ishikawa, Y. (1997). False Drop Analysis of Set Retrieval with Signature Files. IEICE Trans. Inf. & Syst., 80-D(6). Knuth, D. E. (1975). The Art of Computer Programming (Vol. 3). Massachusetts: AddisonWesley Publish Com. Lovins, J. (1968). Development of a Stemming Algorithm. Mechanical Translation and Computational Linguistics, 11, 22–31. Saraiva, P. C. et al.. (2001). Rank-preserving twolevel caching for scalable search engines. Proc. 24th Annual Intl. Conf. on Research and Development in Information Retrieval, New Orleans, LA, USA (pp. 51-58). doi:10.1145/383952.383959 Tsirogiannis, D., Guha, S. and Koudas, N. (2009). Improving the Performance of List Intersection. Proceedings of PVLDB ‘09.

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Zobel, J., & Moffat, A. (2006, July). Inverted Files for Text Search Engines. ACM Computing Surveys, 38(2), 1–56. doi:10.1145/1132956.1132959

ADDITIONAL READING Chen, Y., & Chen, Y. B. (2008). An Efficient Algorithm for Answering Graph Reachability Queries. Proc. 24th Int. Conf. on Data Engineering (ICDE ‘08) (pp. 892-901). doi:10.1109/ ICDE.2008.4497498 Chen, Y., & Chen, Y. B. (2011) Decomposing DAGs into spanning trees: A new way to compress transitive closures. Proc. 27th Int. Conf. on Data Engineering (ICDE ‘11) (pp. 1007-1018). doi:10.1109/ICDE.2011.5767832 Deppisch, U. (1986). S-Tree: A Dynamic Balanced Signature Index for Office Retrieval. Proc. ACM SIGIR conf. (pp 77 – 87). doi:10.1145/253168.253189 Willett, P. (2006). The Porter stemming algorithm: then and now. Program: electronic library and information systems, 40(3), 219 – 233. Zobel, J., Moffat, A., & Ramamohanarao, K. (1998). Inverted Files Versus Signature Files for Text Indexing. ACM Transactions on Database Systems, 1998(4), 453–490. doi:10.1145/296854.277632

KEY TERMS AND DEFINITIONS Interval: A pair of integers [a, b] where b denotes the rank of v in a post-order traversal of a trie. Here the ranks are assumed to begin with 1, and all the children of a node are assumed to

be ordered and fixed during the traversal. In addition, a denotes the lowest rank for any node u in the subtree rooted at v. Its purpose is to check the reachability. Let [a, b] an [c, d] be the intervals associated with v and u, respectively. If [c, d] ⊂ [a, b], then u is reachable from v through a path in the tree. Inverted List: (Also referred to as postings file or inverted file) an index data structure associated with a key word w, storing a set of document identifiers, which contain w. Its purpose is to allow fast full text searches, at a cost of increased processing when a document is added to the database. Search Engine: A software used by the Internet to search data (as text or a database) for specified information; also, a sever on the World Wide Web that uses such software to locate key words in other sites. Set Disjunction: (Also called set union, denoted as A ∪ B, where A and B are two sets) the set of elements which are in A, in B, or in both A and B. Set Intersection: (Denoted as A ∩ B, where A and B are two sets) the set that contains all elements of A that also belong to B (or equivalently, all elements of B that also belong to A), but no other elements. Signature File: A set of signatures (bit strings) with each created for a document by superimposing (bitwise OR) all the word signatures. To find all the documents that contain a set of key words, we will first generate a query signature by superimposing all the query word signatures and then search the signature file to find its matching ones. Trie: (Also called digital tree and sometimes radix tree or prefix tree as they can be searched by prefixes) an ordered tree data structure that is used to store a dynamic set of strings like texts and documents.

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Improving Usability of Website Design Using W3C Guidelines G. Sreedhar Rashtriya Sanskrit Vidyapeetha (Deemed University), India

INTRODUCTION Over the last few years there has been a remarkable increase in use of the World Wide Web (WWW) for a wide and variety of purposes. There was also a fast growth in its applications. This led the Internet users to realize the importance and the benefits gained from a globally interconnected hypermedia system. On the other hand it causes a larger number of useless, meaningless and badly designed websites on the Internet world causing unwanted additional traffic; this is all because of an unorganized non-planned websites development processes. Due to the unceasing growth of web sites and applications, developers and evaluators have interesting challenges not only from the development but also from the quality assurance point of view.

BACKGROUND As we know, the quality assurance was and is one of the challenging processes in software engineering as well as for the web engineering, as a new discipline. Although there exists many design guidelines, and metrics for the evaluation of web sites and applications, most of them lack a well-defined specification framework and even worse a strategy for consultation and reuse. Some initial efforts have been recently made to classify metrics for some entity type as for example metrics for software products. Particularly, in last few years a set of web site metrics were defined and specified based on the data collection point

of view. The quality model must be able to assess the quality of each and every aspect of the website and it should cover the process of all web engineering activities. A set of guidelines are evolved to build a qualitative model of a website. According to Drefus P (1998) a guideline consists of a design and evaluation principle to be observed to get and to guarantee a usable user interface [1]. Guidelines can be found in many different formats with contents varying both in quality and level of detail, ranging from ill-structured common sense statements to formalized rules ready for automatic guidelines checking. Certain rules are validated by experimental results provided by user tests, experiments in laboratory or other techniques. Guidelines can be classified (Figure 1) by type ranging from the most general to the most specific: principles, guidelines and recommendations. Principles are general objectives guiding conceptual User Interface (UI) decisions. They reflect the knowledge around human perception, learning and behavior and are generally expressed in generic terms like “Use images and metaphors consistent with real world” so that they can be applied for a wide range of cases. Guidelines are based on principles specific to a particular design domain. For example, a web design rule can stipulate to “use a consistent look and a visual language inside the site”. Some guidelines have to be interpreted more and altered to reflect the needs of a particular organization or a design case. Recommendations determine conceptual decisions specific to a particular domain of application and should reflect the needs and the terminology of a given organization. They are unambiguous state-

DOI: 10.4018/978-1-5225-2255-3.ch696 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Figure 1. Website Guidelines and Sources

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ments so that no place for interpretation is left. Recommendations include ergonomic algorithms, user interface patterns and design rules. Design rules are functional and operational requirements specifying the design of a particular interface, e.g. “Every web page needs an informative title”. Beirekdar A et al (2002) developed a framework to define a Guideline Definition Language (GDL) to investigate quality evaluation procedure. The GDL expresses guideline information in a sufficiently rich manner so that evaluation engine can perform GDL-compliant guideline. U (p ) = = =

fkwaresmi (Web _ page,UES i, j ) EXEC (EC i, j {INST _UESi, j }) {" Re spected " | "Violated " | " Partially Re spected "} (1)

Where UESi,j be the set of evaluation sets associated to the guideline i in the source j and that will be used for the evaluation of the evaluated web page. ECi,j be the set of evaluation conditions associated to UESi,j. INST_UESi,j is the set of captured instances of UESi,j in the evaluated page. In practice, the f (Web _ page,UESi, j ) executes each ECi,j condition and then it combines the results to have the overall result for the guideline

i. We say that a web page satisfies a guideline Gi,j, if the execution of all ECi,j on all the INST_UESi,j is true. Using the above evaluation parameters allows us to define a kind of quality model to balance the evaluation result. In the accessibility field, Bobby, Valet, and EvalIris define a set of accessible evaluation tools. All these tools are based on accessibility guidelines. It does this through automatic checks as well as manual checks. It also analyzes web pages for compatibility with various browsers (eq. 3.2). Accessibility tools use a binary model to evaluate the accessibility of web pages (eq. 3.2). Accessibility errors =

guidelines

∑ i =1

ai x i

(2)

where ai is 0 when guideline is violated and 1 when guideline is not violated and xi is a guideline. A set of guidelines are considered to establish the procedure for Correctness of the Website. The World Wide Web Consortium (W3C) is an open source organizations and it defines various web standards for designing a website. The W3C is led by web inventor Tim Berners-Lee and CEO. The standards defined by W3C are considered as guidelines and these guidelines help in assessing

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Improving Usability of Website Design Using W3C Guidelines

the quality of website content in presenting the web content. The guidelines are as summarized as follows. Guideline 1: Provide a text equivalent for every non-text element. This includes images, graphical representations of text, image map regions, animations, applets and programmatic objects, frames, scripts, spaces, audio and video files. Guideline 2: Do not rely on color scheme only. The content of web page must match with foreground and background color. Also provide sufficient contrast to the content for visibility. Guideline 3: Use markup and style sheets instead of images to convey information. Style sheets controls the layout and presentation of the web page and decreases the download time of the web page. Guideline 4: Clearly mention the text information of web page with natural language. Specify the expansion of each abbreviation or acronym in the document. Guideline 5: Use tables properly in the web document. For data tables, clearly specify row and column headers and number of rows and columns exactly. Guideline 6: Ensure that web pages featuring new technologies transform gracefully. When dynamic contents are updated, ensure that content is changed. Ensure that pages are available and meaningful when scripts, applets or other programmatic objects are not supported by the browsers. If this is not possible, provide equivalent information as alternative in the web page. Guideline 7: Ensure user control of time sensitive content changes. Until user agents provide the ability to stop the refresh, do not create periodically auto-refreshing pages. Guideline 8: Ensure direct accessibility of embedded user interfaces. Make programmatic elements such as scripts and applets directly

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accessible or compatible with assistive technologies. Guideline 9: Design for device-independence. Ensure that any element that has its own interface can be operated in a device-independent manner. Guideline 10: Provide context orientation information. Title each frame to facilitate frame identification and navigation. Divide large blocks of information into more manageable groups wherever appropriate. Guideline 11: Provide clear navigation mechanisms. Clearly identify the target of each link. Provide information about the general layout of a site such as site map or table of contents. Guideline 12: Ensure that documents are clear and simple. Create a style of presentation that is consistent across pages.

METHODOLOGY In optimizing the correctness of website content different qualitative measures need to be investigated. These measures derived from the Web page errors that are generated using the W3C Validation Service. This process uses the standard web tool W3C HTML Validator to validate and identify the number of different errors according syntax errors of HTML tags, properties of web page and standards mentioned by various organizations such as W3C. Most pages on the World Wide Web are written in computer languages (such as HTML) that allow Web authors to structure text, add multimedia content, and specify what appearance or style, the result should have. As for every language, these have their own grammar, vocabulary and syntax, and every document written with these computer languages are supposed to follow these rules. Markup languages are defined in technical specifications, which generally include a formal grammar. The tool compares HTML document

Category: Web Technologies

to the defined syntax of HTML and reports any discrepancies. The outputs of the Markup Validator are a list of error messages and their interpretation W3C HTML Validator helps to ensure that documents are free of potential problems that can result in unexpected output when users view the bad documents with different browsers. A screenshot of W3C HTML Validator is shown in figure 2. The errors related to website content cause incorrect display of some components of Web pages. These errors include: 1. Table Tag Errors (TTE): All the sub tags in table tag should be properly used in the web page design. Errors in table tag cause for display problems of web page. 2. Body Tag Errors (BTE): Body Tag Errors cause the errors in displaying the contents of the web page.

3. Image Tag Errors (ITE): Image Tag Errors cause for errors in downloading the image in a website. 4. Head Tag Errors (HTE): Head Tag Errors cause for errors in displaying heading and title of the web page. 5. Font Tag Errors (FoTE): Font Tag Errors cause the errors in textual display of the web page. 6. Script Tag Errors (STE): Script Tag Errors cause the errors in programming at client side scripting. 7. Style Tag Errors (StTE): Style Tag Errors cause errors in dynamic display features of the web page. 8. Form Tag E rrors (FmTE): Form Tag Errors cause errors in input and output display of the script programming in a web page.

Figure 2. W3C HTML Validator

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9. Link Tag Errors (LTE): Link Tag Errors cause errors in linking various web components. The web content errors are occurred due to non-standards of web site design. The developer must be attentive in using HTML tags so that appropriate tags should be used in web design process. All the tags and their attributes properly set and closed accordingly. This will reduce the problem of web page display and avoids the problem in downloading of the web page. Website optimization is required in the following areas in order to improve the correctness of the website. 1. Text Presentation: Text Presentation is an important issue in display of the web content. These issues should be properly handled in presenting 100% correct text presentation. Several literature sources provide guidance about appearance of text on the web page. To format the text on web page the developer should consider following properties in text presentation. ◦◦ Fonts must be chosen among the most readable ones. ◦◦ Font size must be defined as relative size ◦◦ Use fonts designed for computer screens rather than fonts designed for print ◦◦ In a single page, the number of different fonts must be limited. ◦◦ When using different fonts and font sizes, they should have some specific meaning (e.g. notes, links, and navigation location). ◦◦ Avoid italicizing and underlining text. These properties can be detected and measured by parsing both the text and CSS. The above properties are defined as attributes in various HTML tags. These tags include , and tags. The errors in attributes of tag

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errors cause incorrect or semi correct display of text on web page. The algorithm QAPM identifies the errors related to text presentation. Thus the Head Tag Errors (HTE), Font Tag Errors (FoTE) and Body Tag Errors (BTE) identify the problems in the text presentation of web page. 2.

Link Presentation: The link presentation is important aspect in organization of web pages. The links in a website may be internal or external links. There are differences among the internal and external links. While internal links must all be valid and links pointing to external domains are out of control of the webmaster, but can be checked. Link topology is an often neglected aspect. Some sites are just trees of nodes, with links from a node pointing to children and to ancestors. Some others have a much more complex link topology, with many horizontal or traverse links. To format the links on the web page perform following functions. ◦◦ Use moderate levels of breadth with minimal in the information architecture. ◦◦ Minimize depth. ◦◦ Avoid broken links. ◦◦ Use corresponding text links. ◦◦ Redundant links may cause confusion and avoid them. ◦◦ Effective navigation requires small pages, few clicks between pages and strong scent.

Thus Link Tag Errors (LTE) and broken links identify the problems in link presentation. 3.

Page Layout: The page layout is probably the principal characteristic perceived by the user. Layout must be clean, and the whole content should be well structured. A page layout is designed using tables, tag or tag. Layout must adaptable to different devices. This implies that page must

Category: Web Technologies

4.

avoid making reference to specific device settings, like screen resolution or fixed size page components. An automated analysis of CSS usage and coding can supply information about the layout and the adoption of an organization wide standard. The algorithm QAPM generates table tag errors (TTE), frame tag errors (FTE), style tag errors (StTE), font tag errors (FoTE), frame tag usage errors and document type declaration errors if any attribute of tag element deviate the properties of page formatting. The Page Formatting Measures assess the following features of website. ◦◦ Use browser-safe colors. ◦◦ Use no more than 6 discriminable colors. ◦◦ Use 256 (8-bit) color palettes. ◦◦ Avoid using black backgrounds. ◦◦ Use high contrast between background and text. ◦◦ Keep line lengths to 40-60 characters ◦◦ Keep text between 9 to 15 words per line. ◦◦ Avoid using framesets. ◦◦ Text should cover no more than 2530% of the screen. ◦◦ Greater text density facilitates page scanning. Graphics Presentation: The graphic presentation is the important issue in presenting pictorial and multimedia components. To format Graphics on the web page perform following properties should be followed. ◦◦ Avoid using graphical text links. ◦◦ Use corresponding text links instead of graphical links. ◦◦ Avoid using animation unless it is appropriate. ◦◦ Proper contrast between foreground image and background (color or image).

The image tag error (ITE), body tag errors (BTE) and image load errors related to image

identifies the errors in display of images and hence Graphic Element Measures to be evaluated. Graphics Element Measures developed for assessing the following features of web interfaces. 5.

Page Performance: Correctness is a merely technical aspect, which can be easily checked. In many cases inconsistent behaviour with different browsers can be originated by lack of conformance to the published grammars (HTML, XHTML) and the default actions taken by the browsers themselves. Correctness is easily checked as an internal quality factor. The important aspects to consider in some environments are the professionalism and effectiveness of the web site that could be measured through how many different platforms are supported and it supports adaptivity and adaptability for a personalization Etnoteam et al (2000), M.Y. Ivory (2001). The form tag errors (FmTE), script tag errors (STE) and title tag with no keyword errors identify the need of page performance measure. The page performance measures developed to answer the following questions related to the website. To increase the performance of the web page following guidelines are considered. ◦◦ Minimize the use of video. ◦◦ Avoid using sound files. ◦◦ Effective navigation requires small pages. ◦◦ Avoid using ‘Click Here’ for link text.

The contents of website must be presented in such a way that the user has to get 100% satisfaction in viewing all web pages of the website so that complete content of website visible and all pages are accessible. At this part of the research paper, it is tried to evolve 10-point scale. Thus 10-point scale is a metric towards defining quality of web content. In this connection it is interpreted the 10-point scale indicates such that ‘0’ always represent poorer side and ‘10’ always represent the best side of quality aspect. The 10-point scale metrics

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for various qualitative measures are formulated using empirical evaluation. Here 10-point scale of web content depends on the value computed using measures and its level competence based on performance.  n    10 

=

k

Correctness

* No. of Pages with errors

Performance

=

Very Good

if Correctness ≥ 9

Good

if Correctness ≥ 8

Betterthan Average

if Correctness ≥ 7

Average

if Correctness ≥ 6

Below Average if Correctness ≥ 5

(3)

(10 − k )

=

where n is number of Web Pages in a Website

(4)

Poor

if Correctness ≥ 4

Very Poor

otherwise

(5)

Table 1. Quality Status of University Websites in India S. No

University

No. of Pages With Errors (Per 100 Pages)

Correctness

Performance

1

English and Foreign Language University, Hyderabad

29

7.1

Better than Average

2

Moulana Azad National Urdu University, Hyderabad

93

0.7

Very Poor

3

University of Hyderabad, Hyderabad

96

0.4

Very Poor

4

Rajiv Gandhi Nagar University, Itanagar

14

8.6

Good

5

Assam University

38

6.2

Average

6

Tezpur University

45

5.5

Below Average

7

Central University of Bihar

87

1.3

Very Poor

8

Nalanda University

13

8.7

Good

9

Central University of Haryana

45

5.5

Below Average

10

Central University of Himachal Pradesh

8

9.2

Very Good

11

Central University of Jammu

12

8.8

Good

12

Central University of Kashmir

7

9.3

Very Good

13

Central University of Jharkhand

13

8.7

Good

14

Central University of Karnataka

19

8.1

Good

15

Central University of Kerala

87

1.3

Very Poor

16

Guru Ghasidas Visvavidyalaya, Bilaspur

17

8.3

Good

17

Dr. Harisingh Gour Viswavidyalaya, Sagar

54

4.6

Very Poor

18

Indira Gandhi National Tribal University

36

6.4

Average

19

Mahatma Gandhi Antharashtriya Visvavidyalaya

18

8.2

Good

20

Central Agricultural University, Manipur

22

7.8

Better than Average

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Category: Web Technologies

EVALUATION The study was conducted on 20 University websites using the methodology discussed in section 3 and the results are shown in Table 1.

CONCLUSION The main theme of the chapter is to provide optimization techniques to improve the correctness of the website. It is observed that website must be informative and all contents of the website must be accommodated in page layout according to standard guidelines. An attempt is made to enhance the quality of website content and layout so that web designer shall follow the quality of content in designing a web site.

FUTURE RESEARCH DIRECTIONS The chapter shows the way for the development of quality web designing in all aspects of websites. This is very much necessary to provide Quality of Service (QoS) to the online users. The chapter is well illustrated with a case study so that one can easily identify the defects in their own websites. The research work done in this chapter is the basis path for further extending the research in web engineering.

REFERENCES Beirekdar, A., Vanderdonckt, J., & NoirhommeFraiture, M. K. (2002). Knowledge based Web Automated Evaluation with reconfigurable guidelines optimization. LNCS, 2545, 362-376. Bobby. (n.d.). Retrieved from http://webxact. watchfire.com/

Dreyfus, P. (1998). Principles of usability. Retrieved from http:// www. devedge.netscape.com/ viewsource /arcive/editior 98 _3_23. html Etnoteam, S. P. A. (2000). Retrieved from http:// ww.etnoteam.it/webquality EvalIris. (n.d.). Retrieved http://www.sc.ehu.es/ acwbbpke/evaliris.html Ivory, M. Y. (2001). An Empirical foundation for Automated Web Interface Evaluation, doctoral dissertations, University of California. Berkeley, CA: Computer Science Department. Valet. (n.d.). Retrieved from http://valet.webthing. com/access/url.html

KEY TERMS AND DEFINITIONS Correctness: A merely technical aspect, which can be easily checked. Correctness is easily checked as an internal quality factor. The important aspects to consider in some environments are the professionalism and effectiveness of the web site that could be measured through how many different platforms are supported and it supports adaptivity and adaptability for a personalization. Graphics Presentation: The graphic presentation is the important issue in presenting pictorial and multimedia components. Guidelines: Consists of a design and evaluation principle to be observed to get and to guarantee a usable user interface. Link Presentation: The link presentation is important aspect in organization of web pages. The links in a website may be internal or external links. Text Presentation: Text Presentation is an important issue in display of the web content. These issues should be properly handled in presenting 100% correct text presentation.

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W3C HTML Validator: The outputs of the Mark up Validator are a list of error messages and their interpretation W3C HTML Validator helps to ensure that documents are free of potential problems that can result in unexpected output when users view the bad documents with different browsers.

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Website Content: The collection of web pages organized on a Web server. Web pages are of two types, static and dynamic. Website Errors: Website errors related to website content cause incorrect display of some components of Web pages.

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Lars Konzack University of Copenhagen, Denmark

INTRODUCTION

INTERNET MEMES

Internet phenomenon is a new field of research. An internet phenomenon is an occurrence on the internet about somebody, a website, or a picture that for some reason captures the attention of numerous internet users and develops a craze that fast-spreads through the internet. The most common internet phenomenon is an internet meme, but also internet celebrities, political campaigns, or simply something out of the ordinary.

The most common internet phenomenon is an internet meme. The idea of memes takes it root in the memetics of Richard Dawkins but the concept of internet memes have evolved since then. Internet memes has become part of everyday life on the internet. Research has been done to understand this internet phenomenon as regards the development of internet memes, categorization of memes, and how they work. An Internet meme is defined as a motif that is virally disseminated through the Internet. The motif often undergoes lots of variations (mashups) and may consist of sound, picture, movie clip, game and written text, or as is mostly the case, a combination by two or more modalities. Moreover the motif can be connected to only one of these modalities but need not be and in such case may enter different kinds of modalities. It is difficult to pinpoint the first internet meme. One could argue that the emoticon introduced as the smiley in September 19th 1982 with all the variations of the theme is in fact the first internet meme (Rosenträger, 2008). The term meme stems from Richard Dawkins controversial work The Selfish Gene referring partly to gene and partly to mimeme, which means to imitate. In his use of the term it is considered as any cultural idea or behavior such as fashion, language, religion, science and sports – cultural DNA reproducing itself (Dawkins, 1976). It is unclear whether Richard Dawkins comprehends the meme as an objective structure, or a metaphor for cultural practices. However, recent use of the term of internet meme has outgrown Richard Dawkins and has become a phenomenon in its own right (Stryker, 2011). According to Mole Empire the

BACKGROUND Before the internet there were the existence of folk tales and urban legends, folk songs and oral poetry as a way share content (Duggan, Haase, & Callow, 2016). Internet phenomenona have often been compared to folklore and urban legends; however there is one significant difference in that folklore was passed on in an oral culture of illiterates. Internet on the other hand sharing are mostly done among 21st Century literates and often stored on servers for other people to see. In this sense sharing internet phenomenona are closer to chain letters except the internet technology makes the process a lot easier and faster and may spread globally within minutes. “Ideas are transmitted, often without critical assessment, across a broad array of minds and this uncoordinated flow of information is associated with “bad ideas” or “ruinous fads and foolish fashions.” (Jenkins, Ford, & Green, 2013, p. 307) With 21st Century computer technology any idea or creation has the potential to spread like wildfire globally on the internet and if they do they become internet phenomena. DOI: 10.4018/978-1-5225-2255-3.ch697

Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Internet Phenomenon

ten most famous internet memes as of 2011 are as follows Keyboard Cat, Three Wolf Moon, Om Nom Nom, Auto Tune, The David After Dentist, Penaut Butter Jelly Time, Christian Bale Rant, Fail, O RLY, and Numa Numa (Smith, 2011). While this of course is by no means based on real academic research, it still gives a clue as to what these internet memes are. A more systematic approach comes from Know Your Meme (http://knowyourmeme.com/), given that they try to accommodate a database of all known internet memes, and as of 2015 they have collected more than 13,000 meme entries of which at least 2,400 are confirmed, and they have categorized them as regards to confirmation status, what year it came to be, and where on the internet it originated. Wikipedia has descriptions of some of the most famous internet memes. An internet researcher may likewise find descriptions of internet memes on Oh Internet (http:// ohinternet.com). On a far more chaotic scale it is possible to find information about internet memes on Encyclopedia Dramatica (http://encyclopediadramatica.se) although it requires skill to understand the in-jokes and to select the right bits of information and knowledge about internet memes. However, with the skill to comprehend Encyclopedia Dramatica, there is indeed information as regards to origins and explanations to a lot of these memes, information that may be difficult if not impossible to achieve by other means, and Encyclopedia Dramatica provides the right context and attitude for these memes, which the other catalogues do not. Observation of how memes develop can be done, at websites such as YouTube, 4chan, 9gag, reddit, YTMND and Tumblr. While a lot of these memes originates from YouTube or 4chan the key to their success is the viral dissemination through e.g. E-mails, Facebook or Twitter. Memes can also be made by the use of so-called meme generators. These are internet services on which the user can upload images or use a wide range of ready-made images and put in a text-caption.

