Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education: Proceedings of the 5th International Conference on Smart Learning Ecosystems and Regional Development [1st ed.] 9789811573828, 9789811573835

Table of contents :
Front Matter ....Pages i-xiv
Front Matter ....Pages 1-1
Classroom Lighting and Its Effect on Student Learning and Performance: Towards Smarter Conditions (Jordi Mogas-Recalde, Ramon Palau)....Pages 3-12
Alive in Smart Countryside (Pekko Lindblom, Eeva Nygren, Jukka Heikkonen, Erkki Sutinen)....Pages 13-26
Newcomer Integration in Online Communities: Chronemics in Asynchronous Collaborative Discussions (Iulia Pasov, Nicolae Nistor, Mihai Dascalu, Stefan Trausan-Matu)....Pages 27-38
An Analysis of Alternation Schemes to Increase Student Employability and the Smartness of Secondary Schools (Carlo Giovannella)....Pages 39-51
What Makes New Technology Sustainable in the Classroom: Two Innovation Models Considered (Janika Leoste, Mati Heidmets, Tobias Ley)....Pages 53-65
Conceptualization of Hypersituation as Result of IoT in Education (Filipe T. Moreira, Mário Vairinhos, Fernando Ramos)....Pages 67-73
Front Matter ....Pages 75-75
A Territorial Learning Ecosystem for Parents’ Participation and Cooperation (Roberto Araya)....Pages 77-89
Escape from Dungeon—Modeling User Intentions with Natural Language Processing Techniques (Stefan Toncu, Irina Toma, Mihai Dascalu, Stefan Trausan-Matu)....Pages 91-103
Effect Induced by the Covid-19 Pandemic on Students’ Perception About Technologies and Distance Learning (Carlo Giovannella)....Pages 105-116
Visible Teacher Thinking and Group Learning (Maria Guida, Letizia Cinganotto)....Pages 117-129
People-Centered Benchmarking of Smart School Ecosystems: A Study with Young Students from Aveiro Region (Óscar Mealha, José Nunes, Carlos Ferreira, Fernando Delgado Santos, João Ferreira)....Pages 131-143
Smart Visual Identities: A Design Challenge for Smart Learning Environments (Catarina Lelis)....Pages 145-154
Digital Making and Entrepreneurship. Imagine the Future (Annalisa Buffardi)....Pages 155-163
Toward a Recommender System for Planning Montessori Educational Activities (Cristi Nica, Alexandru Olteanu, Emil Racec)....Pages 165-170
Front Matter ....Pages 171-171
Cohesion Network Analysis for Predicting User Ranks in Reddit Communities (Catalin-Emil Fetoiu, Maria-Dorinela Dascalu, Mihnea Andrei Calin, Mihai Dascalu, Stefan Trausan-Matu, Gheorghe Militaru)....Pages 173-185
Tracing Humor in Edited News Headlines (Dan Alexandru, Daniela Gîfu)....Pages 187-196
Data-Driven Intelligent Tutoring System for Accelerating Practical Skills Development. A Deep Learning Approach (Robert Marinescu-Muster, Sjoerd de Vries, Wouter Vollenbroek)....Pages 197-209
Exploratory Analysis of a Large Dataset of Educational Videos: Preliminary Results Using People Tracking (Eduard Cojocea, Traian Rebedea)....Pages 211-223
A Serious Game for Lean Construction Education Enabled by Internet of Things (Lavinia C. Tagliabue, Silvia Mastrolembo Ventura, Jochen Teizer, Angelo L. C. Ciribini)....Pages 225-233
Semantic Recommendations of Books Using Recurrent Neural Networks (Melania Nitu, Stefan Ruseti, Mihai Dascalu, Silvia Tomescu)....Pages 235-243
Supporting Multiple Programming Languages in an Online Judge (Ioana-Teodora Tica, Alexandru-Corneliu Olteanu, Emil Racec)....Pages 245-252
A Quantitative Analysis of Romanian Writers’ Demography Based on the General Dictionary of Romanian Literature (Laurentiu-Marian Neagu, Irina Toma, Mihai Dascalu, Ștefan Trăușan-Matu, Laurențiu Hanganu, Eugen Simion)....Pages 253-261
Chatbot, the Future of Learning? (Bogdan-Ioan Ouatu, Daniela Gifu)....Pages 263-268
Back Matter ....Pages 269-270

Citation preview

Smart Innovation, Systems and Technologies 197

Óscar Mealha Matthias Rehm Traian Rebedea   Editors

Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education Proceedings of the 5th International Conference on Smart Learning Ecosystems and Regional Development

123

Smart Innovation, Systems and Technologies Volume 197

Series Editors Robert J. Howlett, Bournemouth University and KES International, Shoreham-by-sea, UK Lakhmi C. Jain, Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia

The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability. The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form. Volumes on interdisciplinary research combining two or more of these areas is particularly sought. The series covers systems and paradigms that employ knowledge and intelligence in a broad sense. Its scope is systems having embedded knowledge and intelligence, which may be applied to the solution of world problems in industry, the environment and the community. It also focusses on the knowledge-transfer methodologies and innovation strategies employed to make this happen effectively. The combination of intelligent systems tools and a broad range of applications introduces a need for a synergy of disciplines from science, technology, business and the humanities. The series will include conference proceedings, edited collections, monographs, handbooks, reference books, and other relevant types of book in areas of science and technology where smart systems and technologies can offer innovative solutions. High quality content is an essential feature for all book proposals accepted for the series. It is expected that editors of all accepted volumes will ensure that contributions are subjected to an appropriate level of reviewing process and adhere to KES quality principles. ** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, SCOPUS, Google Scholar and Springerlink **

More information about this series at http://www.springer.com/series/8767

Óscar Mealha Matthias Rehm Traian Rebedea •

•

Editors

Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education Proceedings of the 5th International Conference on Smart Learning Ecosystems and Regional Development

123

Editors Óscar Mealha Department of Communication and Art, Digital Media and Interaction Research Center University of Aveiro Aveiro, Portugal

Matthias Rehm Department of Architecture, Design, and Media Technology Aalborg University Aalborg, Denmark

Traian Rebedea Department of Computer Science University Politehnica of Bucharest Bucharest, Romania

ISSN 2190-3018 ISSN 2190-3026 (electronic) Smart Innovation, Systems and Technologies ISBN 978-981-15-7382-8 ISBN 978-981-15-7383-5 (eBook) https://doi.org/10.1007/978-981-15-7383-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Organization

General Chair Mihai Dascalu, University Politehnica of Bucharest, Romania Conference Co-chairs Donatella Persico, CNR—Institute of Educational Technology, Italy Elvira Popescu, University of Craiova, Romania Honorary Chairs Carlo Giovannella, Università di Roma Tor Vergata, Italy Fernando Ramos, University of Aveiro, Portugal Ştefan Trăuşan-Matu, University Politehnica of Bucharest, Romania Special Events and Tracks Chair Daniela Gîfu, Alexandru Ioan Cuza University, Romania Publishing Chairs Óscar Mealha, University of Aveiro, Portugal Traian Rebedea, University Politehnica of Bucharest, Romania Matthias Rehm, Aalborg University, Denmark Local Organizing Committee Gabriel Gutu-Robu, University Politehnica of Bucharest, Romania Carolina Abrantes, University of Aveiro/DigiMedia, Portugal Maria-Dorinela Dascalu, University Politehnica of Bucharest, Romania Laurentiu Neagu, University Politehnica of Bucharest, Romania José Nunes, University of Aveiro/DigiMedia, Portugal Stefan Ruseti, University Politehnica of Bucharest, Romania Irina Toma, University Politehnica of Bucharest, Romania

v

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Organization

Student Design Contest Committee and Jury Diana Andone, Polytechnical University of Timisoara, Romania Giordano Bruno, Fondazione Morfe’, Italy Patrizia Marti, University of Siena, Italy Carlo Giovannella, University of Rome Tor Vergata, Italy Giuseppe Roccasalva, Politecnico di Torino, Italy Jelle Saldien, Ghent University, Belgium Program Committee Diana Andone, Polytechnical University of Timisoara, Romania Vincenzo Baraniello, University of Rom Tor Vergata, Italy Rosa Maria Bottino, CNR—Institute of Educational Technology, Italy Giordano Bruno, ISIA Roma e Fondazione Morfe’, Italy John M. Carroll, Penn State’s College of Information Sciences and Technology, USA Stefano Cacciamani, University of Valle d’Aosta and CKBG, Italy Ines Di Loreto, Université de Technologie de Troyes, France Tania Di Mascio, Università de L’Aquila, Italy Monica Divitini, Norwegian University of Science and Technology, Norway Gabriella Dodero, ASLERD, Italy Rosella Gennari, Free University of Bozen, Italy Ralf Klamma, RWTH Aachen University, Germany Stefania Manca, CNR—Institute of Educational Technology, Italy Patrizia Marti, University of Siena, Italy Alke Martens, University of Rostock, Germany Viktoria Pammer-Schindler, Know-Center, Austria Marcello Passarelli, Institute of Educational Technology, Italy Luis Pedro, University of Aveiro, Portugal M. Paloma Diaz Perez, Universidad Carlos III de Madrid, Spain Juliana Raffaghelli, Universitat Oberta de Catalunya, Spain Kasper Rodil, Aalborg University, Denmark Lara Sardinha, University of Aveiro, Portugal Carlos Santos, University of Aveiro, Portugal Antonio Teixeira, Universidade Aberta, Portugal Marco Temperini, Università di Roma La Sapienza, Italy Benedetto Todaro, Quasar Institute for Advanced Design, Italy Radu Vasiu, Polytechnical University of Timisoara, Romania Annika Wolff, Lappeenranta University of Technology, Finland Imran Zualkernan, American University of Sharjah, United Arab Emirates

Preface

After half of the decade of growing our community, this year we are proud to present the proceedings of the fifth International Conference on Smart Learning Ecosystems and Regional Developments (SLERD 2020). After a complete European tour, with the first edition in the East—Timisoara, Romania (2016), the second edition in the West—Aveiro, Portugal (2017), the third edition in the North —Aalborg, Denmark (2018), and the fourth edition in the South—Rome, Italy (2019), we have returned to the East of Europe for the fifth edition of SLERD, virtually hosted in Bucharest, Romania (2020). This fifth edition was organized by University Politehnica of Bucharest, Faculty of Automatic Control and Computers and, as for the previous editions, supported by the Association for Smart Learning Ecosystem and Regional Development (ASLERD) on May 25–27, 2020. As our lives, work, and travel were affected by the COVID-19 pandemic, we decided that SLERD 2020 to continue as a virtual conference that takes advantage of the technologies empowering us to organize our work, meetings, and scientific events mostly online. The conference welcomed researchers and practitioners from all over the world involved in the development of Smart Learning Ecosystems and Smart Education, as engines of social innovation and territorial development. At the core, the adjective smart comprises terms like intelligent, purpose oriented, supportive, artful, clever, and the like. Thus, smart does not necessarily include the usage of technology (neither does it exclude technology!). When referred to learning ecosystems in ASLERD and SLERD contexts, smart does not simply mean “technology enhanced” (to include expert systems or AI). The smartness is a more complex multilayered construct related to the well-being of the players operating in the ecosystems that hopefully are also in relation to the territory—see Declaration of Timisoara, the Wikipedia page of ASLERD, the proceedings of the previous SLERD conferences published by Springer (Aveiro, Aalborg, Rome), and the special issues (N.16, N.17, N.20, N.27, N.31, N.35, N.39, and N.43 in preparation) devoted to SLERD by IxDA journal. Smartness is affected by the improvement of any relevant aspects of the learning processes and ecosystem functionality, especially if connected with territorial development and social innovation. Technologies vii

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Preface

are just mediators. Hopefully, they should be included in most smart learning solutions and ecosystems, but they are not a “sine qua non.” The achievement of the learning ecosystems’ smartness is a process that needs a long-term vision, multidisciplinary competences, an attitude to understand people and contexts and to mediate point of views, a dynamic resilience to keep on track to achieve, step by step, the foreseen goals: in short, a design literacy that fosters projects and processes capable of reifying them, all aimed at achieving a people-centered smart education, social innovation, and territorial development. Overall, we received 29 unique submissions from ten countries, demonstrating the wide interest for this research area and for the SLERD 2020 conference, even in the complex context that changed our world this year. Out of the total submissions, after a rigorous double-blind peer-review and meta-review process, we accepted 13 full papers and six short papers. Additionally, we included three extended abstracts as position papers to show the breadth of the work and new directions relevant to this research community. The selected scientific papers published in this volume aim to understand, conceive, and promote innovative human-centric design and development methods, smart education and training practices, informal social learning, and citizen-driven policies in education. The papers are organized in three main topics, each of them consisting of several long and short contributions and a position paper. In the first part, the focus is on improving the learning and workplaces for smart education, in both formal and informal environments. The second part is centered on proposing new methods and practices for people in place-centered design for smart education. Finally, in the third part of the volume, we present a wide range of analytical technologies and tools for supporting smart education. With all these contributions, SLERD 2020 aims to foster social innovation sectors, identifying and discussing ICT and economic development strategies alongside with new policies for smarter proactive citizens, teachers, and learners. Thus, the proceedings are relevant to both researchers and policy makers. In summary, SLERD 2020 offered an exciting program that provided an excellent overview of the state of the art in smart learning ecosystems and was an occasion for bringing research forward and creating new networks. We are very proud of the final selection of papers, which would not have been possible without the effort and support of our excellent Conference and Program Committees, consisting of more than 50 international researchers. We would like to thank all the ones who, in different roles, have contributed their time to organize the event with enthusiasm and commitment. Aveiro, Portugal Aalborg, Denmark Bucharest, Romania June 2020

Óscar Mealha Matthias Rehm Traian Rebedea

Contents

Places for Smart Education Classroom Lighting and Its Effect on Student Learning and Performance: Towards Smarter Conditions . . . . . . . . . . . . . . . . . . Jordi Mogas-Recalde and Ramon Palau Alive in Smart Countryside . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pekko Lindblom, Eeva Nygren, Jukka Heikkonen, and Erkki Sutinen

3 13

Newcomer Integration in Online Communities: Chronemics in Asynchronous Collaborative Discussions . . . . . . . . . . . . . . . . . . . . . . Iulia Pasov, Nicolae Nistor, Mihai Dascalu, and Stefan Trausan-Matu

27

An Analysis of Alternation Schemes to Increase Student Employability and the Smartness of Secondary Schools . . . . . . . . . . . . . . . . . . . . . . . . Carlo Giovannella

39

What Makes New Technology Sustainable in the Classroom: Two Innovation Models Considered . . . . . . . . . . . . . . . . . . . . . . . . . . . . Janika Leoste, Mati Heidmets, and Tobias Ley

53

Conceptualization of Hypersituation as Result of IoT in Education . . . . Filipe T. Moreira, Mário Vairinhos, and Fernando Ramos

67

People in Place Centered Design for Smart Education A Territorial Learning Ecosystem for Parents’ Participation and Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roberto Araya

77

Escape from Dungeon—Modeling User Intentions with Natural Language Processing Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stefan Toncu, Irina Toma, Mihai Dascalu, and Stefan Trausan-Matu

91

ix

x

Contents

Effect Induced by the Covid-19 Pandemic on Students’ Perception About Technologies and Distance Learning . . . . . . . . . . . . . . . . . . . . . . 105 Carlo Giovannella Visible Teacher Thinking and Group Learning . . . . . . . . . . . . . . . . . . . 117 Maria Guida and Letizia Cinganotto People-Centered Benchmarking of Smart School Ecosystems: A Study with Young Students from Aveiro Region . . . . . . . . . . . . . . . . 131 Óscar Mealha, José Nunes, Carlos Ferreira, Fernando Delgado Santos, and João Ferreira Smart Visual Identities: A Design Challenge for Smart Learning Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Catarina Lelis Digital Making and Entrepreneurship. Imagine the Future . . . . . . . . . . 155 Annalisa Buffardi Toward a Recommender System for Planning Montessori Educational Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Cristi Nica, Alexandru Olteanu, and Emil Racec Supportive Technologies and Tools for Smart Education Cohesion Network Analysis for Predicting User Ranks in Reddit Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Catalin-Emil Fetoiu, Maria-Dorinela Dascalu, Mihnea Andrei Calin, Mihai Dascalu, Stefan Trausan-Matu, and Gheorghe Militaru Tracing Humor in Edited News Headlines . . . . . . . . . . . . . . . . . . . . . . . 187 Dan Alexandru and Daniela Gîfu Data-Driven Intelligent Tutoring System for Accelerating Practical Skills Development. A Deep Learning Approach . . . . . . . . . . . . . . . . . . 197 Robert Marinescu-Muster, Sjoerd de Vries, and Wouter Vollenbroek Exploratory Analysis of a Large Dataset of Educational Videos: Preliminary Results Using People Tracking . . . . . . . . . . . . . . . . . . . . . . 211 Eduard Cojocea and Traian Rebedea A Serious Game for Lean Construction Education Enabled by Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Lavinia C. Tagliabue, Silvia Mastrolembo Ventura, Jochen Teizer, and Angelo L. C. Ciribini Semantic Recommendations of Books Using Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Melania Nitu, Stefan Ruseti, Mihai Dascalu, and Silvia Tomescu

Contents

xi

Supporting Multiple Programming Languages in an Online Judge . . . . 245 Ioana-Teodora Tica, Alexandru-Corneliu Olteanu, and Emil Racec A Quantitative Analysis of Romanian Writers’ Demography Based on the General Dictionary of Romanian Literature . . . . . . . . . . . . . . . . . 253 Laurentiu-Marian Neagu, Irina Toma, Mihai Dascalu, Ștefan Trăușan-Matu, Laurențiu Hanganu, and Eugen Simion Chatbot, the Future of Learning? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Bogdan-Ioan Ouatu and Daniela Gifu Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269

About the Editors

Óscar Mealha is Professor at the Department of Communication and Art, University of Aveiro, Portugal. He develops his research in the area of “Information and Communication in Digital Platforms” in the context of “Knowledge Media and Connected Communities” with several projects, masters and doctoral supervisions and publications on interaction design and analysis techniques and methods, namely for UX design and evaluation, usability evaluation, and visualization of interaction / infocommunication activity. He is involved in infocommunication mediation projects such as “Unified Communication & Collaboration” with IT companies, “Visualization of Open Data Dashboards for Citizen Engagement and Learning” in municipalities and smart territories, and “Knowledge Interface School-Society (KISS)” with school clusters within the scientific network ASLERD - www.aslerd.org. He is the Director of the Doctoral Program on Information and Communication in Digital Platforms, a joint program of the University of Aveiro and University of Porto, 2014 -. co-Founder and member of the Scientific Committee of this doctoral program, 2008 -. He was the elected Dean at the Department of Communication and Art, University of Aveiro, 2005-2011. Elected Senate member, in representation of Professors at the University of Aveiro, 2004-2008. Elected Vice-President of the Pedagogical Council at the University of Aveiro, 1998-2002. Matthias Rehm is Professor for Human Machine Interaction at the Dept. of Architecture, Design, and Media Technology and the director of the crossdepartmental Human Robot Interaction lab at the Technical Faculty of IT and Design at Aalborg University. He received his Diploma and Doctoral degrees (with honors) in 1998 and 2001 respectively from Bielefeld University in Germany. In 2008, he successfully completed his habilitation process in Informatics at the University of Augsburg in Germany. His research is focused on modeling social, affective and cultural aspects of everyday behavior for intuitive human machine interactions. He has over 120 peer reviewed publications in the area of social robotics, human machine interaction, multimodal interaction, and smart learning technology. In 2010, he became founding and steering group member of Aalborg University’s Robotics Center (http://robotics.aau.dk). In 2014 he co-founded an international, xiii

xiv

About the Editors

Canadian-based startup that is actively pushing the limits in smart learning technologies. In 2015, he was elected vice president for the International Association for Smart Learning Ecosystems and Regional Development (http://aslerd.org). Traian Rebedea is Associate Professor at University Politehnica of Bucharest, with a Ph.D. in natural language processing defended in 2013 (at UPB). The thesis focused on offering detailed feedback to learners engaged in multi-party computer supported collaborative conversations (using chats, discussion forums). In the last couple of years, Dr. Traian Rebedea was involved in several applied projects involving information extraction, NLP, and machine learning with applications in opinion mining, information extraction from public data about companies and persons, conversational agents and question-answering systems. He has coordinated two research projects in collaboration with companies (Bitdefender and Autonomous Systems), as well as an innovation grant for startups offered by the European Commission through the Open Data Incubator (Wholi project). He co-founded Roboself Technologies in 2019, a technological startup developing virtual personal assistants that already received a 200k Euro private investment.

Places for Smart Education

Classroom Lighting and Its Effect on Student Learning and Performance: Towards Smarter Conditions Jordi Mogas-Recalde

and Ramon Palau

Abstract The main objective of this study was to determine which lighting factors intervene in the learning processes taking place in a physical classroom, in regard to smart classroom conditioning. It was performed by means of a systematic literature review. Two research questions were posed: What aspects of classroom lighting have studies focused on? And how factors of classroom lighting influence learning processes? From a sample of 130 papers chosen, we identified seven aspects of classroom lighting. One of the aspects is “cognitive processes”, treated in eighteen of the papers. Classroom lighting does affect cognition, and it is proven in terms of academic achievement, attention rates, working speed, productivity and accuracy among other reported effects. LED lighting appears to be the most suitable to improve psychological and cognitive processes in the classroom. Particular importance is given to using higher correlated colour temperature (CCT) and the balance between daylight and artificial light. From the results, it is clearly stated that a dynamic lighting is necessary to host different activities in classroom. Research is now focusing on automation of a dynamic lighting system as the first step towards smart classroom lighting. Keywords Lighting · Educational lighting · Cognitive processes · Classroom design · Smart classroom

J. Mogas-Recalde (B) · R. Palau Departament de Pedagogia, Universitat Rovira i Virgili, ctra. de Valls, s/n, 43007 Tarragona, Catalonia, Spain e-mail: [email protected] R. Palau e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_1

3

4

J. Mogas-Recalde and R. Palau

1 Introduction In educational contexts, the lighting within a classroom is particularly important. Monteoliva et al. [1] wrote that “lighting is one of the most critical physical characteristics in a learning space”. Barrett et al. [2] identified lighting and flexibility as considerable factors talking about educational spaces. Thus, lighting should be controlled in educational spaces, namely in classrooms, since it causes a direct impact on the learners’ performance and achievement. Developments in lighting allow new possibilities in education. Particularly, LED technology has become popular because they present a highly efficient system, are small sized, long-lasting, allow saving energy in comparison to other lamp types, are environmentally friendly and have a real-time tuneable spectrum. Although many other factors are also to be taken into account [3]. Lighting in education is exceptionally valuable when it is related to smart classrooms. The smart classroom concept is part of smart learning environments, places endowed with adaptive devices to promote better and faster learning [4, 5]. From this conception, technology brings new possibilities to the learning environments by means of automation and personalisation [6]. Lighting is considered an integral part and characteristic of smart classrooms, being a primary characteristic of the ambient factors [7]. In this study, we show the process of literature review relating lighting with educational contexts and classrooms, followed by the results and discussion to reveal what factors of classroom lighting influence the cognitive processes. Finally, we draw conclusions with special attention to the possibilities offered by smart classrooms.

2 Method 2.1 Research Questions The main objective of this study was to determine which lighting factors intervene in the learning processes that take place in a physical classroom, according to the scientific research published to date. Two research questions were addressed: • What aspects of classroom lighting have studies focused on? • How factors of classroom lighting influence the learning processes?

2.2 Literature Search The studies on lighting in educational contexts have different focuses. Some focus on one particular variable which they study in depth, whereas others consider a range of variables to provide a broader view. This study makes a systematic review

Classroom Lighting and Its Effect on Student Learning …

5

following the steps used by Vangrieken et al. [8]. The literature search is divided into two phases. The first phase aimed to retrieve all the documents studying all forms of classroom lighting in order to get an overall understanding of all the focuses. This holistic view is useful for identifying possible third intervening variables in further studies. The searches were performed using three combinations of keywords: (1) lighting “learning processes”, (2) lighting “learning environment” and (3) classroom lighting. The second phase consisted of selecting the studies that clearly focused on the relationship between classroom lighting and student cognition (learning processes, cognitive impact, concentration, etc.). Two of the main databases in educational science were consulted: Web of Science (WoS) and Scopus. Palau and Mogas [7] have already performed research using these two databases for a characterisation of the smart learning environments, and regard the results obtained as being qualitatively successful.

2.3 Selection Criteria and Selection Process The first phase of the systematic review only included papers published in the last ten years (i.e. from 2009 to the present). The main subject was lighting in education, but all papers studying classroom lighting were considered with the only limitation of the year of publication. The word combinations applied to the article’s topic, title, abstract or keywords. A total amount of 3362 articles were initially retrieved, 2919 in WoS and 443 in Scopus (Table 1). A preliminary selection was made by analysing the titles and discarding those differing from the purpose of this study. The main reason for rejection was discordance with the focus, even if there was some lexical matching. Although some articles were found twice, either by different searches of the same database or because they were in both of the databases used, it was decided to include them all to guarantee that the documents were systematically counted during the process, and discard them at a later stage. This procedure considered 219 articles to be eligible, and the original documents were downloaded using the institutional licenses for access to scientific databases. 25 had to be discarded for one of two reasons: some were written in languages we were unable to understand (mainly Korean and Bahasa Melayu) and others could not be downloaded. Previous downloading, 64 of the papers were identified duplicated. Thus, a total of 130 documents were finally selected for review. The filtering criteria used during the second phase was an attentive read of the abstracts to determine the main underlying focus and to classify the documents. Up to seven aspects of lighting in classrooms or educational spaces were distinguished (see Table 2, in results and discussion).

189

359

2371

2919

1. Lighting “learning processes”

2. Lighting “learning environment”

3. Classroom lighting

Total 3.6%

105

4.0%

95

2.8%

10

0%

0

15

12

3

0

3.1%

90

3.5%

83

1.9%

7

0%

0

443

350

42

51

Scopus Selected

Retrieved

Discarded

Retrieved

Eligible

Web of science

Table 1 Results from the literature search in both databases

25.7%

114

27.4%

96

35.7%

15

5.9%

3

Eligible

10

8

1

1

Discarded

23.5%

104

25.1%

88

33.3%

14

3.9%

2

Selected

6 J. Mogas-Recalde and R. Palau

Classroom Lighting and Its Effect on Student Learning …

7

Table 2 Classification of the aspects studied n

Aspect

Description

31 Automation

Wide range of studies conclude that automation is fundamental in the move towards the smart classroom (in terms of lighting, at least)

22 Student comfort

Many studies focus mainly on users’ self-perception of comfort, especially in terms of classroom lighting. The most commonly used technique is the questionnaire

21 Sustainability

Increasing number of studies focus on saving energy in classrooms or more efficient lighting systems. They are related to the smart classroom since the concept “smart” implies per se caring about sustainability

18 Cognitive processes (This aspect is given special attention in the present study, in next section.) 17 Technical issues

Some studies discuss classroom illumination from a technical point of view and deal with such issues as daylight and light reflection

16 Space design

Some papers adopt architectonic points of view and discuss how lighting might be adapted to space designs.

5

Given articles study the impact of lighting on student health. Most deal with myopia

Impact on health

By this time, the first research question of the paper had been answered. To answer the second one, we restricted our documental analysis to those papers with a “cognitive process” focus. The research question focused on learning processes, but we decided to broaden this view because the papers on learning processes also report meaningful data on psychological and cognitive processes in general.

2.4 Literature Analysis A narrative method was used to analyse the content of the retrieved documents and extract the relevant information. First, the main ideas were identified so that the results could be structured. Then, a substantial amount of information was extracted from each of the documents. Of the papers retrieved on cognitive processes and classroom lighting, all but one provided some sort of highlight of interest. Despite having passed the previous filters, that one was later discarded from this literature review.

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3 Results and Discussion 3.1 Aspects of Classroom Lighting Society is becoming more and more aware of the importance of the architectural and physical elements of learning spaces to the productivity and efficiency of educational practices. Classrooms are the typical spaces in which learning processes take place and, in consequence, their design must take into account a variety of intervening factors. Of these, lighting has always been considered one of the most meaningful. From our literature review, we identified that seven different aspects of classroom lighting have been studied as main focus (Table 2). Each aspect has its own characteristics. Most of the documents adopt one main focus but also have some secondary focuses. To give an example, automation and technical issues are so closely related that it is difficult to deal with one of them without mentioning the other. This classification considers only the predominant focus per paper in order to determine the main aim. Even so we do not underestimate the complexity of the relations between focuses. Our main interest is to understand learning processes so the part of this classification that is of most interest is the focus on cognitive processes. This is the subject of the next section.

3.2 Classroom Lighting and Cognitive Processes The effects of classroom lighting are addressed in terms of its psychological and cognitive implications such as academic achievement [9, 10], attention [11], concentration [12, 13], student motivation to learn more [14], engagement [15], visual pleasantness or comfort (as can be seen in the 22 papers identified, see Table 2; e.g. [16]). The effects on cognitive performance in general terms are also assessed [1]. Furthermore, Choi and Suk [3] report other positive effects of controlling lighting such as working speed, productivity and accuracy. The sort of light that most improves or promotes learning processes is a point of debate. Several studies have shown that LED lighting is the best option for a positive impact on student learning processes. In this respect, Pulay and Williamson [14] compared the engagement of early childhood students in classrooms equipped with LED lighting and fluorescent lighting. The results revealed better engagement in the classroom lit with LED lighting, and the difference was even greater in students with developmental disabilities. Likewise, after some experimental research in a classroom of psychology students, Eo and Choi [17] also found that LED lamps had a better impact on learning and feelings than fluorescent lighting. As far as the basic technical measurements of lighting are concerned, interest focuses on the effects of the correlated colour temperature (CCT), measured in degrees Kelvin (K) and the intensity or the illuminance, measured in lux (lx). Most

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of the papers retrieved in the systematic search are on the colour temperature, while just a few deal with the intensity (and always as a complement to CCT). CCT ambient light has a direct impact on learning scenarios because it can affect student alertness. Low CCT, associated to warmer atmospheres with light in the red/orange/yellow spectrum, is in general less appropriate for usual activities held in classrooms, whereas higher CCT, associated with blue light, can promote alertness and better performance. Thus, for classrooms, it is ideal to use neutral and higher CCT light, but it might depend on the performed task. In this regard, Hartstein, LeBourgeois and Berthier [11] have shown the effect of exposing preschool-age children to different levels of CCT. Their results show that task switching is greatly improved at higher CCTs (combining 3500 and 5000 K): that is to say, the use of more blue light improves the alertness and performance of young pupils. Keis et al. [18] obtained results that showed the benefits of high CCT on student performance, in particular faster cognitive processing speed and better concentration. They claim that “the blue-enriched white lighting seems to influence very basic information processing primarily, as no effects on short-term encoding and retrieval of memories were found”. In fact, lighting values should not be fix. Rather it should vary with the activities being performed in the classroom. Choi and Suk [3] propose a dynamic lighting system for a smart learning environment. They carried out three studies (with three lighting presets: 3500, 5000 and 6500 K) and concluded that dynamic lighting can make learning environments smarter. Dynamic lighting has also been explored by other authors [12] who made three studies of how a dynamic lighting system affects the concentration of Dutch elementary school children. They also concluded that there was a positive influence. It is stated that there must be three or four settings of CCT: calm setting (2900 K), normal or standard setting (3000–4000 K), focus setting (6500 K) and a possible energy setting (12,000 K) [12, 19], but studies only prove empirically the effects of normal and focus settings, not deepening on the convenience to adopt calm settings and how this really has impact on students’ cognition. Another research trend is the contrast between daylight and artificial light. Research done by Barrett et al. [2, 20, 21] identified naturalness as an important factor in the physical design of classrooms since it is directly related to student progress. They analysed seven parameters of classroom design, and lighting reported the highest correlation with overall progress. Their study shows that “the highest quantity of natural and electrical light, but without direct sunlight, was found to be optimum. Too much direct sunlight into the classroom was found to cause a glare problem” especially if using interactive whiteboards. Studies done by Mott et al. [19] also support the thesis that “artificial lighting plays a key role in helping to create an effective learning environment to ensure children reach their full potential”. In their case studies, students perform better under high intensity (yet glare free) lighting which is referred to by the authors as focus lighting. Even so, the authors themselves accept that other scientific research [2] proves the contrary: i.e. that daylight has been found to be positive for learning in schools. Thus, the challenge is to find the balance for good ambient lighting conditions.

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The most innovative aspects of lighting in classrooms tend to focus on automated systems. This is the case of the study by Lee et al. [13, 22], who developed and implemented a lighting control system based on context awareness. The authors aimed to improve learning efficiency in the classroom, but the data they collected only allowed them to make a proposal about the system, and they had to leave the study of efficiency and attentiveness for further research. Other researchers have proposed automation systems (see the most common focus in Table 2), but none have analysed its direct effect on cognitive processes. All the papers we have analysed report a direct impact of classroom lighting on student performance (concentration, attentiveness, achievement, etc.). Only the study by Murillo and Martínez-Garrido [23] claims that there is a lack of correlation between lighting and any learning variables. Although their research is scientifically well prepared, their methodology does not seem to justify such a conclusion. So, our systematic literature review confirms that lighting does have an impact on learning.

4 Conclusions The first research question aimed to determine what aspects of classroom lighting classroom had been studied. The method applied found the response during the process of document retrieval, while the subsequent documental analysis confirmed the classification. The aspects we identified are automation, student comfort, sustainability, cognitive processes, technical issues, space design and impact on health. Our findings were more extensive than we had initially expected, and we concluded that a good design of classroom lighting attempts to satisfy multiple variables. As far as the second research question is concerned, the study focused on using “cognitive processes” to identify how factors of classroom lighting influence the learning processes. The authors reviewed study the following: academic achievement, attention, concentration, student motivation to learn more, engagement, visual pleasantness or comfort, cognitive performance in general terms, working speed, productivity and accuracy. This main focus is combined with others to provide as much information on impact as possible. Namely: • LED lighting appears to be the most suited to improving psychological and cognitive processes in the classroom, such as engagement, learning and feelings. • Ideally schools need to use higher correlated colour temperature (CCT) ambient lighting in their classrooms to boost alertness and performance, whereas lower CCT can be beneficial for more relaxed activities. • In consequence, dynamic lighting appears to be necessary. In classrooms, different kinds of activity are performed and their needs may vary, so lighting should be adaptive. • The balance between the use of daylight and artificial light is a tricky issue. This research indicates that artificial and controlled light is the most common option and the one that has been most studied. Despite this, there is no consensus and

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some believe that by modifying levels of CCT and intensity, and bearing in mind other factors like reflections on screens, both can be used. • Automation is also a major focus of the studies on classroom lighting. Some of them point at the concept “smart”. A smart classroom would include all aspects of lighting in the classroom so that adaptive and automated solutions can be given to students’ needs in each learning situation. However, a considerable amount of research still has to be done before the real impact of (smart) lighting on cognitive processes is fully understood. We believe that this literature review has made a contribution to educational research because, although various studies have been published to date on classroom lighting, there was a need to bring all the data together to determine which factors have been focused on and how lighting can affect educational and cognitive processes. The main limitation of this paper is that it is based exclusively on the papers we retrieved from a systematic review: its value is that it provides a conceptual framework. Future research must endorse the theory underlying the dynamic lighting (i.e. empirical research proving how low CCT has an impact on cognitive processes, to sustain the dynamic lighting theory). Further, research could aim to exploit automation, combining the information with that obtained from other focuses (e.g. cognitive processes), to provide a better definition of the smart classroom concept. Considering the overall trend, we believe that this would be a meaningful and innovative way forward. Acknowledgements This work has been possible with the support of the Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya, the European Union (EU) and the European Social Fund (ESF) (funding reference number 2017FI_B_00085).

References 1. Monteoliva, J.M., Korzeniowski, C.G., Ison, M.S., Santillán, J., Pattini, A.E.: Estudio del desempeño atencional en niños en aulas con diferentes acondicionamientos lumínicos. Rev. CES Psicología 9(2), 68–79 (2016). https://doi.org/10.21615/cesp.9.2.5 2. Barrett, P., Davies, F., Zhang, Y., Barrett, L.: The holistic impact of classroom spaces on learning in specific subjects. Environ. Behav. 49(4), 425–451 (2017). https://doi.org/10.1177/001391 6516648735 3. Choi, K., Suk, H.J.: Dynamic lighting system for the learning environment: performance of elementary students. Opt. Express 24(10), A907–A916 (2016). https://doi.org/10.1364/OE.24. 00A907 4. Koper, R.: Conditions for effective smart learning environments. Smart Learn. Environ. 1(5) (2014). https://doi.org/10.1186/s40561-014-0005-4 5. Spector, J.M.: Conceptualizing the emerging field of smart learning environments. Smart Learn. Environ. 1(2) (2014). https://doi.org/10.1186/s40561-014-0002-7 6. Domínguez, S., Palau, R.: Smart learning environments. Comunicación y Pedagogía 293, 34–38 (2017) 7. Palau, R., Mogas, J.: Systematic literature review for a characterization of the smart learning environments. In: Cruz, A.M., Aguilar, A.I. (eds.) Propuestas multidisciplinares de innovación

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Alive in Smart Countryside Pekko Lindblom, Eeva Nygren, Jukka Heikkonen, and Erkki Sutinen

Abstract We suggest that the rapidly desolating Finnish countryside can become a creative zone for education and development where new ideas can be conceptualized, piloted, and tested, and new technologies developed more freely than in urban areas. We used design science as a research approach to develop the “Alive in Smart Countryside” concept and business development process involving user communities and SMEs not only as observed subjects but especially as co-designers. Faculty and students from the University of Turku created a Living Lab “Alive in Smart Countryside” to the rural municipalities Juuka and Kitee, located in North Karelia, Finland. Living Lab promoted disruptive innovation in order to design solutions toward smart countryside. In addition to analyzing and examining the design endeavor, we discuss the project as an informal learning initiative. Having a folk high school as the coordinator of the project brought in the pragmatic orientation of informal adult education and connected different actors in the region. Keywords Smart countryside · Co-design · Regional development · Small and medium enterprises · Informal learning · Design science

1 Introduction The Finnish countryside1 has become less populated because of rapid urbanization over the past decades [1]. Prevailing, often statistically motivated regional research demonstrates that the curve of migration loss in rural areas is gaining strength. Based on Statistics Finland’s latest population structure and migration data [2], it is likely that the provincial population develops so that the countryside and lagging regions continue to lose inhabitants to neighboring cities at an even faster pace, and the major 1 In this paper, we use the terms countryside, rural area, and sparsely populated areas interchangeably.

P. Lindblom (B) · E. Nygren · J. Heikkonen · E. Sutinen Department of Future Technologies, University of Turku, 20014 Turku, Finland e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_2

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growth will concentrate to few provinces in the country. The Helsinki metropolitan area together with Turku and Tampere city regions with major university clusters seem to attract the largest portion of national migration also in the future. This scenario is notably challenging for the rural areas, especially now as the policymakers have also directed the attention and the regional development investments’ spearheads toward the buzz and activities of the cities [3]. The stakeholders, policymakers, and ordinary people in rural areas are upset when shops, post offices, and schools are closing their doors. Problems can easily lead to the apathy and passivation of inhabitants. However, rural areas can also act as an engine of development if the aspirations and motivation meet proper resources. The countryside can become a creative zone for education and development where new ideas can be conceptualized, piloted, and tested, and new technologies developed more freely than in urban areas. A notable prerequisite for the development of the countryside can be found from bold and free thinking about the new avenues for local development, with little or no burden from existing conventions or traditions. Rural areas should become aware and thus proud of their own strengths, potential, and resources rather than comparing their conditions and resources to those in urban settings. Technology has long been expected to enable life, work, and progress independently from location. However, the expectations have largely not been met. Telecommuting in the countryside is still somewhat rare, village schools have been closed rather than transformed into supported learning centers or other kind of modern social hubs. The notion smart rural, in contemporary practical meaning would encompass to harnessing applicable empty spaces to shared common benefit, e.g. operated by either visiting teachers or online tutoring and technologies. Aspirations for remote health care or subsidized workspace-hubs are still in their infancy. Globally, the potential of rural areas is most visible when reflected on the problems of urban milieu. In the Global North, demographics indicate that the diminishing young generations are moving to urban centers for studies and work, thus leaving villages to the hands of the older generations, with consequences of cheap housing. The fast-growing megacities of the Global South are increasingly fragile and pose threats to the population in terms of worsening air quality, virus outbreaks, and terrorism, rare in the sparsely populated areas. The recent renaissance of artificial intelligence (AI) as one of the key technologies and factors that have disruption potential via digitalization, e.g., in shared economy, have raised new hopes of rural areas as attractive places to live and work remotely. We present a real-life approach that we initiated to make use of AI for revitalizing countryside, or, as we call it, smart countryside. To answer the challenge to empower sparsely populated areas, we used design science as a research approach [4, 5] to develop the “Alive in Smart Countryside” concept. Design science research (DSR) is an approach aiming at delivering a concrete solution (called an artifact) to a real-life problem by interacting with a given, local environment (relevance cycle) by an iterative design cycle that makes use of and input the results of the design to the state of the art, global knowledge base (rigor cycle) (Fig. 1). While usually used in designing digital services, design

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Fig. 1 Design science research cycles in the project [14]

science research can also be understood as a method for making a difference by informal learning. The “Alive in Smart Countryside” concept involves user communities, not only as observed subjects but especially as co-designers–researchers, students and local people form the whole community of citizen science practitioners. The idea was to involve faculty and students from University of Turku, computer scientists from the Department of Future Technologies and economists from the Turku School of Economics, to live and work with two rural municipalities to promote bold, disruptive innovation in order to design solutions toward smart countryside. They created Living Labs2 to the rural municipalities Juuka and Kitee, North Karelia, Finland [6]. Living Labs offered local small- and medium-sized enterprises (SMEs) digital co-design services based on business and customer desires, requirements and expectations. In this paper, we analyze and examine the design and implementation of the first phase of the “Alive in Smart Countryside” concept as a six-month project. Hence, we first describe an overview of the problem domain and provide background on the rural areas in Finland, North Karelia, in particular. In Sect. 3, we propose one solution how to implement digital transformation for this specific context. In Sects. 4 and 5, we discuss how this solution benefits various stakeholders and could be further developed.

2 There

are several definitions of the term Living Lab—common to all definitions is that involvement of people is the source of inspiration and the main methodologies are co-creation and user engagement.

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2 Rural Areas in Finland The diversity of the Finnish landscape, cultural differences among the provinces, and the peacefulness of the countryside are commonly considered as important values and characteristic. The downside of the long distances and quietness is, on the other hand, the need and sustainability issues concerning the availability of the public services. The rural areas are at risk of being left behind in the competition of attracting people and businesses to the trending growth centers. The national migration in Finland is of course not a new phenomenon but at present the effects of migration loss hit sparsely populated areas especially hard. This has to do with tightening economic resources and diminishing public funding together with lower tax income due to shrinking population. It has become clear to the legislators and local policymakers that it is demanding to keep up the service standards in the whole country. Therefore, a cohort of municipalities and even provinces in Finland have executed merger plans and other reforms to meet economic challenges. Nevertheless, success stories of local businesses tend to boost the self-esteem of the regional economic life. In rural settings, unexpected positive phenomenon has a better possibility to come across and even impact the prevailing attitude.

2.1 North Karelia North Karelia is the easternmost province in Finland having approximately 165,000 inhabitants, of which 75,000 live in the university city of Joensuu. The province has four other cities and nine municipalities. The total area of North Karelia is 21,585 km2 , there are 2200 lakes and two-thirds of the area is covered by forest. One of Finland’s best-known national landscapes is the Koli area with its hills. In North Karelia, the four seasons have a clear rhythm, and the winter is snowy [7, 8]. The most important industrial fields are lumber, wood, food, plastic, metal, stone, and tourism. Creative industries, especially film and game industry, are rising sectors. Sharing 302 km of border with Russia brings possibilities, for example, in the fields of business cooperation and tourism [9]. Historically, North Karelia has been a meeting point of eastern and western cultures and forms of faith, and a place where the Finnish national epic, the Kalevala, was created. Nowadays, the area has a lively cultural life. Internationally famous artists from North Karelia are heavy music band Nightwish and folk music ensemble Värttinä. In the summer, there are music festivals, e.g., Ilosaarirock and Kihaus Folk Music Festival [8]. The small municipality Juuka with 5000 inhabitants is located on the western shore of Pielinen, the fourth biggest lake in Finland. Juuka’s nature is variable with high hills, a wide Pielinen, and the archipelago of Paalasmaa. The city of Kitee is located in the center of Central Karelia with 10,500 inhabitants. Kitee has a city center with modern services and a sparsely populated countryside near the Russian border. During the summer, both Juuka and Kitee are popular locations

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for leisure accommodation; their population increases by more than 10% during the summertime. The unemployment situation in the province of North Karelia is the worst in Finland. In the late 2019, the provincial unemployment rate was 14.0%, while the unemployment rate for the whole country was 9.8%. The unemployment rate of Juuka was 18.6% and Kitee 14.2% [10]. The region’s other challenges are high retirement rate, together with national migration and lack of working age women. The region’s working age population is declining at a significantly faster rate than the national average, which is already evident as a labor shortages in some sectors [11].

2.2 SWOT Analysis of Moving to Rural Areas Skillful human capital is a crucial factor of regional growth and development [12]. For sparsely populated areas to develop and be viable, they need to be attractive places to live. In Table 1, we analyze Finnish rural areas as a living milieu, based on our co-design process during the Alive in the Smart Countryside Living Lab events. Table 1 A SWOT analysis of rural areas as a living milieu Strengths

Weaknesses

• Affordable living costs (e.g. housing) • Working infrastructure • Clean environment • Neighbors, social life • Less stress and competition • Pleasant and safe living environment • High-level and versatile education

• Transportation (e.g. logistics, limited public transport) • Generations of unemployment in the family • Conventional (non-digital) services located far away • Limited employment opportunities • Limited leisure activity possibilities

Opportunities

Threats

• Digitized services: schooling, health care, shopping, entertainment • Self-employment, remote work • Shared economy • Periphery is a good place for out-of-box inventions (historically, a place for major inventions), free zone to design and pilot not-yet-existing practices • Available workforce • Development of information technology

• Prejudices toward newcomers • Conventional or outdated thinking of authorities • Neighborhood watch (could also be an opportunity)

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3 Creating Living Lab “Alive in Smart Countryside” to North Karelia To answer the challenge to bring sparsely populated areas to life, we used design science as a research approach [4, 5] to develop the “Alive in Smart Countryside” concept. We tested the first version of the concept in a six months’ project managed by the Evangelical Folk High School of Kitee and implemented in close cooperation with the Municipality of Juuka, the City of Kitee and the University of Turku, Department of Future Technologies and Turku School of Economics. Project was funded partly by the Future Funds of the Regional Council of North Karelia. The preliminary anticipation of the project was to gain a contemporary picture and understanding about the local SMEs’ capabilities and use of digital business solutions. We focused on SMEs as they are often a driving force for innovation and knowledge dissemination, respond to new demands and social needs, and contribute to empowerment in society [12]. We were keen to learn how broad a selection of stakeholder companies would be eager to participate and learn from our agile thoughts to harness the ins-and-outs of digitalization in their everyday business. Also, we wanted to see what kind of concrete, perhaps disruptive impact our project would have to the region.

3.1 Methodology Design science research (DSR) aims at the creation of innovative, purposeful artifacts for a special problem domain using several iterations of build-and-evaluate loops [13]. Based on the first iteration of the project, we analyzed the application domain consisting of the people, organizational systems, and technical systems interacting and forming the rural ecosystem of North Karelia [14]. We identified and represented opportunities and problems in the relevance cycle to produce better solutions to the actual needs of people and SMEs in the sparsely populated North Karelia in Finland. The central design cycle iterates between the core activities of building and evaluating the design artifacts and processes of the research [4, 14]. The design artifacts were in this case the Alive in Smart Countryside concept and other methods (algorithms and practices) and instantiations (implemented and prototype systems) which were developed and tested during the project. The rigor cycle provides theories and methods used in the project along with domain experience and adds the new knowledge generated during the project to the growing knowledge base.

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3.2 Aim of the Project The aim of the Alive in Smart Countryside project was to improve the utilization of information and communication technologies in SMEs context in rural regions to meet the challenges of digital transformation. Based on the co-design approach, we wanted to produce a process model to understand and therefore enhance the digital capabilities of the participant SMEs. The project brought digital intelligence and know-how to North Karelia, Finland, by providing local companies digital co-design services based on business and customer desires, requirements and expectations. The focus areas were, in accordance with the North Karelia’s Regional Strategic Programme “POKAT 2021 for 2018–2021” [11], especially crafts, tourism, food production, and material technology. Hence, the largest representation of our selected participant SMEs by characteristics was also in line with the local strategy. User communities were involved not only as observers but also as creative actors of the projects.

3.3 Implementation of the Project Relevance cycle. During the project, the multi-disciplinary staff and students of the University of Turku (Department of Future Technology and Turku School of Economics) dismounted to two municipalities of North Karelia, Juuka and Kitee. In practice, selected students moved to the region and set up two Living Labs to provide on-demand digital co-design services to local businesses for four months from May to August 2019. The timeline of the project is visualized in Fig. 2. The Living Lab was set up in the premises of Kivikeskus, Juuka, and in Kitee another team worked partly in the Kitee Evangelical Folk High School and partly in the city premises. Information, marketing, and engagement on the project started locally on a fast schedule. Advertising was mainly done by using the local newspapers, mailing lists, and social media. However, municipal employees were key actors attracting customers to the project. The director of business development at Kitee had made a comprehensive contact list and mapping of the SMEs ICT development challenges

Fig. 2 Timeline of the Alive in Smart Countryside project

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Fig. 3 Methodology of the Alive in Smart Countryside concept

in the Kitee area in fall 2018. Similarly, the municipal business planner of Juuka played an important role in attracting the project participants in Juuka. In the summer of 2019, 30 companies were served in Kitee and 18 in Juuka, so the project reached altogether 48 companies. Nature of the work with companies varied; some had a preliminary discussion on cooperation opportunities, while some SMEs had already development ideas and were involved in more extensive development tasks. The project proved to attract wide interest among local businesses, but we had to limit the number of cases we eventually selected. Our Living Labs offered companies recurring, caring, short-term assistance regarding digital tools and business services. Within a longer project, the development could be more extensive and diverse. Rigor cycle.3 The project used various methods (Fig. 3). One functional example of our interaction design method was the hackathon concept which proved to activate and bind the participants to the actual co-development process. The local actions started with hackathon events in Kitee and Juuka in May 2019, where digital concepts were discussed, starting with the specifics of the locality, with entrepreneurs, municipal decision-makers, and other stakeholders involved in the project. Hackathons were more easily understood as digital development and brainstorming sessions 3 We

utilized the foundations of the knowledge base as a reference. See Sect. 3.1, Fig 1. Design Science Research Cycles in the project.

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where local people participated actively in the discussion and expressed their wishes for the project during the summer. The university representatives filtered the specific needs and challenges constructively of the stakeholders and in their business environment. This refreshed discussion, for example, between tourism networks and operators. A total of 35 persons from various organizations participated in the kick-off seminar. The Living Lab provided co-design services and ICT advice to local businesses on a variety of issues. Preliminary plans of the Living Lab process proved to overestimate the digital capabilities of the SME focus group in some cases. Together with SME staff, the project workers made pilots testing new software and processes. In addition to working in fixed premises, Living Lab toured as a design caravan visiting entrepreneurs across the region to maximize access to these services. Thus, the use of a private car was essential. Furthermore, there were capacity building activities. Project workers organized several open training sessions on the projects’ themes, such as modernizing Web pages and creating social media ads. An important principle was agile development, because the first phase of the project lasted only for the summer and the time available per company was limited. Hence, we provided companies with tools for rapid deployment. The range of used technologies was wide, including virtual reality (VR), augmented reality (AR), Internet of things (IoT), digital storytelling, gameplay, mobile technologies, and various Web technologies. Existing e-platforms were used for marketing purposes. The students acted as consultants quite independently and blended well to the local communities. The university researchers followed the process and guided the students through remote tools and visited regularly the province. Design cycle. The two iterative tasks of the design process were building and evaluation of artifacts using co-design—in this case the “Alive in Smart Countryside” concept and several individual digital artifacts for individual stakeholders. In developing a concept like this, it is essential to engage the network in a new way from the outset, so special attention was paid to local information and prospectus (Table 2). The evaluation of the developed concept was made in numerous ways. The project had a steering group that met regularly online and few times “face to face." In August, we organized concluding public events and open evaluation seminars both in Kitee and Juuka where stakeholders, media, and other affiliates with interest were invited. The atmosphere at the final seminar was positive and we received productive feedback from the local entrepreneurs, but also from neighboring municipalities. The project raised hopes for a continuation with advanced technologies that were mentioned above. The steering group analyzed the feedback received and reflected it on the model created. Throughout the project, we collected written and oral feedback from participants. We noticed that only a restricted but very committed amount of companies participated and followed through our entire tutoring process; the main reason being obviously the lack of time resources. The motivational factors in participating from the SMEs’ side were somewhat below the project team expectations. We analyzed

22 Table 2 Participants and their primary roles in the project

P. Lindblom et al. Participant

Role in the project

University of Turku

• Project planning and evaluation • Expertise from researchers • Guidance and relocation of 9 students

Evangelical Folk High School • Project planning and of Kitee evaluation • Project management • Primary point of contact for stakeholders • Facilities for actions in Kitee • Key facilitator of informal learning Kitee city and Juuka municipality

• Project planning and evaluation • Advertisement and engagement • Participant selection (SMEs) • Participation in hackathons

SMEs in Kitee and Juuka

• Participation in co-designing digital services, hackathons • Evaluation

that this had to do with the fact that the project was launched in free-of-charge basis; the motivation to take full advantage of the seminars and consultation was not tied to direct monetary or time-based value. Nevertheless, according to the received feedback, the active participants rated the project as a significant development forum.

3.4 Connection to the Local Development Strategy The Alive in Smart Countryside concept responds well to the development objectives that have been set in the statutory regional strategic programme “POKAT 2021 North Karelia Regional Strategic Programme for the period 2018–2021” [11]. The POKAT programme steers the use of EU funds and other resources designated to North Karelia. Table 3 summarizes the connections between the POKAT 2021 strategy and aspects of the Alive in Smart Countryside concept.

Alive in Smart Countryside

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Table 3 Connection to the POKAT 2021 strategy [11] POKAT 2021 focus areas Growth from renewal—diverse, sustainable, and job-friendly economic structure

Alive in Smart Countryside concept Economic competitiveness

Use of digital services will improve competitiveness Revising sales and marketing; for example, a model for small businesses to market their products New smart solutions

Smart specialization choices

Smart specialization platforms for use Seizing growth opportunities

Vitality from regional networking—A competitive and attractive operating environment

Accessibility, transport routes and links

Development of joint marketing Utilization of digital platforms

National and international networks

Building local trust and cooperation The University of Turku has an extensive cooperation network that can be utilized in the project and beyond

Well-being from partnerships—Comfortable living

Education and competence

Availability of university level research Future technologies easily accessible by local businesses

Well-being and inclusion

e-Inclusion opportunities (digital, AR/VR) Utilizing the hackathon concept for a broad inclusive experience

Culture and identity

Bringing local culture forward with digital tools, e.g., VR application for folk music

4 Design Artifacts from Stage 1 The main design artifact from Stage 1 was the “Alive in Smart Countryside” concept. Besides the concept, the project resulted in several individual digital artifacts for targeted stakeholders. Although the project aimed at integrating or even strengthening the use of AI as a key toolbox in the Alive in Smart Countryside concept, we soon realized that we needed to step down and start from the preliminaries of digitalization: co-designing elementary services, like e-shopping platforms or Web sites for local entrepreneurs. However, throughout the project, stakeholders indicated their interest in, for instance, analyzing existing user data for improving their marketing and sales. This phenomenon applied particularly to larger companies in the region. Examples of typical project tasks included: Web site design and maintenance (in Finnish and English), designing and implementing technical solutions and digital

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platforms, training IT skills, comparison of ERP systems, implementation of reservation systems, cross-platform integrations, acting as an agent in communications between subscriber–producer, social marketing advisory tasks, keyword optimization and Google Ads advisory tasks, and event production financial planning support and marketing advice. We intend to apply for EU funding for a larger-scale project, which will implement and deepen the same concept, including the intersectoral dimension. The Department of Future Technologies of the University of Turku is interested in continuing cooperation with North Karelia stakeholders. The idea is to use hackathons more effectively in the possible future projects. Some practical development ideas had to do with, e.g., VR/AR technologies and telepresence solutions in future hackathons. The hackathon will select a few locally considered concepts that will be developed over the next three months using the agile development method. Such a concept could be, for example, a model for small businesses to market their products directly to Central Europe through digital platforms, or an electronic road map (interactive road map) of services and products offered to travelers in the community.

