Theorising and Implementing Mobile Learning: Using the iPAC Framework to Inform Research and Teaching Practice [1st ed.] 9789811582769, 9789811582776

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Matthew Kearney Kevin Burden Sandy Schuck

Theorising and Implementing Mobile Learning Using the iPAC Framework to Inform Research and Teaching Practice

Theorising and Implementing Mobile Learning

Matthew Kearney Kevin Burden Sandy Schuck •

•

Theorising and Implementing Mobile Learning Using the iPAC Framework to Inform Research and Teaching Practice

123

Matthew Kearney Faculty of Arts and Social Sciences University of Technology Sydney Broadway, NSW, Australia

Kevin Burden Faculty of Arts, Culture and Education University of Hull Yorkshire, UK

Sandy Schuck Faculty of Arts and Social Sciences University of Technology Sydney Broadway, NSW, Australia

ISBN 978-981-15-8276-9 ISBN 978-981-15-8277-6 https://doi.org/10.1007/978-981-15-8277-6

(eBook)

© Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Foreword

How can mobile learning enhance our diverse students’ learning experiences? And how do we prepare teachers for their technology-rich students? The simplicity of these questions belies the complexity of the answers and the many adaptations that will be required of educators and other stakeholders, including learners and their families. Given the ubiquity of mobile devices and their rapid deployment to address current issues, this scholarly text is timely. For example, what types of online learning are evolving during the coronavirus pandemic and how can m-learning be exploited, mediated and enhanced by the iPAC Framework? This book provides well-researched and theorised illustrations of how m-learning pedagogies can expand the learning experiences of school students and future teachers. The behaviour of teachers is pivotal as the central ‘keystone species’ in our educational ecosystems (Davis, 2019), so additional frameworks that can be deployed to inform pedagogy are particularly welcome. Central to the text is a succinct theoretical model called the iPAC Framework that highlights distinctive socio-cultural features of mobile pedagogies. The three main pedagogical dimensions of personalisation, authenticity and collaboration have remained robust over a decade of research with refinements to their sub-dimensions that improve clarity and usability. Since 2008, the authors and their collaborators in Australia, Europe and elsewhere have researched mobile learning using a range of relevant methodologies, including design-based research, case study methods and a critical systematic review of relevant literature. The iPAC Framework is also set within a review of theoretical models, including the ‘third space’ theory as originally proposed in the wider literature of cultural theory. The blurring of boundaries and vigorous hybridity of the behaviour that can evolve when released from the constraints of tradition underlies the future potential for this perspective on mobile learning. At the time of writing in 2020, both the authors and I recognise that we are experiencing an unprecedented change in schools and tertiary learning due to the coronavirus pandemic. Despite many previous reservations about mobile learning, most educational systems will be keen to promote online learning for many of their v

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Foreword

students. Only this week Aotearoa New Zealand has moved quickly nationwide to implement remote learning from home for primary and secondary school students; online learning with mobile devices is a preferred mode that blends with other activities. Worldwide, school and tertiary students will also be learning with mobile devices in contexts where boundaries are blurring, not least between home and campus. The preparation of future teachers is also challenged by the limited opening of school campuses. My hope is that the iPAC Framework will strategically inform this rapid evolution of practices so as to enable educators, parents and other stakeholders to make the most of the significant investments made to mitigate the impacts of this disaster. The authors recognise that pedagogical changes and related professional development can be speeded by iPAC mediation in the form of a mobile learning toolkit, courses and case studies in addition to this seminal book. Kearney, Burden and Schuck are to be congratulated for having made a selection available, along with a portal (https://www.ipacmobilepedagogy.com/) ‘where research and case studies can be reported and publicised’. Thus, we are invited to contribute and join in the future of iPAC and that collaboration can better inform the complex and rapid evolution of education for years to come. Niki Davis Emerita/Adjunct Professor of e-Learning UC Child Well-being Research Institute University of Canterbury Christchurch, New Zealand

Reference Davis, N. E. (2019). Digital technologies and change in education. The Arena Framework. New York, USA: Routledge.

Acknowledgements

We would like to acknowledge a number of people who supported our writing of this book and contributed to its production. Terry Fitzgerald worked tirelessly at proofreading, checking references and formatting the chapters. University of Hull doctoral student, Rebecca Kelly, provided insights and references that were very useful in Chap. 4. Professor Didar Zowghi and Dr. Muneera Bano provided valuable input through a conference paper on which Chap. 10 was partially based. Associate Professor Paul Burke made a significant contribution to the article on which Chap. 11 was based. Associate Professor Burke and Professor Peter Aubusson were guest authors for Chap. 12 and provided invaluable insights and research data to the chapter. We would like to thank the teams of three major projects that were influential in the ongoing development of the iPAC Framework that is the focus of this book. In particular, we would like to acknowledge the significant role played by Professor Peter Aubusson in his leadership of the Optimising Teaching and Learning with Mobile-Intensive Pedagogies project. His insights and contributions to the development of the Framework cannot be overstated. Teachers and project participants in the Mobilising and Transforming Teacher Educators’ Pedagogies (MTTEP), Developing and Evaluating Innovative Mobile Pedagogies (DEIMP) and Optimising Teaching and Learning projects provided valuable feedback which informed a number of chapters. Thank you to all the participants in our research and users of our Framework. We cannot name you but your thoughts, opinions and insights have influenced the discussion in this book.

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Contents

1

Introducing This Book . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Our iPAC Framework: A Decade of Research . . 1.3 An Outline of the Book’s Structure and Contents 1.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part I 2

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The Current Context

The Digital Landscape of Education . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Current Context of Digital Technologies in Education . . . . . 2.3 The Issues, Challenges and Benefits of Technology Use in the Current Educational Landscape . . . . . . . . . . . . . 2.4 Future Trends in Use of Digital Technologies in Education . 2.5 Implications for Teacher Education and School Education . . 2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Mobile Learning and Ubiquitous Learning . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Drivers of M-Learning . . . . . . . . . . . . . . . . . 3.3 M-Learning and TEL . . . . . . . . . . . . . . . . . . 3.4 Trends in M-Learning: Our Research Projects 3.5 U-Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Implications for Teaching and Learning . . . . . 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Seamless Learning—Mobile Learning in the Third Space 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Third Space Constructs . . . . . . . . . . . . . . . . . . . . . . . 4.3 The Third Space in Educational Settings . . . . . . . . . . 4.4 Third Space Learning with Digital Technologies . . . . . 4.5 M-Learning in the Third Space . . . . . . . . . . . . . . . . . 4.6 Implications of M-Learning in the Third Space for Teaching and Learning . . . . . . . . . . . . . . . . . . . . 4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Frameworks for Understanding Mobile Learning

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Rationale for a Mobile Pedagogical Framework . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Development of the Mobile Pedagogical Framework . 5.3 Sub-dimensions of the MPF . . . . . . . . . . . . . . . . . . 5.3.1 Personalisation . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Authenticity . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Collaboration . . . . . . . . . . . . . . . . . . . . . . . 5.4 Next Steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Unpacking Authenticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Why Is Authentic Learning Important? . . . . . . . . . . . . . 6.3 Defining Authenticity . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Authentic Learning and Mobile Technologies . . . . . . . . 6.5 Unpacking Authentic Learning . . . . . . . . . . . . . . . . . . 6.5.1 Participatory Contexts . . . . . . . . . . . . . . . . . . . 6.5.2 Simulated Contexts . . . . . . . . . . . . . . . . . . . . . 6.5.3 Hybrid Contexts . . . . . . . . . . . . . . . . . . . . . . . 6.6 Is Authentic Mobile Learning Predefined or Emergent? . 6.6.1 Personal Commitment of Learners . . . . . . . . . . 6.7 Discussion and Implications . . . . . . . . . . . . . . . . . . . . 6.7.1 How Does the Model Work? . . . . . . . . . . . . . 6.7.2 Returning to Research Questions . . . . . . . . . . . 6.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Evolution of the iPAC Mobile Pedagogical Framework . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Revisiting the Original Mobile Pedagogical Framework 7.3 Evolution of the iPAC 1.0 Framework . . . . . . . . . . . . .

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7.4 Appropriation of the MPF and iPAC 1.0 Framework 7.5 Evolution of the iPAC 2.0 Framework . . . . . . . . . . . 7.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

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Differentiating Mobile Learning Frameworks . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 What Are Theoretical Frameworks and Models? . . . . . . . . 8.3 Ways of Conceptualising Mobile Learning (Pre-2012) . . . 8.4 Mobile Learning Frameworks and Models (Post-2012) . . . 8.4.1 The M-COPE Framework . . . . . . . . . . . . . . . . . . 8.4.2 The Mobile Learning Ecology Framework (2015) 8.4.3 A Framework for Designing Transformative Mobile Learning . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Part III 9

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Tools for Investigating Mobile Learning

The Development and Use of the Mobile Learning Toolkit . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 The Mobilising and Transforming Teacher Educators’ Pedagogies (MTTEP) Project . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Toolkits for Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Principles for Developing a Pedagogical Toolkit . . . . . . . . . . 9.5 How Does the Mobile Learning Toolkit Work? . . . . . . . . . . 9.6 Initial Reception by Users and Subsequent Modifications . . . 9.7 Impact on Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.7.1 Objective 1: Evidence that Teachers and Teacher Educators Now Underpin Their Use of M-Learning with Sound Theoretical Principles, Derived from the Toolkit . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.7.2 Objective 2: Teachers Find the Tools and Instruments Provided by the Toolkit Help Them to Be More Effective in Designing Lessons that Exploit the Unique Affordances of M-Learning . . . . . . . . . . . . . 9.7.3 Objective 3: Teachers and Teacher Educators Use a Wider Range of M-Learning Pedagogies . . . . 9.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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10 Evaluating Education Apps from a Sociocultural Perspective . . . . . 129 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 10.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

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10.2.1 Current Challenges with Evaluating Education Apps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.2 The Education App Landscape . . . . . . . . . . . . . . 10.2.3 Rubrics for Evaluating Apps . . . . . . . . . . . . . . . . 10.2.4 Use of Sentiment Analysis for Evaluating Apps . . 10.3 Initiative One: Development of an Online App Evaluation Rubric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.1 Current Structure . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Initiative Two: Use of Feature-Based Sentiment Analysis for App Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.1 Study Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6 Contributions and Future Directions . . . . . . . . . . . . . . . . . 10.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix 1: EVALUATION RUBRIC: Education Apps . . . . . . . Appendix 2: EVALUATION RUBRIC: Notes/Sample Features of Apps (to Assist with Rubric Responses) . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 iPAC Survey Development: Capturing Mobile Pedagogical Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 The iPAC Framework . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Earlier Versions of iPAC Surveys . . . . . . . . . . . . . . . . 11.4.1 Investigating Mobile Pedagogies Adopted in a Specific Task . . . . . . . . . . . . . . . . . . . . . . 11.4.2 Investigating Mobile Pedagogical Approaches Typically Adopted in Tasks . . . . . . . . . . . . . . 11.5 Scale Development . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5.1 Classification Exercise . . . . . . . . . . . . . . . . . . 11.5.2 Testing the iPAC Scale . . . . . . . . . . . . . . . . . . 11.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6.1 Personalisation . . . . . . . . . . . . . . . . . . . . . . . . 11.6.2 Authenticity . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6.3 Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . 11.6.4 Overall M-Learning Experiences . . . . . . . . . . . 11.6.5 Discriminant Validity . . . . . . . . . . . . . . . . . . . 11.6.6 Differences Across Survey Versions . . . . . . . . 11.6.7 Structural Model of M-Learning Practice and Experience . . . . . . . . . . . . . . . . . . . . . . . .

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11.7 Current Use of the Surveys and Future Directions 11.7.1 Current Online Surveys . . . . . . . . . . . . . 11.7.2 Plans for Further Survey Development . . 11.7.3 Future Research Directions . . . . . . . . . . . 11.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix: Final Validated iPAC Scales . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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12 Mobile Pedagogies in Mathematics and Science Education . . 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.1 Mobile-Intensive Pedagogies . . . . . . . . . . . . . . . 12.2.2 Mobile Pedagogies in Mathematics and Science . 12.3 Study Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3.1 Development of the Survey . . . . . . . . . . . . . . . . 12.3.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3.3 Demographic Data . . . . . . . . . . . . . . . . . . . . . . 12.4 Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4.1 Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4.2 Personalisation . . . . . . . . . . . . . . . . . . . . . . . . . 12.4.3 Authenticity . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4.4 Location of M-Learning Activities . . . . . . . . . . . 12.4.5 Overarching Approaches . . . . . . . . . . . . . . . . . . 12.4.6 Summary of Findings . . . . . . . . . . . . . . . . . . . . 12.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Part IV

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Case Studies and Projects

13 Use of the iPAC Framework in Schools and Teacher Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 The Nature of Impact . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3 The Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4 The Impact of the iPAC Framework in Schools and in Teacher Education . . . . . . . . . . . . . . . . . . . . . . . 13.4.1 Level 1: Raising Awareness and Understanding About Mobile Learning . . . . . . . . . . . . . . . . . . 13.4.2 Level 2: Changes in Attitudes and Thinking About Mobile Learning . . . . . . . . . . . . . . . . . . 13.4.3 Level 3: Changes in Capacity and Preparedness to Use Technology in the Classroom . . . . . . . . . 13.4.4 Level 4: Changes in Behaviours and Practices . .