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Since internet memes are a new phenomenon only few studies of have been made and they have all been experimental in their approach, some of which are presented here.

STUDYING MEMES A study of Internet memes was conducted by Michele Knobel and Colin Lankshear (Knobel & Lankshear, 2007). They suggest an approach to memes based on three characteristics of memes that according to Richard Dawkins is the key to a successful meme: fidelity, fecundity, and longevity. Fidelity is how replicable the meme is. Fecundity is the dissemination speed. And longevity is the staying power of the meme. Furthermore the memes have been analyzed using three general axes: referential or ideational system, contextual or interpersonal system, and ideological or worldview system. During their research, they found that successful memes had three key components: humor, rich intertextuality (references to other works of art), and anomalous juxtaposition (mash-ups of deliberate provocative or off-guard instances of absurdity). Furthermore they made a categorization of internet memes primarily dividing into three groups: 1) social commentary purposes, 2) absurdist or humor purposes, and 3) otaku and manga fan purposes. Another approach comes from Colin Stryker that conducted his own research of the meme life cycle that he lays out as seven stages (Stryker, 2011). The seven stages are as follows: birth, discovery, aggregation, word of mouth, blog pickup, mainstream exposure, commercialization, and death. It must be added that Colin Stryker says that not all memes goes through this exact life cycle. Some never become mainstream and other jump over certain stages, or follow a different path in order of sequence. The birth of the meme is where and how it originated. A discovery of the meme could be that if someone posts the meme on a web–com-

Category: Web Technologies

munity like 4chan and is immediately picked up by the other users with mash-ups and comments. Aggregation begins when the meme jumps from the web-community that first discovered the meme to other web-communities like Reddit. Word of Mouth is actually more like dissemination through means of electronic written text and uploading images or movies, using blogs, tweets, status updates and instant messaging. It only reminds of actual word of mouth in the sense that it is distributed via informal means. Blog pickup happens when the specialized internet sites like 9gag, YTMND, Tumblr, and Know Your Meme receives and propagates the meme. It is at this stage the meme becomes a part of the general internet culture with self-referential jokes and clever mash-ups. Mainstream exposure of the meme occurs when old media like television, radio and newspapers distributes the story of the meme. It is at this the stage the main stream population outside the internet culture learns about the internet meme, and it is paradoxically at that point where the inner circle of the internet culture may lose interest in the meme. Then there is only commercialization left (e.g. turning the meme into some sort of merchandise) before the meme is almost death, disappeared and forgotten. In a study of YouTube memes conducted by Limor Shifman various meme features are recognized (Shifman, 2011). First of all Shifman discusses the difference between a viral video and a memetic video, in which a viral video is a video clip that spreads without significant change, while a memetic video is a popular video clip that ‘lures extensive creative user engagement in the form of parody, pastiche, and other derivative work’ (p. 190). The recognized Limor Shifman key features of YouTube memes are as follows: They relate to ‘ordinary’ people, and are often about flawed masculinity. They use humor, simplicity and repetitiveness, and portray whimsical content. Patrick Davison argues that in order to understand internet memes, we will have to specify what exactly is being replicated (Davison, 2012).

According to Davison a meme can be separated into three components: manifestation, behavior, and ideal. The manifestation is the actualized as an observable, external phenomenon in time and space. The behavior is the action taken by users and producers of the meme. And the ideal of the meme is the idea conveyed as a conceptual purpose. The ideal determines the behavior which again resolves into the manifestation. All of these approaches to internet memes demonstrate first of all that it is a new field that needs to be further studied but it also shows that these people take it very seriously and come up with ways to grasp and comprehend internet memes. They are trying to categorize them, understand how they function, how they develop over time, and how we should perceive them as cultural expression.

INTERNET CELEBRITIES An internet celebrity is a celebrity known on the internet, and maybe even in old media as well, through at least one internet meme. In many cases this celebrity status has been notorious or ill-famed rather than the more positive kind of renown and fame. Due to the spreadability of internet memes, these meme celebrities have got lots of attention (Jenkins, Ford, & Green, 2013). In November 2002, a Canadian high school student, Ghyslain Raza, made a video of himself playing with a golf ball retriever as if he was fighting like Darth Maul from Star Wars Episode 1: The Phantom Menace (Knobel & Lankshear, 2007). The video was uploaded to Kazaaby some of his classmates without his knowledge. The video became known as Star Wars Kid, went viral as a global hit, because people found it hilarious to see how a heavy-built kid was using all his energy to immerse himself into the role of a Sith Lord. The video was manipulated in different ways with sound and light effects, and lots of images were made based on the original video. Ghyzlain Raza

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had unknowingly become an Internet sensation. Ghyzlan became the laughing stock of millions and did not get anything else out of it (Stryker, 2011). Numa Numa is the popular name of the music video Dragostea din tei by the Moldovan pop-group O-Zone, and it became notorious when overweight 19 year old Gary Brolsma in December 2004 lip synched to the video, waving his arms around. He later appeared in Good Morning America, and in 2006 he made his own website with merchandise and a remix of the original song (Stryker, 2011). Although Brolsma became laughed at he could at least cash in his fame. David After Dentist is a video clip by his seven year old son David after his visit to the dentist, filmed by his father in May 2008. Seven months later he uploaded the video to YouTube (Prout, 2010). It became an instant sensation, and David was considered funny as well as cute. David along with his father appeared in Today Show, Tyra Banks Show and The O’Reilly Factor. The father detected a business opportunity, got a website, and made more video clips of his son, selling T-shirts and other merchandise. There has been controversy as to whether or not it was ethical for his father to exploit his own son like that (Linkins, 2009). Another internet phenomenon was Boxxy. She made a couple of videos about herself to internet friends from a website called Gaia, and uploaded the videos to YouTube as BoxxyBabee in January 2008. This wasn’t dramatic until 4chan came across the videos a year later and decided it was either the worst they had ever seen or the cutest girl on the internet. Memes were made in her honor as either cancer or as queen. She was portrayed as a girl version of Che Guevara and sold as T-shirts by meme merchandise internet shops. Little did she know what was going on until her mail account was hacked and all of her personal files was spread across the internet. Later she tried with mixed luck to cash in her sudden fame (Stryker, 2011). Rebecca Black on the other hand tried to be famous by making a music video. On February 2011, the 13 year old girl uploaded the music video

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to YouTube and it turned viral. And people hated it. The song Friday came to be the most hated song on the internet and viewed by millions. The ill-famed girl was ridiculed through countless of memes (Stryker, 2011). She did however make an appearance as Katy Perry’s friend in Katy Perry’s music video for Last Friday Night (T.G.I.F.). The face of Laina Morris became known as Overly Attached Girlfriend (Lagore, 2015). It was based on a video uploaded June 6th 2012 named JB Fanvideo for a Justin Bieber song competition. The starring eyes of Laina Morris and the song about an overly attached girlfriend made her face known to the internet. She has coped with the fame by posting other videos, and on March 11th 2013 she appeared on the show Late Night with Jimmy Fallon. But not only people become famous. Products may become well-known as well. Three wolf Moon was a black T-shirt with three wolves howling at the Moon sold on Amazon. The T-shirt sold well but wasn’t anything in particular, when suddenly a guy made a false review of the T-shirt stating that you would get all the girls you wanted if you wore it. The review was written in a humorous tone and others joined in, making their own reviews. After a while it spread across the internet with images superimposing the T-shirt to famous people and some music videos were made as well, and the T-shirt became a bestseller (Stryker, 2011). For this reason or just for the fun of it many internet users have tried to create their own internet memes, hoping they will catch on. These are made on purpose and for the most part they fail. It is much more fun if the internet meme seems to come out of nowhere, like a coincidence or accident. These pushed memes are considered unfunny and are called forced memes. Within the depths of internet culture forced memes are frowned upon (Nissenbaum & Shifman, 2015). Since internet phenomenons have had such a tremendous impact, they have been used for propaganda purposes as well. Anonymous, the freedom fighters from 4chan, have used internet memes countless of times to spread their point of

Category: Web Technologies

view across the internet. On Project Chanology, their ongoing revolt against Scientology, they used the Guy Fawkes Mask (Stryker, 2011). It is correct that it originates from Guy Fawkes and the comic book and the later movie V for Vendetta. However, it was actually used at the time as a meme called Epic Fail Guy, which featured a dancing cartoon-like figure wearing the mask. The message to scientology was that they were epic fail. They used other known memes like Rickrolling, and Longcat as well. As Anonymous progressed into support for WikiLeaks, Arab Spring, and the Occupy Movement it became a symbol of an internet youth rebellion. It became a sign of the times (Coleman, 2014). Internet memes have been used in the American election for either candidate as far back as 2008, and have become a part of the campaign strategy in which each candidate produce forced memes to either a positive portrayal of their own candidate or a negative portrayal of the other candidate (Tay, 2015).

OTHER INTERNET PHENOMENONA There are lots of internet phenomena and it is not possible to show all them here. This is just a few examples to show the range of diversity of internet phenomena. Beany Babies have been said to be the first internet phenomenon. In the 1990s Beanie Babies attracted bidders on eBay more than any other item category. In May 1997, the average price for a Beany Baby was $30, generating a total Beany Babies turnover of $500.000 on eBay. By the end of the decade the sales plummeted to a more reasonable price around 50 cents apiece (Bissonnette, 2015). Creepypasta are horror stories spread on the internet that are creepy. They are very close to urban legends except they are spread not through word-of-mouth but via internet as written or au-

diovisual communication that can be saved. The term creepypasta comes from a portmanteau of creepy and copypasta – copypasta meanining stories that are copied and pasted. The most common creepypasta is Slender Man which is a character of a tall man abducting children. It took the world by storm and made a lot of people scared or anxious. Slender Man was originally created by Erik Knudsen who in June 2014 apologized that it was taken too seriously (Delaney & Madigan, 2016). Another internet phenomenon is Let’s Play (or LP). LP was promulgated by the website Something Awful and is video footage of someone playing and commenting a videogame – often shared on YouTube or. The focus is not on the player but what happens in the game. It’s a mixture of review, general commenting on the game and how it is played and advice on how to play the game. Most notably is PewDiePie, whose real name is Felix Arvid Ulf Kjellberg, with almost 50.000 subscribers (Berry, 2015). Kony 2012 was a failed attempt at arresting Joseph Kony, an Ugandan war criminal, by the end of 2012. Kony 2012 was the title of a short documentary that was shared on the internet with over 100 million views. Although the video got a lot of attention and a resolution condemning the terror of Joesph Kony from American senators, and even President Obama offering a reward of $5 million for his arrest, Joseph Kony has still not been captured (Mahoney & Tang, 2016). In 2014, Ice Bucket Challenge arose among people to donate money to amyotrophic lateral sclerosis (ALS). They should take the challenge of having ice cold water poured over them and challenge three others to donate and be poured over by cold water. This chain letter approach meant that a lot of people came in contact with the Ice Bucket Challenge and eventually a lot of celebrities participated. The Ice Bucket Challenge was often shared as YouTube videos on the internet and raised $114 million dollars (Hutchins & Tindall, 2016).

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FUTURE RESEARCH DIRECTIONS There is still lot to be done as far as future research in the field of internet meme, and we have yet to see their cultural, sociological and political impact. Internet phenomena are used, reproduced and part of a participating internet culture. One of the most interesting future research directions would be to see the development of political internet memes and the link between geek culture and popular culture in relations to internet phenomena. There is a lot of internet phenomena that cannot be analyzed by a single individual. Future research will demand digital humanities solutions to delve into the many aesthetic and cultural aspects of internet phenomena.

CONCLUSION As we have seen internet phenomena can be analyzed as far as categorization, functionality, development, and as a socio-cultural phenomenon. Research has shown how internet memes are born; how they develop over time; and how they eventually die. It has shown how internet memes may be categorized into three major subgroups: 1) social commentary purposes, 2) absurdist or humor purposes, and 3) otaku and manga fan purposes. An internet meme consists of three components: 1) manifestation, 2) behavior, 3) and ideal. The spreadability of internet phenomena means that indeed internet celebrities have arisen, and we have seen some examples of these. Furthermore a range of different internet phenomena has spawned and disappeared again.

REFERENCES Berry, J. (2015). PewDiePie: The Ultimate Unofficial Fan Guide to The World’s Biggest Youtuber. London: Hachette.

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Bissonnette, Z. (2015). The Great Beanie Baby Bubble: Mass Delusion and the Dark Side of Cute. New York, NY: Penguin. Blackmore, S. (1999). The Meme Machine. Oxford, UK: Oxford University Press. Coleman, G. (2014). Hacker, Hoaxer, Whistleblower, Spy: The Many Faces of Anonymous. New York, NY: Verso. Culture, L. L. (n.d.). Tim Delaney. Tim Madigan. Davison, P. (2012). The Language of Internet Memes. In M. Mandiberg (Ed.), The Social Media Reader (pp. 120–134). New York: New York University Press. Dawkins, R. (1976). The Selfish Gene. Oxford, UK: Oxford University Press. Delaney, T., & Madigan, T. (2016). Lessons Learned from Popular Culture. Albany, NY: SUNY Press. Duggan, A. E., Haase, D., & Callow, H. J. (2016). Folktales and Fairy Tales: Traditions and Texts from around the World, 2nd Edition: Traditions and Texts from around the World. Santa Barbara, CA: ABC-CLIO. Hutchins, A., & Tindall, N. (2016). Public Relations and Participatory Culture: Fandom, Social Media and Community Engagement. New York, NY: Routledge. Jenkins, H., Ford, S., & Green, J. (2013). Spreadable Media: Creating Value and Meaning in a Networked future. New York: New York University Press. Knobel, M., & Lankshear, C. (2007). Online Memes, Affinities, and Cultural Production. In M. Knobel & C. Lankshear (Eds.), A New Literacy Sampler (pp. 199-227). New York: Peter Lang Publ.

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Lagore, J. (2015). Self-Promotion for All! Content Creation and Personal Branding in the Digital Age. In D. C. Sarver & S. Collister (Eds.), Debates for the Digital Age: The Good, the Bad, and the Ugly of Our Online World (pp. 221–240). Santa Barbara, CA: ABC-CLIO. Linkins, J. (2009, July 9). Bill O’Reilly Goes After Father Of ‘David After Dentist’ For Some Reason. Huffington Post. Retrieved from http://www. huffingtonpost.com/2009/06/08/bill-oreilly-goesafter-f_n_212635.html Mahoney, L. M., & Tang, T. (2016). Strategic Social Media: From Marketing to Social Change. Oxford, UK: John Wiley & Sons. doi:10.1002/9781119370680 Nissenbaum, A., & Shifman, L. (2015, October 9). Internet memes as contested cultural capital: The case of 4chan’s /b/ board. New Media & Society, 1–19. Prout, S. (2010). The Power of Influence: The Easy Way to Make Money Online. Milton: Wrightbooks. Rosenträger, S. (2008). Emoticons as a New Means of Communication in Italy and Germany. Munich: GRIN Verlag. Shifman, L. (2011, October 3). An anatomy of a YouTube meme. New Media & Society, 14(2), 187–203. doi:10.1177/1461444811412160 Smith. (2011, December 14). The History Of The Most Famous Internet Memes So Far. Mole Empire. Retrieved from http://molempire. com/2011/12/14/the-history-of-the-most-famousinternet-memes-so-far/ Stryker, C. (2011). Epic Win for Anonymous: How 4chan’s Army Conquered the Web. London: Overlook Duckworth.

Tay, G. (2015). Binders full of LOLitics: Political humour, internet memes, and play in the 2012 US Presidential Election (and beyond). The European Journal of Humour Research, 2(4), 46–73. doi:10.7592/EJHR2014.2.4.tay

ADDITIONAL READING Bauckhage, C. (2011, May). Insights into Internet Memes. In ICWSM. http://www.aaai.org/ ocs/index.php/ICWSM/ICWSM11/paper/download/2757/3304 Burgess, J. (2008). All Your Chocolate Rain Are Belong to Us. Viral Video, Youtube and the Dynamics of Participatory Culture. In G. Lovink & S. Niederer (Eds.), Video Vortex Reader: Responses to YouTube (pp. 101–109). Amsterdam: Institute of Network Culture. Dibbell, J. (2010). Radical opacity. Technology Review, 113(5), 82–86. Guadagno, R. E., Rempala, D. M., Murphy, S., & Okdie, B. M. (2013). What makes a video go viral? An analysis of emotional contagion and Internet memes. Computers in Human Behavior, 29(6), 2312–2319. doi:10.1016/j.chb.2013.04.016 McConnell, B., & Huba, J. (2007). Citizen marketers: When people are the message. Berkshire: Kaplan Pub. Shifman, L., & Thelwall, M. (2009). Assessing global diffusion with Web memetics: The spread and evolution of a popular joke. Journal of the American Society for Information Science and Technology, 60(12), 2567–2576. doi:10.1002/ asi.21185

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Wang, L., & Wood, B. C. (2011). An epidemiological approach to model the viral propagation of memes. Applied Mathematical Modelling, 35(11), 5442–5447. doi:10.1016/j.apm.2011.04.035 Zimmer, B., & Carson, C. E. (2011). Among the new words. American Speech, 86(4), 454–479. doi:10.1215/00031283-1587259

KEY TERMS AND DEFINITIONS Internet Meme: A motif that is virally disseminated through the Internet. The motif often undergoes lots of variations (mash-ups) and may consist of sound, picture, movie clip, game and

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written text, or as is mostly the case, a combination by two or more modalities. Moreover the motif can be connected to only one of these modalities but need not be and in such case may enter different kinds of modalities. Internet Phenomenon: An occurrence on the internet about somebody: a website, or a picture that for some reason captures the attention of numerous internet users and develops a craze that fast-spreads through the internet. Manga: Japanese comics style. Otaku: A Japanese term for obsessive collector – often manga-related items. It is commonly used as the Japanese term for a geek or nerd. Spreadability: The ability to disseminate information.

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An Overview of Crowdsourcing Eman Younis Minia University, Egypt

INTRODUCTION During the past decade, there were rapid developments in the Internet, computing technologies, wide-spread and use of location-aware technologies such as GPS and mobile phones. These developments influenced how people communicate and share their opinions, views, knowledge, maps, and many others throughout software platforms. These technologies have participated in the creation of what is now called Web 2.0, which is a new era of web technologies enabling users to play an active role in adding contents to the web in a collaborative way, instead of just consuming the web contents. People are now easily sharing social media posts, blog posts, product reviews, ideas, opinions, maps and others. Crowdsourcing is characterized as a phenomenon that appeared due to enabling web users to contribute to the web. There are many online Crowdsourcing applications such as Amazon Mechanical Turk, Open Street Map, and Yahoo Answers amongst others. Thus, crowdsourcing is a collaborative process which involves four main components (requester, crowd, open call and platform). People might benefit financially or intellectually from participation in crowdsourcing. This chapter serves as a general overview of crowdsourcing research and envisages future research directions.

BACKGROUND Due to the invasion of pervasive computing and Web 2.0 technologies (Brabham, 2013), users can not only consume information on the web

by search and navigation but also, contribute their own contents. Crowdsourcing is also called fan sourcing, crowd casting, mass collaboration, socio-technical systems (Geiger, D. et al. 2012), collective intelligence, smart mobs, peer production, citizen science and user generated content (Haythornthwaite, 2009). Many applications have been created to make use of the online crowds such as Question Answering, Mapping, Software Engineering, Healthcare and many more. Examples of Crowdsourcing systems are yahoo answers, Open Street Map, Amazon Mechanical Turk, among others. Crowdsourcing can be classified according to (Howe, J. 2008) into co-creation, crowd creation, crowd voting, crowd wisdom, and crowdfunding. There is always confusion between the terms crowdsourcing and outsourcing. Here we distinguish between these terms. Wikipedia has created a debate; some researchers do not consider it a crowdsourcing site. Because it is not satisfying their 8 criteria listed in (Estellés-Arolas et al. 2012). On the other hand (Haythornthwaite, 2009) considers Wikipedia a crowdsourcing platform, contributed by a collection of volunteer contributors. This chapter presents a general overview of the crowdsourcing field. It is structured as follows; it begins with the analysis of definitions of the term from literature followed by a classification of crowdsourcing activities. Then, a comparison between crowdsourcing and outsourcing is presented. After that a discussion of crowd-user motivations is presented. It also presents the benefits and problems of crowdsourcing, its applications, online crowdsourcing systems, current and future research avenues.

DOI: 10.4018/978-1-5225-2255-3.ch698 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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CROWDSOURCING DEFINITIONS Crowdsourcing is not a new phenomenon; it has been used in the past in many cases for collecting users’ participations (Howe, J. 2008). But, until now there is no standard agreed upon definition of crowdsourcing. The term was first proposed by Jeff Howe’s in a Wired Magazine article (Howe, J. 2008) and defined as: the act of a company of institution taking a function once performed by employees and outsourcing it to an undefined and generally large network of people in the form of open call. After that, there were several other attempts to define it in the literature such as (Ambati et al. 2012 Azzam, T. et al. 2013, Von Ess 2010, Bell, 2009, Doan et al. 2011, Lebraty et al. 2013, Brabham, 2013, Estellés-Arolas et al. 2012, Sharma et al. 2014 and many more). These definitions are different in their view regarding the targeted application of crowdsourcing. The definitions of Crowdsourcing are unified in terms of the general concepts and components, but, targeting different applications.

Figure 1. The Crowdsourcing process

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Figure1 depicts the crowdsourcing process, showing various components. The first component is the sourcer or the requester, which can be an organization or an individual. The requester wants to accomplish a specific goal or solve a problem. This goal can be to create, test, or rate a product or a service or it can even be to collect capital investment to initiate a project. The second component is an open call (this is the call for participants to contribute to achieving the requester’s goal). The third component is the crowd (the anticipated contributors to the open call). The last component is the platform that facilitates the process of interaction between the requester and the crowd. Figure1 depicts the crowdsourcing process, showing various components. It shows that the crowdsourcing process is initiated by the requester, which may be a company, an institution or an individual, having some problem to solve. This requester sends an open call for participations to the crowd. The diversity and heterogeneity of the crowd capabilities are an advantage, which produces variations of the presented solutions. The crowd participates in the open call by providing solutions to the problem. The requester receives and evaluates the presented solutions.

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CLASSIFICATION OF CROWDSOURCING ACTIVITIES Crowdsourcing activities can be classified into the following categories (Howe, J. 2008): •

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Crowd Wisdom: There are some tasks which require human intelligence, such as text or image classification, tagging or annotation. The crowdsourcing system collects several human- annotated examples from the crowd to be used for computer learning. In such applications, the crowd wisdom has been used to help the computer solving a hard problem. As an example, Amazon Mechanical Turk (www. mturk.com) is offering paid work for the crowd to solve human Intelligence tasks (HITs) called micro-tasks. Another example is (https://www.google.com/recaptcha), where users are acting voluntarily to support Google performing intelligent tasks such as text and image recognition automatically. These kinds of tasks are essential for Machine Learning applications. Crowd Creation: In this type of crowdsourcing, the crowd is asked to contribute some kind of artifacts, designs, maps or even a question and its answer. For example (threadless.com), is a company offering its customers a share of profit for the best T-shirt design. Another example in mapping is (Open Street Map), which is an online free mapping platform, where anyone can share their own maps or use existing maps. It is mainly based on the volunteer contributions from the crowd. Moreover, Question Answering Services such as (https://answers.yahoo.com/) are depending on the crowd for creating questions and answers. Wikipedia is another example of crowd creation tasks, where the crowd collaborates to create an encyclopedia. Crowdfunding: It is a recent phenomenon depending on collecting financial funding

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for establishing a project or financing an idea. The idea behind its success is finding the interested crowd in the suggested project or idea. An example is (sellaband.com) asking for donations from their crow-fans to sponsor their music albums. Crowd Opinion or Crowd Rating: In these systems, the crowd worker is required to provide his/her feedback or opinion in an artifact, design or even rate a piece of software. This type of systems can be used to evaluate the outputs of the previous types of systems using the crowd opinions. Crowd Solving: This type of crowdsourcing usually involves a contest for the crowd to solve a specific problem. For example, (www.kaggle.com) is an online crowdsourcing system for scientific problem solving. It encourages participants by giving financial prizes for the best solutions. Another example in the scientific field is (www.researchgate.net), where any researcher can send his problem and receive answers from his/her peers.

Although Howe, J. 2008 first presented crowdsourcing as an outsourcing activity, there are potential differences between those two concepts. Despite being aiming to get a task or a job done by people outside the organization boundary, there are still differences between the two terms. The next section shows the differences between them.