5 Discussion The stakeholders, i.e., the municipal leadership and the active entrepreneur participants wanted a real and sustainable impact from the project. The project was expected to contribute to transforming the rural area to become more attractive for young entrepreneurs. Table 4 summarizes the process and its results in terms of four aspects of sustainability: (1) economical, (2) environmental, (3) cultural, and (4) ethical aspects. While a design endeavor, the project can also be understood as an informal learning initiative. Having a folk high school as the coordinator of the project brought in the pragmatic orientation of informal adult education for making a difference in a community. Folk high schools have a long and widely respected success story for the progress and welfare of rural areas. In their pedagogy based on the ideas of Danish Table 4 Sustainability aspects of the design process Aspect

Process

Results

Economical

Fair sharing of public regional funding and stakeholders’ own time

Enhanced capabilities and business revenue to take on new digital tools and platforms

Environmental

Taking green values into account in everyday consumption

Digital marketing materials, careful consideration of resources

Cultural

Researchers live in and interact with local people

Trust and belief, actual caring about the condition of local business life

Ethical

Appreciating the customer base, GDPR, sharing economy

Technology as a tool for people to master

Alive in Smart Countryside

25

N. F. S. Grundtvig (1783–1872), the folk high schools build on their boarding school format, which promotes both communality and individual support as well as the informal relations between teachers and students [15]. The important new role of folk high schools in modern society is uniting different partners in informal learning and offering a flexible platform for different research and development activities. Both folk high school pedagogy and design science research get inspiration from real-life problems. Our project shed light on how busy SME stakeholders really can take advantage of the assistance concerning modern ICTs in the North Karelian rural environment. This kind of regional business development project represents a promising way to bring value to otherwise busy entrepreneurial 24/7 work. Extra R&D workforce gives companies a possibility to roll out new business projects, software, and visuals. However, the regional network is vital. When inviting companies to join this kind of activities, coordination and project leadership has a great deal to do with the success of the project.

6 Conclusion The basis of our research team’s approach to empower the sparsely populated areas was clearly distinctive from the mainstream regional policy interpretations and negative statistical expectations of lagging countryside. We wanted to explore the potential toward smart development, the utilization of information and communication technologies among the SMEs in rural areas of Finland by means of agile and digital solutions. Based on the actual feedback from the stakeholders and concrete sustainable impacts, such as new digital footprints in the North Karelia region after the Alive in the Smart Countryside project, it can be observed that the concept produced a positive output. We believe that the more motivated and actively involved local user communities are in the project, the more effective the new knowledge implementation will be. The digitalization of local businesses and empowered ability in business transformation will enhance the cohort of enterprises as well as the region’s attractiveness in general. The impact of learning processes was evident during the project. Participant SMEs developed, e.g., new e-commerce platforms, enhanced ways in digital marketing and sales, established new kind of Web presence and developed other vital competences in the area of content development. These factors may very well attract new investments and improve the accessibility of tourism and accommodation services. To conclude, we consider that our agile way of working with the local businesses is an appropriate approach, when the attendees are coordinated and filtered properly beforehand. This can be done from the behalf of the regional authorities to certain target groups. Also, the project revealed that nearly with every active stakeholder there lies a desire to enhance the revenue and efficiency with new digital tools and take hold of better new practices in everyday work. In that sense there

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is obviously potential in delivering projects to the rural SMEs’ doorstep; something the regional business development authorities may want to explore, as small entrepreneurs seldom have resources to search ways to expand their digital business capabilities and functions.

References 1. Tervo, H., et al.: Onko urbanisaatio maaseudun turma?: kaupunkien väestönkasvun vaikutukset erityyppisen maaseudun väestökehitykseen Suomessa ajanjaksolla 1990–2015. Yhteiskuntapolitiikka 83(3) (2018) 2. Statistics Finland: Statistics Finland 2018 data (2018). Available from http://stat.fi/index_en. html 3. Helsinki City Executive Office: Strong and effective urban policy strengthens the whole Finland, Joint statement by the C21—group of the biggest cities in Finland (2017). Available from https:// www.hel.fi/uutiset/fi/kaupunginkanslia/c-21-kaupunkien-kannanotto 4. Hevner, A.R., et al.: Design science in information systems research. MIS Q. 75–105 (2004) 5. Hevner, A., Chatterjee, S.: Design science research in information systems. In: Design Research in Information Systems, pp. 9–22. Springer (2010) 6. The user engagement for large scale pilots in the internet of things (U4IoT). Living Lab Methodology Handbook (2017) Available from https://u4iot.eu/pdf/U4IoT_LivingLabMethodology_ Handbook.pdf 7. Karelia Expert Matkailupalvelu Oy. Available from https://www.visitkarelia.fi/ 8. Regional Council of North Karelia. Regional council of North Karelia (2020). Available from https://www.pohjois-karjala.fi/web/english/north-karelia 9. The Board of Euregio Karelia. Main Directions of Euregio Karelia 2020 (2014) 10. Ministry of Economic Affairs and Employment of Finland. Employment Bulletin (2019). Available from https://www.temtyollisyyskatsaus.fi/graph/tkat/tkat.aspx 11. Regional Council of North Karelia. POKAT 2021 North Karelia’s Regional Strategic Programme for 2018–2021 (2018) 12. OECD Rural Policy Reviews OECD Mining Regions and Cities Case Study Outokumpu and North Karelia, Finland: Outokumpu and North Karelia, Finland. OECD Publishing (2019) 13. Mullarkey, M.T., Hevner, A.R.: An elaborated action design research process model. Eur. J. Inf. Syst. 28(1), 6–20 (2019) 14. Hevner, A.R.: A three cycle view of design science research. Scand. J. Inf. Syst. 19(2), 4 (2007) 15. Lövgren, J., Nordvall, H.: A short introduction to research on the Nordic folk high schools. Nord. Stud. Educ. 37(02), 61–66 (2017)

Newcomer Integration in Online Communities: Chronemics in Asynchronous Collaborative Discussions Iulia Pasov, Nicolae Nistor, Mihai Dascalu, and Stefan Trausan-Matu

Abstract Online knowledge building communities (OKBCs) continue to exist by constantly integrating newcomers. Previous research on newcomer integration focuses on participants’ behavior and discourse, while ignoring chronemics, i.e., the time-related features such as pauses and their role in communication. Pauses can either serve as reflection spaces within the asynchronous online discussion or can be associated to unrelated offline activities. We interviewed N = 40 members from different blogger OKBCs to better understand the role of pauses in asynchronous collaborative dialog, especially related to newcomer integration. Old-timers consider that newcomers take longer pauses in discussions than the average community members, or act as silent members. Also, newcomers with high frequency in contributions are more likely to be integrated. The findings suggest a close relationship between chronemics and newcomer integration in online communities. This qualitative study was validated by a quantitative study on N = 1431 members from five blogger communities and argues that chronemics can be used to predict newcomer integration in knowledge communities, which can be in turn perceived as collaborative environments for informal learning. I. Pasov · N. Nistor Ludwig-Maximilians-Universität, Leopoldstr. 13, 80802 Munich, Germany e-mail: [email protected] N. Nistor e-mail: [email protected] I. Pasov · M. Dascalu (B) · S. Trausan-Matu University Politehnica of Bucharest, 313 Splaiul Independent, ei, 60042 Bucharest, Romania e-mail: [email protected] S. Trausan-Matu e-mail: [email protected] N. Nistor Walden University, 100 Washington Avenue South, Suite 900, Minneapolis, MN 55401, USA M. Dascalu · S. Trausan-Matu Academy of Romanian Scientists, Str. Ilfov, Nr. 3, 050044 Bucharest, Romania © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_3

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Keywords Chronemics · Newcomer integration · Online learning

1 Introduction Online knowledge building communities (OKBCs) are an important part of any smart learning ecosystem, as they not only favor collaboration and participation between members but they also grant access to the history of communications, which learners can analyze, visualize, or challenge. In OKBCs [1], learning emerges from members’ participation in a collaborative dialog carried out around topics of interest. As no member stays indefinitely within a community, it is important that OKBCs constantly integrate newcomers. Newcomer integration has been studied in face-to-face settings by Eberle, Stegmann and Fischer [2], and in OKBCs by Nistor and Serafin [3]. While there is a number of actual newcomer integration studies, most research was focused so far on members’ contributions and dialogue quality [4], while disregarding the chronemics, i.e., the time-related features of the dialog.

1.1 Chronemics Chronemics, as defined by Bruneau [5], consists of “the study of human tempo as it relates to human communication” (p. 114) or, more precisely, the study of time in communication. Pauses in computer-mediated conversations, defined as length of time between consecutive contributions, have been addressed by Walther [6] who found that, in asynchronous discussions, longer pauses can be associated with more time for contemplation, planning, and editing content. Wise et al. [7] first addressed the idea of temporal considerations in pedagogy, as well as the lack of theorization, in current research, for temporal properties, temporal processes, and their impact on online learning. While focusing on identifying the most important time properties in online discussions, they proposed the analysis of duration, sequence, pace and salience as temporal characteristics. Literature on chronemics [6–8] in asynchronous online discussions describes different reasons to make pauses in communication: either to reflect and do additional research on the topic discussed and the language used, or simply to switch to their offline activities. These activities can influence the length of pauses, i.e., the dialog chronemics. These, in turn, can influence the way in which community members are perceived by their communication partners [6]. Reflecting on content Asynchronous online communications provide several advantages to participants [6, 7]: time to either revisit and retrieve the discussion’s history, or time to reflect and research before responding, without causing a break in the topic. Moreover, participants in asynchronous discussions can get distracted by the content available on the Internet and, although reading more information on

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the same topic is beneficial to their development, the time necessary for this activity increases the pause until their follow-up message. Perceiving communication partners The length of pauses in computer-mediated discussions is statistically associated with user personality [8]: extroverts are more likely to reply faster, their pauses will be shorter, and they will generate more content. A considerate and friendly person is expected to communicate more gently, i.e., with longer pauses, as compared to an aggressive one. Besides personality, further individual, situation and context variables may also influence communication chronemics, for instance time zone differences, personal schedules, or technical issues [6]. Altogether, persons will be perceived by their online communication partners not only as the sum of the communication contents they produce and send but also through the lens of their individual communication chronemics, including typical pause length. Both contents quality and chronemics are directly perceived by communication partners, then interpreted as social cues reflecting personality. Kalman et al. [8] emphasize the evaluative part of this perception: in their study, longer pauses were associated with less trust, i.e., users trusted more the communication partners who made shorter pauses in their online discussions. Chronemics and newcomer integration The perception and evaluation of OKBC newcomers as individuals are essential for their integration. Old-timers will expect newcomers to have certain knowledge and to contribute to discussions, to be available and open for collaboration, to be trustworthy [2]. Research in the direction of social influence [9] shows that, within a group, a minority which proves consistency may succeed to influence the majority. Therefore, in online communication, chronemics may complement dialog contents and quality, and influence newcomer integration. This relationship has been so far insufficiently examined,and makes the object of the study presented in the following.

1.2 Research Questions Considering the proposal by Wise et al. [7] to define time in asynchronous online discussions by four features, we define pauses in discussions as lengths of time (duration) between consecutive interactions (sequence, pace) as perceived by community members (salience). Given the gap in literature outlined above, this study addresses the following research questions: • RQ1: How is the newcomers’ pauses length perceived by old-timers in online discussions, in relationship with dialogue quality and participation? • RQ2: What are the differences in pauses length between different members in online communities as perceived by other community members?

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2 Method 2.1 Qualitative Analysis The stated research questions were examined by means of basic qualitative methodology. The target group consisted of 40 members of online communities (55% female), aged between 16 and 54 (M = 33, SD = 8.69), coming from different locations (23—Germany, 6—USA, 3—UK, 3—Canada 2—Austria, 1—Switzerland, 1—China, and 1—Sweden) and having varied professions (e.g., teachers, journalists, students), interests (e.g., mathematics, lifestyle, literature, cooking, politics), or statuses in their communities (16 central, 15 active, and 9 peripheral). A questionnaire was developed to be administered as in interview for targeted members in blogger communities, following the research questions, and divided into three parts: (a) the first part covered basic demographic questions (e.g., age, gender, location, background), as well as preferences about online communities topics; (b) the second part included questions about their pauses in online discussions (length, reasons, and returning to discussions after pauses), as well as the pauses of other members; (c) the third part considered the observed relationships between pauses in discussion and the integration of newcomers. Interviews were conducted via voice calls, in both English and German, guided by the content of the questionnaire. Before the interview, the interviewers offered information on the questions, and consensus was obtained for recording the discussions. Each session, taking between 25 and 50 min, was recorded with the consensus of the interviewee, and participants were given as much time as required to answer the questions. Each call was conducted by a different interviewer. In order to verify the results, each recording was transcribed and segmented according to the three stages of the questionnaire. Data were collected during May–July 2018 by interviews with blogger community members on the topic of pauses in online asynchronous discussions, as well as and their relationship with newcomer integration.

2.2 Quantitative Analysis A quantitative study was carried out on N = 5 publicly available blogger communities to validate the results from the qualitative study (see Table 1). All communities were built around the same topic—the study of English language, teaching and learning practices. Each community had between 88 and 1600 participants, from which 10 to 500 members contributed more than once. Participants were involved in almost 2000 threads of discussions with more than 11,000 messages. Each thread was discussed by maximum 68 participants (M = 3.9, SD = 4.5). For every community, we extracted the history of all blog posts and corresponding messages (5–11 years). Between 300 and 4000 contributions (M = 1554, SD =

Blog lifespan (years)

7

8

10

5

9

Community name (URL)

1. Vicky Loras’ Blog (vickyloras.wordpress.com)

2. Chiasuanchong (chiasuanchong.com)

3. English with Jennifer (englishwithjennifer.wordpress.com)

4. Reading all the books (readingallthebooks.com)

5. What Ed Said (whatedsaid.wordpress.com)

General details

Table 1 Community details

25

3

20

30

30

Owner peak freq. (#/month)

462

11

708

119

131

Members

Active members

1.4

14.0

4.1

1.0

3.1

2.7

3.2

2

1.5

2.8

Pause (90th % in Avg. lifespan days) (years)

5

3

3

10

5

Avg. peak freq. (#/month)

30

2

5

18

9

#

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1200) were collected for each of the five communities, summing up to almost 1 million words. Community members were identified by their public username and no personal data regarding the participants was collected. Two metrics were computed for the community members: (a) the frequency of each participant’s messages, defined as an array of number of contributions per month, and (b) participant’s pauses in discussions, defined as an array of pauses between consecutive posts. When comparing pauses between participants, the values were computed for the same periods of time, within the same community, as values differ across time and between communities (see Table 1). The analysis was performed under the following assumptions: (a) the blog owner is a community old-timer, (b) all the other members started as newcomers, (c) members have integrated into the community when they have spent an average lifecycle (time between first and last contribution in the community) and are among 10% most active members.

3 Results 3.1 RQ1—Perceptions in Terms of Pause Length When asked about differences between newcomers and old-timers regarding pauses’ length, 27 (67.5%) out of 40 bloggers accepted to express their opinion. Most members (18, representing 66.7%) found that it takes longer for newcomers to reply: 12 admitted that newcomers make longer pauses than regular community members, while six participants reported to find newcomers more as silent members. Moreover, one participant remembered that during her time as a newcomer, she also started by reading others’ contributions. • I have noticed that newcomers don’t post often. They may start to be active in a while […] after knowing everything about the group, when they see how it goes, what kind of discussions are there. They check and then start asking questions or making any comments. • Newcomers. They don’t really participate in the discussion. They just take and leave. I think this happens very often.

Only one participant reported to find pauses of newcomers shorter when they desire a rapid integration. However, several participants (8, representing 29.6%) considered that each person has his/her own pace, and that there is no connection between the pauses of newcomers and old-timers.

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3.2 RQ2—Differences in Pause Lengths When asked about the average length of pauses in their preferred communities, the interviewees proposed a varied range of values, from minutes to days, or even weeks. Central members make pauses of maximum one day, while pauses can reach days or even a week in the case of active and peripheral members. • I think a two-minute break is enough. That doesn’t have to be half a day. • I am generally engaging with the community a couple times a day. • The break between two comments can take several weeks. […] It always depends on how much I am interested in the article.

Members who admitted making pauses in communication longer than a day are either peripheral members, or they participate in small communities (4–5 comments per blog post). Members who participate in the blogger OKBC of mathematics teachers reported that they often need time to sketch the solution on a proposed problem while this can only be done in a quiet environment (e.g., not on a bus or during lunch break). One member belonging to a community of gamers said that sometimes, in order to be able to reply to an open topic, he needs to replicate the described situation (e.g., play the game, change settings), which might take hours. Another member, who participates in cooking OKBCs, admitted that often, when a new recipe is proposed by a fellow member, she needs to test it in order to be able to contribute. In her situation, the activity can happen after hours or even days. Members of communities on sensitive topics (e.g., religion, people with disabilities, politics) considered longer pauses benefic, if participants reflect on their own messages, before sending them, in order to avoid hurting their peers. In contrast, participants from communities on visual topics (e.g., beauty, fashion, lifestyle) expect short pauses from their peers, as no offline actions are required. Out of 40 interview participants, 36 (90%) declared that they made pauses for research and reflection, 16 (40%) for offline activities (e.g., going to work, school, social activities, vacations, etc.), while 5 (12.5%) admitted making pauses to structure their response. Also, most members (28, representing 70%) reported to find it simple to return to discussions after pauses in communication, while some (8, representing 20%) considered it to be connected to the length of the pause and the difficulty or novelty of the discussed topic. Most interviewees (29, representing 72.5%) assumed that other members make pauses for reflection and research, while the remaining ones provided no concrete arguments. When questioned about newcomers’ pauses, only 30 valid observations were made. Most participants (20, representing 66.7%) agreed that in order to gain the community’s interest and attention, newcomers need to stand out by contributing frequently, with short pauses, even if the quality of the contribution is not high (3 participants). • Only by many contributions, one can draw attention to oneself. With a few contributions or long pauses in a heated discussion, I suppose, someone will be quickly forgotten.

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Moreover, eight interviewees (26.7%) specified that long pauses have a negative impact on newcomer integration, since it is likely that they are not observed and later on forgotten. • If they are participating and […] have shorter pauses […] build more like a habit of participating […], their membership or involvement would be greater than of someone who has longer communication pauses. • I think (longer) communication pauses could have a negative impact.

Especially, in larger communities, more content from a newcomer is perceived better than high quality content, as it enables others to remember the author, while showing interest for the community. High quality content is expected from more experienced members, who are not expected to participate as frequent. However, 10 (33%) interviewees mentioned that pauses shouldn’t be too short either, and that those breaks should be well spent on formulating messages, language checks, and emotional detaching from the content in order to avoid building a bad reputation. • I think we should have these pauses, but not for long. The first thoughts that come up are the best. I believe in it. When you have the thoughts, you just write them and, before sending, reread it. This way, you will not lose your thoughts, because you already have them written down. But when you reread it, maybe you still can improve something.

3.3 Quantitative Analysis Results The analysis of pauses between consecutive contributions shows that an average of 91.14% of newcomers take longer pauses between contributions than active members. However, most newcomers only contribute once (see Table 1) and, thus, have no pauses in discussions. Blog owners have a similar behavior in regards of pauses between contributions: several peaks of higher pauses, but mostly the same pause length, slowly increasing in time (see two representative showcases in Fig. 1). The frequency of contributions from blog owners increases with the frequency of other members (see Fig. 2: 2011–2013, where P denotes anonymized community participants, selected from those who participated longer than the active members’ average lifespan). Similarly, their pauses increase toward the end of the lifespan of the community (when fewer members are still active). In addition, the number of contributions for active community members in their first year is correlated with the number of active days (p = 0.47, t < 0.001). The positive correlation proves that members who contribute frequently are more likely to be integrated and participate for longer periods of time. The top 5% most active members (see two samples of representative members in Fig. 3) start with frequent messages (short pauses), while their pauses increase toward the moment when they leave the community, by no longer making active

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(a) Blog 3

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(b) Blog 5

Fig. 1 Examples of two blog owners’ pauses between consecutive contributions

Fig. 2 Contributions per month for active members from one blogger community

Fig. 3 Samples of pause length across time for 2 out of top 5% most active members

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Fig. 4 Samples of pause length across time for 2 out of top 1% most active members

posts. For 88.48% of those community members, the trend of pauses between contributions is positive, which means that their pauses increase in time. Their average pauses throughout their lifespan are shorter than the ones of blog owners. Such members join the community for a limited period of time, when they contribute with a high frequency. They typically contribute with multiple messages in relatively few conversation threads. Active members with numerous contributions (see selected representative samples in Fig. 4), selected as the top 1% most active members, are more likely to have peaks of participation, followed by periods of rest. Similar to the previous groups, their most recent activity decreases and their average pauses are shorter than the ones of blog owners; 94.44% of them, representing 34 of 36 such members of all five communities, present a positive pause trend across time within the community.

4 Discussion and Conclusions This study considered the chronemics perspective [6, 8] with regard to examining the collaborative dialog of blogger OKBCs, and was aimed to understand the role of pause lengths in newcomer integration. Our qualitative study revealed that communication pauses for newcomers are perceived to be longer than the ones of community old-timers due to the need for reflection on content and language; being less familiar with the topic and the community, newcomers are more likely to put more work in documenting their contributions and textual formulation. The quantitative study supports this result, as more than 90% of newcomers contribute, with longer pauses than active members, while considering the same timeframes. In many communities, newcomers were perceived by other members as silent participants, who only start contributing once they gain trust, and the length of their pauses cannot be generalized across different communities. Long pauses are expected and easily understood in communities which require offline actions of different nature in order to provide adequate answers. Nevertheless, newcomers’ pauses tend to be longer when taking into account the average community pause or the pauses between

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the contributions of active members from the same community. In addition, the length of pauses is associated both to the topic of the community, as well as to the membership type. Given that newcomers’ pauses in discussions are longer than the ones of oldtimers, it is expected that they will decrease, as members are integrated in the community. Therefore, a newcomer who makes short pauses is expected to be interested in the community. As longer pauses are associated with lower trust [8], newcomers, who reply more often and with fewer pauses, are more likely to gain the community’s trust, which makes integration easier for them. Moreover, very long pauses are considered to act as a reset in newcomer’s interaction with a community. Although it might seem that a linear strategy for newcomer integration exists (i.e., shortest pauses meaning faster integration, supported by a positive correlation between frequency and lifespan in the community), our results argue for a more elaborated context in which no straightforward gold strategy can be established. In addition, many of the old-timers recommend to new members to carefully read their messages before sending them, especially in heated discussions, in order not to offend other members or to post ambiguous messages. In addition, community owners exhibit an interesting behavior: they contribute frequently when establishing the community, probably aiming to attract new members and make up for the few participants. Afterwards, their pauses increase over time, as more members become regulars and contribute to the community’s knowledge base. The quantitative study provides also valuable insights that pauses of community members before leaving the community (i.e., not having further traces of their involvement) increase in time (see Figs. 3 and 4). This observation could support future community abandon analyses for members of online communities. The results of these studies confirm a new dimension which can be used to predict newcomer integration in online knowledge communities. The properties of chronemics in asynchronous messages makes them ideal candidates as features for prediction algorithms in learning analytics. The challenge is to find the optimal moment when newcomers should be encouraged to participate more by moderators in order to be integrated. It is also possible to explore the implications of chronemics from the beginning (newcomer integration) to the end (community abandon) for each member within the community. Further analysis should be performed to explore correlations between chronemics and people’s membership status in online communities over time. Numerical values can be obtained from the actual length of pauses, or its relative value to the average community pause or blog owner’s average pause length, while information about increasing or decreasing pauses in time can be obtained from the trend of the time series. A tool which can predict members’ integration is of high interest as smart learning environments aim to provide automated tools to support learning processes, especially for the assistance of struggling learners. Newcomers can become active members in the environment with support at the appropriate time, thus further increasing the community’s “smartness” over time.

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Acknowledgements This work was supported by a grant of the Romanian Ministry of Research and Innovation, CCCDI—UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0689/„Lib2Life - Revitalizarea bibliotecilor si a patrimoniului cultural prin tehnologii avansate”/“Revitalizing Libraries and Cultural Heritage through Advanced Technologies”, within PNCDI III.

References 1. Scardamalia, M., Bereiter, C.: Knowledge building: theory, pedagogy, and technology. In: The Cambridge Handbook of the Learning Sciences, pp. 97–115. Cambridge University Press, New York, NY, USA (2006) 2. Eberle, J., Stegmann, K., Fischer, F.: Legitimate peripheral participation in communities of practice: participation support structures for newcomers in faculty student councils. J. Learn. Sci. 23(2), 216–244 (2014) 3. Nistor, N., Serafin, Y.: Newcomer integration strategies in blogger online knowledge building communities: a dialog analysis. In: Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning. International Society of the Learning Sciences, Philadelphia, PA (2017) 4. Nistor, N., et al.: Automated dialog analysis to predict blogger community response to newcomer inquiries. Comput. Hum. Behav. 2018(89), 349–354 (2018) 5. Bruneau, T.: Chronemics and the verbal-nonverbal interface. In: The Relationship of Verbal and Nonverbal Communication, pp. 114–118. Mouton Publishers, The Hague, The Netherlands (1980) 6. Walther, J.B.: Interpersonal effects in computer-mediated interaction: a relational perspective. Commun. Res. 19(1), 52–90 (1992) 7. Wise, A.F., et al.: Temporal considerations in analyzing and designing online discussions in education: examining duration, sequence, pace, and salience. In: Elena, B., Peter, R. (eds.) Assessment and Evaluation of Time Factors in Online Teaching and Learning, pp. 198–231. IGI Global, Hershey, PA, USA (2014) 8. Kalman, Y.M., et al.: Online chronemics convey social information. Comput. Hum. Behav. 29(3), 1260–1269 (2013) 9. Moscovici, S., Mugny, G.: Minority influence. In: Paulus, P.B. (ed) Basic Group Processes, pp. 41–64. Springer, New York, NY (1983)

An Analysis of Alternation Schemes to Increase Student Employability and the Smartness of Secondary Schools Carlo Giovannella

Abstract In this contribution, we report on the outcomes of a project that aimed at comparing high quality alternation schemes that have been designed to reduce the mismatch between the skills demanded by the job market and those that can be developed nowadays attending either high or vocational schools. Despite the differences among schemes and experiences that have been investigated, we have detected an overall positive level of the student satisfaction, either in the cases of projects designed for small groups of students and for massive alternation schemes. A critical analysis of the experience allowed to identify commonalities and criticalities and to extract a set of guidelines and recommendations on how to improve the alternation schemes and satisfy the expectations of all actors (students, companies, family, schools) and, thus, to increase the smartness of the educational processes and, as a consequence, that of the schools. Keywords Alternation scheme · Student employability · Skill mismatch · Life skills · School smartness

1 Introduction Training initiatives aimed at increasing the employability of students is one of the priorities of European governments [1–3] and, in particular of the Italian one that in 2015 launched—following the approval of the so-called Buona Scuola (Good School) law [4]—an important school-work alternation program. This program, however, due to its massive character, met many obstacles in its practical realization [5, 6]. Despite of the criticalities that have been detected, it is still a fairly common belief that schoolwork alternation activities can contribute enormously to reduce the skill gap. It is, C. Giovannella (B) University of Rome Tor Vergata—Dip. SPSF, Rome, Italy e-mail: [email protected] ASLERD, Rome, Italy © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_4

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thus, very important to dedicate an adequate effort in experimenting with high quality and viable alternation schemes to extract guidelines on how to implement them in as many as possible secondary schools. A recent survey conducted by Unindustria [7], preliminarily to the research described in the next sections, has photographed the state of the art and confirmed that there exists a very relevant gap between the skills demanded by companies and those that students can actually develop during their secondary school curriculum. Such skill mismatch does not concern only the “hard” skills (i.e., the vertical and specialized skills relevant only for a given working domain) but also the “soft” skills, that are expected to be part of the basic “tool-kit” needed by everyone to get a job and, even more, to get a qualified one. Such “soft” skills include individual, sociorelational and managerial skills that all together make up the so-called LIFE skills (see Fig. 1) [8, 9] and, as well, the basic digital skills that, according to the European DGComp [10], must be owned to be considered digital literate. Figure 1 highlights the LIFE skills that, classified under different categories (see legend), are deemed by the Technology for Employability report [11] the most relevant ones for job placement and, as well, those required at most by Italian companies (according to the study conducted by Unindustria [7]). Designing high quality alternation schemes to support the development of an adequate set of skills is therefore essential to foster an increase of the students’

Fig. 1 Taxonomic table of essential LIFE skills organized by macro-areas—individual, sociorelational and management skills—and micro areas (e.g., problem setting, leadership and team management, process monitoring, etc.). The colored backgrounds highlight those LIFE skills— basic, professional, high level, key individual and LL employability—considered relevant for employability by Ref. [11]. The orange frames, on the other hand, identify the LIFE skills required at most by Italian companies [7]

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employability either at local and at global level. At the same time, it represents also a contribution to achieve the UNESCO SDG 4 for 2030 [12]. Goal 4, in fact, aims at achieving high quality education that should not be only a prerogative of everyone, but also capable—target 4.3—to promote a reduction in the skill gap, starting from secondary schools. The skill gap reduction is deemed important not only to foster a more adequate job placement but also, and above all, the achievement of more sustainable styles of life to include global citizenship, respect for human rights and gender equality, rejection of violence and appreciation of multiculturalism. In accordance with the above premises, the research described in the next sections has been developed along two lines that were deemed relevant to extract guidelines and recommendations for the future: (a) an investigation and comparison of the outcomes generated by a set of different alternation schemes (AS) implemented during one school year; (b) a more detailed and long-term (3 years) investigation of the outcomes generated by a massive alternation scheme—the Incubator of Projectuality (IP, see Ref. [6])—designed to satisfy the Buona scuola law [4]. The overall goals of the research are to: (i) identify commonalities, positive factors, and criticalities of the alternation schemes that have been investigated; (ii) verify the feasibility of alternation schemes on a massive scale. The lesson learnt will serve, hopefully, to guide a better implementation of future alternation schemes that could satisfy the expectations of all actors involved in the educational process (students, companies, family, and schools) and, thus, contribute to the increase of the secondary schools smartness [13, 14].

2 Alternation Schemes During this work, we have investigated four alternation schemes characterized by very different design approaches and, as well, by the different backgrounds that characterize the ICT companies that have been involved. Scheme 1: this scheme has been implemented by a company—Company A—that was involved for the first time in an alternation experience. The core business of Company A is the design and implementation of physical networks, with a specific focus on network migration. The goal of the experience has been mainly the student orientation. This alternation scheme has involved 10 students divided in two groups and it has been composed by four phases: (a) f2f lectures and shadowing to get confident with the core business of the company and, as well, the working daily routine (3 weeks); (b) competitive business game on a realistic case study (1 week); (c) video-storytelling of the experience (1 week); (d) production of an oral presentation supported by slides (1 week). Scheme 2: this scheme has been implemented by a medium size company— Company—also involved for the first time in an alternation experience. The company’s activities cover many different IT sectors (mainly services). The project, 6 weeks long, involved eight students. The experience started with a co-design phase

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leaded by school teachers and company tutors. At the end of this phase, the students have been asked to visit the company and, as well, to attend a set of f2f lectures—hold at school—aimed at introducing them to a classical waterfall process and to a blog implementation by means of Drupal. The students have been asked, then, to organize the process and realize a gantt. The development of the blog has been carried on at school under the supervision of both school’s teachers and company tutors. The experience ended with the production of an oral presentation supported by slides. Scheme 3: this scheme has been designed and realized by a company—Company C— with a long standing experience in alternation activities. Company C has a dedicated team devoted to the design and implementation of alternation schemes. Few months in advance, the team performed a benchmarking to identify technologies on which it could be worth to invest and that could be used to implement, at least, well-targeted prototypes. This year alternation experience has been focused on the use of drones for video surveillance and verification of authorized car parking. The software used to control the drones were expected to be integrated with pre-assembled computer vision technologies—i.e., APIs to recognize car plates—and with standard client–server technologies. The intention of company C was to expose the students to a process of integration of open-source software and cloud environments. Once that the design of the experience was completed, it has been presented to and discussed with school’s teachers who selected 26 candidates. Afterwards, on the basis of their propensities, the students were assigned to different tasks and roles (e.g., software developer, designer, data collection, communication). The experience, almost fully designed in advance, has been carried on in five days distributed over 2 weeks, partially inside the company premise and partially outside. Tutorship has been provided both f2f and online by company tutors and peers (students attending the last, V year, of the vocational school) and that have been already involved in a similar experience the year before. At the end, the students were asked to produce a movie and a slide presentation to narrate the experience. Scheme 4: this scheme was co-designed by company D—having characteristics very similar to those of company B—school’s teachers and ASLERD with the aim to: (a) involve the largest as possible number of students and, at the same time, minimize the company effort; (b) foster the development of a considerable number of LIFE skills by exposing the students to the simulation of an innovation process, distributed along three years. The first step was to meet students and their parents to explain them the overall philosophy of the alternation scheme in order to avoid possible future contrastive attitudes, in particular by parents. As second step, the students have been asked to fill a questionnaire and undergo a motivational interview. The questionnaire served also to produce a Moreno diagram [15] and the best composition of the working groups. Immediately after the members of the groups had to elect the group leader and identify for each member complementary roles. To make them better understand how relevant is the efficient organization of the team, the students visited company D where they got lectures on company organization and team working.

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The third-year students, a group of 27 students, were required to identify a domain of interest to be used as target of the innovation process and, then, to explore it with the aim to identify and document potential problems/expectations (problem setting). Immediately after they were asked to identify possible solutions and these latter were critically reviewed and discussed with internal, company and ASLERD tutors in order to identify the most viable one. The students, then, were asked to: (a) narrate problems and solution by mean of a short video; (b) speculate on the image of a possible start-up and propose its name, logo, mission and vision; (c) prepare a pitch. The domains explored by the students varied quite a lot: social parking finding, differential garbage collection, optimization of the queue at the school cafe/cantine, support against dependencies, etc. The students of the fourth and fifth years, about 25 students, were organized in larger groups, within which all students had to assume a specific role. The groups were asked to develop an executive design of a concept, chosen among those that were elaborated during the previous year. Those that were able to complete the executive design were also asked to develop a medium fidelity prototype. All groups had to produce a movie, a website, and a pitch to promote their project. They had also to attend two short technical courses dedicated to the development of client–server applications and to the preparation of a business plan.

3 Outcomes of the Alternation Schemes The differences in types, structure, and duration of the alternation schemes described in the previous section, as well as several problems generated by a heavy bureaucracy, made impossible to apply homogeneous procedures and metrics to measure in details the effects produced by each one of them. Only in one case, that of the alternation scheme 4, it has been possible to compare the students’ satisfaction level with that generated by all other alternation schemes adopted by the school. The outcomes of this detailed investigation that, moreover, has been conducted over three years will be described separately in the next section. In this section, we concentrate on the outcomes of an evaluation questionnaire that all students that took part in the four alternation schemes were asked to fill. The analysis of the answers provided by the students returned a generalized appreciation of all experiences, that resulted to be higher in the case of activities conducted in small groups and entirely within the company premise (average score 9.0 over 10.0 in case of AS 3), and lower in the case of alternation schemes realized partially at schools or characterized by less practical activities (average score 7.8 in case of AS 2). The students appear very eager to “live” the companies and appreciate the orientation on job opportunities that may derive from the activities carried on during the alternation schemes. They wish to learn about professional profiles and roles, company organization, project planning and management, and best practices. In addition, they would like to receive guidelines on how one should behave at work

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and hints about critical aspects of working in a company. Because of this, they would feel free to interview the employees and be able to develop their own interpretative framework. Moreover, to enrich this latter, and broaden their own horizons, students would like to spend time in many different companies also with the hope to initiate the development of their own personal system of relationships. A part of them was also very keen to know about opportunities and threats of being a start-upper and how to keep an enterprise competitive in a so quickly evolving market. On the other hand, students do not like situations in which they perceive a significant distance between the proposed activities and truly working situations (as may happens, sometime, in the case of simulations). The venue where the activities take place is also deemed very relevant. Those carried out at schools, although under the supervision of company’s tutors, are hardly perceived as part of a working process. Not strange, thus, that the mean value assigned by the students to the meaningful use of technologies goes from 8.9 in the case of AS 3 to 3.8 in the case of AS 1. Going into the details of the AS activities, students show a certain impatience toward any form of transmissive/non-practical activity: presentations (even introductory slides), regulations and standards, etc. Also reporting is not very appreciated by some of them. They love “learning by doing” and when are faced with the limitations imposed by the level of their technical skills, all in all, are available to participate in less technologically advanced and attractive projects (e.g., a blog implementation), as long as such projects lead to practical results and they feel protagonists of activities that goes beyond school routines. The students appear to be also very demanding about organizational and planning aspects. They expect AS experiences to be carefully planned (contents and timing). Dates are expected to be established well in advance and respected, with no overlaps with other activities. They also expect a full and efficient level of coordination among all actors of the AS, in particular between school and companies. The mean value assigned by the students to the organization and management of the AS activities goes from 8.7 in the case of AS 3 to 7.2 in the case of AS 1. Students feel themselves ready to get involved in design and development activities but, on the other hand, they expect to be assisted by a continuous and expert tutorship. They wish to communicate effectively, not feeling neglected, abandoned to themselves or, even worse, be offloaded to someone else. They cannot bear that someone attempts to take advantage of them and are extremely sensitive to the courtesy/kindness of relationships. Students like a relaxed, if not even fun, working atmosphere. Such wishes and expectations are fully sharable but suggest also a certain degree of fragility and psychological unpreparedness that should be tackled with by the tutors to help students to face with real life problems and settings. Despite such potential weakness, students are eager to be challenged and have the possibility to demonstrate their own value. It emerges clearly the need to increase their self -esteem while remaining in what we may call an assisted “flow” zone (i.e., in a zone where challenges are adequate to the level of skills owned by the students and capable, at the same, time to offer perspective of assisted growth) [16].

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As far as the LIFE skills whose development have been fostered by the participation in AS activities, not all students deserve a high level of awareness. Nonetheless: • the ability to get in relation and to collaborate with others are statistically the two skills indicated at most, likely because they are connected with team working; working in a group with the aim to complete a project requires coordination, cooperation, taking responsibility and, because of this, is perceived as enriching and capable to introduce them into a novel dimension of the collaborative work, that goes far beyond the group work they are used to in the school setting; it is no by chance that other skills frequently indicated are the ability to organize and self -organize and, as well, flexibility and adaptation; • the practical nature of most activities led many students to indicate designing and problem solving accompanied, albeit in a side position, by problem setting and critical thinking; • due to the situations presented by the ASs, that are somewhat atypical with respect to the school routines, one of the most mentioned skills is the capability to deal with the unexpected accompanied, in some cases, by the capability to manage processes. From the answers given to the questionnaires emerges also a request for a follow up of the AS experience, either to complete what has been started or to diversify the experience, together with a widespread request for a skill certification.

4 Detailed Evaluation of Alternation Scheme 4 AS4—the “incubator of projectuality” (IP)—is a particularly interesting case due to its massive character. In the case of the IP, we had the opportunity to: (a) compare the evaluation provided by the students involved in the IP with those given by students involved in all other AS adopted by the same school in the same year; (b) monitor the process along three years. This means that we were able to submit IP to a deep scrutiny and, thus, to check the viability of massive AS that, considering the outcomes of Sect. 3, one can expect to be much more complex to design and implement. It is not strange, thus, that average values of the indicators measured for the past three years, and reported in Table 1, are lower than those reported in the case of all other AS considered in Sect. 2. However, when we compare such values with the average satisfaction achieved in the case of all the other AS, adopted by the school along the years, we observe a significant improvement of about 1.5 points out of 10. This year, for the first time, it was possible to operate an integrated evaluation of the IP over the three years of the IT curriculum. The full distributions of the students’ satisfaction are reported in Fig. 2. It is quite evident that in the IP case, both the mode and the average value, 6.36, are far higher than those characterizing all other AS. The shape of the distribution of satisfaction about IP does not show significant tails at very high and, above all, at very low values. It is also characterized by a double

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Fig. 2 Distributions of the student satisfaction about alternation experience. All alternation schemes triennio (III, IV, and V classes) 2016 in blue, triennio 2017 in red, triennio 2019 in light green, and III-IV-V classes that participated in the IP activities in 2019 in violet

peaks. These latter indicates that, at least up to now, it has not been possible to attract and motivate in the same manner all participants. Our impression, as confirmed by the text analysis of comments and open answers (see below), is that a minor part of the students feel attracted only by technological implementations (coding), although they do not have strong enough technological and methodological skills to carry them on with success; and this may also lead to a partial frustration and, thus, to a decrease in the satisfaction level. As shown by Fig. 3, it seems that the double peaks in the distribution of the students’ satisfaction tend to developed since the first year 0,35 0,3 0,25 III 2016

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Fig. 3 Distributions of the student satisfaction about alternation experiences. All alternation schemes only III classes 2016 in blue, 2017 in red, 2019 in light green, and III class that participated in the IP activities in 2019 in violet

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Table 1 Mean values of the indices worked out from the ASLERD questionnaires (quantitative answers) [14] Index

IP III (2017)

IP III (2019)

All AS 2016

All AS 2017

All AS 2019

Satisfaction

5.67

6.76

4.30

5.18

5.00

Significance

5.77

6.50

–

5.36

5.27

Governance

5.62

5.62

–

5.27

5.07

IP values are referred only to the III year. All AS values have been averaged over the last three years of the IT curriculum (III, IV and V)

of the participation in the AS activities (III year of the curriculum), regardless of the characteristics of the AS and becomes more and more evident year after year. The continuous redesign to which the IP experience has undergone along the last three years has certainly generated positive effects, see Table 1. The improvements are also confirmed by the comparison among the distributions of the students’ satisfaction measured along several years (see Fig. 4). It can be seen that the lower peak has moved from 3 to 5, the tail at very low satisfaction values has disappeared, while the upper peak has moved from 7 to 7–8. Despite such meaningful improvements, however, the gap between the two groups of students (i.e., the two peaks) has not yet been eliminated and one may wonder if, in the case of massive AS, it is worth to continue to invest in all students including those that show after the first year of experience a limited interest/satisfaction. In fact, if we consider only the higher peak of the distributions shown in Figs. 2, 3 and 4, the satisfaction level aligns with those reported in Sect. 3 for most of the others AS whose participants were selected a priori. Possibly, IP students should undergo a selection after the first year to skim those that are not fully motivated. 0,35 0,3 0,25 III IP 2017

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Fig. 4 Distributions of the student satisfaction about IP alternation scheme. only III class 2017 in blue, only III class 2019 in light green, only V class 2019 in red

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To partially explain the difference in the mean value of the student satisfaction among AS 4 and the others AS, one can also consider that in the case of massive alternation schemes, the activities were carried on mainly at schools and this, as underlined in the previous section, is not very appealing for the students, despite the significant advantages for companies (reduces investment of human resources, time and logistics). In addition, we may also note that in the case of the AS1-3, only 60% of the students took part in the evaluation of the AS activities, possibly the more motivate ones.

4.1 Text Analysis The text analysis of students’ comments and open answers shows that most of the third-year students consider IP activities interesting, useful, personalized, and even fun. They also think that the process has the merit to show how challenging to innovate could be. Going into the details, it emerges that the most positive aspects of the IP experience are: p1) orientation to entrepreneurship and to work organization; p2) a “team working” that goes beyond school group working due to assumption of roles and responsibilities, brainstorming aimed at integrating different views, cooperative efforts to achieve goals and respect deadlines; p3) stimulus to creativity and free initiative (albeit supported by a careful tutorship); p4) the acquisition of greater self -confidence that, in some cases, can even lead to decision making and the emergence of a leadership; p5) continuous project improvement based on perseverance and continuous problem solving; p6) the feeling to be active part of a carefully planned process. The students of the fourth and fifth years, on the other hand, appreciated at most the technological verticalizations. Not by chance they have also appreciated the use of online collaborative working tools (e.g., gdrive and google suite), which made work sharing and reviewing more immediate and allowed students to get used with smart working. They were also fully satisfied with job orientation and the development of transversal skills. It is worth noting that these latter are, actually, the two key goals of the recent transformation that the alternation schemes have undergone. The Ministry of Education (MIUR), in fact, has change their name from school-work alternation schemes (ASL) to schemes for job orientation and horizontal competences (PCTO). To better understand the reasons for the lower peak that emerges in the distributions of Figs. 2, 3 and 4, beside positive aspects, we have also collected the negative ones expressed by a minority and, that coherently with some of the observation reported in Sect. 3, include: n1) organizational issues connected to school internal communication processes and, sometime, conflicting overlaps among curricular and AS activities; this criticality was mainly evidenced by students of the V year involved in the preparation of the bachelor exam; n2) the too low number of hours spent in a company environment and premise; n3) the limited contribution given

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by less motivated students (with repercussions on team working and, sometime, on final outcomes); n4) the too short time to experience practical developments and prototyping, that in some students produced a latent feeling of limited concreteness. All in all, despite the fact that the IP requires further adjustments, the result of Table 1 and the positive nature of most of the students’ comments underline the effectiveness of the improvements we introduced with respect to the previous years. Among them: (1) the introduction of the motivational test and of the student interviews; (2) the identification of students’ affinities to define the teams’ composition; (3) the preliminary brainstorming with parents; (4) the reduction and dilution of the theoretical lectures; (5) adaptation of the development environments to the technical skills provided by the school; (6) the introduction of easy to use online collaborative working environments. The motivational test and the detection of the student affinities, in fact, allowed to identify and avoid potential situations of conflict and, at the same time, to integrate propensities and match interests. As consequence, the number of problematic situations in the third year has been largely reduced. The preliminary meeting with the parents made possible to eliminate preconceptions regarding alternation schemes and get them aware about the potential advantages for the students. It has proved essential to eliminate contrastive attitudes from families. Unfortunately, the dilution and the reduction of the theoretical pills were deemed not yet sufficient by a small number of students. The request for their further reduction, however, conflicts with the parallel request to introduce more sophisticated technological tool-kits and, as well, with the reduction by more than 50% of the amount of hours allocated for AS experience that has been recently decided by the MIUR.

5 Conclusions, Lessons Learnt, and Recommendations The analysis of the four alternation schemes described in Sect. 2 shows, without any doubt, that schools and companies can collaborate profitably to organize activities perceived as significant, or very significant, by secondary school students, at least in the case of vocational IT schools. It is also evident that the students’ satisfaction is influenced by the following main factors: (1) pre-selection of most motivated students; (2) activities organized in small groups; (3) detailed and careful pre-design of the process; (4) use of the company premise as “working” environment; (5) coordinated tutorship (school and company tutors), better if continuous but discreet; (6) minimization of theoretical notions and maximization of laboratory practices; (7) involvement in the development of concrete applications; (8) development, beside LIFE skills, of a relevant set of technical skills; and last but not least (9) relaxed working atmosphere, when not fun. These factors may be transformed in guidelines for the design of high quality AS. Some of them, however, can potentially get in conflict with the request by the “Buona Scuola” law to implement alternation schemes for all (that necessarily should have

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a massive character). In fact, even medium-large companies do not have adequate logistic and human resources to host at their premises all students of one or more classes. Even less, they have the possibility to satisfy the needs of a whole school or those of all schools of a given geographical area. This implies the need to develop a mixed AS model that, after the acceptance of all students (massive phase of the AS, to be delivered at schools), is followed by a selection (better if a self-selection) of the most motivated students. A model that does not exclude students a priori and that is also capable to valorize passion and commitment. Of course it is very important to provide, since the beginning, adequate information to students and families so that they can get aware of the required efforts and commitment and, as well, of opportunities. This should also prevent the onset of contrasting attitudes in case students will be forced, at any point of the process, to switch toward paths of lower quality, due to lack of commitment. All the elements that emerged so far lead also to the formulation of a recommendation for the Italian Ministry of Education (MIUR): to mitigate the impact of massive alternation schemes on the productive system, the best strategy is not to reduce for all students the hours allocated for AS by more than 50% but, rather, to established a fork, min-max number of hours, to allow less motivated students to decide for shorter and less significant AS activities while more motivated students could feel free to get involved in experiences that from one side require greater commitment and from the other are also more enriching. To support this recommendation, we can underline that many students have stressed how AS activities are too compressed to develop demos that could go beyond the medium-profile prototyping phase. As we have seen a problem common to all alternation schemes concerns the amount and the duration of the theoretical pills. Probably the solution is to integrate better the AS with curricular activities. The contents of the latter, in fact, should be updated in order to provide more advanced theoretical and practical bases, without renouncing to the development of an adequate set of LIFE skills (i.e., without renouncing, for example, to foster the acquisition of the problem setting skill in favor of a more technologically advanced problem solving). In addition, it would be very important to foster the involvement in AS activities of the curricular teachers that are not teaching ICT topics. Indeed, there exist many potential contact points to involve native and foreigner language teachers for communication activities, maths teachers for data analysis, etc. The involvement of curricular teachers could also reduce potential activities overlapping and foster a better timing organization.

References 1. Council Recommendation of 22 April 2013 on establishing a Youth Guarantee 2013/C 120/01 (2013). http://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:32013H0426% 2801%29. Accessed 20 Feb 2020 2. Europe 2020 a strategy for smart sustainable and inclusive growth. http://eur-lex.europa.eu/ legal-content/EN/TXT/PDF/?uri=CELEX:52010DC2020&from=EN. Accessed 20 Feb 2020

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3. Chatzichristou, S., Ulicna, D., Murphy, I., Curth, A.: Dual education: a bridge over troubled water EU DG for internal polices—culture and education (2014). http://www.europarl.europa. eu/RegData/etudes/STUD/2014/529072/IPOL_STU(2014)529072_EN.pdf. Accessed 20 Feb 2020 4. La Buona Scuola (2014). https://labuonascuola.gov.it/documenti/La%20Buona%20Scuola. pdf. Accessed 20 Feb 2020 5. Giovannella, C.: Incubator of projectuality: an innovation work-based approach to mitigate criticalities of the Italian massive alternance scheme for the school-based educational system. IJDLDC 8(3), 55–66 (2017) 6. Giovannella, C., Crea, I., Brandinelli, G., Ielpo, B., Solenghi, C.: Improving Massive Alternance Scheme: the paradigmatic case history of the incubator of projectuality at the Ferrari school of Rome. In: The Interplay of Data Technology, Place and People for Smart Learning, pp. 3–14. Springer Publisher (2018) 7. DIGICOMP. https://ec.europa.eu/jrc/en/digcompedu. Accessed Feb 2020 8. Giovannella, C.: Schools as driver of social innovation and territorial development: a systemic and design based approach. IJDLDC 6(4), 64–74 (2016) 9. http://disco-tools.eu/disco2_portal/terms.php. Accessed 20 Feb 2020 10. Carretero Gomez, S., Vuorikari, R., Punie, Y.: DigComp 2.1: The Digital Competence Framework for Citizens with eight proficiency levels and examples of use. Publications Office of the European Union (2017) 11. Chatterton, P., Rebbeck G.: Technology for employability. JISC (2015) 12. UNESCO: https://en.unesco.org/education2030-sdg4/targets. Accessed 20 Feb 2020 13. Giovannella, C.: “Smartness” as complex emergent property of a process. The case of learning eco-systems. In: ICWOAL, pp. 1–5. IEEE Publisher (2014) 14. Giovannella, C.: Participatory bottom-up self-evaluation of schools’ smartness: an Italian case study. IxD&A J 31, 9–18 (2016) 15. Moreno, J.L.: Who Shall Survive?. Beacon House, New York (1934) 16. Czisikszentmihalyi, M.: Flow—The Psychology of Optimal Experience. Harper & Row (1990)

What Makes New Technology Sustainable in the Classroom: Two Innovation Models Considered Janika Leoste, Mati Heidmets, and Tobias Ley

Abstract Sustaining technology-enhanced learning (TEL) in the classroom and the necessary teaching practices after initial research funding ends is often perceived to be a challenge. This paper examines whether using the flexible process user innovation (FPUI) model in implementing a new TEL method would increase the likelihood of sustainability compared to using the linear process closed innovation (LPCI) model. We evaluate teachers’ knowledge appropriation as an important proxy for sustainability of a TEL method called Robomath in two different implementation cases that are based on different innovation models. The first case followed the LPCI model: 42 basic school teachers applied the Robomath method during a school year in their math lessons while using ready-made learning designs. The second case followed the FPUI model: 25 basic school teachers applied the Robomath method in their math lessons while they simultaneously participated in a ten-month teacher professional development program and together with university researchers co-created learning designs for the method. We used the Knowledge Appropriation Model for analyzing the potential sustainability of the Robomath method in both cases. Our study indicated that intended adoption and knowledge appropriation are significantly higher when using the FPUI model compared to using the LPCI model. Using a similar approach for improving the adoption of innovative methods in other TEL learning settings and STEAM disciplines is a subject for further studies. Keywords User innovation · Sustainability · Educational robot · Technology-enhanced learning

J. Leoste (B) · M. Heidmets · T. Ley Tallinn University, Tallinn 10120, Estonia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_5

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1 Introduction Technology-enhanced learning (TEL) offers numerous possibilities to make teaching and learning more efficient, and increase student motivation. This is evident both in research [1, 2] and in the experience of enthusiast teachers. Despite of the opportunities that TEL methods provide, studies also suggest that implementing innovative TEL methods is generally not enough wide-spread and is often limited to a few technology-enthusiast teachers [3]. TEL innovation projects tend to start with a great enthusiasm but instead of becoming sustainable classroom practices, they will fade after their external support ends, without significantly changing educational activities [3]. There are several potential reasons for TEL method innovations to fail—insufficient funding, poorly developed pedagogical dimension, or immaturity of technology [3, 4]. Little attention has been paid to the question of how the acceptance and sustainability of innovations depend on the choice of an innovation model. In this paper, we are observing how different innovation models influence the readiness of teachers to continue using an innovative TEL method beyond the end of external support. In the first part of the article, we are comparing different innovation models, focusing on their emphasis on innovation sustainability. The second half of the article presents an empirical study that compares: (a) the readiness of two groups of teachers to continue using an innovative TEL method after the end of the implementation period; (b) the teachers’ readiness of sharing their innovation-related experience with other teachers.

2 Theoretical Background: Innovation Models 2.1 Business Innovation: Moving from Closed to Open Models Innovation is most thoroughly studied in the business sector due to constant competitive pressure. There is a clear distinction between invention and innovation, the latter being a crucial process for making inventions usable for end-users. Sustainability is one of the goals of innovation [5–8]. Innovation processes follow innovation models—value-laden conceptual frameworks that describe different aspects of innovation process, e.g., distribution of power and agency, social relations and order, etc. [7]. During the nineteenth and twentieth centuries, the closed innovation model was favored. With this model the ideas and solutions originate from and stay inside the organization that developed them [7, 9]. Closed innovation is usually based on linear process, fixing the desired outcome at the early stages of the innovation process and allowing progression from stage to stage only after the completion of the previous stage [5, 10]. Following the linear process, closed innovation model is suitable for

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organizations that operate in environments where actions and resources are easy to plan [8]. The ICT revolution and accelerated pace of globalization created conditions for different kinds of innovation models [11]. Since 1970s, companies were looking for innovation strategies that could sustainably provide customers with more customized results while being able to cope with customers’ needs that could not be foreseen at the start of the innovation process [12–14], resulting in emergence of open innovation models [7–9]. Contrasting to closed innovation, with open innovation, the ideas and solutions can move across the boundaries of a single organization, speeding up innovation processes [5, 9]. Also, open innovation models allow more easy employment of flexible process where improvements can emerge from any source at any stage of an innovation process and no stage is locked earlier than absolutely necessary [5]. Implementing open innovation models can be difficult for some organizations as it requires, compared to closed innovation, a different organization culture, causes loss of full control over development process, and increases coordination costs [15, 16]. However, openness can stimulate innovation by combining the efforts of different stakeholders, and generally leads to increased product diversity with better matching of products and consumer preferences [13, 15, 17]. Besides being able to make better use of flexible innovation process, open innovation is more efficient in employing the knowledge, experience, and skills of different stakeholders, especially those of end-users (Table 1). The user innovation model that became an important branch of open innovation models was recommended by Von Hippel [14] in 1970s. The user innovation model stresses the idea that users usually have more knowledge about the needed quality of the product. Involving users in innovation process could potentially lead to novel, improved, or refined Table 1 Comparison of closed innovation and open innovation models [5, 10, 13–15, 17] Innovation model

Process flow

Stakeholder engagement

Closed innovation Process is linear. Low Each stage must be finished before the next one can be initiated. Easy to plan Open innovation

Knowledge sharing

Keywords

Knowledge and Closed technologies are Linear and rigid protected and kept Uncooperative safe from external influences

Process is Stakeholder Takes advantage Open flexible. Several involvement high, of outside critical Flexible stages can run including users knowledge. Cooperative concurrently. Sharing New information knowledge can be may cause beneficial reinitiation of previous stages

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products with greater potential to succeed and become sustainable. Research has demonstrated that user involvement can increase user motivation and commitment, and thus result in the development of more sustainable usage practices [13, 15, 17]. The development and implementation of innovations happen increasingly in partnerships of researchers and practitioners, using the user innovation strategy [18]. In such partnerships, the users are often involved in co-creation of innovation artefacts and they are considered as co-creators and partners in this process [18–20]. Therefore, in the business sector, one may observe a gradual movement toward open innovation models, especially toward using the user innovation model. Both researchers and practitioners have reached an understanding that involving users as co-creators into an innovation process can help overcoming the research-practice gap, i.e., make technology more mature and efficient while ensuring its better sustainability.

