Innovations in Technology Enhanced Learning [1 ed.] 9781443883191, 9781443866293

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Innovations in Technology Enhanced Learning

Innovations in Technology Enhanced Learning

Edited by

Anton Ravindran and Liz Bacon

Innovations in Technology Enhanced Learning, Edited by Anton Ravindran and Liz Bacon This book first published 2015 Cambridge Scholars Publishing 12 Back Chapman Street, Newcastle upon Tyne, NE6 2XX, UK

British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library

Copyright © 2015 by Anton Ravindran, Liz Bacon and contributors Published by Cambridge Scholars Publishing in collaboration with Global Science and Technology Forum

All rights for this book reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the copyright owner. ISBN (10): 1-4438-6629-6, ISBN (13): 978-1-4438-6629-3

TABLE OF CONTENTS

Preface ....................................................................................................... vii Anton Ravindran Foreword .................................................................................................... ix Liz Bacon Developing Next Generation Online Learning Systems to Support High Quality Global Higher Education Provision ................................................ 1 Lachlan MacKinnon and Liz Bacon Facilitating Communities of Inquiry Online via Types of Teaching and Learning .............................................................................................. 43 Amy L. Skinner and John M. Peters Social Media-Enabled Learning: A Review of the Research in Higher Education ................................................................................................... 63 Nicole C. Green, Brenda Wolodko and Roslyn Foskey Social Networks and E-Learning: Key Determinants Identification ......... 85 Vimala Balakrishnan and Huck-Soo Loo Exploring the Factors Impacting Facebook Use in Education: Personality Traits and Acceptance Technology Model ........................... 100 Sima Sharifirad and Mohammad Sadegh Sharifirad Factors Affecting the Adoption of e-Learning Systems: Implications for Language Learning in LMSS ............................................................. 131 Mohamed Amin A. Mekheimer and Hamad Shabieb Aldosari The Process of e-Learning Adoption in Higher Education ...................... 177 Natália Fernandes Gomes, Antonio Víctor Martín García and María José Hernández Serrano

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Perceptions of Online Learning in an Australian University: An International Students’ (Asian Region) Perspective – Enjoyment ..... 210 Renee Chew Shiun Yee Scalable and Personalized Blended Learning for Software Education .... 244 Chris Boesch and Sandra Boesch Complete Reading System for the Vision Impaired ................................ 262 Azadeh Nazemi and Iain Murray An Ultra-lightweight Java Interpreter for Bridging CS1 ......................... 299 P. A. Stocks Monitoring Students’ Engagement with an Online Course: Reflections and Implications for Practice................................................... 327 Aneta Hayes and Amal Al-Gallaf Contributors ................................................................................................ 358

PREFACE

With the unabating acceleration of change, both in technology and knowledge transfer, it is clear that individuals and corporations have only one option: either continue to learn, unlearn and relearn or permit our skills sets and knowledge to become redundant and obsolete as the shelf life of knowledge erodes fast. Enterprises that fail to cultivate and support an environment for continuous learning by their employees will quickly lose their competiveness in the global market place and slide into oblivion. Thus technology-based learning and education is becoming increasingly important in the 21st century. Indeed, until recently, all teaching methodologies developed for students up to their tertiary education were largely based on traditional delivery models. However, a significant amount of research has now been done on the best ways to teach adults as well as how to leverage the best from technology to deliver such training. Corporations are constantly on the lookout for more cost effective, as well as standardized and convenient ways to deliver training to their employees in a self-paced manner. Online learning models including MOOCs, are radically changing both the delivery model as well as the learning experience. This book consists of a collation of selected innovative research findings, which were further developed into book chapters focusing on technology based training and education. Undoubtedly, online learning has significantly increased learning options and has introduced new dimensions for education and training. The key factor that differentiates traditional education from online education is its inherent flexibility and global reach as it is not confined to traditional classrooms but enables learning from anywhere and at any time. It is an attractive proposition given the increasing need for lifelong learning. Online education presents participants with an opportunity for global connection and interaction and discussion in a ubiquitous real time manner. While earlier versions of computer-based training and education were focused on individualized learning, with the advances in technology, today’s online education is more focused on various types of interaction and collaboration by the participants, thus enriching the learning experience. This is comparable to the dialogue that would take place in a

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classroom environment during traditional delivery, but has the potential to allow contributions from around the globe. Although there has been long debated discussions that learning online is different from learning in a traditional classroom, it is neither better nor worse pedagogically but rather an alternate model to complement the traditional learning environments to meet growing need and demand for lifelong training and education. Online technologies will continue to significantly transform the way that such learning takes place This book, written by 24 authors contains 12 comprehensive chapters under 4 broad themes: Socialisation; Technology Acceptance; Adaptive, Adaptable, Personalised & Engaging Environments and Metacognition and Monitored Environments. We have presented research findings from 10 countries in 4 continents. The book covers major issues about technology based learning, provides ample resources on diverse aspects of online education technologies and gives unique insights about online education. We hope the contents of the book will help to widen the perspectives and better equip academics, education and training professionals and practitioners alike with a more advanced vision of online education. Dr. Anton Ravindran

FOREWORD

This book consists of a collection of state-of-the-art research papers discussing innovations in the area of technology enhanced learning in adult education. It was inspired by ideas presented at the annual Computer Science Education: Innovation and Technology conferences, organised and administered by Global Science and Technology Forum (GSTF), at which Professor Liz Bacon has been a regular speaker. Input for the twelve chapters have been sourced from ten geographically dispersed countries from across the world: USA, Spain, Portugal, UK, Bahrain, Saudi Arabia, Malaysia, Singapore, Iran and Australia, providing a truly international perspective on the field. With rapid developments in the technology and delivery mechanisms including the development of MOOCs (Massive Open Online Courses), online learning is in the process of revolutionising higher education, which makes this book all the more relevant and timely. The intended audience for this book are academics, university students, particularly PhD students, researchers and industry professionals working in this area. However, it may also be relevant to learners interested in developing their online learning skills. It provides an illuminating insight for anyone interested in this domain, in particular the way in which the nature and practice of education is being transformed across the globe. This transformation is impacting every level of education, both formal and informal, and throughout life. Chapter 1 introduces the author's views on this subject, discussing the worldwide demand for technology enhanced learning now and in the future. It reviews 4 key areas, identified in research literature and through research undertaken by the authors, as the key to the success of online learning, these being: metacognition and learning strategies; engaging and immersive environments; socialisation; and adaptive, adaptable and personalised environments, all of which are expanded upon in the other chapters of this book. The chapter then moves on to discuss the need to develop metacognitive students who are able to cope with the more flexible and open nature of online learning in higher education, which many students struggle with, discussing the need for higher education to take a more andragogic / heutagogic approach to teaching. This approach also opens up opportunities to free up the learning process by enabling students to develop their skills in a monitored and authenticated digital

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learning environment. Such an environment would also enable students to develop an active portfolio and CV, and support the move toward assessment when ready as opposed to assessment at fixed scheduled times. In addition to the themes picked up from chapter 1, another theme which emerges across the work described in the rest of the book is on evaluation, or rather technology acceptance, as it is described in the models. This addresses the rationale for the continued use of learning technologies by students. The subsequent chapters in the book are organised relevant to these 4 broad themes, although the research described in some chapters will contribute to more than one theme. The chapters themselves and their thematic relationships are briefly described below:

Theme 1: Socialisation The first theme is that of socialisation. Chapter 2 explores the need to adapt our traditional ways of teaching for delivery in online learning environments and investigates the development of communities of learners in different types of teaching and learning environments. Conclusions from the research emphasise the need for students to become not only more independent, but collaborative and social learners in an online learning environment. It also discusses the challenges this brings for students who are traditionally unfamiliar with this type of learning and the need for the role of the teacher to change in order to become both a facilitator and co-learner with the students. Chapter 3 provides a comprehensive literature review of social media-enabled learning and associated environments in higher education. It examines the design methodologies that move higher education from a focus on content provision to a dynamic, open, flexible, collaborative and learner-centred approach. It then discusses the development of knowledge creation and understanding, and the need to develop the collective intelligence of students through social interaction. Chapter 4 provides a perspective from Malaysia where the use of social networks in e-learning tends to be less well used in education than in many other parts of the world. The study, based on the Push-Pull Mooring framework, investigates factors that influence the use of social networks in e-learning. In this research, the Push- Pull-Mooring framework was used to identify the factors that push people towards e-learning (perhaps reluctantly), the pull factors that attract people towards e-learning and the mooring factors which encourage delay in movement. Results revealed e-learning Perception, Ease of Use, Convenience, Academic Reasons and Social Networking as key factors in

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determining the use of social networks in e-learning. Chapter 5 continues the social networking theme with an examination of the use of Facebook in an educational setting and its impact on Iranian student life given its use is currently prohibited. The chapter discusses the reasons why students use social networking sites, and in particular Facebook. The student survey undertaken focuses on establishing the reasons why students adopt a technology (based on the Technology Acceptance Model - TAM) and the impact of their personality on their use of social networking sites.

Theme 2: Technology Acceptance Chapter 6 describes a study, based on male students studying English as a Foreign Language in a college in Saudi Arabia, which focuses on determining whether voluntary or mandatory use of an e-learning management system (LMS) affects adoption of that technology. The questions in the study are based on the Technology Acceptance Model (TAM) and it focuses on answering the research question “what are the factors that impact the adoption of an e-learning system in voluntary and mandatory settings?”. Chapter 7 presents the literature on technology acceptance models and discusses how e-learning has been adopted in the Spanish and Portuguese Higher Education Systems, focusing on the adoption of e-learning, in the form of blended and mobile learning. Results of the analysis and examples from two institutions, one in Spain and one in Portugal, have both concluded that one of the biggest challenges faced by higher education institutions in moving forward with e-learning is the need to train and motivate teachers, and their subsequent willingness to integrate new approaches into their teaching.

Theme 3: Adaptive, Adaptable, Personalised and Engaging Environments Chapter 8 discusses the results of a study carried out at an Australian university to establish if cultural and language differences affect students’ online interactions and communications. The study used a modified Online Learning Environment Survey (OLES) to collect data from undergraduate students from 14 countries: studying a range of disciplines including Health, Law, Education, Science & Technology, Creative Industries, Business, Built Environment & Engineering. This chapter analyses the differences between the cultures, their expectations, and their motivation for online learning, arguing the need to provide adaptable and personalised systems that support the needs of international students based on their

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culture, and also to recognise the support required for lecturers to provide this personalisation. Chapter 9 describes the development and use of a platform to support students in learning how to program computers and can support several programming languages. In some modes of the platform, the students can use a drag ‘n’ drop interface akin to puzzle games to help them develop their code. As the students work their way through a set of programming problems, they unlock videos which provide additional information, encouragement, and motivation. All modes provide a personalised and engaging learning experience supporting the development of students at their own pace. The platform is used by the author to hold weekly programming tournaments at the start of a class to support a combination of competition, personalised learning and peersupported learning. This approach has been shown to motivate students to come well-prepared to class, in addition to providing the teacher with a wealth of information about how the knowledge and skills of their students are developing. Chapter 10 describes the challenges of reading electronic documents for the vision impaired. Although standards have been developed for the markup of electronic documents, many online documents do not provide this and readers consequently encounter unstructured text and scanned documents which require optical character recognition in order to determine their content. This approach includes the additional challenges and complexities of reading multi-dimensional material such as mathematical formulae which can contain subscripts and superscripts etc. The author describes the development of a personalised Complete Reading System designed to be portable, low powered, simple to use, standalone and affordable, which has been designed to address the identified challenges, and is specifically aimed at use in developing countries.

Theme 4: Metacognition and Monitored Environments As touched on in the description of chapter 2 above, this theme develops one of the key success areas identified in chapter 1 for online learning, that of metacognition, discussing the need to develop metacognitive learners. This theme is further explored in chapter 11 which picks up on another discussion initiated in chapter 1, that of monitoring, supporting and capturing the student experience in digital environments. This chapter describes the development of JULI, a Java Ultra- Lightweight Interpreter for use in teaching introductory programming, a topic that traditionally has high failure rates for first year computer science students. JULI is an environment that provides personalised guidance on errors to students and its use by students in Australia has resulted in a marked

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improvement in their success rates. The JULI environment provides an analysis of error logs which helps the author discover common errors and mistakes made by students, thus providing him with insight into student learning and enabling him to develop enhancements to JULI to further support student learning and metacognitive development. Finally, chapter 12 describes a technology enhanced learning environment used at a medical university in Bahrain with the aim of both supporting the learner with inspiring content to help them learn non-core curricula, and also to provide a monitoring tool for the lecturers, and a facility to control access to content in order to help students organise their learning and develop their metacognitive skills. As identified in some previous chapters, despite being digital natives, students do not automatically know how to develop and manage their learning online; in other words students cannot automatically be assumed to be metacognitive. In this study, the Microsoft Learning Content Development System (LCDS) was used to help create inspiring customised content by providing templates to develop games, quizzes etc. A particular focus of the work is on Test Yourself Activities, in addition to the use of a checklist, which helped staff to monitor student engagement and progress through the module.

Conclusion This book provides a truly international and cutting edge perspective on developments in technology enhanced learning. Supporting higher education students in the move towards maturity in their learning, sufficient to cope with the typical flexibility, open nature and choices provided by technology enhanced learning, is not trivial and their ability to thrive in such an environment will be greatly impacted by their metacognitive development. Similarly, as several chapters in this book identify, the move towards technology enhanced learning is fundamentally dependent on motivated and skilled teachers developing and supporting learning materials and assessments for students. In order for both teachers and learners to be successful in the online world, it is important that they understand those factors which will impact on their success. For teachers, this is in the ability to develop effective and engaging learning materials for delivery in online environments and to provide relevant engagement and support to their students. For learners, they are subject to the same factors that they would experience in traditional face-to-face learning such as personal issues, motivation, personality traits etc., but they also need to be mature enough as learners, i.e. metacognitive, to be able to cope with the demands of a purely online service. For many of those involved in

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higher education, a common belief is that online provision is a poor second compared to face-to-face interaction between teachers and students, and this inevitably impacts the acceptance and take up of online facilities. However, online learning developed and supported by committed, motivated and skilled teachers for students with good levels of metacognitive skills, can offer a rich, flexible, individual learning experience that can exceed that offered in traditional face-to-face environments. The global requirement for higher education is growing at such a rate that online learning currently offers the only viable solution to meeting the needs of vast numbers of learners. These online services need to be managed across a wide range of delivery mechanisms, from simple mobile devices to advanced computing facilities. The combination of many of the technologies described in this book, together with the skills, motivation and enthusiasm of online teachers, and the support for the range of delivery mechanisms, must all come together to ensure that the provision offered to those learners gives them the best possible learning experience. We hope that this book will encourage you to become involved in online and technology enhanced learning, whether as a teacher, a learner or in some other capacity. The technologies described within these chapters will become redundant as technology evolves swiftly and novelty, more alluring, but the learning models and human issues will remain relevant and important for the foreseeable future, and must be addressed if we are to successfully provide support for the next generation of online learners. Professor Liz Bacon PhD, CEng, CSci, CITP, FBCS, FHEA, MACM

DEVELOPING NEXT GENERATION ONLINE LEARNING SYSTEMS TO SUPPORT HIGH QUALITY GLOBAL HIGHER EDUCATION PROVISION LACHLAN MACKINNON AND LIZ BACON UNIVERSITY OF GREENWICH, LONDON UK

Abstract The world’s demand for higher education, particularly in the developing world, is increasing. However, demand is often not co-located with supply and most students cannot afford to study abroad. In the short term this demand can only realistically be met through online distance learning provision. However, currently online distance learning students face many challenges in completing their courses and success rates are often poor. This chapter reviews the factors affecting the success rates of online distance delivery from both the student and education provider perspectives. The results of this analysis identify four factors that the literature has shown to be key to success in online learning, all of which the authors have been researching for the past decade. This analysis is followed by a discussion of their research contribution in these areas, set in the context of the literature. It is then argued that a primary goal of higher education, and one that is key to the success of online learning, is to produce students who are metacognitive and able to develop their skills into lifelong learning and that, in order to do this, the educational model needs to move towards a more andragogic/heutagogic approach. In doing so, this could provide opportunities to free up the learning process by enabling students to develop their skills in a monitored and authenticated digital learning environment, providing students with an active portfolio of achievement and CV, supporting the move toward assessment when ready as opposed to assessment when scheduled.

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Keywords: Metacognition, games-based learning, adaptable, adaptive, personalised learning, socialisation, learning strategies.

1. Introduction The world we now live in is predominantly governed by the growing and ubiquitous digital technologies that allow us to communicate, carry out our business, develop Government structures, be entertained, network with colleagues, family and friends and, to a large extent, live our lives. For years we have been driven by Moore’s law (Moore 1965) and, as a result, all the advances that we have seen in hardware and software have been promoted on the basis of technological arguments such as greater speed, greater functionality, more power and more storage. However, in the last few years we have seen the move begin from technology drivers, focused on developers and technologists, to design drivers focused on end users. It can be argued that the arrival of new technology such as netbooks and the iPad (Apple) represent design models utilising existing technology in ways that more readily fit the needs of users, while new technologies such as Wii (Nintendo) represent attempts to produce technologies that fit more readily with the lifestyle choices of users. This fundamental shift away from technology drivers to design drivers associated with the ubiquity and pervasiveness of computing technology within society not only changes the way that users think about the technology, but must also impact on the way teaching and learning can, or should, occur utilising that same technology. Current models of higher education in the UK, and across the world, are predominantly based around traditional lecture and tutorial models, with assessment by submitted and marked coursework and exams, supporting a pedagogic model developed in the nineteenth century. Even subjects that one would anticipate to have moved away from this approach to embrace modern technological approaches, such as Computer Science, still predominantly maintain this model. As tools and technologies have developed to support Technology Enhanced Learning (TEL), we have not seen a commensurate development of twenty-first century teaching models and practices, and much of the existing TEL development has focused on repeating existing pedagogical models and practices. Attempts to introduce new large-scale models, such as MOOCs, suffer from significant non-completion rates, and therefore do not currently provide a viable alternative to traditional face-to-face teaching or online distance learning approaches. However, there is a significant and rapidly growing requirement for higher education provision in developing countries, which

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already far outstrips the ability of those countries to respond by increasing traditional provision. It has been argued that the ubiquity of computing and telecommunications technology should permit the existing higher education providers in developed countries to meet this need through online distance provision, but this really requires the development of new teaching models and practices. The authors have been jointly researching in this area for over a decade, and have identified four key factors that should be addressed in developing new teaching models and practices to ensure successful technology enhanced learning, whether undertaken at a distance or in support of face-to-face provision. This chapter provides a general background to the issues of TEL, using this to identify and clarify the key factors to be addressed. It then discusses each of the four key factors, which are: metacognition and learning strategies; engaging and immersive learning environments; socialisation; and adaptive, adaptable and personalised environments. This is then followed by a consideration of the major research context and our own research, to give a clear rationale for the key nature of each factor and how it can be addressed in new teaching models, providing examples of successful research outcomes that demonstrate the potential benefits for students in each of these areas. The final section of the chapter reflects on one of the key objectives of higher education: the desire to develop metacognitive students so that they can successfully continue their lifelong learning once they leave formal education. In order to achieve this, the educational model needs to become more andragogic or heutagogic in nature to ensure that students develop the skills to direct and manage their own learning, a skill even more essential today if students are to benefit from online distance learning. Finally, with the move to more learning taking place in digital environments, it is argued that these environments should provide the capability to identify the user, monitor their learning, provide an authenticated portfolio of achievement and CV and support the move toward assessment of individuals when they are ready as opposed to requiring knowledge and skills to be demonstrated at specific points in time, scheduled typically at the convenience of the awarding institution.