CROWDSOURCING VERSUS OUTSOURCING Although Jeff How’s definition of Crowdsourcing presented earlier as an outsourcing activity, outsourcing is a different concept. Outsourcing is the process of hiring an external company to provide a product or a service. Here we want to clarify that outsourcing is different from crowdsourcing in many aspects. First, outsourcing is the process throughout which the company contract

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with other external company to provide a product or a service, whereas in the crowdsourcing process the company sends an open call of participations to the crowd. Thus, the differences between both processes involve three points (Sharma and Padmanaban, 2014): 1. Contract: In the outsourcing process, there is a contract between the main company and the specified outsourcing company. Whereas, in the crowdsourcing process, there is no contract between the sourcer and the crowd. 2. Cost: The contract entitles the sourcer company to pay a pre-determined amount of money according to the contract. The crowdsourcing could involve volunteering work from the crowd or pay a nominal amount of money. 3. Commitment: The company involved in the outsourcing is committed to abiding by all the contract conditions by delivering product or service on time and specifications required. But, the crowd is free of any obligations to participate or give good quality outputs.

Figure 2. Crowdsourcing versus outsourcing

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Figure 2 shows the difference between crowdsourcing and outsourcing.

USER MOTIVATIONS FOR CONTRIBUTION It is important to know why people are engaged in crowdsourcing activities. Motivations are the stimulating factors that encourage people to do actions. Ryan et al. 2000 presented the crowd motivations to participate to Intrinsic and extrinsic motivations. The intrinsic motivations are defined as those leading to enjoyment or any type of satisfaction for the participant. Extrinsic motivations are those triggered by a separate outcome such as a reward. Pilz et al. 2013 discussed different incentives of participants in crowdsourcing. They further divided the user incentives more finegrained classes out of the essential intrinsic and extrinsic proposed by (Ryan et al. 2000). They further divided the intrinsic motivations into enjoyment-based containing (skill variety, task identity, task autonomy, time killing and feedback) and community- based including (community

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identification and social contact). Whereas, the extrinsic motivations were further sub-divided into immediate payoff (payment), delayed pay-off (Signaling and human capital advancement) and social motivation (action significance by external values, action significance by external obligations and norms and Indirect feedback from the job). Regarding the company, there are also gains out of crowdsourcing which will be discussed in the next section.

BENEFITS OF CROWDSOURCING There are a lot of benefits for the company to use crowdsourcing. These benefits as presented by (Stol and Fitzgerald, 2014) are as follows: 1. Cost Reduction: Using crowdsourcing for doing a task is financially effective, as the cost of hiring online crowd is much less than hiring internal employees for performing the same task. The crowd may even do the work voluntarily. In addition to the cost reduction, using the crowd gives indications of the actual needs of the customers. 2. Faster Time-to-Market: Using the crowd makes the production process more effective, as the task can be done in less time. 3. Higher Quality: Crowdsourcing allows harnessing the diversity of experiences and knowledge of the crowd. This leads to higher quality solutions. 4. Higher Creativity and Innovation: By receiving many contributions from the crowd, which are having a wide variety of experiences, mentalities and geographically distributed different cultures. This gives more opportunities to have more creative and innovative ideas.

PROBLEMS OF CROWDSOURCING 1. It is difficult to estimate the project budget, as the participants will vary in size.

2. It is challenging to produce a precise specification of the problem, to present it to the crowd. 3. The crowd structure is missing. 4. Legal considerations, regarding who has the intellectual property of the contents. 5. Balancing the incentive motivations of the crowd (Extrinsic and intrinsic). 6. Problems regarding using crowdsourcing for software development are discussed in (Stol et al. 2014) as task decomposition, coordination and communication, planning and scheduling, quality assurance, Knowledge, and intellectual property.

CONCEPTUAL MODELS OF CROWDSOURCING Conceptual models are abstractions and simplifications of the real world systems. Conceptual models are different from software design and implementation. An example of a conceptual model is the E-R (entity - relationship) diagram in database design, which is the logical design of the database, and then it is used for creating the physical database. Pedersen, J. et al. 2013, presented a conceptual model for crowdsourcing from the information systems perspectives. This conceptual model presents the basic input processing output components of Crowdsourcing. It is important to classify the research relating it to the information systems research community. The basic components of this proposed model are presented in Figure 3. 1. Problem: The specifications of the current situation and the tasks required to obtain a solution. The problem can be co-creation, crowd creation, crowd wisdom or crowd funding (Howe, J. 2008). Research related to the problem is concentrating on goals and tasks (Wiggins et al. 2012), and mechanisms for the evaluation of ideas (Blohm et al. 2011)

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Figure 3. Crowdsourcing Conceptual Model (Pedersen, J. et al. 2013)

and user selection of a problem to contribute (Peddibhotla, 2013). 2. Process: Is the actual plan to solve the problem. The problem can be simple, one stage solution, such as product design or voting. It also can be a complex problem that requires a multiple-stage solution such as software development. Research targeting the processes includes collaborative e-science design (Farooq et al. 2009) and modeling information sharing process (Grant et al. 2010). 3. Technology: Acts as the enabling component to the crowdsourcing process. The technology involves not only the software platform, which facilitates the interaction between the sourcer and the crowd but also, tools supporting the online community creation. ICT technology and crowdsourcing (Davis, 2011), (Tilson et al. 2010). 4. Governance: These are the rules and regulations that organize the whole process of crowdsourcing. It also includes the rules for consistency preserving and quality assurance. Research involves quality control (Lasecki et al. 2012), (Lease, 2011) and crowdsourcing activity coordination (Zhang et al. 2011).

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5. People: People participating in the crowdsourcing process are divided into three classes. The first is the crowd, all the possible participants. Second, the individual is the person who participates in the crowdsourcing process. Third, the problem owner can be an individual, an organization or a company having a problem aiming to solve. Research tackled the types of participants (Cullen et al. 2011), user motivations (Pilz et al. 2013) 6. Solution: The output of the crowdsourcing process. It can be a product, a design, a piece of software, or a rating of any of the previous artifacts. Research address the sourcer feedback (Moon et al. 2008), (Bao et al. 2011) and solution trustworthiness (Zhai et al. 2012). Additionally, Malone et al. (2010) suggested a gene-based model for studying crowdsourcing systems. His model is composed of the following questions: 1. What? (Goal or Mission) In this question the requester decides which tasks need to be performed, either create a new product or service or decide between different available options.

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2. Who? (Staff) The answer to the first question helps to decide the audience. Who will be the targeted participants and also determine the suitable software platform for the required task. If the task can be done by organization employees, the tasks can be assigned to them. Otherwise, it will be better to use crowdsourcing, where there are more varieties of tools and experiences. 3. How? (Process) This refers to how the task will be accomplished, the useful tools and the results evaluation criteria. Considering the way in which the problem will be divided into smaller tasks. Moreover, deciding on the integration of the results to form a solution to the problem. 4. Why? (Incentives or Motivations) This involves incentives of the crowd to participate. There should be a balance between intrinsic motivations, which are related to the participants and extrinsic, which is something external such as prize or money.

CROWDSOURCING APPLICATIONS Crowdsourcing has been applied in many applications 1. Software Development, Testing, and Engineering: Using crowdsourcing for software development is a challenging process as it is a complex process, compromising of multiple stages and activities. It has been extensively discussed in (Stol et al. 2014), (LaToza et al, 2016) and Sharma et al, 2014). 2. Question Answering: Questions answering Systems (QAS) are software systems accepting user questions in natural language and presenting exact answer. They are becoming more popular with crowdsourcing user questions and answers. Yahoo Answers and Google Answers are examples of community QAS.

3. Geospatial Mapping: The term volunteered geographic information (VGI) suggested by Goodchild, 2007 has become very popular, with the spread of geographic mapping platforms, which allows the public to contribute their own maps and GPS traces. Examples are Open Street Map and Google My Map. 4. Scientific Research: There are many platforms that facilitate the research and collaboration in scientific problem solving such as (innocetive.com) and (researchgate. net). 5. Decision Support: Organizations learning from the crowd (Schlagwein et al, 2014) and (Chiu et al 2014) presented a framework for using the crowd to enhance the decision support for organizations. 6. New Product Design: Poetz et al, 2012 compared between using the crowd and employees for designing tasks. He concluded that crowdsourcing is a promising approach to product design. 7. Medical Applications: King et al. 2013 held a comparison between conventional methods and crowdsourcing methods for Skin self-examination. He argued that using crowdsourcing has a potential in the cancer detection task. Brabham et al. 2014 presented a framework for using crowdsourcing in public health applications. Meyer et al. 2016 proposed the use of crowdsourcing for medical diagnosis. 8. Machine Learning and Prediction: Tong et al. 2014 presented an approach to use crowdsourcing for race results prediction. Demartini, et al. 2013 proposed an approach to use the crowd for identifying similar items in the Linked Data. Scharl et al. 2012 presented an approach for the use of crowdsourcing in the acquisition of multi-lingual language data sets. Zhang et al. 2012 demonstrated using the crowd for web page clipping, which is useful for pattern mining applications. Sprugnoli, et al,

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2013 presented the use of crowdsourcing foe speech translation. 9. Sales and Design: Crowdsourcing has been used in product design to boost sales. This has been the idea used by (Threadless.com) by giving its customers the chance to send their own T-shirt designs. Then, different designs were evaluated and the designs with most sales are entitled to share profit with the company (Howe, J. 2008). 10. Crowdsourcing in Media: Nylund, et al. 2014 demonstrated the role of crowdsourcing in the media industry. The authors claimed that the spread of social media sites among the crowds has created the potential for growFigure 4. Crowdsourcing platforms

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ing the citizen journalism. In other words, it has become easier for the public to capture the events as soon as it happens and spread it out throughout social media sites. 11. Marketing: Doing marketing research and collecting crowd ideas and opinions about products or services during or after development is essential for business. (Whitla, 2009) 12. Optimization: (Yi et al. 2011) demonstrated the use of crowdsourcing for the traveling salesman optimization problem. 13. Disaster Management: (Gao et al, 2012), suggested the use of social media as a crowdsourcing platform for disaster relief.

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CROWDSOURCING PLATFORMS ON THE WEB There are a plethora of Crowdsourcing platforms available online. Table1 shows different available crowdsourcing platforms with their associated tasks and user motivations. Crowdsourcing platforms are different from crowdsourcing systems also in the user motivations. All the crowdsourcing platforms have extrinsic motivations.

solution are needed. Moreover, it is essential for the organization or the individual to ensure that the intended goals of crowdsourcing have been achieved. Another issue is how to detect and avoid cheating from the crowd and create trust among the crowd members and between the requester and the crowd. Regarding applications, it is of interest to investigate the use of crowdsourcing with the semantic web and showing the differences imposed by these technologies.

FUTURE RESEARCH DIRECTIONS

CONCLUSION

Crowdsourcing has been an active research area for ten years. Although a lot of research has been done in many areas such as business, behavioral science, and computer science, there is still more to be done. First regarding the problem, it is critical to present the calls for participations in a way suitable for the intended audience and finding the suitable audience for the problem. This needs to be per application customization. A possible research question can be what are the criteria that determine the suitability of the individual to participate in certain crowdsourcing application? Second, regarding the process, there is a need for a unified set of processes for each crowdsourcing application context. Third, technology, it is important to integrate web2.0 technologies such as wikis blogs and social media with new technologies for crowdsourcing such as sensors, which are now widely available in mobile phones. Fourth, governance, it is crucial to control the whole process throughout presenting evaluation methods for every element in the systems starting with the problem specification to the final output. Fifth, people, studying user motivations for contributions is an important aspect, it needs to be studied per application, as it helps in future systems design. Finally, the output is the final solution to the problem presented by the crowd. It is essential to quantify the quality of the proposed solutions, in order to, select the best one. Thus, a concise set of procedures for measuring the quality of the

Crowdsourcing is a promising research area which has proven success in many applications. It has been an active research area over the past ten years and it is anticipated to grow in the future, due to its endless applications. Using the crowd thinking and experience could give the organization new and innovative ideas that could never be possible using only the internal organization resources. Thus, crowdsourcing presents great opportunities for value creation in areas such as business, Information systems development, machine learning data collection and many more.

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Bell, D. (2009). The Crowdsourcing HandbookTHE How To on Crowdsourcing, Complete Expert’s hints and tips Guide by the leading experts, everything you need to know about Crowdsourcing. Emereo Pty Ltd.

Farooq, U., Ganoe, C. H., Carroll, J. M., & Giles, C. L. (2009). Designing for e-science: Requirements gathering for collaboration in CiteSeer. International Journal of Human-Computer Studies, 67(4), 297–312. doi:10.1016/j.ijhcs.2007.10.005

Brabham, D. C. (2013). Crowdsourcing. MIT Press.

Gao, H., Barbier, G., Goolsby, R., & Zeng, D. (2011). Harnessing the crowdsourcing power of social media for disaster relief. Arizona State Univ Tempe.

Brabham, D. C., Ribisl, K. M., Kirchner, T. R., & Bernhardt, J. M. (2014). Crowdsourcing applications for public health. American Journal of Preventive Medicine, 46(2), 179–187. doi:10.1016/j. amepre.2013.10.016 PMID:24439353 Chiu, C. M., Liang, T. P., & Turban, E. (2014). What can crowdsourcing do for decision support? Decision Support Systems, 65, 40–49. doi:10.1016/j.dss.2014.05.010 Cullen, R., & Morse, S. (2011, January). Who’s contributing: Do personality traits influence the level and type of participation in online communities. In System Sciences (HICSS), 2011 44th Hawaii International Conference on (pp. 1-11). IEEE. Davis, J., & Lin, H. (2011). Web 3.0 and Crowdservicing. AMCIS. Demartini, G., Difallah, D. E., & Cudré-Mauroux, P. (2013). Large-scale linked data integration using probabilistic reasoning and crowdsourcing. The VLDB Journal, 22(5), 665–687. doi:10.1007/ s00778-013-0324-z Doan, A., Ramakrishnan, R., & Halevy, A. Y. (2011). Crowdsourcing systems on the world-wide web. Communications of the ACM, 54(4), 86–96. doi:10.1145/1924421.1924442 Estellés-Arolas, E., & González-Ladrón-de-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science, 38(2), 189–200. doi:10.1177/0165551512437638

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Geiger, D., Rosemann, M., Fielt, E., & Schader, M. (2012). Crowdsourcing information systemsdefinition, typology, and design. Academic Press. Geiger, D., & Schader, M. (2014). Personalized task recommendation in crowdsourcing information systems—Current state of the art. Decision Support Systems, 65, 3–16. doi:10.1016/j. dss.2014.05.007 Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69(4), 211–221. doi:10.1007/s10708-007-9111-y Grant, T. J., & van den Heuvel, G. G. A. (2010, May). Modelling the Information Sharing Process in Military Coalitions: A work in progress. Proceedings of the 7th International ISCRAM Conference. Haythornthwaite, C. (2009, January). Crowds and communities: Light and heavyweight models of peer production. In System Sciences, 2009. HICSS’09. 42nd Hawaii International Conference on (pp. 1-10). IEEE. Howe, J. (2006). The rise of crowdsourcing. Wired Magazine, 14(6), 1-4. Howe, J. (2008). Crowdsourcing: How the power of the crowd is driving the future of business. Random House.

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King, A. J., Gehl, R. W., Grossman, D., & Jensen, J. D. (2013). Skin self-examinations and visual identification of atypical nevi: Comparing individual and crowdsourcing approaches. Cancer Epidemiology, 37(6), 979-984. Lasecki, W. S., & Bigham, J. P. (2012, October). Online quality control for real-time crowd captioning. In Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility (pp. 143-150). ACM. doi:10.1145/2384916.2384942 LaToza, T. D., & van der Hoek, A. (2016). Crowdsourcing in Software Engineering: Models, Motivations, and Challenges. Software, IEEE, 33(1), 74–80. doi:10.1109/MS.2016.12 Lease, M. (2011). On Quality Control and Machine Learning in Crowdsourcing. Human Computation, 11, 11. Lebraty, J. F., & Lobre-Lebraty, K. (2013). Crowdsourcing: One step beyond. John Wiley & Sons. doi:10.1002/9781118760765 Malone, T. W., Laubacher, R., & Dellarocas, C. (2010). The collective intelligence genome. MIT Sloan Management Review, 51(3), 21. Meyer, A. N., Longhurst, C. A., & Singh, H. (2016). Crowdsourcing Diagnosis for Patients With Undiagnosed Illnesses: An Evaluation of CrowdMed. Journal of Medical Internet Research, 18(1), e12. doi:10.2196/jmir.4887 PMID:26769236 Moon, J. Y., & Sproull, L. S. (2008). The role of feedback in managing the Internet-based volunteer work force. Information Systems Research, 19(4), 494–515. doi:10.1287/isre.1080.0208 Nylund, M., & Gopalkrishnan, A. (2014). Crowdsourcing in Media. Academic Press. Peddibhotla, N. (2013). How individuals choose topics to contribute at an online context. Electronic Markets, 23(3), 241–250. doi:10.1007/ s12525-013-0125-7

Pedersen, J., Kocsis, D., Tripathi, A., Tarrell, A., Weerakoon, A., Tahmasbi, N.,... De Vreede, G. J. (2013, January). Conceptual foundations of crowdsourcing: A review of IS research. In System Sciences (HICSS), 2013 46th Hawaii International Conference on (pp. 579-588). IEEE. doi:10.1109/ HICSS.2013.143 Pilz, D., & Gewald, H. (2013). Does Money Matter? Motivational Factors for Participation in Paid-and Non-Profit-Crowdsourcing Communities. Wirtschaftsinformatik, 37. Poetz, M. K., & Schreier, M. (2012). The value of crowdsourcing: Can users really compete with professionals in generating new product ideas? Journal of Product Innovation Management, 29(2), 245–256. doi:10.1111/j.1540-5885.2011.00893.x Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. doi:10.1006/ceps.1999.1020 PMID:10620381 Scharl, A., Sabou, M., Gindl, S., Rafelsberger, W., & Weichselbraun, A. (2012). Leveraging the wisdom of the crowds for the acquisition of multilingual language resources. Academic Press. Schlagwein, D., & Bjørn-Andersen, N. (2014). Organizational learning with crowdsourcing: The revelatory case of LEGO. Journal of the Association for Information Systems, 15(11), 754. Sharma, M., & Padmanaban, R. (2014). Leveraging the Wisdom of the Crowd in Software Testing. CRC Press. doi:10.1201/b17483 Sprugnoli, R., Moretti, G., Fuoli, M., Giuliani, D., Bentivogli, L., Pianta, E.,... Brugnara, F. (2013, May). Comparing two methods for crowdsourcing speech transcription. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 8116-8120). doi:10.1109/ICASSP.2013.6639246

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Stol, K. J., & Fitzgerald, B. (2014, May). Two’s company, three’s a crowd: a case study of crowdsourcing software development. In Proceedings of the 36th International Conference on Software Engineering (pp. 187-198). ACM. doi:10.1145/2568225.2568249 Tilson, D., Lyytinen, K., & Sørensen, C. (2010). Digital Infrastructures: The Missing IS Research Agenda. Information Systems Research, 21(5). Tong, Y., Cao, C. C., & Chen, L. (2014, August). Tcs: efficient topic discovery over crowd-oriented service data. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 861-870). ACM. doi:10.1145/2623330.2623647 Von Ess, H. (2010). Crowdsourcing: How to find a crowd. Berlin: ARD/ZDF Medien Akademien. Whitla, P. (2009). Crowdsourcing and its application in marketing activities. Contemporary Management Research, 5(1). Wiggins, A., & Crowston, K. (2012, January). Goals and tasks: Two typologies of citizen science projects. In System Science (HICSS), 2012 45th Hawaii International Conference on (pp. 3426-3435). IEEE. Wu, B., Zhong, E., Tan, B., Horner, A., & Yang, Q. (2014, August). Crowdsourced time-sync video tagging using temporal and personalized topic modeling. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 721-730). ACM. doi:10.1145/2623330.2623625 Yi, S. K. M., Steyvers, M., Lee, M. D., & Dry, M. (2011). Wisdom of the crowds in traveling salesman problems. Memory & Cognition, 39, 914–992. PMID:21264578

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Zhai, Z., Hachen, D., Kijewski-Correa, T., Shen, F., & Madey, G. (2012, January). Citizen engineering: Methods for” crowdsourcing” highly trustworthy results. System Science (HICSS), 2012 45th Hawaii International Conference on, 3406-3415. Zhang, H., Horvitz, E., Miller, R. C., & Parkes, D. C. (2011). Crowdsourcing general computation. Academic Press. Zhang, L., Tang, L., Luo, P., Chen, E., Jiao, L., Wang, M., & Liu, G. (2012, August). Harnessing the wisdom of the crowds for accurate web page clipping. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 570-578). ACM. doi:10.1145/2339530.2339621

ADDITIONAL READING Brabham, D. C. (2008). Crowdsourcing as a model for problem solving an introduction and cases. Convergence (London), 14(1), 75–90. doi:10.1177/1354856507084420 Doan, A., Ramakrishnan, R., & Halevy, A. Y. (2011). Crowdsourcing systems on the world-wide web. Communications of the ACM, 54(4), 86–96. doi:10.1145/1924421.1924442

KEY TERMS AND DEFINITIONS Crowd: A group of people from geographically distributed locations, different professions, and different cultures. Crowdsourcing Platform: A more generic online crowdsourcing system, designed for multipurpose. It can be used for multiple tasks such as product design, rating or voting and data collection tasks. An example is Amazon Mechanical Turk.

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Open Source: The act of developing software for free usage and distributions. It is not only for free usage, but also, for free modifications. Outsourcing: The process used by companies to get a product or a service done externally by other people or companies. User Incentives: Motivational factors that are used to encourage the crowd to participate in a crowdsourcing activity.

Web 2.0: A set of tools such as social media, wikis and blogs, which made it possible for people to create and share their own contents on the web. The further advancements of using the Semantics and linkage are called Web 3.0.

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Revisiting Web 2.0 Michael Dinger University of South Carolina Upstate, USA Varun Grover Clemson University, USA

INTRODUCTION Though well over a decade old now, the term Web 2.0 was intended to capture a wave of technologies that indicate advancement beyond the original Web 1.0, which was dominated by tightly controlled, relatively static websites. As such, Web 2.0 represents a set of technologies that enable high levels of interactivity and participation via the internet. It is an umbrella term that describes a variety of dynamic and community-based web initiatives that place value on the power of distributed knowledge, leverage data, and provide users with rich multimedia experiences (O’Reilly, 2005). Web 2.0 technologies have given rise to the dominance of social media which over 65% of U.S. adults use (Perrin, 2015). Furthermore, Web 2.0 technologies are now inherent throughout the modern web, but are also increasingly embedded throughout the milieu of digital technology, including mobile platforms and cloud services. Web 2.0 technologies and their associated changes in internet usage enable new forms of data collection and data analytics, including social network analysis and social media analytics (Chen, Chiang, & Storey, 2012). As a result, it is useful to revisit the core technological components of the Web 2.0 wave and to consider how these elements have become embedded throughout modern business applications and how the technologies can add value for consumers and businesses. Cutting edge firms can leverage such technologies to create integrated solutions that improve their relationships with customers and enrich customer experiences.

Many businesses create additional value for users and customers by leveraging the voluntary participation of users through interactive technologies. For example, Amazon offers extensive product support through an interactive set of tools supporting customer interactions. Customers can leave product reviews for their past purchases. However, Amazon also provides a question and answer system, wherein prospective customers can ask questions about a product and previous buyers are able to respond, with the answers posted on the product listing page. The application of interactive technologies enables Amazon to create value without extensive organizational labor, but instead harnesses the power of their existing customer base to create valuable content. Businesses continue to capitalize on this set of technologies in a variety of ways. Many companies leverage interactive technologies by capturing customer data and leveraging it to generate instantaneous, custom-tailored customer experiences (Bodoff & Ho, 2015). For example, Netflix aggregates and analyzes subscriber movie preferences in order to provide accurate movie recommendations. Similarly, Pandora, the internet music service, creates customized radio stations for individuals based on their expressed preferences. However, Pandora also mines user data to customize targeted advertising to each user (Singer, 2014). Furthermore, businesses can leverage Web 2.0 technologies in order to dynamically cooperate with customers and partners in efforts to generate new design innovations (Roberts & Dinger, 2016; Roberts & Grover, 2012; Wong, Peko, Sundaram,

DOI: 10.4018/978-1-5225-2255-3.ch699 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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& Piramuthu, 2015). Both online and traditional businesses must understand how to navigate and capitalize on the changing internet terrain to stay competitive in the Web 2.0 era.

BACKGROUND Web 2.0 thinking emphasizes the distributed and interactive nature of information technologies. Therefore, the core concept of a web page is altered to allow for quick and efficient interaction from users. This mindset is represented in the way that users can create, remove or edit informational content on wikis, comment on blogs or content aggregation sites like Reddit, or drive the content of media-sharing sites like YouTube. The distributed nature of Web 2.0 technologies allows many users to create and participate while needing little technical knowledge.

Characteristics of Web 2.0 Web 2.0 technologies can be identified by a number of common characteristics. These technologies generally capitalize on the ability of website users to actively participate, including the capacity to dynamically contribute content and network with other users. Web 2.0 initiatives are dynamic in nature, enabling constant change and updates. Also, Web 2.0 technologies regularly include social networking elements which enable users to form connections with one another. Finally, these endeavors are noted for their reliance on the distributed contributions of many participants.