2.2 Innovation Models in Education Innovation models in the business sector are also shaping innovation processes in the education sector. The innovations that target teachers or students try to change user behavior so that the promising teaching or learning tool or method becomes a natural part of user’s daily routines. This is similar to the admission, pointed out in the Concerns-Based Adoption Model (CBAM): changes are brought about by focusing on individuals, and the process of change is a personal experience [21]. Different stakeholders have put an immense pressure on education to enhance teaching and learning with modern technology. Thus, in recent decades, educational institutions have been flooded by promising TEL innovations which, however, fail to become useful, and are unable to leave an impact on classroom practices. The best examples of TEL innovations becoming part of classroom routine tend to be small-scale exceptions that do not represent the field as a whole [22, 23]. There are several theoretical frameworks that are used in the education context to describe how TEL innovations reach the teaching and learning practices [24]. Each of these frameworks is focused on certain aspects of the innovation process: (a) how innovations are generally spread—Innovation Diffusion Theory [25]; (b) how users come to accept and use technology—the Technology Acceptance Model [26]; (c) how organization’s structure and its resources influence its technology acceptance—the Technology-Organization Environment framework [27]; (d) what influences users’ intentions to use an information system and their later usage—the Unified Theory of Acceptance and Use of Technology [28]; (e) how people develop in the innovation process and what are the levels of this development—Concerns-Based Adoption Model [29]; and (f) knowledge that teacher needs for effective pedagogical practices in a TEL environment—Technological Pedagogical Content Knowledge [30]. All these theoretical frameworks are focused on particular aspects of innovation process [31], paying comparatively little attention to the sustainability issues of the implemented method or approach. Similarly to the business sector, there are also in

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the education sector calls to move toward open innovation models, especially toward the user innovation model. It is hoped that this can make TEL innovations in the classroom more attractive, engaging both students and teachers, and consequently making change more sustainable [32].

2.3 Knowledge Appropriation Model For analyzing innovation and social learning processes, we have employed a theoretical framework, called Knowledge Appropriation Model (KAM), and applied it to the adoption of TEL innovation [33]. KAM is meant to describe the interaction and learning processes between professionals during the innovation process in its different stages in a way that it supports more sustainable changes, e.g., in classroom practices. Compared to other frameworks that describe ICT adoption in teachers’ practices, KAM emphasizes the importance of the social practices of knowledge creation and learning to explain and predict innovation adoption. KAM is built on the knowledge maturation model that describes how new knowledge is created in collaboration. In the education context, the focus is on the individual experience of a teacher, how it is shared inside a community and then further matured and formalized so that it is accessible to others in the organizations, for example, how the learning designs for innovative methods are created, shared and improved. The maturation process consists of five levels [34]: (1) Appropriation of an idea (concerning new method, procedure etc.); (2) Sharing and discussing the idea with others (researchers, experts, teachers); (3) Co-creating innovation-related artefacts (for example lesson designs and teaching practices, jointly created by university experts and teachers); (4) Formalizing co-created artefacts (documenting procedures); and (5) Standardizing new practices, enabling innovation’s wider distribution. The KAM model adds to this collective learning perspective a focus on scaffolding, in order to help the innovation to become adopted. Scaffolding practices describe how individuals seek help and are then guided by other professionals or experts to apply the created knowledge in their work context.

3 Empirical Study 3.1 Aim and Research Questions The aim of the empirical study was to compare two groups of teachers. With the first group, an innovative TEL method was implemented by following the LPCI model (Case 1). With the second group, the FPUI model was followed (Case 2). With the groups, two variables were compared: (a) innovation sustainability (the degree of

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teacher’s readiness to continue using the method after the end of the implementation process); and (b) knowledge sharing (how much does the teacher share the methodrelated knowledge with other teachers). The innovative TEL method used in this study was robot-supported math teaching (Robomath). Robomath is a method that uses educational robots as learning tools for helping students to better perceive abstract math concepts (e.g., shapes, time, and distance), and teachers to create meaningful functional environment for students to apply their math knowledge [35, 36]. Two research questions were set: 1. Do the teachers who participated in Case 1 (that followed the LPCI model) and the teachers who participated in Case 2 (that followed the FPUI model) differ in their readiness to continue using the Robomath method after the implementation project was completed? 2. Do the teachers who participated in Case 1 (that followed the LPCI model) and the teachers who participated in Case 2 (that followed the FPUI model) differ in the way of sharing their experience with other teachers?

3.2 Sample and Procedure Two groups of teachers (Case 1 and Case 2) participated in the study. With both groups of teachers, the learning and testing of the Robomath method took place in a context of a school-university partnership (SUP) project aiming to prepare teachers for using the method in their math lessons. Case 1: 42 teachers teaching math in the grades 3 and 6, from 42 Estonian schools participated. All teachers were female with average age of 56 years. Their average length of service as a teacher was 21 years. During the school year of 2018/2019, a SUP innovation program based on the LPCI model was conducted aiming to introduce the Robomath method to Case 1 teachers. University experts provided these teachers with an initial training and lesson designs for conducting Robomath lessons. Teachers were expected to keep notes about the conducted lessons (up to 15 lessons), and they did not participate in developing the learning designs or analyzing the experience of other participants. For technical support, an online scaffolding environment eDidaktikum1 was used. Each lesson included three robotics exercises and one related math problem. In Case 1, the program’s innovation process was linear (Table 2): the Robomath method-related learning designs and recommended teaching practices were designed before they reached teachers. During the course of the study, the learning designs did not change. The innovation process followed the closed innovation model: the Robomath method-related learning designs and recommended teaching practices were developed by researchers. Teachers were encouraged to use the Robomath method as it was. 1 https://edidaktikum.ee/en/home.

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Table 2 Innovation model characteristics of Case 1 and Case 2 Case 1

Case 2

Innovation model Closed innovation

User innovation

Evidence

The method was developed from an idea to the final stage of a classroom-ready method by researchers. In the initial stages a few teachers were marginally involved as subject experts but they did not participate in making the method classroom-ready, and they did not participate in Case 1

The method was initiated by researchers. However, in all following stages, the participating teachers were directly involved in method’s development: they tested different approaches, and the method’s development depended on their classroom experience

Process flow

Linear process

Flexible process

Evidence

The method was developed by researchers. The teachers’ feedback was only considered as an input for technical support, but not as a basis for further development

The teachers’ feedback resulted often in reviewing the existing materials and practices that constituted the method. The development of the method lasted until the end of the study

Case 2: 25 teachers teaching math in the grades 3 and 6, from 25 Estonian schools participated. 69% of teachers were female. Their average age was 45 years, and their average length of service as a teacher was 13 years. In the second case, a SUP innovation program based on the FPUI model was conducted from January 2019 to December 2019. Again the aim of the program was to promote the Robomath method to become a part of teaching practices of participating teachers. The Case 2 teachers were scaffolded with a teacher professional development (TPD) program. The purpose of the TPD program was to provide an environment for teachers to co-create with university researchers the learning designs and teaching practices necessary for conducting Robomath lessons. The TPD consisted of monthly contact days in university’s facilities and intermediate periods. During the contact days, researchers provided teachers with methodological, didactical, and technological knowledge that supported the Robomath method. Based on this knowledge, the participants co-created in smaller teams learning designs for the method. By doing this, they also built common understanding about the concepts introduced. During the intermediate period, the teachers tested their co-created learning designs in their classes and gathered evidence about revealed practices. This gathered evidence was then reflected during the next project day and through joint discussions, the best practices for the Robomath method were co-created. In Case 2, the innovation process was flexible (Table 2): the Robomath method was introduced to teachers as a concept. Through several iterations of co-creation and testing, the necessary practices and learning designs were designed, taking into consideration the experience the teachers collected from their classrooms. The innovation process followed the user innovation model: while researchers provided

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teachers with theoretical background knowledge, the actual development of practices and learning designs saw teachers and researchers working jointly as equal partners. In conclusion: the teachers participated as development partners, and the materials and practices needed for using the Robomath method took shape during the course of the study.

3.3 Method To answer the research questions, Internet-based survey was carried out including both scales and open-ended questions. We used three scales, developed by authors and aimed to measure teacher’s attitudes concerning the innovative methods in a classroom: (a) Teacher confidence about their knowledge related to the Robomath method. The subscale consisted of three statements, rated on a five-point Likert scale, e.g., “I know very well how to use Robomath technical tools”; (b) Teacher evaluation on the usefulness of the Robomath method. The subscale consisted of three statements, rated on a five-point Likert scale, e.g., “Robomath is an effective method for teaching a discipline”; and (c) Teacher readiness to continue using the Robomath method. The subscale consisted of three statements, rated on a five-point Likert scale, e.g., “I am sure that I am going to use the Robomath method also after the end of the study.” The questionnaire was shared to participants via e-mail, and the results were calculated with the TIBCO Statistica software. Also six open-ended questions, indicating teacher’s practices of sharing the new knowledge, were asked to describe teacher’s attitudes and behavior in the context of Robomath training sessions: Please bring some examples of the practices you used in your school for supporting implementation of the Robomath method, including: (1) Helping each other when using method (guiding etc.); (2) Sharing ideas (sharing your ideas or knowledge with other research participants); (3) Cocreating learning designs (e.g., designing math word problem, creating robotics exercises); (4) Extended sharing of learning designs and information about Robomath (e.g., documenting learning designs so that these can be shared with other teachers in your school, writing an article for your school newspaper, revising with other teachers the procedures of using (learning) technology, etc.); (5) Promoting new knowledge in your school (e.g., conducting a demo-lesson, talking with the management or colleagues, etc.). Question indicating teacher’s intention to continue using the Robomath method: (6) Do you believe that it is necessary and possible for you to continue with Robomath lessons during the next school year? Teacher’s answers to the open-ended questions were anonymized and coded. For categorizing the practices, described by the teachers, we used KAM as a deductive analytical tool. The codes were based on social practices that, according to KAM, are present when individuals share information on different levels of formalization.

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4 Results We used the results of both scales, developed by authors and open-ended questions to answer the first research question (Do the teachers who participated in Case 1 and the teachers who participated in Case 2 differ in their readiness to continue using the Robomath method after the implementation project was completed?). The average values, standard deviations, and reliability of the three scales used are presented in Table 3. When comparing the data of two groups (Case 1 and Case 2) with t-test, it became evident that the Case 2 respondents valued the Robomath method’s usefulness more highly (M = 3.91) than the Case 1 respondents (M = 3.42), t(67) = 1.89, p < 0.05. In additions the respondents of Case 2 expressed higher preparedness to continue using the Robomath method (M = 4.08) compared to the respondents of Case 1 (M = 3.25), t(67) = 3.24, p < 0.05. There were no significant differences in the evaluations of both case respondents on their confidence in method-related knowledge. The higher readiness of the Case 2 respondents to continue using the Robomath method after the end of the project was also confirmed by responses to the openended question 6. Almost all (92%) of Case 2 teachers responded that they were going to use the method in the future compared to half of the Case 1 teachers. Thus, the results give some indication about higher readiness of the Case 2 teachers to continue with the Robomath method compared to the teachers of Case 1. To answer the second research question (Do the teachers who participated in Case 1 and the teachers who participated in Case 2 differ in the way of sharing their experience with other teachers?) the responses to the open-ended questions 1–5 were used. The interview answers indicated following differences in how much teachers shared their Robomath experience with other teachers: • All the Case 2 teachers shared their ideas with their peers, compared to 69% of the Case 1 teachers), for example (a Case 2 teacher): “I conducted demo-lessons, and talked to the colleagues and management. Management is very supportive. The colleagues who are already using digital tools in their lessons are very open, but others are more skeptical as preparing (lessons) can be time-consuming.” • Half of the Case 2 teachers (compared to 14% of the Case 1 teachers) had formalized their co-created designs and practices, in order to make it possible for their peers to start using these. For example (a Case 2 teacher): “I have put all the worksheets into google-drive environment, my colleagues have the chance to use these worksheets in their work.” Table 3 Average values, standard deviations, and internal consistency of the three scales Scale

N

Mean

Std. Dev.

Cronbach’s alpha

Confidence in knowledge

67

3.06

1.04

0.81

Evaluating method as useful

67

3.71

0.77

0.79

Readiness to continue using

67

3.41

0.88

0.84

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• Two-third of the Case 2 teachers had exchanged their method-related experience with other teachers by participating in co-creating lesson designs and teaching practices (compared to 21% of the Case 1 teachers), for example (a Case 2 teacher): “The effort from each team member makes the designed robotics exercise more perfect.” Thus, there is also some indications about higher involvement of the Case 2 teachers in sharing their knowledge about the Robomath method with other teachers, compared to the Case 1 teachers.

5 Discussion Any developer of an innovative TEL method dreams of their innovation to reach classrooms and to become sustainable there. Our study indicates that a novel TEL method has greater chances to become sustainable when it is not served to teachers in a finalized form, and instead it is allowed to acquire its form in a collaboration with its end-user, i.e., with teacher. Involvement of teachers as partners in co-creating innovation-related artefacts like learning designs or teaching practices is important. This assertion is supported by various studies [18–20, 37–39]. Yet, the studies that empirically compare different innovation implementation strategies (in our case, the LPCI model vs. the FPUI model), are rare. The user innovation approach toward TEL innovations may offer following added value: Firstly, the development process of an innovation’s artefact is influenced by the teacher’s knowledge about the real classroom teaching. The chance for researchers to impose an unpractical, though academically promising, solution is thus lowered. Secondly, taking part in co-creating an innovative product will cause teachers to develop a “psychological ownership” toward that product. The “that’s my baby” feeling is a considerable motivator to continue using the novel product also after the implementation period (implementing project) ends. Thirdly, as indicated by our study, involving teachers will increase their enthusiasm to share the novel approach with other teachers, to promote its use to their colleagues. Most TEL innovations still follow the principles of closed innovation, e.g., researchers developing a theoretically promising tool that is doomed to fail as it lacks the involvement of potentially interested stakeholders [4, 40]. It appears that, similarly to the business sector, it is also in the education sector the just time to start paying attention to the user innovation model-based implementation of innovative TEL methods. This strategy is also advised by several recent studies indicating the user innovation procedures to be critical factors for the adoption and survival of innovations. Teacher involvement has an additional benefit of enhancing their ownership regarding an educational innovation [41]. The beneficial role of psychological

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ownership in accepting innovations is also discussed by [42–44]. Through the social practices, that are nurtured by co-creation of lesson designs and teaching practices, the teachers develop control over the innovative method, get to know it closely and invest considerably their time and energy, acquiring the psychological ownership of the innovative method [44]. Our study supports the notion of necessity to move toward using the user innovation approach in education. Nevertheless, the limits and particularities of the study have to be considered. The sample of our study was relatively small. Only one open innovation model (the user innovation model) was observed. The results could have been influenced by the age difference of Case 1 and Case 2 teachers. Further studies in this field should involve different TEL method innovations (in our study there was one: the innovative Robomath method) and different school levels. Nonetheless, we can cautiously conclude that conscious application of the principles of the FPUI model may help education system to achieve higher adoption and sustainability rates of TEL innovations—needed for modernizing the teaching practices, especially in STEAM subjects with well-established teaching traditions, like math. Acknowledgements Project “TU TEE—Tallinn University as a promoter of intelligent lifestyle” (nr 2014-2020.4.01.16-0033) under activity A5 in the Tallinn University Center of Excellence in Educational Innovation. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 669074.

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10. Schuurman, D.: Bridging the gap between Open and User Innovation? Exploring the value of Living Labs as a means to structure user contribution and manage distributed innovation. Ghent University and Vrije Universiteit Brussel (2015) 11. Jorgenson, D.W., Vu, K.M.: The ICT revolution, world economic growth, and policy issues. Telecommun. Policy 40(5), 383–397 (2016) 12. Sousa, M.: Open Innovation Models and the Role of Knowledge Brokers. Inside Knowledge (2008) 13. Von Hippel, E.: The Sources of Innovation. Oxford University Press, Oxford (1988) 14. Von Hippel, E.: The dominant role of users in the scientific instrument innovation process. Res. Policy 5(3), 212–239 (1976) 15. Almirall, E., Casadesus-Masanell, R.: Open versus closed innovation: a model of discovery and divergence. Acad. Manage. Rev. 35(1), 24–47 (2010) 16. Greenstein, S.: Invisible hands versus invisible advisors: Coordination mechanisms in economic networks. In: Noam, E., Nishuilleabhain, A. (eds.) Public Networks, Public Objectives, pp. 135– 160. Elsevier Science, Amsterdam (1996) 17. Gabison, G., Pesole, A.: An Overview of Models of Distributed Innovation. Publications Office of the European Union, Luxembourg (2014) 18. Metz, A., Boaz, A., Robert, G.: Co-creative approaches to knowledge production: what next for bridging the research to practice gap? Evid. Policy J. Res. Debate Pract. 15(3), 331–337 (2019) 19. Könings, K., Seidel, T., Van Merrienboer, J. J. G.: Participatory design of learning environments: integrating perspectives of students, teachers, and designers. Instr. Sci. 42(1) (2014) 20. Prieto-Alvarez, C.G., Martinez-Maldonado, R., Dirndorfer Anderson, T.: Co-designing learning analytics tools with learners. In: Lodge, J.M., Cooney Horvath, J., Corrin, L. (eds.) Learning Analytics in the Classroom. Translating Learning Analytics Research for Teachers. Routledge (2018) 21. Sherry, L.: Sustainability of Innovations. JILR. 13(3), 209–236 (2003) 22. Fullen, M., Donnelly, K.: Alive in the swamp: assessing digital innovations in education. NESTA (2013) 23. Niederhauser, D.S., Howard, S.K., Voogt, J., Agyei, D.D., Laferriere, T., Tondeur, J., Cox, M.J.: Sustainability and scalability in educational technology initiatives: research-informed practice. Technol. Knowl. Learn. 23, 507–523 (2018) 24. Batiibwe, M.S.K., Bakkabulindi, F.E.K.: Technological Pedagogical Content Knowledge (TPACK) as a theory on factors of the use of ICT in pedagogy: a review of literature. Int. J. Educ. Res. 4(11), 123–138 (2016) 25. Rogers, E.M.: Diffusion of Innovations, 5th edn. Free Press, New York (2003) 26. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989) 27. Tornatzky, L., Fleischer, M.: The process of technology innovation. Lexington Books, Lexington, MA (1990) 28. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003) 29. Hall, G.E., Hord, S.M.: Change in Schools: Facilitating the Process. State University of New York Press, New York (1987) 30. Koehler, M.J., Mishra, P.: What happens when teachers design educational technology? The development of technological pedagogical content knowledge. J. Educ. Comput. Res. 32(2), 131–152 (2005) 31. Leoste, J., Tammets, K., Ley, T.: Co-creating learning designs in professional teacher education: knowledge appropriation in the teacher’s innovation laboratory. Interact. Des. Architect. 42, 131–163 (2019) 32. Arnkil, R., Järvensivu, A., Koski, P., Piirainen, T.: Exploring quadruple helix outlining useroriented innovation models. University of Tampere (2010) 33. Ley, T., Maier, R., Thalmann, S., Waizenegger, L., Pata, K., Ruiz-Calleja, A.: A knowledge appropriation model to connect scaffolded learning and knowledge maturation in workplace learning settings. Vocat. Learn. 13, 91–112 (2020)

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34. Ley, T., Poom-Valickis, K., Eisenschmidt, E., Tammets, K., Hallik, M., Leoste, J., Sarmiento, M., Rodriguez-Triana, M.: Research Model: Co-creation and Innovation Adoption. Tallinn University (2018) 35. Leoste, J., Heidmets, M.: The impact of educational robots as learning tools on mathematics learning outcomes in basic education. In: Väljataga, T., Laanpere, M. (eds.) Digital Turn in Schools—Research, Policy, Practice: ICEM2018; Tallinn; 5–7 Sept 2019, pp. 203–217. Springer Nature, Singapore (2019) 36. Leoste, J., Heidmets, M.: Factors influencing the sustainability of robot supported math learning in basic school. In: Silva M., Luís Lima J., Reis L., Sanfeliu A., Tardioli D. (eds.) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol. 1092. Springer, Cham (2020) 37. Botha, A., Herselman, M.: Teachers become co-creators through participation in a teacher professional development (TPD) course in a resource constraint environment in South Africa. Electr. J. Inf. Syst. Dev. Countries 84, e12007 (2018) 38. Voogt, J., Laferrière, T., Breuleux, A., Itow, R.C., Hickey, D.T., McKenney, S.: Collaborative design as a form of professional development. Instr. Sci. 43(2), 259–282 (2015) 39. Rodriques-Triana, M.J., Prieto, L.P., Ley, T., De Jong, T., Gillet, D.: Tracing teacher collaborative learning and innovation adoption: a case study in an inquiry learning platform. In: International Conference on Computer Supported Collaborative Learning. Proceedings (2019) 40. Gunn, C.: Sustainability factors for e-learning initiatives. ALT-J. 18(2), 89–103 (2010) 41. Michos, K., Hernández-Leo, D., Albó, L.: Teacher-led inquiry in technology-supported school communities. Br. J. Educ. Technol. 49(6) (2018) 42. Aga, D.A., Noorderhaven, N., Vallejo, B.: Project beneficiary participation and behavioural intentions promoting project sustainability: the mediating role of psychological ownership. Dev. Policy Rev. 2018(36), 527–546 (2018) 43. Han, T.S., Chiang, H.H., Chang, A.: Employee participation in decision making, psychological ownership and knowledge sharing: mediating role of organizational commitment in Taiwanese high-tech organizations. Int. J. Hum. Resour Manage. 21(12), 2218–2233 (2010) 44. Yim, J.S., Moses, P., Azalea, A.: Effects of psychological ownership on teachers’ beliefs about a cloud-based virtual learning environment. Res. Pract. Technol. Enhanced Learn. 13, 13 (2018)

Conceptualization of Hypersituation as Result of IoT in Education Filipe T. Moreira , Mário Vairinhos , and Fernando Ramos

Abstract With the emergence of new technologies and their use in different areas, new experiences emerge. In the context of the use of IoT in educational contexts, the potential of hypersituation has been considered by several authors as the greatest potential of these technologies for this field of study. However, despite several references to this fact, this term still lacks further conceptualization and the drawing of guidelines to achieve it. Thus, this paper aims to present an interpretation and definition of the term hypersituation indicating potentials, challenges, and ways to achieve it. Keywords Hypersituation · Internet of things · Education

1 Introduction We are experiencing a moment where the amount of data, knowledge, and technological devices are changing the mindset of institutions in their teaching and learning processes [1] and the way they communicate, interact, and present themselves. This is especially pertinent when we have access to the Internet of things (IoT) technologies that can provide lots of data and create possibilities to generate new knowledge about different areas. This is relevant if we think that with the dissemination of the IoT technologies humans cannot only communicate with other humans, but also with machines and access information of machine-to-machine communications [2]. In this framework, F. T. Moreira (B) · M. Vairinhos · F. Ramos DigiMedia, DECA, University of Aveiro, Aveiro, Portugal e-mail: [email protected] M. Vairinhos e-mail: [email protected] F. Ramos e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_6

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some studies and reports have shown some potential uses of IoT technologies in education. With this, there is a potentiality that seems to have gained greater preponderance, asserting itself as the one that has aroused the greatest interest, which is the ability to create environments of “hypersituation” [3]. The term “hypersituation” applied in the context of the use of the Internet of things in Education became widely known when it appeared in the NMC Horizon Report 2015 [3]. However, the first time its meaning was used in this context was through an opinion article by Mara Hancock in which the author presents it as hypersituating. The author mentions that one of the components of IoT, that will open new opportunities for education is the concept of hypersituating thus listed by the author in the absence of a terminology that best fits the context. Thus, hypersituating is defined as: The ability to amplify access to knowledge of a device user’s current location. This is where a mobile device and/or sensor can correlate with personal and diverse information to augment and deepen one’s understanding of the surrounding physical world. The learner can both consume and create [4].

Nevertheless, although many academic publications highlight hypersituation as the main asset of IoT in education [2–8], its conceptualization appears underdeveloped and no ways of reaching this stage are indicated. Thus, in this paper, we present the definition of hypersituation and the main possibilities of this reality to promote more effective educational environments.

2 Hypersituation One of the aspects that may refer to some difficulty in interpreting the term hypersituation is the fact that it is composed of a prefix—hyper—and by a name—situation. Using Oxford Learner’s Dictionaries, it can be stated that “hyper” refers to something “more than normal”1 or “too much” and that the name “situation” is referred to “all the circumstances and things that are happening at a particular time and in a particular place” (see Footnote 1). Thus, this conjugation seems to refer us to another dimension in which new layers are added to the reality of the individual, or at least to a certain situation in the individual’s life. Sometimes the discussion on hypersituation or hypersituated experience refers us to the concept of hyperreality, this being a “reality” that is also obtained through technological mediation in conjunction with the physical reality of the individual. In this framework, it is considered that hyperreality technology can create realities that are added to natural reality, thus creating different layers of reality. An event that takes us back to the thought of “Eco [9]—technology can give us more reality than nature can.” At this stage of reality, the individual may even find himself unable to distinguish reality from a simulation of reality [10]. As opposed to this, hypersitu1 Definitions obtained in Oxford Learner’s Dictionaries available at https://www.oxfordlearnersdict

ionaries.com/definition/english/hyper_2 viewed at January 30, 2020.

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ation does not add realities, it only facilitates different interpretations of reality or ultimately provides different perspectives on the same reality, as will be explained later in this paper. Then, in our perspective, hypersituation can be interpreted in analogy with hypertext which from the perspective of Smith and Weiss [11] gives great flexibility to the text. In this line, hypersituation is considered to provide great flexibility in interpreting reality or a simple situation, since it allows access to real-time data from the surrounding environment. This allows the individual to access complementary information in a targeted way, similar to hypertext. Thus, without technological mediation, each individual was circumscribed to his or her senses, previous knowledge, beliefs, and/or knowledge of peers (which could be transmitted to him or her) for the interpretation of the environment. In hypersituated environments, the individual will have access to layers of data and information about certain situations in real time, which can be provided in a contextualized manner with their age group, interests, culture, or location. This contextualization will be facilitated by the semantic Web. To exemplify the concept of hypersituation, two figures were created. In Fig. 1, a situation without technological mediation, where the individual depends on the aforementioned factors to interpret the situation in real time. In contrast, in Fig. 2, the individual has access to a whole set of data and information in real time, allowing a more complete interpretation of the situation in different layers. It can thus be said that hypersituation allows new dimensions to be assigned to each situation. However, it is important to highlight that to experience hypersituation there is a need for data or information provided to him/her to be contextualized. That implies the use of different technologies, highlighting artificial intelligence and the semantic Web.

Fig. 1 Situation

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Fig. 2 Hypersituation

Thus, from our perspective, the hypersituated experience may contribute on a large scale to the emergence of new pedagogical situations and contexts. These situations will be originated by a new paradigm where the temporality of the physical and digital world will be identical. This temporality will be possible through the connection of a new and particular type of digital technology. These technologies include augmented reality, IoT, big data, virtual reality, and artificial intelligence, which, to a greater or lesser degree, all can incorporate or superimpose information in real time in the experience. The overlapping occurs, depending on the technology in question, in several planes or dimensions of the experience, the most important being the perceptive, informational, and narrative integration. In this context, the perspective dimension occurs at the individual level, informational is made possible by IoT technologies while the narrative dimension is based on the educational project that frames the use of technology in the pedagogical experience and stands out for its role in ensuring that digital information has an auto-referential character.

2.1 How to Achieve To make hypersituated experience reality in educational contexts, it will be necessary to develop a network involving a whole set of technologies as well as a platform that allows contextualization by the individual, i.e., each individual will have access to different layers of the same situation according to their reality. This combination of technologies is illustrated in a simplified manner in Fig. 3.

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Fig. 3 Hypersituation technologies scheme

Ubiquitously reaching this stage is now an impossibility, however, with 5G technologies in expansion, it is believed that within a few years it will be a reality. However, it is not enough to have an environment with hypersituation potential to have a hypersituated experience. For this experience, it is necessary that in addition to the technologies, three essential factors must be fulfilled: perception, information, and narrative, as previously mentioned. Thus, in Fig. 3, the technologies that will allow the narrative are not indicated, because it is considered that they will operate at the platform level.

2.2 Potentialities and Challenges The hypersituation environment allows the student to have an hypersituated experience. In this experience, it is possible to address real and concrete data. With this, it will be easier for students to assume a more active role in searching, sharing, and processing of data, where the teacher will have mainly a guiding role. In hypersituated environments, the classroom may become an “open” space, where the physical limitations will not be relevant to the interpretation of the outside world, which can be monitored, analyzed, and studied in real time. Also, other educational resources may change, becoming more dynamic and contextualized with the reality of each student.

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About main challenges we identified: The difficulty of creating these hypersituated environments, since it will be necessary to combine a set of technologies and to develop a platform that allows the user (especially students) the possibility of accessing data and information in an easy and targeted way; teacher training in this area will need to be reinforced so that they feel comfortable and able to take full advantage of these environments; many other teaching methodologies and educational resources will need to be rethought, in particular, the school books; considering that the students through their devices will also be data senders, there will have to be a greater control and security guarantee to guarantee the total protection of those involved.

3 Final Remarks Considering what has been described, in our understanding, hypersituation is characterized by being a stage mediated by different technologies where students have the possibility of perceiving each situation with different layers of data that can be converted into information. These data can contribute to interpretations of the surrounding environment by different prisms and layers of complexity, always considering the characteristics of the students and their location. With this, students can have a better understanding of each life situation, and in sum they can have a better comprehension of the world. However, despite the consensus that seems to exist in the scientific community about hypersituation being the great potential of IoT for education [3–8], there is a need for empirical studies that attest to this idea. There is also a need to specify pragmatic ways to reach this stage, considering the existing resources, mainly in schools. As a result of these studies, it will also be necessary to carry out studies to understand the real impact of this experience mediated by technology on the learning and motivation of students, and it is estimated that this will be an issue under analysis in the coming years.

References 1. Marquez, J., Villanueva, J., Solarte, Z., Garcia, A.: IoT in education: integration of objects with virtual academic communities. In: Rocha, Á., Correia, A.M., Adeli, H., Reis, L.P., Mendonça Teixeira, M. (eds.) New Advances in Information Systems and Technologies, pp. 201–212. Springer International Publishing, Cham (2016) 2. Ramlowat, D.D., Pattanayak, B.K.: Exploring the internet of things (IoT) in education: a review. In: Advances in Intelligent Systems and Computing, pp. 245–255. Springer (2019). https://doi. org/10.1007/978-981-13-3338-5_23 3. Johnson, L., Adams Becker, S., Estrada, V., Freeman, A.: Horizon Report: 2015 Higher Education Edition (2015). ISBN 978-0-9906415-8-2 4. Hancock, M.: Ubiquitous Everything and Then Some. https://er.educause.edu/articles/2014/9/ ubiquitous-everything-and-then-some. Last accessed 2 Sept 2019

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5. Moreira, F.T., Vairinhos, M., Ramos, F.: Internet of Things in education: a tool for science learning. In: Iberian Conference on Information Systems and Technologies, CISTI (2018). https://doi.org/10.23919/CISTI.2018.8399234 6. Moreira, F.T., Varirinhos, M., Ramos, F.: Enhancing learnings with Internet of Things: PAprICa project. In: 2019 14th Iberian Conference on Information Systems and Technologies (2019). https://doi.org/10.23919/CISTI.2019.8760610 7. Kloos, C.D., Munoz-Merino, P.J., Alario-Hoyos, C., Estevez-Ayres, I., Ibanez, M.B., CrespoGarcia, R.M.: The hybridization factor of technology in education (2018). https://doi.org/10. 1109/EDUCON.2018.8363465. http://search.ebscohost.com/login.aspx?direct=true&db=eds eee&AN=edseee.8363465&site=eds-live 8. Bachir, S., Gallon, L., Abenia, A., Aniorte, P., Exposito, E.: Towards autonomic educational cyber physical systems. In: Proceedings—2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019, pp. 1198–1204. Institute of Electrical and Electronics Engineers Inc. (2019) 9. Eco, U.: Travels in Hyperreality. Picador, London (1987) 10. Baofu, P.: The Future of Post-Human Mass Media A Preface to a New Theory of Communication. Cambridge Scholars Publishing, Newcastle (2009) 11. Smith, J.B., Weiss, S.F.: Hypertext. Commun. ACM 31, 816–819 (1988). https://doi.org/10. 1145/48511.48512

People in Place Centered Design for Smart Education

A Territorial Learning Ecosystem for Parents’ Participation and Cooperation Roberto Araya

Abstract Parents are critical to student attainment and learning. It is therefore key to foster their active participation in their children’s education and provide them with tools to be effective. This paper describes the implementation of a territorial learning ecosystem that provides K12 parents with the facilities to support their children’s learning at home, track progress against the national curriculum, and network and share their experience with other parents. The territorial learning ecosystem includes an interactive map of the country. This map displays indicators of the level of overall activity in the subject, as well as in each of the specific learning objectives defined by the national curriculum. The ecosystem provides indicators at four different levels: region, district, school, and classroom. During the second semester of 2019, a total of 1235 first grade classes voluntarily adopted the system. In mid-October, educational videos made by parents were uploaded to the system, in which they shared examples of homemade activities and teaching strategies. 98 schools from the most populated region submitted 98 parent-made videos to be shared during a public event. 82% of the videos showed a mother with her child learning to read or write, 49% showed word segmentation activities, 48% showed homemade educational games, and 26% showed activities using glove puppets. Experts rated the communication and educational quality of the videos. We found that a much higher proportion of videos involving glove puppets were rated as “very good" when compared to the other types. Keywords Territorial ecosystem · Supportive technologies · Social innovation

R. Araya (B) Center for Advanced Research in Education, Institute of Education, Universidad de Chile, Periodista José Carrasco Tapia Nº 75, Santiago, Chile e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_7

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1 Introduction 1.1 Parental Participation Parents are critical for improving students’ attainment and learning outcomes. In the primary years, family influences have a more powerful effect on children’s attainment and progress than school factors. At age 7, parent effects explain 29% of the variance in attainment, while school effects explain only 5% of the variance [1]. Therefore, foster their active participation in their children’s learning is key. On the other hand, parents of the lowest socioeconomic status (SES) segments, on average, have much lower expectations of attainment than other parents [2, 3]. Therefore, it is particularly important to devise ways to engage low SES parents. However, parents do not have effective strategies for supporting learning at home. Furthermore, such strategies are very difficult to discover or learn independently. First, there are two rival strategies for teaching reading that have been causing a pedagogic dispute for nearly a century, the so-called reading wars. While one of them is effective according to empirical evidence, many teachers and parents inadvertently use the wrong strategy or an inefficient mix of the two. Second, parents do not have the time nor the training to search for empirical evidence. Third, some books recommend several strategies but it is not clear how to implement said strategies. For example, Willingham [4] encourages parents at home to adopt strategies such as talking and asking specific questions about what happened in the classroom, reading more non-fictions texts aloud to the child, and playing games, such as board games or homemade games. However, the problem is that identifying effective games or types of books based on the child’s gender and interests is not a simple task.

1.2 The Secret Is Collective Brains The challenge is identifying strategies that help parents of elementary school students to contribute to their children’s learning. We propose an ecosystem to foster parental participation, sharing and cooperation. Since we are social animals with particularly powerful adaptations that help us learn from others, it is, therefore, important to facilitate that type of learning. According to Sloman et al. [5], individual intelligence is overrated, and in the real world nobody operates in a vacuum; we work in teams, and we let our group to do our thinking for us. There is growing empirical evidence on the power of learning in communities. For example, after studying different hunter-gatherer societies all over the world, Henrich [6] concluded that larger and more interconnected groups generate more tools, expanded bodies of know-how, and fancier techniques. If the community is very small, it can experience the “Tasmania effect.” This is a regression that occurred in Tasmania. As the size of the community was reduced 12,000 years ago as a result of rising sea levels that cut

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them off from Australia, the aborigines were losing their know-how to make tools, weapons, clothing and boats, and going back to more primitive cultural tools. Humans are cultural animals, animals that have evolved through natural selection to participate in a community, where individuals not only relate to each other as individuals but are also shaped by their social network [7]. Our societies and social networks act as collective brains [8], where individuals selectively transmit and learn ideas that can produce complex solutions without the need for a designer. We therefore explore the effect on parents of using technology to connect parents and teachers in a learning community that collaborates in order to improve the children’s learning.

2 The Evolutionary Mechanism for Learning 2.1 Imitation One critical mechanism for learning is imitation. This mechanism is widely present throughout the animal kingdom and is an essential force for animal and human societies [9]. Imitation is a key component of cultural transmission. It is favored in relatively stable environments, when the error rate associated with it is lower than the error rate associated with individual learning. A learning ecosystem should therefore help parents imitate strategies that have already been implemented by other parents. Once parents start imitating the strategies and ideas, then we have to consider that copies are not completely identical to the originals. Mutations invariably occur and therefore new strategies naturally begin to appear. Inevitably, a process of recombination of ideas also starts to emerge at the same time. As Ridley [10] describes it, ideas start having sex with each other. This is the engine of human progress: mating of ideas to make new ideas [10]. Thus, it is critical that a learning ecosystem promotes and fosters the sharing and exchanging of didactic strategies, games, and tools. This is the basic mechanism for social innovation. The whole process is a typical evolutionary mechanism, but it needs to start from an initial population of strategies [11]. This initial set of strategies and ideas will jumpstart an iterative process. Therefore, the learning ecosystem must also provide an initial set of strategies. The population of strategies in each iteration is called a generation. In each generation, the fitness of every strategy needs to be somehow evaluated. This is a critical step to ensure the emergence of more effective strategies. These are strategies that are both highly motivating to young children and at the same time effectively help their learning. This means parents need to have feedback on the strategies, and so a learning ecosystem must provide clear metrics that help them assess the effect of their strategies.

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2.2 The First Generation of Strategies What are the initial ideas provided by the learning ecosystem? The system provides three types of strategies. The first strategy proposed is play. This is suggested together with different didactic games. This is considered one of the key tools to be imitated at home. It is very powerful since play is an ecologically valid educational strategy used by mammals and several other animals [12]. Mother--offspring play among humans and non-human primates is widespread and central to offspring development. Indeed, such play has structural similarities across these species, like exaggerated movements and self-handicapped (slower and weaker) behavior of adults to facilitate offspring learning. Play is proposed through games, such as using dice with letters to challenge the child to form words and, eventually, sentences. Other games propose using whistles to help separate the different sounds in a word. A second strategy proposed is the use of glove puppets or imaginary companions. Children are frequently exposed to anthropomorphic books, TV shows, and narratives. They are used to play with non-living objects, such as hand puppets and stuffed toys, and perceive them to be agents that are worthy of social interaction [13]. Imaginary friends are used by 67% of children [14]. Several studies suggest [14] that having an imaginary friend confers a developmental advantage in a number of important sociocognitive areas. Children with imaginary friends produced a range of more complex sentence types in a narrative task than children in a control group [15]. They produced significantly more adverbial clauses, relative clauses, and compound sentences, where clauses were connected using “and” or “but.” Using glove puppets as a tool for learning at home is therefore a reasonable strategy. If it is their own favorite glove puppet then the effect can be increased, since young children are less likely to accept an identical replacement for an attachment object than for a favorite toy [16]. This may be due to the fact that children believe that their favorite toy or puppet has a hidden and invisible property that distinguishes it from everything else. Play and glove puppets can facilitate the development of powerful cognitive strategies in children, such as role-reversal imitation and transmission chains, in which children learn something and then teach another child [17]. However, in this case, the child instructs their glove puppet. This in turn facilitates the internalization process, where a child not only follows instructions but also self-regulates their own problem-solving activities and instructs themselves. A third strategy proposed is using coloring books with instructions on how to color. This strategy is particularly recommended for fostering reading comprehension. It starts with simple instructions to color one of the objects shown on the page, such as “color the piano blue.” Throughout the book, there is a sequence of coloring activities that become increasingly more complex. For example, instructions with existential quantifiers, such as “in each box, color at least two balls blue,” printed on a page depicting several boxes, each containing several balls. A more complex set of instructions is given in a series of sentences articulated by different characters shown on the page. Each of them gives certain hints about the location of where a

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particular object is hidden. Connecting the sentences, the reader should be able to infer where the object is hidden and color its location.

2.3 The Territorial Component In-group favoritism and ethnocentrism are human universals [18]. Humans are innately tribal. Experiments with children, adults, and even monkeys with reaction time tests reveal negative associations with out-group members. However, markers can be created flexibly, extending beyond language, ethnicity or even race [19]. This powerful social force has been recognized in different ways since ancient times. For example, a related concept is cohesion, asabiyyah [20], or social solidarity. They emphasize group consciousness and a sense of shared purpose. One powerful groupmarker is territory. Capello [21] highlights that territorial identity is rooted in similarity and solidarity, which form the basis of identity. Therefore, a powerful learning ecosystem should include territorial facilities that use the in-group mechanism that fosters sharing and collaboration between parents from the same district.

3 Implementation During the second semester of 2019, a total of 1235 low socioeconomic status (SES) Chilean schools voluntarily adopted ConectaIdeas Express, a smartphonebased support system, in order to help teach first graders how to read [22, 23]. At the end of the semester, the app was being used by almost 50% of schools that were effectively using the Ministry of Education’s official textbook for teaching reading. From these schools, 988 teachers used the app to assess 30,158 students in 1022 first grade classes. Teachers used exit tickets in order to assess their students. These onequestion tickets correspond to formative assessments that allow teachers to quickly know how well their students understand the material they are learning. For this reason, toward at the end of the session, students answered the exit ticket orally or in writing. Later, each teacher inputted the information into their smartphone. The tickets were designed by the Ministry of Education and shown in the textbook. Each ticket is associated with a specific learning objective (LO) on the national curriculum. Most of the schools that adopted the app serve a population of low SES students from all over the country. They represent 15% of the country’s students at that grade level. Most of the schools have poor technological infrastructure and unreliable Internet, particularly rural schools. All training was done during the second semester via a weekly email with tips on how to install and use the app, as well as links to oneminute videos. On average, each teacher used the app for 22 sessions. This means that it was used approximately twice a week. The territorial learning ecosystem was implemented as a geographic information system, with maps showing the activity by region, district, and classroom. All the

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Fig. 1 Screenshot with the map of the country showing the average number of questions answered per class in each region of the country. Darker regions mean a higher number of questions per class. To the right is the list of regions with the average number of questions answered by each class

information on every class was shown in the territorial learning ecosystem for the whole country (Fig. 1), with statistics on curriculum coverage per region. At the country level, an interactive map shows the average number of questions answered per class in each region of the country. Each parent can access the map and zoom into any region (Fig. 2). In each region, different districts are shown with their own activity indicators. Darker zones correspond to districts where more tickets have been completed per class. Any parent can zoom into a district and see all of the schools and classes in that district. Black dots are schools without first grade (Fig. 3). White dots are schools not in the Ministry program. The cell phone icons are schools using ConectaIdeas Express. Red cell phones are classes where parents have produced a video and the teachers have uploaded it to the system. If the user clicks on the smartphone the information on the class is displayed, along with the number of tickets and curriculum coverage (Fig. 4). Moreover, if the smartphone is red it means that there is a parent-made video that has been uploaded for at least one class at that school. In this case, the video is shown to the left of the screen (Fig. 4). Parents can access all of the maps from their smartphones and share them through WhatsApp. While the territorial learning ecosystem can also be accessed via the Web, parents tend to prefer the mobile version. In order to assess the impact of the territorial learning ecosystem on parents and the quality of their videos and interventions, 170 schools from the metropolitan region were invited to a breakfast to share experiences and submit one parent-made video per class. The breakfast was held near the end of the semester, on October 11.

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Fig. 2 Screenshot with map of the metropolitan region showing the average number of questions answered per class in each district of the region. Darker districts mean a higher number of questions per class. To the right is the list of districts in this region with the average number of questions answered by each class

Fig. 3 Screenshot with a map of the Lo Prado district in the metropolitan region showing the average number of questions answered per class at each of the schools in the district. To the right is the list of schools in the district with the average number of questions answered by each class

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Fig. 4 Screenshot with parent-made video displayed to the left of the screen after clicking on one of the red smartphones on the map. Information on individual students is never displayed

Parents had access to a set of initial videos showing two of the three different strategies mentioned previously: play and hand puppets. The coloring book strategy was proposed later on in November and its impact is not analyzed here. Therefore, only two kind of strategies are included in the videos from the initial generation that jumpstarts the evolutionary mechanism.

4 Results Ninety-eight schools from the metropolitan region sent their parent-made videos. This is then the second generation of didactic videos. A team of four independent teachers watched the videos in order to detect the presence of different features. Some of these features and the frequencies with which they were detected can be found in Table 1. The most frequent strategy was videos showing a mother with their Table 1 Parent-made didactic videos uploaded to the territorial learning system

Percentage (%) Description 82

Videos showing a mother with their child

49

Videos of word segmentation activities

48

Videos showing play with didactic homemade games

26

Videos showing glove puppets

13

Videos showing magic boxes

9

Videos showing more than one child

8

Videos showing whistles

3

Videos showing didactic dice

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child (Table 1). Videos of word segmentation activities and with homemade games were also frequent. An independent, experienced teacher and a video communication expert rated each video based on its didactic value (i.e., whether or not it promoted validated teaching strategies) and its communication quality (i.e., whether or not it was engaging and easy to understand and replicate). The videos were rated using a three-level system: unsatisfactory, satisfactory, and very good. • 5% of the videos were rated as unsatisfactory • 30% of the videos were rated as satisfactory • 65% of the videos were rated as very good. We found that some features are more frequent among very good videos. Conversely, we also found that the proportion of very good videos is much lower when the videos do not have these features. More specifically, we found that: • 94% of videos involving a didactic game are rated as very good, versus 41% of videos without didactic games. • 96% of videos involving glove puppets are rated as very good, versus 56% of videos without glove puppets. • 88% of word segmentation videos are rated as very good, versus 46% of videos without word segmentation. • 75% of videos showing a mother and their child together are rated as very good, versus 28% of videos not showing the two of them. • Additionally, we found that 91% of videos without glove puppets but involving a game are rated as very good. Conversely, only 40% of videos without a glove puppet or a game are rated as very good. • Moreover, 51% of videos without a glove puppet or a game but that show a mother and their child together are rated as very good, versus only 13% of videos where all three of these features are missing. We ran a decision tree classification algorithm in order to get better insights into what combinations of features make a good video. Figure 5 shows the resulting decision tree that classifies the videos into two categories: very good and not very good. The most discriminatory variable according to the Kolmogorov-Smirnov (KS) metric is the presence of a glove puppet. Virtually all (96%) videos with glove puppets are rated as very good. In those without glove puppets, the variable indicating the presence of a didactic game is the one that best discriminates. If there is a didactic game, then 91% of the videos are very good. If not, then the variable indicating the presence of a mother and their child in the video is the one that best discriminates. If mother and child are present in the video, then 51.4% of the videos are very good. If they are not, then only 13.3% of the videos are very good. In summary, a recommendation for parent-made videos is to show the mother and offspring playing a didactic game together and using a glove puppet. The analysis of the videos was carried out in January 2020 during the school summer holidays (January and February). In April 2020, a Web book with all of

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Fig. 5 Results of the decision tree induction algorithm, classifying combinations of features in order to make a good video

the videos was published for parents, highlighting the ideas or strategies included in each one (Fig. 6). During April and May, the Web book and videos are being sent to parents and teachers. The Ministry of Education continued with the application of the system in 2020, while the Web book and video are currently being used by teachers and parents for home-based activities to foster reading instruction during the schools closures caused by the COVID-19 pandemic.

5 Conclusions According to a recent 2019 OECD report [24], pedagogical innovation among OECD nations has been moderate at the system level. The biggest innovations have been seen in independent knowledge acquisition and homework practices, followed by both rote learning and active learning practices. The report concludes that in order to produce good results, good pedagogical practices must be supported. Given this trend toward homework practices and the importance of the effect of parents, it is then critical to disseminate effective teaching strategies for home use. In particular, these should be strategies that foster collective brains that connect with each other in order to explore, test, imitate, improve, and recombine strategies, and thus discover new effective strategies. In this paper, we have described the implementation of a territorial learning ecosystem that provides K12 parents with the facilities to support their children’s learning at home, track the class progress according to the national curriculum, and network and share their experience with other parents.

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Fig. 6 Screenshot of half a page from the Web book for parents that shows and highlights the strategies in each of the videos, in order to facilitate imitation and recombination of didactic strategies by parents

The plan was to create a community and an ecosystem that fostered parent learning using an evolutionary mechanism for the transmission and improvement of strategies. Starting with a population of a first generation of videos with initial teaching strategies, the participation of parents was encouraged by imitating the videos, sharing them, exchanging ideas, and adjusting them based on their own needs. The territorial learning ecosystem also promotes territorial motivations. It includes an interactive map of the country, which displays indicators of the level of overall activity in the subject, as well as in each of the specific learning objectives defined by the national curriculum. The ecosystem displays indicators at four different levels: region, district, school, and classroom. During the second semester of 2019, a total of 1235 first grade classes voluntarily adopted the system. In mid-October, educational videos made by parents began to be uploaded to the system. Through these videos, parents were able to share their own homemade activities and teaching strategies. 98 schools from the most populated region submitted parent-made videos for sharing at a public event. 82% of the videos showed a mother with her child learning to read or write, 49% showed word segmentation activities, 48% showed homemade educational games, and 26% showed activities involving glove puppets. Experts rated the communication and educational quality of the videos. We found that a

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much higher proportion of videos involving a mother and their child playing a game with a glove puppet were rated as “very good." This year, the system is being applied by the Ministry of Education for all first and second graders. This will give us the opportunity to observe new generations of videos, strategies, games, and materials, as well as to verify whether the degree of enthusiasm and participation among parents persists and extends to other subjects. Acknowledgements Support from ANID/PIA/Basal Funds for Centers of Excellence FB0003 is gratefully acknowledged.

References 1. Gross, J.: Parental engagement how to make a real difference. https://cdn.oxfordowl.co.uk/ 2017/09/05/12/40/32/855/bp_gross_parentalengagement.pdf (2017) 2. Child Trends Databank: Parental Expectations for Their Children’s Academic Attainment. Online at: http://www.childtrends.org/?indicators=parental-expectations-for-their-childrensacademic-attainmen (2015) 3. Araya, R., Gormaz, R., Ulloa, O.: For which socioeconomic segments are parental expectations of educational attainments more sensitive to their children performance? In: Proceedings of the 6th International Conference on Sustainability, Technology and Education, Sydney, Australia (2017) 4. Willingham, D.: Raising Kids who Read. What Parents and Teachers Can Do. Jossey-Bass, San Francisco (2015) 5. Sloman, S., Fernebach, P.: The Knowledge Illusion, Why We Never Think Alone. River head books, New York (2017) 6. Henrich, J.: The secret of our success. How culture is driving human evolution, domesticating our species, and making us smarter. Princeton University Press, Princeton (2016) 7. Baumeister, R.: The cultural animal. Human nature, meaning, and social life. Oxford University Press, New York (2005) 8. Muthukrishna, M., Henrich, J.: Innovation in the collective brain. Phil. Trans. R. Soc. B 371, 20150192 (2016) 9. Dugatkin, L.: The imitation factor. Evolution beyond the gene. The Free Press, N.Y. (2000) 10. Ridley, M.: The rational optimist. How prosperity evolves. Harper, New York (2010) 11. Holland, J.: Hidden Order. Addison Wesley, How Adaptation Builds Complexity (1995) 12. Pellegrini, A.: The role of play in human development. Oxford University Press, New York, N.Y. (2009) 13. Tahiroglu, D., Taylor, M.: Anthropomorphism, social understanding, and imaginary companions. Br. J. Dev. Psychol. 37, 284–299 (2019) 14. Roby, A., Evan Kidd, E.: The referential communication skills of children with imaginary companions. Dev. Sci. 11(4), 531–540 (2008) 15. Bouldin, P., Bavin, E.L., Pratt, C.: An investigation of the verbal abilities of children with imaginary companions. First Lang. 22, 249–264 (2002) 16. Hood, B., Bloom, P.: Children prefer certain individuals over perfect duplicates. Cognition 106, 455–462 (2008) 17. Tomasello, M.: Becoming Human. A Theory of Ontogeny. Harvard University Press, Cambridge (2019) 18. Greene, J.: Moral Tribes. Emotion, Reason, and the Gap Between US and them. The Penguin Press, New York (2013)

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19. Kurzban, R., Tooby, J., Cosmides, L.: Can race be erased? Coalitional computation and social categorization. PNAS 98(26), 15387–15392 (2001) 20. Turchin, P.: Historical Dynamics: Why States Rise and Fall. Princeton University Press, Princeton (2003) 21. Capello, R.: Interpreting and understanding territorial identity. Reg. Sci. Policy Pract. 11, 141–158 (2019) 22. Araya, R.: Mobile performance support system for teachers and parents teaching first graders to read. In: 16th Mobile Learning 2020 Conference 23. Araya, R.: Early detection of gender differences in reading and writing from a smartphone-based performance support system for teachers. In: 10th International Conference in Methodologies and intelligent Systems for Technology Enhanced Learning (MIS4TEL 2020) 24. Vincent-Lancrin, S., et al.: Measuring Innovation in Education 2019: What Has Changed in the Classroom? Educational Research and Innovation. OECD Publishing, Paris (2019)

Escape from Dungeon—Modeling User Intentions with Natural Language Processing Techniques Stefan Toncu, Irina Toma, Mihai Dascalu, and Stefan Trausan-Matu

Abstract Educational games are a powerful solution for pedagogical problems, both from students’ and teachers’ points of view. Students may experience the inability to focus on lectures in a traditional learning environment, as they cannot understand the lecture materials, are not motivated to study, or the subjects are not challenging enough. Research on learning strategies shows that students are more likely to remain focused and engaged in a smart learning environment that makes use of gamification, instead of a classical classroom scenario, where teachers present formal lectures. Our game, Escape from Dungeon, falls in the category of serious games for problem solving that integrate natural language processing (NLP) techniques adopted to model user intentions. We focused on ensuring appealing graphics and ease of interaction, while relying on novel technologies. The main character of the game is controlled through vocal commands that are interpreted using NLP tools. The game was tested by ten users throughout a pilot test. Users considered the game innovative and entertaining. However, users suggested additional game scenes for an extended gameplay, as well as more actions and intents to be covered within the interaction with the character. Keywords Serious games · Smart learning environment · Natural language processing · Voice recognition · Virtual reality S. Toncu · I. Toma · M. Dascalu (B) · S. Trausan-Matu University Politehnica of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania e-mail: [email protected] S. Toncu e-mail: [email protected] I. Toma e-mail: [email protected] S. Trausan-Matu e-mail: [email protected] M. Dascalu · S. Trausan-Matu Academy of Romanian Scientists, Str. Ilfov, Nr. 3, 050044 Bucharest, Romania © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_8

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1 Introduction Games are one of the oldest means of relaxation and entertainment, as they offer participants the opportunity to enter a unique universe, transcending everyday reality [1]. Most games share common features, such as rules and goals: players agree upon game rules and respect them in order to achieve the predefined goals. The gaming process involves competition to a certain degree, while the outcome is uncertain. Some games include elements of chance, fiction, puzzle solving, but all games should consider leisure elements to ensure personal entertainment, satisfaction or self-fulfillment. Even though games are designed to entertain players, their goals can include more than the act of play. Participants can exercise previous knowledge or acquire new information during the gameplay, by being actively involved in a learning ecosystem. This process is more tiresome to be performed during classes or seminars. Gamebased learning [2] is a method applied by teachers when presenting information to their students in a fun and interactive manner, while keeping students focused on the presented knowledge, rather than on the game core and mechanics. Thus, the traditional learning process, when theory is presented in a mechanical way, is transformed into an attractive activity, focused on understanding concepts while practicing theoretical notions [3]. This article focuses on serious games based on natural language processing (NLP) for interactive storytelling [4] and dialogue systems [5]. NLP dialogue systems for serious games can be separated into natural language understanding (NLU), which extracts intents and entities from data based on context and grammar, and natural language generation (NLG), responsible for generating text based on structured data [6]. NLU is of interest for the scope of this study because our serious game—Escape from Dungeon—integrates speech recognition functionalities and NLP techniques for intent and entity identification. The usage of intent identification is a growing market, as more products incorporate voice assistant features for everyday use. Intents vary from simple informative commands, such as “play music” or “call somebody,” to more complex intents that require several connected systems in order to fulfill the request (e.g., “turn off the light,” or “turn on heating system”) [7]. For each spoken request, referred to as an utterance, an intent identification system performs the following tasks: domain classification, intent detection, and slot filling [8]. These three tasks are preceded by a preprocessing step that converts speech to text. Several models based on deep neural networks classifiers [9, 10] are trained for user intent detection. The problem arises when users express different intentions than the ones available in the training set. One solution to this problem is to simply ask users to rephrase their intent or tell them that the system does not understand the request. A second approach is to use an intent detection system that supports zero-shot learning [11]. The system goes beyond the manually labeled intentions and gathers data from external resources, such as labeled ontologies or manually defined attributes that describe intents, followed by associating existing or emerging intents with extra annotations [12].