2. Why teaching students face-to-face has to change Whilst there are pockets of good practice around the sector in terms of the use of technology to support teaching, the vast majority of teaching, particularly in STEM subjects, is delivered using a very traditional lecture based model of face-to-face teaching alongside examinations that are

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relatively traditional in their form. Our students are expected to attend classes in the manner of school pupils who attend to receive instruction. The educational models that we currently use tend to be primarily didactic and pedagogic, focused around the idea of telling the student what they need to know in a teacher-pupil or parent-child relationship. We have developed timetables, class module structures, teaching models and delivery mechanisms which fundamentally support an instructivist, didactic, pedagogical model of learning. However, we also expect students to take responsibility for their own learning and think for themselves in the manner of adults and emerge from this process as adults, who are meta-cognitive and have a constructed view of reality which permits them to manage their own learning and development. There are undoubtedly subject areas where this style of teaching is appropriate and relevant. For example, in teaching a fire drill there is no value in a philosophical discussion about the nature of fire from first principles, since what we wish to achieve is an operant conditioning that results in our subjects unthinkingly establishing a pattern of behaviour relative to a stimulus, i.e. they vacate the building upon hearing a fire alarm without question or debate. However, for the majority of Higher Education teaching, such an approach would be completely inappropriate. An obvious point that could be argued here is that there is nothing wrong with the status quo. We have evolved and changed and perhaps the world is unfolding as it should. You only have to look at the growth of VLEs, eLearning technologies, distance learning systems and virtualisation to see that progress is being made. However, although all of this is true, we do need to be sure that what we are doing represents a move to an educational model that maximises the potential of technology to support learning, and is not just a case of using the technology as an alternative means of delivery. When we first started using VLEs, the most prevalent viewpoint was undoubtedly that this was simply a way of delivering material to students, and the majority of the early adopters simply loaded their existing materials, in whatever form they had them, on to the new system and the term “shovelware” (Khoo et al. 2010) was coined. Clearly we have moved on, and there are undoubtedly academics who have developed their practice in the use of VLEs, prepare their materials as learning objects, use well-formed eAssessment instruments and utilise modern multimedia and social networking technologies to support student learning, but these are still few and far between. It is still the case that the majority of academics prepare materials in relatively traditional ways, and make limited concessions to the new technologies in the way that they develop and design their materials. For example, lab

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classes still tend to be run on the basis of textual instructions to carry out tasks utilising the relevant technologies, rather than through software walkthroughs, vodcast instructions or remote control experimentation, e.g. network simulation or physical control of a robot. This situation is not going to diminish in the future and we are already in the situation where those who study, particularly in subject areas like computer science, do so almost entirely within digital environments but, as discussed above, we still teach predominantly according to traditional models. In the example of computer science we therefore find ourselves in the situation of teaching a subject area fundamental to the development of the twenty-first century, changing the very nature of how we live our lives, and yet seeking to do so using a nineteenth century educational model. What is therefore immediately apparent is that we have to change and we have to change now, because our students are already living and working differently from the mechanisms we seek to impose on them. They arrive at university having been fully immersed in a digital world and in many instances their understanding of the technologies far exceeds that of their instructors. These are important issues for the future development of higher education, not just in computer science but across all subject areas, as the technology makes it possible to completely revise the educational model. Higher education must utilise twenty-first century techniques, appropriately supported by technology, in order to create efficient, effective, personalised teaching for students that develops truly metacognitive adult learners.

3. The worldwide demand for higher education and the need for online distance delivery The current world population is approximately seven billion people. The United Nations is predicting that by 2100 there will be 11 billion people on the planet (United Nations 2011), despite the fact that birth rates have decreased since 1990 (the peak birth rate for any year in the worlds history). Regardless of whether the population increases or decreases, throughout the world the demand for higher education is likely to continue to increase as more people choose to study to that level than ever before (Dorling 2013). In the western world, the percentage seeking to study in higher education is relatively stable or growing slowly, despite there being a political will to increase numbers in many countries. Western higher education institutions are generally mature, and funded to cope with this demand. This is not true of the developing world. Although many

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countries have some outstanding universities, in-country capacity is often unable to meet the demand for higher education which has massively increased in recent times; that is to say that demand far outstrips supply in the developing world, which cannot build sufficient universities to cope with this demand in the short term. For example India, which is home to a third of the world’s poorest, education can make a huge difference and the country therefore has ambitions to increase those in higher education by 10 million in the next five years, from the 25.9 million currently in higher education. Their target by 2016/17 is to educate over 25% of 18 to 23 year olds. By comparison, in 2011/12 only 17.9% of the target demographic entered higher education (Times Higher Education 2013). Growing higher education provision by 40% over five years is a challenge by any standards, and most developing countries have similar ambitions. Given this scenario there is a need to investigate and develop alternative models of higher education, for both the developed and developing worlds, to help meet this huge demand. Potential students are no longer constrained by geography. Increasing wealth in the developing world is enabling more students to study abroad than ever before. However, for those that can’t afford it, internet access is providing access to higher education in a way that was not possible in the past. Considerable research has been undertaken to support the development of technology enhanced learning (TEL) for both distance and face-to-face students. However, a big change is that online distance education can now offer a route to provide high quality educational resources from the developed world to learners in developing countries locally and at an affordable cost. As a result of this increasing demand for higher education and availability of supply alternatives, higher education providers need to compete for students at the global, as well as national and local levels. The result is that the sector has to substantially rethink its model of the delivery of higher education, and this has already begun in a significant way, for example with the introduction of MOOCs (Massive Open Online Courses). Online education is not only designed for those studying at a geographical distance from their lecturers; it has become an important part of higher education and its role is increasing, with many students now taking an online course as part of their studies. In the US in 2010, 30% of students took an online course at some point during their college career and the trend is increasing (Hachey et al. 2012). The success rates of online courses can, however, vary tremendously and have been shown to be particularly poor for MOOCs, the success rate of which is often at less than 10%. There are a variety of factors affecting the success of online course delivery and research studies have often

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reported inconsistent findings. However, the following factors have been shown to affect success rates from the student perspective: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Prior experience of online courses Age Gender Ethnicity Motivation Intrinsic interest in the subject Personal and financial issues Learning styles and learning strategies Social and cultural characteristics including an ability to make friends online 10. Metacognition

From the perspective of the education provider, offering the following facilities to students have all been shown by research to support the learning process: 11. Engaging and immersive learning environments 12. Provision of adaptive interfaces and personalisation of the environment including the learning materials 13. Ability to support students in forming social groups for mutual support and collaborative learning 14. Retention of some physical contact with other staff and students, i.e. mode of delivery being more blended learning than completely online at a distance. It is not possible to address the first seven factors within the teaching environment, which is our focus here (though some can be addressed within the support environment). With regard to 1, it has been shown that success rates of online distance courses are influenced by familiarity and prior experience with online environments. This will increase over time; however the students arrive at the start of a course with whatever experience they have and this can’t be changed overnight. In terms of 14, this is about physical contact with other staff and students, and our focus here is about the nature of the online aspects of delivery, regardless of whether or not they are supported by face-to-face contact. This leaves us with six factors, which we have been investigating through our research for a number of years. With regard to learning styles and learning strategies, we believe from our research that learning

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strategies have a far greater influence on student performance than learning styles, which are not without problems. There has been considerable debate in the literature with regard to the use, definition and approaches to determining learning styles, over several decades. There is no universally agreed definition of learning styles, but two that reflect a general view are Brusilovsky and Millán (2007) who define learning styles as “the way people prefer to learn” and Dunn et al. (1989) who define learning styles as “a biologically and developmentally imposed set of personal characteristics that make the same teaching method effective for some and ineffective for others”. Despite the disagreements, the majority of learning styles research agrees there is a distinction between visual, auditory and kinesthetic learners, and some research, including our own, has successfully incorporated their use into a variety of eLearning systems, for example Graf and Kinshuk (2007), Cemal Nat et al. (2009) and Peter et al. (2011). That said, we will not focus on them in this chapter for several reasons: they are not in wide use today and the functionality offered is considered inflexible, and there is a body of research that suggests the basic premise of the research, theories, findings and implications for teaching using learning styles is flawed, as learning is a significantly more complex process than can be expressed by learning styles. Some argue that it is the learning preferences of students that are being measured, not their learning styles, and question the objective measurement of the self-reporting subjective judgements students make about themselves. Whilst learning styles might have their use and place, we do not see them as playing a significant role in online learning in the future and will therefore focus our discussion on the remaining success factors as identified above. The discussion about learning strategies will be included with metacognition, which is where we believe they truly exist. We also believe the two socialisation factors identified above from the student and education provider perspectives can safely be combined. Therefore, to summarise, the four key areas we intend to focus on are: 1. 2. 3. 4.

Metacognition and learning strategies Engaging and immersive learning environments Socialisation Adaptive, adaptable and personalised environments

The following section of this chapter reviews these four key areas, including our own work in this space, and demonstrates why we believe it is essential to address these areas to provide and support successful and effective online learning.

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4. Review of Key Success Factors in online learning As outlined above, the four key areas upon which we focus – metacognition and learning strategies; engaging and immersive environments; socialisation; and adaptive, adaptable and personalised environments – have been shown to contribute to student success in online learning environments. This section discusses the literature and our research in these areas.

4.1 Metacognition In this context metacognition is defined as the knowledge someone has about how they personally learn. This includes strategies about when and how to use particular learning techniques, including how to make selections between learning objects and materials, i.e. someone’s “learning strategy”. In face-to-face education this is not something that is generally taught; it is somehow expected to emerge as students’ progress through their education and, by the time they reach higher education, it is assumed that students are generally metacognitive. It is likely that those who are will fare better. Although it is generally expected that this understanding just emerges over time, for many learners it does not, but most still manage to make it to the finish line when participating in traditional modes of face-to-face teaching. In common with face-to-face teaching, online distance education typically does nothing more to help the students develop their metacognitive skills, and often this type of learning can accentuate problems that arise when students lack such skills. Traditional classroom learning is what students are most familiar with and have experienced all their life so most students develop learning strategies to cope with that approach. In online learning students can be confronted with an unfamiliar style and often a choice of learning materials, typically represented as text, graphics, animation, audio and video. These are commonly presented in a nonlinear way (Azevedo, Cromley and Seibert 2004; Mulwa et al. 2010), which is a model that is unfamiliar to them. As a result, this requires students to take more control over their learning, as it provides/requires a more heutagogic, as opposed to pedagogic/andragogic, model of learning and therefore works best for students who are motivated, self-directed, well-organised and strategic. However, if they are not metacognitive, then they will typically flounder more than in a traditional classroom setting as they not only have to understand how they learn, but they must maintain more self-management and self-discipline in their learning to ensure they remain motivated to finish the course. The

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generally lower success rates for online distance learning courses are reflective of the fact that many students find this a significant challenge and need the regular discipline of attending class to help maintain motivation. With online education, where more self-discipline and selfmanaged learning is required, development of metacognition for online students is more important than ever. High quality students will generally always succeed regardless of how they are taught. However, for weaker students it is far less likely with current practice that we will get them to the level of independent metacognition that we desire, and this is a failing in our practice. In the tradition of humanities and the classics, the teaching of rhetoric and reasoning focused on the ability of the student to understand the process of learning, but to some extent in modern higher education this tradition has diminished over time. In science and engineering the basis of learning has tended to be much more ontological, with epistemology reserved for proof modelling to demonstrate the correctness of ontological facts or to add to the body of knowledge. However, in more recent times we have seen a renewed emphasis on the consideration of intellectual reasoning as a component of higher education. For example, in computer science considerable activity has focused on the concepts of computational thinking (Wing 2006). It can reasonably be argued that computational thinking, like rhetoric and reasoning before it, is about helping students to understand how to organise their thinking relative to a particular approach to learning, in this case computational problem solving. Perhaps the most important point is that this once again enables us to reflect on the metacognitive skills of students as they apply reasoning approaches to the learning tasks they undertake. Some of our recent work (Kazimoglu et al. 2012) has produced empirical evidence of the impact of a computational intelligence approach in learning technical skills and knowledge in computer science, and this lends weight to the argument that supporting the development of metacognitive skills is one of the key factors for student success in developing learning systems. Within the consideration of reasoning approaches such as computational thinking we also see many of the elements of social constructivism, particularly the idea of learners constructing their own world view of a subject area, learning being a social activity, and the role of the tutor as a guide rather than the font of all wisdom. The literature has shown a number of ways that metacognition can be developed and supported. A few key examples of these are:

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x The need for a well-designed learning environment (Kirsh 2005). A good visual design that provides an appropriate structure, well-written easy to understand sentences which require less cognitive effort to comprehend, ensuring that links to learning materials and support tools, such as chat tools, are easily visible to students and are not missed, can all make a significant difference to the effectiveness of metacognitive development. x That not all students have the ability to manage and regulate their learning, and deploy relevant strategies at the right time, monitor their own progress etc. (Azevedo and Cromley 2004). In order to be successful, students need to be aware of their own thought processes and be able to monitor the effectiveness of their learning strategies in order to develop the ability to self-regulate (Zimmerman 2008). This ability to undertake self-regulated learning is a form of metacognition and also includes the ability to translate knowledge, skills and attitudes from one learning environment to another (Boekaerts, 1999). The presence of a tutor in a technology enhanced environment has also been shown to assist with the development of metacognition (Azevedo and Cromley 2004). x That the educational psychology literature has also questioned whether individual metacognitive abilities are as a result of biological differences or different learning experiences, i.e. nature vs nurture (Woolfolk and Margetts, 2007). In terms of biological differences, the research on this is unclear. However, several researchers have demonstrated that metacognitive skills training and support for selfdevelopment can help (Wagster et al. 2007; Gunter et al. 2003), in addition to the fact that students develop metacognitive abilities as part of their usual learning and observation experiences (McInerney and McInerney 2006). Some of our recent research work in this area has focused on determining the impact of student metacognitive skills by assessing a student’s recall and retention of information within a formally designed and technology enhanced learning environment (Cemal Nat 2012). In this research, a group of students were provided with a range of learning materials in different formats – e.g. text, audio and video – and were allowed a choice of what materials they used to learn a specific topic. Prior to starting the experiment students were asked to answer a questionnaire, based on the Felder and Silverman Learning Style Model (FSLSM) to determine their learning style. The learning outcomes were assessed using a recall-type assessment test immediately after the students

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completed the learning. This was followed up with a retention test that took place two weeks later. The performance of the students who studied using materials consistent with their learning style (matched group) were compared to those who did not (mismatched group) in order to determine which group performed better. Some analysis of external factors that might have affected the learning, such as prior knowledge of the subject, were assessed and taken on board in drawing conclusions from this study. Key findings from the experiment were as follows: x The results that show statistically significant achievements in learning performance were associated with both the matched and mismatched groups. The matched group that performed better showed evidence of an effective use of metacognitive skills in that they found a route through to be successful. x Where the mismatched group performed better, there was evidence of the students applying appropriate learning strategies and thus demonstrating metacognitive abilities. In addition, the findings regarding student ability to use or develop effective metacognitive skills in the event of a mismatch between their needs and the learning environment, can be used to guide educationalists in building learning environments that encourage students to learn how to learn. x This experiment represented the first attempt to establish a relationship between metacognition and FSLSM; however what it did show was that learning styles were not a good arbiter to use for designing TEL environments. It did however generate some evidence for the use of cognitive strategies and demonstrated the importance of metacognition in the student selection of learning materials. It also defined a methodological approach to design a TEL environment that could be used to help students to develop metacognitive skills and, thereby, cognitive strategy. x Experimental data collected through the TEL environment showed the learning behaviours of students, thus it is possible to determine the learning strategies of students. For example, the skills and strategies that students in the mismatched group used may help other students to develop their learning strategies. Therefore, advice can be provided to students on the basis of what works for them, by using this experimental model with different course materials. In addition, individual feedback can be given to those who do not perform well by analysing their learning behaviours.

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4.2 Engaging and Immersive Environments In a face-to-face classroom learning environment a good teacher will engage students in the subject, find ways to explain concepts differently to students on the fly when confronted with struggling students, and reinforce/repeat key points throughout a teaching session as appropriate. Outside of lectures, teachers can engage in a one to one dialogue easily, and adapt their explanation and the support they provide to an individual. However, one of the key drawbacks of the massification of higher education in developed countries has been the depersonalisation of the learning experience for students at all levels. Clearly it is not possible for an individual classroom teacher to provide a personalised experience for every one of the 20 to 30 primary or secondary school pupils in their class and it becomes even less likely in higher education where lecturers are often faced with classes of 200-plus students. With online environments, given the tutor is at a distance, it is considerably harder to inspire students with standard learning materials and provide personalised tutoring by the teacher. However, online learning has the potential to provide a different range of learning experiences that could not be provided by a teacher in a classroom. For example, areas that have been shown to be very successful in engaging students and improving learning are the use of rich multimedia environments, immersive simulation environments, and the use of games. It can be argued that games offer the ideal medium for TEL because they are inherently constructivist, engaging and immersive. Games have formed a basis for educational activities for centuries, and gamers return to play them because they provide fun and enjoyable experiences. The social nature of games is also well understood, providing opportunities for educational cohort effects such as peer bonding, expert-novice tutoring and vicarious learning. They also provide personalised experiences with players taking their own individual routes through a game, receiving feedback as they progress. Games by their very nature, since players will rarely be successful on their first attempt at a game, provide opportunities for repetition (a key aspect of learning), reinforcing and building on concepts learned earlier in the game. Games alone cannot, and should not, be used to teach all aspects of all subjects. They can however form a significant, novel and important part of higher education and can be used to support, engage and retain online distance learners in an immersive manner that is not available to classroom teachers. Rich multimedia environments, in particular serious games, offer opportunities to develop realistic immersive games and computer-based simulations which provide

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fun and engaging learning environments. Much of the serious games industry has grown up around the concept of “Serious Games for Serious Training” (Chan 2007) with applications being developed for a range of situations from military and security, through health and education (Graven and MacKinnon 2008), all the way to politics (Ochalla 2007). Recent research by the authors in this area has utilised games and multimedia, augmented reality, and simulation environments. Three recent projects are briefly described: 4.2.1 Maritime City Maritime City (Flynn et al. 2010; MacKinnon et al. 2012) is a tool currently being used to train Social Workers using an immersive, serious game. The need for this game stems from the requirements to meet a set of UK Social Work standards for practice in order to qualify. Whilst all students should have a placement during their education in order to gain practical experience of dealing with real world scenarios, a placement cannot guarantee to provide a full range of experiences that a student should encounter by the time they graduate. Traditionally role-play activities are used to fill the gap. However the quality of these can be variable as they depend on the acting abilities of the students, their enthusiasm to engage etc. Due to the immersive and engaging nature of games, it was decided to develop a game-based training environment that could be used instead of, or in addition to, role-play activities. The game environment presents the player, who takes on the role of the Social Worker, with a virtual city, as shown in figure 1, and various scenarios are presented to the player in which they have to make decisions about actions to take in their capacity as a Social Worker. In the first scenario (see figure 2) the Social Worker is confronted with a challenging child-abuse case, which is based on a real case in the UK, where the child ultimately died. The player has to make decisions about how to deal with difficult and abusive parents and, given there are two children involved, which one to focus their immediate attention on etc. A particularly important aspect of the game was the need to convey facial characteristics and body language as Social Workers must be able to develop not only their skills to interpret both verbal and non-verbal forms of communication, but a non-judgemental, objective view of client emotions. Whilst an avatar clearly does not look as real as a human, research has shown that avatars can elicit an emotional response similar to that of a real human face (Mosera et al. 2007) and therefore, coupled with the voice of professional actors, it was felt this approach could be effective.