Distributed Contributions The primary driver behind Web 2.0 technologies are the ability of firms to harness the value of distributed contributions from many users. Wikis inherently rely on the contributions and efforts of many users. The intent of wikis is to represent a culmination of the knowledge of all participating users. Media platform sites like YouTube, Instagram, Tumblr and Flickr entirely consist of user

contributions. Facebook has opened up its software platform so that users can create and contribute original applications (developers.facebook.com). Similarly, Apple has enabled a wide range of developers to create new and innovative applications for the iOS ecosystem (developer.apple. com) as has Google with the Android ecosystem (developer.android.com). The ability to harness the distributed contributions of many participants plays a significant role in a firm’s ability to generate value from these initiatives.

Dynamic Nature The dynamic nature of Web 2.0 technologies is driven by their ability to be quickly changed. A core design element of wikis is the ability to add, remove or change content quickly. On social networking sites, users are able to add, modify and remove content ease. Content platforms like Twitter, Instagram, and Vine thrive on the speed of content creation and sharing. By focusing on a small piece of content, users can rapidly create and share content, such as a 140 character post, an image, or a 6 second video, respectively. The speed of movement can be a double-edge sword, as it enables companies to quickly interact with customers to resolve problems, or for upset users to share stories of bad customer service that go viral. For example, a United Airlines customer created a YouTube video to share how the airline broke his guitar and would not reimburse any of the damages, and a $1,200 guitar ended up costing the airline far more in bad publicity (Sawhney, 2009). In another case, a Comcast customer service representative would not simply comply with a customer’s request to cancel their service. The customer recorded the call and posted it online on SoundCloud, where it went viral with bad publicity for Comcast (Diamond, 2015).

Rich Media Rich media is a common characteristic of Web 2.0 technologies. Many online sites and app platforms are compiled solely of user-generated 8037

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content, including YouTube, Instagram, Tumblr and Flickr, but rich media can enhance any user experience. For years, many news sites, such as CNN.com and ESPN. com, have embedded video in conjunction with print stories. However, the technology has become ubiquitous, with local TV channels and newspapers commonly posting video in conjunction with news stories.

Social Networking Elements Social networking leverages people-to-people interactions. For example, blog users can form connections to other bloggers. Analysis suggests that users of media sharing websites, specifically YouTube, can engage in social networking activity through the manner in which they manipulate access to their contributed media (Lange, 2007). Finally, social networking platforms can be embedded in a variety of different websites.

ARCHETYPES OF WEB 2.0 The Web 2.0 trend popularized three general archetypes of technology: blogs, wikis, and social networking sites (SNS). Blogs, short for weblogs, are perhaps the most traditional of the technologies and are essentially websites that can be dynamically and regularly updated. Wikis are information-distribution sites that exist through the collective efforts of many users. Wikis are intended to efficiently harness the collective knowledge of all willing participants. In general on a wiki, all users can generate original content and edit or remove published content. The core concept of the wiki is that all changes can be made, or undone, quickly. Social networking sites represent the ability of internet technologies to connect users and provide a platform that enables these connections.

Blogs Blogs are simple websites that are largely defined by the fact that they are updated easily and regu8038

larly. Blogs are inherently flexible and can be used for a variety of purposes, ranging from knowledge management initiatives (Sultan, 2013) to customer engagement tools (Brodie, Ilic, Juric, & Hollebeek, 2013) or potential sources of business intelligence (Chau & Xu, 2012). In fact, blogs have long been challenging traditional media outlets for the attention of many online users (Singh, Veron-Jackson, & Cullinane, 2008). Blogs can serve as rallying points for people of similar interests, such as consumer advocates at the Consumerist (www.consumerist.com), and organizations can also offer their own blogs as a platform on which to interact directly with customers. Blogs can serve as a platform to gain the attention of an organization (Roberts & Dinger, 2016), as they not only enable individuals to publically voice their own concerns, but entire communities can form regarding a common cause. For example, in 2005, a writer for BusinessWeek chronicled his complaints with Dell on his own personal blog, called Dell Hell (Jarvis, 2007). Eventually, he penned an open letter to Michael Dell and challenged him to respond to the public complaints and concerns of bloggers. The following year, Dell capitalized on the chance to positively interact with their customers by creating their own blog (Fournier & Avery, 2011).. Within-firm blogs offer a platform for individual employees to express themselves inside the firm. These repositories can be used to store thoughts or voice opinions, and as such, internal blogs can be used as s a knowledge repository and to enhance knowledge sharing (Baxter & Connolly, 2013). Executives have been regularly using internal blogs to publicize statements within firms and connect with their own employees (Gaines-Ross, 2013). Firms and CEOs can use a public blog in order to provide a ‘face’ for the organization (Vidgen, Mark Sims, & Powell, 2013). The publication of a blog enables the firm to interface directly with consumers. By allowing customers to interact directly with the firm, the organization may seem more human and downto-earth (Singh et al., 2008). While the blog can be used for impression management, it can also be

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used to gauge consumer reactions to changes or investigate the potential for new products. For instance, Facebook makes public announcements on their blog and updates users on potential changes to Facebook (blog.facebook.com).

Wikis Wikis are a type of technology that allows many users to combine their collective knowledge (Gaines-Ross, 2013). The wiki is a type of technology that also embodies a specific mindset towards the accumulation of knowledge. Wikis are designed such that participants can quickly and easily create new wiki pages, edit existing wiki pages, and trim unnecessary wiki pages. Wikis offer the ability for many users to converge on a given topic. As such, wikis represent an excellent technological platform for knowledge generation and can be harnessed to inspire innovations and growth (Biasutti & Heba, 2012). Wiki technology enables users to search for information, link other articles, author new articles, and tag articles (McAfee, 2006). Furthermore, wikis can use extensions to enhance user ability to find related content and set up signals which notify users of new content (McAfee, 2006). A popular source of online information, Wikipedia hosts over 5.1 million articles in English alone. However, the collaborative nature of public wikis raises concern about the validity of information contained in such a source (Reavley et al., 2012). Therefore, a number of checks are implemented to insure the validity of contributions to a given wiki topic. A common wiki tenet is to make it easy to correct mistakes. Changes are tracked and edits are easily undone. This makes it a simple task to undo vandalism. Firms have the opportunity to derive value from the collective of the public that participates in wikis. Wikis can form around any given subject. For example, users of the Linux-based operating system Ubuntu operate a wiki to assist all Ubuntu users (wiki. ubuntu.com). Firms have the opportunity to harness the power of their consumers by creating and driving

wikis that feature the firm’s products. By allowing consumers to create content revolving around the firm’s products, the firm is both developing the core consumer base in a positive manner and enabling the creation of additional value for any consumer that can benefit from this knowledge and experience. For instance, fans of Marvel properties have created the Marvel Database (marvel.wikia. com), which hosts over 150,000 articles created and moderated by the fan community. Internal wikis can be applied to a number of possibilities. Wikis offer value as tools for collaborative knowledge management (GainesRoss, 2013). The wiki becomes a repository of knowledge that users can improve over time. For example, one report suggests that the U.S. Government leverages wikis in a variety of ways, including an Army-based wiki on Afghanistan, an intelligence wiki full of non-classified information for government personnel (Intellipedia), and a wiki for diplomats (Diplopedia) (Bell, 2009). Wikis are relatively easy to set up and modify, which makes them ideal for projects and routine use. Wikis can be created at the beginning of a project and then modified according to project progress. Alan Mansfield, a small business owner, estimates that using wikis to manage projects cuts down time spent on each project by 25 percent (Miller, 2006). In running his literary agency, Mansfield uses wikis to “store drafts, e-mails, proposals, contracts, contacts, and anything else to do with the project” (Miller, 2006).

Social Network Sites Social network sites (SNS) use internet-enabled technologies to allow users to form or maintain social connections. Boyd and Ellison define social networks as “web-based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system” (2007 p 211). The core factors driving SNS are the platforms on which many users can 8039

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engage and interact with one another. SNS have become so extensive that firms are having trouble with employees who insist on accessing social networking sites during regular working hours (Andreassen, Torsheim, & Pallesen, 2014). While firms may view social networking as a distraction to employees, firms can seek to engage with social networking software in a valuegenerating manner. Many companies leverage social networking software internally in order to facilitate connections and exchanges between employees (Leonardi, Huysman, & Steinfield, 2013) and enhance knowledge sharing (Ellison, Gibbs, & Weber, 2014). Social networks also facilitate connecting potential participants for distributed innovation processes (Lee, Olson, & Trimi, 2012). As recruitment tools, social networks present a valuable tool that can be leveraged by recruiters to more thoroughly assess potential recruits (Davison, Bing, Kluemper, & Roth, 2016). Organizations can also use social networks and social media to engage in direct market research (Chen et al., 2012; Fan & Gordon, 2014). Also, social networks enable firms to create higher levels of customer engagement through positive discourse with customers (Sashi, 2012).

A FRAMEWORK FOR WEB 2.0 INITIATIVES This framework attempts to simplify and direct Web 2.0 initiatives. Due to the broad range of potential Web 2.0 initiatives, we propose a framework for understanding the value generated from these initiatives.

Value Derived within the Firm This part of the framework addresses the ability of the firm to generate value through Web 2.0 initiatives. These applications may take a variety of forms, but the focus is on the benefits of the internal applications of the Web 2.0 applications. These applications may take form in terms of

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knowledge management initiatives (Gaines-Ross, 2013; Sultan, 2013), project management efforts (Miller, 2006), and social networks that connect employees (Leonardi et al., 2013). Firms must understand the type of benefit they intend to achieve before installing Web 2.0 technologies. The application of multiple Web 2.0 endeavors allows for firms to integrate across technology, thus potentially giving rise to super-additive value due to the complex interactions between multiple Web 2.0 platforms (see Figure 1).

Value Derived from External Sources The firm should consider how to derive value from Web 2.0 instances that exist in the public space. The firm should evaluate all potential sources of access to these instances. Once the sources are identified, the firm can approach the issue of deriving value from these websites. For instance, firms can leverage social network sites to evaluate job applicants or to actively recruit individuals (Davison et al., 2016). Firms can also capitalize on externally-run blogs in order to understand the mindset and concerns of customers, as external sites provide access to the unfiltered complaints of customers (Fournier & Avery, 2011). Due to the open and simple nature of Web 2.0 technologies, many users are participants on multiple platforms. Furthermore, users, and Web 2.0-savvy businesses, form connections between multiple Web 2.0 initiatives. For instance, Facebook enables Instagram users to link their account to their Facebook account and post content to both Instagram and Facebook simultaenously. The challenge for firms is to identify potential sources of value and to effectively funnel value from those sources into the firm (see Figure 2).

Value Derived from the Interaction Between the Firm and Public Firms should evaluate how to generate value from dynamic interactions with their customers or other public sources. This proposition deals

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Figure 1. Value derived within the firm

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Figure 2. Value derived from external sources

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with two fronts: social and technical. The social aspect of this interaction deals with the actual exchange between firm and public. For instance, Dell responded to customer complaint blogs directly (Jarvis, 2007). Beyond interacting directly with customers and other external parties, a firm may choose to create a platform to facilitate this interaction. For instance, Dell chose to publish a blog (a technical issue) for the purpose of interacting with customers (a social issue) (Fournier & Avery, 2011; Jarvis, 2007). This suggests that firms must first understand and engage Web 2.0 technologies from a technical perspective before progressing to capitalizing on potential social interactions via firm-driven technologies. Furthermore, firms can capitalize on direct involvement with customers and third parties by creating a platform to drive distributed innovation (Lee et al., 2012). Many design initiatives focus

on distributing design and innovation processes within the firm. However, firms can leverage these technologies and allow customers to become involved in the creation processes (Frow, Nenonen, Payne, & Storbacka, 2015). In doing so, firms can engage in dynamic value generation in conjunction with customers and third parties. Firms can seek to integrate technologies within the firm and with external sources in order to reach out and provide a platform for dynamic social interactions and distributed innovation processes (see Figure 3).

Value Derived from Innovative Business Models Firms must stay current amidst the constant change of business models. For instance, firms are attempting to develop retail environments within online virtual worlds (Frow et al., 2015).

Figure 3. Value derived from the interaction between the firm and public

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Furthermore, firms may consider how to leverage social media and mobile applications to advance firm goals and create additional value for consumers and the firm (Aksoy et al., 2013). Online firms may focus on implementing aspects of Web 2.0 technologies into their existing business models. For instance, online retailing environments may enable individual sellers to connect their web stores, thus leading to a virtual mall for online shoppers (Stephen & Toubia, 2010). Firms, both online and traditional, must stay on top potential changes to their business model in order to be competitive in this fast-paced environment.

Risks Posed by Web 2.0 Initiatives In seeking to gain value from Web 2.0 endeavors, firms must not ignore the risks associated with these technologies. Primarily, firms must control their exposure regarding information technology security. While these technologies inherently connect individuals within the firm and may connect the firm to outsiders, firms must maintain tight security standards in order to prevent unauthorized access or the introduction and spread of harmful computer viruses. Similarly, firms should be wary of potentially unreliable information available from external sources. The potential for bad information in online settings is always a concern and is particularly relevant regarding wikis (Reavley et al., 2012), however, recent trends have tilted towards analytical study of large data sets derived from social media, which should iron out small fluctuations in data accuracy (Chen et al., 2012; Fan & Gordon, 2014).

CONCLUSION Web 2.0 technologies have not only changed the landscape of the internet, but continue to create a dynamic and changing competitive environment for many firms. Firms that aggressively implement Web 2.0 and social media technologies may

successfully develop competencies that create additional value and drive competitive advantage.

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Brodie, R. J., Ilic, A., Juric, B., & Hollebeek, L. (2013). Consumer engagement in a virtual brand community: An exploratory analysis. Journal of Business Research, 66(1), 105–114. doi:10.1016/j. jbusres.2011.07.029 Chau, M., & Xu, J. (2012). Business intelligence in blogs: Understanding consumer interactions and communities. Management Information Systems Quarterly, 36(4), 1189–1216. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. Management Information Systems Quarterly, 36(4), 1165–1188. Davison, H. K., Bing, M. N., Kluemper, D. H., & Roth, P. L. (2016). Social Media as a Personnel Selection and Hiring Resource: Reservations and Recommendations. In Social Media in Employee Selection and Recruitment (pp. 15–42). Springer. doi:10.1007/978-3-319-29989-1_2 Diamond, M. L. (2015). Viral power: Negative social media bad for business. Retrieved June 27, 2016, from http://www.usatoday.com/story/ money/business/2015/01/10/viral-power-negative-social-media-bad-for-business/21570851/ Ellison, N. B., Gibbs, J. L., & Weber, M. S. (2014). The use of enterprise social network sites for knowledge sharing in distributed organizations the role of organizational affordances. The American Behavioral Scientist. Fan, W., & Gordon, M. D. (2014). The power of social media analytics. Communications of the ACM, 57(6), 74–81. doi:10.1145/2602574 Fournier, S., & Avery, J. (2011). The uninvited brand. Business Horizons, 54(3), 193–207. doi:10.1016/j.bushor.2011.01.001 Frow, P., Nenonen, S., Payne, A., & Storbacka, K. (2015). Managing Co‐creation Design: A Strategic Approach to Innovation. British Journal of Management, 26(3), 463–483. doi:10.1111/14678551.12087

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Gaines-Ross, L. (2013). Get Social: A Mandate for New CEOs. MIT Sloan Management Review, 54(3), 1. Jarvis, J. (2007). Dell Learns to Listen. BusinessWeek. Lee, S. M., Olson, D. L., & Trimi, S. (2012). Co-innovation: Convergenomics, collaboration, and co-creation for organizational values. Management Decision, 50(5), 817–831. doi:10.1108/00251741211227528 Leonardi, P. M., Huysman, M., & Steinfield, C. (2013). Enterprise social media: Definition, history, and prospects for the study of social technologies in organizations. Journal of Computer-Mediated Communication, 19(1), 1–19. doi:10.1111/jcc4.12029 McAfee, A. P. (2006). Enterprise 2.0: The Dawn of Emergent Collaboration. Sloan Management Review, 47(3), 20–28. Miller, D. I. (2006). Collaborating through wikis. CNet. O’Reilly, T. (2005). What Is Web 2.0: Design Patterns and Business Models for the Next Generation of Software. Retrieved from http://www.oreillynet. com/pub/a/oreilly/tim/news/2005/09/30/what-isweb-20.html Perrin, A. (2015). Social Media Usage: 20052015. Retrieved May 15, 2016, from http://www. pewinternet.org/2015/10/08/social-networkingusage-2005-2015/ Reavley, N. J., Mackinnon, A. J., Morgan, A. J., Alvarez-Jimenez, M., Hetrick, S. E., Killackey, E., & Jorm, A. F. et al. (2012). Quality of information sources about mental disorders: A comparison of Wikipedia with centrally controlled web and printed sources. Psychological Medicine, 42(08), 1753–1762. doi:10.1017/S003329171100287X PMID:22166182

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Roberts, N. H., & Dinger, M. (2016). The Impact of Virtual Customer Community Interactivity on Organizational Innovation: An Absorptive Capacity Perspective. IEEE Transactions on Professional Communication, 59(2), 110–125. doi:10.1109/ TPC.2016.2561118 Roberts, N. H., & Grover, V. (2012). Leveraging Information Technology Infrastructure to Facilitate Firms Customer Agility and Competitive Activity: An Empirical Investigation. Journal of Management Information Systems, 28(4), 231–270. doi:10.2753/MIS0742-1222280409 Sashi, C. (2012). Customer engagement, buyer-seller relationships, and social media. Management Decision, 50(2), 253–272. doi:10.1108/00251741211203551 Sawhney, R. (2009). Broken Guitar Has United Playing the Blues to the Tune of $180 Million. Retrieved August 3, 2015, from http://www. fastcompany.com/1320152/broken-guitar-hasunited-playing-blues-tune-180-million Singer, N. (2014). Listen to Pandora, and It Listens Back. Retrieved June 2, 2016, from http://www. nytimes.com/2014/01/05/technology/pandoramines-users-data-to-better-target-ads.html?_r=0 Singh, T., Veron-Jackson, L., & Cullinane, J. (2008). Blogging: A new play in your marketing game plan. Business Horizons, 51(4), 281–292. doi:10.1016/j.bushor.2008.02.002 Stephen, A. T., & Toubia, O. (2010). Deriving value from social commerce networks. JMR, Journal of Marketing Research, 47(2), 215–228. doi:10.1509/jmkr.47.2.215 Sultan, N. (2013). Knowledge management in the age of cloud computing and Web 2.0: Experiencing the power of disruptive innovations. International Journal of Information Management, 33(1), 160–165. doi:10.1016/j.ijinfomgt.2012.08.006

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KEY TERMS AND DEFINITIONS Blogs: Short for ‘weblogs,’ blogs are simple, content-driven sites that are updated regularly. Distributed Contributions: The practice of leveraging the willing participation of users. Dynamic Content: Internet content that can be modified and uploaded quickly which keeps users ‘up-to-the-minute.’ Rich Media: Media, particularly images, video and sound, conveyed via internet technologies that provides a deeper user experience than simple text. Social Networking Elements: Web 2.0 applications embedded in web sites that enable users to uniquely identify and form connections with one another. Social Networking Sites (SNS): Web-based platforms which enable many individuals to create individual profiles, find and connect with other users. Web 2.0: An umbrella term that refers to an assortment of advances in internet technologies, marked by increases in rich media, dynamic content, social networking elements, and distributed contributions. Wikis: Content-driven sites which are editable by all participants and focus on harnessing the collective knowledge of all users.

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Search Engine Optimization Dimitrios Giomelakis Aristotle University of Thessaloniki, Greece Andreas A. Veglis Aristotle University of Thessaloniki, Greece

INTRODUCTION The extensive range of information resources and services is certainly one of the most important features of the Internet while at the same time, web search is considered as a crucial application for managing the massive volumes of distributed web content. Beyond argument, search engines have made an enormous contribution to the web by making the process of finding information online a very quick and easy process. Today, major search engines are considered to be the most common and trusted tool or service to retrieve information from the Internet (Spink & Xu, 2000). Also, they are the primary method used for navigation for hundreds of millions of users worldwide and one of the most common online activities (Purcell, 2011; Purcell, Brenner, & Rainie, 2012). The majority of Internet traffic depends largely on them (Safran, 2013) and thus, web search is one of the best sources of traffic for any website. However, it is true that the vast majority of all search traffic comes from the first or the first pages of search results as users usually focus on the top ranks. There are two ways an online user – customer will find a business website via a search engine: through a pay-per-click campaign (PPC) or through an organic result listing that is based essentially on what is called Search Engine Optimization or briefly, SEO. The latter can be defined as the process of affecting - improving the visibility of a website (or a web page) so that it ranks well for particular keywords in a search engine’s “natural” or “organic” (un-paid) search results (Ledford,

2009; Potts, 2007). Generally, the earlier, and more frequently a site appears in the search engine results page, the more visitors it will receive from the search traffic. In other words, it is a set of techniques that take into account the evaluation criteria of search engines regarding website content and structure (Giomelakis & Veglis, 2015a). There have been plenty studies regarding online users’ click behavior on search engine results pages. According to the results, 90 percent of search engine users never read beyond the third page of search results (iProspect, 2006). Also, the top listing in Google’s organic search results receivers 32.5 percent of the traffic, compared to 17.6 percent for the second and 11.4 percent for the third position. Finally, websites listed on the first page in Google’s results generate 92 percent of all traffic from an average search (Chitika, 2013). From all the above, it is evident that if a website is not in the first search results page or even worse is absent from the top 30, it has almost no chance of being read by a user (Clay, 2006). As a consequence, and while more and more websites are indexed by search engines and compete one another to ensure their own market share, it is clear that factors as the highest ranking and top of the results page become increasingly essential for businesses of all kinds (Enge, Spencer, & Stricchiola, 2015; Giomelakis & Veglis, 2015a). This chapter provides an overview of Search Engine Optimization, with a focus on its different characteristics as well its history and how it has evolved over the years. In order to give a better understanding of the importance of SEO in the

DOI: 10.4018/978-1-5225-2255-3.ch700 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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current state of the Internet and in information search, basic knowledge of how search engines operate along with their recent updates are also provided.

BACKGROUND Search engines are software that catalogs the World Wide Web and provides search using keywords into their vast databases containing full-text indexes of web pages. Users actually search this database of retrieved web pages, not the World Wide Web itself. As a consequence, they manage to take rapid results something that would be impossible if engines were trying to search billions of pages on the web in real time (Veglis, Pomportsis, & Avraam, 2004). When users click on search results they retrieve the current version of a web page. It is worth mentioning that search engines consist of three parts: the web crawler or spider, the indexer and also the query processor (Mudgil, Sharma, & Gupta, 2013). The crawler systematically browses the World Wide Web, looks at every URL (Uniform Resource Locator) collecting keywords and phrases on each page, which are then included in a massive database. The crawler is also responsible for keeping indexed pages up to date (Ledford, 2009). Search engines start with a set of very high quality sites and then visit the links on each page (of those sites) to discover other web pages. This complex process repeats over and over again until the crawling is complete. Through links, web crawlers (i.e. automated robots) can reach the many trillions of interconnected documents (Enge, Spencer, & Stricchiola, 2015). Search engines use algorithms so as to find and collect information about web pages. Generally, a search algorithm can be characterized as a problem-solving procedure that takes the problem (i.e. the users’ word or phrase), sifts through a vast database of cataloged keywords with their URLs, and then returns a listing of best-matching web pages according to the search criteria. Search results (i.e. page ranking) usually depend on the

perceived quality-importance of the page (quality score - links from other sites) conforming to the algorithm that’s being used as well as on relevance (relevant terms/links) and several other factors such as frequency of keywords, location, age or click- through rates (Ledford 2009; Enge, Spencer, & Stricchiola, 2015). It is worth mentioning that in the case of Google, the famous PageRank (one of its oldest algorithms) is a quality metric used in ranking (one out of many) that measures the importance of web pages by counting the number and quality of links to a page (Page, Sergey, Rajeev, & Winograd, 1998; Grappone & Couzin 2008). PageRank (PR) is a numerical measurement (0–10, the higher the better) and in the past it was one of the most important factors. Google has continuously improved the way it uses links to impact rankings (one reason is to deal with link spam), and its current algorithm is not based on PR as it was originally defined (Enge, Spencer, & Stricchiola, 2015). However, it still has some value in SEO (Grappone and Couzin 2008; Slegg 2013; Giomelakis & Veglis, 2015b). Also, it is true that PR had become an obsession for many SEO workers worrying about their numbers and examining closely the quality of every new link. The last Pagerank update was in December 2013 and on April 15, 2016 the search engine has officially shut down the PageRank data to public (Southern, 2016; Schwartz 2016). In brief, search algorithms can be classified into three broad categories: on-page algorithms that measure onpage factors looking at the elements of every page (e.g. keywords in content or meta tags), wholesite algorithms that focus on the relationship of site elements (e.g. anchor text, linking between pages or architecture of pages) and finally, offsite algorithms that explore the links between the websites. All these three types are generally part of a larger algorithm (Ledford, 2009). Search Engines have evolved dramatically over time and they constantly “crawl” the web to update their databases with new information. According to statistics, Google is the world’s most popular search engine having the vast majority

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(over 2/3) of the global search engine market followed by Bing, Yahoo and Baidu, China’s most popular search engine (Netmarketshare, 2016). In a comScore survey (2015) data showed that Google sites led the U.S. explicit core search market with 64.5 percent market share, followed by Microsoft sites with 19.8 percent and Yahoo! sites with 12.8 percent.