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Contents

13.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 14 Innovative Mobile Pedagogies with School-Aged Learners . 14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.1 Innovation and Disruption . . . . . . . . . . . . . . . . 14.2.2 Innovation with Mobile Technologies . . . . . . . 14.3 Investigating Mobile Pedagogical Innovation and Disruption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.4 Emerging Innovation Principles and Links to the iPAC Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.5 Illustrative Examples . . . . . . . . . . . . . . . . . . . . . . . . . . 14.6 Implications for School and Teacher Education . . . . . . 14.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part V

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Future Possibilities for Mobile Learning

15 Considering iPAC in a Mobile-Intensive Future . . . . . . . . . 15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2 The Impact and Achievements of the iPAC Framework to Date . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.1 Who Might Benefit from iPAC? . . . . . . . . . . . 15.3 Future Research Agendas . . . . . . . . . . . . . . . . . . . . . . 15.3.1 Mediation . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.4 Applicability and Usefulness of the Framework . . . . . . 15.5 Formal Versus Informal Settings . . . . . . . . . . . . . . . . . 15.6 Looking Forward: What Next? . . . . . . . . . . . . . . . . . . 15.6.1 iPAC in a Post-Pandemic World . . . . . . . . . . . 15.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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242 242 244 244 246 247 248 249 251 252

About the Authors

Dr. Matthew Kearney is an Associate Professor of Educational Technology in the Faculty of Arts and Social Sciences (FASS) at the University of Technology Sydney (UTS), Australia. For over 20 years, his research projects in the field of technology-enhanced learning have investigated how new and emerging learning technologies can be used in pedagogically transformational ways, particularly in school and teacher education contexts. He has worked on a number of interdisciplinary teams with researchers from marketing, education and software engineering, and recent publications are co-authored with scholars from these fields. He has led five funded projects and has contributed as co-researcher to numerous other funded projects. Outputs include 4 major project reports and over 60 refereed publications. He is currently leader of the Initial Teacher Education cluster in the School of International Studies & Education at UTS and serves on the editorial team for the Australasian Journal of Educational Technology. He was recipient of the 2019 FASS Award for Research Impact and was part of an international team recognised for its impact in the development of the iPAC Framework and associated materials (2019 e-Learning Excellence Award). Dr. Kevin Burden is Professor of Educational Technology in the Faculty of Arts, Cultures and Education at the University of Hull, UK. Over the past 20 years, he has participated in and led many national and international technologies in education-related projects and initiatives and has secured funding in excess of £1.7m from external sources. His primary research focus and work over this period has focused on the professional development and learning of educators and the role that technology can play in mediating and supporting this learning. He has worked with a large number of national UK providers and government agencies, including NESTA, the New Opportunities Funding ICT for Teachers project, the Training and Development Agency (TDA, DfE), Becta, BBC, HEA and JISC. Many of these agencies commission Professor Burden and his research team to undertake research on their behalf, and he has considerable experience and expertise in managing large funding awards and research teams. He currently leads an Erasmus + project titled Developing and Evaluating Innovative Mobile Pedagogies (DEIMP) and recently xv

xvi

About the Authors

led an international team recognised for its impact in the development of the iPAC Framework and associated materials (2019 e-Learning Excellence Award). Dr. Sandy Schuck was Professor of Education in the Faculty of Arts and Social Sciences (FASS) at the University of Technology Sydney until her retirement in May 2020. She is currently Adjunct Professor of Education in FASS. She was Director of Research Training in the Faculty of Arts and Social Sciences at the University of Technology Sydney 2011–2018 and a founding Director of the STEM Education Futures Research Centre at UTS. Her research interests are all related to her interest in enhancing teacher practice and preparation. They include learning and teaching with new media, the development of mobile pedagogies, teaching and teacher education futures, beliefs and practices in mathematics education, teacher professional learning, and mentoring, retention and induction of early career teachers. She has authored or co-authored over 100 publications, including the co-authoring or co-editing of six scholarly academic books and numerous book chapters and journal articles in leading journals. She has been awarded over two million dollars in competitive research grants. She mentors early career researchers and collaborates extensively with colleagues in multi-disciplinary projects. She was awarded the inaugural Researcher Developer award in the University of Technology Sydney Excellence in Research Awards in 2010 and was part of an international team awarded for its impact in the development of the iPAC Framework and associated materials (2019 e-Learning Excellence Award).

List of Figures

Fig. 2.1 Fig. 5.1

Fig. 5.2

Fig. 5.3

Fig. 5.4 Fig. 5.5

Fig. 6.1 Fig. 6.2 Fig. 7.1

Fig. 7.2

Fig. 7.3 Fig. 7.4

The TPACK framework source http://tpack.org (Reproduced by permission of the publisher, © 2012 by tpack.org) . . . . . . A two-way relationship between the organisation of time-space and mobile learning experiences (socio-cultural perspective) (Kearney, Schuck, Burden, & Aubusson, 2012) . . . . . . . . . . . Use of a prototype framework to analyse one of our project teaching trials (Kearney, Schuck, Burden, & Aubusson, 2012) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Another prototype framework presented at a university teaching conference, 2009 (Kearney, Schuck, Burden, & Aubusson, 2012) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Penultimate framework presented at mLearn 2010 (Kearney, Schuck, & Burden, 2010) . . . . . . . . . . . . . . . . . . . . The 2012 Framework comprising three distinctive characteristics of mobile learning experiences, with sub-scales (Kearney, Schuck, Burden, & Aubusson, 2012) . . . . . . . . . . . A conceptual model of authentic mobile learning . . . . . . . . . . Authentic mobile learning examples . . . . . . . . . . . . . . . . . . . . The original representation of our Mobile Pedagogical Framework (MPF) comprising three distinctive features of mobile learning experiences (from Kearney et al., 2012, p. 8) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iPAC 1.0. An updated representation of the MPF, which became known as the ‘iPAC’ Framework (reprinted with permission from Burden & Kearney, 2018, p. 92) . . . . . . . . . Six continua were developed to help educators’ interpretation of each sub-dimension of the iPAC 1.0 Framework . . . . . . . . An early appropriation of the MPF by Bartlett-Bragg and Dellow (2012) for business education purposes . . . . . . . .

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Fig. 7.5

Fig. 7.6 Fig. 7.7 Fig. 8.1

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Fig. 9.3 Fig. 10.1

Fig. 10.2 Fig. 10.3 Fig. 11.1

Fig. 11.2 Fig. 11.3

Fig. 11.4

Fig. 11.5

Fig. 11.6 Fig. 11.7 Fig. 11.8 Fig. 12.1 Fig. 12.2

List of Figures

An appropriation of the MPF from Townsend (2017, p. 215, with permission). Juxtaposition of Aboriginal and Torres Strait Islander cultural philosophies and a pedagogic framework of mobile learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The circular representation of iPAC 1.0 by German MTTEP project members . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iPAC 2.0 Framework (reproduced with permission from Kearney et al., 2019, p. 754) . . . . . . . . . . . . . . . . . . . . . . . . . The FRAME framework: From Koole et al., (2018), p. 3 (Creative Commons licensed. See https://www.mdpi.com/ 2227-7102/8/3/114) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The updated iPAC Framework (Reproduced with permission from Kearney, Burke & Schuck, 2019, p.754) . . . . . . . . . . . . Visual polar charts of teacher and student results are generated in reports from the toolkit’s survey instruments (Reproduced with permission from Burden & Kearney, 2018, p. 93) . . . . . The collaboration items in the online rubric tool . . . . . . . . . . Screenshot of the personalisation items in the online rubric (See http://www.mobilelearningtoolkit.com/app-rubric1. html) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Word clouds relating to the three dimensions of the iPAC Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Feature-based sentiment analysis results . . . . . . . . . . . . . . . . . Current representation of the iPAC framework (reproduced with permission from Kearney, Burke & Schuck, 2019, p. 754) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structural model and estimates . . . . . . . . . . . . . . . . . . . . . . . . Screenshot of iPAC items from teacher survey (specific task). Authenticity dimension. Nominated year group and subject area is Year 7 English. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Screenshot of iPAC items from student survey (specific task). Authenticity dimension. Nominated subject area is English . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Screenshot of innovation items from teacher survey (specific task). Nominated year group and subject area is Year 7 English . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Screenshot of innovation items from student survey (specific task). Nominated subject area is English . . . . . . . . . . Sample screenshot of polar chart provided in the Teacher Report from (specific task) survey . . . . . . . . . . . . . . . . . . . . . Screenshot of sample Teacher Report from (specific task) survey, including an innovation score . . . . . . . . . . . . . . . . . . . Quality of Wi-Fi access at teachers’ schools . . . . . . . . . . . . . . Ownership of m-devices as reported by teachers . . . . . . . . . .

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List of Figures

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xix

Nominated cohorts by teacher participants . . . . . . . . . . . . . . . . . 189 Lesson observation tool developed as part of the European Tablet Teacher project in 2019 (Used with permission from Stoller, Hughes & Wadsworth, 2019, p. 18). . . . . . . . . . . . 213 Innovation Continuum—breakdown of final set of papers according to the level of innovation (Reproduced with permission from Burden, Kearney, Schuck & Hall, 2019, p. 92) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

Chapter 1

Introducing This Book

Abstract We introduce in this chapter our book on mobile learning, the iPAC Framework and its use by educators. The book presents a context for mobile learning and then introduces, outlines and develops a Mobile Pedagogical Framework, now known as the iPAC Framework. The Framework presents a socio-cultural approach to using mobile technologies for learning and also scaffolds teachers’ use of mobile devices with their students in mobile learning. It was developed during two research projects on mobile device usage for education and then applied in further projects, over a decade of sustained research. The Framework was modified according to user feedback and findings from these projects. This chapter describes these research projects and then presents an outline of the book, introducing each chapter and its place in the book. Keywords iPAC · Mobile learning · M-learning · Socio-cultural theory · Mobile pedagogical framework

1.1 Introduction This book introduces readers to a socio-cultural framework for mobile learning and pedagogy. A socio-cultural theoretical perspective suggests that learning is affected and modified by the tools used for learning, and that reciprocally the learning tools are modified by the ways in which they are used for learning. Central to our position here is the notion that learning is a situated, social endeavour, facilitated and developed through social interactions and conversations between people (Vygotsky, 1978), and mediated through tool use (Wertsch, 1991). The content of this book is presented from a socio-cultural perspective, and this theory underpins the mobile learning framework that is central to the development of this book. The framework, initially known as the Mobile Pedagogical Framework (MPF), was developed through a decade-long research program led by the authors. The book traces the development of the Framework into its current manifestation as the iPAC Framework. It considers the context of mobile learning to the present day, develops the theory underpinning the Framework and then goes on to consider how the Framework is used by teacher educators, teachers and other educational practitioners. It outlines the development © Springer Nature Singapore Pte Ltd. 2020 M. Kearney et al., Theorising and Implementing Mobile Learning, https://doi.org/10.1007/978-981-15-8277-6_1

1

2

1 Introducing This Book

of related resources for researchers and practitioners to facilitate their examination of mobile pedagogies. The use of mobile devices for learning is becoming a contested field. Experts in the mobile learning (or m-learning) field have espoused the benefits of m-learning for students, in particular, the access to experts, the ability to share with others and the autonomy afforded by these devices (Chee, Yahaya, Ibrahim, & Noor Hassan, 2017; Kearney, Schuck, Burden, & Aubusson, 2012; Schuck, Kearney, & Burden, 2017). These characteristics of m-learning are discussed in Chap. 3 of this book. The malleable nature of space and time is also a feature of m-learning environments that is valued by educators. A discussion on the ‘Third Space’, in which time and space are central features, is reported in Schuck et al. (2017) and developed further in Chap. 4. On the other side of the debate are those who see mobile devices as having numerous negative impacts on the well-being of students, including ‘phone addiction’, anxiety and cyberbullying. Often missing is the middle ground in these discussions, in which the benefits for learning are highlighted at the same time as the strategies that support students to manage the negative aspects and to self-regulate their use of devices. The focus in this book is not on the negative aspects of mobile use but on the research reporting the educative value of m-learning. As educators, rather than psychologists, we concentrate on investigating the ways in which m-learning can expand the learning experiences of students. We also look at how teacher educators and school teachers can develop pedagogical strategies encompassing m-learning, and how teaching itself has the potential to change, given the ubiquity of mobile devices. We express concern at the rise of bans of mobile devices in schools, because m-learning can be such a positive contribution to a student’s experience (Burden, Schuck, & Kearney, 2019c). The evidence we present in this book aims to encourage deeper consideration and critique of the pedagogical aspects of m-learning from a socio-cultural perspective. The above discussion is not to say that we reject the risks that have created the panic that leads to banning, but rather, we suggest that the experiences of students using mobile devices cannot be simply designated as good or bad. If used appropriately, these devices can enhance students’ learning. If used inappropriately, they have the potential to cause some harm. And so, one of the purposes of this book is to present evidence of how, with the support of our Framework, teachers’ pedagogies can be enhanced to benefit their students’ m-learning. For further discussion of the worth or dangers of student mobile device use, we refer to a dispassionate and evidence-based article by Turvey and Pachler (2018).