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Figure 1 – Maritime City

Figure 2 – Screenshots from Scenario 1 of parents and abused child in Maritime City

In terms of the evaluation, the first version of the first scenario was tested with ten qualified Social Workers (not trainees) who were undertaking a Masters degree at the University of Greenwich in London. They completed the game and then undertook some role-play, both related to the same scenario. Their feedback was gathered through a combination of a survey, using a Likert-scale, and open-ended responses to questions. Overall the results were extremely positive. 37.5% found the game “very realistic” and over 62.5% found the game easy to use and good at communicating the lessons to be learned. In terms of realism, in the initial version the characters were thought to be a bit too “clean”. However, the students found the game compelling and engaging, and the emotions displayed by the avatars consistent with the voices of the professional actors.

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4.2.2 The Pandora Project Pandora was an EU FP7 Project (Bacon et al. 2012; MacKinnon et al. 2013) that was completed in March 2012. The software and associated training environment that was developed is now being further developed and deployed by our eCentre research group at the University of Greenwich, as part of the Pandora commercial training toolset. The purpose of Pandora is to train Gold (strategic level) Commanders in crisis management through an augmented reality, immersive environment designed to use emotional affect in order to impact decision-making. Gold Commanders in the UK are senior individuals with executive responsibility, who are required to take responsibility and make strategic decisions about their organisations’ services and facilities in the event of a crisis. Example organisations are likely to include Fire, Police and Ambulance Services, and Local Authorities. In the event of a crisis, these individuals would be expected to work together to provide strategic solutions to an unfolding crisis. Example crises could be a plane crash, a health pandemic, an emergency caused by extreme weather, or a combination of these. However, they are all crises that require solution by more than one agency. During a crisis, Gold Commanders are typically colocated in a room and not at the scene of the emergency. They will give instructions and direction to others who will find on-the-ground solutions to implement the strategy. Gold Commanders have a number of priorities to take on board in setting the direction such as protecting their own staff whilst containing the emergency, relieving suffering, protecting people and property, ensuring that the supply of critical services continues or is restored if already lost. They will also be involved in the evaluation of a crisis in order to identify lessons learned once the crisis has been brought under control. Traditionally Gold Commanders are trained in the following two ways: either through table-top, mostly paper-based exercises or through real simulation exercises. In table-top exercises, for the most part the participants have to imagine the events in the scenario that are being described to them on paper or verbally by an instructor which, no matter how enthusiastic all participants are, the instructor included, leaves it very challenging to experience the stress and emotional engagement that would naturally occur in a real crisis situation. With regard to a real simulation exercise, whilst these provide the realism of a genuine crisis situation, they are very expensive to organise and run and realistically can only simulate a few events from a crisis scenario, with a limited set of outcomes.

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The decisions made by Gold Commanders will invariably include life and death decisions and the views of the different agencies about how to solve a crisis might be very different. It is therefore critical to provide realistic training that enables trainees to experience the full range of emotions and intensity of a real emergency, and this is what Pandora aims to provide using computer simulations which utilise multimedia and augmented reality technologies. Within Pandora, the trainee behaviour and emotional states are captured prior to the start of the training through a pre-event questionnaire. The trainees are then monitored throughout the training session via biometric sensors, self-reporting and trainer input. The emotions of the trainees are then manipulated as the training progresses, through affective media effects, often dynamically generated on the fly, to increase or decrease stress levels of individuals or the group as appropriate. The Pandora system is able to record the entire training session and provides facilities for post-hoc reviews and feedback either to the group or an individual. A severe weather scenario was developed for the Pandora System and was trialled with 13 real-life Gold Commanders at the UK Government Cabinet Office Emergency Planning College in the UK over a three-day period. The evaluation used a combination of Likert-scale and open-ended questions to obtain feedback about the learning climate, self-perception of learning, oral instructions and tutorials, security and privacy issues, the user Interface and the acceptance of the technology. The vast majority of the scores were very positive and the verbal feedback from all three events was resoundingly positive. All the trainees reported feeling emotionally engaged throughout and experiencing stress, particularly during decision making periods, the evidence for which was backed up by the biometric data. The trainers were equally enthusiastic about use of the Pandora system as it provided them with a novel training experience as the trainers had the ability to adapt the scenario on the fly, dynamically injecting new events, compressing timelines, and monitoring the biometric data to see how stressed their trainees really were, given that many were able to disguise any outward signs of stress. 4.2.3 Program Your Robot – a serious game for learning computational thinking and introductory programming Our final example in this section provides an overview of a game designed to help computer science students learn the art of computational thinking and the constructs of introductory programming, which most computer science students find challenging (Kelleher and Pausch 2005;

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Coull and Duncan 2011), evidenced by worldwide failure rates of 30-50% in introductory programming classes being “reported for decades” (Guzdial 2012). The reasons for the failure rates are numerous and varied. However, the nature of programming itself is considered to be difficult; it is not a single skill but requires a student to understand the logic of constructs, the syntax of a programming language, the grammar rules of the language etc. (Li and Watson 2011) and in order to be successful, students need to develop a series of complex cognitive abilities. Above all, it requires practice, logical thought and, frequently, the sheer determination not to give up but to persist in finding a solution. The game, called “Program Your Robot” is publicly available at http://www. programyourrobot.com and requires players to help a robot escape a board-like platform by designing an algorithm to help the robot move around the board picking up collectable items along its way. The player has to construct an algorithm by selecting a correct sequence of action or programming commands in order to achieve this goal. Action commands instruct the robot to move forward or turn around, for example, and the programming commands provide facilities to repeat moves, make a decision, call a function which contains a separate group of instructions for the robot etc. A screen shot from the game is provided in figure 3.

Figure 3 – Screen shot from the “Program Your Robot” game

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There has been little empirical evidence that supports the idea that serious games are effective tools for learning computational thinking and introductory computer programming constructs (Hainey et al. 2011; Kazimoglu et al. 2012) and one of the key objectives of this research was to provide that evidence that games are useful educational tools. Higher education students studying the first year of a BSc in Computing Science (or related programme), and registered for an introductory programming module, were asked to play the game, after filling out a pre-game questionnaire to establish, for example, prior knowledge. The game was played about five weeks into their course. They were also asked to fill out a post-game-playing questionnaire so they could provide feedback on the game and how it may, or may not, have affected their perception of their computational thinking and programming abilities. Valid results were received from 68 students studying at a university in Cyprus and 145 at the University of Greenwich. The results are extensive (Kazimoglu 2013). However, key findings showed that not only did students feel programming was easier after playing the game, but they perceived significant improvements in the following areas: a) Their intrinsic motivation to learn computer programming; b) Their attitude to learning computer programming through playing games; c) Their knowledge regarding how key computer programming constructs (i.e. programming sequence, functions, decisions and loops) work; d) Their problem solving abilities; e) Their ability to visualise programming constructs from given problems. This positive influence was proven through detailed statistical analysis of multivariate results, providing a strong base of empirical evidence.

4.3 Socialisation In a face-to-face classroom learning environment where students and staff are co-located in a physical space during the core teaching/learning experience, it is well-known that forming social groups can be important to the retention and success of students. The evidence suggests (Boyle 1997) that those students who do not make friends on a course are more likely to give up when faced with difficulties either of a personal nature or in understanding the learning material. Students, having made friends on

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their course, will often work together outside of the classroom, bouncing ideas off each other, to help each other understand new concepts etc. In other words there is a cohort effect of mutual support and peer pressure etc. Students also tend to know the teacher well and will generally be confident in asking questions as needed. With online distance learning, the isolation of learners is one of the key challenges faced by students and a focus of considerable research. Some students who do not socialise well in a face-to-face context may thrive in an online environment as they have more choice about when and with whom to socialise, as opposed to being forced to socialise because they happen to be sitting next to someone in class. It is also the case that many students may feel more comfortable communicating in an online world having lived in an online social world for many years. In our research on games-based learning (Graven and MacKinnon 2009; Kazimoglu 2013), we have investigated the use and provision of social support constructs within learning environments, and found clear evidence of the benefits in peer discussion and support over time, once students have become familiar with the environment. In summary, the research shows that the less isolated and the more of a cohort effect that can be created in online learning, i.e. the more students make friends with other students on the course, the more they are likely to succeed, which is why this is an important topic, as is shown by the fact that there are several papers on the subject in this book. The literature on this topic will not be repeated here as the reader is referred to the other chapters for an extensive analysis of the literature and strategies employed to facilitate socialisation in online environments.

4.4 Adaptive, adaptable and personalised environments In face-to-face classroom learning, adaptive and personalised learning is limited by the time a teacher can provide for individual discussion and tuition. There are however two circumstances where an individual will receive more personalised tuition than normal: firstly, when undertaking a PhD, the research for which will be completely personalised, but as a percentage of the population only a fraction make it to this level; secondly, if a student has some form of special needs, which require more personal support, but these can also have some form of stigma associated with them. Nonetheless, it is interesting to speculate on the growth of recognition of special needs conditions, such as dyslexia, in higher education, which result in more personalised educational treatment. In online learning, however, an environment can be built to adapt to an

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individual’s needs, the environment can more easily provide a range of learning materials, and support tools have the potential to adapt responses to a learner and provide a more personalised learning experience. The level and type of adaptation and personalisation will be different and potentially better than that which a teacher can provide, albeit less intelligent. So, at a lower level, it can be more flexible and provide a range of options and support, but not as sophisticated as that provided by a teacher. In terms of the literature in this research area, it is important to first of all define the terms adaptive, adaptable and personalised. There is no universally agreed definition of any of these terms; however the literature provides a fairly common understanding of the first two: Adaptable systems These systems that allow the user to change certain system parameters and adapt their behaviour accordingly, are called “adaptable” (Santally 2005). An example is Alfanet (Santos et al. 2003), which stands for Active Learning for Adaptive Internet, and provides the facility to allow predefined courses to be altered during run-time according to specific user requirements. It has three key ways that it can adapt: x Adaptation of the instructional design to provide different course contents, activities and services to the learner. x Adaptation of the support provided to the learners while interacting with a course x Adaptation of the user interface presented to each learner according to his/her model Adaptive systems Systems that adapt to the users automatically, based on their assumptions about the user needs are called “adaptive” (Santally 2005). Many stress this as an important area for eLearning systems to address, for example Brusilovsky (1996) and Santally (2005). Brusilovsky (1996) argues that adaptivity is important in an eLearning system because different types of users, from a variety of backgrounds, with different learning goals, learning styles, knowledge, preferences etc. will use the system. He argues that adaptive hypermedia is an alternative to the traditional “one-size-fits-all” approach and that it is important that systems help users to navigate different paths through a course of study based on their needs and preferences. In order to do this, adaptive hypermedia systems should build a user model including details of a user’s goals,

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preferences, interests, cognitive style (how a learner prefers to organise and represent information) and knowledge (Brusilovsky and Millán 2007). Brown et al. (2009) argue that this functionality should adapt both the content (adaptive presentation) and the link level (adaptive navigation). Personalisation Personalisation is a wide topic and can cover a range of areas such as personalisation of the user interface and content, how content is accessed, the media forms provided, method of instruction employed, learning styles supported, and personalisation of feedback. Personalised learning systems should be capable of adapting automatically to changes in an individual’s learning characteristics as the learning experience progresses (Karagiannikis and Sampson 2004). Learner characteristics on which to base personalisation could include a learner’s background, expertise, prior knowledge, skills, requirements and preferences (Brusilovsky 2001) and some, such as De Bra and Calvi (1998), argue that these systems should go further and provide mechanisms to infer learners’ characteristics. Many researchers have identified personalisation as one of the key challenges for learning technologists to develop (among others, Eklund and Brusilovsky 1998; Sampson, Karagiannidis and Kinshuk 2002; and Voigt and Swatman 2003). Two projects outlining recent research in this area by the authors are briefly described: 4.4.1 The use of tagging to support the authoring of personalisable learning content This research focused on the personalisation of learning platforms. The background literature review revealed that whilst many learning platforms provided some aspects of personalisation, they were often at the superficial end of the spectrum e.g. colour or aspects of the user interface. Nine of the most commonly used learning platforms were evaluated against a number of personalisation criteria, which included the instructor’s ability to manage and sequence course material and monitor learners, a learner’s ability to search for learning objects, whether the system can structure learning according to needs, and the ability of the system to adapt the user interface and adapt to a learner’s goals, behaviours and learning styles. None of the eLearning platforms evaluated were considered to offer a truly personalised learning experience for the learner. The outcome of the background research suggested that one potential solution to the issue of providing adaptable and personalised

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content could be the development of a tagging system, utilising concepts from adaptive hypermedia systems. In order to determine whether it was possible to build such a system, a discriminatory tagging methodology, to allow authors to tag learning objects for personalisation and reuse, was developed. The main focus for the evaluation of this tagging methodology was the authoring side of the tagging and included the facility to allow learning objects to be tagged according to their subject and topic, learning style, level, object file type and object resource type, ensuring that multiple representations of learning objects were kept consistent. This also provided the potential for learners to personalise their own space based on their own individual requirements. The research demonstrated that learning styles could be used as an example of a discriminatory type, although as discussed above, they were not necessarily the best discriminator. However, the methodology and tags devised are flexible and could be applied to any discriminatory concept considered appropriate. 4.4.2 Impact of learner control on learning in adaptable and personalised eLearning environments This research (Mustafa 2011) focused on investigating the impact of allowing learners to have a measure of control over their learning while working in different online learning environments. This aspect, in combination with a structured learning material selection process, based on student learning preferences, was analysed to study the impact on student learning. A qualitative study was carried out to understand how different learning philosophies, learning environments and learning preferences, correlated with learner control over their learning environment and the impact on their learning performance. An adaptable personalised eLearning system was developed to provide an environment in which qualitative measurements could be captured. Experimental data was then gathered from two cohorts of MSc students over two semesters using this online learning environment. During the registration process, learners were randomly assigned to either the adaptable eLearning system or the personalised eLearning system. The learning materials were provided in several forms based on Fleming’s (Fleming 2001) VARK learning style (Visual, Aural, Reading and Kinesthetic Learning). A switchboard model was then developed and used to manage the process of providing relevant materials to students, dependent on different aspects of their learning style and whether they were in the adaptable or personalised group.

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The assessment was categorised into three types: Recall, Competency and Understanding. The results showed that a personalised eLearning system performed better in supporting learning that required students to recall and understand what they learned, whereas in the adaptable eLearning system, students showed a marked improvement in the assessment based on competency. Thus key findings from the analysis of the resulting data demonstrated that certain types of learning environment are better suited to certain types of learning behaviour. The outcomes from this research could provide a basis for the future design of eLearning systems, utilising different models of learner control, based on underpinning educational philosophies, in combination with learning preferences, to structure and present learning content according to type.

4.5 Section summary and conclusions This section has summarised the key literature and research work of the authors over the past ten years, primarily undertaken in conjunction with their PhD students and research assistants, in the four key areas of 1) metacognition and learning strategies, 2) Engaging and immersive environments, 3) socialisation, and 4) adaptive, adaptable and personalised environments, all of which have been demonstrated to show benefit to students in online learning environments. Regardless of whether future education is face-to-face or online, in order to develop metacognitive students, which neither mode currently supports well, there needs to be a change in the nature of the studentteacher relationship to support this development and, given the importance of this topic for online learning, the following section discusses this issue. Finally, having argued that the world needs online education, regardless of whether it is supported by face-to-face teaching or not, there is a significant issue to address regarding the increasing use of online environments, particularly the use of online assessments, and that is authentication of the person undertaking the assessment. Whilst there has been a lot of excellent work on the prevention of cheating and plagiarism (Carroll 2009) and this work is vital and important both as a deterrent and to prevent accidental plagiarism by students, there remains a pool of students who will, for example, pay others to undertake assessments for them no matter how well an assessment is designed, and this type of offence cannot be completely eliminated, especially in on-line assessments, without considerable resources. With the increasing use of online assessments, this is an issue that institutions need to take seriously if they

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are to continue to ensure the standard of their awards (The Chronicle of Higher Education 2010; Macdonald and Carroll 2006). The challenges around this issue are discussed in section 6.