HISTORY OF SEO SEO constitutes a part of Search Engine Marketing (SEM) and it is considered one of the leading and most influential activities in the field of online marketing, hence there is a wealth of information and tools designed to train and support the community (Potts, 2007; Nigro, Balduzzi, Cuesta, & Cisaro, 2012). Historically, the actual phrase “Search Engine Optimization” was possibly first coined in 1997 by an unknown person. Search guru Danny Sullivan found for the first time the term used in May 1997 in a meta tag on his website, Search Engine Watch. However, it is quite possible that it was used before that. Sullivan acknowledges Bruce Clay as one of the first people in the industry to popularize the term (Sullivan, 2004; Dover, 2011). SEO has always been associated with the influence of search engine results and its creation is rationally connected with the creation of the first search engines in the early-mid 90’s. Danny Dover describes (2011) that early SEO experts used to take advantage of alphabetical (“AAA”, “1ForU”) and chronological (submit websites at certain times) order to get to the top of rankings. Since then, things have changed considerably. Search engines improved using more complicated algorithms and at the same time, SEO became a more profitable business. Over time, site owners started to identify the worth of having their websites highly ranked and visible in online search results. They also noticed that the higher their website ranked, the more people would click on their content. From a simple URL submis-

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sion initially and use of unsophisticated tactics, companies started to implement strategies such as keywords insertion, link building (process of obtaining hyperlinks from other websites), and recently social media utilization. From the early days of search engines until today many SEO users have attempted to deceive search engines in order to artificially improve their visibility. That is usually called as “blackhat SEO” and pertains to the use of strategies or tactics (e.g. excessive use of keywords, hidden text or pages) that are used to get higher rankings in an unethical manner, doesn’t obey search engines guidelines for webmasters or involve deception. In general, it is a practice that violates the search engines’ terms of service and possibly results in a website being banned from a search engine and affiliate sites (Grappone & Couzin 2008; Ledford 2009). A remarkable example of penalizing was Google removal of BMW Germany and Ricoh Germany for using misleading tactics in 2006 (Cutts, 2006). As stated, search engines have evolved tremendously in recent years and one of the most basic reasons that their algorithms are in a constant change is due to the need to address “Black Hat SEO” and provide the best possible results. Thus, they developed more complex ranking algorithms, considering additional factors that were more difficult for webmasters to manipulate.

SEO CHARACTERISTICS There are definitely myths about search engines. For example, that attaining a top spot in the organic, natural results requires actually paying Google and other search sites. On the contrary, a high position in search results requires actually worthy content coupled with time, effort, and skill (Potts, 2007). SEO can be applied to many different websites to some degree and can target different types of search, including image - video search, local, news or academic search (Beel, Gipp, & Wilde, 2010; Giomelakis & Veglis, 2015b; Wikipedia, 2016). Arguably, it is very closely connected to

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e-Commerce websites (Malaga, 2007). According to a relevant study, 89 percent of consumers turn to search engines to find information on products, services or businesses before making purchases (Fleishman-Hillard & Harris Interactive, 2012). Given that it takes into account how search engines work and the actual terms that people search for, there are several factors that affect SEO but unfortunately, there is no magic recipe for the first page and certainly no guaranteed results. By contrast, there is a variety of things to consider that in conjunction can bring good results. Some of them can directly affect SEO while others can have an indirect impact on search rank. Everything has its value and Google, Bing or other search engines use them to evaluate and rank web pages in their results. Although there are differences in the ways various search engines work, the basic rules remain similar and a well-planned SEO strategy can usually bring good results in all major search engines (Giomelakis & Veglis, 2015a). SEO strategies can be divided into four main categories: keyword research/selection, search engine indexing, on-page optimization and offpage optimization (Malaga, 2008). On-page optimization refers to the management of all factors related directly to someone’s website (content – appropriate keywords, architecture and internal link structure as well as html elements). More specifically, this category contains among others, page titles (or HTML titles tag that usually appear in search results), on-page headlines, description of web pages (or meta description tag) and URL’s. Using main, appropriate words/ phrases that people often use in their searches in all the above elements is crucial to SEO. However, it should be noted that the use of keywords to a great extent in the actual content of a web page is by all means not a key requirement. In contrast, it is effective when this happens naturally without boring the readers. Besides, keyword stuffing (i.e. the practice of overloading a web page’s content with keywords) can bring unpleasant results to a website (Giomelakis & Veglis, 2015a). Further-

more, as mobile devices become more prevalent in daily life, a good user experience across all devices (desktop, tablet or smartphone) has become a significant consideration in SEO strategy (mobile SEO). Google has updated its algorithm in this direction (it took the name “Mobilegeddon”) given that in many countries around the world including the US and Japan, more searches take place on mobile devices than on computers (Makino, Jung, & Phan, 2015; Dischler, 2015).

Examples of SEO Practices 1. Page titles/headlines a. World Cup 2014: Brazil humiliated by Germany 7-1 in semi-final / Novak Djokovic beats Roger Federer in epic Wimbledon 2014 men’s final (SEO-Friendly) b. Germany’s triumph in the semi-final / Djoko did it again! (Non-SEO Friendly) 2. Url’s www.newsandseo.com/2014/seo-bestpractices-for-structuring-urls.html (SEO-Friendly) www.newsandseo.com/2014/how-to-createseo-friendly-urls.html (SEO-Friendly) www.newsandseo.com/2014/new/item. aspx?itemid=3051.html (Non-SEO Friendly) www.newsandseo.com/2014/forum/ viewtopic/m-159327.html (Non-SEO Friendly) 3. Different titles/headlines This practice is used by several news organizations (e.g. BBC News, Guardian, Huffington Post or New York Times) and the most common is the different headlines between home page or other Web site indexes and the story page itself (see figure 1). The latter that appears in search results, is usually more specific including more keywords for the topic.

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Figure 1. The dual headline system in BBC news

Regarding off-page optimization, it includes all the efforts made away from someone’s website like link building or social signal strategy. Links from other websites (called backlinks or inbound links) have great importance, serve as votes for the value and are a basic requirement for a web page to be found both from search engines and online searchers. In addition, it is highly important for inbound links to be qualitative, coming from well-known, trusted websites in order to give much more value and better evaluation. There are many different ways to gain inbound links such as articles/domain submission to directories (e.g. Dmoz), forum, blogs, and news aggregators as well as building relationships and links exchange with other websites. It is true that for a website with quality content, it is usually a matter of time until it gains many natural and qualitative backlinks. However, not only inbound links but also external links that lead to other relevant web pages (preferably credible and high-ranking sites) can contribute positively especially if a website is new (Giomelakis & Veglis, 2015a).

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SEO, SOCIAL MEDIA AND CONTENT The development of the World Wide Web (Web 2.0) over the last decade has affected and brought significant changes in web content such as usergenerated content and social web. Furthermore, search technology itself evolved over the last years with the growth of contemporary search engines something that has arguably affected SEO. Nowadays, the optimization for search engines is not anymore only about link-building and keywords. Social media and social signals (such as Facebook Shares/likes) have increasingly become one of the many factors search engines take seriously into account. In order to enhance their perfect reach, many search engines incorporate information from Web 2.0 services into their searchable indexes (Zimmer, 2008). For instance, active Facebook profile pages have many ranking possibilities for branded, subject-based and personal name-based queries at Google results. Also, due to Twitter’s domain authority, its profile pages also rank highly for relevant searches. Results from Twit-

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ter can be used for queries where real-time data are helpful (Dover, 2011). Regarding Google+, its characteristics make it a far superior platform for SEO and sharing content on it can possibly influence search rankings in significant ways (Shepard, 2013). In this context, recent studies from Searchmetrics (2013) and Moz’s experts (Peters, 2013) confirmed how important social media signals are to SEO ranking, especially Google +1s. Social signals, in general, have a positive impact on websites and are considered as the new link building metric as search engines increasingly search for social signals to help the ranking of pages (Rayson, 2013; Frasco, 2013). The more referrals someone has across the web, the more search engine spiders notice and categorize their content (Ehrlich, 2013). Social media and SEO do overlap, and the former can contribute to the total organic success of websites in several ways (Pascale, 2014). Today, recent algorithm changes from search engines especially in Google (e.g. Penguin, Panda, and Hummingbird) have added more value to quality as well as content marketing that many experts have called it as the new SEO (DeMers, 2013). For example, the change of Panda algorithm (February 2011) was made in order to eliminate low-quality websites in favor of those with frequently updated, in-depth content. Also, Google Penguin (April 2012) was the response to “Black-hat” SEO and web spam and finally Hummingbird (late 2013) added the contextual search, a way to interpret content by looking at the relationship between terms (Rampton & DeMers, 2014). The world of SEO is constantly evolving and thus, SEO workers have to be always up to date on new developments and also utilize useful tools and services for their work. Tools such as Google Analytics (the most widely known web analytics package), Google PageSpeed Insights (analyzes the performance of a web page providing information on how to improve the load time), Google Search Console (previously Google Webmaster Tools that enable webmasters to monitor and maintain their presence in search results), Google Trends (a tool to iden-

tify “hot” topics), SEMrush (software for online marketing) or finally MozBar (for the page and domain authority of a website) are very popular in SEO industry. It is crucial to understand that SEO is not only about engines but also making someone’s website (and its content) user-friendly and better for people. The content is still the most important thing for any website and this has to be reliable and interesting. Even search engines look now for unique, qualitative and useful content that will satisfy and motivate readers to share it through social networks, blog posts or forums. In spite of how good is SEO, it is not likely for any website with useless content to get ranked well in the long term because no one is ever going to visit or look for it. Besides, Google and other search engines got much smarter and can distinguish between content with real value and content that exists only for SEO purposes.

FUTURE RESEARCH DIRECTIONS Beyond argument, there are several important aspects in this field that require further study. For instance, the increasing involvement of social media in SEO strategy is an area of research that has to be studied more in order to better clarify the relation between them. And that’s because there is a strong indication that social networks and social media, in general, will continue to increase their significance and presence in the internet population. It is true that web search is increasingly becoming more personalized and user influenced. For many years, SEO and search results were all about keywords and link building. Nowadays, things are changed, the search results are arguably better and the search experience is much more advanced. SEO experts need to take the audience, their interests, user experience or what device they are using more into account when developing their strategies. All the above are issues that deserve more careful attention in future research because these are elements intimately related with search engines. In this context

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and as the web ecosystem evolves, the so-called mobile, local or vertical search-SEO has grown considerably gaining significant attention from field experts. Also, we would say that the mobile landscape has evolved to such an extent that it has become a completely unique (mobile-first) search environment. All these new types of SEO along with the opportunities they offer in online industry are areas that need to be addressed in future studies.

ing imperative to get your information seen and keep ahead of the competition, companies will continue to figure out ways to use SEO-related techniques for higher rankings. The good use of SEO certainly does not guarantee high traffic and top rank. Nevertheless, an effective strategy can play its role by helping companies to obtain the best results and take advantage of the benefits provided through the utilization of search engines and web search in general.

CONCLUSION

REFERENCES

Undeniably, search engines have come a long way since their appearance in the early 90’s. In this context, SEO grew out of the development of search engines and the World Wide Web. As the years pass, there is no question that search engines will continue to evolve their algorithms and make improvements in their ability to crawl and index new types of content such as multimedia or data coming from social media. Over time, there have been fundamental shifts in the SEO landscape and field experts see a shift away from traditional ranking factors to deeper analysis and factors such as quality, multi-form content and social signals (DeMers, 2013). Within this progress, social media have increasingly become one of the many factors search engines take into account and content is still the king (Zimmer, 2008; Dover, 2011). It might be thought that SEO will always have a place in a web strategy as long as Google and other search engines continue to drive a great amount of traffic on the Internet. The tactics involved in optimization may change, but the need will always remain. It is evident that every business that has a web presence (or wants to get clients from the web) would be wise to think about a SEO strategy as well as the potential it creates in order to increase or maintain its position within the search results. Implementing SEO into a website is becoming more and more necessary in today’s digitalized world. Given that people will always be on a quest for information, and given the market-

Beel, J., Gipp, B., & Wilde, E. (2010). Academic Search Engine Optimization (ASEO): Optimizing Scholarly Literature for Google Scholar and Co. Journal of Scholarly Publishing, 41(2), 176–190.

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Chitika Online Advertising Network. (2013, June 7). The Value of Google Result Positioning. Retrieved February 24, 2016 from http://chitika. com/google-positioning-value Clay, B. (2006). Put SEO in your site design. Bruce Clay. Retrieved March 13, 2016, from http://www. bruceclay.com/eu/seo/articles/seo_sitedesign.htm ComScore. (2015, March 17). comScore analysis of the U.S. search marketplace (February 2015). Retrieved February 15, 2016 from http:// www.comscore.com/Insights/Market-Rankings/ comScore-Releases-February-2015-US-DesktopSearch-Engine-Rankings Cutts, M. (2006, February 4). Ramping up on international webspam [Web log comment]. Retrieved February 10, 2016 from https://www.mattcutts. com/blog/ramping-up-on-international-webspam DeMers, J. (2013). SEO in 2013: 7 Surprisingly Simple Factors That Will Take The Lead. Search Engine Journal. Retrieved March 13, 2016 from http://www.searchenginejournal.com/seo-in2013-7-surprisingly-simple-factors-that-willtake-the-lead/57092

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Dischler. (2015, May 5). Building for the next moment [Web log comment]. Retrieved February 10, 2016 from http://adwords.blogspot.gr/2015/05/ building-for-next-moment.html Dover, D. (2011). Search Engine Optimization Secrets. Indianapolis, IN: Wiley. Ehrlich, S. (2013). SEO Best Practices: The Impact of Social Media on Search Engine Optimization. Bulldog reporter. Retrieved March 10, 2016 from https://www.bulldogreporter.com/ seo-best-practices-the-impact-of-social-mediaon-search-engine-opti/ Enge, E., Spencer, S., & Stricchiola, J. (2015). The Art of SEO. Mastering Search Engine Optimization (3rd ed.). O’Reilly. Fleishman-Hillard International Communications and Harris Interactive. (2012, January 31). Digital Influence Index. Retrieved March 5, 2016 from http://fleishmanhillard.com/2012/01/31/2012digital-influence-index-shows-internet-asleading-influence-in-consumer-purchasingchoices/ Frasco, S. (2013). 6 Reasons Social Media is Critical to Your SEO. Search Engine Watch. Retrieved March 13, 2016 from http://www. socialmediatoday.com/content/6-reasons-socialmedia-critical-your-seo Giomelakis, D., & Veglis, A. (2015a). Employing Search Engine Optimization Techniques in Online News Articles. Studies in Media and Communication, 3(1), 22–33. doi:10.11114/smc.v3i1.683 Giomelakis, D., & Veglis, A. (2015b). Investigating Search Engine Optimization Factors in Media Websites. Digital Journalism, 4(3), 379–400. do i:10.1080/21670811.2015.1046992 Grappone, J., & Couzin, G. (2008). Search Engine Optimization - an Hour a Day (2nd ed.). Wiley Publishing.

Iprospect. (2006, April). Search Engine User Behavior Study. Retrieved March 8, 2016 from http:// district4.extension.ifas.ufl.edu/Tech/TechPubs/ WhitePaper_2006_SearchEngineUserBehavior. pdf Ledford, J. L. (2009). Search Engine Optimization Bible (2nd ed.). Indianapolis, IN: Wiley. Makino, T., Jung, C., & Phan, D. (2015, February 26). Finding more mobile-friendly search results [Web log comment]. Retrieved February 11, 2016 from https://webmasters.googleblog. com/2015/02/finding-more-mobile-friendlysearch.html Malaga, R. A. (2007). The Value of Search Engine Optimization: An Action Research Project at a New E-Commerce Site. Journal of Electronic Commerce in Organizations, 5(3), 68–82. doi:10.4018/jeco.2007070105 Malaga, R. A. (2008). Worst Practices in Search Engine Optimization. Communications of the ACM, 51(12), 147–150. doi:10.1145/1409360.1409388 Mudgil, P., Sharma, A. K., & Gupta, P. (2013). An Improved Indexing Mechanism to Index Web Documents. Proceedings of the 5th International Conference on Computational Intelligence and Communication Networks (CICN). doi:10.1109/ CICN.2013.101 Net Market Share. (2016). Desktop Search Engine Market Share - February 2016, Market Share Statistics for Internet Technologies. Retrieved March 2, 2016 from https://www. netmarketshare.com/search-engine-market-share. aspx?qprid=4&qpcustomd=0 Nigro, H. O., Balduzzi, L., Cuesta, I. A., & Císaro, S. E. G. (2012). Knowledge Based System for Intelligent Search Engine Optimization. In F. L. Gaol (Ed.), Recent Progress in Data Engineering and Internet Technology (pp. 65–72). Berlin: Springer. doi:10.1007/978-3-642-28798-5_10

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Page, L., Sergey, B., Rajeev, M., & Winograd, T. (1998, January 29). The PageRank Citation Ranking: Bringing Order to the Web. Technical Report, Stanford InfoLab. Retrieved March 5, 2016 from http://ilpubs.stanford.edu:8090/422

Schwartz, B. (2016). Google Toolbar PageRank officially goes dark. Search Engine Land. Retrieved November 05, 2016 from http://searchengineland. com/google-toolbar-pagerank-officially-goesdark-247553

Pascale, A. (2014). 7 Legitimate Ways That Social Media Impacts SEO. Clickz. Retrieved March 10, 2016 from https://www.clickz.com/clickz/ column/2342211/7-legitimate-ways-that-socialmedia-impacts-seo

Search Engine Optimization. (n.d.). In Wikipedia. Retrieved from https://en.wikipedia.org/wiki/ Search_engine_optimization

Peters, M. (2013, July 9). 2013 Search Engine Ranking Factors (Moz’s Study) [Web log comment]. Retrieved February 10, 2016 from http:// moz.com/blog/ranking-factors-2013 Potts, K. (2007). Web Design and Marketing Solutions for Business Websites. New York: Apress. Purcell, K. (2011). Search and email still top the list of most popular online activities. Pew Research Center. Retrieved March 2, 2016 from http://www.pewinternet.org/2011/08/09/searchand-email-still-top-the-list-of-most-popularonline-activities/ Purcell, K., Brenner, J., & Rainie, L. (2012). Search Engine Use 2012. Pew Research Center. Retrieved March 2, 2016 from http://www.pewinternet.org/2012/03/09/search-engine-use-2012/ Rampton, J., & DeMers, J. (2014). The 2014 Beginner’s Guide to SEO. Search Engine Journal. Retrieved from https://www.searchenginejournal. com/seo-guide/

Searchmetrics. (2013). Search Metrics Ranking Factors and Rank Correlation. Retrieved March 2, 2016 from http://www.searchmetrics.com/en/ knowledge-base/ranking-factors-us-2013/ Shepard, C. (2013, August 20). Amazing Correlation between Google +1s and Higher Search Rankings [Web log comment]. Retrieved February 12, 2016 from http://moz.com/blog/google-pluscorrelations Slegg, J. (2013). Is Google Getting Ready to Retire PageRank? Search Engine Watch. Retrieved October 28, 2016 from http://searchenginewatch. com/article/2299954/Is-Google-Getting-Readyto-Retire-PageRank Southern, M. (2016). Google Confirms Toolbar PageRank is No More. Search Engine Journal. Retrieved November 05, 2016 from https:// www.searchenginejournal.com/google-pagerank/159112/ Spink, A. & Xu, J. L. (2000). Selected results from a large study of Web searching: The Excite study. Information Research, 6(1).

Rayson, S. (2013). The Social Media Optimization (SMO) of SEO: 7 Key Steps. Social Media Today. Retrieved March 11, 2016 from http://www. socialmediatoday.com/content/social-mediaoptimization-smo-seo-7-key-steps

Sullivan, D. (2004, June 14). Who Invented the Term “Search Engine Optimization”?. Search Engine Watch. Message posted to http:// forums.searchenginewatch.com/showpost. php?p=2119&postcount=10

Safran, N. (2013, June 25). Natural Web Site Traffic Accounts for Nearly Half of All Traffic [Web log comment]. Retrieved February 6, 2016 from http://www.conductor.com/blog/2013/06/webtraffic-natural-search-data/

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Zimmer, M. (2008). The Externalities of Search 2.0: The Emerging Privacy Threats When the Drive for the Perfect Search Engine Meets Web 2.0. First Monday, 13(3). doi:10.5210/fm.v13i3.2136

ADDITIONAL READING Adams, R. (2015). SEO 2016: Learn Search Engine Optimization. CreateSpace Independent Publishing Platform. Clarke, A. (2015). Search engine optimization 2016: Learn SEO with smart internet marketing strategies. CreateSpace Independent Publishing Platform. Dick, M. (2011). Search Engine Optimisation in UK News Production. Journalism Practice, 5(4), 462–477. doi:10.1080/17512786.2010.551020 Google. (2010). Search Engine Optimization Starter Guide. Retrieved February 15, 2016 from http://static.googleusercontent.com/media/www. google.com/en//webmasters/docs/search-engineoptimization-starter-guide.pdf McDonald, J. (2015). SEO Fitness Workbook, 2016 Edition: The Seven Steps to Search Engine Optimization Success on Google. CreateSpace Independent Publishing Platform. Moz. (2015). Moz’s Search Engine Ranking Factors 2015. Expert Survey and Correlation Data. Retrieved March 5, 2016 from https://moz.com/ search-ranking-factors

KEY TERMS AND DEFINITIONS Click-Through Rate (CTR): The ratio of users who click on a specific link compared to the number of total users who view an email, a web page or an advertisement. It is used to measure the success of an advertisement campaign. For example, if an advertisement had 10 clicks and 1000 impressions (number of times it was served), the CTR would be 10% (clicks / impressions x 100). Local Seo: The optimization process that focuses on results that are relevant to a user based on its current location. Organic Results: Also called «natural» or un-paid. The results that appear because of their relevance to the search terms. Social Signals: A kind of recommendations through social media. Social signals are the Facebook shares or likes, the tweets, Google +1s, pins or some other way of social media bookmarking or sharing. User-Generated Content (UGC): Any form of digital content such as images, video, status updates or blogs that is produced and shared by users of an online service or website, often made available via social media websites. Vertical Search: The search accessibility of specific segments and formats of online content. Vertical search services focus on specific topics such as news, restaurants or products. Google provides also vertical search such as Google news or Google images.

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Toward Trustworthy Web Services Coordination Wenbing Zhao Cleveland State University, USA

INTRODUCTION Many Web-based companies such as amazon.com, yahoo.com and twitter.com have been offering Web services to their partners and customers. Through such Web services, new value-added services have been provided and hence higher revenues have been generated. Essentially, the Web services technology is transforming the World Wide Web from a predominantly publishing platform to a programmable platform, which undoubtedly will make it easier to conduct business online, and enable automated business-tobusiness communications (Papazoglou, 2003; Zhao et al., 2008). The Web services technology is particularly useful for Application Service Providers that offer various on-demand services and software-as-a-service (SAAS) to their customers (Chakrabarty, 2007). Such service-oriented computing is attractive to many businesses because they can save valuable resources and money by avoiding installing and maintaining sophisticated enterprise software on-site. Furthermore, most of business interactions are transactional, which require well-defined standardized coordination support. To meet this requirement, a number of specifications have been proposed and rectified by OASIS (Feingold & Jeyaraman, 2009; Little & Wilkinson, 2009; Freund & Little, 2009). Inevitably, the dependability requirement of these Web services has been increasing because of the key role they play in e-businesses. One of the most well-known approaches to enhancing the trustworthiness of Web services coordination is Byzantine fault tolerance (BFT) (Zhao, 2014). The BFT technique employs space redundancy

(i.e., replication) and a replication algorithm that can tolerate malicious faults (often referred to as Byzantine fault) (Lamport et al., 1982; Castro & Liskov, 2002). In this article, we present an overview of our recent works on enhancing the trustworthiness of Web services coordination for business activities and transactions. The approach is based on what we call application-aware Byzantine fault tolerance. We argue that it is impractical to apply general-purpose Byzantine fault tolerance algorithms for such systems in a straightforward manner. Instead, by exploiting the application semantics, much lighter weight solutions can be designed to enhance intrusion tolerance, and hence the trustworthiness of systems that require Web services coordination.

BACKGROUND Web services interactions are becoming more and more complex in structure and relationships. More complex means we need longer time to execute them, because of business latencies and user interactions. The Web Services Coordination specification (WS-Coordination) (Feingold & Jeyaraman, 2009) describes an extensible framework for plugging in protocols that coordinate the actions of Web services applications. Such coordination protocols can be used to support a variety of business applications, including those that require strict consistency and those that require agreement of a proper subset of the participants. The framework enables a Web service to create a context needed to propagate an activity to other

DOI: 10.4018/978-1-5225-2255-3.ch701 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Web Technologies

Web services and to register for a particular coordination protocol. There are two types of business transactions. One follows the traditional atomic transaction semantics, and the other is referred to as business activities, which implies that the atomicity property may be relaxed. The former is suitable for short transactions that require strong atomicity, such as a fund transfer transaction. The latter is more suitable long running transactions, such as those used in supply chain management. Based on WS-Coordination, two specifications, namely Web Service Atomic Transaction (WS-AtomicTransaction) (Little & Wilkinson, 2009) and Web Service Business Activity (WS-BusinessActivity) (Freund & Little, 2009), were standardized by OASIS to address the coordination needs for common types of business transactions.