1.2 Our iPAC Framework: A Decade of Research The first iteration of the iPAC Framework was developed through the implementation and findings of two initial projects in which the authors were involved. The first project was called Mobagogy, which came about when a team of Australian teacher

1.2 Our iPAC Framework: A Decade of Research

3

educators was funded to develop their skills in mobile pedagogies (Schuck, 2015; Schuck, Aubusson, Kearney, & Burden, 2010), that is, pedagogies which include ways of teaching and learning that can only be done with mobile devices. The authors researched the implementation of this 18-month project and theorised what they saw happening in the project. During the same period, another project, titled A Bird in the Hand, was developed and funded by the Teacher Development Agency in the UK to support pre-service teachers to use mobile devices to enhance their teaching in schools. Using the findings of these two research projects, Kearney et al. (2012) developed the first iteration of the Mobile Pedagogical Framework (MPF). Chap. 5 of this book reprises the process. Once the Framework was developed, two small-scale funded research projects underpinned by the Framework were implemented. The first was a component of the Australian Teaching Teachers for the Future (TTF) project in 2012, investigating m-learning in mathematics teacher education (Kearney & Maher, 2013). The second project investigated mobile-intensive pedagogies more broadly in schools, using two case schools in Sydney, one primary and one secondary (Maher, Schuck, & Perry, 2017). The Framework was also applied in a series of major projects, two led by The University of Hull, UK, and one led by UTS in Australia, as shown in Table 1.1. An Erasmus + project called Mobilising and Transforming Teacher Educators’ Pedagogies (MTTEP, see http://www.mttep.eu/) was a 3-year research and teaching project, exploring the impact of m-learning on pedagogies and teacher education. It aimed to create a sustainable m-learning network and a toolkit for educators to use based on our Framework. A survey to help teachers identify how they were using the Framework in a particular task was developed, and an m-learning toolkit was developed. This toolkit is discussed in Chap. 9. The MTTEP project researched how the toolkit was used and how the pedagogies of teacher educators and teachers might have changed as a result of working with the Framework. Case studies from the MTTEP project illustrating how the Framework was used are discussed and presented in Chap. 13. Table 1.1 Authors’ funded projects relating to the iPAC framework (*denotes major projects) Title

Years

Lead institution

Mobagogy

2009–2010

UTS

A Bird in the Hand

2008

Hull

M-learning in Maths Teacher Education

2012

UTS

*Mobilising and Transforming Teacher Educators’ Pedagogies (MTTEP: mttep.eu)

2014–2017

Hull

Investigating mobile-intensive pedagogies in schools

2014–2015

UTS

*Optimising Teaching and Learning with Mobile-Intensive Pedagogies

2015–2019

UTS

*Developing and Evaluating Innovative Mobile Pedagogies (DEIMP: deimpeu.com)

2017–2020

Hull

4

1 Introducing This Book

During the period of implementation of the MTTEP project, the Framework underwent a few changes and became known as the iPAC Framework. These changes and amendments are detailed in Chap. 7. Another major project which used the iPAC Framework was funded by the Australian Research Council (ARC). Titled Optimising Teaching and Learning with Mobile-Intensive Pedagogies (subsequently abbreviated to Optimising Mobile Pedagogies), this project investigated how schools could enhance their students’ learning in mathematics and science through m-learning (Bano, Zowghi, Kearney, Schuck, & Aubusson, 2018). A scale was developed to research how teachers perceived their integration of iPAC in their typical m-learning tasks. Discussion of how mathematics and science teachers varied in their use of iPAC from teachers of other subjects is discussed in Chap. 12. A student survey was also developed. A final output of this project was a study of the four scales developed thus far and the evaluation of iPAC use in typical and specific m-learning activities for both teachers and students (see Kearney, Burke, & Schuck, 2019). The findings from this part of the study are expanded in Chap. 11. The most recent project which informed aspects of this book is the research and teaching project, Developing and Evaluating Innovative Mobile Pedagogies (DEIMP, see http://www.deimpeu.com/), which is in its final stages at the time of writing. The DEIMP project is an Erasmus + project. Its focus is on innovation and investigates how mobile pedagogies might be developed that show characteristics of innovative practice. In Chap. 14, we consider how the associated principles discussed in Burden, Kearney, Schuck, and Burke (2019b) align with iPAC dimensions. This section has outlined how research projects in which the authors engaged over the last decade have led to the development of the iPAC Framework, informed its use and subsequent amendments, and supported the development of tools to both assist in research on teacher use of m-learning and guide teachers’ pedagogical practices with mobile devices. The next section outlines the structure of the book.

1.3 An Outline of the Book’s Structure and Contents This book is divided into four parts, each focusing on a particular theme. The first part, which comprises Chaps. 2, 3 and 4, sets the context for the remainder of the book. It considers the role of technology in education and then focuses on mobile technologies and m-learning in particular. It examines the characteristics of time and space that are features of m-learning. Chapter 2 investigates the role of technology-enhanced learning. It considers how prepared teachers are to implement educational technologies in their pedagogies. It proposes benefits of technology-enhanced learning, noting the constraints and barriers that might operate. Chapter 3 then focuses on mobile technologies and m-learning. It describes the pedagogical affordances of mlearning and their potential contributions to students’ learning experiences. Chapter 4 deconstructs the notion of a third space for mobile learning and shows how this third

1.3 An Outline of the Book’s Structure and Contents

5

space bridges the binaries formerly articulated in concepts of virtual versus physical, classroom spaces versus home or social spaces, and formal learning versus informal learning. These three chapters set up the context for the remainder of the book, which focuses on m-learning and the use of the iPAC Framework by teachers, teacher educators and students. Part II, comprising Chaps. 5–8, moves into the theory-building concerning m-learning. The section articulates the development of the Mobile Pedagogical Framework (Chap. 5), outlines research on how an earlier version of the authenticity dimension of this Framework was challenged in teachers’ understandings of authenticity (Chap. 6) and explicates amendments to the Framework based on further research and reports its consequent naming as iPAC (Chap. 7). The contexts and contributions of other mobile frameworks that existed prior to and after the iPAC Framework are also discussed (Chap. 8). In the third part, we consider the tools developed for investigating m-learning using the iPAC Framework. These tools are covered in Chaps. 9–11. Chapter 9 outlines the Mobile Learning Toolkit developed in the MTTEP project, showing how it was used by participants in the project. Chapter 10 investigates two new initiatives for evaluating education apps that were developed in the MTTEP project, including a pioneering rubric that supports teacher educators’ and teachers’ pedagogies. Chapter 11 discusses the validation of four surveys, two for teachers and two for students, investigating the typical and specific m-learning tasks that teachers implemented with their students. The validated surveys serve as both research tools and scaffolds for teachers in the use of iPAC. The fourth part comprising Chaps. 12–14 provides the findings from the Optimising Mobile Pedagogies project, the MTTEP project and the DEIMP project. It outlines the findings on the use by stakeholders of the iPAC Framework and the accompanying surveys. Chapter 12 considers the results of surveys given to teachers nationally in Australia regarding their use of iPAC and m-learning tools more generally. It distinguishes between use by mathematics and science teachers and teachers of other subjects and compares their mobile pedagogical practices. Chapter 13 investigates the perspectives of teachers and teacher educators who participated in either the MTTEP project or other opportunities to use the iPAC Framework. It provides case studies of their practice, noting the experiences and responses of participants. Chapter 14 considers the principles for innovative practice identified in the DEIMP project and investigates their alignment with the iPAC dimensions. Chapter 15 concludes the book with a discussion on what we have learned and future directions for research on the iPAC Framework.

1.4 Conclusion This chapter has set the context for this book and provided a rationale for it. It outlines the research projects that underpinned and informed the work covered in this book, as well as the structure and content that the reader will encounter in each chapter.

6

1 Introducing This Book

Readers are able to access a website with details of the Framework, as well as tools and links to the projects discussed in this book, via https://www.ipacmobilepe dagogy.com. We hope that this book will stimulate interest in the tools and resources available on this website and will enhance m-learning practice and understanding. M-learning offers emerging and sometimes unprecedented opportunities for innovative educational practices (Burden, Kearney, Schuck, & Hall, 2019a). This book was completed during the global pandemic of 2020, a time in which remote learning occurred at scale throughout the world. We hope the iPAC Framework and associated ideas and resources presented in this book prove useful for educators adapting to online learning and teaching during and post the pandemic period (Kearney, Burden & Schuck, 2020). The iPAC Framework identifies some of the important characteristics of m-learning from a socio-cultural perspective and acts as a lens for educators seeking to exploit these opportunities. We trust that the readers of this book will gain ideas on how to use the iPAC Framework and evaluate their own practices with m-learning. We believe and hope that this book will contribute to the work of policymakers, teachers, teacher educators and educational researchers. Our aim is for the richness of m-learning to become apparent to all who read this work.

References Bano, M., Zowghi, D., Kearney, M., Schuck, S., & Aubusson, P. (2018). Mobile learning for science and mathematics school education: A systematic review of empirical evidence. Computers & Education, 121, 30–58. https://doi.org/10.1016/j.compedu.2018.02.006. Burden, K., Kearney, M., Schuck, S., & Hall, T. (2019a). Investigating the use of innovative mobile pedagogies for school-aged students: A systematic literature review. Computers & Education, 138, 83–100. https://doi.org/10.1016/j.compedu.2019.04.008. Burden, K., Kearney, M., Schuck, S., & Burke, P. (2019b). Principles underpinning innovative mobile learning: Stakeholders’ priorities. TechTrends, 63(6), 659–668. https://doi.org/10.1007/ s11528-019-00415-0. Burden, K., Schuck, S., & Kearney, M. (2019c, January). Should we be concerned about mobile devices in the classroom: What does the evidence say? Impact. Journal of the Chartered College of Teachers. Special Issue. Retrieved from https://impact.chartered.college/article/mobile-devicesschools-really-innovative-what-does-evidence-say/. Chee, K. N., Yahaya, N., Ibrahim, N. H., & Noor Hassan, M. (2017). Review of mobile learning trends 2010–2015: A meta-analysis. Educational Technology & Society, 20(2), 113–126. Kearney, M., & Maher, D. (2013). Mobile learning in maths teacher education: Driving pre-service teachers’ professional development. Australian Educational Computing, 27(3), 76–84. Kearney, M., Schuck, S., Burden, K., & Aubusson, P. (2012). Viewing mobile learning from a pedagogical perspective. Research in Learning Technology, 20(1). https://doi.org/10.3402/rlt. v20i0.14406. Kearney, M., Burke, P., & Schuck, S. (2019). The iPAC scale: A survey to measure distinctive mobile pedagogies. TechTrends, 63, 751–764. https://doi.org/10.1007/s11528-019-00414-1. Kearney, M., Burden, K., & Schuck, S. (2020). Designing personalised, authentic and collaborative learning with mobile devices: Confronting the challenges of remote teaching during a pandemic. In R. Ferdig, E. Baumgartner, R. Hartshorne, R. Kaplan-Rakowski & C. Mouza (Eds). Teaching,

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technology, and teacher education during the covid-19 pandemic: Stories from the field (pp. 661– 666). Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/p/216903/. Maher, D., Schuck, S., & Perry, R. (2017). Investigating knowledge exchange amongst school teachers, university teacher educators and industry partners. Australian Journal of Teacher Education, 42(3), 73–90. Schuck, S. R. (2015). Mobile learning in higher education: Mobilizing staff to use technologies in their teaching. eLearn Magazine. https://doi.org/10.1145/2749476.2749226. Schuck, S., Aubusson, P., Kearney, M., & Burden, K. (2010). Mobagogy: Mobile learning for a higher education community. In I. Sánchez & P. Isaías (Eds.), Proceedings of the IADIS mobile learning, 2010 conference (pp. 69–76). Porto, Portugal: IADIS Press. Schuck, S., Kearney, M., & Burden, K. (2017). Exploring mobile learning in the third space. Technology, Pedagogy and Education, 26(2), 121–137. https://doi.org/10.1080/1475939X.2016. 1230555. Turvey, K., & Pachler, N. (2018). Tablet devices in education—Beyond face value. In R. Luckin (Ed.), Enhancing learning and teaching with technology: What the research says. London: UCL IOE Press. Vygotsky, L. S. (1978). Mind in society. Cambridge, MA: MIT Press. Wertsch, J. V. (1991). Voices of the mind: A sociocultural approach to mediated action. Cambridge, Mass: Harvard University Press.