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Our serious game—Escape from Dungeon—aims to develop players’ sense of observation and general knowledge. The game integrates speech recognition functionalities, NLP techniques for intent identification, and can render scenes into a virtual reality environment with the help of a VR headset.

2 Serious Games Based on NLP Serious games represent a small percentage of the gaming market [13] when compared with the rest of the entertainment gaming industry [14]. The discrepancy is caused by the game objectives, as it is more likely for a user to play a game for entertainment than for educational purposes. Serious games based on NLP dialogue systems process and generate natural language to communicate with the players. The whole process is complex because the game must handle metaphors, bad spelling, grammar mistakes, and contextual input. The following section details three existing games, Façade [15], LifeLine [16], and Crystal Island [17]. Each of the games is of interest for the current paper as they use NLP techniques for interacting with users, voice commands, or have a solid educative scenario. In addition, their specific features were further used to establish a starting point for developing our serious game.

2.1 Façade Façade is a serious game introducing the players in the universe of a married couple. The game is considered an interactive drama: the player is invited by two friends, Trip and Grace, to their apartment for dinner. Here, he/she participate to a couple fight and interact with their virtual friends using natural language as input. The story is not pre-scripted, but it instead is shaped based on the dialogue (see Fig. 1). Users can try to help their friends solve their conflict or, on the contrary, intensify it. Depending on what the player says to the two non-playable characters (NPCs), the story branches out. Thus, the game can have multiple endings: the marriage conflict ends with the spouses reunited or with their separation; the player is asked to leave the apartment, either Trip or Grace leaves the apartment, or their relationship can remain unchanged if the users interaction does not affect them negatively. The input provided by users is mapped into predefined sets of feelings, and the responses are selected accordingly, from a predefined pool of context-appropriate texts.

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Fig. 1 Façade gameplay [15]

2.2 Lifeline Lifeline (see Fig. 2) begins with several monsters attacking a space station where the main character named Rio lives. The purpose of the game is to guide and help Rio escape from the space station. The novelty of the game consists of using vocal commands to control the main character, instead of a standard keyboard. However, Lifeline does not use NLP to evaluate the commands, but maps the user’s instructions to a list of approximately 5000 words and 100.000 phrases, such as: “walk,” “run,” “stop,” “go back,” “shoot,” or questions—e.g., “what should we do?”, “where am I?”.

Fig. 2 Lifeline gameplay [16]

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2.3 Crystal Island Crystal Island (see Fig. 3) is a narrative-centered learning environment, developed specifically for eighth-grade microbiology students of the Center for Educational Informatics in North Carolina State University [17]. The purpose of the game is to identify the source of an infectious disease that spreads through a research center. The main character can gather and manipulate objects, take notes, view posters, read books, use laboratory equipment to test different items for pathogenic compounds, and keep a diagnosis worksheet of the gathered knowledge. At the end of the game, players must diagnose the source of the disease and synthesize a cure based on the knowledge acquired during gameplay. This serious game does not use NLP to interact with the characters, but it is of interest for the current work from design and object-interaction perspectives.

3 Escape from Dungeon—A Serious Game Using NLP The first two games presented in the previous section, Façade and Lifeline, are designed to entertain users by controlling the protagonist using vocal commands in order to solve puzzles and win the game. One issue is that the commands are predefined, hardcoded, and contextualized for the current situation. The last game, Crystal Island, is a serious game that teaches users microbiology in an interactive

Fig. 3 Crystal Island gameplay [17]

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and entertaining way. Yet, the game is not flexible regarding the content of the story and it is limited to learning only one discipline, namely microbiology. Escape from Dungeon is designed to overcome the above drawbacks. The game supports interactions in natural language and tests users through a predefined general knowledge questionnaire. The main advantage from other existing serious games is the possibility to change the content of the preloaded questions to match the targeted educative content.

3.1 Gameplay and Story Escape from Dungeon is a first-person game. The graphical interface is viewed in a first-person view from the player’s perspective; therefore, the environment is more realistic, users can better relate to the surroundings and feel as if they are part of the game. The purpose of the game is to escape from a dungeon by solving puzzles and answering to general knowledge questions. The solutions to puzzles are found by interacting with objects in each room, using only vocal commands. The background story is that you are only capable to observe the environment and ask a “butler” who is with you to perform actions on your behalf. Currently, players can explore two rooms. The first room contains a puzzle that must be solved to progress to the second room. Instructions on movement and interactions using voice commands are given at the start of the game using a computer-generated voice. Players are encouraged to explore and test different commands such as: “go,” “stop,” “go to the table,” “pick up the key,” etc. Support for Google Virtual Reality was integrated into the game to enhance the visual experience. When deployed on Android, any virtual reality headset can be used as an extension to create a virtual world around the player, as seen in Fig. 4.

3.2 Integrated Technologies Escape from Dungeon is based on Unity Game Engine [18] and integrates various technologies: Wit.ai [19] for extracting actions and intents from the text commands received from players, IBM Watson [20] for human voice recognition and Google VR [21]—for virtual reality support. Unity Game Engine is a 2D and 3D system that allows developers to build game scenes, import and build assets and animations, interact with objects, etc. The games developed using Unity Game Engine can be deployed across desktop, mobile, console, or VR devices. This tool was chosen for development due to its ease of use. Wit.ai. is a tool that uses NLP techniques to transform text input into structured data. The user interface available to developers contains multiple menu items, but the most important one is the “understanding” area, where developers use training

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Fig. 4 Escape from Dungeon—virtual reality screenshot

examples to identify locations, emotions, or custom defined entities and intents. Intents are used to understand the meaning of a sentence, while entities are variables that define the details of a user task. Entities and intents define the application’s behavior and shape how it interacts with users. The NLP model must cover all possible intentions and understand and predict the most likely intents and corresponding entities received as input. The recommendation from Wit.ai documentation is to use built-in entities when possible, as these are trained across the whole Wit.ai dataset. An example of training using the Web interface is shown in Fig. 5. Given the phrases: “I would like to be transported to the door. Thank you.”, the engine considers that the input contains an intent for the action of “going” (from the verb “transported”). It also found a positive sentiment (“thank you”) and a user-defined entity

Fig. 5 Wit.ai Web training interface [19]

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Fig. 6 Wit.ai user-defined entities and intents [19]

“door.” Wit.ai is confident in the results, hence the high probabilities of 99.8%, 86.6%, and 95.8%, respectively. The intents and entities predefined and user-defined in the application are displayed in Fig. 6. Besides text processing, Wit.ai also offers speech understanding tools: audio clips encoded in wav, mpeg3, ulaw, or raw formats. The files can be sent to Wit.ai, converted to text, and then processed by the pre-trained model. However, a major drawback of using Wit.ai for speech-to-text is the long period of time between the moment of speech and the moment the actions are executed. Saving the audio clip to a file, importing it back into Unity, transferring it to Wit.ai, and deciphering the text out of the audio file takes several seconds, delay which is neither practical, nor acceptable, making the game flow slow-paced. The solution for this issue was to use a streaming speech recognition tool. IBM Watson: speech-to-text and text-to-speech. This framework can recognize words while they are spoken and corrects misunderstood words, depending on context; after a short period of pause in speech, it automatically forms the sentence and returns it as a final JSON object, containing every word and its correctness probability. Watson speech-to-text also includes a Unity Game Engine plugin, making the integration process easier. Google VR is a virtual reality platform developed by Google. In a VR world, players can interact with objects in a three-dimensional manner, creating the perception that they are part of the scene. Currently, three types of VR simulations are available: non-, semi-, or fully immersive. Fully immersive simulations are commonly used for entertainment purposes and were selected for our game. In this category fall games requiring head-mounted displays, gloves, headphones, etc. Google produces a head-mounted display called Google Cardboard [22], which is affordable and easy to assemble by end-users.

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Fig. 7 Intents for picking up a key and a lamp

3.3 Game Flow and User Interface The commands spoken by users are passed through IBM Watson to Wit.ai system. When responses are received from Wit.ai, all intents are searched through the implemented action methods and coupled with entities parameters. For example, a command like “Move to the door and open the door” will find “door” as a parameter entity, “move” and “open” as intents. For every intent, a corresponding method is applied for each parameter. If an intent cannot be identified, the player’s natural language input is ignored, and he/she is asked to repeat the desired command. Unity Game Engine supports tag definition and allows attaching tags to any virtual object rendered in the game. The link between parameter entities and game objects is made through tagging: each parameter is searched by its tag in the active scene. Similar to the case of intents, only the parameter with a corresponding tag is used, while the rest are ignored. For example, the player wants to pick up the key and the lamp in Fig. 7. The entity is represented by the word “key,” while the object with the same tag name is searched in the room. If the object exists and is in the visual range of the character, within a reachable radius, the object is placed into the player’s inventory. In the current version, only one item is allowed in the inventory—when reaching for another item, the object currently stored in the inventory is dropped on the floor and the player is notified accordingly. While the first room was oriented toward entertaining, familiarizing the player with possible actions and testing creativity, the second room requires correct answers to general knowledge questions (see Fig. 8). The player is asked to choose from a range of quiz domains, such as: paintings, cars, actors, cartoons, etc. Upon choosing a category, four images will appear one after the other in the picture frame. The user must correctly guess what the image represents to escape the dungeon. Four choices are displayed next to the image and users must pick one of them and say the chosen number out loud. If the answer is correct, the next image will appear.

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Fig. 8 Second challenge room

In case the player makes a wrong choice, a message is shown, and the user is offered the possibility to answer again. If the user has no knowledge of the content presented in the picture frame, he/she can guess all the possible answers until he/she learns the correct answer. This is a general example of learning while playing and can be developed furthermore with other quiz examples, replacing the pictures with popular music, lines from classic movies, mazes of increasing complexities with potential traps, or specific information taught during class lectures.

4 Results A survey was conducted with ten users, ix males and four females, three with ages lower than 18 years old, five aged 18–25, while the rest were above 25 years old. The users were asked to respond to a 15-question survey with ratings on a 5-point Likert Scale (1–completely disagree; 5–completely agree) and three free input questions, covering their perspective on our serious game. Out of the first 15 questions, questions one to three ask for a general feedback: the impressions of the game, if the users understood the game’s purpose, and if they fulfilled it. The next three questions refer to the graphics, VR experience, and the user interface. Questions seven to ten focus on NLP, action parsing, and understanding, while the last questions concentrate on game content and future development. The three free input questions allowed users to express themselves about the game. In terms of reliability statistics, intraclass correlation coefficients [23] and Cronbach’s alpha [24] were computed based on the first 15 questions of the survey. The corresponding values, 0.437 for ICC and 0.625 for Cronbach’s alpha, denote a low

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level of agreement between the users, mostly regarding the accessibility of the game and game mechanics. The feedback gathered from the free input questions was positive. The users emphasized the fun and innovative aspects of the game, required more scenes to play and increasing the difficulty level of the scenes. 50% of the users faced problems while interacting with the character, as the voice recognition software did not respond as expected. Users complained that their voice commands were not properly understood, or the character did not follow all commands.

5 Conclusion and Future Development Compared to the entire gaming industry, the number of serious games is very small and the major drawbacks are represented by the limited user interface and interaction—lack of good animations or textures; lack of flexibility by focusing only on one educational field; the usage of predefined range of options, without giving users the opportunity to freely interact with the game. The current paper introduces our serious game—Escape from Dungeon—that uses natural language processing techniques including speech recognition and speech generation, as well as virtual reality interaction using Unity. The game environment is represented from a firstperson view, allowing users to immerse in the game. The main character is controlled through vocal commands, from which user intents and entities are extracted. These and later on mapped on the available scene elements, coupled with possible player actions. The game was tested by ten users throughout a pilot test and it was considered fun and innovative. From the user feedback, the game scenes require minor improvements: more objects should be available in the rooms and serve as escape tools, the escape scenario should be more complex and should require the usage of multiple objects. Another user idea is adding a mechanism for combining two or more raw materials to create different objects. New actions could be introduced for a more realistic look, namely: “throw”—could be an action used to throw the object contained into inventory in front of the character or to a designated target; “break”—where the user asks to destroy an object in the scene using an item from his inventory or with his bare hands; “craft”—where the main character uses items in his inventory to create improved objects or repair already crafted ones. The game can be enhanced by adding multiplayer support that encourages collaboration through the implementation of a virtual classroom, where teachers are offered the possibility to create personalized questions and quizzes, set a time limit for exiting the dungeon or disabling the use of some objects. This feature could also include template facilities to ease the online sharing of the content between teachers. Acknowledgements This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS—UEFISCDI, project number PN-III 72PCCDI/2018, ROBIN—“Robot, ii s, i Societatea: Sisteme Cognitive pentru Robot, i Personali s, i Vehicule Autonome”

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and by the Operational Program Human Capital of the Ministry of European Funds through the Financial Agreement 51675/09.07.2019, SMIS code 125125.

References 1. Susi, T., Johannesson, M., Backlund, P.: Serious Games—An Overview. Technical report, HS-IKI-TR-07-001, Sweden (2006) 2. Eck, R.Van: Digital game-based learning: it’ s not just the digital natives who are restless. Educause Rev. 41, 1–16 (2006). https://doi.org/10.1145/950566.950596 3. Westera, W., van der Vegt, W., Bahreini, K., Dascalu, M., van Lankveld, G.: Software components for serious game development. In: Connolly, T., Boyle, L. (eds.) 10th European Conference on Games Based Learning, pp. 765–772. Reading UK, Paisley, Scotland (2016) 4. Kampa, A., Haake, S., Burelli, P.: Storytelling in serious games. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 521–539. Springer (2016). https://doi.org/10.1007/978-3-319-461526_19 5. Merdivan, E., Singh, D., Hanke, S., Holzinger, A.: Dialogue systems for intelligent human computer interactions. In: Electronic Notes in Theoretical Computer Science, pp. 57–71. Elsevier B.V. (2019). https://doi.org/10.1016/j.entcs.2019.04.010 6. Hennigan, B.: Making the case for NLP in dialogue systems for serious games. In: 8th international Conference on Natural Language Processing (JapTAL), 1st Workshop on Games and NLP (2012) 7. Hoy, M.B.: Alexa, Siri, Cortana, and more: an introduction to voice assistants. Med. Reference Serv. Q. 37, 81–88 (2018) 8. Tur, G., De Mori, R.: Spoken language understanding: Systems for extracting semantic information from speech. John Wiley & Sons (2011) 9. Zhang, C., Fan, W., Du, N., Yu, P.S.: Mining user intentions from medical queries: a neural network based heterogeneous jointly modeling approach. In: Proceedings of the 25th International Conference on World Wide Web, pp. 1373–1384 (2016) 10. Xu, P., Sarikaya, R.: Convolutional neural network based triangular crf for joint intent detection and slot filling. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 78–83. IEEE (2013) 11. Lampert, C.H., Nickisch, H., Harmeling, S.: Attribute-based classification for zero-shot visual object categorization. IEEE Trans. Pattern Anal. Mach. Intell. 36, 453–465 (2013) 12. Bucher, M., Herbin, S., Jurie, F.: Improving semantic embedding consistency by metric learning for zero-shot classiffication. In: European Conference on Computer Vision, pp. 730–746. Springer (2016) 13. Sonawane, K.: Serious Games Market Outlook: 2023, https://www.alliedmarketresearch.com/ serious-games-market. Last accessed 2 Oct 2020 14. Wijman, T.: The Global Games Market Will Generate $152.1 Billion in 2019 as the U.S. Overtakes China as the Biggest Market. https://newzoo.com/insights/articles/the-global-gamesmarket-will-generate-152-1-billion-in-2019-as-the-u-s-overtakes-china-as-the-biggest-mar ket/. Last accessed 20 Oct 2020 15. Mateas, M., Stern, A.: Façade: an experiment in building a fully-realized interactive drama. In: Game Developers Conference, pp. 4–8 (2003) 16. Konami: LifeLine. https://www.playstation.com/en-us/games/lifeline-ps2/. Last accessed 10 Feb 2020 17. Rowe, J., Mott, B., McQuiggan, S., Robison, J., Lee, S., Lester, J.: Crystal island: a narrativecentered learning environment for eighth grade microbiology. In: Education, pp. 11–20 (2009)

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18. Brodkin, J.: How Unity3D Became a Game-Development Beast. http://insights.dice.com/2013/ 06/03/how-unity3d-become-a-game-development-beast/ 19. Wit.ai Documentation. https://wit.ai/docs. Last accessed 5 Feb 2020 20. High, R.: The era of cognitive systems: an inside look at IBM Watson and how it works. Int. Bus. Mach. Corporation 1, 1–14 (2012) 21. LLC, G.: Google VR. https://arvr.google.com/vr. last accessed 2 May 2020 22. Yoo, S., Parker, C.: Controller-less interaction methods for Google cardboard. In: Proceedings of the 3rd ACM Symposium on Spatial User Interaction, p. 127 (2015) 23. Koch, G.G.: Intraclass correlation coefficient. In: Kotz, S., Johnson, N.L. (eds.) Encyclopedia of Statistical Sciences, pp. 213–217. Wiley, New York, NY (1982) 24. Cronbach, L.J.: Coefficient alpha and the internal structure of tests. Psychometrika 16, 297–334 (1951)

Effect Induced by the Covid-19 Pandemic on Students’ Perception About Technologies and Distance Learning Carlo Giovannella

Abstract This paper represents the first investigation conducted in Italy at university level to detect the effects induced on students by the swap of the educational processes from physical to fully virtual, caused by the Coronavirus epidemic. The study involved 101 students attending a bachelor course in Educational Science. The results show that, although students seem to miss physical settings and faceto-face activities, the sudden switch from physical to fully virtual setting has been positively absorbed. The overall emerging scenario indicates that a large part of the present generation of university students is ready for novel educational processes, largely grounded on blended learning activities. The results of this study, beside representing an historical documentation, question the nowadays organization of the physical learning ecosystems, that date to few centuries ago and suggest to re-think both their organization and functionalities. Keywords Distance education · Distance learning · Perception about technologies · Smart learning ecosystems · Covid-19 pandemic

1 Introduction Due to the rapid diffusion of the Covid-19 pandemic, a large part of the world is experiencing a lockdown that affected also all educational ecosystems [1]. The closure of the educational institutions affected more than 70% of the world’s student population [2] and in Italy is has been adopted since the beginning of March as a nonpharmaceutical intervention to contain the spread of the pandemic. Consequently, all didactic activities, suddenly, had to be transferred within virtual settings. The swap from physical to virtual has been quite fast: usually few days at university level, where C. Giovannella (B) University of Rome Tor Vergata—Dip. SPSF, Rome, Italy e-mail: [email protected] ASLERD, Rome, Italy © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_9

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the decisions have been largely centralized; from one to few weeks in the schools due to several concurrent criticalities (lack of technical competences and support, lack of already available local or cloud webservices, lack of a vision and/or of an expert governance of “digital” aspects, etc.). One month after the lockdown, however, also the schools start to be almost fully operational, while at university we are already in the position to perform preliminary studies on the effects induced by the “swap to digital.” This situation can be considered, in fact, a unique “experimental setting” because without the pandemic it would not be ever possible to force institutions, professors and students to swap, in a so short time, from physical to virtual. We decided, thus, to investigate: (a) to which extend a shock like the abrupt swap of the educational processes from physical to fully virtual has been metabolized by students and (b) how, after one month, such shock has impacted on their opinions on distance learning. We decided, thus, to prepare a questionnaire and submit it to a consistent sample of students attending the bachelor course in Educational Science of the University of Rome Tor Vergata. This choice has been guided by the fact that such students are expected to work as educators in either schools, or social challenging environments or private companies. They have been deemed more motivated than other students to reflect on this new and unexpected situation and, thus, capable to fill a questionnaire with a sufficient level of commitment. Due to the uniqueness of the “experimental setting,” we have no previous works or guidelines to refer to: the last disruptive pandemic was the Spanish flu that outbroke about one century ago and, at that time, Internet was far to be even imagined. In the literature, we were able to found only few papers devoted to the effect of pandemic outbreak on schools written during the Internet age—e.g., [3]—all dated at the time of the SARS (2003) and of the swine flu (H1N1-2009). Most of them deal with the closure of the schools as a “non-pharmacological” measure for the containment of the epidemic and do not consider learning processes and/or the effects induced by the use of technologies [4, 5]. Only in a one of them [6, 7], the author reports on “teachers’ perspectives about the role played by digital technology to meet the challenge presented by the closure of 1302 schools in Hong Kong.” The paper is based on eight interviews to teachers and narrates about the difficulties to adapt the learning strategies in an era (2003) where most of the potentialities of Internet were not yet fully exploited. At that time, still very relevant was the use of phone and post as communication channels, since many teachers did not feel trained enough to use Internet. At that time the use of this latter was dominated by email exchanges and forum just started to be perceived as a tool capable to support collaborative learning. With regard to the present pandemic, we have found only one recent paper dedicated to the effects of Covid-19 on education but it focus mainly on emergency policies, their implementation and the associated risks [8]. After the completion of this paper, an additional couple of preprints started to circulate but their focus is on guidelines to manage at best the process to swap to distance learning and, thus, not relevant to the present work. In absence of any helpful guideline, we decided to focus the questionnaire on the following aspects: (a) the operational conditions; (b) the feeling about activities

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and settings; (c) the change of perception about technologies; and d) the expectations for the future (e.g., about learning ecosystems and the individual professional placement). In the following, we will discuss the outcomes of the survey for each of such aspects, together with the implications that they may have for the future of the learning ecosystems and their smartness [9].

2 Experimental Setting The questionnaire is composed by 40 questions and includes qualitative (open answer), quantitative (linear scale plus open comment) and multiple-choice questions. The questionnaire has been realized and made available, after one month from the lockdown, in an electronic form within the learning environment LIFE, because it was already in use to support the course on didactic technologies and all students were already registered in. The student-set that tooks part in the survey was composed by 101 elements attending the bachelor in Education Science of the University of Rome Tor Vergata. Almost all the students were women (98%) attending either first (15%), second (40%) and third year (46%) of the bachelor course; 40% were younger than 22 years old, 50% between 22 and 26 years old and the remaining 10% older the 26 years. The questionnaire has been filled anonymously.

3 Feelings About Operational Conditions The first interesting effect of the lockdown is that laptops replaced smartphones as preferred device to connect and participate in online learning activities. As shown by Fig. 1 before the lockdown students, being free to get out of their home and most of the

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time on move, preferred to connect mainly or exclusively by smartphone, 90%. After the lockdown, the percentage of students that continue to use also the smartphone reduced to 45% while that using also, or predominantly, a laptop increased from 53% to about 90%. Coherently, we observed also a decrease in the use of tablets and an increase in that of desktop computers, although the use of these latter can be considered nowadays overall marginal. A large part of students, 65%, resulted to be engaged in online learning activities between 2 and 4 h, and slightly more than 70% think that the swap to distance education did not increase their working load. Indeed, some of them feel that they are sparing time: that usually spent in commuting and/or in waiting for lectures (due to a not optimized timetable of the face-to-face (f2f) activities). Most of the students did not feel to have undergone particular changes, either because some courses were already delivered in blended configuration or because a certain amount of teachers tend to reproduce the classroom dynamics, i.e., ex-cathedra lectures by mean of videoconference tools.

4 Feelings About Activities and Settings The observations reported in the previous section already allow us to answer the first of our questions. Infact the lock-down and the closure of the campus did not shocked the students that, apparently, switched smoothly from a physical to a fully virtual educational process. This is also due to the prompt answer of the learning ecosystem that in few days swapped between the two modalities (it is worth noting that the average swapping time for the Italian campuses has been one week). In our case, both the campus promptness and the quality of the technological setting were judged quite good (see Fig. 2), with a mean value of respectively 7.39 ± 0.14 and 7.22 ± 0.13 over 10. Also the technological and didactic adequacy of the teachers have been judged positively—respectively with a mean values of 7.22 ± 0.16 and 7.43 ± 0.15. The initial difficulties of some teachers to adapt themselves to the new technological setting have been noticed by the students and clearly affect the distribution shown in Fig. 3 (small peak at 5). On the other hand, teachers have been deemed capable to adapt promptly their didactics to the new situation although, in some cases, by simply reproducing the traditional classroom dynamics. Students appear not to have experienced any technological barrier: about 90% of them declared that the digital skills they already owned were enough to swap to the new setting. Although all—institution, teachers and students—reacted positively to the shock, most of the students start now to miss the physical setting, 7.28 ± 0.21, and declare to prefer face-to-face (f2f) setting with respect to the virtual one for all categories of didactic activities considered by our questionnaires, a feeling that increases in going from lessons to practice, to question time and, finally, exams, see Fig. 4.

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UTOV ability to react 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0

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Fig. 3 Distribution of the students’ perception about professors’ technological smartness to swap from physical to fully virtual learning processes (mean value ± standard error: 7.22 ± 0,16) and their corresponding didactic smartness (mean value ± standard error: 7.43 ± 0.15)

The f2f contact with the teacher is still deemed very relevant, despite the possibility offered by videoconference tools to answer questions both publicly and privately. Also the large preference for f2f exams is somewhat surprising if one considers that one of the main concerns arisen by institutions against distance exams is the possibility for students to cheat. By analyzing the comments, one realizes that the main reasons to prefer f2f activities are: (i) the fear about possible malfunctioning of the connection; (ii) a possible reduction in concentration that could be induced by other concurrent stimuli; (iii) the concern about the coldness of the interaction with the teachers, due to the limited possibility to use the non-verbal cues of the human communication; this despite of the recognized comfort offered by connecting from home. As far as the distance exams, unexpected were the concerns about efficacy

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Preferences 100 80 60 40 20 0 Lessons

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Fig. 4 Students’ preferences (%) about educational setting (distance versus f2f) for some categories of didactic activities

and correctness of the evaluation that are deemed to be higher in the case of f2f exams. These latter, moreover, are considered also as an experience capable to foster a personal growth. Possibly it can be ascribed, again, to the incompleteness of the virtual communication that is considered to prevent the establishment of a fully psychological contact and contract between student and professor. This is also the reason that makes students prefer largely f2f Q&A events. One may note, however, that during these latter the visual contact is often loosen because people switch off the camera to preserve “good” connections or preserve their own privacy. These outcomes underline the need of additional efforts to develop further the communication cues offered by the online environments and justify, as well, the growing relevance assumed by the study on human--humanoid interaction. Despite of the above criticalities, about 60% of the students declare that there are no f2f activities to which they cannot renounce to. Moreover when students have been asked which would be their preferred configuration for their future educational activities, only 32% indicate the f2f one; the large majority, 56%, indicate the blended one, while the fully online would be preferred only by 12% of the students. This confirms the positiveness of the present experience that, although have been forced by the pandemic, allowed students to reflect on weaknesses and strengths of online activities that, now, start to be considered as a meaningful integration of f2f ones. Among the strength of the online activities, the students indicate the comfort and the spare of time, the possibility to follow more courses than in f2f configuration, the avoidance of crowded halls and the impression to sit always in the first row, the straightforward availability of contents, the possibility to see again recordings also to counteract possible timetable overlapping, the possibility to interact in parallel with peers without disturbing the lesson (despite the possible payback of a loss of concentration).

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5 The Change of Perception About Technologies and e-Maturity As noted in the previous sections, a large part of the teachers were not fully prepared to redesign and adapt their didactic to the new situation. They are possibly lacking of an adequate pedagogical and didactic expertise to tackle with distance learning and, thus, as first answer to the emergency, tend to remain in their comfort zone and reproduce the standard transmissive f2f classroom dynamics (in fact 6.60 ± 0.19 over 10 is the mean value associated by students to the use of classical didactic dynamics during the delivery of online educational processes). Going into details, students ranked the activities carried on by teachers as follow: content sharing 83%, synchronous and asynchronous communication 50%, content production 47%, delivery of recorded lessons 42%, collaborative and team working activities 40%, delivery of real time lessons 37% and so on. Last in the ranking is the personalization of didactics, 4%. Not strange, thus, that students have only moderately changed their overall idea on the educational experience, in average 5.89 ± 0.23. Nevertheless, due to the parallel exposition to alternative approaches and strategies, the detected increase of interest for the didactic technologies is quite high, mean value 6.62 ± 0.18, as well as the perceived increase of their digital skills 6.80 ± 0.16, see Fig. 5. Possibly, this latter tends to influence the former. The overall result is that students consider the use of didactic technologies very useful to increase the level of their digital-self, 72%, while less relevant is considered their contribution to the community building 40%, to the process management 39% and to learning efficiency 31%. Even less relevant is considered the support of technologies to the interaction (largely covered by other parallel communication channels), to the learning efficiency and to the quality of the educational experience.

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Fig. 5 Distributions of the students’ increase of interest in technologies (mean value ± standard error: 6.62 ± 0.18) and of the perceived level of their digital competences (mean value ± standard error: 6.80 ± 0.16)

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With regard to the benefits that technologies may offer in improving learning activities, the answers of the students appear somewhat conservative and are certainly influenced by the teachers’ mainstream usage of the technological settings (see above). Coherently with the answers given to other questions, students ranked, in order of relevance, the support to: content sharing 78%, synchronous and asynchronous communication 50%, production of content 46%, collaborative and team working activities 44%. Less relevant is considered the support that technologies may provide to: diversification of the didactic approaches 39%, management of the processes 30%, self-evaluation 28%, planning of the processes 24%, evaluation and monitoring 24% and, at the bottom, support to personalization 13%. These results clearly demonstrate how relevant could be the preparedness of university teachers about digital pedagogy, that nowadays is, unfortunately, completely overlooked. Despite of that, students declared that applications and devices that up to the lockdown have been used only for amusement and socialization appear now very useful also in didactics, and that they start to glimpse new perspectives. New applications/environments have been “discovered” and their functionalities appreciated. As a consequence, increasing skills in using the devices have been developed and laptops started to be used more frequently than before (see Fig. 1). Overall students got aware of new technological and individual potentialities. These are effects that have been reported also in [6, 7] from the school teachers’ perspective. Getting more in details into the perceived increase of digital skills, the students indicate an increase in: the abilities to take part in virtual classes 58%, digital communication 53%, using digital tools to download, organize and share digital contents 45%, using collaborative working environment 44%, critical analysis and filtering of digital resources 39%, production of digital content 38%, video making 31% and photo elaboration 25%, knowledge representation 26%, managing personal spaces

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Fig. 6 Distributions of the students’ perception on Campus e-maturity, detected during usual time, mid February (mean value ± standard error: 6.83 ± 0.11), and after one month of lockdown, first week of April (mean value ± standard error: 7.09 ± 0.15)

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on the Web 22%, data treatment 19%, process planning 18%, evaluation 15%, use of online tools for the personal productivity 15% … and so on. A side, but not irrelevant, effect caused by the smooth swap of the learning process from physical to fully online is the observation of a meaningful increase of the perceived e-maturity of the campus: the mean value of the distribution, in fact, has jumped from 6.83 ± 0.11 to 7.09 ± 0.15 in less than two months. The e-maturity is actually a complex construct [10] that is determined by several factors—infrastructures, devices, competences of all relevant actors, services and processes, strategic vision—and can be also connected to the campus smartness [9]. Here, it has been evaluated only as a whole and compared with an equivalent overall evaluation that was provided by the students at the beginning of the semester (mid February, Giovannella unpublished).

6 Expectations for the Future Learning Ecosystems and Professional Placement The landscape that emerges from the previous sections demonstrates, all in all, a positive acceptance and an equally positive attitude toward didactic technologies and distance learning. This is confirmed by the positive opinion they have about the impact generated by the didactic technologies on learning processes (mean value: 7.00 ± 0.16). Such positive perception together with the self-perceived increase of digital skills has induced also a modification of students’ perspective about their working future. Before the forced swap to fully online learning activities, the students of the bachelor course in Educational Sciences indicated as target for their future job nurseries and kindergartens or critical social environments like family houses; only few units considered the possibility to work in distance learning. Now, after one month of forced online experience, the situation has changed quite a lot and working in distance learning is not consider any longer a taboo. Figure 7, in fact, shows there are at least 6% of the students that consider it as a very desirable opportunity (values of 9 and 10 on a scale of 10) while about 55% of the students (peaked between 6 and 8) consider it as a viable one. The amount of students that are not considering at all such opportunity is now very low (about 11%). The increased interest for the working opportunities offered by the distance learning is possibly due also to the strong perception of sustainability of the online didactic processes developed during this forced experience: average value 6.92 ± 0.15. As final provocation, we asked to the students if, and to which extend, universities and schools, in the future, could be replaced by their virtual counterparts. Figure 8 shows that while the replacement of universities with virtual campuses is considered somewhat realistic by students, and by some of them even desirable (actually this is already a concrete possibility since few decades), as far as schools

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Fig. 8 Distribution of the desirability of a future full swap from physical to fully virtual of university (mean value ± standard error: 5.70 ± 0.23) and schools (mean value ± standard error: 3.96 ± 0.22)

the opinions are highly controversial and a large part of the students thinks that it is not desirable at all. This latter is a very understandable and sharable position since children and, even more, pupils need physical environments and social relations to develop an equilibrated personality. The point, thus, becomes: to which extend schools could go virtual and how progressively with the age of the students? A question that would imply also a detailed investigation on what activities could be transferred to virtual environments and/or supported by didactic technologies. It would be certainly very interesting and useful to explore such topic in the present lockdown conditions because it offers a unique real setting where one could observe criticalities and identify potentialities. However, such investigation goes beyond the limits and scope of this paper and it is left for future studies.

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Here after having answered to our initial questions and verified that the swap from physical to fully virtual educational processes has not shocked the students but, rather, induced a higher appreciation of didactic technologies and distance learning, we can only speculate on possible future scenarios. Probably universities and schools—as they have been designed accordingly to the Enlightenment principles and expectations of the industrial revolution, and handed down until today—need to be rethought. Despite some attempts to redesign the “physical containers” (building and halls) and transform them in “functional” spaces [11–13], a progressive transition toward a rationalized blended configuration of the activities has not yet been taken in serious consideration, neither the integration between institutional, home and other external spaces to compose an enlarged learning ecosystem. Efforts have been payed mainly to integrate digital devices into physical spaces and to dematerialize the administration. Considering another perspective, we may ask how much all present institutional physical spaces are really needed and, overall, in they need to be used as they are currently used. Probably, we can reduce their number and/or transform most of them in large co-laboratory, coworking and “co-gimn” spaces to be used only for activities that due to their intrinsic nature and/or social function should/could not go virtual. Of course we are also fully aware that schools, apart from their main educational function, have also to: satisfy the “children-parking” function to allow parents to work; exert a mitigation action on teenagers during their period of critical development; and, overall, act as a knot of what we may be considered the last physical territorial network that is still in place (provided that we still need one). Nevertheless we think that, while keeping in mind all societal needs, we should try to optimize the “phigital” spaces and try to support the progressive students empowerment toward self-regulation and full responsibility about their learning and development path. The increase of the percentage of distance learning activities, in fact, should go in parallel with an increasing level of student self-regulation and self-empowerment, that is with the development of skills and competences, rather that simply with the age. Unavoidably, this would imply also the transformation of learning processes and didactics. This latter should pay more attention to the transformation of knowledge and abilities in competences, rather than to the transmission of knowledges and procedures. Any attempt to go progressively virtual, however, will not be possible if we do not recognize that the access to the web should be considered a human and a student right [14] and that SDG4 [15] can be achieved only under such preliminary condition.

References 1. Wikipedia: https://en.wikipedia.org/wiki/Impact_of_the_2019%E2%80%9320_coronav irus_pandemic_on_education. Accessed 9 Apr 2020 2. UNESCO. https://en.unesco.org/themes/education-emergencies/coronavirus-school-closures (2020). Accessed 9 Apr 2020 3. Carlo, J.T.: https://www.texmed.org/template.aspx?id=7808 (2009). Accessed 9 Apr 2020

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4. Klaiman, T., Kraemer, J.D., Stoto, M.A.: Variability in school closure decisions in response to 2009 H1N1: a qualitative systems improvement analysis. BMC Public Health. 11, 1–10 (2011) 5. Cauchemez, S., Ferguson, N.M., Wachtel, C., Tegnell, A., Saour, G., Duncan, B., Nicoll, A.: Closure of schools during an influenza pandemic. Lancet Infect. Dis. 9(8), 473–481 (2009). https://doi.org/10.1016/S1473-3099(09)70176-8 6. Fox, R.: SARS epidemic: teachers’ experiences using ICTs. Education, pp. 319–327 (2003) 7. Fox, R.: ICT use during SARS: teachers’ experiences. J. Technol. Teach. Educ. 15(2), 191–2005 (2007) 8. Zhang, W., Wang, Y., Yang, L.: Suspending classes without stopping learning: china’ s education emergency management policy in the COVID-19 outbreak. J. Risk Financ. Manag. 13, 1–6 (2020) 9. Giovannella, C.: “Smartness” as complex emergent property of a process. The case of learning eco-systems. In: ICWOAL 2014, pp. 1–5.IEEE publisher (2014) 10. Sergis, S., Sampson, D.G.: From teachers’ to schools’ ICT competence profiles. In: Theorizing Why in Digital Learning: Opening Frontiers for Inquiry and Innovation with Technology, pp. 307–327. Springer (2014) 11. Design spaces for effective learning—a guide to 21st century learning space design. http:// www.jisc.ac.uk/whatwedo/programmes/elearninginnovation/learningspaces.aspx. Accessed 9 Apr 2020 12. Bosch, R.: https://rosanbosch.com/en/project/vittra-school-telefonplan. Accessed 9 Apr 2020 13. Indire. http://www.indire.it/quandolospazioinsegna/scuole/vittra/. Accessed 9 Apr 2020 14. U.N. resolution A/HRC/32/L.20. https://www.article19.org/data/files/Internet_Statement_Ado pted.pdf. Accessed 9 Apr 2020 15. United Nations. https://sustainabledevelopment.un.org/sdg4. Accessed 9 Apr 2020/09

Visible Teacher Thinking and Group Learning Maria Guida and Letizia Cinganotto

Abstract This study concerns MLTV, a research project conducted by INDIRE in partnership with Project Zero, Harvard Graduate School of Education, and with three upper secondary schools in Italy, within the Educational Avant-Garde Movement, a network of more than thousand schools aimed at didactic and organizational innovation. The project was meant to experiment, localize, and adapt to the Italian school system the combination of two theoretical frameworks by Project Zero: Making Learning Visible (MLV) and Visible Thinking (VT) whose main features are Thinking Routines, group learning, and documentation. For the purpose of the present study, the research question deals with the use of the Thinking Routines and protocols as metacognitive tools that foster group learning for teachers from a life-long learning perspective. Particular observations of teachers in the staff room, involved in protocol-guided discussions with their colleagues, suggest that the use of TRs and protocols can really generate transformative learning, based on group learning and metacognitive reflections, and consequently a change in their professional practices. Keywords Group learning · Collaborative research · Documentation

This paper stems from the collaborative work of the authors. In particular, Maria Guida is the author of paragraphs 1, 2, 3, 6. Letizia Cinganotto is the author of paragraphs 4, 5, 7, 8. Conclusions and references have been written collaboratively. M. Guida (B) · L. Cinganotto INDIRE, Florence, Italy e-mail: [email protected] L. Cinganotto e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_10

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1 Introduction The collaboration between INDIRE, the National Institute for Documentation, Innovation and Educational Research, Italy [1] and a research group from “Project Zero” (PZ) [2], a research center at the Harvard Graduate School of Education, was set in 2017. Main objective of this collaboration was the activation of the MLTV Project (Making Learning and Thinking Visible in upper secondary schools). This project was meant to experiment, localize, and adapt to the Italian school, some innovative proposals from PZ, mostly the combination of two theoretical frameworks: Making Learning Visible (MLV) and Visible Thinking (VT). These frameworks, experimented and validated in USA for years, have been tested for the first time in Italy within this project. MLTV involved from the very beginning three Italian schools with fifteen teachers and three school principals as co-researchers. Schools have been chosen for the active role they already played as part of the “Avanguardie Educative” Movement (Educational Avant-Garde Movement) (AE). Promoted by INDIRE in year 2014 together with 22 founding schools, it is nowadays a network of more than 1000 schools aimed at school system innovation. The MLTV project is still ongoing and, after the initial phase of testing of tools and protocols, which the present paper is focused on, it is now spreading and scaling up tools and first results to a wider community within AE.

2 MLTV Project: The Theoretical Frameworks MLTV, as mentioned above, derives from the combination of two theoretical frameworks MLV and VT both having as their foundation the idea that nowadays, teachers and students need strategies that provide a more varied view of the complexity of learning in order to develop the twenty-first century skills. PZ, in its research work with teachers, has identified five principles that characterize the classes that work by Making Learning Visible and these principles suggest that strong and meaningful learning is generally directed toward a specific purpose, it is social, it is emotional, it is enabling and based on representation. The most important constructs in MLV are group learning and documentation. A learning group, according to PZ, is a collection of persons who are emotionally, intellectually, and aesthetically engaged in solving problems, creating products, and making meaning, in which each person learns autonomously and through the ways of learning of others [3]. Group learning is promoted through five interconnected strategies: to increase students’ ability to learn together, to design engaging tasks, to facilitate conversations that deepen learning, to form groups intentionally, and to plan an effective synergy between individual, small group, and whole class. Documentation is meant as the practice of observing, recording, interpreting, and sharing through different media the processes and products of learning in order to deepen learning [4]. More than a technical strategy for capturing images of students

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at work, documentation creates a new relationship between teachers and students in the teaching–learning process. Sharing tangible and real documentation between students and teachers strengthens the social process of learning, invites to build multiple perspectives, interpretations, theories, and provides teachers and students with an additional sense of direction of their work. Documentation is usually guided by a specific question regarding the learning process. It involves students and teachers in analyzing, interpreting, and assessing individual and group learning collectively and this requires multiple perspectives. It involves the use of multiple languages. Students have many ways of representing and expressing their thinking which can be captured with various media and symbolic systems other than simple speaking. The documentation is not kept private; it becomes public when it is shared with others, for example, other students, teachers, families, and larger communities. It is not only a beautiful final product and it is not only retrospective, it also looks to the future and shapes the design of future learning contexts. Thinking Routines (TRs) represent the pillars of the second framework, VT, which promotes a culture of exploration and the construction of critical thinking [5]. TRs are a sort of organizational structure, didactic protocols composed of a set of questions and a short sequence of phases to guide students’ mental processes and make visible their thinking. Furthermore not only what students understand but also how they are understanding it can be made visible. TRs encourage students to actively engage on a topic, ask questions, take stock of previous knowledge, connect new knowledge to previous ones. TRs are not only tools to be used more and more in the class, they can be considered also as structures, through which students collectively as well as individually initiate, explore, discuss, document, and manage their thinking. They are also patterns of behavior, adopted to help one use the mind to form thoughts, reason, or reflect [6] [7]. “See-Think-Wonder” is the most commonly used TR whose aim is to help students to distinguish between observation and interpretation, allowing the due time for a silent, close look at an image or any other piece of information. It is composed by three steps, guided by prompt questions and gradually leads students to develop their own ideas. The “See” phase starts with the question: “What do you see?”. No interpretations are allowed at this stage, their place is the next “Think” step where the guiding question is “What do you think about what you see?”, while the “Wonder” step, through the question “What do you wonder about?” pushes toward the students’ reflection on a creative level encouraging them to figure out “what’s next.” MLV and VT have in common a number of assumptions and beliefs. However, thinking and learning are considered fundamentally social endeavors which are distributed across individuals, groups, and cultural artifacts and resources. Another common assumption is that thinking and learning can be made visible. Finally in order for classrooms to be cultures of thinking and learning for students, schools must be cultures of thinking and learning for adults.

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3 Methodology The research refers to the collaborative research model [8, 9] whose pillars are the attention to teacher thinking [10, 11] and the analysis of practices for professional development [12]. It pertains also to a participatory approach as the enquiry actively involves different actors from school and research, namely teachers and principals from the three schools identified and the researchers from INDIRE and Project Zero. The research model foresees a process of collaborative knowledge building where all the actors contribute in a peer relationship but with specific roles in all research phases: cosituating the research, cooperating and co-producing the result [9]. This provides for a constant interaction and negotiation between researchers and actors of the school (not only teachers but also school managers, students, and families). In particular, in the first phase, theoretical references were made explicit and project objectives formulated. Therefore, the research questions were identified. The second phase provided for the agreement on the methodology and the data to be collected and allowed teachers to better analyze and deepen the topic under investigation and researchers to collect information useful for the development of the methodology. Then the research group chose two main focuses, identified in the localization of the framework in Italian schools and in the change in classroom culture. These focuses were being investigated by the means of class observations, focus groups with the students, analysis of teachers’ documentation and protocol-guided discussions, interviews with teachers and school directors. The third phase, still ongoing, consists in data analysis and includes the elaboration of research outputs aimed both to schools and academic community, such as guidelines for other schools willing to adopt the framework but also scientific articles and even a research book [13, 14]. For the purpose of the present study, the research question deals with the use of the TRs and protocols, mainly used by the teachers in classes with their students, particularly as metacognitive tools that foster group learning for teachers and principals themselves, from a life-long learning perspective. Observations of teachers in the staff room with their colleagues, involved in protocol-guided discussions, as we will see below, indicated that the use of TRs and protocols really produces a transformative learning for teachers, based on a metacognitive reflection and consequently a change in their professional practices [15]. This leads teachers, for instance, to use fewer words during their lessons: they do not need to talk as much because they have ready prompts for asking for student thinking. As a consequence, it helps them move to a more constructivist approach to teaching and learning. Furthermore, it encourages teachers to clarify their pedagogical intentions and introduces structures that guide teachers’ attention to students’ learning strategies as much as products or results. Very often teachers have implicit, unrecognized assumptions about learning as linear and teaching as transmissive. In order to change their teaching practice, they need to overcome these assumptions and the use of MLTV tools can help them to do so.

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4 Group Learning and Protocols As already mentioned, according to Project Zero, group learning is meant to be “an assemblage in which each person learns autonomously and through the ways of learning of others” (Making Learning Visible Project, PZ, Harvard Graduate School of Education). The definition shows how collaboration and group learning play a crucial role in education, both for young and adult learners, in a life-long learning perspective, considering not only the cognitive dimension, but also the emotional, social, and affective side, which can strongly impact the learning process as a whole. “No matter where, what, or whom one teaches, creating a learning community is essential to promoting learning at home, school, and the workplace. In order to live, learn, and work together effectively, we need to be able to listen to one another, to work together to identify and solve problems, and to acknowledge and respect diverse points of view” [16]. This means that group learning does not stem from the activity of the sum of the people involved in the group, but from their negotiation, co-construction, and sharing of ideas, thoughts, reflections. PZ researchers identified four features in group learning: The members of learning groups include adults as well as children. Documenting children’s learning processes helps to make learning visible and shapes the learning that takes place. Members of learning groups are engaged in the emotional and aesthetic as well as the intellectual dimensions of learning. The focus of learning in learning groups extends beyond the learning of individuals to create a collective body of knowledge. Group learning was an important dimension INDIRE researchers tried to foster within the teachers and principals from the three Italian schools, in order to enhance their discussion and decision-making processes as an integral part of their teaching activities and in order to transform not only their teaching style, but also their attitudes and beliefs toward their teaching profession within their wider school community. In fact, one of the problems in Italian schools is lack of collaboration among teachers, short time for discussing so many issues related to school life in general. The aim was to have Italian teachers experiment Project Zero protocols and Thinking Routines in order to guide them toward the creation of a real community of practice, helping them transform their idea of learning from an individual action into a collective body of knowledge, engaging, and learning from the others’ thinking skills, feelings, emotions. According to Allen [17], “a protocol is simply a way to structure a discussion so that it supports the learning of all participants. Usually the discussions take place orally, but some may be conducted through writing […] The protocols […] share four core features: • a clear purpose that is made explicit for all participants • an established sequence of steps • a focus on supporting a group’s collaborative thinking and learning as well as the thinking and learning of the individuals within the group

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• the cultivation of habits of thinking and learning that are useful in contexts beyond the protocol itself.” Therefore, a protocol can be defined as a guide to co-construct understanding, thinking, and learning within a group with the aim to mutually enrich both personally and professionally.

5 Norms In order to foster reflection on the process of negotiating norms within a group of adults, INDIRE researchers adopted the “Norms Construction” protocol by Project Zero from School Reform Initiatives and experimented it with the Italian teachers. According to the protocol, participants are given time to reflect and consider aspects of learning that are important when choosing to work and learn in a community. They have some time to reflect and write on sticky notes their personal ideas, first individually, then in pairs and in a small group of four colleagues, through a process of progressive negotiation and adoption of ideas and needs identified as crucial within a community of learners. The final step is to summarize and group the most common ideas, as gradually agreed upon by the groups, on other sticky notes to be collected on a poster. The result will be a collective set of norms, negotiated in small groups, which will be further discussed and negotiated within the whole community, in order to be fully adopted by the entire group. During the kick-off meeting of MLTV project in 2017, INDIRE researchers facilitated the implementation of the “Norms Construction” protocol with the Italian teachers and school principals. The starting point was the following prompt: “think of an important learning experience you had in the past and try to recall the elements which were really powerful and effective.” The following rules were the final result of the protocol, at the end of all the negotiation process: To respect all ideas A comfortable environment To have, give, and receive respect while speaking (debate rules) To be opened to other people’s viewpoints, cultures, experiences, feedback To be listened to Freedom to fail To feel free and relaxed and not judged To learn with humility from other group members Clear goals and understand the processes Time to reflect Short but intense learning period with some coffee breaks To share experiences, goals, concepts To share working methods and materials Cooperation and collaboration

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To feel engaged in useful activities To feel accepted with our own peculiarities (speaking speed, habits, language) Share the air An informal context with enthusiasm, support To have FUN. From the comments collected by the teachers and the school principals it was clear that it was the first time for them to be involved in such a collaborative activity for the setting of norms; the shift from the mandatory setting to the participatory one was also perceived as significant: a new way to think and self-regulate in cooperation with the other group members. The traditional top-down model of delivery usually adopted the mandatory way of setting rules and norms; therefore, this activity represented an important discovery not only in terms of educational, but also social, emotional, and affective pathway. Setting norms through the suggested protocol was the pillar of group learning among the Italian teachers and school principals.

6 Teachers as Researchers in Protocol-Guide Discussion At the very beginning of the project, researchers, teachers, and principals discussed their role in the work to be done. The construct of teacher–researcher is based on the principle that class practice is inseparable from educational theories aimed at improving learning in the context of action, which lead the analysis of class interaction and student production. Teaching, research, and professional development are closely linked and they develop thanks to a mutual interaction. Researchers involved teachers in protocol-driven discussions during the monthly onsite visits to schools in the first year of the project. In MLTV, the protocolguided discussion was a powerful tool in order for teachers to foster the habit of reflect on documentation together with colleagues, acting a collaborative sense making and building at school a democratic community of thinkers. J. I., an English teacher, referred that when she got started using TRs and protocols her feeling was of constraints, overload, time limits but now she thinks that it is an added value being challenged to make visible what it is evident to you but may be not evident at all for others. Regular teacher meeting at school is quite uncommon in Italian upper secondary school, where, contrary to primary school, teachers meet almost only to give marks and define students’ evaluation. A. R., school principal said: “We have institutionalized these moments of meeting and therefore what we do with the researchers. We got used, also via skype, to share pieces of teaching life together, trying to refine the documentation process.” However, through meeting regularly for documentation analysis, teachers consequently acquire an investigative attitude, while performing their teaching activity, but also the vision of their professional development as a continuous process of group learning which is grounded in evidence. A. D., a teacher of Italian, suggested: “I can not structure good learning opportunities if I am alone. If I am comparing different perspectives with colleagues

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I can do this in a more informed way.” Paying more attention to students’ thinking processes induces teachers to pay more attention to their own thinking processes and deepens their metacognitive skills and the ability to recognize the intertwining of conceptions and emotions that influence their way of being teachers. I. N., a teacher of economics, in this regard noticed that students’ thinking is often standardized and it is important to provide them with different points of view. According to R. C., a teacher of informatics, “it is important for students to be exposed to puzzling and uncertain situations so that they might develop divergent thinking and critical thinking. Unexpected situations are a good learning opportunity for them.” The most important aspect of teacher discussion is that their findings and insights inform next lessons, not in order to do something better but to do something different, revising not only methods but even goals, transforming practices and relationships. Teachers– researchers were actively involved in every phase of the project and not just considered like sources of data for researchers. Each of them identified a personal research question, related to Making Learning and Thinking Visible, which could be included under the “overarching question” of the project. Then they engaged in a process of inquiry, of intentional investigation, to explore their research questions, gathering data by documenting learning, particularly learning in groups, analyzing the data together through conversation, making connections to other inquiries into Making Learning and Thinking Visible, and planning new steps to continue exploring the research question. Let us consider the case of A. D., to give an example of what kind of documentation teachers share in their protocol-guided discussion. He brought to his colleagues a short video about his lesson where he asked his students to highlight the words they did not know in a given text, working in small groups. He then decided to follow more carefully group n.2 because it came up with a fewer number of unknown words. This group was interesting because even though it apparently looked more skilled, because of the few words the students had highlighted, it ended up in being less efficient in the task (the students only completed a part of the task in the given time). Sometimes things are not as they seem! At a deeper look, A. D. noticed that the way the lesson goes is often challenged by students’ misconceptions and by such strongly consolidated idea/concepts they do not ever challenge. This is the case when students are convinced they know the meaning of a word but it is wrong. Also, through the discussion, he made explicit that sometimes students are afraid of letting the teacher become aware of their mistakes and/or ignorance. A. D.’s insights informed the prosecution of his teaching activity. Very relevant aspects and teacher reflections came up during other conversations like the one just mentioned. A. C., a teacher of informatics, underlined the importance of feedback and modeling process too when he said: “Researchers showed me how to use a tool for giving feedback. I could feel their appreciation. It was a small and banal thing but it is a small step taken in the right direction and it contributes to my learning path.” With reference to modeling A.D. said that teachers first should learn how to learning in groups as they ask the same to their students. O. C., a mathematics and physics teacher, said she realized that the TRs strength is also in the time set, and these types of thinking and connection need time. Timing factor is important. A.C. replied underlining “the

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mix between time setting and the systematic attitude that TRs give you in using time. TRs are useful, they help you make good use of time. TRs help manage the quantity and quality of time.” Protocol-guided discussions nurtured also the transition from a transmissive teaching style to the role of supporting students’ knowledge building and the creation of a culture of thinking in the classroom. M. G., a teacher of English, watching O. C.’s lesson, noticed: “in this approach to the explanation of science I see the parsimony of words: when I was young the overabundance of words made it complicated. Here you stimulate the intuition with a few words and this facilitates the understanding of the concept. Science transmitted by words sins of abstraction.” Lastly, during a meeting set in one of the experimenting schools, a group of eleven teachers asked to be allowed to assist to a protocol-driven discussion as silent spectators. It came out that they are the whole mathematical department. The teachers glimpsed the potential of the framework in answering their questions and needs and at the end of the discussion expressed their willing to take an active part in MLTV. Starting from this episode, expression of a training need but also evidence of the value that the framework can have in teachers’ opinion, a sort of spin off of the project was developed, named MLTV4MATH [18], aimed at investigating the specific advantages of the framework in math lessons.

7 Protocol-Guided Discussions: The “Fishbowl” One of the protocol-guided discussion suggested by Project Zero and proposed to the Italian teachers and school principals working at MLTV project is called “fishbowl,” referring metaphorically to a fish in a bowl, being watched by observers from outside, according to some certain steps and fixed times, which will gradually lead to a deeper and deeper understanding of a piece of documentation proposed by the “fish,” that is a teacher in our case. This protocol is based on Project Zero’s already mentioned definition of documentation which is strictly linked to learning and assessment: according to Project Zero, rather than a focus on products, assessment should focus on documenting a combination of learning processes and products as a way to inform pedagogical decisions. Therefore, documentation is not meant as an exhibition but, on the contrary, as a way to collect memories of a learning process and to discuss it with students or colleagues as critical friends, in order to find out strengths and weakness, in the perspective of continuous improvement. The fishbowl protocol consists in presenting a piece of documentation by a teacher, related to a teaching activity, letting the colleagues the time to silently observe it, then asking questions or making remarks, while the “fish” being silent and listening. In the following step, after collecting all the questions and comments, the “fish” will reply by providing his/her own point of view related to his/her interpretation of the documentation, in light of the insights stemmed from the colleagues’ remarks. Finally, the colleagues will briefly express what they have learnt from this discussion, that will enrich their professional experience.