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Web Services Atomic Transactions The Web Services Atomic Transactions specification defines a coordination framework for Web services atomic transactions. In a distributed atomic transaction, all participants must reach the same final agreement as to whether the transaction has succeeded or not. This is ensured by the coordination mechanisms specified in WSAtomicTransaction. In WS-AtomicTransaction, there are three actors for each transaction: Completion Initiator, Coordinator and Participants. Each provides a different set of services for the atomic transaction, and they interact with each other via two protocols, the Completion Protocol and the Two-Phase Commit Protocol (Gray & Reuter, 1993) (Tanenbaum & Steen, 2002). The completion initiator is responsible to start and terminate a transaction. It also provides the Completion Initiator Service so that the coordinator can inform it the final outcome of the transaction, as part of the completion protocol. The coordinator provides the following services: •

Activation Service: At the beginning of a transaction, the initiator invokes the

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Activation Service for creating a coordinator object, which will generate a new coordination context for the transaction and return it to the initiator. The coordination context contains a unique transaction identifier and an endpoint reference for the Registration Service. This coordination context will be included in every request messages within the transaction boundary. Registration Service: The participants and the completion initiator use this service to register their endpoint references for other associated participant-side services. Later these endpoint references will be used by the coordinator to contact the participants during the two-phase commit. Coordinator Service: When a participant gets a Prepare request from the coordinator, it places its vote by invoking the coordinator service. The participants also use this service to notify the coordinator their acknowledgments to the commit/abort request. The participants obtain the endpoint reference of the Coordinator Service during the registration step. Completion Service: The initiator invokes this service to notify the coordinator to start a distributed commit. The Completion service, together with the Completion Initiator service on the participant side, implements the WS-AtomicTransaction completion protocol. The endpoint reference of the Completion Service is returned to the initiator during the registration step.

The participant provides the Participant Service, which allows the coordinator to solicit votes from, and to send the transaction outcome to the participants according to the two-phase commit protocol. The Completion Protocol is used by the completion initiator to initiate the atomic termination of a transaction. When the initiator decides that it is time to commit the transaction, it sends a Commit request to the coordinator. The coordinator

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will then launch an instance of the Two-Phase Commit (2PC) protocol to carry out the coordination for atomic commitment of the transaction. When the 2PC completes, the coordinator notifies the initiator the outcome of the transaction (i.e., Committed or Aborted). If the request from the initiator is Rollback instead, the coordinator will abort the transaction directly. The 2PC Protocol is used by the coordinator and participants to guarantee atomic commitment of a transaction, and it executes in two phases. During the first phase, i.e., the prepare phase, the coordinator sends a Commit request to all registered participants soliciting their votes. When the coordinator receives votes from all participants, or a timeout has occurred, it starts the second phase, i.e., the commit phase, to notify the participants the outcome of the transaction. 2PC has two variants used for different resources, Volatile 2PC and Durable 2PC. Volatile 2PC is used for volatile resources such as caches and Durable 2PC focuses on durable resources like a database. Participants must register in an appropriate protocol before the termination of the transaction. A participant can register in more than one protocol. Upon receiving a Commit request from the initiator in the completion protocol, the coordinator begins the prepare phase first for every participant who has registered in the Volatile 2PC protocol by sending a Prepare request to them before it begins the prepare phase for Durable 2PC. The participant that gets the request must respond with a Prepared, Aborted or ReadOnly message based on its own decision. During this period, other participants can continuously register with the coordinator for Durable 2PC, but the registration progress has to be done by the coordinator before the start of the first phase for durable resources. After the prepare phase for Volatile 2PC, the coordinator begins to take care of the prepare phase for Durable 2PC participants. Same as the prepare phase of Volatile 2PC, all participants in the Durable 2PC protocol have to

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respond appropriately before the protocol advances to the second phase, in which the coordinator will issue the Commit requests to all participants for both Volatile and Durable 2PC protocols if all participants have provided positive feedbacks. If there are any negative votes, even if only one, the coordinator has to abort the transaction. After the participants get a Commit notification, they will commit the transaction and send the Committed acknowledgement back to the coordinator. Figure 1 shows an example of a Web services atomic transaction for a travel reservation coordinated by WS-AtomicTransaction. In this example, a client contacts a Travel Agent to make travel arrangement. The Agent, which acts as the completion initiator, is responsible to make flight and hotel reservations in the context of an atomic distributed transaction, on behalf of the client. We assume that the Agent relies on a Flight reservation Web service and a Hotel reservation Web service, for booking a plane ticket and a hotel room for the traveler. To begin this transaction, the client sends a request to the initiator (step 1). The initiator invokes the Activation Service on the Coordinator. This will create a unique coordination context for the transaction (step 2). This context contains the Endpoint Reference for the Registration Service. After the Activation step, the initiator gets the reply back with the transaction context (step 3). In next step, the initiator registers the Completion Initiator Service with the coordinator so that the coordinator could inform the initiator after it obtains the outcome of the transaction (steps 4 and 5). Now the booking service offered by the initiator carries out the reservations for the plane ticket and a hotel room (steps 6 and 10). The flight and hotel booking Web services must register their participant endpoint references with the coordinator (steps 7 & 8 and 11 & 12). After the registration step, these services will send their responses for the reservation requests (steps 9 and 13). Subsequently, the initiator asks the Comple-

Category: Web Technologies

Figure 1. An example Web services atomic transaction

tion Service to commit the transaction (steps 15 – 17). Finally, the Travel Agent sends the result back to the client (step 18).

Web Services Business Activities WS-BusinessActivity differs from WS-AtomicTransaction in that it focuses on the coordination of long running business activities where the atomic transaction model is not appropriate. WS-BusinessActivity is also built on top of the WS-Coordination framework. The WS-BusinessActivity specification describes two coordination types, Atomic-Outcome and Mixed-Outcome, and two coordination protocols, Business-Agreementwith-Participant-Completion and BusinessAgreement-with-Coordinator-Completion. Either protocol can be used with either coordination type. If the Atomic-Outcome Coordination type is used, all participants must reach an agreement

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about the activity outcome (i.e., either to close or to compensate). If the Mixed-Outcome coordination type is used, some participants may be directed to close while others to compensate. All WS-BusinessActivity frameworks must implement the Atomic-Outcome coordination type. In a WS-BusinessActivity framework, a participant registers either one of the two protocols, which are managed by the coordinator of the business activity. The only difference between the two protocols is that the Business-Agreementwith-Participant-Completion protocol assumes that the participants know when the coordinator has completed all the processing related to a business activity and the Business-Agreementwith-Coordinator-Completion protocol notifies the participants when the coordinator has received all requests. A participant who has registered the BusinessAgreement-with-Participant-Completion protocol

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informs its coordinator by sending a Completed notice when it has done its entire works for a business activity. The coordinator should reply with either Close or Compensate message depends on the circumstance. The participant receives a Close instruction if the activity has completed successfully. If it gets a Compensate instruction instead, the coordinator will undo the completed work and will have to restore the data recorded from the initial condition. The participant may encounter a problem or fail during the processing of the activity, in which case, it must signal the coordinator with an error message. If it gets the Fail message, the coordinator will acknowledge the participant by a Failed notification. Upon receiving a CannotComplete notification, the coordinator learns that the participant cannot successfully finish its work. On sending out this message, the participant discards all its pending work and cancels all related executions, and exits the current business activity. On receiving such a message, the coordinator is required to notify the participant with a NotCompleted message. In the active state, the coordinator could cancel any transaction by using the Cancel notification, and the participant will respond with a Canceled message if it receives the message. In the Business-Agreement-with-CoordinatorCompletion protocol, the completion decision comes from the coordinator. The coordinator sends a Complete message to the participants informing them that they won’t receive any new requests within the current business activity and it is time to complete the processing. The participant then replies with a Completed message if it could successfully finish its work

BYZANTINE FAULT TOLERANT WEB COORDINATION In this section, we first introduce the general Byzantine fault tolerance technique. It is followed by a discussion on how to apply the BFT technique for Web services coordination within minimum

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runtime overhead by exploiting the semantics of the Web services coordination.

Byzantine Fault Tolerance A general purpose BFT replication algorithm is designed for a client-server architecture where the clients issue requests to the replicated server replicas. A Byzantine faulty replica may use behave arbitrarily to prevent the normal operations of a replica. In particular, it might propagate conflicting information to other replicas or components that it interacts with. To tolerate f Byzantine faulty replicas in an asynchronous environment, we need to have at least 3f+1 number of replicas (Castro & Liskov, 2002). One replica is designated as the primary and the remaining replicas are backups. The client first sends its request to the primary replica. The primary then broadcasts the request message to the backups and also determines the execution order of the message. To prevent a faulty primary from intentionally delaying a message, the client starts a timer after it sends out a request. It waits for f+1 identical replies from different replicas. Because at most f replicas are faulty, at least one reply must come from a correct replica. If the timer expires before it receives a correct reply, the client broadcasts the request to all server replicas. This enables the correct replicas to detect the primary failure so that a new primary can be elected (this is often called a view change). All correct replicas must agree on the same set of input messages with the same execution order. In other words, the request messages must be delivered to the replicas reliably in the same total order. A BFT replication algorithm, such as PBFT (Castro & Liskov, 2002) is designed to meet this objective. During the normal operation, the PBFT algorithm uses three phases to commit the total order of a request. During the first phase, often referred to as the pre-prepare phase, the primary multicasts to the backups a pre-prepare message containing the client’s request, the current view number, and the sequence number of the request.

Category: Web Technologies

A backup verifies the pre-prepare message and control information proposed by the primary for the purpose message ordering and possibly for controlling nondeterministic operations. If the backup accepts the message, it starts the prepare phase (i.e., the second phase) by multicasting to the backups a prepare message containing the ordering information and the digest of the request message being ordered. A replica (including the primary) waits until it has collected matching prepare messages from 2f other replicas and the corresponding pre-prepare message. It then starts the commit (i.e., the third phase) by multicasting a commit message to other replicas. The commit phase ends when a replica has received matching commit messages from 2f+1 replicas (including the one it has sent). At this point, the request message is totally ordered. The message can be delivered to the server application if the replica has delivered all previously ordered requests.

Applying the BFT Techniques Smartly to Web Services Coordination Because the interactions between the coordinator (replicated) and other components in Web services atomic transactions and business activities must conform to the WS-AtomicTransaction and WSBusinessActivity standards, there is no reason not the exploit the semantics specified in these standards to significantly reduce the runtime overhead of achieving Byzantine fault tolerance. The specific semantics of the Web services coordination enables us to apply the following optimizations (Chai et al., 2013; Zhang et al., 2012): Partitioning of the requests. Without the knowledge of application semantics, all requests received at the replicated coordinator would have to be totally ordered and executed sequentially according to that total order. This would severely limit the system throughput and increase the end-to-end latency experienced by clients under heavy load. With the knowledge of the Web ser-

vices coordination context, however, we can see that the requests that belong to different atomic transactions or business activities are handled by different coordinator objects, hence, they can be executed in parallel without causing inconsistency among the replicas. Using source ordering instead of total ordering for business activities. For business activities, requests within different business activities are handled independently by different coordinator objects. Hence, their relative ordering does not result in conflicting state transitions of the Coordination service. These include all requests for the Registration and Coordinator services, such as the Registration requests. For the Activation requests, even though they are handled by the same object, their relative ordering does not affect how the coordinator objects are created. One concern is the activity context id, which must be unique for each business activity. We modified the implementation such that the coordinator would use a client-supplied unique id to ensure replica consistency. Hence, the requests are commutative with each other even if they belong to the same business activity. Hence, they can be processed in parallel. Deferred Byzantine agreement. Even for commutative requests, all nonfaulty replicas must deliver the same set of requests to ensure replica consistency. Even though the mechanism to ensure this is lightweight, it would nevertheless incur a performance penalty if the number of commutative requests is large. For a sessionoriented application, such as an application uses WS-AtomicTransaction or WS-BusinessActivity, it is possible to defer the Byzantine agreement for the set of commutative requests at the end of the session to reduce the runtime overhead. More specifically, in a Web service atomic transaction, the agreement on the registration requests, which are commutative with each other, are deferred until the transaction commitment time. Furthermore, the Byzantine agreement on the participants set is combined with that for the transaction outcome.

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FUTURE TRENDS

CONCLUSION

The BFT mechanisms described in the previous section are specifically handcrafted for Web services coordination. Although the mechanisms are optimal as a way to enhance the trustworthiness of Web services coordination, they cannot be directly applied to other distributed systems. We see two future trends with respect to developing techniques to enhance Web services and distributed applications in general: (1) automating the application semantics discovery; and (2) reconstructing applications using commutative data structures or software transactional memory. We have been exploring methods to automate the discovery of application semantics, including the dependency analysis of interface specifications, such as that written by the Business Process Execution Language (Fu et al., 2004; Ouyang et al., 2007) for business transactions, source code static analysis, and modeling and pattern recognition of messages exchanges. We have documented a number of interaction patterns as the foundation for automated analysis of application semantics for Byzantine fault tolerance (Chai and Zhao, 2012). An alternative approach to implementing highly efficient solution for Byzantine fault tolerance is by using commutative data types (Shapiro et al., 2011; Preguica et al., 2009). Because all operations on the replicas are commutative, the need for carrying out Byzantine agreement is minimized. We have designed a system that requires only periodic synchronization of operations (Chai and Zhao, 2014). Similarly, the server application can be constructed using software transactional memory (Shavit & Touitou, 1997), which enables automatic analysis for possible concurrent processing of requests without application semantics (Zhang and Zhao, 2012). The biggest benefit for this approach is that the solution is ignorant to application semantics, which means any solution designed will be readily applicable to other applications written using commutative data types. The down side of this approach is that the applications would have to be redesigned and implemented using commutative data types.

In this article, we introduced Web services coordination and its applications to Web services atomic transactions and business activities, and presented a overview of our recent works on enhancing the trustworthiness of Web services coordination for business activities and transactions. We argue that even though it is possible to use a general purpose BFT replication algorithm for Byzantine fault tolerant replication of a Web services coordination service, doing so naively would incur unacceptable runtime overhead. Instead, by exploiting the application semantics, much lighter weight mechanisms can be designed to enhance intrusion tolerance, and hence the trustworthiness of systems that require Web services coordination.

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REFERENCES Castro, M., & Liskov, B. (2002). Practical Byzantine fault tolerance and proactive recovery. ACM Transactions on Computer Systems, 20(4), 398–461. doi:10.1145/571637.571640 Chai, H., Zhang, H., Zhao, W., Melliar-Smith, P. M., & Moser, L. E. (2013). Toward Trustworthy Coordination of Web Services Business Activities. IEEE Transactions on Services Computing, 6(2), 276–288. doi:10.1109/TSC.2011.57 Chai, H., & Zhao, W. (2012). Interaction Patterns for Byzantine Fault Tolerance Computing. In Computer Applications for Web, Human Computer Interaction, Signal and Image Processing, and Pattern Recognition (pp. 180-188). Springer Berlin Heidelberg. doi:10.1007/978-3-642-35270-6_25 Chai, H., & Zhao, W. (2014, June). Byzantine fault tolerance for services with commutative operations. In Proceedings of the 2014 IEEE International Conference on Services Computing (pp. 219-226). IEEE. doi:10.1109/SCC.2014.37

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Chakrabarty, S. (2007). Strategies for business process outsourcing: An analysis of alternatives, opportunities and risks. In E-Business Process Management: Technologies and Solutions (pp. 204-229). Hershey, PA: IGI Publishing. Freund, T., & Little, M., (2007). Web Services Business Activity (WS-BusinessActivity) Version 1.1. OASIS Standard. Fu, X., Bultan, T., & Su, J. (2004, May). Analysis of interacting BPEL web services. In Proceedings of the 13th international conference on World Wide Web (pp. 621-630). ACM. Gray, J., & Reuter, A. (1993). Transaction Processing: Concepts and Techniques. Morgan Kaufmann Publishers. Lamport, L., Shostak, R., & Pease, M. (1982). The Byzantine generals problem. ACM Transactions on Programming Languages and Systems, 4(3), 382–401. doi:10.1145/357172.357176 Little, M., & Wilkinson, A. (Eds.). (2007). Web Services Atomic Transaction (WS-AtomicTransactionomicTransaction) Version 1.1. OASIS Standard. Ouyang, C., Verbeek, E., Van Der Aalst, W. M., Breutel, S., Dumas, M., & Ter Hofstede, A. H. (2007). Formal semantics and analysis of control flow in WS-BPEL. Science of Computer Programming, 67(2), 162–198. doi:10.1016/j. scico.2007.03.002 Papazoglou, M. P. (2003). Web Services and Business Transactions. World Wide Web. Internet and Web Information Systems, 6(1), 49–91. Preguica, N., Marques, J. M., Shapiro, M., & Letia, M. (2009, June). A commutative replicated data type for cooperative editing. In Proceedings of the 29th IEEE International Conference on Distributed Computing Systems (pp. 395-403). IEEE. doi:10.1109/ICDCS.2009.20

Shapiro, M., Preguiça, N., Baquero, C., & Zawirski, M. (2011). A comprehensive study of convergent and commutative replicated data types. Technical report. INRIA. Shavit, N., & Touitou, D. (1997). Software transactional memory. Distributed Computing, 10(2), 99–116. doi:10.1007/s004460050028 Zhang, H., Chai, H., Zhao, W., Melliar-Smith, P. M., & Moser, L. E. (2012). Trustworthy coordination of Web services atomic transactions. IEEE Transactions on Parallel and Distributed Systems, 23(8), 1551–1565. doi:10.1109/TPDS.2011.292 Zhang, H., & Zhao, W. (2012). Concurrent Byzantine Fault Tolerance for Software-TransactionalMemory Based Applications. International Journal of Future Computer and Communication, 1(1), 47–50. doi:10.7763/IJFCC.2012.V1.14 Zhao, W., Moser, L. E., & Melliar-Smith, P. M. (2008). A reservation-based extended transaction protocol. IEEE Transactions on Parallel and Distributed Systems, 19(2), 188–203. doi:10.1109/ TPDS.2007.70727

ADDITIONAL READING Benatallah, B., Dumas, M., & Sheng, Q. Z. (2005). Facilitating the rapid development and scalable orchestration of composite web services. Distributed and Parallel Databases, 17(1), 5–37. doi:10.1023/B:DAPD.0000045366.15607.67 Chai, H., & Zhao, W. (2013). Byzantine fault tolerance for session–oriented multi–tiered applications. International Journal of Web Science, 2(1), 113–125. doi:10.1504/IJWS.2013.056578 Deswarte, Y., & Powell, D. (2006). Internet security: An intrusion-tolerance approach. Proceedings of the IEEE, 94(2), 432–441. doi:10.1109/ JPROC.2005.862320

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Fensel, D., Lausen, H., Bruijn, J., & Polleres, A. (2007). Enabling semantic web services: the web service modeling ontology. Springer. doi:10.1007/978-3-540-34520-6 Garcia, M., Bessani, A., Gashi, I., Neves, N., & Obelheiro, R. (2011, June). OS diversity for intrusion tolerance: Myth or reality? In Proceedings of the IEEE/IFIP 41st International Conference on Dependable Systems & Networks (pp. 383394). IEEE. Mahajan, S., & Singhal, R. (2009, December). Azvasa:-Byzantine Fault Tolerant Distributed Commit with Proactive Recovery. In Proceedings of the 2nd International Conference on Emerging Trends in Engineering and Technology (pp. 659-663). IEEE. doi:10.1109/ICETET.2009.44 Malik, Z., & Bouguettaya, A. (2009). Rateweb: Reputation assessment for trust establishment among web services. The VLDB Journal—The International Journal on Very Large Data Bases, 18(4), 885-911. Min, B. J., & Choi, J. S. (2004). An approach to intrusion tolerance for mission-critical services using adaptability and diverse replication. Future Generation Computer Systems, 20(2), 303–313. doi:10.1016/S0167-739X(03)00146-8 Montagut, F., Molva, R., & Golega, S. T. (2008). Automating the composition of transactional web services. International Journal of Web Services Research, 5(1), 24–41. doi:10.4018/ jwsr.2008010102 Pal, P., Webber, F., Schantz, R. E., & Loyall, J. P. (2000, October). Intrusion tolerant systems. In Proceedings of the IEEE Information Survivability Workshop (pp. 24-26). Rajan, H., & Hosamani, M. (2008). Tisa: Toward trustworthy services in a service-oriented architecture. IEEE Transactions on Services Computing, 1(4), 201–213. doi:10.1109/TSC.2008.18

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Sousa, P., Bessani, A. N., Correia, M., Neves, N. F., & Verissimo, P. (2007, December). Resilient intrusion tolerance through proactive and reactive recovery. In Proceedings of the 13th Pacific Rim International Symposium on Dependable Computing (pp. 373-380). IEEE. doi:10.1109/ PRDC.2007.52 Sousa, P., Bessani, A. N., Dantas, W. S., Souto, F., Correia, M., & Neves, N. F. (2009, June). Intrusion-tolerant self-healing devices for critical infrastructure protection. In Proceedings of the IEEE/IFIP International Conference on Dependable Systems & Networks. (pp. 217-222). IEEE. doi:10.1109/DSN.2009.5270333 Yang, S. J., Lan, B. C., & Chung, J. Y. (2005, February). A trustworthy Web services framework for business processes integration. In Proceedings of the 10th IEEE International Workshop on ObjectOriented Real-Time Dependable Systems (pp. 186-193). IEEE. doi:10.1109/WORDS.2005.13 Young, M., Kate, A., Goldberg, I., & Karsten, M. (2013). Towards Practical Communication in Byzantine-Resistant DHTs. Networking, IEEE/ ACM Transactions on, 21(1), 190-203. Zhao, W. (2007, October). BFT-WS: A Byzantine fault tolerance framework for web services. In Proceedings of the Eleventh International IEEE EDOC Conference Workshop (pp. 89-96). IEEE. doi:10.1109/EDOCW.2007.6 Zhao, W. (2007) A Byzantine fault tolerant coordination for Web services atomic transactions. Proceedings of the 3rd IEEE International Symposium on Dependable, Autonomic and Secure Computing (pp. 37-44). Columbia, MD. Chai, H., & Zhao, W. (2012). Byzantine Fault Tolerance as a Service. In Computer Applications for Web, Human Computer Interaction, Signal and Image Processing, and Pattern Recognition (pp. 173179). Springer Berlin Heidelberg. doi:10.1109/ DASC.2007.10

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Zhao, W. (2009). Design and implementation of a Byzantine fault tolerance framework for Web services. Journal of Systems and Software, 82(6), 1004–1015. doi:10.1016/j.jss.2008.12.037 Zhao, W. (2013, April). Towards practical intrusion tolerant systems. In Proceedings of the IET International Conference on Information and Communications Technologies (pp. 280-287). IET. Zhao, W. (2014). Building dependable distributed systems. John Wiley & Sons. doi:10.1002/9781118912744 Zhao, W. (2015). Optimistic Byzantine fault tolerance. International Journal of Parallel, Emergent and Distributed Systems, 1-14 (preprint). Zhao, W., Moser, L. E., & Melliar-Smith, P. M. (2005). A reservation-based coordination protocol for We services. Proceedings of the IEEE International Conference on Web Services (pp. 49-56). Orlando, FL. doi:10.1109/ICWS.2005.14

KEY TERMS AND DEFINITIONS Atomic Transaction: An atomic transaction in the context of Web services refers to a distributed transaction to be executed atomically. It should exhibit the atomicity, consistency, isolation and durability properties, just like a local transaction. Business Activity: A business activity is usually long running and requires flexible outcomes. A Web service business activity must conform to the

WS-BusinessActivity specification and adopts one of two two coordination types, Atomic-Outcome and Mixed-Outcome, and one of two coordination protocols, Business-Agreement-with-ParticipantCompletion and Business-Agreement-withCoordinator-Completion. Either protocol can be used with either coordination type. Business Process Execution Language (BPEL): It is a computer language used to describe the actions and execution order of these actions within a business process. It is designed specifically for the Web services paradigm. A BPEL for Web services was standardized by OASIS. Byzantine Fault Tolerance: A replicationbased technique used to ensure high availability of an application subject to Byzantine fault. Byzantine Fault: It is used to model arbitrary fault. A Byzantine faulty process might send conflicting information to other processes to prevent them from reaching an agreement. Distributed System: A distributed system is a computer network system, shown to end users as a single machine but actually work with a set of independent computers connected. Replica Consistency: The states of the replicas of an application should remain to be identical at the end of the processing of each request. Replica consistency is necessary to mask a fault in some replicas. Web Services: The Web services technology refers to the set of standards that enable automated machine-to-machine interactions over the Web. The core standards include XML, HTTP, SOAP, WSDL and UDDI.