Part I

The Current Context

Chapter 2

The Digital Landscape of Education

Abstract In this chapter, we consider the general adoption of digital technologies in education and examine the trends in educational technology use. We discuss the current landscape of technology-enhanced learning (TEL) and set the context for future chapters by articulating the issues, challenges and benefits of the current digital landscape for education. We consider the implications for education of the ubiquitous digital presence in society. With a particular focus on Australia, this chapter considers policies and recommendations for adoption and adaptation of digital technologies that currently exist in school and teaching policy documents. This is followed by an identification of the barriers and the enablers to the effective use of digital technologies by teachers and teacher educators, and in schooling more generally. The implications of new and emerging technologies for learning are discussed with particular reference to schooling and teacher education. Changing roles of students and teachers are considered. The chapter concludes by examining what the future might hold for TEL and what the implications of that future might be for schools and teacher education institutions. Keywords Technology-Enhanced learning (TEL) · Digital technologies · Digital learning · Teacher education · Schooling · Barriers and enablers · Digital education · E-Learning

2.1 Introduction This book concerns the development and impact of mobile pedagogies on schooling, learning and teacher education in the current context. The development of mobile pedagogies and the introduction of mobile devices into education cannot be understood without first having an overview of the more general digital landscape and how this is embraced, resisted and valued in school education and teacher education. This chapter provides an overview of the current digital landscape for education, focusing in particular on the contexts of teacher education and teaching in schools. The issues, challenges and benefits of adoption and implementation of educational technologies over the past decade are discussed, and the implications for their future

© Springer Nature Singapore Pte Ltd. 2020 M. Kearney et al., Theorising and Implementing Mobile Learning, https://doi.org/10.1007/978-981-15-8277-6_2

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use in school and teacher education are investigated. The following two chapters in this part discuss the context of mobile learning.

2.2 Current Context of Digital Technologies in Education Digital technologies are increasingly pervading our lives. Recent figures on access indicate that across the globe the use of the Internet stands at 53%, with 88% of Australians accessing the Internet, and that time spent on digital technologies is largely used for accessing social media (Brand, Todhunter, & Jervis, 2017). Given this increasing take-up of digital technologies, it is of interest to investigate how to leverage them for learning. Digital technologies have the capacity to improve our lives and contribute to the functioning of the economy. They allow us to seek information, communicate and collaborate with people in distant places, and potentially to change pedagogies (Camilleri & Camilleri, 2017). Variations of digital technologies are becoming part of the learning landscape in many schools and higher education institutions. In Australian homes, the use of the Internet to find information, participate in electronic games, interact socially and access social media to construct and share identities is almost ubiquitous. Although new information and communication technologies (ICT) are constantly emerging and access to them is rapidly increasing, their educational use has increased only slowly over the last decade and has not led to the radical changes in pedagogy that were expected when they were first introduced (Burden & Kearney, 2017; Kearney, Burden, & Schuck, 2019). It is probably fair to say that work and personal uses of digital technologies have seen much greater innovation and disruption than have educational settings. In non-educational settings, the so-called sharing economy is one example of how digital technologies have disrupted existing practices and created new ones. Examples include car sharing, accommodation sharing and even food deliveries now have new models of practice that have undermined old practices. In education, such disruption is less common, with massive open online courses (MOOCs) being an example of an innovation that was initially expected to radically change higher education practice, but which does not yet appear to have a notable effect on universities (Chiappe, Martínez, & Hine, 2015). It is 30 years since an initial vision of the role of information and communication technologies was brought into Australian school policies by the Ministerial Council on Education, Employment, Training and Youth Affairs (MCEETYA, 1989). This statement was superseded within 10 years by the National Goals for Schooling in the Twenty-first Century (Education Council, n.d.) and later by the Melbourne Declaration on Educational Goals for Young Australians (MCEETYA, 2008). The Melbourne Declaration noted: Rapid and continuing advances in information and communication technologies (ICT) are changing the ways people share, use, develop and process information and technology. In this digital age, young people need to be highly skilled in the use of ICT. While schools

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already employ these technologies in learning, there is a need to increase their effectiveness significantly over the next decade. (MCEETYA, 2008, p. 5).

These goals are not radically different from those in the initial 1989 statement. And more than a decade after the Melbourne Declaration, this statement is still current in its acknowledgement of both the importance of digital technology use in schooling and the need to increase it in appropriate and effective ways. There have been many critiques of statements such as that in the Melbourne Declaration noted above. According to Jordan (2011), critics suggest that such statements reflect the view that the panacea for all educational woes is the embrace of digital technologies, and therefore the reason students are not achieving the required levels of effective technology use is somehow due to teachers not fulfilling their part of the bargain. Critics also complain that these statements are techno-deterministic in nature, do not recognise the intricate interactions that occur between technologies, students and teachers, and lack understanding of “the complexities or the messiness of using ICT in school contexts” (p. 429). It is important therefore, when discussing the current uses of technology in education, that this complexity and messiness be included in our considerations of digital technologies, along with the benefits that they might bring to educational settings. Agency of students and teachers is also important as they interact with these technologies. In this book, we adopt a socio-cultural view of learning and suggest that the ways technologies are used will impact both on design adaptations and on the aims and goals of learning. We note the importance of cultural artefacts and contexts in the design of this learning and we distinguish between digital technologies that are used for information gathering and those that allow users to interact, collaborate and create in ways that are personal and meaningful for them. We acknowledge the current uptake of various digital technologies, as indicated by Brand, Todhunter, & Jarvis (2017) and argue that it is the duty of education authorities to ensure that those with less access to these technologies at home are provided with opportunities to use these tools in their schools as confidently as their peers who have access to them in their personal lives. Given the importance of this access, we need to consider how to support teachers and pre-service teachers (PSTs) to be comfortable with technology use so that they can be confident digital learners and encouraging digital teachers. While we acknowledge the messiness of the educational space in which digital technologies exist, we believe that it is important for both teachers and students to be able to use available technologies in competent ways for their teaching and learning. Students have increased access to computers and other devices at home and can use these to search for information, share, collaborate, socialise and research (Camilleri & Camilleri, 2017). Accordingly, given the tools that students currently have at their disposal, we might need to consider afresh both the curricula we offer and our lesson designs so that we can support students to learn in ways that are relevant and meaningful.

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2.3 The Issues, Challenges and Benefits of Technology Use in the Current Educational Landscape Much of the literature regarding teacher uptake of digital technologies discusses the barriers and enablers that influence it. The seminal work in this area was done by Ertmer (1999), who classified barriers as first-order or second-order and then looked at how these different types of barriers could be overcome. Ertmer suggested that first-order barriers are those external to the teacher, such as their access to support, their training and the provision of technologies. Second-order barriers are the intrinsic barriers to adoption and implementation, and these include teachers’ beliefs about technology use and pedagogies, their attitudes, and their self-efficacy when using technologies. Ertmer regarded both types of barriers as obstacles to technology adoption and adaptation, with the second-order barriers being more intangible and therefore more difficult to overcome. More recently, Ertmer et al. (2012) have suggested that first-order barriers are likely to be less prevalent given the encouragement by policymakers, school executive and other important stakeholders for digital technologies to be incorporated into school curricula and practices. With access to digital technologies of various kinds now commonplace in most Western schools, and professional training often provided by the technology company associated with the product, first-order barriers appear to have diminished. Second-order barriers are more enduring, however, as they involve teachers’ beliefs and attitudes, which are often deep-seated and quite difficult to shift. Teachers (quite rightly) need to be convinced of the pedagogical value of adopting digital technologies in their classrooms, and they can be helped by educational frameworks and models that indicate how pedagogical knowledge and technological knowledge can be integrated to implement technology-enhanced learning (TEL). An example is the TPACK framework (Koehler & Mishra, 2009), which indicates how technology, pedagogy and knowledge are entwined (see Fig. 2.1). Another useful model that has seen strong uptake by teachers is Hunter’s (2015) High Possibility Classrooms, which provides a scaffold for effectively integrating technology into their teaching. As well as concern about their own self-efficacy in teaching with technologies, teachers are constrained by the current structures of schooling. These include international testing, external high-stakes examinations, curricula that were largely developed in a pre-digital era, and requirements of a schooling system that has not kept up with changes in society at large. There remains an expectation that students will attend school each day, work on educational programs that are generally aimed at a hypothetical ‘average’ learner and complete a hand-written exam at the end of their school years that will test their knowledge of facts and processes that quite often can be easily accessed using computers or other digital technologies. While there is much discussion about so-called twenty-first-century skills and literacies, these do not appear in most existing curricula or classrooms. Burden (2010) refers to the four Cs of twenty-first-century learning: collaboration, critical thinking, creativity and communication (P21, n.d.). Discussions about how to develop these skills and

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Fig. 2.1 The TPACK framework source http://tpa ck.org (Reproduced by permission of the publisher, © 2012 by tpack.org)

the tools to use are usually held in universities and do not appear to permeate the school context, even though they are critical skills to learn. Other barriers to effective adoption of digital technologies for learning are the concerns held by parents and schools about the dangers lurking in the use of the Internet by young people. These include the oft-cited ‘stranger-danger’ of young people interacting with inappropriate people through the Internet, cyberbullying occurring through social media and students accessing inappropriate websites. While these fears are well founded, the methods of counteracting them are often knee-jerk reactions that lead to bans and restrictions, rather than to the education of students about how to manage these challenges and be discerning in their use of the Internet and social media (Livingstone, Mascheroni, Ólafsson, & Haddon, 2014; Livingstone, Nandi, Banaji, & Stoilova, 2017). These concerns point to the importance of teachers being competent and confident in areas of TEL and knowledgeable about how to facilitate the safe practices of their students. Related to the management of digital access in safe and productive ways is the need for teachers to support the development of new literacies and numeracies. The traditional skills are insufficient for navigating this new landscape (Lankshear & Knobel, 2003). Technological literacies, critical literacies and multimodal literacies now need to be taught to students alongside the other literacies. Somekh (2004) found that while most households enjoy access to digital technologies, a digital divide exists in the way they are used; households with higher levels of cultural capital provide more guidance to students on how to use digital technologies in safe and productive ways for learning. For a more equitable society, schools need to ensure that this particular digital divide is addressed. Digital technologies still tend to be used in teaching and learning in ways that align with traditional models of teaching. Curricula have barely changed to reflect

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the opportunities occurring with new and emerging educational technologies. The Substitution, Augmentation, Modification, Redefinition (SAMR) model (Puentedura, 2015) indicates that most educational use is at the lowest level, Substitution, which means that the technology is substituted without any change in practice. As well, little of schooling and teacher education occurs at the Redefinition level, the level at which new tasks are created that could only have been done with the technology. It appears, therefore, that educational technologies are still being used to serve current models of teaching and learning. Cranmer and Lewin (2017) have examined the challenges of articulating what technological innovation might look like for teachers and students. They suggest that innovations need to be of value and that context plays an important role in determining the nature of the innovation; what might be regarded as innovative in one context might not be so regarded in another. Kearney et al. (2019) support this view. Criteria for determining the level of innovation were suggested by Law, Chow and Yuen (2005) in their Dimensions of Innovation rubric. Their criteria comprised intended learning outcomes, teachers’ roles, students’ roles, digital technologies used, connectedness and achieved learning outcomes. Burden, Kearney, Schuck, and Hall (2019) suggest similar criteria for innovation in mobile learning (these are discussed in more detail in the next chapter). Interestingly, the highest level of innovation in TEL is not necessarily the most desirable. Rogers (2003) and the OECD (2008) both indicate the value of incremental innovation in small-scale interventions, a view supported by Kampylis et al. (2013), who noted the difficulties in scaling up disruptive or high-level innovations. These disruptive innovations align with the Redefinition level of SAMR, which we have noted as being sparse in implementation. These studies indicate that perhaps the best way to innovate with technologies in teaching and learning is through small and achievable steps. Looking at the other side of the debate about the place of educational technologies in the current landscape, we now turn to why digital technologies are important in today’s schools. As we have noted above, students need to be technologically literate to function effectively in the workplace, yet the digital divide exists, not so much in access to digital technologies, but rather in the ways they are used (Somekh, 2004). Furthermore, digital technologies and the Internet provide access to external experts, collaboration from a distance and diverse information. Augmented and virtual realities allow students to learn in cost-effective ways that would previously have required either expensive simulators or transport to the equipment needed for learning. Free access to collaborative sites that allow students to collectively create documents, spreadsheets and presentations also enables students to work together in seamless ways that do not demand their physical presence in a particular location. This democratisation of education provides them with autonomy and choice. Access to these technologies allows them to collect and investigate information without going through a mediating influence such as a teacher. Of course, there are dangers involved in the free access to views, knowledge and opinions, with students able to interact with people who may influence them negatively. As well, the so-called ‘echo-chamber’ that is encouraged by interacting online only with like-minded individuals, and is promoted by feeds that respond

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to a person’s interests, causes a narrowing of views and perspectives. Again, this narrowing suggests that education about the use of digital technologies is extremely important. Education has a role in ensuring that multiple views are heard and given due consideration. One final argument for the benefits of TEL is economic. Being an efficient and competent user of digital technologies means that a learner can adapt more readily to the skills and requirements of current and future workplaces. As noted earlier, most workplaces have had to change with the times to remain in business. This has meant embracing technologies in order to cut costs, reach markets and remain viable. Teachers have a duty to prepare their students to enter this workplace with the understandings and skills needed to function in a twenty-first-century workplace.