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E. P., teacher of history and philosophy, was the “fish” in the first fishbowl that was held during the mid-term meeting involving all the teachers and school principals of the project. He presented his piece of documentation in three minutes, according to the protocol: it was a poster with pictures and images from the First World War and the Russian Revolution with reflections written by his upper secondary school students. The main difficulty he encountered with this protocol was just to stay in the rigid scan of the times foreseen for each step: in fact, he found it difficult to be synthetic. His colleagues had the opportunity to observe in silence the proposed material for 5 min and then started asking questions, without getting any answers from the “fish” for the moment. E. P. commented that this phase was particularly helpful for him as it allowed him to become aware of some aspects and angles that he had not considered, neither in the realization of the work, nor in its overall evaluation. In particular, his colleagues with their questions pointed out that the choice of the photographs was related to the military technologies of the First World War, while in the part about the Russian revolution the photographs focused on a total different issue: the leaders of the revolution. Some of his colleagues’ observations impressed him a lot, especially those related to the value of the pictures chosen to synthesize a complex or even simple subject. Finally, some of his colleagues suggested that he focused his attention on the process that led the pupils to choose the image, documenting the discussion. After listening to his colleagues’ questions and remarks, he was finally able to reply, highlighting the observations that had struck him the most. Finally, all the members of the group pointed out an idea that emerged from the discussion that they considered useful for their work in the classroom. This experience was of great value and enriched E. P. a lot both professionally and personally: he rediscovered and deepened the value of stepdriven educational dialogue. The protocol, in fact, implies a method of collaboration and sharing that is based on certain criteria and steps for a constructive dialogue. In this way, the overlapping of voices that sometimes may occur during a discussion, was avoided, preventing any possible resentment or hostility among the group. E. P. also commented he had rediscovered the value of active listening as central to his experience as a teacher and educator. At the beginning of the protocol, he could not respond to his colleagues’ observations, this was not easy and the temptation to speak was strong, but having listened to the whole discussion of his colleagues meant that he was able to reply in a more conscious and effective way, less linked to emotionality and instinct.

8 The Teachers’ Overall Impressions and Reactions At the end of the first year of experimentation, teachers were invited to fill in a questionnaire designed by Project Zero and to express their degree of agreement– disagreement through a 6-point Likert scale (1 = strongly disagree; 6 = strongly

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agree) with some statements regarding participation in the MLTV project as a whole and the use of its tools. The general impressions of the teachers were very positive: they all believe MLTV project can help go beyond the traditional and transmissive model of schooling. The project encouraged the teachers themselves to share and work together (also with colleagues from other schools), enhancing group reflection on the documentation, in order to better understand the students’ learning process and to improve the teaching itself. Here are some of the teachers’ answers to the question: “Why MLTV?” “Because it also helps to bring out emotional and social learning in the group and highlights new personal skills and competences related to learning” (A.D.). “Because a good teacher needs significant inputs for teaching that responds to constantly evolving educational needs. Our students do not need top-down lessons, but activities in which they feel like thinking protagonists that help them develop competences and become aware of their learning path” (D.S.). “Because it is a great possibility, a collection of powerful tools that can paradoxically allow us to get out of the routine of teaching and to open up and dare more, using different procedures, techniques, methods but very close to our students and their way of learning and thinking. They also stimulate positivity, discovery, involvement and help build effective and engaging relationships among students, between students and teachers, between teachers” (O.C.). The aforementioned quotations show how group learning has become an important dimension in the teachers’ idea of education and learning: they are more aware of the impact it can have in terms of social relationships, cognitive development, affective engagement.

9 Conclusions and Future Perspectives The paper was meant to briefly present some of the outcomes of a project conducted by INDIRE in collaboration with Project Zero, Harvard Graduate School of Education. After briefly describing the frameworks of the project, some of the tools experimented with the Italian teachers and school principals, in particular some of the protocols for constructing norms and guiding discussions were highlighted. The aim was to help teachers and school principals get aware of the importance of group learning as a collective and reflective learning experience, involving socio-emotional, cognitive and affective dimensions, as described and encouraged in Project Zero frameworks. Joining the project turned out to be a very positive experience for teachers and school principals: They found out the importance of group learning as a new adventure, that can improve their teaching style as well as their lifestyle. MLTV project is still ongoing and the future work consists of a deeper analysis of the collected data, in disseminating tools and first results of the project to a larger

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number of schools scattered throughout the Italian territory and to deepen the aspect of the documentation, barely touched in the first year of the project, both in terms of collection and analysis. As a long-term perspective, the project aims at fostering and spreading innovation among the Italian schools, enhancing active, interacting and student-centered approaches as the ones promoted by MLTV project. Acknowledgements Authors wish to thank all the other members of the MLTV/INDIRE (Elisabetta Mughini, Chiara Laici, Elena Mosa and Silvia Panzavolta); colleagues of PZ (Mara Krechevsky, Carin Aquiline, Daniel Wilson, Claudia Rebesani); teachers and head teachers of the three schools: ISIS Europa (Pomigliano D’Arco), IIS Savoia-Benincasa (Ancona), ISIS A. Malignani (Udine).

References 1. INDIRE Homepage. http://www.indire.it. Last accessed 25 May 2020 2. Project Zero Homepage. http://www.pz.harvard.edu/. Last accessed 25 May 2020 3. Krechevsky, M., Mardell, B., Rivard, M., Wilson, D.: Visible learners: promoting reggioinspired approaches in all schools. Wiley, San Francisco (2013) 4. Krechevsky, M., Perkins, D., Blythe, T.: Putting understanding up front. Educ. Leadersh. 51(5), 4–7 (1994) 5. Ritchhart, R., Palmer, P., Church, M., Tishman, S.: Thinking routines: establishing patterns of thinking in the classroom. Paper presented at the annual meeting of the American Educational Research Association, San Francisco (2006). Retrieved from URL: http://www.ronritchhart. com/Papers_files/AERA06ThinkingRoutinesV3.pdf. Last accessed 25 May 2020 6. Ritchhart, R., Perkins, D.: Making thinking visible. Educ. Leadersh. 65, 57–61 (2008) 7. Ritchhart, R., Church, M., Morrison, K.: Making thinking visible. Jossey Bass, San Francisco (2011) 8. Desgagné, S.: Le concept de recherche collaborative: l’idée d’un rapprochement entre chercheurs universitaires et praticiens enseignant. Revue des sciences de l’éducation 23(2), 371–393 (1997) 9. Magnoler, P.: Ricerca e formazione. La professionalizzazione da professionalizzazione degli insegnanti, Pensa Multimedia, Lecce-Brescia (2012) 10. Tochon, F.: Recherche sur la pensée des enseignants: un paradigme à maturité. Revue Française de Pédagogie, n. 133, 129–157 (2000) 11. Damiano, E.: La Nuova Alleanza. La Scuola, Brescia (2006) 12. Altet, M.: Professionnalisation et formation des enseignants par la recherche dans les IUFM: Avancées et questions vives. In: Clanet, J. (dir) Recherche/formation des enseignants: Quelles articulations? pp. 19–32. Presses universitaires de Rennes, Rennes (2009) 13. Panzavolta, S., Mosa, E., Laici, C., Guida, M., Cinganotto, L.: MLTV, rendere il pensiero e l’apprendimento visibili nella scuola secondaria di secondo grado (MLTV, making learning and thinking visible in upper secondary school) Proceedings of EDEN Conference (2018) 14. Panzavolta, S., Mosa, E., Laici., C.: Making learning and thinking visible, an analysis of the use of thinking routines. In: Proceeding of international ICERI conference (2019) 15. Schön, D.A.: Educating the reflective practitioner: toward a new design for teaching and learning in the professions. Jossey-Bass, San Francisco (1987) 16. Collaboration and group learning. Project Zero website. https://pz.harvard.edu/topics/collab oration-group-learning. Last accessed 17 Apr 2020 17. Allen, D., Blythe, T., Dichter, A., Lynch, T.: Protocols in the Classroom, Tools to Help Students Read, Write, Think, and Collaborate, pp 13–14. Teachers College Press (2018)

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18. Guida, M.: Rendere visibili il pensiero degli studenti ed il loro apprendimento in matematica in D’Amore, B., Sbaragli, S. (eds.) La didattica della matematica, strumento concreto in aula. Proceedings of XXXII Convegno Nazionale Incontri con la Matematica. Castel San Pietro, Bologna, 16–18 XI 2018. ISBN: 88-371-2100-6 (2018)

People-Centered Benchmarking of Smart School Ecosystems: A Study with Young Students from Aveiro Region Óscar Mealha , José Nunes , Carlos Ferreira , Fernando Delgado Santos, and João Ferreira

Abstract This paper proposes a smartness benchmarking process capable of comparing the smartness dimensions in different school ecosystems in each stakeholder’s perspective. The process uses a mixed-method approach supported by ASLERD smartness questionnaires used to gather stakeholder opinions with closed and open questions organized into eight different smartness dimensions. All these dimensions inform stakeholder’s motivations and needs considering their relationship with the ecosystem’s territory, its institutions, and the people that share it. Quantitative data from closed questions is used as a valorization indicator of related opinions in open-ended questions. The methodological approach adopted for the benchmarking process flows as a result of the iterative relational analysis of these three modules: (i) descriptive statistics (opinion valorization) calculation; (ii) statistical deeper probing with nonparametric tests; and (iii) qualitative pertinence processing and clustering of issues/problems per smartness dimension. The benchmarking process was tested Ó. Mealha (B) · J. Nunes Department of Communication and Art/DigiMedia Research Centre, University of Aveiro, Aveiro, Portugal e-mail: [email protected] J. Nunes e-mail: [email protected] Ó. Mealha ASLERD, Rome, Italy C. Ferreira Department of Economics, Management, Industrial Engineering and Tourism/IEETA, University of Aveiro, Aveiro, Portugal e-mail: [email protected] F. D. Santos Agrupamento de Escolas José Estêvão de Aveiro—AEJE, Aveiro, Portugal e-mail: [email protected] J. Ferreira Agrupamento de Escolas de Estarreja, Estarreja, Portugal e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_11

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with three cohorts of seventh-ninth grade students from three different schools in the Aveiro region, Portugal. The data was gathered in May/June 2018 at José Estêvão school (n1 = 156), School No. 2 at São Bernardo (n2 = 60) and at Estarreja school (n3 = 81). Results contain evidence of process validation as a multilevel inspection benchmarking solution and reveal that the process can validate the affordance of the questionnaires. Results depict a misinterpretation of one of the questions in two different data processing phases, situation that was validated with the relational analysis of this data. The mixed-method methodological approach produces different results, shown in this paper with different degrees of complexity, from a holistic perspective to a detailed clustered subjective opinion of a cohort’s specific population. Keywords School smartness · Mixed methods · Benchmarking · Co-design · Communication · Educational community

1 Introduction The need to diagnose and understand the issues that underpin problems that different educational community stakeholders have with a specific school ecosystem has led to the research reported in this paper. The relational issues that are relevant for this approach and inform the smartness concept as defined by ASLERD scientific network within its Timisoara Declaration [1, p. 5] “individuals that take part in the local processes achieve a high level of skills and, at the same time, are also strongly motivated and engaged by continuous and adequate challenges, provided that their primary needs are reasonably satisfied" detail human motivational needs (Maslow) and inform on the level of flow [2], in terms of the expected optimal experience for such an ecosystem. Smartness as a “people-centered approach" [3] also contextualizes this study in a postmodern ideology [4] with a special highlight on the individual’s needs, interests, and wishes [5, 6]. This approach, concerned with the individual’s singularities, is present in some of the postmodern philosophers rationale, such as Lipovetsky [7] that argues, for decades, on the potential that technology-mediated instruments have to configure each individual’s presence online and answer their needs. As a contribution to this research landscape, this paper proposes a mixedmethod approach of a smartness benchmarking process for the school ecosystem. The process was applied and tested with three school cohorts and the main results of this test reported in the second part of this paper. The participants are pre-secondary students and this particular population, and perspective of the smartness studies, also clarifies many issues related with family-school-community relations, that augment on other similar international studies and methods [8].

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2 Related Work The methodological approach that informs the benchmarking process is clearly a bottom-up contribution to study and understand school ecosystems, by gathering the opinions of their stakeholders. As mentioned in former work [9], this smartness benchmarking process should represent a contribution to other top-down studies such as INVALSI for performance in numeracy and literacy, in Italy, and RAV as a school self-evaluation form, promoted by education agencies responsible for educational policies and monitoring the educational ecosystems, namely in Europe at a national context such as OTES in Portugal [10, 11]. Although these studies usually include empirical instruments to inquire different educational stakeholders, they lack complexity and detail capable of representing motivational issues, fundamental for human performance optimization and sense of belonging to institution and place. An initial version of the ASLERD smartness questionnaires is reported in [12–14] by Giovannella et al. and later adapted and applied in European university campuses benchmarking studies [15, 16] and also adapted and applied to basic/secondary school clusters in Italy and Portugal [17, 18]. The smartness studies that took place at the different European university campuses used a mixed-method approach supported by a principal component analysis (PCA), for a global understanding of the smartness landscape considering the different smartness dimensions at stake, and also considered qualitative analysis supported by participant’s opinions in the open-ended questions in each dimension.

3 Smartness Benchmarking Process for School Ecosystems The process proposed in this paper uses a mixed-method approach supported by ASLERD smartness questionnaires used to gather stakeholder opinions with closed and open questions organized into eight different smartness dimensions. All these dimensions inform stakeholder’s motivations and needs considering their relationship with the ecosystem’s territory, its institutions, and the people that share it. Qualitative data from closed questions, that use a 10-point ordinal Likert scale, represents a valorization of related opinions in open-ended questions. The value 1 represents— “very bad” and the value 10—“very good.” The descriptive statistics of the valorization information are used in a first instance to diagnose the holistic benchmarking situation. A multidimensional radar diagram is used to integrate this initial representation of the cohorts scores. For this purpose and considering the ordinal Likert scale used in the closed questions, first, third quartile, and median were chosen as the most appropriate “valorization of opinion" indicators for each cohort. Both these quartile integrate better with the median, considering the ordinal nature of the scale, than a complementary representation of standard deviation. The visual overlay of these diagrams depicts a clear holistic view of cohorts’ differences in each one of the questions/inquiry dimension.

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The next benchmarking step goes into deeper statistical probing, with nonparametric statistical functions [19] and multivariate data analysis [20] due to the ordinal nature of the closed questions’ scale, to understand the pertinence of each dimension in the cohort’s perception of smartness. The functions that are proposed in this process, for different analysis purposes, are: (i) Mann-Whitney U test; (ii) Kruskal-Wallis independent sample test; (iii) pairwise comparison of samples; (iv) Spearman correlation; and the (v) hierarchical Ward type dendrograms. The contribution of these nonparametric tests corresponds to more detailed information of how the sample’s population scored their smartness dimensions. This has a direct effect on guidance and detail in the qualitative benchmarking procedure, a fundamental contribution to the final qualitative data analysis and overall benchmarking rigor. The qualitative data obtained from the open-ended questions is clustered and organized into taxonomies of reported issues/problems, aggregated into positive and negative opinions. The relative frequency of these opinions, considering the total number of participants in each cohort, is used as a pertinence indicator of opinion.

4 Smartness Benchmarking of Three Different Schools The validation of this smartness benchmarking process took place in May/June 2018 in three different schools of the central Portuguese coastal region with Aveiro as its regional capital. One cohort, nEST = 81 (Male = 40; Female = 41), was obtained at the Estarreja (EST) school cluster; the other two cohorts, nJE = 156 (M = 61; F = 95) and nSB = 60 (M = 31; F = 29), were obtained, respectively, at the José Estêvão (JE) and São Bernardo (SB) schools, both from Aveiro’s José Estêvão school cluster. For the purpose of this process’ validation, and for the sake of this paper’s size, only the seventh-ninth grade student population was used and only five of the eight dimensions of inquiry were selected for a first empirical test of the smartness benchmarking framework. The study was approved by the Ministry of Education of the Portuguese Republic (n.º 0576100001) that included ethical issues and procedures and was also authorized by the Director of AEJE and Director of Estarreja school clusters. All these participants received parent authorization and signed a consent form. The questionnaires were applied during “Citizenship" classes with a short briefing concerning the relevance of this study. The seventh-ninth grade ASLERD smartness questionnaires are organized into a first sociodemographic data section and eight inquiry dimensions, comprising a total of 34 closed questions (CQ) and 21 comments or open-ended questions (OQ). The five dimensions considered in this paper were chosen because they revealed a diversity of opinions and contain a very different amount of questions in each dimension: (i) food services (CQ =1, OQ = 1) what is your score for the canteen service?; (ii) security (CQ = 2, OQ = 1) to what extent do you feel safe in your school? and outside of school? what are the main security problems within your school?; (iii) people and space (CQ = 11, OQ = 5) indicate how well you feel at your school? (w/cmnts)? to what extent you get along with your colleagues? (w/cmnts)?

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how well is your relationship with the teachers? (w/cmnts)? how well you feel with your foreign colleagues? would you like to know more about the country and the habits of your foreign colleagues? do you discuss with your colleagues? (w/cmnts) how often your colleagues make fun of you when you are not around? indicate the time you play/talk with your colleagues in a recreational way? how much help you have to do your homework? how frequently you meet your colleagues to play games, to do homework and other types of socializing? how much do your classmates value you for your results in school, sports, or other activities? (w/cmnts); (iv) sociability (CQ = 6, OQ = 0) please rate your colleagues’ behavior regarding compliance with rules of mutual respect? to what extent do you think your colleagues create positive relationships? to what extent do you think your colleagues include other colleagues (outside your circle of closest friends) in your activities or conversations? to what extent do you think your colleagues respect differences? to what extent do you think your colleagues have respect for the law? to what extent do you think your colleagues have a sense of responsibility?; and (v) interaction with family (CQ = 1, OQ = 1) to what extent would you like your parents to collaborate with the school? give some examples of activities you would like your family to do at school? The radar in Fig. 1 represents an overlaid perspective of the median (MED) lower (Q1 ) and higher (Q3 ) quartile given by the seventh-ninth grade participants in each one of the 21 closed questions that integrate the smartness dimensions of this study. The radar axis number identifies the closed question and the caption establishes the question—study dimension relation. The color coding used in Fig. 1 is adopted for all the paper with JE data in blue, SB in green, and EST in purple.

5 Findings Results in this section clearly diagnose and confirm the most relevant smartness dimensions in each cohort that are complementary informed with the pertinent subjective clustered opinions of each related issue or problem. This benchmarking process also confirms the consistency of the question organization in each dimension with the hierarchical Ward type dendrograms, and in fact even identified problems in a question formulation/perception and so also corrected and improved the complexity of the inquiry instruments. The following sections detail the most relevant results obtained with the application of the benchmarking process, proposed in this paper, to three cohorts of seventh-ninth grade students of three different schools in the Aveiro region, Portugal.

5.1 Food Services Mann-Whitney U test of independent samples was applied to the population of the three cohorts to test the hypothesis concerning the opinion pattern of male and female

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Fig. 1 Median, first quartile (Q1 ), and third quartile (Q3 ) of seventh-ninth grade student scores on each question (21 axis) organized by five inquiry dimensions and three cohort overlays, blue—José Estêvão, green—São Bernardo, purple—Estarreja

participants, suggests no difference in all cohorts. At JE the p-value = 0.901; at SB p-value = 0.764; and at EST p-value = 0.315, all well above the significance level of 0.05. Kruskal-Wallis test on independent samples was used to test the hypothesis of difference in the opinion patterns of the three different population ages, seventh, eighth, and ninth grade students. Results reveal that these three different grades in the three cohorts do not have different patterns of opinion. At JE, p-value = 0.501; at SB, p-value = 0.185; and at EST p-value = 0.838, again all well above the significance level of 0.05. Curiously if the Kruskal-Wallis test is applied to data from CQ1 of all three cohorts, it reveals they do not have the same opinion on their school’s food service (p-value = 0.008). The pairwise comparison confirms that São Bernardo School has a different pattern of opinion from the other two schools (JE and EST). EST and JE report the same issues and problems in very similar perceptions of pertinence. The inspection of the qualitative opinions in the questionnaires first open-ended question (OQ1 ) highlights the following issues (Fig. 2) organized in blue—JE,

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Fig. 2 Comparison of opinions in percentage of comments concerning “Food Services” of seventhninth grade students in three different schools

green—SB, and purple—EST. This question received from JE students a total of 135 (86.5%) comments with nJE = 156, from SB students 56 (93.3%) comments with nSB = 60, and from EST, 55 (67.9%) comments with nEST = 81. As detected in the descriptive statistics in Fig. 1 and confirmed in the nonparametric tests, SB school students have an overall larger negative opinion on food services. In fact, these SB students detail their opinion and score high on “bad quality" but also mentioning, “raw food," “cold food," “lack of salt," etc., comments and scores exclusive to SB school and not all represented in Fig. 2. These are the only students who have no participants, whatsoever, mentioning the food is good. As can be seen in Fig. 1 radar, this cohort has the lowest median = 4, narrowest distance between Q1 (first quartile) = 2 and Q3 (third quartile) = 5 with the lowest values; when compared with JE and EST, both cohorts with median = 5, Q1 = 5, and Q2 = 7.

5.2 Security Two questions inquire on security, CQ2 and CQ3 , respectively, inquiring on security inside the school premises and outside school. The first test adopted in this smartness dimension was to test the level of correlation of both questions with the Spearman’s rank correlation coefficient ρ: • JE: ρ = 0.61 (p-value = 0.0001); highly positive correlation between CQ2 and CQ3 ; • SB: ρ = 0.35 (p-value = 0.006); correlates positively CQ2 and CQ3 but less than JE; • EST: ρ = 0.26 (p-value = 0.021); still correlates positively CQ2 and CQ3 but less than the other two schools. After this first correlation information, it is important to apply the Wilcoxon matched sample test to detect the median differences. The JE cohort does not reveal the same perception of safety inside (CQ2 ) and outside (CQ3 ) school (p-value = 0.001). Students have a greater sense of security within the school. To understand

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the perception of safety inside JE school (CQ2 ), gender differences were tested with Mann-Whitney U test of independent samples and no difference in perception was detected (p-value = 0.242). Considering the seventh, eighth, and ninth grade population, no differences were detected with the Kruskal-Wallis test (p-value = 0.124). Both of these tests were applied to the outside security question and detected gender differences, with girls feeling more unsecure outside school (p-value = 0.010) and the eighth grade students (p-value = 0.017), identified with the pairwise comparison, revealing the lowest perception of security outside school. In SB school the perception of security, inside and outside school, reveals a similar perception besides one small detail in the seventh grade students, identified in the pairwise comparison. These students’ opinions concerning indoors security show a greater value (Kruskal-Wallis test (p-value = 0.014)) than the outside school security. The cohort of the EST school does not reveal any difference on security perception in its population, categorized by gender and grade but does reveals different perception of security inside (CQ2 ) and outside (CQ3 ) (Wilcoxon matched sample test (p-value = 0.000)); students have the highest perception of security inside school. Comparing the perception of the three cohorts concerning security inside school with a Kruskal-Wallis test (p-value = 0.002) reveals there is a difference in perception. Pairwise comparison shows a significant difference between SB-JE (p-value = 0.003) with SB school evidencing the lowest perception of security inside school. The inter-cohort comparison for outside school security also shows difference (p-value = 0.000) but with EST school pinpointed as the school with lowest perception of security outside school (p-value = 0.000). As can be seen in Fig. 3, SB school students are the most concerned with internal security or safety issues. The clustering of opinions reveals that SB schools always have the highest scores in major issues. The “security equipment" issue is not relevant at SB school because this is the only school that has specific equipment to control school entrance and only has one school gate for income and outcome traffic of

Fig. 3 Comparison of opinions in percentage of comments concerning “Security” inside and outside school premises of seventh-ninth grade students in three different schools

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people and goods. The other two schools do not have any control equipment at school entrance and besides not mentioning any critical security issue, the lack of this security equipment is a major concern for students at JE and EST.

5.3 People and Space The first test performed in this dimension of questions was to understand their relationship in each cohort using the Spearman’s rank correlation coefficient ρ. Many questions present a strong positive correlation, others, but less, depict a strong negative correlation. A dendrogram with Ward’s hierarchical representation suggests that two groups of questions are formed, considering similarity factors. Questions CQ9 and CQ10 are represented in one group and the other nine into another group. This happens in the JE and SB cohorts and with a small difference concerning the EST cohort, which confirms this dimension is measuring very similar issues. The first five closed questions (CQ4 to CQ8 ) of this dimension are in the more similar group, which suggests one question that inquires on “how well the students feel at school and with their relationship with colleagues and teachers," would be enough. An additional detail occurs in the EST cohort: questions CQ9 and CQ10 , with a very low score in all cohorts, now received in their group questions CQ12 to CQ14 . This is something that really needs deeper qualitative inspection because CQ12 is related to homework, namely support at home to do it, CQ13 is related with socialization and play with friends and CQ14 that measures friends recognition concerning sports or school results and other achievements. The people and space dimension has five open-ended questions to inquire and better understand students’ opinions considering their relation with colleagues, teachers and to also identify their physical comfort at school. Figure 4 systemizes the main issues that were identified and reveal a common pattern of opinions in the three cohorts. The qualitative analysis also confirms that CQ9 , which inquires on the students’ opportunities to discuss with their colleagues, was a misinterpreted question. Students understood the “discussion" term as a disruptive or uncomfortable conversation that could even lead to losing friends, something they avoid or do not do at all. This is not the purpose of this question and needs reformulation in the smartness questionnaires to address the conversational opportunities students promote and the issues they discuss. This also explains the extremely low score this question obtains. The question with the lowest score in all the smartness questionnaire universe of questions formulated is question 10 (CQ10 ) and is expected as so because it asks the student to mention if their colleagues make fun of them at some point. Spearman’s correlation coefficient and Ward’s hierarchical type dendrogram also revealed the results from these two questions tend to relate/group, which makes sense considering “negative essence" of what is asked or perceived as such. The JE school seems to also be an outlier in this dimension, with strong indicators suggesting it as the school with the highest perception of smartness, as an optimized

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Fig. 4 Comparison of opinions in percentage of comments concerning “People and Space” of seventh-ninth grade students in three different schools-human relations with colleagues and teachers and physical comfort

relation of student-school. The radar in Fig. 1 also outlines the descriptive statistics indicators of JE school with higher median and quartile values in most of the dimensions.

5.4 Sociability This dimension has the goal of measuring human proximity namely related to socialization activities exclusively with closed questions. The questions that explicitly inquire the capability of their colleagues to create positive relationships (CQ16 ) or include other colleagues in their conversation or activities (CQ17 ) are the most relevant questions in one of the two groups that are formed with the hierarchical Ward dendrograms. Question CQ18 , that inquires on the student’s colleagues respect for difference, in the EST cohort also groups with the former two questions. Questions CQ15 , CQ19 , and CQ20 (related to compliance with rules, respect for law, and sense of responsibility) group together in the three cohorts. Spearman’s coefficient revealed, in this dimension, a strong correlation of all six questions, evidencing that these questions are consistently grouped, have intentional proximity, and measure a same motivational dimension in the ASLERD smart questionnaires.

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5.5 Interaction with Family The opinion of JE students related with school-family interaction has a common pattern concerning gender that is confirmed with the Mann-Whitney U test (p-value = 0.342) but a 10% significative difference of the seventh grade students scoring higher than the rest of the population (Kruskal-Wallis test, p-value = 0.091). In SB school the Mann-Whitney U test and the Kruskal-Wallis test confirm an overall similar opinion of all the population considering this dimension of inquiry. In EST the same evidence is obtained from both these tests. The application of the Kruskal-Wallis test (p-value = 0.981) to the three cohorts reveals absolutely no difference in their opinions concerning interaction with family. All the comments and suggestions gathered from the population’s opinions match the same issues as listed in Fig. 5. Although there is some relative difference in the specific pertinence of each one of these issues, the opinion pattern of all three cohorts is similar and confirms the nonparametric test’s results. Two exceptions can be pointed out at SB school that do not mention parents’ participation in the parent’s association nor do they mention parents could/should meet teachers. This study continues to confirm that a big majority of students do not want their parents involved in any school activity whatsoever. Most comments just mention they do not want to be controlled by parents at school, nor do they want their parents to get near school or involved with school activities. The lesser information their parents get of them from school the better. Open-ended question (OQ8 ) and its corresponding closed question 21 (CQ21 ) get a second position in the rank of the lowest scored questions of all 21 questions of the smartness inquiry process considered in this study.

Fig. 5 Comparison of opinions in percentage of comments concerning “Interaction with Family” of seventh-ninth grade students in three different schools

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6 Conclusions The bottom-up, people-centered, smartness benchmarking process for school ecosystems proposed in this paper is supported by a mixed-method methodological approach. The paper details the process and its different elements and phases. One of the process’ main elements are the ASLERD smartness questionnaires designed for school ecosystems, explained in the perspective of one of the stakeholders, the seventh-ninth grade students. Descriptive statistics proved to be adequate to valorize the qualitative data obtained from open-ended questions directly related with closed question inquiry. Median, first and third quartile are the descriptive statistics indicators used for valorization purposes, adequate for ordinal Likert scales which is the case in the smartness questionnaires. The first quartile and third quartile also assume a better representation of each sample’s perception of smartness than standard deviation (SD). The radar graph, as a multidimensional polygonal visual representation of descriptive statistics, incorporates properties of simultaneous holistic and detailed analysis and inspection of each inquiry dimension or question, which integrate the smartness questionnaires. The nonparametric statistical functions used to test and compare the cohorts’ data and their population’s scores, in different aspects of smartness, revealed to be useful and produced strategic guidance for the processing, clustering, and analysis of the qualitative data substance, the subjective issues. The application of the smartness benchmarking process to the three cohorts of three different schools produced results with evidence that the process is adequate for a smartness benchmarking process. The nonparametric testing that is proposed in this process augments former processes [17], and clearly produced guidance for an effective and detailed inspection of qualitative data. Besides this contribution to deal with more complexity in smartness data processing, representation, and analysis, this benchmarking process also tested the consistency of the smartness questionnaires’ structure and content, in terms of question formulation and position in the smartness inquiry dimensions. All in all, this paper proposes an innovative contribution in designing and validating a more robust and complex ASLED smartness benchmarking process for the school ecosystem. Acknowledgements A special acknowledgment to all the teachers, parents, and students at the school clusters Agrupamento de Escolas José Estêvão de Aveiro, and Agrupamento de Escolas de Estarreja, Portugal, namely teachers and seventh-ninth grade students that shared their opinion and gave up some of their time for this study. A last word of recognition goes to Cláudia Ramos and Vincenzo Baraniello, grateful for her help with the qualitative data processing and for his help obtaining this paper’s datasets from Life platform.

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References 1. ASLERD: Timisoara Declaration: Better Learning for a Better World through People Centred Smart Learning Ecosystems (2016). http://www.mifav.uniroma2.it/inevent/events/aslerd/docs/ TIMISOARA_DECLARATION_F.pdf 2. Csíkszentmihályi, M.: Flow—The Psychology of Optimal Experience. Harper Collins e-books (1990) 3. Norman, D.: The Design of Everyday Things (2013) 4. Gray, D.E.: Doing Research in the Real World. SAGE Publications, London (2004) 5. Barroca, J., Brito, D., Campolargo, M., Concilio, G., Ferreira, V., Martires, P., Molinari, F., Oliveira, A., Oliveira, M., Peterson, S.A., Sa Couto, A., Rizzo, F.: MyNeighbourhood Concept (2013) 6. Oliveira, Á., Campolargo, M.: From smart cities to human smart cities. In: Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 2336–2344 (2015) 7. Lipovetsky, G.: L’ère du vide: Essais sur l’individualisme contemporain (Les Essais). Gallimard, Paris, France (1983) 8. Crozier, G.: International perspectives on contexts, communities and evaluated practices. Family-school-community partnerships. J. Educ. Policy 29, 280–282 (2014) 9. Giovannella, C.: Participatory bottom-up self-evaluation of schools’ smartness: an Italian case study. IDAJ 9–18 (2016) 10. Pereira, H., Mil-Homens, P., Pinto, M.L., Lourtie, P.: Eleição da Melhor Universidade Pública/Best Public University Election (2001). http://www.dn.pt/ 11. DGEEC: OTES—Observatório de Trajetos dos Estudantes do Ensino Secundário. https://www. dgeec.mec.pt/np4/47/ 12. Giovannella, C.: Territorial smartness and the relevance of the learning ecosystems. In: IEEE International Smart City Conference (ISC2), Guadalajara, Mexico, pp. 1–5 (2015) 13. Giovannella, C.: “Smartness” as complex emergent property of a process: the case of learning eco-systems. In: European Conference on Enhanced Technology Learning—EC-TEL, Dubai, United Arab Emirates, pp. 1–5 (2013) 14. Giovannella, C.: Where’s the smartness of learning in smart territories? IDAJ 59–67 (2014) 15. Galego, D., Giovannella, C., Mealha, Ó.: Determination of the smartness of a university campus: the case study of Aveiro. Procedia Soc. Behav. Sci. 223, 147–152 (2016) 16. Giovannella, C., Andone, D., Dascalu, M., Popescu, E., Rehm, M., Mealha, O.: Evaluating the resilience of the bottom-up method used to detect and benchmark the smartness of university campuses. In: 2016 IEEE International Smart Cities Conference (ISC2), pp. 341–345. IEEE (2016) 17. Mealha, Ó., Nunes, J., Santos, F.D.: Smartness comparison among different age group students in an integrated school: the potential for design and management. In: Rehm, M., Saldien, J., Manca, S. (eds.) Project and Design Literacy as Cornerstones of Smart Education, Proceedings of the 4th International Conference on Smart Learning Ecosystems and Regional Development, vol. 158, pp. 93–107. Springer Nature Singapore Pte Ltd (2020) 18. Giovannella, C.: Participatory bottom-up self-evaluation of schools’ smartness: an Italian case study. In: 1st International Conference on Smart Learning Ecosystems and Regional Developments—SLERD 2016. ASLERD, Timisoara, Romenia (2016) 19. Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference. CRC Press, Boca Raton (2011) 20. Hair, J., Black, W., Babin, B., Anderson, R.: Multivariate Data Analysis. Pearson Education, Harlow (2014)

Smart Visual Identities: A Design Challenge for Smart Learning Environments Catarina Lelis

Abstract Smart environments in the context of education have been embedding technologies easily adoptable by all and that support users in optimising life, greatly resorting in data visualisation tactics. This means every citizen is also expected to develop their design literacy in order to effectively be a data contributor, an information coder/decoder and an evolving knowledgeable human being. This paper speculates on the relevance of investing in brand design activities, in particular within “smart visual identities”, which configure as a meaningful and holistic branding resource: besides serving as a customisable window of the meaningful data collected on the user within a smart learning ecosystem (in this case university campuses), it is also a remarkable asset for the development of bonds between the user and said environment (increased brand loyalty via belonging). Seven brands of campuses that have been used in research of smart learning environments were analysed and four workshops were delivered at two different universities. The results show that the analysed campuses do not rely on smart visual identities and the workshops allowed the identification of features that a customisable smart brand identity should entail. It is argued that by having to customise a highly relatable brand identity (such as the one of a University is to its learners), individuals will increasingly develop their design literacy, while using the campus in a sustainable way. Keywords Visual identity · Design literacy · Learning ecosystems · Smart branded campuses

1 The Background and Its Initial Relevant Data A smart ecosystem is human-centred. It is able to offer optimised infrastructures and customisable technology-mediated solutions, responding to active citizens’ needs and big data usage, fostering adaptable and challenging learning contexts, in a C. Lelis (B) University of West London, London, UK e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_12

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bottom-up approach [7]. It also emphasises learning through curation, creation or authoring digital or physical artefacts, in constantly changing contexts. This is what a smart learning ecosystem promises, and the design thinking informing the way how this brand promise is delivered and the impact it may have in a learner’s life is the foundational question underpinning this research.

1.1 The Role of Design Literacy In 2016, a number of international learning-centred associations signed the Timisoara Declaration, with the joint mission of developing smart learning ecosystems by, firstly, promoting fully interoperable tech-spheres. The signees add that such ecosystems have the potential to emancipate disparate members of a community in becoming active citizens, defending that design literacy is paramount for individuals to develop the ability to manage and solve complex processes, emerging from creative problem solving and diverse sources of data [19]. Therefore, understanding the worth of design seems to be of utmost importance. Design is, in fact, an omnipresent discipline and a default human condition. It is a transformational and inspirational humanist system that constantly shapes the world around us, bridging efficiency, efficacy and experience, balancing technical, commercial and human considerations [4, 16]. In a visually saturated society, coding and decoding information visually are becoming a sort of survival skills, and that, alone, justifies the need for developing design literacy in all citizens globally, namely when the contribution of design to the economy is increasingly valued due to the relevance of design skills to the so-called Fourth Industrial Revolution [18]. In a smartness context, a requirement for the contributing audiences would be a minimum sense of design purpose and basic systems of enquiry in the context of design—design is an impactful and societal activity in every way [1] and several authors defend it should be more extensively explored at the level of other basic literacies [14, 15, 17].

1.2 Smart Branded Campuses Design is, as well, a conscious instrument in the development of a brand. Being a brand a promise of quality (which can be attached to the most explicit features, such as a product’s function, and/or to the most subjective aspects, like feelings and emotions), communication is deemed necessary for the entity behind the brand to gain the desired status in its operating context. Designers have been inspired by the challenge of transforming data into meaningful brand experiences. For example, Uber has been really successful in making available their geographical, mapping, routing, and activity data, providing users with a fast and relevant experience. However, many

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cases fail in doing so, namely when it comes to giving back to their users a datainformed and data-driven interactive result—whilst guaranteeing their visual identity guides all the possible data visualisation interactions. As with every other entity, smart ecosystems (cities, schools, university campuses, museums, libraries, science parks, etc.) always own a name; in many cases, they invest in a tangible visual mark (like a logo) and, therefore, they will be dealing with some sort of branding exercise. Interestingly, at the beginning of this decade, the expression smart brand rose in a limited circle of brand experts’ lexicon, and is now expanding, being used to define all sorts of digital and more or less interactive solutions that can be potentially represented by a) a somehow dynamic brand’s visual identity system [5, 11–13, 20], or b) a very flexible marketing approach [9], without necessarily having any correspondence with citizens’ active participation and their own meaningful construction of knowledge. It is believed that smart brands, as commonly used by some brand and design experts, may actually not involve the smartness design-oriented construct as defined in Timisoara. Since marketing is a field that, by its nature, has not been very popular in human-centred approaches, a designer would probably argue that a smart brand’s origin cannot have any relationship with this much commercial and sales-led field. Hence, one would anticipate it must be more closely related with the design exercise underpinning the conceptualisation and development of a brand. From this perspective, dynamic brand identities may help in clarifying the concept.

2 Methods The main objective of this ongoing research is to suggest a definition and classification of smart brands so that these can rightfully represent and deliver the smartness promise of smart ecosystems, those already acknowledged as such, but also the environments with the potential of becoming it. The research draws on Hermeneutics, informed by Gadamer’s perspective that the individuals can only reach their own truth when they understand or master their own experience [6]. It is rooted in the methodological approach of Grounded Theory. Following the inspirational quote by Glaser that “all is data” [8], p. 8, the research considers Charmaz’s guidelines to ensure adequacy of data quality [2], hence capturing a range of contexts, perspectives and timeframes. Greatly informed by inductive and abductive reasoning supporting a speculative design exercise, at this initial stage, the research will resort mostly in secondary research (fieldnotes, scholarly literature) and the analysis of visual artefacts resulting from creative workshops. The question guiding it is: “How smart can a smart campus’ visual identity be?”.

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2.1 The Exploratory Stage Last decade, a couple of authors seem to agree on the main categories that explain the different kinds of what is widely known as Flexible/Dynamic Visual Identities: visual identities that, unlike conventional ones, allow variations and permutations to their visual identity systems (e.g. by changing colours, patterns, typefaces, backgrounds, etc.), but still guaranteeing they are fully recognised. In 2010, Ulrike Felsing puts forward five different categories as a possible taxonomy [5]; in 2012, Irene van Nes proposes a framework of six [20] which is, one year later, confirmed and augmented by Emanuel Jochum [10]. From these five or six, two are absolutely common and can help in defining what a smart brand could entail, namely by looking at its visual identity system which can be fully data-informed: the category that defines brands that are open to personalisation strategies, usually involving their audiences in the creation and representational process such as the case of the visual identity developed to celebrate the 450th anniversary of Rio de Janeiro’s foundation as a city (Fig. 1), and the computer-mediated one, which describes brand identities that are dynamically represented by live data, fed into and interpreted by special software. Some cases falling under this category are the new visual identity of Dataveyes which originates from the live relations between its staff members, being set in motion by their activity data, or Swisscom’s new logo which shape changes according to the number of visitors in both their stores and website. However, none of these brings any practical advantage to the audiences, beyond the mere fruition and experiential one. Nordkyn is possibly the best example of brand identities that are dynamically represented by live data and that provide the audiences with meaningful information (Fig. 2). In this research, said categories will be labeled as Customisable and Computerised, respectively.

Fig. 1 Customisable brand identity of Rio450

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Fig. 2 Computerised brand identity of Nordkyn, which logo reflects the data received by a feed of weather statistics, real-time changing when the direction of the wind or the temperature changes

2.2 The Analysis of Campuses’ Visual Identities The research initiated with an appreciation of the visual identity systems of some of the HE institutions whose campuses could fall within the smart learning ecosystems’ label and that have been considered in other research, e.g. University of Rome Tor Vergata, Polytechnic of Turin, University Politehnica of Bucharest, University of Craiova, Politehnica University of Timioara, University of Aveiro and Aalborg University. The main objective was to assess each visual identity case against the definition of dynamic visual identity and, if applicable, identify the category in which said identities would have a better fit. The framework for analysis was Chaves and Belluccia’s which identifies the parameters to evaluate the graphic quality of a brand [3]. The cases would be considered for the purposes of understanding smart visual identities of brands, if both categories Customisable and Computerised were to be recognised.

2.3 The Field Studies A group of four workshops was organised with the intent to capture the possible avenues for Universities’ visual identities to be used as smart devices, embedding the smartness ideals defined at the Timisoara Declaration. Three workshops were delivered at the University of West London (UWL) in London, with students enrolled in creative-based courses—BA Advertising and Public Relations, BA Graphic Design, and MA Advertising, Branding and Communication (Fig. 3)—and a fourth one that took place at the Manchester Metropolitan University (MMU) in Manchester, with M.Phil/Ph.D Design students, all of them teaching academics (Fig. 4). In total, 57 individuals participated in these workshops, in which, after (1) collaboratively creating mindmaps on what a smart campus should be from the participants’

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Fig. 3 A sample of the outputs created at the UWL workshops

Fig. 4 Creative stage at the MMU workshop

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perspective, and (2) breaking down Maslow’s pyramid to detect their own needs as campus users, a speculative design exercise allowed them to anticipate scenarios of meaningful campus-related data visualisation through the brand’s identity.

3 Findings and Discussion None of the seven analysed smart HE institutions have anything to do with the suggested conceptualisation of a smart visual identity. In none was found any elements that could indicate a design approach based on the graphical and informational premises of categories Customised and Computerised. Therefore, if smartness is meant to be informed by design practices and a promise delivered by specific learning environments, the way it is represented and branded should consistently and coherently crystallise the inherent desire/need of promoting design as a pivotal condition for humans to develop and innovate. And that seems to not be the case. The workshops, grounded on a preliminary understanding of what a smart campus entails, and after analysing their users’ needs and understanding of how they use information, supplied an incredibly rich body of data which is still currently being analysed (Fig. 5). The most obvious insight is that the information a smart visual identity could give back to its users is geolocation-related, working almost as a “within the campus compass”, entailing such a level of complexity that would not just provide users with GPS-like directions for their next meeting/class, avoiding busy corridors or highlighting lifts out of order, but also taking them through a route that would include a hint towards, for example, the nearest toilet/cafe in case the user had not been there for more than a definable number of hours. In these cases, the visual identity would adopt a flexible and movable graphical approach (likely shapebased), such as the one implemented by Nordkyn. Other relevant data visualisations for an optimised campus experience had clearly to do with (1) achievement and success, since there were several instances that were studies’ progression related (e.g. marks and deadlines, made explicit within the brand logo via colour coding), and (2) sense of belonging, as several participants created a visual identity version that would give them a clear sense of, for example, how many country fellows would be, simultaneously on campus. It was also mentioned by participants that, by having to customise a highly relatable brand identity, they would develop their sense of belonging towards the university, which in brand equity terms would be seen as increased brand loyalty. So far, this research allowed the visual and interaction properties’ analysis of some of the so-called smart brands; Nordkyn is one of the best examples in which useful and meaningful information is being visually depicted in a brand’s visual identity but it fails on the customisation level. It is the researcher’s understanding that smart brands would fall within a specific type of flexible/dynamic brand that, among other strategic features, makes use of real-time data to implement transmogrifying conditions to their visual attributes or elements (colour, type, shape, relation with space, etc.). However, the conducted exploratory analysis suggests that, data

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Fig. 5 A customised representation of UWL’s logo

visualisation-wise, the brand identities of smart learning environments do not include citizens as active participants, do not take any advantage of smart/big data in order to promote personalised experiences in the context of learning and continuous knowledge creation, and only a few could potentially promote design literacy and creativity, not just by telling, but by doing and leading by example—and the visual assets of a brand are great showcase displays. With this research, it is expected to demonstrate that one of the defining attributes of smart brands is to include a discourse based on broader dimensions of design. On the other hand, it is being presupposed that the existing smart learning environments do not communicate through a smart brand (identity) approach, and that their promotion of design literacy is not actually grounded on a systematised or holistic design-based exercise.

4 Contribution and Future Steps Entertaining profiles, participatory strategies and memorable experiences, enabled and promoted through proper and unique interaction schemes based on meaningful

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use of data, may be exciting ways to overcome the rigid and numerical nature of smart data in which smart environments heavily rely and which is massively underused by the contributing individuals. Hypermodern brands, in a way, bring to life McLuhan’s idea that it is the medium that defines the message and its contents, being the brand, in this sense, a continuous metaprocess. This is even more true when these allow onthe-fly processing of and reaction to data, but also all the asynchronous interaction possibilities which, together with real ordinary people, provide brands with a highly significant character and people with meaningful and relevant experiences. Smart visual identities (of campuses) may well be defined by having the responsibility of bringing data back to people, optimising the underlying conditions for learning. They can be used to dynamically represent the level of smartness of the campus and to help its users to have a smarter experience. They could actually integrate campus-related data with the user’s specific needs outside campus but still campus related (e.g. the next and nearest public transport to get them to the campus, by visually expressing data retrieved from apps such as Citymapper), hence possibly optimising the user’s ecosystem at a broader scale. Working together, campuses’ brands and designers can transform data into beautiful, expressive but also functional experiences. Data can truly become visual, physical and experiential interactions but, by nature, it is inherently invisible. Brands and their visual-based language resources can be the leading asset in making the invisible visible, namely with the added-value that the data being used is unique to each brand. The expected next step would be to complete the analysis of the corpus obtained at the workshops to identify the defining attributes of smart visual identities, through a binary checklist, for example. In order to avoid any bias, this assessment would be further performed by two other academics, preferably from the subject fields of design and learning technologies. This can also, hopefully, define a framework for higher education entities to implement their own smart brands, developing creative and design thinking-based learning environments, and authentically become smart learning ecosystems.

References 1. Brown, T.: Change by design: how design thinking transforms organizations and inspires innovation. Harper Business, New York (2009) 2. Charmaz, K.: Constructing grounded theory: a practical guide through qualitative analysis. Sage, London (2014) 3. Chaves, N., Belluccia, R.: La marca corporativa: gestión y diseño de símbolos y logotipos. Paidós, Buenos Aires (2003) 4. Dabner, D., Stewart, S.: Graphic design school: a foundation course for graphic designers working in print, moving image and digital media, 6th edn. Thames and Hudson, London (2017) 5. Felsing, U.: Dynamic identities in cultural and public contexts. Lars Muller Publishers, Zurique (2010) 6. Gadamer, H.-G.: Truth and method, 2nd edn. Sheed and Ward, London (1989)

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7. Giovanella, C.: Where’s the smartness of learning in smart territories? Interact Des Archit J (22), 60–68 (2014) 8. Glaser, B.G.: Doing grounded theory: issues and discussions. Sociology Press, Mill Valley, CA (1998) 9. Hollis, N.: Brand premium: how smart brands make more money. Palgrave McMillan, New York (2013) 10. Jochum, E.: Dynamic brands: how flexible design systems turn turn brands into dynamic visual identities. (Published master’s Thesis) Zurich University of Arts, Zurich (2013) 11. Jones, R.: Five ways branding is changing. J Brand Manag 20(2), 77–79 (2012). https://doi. org/10.1057/bm.2012.51 12. Kreutz, E.: Identidade visual mutante: uma prática comunicacional da MTV. Ph.D Thesis, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brasil (2005) 13. Leitão, S., Lélis, C., Mealha, O.: Marcas dinâmicas: haverá forma de as orientar? In: Paper presented at the 1st International Congress on Branding, ESTG, 2–4 Octr 2014, Leiria, Portugal (2014) 14. Nielsen, L.M., Braenne, K.: Design literacy for longer lasting products, Stud. Mater. Thinking 9, 7 15. Nielsen, L.M.: Design literacy in general education. Des. Technol. Educ. Int. J. 22, 1 (2017). Retrieved from: https://ojs.lboro.ac.uk/DATE/article/view/2193 16. Norman, D.: The Design of Everyday Things. MIT Press, Boston (2013) 17. Pacione, C.: Evolution of the mind: a case for design literacy. Interactions 17(2), 6–11 (2010) 18. The Design Council.: The design economy 2018: the state of design in the UK. Retrieved from https://www.designcouncil.org.uk (2018) 19. Timisoara Declaration.: Better learning for a better world through people centred smart learning ecosystems. Timisoara: Aslerd. Retrieved from http://www.mifav.uniroma2.it/inevent/events/ aslerd/docs/TIMISOARA_DECLARATION_F.pdf (2018) 20. Van NES, I.: Dynamic identities: how to create a living brand. BIS, Amsterdam (2012)

Digital Making and Entrepreneurship. Imagine the Future Annalisa Buffardi

Abstract The contemporary dynamics of cultural, social, and economic change address us toward a global recall to a new educational design, which should be able to enhance and create new competencies for the worker and the citizen of the future. This paper proposes a reflexion on the innovations that characterize the ongoing transformation and on the challenges that educative institutions have to face. In particular, we will focus on some twists among possible educative scenarios, based on the digital making and focused on the entrepreneurship competence framework, always with a sight to the connections between technological innovation and education. The paper recalls the laboratories of digital fabrication as expression of a dynamic that put together the cultural technological characteristics and the technical development that take shape on the new social and instances. Within this frame, the paper presents some results of the research carried out by the National Institute for Documentation, Innovation and Educational Research (INDIRE) within the 2014–2020 National Operational Programme. Focusing on the development of the competencies to participate in the twenty-first century, the research analyzed ten case studies in the upper secondary school. This practice of teaching and learning is based on the increasing affordances of educational technologies combined with traditional science labs, and maximises the value and potential of digital culture by encouraging participation and creativity. Keywords Digital making · Entrepreneurship · Work-based learning

1 Introduction This paper proposes a reflexion on the innovations that happened during the spread of digital systems in the contemporary society context and on the challenges that educative institutions have to face. The ongoing transformation will be examined A. Buffardi (B) Indire, Naples, Italy e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_13

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starting from openness dynamics, connectivity and collaboration, which characterize the Web culture. In particular, we will focus on some twists among possible educative scenarios and social and cultural contexts. These scenarios seem to be identified through the characteristics of participation and making. Also by looking to productive changes, they expand their competencies to the capacity to “anticipate innovation” and to transform creative ideas in original solutions, services and products for social wealth and sustainability development. These characteristics will be recalled referring to the cultural setting, to new knowledge and economic-productive models, always with a sight to the connections between technological innovation and education. Starting from these point of view, and referring to the new scenarios made possible by technologies, the following paragraph links the public participation instances with the new availability of access to information, data, scientific research, and to techniques and production technologies. A scenario in which the new availability and the easier access to technologies and knowledge seem to increase the possibilities to the choices and to the construction of the “world we want.” It is a theme that recalls new opportunities and new divides among who is capable to participate to the vision of change and who is excluded, and involve the role of the educative system in the developing of the necessary competencies to participate to the contemporary society challenges. Within this frame, the last paragraph recalls the FabLabs educative model that, in its different declination, performs the creative making and takes shape on “imagination, passion and art,” as in the tradition of the scientific laboratory, and it leads to innovation through the passionate activity of “hands and minds deeply imbued” [16]. We then present some work-based learning experiences, carried out by Italian upper secondary school, based on creation of digital artefacts and focused on the entrepreneurship competence framework. They represent an example of learning where technologies and openness, “creating” and learning work in tandem to develop new competencies for the citizens of the future.

2 Opening, Networking, and Participation The diffusion of what has been called, with a criticized expression, web 2.0, inaugurates a new Web phase, in which the participation, the interaction, the users involvement, the forms of connective intelligence and the sharing of the thinking processes become evident. The increasing availability of technologies also for the material production led in a parallel way to what Gershenfeld [9] has defined the “democratization of manifacturing.” In the Fourth Industrial Revolution, characterized by new instruments (such as 3D printers), by open-source processes and by the use of the huge amount of data available today, the economy adds value to the role of knowledge, of the ideas, of the research and of the human capital in productive development processes. The increasingly common availability of technologies for the production, associated with the logic of the network, draws possible sceneries that in combining the connective value of the net with the radical innovation of the

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production, give value to the participation, creativity, and innovation. On these foundations, they push toward the entrepreneurial skill of young people, risking however to propose again a paralyzing rhetoric and to create new gaps, to “unfairly shift much of the responsibility for job creation and labour market performance away from the larger public and private sectors to young people, which can leave many youth vulnerable” [20]. The easier access to knowledge, data, and digital production technologies opens to scenarios in which the logic of connective thinking is translated into “creative connecting making” [5]. A perspective that proposes again the main characteristics of the Internet culture. Nowadays, we are in a phase in which the diffusion of the social media has spread “the hacker ethic” [12]—intended as culture of sharing, participation, collaboration—to a wider community of users, thus having a great impact in the different social life settings. Openness and participation reflect the Internet culture and according to some researcher [7, 13], they try to pass the gap between educative institutions and daily life especially in young people. Also the “networked information economy” is based on technological and cultural changes, allowing an enhanced inter linkage among peer and adding value to the production of information and culture, offering an “alternative solution for people who want to work together and develop the limits of their market and stated systems” [2]. The connective construction of ideas, products, and services is also based on a vision of the future oriented toward a possibility of identifying sustainable opportunities in social, cultural, and economic terms, for the community, the environment, and the market, and the ability to identify solutions that respond to the challenges of change, combining resources and knowledge. A vision that feeds on the opportunities offered by technologies, from the growing capacity for generating, storing and connecting data from the connected world. In the ideology of open models, a shared development represents a connective thought-action on an object, an idea, a service, in order to generate value, in the different fields of application.

3 Science, Technology, and Entrepreneurship for the World We Want The diffusion of web 2.0 carries and sustains the challenge toward an active and proactive public participation, with reference to the relationship between science and technology and their impact in our daily life. In the words of de Kerckhove, the true challenge today is the participation to the global discourse about what is necessary to do, an assumption of responsibilities. This, for example, is evident in the ecologic setting: the assumption of responsibility toward the destiny of our world” [4]. A transition that fits in, and is reflected in opportunities, and pushes for a new entrepreneur in which creativity, technology, knowledge and innovation are deeply linked. The progressive diffusion of production technologies—the democratization of manufacturing—within this cultural framework, widens the possibilities of participation in the effective possibility of giving shape to ideas and mold things [9, 10].

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Potentially, the easiest access to tools and knowledge seems to expand the possibilities to contribute to choices, to think and design solutions. The ever-growing availability of manufacturing technologies, associated with the networking features that define the digital environment, enhances participation, creativity, and entrepreneurship in the scenarios of social, cultural and economic-productive development whilst combining the connective value of the network with radical production innovations. Elements that represent significant factors of attention for educational policies in the context of the formation of “young people who will change the world” [21]. “The world we want,” the world we would like to create [14] is generally open to creative and participative possibilities of anyone has the conscious access to technologies and knowledge, of anyone has the opportunity to identify possible choices for their life path. The theme of entrepreneurship is effectively placed in this framework and allows us to glimpse landscapes where it is possible to devise, design, and create products and services capable of responding to new emerging needs. With reference to the capability approach, “understanding the entrepreneurship” in younger people means, in educative and formative contexts, to individuate the ways to an expansion of areas of freedom and of individual agency, to go toward the promotion of innovation processes and, in the same time, toward an expansion of individual possibilities of ideation and realization of professional and life projects [19].