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Usability of CAPTCHA in Online Communities and Its Link to User Satisfaction Samar I. Swaid Philander Smith College, USA

INTRODUCTION CAPTCHA- Completely Automatic Public Turing test to Tell Computers and Humans Apart- is a security application that is used by many websites to avoid spamming and hacking. CAPTCHA provides a test that humans are able to solve, but computer programs cannot, to defeat websites and stop spammers and hackers. CAPTCHA test can be viewed as a function of random input that generates a challenge test and a solution. Previous research found that website users spend on average 10 seconds per CAPTCHA (Sutherland, 2012), which suggests for 200 million CAPTCHA that are solved by human users every day, more than 5000,000 hours on daily basis are lost productivity (Sutherland, 2012). CAPTCHA is developed as an instrument to limit misuse of websites that offer free services of email creation, weblogs posting, social networking, online voting, online games online banking, or chat rooms, etc. With this aim in mind, several CAPTCHAs (Boshmaf et al., 2012; Coats and Baird, 2001; Chellapilla and Simard, 2005a; Von Ahn and Blum, 2005) are developed. CAPTCHAs share a number of common characteristics: (i) automated test; (ii) publicly available; (iii) separability of test- generation and solution; and (iv) defeating attacks (Fidas et al., 2011, Swaid, 2013). A number of different techniques for generating CAPTCHAs have been developed, each satisfying the criteria of CAPTCHA test described above. CAPTCHA types can be categorized to five main types: (i) text CAPTCHA such as reCAPTCHA and Gimby CAPTCHA (ReCaptcha, 2015);

(ii) image-based CAPTCHA such as ASIRRA (Elson et al., 2007), Bongo (Bongo, 2015)and Pix (Pix, 2015) that rely on image-recognition techniques (Vikram et al., 2011); (iii) animationbased CAPTCHA; (iv) audio CAPTCHA (e.g., ReCaptcha Audio Captcha, Digg, and (v) other (e.g., mathematical functions, games, multiple choice, and cognitive based CAPTCHA) (Hernandez-Castro and Ribagorda, 2010; Yamamoto et al., 2010). Text CAPTCHA is the most common type where the user is presented with a challengeresponse test in a form of numbers and letters, and user needs to strike in the right characters in the given text box. In this chapter, the author uses text CAPTCHA and CAPTCHA alternatively focusing on usability of text-based CAPTCHA that is formed of randomly generated sequence of letters and/or numbers that appear as a distorted image.

Cracking CAPTCHAs Cracking CAPTCHA and bypassing the CAPTCHA tests have attracted a number of studies to examine methodologies used in solving CAPTCHA. Generally, text CAPTCHA cracking needs three steps. First phase is pre-processing CAPTCHA by removing background, color and added noise. Second, a segmentation process is used to locate individual character in CAPTCHA challenge. Finally, a classification or recognition phase is applied to solve CAPTCHA, such as using standard OCR software (Serrao, et al., 2013; Sutherland, 2012), CAPTCHA farms (Serrao, et al., 2013), or CAPTCHA smuggling (Egele et al., 2010). In addition, there are other alternatives of

DOI: 10.4018/978-1-5225-2255-3.ch702 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Category: Web Technologies

commercial software that are developed to crack CAPTCHA such as Death BY CAPTCHA, CAPTCHA Sniper, Automated CAPTCHA Bypass, CAPTCHA Monster or PWNTCHA (Serro et al., 2013). Exploring mechanisms to crack CAPTCHA and its relation to usability is also important and of practical relevance, but beyond the scope of this chapter.

CAPTCHA Usability and Online Communities In general, we may define the concept of usability as the effort required to use a computer system. For instance, Nielsen (2003) suggests that usability concerns several aspects such as the ease with which the user is capable of learning to manage the system, the ease of memorizing the basic functions, the grade of efficiency with which the site has been designed, the degree of error avoidance and the general satisfaction of the user in terms of manageability. Therefore, greater levels of usability will be associated to lower levels of difficulty to manage that functionality (Davis, 1993) and, as a result, usability is traditionally considered a key factor for predicting intentions to use a system (e.g. Davis, 1993). More specifically, text CAPTCHA usability might reflect perceived ease of solving CAPTCHA test (Swaid, 2013) explaining satisfaction. Designing successful CAPTCHA that is unsolvable by CAPTCHA solving software, yet, a user friendly one, is a challenging task (Ahamd and Yan, 2010). Several proprieties to make CAPTCHA resistant to Optical Character Recognition (OCR) software can make CAPTCHA unusable

(Sutherland, 2012) and therefore, suggest users’ dissatisfaction. Today, millions of people join online social gathering spheres to chat, to debate, to play games, to ask for information, or to find social support (Preece, J. and Maloney-Krichmar, 2003). Some forms of such online gatherings come in the form of forums, discussion groups, and social networking sites among others. These online social gatherings are known by a variety of names including ‘online community’, that is described as ‘cultural aggregations that emerge when enough people bump into each other often enough in cyberspace’ (Rheingold, 1994, p. 57). To eliminate automated spammers and other computer agents who are attempting to violate community norms, online communities ask applicants to complete a CAPTCHA test before subscribing. Studies showed that new comers to online communities are unable to join due to the entry barrier posed by visual CAPTCHAs (Chandrasheka, 2010). A CAPTCHA that is robust, yet, unsolvable by humans is useless. The issue of usability has been studied focusing on the functional level (Ahmad and Yan, 2010; Burstein et al., 2010), however, the ultimate effect of CAPTCHA usability on legitimate goal-oriented users is not well documented in literature (Motoyama et al. 2010). According to Ellson et al. (2007), even relatively simple challenge can drive away a substantial number of potential customers to websites. Thus, a methodological analysis is needed for CAPTCHA schemes used in websites for online communities to understand the correlations between design characteristics and users satisfaction.

Figure 1.

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Background Over the past decade, a number of CAPTCHAs have been created to differentiate humans from computer programs. Unsurprisingly, CAPTCHAs have been evaluated in terms of its robustness (Azad and Jain, 2013; Chew and Baird, 2003; Chellapilla et al., 2005a, 2005b), its economic context (Motoyama et al., 2010), but limited number of studies focused on usability issues of text CAPTCHA. One study by Ahmad and Yan (2008) analyzed CAPTCHA technically to find attributes of content, presentation and distortion to explain usability. In another study by the same authors they shed light on the role of color on CAPTCHA Table 1.­

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usability (Ahmad and Yan, 2010) (see Table 1). A study by Sutherland (2012) focused on analyzing different properties of layout CAPTCHA in terms of background color and integration with the website. In an attempt to maximize the usability of CAPTCHA, security features of visual, antisegmentation and anti-recognition were identified and applied to create two new CAPTCHA schemes for Google (Bursztein et al., 2014; 2011). In the work of Swaid (2013), a think-a-loud protocol and a sorting task are conducted to understand the dimensionality of CAPTCHA usability. The study applied a formative approach in operationalizing the latent construct of usability (see Table 1).

Category: Web Technologies

CAPTCHA USABILITY: THE STUDY To understand the relationships among dimensions of CAPTCHA usability and behavioral variables of user satisfactions, the study is designed as twophase one. First phase would focus on surveying the top 50 most popular online communities’ websites to identify CAPTCHA use; while second phase would collect users’ perceptions on CAPTCHA usability and their satisfaction with the registration process.

CAPTCHAs in Top 50 Online Communities: A Content Analysis Approach To gather exiting text CAPTCHAs exit in top 50, most visited online communities websites, the author uses Alexa (Alexa.com, 2015), a web tracking and statistics gathering service, to determinate the top 50 most popular websites for online communities visited as of February 2015. Of the top 50 online communities, the ones that follow the criteria below are selected: 1. Website is one of the online communities’ platforms. 2. Website offers registration, blogging, or somehow updating activity. 3. No membership fee is required. 4. English-speaking website. 5. Challenge users with static text CAPTCHA. Text CAPTCHAs with animation or other types of CAPTCHAs (e.g., CAPTCHA used by Yahoo) are excluded from the study.

Applying the criteria listed above, the author confirmed findings of previous study on CAPTCHA usability using qualitative approach (Swaid, 2013). Analysis revealed that there are four main text CAPTCHAs used by the most popular online communities websites (see Table 2). Unsurprisingly, ReCAPTCHA was the dominant CAPTCHA used by most of the online communities, while SolevMedai CAPTCHA and Mollom were realized in less number of online communities. This is in line with other market analysis reports that found ReCAPTCHA market share is about 99% (WebAnalyzer, 2016). Some of the CAPTCHAs were excluded for reasons of animation added, non-text interactive CAPTCHA or image orientation CAPTCHA (see Figure 2). These mainly were found at Yahoo.com, Namepros.com and Care2. com. Next, a content analysis to CAPTCHA is applied, as follows. Content analysis as a technique is used to examine artifacts of social communications that might come in the form of written documents, multimedia content and any form of transcriptions of recorded verbal. As Holsti noted, content analysis is “any technique for making inferences by systematically and objectively identifying special characteristics of messages”‎. Based on this definition, content analysis can be applied on messages that come in form of written text, photographs, and/or multimedia content, to identify specific characteristics of the message. Similarly, Krippendorff defined content analysis as a research technique for making valid and reliable inferences from data to their context ‎(Krippendorff, 2013). Weber (1990) defied content analysis as research methodology that utilizes a set of procedures to

Figure 2.

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make valid inferences from text. Content analysis is most conveniently used with textual types of data such as online comments, online content, blogs, and open-ended survey questions ‎ ‎(Krippendorff, 2013).

Dimensions of CAPTCHA Usability Text CAPTCHA found in online communities exhibits four main dimensions that can be used to measure the usability of CAPTCHA. Based on previous research (i.e., Al-Ahmad and Yah, 2010, Burztein et al., 2014; Swaid, 2013), it is suggested that usability of CAPTCHA is a function of four dimensions: Content, Visual Layout, Distortion and Service. To understand the relationships among dimensions of CAPTCHA usability and perceived usability and satisfaction with the registration process, dimensions of CAPTCHA usability are developed as reflective latent constructs represented by at least three measures (Hatcher, Table 2.­

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1994) (see Table 3). The conceptual model of the study also examines the effect of perceived usability on satisfaction (see Figure 3). Following the recommendation of Churchill (1979), this study adopts the following steps: (i) latent constructs are conceptualized by defining the domain of the constructs; (ii) dimensions are operationalized focusing on the content validity of the dimensions, (iii) data are collected using the developed scale, (iv) reliability analysis and factor analysis are applied to assess the reliability and validity of the developed model. Endogens variable of overall CAPTCHA usability is adopted from the System Usability Scale (SUS) modified by Lewis and Sauro (2009). Satisfaction with registration process of website is adopted from Flavia et al. (2006). Confirmatory factor analysis (CFA) and Structural Equation Modeling (SEM) are conducted to examine the reliability and validity of the measurement model and to test associations hypothesized in research model.

Category: Web Technologies

Figure 3.

RESULTS The 13-Item battery of CAPTCHA usability is arranged in a survey instrument to be measured using a seven-point Likert scale ranging from (1) strongly disagree to (7) strongly agree. In addition, a 5-item construct representing usability and 3-item construct representing satisfaction are embedded in the survey. Total of 124 college students have been invited to take part in the study for a credit in one of the universities in the Mid-South of the United States. About 60% of the participants are females, whom ages range from 19 to 35 years old. All participants have some type of participation in online communities such as forums, discussion groups, and social networking websites (see Figure 4).

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The participants are first presented with questionnaire asking their experience with web browsing, experience with CAPTCHA, and level of engagement in online communities. Then, participants are asked to solve survey questions after they are challenged with three CAPTCHAs to solve.

Measurement Model The measurement model is assessed by CFA to confirm the dimensionality of CAPTCHA usability. Chi-Square value is 2.18 less than the three recommended value by Bagoozi and Yi (1988). Other fit indexes show good fit for the measurement model. The GFI is 0.92, greater than the cut-off value of 0.90 recommended (Hatcher,

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Usability of CAPTCHA in Online Communities and Its Link to User Satisfaction

Figure 4.

1994). The adjusted goodness of fit (AGFI) is 0.90 (Bagozzi and Yi, 1988). The root mean square error of approximation (RMSEA) is 0.049, which also suggests a good fit with data collected. Reliability was assessed using Cronbach’s alpha which ranges from 0.711 to 0.889, exceeding the 0.70 threshold, recommended by Nannally and Bernstein (1994) (see Table 3). As factor loadings exceed 0.70, this indicates acceptable item convergence on its intended constructs, indicating validity (Bagoozi and Yi, 1988).

Structural Model Hypotheses of research model are tested using the structural equation modeling. Overall fit statistics suggest that the model has adequate model fit (GFI = 0.91; AGFI = 0.90; CFI = 0.91; RMSEA = 0.043). The model fit indexes all exceeded their respective acceptance levels, indicating that the structural model fits the data well. In order to test research hypotheses, structural coefficients for model paths are calculated (see Table 4).

Discussion This study examined the dimensionality of text CAPTCHA usability and examined how CAPTCHA usability dimensions affect perception of CAPTCHA usability users’ satisfaction with

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registration process. The analytical results of this study are discussed below. First, the analytical results shows that CAPTCHA usability can be explained by CAPTCHA content, visual layout, distortion and service. Usability is most strongly affected by visual layout. Online communities thus need to ensure that visual layout introduced by CAPTCHA text is appropriate and solvable to users. Second, the dimension of content significantly affects CAPTCHA usability perception. This finding might be exaplianed by the fact that users expect CAPTCHA with content from character set and symbols they are familiar with. Third, the distortion dimension is a significant predictor of text CAPTCHA usability. Other studies that analyzed the CAPTCHA suggest that distortion might affect the perception of CAPTCHA usability. Therefore, to enhance users satisfaction with the registration process, online communities should use CAPTCHA that applies distortion technique in a way that would not affect CAPTCHA usability of CAPTCHA, and still can be used to stop spam. Next, although service dimension had only a mild effect on CAPTCHA usability perception in this study, its importance should not be underestimated. Managers of online communities should pay careful attention to this aspect. Particularly, CAPTCHA should be designed with features that facilitate its solvability such as explaining reasons

Category: Web Technologies

Table 3.­

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Table 4.­

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behind using CAPTCHA and providing users with the ability to switch to other non-text CAPTCHA (e.g., audio CAPTCHA) to enhance accessibility (Kuzma et al., 2011). Additionally, this study found a positive relationship among CAPTCHA usability and satisfaction with registration when using online communities.

IMPLICATIONS AND FUTURE RESEARCH DIRECTIONS This study has the following implications for practitioners initiating or currently using CAPTCHA in their online communities. First, this study suggests that to enhance users’ satisfaction, online communities should employ CAPTCHAs that are carefully tested in terms of its usability, addressing each of CAPTCHA dimensions of content, visual layout, distortion and service. Practitioners are advised to use the scale to measure each dimension while testing the appropriate mix of features in an iterative fashion to identify aggressive distortion, unnecessary characters or inefficient collapsing to exclude from CAPTCHA test. The scale might be used as a starting point to understand the non-linear effect of interactions (Bursztein et al., 2014) on CAPTCHA’s usability. Secoindly, there are variety of commercial CAPTCHA. Managers are advised to use CAPTCHAs after careful testing. Thirdly, practitioners are advised to employ CAPTCHAs that offer features enhancing accessibility such as switching to audio CAPTCHA for users with vision problems or contacting a technical person to help in bypassing CAPTCHA test. Such services would improve the perception of responsiveness in web-based service-oriented systems (Swaid and Wigand, 2009) such as online communities. Although the study develops a scale following a systematic approach and validity testing, limitations need to be addressed. First, participants in this study are college students. Although online communities’ population resembles the college students, replicating the study with non-college students might suggest additional findings. Sec-

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ond, in this study, the author focused on perception of usability and satisfaction as behavioral variables. Future research can test relationships between CAPTCHA usability and other variables such as intentions to recommend and loyalty to online communities. Third, the growth of the internet and online communities will continue, and future research can replicate the study solely involving objective measures to assess solvability and usability of CAPTCHA such as time consumed and number of errors while solving CAPTCHA test. Forth, evidence exists that the relative importance and possible meaning of CAPTCHA usability may differ across cultures (Zhu and Keough, 2014). Thus, the study can be replicated to provide cross-cultural comparisons. Similarly, considering the cognitive characteristics of individuals, their learning styles (Belk et al., 2013, Germanakos et al. 2009; Grafs et al., 2005) or demographic variables (Bursztein et al., 2010) might point out other fruitful findings in terms of how to improve usability. Finally, as smartphones are widely used to access web services offered by online communities, it is worthy to address usability issues of text CAPTCHAs on smart phones. This chapter is intended to provide an essential step in quantifying usability of text CAPTCHA contributing to the limited knowledge in this area. There is more work to be done to understand CAPTCHA usability considering a number of socio-technical and cultural challenges that relates to CAPTCHA engineering.

CONCLUSION The conclusions drawn from this study make contributions in two main areas. First, this study is the first one to develop an dimension-based instrument to measure text CAPTCHA usability applying the reflective approach in dimension construction. Second, this study identified text CAPTCHA usability dimensions that affect CAPTCHA usability perception, which in turn is significantly related to users’ satisfaction. This chapter is intended to

Category: Web Technologies

provide an essential step in quantifying usability of text CAPTCHA contributing to the limited knowledge in this area. Clearly, there is much more work to be done to understand CAPTCHA usability considering the multiple aspects of its dimensions that would support creating not only robust CAPTCHAs but also usable and effective ones. This, however, boils down to solving a number of socio-technical challenges that relate to CAPTCHA engineering.

REFERENCES Acquire. (2014). Embrace Social Content with Confidence. Retrieved from: http://www.acquia.com/sites/default/files/library/attachment/ mollom-data-sheet.pdf Ahmad, A., & Yan, J. (2010). Color, Usability, and Security: A Case Study. Retrieved from: http://www.computer.org/csdl/mags/ic/2012/02/ mic2012020044-abs.html Ahn, L., Blum, M., Hopper, N., & Longford, J. (2013). CAPTCHA: Using Hard AI problems for Security. The 22 International Conference on Theory and Applications on Cryptographic, 249-311.

Belk, M., Germanakos, P., Fidas, C., Holzinger, A., & Samaras, G. (2013). Towards the Personalization of CAPTCHA Mechanisms Based on Individual Differences in Cognitive Processing. Proceedings of the International Conference on Human Factors in Computing & Informatics, 409-426. doi:10.1007/978-3-642-39062-3_26 Bongo. (2015). The CAPTCHA Project. Retrieved from: http://www.captcha.net/captchas/bongo/ Boshmaf, Y., Muslukhov, I., Beznosov, K., & Ripeanu, M. (2012). Key Challenges in Defending Against Malicious Socialbots. Retrieved from: https://www.usenix.org/system/files/conference/ leet12/leet12-final10.pdf Burstein, E., Bethard, S., Fabry, C., Mitchell, J., & Jurafsky, D. (2010). Hoa Good Humans at Solving CAPTCHAs? A Large Scale Evaluation. Retrieved from: http://web.stanford.edu/~jurafsky/burszstein_2010_captcha.pdf Burstein, E., Moscicki, A., Fabry, C., Bethard, S., Mitchell, J., & Jurafsky, D. (2014). Easy Does It: More Usable CAPTCHA. CHI 2014, Toronto, Ontario, Canada.

Alexa. (2015). Actionable Analytics for the Web. Retrieved from: http://www.alexa.com/

Burszteein, E. (2011). Text-Based CAPTCHA Strengths and Weaknesses. ACM Computer and Communication Security (CSS’2011). Retrieved from: https://cdn.elie.net/publications/text-basedcaptcha-strengths-and-weaknesses.pdf

Azad, S., & Jain, K. (2013). CAPTCHA: Attacks and Weaknesses against OCR Technology. Global Journal of Computer Science and Technology Neural & Artificial Intelligence, 13(3).

Chellapilla, C., Larson, K., Pimard, P., & Czerwinski, M. (2005a). Building Segmentation Based Human-friendly Human Interaction Proofs. LNCS, 3517.

Bagozzi, R., & Yi, Y. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16(Spring), 74–94. doi:10.1007/BF02723327

Chellapilla, K., Larson, K., Simard, P., & Czerwinsk, M. 2005. Computers beat Humans at Single Character Recognition in Reading based Human Interaction Proofs (HIPs). Second Conference on Email and Anti-Spam (CEAS).

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Chellapilla, K., & Simard, P. (2005b). Using Machine Learning to Break Visual Human Interaction Proofs (HIPs). Retrieved from: http://papers.nips. cc/paper/2571-using-machine-learning-to-breakvisual-human-interaction-proofs-hips.pdf Chew, M., & Baird, H. S. (2003). Baffletext: A Human Interactive Proof. Proceedings of the Society for Photo-Instrumentation Engineers, 305–316. doi:10.1117/12.479682 Churchill, G. (1979). A paradigm for developing better measures of marketing constructs. JMR, Journal of Marketing Research, 16(February), 64–73. doi:10.2307/3150876 Coats, A., Baird, H., & Fateman, R. (2001). Pessimal Print: A Revese Turing Test. Proceedings of 6th International Conferee on Document Analysis and Recognition, 1154-1158. Davis, F. (1993). User Acceptance of Information Technology: Systems Characteristics, User Perceptions and Behavioral Impacts. International Journal of Man-Machine Studies, 38(3), 475-487. Egele, M., Bilge, L., Kirda, E., & Kruegel, C. (2010). CAPTCHA Smuggling: hijacking Web Browsing sessions to Create CAPTCHA Farms. Proceedings of the 2010 ACM Symposium on Applied Computing. Retrieved from: https://iseclab. org/papers/manuel-captcha.pdf Ellson, J., Douceur, J., Jowell, J., & Saul, J. (2007). Asirra: A CAPTCHA that Exploits InterestAligned‎ Manual‎ Image‎ Categorization.‎ CCS’07,‎ Alexandria, VA. Fidas, C., Voyiatzis, A., & Avouris, N. (2011). On the Necessity of UserFriendly CAPTCHA. CHI 2011, 2623-2626. Flavia´n, N., Guinalı´u, M., & Gurrea, R. (2006). The Role Played by Perceived Usability, Satisfaction and Consumer Trust on Website Loyalty. Information and Management, 43, 1-14.

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Gossweiler, R., Kamvar, M., & Basluja, S. (2009). What’sUp CAPTCHA? A Captcha Based on Image orientation. The International World Wide Web Conference Committee (IW3C2), Madrid, Spain. Grafs, S., & Kinshuk. (2005). Improving Student Modeling: The Relationship between Learning Styles and Cognitive Traits. Proceedings of the International Conference on Cognition and Exploratory Learning in Digital Age, 37-44 Grafs, S., & Kinshuk. (2009). Advanced Adaptivity in Learning Management Systems by Considering Learning Styles. International Workshop on Social and Personal Computing for Web-Supported Learning Communities, 235-238. Hatcher, L. (1994). A step-by-step approach to using the SAS ® System for factor analysis and structural equation modeling. Cary, NC: SAS Institute Inc. Holsti, O. (1968). Content Analysis. In G. Lindzey & E. Aronson (Eds.), The Handbook of Social Psychology (2nd ed.; vol. 2, pp. 596-692). New Delhi: Amerind Publishing Co. Hwang, K., Haung, C., & You, G. (2013). A Spelling CAPTCHA Systems By Using Click. International Symposium on Biometrics and Security Technologies, 2(4), 1-8. Krippendorff, K. (2013). Content Analysis: An Introduction to Its Methodology. Sage. Kuzma, J. (2011). CAPTCHA Accessibility Study of Online Forums. Retrieved from: https://eprints. worc.ac.uk/1414/2/captchaapril10.pdf Kvalseth, T. (1989). Note‎on‎Cohens‎kappa. Psychological Reports, 65(1), 223–226. doi:10.2466/ pr0.1989.65.1.223 Lewis, J., & Sauro, J. (2009). The Factor Structure of the System Usability Scale. Retrieved from: https://www.measuringu.com/papers/ Lewis_Sauro_HCII2009.pdf

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Mollom. (2010). Mollom CAPTCHA are intelligent. Retrieved from: https://mollom.com/blog/ mollom-captchas-are-intelligent

Sutherland, C. (2012). Usability and Security of Text-Based CAPTCHAs UMM, CSci Senior Seminar Conference. Morris.