2.4 Future Trends in Use of Digital Technologies in Education In this chapter, we argue that digital technologies have much to offer students in their learning, interactions and understandings. We have noted that not much has changed in the educational landscape in the past three decades. Policymakers are still noting the importance of digital technologies for learning, but school curricula have not radically changed, and they tend to suggest the use of technology in ways that are principally substitutions of former techniques and tools (Puentedura, 2015). Teachers and teacher educators remain reluctant to use digital technologies in ways that might transform teaching and learning. In this section, we consider what the future may look like in schooling that uses TEL to its fullest potential. We ask where digital technologies might make large contributions and in what new directions they may take us. We then consider the feasibility of these new directions. One of the more recent developments (in the educational arena, at any rate) is the emergence of smart technologies. These technologies are so-called because they are adaptive to the learner’s needs, responses and stages (Kim, Cho, & Lee, 2012; Lee, Zo, & Lee, 2014; Middleton, 2015). Smart technologies allow teachers to customise the curriculum for each student so that they can learn at the pace and the level that is most suited to their learning characteristics. Aligned with these technologies are those that can provide teachers with instant tracking of students’ progress and diagnoses of their strengths and weaknesses. A classroom in the future may consist of students working at their own pace on their chosen topic of interest and collaborating with others who are similarly engaged. Indeed, such schools do already exist, relying on digital technologies to allow customisation and autonomy. Widespread access to smart technologies will simply make the task easier for schools already engaged in these practices. Of interest for the future are questions about the need for subject content knowledge, given the ready access that technologies will offer to many areas (Schuck,

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Aubusson, Burden, & Brindley, 2018). For example, will students need to have language classes, given the prevalence of translation applications and software? Might students learn topics that will offer philosophical and intellectual growth rather than learning how to enact a procedure or routine that can be done efficiently by technologies? Other questions concern the roles of the teacher. We already view teachers’ roles as being more about facilitation of learning than direction of learning, but how will such facilitation change with easy access to experts beyond the school? If students are being guided by data analytics that they can access without a teacher acting as mediator, will teachers’ roles as assessors be reduced? These questions are expanded in Chaps. 3 and 4, where we discuss the impact of mobile technologies on learning and on the flexibility of time and place in which learning can occur. Given these possibilities, what might a school and a classroom of the future look like? To answer this question, we must consider a number of factors in addition to the digital technologies we are concerned with in this chapter. Stylianides and Pashiardis (2007) suggest there are five drivers that impinge on the structure and processes of schools: 1. 2. 3. 4. 5. 6.

technological and informational developments; political structures and patterns of legal norms; social conditions; cultural values; economic and market factors; population and demographic characteristics. (p. 385)

These drivers are interrelated (Schuck et al., 2018). As indicated earlier in this chapter, underlying social conditions, economic factors and politics have all led to policymakers and stakeholders urging schools to adopt technological developments. Conversely, cultural values are likely to restrain these changes, as teachers resist the imposition of technologies on their teaching and in their classrooms. We can only speculate on what a school of the future may look like. If we are imagining schooling decades into the future, we can only look at the drivers now current (as noted above) and then use these to develop scenarios of possible schools. In previous work, we have developed a series of scenarios (Schuck et al., 2018) that may possibly describe schools in 2030. These are broadly based on the OECD schooling scenarios (CERI/OECD, 2001) but are modified to fit with current drivers rather than the ones operating at the time of the OECD scenarios. In our 2018 scenarios, we identified different models of schooling that would align with the technologies anticipated to be in existence in 2030. These incorporated models of schooling on a continuum from conservative to highly disruptive. At one end are schools that look much like the ones that exist today, where the substitution version of technology use is widespread. At the other end of the continuum are models of learning that acknowledge the critical role of new and emerging technologies and are products of the disruption caused by them. In such scenarios, the likelihood of access to artificial intelligence and smart technologies suggests that students do not need to learn many of the items currently in school curricula and must instead concentrate on emotional

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intelligence, critical thinking skills, creativity, and relationship building—areas in which humans are likely to do better than machines (Schuck et al., 2018). Such scenarios have great potential to disrupt teacher education and schooling. However, we do not know at which point on the future learning scenarios continuum we will arrive in 2030. Certainly, in research done on such scenarios with teacher educators, the disruptive end of the continuum did not seem to enjoy much prominence (Aubusson, Pannazon, & Corrigan, 2016, Aubusson & Schuck, 2013). It might well be the case that even with the hugely disruptive forces brought about in society by smart machines, education will once again lag behind in adoption and adaptation of such technologies. On the other hand, teachers’ and teacher educators’ decisions about how much of the new technologies to adopt might be removed from them by a wave of popular use that renders the older practices obsolete. As with disruptive practices that have subverted other mainstream practices such as those mentioned earlier, it may well happen that teachers and teacher educators who reject the new technologies will become irrelevant in the new order of education.

2.5 Implications for Teacher Education and School Education The discussion above indicates that although we cannot predict the future of teacher education and of schooling, the experiences of disruptive industries in non-education fields point to the danger of assuming that the business-as-usual model is sustainable enough to guide us through rocky shoals. It seems likely that disruption of some sort or another is around the corner as smart educational technologies continue to be developed. Consequently, it seems sensible to consider what the implications may be for both school and teacher education in the era of smart pedagogies and artificial intelligence. While education is by nature a somewhat reactive industry, and large disruptions are unlikely to gain hold rapidly, we must consider the likelihood of disruption occurring in schools as teachers grapple with how best to use smart pedagogies. Of course, such disruption has great implications for how we prepare teachers to use the new technologies. This section considers how to disrupt teacher education with a view to better preparing teachers for practice 10 or 20 years hence. The first step in changing teacher education courses to meet new requirements must be the preparing of teacher educators to better respond to the possible scenarios. As indicated above, recent research (Aubusson et al., 2016; Aubusson & Schuck, 2013) indicates that teacher educators are invariably not the pioneers of learning with new technologies. Indeed, they are often criticised for lagging behind schools in their uptake of new technologies in appropriate and defensible ways that change their roles, students’ roles and the nature of pedagogical activities (Kearney et al., 2019). Therefore, it is important that teacher educators are both encouraged to embrace

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technological change and supported in reflecting on what this might mean for their own practices and for the practices of their PSTs. One problem in Australia at the moment is the age demographic composition of teacher education staff in university faculties. Reduced funding to universities and perceptions of the place of teacher education in university hierarchies have led to a general lack of staff renewal in these faculties. Staff are generally at the more mature end of the age continuum and new opportunities for younger people to enter teacher education are few and far between. While it would be glib to make the adoption of new technologies an issue about age, it is likely that younger academics who are entering teacher education are more likely to seek and develop new practices, and these practices are likely to include TEL. Apart from the renewal of teacher education staff, other factors will facilitate the uptake of digital technologies for teaching and learning. These include recruitment and promotion of visionary leaders who realise the importance of such change and provide staff with time and support to adopt new practices; and provision of a blueprint for schooling that shows how such practices will be used in future decades (Bereiter, 2002; Burke & Foulger, 2014; Law, 2008). Having taken the first step of ensuring that teacher educators both share the vision of the future and have the confidence and competence to bring it to their PSTs, teacher education programs can then introduce courses that will attract PSTs who embrace this digitally integrated vision. The outcome of the selection of more technologically adept PSTs should be the graduation of early career teachers (ECTs) who are motivated to teach using TEL. Their task on entering schools may not be easy, however. While ECTs are often challenged by the practices and attitudes of their more senior colleagues in schools, an additional challenge will be the expectation from teacher educators and school management teams that these newcomers will be able to effect change in schools through TEL. ECTs will need to act as agents of change, bringing their newly developed competencies to schools and leading the way in changing the practices there. It is up to teacher educators to prepare their PSTs for these challenges and provide them with tools and strategies for developing resilience, demonstrating leadership, and increasing their communicative and negotiating skills. In schools, new ways of conceptualising the roles of teacher and student will be essential, and these new conceptions must include understanding the influence of available digital technologies. A drive to encourage students to use those technologies in order to act autonomously as learners and to behave in principled ways should ensue. It is essential that students are guided by their teachers to use the abundance of available digital technologies in ways that are beneficial for their learning, ethical in approach and productive for society. While many students are already accessing technologies in such ways, equitable education must ensure that all students are able to enjoy the benefits of new technologies in effective and principled ways.

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2.6 Conclusion This chapter has analysed the role of digital technologies in schools. It has discussed the barriers, enablers and benefits associated with increasing TEL in schools and teacher education institutions. Along the way, we have noted the importance of understanding the constraints, complexities and messiness of schools and school systems when technologies are imposed on teachers. We outline the benefits of using digital technologies in education in this chapter and suggest that there are a range of possibilities for enhancing teaching and learning with technologies. However, prior to the global pandemic of 2020, these possibilities were not realised in the main. It is too soon to judge whether teaching with digital technologies has changed during pandemic times. What is clearly apparent is that for education to serve all, digital technologies must be accessible to all and students must be adequately prepared to use these technologies in effective ways to improve learning. Consequently, teachers should be given full support in sharing principled use of technologies with their students to ensure that learning is robust and current. We now move to the focus of this book, the role of mobile technologies for learning. The next chapter articulates our understanding of mobile learning and indicates the barriers and enablers to the adoption and use of mobile pedagogies.

References Aubusson, P, Panizzon, D., & Corrigan, D. (2016). Science education futures: ‘Great potential. Could do better. Needs to try harder. Research in Science Education, 46(2), 203–221. Aubusson, P., & Schuck, S. (2013). Teacher education futures: Today’s trends, tomorrow’s expectations. Teacher Development, 17(3), 322–333. Bereiter, C. (2002). Design research for sustained innovation. Cognitive Studies, 9(3), 321–327. Brand, J. E., Todhunter, S., & Jervis, J. (2017). Digital Australia Report 2018. Eveleigh, NSW: Interactive Games and Entertainment Association (IGEA). Retrieved from https://www.igea.net/ wp-content/uploads/2017/07/Digital-Australia-2018-DA18-Final-1.pdf. Burden, K. J. (2010). Conceptualising teachers’ professional learning with Web 2.0. Campus-Wide Information Systems, 27(3), 148–161. Burden, K., & Kearney, M. (2017). Investigating and critiquing teacher educators’ mobile learning practices. Interactive Technology and Smart Education, 14(2), 110–125. https://doi.org/10.1108/ ITSE-05-2017-0027. Burden, K., Kearney, M., Schuck, S., & Hall, T. (2019). Investigating the use of innovative mobile pedagogies for school-aged students: A systematic literature review. Computers & Education, 138, 83–100. https://doi.org/10.1016/j.compedu.2019.04.008. Burke, D. M., & Foulger, T. S. (2014). Mobile learning in teacher education: Insight from four programs that embraced change. Journal of Digital Learning in Teacher Education, 30(4), 112– 120. https://doi.org/10.1080/21532974.2014.927208. Camilleri, M., & Camilleri, A. (2017). Digital learning resources and ubiquitous technologies in education. Technology, Knowledge and Learning, 22, 65–82. https://doi.org/10.1007/s10758016-9287-7. CERI/OECD. (2001). Scenarios for the future of schooling. In CERI/OECD, Schooling for tomorrow: What schools for the future? (Chapter 3, pp. 77–98). Paris: OECD.

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Retrieved from https://www.oecd.org/site/schoolingfortomorrowknowledgebase/futuresthink ing/scenarios/overviewofthesixsftscenarios.htm. Chiappe, A., Martínez, J., & Hine, N. (2015). Literature and practice: A critical review of MOOCs. Comunicar. 44. https://doi.org/10.3916/c44-2015-01. Cranmer, S., & Lewin, C. (2017). iTEC: Conceptualising, realising and recognising pedagogical and technological innovation in European classrooms. Technology, Pedagogy and Education, 26(4), 409–423. https://doi.org/10.1080/1475939X.2017.1299791. Education Council. (n.d.). The Adelaide declaration on national goals for schooling in the twentyfirst century. Retrieved from http://www.educationcouncil.edu.au/EC-Publications/EC-Publicati ons-archive/EC-The-Adelaide-Declaration.aspx. Ertmer, P. (1999). Addressing first- and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47–62. Ertmer, P. A., Ottenbreit -Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs and technology integration practices: A critical relationship. Computers & Education, 59(2), 423–435. Hunter, J. (2015). Technology integration and high possibility classrooms: Building from TPACK. New York, USA: Routledge. Jordan, K. (2011). Framing ICT, teachers and learners in Australian school education ICT policy. Australian Educational Researcher, 38, 417–431. https://doi.org/10.1007/s13384-011-0038-4. Kampylis, P., Law, N., Punie, Y., Bocconi, S., Breˇcko, B., Han, S., … Miyake, N. (2013). ICTenabled innovation for learning in Europe and Asia: Exploring conditions for sustainability, scalability and impact at system level. Luxembourg: Publications Office of the European Union. Kearney, M., Burden, K., & Schuck, S. (2019). Disrupting education using smart mobile pedagogies. In L. Daniela (Ed.), Didactics of smart pedagogy: Smart pedagogy for technology-enhanced learning (pp. 139–157). Cham, Switzerland: Springer. Kim, T., Cho, J. Y., & Lee, B. G. (2012, July). Evolution to smart learning in public education: A case study of Korean public education. In IFIP WG 3.4 International Conference on Open and Social Technologies for Networked Learning (pp. 170–178). Berlin, Heidelberg: Springer. Koehler, M. J., & Mishra, P. (2009). What is technological pedagogical content knowledge? Contemporary Issues in Technology and Teacher Education, 9(1), 60–70. Lankshear, C., & Knobel, M. (2003). New literacies: Changing knowledge and classroom learning. Open University Press. Law, N. (2008). Teacher learning beyond knowledge for pedagogical innovations with ICT. In J. Voogt & G. Knezek (Eds.), International handbook of information technology in primary and secondary education (pp. 425–435). New York: Springer. Law, N., Chow, Y., & Yuen, H. K. (2005). Methodological approaches to comparing pedagogical innovations using technology. Education and Information Technologies, 38, 7–20. Lee, J., Zo, H., & Lee, H. (2014). Smart learning adoption in employees and HRD managers. British Journal of Educational Technology, 45(6), 1082–1096. https://doi.org/10.1111/bjet.12210. Livingstone, S., Mascheroni, G., Ólafsson, K., & Haddon, L. (2014). Children’s online risks and opportunities: comparative findings from EU Kids Online and Net Children Go Mobile. London, UK: EU Kids Online, LSE. Livingstone, S., Nandi, A., Banaji, S., & Stoilova, M. (2017). Young adolescents and digital media: Uses, risks and opportunities in low- and middle-income countries: A rapid evidence review. London, UK: Gage. Retrieved from http://eprints.lse.ac.uk/83753/. MCEETYA. (1989). The Hobart Declaration. Retrieved from http://www.educationcouncil.edu. au/EC-Publications/EC-Publications-archive/EC-The-Hobart-Declaration-on-Schooling-1989. aspx. MCEETYA. (2008). The Melbourne Declaration on educational goals for young Australians. Retrieved from http://www.educationcouncil.edu.au/site/DefaultSite/filesystem/documents/Rep orts%20and%20publications/Publications/National%20goals%20for%20schooling/National_ Declaration_on_the_Educational_Goals_for_Young_Australians.pdf.