4 FabLabs Model. Engaging the Digital Generation In the description of twenty-first century competencies, the European Union, the Organization for Economic Cooperation and Development, and various world organizations have defined the areas regarding the collaboration, creativity, innovation, problem solving as the one needed to contribute and to participate in the challenges of contemporaneous society [17], to the personal growth of the citizens and to promote an economic growth based on knowledge and innovation. Regarding creativity and innovation, the areas are described through elements such as “recognizing the fields of innovation,” “developing innovative and creative ideas that can have an impact and be adopted,” “work in a creative way with others, by implementing and communicating new ideas in an effective way” [17]. Competencies that widen the front of the public participation to the ability to recognize the sectors of innovation, planning, and developing original ideas to reply to the social exigencies, to transform creative ideas in products and services. These seem as the “human skills that artificial intelligence (AI) and machines see unable to replicate,” as detected in the recent large-scale canvassing of technologists, scholars, practitioners, strategic thinkers, and education leaders, made by Pew Research Center and Elon’s Imagining the Internet Center. Many experts surveyed claim that “these should be the skills developed and nurtured by education and training programs to prepare people to work successfully alongside AI” [18].

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The digital and entrepreneurial competencies have, in this context, a particular relevance. The digital competence is formed, as synthetized in the European framework DIGCOMP, in knowledge, abilities and attitudes necessary to use technologies and through them, to realize activities, solve problems also with original solutions, communicate, collaborate, manage information, create, sharing, and build knowledge. The framework 2016 and DigComp 2.1 update reinforce moreover the concept in the direction of how to use digital technologies in a creative and conscius way to create a new knowledge, and innovate processes and products. In this sense, they recall the level, defined as “digital transformation,” in which we are able to use the digital opportunities for innovation and improvement, thanks to their creative use. Regarding the entrepreneurial competencies, they always seem more relevant in the programmatic indications of the different national governments and of the European Union [8]. In the European framework EntreComp of 2016, the ability of innovation, of transforming the ideas in actions, of assuming risks, represent constitutive elements, in a frame that enriches itself from the capacity of “spotting opportunities” and to answer to the challenge of change; of “Vision”, intended as predisposition to imagine a desirable future and to use this vision to guide change processes. The competence recalls the areas of problem solving, group work, communication, sustainable thinking, and creativity. As Blikstein argues [3] “every few decades or centuries, a new set of skills and intellectual activities become crucial for work, conviviality, and citizenship. Today, there are calls everywhere for educational approaches that foster creativity and inventiveness. Simultaneously, digital fabrication technology became better and more accessible, and the intellectual activities enabled by the new technology became more valued and important.” Starting from the concept of FabLab introduced by Gershenfeld, in 2009 Blikstein launched the FabLab@School at the Stanford University, a new type of digital fabrication laboratory especially designed for school and children. The origin of this model is the technology value “not only as a way to optimize the existing educational system, but as a transformative force that can generate radically new ways of knowing and learning” [3]. The FabLab model, and all the experiences created to promote networking, starting from a major accessibility of technologies for the production, represent an educative scenario that takes its form on the creative potential of new technologies, by creating an authentic context for learning, that allows children to experiment and play with their own ideas, giving them permission to create, imagine, and build [10].

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4.1 Work Based Learning, Digital Making and Entrepreneurship In Italy, the research carried out by Indire shows that some work-based learning experiences in the upper secondary schools go in this direction.1 Focusing on the development of the competencies for the citizen and the worker of the future, we selected some projects oriented to engage the students in the development of innovative and creative ideas, to transform these ideas into action, services, prototype, or products. Many of these projects are promoted by school participating in Territorial Laboratory for Employment.2 In other cases, they are connected with national competition promoted by Companies or Italian Minister of Education; in still other cases, the projects start from the involvement of local partners. Anyway, they reflect an identity of the school based on the interconnession between school and territory, companies and institutions. They are part of a wide pedagogical vision that gives value to the use of technologies to realize activities, create artefacts or generate new services, engaging the students in an authentic context for learning, also building and sharing knowledge and competencies. Inside and outside the school, technologies play an important role giving shape to relation between making and learning. In some cases, projects are developed in the laboratory school. It is the case, for example, of the electric bike to visit and to support medical help in the Aspromonte National Park in Reggio Calabria, produced by the students of the Panella Vallauri Institute, also in collaboration with a local company; Furthermore, it is the case of the “Celerifero”, an electric velobike developed by the students of the Ferrari Institute in Maranello-Modena, intended for the urban mobility and also aimed to foster stronger community engagement in the field of sustainability. They are projects strongly focused on this issue. Moreover, it is the matter of “Ent”, an AI system in the shape of a tree, produced in the laboratory school at the Gae Aulenti Institute in Biella, by its students with their teachers, with the aim of the dissemination and preservation of the environmental culture. Local and big companies participate, in different ways, in the design activities and they are also involved in the training of teachers and students. In some cases, they offer tools and support to the students in research and development. In the case of the Campus Came Project, ideated by the Came Spa, the Company provides tools and training for students and teacher inherent its own systems, in the field of smart and home automation. Furthermore, it is the case of the robotic arm produced by the students of the Altamura-Da Vinci Institute in Foggia. The local partner provided 1 .Project

“Modelli innovativi di Alternanza Scuola Lavoro,” carried out by Indire within the 2014-2020 National Operational Programme “Per la scuola. Competenze e Ambienti per l’apprendimento”. 2 Territorial Laboratories for employement are realized in the schools with the collaboration of institutions and associations, universities, foundations, private entrepreneurships. They are proposed as “spaces with a high innovative profile that are available to more schools in the territory where to develop advanced didactic practises together with local politics for the job and entrepreneurships (DM657/2015). When we carried out the field research, Territorial Laboratories were not active in many of the school selected, but only in the planning or financing phase.

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particularly mentorship for the students, both in school laboratory and its laboratory, both in research and development. Also it is the case of the t-shirt called T-health, a wearable device creating by the students of the Costanzo Institute in Catanzaro with the aim to monitor heart health and track blood pressure. The T-health is developed in the school laboratory, in the textile laboratory of a local partner and also in the innovation laboratory of the center for biotechnology and life science, engaged in the partnership, that provided tutorship through their researchers to support and stimulate the students. The “well 4.0 for Africa” project, conducted by the students of the Nelson Mandela and Cattaneo Dall’Aglio Institutes (Castelnovo ne’ Monti-Reggio Emilia), is also an interesting case that show a strong collaboration between school and companies. The idea came from young African ex student of the Nelson Mandela Institute, who wanted to aid his native village. The project engaged two schools and some their students, in collaboration with Siemens and other local partners, that provided technologies, working together with the students to develop a photovoltaic power well to improve water in Tomora Village, in Mali. In this case, the activities were carried out in the area adjacent from an old well. Lab work is always central to this kind of teaching, as a place and a model for the generation of ideas and their development, guided by knowledge and research. Collaboration and group work are important, to promote social and personal competencies. “Working in a creative way with others” to implement new ideas with potential social impact is a key constant in the case studies explored. In the projects investigated, the students created, designed, and developed, collaborated and contributed to solve anomalies during the realization, both by resorting to creative strategies and through theoretical and technical studies; they used software and instrument available in the laboratories or identified online open solutions to create their prototypes; they carried out research on technical procedures and applications, to evaluate the impact of the project idea, including analysis of both technological innovation and social issues related. However, we could not observe the specific role of the above-mentioned projects in improving digital competences. The schools involved in the survey are usually active in promoting laboratory activities. Technologies are generally part of a learning process based on the practical implementation of a product, stimulating students to plan, create, program, collaborate, and solve problems. These projects are based on a wider educational vision based on the intersection between technology, culture, and society. In these experiences, making and learning represent the starting point to enable the students in new skills and generative capacities in order to strengthen the ability to see their own future and to express their own realization of direction through practical choice, engaged them in developing of the future. The findings suggest more in-depth analyses regarding this complex and multidimensional phenomenon. According to Livinsgtone and Helsper [15], “the academic debate has reframed the digital divide in terms of the social inclusion agenda, refocusing attention on digital inclusion.” They suggest to study digital inclusion not through a binary divide but as a progression from non-use, through low use, to more frequent use [15]. Many researchers have argued that social and cultural factors, expertise, and skills play a role in the differential use of the Internet and digital technologies, which translates into “a progression in the take-up of online

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opportunities” [15]. As stated elsewhere, the school also can play a relevant role. Previous analyses showed the relationship between classroom digital activities and the Internet usage, in particular with reference to certain creative, collaborative, and cognitive usages of the Web [6]. Furthermore, students more engaged in digital learning activies appear “all round users,” as defined by Livingstone and Helpser [15]. They use the Internet, more than other, for leisure, participation, learning, socializing and to solve problems, to communicate, to manage information, to collaborate, to create, and share content: skills that are identifies within the area and the levels of the DIGCOMP [6]. In a differential divide between have and have plus (opportunities) and makes and who makes plus, teachers and scholastic institutions have a decisive role to play, and a clear necessity to be supported in interpreting the ongoing changes and in guiding youngsters to seize the opportunities that new technology offers.

5 Conclusions These experiences indicate a path in which the educative systems take part in the ongoing transformations. A way that is building up actually, so it happens to other settings. As Gershenfeld [11] says “also at an economic level, the FabLab production model cannot yet compete with the mass production (…). Nevertheless, also if local production doesn’t substitute the serial ones, they will change the world.” In the same way, the educative scenarios that take their shape within the dimension of the School Fabrication Laboratory, based on the use of digital technologies of production and on the networking logics, cannot compete with the yet prevalent fordist model, but represent an emergent model that incorporates the technologies and its culture. And that maybe, paraphrasing Gershenfeld, will end up changing the school. Starting from Laboratories of Digital Fabrication, these various experiences represent one of the forms through which the intersection between the values of the digital culture and some consolidated principles of the pedagogical tradition is expressed. This would lead towards the direction of one of the possible way of social development offered by the diffusion of digital technologies. As Baldacci [1] underlines, by following some “deweyane suggestions,” “the laboratory is a didactic strategy: it has a role and a strategic relevance in school curriculum, as an indirect formation of the mind. It structures a context that has long term and second level effects on mindsets, creating the appropriate conditions for the reflexive thinking.” In the contemporary context, school making laboratories express a cultural educative space embedded in the contemporary transformations that can create the appropriate conditions for the (reflexive and) sustainable thinking.

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References 1. Baldacci, M.: Il Laboratorio come strategia didattica. Suggestioni deweyane. In: Filograsso N., Traviglini R. (eds.) Dewey e l’educazione Della Mente, pp. 86–95. Franco Angeli: Milano (2004) 2. Benkler, Y.: Open development. networked innovations in international development. MIT Press, Cambridge, MA, MIT Press (2013) 3. Blikstein, P.: Digital fabrication and ’making’ in education the democratization of invention. In: Walter-Herrmann, J., Büching, C. (eds.) FabLabs: of machines. Makers and Inventors. Transcript Publishers, Bielefeld (2013) 4. Buffardi, A., de Kerckhove, D.: Education overload, nuove sfide per l’apprendimento. In: Savonardo L. (ed.) Bit Generation. Culture giovanili, creatività, social media. (pp. 85–99) Franco Angeli, Milano (2013) 5. Buffardi, A., Savonardo, L. (eds.): Culture digitali, innovazioni e start up. Il modello Contamination Lab. Milano, Egea (2019) 6. Buffardi, A., Taddeo, G.: The Web 2.0 skills of Italian students: an empirical study in Southern Italy. Ital. J. Sociol. Educ. 9(1), 45–76 (2016). https://doi.org/10.14658/pupj-ijse-2017 7. Deimann, M., Friesen, N.: Exploring the educational potential of open educational resources. E-Learn. Digit. Media 10(2), 112–115 (2013) 8. European Commission/EACEA/Eurydice: Entrepreneurship Education at School in Europe. Publications Office of the European Union, Luxembourg (2016) 9. Gershenfeld, N.: When things start to think. Henry Holt, New York (1999) 10. Gershenfeld, N.: How to make almost anything. The digital fabrication revolution. Foreign Aff 91–96 (2012) 11. Gershenfeld, N.: La libertà del web rivive nel mondo reale, La Repubblica, oct. 10 (2016) 12. Himanen, P.: The information society and the welfare state: the finnish model. Oxford UP, Oxford (2001) 13. Jenkins, H.: Confronting the challenges of participatory culture: media education for the 21th century. The MIT Press, Cambridge (2006) 14. Kingwell, M.: The world we want: virtue, vice, and the good citizen. Penguin, Penguin, Canada (2001) 15. Livingstone, S., Helsper, E.: Gradations in digital inclusion: children, young people and the digital divide. New Media Soc 9(4), 671–696 (2007) 16. Medawar, P.B.: Is the scientific paper fraudulent? Saturday Rev 1, 42–43 (1964) 17. Niemi, H., Harju, V., Vivitsou, M., Viitanen, K., Multisilta, J., Kuokkanen, A.: Digital storytelling for 21st-century skills in virtual learning environments. Creative Educ 5, 657–671 (2014). https://doi.org/10.4236/ce.2014.59078 18. Rainie, L., Anderson, J.: The future of jobs and jobs training. Pew Internet Res. Cent. http://www.pewinternet.org/2017/05/03/the-future-of-jobs-and-jobs-training/. Last access 19 2017/10/19 (2017) 19. Strano, A.: Capacitare entrepreneurship per l’attivazione professionale dei giovani. In: Formazione e Insegnamento. Rivista Internazionale di Scienze dell’Educazione e della Formazione, 13(1), 109–116 (2015) 20. United Nations: Youth civic engagment. World Youth Report. United Nation Publications, New York (2016) 21. Wagner, T.: Creating innovators: the making of young people who will change the world. Scribner/Simon & Schuster, NewYork (2013)

Toward a Recommender System for Planning Montessori Educational Activities Cristi Nica, Alexandru Olteanu, and Emil Racec

Abstract In recent decades, learning methods have evolved, adapted, and reinvented. With these changes, the curriculum has become increasingly complex and there is an opportunity for technology to offer a helping hand in providing superior educational experiences. In this paper, we highlight the need for a recommender system within the Montessori kindergartens while exploring the main techniques of the recommender systems used in large environments such as YouTube, LinkedIn, or Amazon. Our ultimate goal is to obtain a recommender system—similar to an intelligent assistant—that helps teachers in planning learning paths and guides students in making progress. Keywords Recommender system · E-learning · Institutional learning · Montessori · Adaptive learning

1 Introduction 1.1 Context In the last decades, the researches in different fields have led to remarkable progress. Education is no exception: new learning methodologies have been discovered, new student scoring metrics have been applied, and their evolution has been tracked based on numerous criteria. Taken all this into account, the traditional work of a teacher has C. Nica (B) · A. Olteanu Politehnica University of Bucharest, Bucharest, Romania e-mail: [email protected] A. Olteanu e-mail: [email protected] E. Racec Devmind, Bucharest, Romania e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_14

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become increasingly challenging. From this point of view, kindergartens and their employees have had an even harder mission. Children at an early age are constantly active and need guidance in all the activities they do. When an educator guides a child he/she should know as many details as possible about the child’s previous activities and his/her talents in different areas such as arts, math, or practical life. Consequently, the need for a digital assistant appears: a system that will provide teachers with all the necessary information about a particular child and with recommendations regarding the next educational steps.

1.1.1

The Montessori Method

Before going through all the current approaches, we need to briefly introduce the Montessori method [1]. The Montessori educational method has existed for over 100 years but this has become more and more popular in recent decades. It is characterized by providing a prepared environment: tidy, pleasing in appearance, simple, where each element exists for a reason in order to help in the development of the child. The prepared ecosystem provides opportunities to commit to interesting and freely chosen work, consisting of long periods of uninterrupted concentration. Children could develop basic cognitive abilities working with concrete materials, scientifically designed. As can be seen, all this methodology comes with a large number of responsibilities and a remarkable amount of information. Hence, with the passage of time, various platforms have emerged in order to bring advantages to all parties involved. However, none of the existing platforms deal directly with the problem mentioned above. Therefore, teachers are required to guide the children only after going through the entire history of previous activities—which is time-consuming. The main purpose of this paper and the future research is to find the best way to develop such a recommendation system for Montessori education and to integrate it within our existing platform: Montessmile. Montessmile is an online educational management platform for Montessori kindergartens which address the needs of both kindergarten employees and parents in order to bring a better educational experience (through it children and parents could be managed, lessons could be scheduled— even in an online environment, children’s progress is tracked and reports will be generated based on it). It is important to note that this project is based on the constant feedback received from five Montessori kindergartens in Bucharest and on their requirements—which also include the recommender system.

1.2 Recommender System—Data Model Format Considering all the previous issues, the need for a recommendation system specialized on the Montessori methodology is clearly outlined. There is an essential aspect

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Fig. 1 Fragment of DAG example for practical life area

that we must take into account during the subsequent research: the data format within Montessori methodology. Besides the well-known actors which take part in such a system (teachers, children, and parents), the Montessori materials and lessons have a special structure that allows them to be represented like a directed acyclic graph (DAG). For each area, there are certain root nodes (lessons) and the other nodes are preceded by one or more nodes— which may or may not be root type. For a better follow-up of the curriculum, it is recommended that the lessons should be done in the order represented by the graph. Skipping a step can lead to deficiencies in the child’s education and those deficiencies may block him/her in the following exercises. A sample of such a DAG could be seen in Fig. 1.

2 Current Approaches Recommendation systems are software tools and techniques whose goal is to make useful and sensible recommendations to a collection of users for items or products that might interest them [2, 3]. As described earlier, there is no recommender system for the proposed problem so we did a research of existing methods in other areas to see which one fits the best for our need. Recommender systems can be divided into several broad categories [4].

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2.1 Collaborative Filtering Collaborative filtering [5, 6] is based on the idea of “word of mouth” promotion: the opinion of friends plays a major role in personal decision making. Moving this behavior into the online environment, friends become equivalent to other users who have similar behavior or preferences. Therefore, collaborative filtering is based on two main types of data: a set of users and a set of elements/items—in our case, users will be represented by the students while elements will be represented by the lessons. More exactly, the relationship between these two entities is the one that matters. This relationship is expressed in terms of ratings provided by user and used for further recommendations to others. There are two types of collaborative filtering: user-based and item-based. The first one is the most used of these. In short, user-based collaborative filtering identifies the X-nearest neighbors of the current user using the similarity between those [7] and will try to predict the current user rating for a specific item.

2.2 Content-Based Filtering Content-based filtering relies on the fact that user preferences remain constant over time. For example, if one student is interested in “Polishing,” he/she will not change this interest from one day to another. At the same time, there is a very high chance for him to be interested in other lessons in the same area, with the same specificity. This is the main reason why content-based filtering is used when it comes to the recommendation of websites [8]. Unlike collaborative filtering, content-based uses two different types of data: a set of users and a set of categories that have been assigned to available items—in our case, users will be represented by the students while categories will be represented by the areas which correspond to lessons. Categories are also known as keywords. The mechanism compares all the items that are available to be recommended with those that have been already consumed. Thus, we will know whether they are similar or not. Almost all the time, similarity depends on keywords extracted from the item description or on categories in the case that items have been annotated.

2.3 Markov-Based Approaches for Adaptive Learning During the research for this paper, we discovered some truly interesting approaches for recommender systems used in adaptive learning, based on Markov chain [9–11]. An adaptive learning system aims at providing instruction tailored to the current status of a learner, differing from the traditional classroom experience. A key

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component of an adaptive learning system is a recommendation system, which recommends the next material (video lectures, practices, and so on, on different skills) to the learner, based on individual characteristics. Therefore, as it can be seen there are a lot of similarities between adaptive learning and Montessori methodology. The main problem which this approach raises up and takes into account is the fact that the traditional memory-based and model-based course recommender systems do not consider the sequence of courses taken by the students. In the context of course enrollment/activity freely chosen by the student, the order in which courses are taken plays an important part in advising and developing a student’s academic plan. At the same time, taking into account the order in which students prefer to do the activities, can lead to new approaches in the Montessori methodology.

3 Discussion and Future Work The work presented in this paper is meant to assist in the development of Montessmile —an online educational management platform for Montessori kindergartens. • Collaborative filtering: the two main types of data will be the students and the lessons. The system will decide a degree of similarity between each student and his/her colleagues and will recommend to the subject lessons/activities done by the most similar K colleagues. • Content-based filtering: the two main types of data will be represented by the students and categories/areas for each lesson. In order for the system to work as best, the division of lessons into categories and subcategories must be done as well as possible. For this, we will ask the opinion of some accredited Montessori experts. • Markov chain solution: it is more like a hybrid between collaborative filtering and a Markov chain. In this scenario, the recommendations will be given according to the current status of the student (which lessons is he/she going through right now), other similar lessons and which are the previous steps he took in the DAG structure that the lessons have. Although it is just the beginning, this survey is extremely important. It will help us decide which models fit our problem so we can try to create a prototype for them. Based on this, we will choose the final approach for the recommender system. A careful analysis of all solutions will save us from further problems. Our test data set will be composed of anonymized data from the Montessori kindergartens in Bucharest. We are currently establishing the test data set. The strategy for the future is divided into next stages: • Build some prototypes based on the most relevant models obtained in this survey. When doing it we will also have to decide on the metrics that we will use within the system and formulas for them (similarity, rating, etc.).

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• Analyzing the performance the prototypes will have on the data set, the architecture, the programming language, the hardware resources, and the metrics on which the final system has to be based will be decided. • Starting from the most advanced prototype, a first stable version of the recommender system will be implemented. From this point, each version will be tested in the partner kindergartens and will be constantly improved. Acknowledgements This work would not have been possible without the constant feedback and help in understanding the needs of a Montessori system given by Mr. Adrian Nache and Mr. Dan Tarko. We are also truly grateful to Andrei Stanila for his help in developing the Montessmile platform.

References 1. Marshall, C.: Montessori education: a review of the evidence base. npj Sci. Learn. 2(1), 1–9 (2017) 2. Jain, S., Grover, A., Thakur, P.S., Choudhary, S.K.: International Conference on Computing, Communication and Automation (ICCCA 2015) 3. Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. Springer, Berlin (2011) 4. Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S., Stettinger, M.: Basic Approaches in Recommendation Systems 5. Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. In: Foundations and Trends in Human-Computer Interaction (2011) 6. Burke, R., Felfernig, A., Goeker, M.: Recommender systems: an overview. AI Mag. 32(3), 13–18 (2011) 7. Item-Based Collaborative Filtering Recommendation Algorithms reading report. https:// haelchan.me/2017/11/03/Item-Based-CFRA-reading-report/. Last accessed 15 Jan 2020 8. Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997) 9. Chen, Y., Li, X., Liu, J., Ying, Z.: Recommendation system for adaptive learning. Appl. Psychol. Measur. 42(1), 24–41 (2018) 10. Adaptive Learning in the Classroom and Beyond. https://edtechnology.co.uk/Blog/adaptivelearning-in-the-classroom-and-beyond/. Last accessed 24 Jan 2020 11. Khorasani, E.S., Zhenge, Z., Champaign, J.: A Markov Chain Collaborative Filtering Model for Course Enrollment Recommendations (2016)

Supportive Technologies and Tools for Smart Education

Cohesion Network Analysis for Predicting User Ranks in Reddit Communities Catalin-Emil Fetoiu, Maria-Dorinela Dascalu, Mihnea Andrei Calin, Mihai Dascalu, Stefan Trausan-Matu, and Gheorghe Militaru

Abstract Social media consists of interactive applications which bring together people from different geographical regions through technology. Online communities have become increasingly popular due to their capabilities to virtually connect people with similar interests. Based on their activity, a social rank is computed to measure how users are perceived within the community. The aim of this paper is to perform an in-depth analysis of a debate community from Reddit. Our method provides tailored services capable to analyze user behavior based on regularity measures, model the interactions between participants, and predict a social rank for users based on their participation. The ReaderBench framework has been used to generate multiple indices, including those for textual complexity, reflective of writing style specificities. Various regression models were trained and evaluated in order to predict users’ rankings, which are reflected in the number of votes they receive from their peers. The results show that the user ranks are predicted with a precision of 15 votes by using MLP neural networks. C.-E. Fetoiu · M.-D. Dascalu · M. A. Calin · M. Dascalu (B) · S. Trausan-Matu · G. Militaru University Politehnica of Bucharest, Splaiul Independent, ei 313, Bucharest 60042, Romania e-mail: [email protected] C.-E. Fetoiu e-mail: [email protected] M.-D. Dascalu e-mail: [email protected] M. A. Calin e-mail: [email protected] S. Trausan-Matu e-mail: [email protected] G. Militaru e-mail: [email protected] M. Dascalu · S. Trausan-Matu Academy of Romanian Scientists, Str. Ilfov, Nr. 3, Bucharest 050044, Romania © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_15

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Keywords Rank prediction · ReaderBench framework · Online community · Sociograms · Regularity measurements

1 Introduction Social ranking represents a way to characterize and categorize people based on their activity on social media channels. Social ranking has become more and more popular due to the exponential growth of social media in the last years, and the increased involvement of people who create multiple communities, post ideas, comments, photographs, videos, interact with others, etc. Users’ activity in social media, such as stream data, posts, comments, is used to create recommender systems to match users’ expectations. Online communities have evolved in the last years, due to their easy access to information and their capability to connect people from different parts of the world. Online courses and learning communities facilitate access to education from multiple regions of the world, easier correlation between teacher and student schedules, as well as the drastic reduce in cultural and geographic barriers in education. New opportunities to improve the areas of education and research emerged, by using online communities for learning purposes and sharing ideas. Multiple frameworks which use natural language processing (NLP) [1] techniques and machine learning algorithms were developed to analyze online communities. The ReaderBench framework [2] supports both tutors and learners by exposing various services, such as automated assessment of participation and collaboration in online communities, or automated evaluations of student responses in blog and microblog posts [3]. Besides learning communities focused on educational processes, there are different online communities that have informational purposes and are centered on sharing ideas. Reddit, for example, is a social media website that consists of more than 138 thousand communities [4] and has more than 330 million users [5]. Reddit is built as a massive collection of individual forums, where users can share news, articles, images, or ideas and comment on the posts of other users. Each individual forum is called a “subreddit” (e.g., /r/AskReddit is the main subreddit used for asking questions from various topics). In each subreddit, users can create discussion threads called posts which are the starting point of conversations between users centered on the current topic of interest. Each user registered on Reddit has a social rank, called karma, which denotes how the user is perceived by the community. This score is computed by upvoting or downvoting posts and comments made by a specific user. Based on their cumulative score and comments, posts are grouped in four categories: hot (the most active posts at the current moment), trending, new or controversial. In addition, Reddit contains communities that help users develop certain skills, overcome problems, or share and debate ideas, ranging from educational or career advices to communities that deal with depression recovery or political debates. The aim of this paper is to perform a comprehensive analysis of a debate community from Reddit, which is focused on discussing politic ideas and beliefs related

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to socialism and communism. Our method relies on advanced NLP techniques to model the interaction between participants, analyze user behaviors based on regularity measures, extract the most discussed topics, and predict users’ karma based on their participation. The ReaderBench framework is used to generate various textual complexity and cohesion network analysis (CNA) indices [2] that take social network analysis (SNA) metrics [6] a step further by considering text cohesion as a liaison between user posts. Participants were grouped in three clusters (central, active, and peripheral) using the CNA indices provided by the ReaderBench framework, while three types of sociograms were generated to observe interactions between users, within the associated clusters. This experiment introduces new social media analytics tools useful to rank users based on their online posts. The subreddit selected for analysis—DebateCommunism—had around 22.000 users at the time of our data extraction. The paper is structured as follows. The next section presents existing approaches in analyzing interactions in online communities and social ranking. The third section is centered on our corpus and the integrated approach, together with corresponding functionalities. Afterward, the fourth section is focused on presenting our results, followed by conclusions and future work.

2 State of the Art Social media analytics (SMA) is the process of gathering data from multiple social networks like Twitter, Reddit, etc.m in order to analyze and process information, and provide recommendations together with monitoring services. SMA helps producers to find out what buyers like, to learn market trends, or to identify what people think about their products in order to improve them. The impact of SMA in business has steadily increased and almost all businesses rely on this process which consists of three main stages: capture, understand, and present [7]. Various social media techniques for analytics, modeling, and optimization were developed [8]. Online communities are part of the social media and represent an important data source for analysis. Multiple researches were made using online communities’ data, starting from longitudinal analyses [9], modeling users’ behaviors [10], or improve interaction between learners based on SNA [11]. Cohesion network analysis (CNA) [12] is a method used to represent and evaluate user participation and interactions based on text cohesion in computer-supported collaborative-learning (CSCL) environments. One of the main ideas of CSCL is that the learning process can be seen as having two principal directions: personal and social knowledge-building processes [13] in which participants contribute collaboratively to the learning. CSCL platforms encourage the sharing of ideas and open discussions between students, having as consequences expertise transfer and reducing educational gaps. Tutors can provide problem-solving tasks with associated discussion threads. In order to ensure the success of the learning process, tutors need to keep track of different indices for each participant, such as performance,

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participation (i.e., active involvement), and collaboration with peers. Because this is a very time-consuming task, multiple methods were developed to automatically evaluate involvement [2, 14]. The objective of CNA is to create a model which can be used to analyze conversations with multiple participants and multiple discussion threads by also considering the relationships between different threads. This is based on the polyphonic model [15], in which each participant is expected to generate comments relevant to the topic of the discussion, but also consistent with the responses of other members of the community; thus, multiple discourse structures (i.e., cohesion graphs) are generated and used in follow-up analyses. In order to compute the semantic similarity between words or concepts, various semantic models are used, like latent semantic analysis (LSA) [16], latent Dirichlet allocation (LDA) [17], and Word2vec [18]. In CSCL communities, a high cohesion indicates a consistent communication and discourse between the members of the community, while a low cohesion indicates the presence of too many discussion topics, unrelated discussion threads, or comments that do not relate to the original intention of a discussion thread. A CNA sociogram represents a directed graph structure, where nodes denote participants and the edges are the interchanged messages between them. Multiple types of sociograms were proposed by Sirbu et al. [14, 19, 20] in which the weight of a link between two members of the community is equal to the sum of the scores of their individual comments multiplied by the scores of cohesion links between the two post. This approach takes into account the quality of dialogue. In terms of prediction, one of the well-known and frequently used types of neural networks is the multilayer perceptron (MLP) [21], which consists of a set of sequential layers, namely input, hidden, and output layers, each one containing multiple computational units called perceptrons. The perceptron computes a weighted sum of the inputs, and the result is passed to its activation function in order to compute the result which is transmitted to the following layer. We selected MLP as ranking algorithm because data was not normally distributed in all the cases (e.g., long-tail distributions were encountered), features varied greatly in terms of scale, and MLP networks offer in general a good flexibility in combining various features.

3 Method 3.1 Corpora Our corpus was collected from the subreddit called “DebateCommunism,” which is a debate community focused on discussing politic ideas and beliefs related to socialism and communism. The main reasons for selecting this community were its appropriateness for the historical context related to the Romanian landscape, coupled with its adequate size and number of posts and comments. Adequate size relates to a number not exceedingly small or large in our case, as small communities

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are not that relevant when training ranking algorithms, whereas extremely large ones lose focus and have too many peripheral members, with few contributions. Using the Python Reddit API wrapper (PRAW—https://praw.readthedocs.io/en/lat est/), the last 200 discussion threads were extracted, which represent the threads with the greatest number of votes, comments, and interactions. The names of the users were automatically anonymized by PRAW. We extracted from each submission the following data: comment, the identifier for the user who wrote the comment, parent comment or submission identifier, and timestamp. The analyzed timeframe was of 9 weeks, more precisely 60 days, between April 11, 2019, and June 10, 2019, and the used language was English. Approximately 23.5 thousand users were subscribed to our analyzed community, out of which 980 were active during the selected period, contributing with posts and comments. This behavior is present in many Reddit forums due to the fact that some people are subscribed only to follow the discussion and upvote or downvote, without making any posts. The minimum number of involved users in a post was 0, while the maximum reached 50. The average number of users in all the threads was 24.99, while the average number of posts made by active users was only 0.204. For all discussion threads, the average number of comments was 30.85, resulting in a total of 6170 comments. The maximum number of comments made by a user was 133. The average number of words used in a comment was 86.28, which confirms our assumption that politic topics generate complex responses. The Reddit formula used to compute a user’s score is not public; thus, a custom formula based on the number of upvotes and half the number of downvotes for all corresponding comments was used as baseline in order to model the users’ karma in the selected timeframe. Based on the previous formula, the best user score was 423.0, while the minimum score was −247.0.

3.2 Extensible Architecture Figure 1 presents our system architecture with all pipeline components designed and implemented, as well as the interactions between them. Three types of services are provided by our system, namely Data Acquisition and Storage, Data Processing and Visualization, and Rank Prediction. Each of these services can be used separately. First, Data Acquisition and Storage consists of three components. The first component is the actual Reddit community (i.e., “DebateCommunism” for our analysis) from which data is extracted. The second component is represented by a crawler written in Python programming language that uses PRAW for extracting posts and comments from a certain subreddit. After extracting the relevant fields for each comment, a JSON object which contains information about all the comments from a discussion thread is created and sent for indexing into Elasticsearch, an enterprise full-text search engine (https://www.elastic. co/products/elasticsearch). An individual index in Elasticsearch is created for each community to separate data between communities, while facilitating text retrieval and

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Fig. 1 Processing pipeline architecture

search on specific keywords. Elasticsearch is used by the ReaderBench framework to retrieve data for follow-up processing. Second, Data Processing and Visualization includes three components, each taking data from Elasticsearch. The regularity indices are computed based on user’s activity (posts and comments). The user’s activity over a certain time window W is computed according to Eq. 1, with T a set with timestamps when the actions took place.  FW (x) =

  1, if x = Wt , where t is a timestamp in T 0, otherwise

(1)

Two measures, called PDH and PWD, were computed based on the entropy of the user’s activity in time. The first measure (PDH, see Eq. 6 which relies on Eqs. 2 and 4) checks if user’s comments are concentrated around a certain hour each day, while the second measure (PWD, see Eq. 7 which relies on Eqs. 3 and 5) checks if user’s comments are concentrated around a certain day each week. The intuition behind PDH and PWD is to explore the variance in user’s behavior across the time of day or the day of week in which they post and observe different interaction patterns (i.e., they regularly post within the same time intervals/same days across the week, or they exhibit irregularities).

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D(h) =

L d −1

179

F60 (24i + h), for each h in {0, 1, . . . , 23}

(2)

F60×24 (7i + d), for each d in{0, 1, . . . , 6}

(3)

i=0

W (d) =

L w −1 i=0

ED = −

23 

D(h) ∗ log(D(h))

(4)

W (d) ∗ log(W (d))

(5)

PDH = (log(24) − E D ) ∗ max D

(6)

PWD = (log(7) − E W ) ∗ max W

(7)

h=0

EW = −

6  d=0

Further, three regularity indices were computed based on the similarity of user’s activity patterns between different weeks: WS1 , WS2 , and WS3 . These indices rely on P(d, k) which returns the count of hours during week k and day d when the user had activity. Based on this, a profile is constructed for each week as a column vector with seven elements, one for each day of that respective week. Another indicator is Active(k), which stores the set of active days of the user during a certain week. The first similarity function (WS1 , see Eq. 8) measures the degree in which a user posts in the same time of day during the week. The second type of similarity (WS2 , see Eq. 9) compares the normalized weekly profiles, based on the Jensen–Shannon divergence (JSD) [22]. The last function of similarity (WS3 , see Eq. 10) is used to compare the daily patterns for each day of the two weeks which are analyzed. In addition, we computed for each user a score based on the number of upvotes and downvotes of his/her comments, which is used afterward for prediction. WS1 (i, j) =

|Active(i) ∩ Active( j)| max(|Active(i)|, |Active( j)|)

WS2 (i, j) = 1 −

JSD(P(i), P( j)) log(2)

 6   P(d, i) − P(d, j) 2 1 ∗ WS3 (i, j) = 1 − |Active(i) ∪ Active( j)| d=0 P(d, i) + P(d, j)

(8) (9)

(10)

The ReaderBench framework computes various indices related to collaboration and participation (CNA; e.g., in-degree and out-degree CNA indices that reflect the sum of scores for in- and out-bound edges in the CNA graph), as well as textual complexity reflective of writing style [23], which are then used by the Rank Prediction

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service. Three types of sociograms are generated using CNA indices provided by the ReaderBench framework to depict interactions between participants. Based on their in-degree and out-degree scores, the participants are grouped in three clusters [14]: central, active, and peripheral. The sociograms are integrated in the ReaderBench website, and they can be visualized at http://readerbench.com/demo/community. In addition, weekly snapshots were generated to see the evolution of the community from one week to the upcoming one, together with the most active participants during each week. Further, a heatmap with the most discussed topics was generated to analyze how different discussion topics impact the overall activity within the community. The last set of components are represented by the Rank Prediction which consists of machine learning models implemented using the scikit-learn (https://scikit-lea rn.org) [24] Python framework. The selected and most predictive model for this study was MLP, adequate for the size of our dataset. Labels are user rankings based on comment votes, a reliable indicator of the influence and perceived user image within the community. All features regarding CNA, regularity, and textual complexity are used individually and combined as input. For each set of indices, the most predictive ones were chosen using feature selection from scikit-learn.

4 Results 4.1 Community Visualizations Three types of sociograms were generated using the CNA indices provided by the ReaderBench framework, as well as the AngularJS (https://angularjs.org/) and d3.js (https://d3js.org/) libraries. In the first type of view—the Force-Directed Graph— members represent the nodes and edges are the posts exchanged between two members. The size of nodes is direct proportional with the average value of indegree and out-degree CNA indices, while the width of edges is direct proportional to the number and relevance of messages exchanged between the two members. Users are grouped into three clusters and marked as follows: central—blue color, active—green color, and peripheral—orange color (see Fig. 2.a centered on the 5th week within our timeframe). The Clustered Force Layout is the second type of view which presents the position of each member in the hierarchical structure (central—red color, active—gray color, and peripheral—white color) using circles directly proportional to their participation. Two forces are used to design this view: cluster—pushes nodes toward the largest node of the same color, and collide—prevents circles from overlapping by detecting collisions. Therefore, central members are place in the middle, surrounded by active members, which are then surrounded by peripheral members (see Fig. 2b with all participants from the 2nd week).

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(a)

(b)

(c)

Fig. 2 a Force-directed graph view of week 5. b Clustered force layout view of week 2. c Hierarchical edge bundling view of week 7

The last type of view is Hierarchical Edge Bundling which shows the dependencies between participants in a radial manner that groups the edges to improve views for communities with a large number of members and interactions. Members are placed on a circle, grouped in three semicircles based on their cluster: central—red color, active—black color, and peripheral—grey color. The connections between adjacent pairs of members on the circle are grouped together in bundles to simplify the community graph. On a mouse-over event, the incoming (dark blue color) and outgoing (red color) links are displayed (see Fig. 2.c for the 7th week). By correlating user identifiers with the computed rankings, we observed that users with the highest-ranking scores tend to be in the central and active clusters in most weeks.

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week

keyword

Fig. 3 Discussed topics heatmap

Further, a heatmap visualization with the most discussed topics was generated by using the keywords extraction mechanism from ReaderBench (see Fig. 3). The first 10 most discussed topics were extracted from each week and integrated in the heatmap visualization. It can be observed that most of the words are related to the political topic of the discussions (e.g., “society,” “capitalism,” “socialism,” or “economy”).

4.2 Social Ranking Prediction Table 1 includes the results obtained after training and evaluating the MLP-1 network using the regularity, CNA, and textual complexity indices to predict user rankings. One hyperparameter (i.e., the hidden layer size of the MLP-1 network) was incrementally set to 4, 8, 16, 32, and 64 to observe the variation of results. Mean absolute error (MAE) was used for evaluation. The results are considered better as MAE decreases, implying that the predicted labels are closer in average to the real ones. In general, MAE values decreased when the number of neurons in the hidden layer was Table 1 Results obtained with features from the 602 feature extended set Hidden layer size for MLP-1 Regularity features

4

8

16

32

64

15.68

16.05

15.74

15.68

15.70

CNA indices

15.66

16.46

16.01

15.67

15.66

Textual complexity indices

15.77

15.64

15.73

16.18

15.64

All features together

15.79

15.68

15.78

15.71

15.64

Bold denotes the lowest value for MAE (i.e., the best model)

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increased, with slights increases that are justifiable given the random initializations of the networks and potential overfitting on training data. The reduction of features after applying the variance threshold of 0.5 dramatically reduced the number of features that were considered as inputs to the neural network. Only the first two regularity features were selected (i.e., PDH and PWD), whereas the other three related to weekly measures showed a small variance which was not helpful to the models. The individual CNA indices were reduced from 10 to 8 due to multi-collinearity, while the textual complexity indices were filtered to 216 from a total of 1111 features related to writing style. All sets of features produced similar behaviors. More features are in general beneficial to the final prediction, as they consider different, complementary facets of the overall evaluation; however, fewer features as in the case of regularity and CNA indices were capable to generate accurate predictions. The results show that rankings were predicted with a precision of about 15 votes (i.e., around 4.5 votes on the logarithmic scale). Our results are valuable taking into account the total range of rankings of almost 700, with a maximum of 423 and a minimum of −247.

5 Conclusions and Future Developments Social media has seen a huge increase in recent years due to its ease and speed to connect people from different parts of world. The Reddit platform continues to grow, attracting more users and creating a huge variety of online communities, where people can share and discuss news, ideas, and build connections. Reddit provides a valuable pool of insights for academic research, in areas like natural language processing or social network analysis, due to the variety of people, discussed topics, and the wide range of community sizes. The aim of this paper was to perform a comprehensive analysis of a debate community from Reddit using the ReaderBench framework, by creating an automated pipeline that generates various indices used to predict a social rank, alongside interactive visualizations. Our system provides three types of services, namely Data Acquisition and Storage, Data Processing and Visualization, and Rank Prediction which can be used independently. Data was extracted from Reddit using PRAW and afterward indexed in Elasticsearch. The ReaderBench framework was used to generate various textual complexity and cohesion network analysis (CNA) indices, whereas multiple regularity indices were computed based on user’s activity. The participants were grouped in three clusters—central, active, and peripheral— based on their CNA in-degree and out-degree scores. Three types of sociograms were created in order to see the interaction between members and the associated clusters. In addition, weekly snapshots were generated to analyze the trends and to observe the evolution of the community throughout consecutive weeks. Results showed that users with the highest rankings tend to be part of the central or active groups. Moreover, a heatmap highlighting the most discussed topics was generated in which words related

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to the political topic of discussion were frequently used (e.g., “society,” “capitalism,” “socialism,” or “economy”). The results showed valuable connections between the ReaderBench indices, regularity indices, and user rankings. The predicted rankings computed using MLP were close to the actual ones, with a small difference in upvotes/downvotes considering the size of the whole range of rankings values. As follow-up directions, we plan to extend the current analyses to empower automated rankings of users in smart learning environments, mechanisms that can be used as incentives to encourage learners to be more actively involved (e.g., a gamified context in which users gain badges based on their ranks in the learning community). Moreover, we will also consider connections between weekly evolutions (i.e., trajectories that transcend snapshots) and overall rankings. Acknowledgements This work was funded by the Operational Programme Human Capital of the Ministry of European Funds through the Financial Agreement 51675/09.07.2019, SMIS code 125125 and by a grant of the Romanian Ministry of Research and Innovation, CCCDI—UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0689 /”Revitalizing Libraries and Cultural Heritage through Advanced Technologies.”

References 1. Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge, MA (1999) 2. Dascalu, M., Trausan-Matu, S., McNamara, D.S., Dessus, P.: ReaderBench—automated evaluation of collaboration based on cohesion and dialogism. Int. J. Comput-Support. Collaborative Learn. 10(4), 395–423 (2015) 3. Dascalu, M., Popescu, E., Becheru, A., Crossley, S.A., Trausan-Matu, S.: Predicting academic performance based on students’ blog and microblog posts. In: 11th European Conference on Technology Enhanced Learning (EC-TEL 2016), pp. 370–376. Springer, Lyon, France (2016) 4. Molina, B.: Reddit is extremely popular. Here’s how to watch what your kids are doing. Retrieved Sept 25, 2019, from https://eu.usatoday.com/story/tech/talkingtech/2017/08/31/red dit-extremely-popular-heres-how-watch-what-your-kids-doing/607996001/ (2017) 5. Pardes, A.: The inside story of reddit’s redesign. Retrieved Sept 25, 2019 from https://www. wired.com/story/reddit-redesign/ (2018) 6. Wasserman, S., Faust, K.: Social network analysis: methods and applications. Cambridge University Press, Cambridge, UK (1994) 7. Fan, W., Gordon, M.D.: The power of social media analytics. Communun ACM 57(6), 74–81 (2014) 8. Leskovec, J.: Social media analytics: tracking, modeling and predicting the flow of information through networks. In: Proceedings of the 20th International Conferences Companion on World Wide Web, pp. 277–278. ACM (2011) 9. Schoberth, T., Preece, J., Heinzl, A.: Online communities: a longitudinal analysis of communication activities. In: 36th Annual Hawaii International Conferences on System Sciences, pp. 10. IEEE (2003) 10. Angeletou, S., Rowe, M., Alani, H.: Modelling and analysis of user behaviour in online communities. In: International Semantic Web Conferences pp. 35–50. Springer (2011)

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11. Wang, Y., Li, X.: Social network analysis of interaction in online learning communities. In: 7th IEEE International Conference on Advanced Learning Technologies (ICALT 2007), pp. 699– 700. IEEE (2007) 12. Dascalu, M., McNamara, D.S., Trausan-Matu, S., Allen, L.K.: Cohesion network analysis of CSCL participation. Behav. Res. Methods 50(2), 604–619 (2018) 13. Stahl, G.: Group cognition, computer support for building collaborative knowledge. MIT Press, Cambridge, MA (2006) 14. Sirbu, M.-D., Panaite, M., Secui, A., Dascalu, M., Nistor, N., Trausan-Matu, S.: ReaderBench: building comprehensive sociograms of online communities. In: 9th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2017), pp. 225–231. IEEE, Timisoara, Romania (2017) 15. Trausan-Matu, S., Stahl, G., Sarmiento, J.: Polyphonic support for collaborative learning. In: Groupware: Design, Implementation, and Use, 12th International Workshop (CRIWG 2006), vol. LNCS 4154, pp. 132–139. Springer, Medina del Campo, Spain (2006) 16. Landauer, T.K., Dumais, S.T.: A solution to plato’s problem: the latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychol. Rev. 104(2), 211–240 (1997) 17. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach Learn Res 3(4–5), 993–1022 (2003) 18. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representation in vector space. In: Workshop at ICLR, Scottsdale, AZ (2013) 19. Sirbu, M.D., Dascalu, M., Crossley, S., McNamara, D.S., Trausan-Matu, S.: Longitudinal analysis of participation in online courses powered by cohesion network analysis. In: 13th International Conferences on Computer-Supported Collaborative Learning (CSCL 2019), pp. 640–643. ISLS, Lyon, France (2019) 20. Sirbu, M.-D., Dascalu, M., Crossley, S.A., McNamara, D.S., Barnes, T., Lynch, C.F., TrausanMatu, S.: Exploring online course sociograms using cohesion network analysis. In: 19th International Conferences on Artificial Intelligence in Education (AIED 2018), Part II, pp. 337–342. Springer, London, UK (2018) 21. Gardner, M.W., Dorling, S.: Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32(14–15), 2627–2636 (1998) 22. Majtey, A., Lamberti, P., Prato, D.: Jensen-Shannon divergence as a measure of distinguish ability between mixed quantum states. Phys. Rev. A. 72(5), 6 (2005) 23. Dascalu, M., Crossley, S., McNamara, D.S., Dessus, P., Trausan-Matu, S.: Please readerbench this text: a multi-dimensional textual complexity assessment framework. In: Craig, S. (ed.) Tutoring and intelligent tutoring systems, pp. 251–271. Nova Science Publishers Inc, Hauppauge, NY, USA (2018) 24. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V.J.J.o.m.l.r.: Scikit-learn: machine learning in python. 12(Oct), 2825–2830 (2011)

Tracing Humor in Edited News Headlines Dan Alexandru

and Daniela Gîfu

Abstract Due to the diversity of language interpretation capacities, the computational natural language understanding (NLU) systems should be able to recognize semantic complex situations. In terms of natural language processing (NLP), computational humor detection is still one of the major challenges, having several applications, especially on social media. In order to observe how easily it is to create humor out of a serious title, in this paper, we aim at the development and comparison of machine learning and neural network models on the humor prediction task in news headlines, using the dataset provided by SemEval-2020. The experimental results demonstrate that the proposed approach is able to improve the humor detection performance, generated by applying short edits to headlines. Keywords Humor detection · Humor scoring · Neural networks · Natural language processing

1 Introduction The figurative language of media (e.g., irony, sarcasm, humor) is so frequently used that it becomes a significant problem for NLP systems [1], since it requires a multidisciplinary approach for its detection. Especially in the context of artificial intelligence (AI), the figurative language research aims at modeling it in a computationally tractable way [2]. Part of the previous work has been done in attempting to identify humor in text, extending whether or not a joke is humorous [3]. D. Alexandru (B) · D. Gîfu Alexandru Ioan Cuza University of Iasi, General Berthelot 16, Iasi 700483, Romania e-mail: [email protected] D. Gîfu e-mail: [email protected] D. Gîfu Romanian Academy—Iasi Branch, Codrescu 2, Iasi 700481, Romania © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_16

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One of the latest trends in the media is to create humorous situations through editing news headlines. It is well known that the headlines direct readers to construct the desired context for interpretation [4]. The goal of this paper is to study how the humor is generated by changing a word/group of words to headlines and how we can make the machine to understand the level of humor in the modified headlines. In order to find solutions for both tasks, we build multiple models able to predict whether or not an edited headline is funny. We use the dataset provided by SemEval-2020, and we annotate it according to the intensity of the humor. This process has been previously modified manually by annotators by replacing specific words/group of words with others in order to make them funny or humorous. Finally, we classify and estimate the humor intensity of those headlines, based on the described methods. The remainder of this paper is organized as follows: Sect. 2 briefly presents existing approaches to humor detection, Sect. 3 describes the dataset and methodology of our system, and Sect. 4 discusses the results. Finally, we conclude with an assessment of the humor detection on short text, and we identify avenues for future research.

2 Previous Works We are witnessing an increasing interest in the inclusion of humor in online media headlines. In recent years, humor takes different forms: comedy-based computing such as Manatee [5], the joke writing computer, STANDUP—System to Augment Non-Speakers’ Dialogue Using Puns [6, 7], SASI the sarcasm detector [8] or DEviaNT [9] were developed with promising results [10]. In addition, humor raises new forms of uncertainties in media. Recognizing humor has great applicability in social networks and human–computer interactive (HCI) systems. Moreover, generating humor is becoming a complex and challenging problem. For instance, funny acronyms [11] or jokes as: “I like my coffee like I like my war, cold” [12]. Many computational linguistics specialists have also automatically analyzed conversational behavior on social media (e.g., tweets). Jihen Karoui and her team developed a multilevel annotation schema [13]. After building the dataset, they discovered that there were words that contradicted their initial hypothesis. On the other hand, based on [14, 15] studies, Francesco Barbieri and Horacio Saggion [16] presented a system that analyzes irony and humor extracted from different tweets. In this sense, they implemented a system that analyzes emojis and the punctuation marks used in those tweets focusing on ironic style detection. They have also analyzed different news headlines using the same method. Furthermore, Veale and Hao [17] developed an algorithm that separates the usual or common structures from those that use irony. Another model recognizes the structures in which ironic terms appear, and they grouped them in four different categories: signatures, unexpected situations, style and emotional situations [18].

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As we previously mentioned, Mihalcea and Strapparava studied those short and funny structures used in headlines. They created a database with different structures that contain the symbolic words, such as “laughter,” “humor” and “joke” from different websites’ URLs. Knowing that the British National Corpus provides for this kind of survey different riddles, sentences and significant titles, they used these as a benchmark. In order to detect the irony’s structure, the study was based on decision trees and random forests. In addition, part of the researchers applied the same algorithms, for four types of texts: short texts (Twitter), long texts (such as discussion forum posts), transcripts (TV or call center conversations) and miscellaneous datasets [19]. On one side, they formed a set of features for alliterations, antonyms and slang that characterizes certain linguistic features. On the other side, they created an algorithm that classifies the texts according to a few symbolic words. In conclusion, the word-based classifier is better that the one based on pre-established rules. Joshi et al. [20] focused on identifying sarcasm through specific evidence meaning. The form and the rules that extract similes (of the form “* as a *”) were captured using Google searches. The method used nine steps, including an error analysis corresponding to each of them. A hashtag tokenizer was used to split the hashtags made of concatenated words. Also, two rule-based classifiers that identify sentiment of situation phrases, highlighting the sarcasm from interjections and intensifiers, proved a promising way. Basically, most previous algorithms are based on SVM-based classifiers, naïve Bayes or random forest. Following this, we decided to compare both algorithms with a neural network in order to gauge the differences.

3 Dataset and Method In this section, we propose two methods to automatically assess humor on the dataset provided by SemEval-2020 Task 7: “Assessing Humor in Edited News Headlines.” Table 1 contains sample rows from the training set which has been cleaned in order to develop the model described in Sect. 3.2. Table 1 Sample data points from the training set Original headline

Substitute

President vows to cut taxes

hair

Bill aiming to protect Christians, other minority groups in Pakistan may soon be marry law Oklahoma isn’t working. Can anyone fix this failing American state?

okay

Facebook defends advertising “principles” after Russia, discrimination

kindergarten

If America is great again, why is the dollar slowly sinking?

intelligence

Italian president blocks eurosceptic coalition govt

dog

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3.1 Dataset The present dataset, called Humicroedit, was presented by Hossain et al. [21], and it consists of news headlines from news media posted on Reddit (reddit.com). These headlines have been edited (over 15,000) for a humorous effect by expert annotators from Amazon Mechanical Turk. Due to the importance of headlines in news, this dataset focused on humorous headlines could be considered a significant start for computational humor research. In fact, humorous headlines offer essential and deep content given their length. Based on these considerations, the humor comprehension requires the development of NLP tools that are both capable to recognize humor pattern and to support alternative guidance for deeper (semantic) understanding. Each edited headline is rated by multiple reviewers (see Table 2), and each reviewer gives a score on a 0–3 scale, having the following meaning: 0—Not funny, 1—Slightly funny, 2—Moderately funny and 3—Funny. The dataset is heavily skewed toward five reviewers. Since this is the dominant sample, we select only the headlines that are reviewed by exactly five reviewers. We can observe that the dataset is imbalanced (with a strong tendency toward “unfunny” headlines in the group’s view). For classification, we have considered “funny” the headlines with a reviewer score sum ≥7 (which is also Q3 based on this reviewer score sum histogram—selected for headlines with five reviewers) (Fig. 1). We have built models for both: • Classification—the easier sub-problem, we consider a headline humorous if it has a total score of at least 7, i.e., at least 2/5 reviewers thought it is funny and 1/5 that it is slightly funny or other combinations of the scores. • Regression—more difficult to have an accurate model, we use the reviewer score sum for target (to predict). In order to generalize the solutions built for this dataset, we can augment it using previous datasets such as the one described by Khodak et al. [22] (scraped from Reddit posts) or IberLEF 2019 “Humor Analysis Based on Human Annotation” challenge (description of the challenge and the winning solution [23]). Table 2 Correspondence between reviewers and headlines from the dataset

Number of reviewers

Counts

1

523

5

8987

10

136

15

6

Tracing Humor in Edited News Headlines

Fig. 1 Reviewer score sum histogram—based on the sample of five-reviewed scores

Fig. 2 Some headlines that are considered funny using the sum grade ≥7

Fig. 3 Some headlines that are not considered funny using the sum grade ≥7

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Fig. 4 Naïve Bayes and its accuracy for classification on our dataset

Fig. 5 SVM and its accuracy for classification on our dataset

3.2 Method The phrases have been cleared of stop words and have been tokenized and stemmed. The stemmed phrases and scores are used for the training data and target, label encoded and processed further with TF-IDF. In order to establish a baseline, we have initially used naïve Bayes and SVM for the classification sub-problem. Intuitively, the classification problem is easier than regression and as such accuracy scores obtained are very high (Figs. 4 and 5). The next step was to build several neural network models and see how they score for both classification and regression. We have picked shallow neural networks as a starting point in order to establish a baseline for neural networks on this dataset. We have used ReLU activation, dropout, batch normalization and the ADAM optimizer. The final layer activation function is sigmoid for classification and linear for regression. The design for classification has been based around the neural network extrapolating the identified features in the TF-IDF results and making the decision based on that (Fig. 6). The design for regression has been based around the network making a representation of the original data and compressing it, reducing to a linear result (Fig. 7).