5. Reflections on a new Educational Model for Adults 5.1 Developing metacognitive students Somewhere along the line, we have to establish the idea that adult learning is a contract or a partnership between the learner and the tutors with whom they engage. We can think of this in the same terms that we would think of somebody going to a lawyer or a doctor as a client of a professional service. It is not the case that the student is a customer who knows what they want and is by definition already in possession of the relevant knowledge and skills to determine that what they receive is appropriate. Rather, like any client, they know the goal they wish to achieve but not the mechanism by which it might be achieved, for which they are reliant on the professional service. We do wish to both engage in debate about the nature and form of our subject materials, and to discuss the ideas and concepts, and it should not be the case that we seek simply as tutors to impose our view and understanding of our subject area on those we teach. We should be seen to be encouraging students to develop their own understanding and question in those areas where things seem unclear. If we adopt this approach we begin to move from a pedagogic model of education towards an andragogic (Knowles 2005) or heutagogic (Hase 2001) model, allowing the potential for our students to be treated as adults, allowing our teaching to become constructivist in its nature and our tutorial style to become scaffolding for the construction of the students’ own metacognitive understanding in their subject area (Liu and Matthews 2005). By way of an example, in computer science there is no prospect of individual academics claiming subject mastery accumulated over many years of experience, or of passing this on to students who likewise will have to take a similar number of years to gain the level of experience of their tutor. In digital technologies, progress and change is swift and technologies become redundant and outdated in relatively short periods of time. So for academics in this discipline, long standing experience either equates to redundancy of knowledge or to constant re-invention and updating of skills to ensure currency whilst understanding the history of the subject. It is not necessary that our students know how the technologies they are working with came into being, what the pre-cursors

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were, or the problems and difficulties their predecessors experienced; fundamentally their interest is in mastering the current technology and developing their skills to be successful in the marketplace of today. They also need to develop the necessary learning skills and capabilities to continue to update themselves as technologies develop, evolve and are replaced. This is not to say that there is no value in understanding how systems have been developed and evolved and, particularly in areas such as algorithm design, it can be strongly argued that understanding the process by which solutions were achieved will avoid the repetition of historical errors. However, it is not the case that this is necessary for all aspects of the subject area, nor is it the most important aspect of the capabilities of the tutor. This takes us into an interesting area: if we make the argument that the role of the tutor changes, that we move towards a more andragogic/heutagogic model of education, which requires students to become more metacognitive, does this also imply that we change the model of delivery, that we change the timing and sequence of that delivery and that we necessarily and importantly surrender control of the process, in whole or in part, to the student?

5.2 Educational Model We need to revisit the basic educational model that we apply in higher education. If we truly have the goal that our students, on graduating, should be metacognitive with well-constructed learning patterns, then we must address them as adults in some form of andragogic/heutagogic model throughout their learning experience. This applies irrespective of whether they have come into higher education directly from School, or arrived as a more mature learner. This is not to say that all learning materials in all subject areas must be taught from an andragogic/heutagogic perspective; as already discussed, there are situations where a pedagogic approach and even a didactic approach are called for. However, the balance needs to shift to a consideration of the student as an adult, a client of a professional service with whom the learning process needs to be negotiated and constructed. Our new technologies make it much easier for us to deal with students on an individual basis even within a large cohort, and while it may be administratively efficient to lecture to large classes, the realities of learning demand a much more individualised approach. So VLEs should not be seen as a convenient delivery mechanism for existing teaching materials and style, but rather as a mechanism that frees the academic to

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develop new materials to support a different style of teaching on a far more individual basis. This educational model is based on lessons learned from the games community where the focus is on providing a rich, complex, immersive environment that allows the game player to progress at their own pace. In this situation our solution would look like a learning environment with learning objects specifically designed for delivery through that environment and linked in to electronic assessment models and instruments regarded as appropriate for the subject area. Students would receive regular, near instantaneous, feedback on their activities and assessments from the system with academic staff receiving regular consolidated reports detailing student activity and performance. In this scenario academics meeting with students could concentrate on helping the students to develop their learning capabilities and subject knowledge by offering guidance on appropriate techniques, additional areas for selfguided study and also introducing opportunities to gain additional experience in a variety of other activities. Given that the educational model is intended to be andragogic/heutagogic, conversations between tutors and students could be focused much more on the professional advice given by the academic on how the student could improve or develop their performance to meet their own learning goals. Obviously, where a student is clearly in breach of the contract, be it explicit or implicit, that they have with the institution, the academic role would be to advise them of that breach, the remediating action needing to be taken and the implications if the student chooses not to.

6. Monitoring, Authentication and Assessment of Student Learning Any solution that we would wish to propose based on the preceding arguments has to have the ability to monitor and capture student experience in digital environments and must then link into a mechanism that will allow that experience to be quantitatively and qualitatively analysed. For example, there is no point in capturing the period of time that a student is logged in to a system without determining some measure of activity during that time. Otherwise we could be giving academic credits for drinking coffee, eating meals or for a student just leaving themselves logged in. We also need to have some understanding of the relative value of types of activity and the periods of time spent undertaking them. So if a student spends two hours developing and testing

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code in an advanced programming language, successfully achieving compilation and bug fixing, then that may well be regarded as being of greater value than two hours spent web-surfing on the computer looking for pre-existing solutions. However, both activities may be relevant and appropriate to the task in hand. Assuming that we can capture and assess information about the student experience, as they work and learn, then the arguments presented about the use of different assessment instruments and assessment based on modelling the student experience become relevant. Any solution system would also have to provide an effective and verifiable identity checking system, almost certainly biometrically-based, to ensure that students are accurately identified for assessment purposes. Where necessary environmental information or controls on assessment points to eliminate external influence, webcam and microphone based monitoring during a timed assessment (in case someone is telling them the answers), could easily be utilised to verify student completion of an assessment task.

Figure 4 – Example Monitoring and Assessment Environment.

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Figure 4 outlines a possible solution to authenticate student work/assessment where it is undertaken without human supervision. The person taking the assessment would need to biometrically authenticate themselves to confirm their identity. This could be done through a variety of mechanisms such as fingerprint login or facial blood vessel–scanning. However, once authenticated, the system needs to ensure first of all that the person engaging with the assessment remains the person who was authenticated at the start of the process. Even if a method such as use of a typing signature ensures the student is typing at the keyboard, a system needs to be sure they are are not being told what to type by someone else, either in the room or electronically: the computer and the physical environment need to be controlled if we are to be absolutely confident the work is entirely that of the student. In order to address the issue of someone else being physically present in the room and ensuring that no verbal communication takes place through a phone, for example, constant video and audio could be captured so that conversations with others, either in the room or by some other means, are recorded. A webcam in the room ensures no one else is present and a webcam directed at the person taking the assessment ensures they remain at the computer. Audio recording and monitoring software ensure questions are not transmitted to others for advice. Any gap in transmission of either audio of video could be deemed to invalidate the assessment although there could be legitimate problems and a view could be taken about this, based on length of disconnection for example. For some tasks, such as programming, student solutions could also be analysed in terms of their digital footprint. By this we mean their programming style, strategy for developing solutions, approach to debugging, etc. All these could be used to uniquely identify an individual having observed them over a period of time and the analysis could be automated. In order to ensure this works for students in remote places, standard, readily accessible technology would have to be used and the increasing use of mobile technology for learning, in some cases enforced due to poor internet connections, e.g. in many places in Africa, makes the task of authentication even more challenging. Having said this, no system is foolproof; for example, students could have a small camera pointed at the screen with hidden earpieces wired to a friend. We can however make it highly automated and challenging to defeat! Once we are confident that we can monitor student behaviour and performance in assessment situations, as well as their learning activities in digital environments, we have a basis for a different model of educational activity.

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This approach does however raise issues of “Big Brother” (Orwell 1949) vs privacy. We have to accept that there is a trade-off between privacy and the development of accurate information on student activity and performance. Our existing systems can be seen to be failing not only in terms of the accuracy of their representation of student skills and abilities, but also in their inability to deal with those who would seek to defraud or defeat the existing mechanisms. It may be argued by some that this demonstrates a lack of trust within the system but this is a nonsense response. The reality is that this is an adult approach to deal with a known, significant and growing problem, as evidenced by a journal special issue (IEEE ToE 2008). Implying that our existing approaches deter untrustworthy behaviour and are already well established is simply facile and over optimistic. In the world in which we now live, our reliance on, and near permanent engagement with, digital systems means that we do have the ability to address these concerns by developing new monitoring systems which will infringe some of the existing privacy we allow students. Whilst we would regret the fact that we do not live any longer in an entirely trust based system, if ever we did, we can at least argue that, in accepting that there will be those who seek to abuse the system and putting in place preventative mechanisms, we are at least making the system fairer.

6.1 Assessment This approach also demands that we reconsider the mechanisms by which we assess learning and, in particular, how we model and monitor student progress within our learning environments and the other digital environments in which they develop their skills. There is significant evidence of massively widespread cheating by students from the simple collusion to develop solutions within a group on individual coursework all the way through to purchasing bespoke solutions to final degree projects. There is no way to tackle this without more effective monitoring of student behaviour and performance. We can set intermediate targets and regular weekly deliverables, and utilise a variety of other mechanisms to attempt to trap the malefactors. But perhaps this is all unnecessary; they are living and working in a digital world, and therefore we should use the tools at our disposal to develop effective monitoring systems which, for example, will show us when a student suddenly moves from zero completion to a 100 page dissertation over night. Part of the condition of being a student, one of the things we can enforce, would therefore be a contract that not only specifies that their work will be their own, but

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confirms that any machine that they use to develop work will host monitoring software from their institution. This can be established on a simple client server basis, utilising existing internet technology.

6.2 Assessment Approaches We should offer our services professionally and explain how and when assessment processes will be undertaken and what evidence will be used to determine student performance. In such a situation it becomes possible to consider assessment from a number of perspectives so we can consider student performance over a period of time in undertaking assigned tasks, for example in programming or mathematics, and determine on that basis that they have reached a particular standard that demonstrates the required level of skill and competency without the need for any further form of assessment. In fact, we know from studies done on programming (Graven and MacKinnon 2009) that it is the repeated exercise of undertaking programming tasks that develops the skill set, so monitoring this with the knowledge that the student concerned could be authentically proven to have undertaken these tasks would be sufficient for assessment purposes. There are other situations where other assessment instruments may be more appropriate, including timed examinations, but another problem with examinations is the whole nature of the management, control and marking of these large scale events and this still does not eliminate in any sense the issues of cheating. An alternative model for timed examination could be online verifiable presence, based on question bank materials where students could undertake the exam within a specified period, each exam would being equivalent but not identical, and student identity being physically or electronically verified through a variety of mechanisms as discussed above. However, we should only seek to use this kind of examination where there is an academic justification for it as the best form of assessment. We should be building new and better electronic assessment tools that allow academics to develop appropriate assessments relative to the subject knowledge, skills and/or competences that they wish to determine, where the system can provide near immediate feedback to the student and offer opportunities for reassessment or mitigation as appropriate.

6.3 Assessment When Ready We should try to remember that the purpose of education is to enable an individual to develop their knowledge, understanding and skills to a

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particular level. It is not a hurdles race, so the fact that a student may fail a unit of assessment should not mean that their education is over or that their potential to learn the relevant knowledge and skills in that area is zero. It is more akin to the high jump where they are allowed multiple opportunities to demonstrate growth of knowledge and skills in their area. There are many examples of students who, having failed through traditional assessment mechanisms, have then gone on to achieve great success in the same subject area, which could suggest that the academics got it wrong rather than the student. However, we can also remove the competitive-race characteristics common in our existing systems, as we can remove the requirement to complete a body of study within a specified time period as long as the subject studied remains relevant within the time period within which it is achieved. This would support students operating on a part-time basis, lifelong learning, the idea of “assessment when ready”, and even the concept of reassessment for grade. If we accept the idea that education is a lifelong process enabling individuals to achieve the best possible outcomes in developing knowledge and skills within their chosen subject areas, then the idea that this should be limited by location, time, or number of attempts should become historical. Moving this argument on further, if we managed to provide fully authenticated environments in which we can track and be confident of an individual’s work, subsequent assessment may not even be needed as the environment can demonstrate student achievement of the learning outcomes.

6.4 Implementation At one level, the technology for much of what is described already exists. Almost all digital software systems offer some sort of accounting on usage and we obviously already commonly track usage characteristics on server load, web aspects and numerous other aspects of our digital environments. These are regularly reported. However, it is not often the case that a contiguous report of all such activities is produced and especially not one focused on individual users. So we need to develop integration mechanisms to pull together this existing information. Where there are shortfalls in the available information that would allow us to monitor individual activities and performance, there is a need to develop new tools that would also allow us to build in additional verification and validation mechanisms. As identified earlier, we also need to develop appropriate protocols for user identification and validation, especially for

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assessment, which can incorporate the use of hardware and software available as standard on laptop/desktop PCs.

6.5 Benefits for Academics, Students and Employers The major issue in moving forward with a plan of this type is in achieving buy-in from academic staff and students, and to achieve this we need to identify the key benefits that can be anticipated from using such a system for academics, students and employers. 6.5.1 Benefits for Academics x The advantages in automated assessment mechanisms of accurate student identification and validation of work, reduction in the assessment burden, and the ability to individually target student learning are all obvious benefits of such a system. x Having better and more accurate information about student activity offers the potential for early intervention to attempt to deal with academic problems and where evidence of disengagement becomes apparent. 6.5.2 Benefits for Students x The potential to produce a detailed portfolio of their work, accurate representations of their skills and abilities relative to tools and technologies with which they have engaged, and more detailed performance information can all be included in a far more detailed, accurate, useful and authenticated CV. x For good students, this is a significant benefit in selling themselves in the marketplace whereas for weaker students this may provide the incentive to undertake further development, improve their skills and work harder to achieve better outcomes. x It is often the case that students complain about perceived injustices within our existing systems on the basis of other students essentially defrauding the system, either by getting good grades for poor work, contributing less in team situations, or just plain cheating. Introducing an effective monitoring system of the type described here would undoubtedly reduce the volume of such complaints, although we would have to anticipate that those complaints may be replaced by others such as the intrusiveness of being monitored.

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6.5.3 Benefits for Employers x For employers, there is the benefit of being able to receive far more accurate information about the capabilities of potential staff, and if a similar system were deployed within the workplace to capture ongoing information about staff capabilities and development, this would aid in project bidding, team selection and promotion. x If we were able to achieve widespread acceptance within higher education for the use of such systems, we would be in a position to develop national verification systems that could confirm the qualifications, skills, knowledge and capabilities of individuals as they move through education and qualification systems into the world of work. x Similar systems operating in the workplace would likewise provide a national database defining the ability of the workforce within specified sectors and would support far more accurate mobility and selection of workers, project sign off, professional liability, and the numerous other aspects which a high quality, highly skilled knowledge economy requires.

7. Summary, Conclusion and Future Work This chapter began with a look at the worldwide increasing demand for technology enhanced learning, both in support of face-to-face and online distance delivery. The demand for higher education worldwide is increasing. However, in many places, particularly in the developing world, there is insufficient local provision and the existing international provision cannot meet this need and many potential students would be unable to access international education anyway. A significant component of the solution in the near future will inevitably be to increase delivery of our online distance learning provision and, coupled with the expected increasing use of technology in education for face-to-face students, the use of technology enhanced learning in higher education has never been more important. The success rates of online distance learning can often be very poor, as evidenced by recent activity with MOOCs for example. This chapter has identified four key areas that affect the success rates in online learning and discussed the existing research literature and the authors’ contributions to this research. A conclusion of this discussion was that in order for students to be successful in online learning and continue their education into lifelong learning, they have to understand how they learn, i.e. they must be

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metacognitve. In order to address this effectively, we argued a need to change the educational model and to move more towards an andragogic/heutagogic approach, engaging with students as adults in charge of their own learning and at the centre of the learning experience, and acting as clients to a professional service offered by academic tutors, whether face-to-face or online. How and when to develop metacognition in students is a subject for future work. For example, does the higher education sector expect them to arrive at university already metacognitive or do they prepare students through their first year, ready to take on the challenges of more independent learning from year two? Another conclusion of this research is that we would also wish to free up the learning and assessment process, by using appropriately designed learning objects in a monitored digital environment to provide evidence of student activity and progress. This would give the student control of the process through the production of an active, authenticated portfolio and CV. Evidence of student learning could be provided through a combination of monitoring information and verified online assessment, with appropriate controls in place to deal with plagiarism, personation and other forms of cheating. Building TEL systems to support this approach will permit academic focus on intellectual and metacognitive development of students, and better prepare students for lifelong learning. We also argued that using technology more effectively to support learning in this way can remove the “competitive-race condition” that currently applies in higher education, where learning must take place within defined timescales and by clearing defined hurdles at specified times, and begin to move to a model of learning that focuses on individual achievement. This approach is currently under development in the DECADE (Domain Expertise Capture in Authoring and Development Environments) project (MacKinnon and Bacon 2009), and we are investigating the use of existing software accounting mechanisms, tool development and automated information capture, to build a model of student performance. Future plans for the DECADE system include the ability to provide a personalised, adaptable and adaptive environment, designed to provide individual and personalised support for students, providing tailored guidance and personalised feedback. It is also anticipated that this will support the development of their metacognition and learning strategies. Other future work includes continuing the research in the area of engaging and immersive environments. Both the Pandora and Maritime City projects are continuing development, with plans for the latter to move into the area of training health professionals to recognise when patients are experiencing pain, in particular, those patients who are unable to

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articulate their condition, for example people suffering from dementia. Our eCentre research group is also beginning some research into gender specific issues in relation to software development and gamification. We are also looking at rolling out practical models for online learning within a distributed socially mediated learning environment and are engaged in working with partners on this. We are interested in models that combine the benefit of the cohort effect with students, addressing the need for socialisation, with high quality on-line learning resources such as that provided by the Minerva Project (Minerva 2013) where students study online but are physically co-located in cohorts. A new area of development is the investigation of mobile technology for effective eLearning. Although not specifically addressed in this paper, the use of mobile devices is growing exponentially with over 38% of media transactions now occurring on smart phones and Facebook having over 100 million accesses per day through mobile devices from the US alone (TechCrunch 2013). In some parts of the world such as Africa and China, internet access can be slow and has not penetrated rural areas due to cost and lack of infrastructure, resulting in mobile telephony dominating over fixed-line networks. In these situations, delivery of education through mobile devices is the only option for online education. Even in the developed world, there are now high expectations amongst mobile users for access to services through their devices, including education. The anticipated demand for online learning will not only impact on existing technology used in this arena, but as already identified will extend into mobile devices and will require cloud and big data services as it develops. Following on from our work on the DECADE project, this environment, like many others, will begin to capture huge amounts of data about how people learn and analysis of this will hopefully support the development of new, personalised educational models for technology enhanced learning. In conclusion, success in online learning will result in greater access to higher education worldwide. This development will result in the creation of effective online learners, and will enable teachers and educators to develop a range of skills and techniques appropriate to the environment in which they are used. Online learning systems have the ability to provide sophisticated and novel approaches to the development of engaging, social and personalised learning. These systems can allow a learner to develop at their own pace, enabling them to be assessed when ready in an authenticated learning environment. With this approach we can support students in the development of a learning ethic, based on their

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metacognitive skills and reinforced through the use of learning contracts, to enable them to achieve effective lifelong learning.

Acknowledgements The authors would like to acknowledge the contribution of their PhD students: Musser Cemal Nat, Ryan Flynn, Olaf Graven, Cain Kazimoglu, Alan Mustafa and Sophie Peter. The authors would like to thank colleagues from the other partners in the PANDORA project for their contributions to the work presented, those partners being CNR (Italy), CEFRIEL (Italy), XLAB (Slovenia), FUB (Italy), UEL (U.K.), ORT (France) and EPC (U.K.). We would also wish to thank the EU for funding this work under FP7-ICT-SEC-2007-1 grant number 225387.