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Middleton, A. (Ed.). (2015). Smart learning: Teaching and learning with smartphones and tablets in post compulsory education. Media-Enhanced Learning Special Interest Group and Sheffield Hallam University Press. Organisation for Economic Co-operation and Development (OECD). (2008). Innovating to learn, learning to innovate. Paris: OECD Publishing. P21. (n.d.). Framework for 21st century learning. Battelle for Kids. Retrieved from http://www.bat telleforkids.org/networks/p21/frameworks-resources. Puentedura, R. (2015). The SAMR model on digital learning. Retrieved from http://hippasus.com/ blog/wp-content/uploads/2015/02/SAMRModelAndDigitalLearning.pdf. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York, NY: Free Press. Schuck, S., Aubusson, P., Burden, K., & Brindley, S. (2018). Uncertainty in teacher education futures: Scenarios, politics and STEM. Dordrecht: Springer. Somekh, B. (2004). Taking the sociological imagination to school: An analysis of the (lack of) impact of information and communication technologies on education systems. Technology, Pedagogy and Education, 13(2), 163–179. https://doi.org/10.1080/14759390400200178. Stylianides, M., & Pashiardis, P. (2007). The future of our schools: An example of the Delphi technique in action and the case of Cyprus. International Journal of Educational Management, 21(5), 384–406. https://doi.org/10.1108/09513540710760174.

Chapter 3

Mobile Learning and Ubiquitous Learning

Abstract In this chapter, we outline the recent emergence of mobile technologies and consider their place in the educational landscape. We define and discuss mobile learning and mobile pedagogies and consider what is different about mobile learning from other technology-enhanced learning. We introduce the term ubiquitous learning and discuss what this entails. Our projects in the area of mobile learning are outlined. We discuss the value of mobile learning and highlight learning benefits facilitated by mobile pedagogies. Implications of mobile learning for school learning and for teachers as designers of learning tasks are noted, thus leading into the next chapter on seamless learning. Keywords Mobile learning · Mobile pedagogies · Ubiquitous learning · M-learning · U-learning · Student autonomy · Authenticity · Learning tasks

3.1 Introduction The previous chapter discussed trends in technology-enhanced learning (TEL) and the implications for teaching and teacher education. We now turn to the focus of this book, which is mobile learning or m-learning. We discuss the terms mobile learning and mobile pedagogies and consider how m-learning may differ from or add to TEL. The term ubiquitous learning or u-learning is introduced and unpacked to consider its relationship to m-learning. After examining the drivers of m-learning and u-learning, we explore the trends in m-learning and consider implications for future learning. M-learning is the term used to describe any learning that is facilitated through the use of mobile devices. Such devices have as their central characteristic an ability to be used anywhere, that is, they are portable. Such portability implies that the device is easily carried, and consequently physical restrictions on where it can be used are generally removed. Examples of mobile devices include smartphones, tablets, netbooks, laptops and two-in-one devices (i.e. those that function as both computers and tablet devices). In this book, we will also be using the term ‘mobile pedagogies’ to refer to pedagogies which include ways of teaching and learning that can only be done with mobile devices.

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Many studies about m-learning indicate its value for encouraging seamless learning, that is, learning anywhere and at any time, and for connecting to the outside world (Cristol & Gimbert, 2014; Melzer et al., 2009). It is suggested by many authors (for example, Chee, Yahaya, Ibrahim, & Noor Hassan, 2017; Traxler, 2010) that mobile pedagogies and m-learning have the potential to disrupt current learning paradigms and offer new and different opportunities for learning. Much of the research literature suggests that m-learning is on the increase, as devices become more affordable and accessibility and connectivity for personal use improve (Bano, Zowghi, Kearney, Schuck, & Aubusson, 2018; Burden, Kearney, Schuck & Hall, 2019). Sixty-eight percent of the world population are mobile users (Brand, Todhunter, & Jervis, 2017), and smartphone penetration in Australia is 91% (Deloitte, 2020). However, with this increase in mobile use, debates on how mobile devices should be used in schools are now occurring. On the one side of the debate, influenced by fear of cyberbullying, distraction from studies, and the ever-present ‘stranger-danger’ that mobile usage appears to facilitate, are those who argue that mobile devices should have no place in schools, with some countries going so far as to ban their presence in classrooms. France has banned the use of mobile devices in schools, including smartphones and tablets for students under 15 years of age (Hudson, 2018). School systems in Australia have recently reviewed the use of mobile devices in schools and provided guidance to minimise or ban their use (e.g. Carr-Gregg, 2019). A recent New York Times article by Bowles (2018) indicates a surprising new digital divide: parents in higher SES groups are requesting that their children are banned from using mobile phones at school, and lower SES groups have students using their mobile phones to a greater degree outside of school. On the other side of the debate are the proponents of the potential of mobile devices for learning. They critique the banning movement for a number of reasons. First, the pervasive nature of such devices makes such bans largely ineffectual, as prohibition on their use for the six school hours of the day is not likely to mitigate any of the concerns indicated above. Given their portability, their use is likely to be widespread beyond school hours. Second, the proponents of mobile use for learning argue that students would be denied opportunities to use such devices in valuable and effective ways for learning if the usage was restricted to out-of-school, non-learning purposes. A different digital divide from the one referring to technology access concerns the use of the device. Somekh (2004) has called this the second digital divide. She showed that in homes with high cultural capital, students are more knowledgeable about how to effectively use their digital and other technologies for learning, whereas those in homes with less cultural capital tend to use them in limited ways. This finding is consistent with the point made by the pro-ban lobby: low SES students may well be using their devices more than high SES students, but the use is likely to be for purposes other than learning. More recent research has found that a more effective way of managing the dangers of mobile usage would be to educate students on how to use them for learning, and that the role of the school is to do exactly that (Burden et al., 2019; Callow & Orlando, 2015; Orlando, 2018).

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The approach taken in this book is data-driven. Based on evidence collected throughout the variety of projects conducted by the authors on m-learning, the chapters of this book will indicate the theory underpinning our m-learning initiatives and explore the benefits of m-learning we found in our projects. While we do not deny the possible negative outcomes indicated by the pro-banning lobby, we present evidence of the counterbalancing value of m-learning and mobile pedagogies and argue that the positive outcomes outweigh the negative ones. Ways of managing the negative outcomes are not the subject of this book and we acknowledge the need for such management to occur and to be studied. However, we hope that the case made in our various chapters clearly indicates the value of m-learning.

3.2 Drivers of M-Learning In this section, we discuss the drivers of m-learning. By drivers, we mean the trends that are prevalent in TEL and the affordances or characteristics of mobile devices that are likely to align with these trends. We note, however, that our underpinning theoretical framework is a socio-cultural one and therefore the relationship between mobile affordances and societal trends is not straightforward. There is a complex relationship governing whether the affordances of mobile devices have been developed as a result of the current drivers or whether the current drivers have evolved as a result of the tools that are available. In terms of learning, we consider the literature that suggests learning outcomes can be improved if a number of factors are present, and we then consider whether these factors are facilitated by m-learning tools. Various m-learning studies discuss factors that are thought to enhance engagement and learning (Cristol & Gimbert, 2014). These factors include student agency in choosing the topic of interest, the mode of learning and the way the student chooses to conduct the learning. Currently, in Australia, a movement called Big Picture Education Australia (BPEA) is responding to students’ widespread disengagement with school education by creating smaller classes, allowing students to develop their own individual learning plans and designing subjects that take student interests as the context for the content (BPEA, 2016). It is a form of project-based learning that is enjoying success with many students who were previously disengaged from learning. Although not suggested by BPEA, this kind of project-based learning, with its individual and personalised subject matter, aligns well with our m-learning framework, which we introduce in Chap. 5 (Kearney, Schuck, Burden, & Aubusson, 2012). Another important factor is the relevance or authenticity of the task. This includes the location of the task and its requirements; authentic learning tasks are regarded as seamless and not restricted to classrooms. They need to be coherent and integrated because student self-regulation and meaningful learning tasks and activities are thought to play an important role in learning (Melzer et al., 2009). We argue in this book (and are supported by other proponents of m-learning, for example, Chee et al., 2017; Crystol & Gimbert, 2014; Ng & Nicholas, 2013; Royle, Stager, & Traxler, 2014; Traxler, 2010) that m-learning is well suited to learning tasks that seek to

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include these factors. The ability to personalise, collaborate and create authentic tasks underlies the mobile pedagogical framework that is central to this book. We recognise and agree with critiques of affordances of m-learning (e.g. Wright & Parchoma, 2011) that focus on the technology rather than the learning activity, and with Nouri, Spikol, and Cerratto-Pargman (2016), who argue that the emphasis in design-based learning and research should not be on the technologies themselves but on the learning tasks. Our argument is that if learning activities are to effectively engage students and produce valuable learning outcomes, they should be authentic, personalised and relevant, and offer opportunities for collaboration. We suggest that while mobile devices are not the only tools that will facilitate such opportunities, they are able to be used in ways that will encourage these factors to be present in learning activities (Kearney et al., 2012). Although academics in the field of m-learning tend to extol its virtues for facilitating activities that are meaningful, personalised and collaborative, m-learning is not commonplace in schooling today. This is perhaps unsurprising given the complexity of school systems, teaching and assessment (Jordan, 2011) that were discussed in Chap. 2. Furthermore, the very nature of mobile devices, which allows students to use them in all sorts of settings, also encourages their use outside the school context. It is therefore challenging for teachers to incorporate in their daily schedules learning activities that call for student autonomy and choice, yet can be conducted in out-of-school times and environments. The drivers noted above emphasise the need for students to engage with opportunities to personalise their learning, collaborate with others beyond the school, and work on topics that are meaningful and relevant to them. For these activities to happen, teachers need to include in their teaching repertoires learning tasks that leverage the affordances of mobile devices. Some schools are now offering their students m-learning opportunities, and these are reflected in the changing designs of schools, where classrooms are often no longer the most common and central places for learning activities, and more informal spaces are provided where students can work at their own pace using their devices (Schuck, Aubusson, Burden, & Brindley, 2018). Other schools offer tablet programs for their students that are built on the need for personalisation and collaboration that underpins the educational philosophy of the school. Other drivers that point to a need to take advantage of available educational technologies are discussed by Schuck et al. (2018). These include the ready access to the Internet, the ease with which communication can be enacted with individuals around the world, and technologies that are responsive to students’ queries. These authors highlight three technological trends that may have a radical and disruptive effect on schooling as we currently know it: 1. the ubiquity of pervasive computing, 2. big data and learning analytics (LA), and 3. augmented realities (AR). (p. 46) The first and third of these are supported specifically by m-learning, while the second might also involve technologies that are not necessarily mobile. The first

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trend concerns the ubiquity of access. The pervasiveness of mobile devices, with their connectivity and computing power, suggests that students who use them will be able to enact learning activities not previously possible. Teachers will need to support students to do this if they are to gain the outcomes possible. Teachers will also have an important role to play in teaching students how to manage issues concerning safety, security, privacy and responsibility. The learning made possible through pervasive computing is often called ubiquitous learning (u-learning). This is discussed in more detail later in this chapter. Similarly, the development of augmented realities (AR) might provide the realistic and authentic experiences that promote engagement in learning. AR can occur when the user experiences the additional layers that augment what they are already viewing (Schuck et al., 2018). These ‘realities’ need mobile devices to enact them. Again, it is suggested that experiences involving AR have the capacity to engage learners in more authentic activities, thus resulting in better learning outcomes. However, research on the outcomes of AR on learning is still in its infancy, particularly for school-aged students, and is not conclusive at this point (Wang, Callaghan, Bernhardt, et al., 2018). Other drivers of contemporary and future learning are not restricted to the use of mobile technologies but tend to include a variety of other educational technologies. This is the case in the second trend mentioned earlier, where different technologies will facilitate learning analytics. We now explore how m-learning may differ from other forms of TEL.