4 Results Multiple metrics have been collected through Keras and TensorBoard during training (through Keras TensorBoard callbacks). We can observe a big difference in training time—the training time for the regression model is three times longer. All training occurred in a local nvidia-docker container, and TensorBoard was accessed through a ngrok tunnel (Figs. 8 and 9).

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Fig. 6 Shallow neural network architecture for humor classification

Fig. 7 Shallow neural network for humor scoring (regression)

We can see how brittle is the solution by shifting the scores in the dataset or adjusting the train–test split. Further work has to be done on cross-validation. BERT embeddings have also been used as part of this research, though more investigation on the results is needed. Since humor is highly dependent on culture, for instance in religious speech [24, 25], a more refined approach (at the expense of the risk of overfitting or increasing variance) could be made by exploring how frequently idioms are used in this specific dataset and their impact on the classification model. A collection of approaches in this direction has been previously identified by Salton (2017) [26].

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Fig. 8 Using TensorBoard for tracking metrics during training. We have used it to aggregate logs from all architectures explored and compared the results

Fig. 9 Tracking the accuracy of our models. It can be seen that our shallow neural network models plateau fast (and classification tends to overfit here)

5 Conclusion This paper reflects the fact that while humor classification is an explored and easy problem (in general, but results can vary depending on the dataset used), regression/scoring is significantly more difficult to do with a reliable accuracy. Here, the

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Humicroedit dataset, consisting in over 15,000 headlines with simple edits (word by word, designed to make them funny) was leveraged by building several features upon it. We showed how the present dataset supports, in a quantitative way, different humor evaluation approaches. Indeed, the results obtained with baseline classifiers are promising, given the fact that the training dataset is still small. In fact, these classifiers show how easily we can predict (1) the mean funniness of the edited headline or (3) which edited version is the funnier of the two. Taking this into consideration, we have to consider more improvements for this model: tracking more metrics, augmenting the original dataset or comparing to current benchmarks done with BERT. But the most challenging research direction yet to be investigated is how to incorporate the cultural dimension for humor detection on social media.

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Data-Driven Intelligent Tutoring System for Accelerating Practical Skills Development. A Deep Learning Approach Robert Marinescu-Muster, Sjoerd de Vries, and Wouter Vollenbroek

Abstract Our data-driven intelligent tutoring system presents promising results in supporting and accelerating the skills acquiring process. For example, mapping of the common latent variables enables the instructors and curricula designers to understand better the relationships between different exercise items and thus to create improved training scenarios. The case study results also reveal significant improvements in accelerating the process of training welders: participants gradually started to improve their welding skills after only 15 trials (approximately 1 hour of training using the system). Keywords Intelligent tutoring systems · Deep learning · Practical skills mastery · Data-driven education module

1 Introduction In the last years, the advancements in computing capabilities coupled with increased availability of huge volumes of data and deeper knowledge on computer algorithms, revitalized artificial intelligence (AI) as a key component in various domains, from automotive industry to medicine. In itself, artificial intelligence is an umbrella term describing machines that simulate intelligence by performing large amount of computations at very high speeds. For instance, a self-driving car is basically a computer program which learns about the environment by detecting patterns from large sets of images containing vehicles, people, traffic signs, etc. It is then able to “see and understand” data from sensors by calculating in real time the probabilities that an object on the road fits in one of the learned categories: vehicles, pedestrians, traffic signs, etc. The ability to uncover patterns from past information and to recognize them in new data made artificial intelligence extremely useful in education. Predicting students R. Marinescu-Muster (B) · S. de Vries · W. Vollenbroek University of Twente, Drienerlolaan 5, 7500AE Enschede, The Netherlands e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_17

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achievements, knowledge tracing, automatic evaluation of students’ answers in open questions, or intelligent tutoring systems are just a few applications of AI in the education field. In particular, intelligent tutoring systems (ITS) powered by AI have shown impressive results in decreasing training time and improving education quality [1, 2]. Therefore, there was a vast interest in the last three decades for establishing intelligent tutoring systems, designed specifically to support cognitive skills development such as mathematics, physics, and chemistry. However, little research was done recently regarding the development and implementation of ITS for supporting practical skills mastery. This is in spite of the fact that early studies have shown that the length of learning sessions is reduced, while the quality of skill development is improved considerably when practical training is supported by ITS [3]. For instance, Lesgold et al. [4] showed that 20 h of training using Sherlock system (a ITS for detecting plane breakdowns) was equivalent to 4 years of experience. Other studies in this area showed significant improvements in acquiring rhythm and pitch skills with piano [5], significant decrease of required instructor need and increased learning quality for power plant operator trainees [6], and promising results from improving welding skills by using virtual reality space as a training method [7]. In general, intelligent tutoring systems designed for practical skills development are seen as highly beneficial, especially for addressing the need for highly skilled workforce [3, 8]. The gap in research and development of intelligent tutoring systems for professional practical skills mastery is apparent, most of the research being done in the late nineties and early two thousands. Julian and Smith [9] also noted recently about the lack of research and development of intelligent tutoring systems for medical training, specifically in procedural and practical skills (i.e., training for robotic assisted laparoscopic surgery). We believe that there is a need for revitalizing research on this topic because the current state of the art in artificial intelligence allows for new insights in this field. This study aims at bridging this gap by proposing a data-driven method using deep learning algorithms for designing an intelligent tutoring system to support practical skills development. The paper is structured as follows: In the next section, we present our approach using recurrent neural networks (a class of deep learning unsupervised artificial intelligence neural networks [10]) for analyzing trainee activity and uncovering relevant patterns of performance. Then, we describe a data-driven monitoring infrastructure of an intelligent tutoring system and outline the results of our approach in a case study of a deep learning training module for shielded metal arc welding (SMAW) intelligent tutoring system-based training. We conclude with a short reflection on future perspectives of such approach.

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2 Data-Driven Deep Learning ITS Framework The main goal of an intelligent tutoring system is to support both students and instructors in achieving higher learning quality. The benefits of ITS as support for the traditional tutoring methods are related mainly to the possibility of continuous student activity data processing for uncovering relevant patterns of performance.

2.1 Data-Driven ITS In the context of practical skills mastery, the generated data stream is usually high-dimensional and unstructured. Activity data is typically collected through a matrix of various sensors which include motion sensors, cameras, wearables, etc. Processing such amount of data is often one of the common deterrents in developing advanced ITS targeting practical and procedural skills mastery. Additionally, the typical machine learning approach on performance pattern recognition requires supervised machine learning processes in which data needs to be classified a priori and all relevant features need to be provided to the system. This is often a tedious process which requires extensive collaboration between instructors, researchers, and ITS developers. Moreover, it also runs the risk of omitting relevant features which describe trainee’s learning model. There are specific requirements and limitations of traditional tutoring methods that make ITS extremely beneficial: • First, the instruction time is often long because of trial-and-error practice cycle, especially for novices; • Second, the experiential nature of the practical tasks induces a limitation on the number of trainees an instructor can train at a given time; • Third, the amount of feedback loops is inversely proportional with the group size,as a result, instructors often focus on one particular group of trainees in detriment of the others (e.g., focusing on the ones who make more errors, either on the ones who make the most progress); • Fourth, the costs of traditional practical training are extremely high, especially in industrial or medical contexts. Effective intelligent tutoring systems typically address most of these problems by continuously monitoring the trainee activity, provide instant feedback and correction methods and provide instructors with detailed performance dashboards of their trainees. The key aspects of effective ITS are personalized feedback resembling one-to-one tutoring and the ability to generate personalized learning paths for optimizing the instruction time and the learning outcome [11, 12]. In order to achieve these, ITS require continuous access to data streams.

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2.2 Recurrent Neural Networks The advent of recurrent neural networks (RNN) offers promising advantages as compared to traditional machine learning algorithms. First, RNNs are a chained set of artificial neurons in which information is propagated recursively over time. This makes RNNs extremely suitable for analyzing sequential data. Second, in RNNs, the hidden layers of the network develop recursively on both the system input and on their previous state. This makes RNNs suitable for learning complex patterns that require also a form of memory over time [13], i.e., retaining previous states and either use it or forget it when needed. This is a particular class of RNN named Long Short-Term-Memory (LSTM). Third, RNNs perform particularly well in fitting continuous, high-dimensional time series (or sequential) data as input and predicting outcomes at later points in time. This renders RNNs as state-of-the art algorithms in speech recognition and translation [14], facial recognition [15], medical diagnostics [16, 17], or knowledge tracing in cognitive skills ITSs [18]. Building on these advantages, we developed a data-driven RNN framework as a basis for an intelligent tutoring system designed to accelerating the instruction time for mastering practical skills. We propose the LSTM variation of RNNs to predict trainees’ performance in future trials based on their previous activity.

2.3 Deep Learning Framework At the core of the proposed framework lies the ability to detect performance patterns from unstructured activity data collected by sensors in past training trials and to predict the performance in the next trial. In general, if the predicted outcome is over a particular threshold (e.g., 80%) in the next trial, the system advises advancing to the next practical exercise. Conversely, if the predicted performance falls below the threshold, then the system advises repeating particular exercises linked to the actual performance. There are four main outcomes of our proposed model: 1. map the exercise relationships—discovering the relationship between learning items can provide information about the latent structure of learning concepts related to practical skills, allowing clustering them based on direct influence between items; 2. generate a personalized skills training curricula—generating the optimal path for accelerated skill acquiring and stabilization of skills; 3. predict the probability of a failure in the next trial—given that the parameters remain constant, the model predicts the probability of failing to achieve a minimum success ratio in the next trial;

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4. develop a personal attribution model of practical skills—each trainee has its own personal style, which becomes apparent from the clustering of the main parameters across trials. This can reveal valuable insights for optimizing the practical skills training path based on the continuous follow-up on the evolution of such clusters. For instance, it can be seen that two parameters are directly influenced. This means that when the trainee makes mistakes in one parameter, the other will also be affected negatively. Such information can therefore provide personalized training advices by the instructor. LSTM Model The model is designed from the assumption that there are three stages of acquiring practical skills [4]: 0—initial state, no skills are yet acquired. In this stage, the model offers a set of minimum set of assessment trials for both basic skills acquiring and assessment of the initial knowledge state. 1—the trainee acquired skills derived from practice in blocked batches—namely the trainee has developed specific patterns related to particular situations (through repetition). The model can use the data to predict trainee performance on a set of competencies (clustered skills) and to offer a personalized optimal path for accelerating the skill development and sustaining retention and transfer. 2—the trainee is able to perform in novel, complex and difficult situations without dropping performance. Our model can develop a curriculum for skill retention and transfer. For instance, it increases the complexity and difficulty of the tasks for continuing the skill development (i.e., adjusting the thresholds of the parameters, change the exercise type, add more difficult items, etc.) (Fig. 1). The probability of a failure in the next trial is given by the equation:

Fig. 1 LSTM adaptive deep learning model

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  P(y|x1:n ) = P nnet y (pool{LSTMx1:n })

(1)

The LSTM cell has three gates which control information flow. First gate is the forget gate—this part uses a sigmoid function to decide whether to keep or not the previous cell state (ht −1 ). Next, the cell decides which information to store in the cell state using a sigmoid function (the input gate) and then using a tanh function to generate a new vector of values. Then, a pointwise multiplication is applied to update the cell state. Finally, the output gate filters the information by using a sigmoid layer (outputs 1/0) and then pushing the output through a tanh function (the output in − 1/1). These can be expressed mathematically by the following equations:1 Forget Gate:   f t = σ (W f · h t−1 , xt + bt )

(2)

  i t = σ (Wi · h t−1 , xt + bi )

(3)

    t = tanh WC · h t−1 , xt + bC C

(4)

t Ct = f t ∗ Ct−1 + i t ∗ C

(5)

t Ct = f t ∗ Ct−1 + i t ∗ C

(6)

t Ct = f t ∗ Ct−1 + i t ∗ C

(7)

Input Gate:

Output Gate:

There are three main steps of training the LSTM model: 1. Data preparation—input data is structured as fixed length vectors, and the values are normalized using MinMax normalization method [19]. Error events are computed based on specific skill performance requirements (e.g., if the observed values reach below an accepted predefined tolerance). 2. Parameter initial optimization—stochastic gradient descent optimized for accuracy and loss minimization. Several dropout ratios are applied in successive 1 Parameter

description W f = a matrix of learned weights connecting input neurons to hidden layers (ht ); ht−1 = the previous hidden state x t = the input vector t = timestamp b(.) = scaling factor t = candidate values vector C t = cell state, C it = input vector

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neuron layers to prevent overfitting. To prevent gradient explosion during backpropagation, we propose gradient clipping thresholds for truncating the gradients if they overpass the threshold. 3. Hyper-parameter optimization—needs to be performed based on particular architecture of the model and the data set. We recommend at least 128 hidden units and 200 epochs, in line with findings presented by Piech et al. [18].

3 Data-Driven Infrastructure of a ITS In the practical skills mastery contexts, data is generated from various sensors in real-time or at a high-speed frequency. This puts immense stress on the computing architecture that forms the backbone of the intelligent tutoring system. Considering also the high-dimensionality and the inherent unstructured nature of the data (i.e., sensors can go offline or new sensors can be added based on the training tasks), the system needs to be designed flexible-first. Our data-driven computing architecture for an ITS in practical skills training is as follows: • • • •

sensor data is stored in a distributed file system as raw data files trainees; data and other relevant information are stored in SQL databases the data is processed in parallel clusters for speed and resilience the deep learning module is a standalone machine which communicates through APIs • results are processed and then presented in an Web-based environment. The picture in Fig. 2 illustrates the proposed ITS.

Fig. 2 Computing infrastructure for a data-driven ITS

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4 Case Study—the Augmented Welder Data-Driven Deep Learning Intelligent Tutoring Module Application Standard training method in shielded metal arc welding (SMAW) can be described as a linear learning model: The instructor sets up a batch of exercises in accordance with the level, duration of training and certification required. Typically, exercise items follow a linear path, from easy to difficult, and the welding scenarios are varied similarly. The instructor switches from trainee to trainee to evaluate their performance and provide corrections if needed. The augmented welder (TAW) is a training tool designed by Institut de Soudure-IS (France) to support standard training methods and to speed up welding skills development for beginner welders and to improve the expertise of advanced professionals [20]. In 2019, we implemented our proposed intelligent tutoring system based on the deep learning framework as a data-driven education module in TAW.

4.1 Method The goal of the module was threefold: first, to shorten the training time from absolute beginner to novice and advanced welder; second, to enable “hyper-leaps” on the linear learning process (that is, to enable trainees to jump directly to a more complex scenario if they prove sufficient mastery in the current skill level) and to provide advice in this respect; and third, to provide immediate feedback based on the current performance and to enable access to performance metrics to both instructors and trainees. We used the data from TAW system collected during January–October teaching sessions in 2019 to train our deep learning model. We used a 80–20 training-validation split ratio on all the model variations employed. From the training partition, we kept data from three participants for testing purposes. In the process of optimization, we included three versions of the model based on the trial windows to predict the performance of the trainee in the next exercise: (a) using data from last three trials (b) using data from last trial (c) using data from all trials. The model using last trial window as input data performed best in terms of accuracy and loss minimization (Accuracy = 87%. L = 0.1621). The model using all trials performed the worst, 67% accuracy and a loss minimization result of 0.4451. One potential explanation is that using all trials data could introduce bias toward the beginner welders (thus being more prone to welding mistakes) because they were over-represented in the dataset (more than 50%). On the other hand, using only the last trial window made possible a more appropriate estimation of the ratio between mistakes and successes. We will explore these aspects in future trials.

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Fig. 3 Welding exercises used during trials

4.2 Dataset The dataset for implementing the model consisted in welding results from 20 trainees (17 male trainees, and three female trainees) in 536 trials originating from six welding exercises. The exercises are presented in Fig. 3. In total, there were around 31 h of trial data. Most of the participants used right hand as the dominant (N = 14 participants) and were beginners (N = 11), followed by five experts and four advanced welders. The average height of the trainees was 182 cm (SD = 4.1013 cm.), and their average weight was around 79 kg (SD = 6.6353 kg). Each welding session generated on average approximately 450 data points (at a frequency of 2 s) covering 11 different sensors (angles, speed, arc-height, etc.). The total data pool for training the deep learning model consisted in 139.620 valid observations.

4.3 Results The deep learning data-driven module using last trial data window performed substantially better than the others presented in the method section (AUCLastTrial = 0.87, AUCLastThreeTrials = 0.78 and AUCAllTrials = 0.67).2 The exercise relationship map is providing an overview of the influence between the exercises, moderated by the exercise difficulty and number of trials available in the database. The map is realized by the model from the relationships inferred from the available data. It can be seen that the most difficult exercises are having the most influence on the others and that exercise 10/15 have a considerable influence on the more difficult exercises. The model predicts that performing well in exercise 10/15 (medium difficulty) will likely result in a good performance on exercise 10/22 which is more difficult. Hence, it can advise the trainee to move to the more difficult exercise if sufficient mastery is attained instead of repeating the same exercise. The 2 AUC = area under the receiver operating characteristic curve (ROC), representing the area under the

discretized curve of precision versus recall values (estimating the probability of a binary outcome). More detailed explanations are available in [22].

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model calculates the likelihood that exercise T influences exercise W, in the sense that performance on exercise T will influence the performance on exercise W (because of shared latent attributes). The exercise relationship mapped by the model is presented in Fig. 4. The results of the model are available online to both trainees and instructors. This allows the trainees to investigate their performance and reply particular low performance moments. The instructors can evaluate the trainees on all or partial trials, can rate the performance, and has the possibility to evaluate the quality of the predictions of the deep learning ITS module. An excerpt of the data-driven feedback dashboard can be seen in Fig. 5. The upper right part of the figure illustrates the trial success predictions and the optimal training path for this trainee (Fig. 5).

Fig. 4 Exercise relationship map

Fig. 5 Data-driven ITS feedback dashboard—trainee view. In the picture, red represents higher mistake ratio, while blue means lower. The training expertise gain is visible in the chart by the changing of colors on the majority of the parameters monitored (P1, P3, P4, and moderately improved on P2) after Trial 13

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5 Discussion In this paper, we introduced a full-fledged deep learning framework for developing intelligent tutoring systems in practical skills mastery, together with a case study presentation of a pilot implementation. We built our approach to address the need for developing intelligent tutoring systems for practical skills mastery, which are extremely important in training and continuous professionalization of skilled professionals. The system presents some very promising results. First, the mapping of the common latent variables enables the instructors and curricula designers to understand better the relationships between different exercise items and thus to create improved training scenarios. Second, the system allows for continuous data collection and is designed with flexibility at the heart. Last but not least, the data-driven intelligent tutoring system based on deep learning algorithms removed the need for labeled data. As such, there is no need for the instructors to pre-evaluate and label the trials used to train the models. Instead, the LSTM is able to work with any input that can be expressed as a sequential vector. One limitation of the deep learning framework is the need for substantially large amount of training data. Therefore, it can be suitable in setups which have the possibility of generating large volumes of data (e.g., industry, medical) but not in small groups. We should also consider the interpretability of such recurrent neural network models as an important limitation, especially in the context of predicting human skills acquisition based on past performance where specific temporal events are important. One way to address this is to use a combination of attention and post-analysis methods as suggested in [21]. The implementation of artificial intelligence in developing intelligent tutoring systems for practical skills mastery leaves many directions for future research and development. For instance, further research can focus on different industrial processes which require similar training facilities. Similarly, another direction can explore the impact of such tools in developing continuous professional education programs, for accelerating the mobility from beginner to advanced users. We continue the collaboration with Institut de Soudure for deepening our understanding of the proposed framework and for intensive validation of the model.

6 Conclusion Our data-driven Intelligent Tutoring System presents promising results in supporting and accelerating the skills acquiring process. For example, mapping of the common latent variables enables the instructors and curricula designers to understand better the relationships between different exercise items and thus to create improved training scenarios. The case-study results also reveal significant improvements in accelerating the process of training welders: participants gradually started to improve their

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welding skills after only 15 trials (apprx. 1 hour of training using the system). The data-driven module is a critical module on the TAW platform and is expected to open advanced opportunities for welding training and professional welding. We consider it as an example of a “data-driven skills training platform.” The design is expected also to be applied in other professional skill set domains. Our innovative analytics approach, the distinction between learning, design, and quality analytics, has proven to be applicable and practical.

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Exploratory Analysis of a Large Dataset of Educational Videos: Preliminary Results Using People Tracking Eduard Cojocea and Traian Rebedea

Abstract The impact of data processing and analytics in education has seen a significant increase in the last decade. The volumes of data produced in education are constantly on the rise, not only in online education, but also in more formal settings. Learning analytics has concentrated mainly on mining patterns in student and teaching interactions by using data logs and texts (e.g., discussion forums, chats, social networks) which are widely available in online learning platforms. However, videos from educational settings have received a smaller interest from the learning analytics community. Educational videos are becoming popular, and there are large volumes shared on different social networks and video platforms. At the same time, deep learning provides powerful techniques for understanding the content in educational videos. In this paper, we propose an exploratory data analysis using educational videos from the YouTube-8 M dataset, one of the largest video datasets and most varied to date. Our preliminary study uses state-of-the-art people tracking neural models to extract features that are then used to cluster educational videos based on the number of people involved. This allows us to identify various educational activities in the YouTube-8 M dataset and to estimate the distribution of these activities from videos uploaded by teachers and learners worldwide. Keywords Video learning analytics · Exploratory data analysis · Educational videos · People tracking · Educational activity detection · Deep learning

E. Cojocea (B) · T. Rebedea University Politehnica of Bucharest, 313 Splaiul Independentei, Bucharest, Romania e-mail: [email protected] T. Rebedea e-mail: [email protected] Open Gov SRL, 95 Blvd. Alexandru Ioan Cuza, Bucharest, Romania © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_18

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1 Introduction As digitization and the data-centered industrial revolution are changing our entire society, education is also transformed by the technologies that allow us to record and analyze large volumes of data [1]. The collection of data from educational settings has seen a significant increase in the last decade, with various datasets being stored and analyzed, ranging from educational resources usage logs, learner interactions and outcomes, and online conversations between learners, to social networking usage or educational videos [2, 3]. All this abundance of data has facilitated the development of new methods for analyzing educational traces mostly in online classes but also in formal settings. While educational data mining (EDM) has focused on using data analytics to extract some value and information from large education datasets to better understand learner outcomes and scenarios [2], learner analytics (LA) emphasizes on intelligent data analytics for “optimizing opportunities for online learning” [3] from an educational perspective by providing relevant information for learners and teachers. Dutt et al. [2] highlight that most EDM applications employ clustering and other unsupervised data mining techniques to analyze student motivation and behavior, to better understand learning styles and student outcomes in online learning or in computer-supported collaborative learning (CSCL). At the same time, LA uses data and intelligent processing to facilitate a better learning experience, moving away from summative assessment and a restrictive view on student grades and learning outcomes to provide improved and timely feedback to learners [3]. At the same time, in the last years, advances in deep learning have fueled significant progress in computer vision [4]. A wide range of deep neural models, mainly using convolutional neural networks (CNNs), have increased performance for a wide range of tasks that are also useful in educational video analytics, such as object detection and tracking. Thus, they allow us to develop computer vision applications that do not depend on heavily controlled environments and ideal cases. Neural models for object detection and tracking, but also for extracting features (such as age or even gender) about detected people in videos, must have high performance and speed in order to be used on raw real-world videos, even filmed at a poorer quality. These technologies will have a huge impact in improving human life, as they allow us to develop better systems in various domains, including medicine and education. To this extent, medicine has already greatly benefited from these advancements by improvements in disease detection, assisted surgery, and many others [5]. Video analytics can also greatly improve education by massively mining patterns in videos from various educational settings to determine student engagement or to provide feedback for a wide range of physical activities. Burgess and Green [6] suggest that YouTube and other video sharing platforms are key elements at the foundation of a participatory culture. This directly impacts learning and creativity, with video sharing helping inclusiveness and diversity and promoting “learning with others.” The benefits of video sharing in learning communities are linked to fostering reflection and communication, improving peer and

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collective learning, and providing the support for creative and innovative communities to easily transmit ideas. Of course, there are also disadvantages mainly related to discriminating between “quasi-factual documentary videos” and relevant information in videos. Usage of video analytics has been very sparse in the educational data mining and learning analytics communities until recently. Several factors can explain this lack of exploration of educational video analytics, including the immaturity of technology for complex real-world videos and the perceived privacy invasion related to computer vision applied to the analysis of people-related images and videos, including from real educational contexts [7]. In this paper, we describe the preliminary results of an exploratory data analysis of educational videos extracted from one of the largest video datasets, YouTube-8 M [8]. Using state-of-the-art object detection and tracking neural models, we extract information such as the total number of unique persons in a video and the evolution of the number of persons in time throughout a video. Using videos with specific tags (e.g., “school”) from the YouTube-8 M dataset, even with simple features, such as the aforementioned ones, we are able to extract relevant information about a wide range of educational videos shared from all over the world. Thus, our main assumption is that the number of persons in educational videos can be relevant to identify specific learning activities. As an example, a small number of persons may indicate a course taking place or learning in small groups, while a large number of persons may indicate a sports event, a ceremony, or a school commercial. The paper continues as follows. Section 2 provides an overview of current solutions in educational video analytics and also presents the neural models used for people detection and tracking to analyze educational videos. In Sect. 3, we describe the proposed exploratory data method based on features extracted with neural models for people tracking, together with the underlying dataset of educational videos. The results of our data exploration are presented in Sect. 4, together with a distribution of educational activities discovered in the videos and some video examples. In the final section, we comment on the limitations of the proposed approach and offer some possible future research directions.

2 Related Work In this section, our aim is twofold, to highlight the existing research in educational video analytics and to present the neural models used for object detection and tracking. The main objective of this section is to bridge the gap between the educational perspectives on video analytics and the state of the art for object detection and tracking. This can help the smart learning ecosystems community understand how computer vision can reach its full potential for video learning analytics, as well as the pitfalls and limitations of applying current technologies for educational videos.

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2.1 Educational Video Analytics Learning analytics (LA) and educational data mining (EDM) are using large datasets to extract relevant insights to improve learning and education. However, most of this work has focused on mining logs from various online learning platforms, mostly massive open online courses MOOCs) [9], or investigating learner-learner or learner-teacher interactions, either in texts (chats, discussion forums) [10] or in social networks and communities of practice [7]. However, throughout the current landscape of LA and EDM applications, educational video analytics seems to be a very incipient topic. We consider that leveraging the recent advances in computer vision will benefit educational practitioners to gain valuable information, such as learner engagement and motivation or better understanding different learning activities, to improve learning. While there are some recent studies that emphasize about the importance of analyzing videos from learning settings to improve outcomes of students [11], most of them still use manual annotation. To this extent, we propose an automatic data exploration of large datasets of educational videos, such as the ones included in the YouTube-8 M dataset [8]. Using crowdsourced video collections, such as videos from YouTube, offers us a large coverage of educational activities from all over the world. Moreover, using machine learning and automatic analysis, we can solve some of the difficulties regarding manual data labeling, which are a significant bottleneck in exploring large video datasets. Labeling and exploring the data are especially time consuming when the data consists of a large number of video streams. Thus, automatic tools that facilitate exploring a video dataset are necessary. Nevertheless, there has been progress in educational video analytics in recent years. For example, Chatbri et al. [12] propose to use convolutional neural networks (CNNs) to classify educational videos from several MOOCs by topic. However, the CNN is applied on an image built using keywords extracted from videos using speech to text, and therefore, they do not use the actual data in the videos. Li et al. [13] use a small collection of videos from a classroom to identify 15 different types of actions relevant to a formal learning context using CNNs. Educational videos from YouTube have also been explored in previous work to some extent. In this context, Shoufan [14] analyzes the factors that explain the impact of educational videos, such as the average rating or number of views. The study finds that pretraining, modality, spatial contiguity, and embodiment are the most important factors that explain the cognitive value of a video. However, the proposed factors do not take into account any features extracted using computer vision or educational video analytics. To this extent, we suggest that exploring a large educational video dataset using educational video analytics can offer valuable information regarding the activities taking place. A simplistic approach is to measure the number of people in each video and what other objects are present. The extracted insights can provide us a useful overview of learning activities taking place all over the world.

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2.2 Object Detection and Object Tracking for Educational Videos Object detection is a computer vision task which implies detecting the existence of objects in an image, classifying the objects and estimating their location within the image. In the last decade, CNNs dominated the computer vision field, and in turn, they represented main solution for object detection. There are many CNN architectures that attempt to solve object detection, each with its advantages and disadvantages. Some of the most popular solutions are models based on regions, such as Region-based CNN (RCNN) [15], Fast RCNN [16], Faster RCNN [17], and Mask RCNN [18], which extract regions from the image and process them individually. Another popular family of models is the You Only Look Once (YOLO) series: YOLO [19], YOLO9000 [20], and YOLOv3 [21] which split the image into a grid of rectangles, each being responsible for detecting the dominating objects inside them. These models are generally a lot faster than those in the RCNN family, due to the fact that the input image is traversed only once, in contrast to hundreds or thousands of times, while at the same time approaching state-of-the-art performance. The speed, performance, and ease to implement make them a great choice. On the other hand, object tracking implies being able to recognize if two objects in successive frames are one and the same object or not. A straightforward solution is to use the Euclidian distance between center of masses, where we consider that two objects in two consecutive frames represent the same object if the Euclidian distance between their center of mass is the smallest of all pairs of objects in those two frames. This approach works well when tracking a single object, but when multiple objects are involved, it struggles greatly, especially when objects intersect. Simple online and real-time tracking (SORT) [22] is a model that improves this approach, by using a Kalman filter [23], which enables it to estimate the future trajectory of objects. Wojke et al. [24] propose an improved version of SORT, called Deep SORT. It adds another layer to the tracking solution, where a pretrained CNN on a large re-identification dataset [25] containing over 1,1 million images of 1261 pedestrians, is used to check the similarity of two objects in consecutive frames. This deep association metric increases significantly the performance of the tracking algorithm.

3 Proposed Method 3.1 Dataset YouTube-8 M represents a large video dataset containing video streams uploaded on the YouTube platform. It can be used for searching videos with certain tags. Since we wanted to explore video streams regarding education, tags like school, high school, university are relevant.

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There are 7815 videos with the tag “school” in the dataset, out of which, we downloaded 2756 videos which met some quality requirements regarding resolution and frame rate (at least 1280 × 720 pixels and at least 24 frames per second). We used an online tool for this process. Afterward, for each video, we extracted subvideos from the middle of each stream, in order to avoid any intro and outro scenes, which do not necessary contain relevant data. Also, the subvideos have the length roughly equal to two minutes (it varies by a few seconds for each video because of the video key frames). This is done so that the results reported in our study to have similar lengths in time and to keep the total computational cost to a reasonable amount.

3.2 Object Detection and Tracking After we generated the subvideos, we used the YOLOv3 model in order to detect objects, restricting the detected objects to the “person” class, and Deep SORT for tracking them and counting the number of unique persons in a video. Since many of the videos have many cuts and rapid scene changes, the performance of the proposed ensemble is lower than on videos containing continuous scenes. Nevertheless, it works well enough in order to offer general information regarding each video. After we computed the total number of people in each video, we used K-means and mean shift clustering methods on the one-dimensional array containing these values.

4 Results Using all the data available, we found four clusters. At first glance, the people counting results with very high values looked like anomalies or errors in the algorithm. But actually, most videos with more than 1000 people detected were actually videos containing sports events, dancing activities or concerts, all with big crowds spectating.

4.1 Overall Results Videos containing hundreds of people usually represent large group activities, such as science fairs and school commercials, while most videos containing tens of people contain footage of classroom activities, school commercials presenting only the inside of the school, interviews with multiple persons, interviews with a single person that included pictures with multiple persons. Finally, the videos with less than ten people usually represent tutorials, video journals, footage of teachers, and virtual images (which usually did not contain any people at all).

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Figure 1 represents the histogram that resulted from the use of all videos and clusters extracted and marked with different colors. Taking this into consideration, it is worth noting that most videos with fewer than 700 people contain indoor footage, while videos with more than 700 people tend to contain outdoor footage. If we ignore the videos containing more than 500 people, we obtain a histogram as presented in Figs. 2 and 3 new clusters of activities. Thus, we observed that videos with fewer than around 100 people were mostly composed of classroom activities,

Fig. 1 Histogram of the number of people counted in videos. The resulted clusters are presented in different colors

Fig. 2 Histogram of the number of people counted in videos with fewer than 500 people detected. The clusters resulted are presented in different colors

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Fig. 3 Images extracted from a school-related product presentation—individual activity

lessons, and tutorials. The videos from around 100 to 200 people were usually small school commercials (presenting a few classrooms, cafeteria, principal’s office), while video from 200 to 500 people mostly contained indoor sports activities and slightly more extended school commercials.

4.2 Detailed Exploratory Data Analysis Single person interviews and presentations Videos containing single person interviews usually contained fewer than ten persons. Besides the interviewed person, other people could be detected such as background people, people passing by, or even a few erroneous detections. In Fig. 3, there are presented four images extracted from a video containing footage of a single person presenting school-related products such as notepads, books, pens, glue, and so on. Our system detected a single person in total in this video, which is correct. This is thanks to the fact that the stream is continuous, with no cut scenes, and the person is clearly visible from the waist up, with some partial occlusions. Classroom activities Videos representing classroom activities usually contained tens of people. The people present in these videos were usually several students and a teacher involved in standard classroom activities, with occasional cut scenes toward a person giving explanations related to the class activity. In Fig. 4, there are presented four images extracted from a video containing footage of classroom activities. In this video, frames where multiple students take

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Fig. 4 Image extracted from a standard classroom activity footage

part in classroom study and play alternate with frames of teachers and parents (one by one) talking about the children’s experience in class. Using our counting algorithm resulted in 89 detections, while the ground truth is 78. Most of the people are clearly visible most of the time, while some are occluded, which will impact the counting performance. Indoor activities with large number of participants Videos with indoor activities such as sports, dancing events, or concerts tended to have people in the order of a few hundreds. This is due to the fact that such activities involve a big number of people both in the performance and in spectating. Nevertheless, it is unlikely that more than 1000 people will attend the activity, due to the space limitations of indoor scenes. In Fig. 5, a music band plays before a crowd of children and adults, inside a large room. Our counting algorithm estimated that there are 493 people in this video. Outdoor sport events with crowd Videos with outdoor activities, usually sports and schoolyard dancing or play, tended to have people in the order of thousands. This is due to the fact that such activities involve a big number of people both in the performance and in spectating and the space available is much larger when compared to indoors. In Fig. 6, there are four images extracted from a video containing cut scenes from an American football match. Our counting algorithm estimated that there are 2301 people in this video. Despite not all people in the crowd spectating the match being detected (because of their very small size and visual contrast), more than 2000 people are still detected. This is due to the fact that there are many cut scenes from different angles, there are many people on the side of the playing field, such as coaches,

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Fig. 5 Images extracted from a video containing a band playing in front of a group of students

Fig. 6 Images extracted from an American football match in high school or university

substitutes, medical staff, and cheerleaders, which are mostly successfully detected and tracked. Error analysis The videos in this dataset contain very diverse activities, in very different setups, lighting conditions, the recording angle varies a lot and has many cut scenes. Thus, the YOLOv3 object detector struggles with small faint people (that are usually far into the background) such as people spectating a match from a distance, while the

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Deep SORT object tracker struggles a lot with the cut scenes, which induce abrupt discontinuities in the video stream. Also, it encounters difficulties with people that are visible only from the chest up which happens frequently in classroom activities. The object detector has no problem detecting them (it can even detect a person just by seeing a hand or a palm), but the deep association metric used by the tracker is mainly trained on a dataset where the persons’ bodies are fully visible. Another source of errors is that in many videos, students wear uniforms, which makes it very hard for the tracker to differentiate between them, since except for the head, people in uniform, for all intents and purposes, have the same appearance. The same problem is present in videos with people wearing similar clothing, like in sports teams, music bands, or cheerleading.

5 Discussions and Conclusions The errors presented above tend to overestimate the total number of people detected in educational videos. This is why it is worth taking them into consideration when using our proposed system for exploring any educational video dataset. Counting people in videos containing a single person worked very well, with values from 1 to 3, which indicate that most likely it is a single person in the video stream. The surplus of people detected are usually related to people faintly visible in reflections or people or humanoid shapes on shirts, books, walls, etc. For example, in Fig. 7, some humanoid figurines are considered persons and thus are being counted.

Fig. 7 Humanoid figurines in an educational video detected and counted as persons

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Thus, if we would like to select videos with classroom activities, which we assume they involve 10–30 people, we should expect results in the range 10–50. The counting performance is better with a fixed camera and appropriate lighting conditions. The results vary wildly when more than a few dozen persons appear in a video. Thus, videos with activities like sports events are likely to generate significantly higher count than the ground truth since the tracking performance decreases proportionally with the increase of density of people in a video. Despite this, we can state that videos with hundreds of people are usually sport activities or school commercials. There are many improvements that can be made both in the counting algorithm and in the interpretation of its results. Firstly, the CNN used by Deep SORT to compute the similarity of objects could include in its training dataset people whose bodies are partially occluded. Secondly, YOLOv3 can detect many other objects, such as chairs, desks, blackboards, sport equipment, and so on. Thus, it can be used to detect relevant school-related objects, in order to further understand the scenario in the video. Thirdly, in addition to the total number of people detected in videos, the evolution of this number over time could be another factor to analyze. At the end, it is worth mentioning that manually labeling educational videos for object detection and tracking models could greatly improve the performance. Acknowledgements This research was funded by the MARKSENSE project “Real-time Analysis Platform For Persons Flows Based on Artificial Intelligence Algorithms and Intelligent Information Processing for Business and Government Environment,” contract no. 124/13.10.2017, MySMIS 2014 code 119261.

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A Serious Game for Lean Construction Education Enabled by Internet of Things Lavinia C. Tagliabue , Silvia Mastrolembo Ventura , Jochen Teizer , and Angelo L. C. Ciribini

Abstract Previous studies have shown that the learning performance of students greatly increases once personal experiences are made possible. In construction education, few serious gaming environments are available to boost the students’ thirst of discovery and interaction. As of now, theoretical content like lean principles is often taught by frontal classroom style teaching. This paper describes the development and preliminary testing of a first of a kind serious gaming platform that introduces construction engineering and management students to hands-on learning. The novelty of the developed platform is that it combines the previously separate methods of Building Information Modeling (BIM), Internet of things (IoT), and Lean Construction (LC) in one (serious) game. Technical aspects of the platform and results to its evaluation in a classroom setting are presented. The added value to learning is shown as part of learning curves automatically generated from the players’ data. While such data were previously not available, some still existing limitations and an outlook present the next steps in establishing such serious games in construction education. Keywords Building information modeling · Lean construction · Engineering education · Serious games · Internet of things · Continuous improvement

L. C. Tagliabue (B) · S. Mastrolembo Ventura · A. L. C. Ciribini University of Brescia, 25123 Brescia, Italy e-mail: [email protected] S. Mastrolembo Ventura e-mail: [email protected] A. L. C. Ciribini e-mail: [email protected] J. Teizer Aarhus University, 8000 Aarhus C, Denmark e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_19

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1 Introduction Game-based learning has the potential to improve the education and performance of engineering students. Moreover, research has found that educational games enable the students to engage with the learning material and allow them to explore and interact with the desired concepts effectively [1]. In fact, serious games are useful to teach abstract concepts; however, teaching abstract concepts and reflecting reality poses some difficulties. In addition, the modern approach to education combines theory and practice together. Serious games, therefore, support teaching, by describing real processes in a simple way and helping to transfer methods into practice [2]. This study describes the adoption of a serious game as an educational tool for engineering students in order to introduce them to the basic concepts of Lean Construction (LC). LC is a way to design production systems to minimize waste of materials, time, and efforts in order to generate the maximum possible amount of value [3]. The amount of waste in a traditionally managed construction site leads to highlight the failure and inability of this traditional method to respect delivery times, budget, and desired quality level, which are the three elements that have always inspired and guided research and mainly practice in the field of construction management (CM) [4]. Furthermore, LC encourages collaborations, it invites to share information in time and without waste, it supports users to deal with any problems that may occur [5]. The need to optimize CM approaches, including the need to address unexpected issues that may appear on site effectively, is the reason why researchers focus on the potential of the LC in the construction industry. LC, in fact, aims at removing obstacles and unnecessary processes, removing inefficiency and waste linked with materials supply chain, workforce and equipment management, reducing costs and creating high quality. The general principles are founded on the following task. At first, it is important to reduce variability, to create flexible delivery systems aimed to match owner requirements. Then, the time should be optimized, batch sizes reduced and pull production control adopted. A key factor is to standardize as much as possible, to institute continuous improvement, to use reliable material deliveries and reliable workforce, to promote participation of the downstream players in the upstream decisions. This is crucial to connect design and construction through coordination and by defining clearly the responsibilities of each actor involved; it is also unavoidable to improve customer satisfaction. Moreover, cooperation between all people involved in the project is essential in order to optimize the entire system continuously [6]. Within that context, automation and implementation of digital tools also provide an opportunity to improve the management of construction processes, improving quality in producing, sharing, and managing actual data coming from site. For that reason, Internet of Things (IoT) concepts are also introduced in the proposed serious game. The Global Standards Initiative on Internet of things (IoT-GSI) defined the IoT as “a global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies.” A thing is an object

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of the physical world (physical things) or the information world (virtual things), which is capable of being identified and integrated into communication networks [7]. “Things” can be devices, systems, materials, machinery, works and goods, etc. Fields of applicability are several: energy, safety, security, industry, manufacturing, retail, healthcare, environment, transport, smart cities, smart buildings, entertainment, etc. Correspondingly, in construction, digitization of processes is progressing through Building Information Modeling (BIM) and IoT. Traditional construction is changing into smarter work places, encouraging more and more the involvement of the people in each phase of the process, from the design to the execution process and to economic and technical management of the building construction site. A new generation of sensors and software exists, which allow obtaining information automatically in order to control the progress of the work with high accuracy and precision. Therefore, using IoT in construction is possible to supervise, e.g., safety and emergency management, security systems, equipment and machinery management, material management, location-based crew management [8]. The proposed study describes how engineering students learn the basic concepts of BIM, LC, and IoT through a serious game. The developed technology platform was experimented in tests and the improvements made in terms of both adopted technology and students’ learning are described in the remainder of this paper.

2 Serious Games for Lean Construction Education The objective of the serious game is to introduce engineering students to theory and application of technology at the same time, like they would experience later in their professional lives. It takes its cue from earlier research, e.g., Technion’s LEAPCON™ Management simulation game [9]. However, in addition to LC principles, BIM and IoT are included in order to automatically collect time-related and location-related data during the play of the serious game. These improve decision making while playing.

2.1 Architecture of Developed Serious Game In order to simplify construction sequences by facilitating simulation, the serious game consists of six airplane LEGO® kits (incl. instructions) instead of buildings. The developed game requires four players, who represent each a different trade. Each one with the task of building a sequence of the airplane. Each airplane is built in its own workstation. Only one player per workstation was permitted, forcing the others to wait in the warehouse area. A material storage area (called warehouse) stores all materials (Fig. 1). Both the workstations and the warehouse were simulated and organized in rooms and corridors of a building floor. The game has three rounds of play. While the first player can flow freely in the first round, other players leave the

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Fig. 1 From left to right: the instruction manual, workstation, warehouse, and mobile devices

Fig. 2 Overview of the organization of the simulation game (left) and location of the warehouse (in green) changes from round 1 (middle, top image) to round 2 (middle, bottom image), and real-time data visualization for each player in a BIM model

warehouse with the necessary material to complete their work sequence only if the preceding player has returned. Upon return of the last player, the game ends (Fig. 2). The serious game aims to analyze the effects of (1) working time, (2) travel time, (3) waiting time, and (4) and the time from start to completion. Moreover, the sequence of building all airplanes simulates a repetitive construction process linked to the standardization concept of lean construction. Therefore, a further objective of the simulation game is to underline how learning and repetitiveness could accelerate the construction procedure as well as how incidents impact activities. In order to measure ground truth, each player had a stopwatch for the manual time recording. The developed IoT platform collected time and location data automatically using a custom smartphone App receiving signals from sensors located at workstations and warehouse (Fig. 1). Two additional rounds of play further allowed the optimization of total time. In order to reduce travelling time, in fact, the location of the warehouse was changed to be closer to the center of gravity of the workstations (Fig. 2, middle). In this way, the travel time was reduced. As the players were expected to adjust quickly, the third round implemented interruptions to the production cycle (e.g., redistribution of tasks, unexpected events such as low visibility in one workstation) (Fig. 3, left). Finally, a quality control was done at the end of each play round to verify production quality. This feedback assisted the players focusing on the objectives of each trade.

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Fig. 3 Low visibility conditions affect round 3 in workstation 6 (left), BLE tag and mobile device (middle) and stopwatch and a device scanning a NFC Tag (right)

2.2 Data Sensing and Visualization One of the objectives of this study was to support time-related and space-related data collection through the adoption of IoT technologies. Moreover, a building information model available in the second and third rounds of the game allowed real-time IoT data visualization and feedback (Fig. 2, right). For the developed IoT platform, initially, Bluetooth Low Energy beacons (BLE tags) for collecting time-related and space-related data were selected. It was soon found that errors were high and, thus, BLE was replaced with Near Field Communication (NFC) tags [10] (Fig. 3, middle and right). The following data were recorded using the smartphone as a sensing and communication device: (a) time, (b) location, and (c) light intensity in a customized application for mobile devices. Readers interested in the technical details of the developed BIM-IoT-LC platform are encouraged to read: [10–12]. An important novelty of this work is that it made, for the first time, location data of players available in real-time in a building information model (Fig. 2, right). This feature information helped the players in the second and third rounds of playing the serious game: In fact, they were able to leave the warehouse earlier than before, because they recognized the trade left the workstations.

3 Results and Discussion The serious game has been successfully tested three times at the Ruhr-University of Bochum, Germany, University of Brescia, Italy, and Hong Kong Polytechnic University, Hong Kong. In total, 12 architectural and civil engineering students participated. The play rounds were administered and supervised by four graduate students and two senior level researchers. Every play round was recorded and manual notes were taken.

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3.1 Observations in Data For reasons of space in this paper, “observations in data” are explained to the tests in Bochum only. These observations in Bochum, however, were likely the most existing. For example, during the last (third) play round, an incident occurred (see final spike in top chart, Fig. 4) in the last workstation. The root cause for this spike is that the workstation’s light was switched off (i.e., to limit the visibility of the player completing the task) (Fig. 3, left). This caused an accident (i.e., the airplane broke apart) in the production process, demonstrating that risk is high if working conditions are improper. The accident caused therefore the necessity of rework by all trades, increasing the whole worktime. Further observations were made during the first round of play. Trade number four (i.e., the last player) made a mistake in the construction sequence, reversing construction activities going first to workstation 5 instead to workstation 4. This mistake, anyway, had no impact on the production cycle, because the previous trade luckily had already completed the work steps at workstation 4. In addition, at the time of final quality inspection, 1 out of 6 airplanes did not pass due to the incorrect and improper fitting of the wings. Moreover, the second round was slightly modified and some tasks were redistributed to the trades for resource levelling. Trade 3, for example, was intentionally not informed about the redistribution of tasks, which he discovered once being at the workstation. The purpose, as often observed in construction practice in reality

Fig. 4 Learning curve of three data collection sessions: Bochum, Brescia, and Hong Kong (top to bottom, respectively) (compared against manually recorded ground-truth data)

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[13–16], was to add “lack of communication” as an additional problem to the work processes.

3.2 Learning Curves Despite the issues just described, each play of the serious game at different locations in the world showed a consistent reduction of total work time to complete airplanes at the workstations. In fact, the IoT data allowed drawing learning curves automatically (Fig. 4). It was also possible to analyze which time factor is more dominant than others (i.e., work time, travel time, extra time waiting in warehouse) (Fig. 5). These values are often referred to direct and indirect work times and used in productivity analysis. In the first data collection session (Bochum), the element that has mainly influenced the reduction of completion time from one play round to the next was the waiting time (41%). Travel time in Brescia was higher, because the workstation layout was larger. Worktime in Hong Kong was higher since few players had experience with building bricks. However, these players were very eager to lean and complete the given tasks (seen in the short waiting times). Overall, production time, except in the respective first play rounds, has been steady and similar. Manually measured time data (i.e., orange curve) and automatically gathered data (i.e., blue curve) were compared. Errors, except 3 times when the smartphone malfunctioned, were low. The time to complete each play round of the serious game steadily decreased over the play time (except in Bochum). The curves started steep and became quite constant in the latter workstations or during the third play rounds. This is mainly due to the reduction of the required worktime to complete the task (e.g., players remembered assembly steps) and due to learning process of procedures by the players (Table 1). It is still important to highlight some additional considerations: as noted already, the peak of the curve in workstation 6 in Bochum came from an incident. While in this corresponded to the 54% of increased completion time of this airplane, low visibility still had some impact on productivity in Brescia and Hong Kong (Table 1).

Fig. 5 Reduction in times from the first to second play rounds in Bochum, Brescia, and Hong Kong (left to right, respectively)

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Table 1 Building times and influence of low visibility in workstation 6 Data collection session

Slowest time [mm:ss]

Avg. time [mm:ss]

Fastest time [mm:ss]

Increase in building time at WS 6

Bochum

16:19

07:26

05:08

+54%

Brescia

12:43

06:75

04:42

+13%

Hong Kong

26:17

10:12

06:19

+7%

The slowest building times always related to the assembly of the first few planes in the first play rounds (Table 1 and Fig. 4). A spike can be seen in all learning curves (Fig. 4). This fact demonstrates how experience matters.

3.3 Participant Feedback Opinion-based interviews were conducted with the participants. While these had not the intent to provide any statistical significance, the vast majority of the participants’ feedback was enthusiastic. Few had experience with real-time data visualization in BIM, none with IoT related to LC applications, and few to LC. A few noted that the developed technology could become more reliable in use and recommended to address these issues in the future research. User interfaces and experience were seen positively.

4 Conclusion This study described how engineering students learn BIM, IoT, and LC principles through a serious game based upon a self-developed technology platform. A novelty is that the platform allowed recording and analyzing data that was previously unavailable. By doing so, the student’s leaning progress was visualized in real time. Among other noteworthy lessons learned, progress curves, efficiency monitoring, and observations of the impact of unforeseen incidents, like work flow interruptions or accidents, were quantifiable. Continuous improvement within the serious game was encouraged and supported using mobile digital analytic and display devices. The next steps in research could focus on more reliable location tracking technologies and, once precise data are available, in using prediction algorithms that could forecast and eliminate incidents before they happen. Overall, this study was a successful first step in providing architectural and construction engineering students with digital means that they were unaware of or had not used before. This may lead eventually to fewer resistance in adapting the usefulness of technology, later in their professional careers. However, this point and engaging larger number of students in tests need to be part of future research.

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Acknowledgements Research activities have been partially funded by the DAAD-MIUR Joint Mobility Program and the Erasmus+ for Traineeship Program that the University of Brescia was awarded with the Ruhr-Universität Bochum. The authors would like to acknowledge Valentina Marchetti and Chiara Minucchi, from the University of Brescia, for the precious support to data collection and analysis.

References 1. Castronovo, F., Nikolic, D., Mastrolembo Ventura, S., Shroff, V., Nguyen, A., Dinh N.: Design and development of a virtual reality educational game for architectural and construction reviews. In: ASEE Annual Conference and Exposition (2019) 2. Pellicer, E., Ponz-Tienda, J.L.: Teaching and learning lean construction in Spain: a pioneer experience. In: 22nd IGLC, pp. 1245–1256 (2014) 3. Koskela, L.J., Howell, G., Ballard, G., Tommelein, I.: The foundations of lean construction, in design and construction: building in value, pp. 211–226. B. Heinemann, Oxford (2002) 4. MSU: Lean construction—A Promising Future for MSU. White paper (2008) 5. Abdelhamid, T.S.: Lean construction principles and methods. Lean construction overview. Michigan State University (2008) 6. Sacks, R., Dave, B., Koskela, L., Owen, R.: Analysis framework for the interaction between lean construction and building information modelling. In: 17th IGLC, pp. 221–234 (2009) 7. ITU-T: Global Information Infrastructure, Internet Protocol Aspects and Next-Generation Networks. https://www.itu.int/itu-t/recommendations/index.aspx?ser=Y. Accessed 20/2/4 8. EGF: Evolvea Internet of Things Definition. http://www.evolvea.com/. Accessed 20/2/4 9. Sacks, R., Esquenazi, A., Goldin, M.: LEAPCON: simulation of lean construction of high-rise apartment buildings. Constr. Eng. Manag. 133(7), 529–539 (2007) 10. Teizer, J., Embers, S., Golovina, O., Wolf, M.: A serious gaming approach to integrate BIM, IoT and lean construction in construction education. In: Construction Research Congress, Tempe, Arizona, USA, March 8–10 2020 11. Neges, M., Wolf, M., Poprach, M., Teizer, J., Abramovich, M: Improving indoor location tracking quality for construction and facility management. In: 34th ISARC, pp. 88–95 (2017). https://doi.org/10.22260/ISARC2017/0012 12. Teizer, J., Wolf, M., Golovina, O., Perschewski, M., Propach, M., Neges, M., König, M.: Internet of things (IoT) for integrating environmental and localization data in building information modeling (BIM). In: 34th ISARC (2017). https://doi.org/10.22260/ISARC2017/0084 13. Kuenzel, R., Teizer, J., Mueller, M., Blickle, A.: SmartSite: intelligent and autonomous environments, machinery, and processes to realize smart road construction projects. Autom. Constr. 71, 21–33 (2016). https://doi.org/10.1016/j.autcon.2016.03.012 14. Teizer, J.: Wearable, wireless identification sensing platform: self-monitoring alert and reporting technology for hazard avoidance and training (SmartHat). J. Inform. Technol. Constr. 20, 295–312 (2015). http://www.itcon.org/2015/19 15. Costin, A., Teizer, J.: Utilizing BIM for real-time visualization and indoor localization of resources. In: International Conference on Computing in Civil and Building Engineering Conference, pp. 649–656 (2014). https://doi.org/10.1061/9780784413616.081 16. Costin, A., Pradhananga, N., Teizer, J.: Passive RFID and BIM for real-time visualization and location tracking. Constr. Res. Congr. 169–178 (2014). https://doi.org/10.1061/978078441351 7.018

Semantic Recommendations of Books Using Recurrent Neural Networks Melania Nitu, Stefan Ruseti, Mihai Dascalu, and Silvia Tomescu

Abstract Digital transformations led to the development of supportive technologies, new tools for smart education, and emergent branches of research in the domain of digital library services. This paper introduces a content-based recommender system for Romanian books. The reference documents are old and were digitized via Optical Character Recognition (OCR), a process that generated noise in the conversion. The current prototype version of our system is trained on a corpus of 50 OCRed books which are split into corresponding paragraphs; thus, recommendations of related books to the user’s input query are provided only with regards to these reference documents. The trained neural models consider a bidirectional RNN layer with LSTM or GRU cells over pre-trained Romanian FastText embeddings, followed by a global max-pooling layer. The study shows competitive results on predicting books given an input text, as the proposed model achieves an overall accuracy of around 90%. Keywords Content-based recommendations · Deep learning models · Literary book recommendation

M. Nitu (B) · S. Ruseti · M. Dascalu University Politehnica of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania e-mail: [email protected] S. Ruseti e-mail: [email protected] M. Dascalu e-mail: [email protected] M. Dascalu Academy of Romanian Scientists, Str. Ilfov, Nr. 3, 050044 Bucharest, Romania S. Tomescu Central University Library of Bucharest, 1 Boteanu Street, 010027 Bucharest, Romania e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_20

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1 Introduction Digital library services are evolving to support the readers’ requirements to find relevant information when searching through a wide range of digitalized resources. Thus, researchers develop modern book recommender systems using deep neural network models [1]. The proliferation of deep learning techniques is widely spread in the development of Natural Language Processing (NLP) tasks, such as writing style recognition [1], sentiment analysis [2], or text classification [3], often applied in recommender systems. A survey published by Alharthi et al. [4] shows that the exploitation of books’ textual content in recommender systems is limited. Most research in the domain of content-based recommenders is based on extracting books metadata such as author, title, genre, and only a few systems consider the content of the books. The main reason for the small rate of research on books’ text content is that it might raise copyright issues. To encourage researchers to expand their work in this direction, new initiatives were adopted. The well-known system Google Books receives books from their authors and publishers through Google Books Partner Program [5], and makes available searches in the full text of books for terms that appear in the input query. Another project is the Hathi Trust Digital Library [6] which allows text search in 16 million book volumes, either in the public domain (6 million) or copyrighted works (10 million). Commercial recommender systems that apply NLP techniques on the text of books are now appearing, for example, BookLamp acquired by Apple in 2014 [7]. Although there are several well-performing recommendation algorithms [8], they are not achieving good results on OCRed PDF books. The current paper proposes a method for book recommendations by training a recurrent neural network (RNN) encoder on a different task, namely to predict the book in which a paragraph appears. A comparative study was performed on the two frequently employed RNN architectures, gate recurrent unit (GRU) [9] and long short-term memory (LSTM) [10], along with different text preprocessing techniques. The system is built as a text classification problem using pre-trained word embeddings. Hence, the content-based recommender is a classifier that learns similarities and correlations between items, where the content represents the title, the summary, an outline or even the entire book, as well as the book metadata such as the author. The full textbooks’ content is provided by the Central University Library of Bucharest, which currently hosts over two million physical volumes [11].