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Santos, O.C., Boticario, J.G. and Koper E.J.R. (2003) aLFanet. Second conference on Multimedia and ICT’s in Education (m-ICTE 2003) Badajoz 3-6 December 2003. Schellinger, Paul, Christopher Hudson, and Marijk Rijsberman, eds. 1998. Encyclopedia of the novel. Chicago: Fitzroy Dearborn. Schuman, Howard, and Jacqueline Scott. 1987. Problems in the use of survey questions to measure public opinion. Science 236: 957–9. TechCrunch 2013. Facebook Reveals 78% Of US Users Are Mobile As It Starts Sharing User Counts By Country. Available at: http://techcrunch.com/2013/08/13/facebook-mobile-user-count/ The Chronicle of Higher Education, The Shadow Scholar 12th November 2010, Available at: http://chronicle.com/article/The-Shadow-Scholar/125329/ Time Higher Education Magazine. (2013). Edition 28-24 July 2013, No2, 110, P32-35. United Nations, Department of Economic and Social Affairs, Population Division, World Population Prospects: The 2010 Revision, New York, 2011. Voigt, C. & Swatman., P. (2003). Learning to learn: HCI-Methods for personalised eLearning. 10th International Conference on HumanComputer Interaction, Crete, Greece. Wagster J, Tan J, Wu Y, Biswas G, Schwartz DL. (2007). Do learning by teaching environments with metacognitive support help students develop better learning behaviors? In: The Proceedings of the 29th Meeting of the Cognitive Science Society (pp. 695–700). August, Nashville, USA. Wing, J. M. (2006). “Computational thinking,” Communications of the ACM, 49(2), pp. 33-35, 2006. Woolfolk, A. & Margetts, K. (2007). Educational psychology. Frenchs Forest, NSW: Pearson Education Australia. Zimmerman, B. J. (2008). Investigating Self-Regulation and motivation: Historical background, methodological developments, and future prospects. Am Educ Res J, 45(1):166-183.

FACILITATING COMMUNITIES OF INQUIRY ONLINE VIA TYPES OF TEACHING AND LEARNING AMY L. SKINNER1 AND JOHN M. PETERS THE UNIVERSITY OF TENNESSEE

Abstract This chapter will describe the relationships between Community of Inquiry (CoI) presences (Garrison, Anderson, &Archer, 2000) and the types of teaching and learning described by Peters and Armstrong (1998). Specifically, we describe our experiences exploring the relationship between the elements of CoI (teaching presence, cognitive presence, and social presence) and perceived learning and satisfaction in two different types of online teaching and learning environments with human servicefocused content. The intent is to share some hard-learned insight with educators in mainstream higher education with respect to engaging adult learners in a distance environment in meaningful ways that also maintain the integrity of their academic programs. Keywords: Community of Inquiry, Types of Teaching and Learning, Reflective Practice, critical thinking.

Today’s higher education instructors can select from an unprecedented array of teaching and learning methods and techniques to aid them in planning, implementing, and managing their face-to-face and online learning environments. The past decade has seen increased interest in active learning, collaborative learning, and other high-engagement methodologies that have been shown to contribute to students’ in-depth

 1

Corresponding author: Amy L. Skinner, University of Tennessee, askinner@ utk.edu, (865) 974-8090, 522 Bailey Education Complex, Knoxville, TN 379963452.

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Facilitating Communities of Inquiry Online

learning and critical thinking (Slavich & Zimbardo, 2012; Cross, 1999). One area of increasing interest is the development of communities of learners in online education. This phenomenon reflects not only a consensus that groups of learners can actively engage in collaborative activities that create new knowledge solely utilizing online learning environments, but also what Shea (2006) described as a shift in pedagogical focus from traditional didactic instruction to facilitation of collaborative learning. In some instances, instructors invite students to cocreate knowledge and understanding through interaction with the instructor and other students (York & Richardson, 2012). This chapter describes our experiences facilitating online communities in different types of teaching and learning environments, guided by two models of teaching and learning. One model specifically addresses online learning environments and the other helps frame our selection of types of teaching and learning in multiple environments. The first model is called Community of Inquiry (CoI).

1. Community of Inquiry Community of Inquiry is a conceptual framework for organizing and examining online teaching and learning environments in higher education. Developed over a decade by Garrison and others, (Garrison, Anderson & Archer, 2000; 2010), the framework has extensive support from qualitative studies, especially in the field of education (e.g., Garrison & ClevelandInnes, 2005), and components of the framework have also been examined in quantitative studies (e.g., Arbaugh, 2008; Arbaugh & Hwang, 2006; Baker, 2010). In an online environment, the framework assumes that teaching and learning occur through the interaction of three essential elements: cognitive presence, social presence, and teaching presence. Cognitive presence refers to the extent to which community participants are able to construct meaning. Its’ foundation is Dewey’s (1933) work regarding reflective thought and scientific inquiry. Garrison et al., (2000) described cognitive presence as the means by which learners construct meaning and understanding. In terms of online teaching/learning environments, Garrison et al., suggested that cognitive presence takes place in four phases of learning: a triggering event, exploration, integration, and resolution. During the triggering event, a problem or issue is identified for further exploration. In the second phase, learners explore the problems or issues through inquiry, reflection, or discourse, both individually and together. During integration (phase three), the students construct meaning or knowledge from the information they discovered

Amy L. Skinner and John M. Peters

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during exploration so that they may apply that new knowledge in the fourth phase, resolution. Application of the new knowledge may be in class, on the job, or in their day-to-day lives. Indicators of cognitive presence in an education environment include a sense of puzzlement and the connection of ideas. Social presence is a multidimensional element that describes the ability of members of the CoI to project and share their personal characteristics with the community. It is the ability of members of the community to be ‘real people’ through the medium of communication being used (e.g., computer-based learning). Social presence has been found to be a crucial element in establishing communities of learners (Fabro & Garrison, 1998; Roberts, 2005) and predates the development of the CoI model by more than two decades (Swan, Shea, Richardson, Ice, Garrison, ClevelandInnes, & Arbaugh, 2008). Arguably, it is the most extensively studied of the three presences (Arbaugh 2008). Social presence has been linked to learning outcomes (Williams, Duray & Reddy, 2006), higher levels of perceived learning and satisfaction (Richardson & Swan, 2003), social intelligence (Meyer & Jones, 2012), and quicker mastery of the technological aspects of the distance education medium (Anderson, 2002). Emotional expressions, development of interpersonal relationships, and group cohesion all indicate social presence in educational environments. In distance education settings, lack of visual and/or oral cues present a barrier to establishing social presence; however, it is difficult for critical discourse to take place without it (Garrison & Cleveland-Innes, 2005). Teaching presence has been referred to as the “binding element in creating a community of inquiry for educational purposes” (Garrison et al., 2000, p. 96). In the online environment, development of appropriate cognitive presence and social presence is dependent on the presence of a teacher or some type of facilitator. The teacher influences the other two presences by regulating the amount and type of content to be covered, how discussions are moderated, group size and composition, and the communication medium used during and outside of class. Examples of teaching presence include setting the climate and initiating discussion topics. There is some debate regarding whether teaching presence includes two (designing and facilitating the educational experience) or three (the addition of direct instruction) general functions. However, the construct itself as part of CoI is supported in research, and teaching presence was found to be a significant factor in student reports of perceived learning, satisfaction, and sense of community (Garrison & Arbaugh, 2007). It should be noted that there is a lack of consensus regarding the dimensions

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Facilitating Communities of Inquiry Online

of the presence across students (Garrison, Anderson, & Archer, 2010) and some debate about whether deep and meaningful learning occurs within CoI (Rourke & Kanuka, 2009). Early research relied on qualitative methodologies such as transcript analysis of online discussion forums (Garrison, Anderson, & Archer 2010). The need for quantitative approaches led to initial development of a CoI survey (Akyol & Garrison, 2008) designed to explore learner experiences and perceptions of the three presences. A multi-institutional study using the instrument found it to be reliable in measuring the existence of CoI in online teaching and learning environments (Swan et al., 2008). The instrument also was found to be a reliable and valid measure of both cognitive and social presence (Arbaugh, Cleveland-Innes, Diaz, Garrison, Ice, Richardson, Shea, & Swan, 2008a; 2008b), and support was found for teaching presence as a two-factor construct, including course design/organization and instructor behavior variables. The most recent version of the instrument and a brief note on the collaboration by the authors that led to its development and validation may be found at http://communitiesofinquiry.com/methodology (Arbaugh et al., (2008b). The second model used in this research is three types of teaching and learning described by Peters and Armstrong (1998).

2. Types of Teaching and Learning In Type I (T-I), teaching by transmission, learning by reception, the instructor is the primary source of information and focus is on individual student learning. T-I is dominant in most education and training establishments, especially in institutions of higher education where the instructor is the assumed authority and is viewed as the expert in possession of information the student needs or must acquire. The instructor controls the content, often within the confines of a discipline or institutional controls (e.g., curriculum requirements and accreditation standards), and assesses student gains from the educational experience. The student is expected to take in the information the instructor provides – or may choose not to do so. Students also are expected to accept the instructor’s measure of their achievement (e.g., grades, points, peer reinforcement). The lecture is the most common form of T-I. Most higher education instructors and students come into their teaching or learning careers with significant T-I experience. Type II (T-II), teaching by transmission, learning by sharing, also emphasizes individual learning. The instructor is the primary source of information, but students also serve as sources of information and use their

Amy L. Skinner and John M. Peters

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own experiences to make meaning of the topic. Relationships are established between students and between instructor and students. Examples of T-II activities include the lecture/discussion format, group work, and cooperative learning. As in T-I, the instructor’s role is still that of authority, controlling content and assessment. The instructor facilitates work in dyads or groups from the outside, yet there is room for extended learning as students bring their own experiences into understanding the subject matter. Students may serve in both student and instructor roles as they share their experiences and interpretations of the subject matter with the group. Type III (T-III), collaborative teaching and learning, emphasizes individual learning and group learning collectively. The focus is on joint construction of new knowledge for all community members. Unlike Types I and II, instructors are co-learners and participants; they may have specific knowledge of the topic, but they are not presumed sole expert. Students are expert in their own lives and have experience to bring to the table – experience that may be meaningfully related to the subject matter. In T-II the instructor facilitates works from the outside; in T-III he or she facilitates from the inside as an equal participant. Group and individual members learn through critical reflection on past and present experiences. Because of their interactive nature and their appeal to students’ own experiences, Types II and III appear to be more strongly related to CoI. Even though each type of teaching and learning is distinctive in terms of its purpose and dynamics, the typology should be viewed as a set or grouping of types, not as a hierarchy or as stages of teaching and learning. One type is no more or less important than the other, and each has a place in the scheme of educational experiences. An instructor could use all three types across multiple class sessions or two or more types in one session. The choice of one or more types depends on several factors, such as the subject matter of the class, the purpose of the exercise, the experience of the instructor with one or more of the types, and the disciplinary context in which instructors and learners are working. However, as a rule each type should be seen as serving in harmony with the other two. Strong teaching is a strategic integration of all three (Peters, 2013). Students and instructors already know how to act and what to expect of one another in T-I classrooms and, in some instances, T-II settings. However, these expectations do not fully apply to participation in a T-III experience. Thus, in a T-III experience participants are faced with learning a new way of engaging in teaching and learning that requires them to critically reflect on how they have dealt with teaching and learning experiences in the past and how they engage with others in a truly

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collaborative manner. Instructors choosing T-III can expect students to be initially confused by the new environment. It takes time for most students to adjust, and the instructor may struggle with priorities, such as adjusting the relative emphasis placed on learning process over subject matter content (i.e., in terms of what is to be “covered” by the course). The outcome is usually worth the investment (Cross, 1999).

3. The Research Project2 With the exception of the study by Peters, Taylor and Doi (2010), researchers using the CoI framework have focused on Types I and II online teaching and learning. Given the limited subject matter range of CoI studies and their focus on traditional modes of teaching and learning, there is ample room for additional studies of CoIs in other subject matter areas and alternative types of teaching and learning. This was the motivation of this research; its purpose was to examine the relationships between CoI presences and all three types of teaching and learning described above (Peters & Armstrong, 1998). Specifically, we explored the relationship between the elements of CoI (teaching presence, cognitive presence, and social presence) and perceived learning and satisfaction in different types of teaching and learning environments where the subject matter is an accredited human services field. We explored these relationships in two consecutive semesters, each with two groups, with the first semester serving as a pilot study and the second semester serving as a follow-up study with different groups. Technology used throughout the studies included the Blackboard Collaborate® live online synchronous learning platform (http://www.blackboard.com/platforms/collaborate/overview. aspx) for live weekly class meetings and class support from Blackboard Learn® online education platform (http://www.blackboard.com/platforms /learn/overview.aspx). In the following sections we refer to the four groups as Class 1, Class 2 (both in the first semester and pilot study), and Class 3, and Class 4 (both in the second semester and study two). The guiding premise of our work was that using Types II and III teaching and learning would result in a stronger CoI than would using primarily Types I and II.

 2

From this point forward, we will use the word ‘participant’ instead of the traditional term ‘student’ to denote the active roles they play in educational settings. When necessary for clarity, the person in the original traditional role of ‘teacher’ will be referred to as such, although they are very much participants.

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3.1 Pilot Study, First Semester Two classes of adult participants in their final semester of an 18-24 month Master’s Degree program in rehabilitation counseling at a large university in the southeast United States served as a pilot study to allow us initially to explore our premise. Originally, 26 participants needed to enroll in a course on psychosocial issues of disability required for their program of study during the summer semester. To accommodate all enrollees the course was divided into two sections, taught on two different days per week in 3-hour time slots. Participants were given the opportunity to choose a section in which to enroll. Eighteen participants enrolled in Class 1 (an evening class), and eight participants enrolled in Class 2 (taught during the day). Both classes were presented synchronously via Blackboard Collaborate®, the university’s live online synchronous learning platform. The lead author served as instructor of both sections of the course. The design of Class 1 adhered to a traditional teaching and learning environment (teaching by transmission, or T-I). This is the type of teaching and learning that the participants had experienced throughout their program of study, including approximately 12-14 prior graduate-level classes. Class 1 also included learning by sharing (T-II). Class 1 participants engaged with course materials through lectures, outside readings, group projects, video materials, and small group and class discussions. Materials, announcements, an open discussion board, and individual participant progress could be accessed 24 hours a day on the section-specific Blackboard Learn® online education platform course site. Graded assignments were required, with due dates throughout the semester clearly stated on the syllabus. The Class 2 environment was primarily collaborative, with some sharing (Type III, with some Type II). Outside readings provided a catalyst for each class. On the first day of class participants were informed that everyone, including the instructor, would be serving in the roles of participant and facilitator. Process, i.e., discourse and construction of meaning, would be the goal of the class, and traditional graded assignments would not be used. Instead, participants would decide on specific topics and projects to focus on each week (i.e., a specific question to answer, knowledge to seek). For example, if one participant asked, “Why do we seem to immediately categorize people based on how they look?”), all participants, including the instructor, would jointly explore this question and create possible answers. Class projects would be the results of collaboration between all participants, and the instructor (a co-learner) would provide feedback of the same value as other participants.

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Facilitating Communities of Inquiry Online

Supporting materials (e.g., videos), announcements, discussion boards, and readings – including those submitted by members in the class– were available continuously on the section-specific Blackboard Learn® course site. Tools and techniques of collaborative learning were the foundation on which Class 2 and 4 proceeded. Several aspects of collaborative learning related specifically to facilitating teaching and learning discourse in online environments have been found helpful in research (e.g., Peters & Ragland, 2009; Peters, Taylor, & Doi, 2011) and informed our work. These aspects employ climate building, or creating an environment in which participants have a sense of safety and respect supportive of a collaborative relationship among all participants; questioning, i.e., asking questions that help participants identify their assumptions, clarify their thoughts, and develop fair and balanced expectations of the learning process; skillful listening to participants’ mental models, wants, assumptions, and values; focusing in order to see and hear what participants say and how they say it, moment to moment, individually and jointly; thinking to identify and suspend one’s own frames, assumptions, values, and biases in order to understand viewpoints and behaviors; and facilitating conditions that create and sustain dialogue by participants. Two of the most important features of climate building are respect and trust between students and between students and teachers. As a result of their previous coursework in the program and interaction with the instructor, participants in the summer semester (pilot study) began the class with supportive responses to one another. It is possible that the students’ history contributed to their early-stage expression of trust and respect. Class 4 (second semester study two) participants had no such history, so the instructor had to encourage openness among the participants during the first half of the semester. In the second half, students appeared to be more open with each other and with the instructor, thus enabling their dialogue and willingness to reflect on the group learning that was taking place. Questioning habits (e.g., closed-end, leading questions) brought by participants from a lifetime of experiences with T-I teaching and learning served Class 1 and 3 participants well, but initially were a hindrance to student participants in Class 2 and 4. This is due to the importance of asking open-ended questions in a T-III environment that encourages participants to suspend judgment and inquire into others’ ideas and way of thinking about particular topics. However, like the development of respect and trust, over the course of the semester the incidence of open-ended questions increased.

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The instructor encouraged participants in Classes 2 and 4 to listen more carefully to one another and sought to model how paying close attention to the discourse might improve the ability of all participants to recall what they heard and how they understood one another. This seemed to be a skill that students were unaccustomed to using in their previous course experiences (online and face-to-face) and needed to be developed, slowly, through the instructor’s prompting and modeling as she participated as a member of the group. Student participants were accustomed to focusing on their individual thoughts and actions. However, in T-III, the focus is not only on individual learning but also on group learning. This was yet another topic for exploration by the participants – how they learned jointly, as well as how they learned individually. Class 2 participants seemed to embrace this concept early in the process, perhaps in part due to their shared academic histories, and often used terms such as ‘we’ and ‘us’ in their discussion. When we refer to thinking as a skill of reflective practice or collaborative learning, we are referring to critical thinking. It is not within the scope of this article to explore this widely researched aspect of learning, but we were concerned to promote critical thinking by participants in all the classes, especially within the environments of Class 2 and Class 4. For example, the instructor encouraged student participants to identify and inquire into their own assumptions and suspend them as they inquired into other participants’ assumptions. The goal was to make the participant’s assumptions (and their beliefs, values, rules, and other aspects of their thinking) the focus of dialogue as participants remained open to further inquiry into their own and others’ way of thinking about questions central to the content of the classes. 3.1.1 Results of the Pilot Study At the end of the semester participants in each section were asked to complete a CoI survey (Akyol & Garrison, 2008; Arbaugh et al., 2008b). They were told that survey completion was optional and that there would be no academic or other repercussions with either choice, and that their responses would be anonymous. The survey examines CoI presences and perceived learning and satisfaction. It consists of 36 items: 13 items assess respondent’s perception of teaching presence (e.g., “The instructor helped to keep course participants engaged and participating in productive dialogue”); 9 items assess perception of social presence (“Getting to know other course participants gave me a sense of belonging in the course”); and 12 items assess perception of cognitive presence (“I utilized a variety of

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information sources to explore problems posed in this course”). The remaining two items assess perceived satisfaction and perceived learning, respectively. The instructions request responses to the items on a 5-point Likert-type scale, with 1 = Strongly Disagree through 5 = Strongly Agree. The survey was posted on each Blackboard Learn® section course site as a self-initiated and self-paced assignment with a setting that allowed respondents to be anonymous to the instructor. All 18 participants in Class 1 and all eight participants in Class 2 completed the survey. In the pilot study, no statistically significant differences between groups were found for any of the CoI presences or reported satisfaction and learning using t-tests (at the p Į • .8. The alpha co-efficient was used to test the internal consistency becuase Alpha Cronbach has been demonstrated many times to attain quite high values even when the set of items measures several unrelated latent variables (Zinbarg, Yovel, Revelle, & McDonald, 2006). The calculated coefficient of alpha reliability for the use scale (part one) was .81 and for the technical problems scale (part two) was .8177. (See Table 2) Table 2 Reliability of the survey Item Competing Behavioural Intention (CBI) Behavioural Intention Perceived usefulness Perceived ease of use Perceived content quality Perceived network externality Computer self-efficacy Course attributes Subjective norm



Correlation .134(*) .290(**) .780(**) .744(**) .738(**) .748(**) .641(**) .461(**) .635(**)

sig 0.047 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

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There was a positive correlation between every item and total score on the survey, which indicates the instrument enjoys a high level of internal consistency.