3.3 M-Learning and TEL In the previous chapter, we discussed TEL and current trends and implications for teaching and learning. M-learning is a component of TEL but has certain characteristics that make it distinctive in itself. As noted at the start of this chapter, m-learning involves learning with a mobile device. Learning with a tool of this sort allows the learning activity to have various characteristics that are particular to this type of TEL. As noted earlier, an essential part of m-learning is the ability to learn anywhere. Crompton defines m-learning as “learning across multiple contexts, through social and content interactions, using personal electronic devices” (Crompton, 2013, p. 47, cited in Crompton, 2017). Location is much more fluid when learning with mobile devices. The term ‘untethered’ has been used (Schuck & Maher, 2018; Traxler, 2009) to describe the ability to use the device away from a fixed location such as a desk. The ability to learn in an untethered way is a key feature of m-learning. This untethered nature of the learning is what is meant by ‘learning anywhere’. The other key trend relates to the time in which the learning or interaction takes place. Students are no longer constrained to work at times set by their teacher or school timetable. The ability to immediately connect to others and to tasks via the mobile device has enabled the rise of what Ling and Donner (2009) and other authors call ‘socially negotiated time’, a plasticity of time, a bending of schedules. This does

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not occur with fixed technologies that need access to Wi-Fi and a particular location for access to occur. The decrease in cost of data packages and the increase in student ownership of mobile devices allow both time and place to be untethered from the school schedule. This untethering leads to the ability to ‘learn anytime’. Other aspects of TEL such as personalised learning (or ‘learning any pace’) and choice of mode of learning are supported by both tethered and untethered technologies. It is the time and location of learning allowed by mobile devices that make them distinctive. Furthermore, the ubiquity of such devices has an effect on students’ activities. Their ownership of the devices allows them agency in choosing what apps to upload, whom to have on their contact lists, and how to customise the device. Mobile devices have become commonplace, and students’ ownership of such devices is increasing each year. They provide students with opportunities to access people who are geographically distant and to learn in autonomous ways and in contexts that are meaningful to them. Their teachers are charged with managing such learning; this is a challenge given the autonomy of the students and their ability to interact beyond the classroom. In schools with restricted financial resources, students are often encouraged to bring their own devices (the BYOD policy), which increases the challenges for teachers who will be working with students across a variety of platforms and apps. Furthermore, students are likely to seek information from numerous sources available to them through the device, thus supplanting the teacher as the sole information giver. It is clear that teachers need to re-examine their roles to ensure that how they work with students remains relevant and meaningful to the students (Royle et al., 2014). Some of these challenges are similar to those explicated in Chap. 2, yet others differ. It is no less important to develop skills in m-learning as it is for other forms of TEL. So, the challenge for teachers is to design m-learning activities that have built into them not only student autonomy, but also the ability to personalise student’s interests and encourage collaboration with a global network. Such are the aims of m-learning projects with which we have been recently engaged. The next section provides an overview of these projects, all of which consider aspects of m-learning and mobile pedagogies. We outline these projects to give a sense of contemporary research in m-learning. Other chapters in this book will describe aspects of these projects in more detail.

3.4 Trends in M-Learning: Our Research Projects At the time of writing, the authors are concluding an Erasmus + project, Developing and Evaluating Innovative Mobile Technologies (DEIMP), running from 2017 to 2020. The aim of this project is to support teachers to become designers of effective and innovative m-learning tasks. The first steps in the project were to conduct a scoping study (Burden, Kearney, Schuck & Hall, 2019) that investigated the innovative m-learning practices currently being used with school-aged students. Next, we derived a set of pedagogical principles that underpinned these practices (Burden,

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Kearney, Schuck & Burke, 2019). The aim of these principles is to guide the design of new m-learning activities by teachers. An app showing innovative designs and an online learning course for teachers have also been constructed. The final outcomes of DEIMP will be a set of innovative and effective m-learning tasks that will be available to all teachers (see www.deimpeu.com for more details). It is clear from the scoping study that there are numerous tasks in which mobile pedagogies are being employed and that enhanced student outcomes are occurring as a result (Burden, Kearney, Schuck & Hall, 2019). While such practices are not yet widespread, there are sufficient examples to indicate that mobile pedagogies can be productive and effective. The DEIMP project builds on the success of another Erasmus + project led by the authors, Mobilising and Transforming Teacher Educators’ Pedagogies (MTTEP) (see www.MTTEP.eu/ for further details). This project (2014–2017) had as its aim the support of teacher educators in using mobile pedagogies, the development of a teacher m-learning toolkit, the illustration of exemplary m-learning practices and the initiation of an m-learning network. These aims were all achieved and led to the development of the new project, DEIMP, which focuses on innovation. Teachers are now using the mobile pedagogical framework that we discuss further in Chaps. 5 and 7, and they indicate that the Framework is of value in scaffolding their mobile pedagogies (see Chap. 13). Another of our projects has recently concluded, running separately to the Erasmus + projects, and funded by the Australian Research Council. This project, introduced in Chap. 1, is titled Optimising Teaching and Learning with Mobile-Intensive Pedagogies (2015–2019). Its findings are currently being disseminated. Methodologies used in this project are a blend of qualitative and quantitative methods. Data from case studies of four exemplary schools were collected to explore how these schools use mobile pedagogies and m-learning. Teacher surveys were developed to investigate teachers’ use of mobile pedagogies and their choices of apps for teaching, and a survey of students looked at their perspectives on the use of mobile devices for learning. Different uses of mobile pedagogies by STEM and non-STEM practitioners have been identified, as have variations in the use of m-learning by mathematics and science practitioners. These surveys are discussed in Chaps. 11 and 12. The case studies revealed an emphasis on student personalisation and autonomy as well as opportunities for collaboration, two of the characteristics of m-learning noted earlier in this chapter. We have also been engaged with a project focused on prospective mathematics teachers’ use of mobile devices in their initial teacher education program (Kearney & Maher, 2013). Participants used their mobile devices to facilitate an enhanced awareness of mathematics in everyday contexts, and then used this knowledge to develop rich, contextualised ideas for their own technology-mediated K–6 mathematics tasks. This study was complemented by another small-scale study that investigated how mobile pedagogies might benefit pre-service primary school teachers in their study of mathematics (Schuck, 2016). The later study proposed that mobile pedagogies should be employed by mathematics teacher educators, both to engage

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their pre-service teachers in their teacher education studies and to model mobile pedagogies. We conclude this brief summary of our research projects with a study focused on the use of two-in-one devices by teachers in two case study schools, one primary and one secondary (Maher, Schuck, & Perry, 2017; Schuck & Maher, 2018). Teachers were encouraged to implement action learning projects to develop their thinking about ways of implementing mobile-intensive pedagogies. This study found that the untethering of the classroom was appreciated at both the primary and the secondary schools and that students’ use of mobile devices in their activities promoted autonomy, agency and engagement. All these studies provide counterpoints to arguments regarding the banning of devices in schools. Each has shown that effective and engaging activities are facilitated by the use of mobile pedagogies and that students perceive the use of devices as positive and beneficial for learning outcomes and engagement. They also highlight the need for teachers to gain supported in including effective and innovative mobile pedagogies in their suite of approaches. Other trends in m-learning research are highlighted by Chee et al. (2017) and include the following: studies mostly focus on effectiveness of m-learning in higher education, with fewer looking at elementary or primary education; most studies feature positive outcomes; the majority of studies concern learning of languages and art, followed by science; the smartphone is the device most often used in m-learning; and finally, informal learning is the most preferred method of m-learning. Some of these trends differ from our research findings (Burden, Kearney, Schuck & Hall, 2019), but this may be because in our study the emphasis was on innovation, and we focused on compulsory school-age learning. For example, we found science to be the most common discipline in which innovative m-learning occurred and that most of the examples of innovative m-learning took place within school classrooms and environs, rather than informally. It is likely that as mobile devices become even more prevalent and their affordances for learning are increasingly exploited, teaching and learning may need to include different forms of pedagogies and lead to different kinds of learning. Ubiquitous learning is already being used to describe the type of learning that can occur now with mobile devices and is likely to become even more familiar to future educators.

3.5 U-Learning The term ubiquitous learning (u-learning) was being discussed as early as 2004 (Ogata & Yano, 2004). Capitalising on the ability of mobile devices to be used anywhere and at any time for m-learning in and out of school, u-learning reflects the pervasive presence of mobile devices and captures the concept of learning beyond the school classroom, in contexts that are wide and varied (Schuck, Kearney, & Burden, 2017). U-learning was considered to derive from ubiquitous computing or ubiquitous learning technologies (Yahya, Ahmad, & Jalil, 2010). Ubiquitous computing was first

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described in 1991 as technologies began to allow people to exchange information and access services in any place or time (Weiser, 1991, cited in Yahya et al., 2010). Ogata and Yano (2004) tracked developments in learning from fixed desktop, computer-assisted learning, through m-learning to pervasive learning, and finally u-learning. They suggested that desktop learning provides low mobility and low embeddedness in a learning context, while m-learning increases the mobility and the ability to learn at any time and anywhere. Pervasive learning refers to the gaining of information from the environment through the technology and is thought to be highly embedded but with low mobility. U-learning is regarded as having high mobility and high embeddedness. Ogata and Yano (2004) noted that some researchers suggest that u-learning may include both pervasive learning and m-learning. However, according to Yahya et al. (2010), u-learning was a vague and ill-defined term that did not enjoy a consensus at the time. They suggested that there was a lack of clarity about what u-learning looked like. A more recent description of u-learning is that “ubiquitous learning (u-learning) [is based] on ubiquitous computing and technology supported environments which can be used in all places and at all times” (Virtanen, Kääriäinen, Liikanen, & Haavisto, 2017, p. 2566). This definition encompasses m-learning and also acknowledges the technologies that allow interaction with the environment. Many studies now consider context-aware u-learning, where the technology itself can respond to the environment and interact with it. Given our emphasis in this book on the learning rather than the technology, the aspects of u-learning that are relevant here are the ability for students to have their learning highly embedded in particular contexts, and for them to learn across contexts in highly mobile ways. While our research has generally used the term m-learning, we acknowledge that u-learning is a term that may supplant m-learning, given its emphasis on the ubiquity of connectedness and availability of resources to learn in situ. It is therefore of interest to understand what is happening in this u-learning space. Given students’ abilities to learn in environments outside of the school, u-learning may be very disruptive of current school practices. Numerous studies (see Burden, Kearney, Schuck & Hall, 2019) have shown that students are using context-aware apps to glean information and to enact tasks. These apps provide relevance to tasks and facilitate activities by encouraging autonomy, choice and engagement. This means that the device itself is more than a technology; it is a student learning portfolio, an augmented reality provider, a task provider and a conduit to experts in the field (Schuck et al., 2018). However, the majority of teachers and schools are not engaging with u-learning and still remain highly tethered to classrooms, routines and timetables that do not take advantage of the characteristics of u-learning. The anticipated disruption is occurring in only a few learning environments and remains largely in the domain of higher education (Schuck et al., 2018). We therefore need to consider the implications for learning and for teaching of both m-learning and u-learning.

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3.6 Implications for Teaching and Learning Our scoping study for the DEIMP project identified articles reporting on innovative mobile practices for school-aged students (Burden, Kearney, Schuck & Hall, 2019). Although a systematic literature review conducted with key search terms across the most relevant databases initially found hundreds of possible articles, we found only 57 articles that actually discussed effective and innovative m–learning activities. Within these 57 papers, the innovation occurred on a continuum from low innovation, sometimes called sustaining or incremental innovation, to high innovation, sometimes called radical innovation or disruption (Christensen, Horn, & Johnson, 2008; Law, 2003). Only three articles described disruptive m-learning activities, that is, ones that showed high levels of authenticity, seamlessness, student agency and collaboration with the greater community. The greatest number of articles (29 articles) discussed innovations at the sustaining end of the continuum (Burden, Kearney, Schuck & Hall, 2019). These results are further discussed in Chap. 14 and suggest that, given the complexity of teaching and schooling (Jordan, 2011), it is impractical to expect teachers to embrace disruptive m-learning activities and it is much more realistic and effective to support teachers to achieve incremental innovation (Cranmer & Lewin, 2017). The implications therefore for teaching and learning with mobile devices, and for u-learning, are clear. First, teachers, executive staff, parents and students need to be persuaded of the benefits of including m-learning and u-learning in their suite of learning tasks and activities. We are not suggesting that other ways of learning be replaced, but rather that tasks that are facilitated by m-learning or u-learning be included in students’ learning and teachers’ design of learning. The available literature demonstrating effective learning and high engagement by students in mlearning tasks needs to be publicised and included in conversations about the place of m-learning. Second, once teachers embrace the value of m-learning and, more importantly, emphasise aspects of it that will enhance student engagement, they need to be supported in their design of learning activities or tasks that include use of mobile devices. These aspects include student autonomy, personalisation, and authentic and meaningful tasks. Projects such as the DEIMP project are seeking to support teachers in these ways. Scaling projects of this nature are essential for promoting effective m-learning. Third, we need to listen to student voices. As Traxler (2009) foretold in his thought piece about students and mobile devices, the near ubiquity of devices is enabling students to behave in ways formerly unheard of. They are gaining more independence, finding information from all sorts of sources, working with malleable time and accessing a world of experiences that were never before available. Teachers have an important role to ensure that students learn how to manage this new world effectively and safely. They also need to ensure that the second digital divide (Somekh, 2004) is narrowed in terms of students’ behaviour when they access this portal to the

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world. The rest of this book focuses on how we can support teachers in managing this new, exciting and challenging domain.