Motoyam, M., Levchenko, K., Kanich, C., & McCoy, D. (2010). Re: Captcha- Understanding CAPTCHA Solving services in an Economic Context. Retrieved from: http://cseweb.ucsd. edu/~savage/papers/UsenixSec10.pdf

Swaid, S. (2013a). Scale Development to Measure the Usability of Text-Based CAPTCHA. International HCI 2013, Las Vegas, NV. doi:10.1007/9783-642-39473-7_33

Nielsen, J. (2003). Usability 101: Introduction to Usability. Retrieved from: http://www.useit.com/ alertbox/20030825.html Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill. Oxwall. (2013). Smart Captcha Plug-in. Retrieved from: http://www.oxwall.org/store/item/583 Pix. (2015). The CAPTCHA Project. Retrieved from: http://www.captcha.net/captchas/pix/ Pope, C., & Kaur, K. (2005). Is It Human or Computer? Defeating E-Commerce with Captchas. IT Professional, 7(2), 43–49. doi:10.1109/ MITP.2005.37 Preece, J., & Maloney-Krichmar, D. (2003). Online Communities. In J. Jacko & A. Sears (Eds.), Handbook of Human-Computer Interaction. Lawrence Erlbaum Associates Inc. ReCacptcah. (2015). Google ReCaptcha. Retrieved from: https://www.google.com/recaptcha/ intro/index.html Rheingold, H. (1994). The Virtual Community. Homesteading on the Electronic Frontier. Reading, MA: Addison-Wesley Publishing. Serro, M., Salunke, S., & Mathur, A. (2013). Carcaking CAPTCHAs for Cash: A Review of CAPTCHA Crackers. International Journal of Research and Technology., 2(1). SolveMedia. (2013). SolveMedia Launches in Europe. Retrieved from: http://news.solvemedia. com/post/55617465910/solve-media-europe

Swaid, S. (2013b). Usability Measures of Textbased CAPTCHA: Application of Think-Aloud Protocol. Academic Business World International Conference, Nashville, TN. Swaid, S., & Wigand, R. (2009). Measuring the quality of e-service: Scale development and initial validation. Journal of Electronic Commerce Research, 10(1), 13–28. Vikram, S., Fan, Y., & Gu, G. (2011). DEMAGE: A New Image-based Tow-Factor CAPTCHA. ACSAC’11, Orlando, FL. Von Ahn, L., Blum, M., & Langford, J. (2005). Telling Humans and Computers Apart Automotiacally. Communications of the ACM, 47(2), 57–60. Weber, R. (1990). Basic content anaIysis. London: Sage. doi:10.4135/9781412983488 Yamamoto, T., Tyger, J., & Nishigaki, M. (2010). CAPTCHA Using Strangeness in machine Translation. Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Application, 430-437. Yan, J., & El-Ahmad, A. (2008). Usability of CAPTCHA or Usability Issues in CAPTCHA Design. Proceedings of the 4th symposium on Usable Privacy and Security, 4-52. doi:10.1145/1408664.1408671 Zhu, Q., & Keough, E. (2014). Using CAPTCHAS to Index Cultures Artifacts. Retrieved from: http:// www.cs.ucr.edu/~eamonn/CAPTCHAs_Rock.pdf

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ADDITIONAL READING Germankos, P., Tsianos, N., Lekkas, Z., Mourlas, C., Belk, M., & Samaras, G. (2009). Towards an Adapative and Personalized Web interaction using human factors. In M. Angelides, P. Mylonas, & M. Wallace (Eds.), EDS.) advances in semantic media adaptation and personalization (pp. 247–282). Taylor and Francis. doi:10.1201/9781420076653c12 Hernandez-Castro, C., & Ribogorda, A. (2010). Pitfalls in CAPTCHA Design and Implementaion: The Math CAPTCHA, A Case Study. Computers & Security, 29(1), 141–157. doi:10.1016/j. cose.2009.06.006

KEY TERMS AND DEFINITIONS CAPTCHA: An acronym for “Completely Automated Public Turing test to tell Computers and Humans Apart”. CAPTCHA is a program that protects websites against bots and hackers by

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generating and grading tests that humans can pass but current computer programs cannot. CAPTCHA Usability: Ease of use CAPTCHA scheme and solve its test. Equation Modeling: Structural equation modeling refers to a set of mathematical models, algorithms, and statistical methods that fit networks of constructs to data. SEM includes confirmatory factor analysis, path analysis, partial least squares path analysis, and latent growth modeling, among others. Factor Analysis: Factor analysis is a statistical method for data reduction that describes variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. Online Community: A virtual network of people who communicate with one another through interactive tools such as forums, discussion groups of social networking sites. User Satisfaction: The attitude of a user to the CAPTCHA Scheme adopted by the website.

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Visual Identity Design for Responsive Web Sunghyun Ryoo Kang Iowa State University, USA Debra Satterfield California State University – Long Beach, USA

INTRODUCTION Because the web has moved beyond desktop environments, responsive web design requires reconsideration with regard to the interface design and development workflow (Carver, 2015). It is hard to create the same feeling and look across all devices (Moss, 2015), and maintaining a strong, consistent visual identity in a responsive web design is challenging because of this flexible or liquid layout and navigation. This flexible and dynamic design system is known as responsive web design (Marcotte, 2011). Responsive web design has changed the web eco system. Therefore, fundamental changes in web design are necessary to accommodate this variety in interfaces and platforms. The first impression of a website is affected by the visual design elements of color, typography, and graphic elements (Park, 2016). If the visual design does not immediately connect with a user the likelihood that they will stay on the site is greatly diminished. Therefore, the website must capitalize on a familiar and trusted brand identity or the site must quickly establish a visual identity that is professional and immediately establishes a sense of trust or familiarity in the user (Kang and Satterfield, 2010). The purpose of this chapter is to identify design strategies for establishing and managing the visual identity of a responsive web design. This chapter revisits the article, Design Elements and Principles for Maintaining Visual Identity on

Websites (Kang and Satterfield, 2010), to reflect the changed Web environments with flexible and dynamic web technologies.

BACKGROUND Responsive Web Design To create a successful responsive web design, a layout that can easily be customized for different screen sizes is key (Voutilainen, Salonen, and Mikkonen, 2015). According to Voutilainen, Salonen, and Mikkonen, it is important to consider “proportion-based grids and flexible images, where element sizing takes place using relative units instead of absolute ones, and CSS3 media queries, where different styles can be used for different devices (2015).” Since the web page layout is liquid and flexible, the exact structure of the layout would not be an element that can be used to maintain the web site identity. Using images and design elements that can be modified using CSS media queries and script language while still maintaining the essence of the visual characteristics of the site are essential to a successful responsive web design.

Visual Identity Design According to Perry and Wisnom, the role of visual identity is to translate a company’s brand positioning and verbal identity into a visible vi-

DOI: 10.4018/978-1-5225-2255-3.ch703 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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sual representation (Perry and Wisnom III, 2003, p14). Perry and Winsnom outlined the purpose of visual identity as fourfold: “to bring the brand to life by giving character and personality to the positioning and name,” “to enhance brand recognition and recall,” “to differentiate the brand from competition”, and “to tie all the disparate brand elements together with the same look and feel” (Perry and Wisnom III, 2003, pp95-96). The visual elements such as logos and colors in the identity system can create an immediate sense of recognition for the company, express its character, and build familiarity and trust (Haig and Haper, 1977, p14). According to Kang and Satterfield (2010), “an identity system can be thought of as a tool that is used to maintain a company’s corporate visual image. This system must be flexible enough for different applications, at the same time, tight enough to maintain visual consistency across media.” The most recognizable elements of corporate visual images are the brand logo, corporate colors and the visual identity (or corporate philosophy) as represented through photos, images and other familiar graphic symbols associated with that brand. A logo or corporate symbol is the predominant visual cue for a business and it conveys the company’s philosophy and message. The roles of corporate symbols are to create awareness; to trigger recognition of an organization; and to activate a stored image of the organization (Dowling, 2001, p 167). Logos are used as a powerful tool for marketing. Customers often make buying decisions based on the brand recognition rather than on the specific characteristics of the product itself. Apple, Google, Coca-Cola, and McDonald’s logos are examples of these types of familiar and trusted corporate symbols. Color is another important element in the identity system. Examples of color associations are Coca-Cola red, Kodak yellow, and Barbie pink. Thus, color is an important element in any identity system. According to Dowling (2001), color is easier to read than form or shape and it holds the viewer’s attention longer. Color also has

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cultural and psychological associations. Therefore, the same color can be interpreted in different ways depending on the culture and context of the situation.

THE VISUAL ELEMENTS OF RESPONSIVE WEBSITE IDENTITY Maintaining website identity for a responsive web design can be challenging and requires different design strategies from those used for print or traditional desktop web design. The use of logos, icons, symbols, colors, images, and typography for responsive web design need to maintain the visual identity of a site across a variety of sizes and screen configurations.

Use of Web Site Identifier A site identifier such as the company name, logo, or sometimes both, and a web address (URL) is a unique feature identifying a website and the role is more important in the responsive web design. These site identifiers mainly logos are generally located in the top left corner with fixed location no matter in the flexible or dynamic layouts. Making the brand easy to see and identify is critical in insuring that a user will quickly identify the site and associate it with other sites on other devices and platforms. According to Steve Douglas (2015), 93% of logos from the top 100 brands are simple enough to recognize at small size. For example, Apple and Nike’s logo is very simple but yet unique. It can be recognized with very small size even without the company name. In 2005, Google changed its typeface from serif to sanserif. One of reason for this change is for “scalable mark that could convey the feeling of the full logotype in constrained spaces (https://design.google.com/ articles/evolving-the-google-identity/.)” Therefore, a logo as a site identifier should have a unique appearance to identify the characteristics of a particular web site, easier to recognize and recall, and scalable for different devices.

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Use of Color

Use of Layout

Color becomes the more powerful tool to build web site identity in the responsive web design. Color can be displayed slightly different in each device but it is much easier keeping the same color on the screen compared to print. According to the top 100 brand in 2016 from Forbes (http://www.forbes.com/powerful-brands/list/), blue and red are still the most popular color for cooperate logos. However, Nike uses a black logo and recently their web site (http://www.nike.com/ us/en_us/) used this black and a monochromatic color scheme to integrate their responsive web site designs. The black and monochromatic color scheme enhanced the memorability and made Nike stand out among the numerous more colorful web sites. This emphasizes that the fact that the use of color as “a ubiquitous perceptual experience” (Elliot and Maier, 2007, p250) can be used in unique or distinctive ways to attract a viewer’s attention.

The layout of an interface is a visible form of its information architecture. Most web pages are divided into four main areas: site identifier; navigation; content; and footer (Kang & Lee, 2003) and these four areas have not changed in responsive web environments. According to Kang and Satterfield (2010), “the layout is designed based on the quantity of content information, numbers of organizational categories, and marketing and design strategies.” However, in responsive web design, the layout is flexible and dynamic based on the screen size. Therefore, before a designer begins coding the web site, a layout style should be carefully considered to create an appropriate and consistent visual appearance across all screen configurations and platforms. Figure 1 shows how the layout and design elements translate between the horizontal desktop web screen and the smaller vertical smartphone screen.

Use of Image Graphics and images can be used as visual cues to forecast the characteristics of a website. According to Donald Norman (2004), photography has a strong appeal to human emotions (p50) and photographic images can be used to strengthen the visual appearance of an interface. For example, photographs showing families give a feeling of comfort, security, and trustworthiness and are perceived to be more friendly than photos that feature single men, single women, or couples (Ha and Kang, 2010). Another study (Park, 2016) showed that web users found websites that included photographic images were perceived to be more attractive than websites without photographic images. Thus, photos, graphics, illustrations, or icons can enhance the visual appearance of an interface and give users a more positive impression of the site. In the responsive web design, images must be scalable and set with a percentage, not as a fixed width. This allows images fit into the grid and allows the images to resize without sacrificing any of their aspect ratios.

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Use of Design System The four main areas of a web; site identifier; navigation; content; and footer need to be organized in a system to maintain web site identity across various screen formats and sizes. The navigation bar plays an important role in maintaining visual consistency across multiple devices. Accordingly, design style is also an important element in responsive web design. A consistent visual style gives users a unified feeling between formats. A unified look and feel across the various platforms can also easily create a strong web site identity. The design system should be flexible enough to be applied dynamically to a variety of devices yet strict enough in its use of a design system to maintain visual consistency. It is also important to consider the limitations and variability of web sites. For example, CSS 3 and media queries make it possible to control the web page layout. A grid is a good way to improve usability and give a consistent look and feel to the page (Watzman 2003, pp268-285). However, in re8081

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Figure 1. Desktop Web Screen and Smart Phone Screen

sponsive web design the number of columns in a grid differs between devices and design elements will dynamically move around the web page. In order to compensate for these differences in layout, the use of color, typeface, and design style will be important in maintaining a consistent visual identity. A very tight systematic approach and design strategies are required to maintain an effective visual identity across all platforms in responsive web design.

Strategies for an Effective Responsive Web Site Identity Design The basic strategies required to maintain a strong visual identity in web design remain the same. As Kang and Satterfield (2010) note, “the most important consideration in the creation of web site identity is an understanding of a company’s goals for having the web site and the users’ goals for visiting the web site. The design strategy for a web site comes from an understanding of the business and how to provide appropriate information in this media. It is also determined by the

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target audience and an understanding of the web’s limitations and strengths.” An effective identity design will use visual identifiers such as logos, icons, symbols, colors, images, and typography to maintain the brand identity of a responsive site across a variety of sizes and screen configurations. It will also capitalize on a user’s familiarity and trust in the brand’s identity. In order for the site to gain this advantage it must quickly align itself with its brand through the use of these visual identifiers and through the cohesive use of the four main areas of organization of a responsive web; the site identifier; navigation bar; site content; and the footer. For responsive web design the following are needed for each of these design and organizational elements.

Interaction and User Experience The best web site identity combines effective usability and user experience. As Norman mentioned (2004), “attractive things make people feel good (p19) ” and create positive emotions. Thus, a combination of good usability and providing

Category: Web Technologies

positive experience is essential with the appropriate web site identity elements. “Appropriate visual elements such as logos, images, colors, and typography will create a positive user experiences and build good brand impressions. Understanding the balance between usability and user experience will help designers create successful web site identity systems.” Therefore, usability and user experience are vital to building a successful responsive web site. “In a broad sense, usability can be considered a part of user experience. User experience depends on visual elements, which in turn affect the final appearance of web site design (Kang and Satterfield, 2010). Designing appropriate interactions for a responsive web site is essential to successful usability. Each device requires different interactions. For example, typing on the keyboard of a laptop or desktop computer is a very different experience from typing on a touch screen. According to Kang and Satterfield (2010), “to build a successful web site identity, a designer must understand not only web site usability but also users’ emotions and experiences. Users will experience a web site through navigational interfaces. An interface is a communication tool between the web site and the users. The interaction that takes place as facilitated by the interface is a personalized experience for each individual. Symbols, icons, colors, text, and images are the tools that create this user experience. When a web site has unique icons for navigation, people will remember the icons and identify the web site through them.”

Visual Identifiers and Branding •

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Logos or Branding: Brand symbols and logos must be used consistently and must scale to fit the screen in the site identifier area of the screen. Icons and Symbols: The use of screen icons should remain consistent and the icons should be easily visible and readable in a variety of sizes. The use of complicat-

•

•

ed symbols or low contrast colors should be avoided to maintain a consistent look and an effective readability in all sizes. Color: Color should be selected for best readability in content areas and to create brand recognition. Colors should be used to create emphasis or visual hierarchy in similar ways in a variety of screen formats. Text, icons and symbols should use colors that are highly visible and readable in small sizes on small devices and not overpowering in larger screens. Typography: Type should be in colors that maintain readability in a variety of sizes. The typefaces used should have a variety of weights and styles to support readability and visual hierarchy in various sizes. Color should be used to maintain and reinforce the visual hierarchy of the type such as using darker colors for body copy and lighter colors for headings and subheads. Brand colors may be used to create brand identity or for emphasis.

Web Site Organization Elements •

•

Site Identifier: A site identification area must be clearly established through the use consistent use of brand symbols and logos. It should have the top level of visual hierarchy on the screen and the users should be able to quickly associate this web site with its brand affiliation. Navigation Bar: The navigation bar should quickly and easily orient the user to the site’s structure or information architecture. The location and visual style should be easy to find and readable across all devices even though the location and screen orientation may vary. The navigation system should be clear and consistent in its use of verbal language, symbols and categories of information. The number of categories should be visually compatible with

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•

the amount of space available on a variety of devices and consistent with how the user interprets the information in each category. Site Content: Site content should be designed and written to be easily accessible at a variety of sizes and scales. Writing should be concise and use headings to help users orient themselves in the content and have a clear expectation of what they are reading or seeing. Content should be developed to be accessed with the fewest barriers such as excessive click throughs or long scrolling pages.

Visual hierarchy should be reinforced by selecting a clean, bold font for headings and an easily readable font for content areas and descriptions. The visual hierarchy can be further reinforced by adding color to major headings and using moderate to high contrast between the body copy and the background. Symbols and icons can be added to link text areas to the navigation categories or to create a visual contrast that will draw attention to key information in the body copy and help user’s better understand the content or more quickly find an element of text. Photography and illustration can be used in the content area to establish a sense of connection with the user by enhancing the relatability of the content to their lifestyle or to clarify the verbal content of a site such as including a construction diagram along side a list of instructions. A robust and sensitive combination of visual and verbal information can greatly enhance the perceived value and visual appeal of a responsive web site and should be used to create a strong connection with a company’s corporate branding and philosophy. •

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Footer: The footer should be consistently located at the bottom of the screen or device. It should include information that will allow the user to solve problems or access the company in appropriate ways. The footer should be clearly identified and separate from the content area. Color and

icons or symbols may be used to distinguish the area visually and to identify key associations with other organizations.

CONCLUSION Responsive web design involves teamwork in various areas and it is critical that each individual who involved in the process must understand the roles of usability and user experience in order to build a successful responsive web site identity. For the responsive web, a lean UX (Gothelf and Seiden, 2013) method is recommended for the development process. Testing design ideas rapidly with end users during the design process to adjust the design and usability problems will help to achieve the same look and feel of the visual information in a responsive web environment. Responsive web usability should consider the different ways of navigating the web site across multiple devices, while still maintaining clear information hierarchy, readability and legibility, and consistency in design styles. A consistent user experience is created through the effective use of visual elements such as the brand logo, colors, images, and a unique design style. In order to create successful responsive web site identity, defining the company’s goals and users’ goals for the web site with a team and testing the every step with end users are strongly recommended. “Just like corporate identity systems in traditional media, responsive web site identity must be considered critical to an effective branding effort on the web. At the same time, to design effective web site identity systems, a designer must take into consideration the web’s unique dynamic and flexible characteristics (Kang and Satterfield, 2010)” for multiple screen sizes and platforms.

FUTURE RESEARCH DIRECTIONS In this chapter, visual identity for responsive web design is examined through a variety of sources in a literature review. There are many usability

Category: Web Technologies

and UX studies for responsive web. However, no study has been conducted to determine the role of web site identity in the effectiveness of responsive web design. Future studies to identify best practices for usability and the effective use of visual elements in the responsive web design would be beneficial to both visual web designer and developers.

REFERENCES Carver, M. (2015). The responsive web. Shelter Island, NY: Manning Publications. Douglas, S. (2011). Design secrets of the top 100 brands. Retrieved June 15, 2016 from http:// www.thelogofactory.com/design-secrets-top100-brands/ Dowling, G. (2001). Creating Corporate Reputation: identity, image, and performance. Oxford, UK: Oxford University Press. Elliot, A. J., & Maier, M. A. (2007). Color and psychological functioning. Current Directions in Psychological Science, 16(5), 250–254. doi:10.1111/ j.1467-8721.2007.00514.x PMID:17324089 Gothelf, J. and Seiden, J. (2013). Lean ux applying lean principles to improve user experience. Beijing: O’Reilly Media, Inc. Haig, W., & Harper, L. (1997). The Power of Logos: how to create effective company logos. New York: Van Nostrand Reinhold.

Kang, S. R., & Satterfield, D. (2010). Design Elements and Principles for Maintaining Visual Identity on Websites. In I. Lee (Ed.), Encyclopedia of E-Business Development and Management in the Global Economy (pp. 955–1001). IGI Global. doi:10.4018/978-1-61520-611-7.ch100 Marcotte, E. (2011). Responsive Web Design. New York: A Book Apart. Moss, B. (2015). Is it time to embrace responsive branding? Retrieved June 12, 2016 from http:// www.webdesignerdepot.com/2015/03/is-it-timeto-embrace-responsive-branding/ Norman, D. (2004). Emotional Design: Why we love (or hate) everyday things. New York: Basic books. Park, J. H. (2016). A study of healthcare website’s visual information in service design (Unpublished MFA thesis). Iowa State University, Ames, IA. Perry, A., & Wisnom, D. III. (2003). Before the Brand: Creating the unique DNA of an ensuring brand identity. New York: McGraw-Hill. Voutilainen, J.-P. Salonen, J., & Mikkonen, T. (2015). On the design of a responsive user interface for a multi-device web service. Proceedings of the Second ACM International Conference on Mobile Software Engineering and Systems, 60-63. Watzman, S. (2003). Visual Design Principles for Usable Interfaces. In J. A. Janco & A. Sears (Eds.), The Human-Computer Interaction Handbook. Lawrence Erlbaum Associates.

Kang, S. R., & Lee, E. (2003). Investigating Elements on the E-commerce Homepage: Focus on Business to Customer Web sites. Proceeding CD in The 6th Asian Design Conference.

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KEY TERMS AND DEFINITIONS Brand Identity: The visual elements and company’s identity can distinguish the brand in consumer’s mind and create brand identity. Corporate Identity: Taglines, logos, and marketing elements which could build company’s identity and brand image. Experience Design: The way that the target audience experiences the brand from the first knowledge of the company through the complete consumer process.

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Grid: Organizational structure used to create information templates. Logo: A graphical element composed of a symbol and/or organization name. Monochromatic: Color schemes uses only one color (hue) with various shades and tints. Signatures: Combination of a logo mark and a company name. Visual Identity: Visual elements such as a corporate logo, system elements, color system, typography to build company’s identity.

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Web Site Mobilization Techniques John Christopher Sandvig Western Washington University, USA

INTRODUCTION The introduction of the first Apple iPhone in January 2007 started a historic shift in web browsing from desktop and laptop computers to smartphones and other mobile digital devices. By 2014 two of the largest global shopping sites, Amazon.com and Target.com, reported that the majority of their traffic originated from mobile devices (Sterling, 2015). During the first quarter of 2015 34% of global e-commerce transactions were conducted via mobile devices (“State of Mobile Commerce,” 2015). The number of smartphone users worldwide is forecast to grow from 2.08 billion in 2016 to 2.5 billion by 2019. Over 36% of the world’s population is projected to use a smartphone by 2018 (Statista, 2016). As the number of smartphone uses grows it is becoming increasingly important that web sites provide a good user experience on mobile devices.

BACKGROUND Mobile devices have several physical limitations that require the use of special web design techniques to produce a mobile-friendly user interface. These physical limitations include small screens, virtual keyboards, slow download speeds, and high network latency. To accommodate these physical limitations mobile-friendly web sites often display less information than desktop sites, have larger tap targets such as buttons and form fields, use smaller images, and offer fewer navigation options. Sites that are developed primarily for desktop and laptop computers often require mobile users to zoom in and scroll horizontally, providing a poor user experience. Mobile-friendly web sites

are designed to render well on all devices, not just mobile devices.

MOBILIZATION TECHNIQUES Currently there are three popular techniques for developing mobile-friendly web sites: responsive, dynamic serving, and redirect to separate URL. These techniques are referred to by a variety of names so to avoid confusion this article will adopt the nomenclature utilized in Google’s Mobile Developer Guide (“Mobile SEO Overview,” 2016). All three techniques detect the size of the user’s screen and modify the page content and layout to accommodate the device. The techniques differ in how they detect screen size and the mechanisms by which they modify page content.

Responsive Web Design The responsive design technique delivers the same content (HTML, CSS, JavaScript, images, etc.) to all devices and “responds” to screen size by modifying the layout. For instance, a page design that utilizes a three column layout on a desktop computer may be displayed as one long vertical column on a mobile device. Tabbed navigation on a desktop computer many be displayed as a hamburger menu on a mobile device, and large images may be reduced in size for mobile devices. Responsive is a relatively new technique. The phrase “response web design” was originally defined by Ethan Marcotte in 2007 (Els, 2015). Marcotte states that web pages should be designed with the flexibility to respond and adapt to the capabilities of various devices. At the time Marcotte proposed this approach CSS did not have the

DOI: 10.4018/978-1-5225-2255-3.ch704 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Web Site Mobilization Techniques

ability to detect screen size but the approach could be implemented using JavaScript. This capability was included in the final recommendations for CSS3 and adopted in 2012 by the World Wide Web Consortium (Hargreaves, 2015). It then took a few years for web browser software to implement new standards and for web site developers to employ the new capabilities in web sites. CSS media queries have the ability to detect screen size, thus allowing developers to create different layouts for different size screens. For example the following media query located in a site’s css file would allow a developer to define styles for devices with screen widths between 320px and 400px. @media screen and (min-device-width: 320px) and (max-width: 400px) {       /* styles for small screens go here */ }

The responsive technique is often implemented using any one of several open-source grid frameworks. These frameworks provide a basic structure for page layout, eliminating the need for developers to create designs from scratch. There are over two dozen grid frameworks include Bootstrap, Foundation 3, Skeleton, 1140 CSS Grid, and Less Framework 4 (Jain, 2015). CSS frameworks allow developers to design different layouts for different size screens. For instance, Bootstrap 4.0 defines breakpoints at five different screen widths: extra small (less than 34em), small (34-47em), medium (48-61em), large (62-74em) and extra large (75em and larger) (Oliver 2015). Web designers can then create specific layouts for each of these five screen size ranges. Other styles classes, such as Bootstrap’s. hidden-*-up and.hidden-*-down (where * is xs, sm, md, lg, and xl) allow developers to target specific content to different size screens. Many grid frameworks also provide JavaScript files that

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work in conjunction with built-in CSS classes to provide dynamic features such as dropdown navigation, breadcrumbs, and hamburger menus (Lambert, 2016). The responsive mobilization technique can also be implemented using JavaScript. JavaScript is a programming language that is supported by all modern browsers. It has the ability to detect screen sizes and apply formatting styles. For example the following JavaScript would hide an HTML element for screens widths between 320px and 400px. if(screen.width >= 320 && screen. width