5. Findings 5.1 Results (Part I) The first section in Part III of the survey was designed to tap into the students' attitudes towards competing behavioural intention. To respond to this section, students were asked to rate their responses with regard to two items. The mean values and standard deviations for students' responses to these items are presented in Table 3 below. Table 3 Means and Standard Deviations for the Items of the Use Scale (N=219) Item N Competing Behavioural Intention (CBI): 1. I intend to use other means (such as participation in the classroom, educational CD-ROM or video) to learn instead of using the e-learning system to receive education. 2. I intend to learn by participating in the classroom, using CD-ROM or educational video instead of using the e-learning system to receive education Average

Mean

Std. Deviation

3.6986

.98640

3.8995

.95732

3.80

0.97

According to Table 3, the overall mean score for all items was 3.80, with regard to Competing Behavioral Intention. It is interesting to notice that all items had mean values greater than 3.5. The second section in Part III of the survey was designed to recognize the behavioural intentions of student users. Table 4 below displays the mean values and standard deviations for students' ratings of these items. According to Table 4, the overall mean value for all items was 4.02.



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Table 4 Behavioural Intention Behavioral Intention Mean Assuming I have access to the e-learning sys4.2192 tem, I intend to use it

Std. Deviation .78249

Given that I have access to the e-learning sys3.8174 tem, I plan to use it.

.93528

Average

0.86

4.02

The third section in Part III of the survey was about perceived usefulness by student users. Table 5 below displays the mean values and standard deviations for students' ratings of these items. According to Table 5, the overall mean value for all items was 3.93. Table 5 Perceived Usefulness Perceived usefulness Mean Using the e-learning system improves my 3.3257 learning performance.

Std. Deviation 1.00202

Using the e-learning system increases my 3.8174 learning productivity.

.99700

Using the e-learning system enhances my 4.1826 effectiveness in my learning.

.80905

I find the e-learning system to be useful …

4.3836

.94767

Average

3.93

0.94

The fourth section in Part III of the survey was about perceived ease of use by student users. Table 6 below displays the mean values and standard deviations for students' ratings of these items. According to Table 6, the overall mean value for all items was 3.89.



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Table 6 Perceived Ease of Use

Perceived ease of use Interacting with the e-learning system does not require a lot of my mental effort.

Mean

Std. Deviation

4.2740

.77685

I find the e-learning system to be easy to use.

3.9726

.90319

My interaction with the e-learning system is clear and understandable.

3.5616

1.06641

3.7671

.95572

3.89

0.93

I find it easy to get the e-learning system to do what I want Average

The fifth section in Part III of the survey tapped into the perceived content by student users. Table 7 below displays the mean values and standard deviations for students' ratings of these items. According to Table 7, the overall mean value for all items was 3.94. Table 7 Perceived Content

Perceived content quality I search and share the related course content … My teachers or classmates search and share the related course content from the internet to help my learning. Content on the e-learning system is updated on a regular basis. The e-learning system often provides the updated information. Average

Mean 4.1279

Std. Deviation .90468

4.2648

.76817

3.5114

.94994

3.8402

1.04356

3.94

0.92

The sixth section in Part III of the survey examined the perceived network externality on the part of student users. Table 8 displays the mean values and standard deviations for students' ratings of these items; according to the table, the overall mean value for all items was 3.59.



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Table 8 Student Users

Perceived network externality: Most students use the e-learning system. There will be more students using the elearning... As more and more students use the e-learning system, I think related services will soon be developed. As more and more students use the e-learning system, related software and hardware will soon be developed. Average

Mean 4.0913

Std. Deviation .87308

3.1050

.93501

3.8265

.99403

3.3242

.99074

3.59

0.95

The seventh section in Part III of the survey explored the computer self-efficacy of student users. Table 9 displays the mean values and standard deviations for students' ratings of these items. According to Table 9, the overall mean value for all items was 3.77. Table 9 Computer Self-Efficacy by Student Users Computer self-efficacy: I am able to operate the e-learning system with less support and assistance.

Mean

Std. Deviation

4.1096

.90201

I am confident that I can overcome any obstacles when using the e-learning system.

3.3836

1.09582

I believe that I can use different e-learning software and systems to receive education.

3.8311

.94517

Average

3.77

0.98

The eighth section in Part III of the survey examined the course attributes as assessed by student users. Table 10 displays the mean values and standard deviations for students' ratings of these items; according to the table, the overall mean value for all items was 3.49.



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Table 10 Course attributes Course attributes Functions of the e-learning system support the processes and demands required by the course.

Mean

Std. Deviation

3.0868

.94177

Available functions on the e-learning system support requirements of the course.

3.8950

.98284

Average

3.49

0.96

The ninth section in Part III of the survey was designed to tap into the subjective norms in the use of Blackboard as evaluated by student users. Table 11 below displays the mean values and standard deviations for students' ratings of these items. According to the table, the overall mean value for all items was 3.89. Table 11 Subjective norms Subjective norm My teachers think that I should use the system.

Mean 3.6712

Std. Deviation 1.10944

My friends think that I should use the system.

4.1142

.86773

Average

3.89

0.99

To identify the effects of voluntariness on the acceptance of onlinelearning systems, independent samples t-tests were utilised and in order to check for the equality of means through employing SPSS (vers. 14) as the statistical analysis tool. Results of analysis showed that no significant differences were found between the Yes (voluntary) and No (mandatory) respondents at the alpha = 0.05 level on Competing Behavioural Intention (CBI) and Behavioural Intention (BI). However, mean scores for the voluntary ELS setting group were higher than for the mandatory ELS setting group for all sections of the survey (perceived usefulness, perceived ease of use, perceived content quality, perceived network externality, computer self-efficacy, course attributes, subjective norm and SUMALL); this was significant at (poff, off->on) (rt, rec-toggle) run : Execute the current statements in the context of the current declarations save : Save the current declarations and statements to file. Use if present, otherwise open browser. show : Show the current declarations and statements (sh) statement : Interpret the given code as a statement (s, st, stmt) type : Show the type of the given expression (t)

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An Ultra-lightweight Java Interpreter for Bridging CS1

Appendix B: Usage Errors by Semester Table B.1 JULI usage error classification: May 2011 Error Missing semi-colon Undefined variable Possible loss of precision Illegal start of expression Name already defined .class expected Illegal start of type Can’t apply symbol Not a statement Unclosed character literal Unexpected type Illegal char Else without if Missing return statement Unclosed string literal Uninitialised variable Operator can’t be applied Illegal escape character Reference to non-static Repeated modifier Int number too large Unreachable statement Incomparable types Illegal line end in literal Missing return type Cannot dereference Void not allowed here Missing return value Qualified name not found

Avg. per session 5.59 3.30 1.02 0.88 0.60 0.38 0.33 0.24 0.23 0.21 0.17 0.17 0.16 0.16 0.16 0.12 0.11 0.09 0.09 0.08 0.06 0.05 0.04 0.03 0.03 0.02 0.01 0.01 0.01 Total:

Total

Total %

576 340 105 91 62 39 34 25 24 22 18 18 16 16 16 12 11 9 9 8 6 5 4 3 3 2 1 1 1

0.39 0.23 0.07 0.06 0.04 0.03 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.008 0.007 0.006 0.006 0.005 0.004 0.003 0.003 0.002 0.002 0.001 0.0007 0.0007 0.0007 1477

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Table B.2 JULI usage error classification: May 2012 Error Mising semicolon Undefined variable Illegal start of expression Not a statement Possible loss of precision Name already defined Missing return statement Illegal start of type Else without if Can’t apply symbol Illegal char Unexpected type Unclosed character literal Unclosed string literal .class expected Reference to non-static Repeated modifier Int number too large Illegal escape character Missing return type Empty char literal Operator can’t be applied Missing method body Return outside method Uninitialised variable Class or enum expected Unreachable statement Cannot dereference Incomparable types

Avg. per session 3.19 1.66 0.80 0.42 0.37 0.23 0.20 0.16 0.14 0.13 0.13 0.12 0.11 0.11 0.08 0.07 0.06 0.05 0.05 0.04 0.03 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.01 Total:

Total 530 276 132 70 62 38 34 27 24 21 21 20 19 19 13 12 10 9 8 7 5 4 3 3 3 2 2 1 1

Total % 0.39 0.20 0.10 0.05 0.05 0.03 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.004 0.003 0.002 0.002 0.002 0.001 0.001 0.001 0.001 1376

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Table B.3 JULI usage error classification: September 2012 Error Mising semicolon Undefined variable Illegal start of expression Not a statement Name already defined Undefined method Int too large Illegal start of type Possible loss of precision Illegal char Repeated modifier Unclosed character literal .class expected Missing return statement Operator can’t be applied Unexpected type Argument mismatch Unclosed string literal Else without if Uninitialised variable Incomparable types Illegal escape character Missing method body No method found Cannot dereference Illegal initialiser for type Illegal underscore Invalid method declaration Can’t reference non-static Illegal underscore

Avg. per session 3.36 1.83 0.8 0.48 0.38 0.35 0.25 0.24 0.23 0.22 0.19 0.18 0.17 0.17 0.17 0.15 0.14 0.14 0.13 0.09 0.03 0.03 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.01 Total:

Total 403 219 96 58 45 42 30 29 27 26 23 21 20 20 20 18 17 17 16 11 4 3 3 2 2 1 1 1 1 1

Total % 0.34 0.19 0.08 0.05 0.04 0.04 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.003 0.003 0.003 0.002 0.002 0.001 0.001 0.001 0.001 0.001 1176

MONITORING STUDENTS’ ENGAGEMENT WITH AN ONLINE COURSE: REFLECTIONS AND IMPLICATIONS FOR PRACTICE ANETA HAYES1 ROYAL COLLEGE OF SURGEONS, IRELAND

AND AMAL AL-GALLAF2 MEDICAL UNIVERSITY OF BAHRAIN

Abstract Professional literature on improving learning with technology recognises that developing, integrating and delivering e-learning modules in higher education can improve the ways in which a university works, and contribute to the development of better student skills. However, this literature also recognises that students cannot be expected to acquire sufficient levels of technological, self-learning and interactive skills on their own, especially when they have previously been educated in more traditional models. Hence, current research in the area calls for incorporating ways of online teaching that ensure students’ development and enable lecturers to monitor learner progress, which can result in productive options for using online courses. The authors of this chapter introduce a specifically designed monitoring system in a digitally enhanced module at one medical university. The authors explain the design of specific learning objects and monitoring tools that are used in this module, and discuss how these enable the lecturers at the university to collect evidence of student activity, learning and progress. This chapter will therefore demonstrate how using web-based learning management systems such as SCORM/AICC can help track details of student activity in a 1 2

E-mail: [email protected] E-mail: [email protected]

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particular module by providing accounts of students’ visits to each lecture in that module, attempts that have been made to study each lecture, the overall time a student spends on studying a lecture and whether or not the module has been successfully completed. This chapter will also give special attention to an additional feature of the course which integrates multiple Test Yourself Activities that have been found in literature not only to challenge the students’ abilities and thinking skills, but also to test their knowledge and demonstrate how well they have learnt the subject matter before the official exam. Finally, this chapter will highlight the importance of forums in monitoring students’ progress and will discuss how the process of monitoring can be enhanced by the presence of an instructor, restricting course access and using checklists. It is hoped that presenting these monitoring and learning objects in this chapter will contribute to building online systems that direct the academic focus towards the metacognitive development of students, rather than creating activities with little educational value for which online learning is often criticised. The concluding remarks highlight the importance of accounting for the contexts of students and universities before making decisions about specific monitoring tools.

1. Introduction and Some Reflections The use of modern technology in higher education has been widely accepted by many universities, since its benefits in developing long-life student skills have been proved by many research studies (for example: Tiwari et al, 2006; Thomas, 2000; Ngai et al, 2005). Similarly, online courses have also become an integral part of programmes in higher education because their integration into university curricula helps to shift the focus of teaching from traditional lecture-based models to studentcentred approaches which are believed to produce self-directed graduates who are more mature in their approach to study (see for example: Palloff and Pratt, 1999 or Shank, 2007). In our previous work, we have also explained that online courses can be used to solve problems with overloaded undergraduate curricula, and demonstrated a series of online activities that can be accessed by students at their own convenience, allowing them in this way to stay fully focused on their undergraduate studies (Al-Gallaf and Hayes, 2012b). The question that remains, however, is whether all these innovations really create meaningful learning opportunities, or whether they represent just another section on the university’s virtual learning environment website where students can

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download and upload things or watch videos without really learning anything. The latest response from the literature in relation to innovations such as online courses and technology-supported activities (see for example: Twenge, 2009 or Jones et al, 2010) suggests that students cannot be expected to develop an understanding of the value of online activities on their own, and that faculty staff at universities should take care to put relevant monitoring systems in place ‘to prevent entitled students from unfairly working the system’ (Twenge, 2009, p. 398). This response might be surprising for some of us, because when we consider the current generation entering higher education, we tend to think of our students as technology-literate young people who are probably more proficient in the use of technology than many of us. Tapscott (2008, cited in Jones et al, 2010) for instance, refers to this generation as the Net generation and Prensky (2009, cited in Jones et al, 2010) calls them Digital Natives; both terms refer to young people born in the 80s, which is considered to be the new era of technology. The Digital Natives who are currently in our universities, however, have been found in the literature not to be able to make sufficient use of technology for their learning, which brings the idea of the Net generation under some scrutiny. For instance, Margaryan et al (2011) found that students in the UK could not use the established university technologies for their learning and that they reported low use of collaborative knowledge creation tools, virtual worlds and social networking sites. The students in another study by the same authors (Margaryan and Littlejohn, 2009, cited in Jones et al, 2010) did not show any changes in study patterns after learning in a technology-enhanced environment and confirmed that they preferred traditional lecturing over technology-supported modules. Likewise Kvavik (2005), who conducted a similar study in the USA, also concluded that despite being computer-literate and using Web 2.0 for everyday communication, students did not demonstrate adequate skills for academic study and did not indicate any preferences towards technology use in the classroom. Finally, in South Africa, students were found not to use these technologies for two reasons – that is, because they were not common in students’ lives and because they were not appropriately entrenched in the university courses (Brown and Czerniewicz, 2008). These findings therefore suggest that we cannot generalise and assume that all young people in our universities, simply because they were born in the era of technological boom and because they are exposed to technology in their everyday activities, are able to utilise this technology for learning. Similarly, we cannot assume that once online courses have been introduced

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into the university programmes, their use is appropriate and that our students benefit from them. Both Twenge (2009) and Jones et al (2010) who were cited earlier link this to culture and argue that it is the sociocultural environment of learners prior to coming to university that determines the students’ ability to ‘settle’ in a technology-enhanced learning environment. For example, Twenge (2009) argues that Chinese students who were always told what to do are likely to find it more difficult to learn in online courses because of the high degree of independence and self-monitoring it requires from them. Therefore, it is very important to understand students’ perspectives on the use of technology in learning and their ability to use it for self-study. We followed this advice, and our previous work regarding the online course referred to in this chapter suggested that even though our students are somewhat similar to the Chinese students mentioned by Twenge (2009) in that they also come from a highly centralised and teacher-centred environment, they demonstrated positive attitudes towards the use of ICT technologies in their learning. In the study on exploring the benefits of technology-assisted, project-based learning on students’ attitudinal change towards the module which is currently replaced with the online programme, we demonstrated a considerable impact of technology use on students’ improved attitude to study, their gains in the acquisition of ICT and collaboration skills, as well as an increased understanding of the subject matter (Al-Gallaf and Hayes, 2012a). Jones et al (2010) recommend that before universities decide to change to technology-enhanced or online learning, they should become better informed about the type of students they will be teaching. Our work showed that the type of students that enter our university are pro-technology, which made us continue with the development and implementation of the online course. However, we are aware that to develop a course and expect students to make use of it on their own is not enough and that, as demonstrated by the findings by Brown and Czerniewicz (2008), it can lead to compromised learning. Twenge (2009) recommends that to maintain standards of education and the content of teaching, faculty at university should monitor students’ engagement with online courses and technology use through very specific instructions and monitoring tools which will require learners to follow rules regarding the completion of the course that have been put in place. This, according to Twenge (2009), is very important because educators cannot compromise on the material to be learnt due to students’ inability to use the course appropriately or due to their thinking that online courses are easy because they require watching videos and uploading documents to the website. That is why we developed and implemented a

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series of monitoring tools in our own programme which we will discuss in this chapter. In the subsequent section of this chapter, we will briefly discuss the use of the SCORM/AICC tracking system in monitoring student activity within the online course at our university. We will only mention it briefly because this programme is quite commonly used in Moodle platforms to monitor students’ engagement with online courses and information on it can be obtained elsewhere. However, in the rest of the chapter, we would like to give special attention to the additional features of the online course which integrate multiple Test Yourself Activities, the use of asynchronous forums and instructors' control over the accessibility of course content, and explain their role in monitoring students’ learning. The sections below will therefore integrate examples taken from our own course with indications of how they can be used as monitoring tools. We will present them as ‘Strategies for Success’ but we will also explain how they can be applied in the courses of others.