2 Related Work Table 1 introduces the most representative recommender systems that consider the content of books and integrate various NLP techniques. In tight connection with the evolution and increasing popularity of deep learning methods, new approaches

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Table 1 Representative content-based book recommender systems Tool

Method

Stylometric book recommendations [15] Authors considered features like vocabulary richness, document length, part-of-speech-tag bigrams, and most frequent words. Books were represented using topics of most frequent words obtained by applying Latent Dirichlet Allocation (LDA), useful for authorship attribution by finding patterns of word occurrences Gain: used stylometric features for authorship detection Inconvenient: as long as the writing style matches, documents with different subjects are retrieved; the approach had low performance in contrast to existing methods e-book recommender [16]

The entire textual content is considered to suggest similar authors and e-books. Authors are represented as a four-layer tree, where: (a) the root node contains the author biography; (b) the second level nodes contain books written by the author; (c) the third level represents pages partitioned from the book; and (d) the bottom level contains book paragraphs. Similarity is measured using nodes from the second to fourth levels, while the entire tree is considered for identifying similar authors. Comparative results between two authors are provided based on biography and writing style; the later considers words’ spatial distribution Gain: the use of a multilayer self-organizing map (MLSOM) that brings an improvement of approximately 3% in terms of precision and mean reciprocal rank, when compared to existing recommender algorithms Inconvenient: problems with system scalability

LIBRA [17]

The algorithm considers books metadata (such as title, authors, summary), together with information obtained from collaborative methods, like user reviews or explicit associations to similar authors. The model considered bag of words representations for books and relied on a Naïve Bayes binary classifier to recommend titles Gain: advantage of using collaborative content and reaching a precision of approximately 70% Inconvenient: lower performance when compared to other approaches that currently exceed a precision of 80%

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were also considered within recommender systems. For example, Quiang et al. [12] propose a dynamic attention deep model for article recommendation, using a convolutional neural network (CNN) with a dynamic attention model to recognize editor’s behavior based on articles’ content. Other approaches and techniques were also explored to perform book recommendations. Pera and Ng [13] considered the author’s writing style and their model considered reviews, not the actual content of books. Garrido et al. [14] relied on the text from books to predict social tags which were then used to generate recommendations. However, this approach requires books to be widely read and reviewed.

3 Method The current paper introduces a content-based recommender system for Romanian language books designed as a classification problem to predict similar books, based on their content. Book representations are learned by the RNN classifier and then fed to a recommendation module. For comparison purposes, the performance of our sequence-based model is tested with two state-of-the-art RNN architectures, LSTM, and GRU. We conducted experiments with pre-trained FastText embeddings [18] for Romanian language.

3.1 Corpora Our preliminary corpus was provided by the Central University Library of Bucharest and it consists of 50 OCRed books in PDF format written in Romanian language, with corresponding metadata (e.g., book title, corresponding authors, year of publication). This collections of unformatted PDFs with broken layout formats and residual errors raised several challenges in the preprocessing phase.

3.2 Text Preprocessing A preprocessing pipeline was required to improve and structure the text from the OCRed books. Thus, a paragraph extractor tool was developed to automate the processing and extraction task. The extracted paragraphs are further fed to the classifier. The workflow of the preprocessing tool is presented in Fig. 1 and consists of three main parts. First, the tool cleans content by removing unused text, such as the table of contents, preface, foreword, or editor’s notes. Second, the tool is focused on paragraph detection, reconstruction of broken paragraphs, and merger of hyphenated

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Fig. 1 Workflow for the PDF paragraph extractor tool

words. Paragraphs boundaries were identified using one of the following delimitators [“tab”, “.”, “?”, “!”]. Third, paragraphs are extracted in a format and structure specific for the classifier. Each paragraph is vectorized in a manner that preserves word order. This results in a sample of approx. 18,000 paragraphs extracted from 50 distinct books. The input sample suffers from a high degree of class imbalance because the length of books varies greatly from a few pages to hundreds. Paragraphs were truncated at 256 words each and only books with 250 to 2200 fragments were selected; thus, the majority of books is captured, without causing serious class imbalance among shorter books.

3.3 Word Embeddings Words embeddings represent concepts as vectors in high dimensional spaces, where similar words have similar representations. Practitioners of NLP techniques for text classification often use pre-trained word embeddings to initialize their models. Two of the most popular word embedding models are FastText [18] and GloVe [19]. FastText is an extension of word2vec [20] and considers sub-words by representing each word as an n-gram of characters, instead of directly learning vector representations for words. GloVe was built on a combination of matrix factorization and context window methods to model documents statistically. The major benefit of FastText consists of its capability to generate embeddings for words that do not exist in the training set. In our approach, we converted the previous extracted paragraphs into word embeddings. We conducted the experiments using pre-trained FastText embeddings for Romanian language. The embedding model consists of 2 million word vectors trained on Common Crawl and Wikipedia (approx. 600 billion tokens).

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Fig. 2 Model architecture

3.4 Classification Model A sequential model was built and evaluated to predict the most appropriate book’s title, given a small snippet of text. Two state-of-the-art RNN architectures (LSTM and GRU) were trained using word embeddings as input (see Fig. 2 for model architecture). Bidirectional LSTM and GRU architectures were considered to overcome the vanishing gradient problem and capture long-term relationships in a sequence. The vanishing gradient appears while training traditional recurrent neural networks, as more steps are gradually added. The network becomes harder to train because backpropagation computes gradients using the chain rule and the gradient of loss function approaches zero. A text CNN layer was also added, but it did not improve the prediction accuracy due to the limited size of the dataset. Therefore, the CNN layer has been replaced with a global max-pooling layer that helps reduce the dimensionality of the feature map by computing the maximum value for each patch. The last layer in our model is a dense layer that computes the dot product between the inputs and the kernel, reshaping our tensor to perform the final prediction.

4 Results From the 50 books in the original corpus, only 30 were selected for the following experiments. The selected books contain at least 100 paragraphs to reduce the potential errors in the paragraph splitting process. The corpus was split into train, validation, and test in the 80/10/10 ratio while keeping the same distribution of books in each partition. Only the first 256 words from each paragraph were used for training,

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Table 2 Validation accuracy at different parametrization (Epochs number, hidden layer size, dropout rate) for RNN cells Epochs #

RNN cell type

Hidden layer size

Dropout

Accuracy (%)

30

BiLSTM

64

27

BiLSTM

128

0.5

0

83.98 85.40

48

BiLSTM

128

0.25

87.63

49

BiLSTM

256

0.25

87.94

50

BiLSTM

256

0.4

88.75

49

BiGRU

128

0.25

88.39

48

BiGRU

192

0.25

88.75

45

BiGRU

256

0.25

89.71

48

BiGRU

256,128

0.25

88.49

few paragraphs being longer than this value. The loss was weighted with the inverse of the number of items in each class to compensate the class imbalance. Table 2 reports model accuracy for different configurations and values of hyper parameters and introduces a performance comparison between the GRU and LSTM models with pre-trained word embeddings. Although LSTMs attained similar results with an accuracy of 88.75%, the bidirectional GRU model outperformed the other architecture, achieving 89.71% accuracy. The best accuracy was achieved with GRU architecture having the following configuration: a 0.25 dropout rate, 45 epochs, and hidden layer size of 256. This is an expected result considering the small number of samples in our dataset and the less complex structure of a GRU cell compared to a LSTM cell which has an additional memory unit. Results exhibited the smallest accuracy for books that had the least number of extracted paragraphs from the entire dataset (between 200 and 250). This outlines the direct correlation between the number of text fragments and accuracy of the model, as seen in Fig. 3.

5 Conclusions and Future Work The aim of this paper was to introduce a content-based recommender system that provides users with reading recommendations from a digital library. The system is designed to support educational stakeholders and contributes to the improvement of smart learning ecosystems by providing meaningful guidance in identifying suitable literary resources, from a variety of choices. The current research explores the performance of two state-of-the-art RNN architectures, starting from word embeddings pre-trained on Romanian. The classifier was trained to predict the book, given a small snippet of text. Results showed that the bidirectional GRU model outperformed other models with an accuracy of about 90%. In order to predict the book, the model needs to construct a relevant representation for a given paragraph, which

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Fig. 3 Correlation between classifier accuracy versus number of extracted paragraphs

can be later used for book recommendations. The advantage of our method is that it only requires unlabeled books to learn text representations. As future directions, an extension module for authorship identification based on the stylometry technique will be considered, where the neural network learns the author writing style. Also, a collaborative recommendation mechanism based on users’ ratings for books will be introduced. The system should predict a user rating for an item, based on the average ratings provided by other users with similar profile ratings. Acknowledgements This work was funded by “Semantic Media Analytics—SEMANTIC,” subsidiary contract no. 20176/30.10.2019, from the NETIO project ID: P_40_270, MySMIS Code: 105976, as well as a grant of the Romanian Ministry of Research and Innovation, CCCDIUEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0689 “Revitalizing Libraries and Cultural Heritage through Advanced Technologies,” within PNCDI III.

References 1. Brocardo, M.L., Traore, I., Woungang, I., Obaidat, M.S.: Authorship verification using deep belief network systems. Int. J. Commun. Syst. 30 (2017) 2. Socher, R., Perelygin, A., Wu, Y.J., Chuang, J., Manning, C.D., Ng, Y.A., Potts, C.: Recursive deep models for semantic compositionality over a sentiment Treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642 (2013) 3. He, X., Gao, J., Deng, L.: Deep learning for natural language processing: theory and practice. In: Conference on Information and Knowledge Management (CIKM), Shanghai, China (2014) 4. Alharthi, H., Inkpen, D., Szpakowicz, S.: A survey of book recommender systems. J. Intell. Inform. Syst. 139–160 (2018) 5. Google Books Partner Program. Retrieved 2020/02/10, from https://support.google.com/books/ partner/?hl=en#topic=3424344 6. HathiTrust Digital Library. Retrieved 2020/02/10, from https://www.hathitrust.org/

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7. BookLamp. Retrieved 2020/02/10, from https://www.businessinsider.com/apple-buys-boo klamp-2014-7 8. Lü, L., Medo, M., Yeung, C., Zhang, Y.-C., Zhang, Z.-K., Zhou, T.: Recommender systems. Phys. Rep. 519, 1–49 (2012) 9. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014) 10. Palangi, H., Deng, L., Shen, Y., Gao, J., He, X., Chen, J., Song, X., Ward, R.: Speech, processing, deep sentence embedding using long short-term memory networks: analysis and application to information retrieval. 24(4), 694–707 (2016) 11. Biblioteca Centrala Universitara Carol I. Retrieved 2020/02/10, from http://www.bcub.ro/ home/biblioteca-in-cifre/biblioteca-in-cifre-la-31-decembrie-2018 12. Quiang, L., Shu, W., Wang, L: DeepStyle: learning user preferences for visual recommendations. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, Tokyo, Japan (2017) 13. Pera, M., Ng, Y: Analyzing book-related features to recommend books for emergent readers. In: Proceedings of the 26th ACM Conference on Hypertext and Social Media, New York, USA, pp. 221–230 (2015) 14. Garrido, A., Pera, M., Ilarri, S.: SOLE-R: a semantic and linguistic approach for book recommendations. In: 14th International Conference on Advanced Learning Technologies (ICALT), pp. 524–528 (2014) 15. Vaz, P., Martin de Matos, D., Martins, B.: Stylometric relevance-feedback towards a hybrid book recommendation algorithm. In: Proceedings of the Fifth ACM Workshop on Research Advances in Large Digital Book Repositories and Complementary Media, New York, USA, pp. 13–16 (2012a) 16. Zhang, H., Chow, T.: Organizing books and authors by multilayer SOM. IEEE Trans. Neural Netw. Learn. Syst. 99, 1–14 (2015) 17. Mooney, R., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries, New York, USA, pp. 195– 204 (2000) 18. Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain (2017) 19. Pennington, J., Socher, R., Manning, D.C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1532–1543 (2014) 20. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of the International Conference on Learning Representations (ICLR 2013), Scottsdale, Arizona (2013)

Supporting Multiple Programming Languages in an Online Judge Ioana-Teodora Tica, Alexandru-Corneliu Olteanu, and Emil Racec

Abstract Online judges are Web-based platforms where people can solve programming challenges and have their solutions automatically evaluated, in real time. They can be used for teaching, self study, or recruitment purposes. Online judges are a great resource for students in particular, as a means of practicing for algorithmic competitions, exams, and interviews. Numerous computer science departments from institutions around the world try to integrate online judges into their teaching systems, as a solution for automatic assessment. Some have even developed custom judges, and published papers which elaborate the implementation details and review the impact on students’ performance. Unfortunately, none of them clarifies the methods used to achieve language-agnostic judges. The aim of this paper is to fill in this gap, by surveying different approaches of designing a judge which supports multiple programming languages. Keywords Online judge · Language-agnostic judge · General purpose language · Domain-specific language · Transpiler

1 Introduction Introduced in 2001 by Kurnia et al. [1], the term online judge generally stands for Web-based systems which perform automatic, real-time evaluation of source code, binaries, or textual solutions submitted by users who participate in programming challenges available on the platform [2]. Initially, online judges were emerged from I.-T. Tica (B) · A.-C. Olteanu University Politehnica of Bucharest, Bucharest, Romania e-mail: [email protected] A.-C. Olteanu e-mail: [email protected] E. Racec Devmind, Bucharest, Romania e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_21

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the academic environment [1, 3, 4] as a means of automatic assessment for programming assignments and competitions. The numerous judging systems available nowadays can be suitable for both educational purposes and other scenarios such as competitive programming, recruitment purposes, data-mining purposes, or online compilation [2]. In the academic environment, online judge systems can be valuable to both students and teachers. From the students’ point a view, online judges provide a good training ground, where they can practice for exams, interviews or coding competitions and receive immediate feedback. A teacher on the other hand can integrate coding assignments in an online judge and use it as an automatic assessment solution in order to optimize the grading process. An analysis of existing online judges can give further insights regarding other benefits they might bring in the context of a computer science class. Based on the information from [2, 5, 6], we identified the main features of some popular and widely competitive programming platforms (LeetCode [7], HackerRank [8], CodeChef [9], HackerEarth [10], Codeforces [11], TopCoder [12]), alongside a couple of judges developed by academic groups (Jutge.org [13], Codeflex [5]) (Table 1): The good variety of languages offered by a judge allows students to choose the focus of their practice, be it the study of an algorithm or learning a new language. The code editor can offer both a uniform interface which ensures fairness in the context of a competition and features which can boost productivity, such as syntax highlighting or code completion. The code snippets, a feature supported by most of the analyzed judges, allow students to focus on the task itself and spend less time on boilerplate code, such as handling input and output or other type of functionalities which are not relevant for the problem itself. Unfortunately, some of the aforementioned competitive-programming platforms present several limitations from an instructor’s perspective. Firstly, not all of them

Table 1 Comparative analysis of online judges Jutge.org

Codeflex

LeetCode

HackerRank

CodeChef

Hacker Earth

Code forces

Top Coder

Programming languages

26

4

13

39

43

31

14

4

Code editor

✓

✓

✓

✓

✓

✓

✓

Code snippets

✓

✓

✓

✓

✓

Tournaments

✓

✓

✓

✓

✓

✓

✓

✓

Practice challenges

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

Tournaments simulation Achievements

✓

Ranking

✓

✓

✓

✓

✓

✓

✓

✓

Recruitment Mock interviews

✓

✓

✓ ✓

✓ ✓

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allow outsiders to be problem curators (e.g., LeetCode), and therefore restrict teachers from using custom challenges. In addition to this, most of the problems cover only topics regarding algorithms and data structures, and therefore they are suited for a limited set of classes from a computer science department. Such constraints have motivated groups from several universities across the world to design and implement custom online judges (e.g., Jutge.org, Codeflex) which allow teachers to apply different pedagogical approaches and address other computer science topics [5, 6, 14]. The publications which accompany these projects focus on the overview of the architecture and issues such as performance or security. Unfortunately, neither addresses the topic of designing a language-agnostic judge, i.e., a judge which supports multiple programming languages. We consider this to be among the key features of an online judge, as it provides flexibility for its users, both instructors and students: • Students could choose to focus either on the algorithmic side of a challenge (and solve it using the language they are most confident with), or on learning a new language. • Teachers would be able to reuse certain assignments for different classes, regardless of the language which is part of the topic. The aim of this paper is to identify different approaches of designing a language agnostic judge and explore their advantages and limitations.

2 Problem Specification A language-agnostic judge should provide two features: • A compiler for each programming language supported by the judge. • A set of code snippets written in every supported language, for each problem statement available. As there has been previous research on how to integrate compilers on educational Web platforms [15], this paper will focus on the second concern, surveying different approaches on how to automatically provide code snippets in different languages. While instructors could manually add skeleton code for each language, this approach would be cumbersome and error-prone. It would also allow inconsistencies to occur over time. Therefore, a system which enables the automatic translation of code snippets from a language to another could be a better solution. However, designing such a system is not an easy task, as there are a few questions which come to mind: • What should the input format be? • How would the translation itself be achieved? • What language features should the translation unit be able to handle? In the following section, we will attempt to provide answers to the first two questions.

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3 Approaches for Code Snippets Generation In order to generate code snippets for a problem statement, a teacher should describe the type of actions the code snippets must perform (e.g., provide a hierarchy of classes, simple utility functions for input/output handling, etc.). We will refer to this description from now on as problem specification. A transpiler, i.e., a source-to-source compiler, could be used to translate the problem specification into actual code snippets. Commonly, a transpiler should parse the source code and compute its abstract syntax tree (AST), perform partial symbol resolution, decorate the AST with information specific to the target language, and then generate the desired code from the internal representation [16]. The exact implementation of the transpiler depends in our case on how we decide to encode the problem specification. We have identified two potential solutions: 1. Describe the problem specification using a custom domain-specific language (DSL), with a limited set of functionalities such as declaring classes and functions or implementing primitives for input/output handling. 2. Allow the teachers to provide the problem specification using a general purpose language (GPL) supported by the online judge.

3.1 Providing Code Snippets via a DSL A DSL is a specialized, problem-oriented language designed to accurately describe a certain domain of knowledge [17]. The DSL should hide complexity, design, and implementation details in order to allow its users to focus on domain description [17]. Domain-specific languages can be classified as following: • External DSLs, which introduce new syntax and semantics. When a developer designs an external DLS, he or she needs to implement various tools (i.e., a lexer, parser, interpreter or compiler), so that the language can be readily used. • Embedded DSLs, which are defined in terms of powerful, general purpose host languages. They can be implemented using language-specific features, such as templates in C++ [18], annotations in Java [19], quasi-quotations in Haskell [18], or more general concepts such as combinators [20]. While external DSLs can be more expressive and flexible, embedded DSLs require less design effort as they rely on the infrastructure of the host language (parsing, typechecking, etc.). In order to generate code snippets in various GPLs, the DSL used as an entry point should provide an abstraction over the common features of the GPLs. While it might not be easy to implement, the viability of this approach is reinforced by the fact that some online judges (e.g., HackerRank [21]) already use custom DSLs in order to generate utility code for input/output handling.

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We identified the following potential advantages of using a DSL for our use-case: • Learning designers could use the DSL to define types of assignments suited for certain computer science classes. • A DSL could restrict the use of language-specific features, and therefore not allow unnecessarily complex skeleton code. • Generating all code snippets from specifications written in a DSL could help impose a coding style across the set of problems provided by the judge. • Models designed with DSLs are easier to validate in general [22]. There are also a couple of limitations to this approach: • Some problem curators might be reluctant to learn the syntax of the DSL. • The use of DSL might restrict the expressiveness of the generated code snippets. • In some situations, a certain language and its features can be the part of the study focus. In such cases, teachers might want to provide code snippets that demonstrate the capabilities of the said language.

3.2 Providing Code Snippets via a GPL Providing the problem specification via any of the GPLs supported by the online judge would require additional design and implementation effort, as parsers for all featured languages would be needed. However, the parsers should only support a restricted set of functionalities, common among all languages, in order to ensure that the each language-to-language translation can be performed without issues. The main benefit of this approach is that certain components (i.e., the parsers) could be reused for other features such as syntax highlighting within the code editor, coding style evaluation, and other forms of static analysis of the source code. Furthermore, this approach might be proffered by teachers, as it would not require them to learn a new language in order to provide skeleton code for assignments. The main limitation of this approach is the fact that there are some languagespecific features which might be difficult to impossible to translate in another one. This can be mitigated by defining a set of requirements the input code snippets should respect in order for the translation unit to generate a correct input. Furthermore, this approach can only guarantee consistent coding style to a certain extent, as some code snippets would be manually introduced, while others would be generated.

4 Discussion The motivation behind our research was to gain knowledge needed for the improvement of LambdaChecker [23], the online judge that our group is currently developing. Apart from addressing the challenge of real-time, on-demand code evaluation, LambdaChecker strives to go beyond simple test-based grading. We aim to integrate code

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inspections (e.g., coding style, object-oriented design, code complexity, etc.) that teachers could benefit from when assessing the quality of a solution. Furthermore, we also want to provide in-editor features for students, such as syntax highlighting or code linters, in order to boost productivity and enhance the learning experience. Although our current research focuses on the automatic translation of code snippets, we keep in mind the fact the features we want to introduce in the future rely on the ability to parse and analyze source code written in GPLs. Furthermore, although both approaches presented in Sect. 3 differ in terms of development effort, performance wise they might be similar, as in both cases, the input undergoes the same types of transformations. These two factors made us incline in the favor of taking the input problem specification via a GPL, at least for the first development iterations. While designing the architecture of a proof-of-concept translation unit, we made the following observations: 1. Existing parsers would be more reliable and assure better performance than if we were to implement them ourselves. 2. A parser for a GPL is more likely to offer APIs in the targeted language— this would create the need of using multiple languages for our system. 3. The parsing operation should provide a general AST, which can then be readily used to generate code in any of the other supported languages. These observations determined us to consider a third approach, which combines the first two from Sect. 3. The input could be provided via GPLs and then the parsed AST could be “serialized” into an internal representation described by a DSL. The internal representation would provide the general AST needed for the code generation stage. A potential model of this approach is described in Fig. 1. This approach would not only combine the benefits of the other two described in Sect. 3 but it also guarantees a good decoupling between the components of the code translation unit.

5 Conclusion and Future Work Although online judges initially emerged from the academia, there are still ways of improving the user experience for both students and instructors. Enabling the automatic generation of code snippets in various languages is a feature which can be particularly useful for both parties, as it gives students flexibility in their self-study and it allows teachers to reuse programming assignments of various complexities in multiple classes. In this survey, we identified two ways of designing this feature: either by generating the code snippets from a specification written in a custom domain-specific language, or by translating the code snippet from one language to each of the others supported by the judge. By analyzing their advantages and limitations, we identified the potential benefits which could result from combining the two.

Supporting Multiple Programming Languages in an Online Judge

Cpp Parser

Problem Specification

Cpp Decorator

Target Code Snippets

Java Parser

Python Parser

GPL Parser Facade

GPL AST

Java Decorator

Python Decorator

Decorator Facade

DSL AST

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Serialization Component

Internal Representation (DSL code)

DSL Parser

Fig. 1 A language-agnostic online judge model

This survey is part of a long-term project, with the goal of implementing an online judge which would allow teachers from code inspection and in-depth profiling of the assignments submitted by students. As the current prototype only supports Java, we identify the following next steps: • Implement a proof of concept transplier which parses Java code and translates it into another programming language (e.g., C++). • Validate the transpiler prototype, test it in real-life scenarios, and gather user feedback. • Extend the prototype by integrating parsers for other programming languages. Acknowledgements This survey was conducted in order to assist the development of LambdaChecker, an online judge which strives to go beyond simple test-based grading. We would like to thank all who contributed to project: Stefan Balas and Catalin Dragutescu, for implementing the prototype as their graduation project; Andrei Medar, Serban Ciofu and Gabriel Tuculina, who are currently working to improve the user experience for both teachers and students; George Milescu and his colleagues at AWS for their valuable advice.

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References 1. Kurnia, A., Lim, A., Cheang, B.: Online judge. Comput. Educ. 36(4), 299–315 (2001) 2. Wasik, S., Antczak, M., Badura, J., Laskowski, A., Sternal, T.: A survey on online judge systems and their applications. ACM Comput. Surv. (CSUR) 51(1), 1–34 (2018) 3. Cheang, B., Kurnia, A., Lim, A., Oon, W.C.: On automated grading of programming assignments in an academic institution. Comput. Educ. 41(2), 121–131 (2003) 4. Douce, C., Livingstone, D., Orwell, J.: Automatic test-based assessment of programming: a review. J. Educ. Resour. Comput. (JERIC) 5(3), 4–es (2005) 5. Brito, M., Gonçalves, C.: Codeflex: a web-based platform for competitive programming. In: 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6. IEEE (2019) 6. Petit, J., Roura, S., Carmona, J., Cortadella, J., Duch, J., Gimnez, O., Mani, A., Mas, J., Rodrguez-Carbonell, E., Rubio, E., et al.: Jutge. org: Characteristics and experiences. IEEE Trans. Learn. Technol. 11(3), 321–333 (2017) 7. Leetcode. https://leetcode.com/. Last accessed 10 Jan 2020 8. Hackerrank. https://www.hackerrank.com/dashboard. Last accessed 10 Jan 2020 9. Codechef. https://www.codechef.com/. Last accessed 10 Jan 2020 10. Hackerearth. https://help.hackerearth.com/hc/en-us. Last accessed 10 Jan 2020 11. Codeforces. https://codeforces.com/. Last accessed 10 Jan 2020 12. Topcoder. https://www.topcoder.com/. Last accessed 10 Jan 2020 13. Jutge.org. https://jutge.org/. Last accessed 10 Jan 2020 14. Verdú, E., Regueras, L.M., Verdú, M.J., Leal, J.P., de Castro, J.P., Queirós, R.: A distributed system for learning programming on-line. Comput. Educ. 58(1), 1–10 (2012) 15. Kaya, M., Özel, S.A.: Integrating an online compiler and a plagiarism detection tool into the moodle distance education system for easy assessment of programming assignments. Comput. Appl. Eng. Educ. 23(3), 363–373 (2015) 16. Mu, L.: gLua: A modern Lua transpiler in scheme (2019) 17. Langlois, B., Jitia, C.E., Jouenne, E.: Dsl classification. In: OOPSLA 7th Workshop on Domain Specific Modeling (2007) 18. Czarnecki, K., O’Donnell, J.T., Striegnitz, J., Taha, W.: Dsl implementation in metaocaml, template haskell, and c++. In: Domain-Specific Program Generation, pp. 51–72. Springer (2004) 19. Humer, C., Wimmer, C., Wirth, C., Wöß, A., Würthinger, T.: A domain-specific language for building self-optimizing ast interpreters. In: Proceedings of the 2014 International Conference on Generative Programming: Concepts and Experiences, pp. 123–132 (2014) 20. Freeman, S., Pryce, N.: Evolving an embedded domain-specific language in java. In: Companion to the 21st ACM SIGPLAN Symposium on Object-Ooriented Programming Systems, Languages, and Applications, pp. 855–865 (2006) 21. Hackerrank domain specific language documentation. https://help.hackerrank.com/hc/en-us/ articles/360008561014-HackerRank-DSL-Domain-Specific-Language-Documentation. Last accessed 26 Jan 2020 22. Voelter, M., Kolb, B., Birken, K., Tomassetti, F., Alff, P., Wiart, L., Wortmann, A., Nordmann, A.: Using language workbenches and domain-specific languages for safety-critical software development. Softw. Syst. Model. 18(4), 2507–2530 (2019) 23. Lambdachecker. https://lambdachecker.com/. Last accessed 17 May 2020

A Quantitative Analysis of Romanian Writers’ Demography Based on the General Dictionary of Romanian Literature Laurentiu-Marian Neagu, Irina Toma, Mihai Dascalu, S, tefan Tr˘aus, an-Matu, Laurent, iu Hanganu, and Eugen Simion Abstract Literature is an essential part of a nation’s cultural identity, and each member of the community should be acquainted with the names and significant works of its most representative writers. In schools, literature is taught in traditional teacher-centered methods, using written literary studies or essays that students often memorize and reproduce. With the development of online environments, there is a need to enhance the teaching process, to shift to e-learning environments by providing online materials, statistical studies, and methods for evaluating the acquired knowledge. For Romanian literature, the General Dictionary of the Romanian Literature (DGLR) is a comprehensive work which includes information on authors, publications, institutions, and literary movements. The work is available in 7 written volumes and through the INTELLIT Web platform will be available online, to the general public, as a learning ecosystem. Besides the original information from DGLR, the platform will include statistical information extracted from the DGLR corpus, such L.-M. Neagu (B) · I. Toma · M. Dascalu · S, . Tr˘aus, an-Matu University Politehnica of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania e-mail: [email protected] I. Toma e-mail: [email protected] M. Dascalu e-mail: [email protected] S, . Tr˘aus, an-Matu e-mail: [email protected] M. Dascalu · S, . Tr˘aus, an-Matu Academy of Romanian Scientists, Str. Ilfov, Nr. 3, 050044 Bucharest, Romania L. Hanganu · E. Simion The “G. C˘alinescu” Institute of Literary History and Theory, Romanian Academy, Calea 13 Septembrie, Bucharest, Romania e-mail: [email protected] E. Simion e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_22

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as writers’ demographic information and places visited by important authors with modern visualizations. The purpose of this study is to provide the general audience visual overviews and snapshots of the Romanian literature as a whole. Keywords Quantitative analysis · Statistical methods · E-learning · Online · Learning environment · General dictionary of Romanian literature

1 Introduction Literature is a collection of written and spoken materials, divided usually into poetry, prose, and drama. The Romanian Academy created the General Dictionary of Romanian Literature [1, 2] (referred to as DGLR), which includes information on writers and their publications. DGLR describes all authors that have published in the Romanian language, regardless of their publication location, who are valuable to the Romanian literature. The dictionary was printed in 7 volumes between 2004 and 2008 and a second edition is now under development. The new edition will include 8 volumes, covering authors’ information in alphabetical order. This paper aims at answering the following research question: How can the DGLR be effectively used as a learning environment by students? DGLR becomes a knowledge base accessible to the public through its digitalization within the INTELLIT online Web platform [3]. The current study provides end users with a snapshot of the Romanian literature, displayed through a set of visualizations that represent an overview of the authors’ lives and their published writings. This work represents the foundations for transforming the INTELLIT platform into a smart learning environment, that introduces customized analyses derived from DGLR. The paper is structured as follows. This first section provided background information about the Romanian Academy project---DGLR, together with a short description of the proposed study. The next section includes a State of the Art describing the current approaches on teaching literature to students and new, modern ways of learning based on online resources. Next, the Methods section presents a brief description of the technologies used for achieving our goal, together with a step-bystep approach for extracting the information used in the visualizations. The fourth section illustrates the generated visualizations and debates on their adequacy. The last section presents conclusions and potential future work directions.

2 State of the Art Learning is the act of acquiring knowledge through study. Study can be either driven by the learner, such as individual study or by teachers in a classroom or online environment [4]. The traditional learning environment is a process driven by the teacher. Usually, the taught materials are available as books and include the primary

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information students need to be knowledgeable of. Teachers present the information included in the curriculum in a structured way, so that students remain focused on the given subject and do not get distracted by other information. Besides the written materials, teachers support students by explaining concepts and guiding them to the right answers. Modern learning approaches, for example, e-learning, rely on online resources and individual study [5], guided by a teacher or an online entity. By having large amounts of information available, students develop critical thinking and analytical skills. They filter the information and are also responsible for their learning process. In literature-related courses, teachers present studies written by literary critics about authors or their publications. Students either learn in the classroom, from their teachers, or through individual study, from different books that contain literary studies. Even though in the online environment students can find resources, such as essays on different subjects, most of them are not reliable and contain inaccurate information. For example, looking at an online essay resource1 for the Romanian literature, out of a total of 2665 essays, 1343 are of lower quality, being graded under 7 (from 10). Therefore, students need a solid and easily accessible collection of information, in which they can browse resources, search for the needed materials, or navigate through linked resources. The answer to the previously described need is the INTELLIT platform, which provides the DGLR information in a online environment. Because the DGLR contains vast information about writers and publications, only the search facility can be insufficient. People using INTELLIT could benefit from aggregated views that provide snapshots of different aspects of Romanian literature, such as authors’ biographic information, places visited by important writers, and historical events intertwined with the authors’ lives or publications. The next sections describe several statistical visualizations and the methods used for identifying the contained information.

3 Method The researchers of the Romanian Academy originally constructed a dataset in Adobe InDesign2 format. Thus, a first step implied parsing and storing relevant data in order to apply the quantitative analysis on this corpus. Next, the targeted quantitative studies were selected, which will be presented further.

1 http://www.referat.ro/referate/romana/. 2 https://www.adobe.com/ro/products/indesign.html.

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3.1 The INTELLIT Dataset Our INTELLIT dataset contains monolingual text written in Romanian language. DGLR consists of multiple volumes and contains information about the Romanian writers in alphabetical order. The current work included the parsing of 70 files in Adobe InDesign format, which were converted into HTML for easier processing. The previous files included the authors and publications from Romanian literature corresponding to letters A–O. Remaining letters, P–Z, are currently under development; the researchers from the Romanian Academy expect to finish their scientific work in the third quarter of 2020. Our current analysis considers 2433 authors from the total number of 2529 entries, for which we were able to extract basic information from their description, such as the year of birth. Separately, the Romanian Academy provided a collection of documents, in Microsoft Word format, for the set of canonical authors, describing in detail their life and literary activity, respectively: life chronology, literary activity chronology, quotes from well-known literary critics, quotes from their significant works, and titles of their representative writings.

3.2 Data Extraction, Indexing, and Visualizations Authors’ data from DGLR is stored in an elastic search server, known to be fast for data retrieval and useful in analytics purposes [6]. Several elements were extracted from the writers’ biography and indexed into Elastic search, such as: year and place of birth, year and place of death, list of writings, list of publishing years, authors’ professions, bulk text biography, description, writers’ name, as well as other authors’ critical references. Additional information was indexed for canonical writers: writer’s canonical name (if any), literary activity chronology, life chronology, quotes from their significant works, titles of the most representative writings, and a more comprehensive list of quotes from literary critics. AmCharts,3 a modern JavaScript library, for plotting charts and maps, was used to achieve the desired graphical representations. The amCharts geo maps are represented as SVG using the GeoJSON format [7] for encoding geographic data structures. GeoJSON supports geometry types such as points, lines, and polygons, all grouped into features or collections. Different maps of countries, continents, or regions are readily available in the geodata amCharts package. The GeoJSON format uses latitude and longitude sets of coordinates to represent points on a map; therefore, the identified cities from DGLR were mapped to their coordinates using Geocoder [8]. Geocoder offers a simple Python API that returns a pair of latitude and longitude coordinates for a location.

3 https://www.amcharts.com/.

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3.3 Quantitative Analysis Our current analysis includes the following demographic data: • Birth location for all writers—this information was found and extracted from DGLR; • Canonical authors publication period—20 canonical authors had a literary activity chronology document from which we extracted the first and last publication years; • Number of writings per number of writers per year—number of writings published in a year compared to all active writers in the given year (active author is based on start and last publication year); • Canonical authors journeys—information on visited cities, year by year, was extracted for 20 canonical authors having a detailed life chronology. Some authors lacked one or more details from the previous criteria and were excluded from this analysis.

4 Results and Discussions The first experiment considered the number of publications relative to the number of active writers in one year. Authors are considered active between their first and last publications, based on the list of writings and literary activity specified in the DGLR. If a publication appeared for a longer timeframe (more than one year) in DGLR, it was skipped in the current analysis. The analysis is based on two presumptions: (a) the writers are alive and active within each year and (b) the publications were released in that year. The plot from Fig. 1 includes writers and publications between 1878 (a reference year, the first moment when more than 50 writers were active at once) and 2000. As it can be easily observed, the number of writers has an overall growth in the past

Fig. 1 Active authors per number of publications

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150 years, but the number of writings has not been constant across time and it does not follow a specific pattern. Important drops in the writings timeline are seen during the First World War (13 writings in 1917), after the Second World War (only 48 writings in 1949), and after the Fall of the Communist Regime. The second analysis considers the birth locations of writers from the DGLR. Not all writers were born in Romania, but these individuals added value to the Romanian literature through their contributions. Figure 2 presents the authors born in Romania and in the neighbor countries: Hungary (10 writers), Serbia (21 writers), Bulgaria (3 writers), and Ukraine (47 writers; 30 are from Cern˘aut, i, a city which was part of Romania until the end of the Second World War). There are approximately 100 writers from Republic of Moldova (also a previous Romanian territory), most of them from Chis, in˘au, B˘alt, i, Soroca and Orhei cities. Other writers were born in more distant countries like: Greece (19 writers), Poland (10 writers), France (8 writers), Italy (7 writers), Germany (7 writers), or the Czech Republic (6 writers), etc. The distribution of writers across the Romanian landscape is mostly uniform, with the most important center being Bucharest, the capital of Romania (364 writers were born here). The important historical cities also were shown as clusters for the birth of Romanian writers: Ias, i (135 writers), Cluj (51 writers), Bras, ov (48 writers), Sibiu (48 writers), and Craiova (48 writers). Our third analysis focused on canonical authors for whom we had additional information detailing their literary activity and chronology. For these authors, we plotted the timeframe of that activity to see the distribution and the overlapping intervals between writers. The obtained results are interesting and can be observed in Fig. 3. The most important writers for Romanian literature have mainly published

Fig. 2 Birth location for authors

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Fig. 3 Publication periods for canonical authors ordered by year of birth

between the nineteenth and twentieth centuries, with only two exceptions: Miron Costin (lived and wrote in the seventeenth century) and Dosoftei (who wrote in the eighteenth century). The writers who appear later on are identified as intellectuals and were influenced, as their life chronology shows, by French or German cultures and literature. The intervals shown in Fig. 3 capture writers’ first publication and their last publication. The most recent canonical authors are Petru Dumitriu, Emil Cioran, and Nicolae Breban. As it can be seen, there are many publishing overlaps between writers, even though they might not follow the same literary movement from that historical period. An important overlap from the first years of the twentieth century includes the following authors: Lucian Blaga, Bacovia George, Mircea Eliade, Ion Barbu, Tudor Arghezi, Eugen Ionescu, and others. Our last experiment targeted the journeys and cities where 20 canonical authors traveled or lived, based on their life chronology. The experiment aimed at tracing the wonderings of the most important authors, based on their life events. The representation from Fig. 4 is overlapped on a part of the world map, making it easier for the end user to relate to the writers. Also, the visited cities can be traced in chronological order, as the paths are marked by arrows. As it can be seen, Ion Heliade R˘adulescu traveled mostly in the Central or Western European cities, such as Vienna, Prague, Leipzig, London (2 times), or Paris (3 times), while Dimitrie Bolintineanu lived mainly in Southern Europe (Macedonia, Greece), Asia (Constantinople, Brusa, Palestina), and Africa (Egypt). Both canonical authors intersected each other in Paris, France, in 1949. Figure 4 presents only a snapshot of how the journey of two writers is reflected on the map. Through this experiments, a software tool was also implemented, using the amChart library, which lets the end user select the desired timeframe (e.g., current timeframe is 1800--1870) and, within a list of canonical authors, to plot their travels in that timeframe. The plotted map is interactive: users

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Fig. 4 Journeys of canonical authors

can hover over each stop city and see the exact year (or years) the writers traveled there and, in case of overlapping, to see the exact overlap in the displayed tooltip.

5 Conclusions and Future Work Our goal is to shift the Romanian literature to the digital era and establish a smart ecosystem centered on Romanian literature. The current study presents a quantitative approach relying on DGLR and applies different demographic studies on authors for finding patterns between them, in order to reveal novel information using statistical techniques. Our work also targets the e-learning domain by integrating several visualizations in the INTELLIT learning platform, namely the birth locations of all authors, publication periods for canonical authors, number of writings per number of authors per year, as well as the journeys of canonical authors. The obtained results are promising, showing interesting demographics inside and outside Romanian borders, together with historical correlations (e.g., impact of certain worldwide events on number of authors born in those periods). Our study includes only authors from DGLR within letters from A to O, with the remaining ones still being work in progress. Future work should include the complete list of authors (letters A–Z) and may include additional research paths---for example, the influence factors for each author and common patterns between them. Moreover, we aim to integrate our work in an existing learning environment or publish it as a standalone software to help students learn more about Romanian literary writers. Acknowledgements This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS—UEFISCDI, project number PN-III 54PCCDI/2018,

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INTELLIT—“Prezervarea s, i valorificarea patrimoniului literar românesc folosind solut, ii digitale inteligente pentru extragerea s, i sistematizarea de cunos, tint, e” and by the Operational Programme Human Capital of the Ministry of European Funds through the Financial Agreement 51675/09.07.2019, SMIS code 125125.

References 1. Simion, E.: Dic¸tionarul General al Literaturii Române, vol. VII. Editura Univers Enciclopedic, Bucharest, Romania (2004) 2. Simion, E.: Dic¸tionarul General al Literaturii Române, vol. IV. Editura Univers Enciclopedic, Bucharest, Romania (2016) 3. INTELLIT National Platform. https://intellit.ici.ro/en/about-intellit/. Last accessed 2020/04/20 4. Gowda, R.S., Suma, V.: A comparative analysis of traditional education system vs. e-Learning. In: 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 567–571. IEEE (2017) 5. Roth, A., Ogrin, S., Schmitz, B.: Assessing self-regulated learning in higher education: a systematic literature review of self-report instruments. Edu. Assess. Eval. Acc. 28, 225–250 (2016) 6. Gormley, C., Tong, Z.: Elasticsearch: The Definitive Guide: A Distributed Real-Time Search and Analytics Engine. O’Reilly Media, Inc (2015) 7. Butler, H., Daly, M., Doyle, A., Gillies, S., Hagen, S., Schaub, T.: The GeoJSON Format. In: Internet Engineering Task Force (IETF) (2016) 8. Carriere, D.: Geocoder: Simple, Consistent. https://geocoder.readthedocs.io/. Last accessed 2020/04/20

Chatbot, the Future of Learning? Bogdan-Ioan Ouatu and Daniela Gifu

Abstract Our position is that, in order to improve the quality of Romanian education, an intelligent learning system could become a substantial and versatile tool for aiding the achievement of every student’s potential, always aiding and encouraging him/her. The limits of the conventional learning process and mental health are major issues in many education systems. Chatbots are centered on assisting humans in performing their tasks efficiently and require a low amount of digital literacy to interact with. Chatbots have been shown to be effective in the fields of education and well-being. Due to the substantial difference between artificial intelligence technology waves, the interaction process can occur in several ways depending on the communication interface. How can we combine both traditional and automated educational approaches in the digital age? On one hand, an artificial intelligence tutor does not get angry or annoyed explaining the same problem at the student’s discretion and can also function as a personal therapist, while a human professor could be empathic, trying to keep a balanced teaching method adapted to all students. Keywords Chatbot · Intelligent learning systems · Mental health in education

1 Introduction Traditionally speaking, one of the significant problems for both teachers and students is a way to evaluate learning performance. In this context, a valid solution is the chatbot technology. Why? Because human-level feedback can be achieved with B.-I. Ouatu (B) · D. Gifu Alexandru Ioan Cuza University of Iasi, General Berthelot, 16, 700483 Iasi, Romania e-mail: [email protected] D. Gifu e-mail: [email protected] D. Gifu Romanian Academy—Iasi Branch, Codrescu 2, 700481 Ia¸si, Romania © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5_23

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existent technology [1]. Moreover, an intelligent learning system could become a substantial tool for reaching the potential of every student, always aiding and encouraging him/her, especially when a well-being solution could be deployed along with the digital tutor. The remainder of this paper is organized as follows: Sect. 2 presents briefly the social context which determined the launching of our position. Section 3 offers arguments in favor of an ITS with a well-being module incorporated and describes chatbots prototypes in education and mental health, observing major achievements of different models. Finally, we conclude with an assessment of the use of chatbots in the learning process and we identify avenues for future research.

2 Social Context Romania has the least higher education graduates in the European Union [2]. Furthermore, many high school students choose university programs that have low preexisting knowledge and skill requirements, and this results in producing low-demand specialists on the labor market contributing to the mismatch priority occupations in Romania [3]. Moreover, only 25% of students end up working in domains they studied and this contributes to another underlying socioeconomic problem than many business owners face: low number of qualified personnel. Romanian mental disorder stigma is an underlying issue [4, 5] and is discouraging the youth from seeking therapy. Mental health awareness is low, only 3% of the annual health budget going toward psychiatry, and that there is one psychiatrist for every 20,000 people in the country [6]. A study from 1999 finds that in Romania 11.67% of children and adolescents aged 3–16 suffer psychiatric disorders [7]. However, a study from 2017 found that in the USA, almost half of adolescents aged 13–18 are affected by mental disorders, and about 20% of these are categorized as “severe” [4]. Mental disorders can lead to academic problems, such as poor attendance, poor social integration, trouble adjusting to school, problems with behavior regulation, and attention and concentration issues. This leads to much higher dropout rates and lower overall academic achievement, as well as specific academic and social problems associated with various disorders [5]. Students with anxiety are twice as likely to dropout high school than students who have never had the condition.

3 Why Chatbots? 3.1 Ease of Interaction and Accessibility Chatbots are easy to interact with and humans find it natural [8].

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We consider it important that the chatbot should be platform-agnostic. This is important for the Romanian region since there are individuals who cannot afford a personal computer but could benefit greatly from educational and healthcare applications only needing an account and a smartphone or access to a school computer.

3.2 Proficiency of Automated Tutoring Systems Compared to Human Tutors Most tutors are not trained in teaching techniques and have moderate expertise on the tutoring topic, but are still very effective in producing learning gains in students. Researchers found that the amount of tutoring experience did not significantly predict learning gains [9]. In 2015, in Romania, more than half of teachers teach in schools where the school principals sense the absence of qualified professors or professors that have good results [10]. Access to a digital tutor could be an incentive for students and minimize the disparity between different social classes (e.g., urban and rural school pupils). Intelligent tutoring systems (ITSs) present student modeling capabilities on the basis of the student’s questions, answers and solutions to problems. This is because successful communication is based on shared meaning [11]. Research has shown that convergent meaning is built really slow and the feedback mechanisms between tutor and learner have low fidelity or are even misleading. Misunderstandings frequently occur as the tutor scrambles to piece together a minimal understanding of the student’s knowledge. What is more, information-seeking behavior is shown more by the students who already understand the subject and rarely comes from the lost student who usually nods his head when asked if he understands the matter. This and the large difference between tutor and student knowledge resulting in positive feedback to student contributions that are vague, incoherent, or error-ridden further motivate the implementation of the educational chatbot. AutoTutor [1] is a computer tutor that simulates the discourse patterns and pedagogical strategies of a typical human tutor. It was found that its ability to evaluate the assertion quality in the domain of computer literacy is indistinguishable from intermediate experts. Average learning gains were approximately 0.8 σ above controls who read static instructional materials for an equivalent amount of time, which are similar to expert human tutors in computer-mediated interactions [12]. It is a testimony to the efficiency of chatbots and presents theoretical arguments and practical evidence in this. It is to be noted that despite being implemented more than 20 years ago it still has such results. Artificial neural networks were studied in the 90s extensively but were propelled to the state of the art of artificial intelligence only recently. Similarly, there are more than 30 years of research that have proven multiple times the theoretical efficiency

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of ITSs and helped deconstruct and improve tutoring methods. The last wave of machine learning saw exceptional results in solving problems similar to ITS, mobile phone users have reached almost 4 billion and now children are sufficiently digitally literate [13], such that ITPs could finally break through outside the American school system, be deployed efficiently and perform better than ever.

3.3 Proficiency of Well-Being Chatbots Up to 75% of the college students who need mental health care do not access clinical services. 70% of patients show interest in using mobile apps for self-monitoring and self-managing their mental health [14]. Internet interventions for anxiety and depression have empirical support comparable with a CBT intervention delivered by a specialist. Overcoming stigma is considered a major benefit of Internet-delivered and mobile mental health interventions. Conversational agents get their value from offering a convenient, engaging way of getting the support needed at any time. Woebot is a platform-agnostic fully automated therapy chatbot that uses cognitivebehavioral therapeutic (CBT) for treating depression and anxiety. It is a modern hybrid input app developed by a team of psychologists with extensive knowledge in CBT and AI experts. Using Woebot correlated with a significant reduction in depression as measured by the PHQ-9. The effect size for depression was moderate. It also demonstrated its capacity for high retention (subjects who finished the study), an underlying problem in many such applications. Goal-setting intervention is yet another module that could be easily implemented and delivered through an ITS and it has been shown to significantly improve academic performances and to close both gender and ethnic achievement gap [15].

3.4 Are Chatbots Hard/Costly to Implement? Even if AutoTutor presents many complex modules, there are bots such as ALICE that do already incorporate most of these functions and are easy to work with. ALICE [16] is one of the strongest chatbots. It is a text-based (interaction is made through a chat window) and, despite having a very simple architecture, is relevant even by today’s standards. It may be argued that the implementation of chatbots would produce diminishing returns, but the cost would be countered by the improvement of public mental health, public education, and essential data collection that would otherwise be pricey to obtain. The costs would be furthermore diminished by the ease of deploying nationwide modules such as new optional curriculum or potentially groundbreaking psychology tools like goal-setting.

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3.5 Adaptability and Fast Deployment A chatbot will ultimately have the consequence of less necessity of human labor and will help institutions prioritize where the remaining human power will be allocated. They can streamline the data analysis process and may provide new data and allow for more insights through the analysis process of the data that is in natural language format. Data could be used to design improvements for the chatbot itself. The application could present a testing ground for new curricula, could complete lacks in some schools’ programs, such as health education, which sometimes is omitted. It does not necessitate the reprinting of tens of thousands of schoolbooks for every content mistake and should offer ways of pointing out such mistakes.

4 Conclusion With principles from all these models, we can see how engaging, helpful, and high retention chatbot tutors are more feasible and can be more accessible than previously thought. They can offer a great deal of educational and psychological support at one’s convenience. With new technology, chatbots are easier and more efficient to implement. ITSs could become a very convenient and inexpensive platform for deploying programs such as a goal-setting module which has clear positive effects in academic achievement, a permanent artificial psychiatric therapist, or a new optional curriculum. Future research will focus on in-depth analysis of leveraging existing tools for simpler implementation and finding ways of introducing such ITS programs in schools and colleges for maximizing retention rate, “market penetration” and for minimizing the chances of being used inefficiently.

References 1. Oden, G.C., Graesser, A.C., Wiemer-Hastings, K., Wiemer-Hastings, P., Kreuz, R.: AutoTutor: a simulation of a human tutor action editor (1999) 2. Absolvent, i înv˘at, a˘ mânt superior—Monitor Social. [Online]. Available https://monitorsocial.ro/ indicator/absolventi-invatamant-superior/ 3. Romania: Mismatch priority occupations|Skills Panorama. [Online]. Available https://ski llspanorama.cedefop.europa.eu/en/analytical_highlights/romania-mismatch-priority-occupa tions. Accessed 26-May-2020 4. Merikangas, K.R., et al.: Lifetime prevalence of mental disorders in U.S. adolescents: results from the national comorbidity survey replication-adolescent supplement (NCS-A). J. Am. Acad. Child Adolesc. Psychiatry 49(10), 980–989 (2010) 5. Youth Mental Health and Academic Achievement. National Center for Mental Health Checkups at Columbia University

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6. Ionescu, C.: Depression in post-communist Romania. Lancet 365(9460), 645–646 (2005). https://doi.org/10.1016/s0140-6736(05)17964-1 7. Grigoroiu-Serbanescu, M., Christodorescu, D., Cantilli, L., Jost, L., Nedelcu, H.: Epidemiology of child and adolescent psychiatric disorders in a Romanian nationwide sample. I. Age group prevalence. Rom. J. Neurol. (1999) 8. Procter, M., Heller, B., Mah, D., Jewell, L., Cheung, B.: Freudbot: An Investigation of Chatbot Technology in Distance Education 9. Cohen, P.A., Kulik, J.A., Kulik, C.-L.C.: Educational outcomes of tutoring: a meta-analysis of findings. Am. Educ. Res. J. 19(2), 237–248 (1982) 10. Romania-OECD. [Online]. Available https://www.oecd.org/countries/romania/ 11. Schober, M.F.: Speakers, addressees, and frames of reference: whose effort is minimized in conversations about locations? Discourse Process. 20(2), 219–247 (1995). https://doi.org/10. 1080/01638539509544939 12. Nye, B.D., Graesser, A.C., Hu, X.: AutoTutor and family: a review of 17 years of natural language tutoring. Int. J. Artif. Intell. Edu. 24(4), 427–469 (2014) 13. Bickmore, T., Gruber, A., Picard, R.: Establishing the computer-patient working alliance in automated health behavior change interventions. Patient Educ. Couns. 59(1), 21–30 (2005). https://doi.org/10.1016/j.pec.2004.09.008 14. Torous, J., Wisniewski, H., Liu, G., Keshavan, M.: Mental health mobile phone app usage, concerns, and benefits among psychiatric outpatients: comparative survey study. JMIR Ment. Heal. 5(4), e11715 (2018). https://doi.org/10.2196/11715 15. Schippers, M.C., Scheepers, A.W.A., Peterson, J.B.: A scalable goal-setting intervention closes both the gender and ethnic minority achievement gap. Palgrave Commun. 1(1), 15014 (2015). https://doi.org/10.1057/palcomms.2015.14 16. Shawar, B.A., Atwell, E.: ALICE chatbot: trials and outputs. Comput. Sist. 19(4), 625–632 (2015). https://doi.org/10.13053/CyS-19-4-2326 17. Geant˘a, M: Studiu: atitudini s¸i percep¸tii despre bolile mintale în cadrul popula¸tiei României. [Online]. Available https://adevarul.ro/

Author Index

A Alexandru, Dan, 187 Araya, Roberto, 77

B Buffardi, Annalisa, 155

C Calin, Mihnea Andrei, 173 Cinganotto, Letizia, 117 Ciribini, Angelo L. C., 225 Cojocea, Eduard, 211

D Dascalu, Maria-Dorinela, 173 Dascalu, Mihai, 27, 91, 173, 235, 253

F Ferreira, Carlos, 131 Ferreira, João, 131 Fetoiu, Catalin-Emil, 173

G Gîfu, Daniela, 187, 263 Giovannella, Carlo, 39, 105 Guida, Maria, 117

H Hanganu, Laurent, iu, 253 Heidmets, Mati, 53 Heikkonen, Jukka, 13

L Lelis, Catarina, 145 Leoste, Janika, 53 Ley, Tobias, 53 Lindblom, Pekko, 13

M Marinescu-Muster, Robert, 197 Mastrolembo Ventura, Silvia, 225 Mealha, Óscar, 131 Militaru, Gheorghe, 173 Mogas-Recalde, Jordi, 3 Moreira, Filipe T., 67

N Neagu, Laurentiu-Marian, 253 Nica, Cristi, 165 Nistor, Nicolae, 27 Nitu, Melania, 235 Nunes, José, 131 Nygren, Eeva, 13

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Ó. Mealha et al. (eds.), Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Smart Innovation, Systems and Technologies 197, https://doi.org/10.1007/978-981-15-7383-5

269

270 O Olteanu, Alexandru-Corneliu, 165, 245 Ouatu, Bogdan-Ioan, 263 P Palau, Ramon, 3 Pasov, Iulia, 27 R Racec, Emil, 165, 245 Ramos, Fernando, 67 Rebedea, Traian, 211 Ruseti, Stefan, 235 S Santos, Fernando Delgado, 131

Author Index Simion, Eugen, 253 Sutinen, Erkki, 13

T Tagliabue, Lavinia C., 225 Teizer, Jochen, 225 Tica, Ioana-Teodora, 245 Toma, Irina, 91, 253 Tomescu, Silvia, 235 Toncu, Stefan, 91 Trausan-Matu, Stefan, 27, 91, 173, 253

V Vairinhos, Mário, 67 Vollenbroek, Wouter, 197 Vries de, Sjoerd, 197