2. Strategies for Success and Examples from the Course 2.1 Strategy for Success 1: Using the SCORM/AICC Manager for Monitoring Purposes SCORM and AICC are web-based software applications that are commonly used in Moodle to plan and organise learning in technologyenhanced environments (Fallon and Brown, 2002). Apart from adding materials prepared in PowerPoint or, for instance, in word-processing applications, SCORM and AICC provide a variety of interactive activities that enable students to interact with each other and the instructor. These, just to name a few, include creating electronic assignments, chat rooms, building dictionaries and creating quizzes and surveys (Fallon and Brown, 2002). These activities, however, can also be used to monitor learners’ participation and assess their performance through activating specific settings in the content created by the instructor. In our course, we used additional settings related to being able to view how many attempts the students have made to complete a task or to study the particular course content, when they first and last accessed the course and what their score was on Test Yourself Activities, which are an additional feature of our course and which will be discussed in more detail below. Activating these settings gives the instructor an opportunity to monitor students’ learning through receiving a report that outlines details of their status – that is, which units have been completed by each student, how much time they

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spent on that unit and what they scored on Test Yourself Activities. We found this report very useful in finding possible explanations as to why a student received a certain grade and in monitoring his/her overall progress. The scores on Test Yourself tasks are reflected as 1 or 0, where 1 means that a student submitted correct answers and 0 means that this student could not answer the questions in the Test Yourself tasks correctly. The figure below (Fig. 1) presents a sample report we receive through the settings of our course. We explain how instructors could use it below. This screen shot shows a good example of one student who made two attempts to complete the unit on Bahrain’s Geography and the Test Yourself Activities that followed. The student scored differently because observing his activity on this particular unit shows that the student spent the expected time to complete the unit during the first attempt, which was set by us at about two hours (Figure 2), and answered the related tasks successfully. The same student failed to do so during the second attempt which can be seen in Figure 3. Figures 2 and 3 show the tracked details of the same student with different attempts. The student’s name has been removed from the screen shot for ethical reasons. The elements represented below have values or meanings that a course developer needs to understand, for example: x cmi.core.exit means the course has not been completed or passed. When setting up the reports page you can also predefine values such as suspend, timeout or logout. x cmi.suspend_data there is no specific value here for this element; it depends on the course creator or developer to decide on what they want the report to show. In our course, it represents the correct answers for each question. Therefore, it is important for course designers to add a SCORM package to each online course. As a course coordinator or designer you should have some editing privileges for the site, or for the virtual learning environment as in our case. This can be helpful in creating, presenting and monitoring the course content. When using Moodle, this can be done by simply turning editing on at the top right of your main course page, clicking on the Add an activity menu and selecting SCORM package (Figure 4). Then, your SCORM/AICC package can be edited and the course content that you had previously prepared can be added. Completing the three types of settings on the setting page could also be useful (Figures 5 & 6). As a course designer it is always wise to decide what is required from the students in each activity and choose the setting that best suits this purpose.

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Fig. 1. represents the report’s main page

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Fig. 2: Successful attempts

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Fig. 3: Unsuccessful attempts

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Fig. 4 Adding SCORM/AICC package process

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Fig. 5 General settings.

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Fig. 6 Other settings & common module settings.

In our newly designed online course, we wanted the students to complete the assigned activities successfully. So we chose the Learning Objects Mode as a grading method which basically shows the number of attempts taken to complete or pass the tasks in each unit of the online course we developed. Course designers can also choose different modes to generate different outcomes: for example, the time spent on each activity, number of attempts and the grades students achieved by the completion of the activities.

2.2 Strategy for Success 2: Using Test Yourself Activities to Monitor Students’ Acquisition of Knowledge Test Yourself Activities comprise an important aspect of online courses as they are designed to add an element of fun to the learning experience, check students' acquisition of knowledge before moving to a new unit, provide students with more than one opportunity to test their knowledge,

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challenge students’ abilities and thinking skills, and motivate students to learn the subject matter (Palloff and Pratt, 1999; Shank, 2007). In our course, the Test Yourself Activities include drag and drop, flip the card, multiple choice questions, tile game, shuffle and sequence, as well as sort and categorise tasks. It is important to highlight here that students are provided with a description of how to use each activity before they start, which has been described in literature as an important aspect of online designs (ibid) and which can be useful in preventing problems with not engaging in the course because students will not know what to do. Most importantly, however, we used Test Yourself Activities as a monitoring tool to be able to measure how much of the course content has been mastered by the students and to ensure that our learners familiarise themselves with certain components of the course before progressing to another unit. For example, as can be seen in Figure 7 below, the unit on Bahrain’s geography includes a tile game which asks the students to identify true and false statements about Bahrain’s geography. The students are required to click the tiles in rows or columns until each tile displays only true statements and then submit the answers. This form of activity checks how well students have learnt geographical facts about Bahrain, and the record on the course’s website gives the instructor the names of the students who completed this activity successfully, which is an indication of their progress. Being able to view how many students submitted correct answers enables us to see the level of mastery of the material, and the number of attempts they have made on this activity. In the Test Yourself Activities, the students are not able to submit incorrect answers, so by viewing the attempts we are able to see how hard students have worked to master the content and mark correct answers. As a course designer it is useful to decide what skills or competencies need to be tested and how. For our online course, we decided from the beginning that we will test our students on every set of Historical content per unit. The nature of the subject course designers are developing for online courses will affect the type of activities they create or choose. Our main target was to make medical students enjoy studying a subject that can be described as outside the core curriculum. As a result, we decided to use the Microsoft Learning Content Development System (LCDS) software which helped us create interesting ‘learning snacks’ to meet the students’ needs. LCDS can be very convenient for different types of subjects in preparing Test Yourself questions that focus on different learning outcomes, such as knowledge, comprehension, application, analysis, synthesis or evaluation. The LCDS software enables designers to choose from different

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Fig.7. represents the tile game in Test Yourself Activities

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sets of templates available for developers to input their questions. Using some of these templates, course developers can create highly interactive games or activities such as drag and drop, flip the card, multiple choice questions, tile game, and shuffle and sequence. This software is very userfriendly and requires the developer only to input their questions or data, therefore saving the developer’s time.

2.3 Strategy for Success 3: Applications of Forums in Monitoring Students’ Engagement with the Course The role of online forums within the course in encouraging studentstudent and student-instructor interaction was emphasised in our previous work (Al-Gallaf and Hayes, 2012b). These forums are asynchronous, which leads to the formation of learning communities in which members exchange ideas, experiences and think through specific problems. This is believed to create greater learning opportunities which can be accessed in a flexible manner and at students’ own convenience (Ko et al, 1999; CarrHellman and Duchastel, 2003). Apart from creating interactive learning opportunities, however, asynchronous forums can also play an important monitoring role. In our undergraduate course, they create a place where the instructor can view everybody’s engagement with the course by looking at the amount and type of comments from each contributor. An example of a forum is presented in Figure 8, where students can start a discussion topic but where the instructor can at the same time view the replies, the types of comments and when they were posted. Each student’s contribution is studied carefully and included in the final checklist which is a report on each student’s engagement with the course, which will be discussed in detail in section 2.5 of this chapter. Students can also interact and communicate with the course coordinator and lecturers through the forums. Figure 9 shows an example of a forum where students can directly ask for help or draw their lecturer’s attention to certain issues related to emerging situations that require immediate action.

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Fig. 8: Main page of the Forum

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Fig. 9 shows an example of communication through forums.

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Additionally, considering the broader culture of learning and teaching in Bahrain that has been characterised by more traditional desk-based teaching, it was felt that the inclusion of forums and the availability of the instructor would help students to interact better with the course. The potential of such interactions lies in the fact that they provide continuous support in terms of assignments, progress, feedback and administration of the course (Palloff and Pratt, 1999), which students may need when switching from teacher-based to more independent modes of learning. The findings from studies included in the reflections part of this chapter suggested that students often do not benefit from online courses because they are expected to acquire skills of how to ‘crack’ them on their own. Hence, along with using asynchronous forums for monitoring students’ contribution to discussions, we encourage instructors to stay in touch with the students throughout the course, to help them learn the skills that are required and so that instructors can monitor how well they are doing. This might involve contacting students with an additional explanation about how to carry out a particular activity or, if students are identified as not contributing to the discussion forums, sending reminders to them to do so. Figure 10 shows the VLE forum digest, which every member of the college receives in their inbox by the end of every working day. It is always wise to specify the purpose of the forum developers are creating, because forums can be used differently. If the use of the forum is to encourage your students to interact with instructors and with each other beyond the classroom boundaries, then a forum is an effective method of communication. We found it a transparent method that students can use to facilitate interaction and the acquisition of knowledge. We advise using learning outcomes to help instructors to decide what type of forum they should choose. We used the forum for different purposes and these included discussions and announcements, question and answer, discussion of course content, providing support and posting deadlines and schedules. Again, Moodle offers different types of forums which can be set by Adding an Activity in the dropdown menu and selecting Forum. As a course designer, it is useful to know that a monitoring system can be put in place from the settings page. It can be done through Display options where specific types of discussion threads that best suit the course nature can be chosen. Selecting the forum type to ensure meeting the learning outcomes can be a good monitoring activity. Choosing one main type of forum, for example, Each Person Posts One Discussion because one of the objectives is to engage students in a discussion on a particular topic, allows for a reasonable amount of control over students’ participation in the forum.

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Fig. 10 shows VLE forum digest

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Instructors can see who contributed and who did not to the discussion, which can later be used, for instance, in awarding students’ marks.

2.4 Strategy for Success 4: Blocking the Online Content The rationale behind creating an online course at our university was driven by the fact that the university struggled to accommodate extra hours in the curriculum to deliver local modules that were not part of the core medical programme. Creating an online solution like this was therefore believed to solve problems with overload, by providing students with the opportunity to access resources when it was most convenient for them (AlGallaf and Hayes, 2012b). Despite the advantages of providing a flexible mode of learning, though, this characteristic of online courses is also of concern to course developers because it does not require the students to study regularly and to follow deadlines (Paulsen, 2003). Bearing this in mind, we decided that blocking parts of the online content as the students’ progress through the course could go some way towards addressing these concerns. We found that it was a useful tool in verifying students’ engagement with the course, and an indirect way of making them study the content when it was required. For instance, the first module in our course is Bahrain Geography. At the start of the course, we opened only this unit and we made it accessible to students for two weeks. All other units were blocked. After the two-week period, we blocked the unit on Bahrain’s geography and opened the follow-up unit on The Portuguese Occupation in Bahrain and kept it open for another two weeks. We continued this strategy of making units accessible sequentially until all units of the course were covered in the semester. This made the students focus on a particular component of the course at a time and follow the sequence of the module, leading to better organisation of study. This was also useful in making the students aware that specific deadlines were put in place and that they were required to complete certain tasks on time. Course designers can use Moodle to block course content from students very easily. Turning Editing On makes an eye appear next to the title of a lecture (Figure 11) and clicking on the eye makes the content invisible to students. The blocked content is marked with the ‘closed eye’ which in Figure 11 can be found next to the content on Al Utoub, History of Healthcare System in Bahrain and Print Version of the Online Course.

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Fig. 11 shows ‘Hide’ or ‘Show’ options to block content

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2.5 Strategy for Success 5: Checklist Many novice online users and more particularly students lack the required skills to monitor their learning which results in unsuccessful experiences (Cavanaugh et al, 2012). A checklist can therefore be a good learning management system as it keeps students working in an organised and systematic way (ibid). For our online history course, students are required to structurally progress throughout the online part to connect historical facts. Therefore, random navigation in the online course would result in misinterpretations of key historical facts and would lead to confusion. All elements designed for our online course are interrelated and a properly structured navigation of this course is essential to achieve good final results. During phase one of implementing the online course, we experienced some issues with students who were not able to monitor their learning. They were struggling to progress individually without the help of their lecturer. As a result, we thought of implementing a checklist. Cavanaugh et al (2012) found that students’ attitudes towards work and assignment submission have improved after using a checklist and that students were more consistent with deadlines. We also observed a change in our students' practice when providing them with the checklist. The students found that it is a good tool that assists them in managing their time. This is demonstrated by the survey results which we present in the concluding section of this chapter. Using checklists is a very effective way to monitor students’ learning. Implementing it is easy, too. Course designers should first identify the key components of the course and they should also look at the most important sections of the online course and plan ways of making students remain focused on these sections while progressing through the online course. The students can then tick relevant boxes which will direct them through their study – that is, what to study first and what to study next if they follow the points on the checklist in sequence (Table 1). This will make them realise that the course is structured to achieve specific goals and that the course content is one coherent block. This requires course designers to first plan thoroughly which online course content should be covered by the students following specific steps and which could be practised in preparation for examinations or assessments. We provide an example of our own checklist in Table 1 below.

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Table 1 Online Course Checklist

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3. Concluding Remarks 3.1 Improved Results and Students’ Reflections Taken together, what we have presented in this chapter shows that making decisions about the use of digital technologies in learning should be a thoughtful and thorough process. The reflections presented in the introduction section of this chapter highlight the fact that it cannot be generally accepted that students will automatically benefit from the use of technology in the classroom simply because they were born in the era of new technology. The findings by Margaryan and Littlejohn (2009), Kvavik (2005) or Brown and Czerniewicz (2008) cited above show that differences in technological literacy depend on the broader context of students and their universities. We demonstrated through our previous work that a technology-enhanced environment suits our students, but we also concluded that, because of their educational background, we might have to put certain monitoring objects in place. We explained above that monitoring students’ engagement with the course through SCORM and AICC managing platforms helped us in tracking details of student activity with the course. We also discussed the specific use of Test Yourself Activities as a monitoring object, and related the role of forums and the instructor in managing students’ engagement with the online module. Subsequently, we referred to systematically restricting the online content as the students progress through the course to ensure students’ engagement with the module on a regular basis. We finally presented the checklist that gives an instructor a quick overview of the amount of work individual students have put into the course. Having implemented this monitoring system for the first time in our course, we were eager to see if this resulted in better student results. To that end, we have compared final results before the implementation of the new system with final results after the implementation. The comparison showed a significant positive change in the summative grades of students and indicated that less students achieved borderline grades (between 50-60) and more students obtained better marks in the higher grade categories (61-70, 81-90 and 91-100). This trend is represented in Figure 12 below.

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Fig. 12. Comparison of results before and after the implementation of monitoring tools.

Students’ reflections on their experience with the new monitoring approach were also sought. Students’ feedback presented in Table 2 below shows that the majority of the students agreed that Test Yourself Activities were very useful 'snacks' that supported their learning. Slightly lower numbers were recorded for the use of the checklist and forums, however, the responses were still positive. We expected positive responses regarding blocking the course content, however, the students were neutral in this regard. This suggests specific implications for practice for us and other instructors, and highlights the importance of matching specific monitoring tools with the context of students discussed throughout this chapter. Students’ overall feedback about the design tools showed good levels of satisfaction and indicated that blocking the online content, Test Yourself Activities, using checklists and forums were effective tools in engaging them with the online course. 㻌

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Table 2 Students' Feedback regarding the Use of Specific Tools Average Rank (5-Point Likert Scale) 1= Strongly Disagree, 2=Diagree, 3=Neutral, 4=Agree, 5=Strongly Agree Average Standard Deviation Test Yourself activities were useful in helping 4.1 1.09 me check how much I learned. I found forums a good means of communication 3.8 0.95 between me and my colleagues and the instructors Blocking the online content helped me organise 3 1.34 my study. Using the checklist helped me understand the 3.8 1.12 key information that should be mastered in the course The design tools (blocking the online content, 3.9 1.18 Test Yourself activities, using checklists, using forums) increased my engagement with the online course.

3.2 Recommendations and Future Work As far as future improvements in monitoring students’ learning in online courses are concerned, we recommend that restricting some course access through setting up conditional activities might be useful. We restricted the availability of specific course sections based on the amount of time we would like our students to spend on them, because we wanted our students to study systematically and to develop better time management skills. This approach received neutral responses from our students and we will investigate why in our future work. At the same time, in situations where instructors are more concerned with the actual acquisition of knowledge and mastery of the material, we believe that additional restrictions can also be based on the grades students obtain on a specific task. This can be done through SCORM and AICC managing platforms where these restrictions can be put in place based on activity completion – that is, whether the completion criteria set by an instructor for a specific activity were met. At present, our completion criteria, for example, in Test Yourself Activities do not allow the students to submit the answers if they are incorrect. At the same time, if the students do not get the answers right when they first attempt the activity, they can come back to the course to study a particular

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section again and then make another attempt at the same Test Yourself task. For instructors who wish to be stricter with the number of opportunities they give to students to attempt a particular task, however, it might be more beneficial to introduce access restrictions based, for example, on receiving a certain score. Additionally, for our students, the course status appears as ‘completed’ when the SCORM system recognises that they have completed a specific activity or, in some cases, when students have simply viewed the course content. We recommend that it might also be more advantageous for some instructors to restrict the ‘completed’ status to receiving a specific score, for example, in situations when minimum achievement is required to accumulate a certain amount of credits. In terms of Test Yourself Activities, we are considering including individual scores to enable better monitoring of our students’ progress. At the moment, our Test Yourself Activities encourage the students to keep trying until they ‘get it right’. The educational value of these types of activities lies in the fact that they force the students to interact with the course until they are able to answer all questions correctly, but we are also aware that these activities may also turn into a guessing game which will drive the students’ attention away from the material and will compromise learning. On the other hand, allowing the students to submit their answers, irrespective of whether they are correct or not, could be a better reflection of their actual level of knowledge. These scores, along with other features on the checklist discussed in section 3.5, could be used in building students’ portfolios which, when used by the lecturers at university or perhaps even future employers, could help in assessing individuals’ selfstudy and self-discipline skills. We also recommend that restricting access to forums after a specific period of time could be a useful monitoring tool. We realised that, even though we kept restricting access to specific segments of the course, the students could still use the discussions on the forums as a source of knowledge, without having to study the main content. Alternatively, the general forums could be turned into ‘Question and Answer’ forums where an instructor poses a question but the students will not be able to see the replies of others unless they have themselves replied to this question (Klemm, 1998). Finally, the ideas and recommendations presented in this chapter need to be reflected on in the instructors’ own socio-cultural and educational context. As Jones et al (2010) point out, often the use of technology does not fully correspond with the expectations and perspectives on learning of some students. We support this conclusion, and additionally argue that the

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use of specific monitoring tools may also have to be considered in relation to the broader context of students, for in some cases the tools we presented in this chapter may not be compatible with the ways students in specific contexts learn. Based on the results from our previous work (Al-Gallaf and Hayes, 2012a and 2012b), we concluded that the tools presented in this chapter are necessary to successfully run the online programme at our university. However, we recommend that other instructors assess whether they are also necessary in their own contexts before a decision about their implementation is made.

References Al-Gallaf, A and Hayes, A (2012a) Multimedia-supported Learning and Students’ Skills and Attitude towards Subjects outside Core Areas. In: Global Science and Technology Forum (GSTF) Education and eLearning (EeL 2012)2nd Annual International Conference Proceedings, 17-18 September 2012, Bali, Indonesia (online). Al-Gallaf, A and Hayes, A (2012b) Developing, Integrating and Delivering e-Learning Solutions in the Undergraduate Medical Curriculum. In: Global Science and Technology Forum (GSTF) Education and eLearning (EeL 2012)2nd Annual International Conference Proceedings, 17-18 September 2012, Bali, Indonesia, pp. 62-6 Brown, C. Czerniewicz, L. (2008). In student use of ICTs in higher education in South Africa. In P. A. van Brakel (Ed.) In: Proceedings of the 10th annual conference of World Wide Web applications. 3-5 September, Cape Town, South Africa: Cape Peninsula University of Technology. Carr-Hellman, A. Duchastel, P. (2000). The Ideal Online Course. British Journal of Educational Technology, 31 (3), 229-241. Cavanaugh, T. Lamkin, M. Hu, H. (2012). Using Generalized checklist to improve student assessment submission times in an online course. Journal of Asynchronous Learning Networks, 16 (4), 39-44. Fallo, C. Brown, S. (2002). E-Learning Standards, New York: St.Lucie Press. Jones, C. Ramanau, R. Cross, S. Healing, G. (2010). Net Generation of Digital Natives: Is there a distinct new generation entering univeristy? Computers and Education, 54, 722-732. Klemm, W. R. (1998). Eight ways to get students more engaged in online conferences. The Higher Education Journal, 26 (1), 62-64. Ko, S. M. Kua, E.H. Fones, C. S. L. (1999) Stress and the Undergraduates” SMJ. Available from:

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http://www.sma.org.sg/smj/4010/articles/4010a2.htm Kvavik, R. (2005). Convenience, communications, and control: How students use technology. In D. G. Oblinger, & J. L. Oblinger (Eds.), Educating the net generation [e-book] Available from: http://www.educause.edu/ir/library/pdf/pub7101.pdf, Accessed on 3 Feb 2013. Margaryan, A. Littlejohn, A. Vojt. G. (2011). Are digital natives a myth or reality? University students’ use of digital technologies. Computers and Education, 56(2), 429-440. Ngai, E. W. T. Lok, C.K. Ng, M.W.E W. Wong, Y. K. (2005). Collaborative project across three Hong Kong universities: A case study in ecommerce education. Journal of Information System Education, 16(1), 109-116. Palloff, R. Pratt, K. (1999). Building Learning Communities in Cyberspace: Effective Strategies for the online classroom, San Francisco: Jossey-Bass. Paulsen, M.F. (2003) Online education and learning management systems: global e-learning in a Scandinavian perspective. Oslo: NKI Gorlaget. Shank, P. (2007). The Online Learning Idea Book: 95 Proven Ways to Enhance Technology-Based and Blended Learning, San Francisco: John Wiley & Sons. Thomas, J. W. (2000). A review of research on project-based learning, California: The Autodesk Foundation. Tiwari, A. Lai, P. So, M. Yuen, K. (2006). A comparison of the effects of problem-based learning and lecturing on the development of students' critical thinking. Medical Education, 40, 547–554. Twenge, J.M. (2009). Genarational changes and their impact in the classroom: teaching Generation Me. Medical Education, 43, 398-405.

CONTRIBUTORS

Amal Al-Gallaf graduated in 1997 and has worked in English language teaching since then on both governmental and private educational sectors. Amal is also specialized in the area of educating children with special educational need like gifted and talented or children with learning difficulties and disabilities since 2005. Currently, Amal works for the Language and Culture Unit at the Royal College of Surgeons in Ireland – Medical University of Bahrain as an English language lecturer and History of Bahrain Lecturer & Coordinator. Her research interests lie in leadership and management in higher education, policy development and internationalization of curriculum. Amal is interested in researching and developing solutions to improve, create enjoyment and facilitate learning through the use of technology. One of her preferred sub-areas is the quality assurance of teaching at higher education levels. Amal has always been engaged in developing courses to ensure students’ full interaction and effective learning through modern usage of information technology and interactive learning activities and snacks. Dr. Hamad Shabieb Aldosari is an associate professor of Applied Linguistics (Discourse analysis) and Chairman of the English Department, College of Languages & Translation, King Khalid University, Abha. His major teaching interests are reading and international language tests. His research interests include the investigation of the relationship between culture and language learning, and e-learning in relation to learning and teaching effects, motivational and attitudinal effects. Professor Liz Bacon BSc, PhD, CEng, CSci, CITP, FBCS, FHEA, MACM is President of the BCS, The Chartered Institute for IT, and a Deputy Pro-Vice-Chancellor at the University of Greenwich in London, with a University wide remit leading the development of technology enhanced learning. She is a past Chair of the BCS Academy of Computing, and the CPHC (Council of Professors and Heads of Computing) national committee. Liz is a Professor of Software Engineering with over a hundred publications and a Co-Director of the eCentre research group. She is an experienced journal and conference reviewer, editorial board member, and PhD supervisor, and has been involved in several EU research projects,

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including being Principal Investigator and Project Coordinator for two EU FP7 projects in the past four years. She is an experienced systems designer and developer and has applied her research in technology enhanced learning, software engineering, artificial intelligence and cyberterrorism to a range of application areas such as crisis management and health. Within technology enhanced learning her research has focused on the following areas: smart games-based learning environments; metacognition and learning strategies; and adaptable, adaptive and personalised systems. Liz has been involved in many professional activities during her career which include working with eskills UK, the Science Council, Parliamentary IT Committee (PITCOM), EQANIE (European Quality Assurance Network for Informatics Education), the National HE STEM programme, EKKA (Estonian Quality Assurance Agency), and the University of Cambridge as an ICT Thought Leader for their International Examinations. She also researches, publishes, and is a regular international speaker, on the supply and demand of e-skills to the IT industry. Dr. Vimala Balakrishnan received her PhD in the field of Ergonomics in 2009 from Multimedia University, Malaysia. Both her Masters and Bachelor degrees were from University of Science, Malaysia. She is currently affiliated with the Faculty of Computer Science and Information Technology, University of Malaya as a Senior Lecturer. Most of her research works are in the field of data engineering, opinion mining, information retrieval and social media. She has published various articles in these fields over the past three years. Dr Balakrishnan is also a member of the Medical Research Support (Medicres) group, Global Science and Technology Forum and International Association of Computer Science and Information Technology. Dr. Chris Boesch is an Assistant Professor of Information Systems (Education) at Singapore Management University. Chris has used Tournament-based Teaching and Team-based Learning to teach Java, Javascript, Python, R, Mapreduce, Cloud Computing, and Big Data Analytics to undergraduates, master’s students, and working professionals in Singapore. Chris earned his undergraduate degree in Electrical Engineering from Auburn University and his PhD in Computer Science from Nova Southeastern University. Chris is an author on sixteen filed US patents and a co-author of several papers including the 2011 Case Study on Using a Programming Practice Tool for Evaluating University Applicants and 2012 Tournament-based Teaching both winning Best

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Paper honors at the Computer Science Education: Innovation and Technology Conference. Dr. Sandra Boesch is the founder of Pivotal Expert and creator of SingPath.com. Sandra has helped thousands around the world to practice software languages by launching an online tutoring platform to provide real-time, personalized feedback. Sandra has led and co-led various courses in and around Asia ranging from introductory programming courses to large scale enterprise integration. Sandra earned a Bachelors in Communications from The University of Alabama at Birmingham, an MBA from Mary-Hardin Baylor, and a PhD in Information Systems from Nova Southeastern University. Roslyn Foskey is an adjunct lecturer in the School of Education at the University of New England. Her research interests include adopting creative approaches to broaden inclusion in adult learning, including older adult learners. Dr. Antonio Víctor Martín García is a Lecturer in the Department of Theory and History of Education (Faculty of Education) in the University of Salamanca, Spain. He is a member of the Research Group of Excellence: Processes, Spaces and Educational Practice and of the editorial boards of several scientific journals as well as Deputy Director of EFORA: Journal of Training and Adult Education, He was Associate Dean of the Faculty of Education (2004-2008) and coordinator of the PhD program in Adult Education of the University of Salamanca. He is the author of several books and papers about training processes in virtual spaces, Educational Gerontology, Adult Education and Social Pedagogy. He currently directs the Master "Advanced Studies of Education in the Global Society". Natália Fernandes Gomes is an Assistant Professor at Guarda Polytechnic Institute, Portugal, in the Department of Informatics. She graduated in Computer Engineering (1998), from the Guarda Polytechnic Institute, and in Information Systems (2004) from the University of Oporto. Presently, she is preparing her Ph.D. at the University of Salamanca in the Department of Theory and History of Education at the Faculty of Education. She has several publications related to the Information Society and learning with educational technologies. Her research interests are innovating teaching strategies and the uses of social media in learning environments.

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Dr. Nicole Green is a senior lecturer in the Faculty of Education, and program coordinator for early childhood education at the University of Southern Queensland. Her research interests include children’s active and authentic participation in research, space and place, enacting teacher education pedagogy, and alternative discourses and theoretical frameworks for curriculum and assessment. Dr. Aneta Hayes graduated in 2005 and has worked in education and language teaching since then. Aneta is currently working for the Language and Culture Unit at the Royal College of Surgeons in Ireland – Medical University of Bahrain. Her research interests lie in teaching and learning in higher education, as well as policy improvement and pedagogical expertise. Aneta is interested in researching socio-cultural factors affecting students' learning experience and how this could be used in assessment and curriculum development. One of her favourite sub-areas is the relationship between the foreign language and disciplinary knowledge and levels of literacy in subject areas of language minority students. Aneta has also engaged in work related to the use of modern technology in HE to maximize students’ learning. Dr. Huck-Soo Loo is an Associate Professor in Universiti Teknologi MARA (UiTM). He has 12 years industrial experience and 23 years’ experience in teaching management and engineering subjects. He is currently supervising several postgraduate students, and has published books and international refereed journal papers. He received several national research grants, and won several local, national and international awards including IID Gold Awards (Malaysia), ITEX (International Invention, Innovation & Technology Exhibition) Gold Medal, Best Invention Award (by CIDB Malaysia), and Brussels Eureka Gold Medal (in Europe). He has filed two patents, and is currently in preparation of filing another. Professor Lachlan MacKinnon BSc, PhD, FBCS, CITP, MIEEE, MACM, MAACE is Professor of Computing Science (Strategic Development), Head of the Department of Smart Systems Technology, and Head of the Department of Creative Digital Technologies, in the School of Computing & Mathematical Sciences, University of Greenwich, U.K. He is also Visiting Professor of Information and Knowledge Engineering at the University of Abertay Dundee, U.K. (where he was formerly Head of the School of Computing & Creative Technologies), and Visiting Professor of Games & Multimedia Technology at Buskerud

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University College, Kongsberg, Norway. Professor MacKinnon is Immediate Past Chair of the UK National Committee of the Council of Professors and Heads of Computing (CPHC), and Chair of the Education Committee of the BCS Academy of Computing. He is Chair of the Executive Committee of the British National Conference on Databases, member of a number of national committees on Cybersecurity and Secure Systems, and a member of the UK National Committee of the British Human Computer Interaction Group. His research interests are in computing policy, information and knowledge engineering, smart systems, games and creative technologies, eHealth and eLearning, and computer security. Dr. Mohamed Amin Mekheimer is an assistant professor of Applied Linguistics (TESOL-TEFL education) at the College of Languages & Translation, English Department, King Khalid University. His major academic interests include computer-assisted language instruction research and practice, teaching, researching, and practicing translation, writing research, and researching culture in language teaching and learning. He translated 17 books in different disciplines of knowledge to several publishers and bodies, e.g., UNESCO, World Bank, and University Book House. Dr. Iain Murray received his B.Eng (Hons) in Computer Systems Engineering in 1998 and his PhD titled “Instructional eLearning technologies for the vision impaired” in 2008 both at Curtin University. He has worked in the field of assistive technology for more than 25 years both as a practitioner and researcher. Currently employed as a senior lecturer in the Department of Electrical and Computer Engineering, his research interest include learning environments for people with vision impairment, embedded sensors in health applications and assistive technology. He founded the “Cisco Academy for the Vision Impaired” in 2002 to deliver ICT training to vision impaired people globally. He is a Fellow of the Australian Computer Society and a member of the IEEE. Azadeh Nazemi received her B.S. degree in Computer Hardware Engineering from Shiraz University, Iran. She started Master by Research and Ph.D. in September 2010 and 2012 in Curtin University, WA, Australia respectively. She was awarded a Curtin University Postgraduate Scholarship (CUPS) and an Australian Postgraduate Award (APA) scholarship in 2012, respectively. She is presently working towards a Ph.D. degree in Computer engineering at Curtin University, Perth,

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Australia. Her research area is Assistive Technology and specifically she is working to design Complete Reading System for vision impaired. On November 2012 her research paper has been awarded “Best Student Paper” in 3rd Annual International Conference on Computer Science Education Innovation & Technology (CSEIT 2012) in Singapore. She has nine years of industry experience as an engineer working in consultant companies. Professor John M. Peters is a Professor of Educational Psychology and Research in the Department of Educational Psychology and Counseling, as well as Faculty Scholar and Director, Tennessee Teaching and Learning Center Institute for Reflective Practice. In his role with the Institute, he conducts faculty inquiry groups and professional continuing education workshops in the area of reflective practice for Rule 31 Mediators as well as for the Tennessee Department of Children’s Services. He has been a member of the UTK faculty since 1970, after serving as an assistant professor at North Carolina State University during the three previous years. He received visiting appointments at Cornell University, the University of Technology Sydney, the University of British Columbia, N.C. State University, and the Royal Melbourne Institute of Technology. Dr. Peters has lectured and led seminars at more than 65 colleges and universities around the world, published or presented over 180 conference papers, articles, chapters, and four books. He received numerous awards for teaching, service, and outreach, including the Chancellor’s Outstanding Teaching Award, the L. R. Hesler Award, the Chancellor’s Outstanding Academic Outreach Award, the Association of Continuing Higher Education outstanding faculty award, and a leadership award from the Tennessee Association for Continuing and Higher Education. He was inducted into the International Adult and Continuing Education Hall of Fame in 1997 and currently serves as incoming Chair of its Board of Directors. Dr Anton Ravindran, is currently the CEO and Founder of RapidStart; President of Global Science and Technology Forum (GSTF); Adjunct Professor, Dept of Management, Birla Institute of Technology & Science, Pilani – Dubai Campus and Adjunct Professor at the Faculty of Information Systems at Bina Nusantara University, Jakarta, Indonesia. He was a visiting Prof and Researcher at the Institute for Research in Applicable Computing (IRAC) at the University of Bedfordshire. He is currently the Managing Editor of Journal on Computing (JoC) and Global Business Review (GBR) published quarterly by GSTF in print as well

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Contributors

hosted and published on Springer’s Open Access Platform. He is the author and co-editor of the book “Business Review: Advanced Applications as well as another forthcoming book titled “The ICT Age”. In the industry, he has worked for several major MNCs in technical and management capacities including Computer Associates, Sun Microsystems, Oracle and Singalab (former IBM & NCB JV). He founded and spearheaded 4 IT companies and has won several entrepreneurship accolades and awards. He has been part of various professional bodies and committees including National Internet Advisory Committee (Singapore), Vice Chair Electric, Electronic and Allied Industry Group (EEAI of SMF), Council Member (BCS) Singapore Computer Society and President, Singapore Education International (SEI) amongst others. He holds a BSc (Texas), an MBA (Texas), and a Doctorate degree in IT Management from the University of South Australia, having been awarded the International IT Management Academic Merit Scholarship. He is a fellow of the British Computer Society and a Chartered Engineer (UK). Dr. Maria Jose Hernández Serrano is Assistant Lecturer in the Department of Theory and History of Education (Faculty of Education) at the University of Salamanca, Spain. She graduated in Psycho-Pedagogy (2003) from the University of Salamanca and earned a European Ph.D. (2009) with the University of Manchester. She teaches courses on strategies for information seeking and lifelong learning. She is author of several publications related to learning with educational technologies in the areas of promotion of creative and critical uses of digital technologies and social media, innovating teaching strategies and informal learning through technologies. Mohammad Sadegh Sharifirad, a PhD candidate of organizational behavior at Ferdowsi Mashhad is the author of several journal and conference papers about leadership, innovation, and organizational learning. He is the winner of the best paper award in the 10th International Conference of the Academy of HRD (Asia Chapter). Since he has been involved in teaching for 10 years, using the subjects used in management in education is of great interest to him. Sima Sharifirad started her master in 2013 and has occupied an active role at the department of computer science in the Amir Kabir University of Technology (Tehran Polytechnic).During her BSc she did several conference papers about learning, education and e-learning. Since she has

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been involved in teaching for 5 years, her main interest is education, learning and the impact of new trends and technologies on learning process. Dr. Amy Liddell Skinner, is Associate Professor of Rehabilitation Counseling at the University of Tennessee. She is a Certified Rehabilitation Counselor (CRC), a Licensed Professional Counselor - Mental Health Service Provider (LPC- State of Tennessee), and a National Certified Counselor (NCC). Dr. Skinner has been active in the rehabilitation counseling field for over 17 years, promoting education and advocacy for expanding community and employment opportunities for people with disabilities. She has been teaching and working with students in the graduate program for 13 years. She is a past-president of the Volunteer State Rehabilitation Association and the Southeast Region National Rehabilitation Association. She serves Board of Directors of Goodwill Industries of Knoxville, Inc. (past-Chair), on the Board of the disAbility Resource Center and served on the (Knoxville) Mayor's Council on Disability Issues. She is the winner of the 2011 YWCA – Knoxville’s Tribute to Women Award for Equality. She serves on the editorial board of three rehabilitation counseling journals. Dr. Phil Stocks received his Ph.D. from the University of Queensland in the area of applying formal methods to software testing. He then took post-doctoral research positions at Siemens Corporate Research, Princeton, and later Rutgers University, working on software testing and programming languages. After a stint doing contract programming at Westdeutsche Landesbank in London, he took a lectureship position at Bond University in Australia where he taught programming at all levels and continued research in programming languages and computer science education. Dr. Brenda Wolodko is a senior lecturer in the School of Education and a member of the ECE team at the University of New England. Her research interests include teacher education, teaching through assessment and mathematics education. Dr. Renee Chew Shiun Yee was born in Negeri Sembilan, Malaysia in 1978. She received the B.A. in Multimedia Studies from the University of South Australia, Australia, in 2003, the MoMM in e-learning technologies from the Multimedia University, Malaysia in 2007, and the Ph.D. degree in Education from Queensland University of Technology, Australia, in

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2011. From 2004 to 2010, she was a lecturer with the INTI International University. Since 2011, she has been a Senior Lecturer cum researcher with the Faculty of Science, Technology, Engineering, and Mathematics (IT Division). She is actively involved in the Student Learning Committee, Research Committee, and Innovative Learning Committee of INTI IU. She teaches and supervises undergraduate and postgraduate students in the area of HCI and Multimedia with the focus on development of games and teachware, and implementation of management systems used in the university. Her area of research includes e-learning, cultural diversity in learning, student-centered learning, educational psychology, and instructional design. Dr. Chew was a recipient of the QUT International Doctoral Scholarship in 2007, the QUT Postgraduate Research Award in 2009, and the Golden Key Honour Society Excellence Scholastic Award in 2003.