3.7 Conclusion This chapter has introduced the concepts m-learning and u-learning and discussed the different viewpoints regarding their value. Their distinctive characteristics have also been articulated along with some of their associated challenges. We have presented the benefits of m-learning and outlined various projects we have conducted over the past decade to study how best to implement such learning. We note how important it is to support teachers to include m-learning tasks that feature student autonomy, authenticity, relevance, ownership and collaboration. In the rest of the book, we explicate the framework for m-learning that we have developed, and we outline how the Framework has been implemented in projects around the world. We also discuss the results of some of those projects and indicate both the benefits of m-learning and the types of support that are available for teachers to develop their mobile pedagogies. Given that one of the most useful features of m-learning is the portability of devices and the consequent enabling of learning anywhere and at any time, it is important to explore the implications of this untethering of learning. The next chapter considers this untethering and introduces the concept of the third space of mobile learning, a metaphor used by the authors to describe the types of learning now possible (Schuck et al., 2017).

References Bano, M., Zowghi, D., Kearney, M., Schuck, S., & Aubusson, P. (2018). Mobile learning for science and mathematics school education: A systematic review of empirical evidence. Computers & Education, 121, 30–58. Big Picture Education Australia (BPEA). (2016). The big picture design for learning and school. The-big-picture-brochure-oct16. pdf. Accessed from www.bigpicture.org.au Bowles, N. (2018, October 26). The digital gap between rich and poor kids is not what we expected. The New York Times. Retrieved from https://www.nytimes.com/2018/10/26/style/digital-dividescreens-schools.html Brand, J. E., Todhunter, S., & Jervis, J. (2017). Digital Australia Report 2018. Eveleigh, NSW: Interactive Games and Entertainment Association (IGEA) Burden, K., Schuck, S., & Kearney, M. (2019). Should we be concerned about mobile devices in the classroom: What does the evidence say? Impact. Journal of the Chartered College of Teachers. January, 2019. Retrieved from https://impact.chartered.college/article/mobile-devices-schoolsreally-innovative-what-does-evidence-say/ Burden, K., Kearney, M., Schuck, S., & Burke, P. (2019). Principles underpinning innovative mobile learning: Stakeholders’ priorities. TechTrends, 63(6), 659–668.

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Burden, K., Kearney, M., Schuck, S., & Hall, T. (2019). Investigating the use of innovative mobile pedagogies for school-aged students: A systematic literature review. Computers & Education, 138, 83–100. https://doi.org/10.1016/j.compedu.2019.04.008. Callow, J., & Orlando, J. (2015). Enabling exemplary teaching: A framework of student engagement for students from low socio-economic backgrounds with implications for technology and literacy practices, Pedagogies: An International Journal, 10(4), 349–371. https://doi.org/10.1080/155 4480x.2015.1066678 Carr-Gregg, M. (2019). Review into the non-educational use of mobile devices in NSW schools – report. Department of Education. https://education.nsw.gov.au/about-us/strategies-and-rep orts/our-reports-and-reviews/mobile-devices-in-schools/review-into-the-non-educational-useof-mobile-devices-in-nsw-schools Chee, K. N., Yahaya, N., Ibrahim, N. H., & Noor Hassan, M. (2017). Review of mobile learning trends 2010–2015: A meta-analysis. Educational Technology & Society, 20(2), 113–126. Christensen, C. M., Horn, M. B., & Johnson, C. W. (2008). Disrupting class: How disruptive innovation will change the way the world learns. New York, NY: McGraw-Hill. Cranmer, S., & Lewin, C. (2017). iTEC: conceptualising, realising and recognising pedagogical and technological innovation in European classrooms. Technology, Pedagogy and Education, 26(4), 409–423. https://doi.org/10.1080/1475939X.2017.1299791. Cristol, D., & Gimbert, B. (2014). Academic achievement in BYOD classrooms. Journal of Applied Learning Technology, 4(1), 24–30. Crompton, H. (2017). Moving toward a mobile learning landscape: Presenting a m-learning integration framework. Interactive Technology and Smart Education, 14(2), 97–109. https://doi.org/ 10.1108/ITSE-02-2017-0018. Crompton, H. (2013). Mobile learning: New approach, new theory. In Z. L. Berge & L. Y. Muilenburg (Eds.), Handbook of mobile learning (pp. 47–57). Florence, KY: Routledge. Deloitte, (2020). Mobile consumer survey 2019: Unwired. Unrivalled. Unknown. Deloitte Touche Tohmatsu, UK. Hudson, I. (2018, September 6). France school mobile phone ban takes effect. The Sydney Morning Herald. Retrieved from https://www.smh.com.au/world/europe/france-school-mobilephone-ban-takes-effect-20180906-p5023k.html Jordan, K. (2011). Framing ICT, teachers and learners in Australian school education ICT policy. Australian Educational Researcher, 38, 417–431. https://doi.org/10.1007/s13384-011-0038-4. Kearney, M., & Maher, D. (2013). Mobile learning in maths teacher education: Driving pre-service teachers’ professional development. Australian Educational Computing, 27(3), 76–84. Kearney, M., Schuck, S., Burden, K., & Aubusson, P. (2012). Viewing mobile learning from a pedagogical perspective. Research in Learning Technology, 20. Retrieved from https://journal. alt.ac.uk/index.php/rlt/article/view/1225/pdf Law, N. (2003). Innovative classroom practices and the teacher of the future. In C. Dowling & K. W. Lai (Eds.), Information and communication technology and the teacher of the future (pp. 171–182). Dordrecht: Kluwer. Ling, R., & Donner, J. (2009). Mobile communications. London: Polity. Maher, D., Schuck, S., & Perry, R. (2017). Investigating knowledge exchange amongst school teachers, university teacher educators and industry partners. Australian Journal of Teacher Education, 42(3), 73–90. Melzer, A., Hadley, L., Glasemann, M., Günther, S., Winkler, T., & Herczeg, M. (2009). Iterative design of mobile learning systems for school projects. Technology, Instruction, Cognition & Learning, 6(4), 235–251. Ng, W., & Nicholas, H. (2013). A framework for sustainable mobile learning in schools. British Journal of Educational Technology, 44(5), 695–715. Nouri, J., Spikol, D., & Cerratto-Pargman, T. (2016). A learning activity design framework for supporting mobile learning. Designs for Learning, 8(1), 1–12. https://doi.org/10.16993/dfl.67

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Ogata, H., & Yano, Y. (2004). Context-aware support for computer-supported ubiquitous learning. In Proceedings of the 2nd IEEE International Workshop on Wireless and Mobile Technologies in Education (pp. 27–34). Orlando, J. (2018, June 28). Response to: We asked five experts: should mobile phones be banned in schools? The Conversation. Retrieved from http://theconversation.com/we-asked-five-expertsshould-mobile-phones-be-banned-in-schools-98708 Royle, K., Stager, S., & Traxler, T. (2014). Teacher development with mobiles: Comparative critical factors. Prospect, 44, 29–42. Schuck, S. R. (2016). Enhancing teacher education in primary mathematics with mobile technologies. Australian Journal of Teacher Education, 41(3), 126–139. Schuck, S., & Maher, D. (2018). Creating opportunities for untethered learning. Technology, Pedagogy and Education. (online version) 1–12. https://doi.org/10.1080/1475939x.2018.151 0788 Schuck, S., Aubusson, P., Burden, K., & Brindley, S. (2018). Uncertainty in teacher education futures: Scenarios, politics and STEM. Singapore: Springer. Schuck, S., Kearney, M., & Burden, K. (2017). Mobile learning in the Third Space. Technology, Pedagogy and Education, 26(2), 121–137. Somekh, B. (2004). Taking the sociological imagination to school: An analysis of the (lack of) impact of information and communication technologies on education systems. Technology, Pedagogy and Education, 13(2), 163–179. https://doi.org/10.1080/14759390400200178. Traxler, J. (2009). Learning in a mobile age. International Journal of Mobile and Blended Learning, 1(1), 1–12. Traxler, J. (2010). Students and mobile devices. Alt-j, 18(2), 149–160. Virtanen, M. A., Kääriäinen, M., Liikanen, E., & Haavisto, E. (2017). The comparison of students’ satisfaction between ubiquitous and web-based learning environments. Education and Information Technologies, 22(5), 2565–2581. Wang, M., Callaghan, V., Bernhardt, J. K., White, A., & Peña-Ríos, A. (2018). Augmented reality in education and training: pedagogical approaches and illustrative case studies. Journal of Ambient Intelligence and Humanized Computing, 9, 1391–1402. https://doi.org/10.1007/s12652017-0547-8. Weiser, M. (1991). The computer for the 21st Century. Scientific American, 265(3), 94–105. Wright, S., & Parchoma, G. (2011). Technologies for learning? An actor-network theory critique of ‘affordances’ in research on mobile learning. Research in Learning Technology, 19(3), 247–258. Yahya, S., Ahmad, E. A., & Jalil, K. A. (2010). The definition and characteristics of ubiquitous learning: A discussion. International Journal of Education and Development Using Information and Communication Technology, 6(1), 117–127. Retrieved from https://www.learntechlib.org/d/ 188069

Chapter 4

Seamless Learning—Mobile Learning in the Third Space

Abstract This chapter introduces the concept of the third space for mobile learning, as previously articulated by the authors. It starts by analysing third space theory as originally proposed in the wider literature of cultural theory. We explain the underlying concepts inherent in third space literature, such as hybridity and seamlessness of boundaries. The next step is to examine third space concepts in education. We analyse the contributions of these concepts to education thinking, including to ideas of university-school partnerships. Following an analysis of third space as used in education more broadly, we consider the use of third space thinking in digital education. We then focus on mobile learning and the third space. We investigate the particular characteristics of mobile learning that make it seamless and suitable for third space learning. The construct of third space as a metaphor for new ways of learning and teaching using mobile pedagogies is explained. The authors’ previous work on third space learning is outlined, and the implications for education are discussed. These implications consider the changing roles of students and teachers, and the changing structures of schools. Provocative suggestions regarding curriculum, schooling and teaching are raised in the light of the affordances of mobile learning in the third space. Keywords Third space · Mobile learning · M–learning · Seamless learning · Hybrid learning · School structures · Time-Space

4.1 Introduction This chapter is the final chapter in the part on current contexts of learning with technologies. Chapter 2 considered the current and past trends for learning with digital tools, and Chapter 3 focused on one particular type of digital learning, that of mobile learning. It explored the place of mobile learning (m-learning), the reasons for adopting m-learning and mobile pedagogies, and the contributions of m-learning to quality teaching and learning. Constraints and barriers were also discussed. Implications of both technology-enhanced learning (TEL) and m-learning were discussed in these two chapters with reference to the changing roles of teachers and students.

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In this chapter, we consider m-learning in the third space. The construct of the third space will be examined, from its early origins to its use in the area of mlearning. The key concepts inherent in the notion of third space will be articulated and discussed. The discussion will then consider the value of third space thinking in education, where the concept has been used in a variety of settings. We then turn to a consideration of this third space metaphor for understanding m-learning, its features and characteristics. Features of m-learning that link to third space ideas of hybridity and boundary crossing will be articulated. The need to change the roles of teachers and students in the third space will be explored, supplemented by discussion about the changing roles of the institution of school and some of the characteristics of contemporary schools, such as classrooms and timetables.

4.2 Third Space Constructs A number of theorists are regarded as the originators of the third space concept. The concept of third space was originally used by Bhabha (1988, 1994, 1996) in his discussion of cultural difference and notions of colonisation. According to Bhabha, the fixed and immutable positions and identities of the colonising authority and the colonised or ‘other’ cause antagonism and inequity. He suggested that this type of binary thinking is dangerous and proposed a third space in which the cultural difference between coloniser and colonised is located ‘in-between’ the two subject positions and is dynamic and changeable. It is in this hybrid space that disruption and displacement of the hegemonic colonising position can occur. For Bhabha (1988), this in-between space or third space is where the “cutting edge of translation and negotiation” (p. 22) can exist. In cultural theory, this space lies between a hegemonic authority and a subjugated other, and is a space in which a negotiation of position and identity can take place for both. Bhabha’s thoughts provide important concepts which have been used in a variety of other domains beyond cultural theory. One important idea is that of the hybridity that exists in this in-between space. The importance of this hybridity lies in its critique of essentialism and its ability to create opportunities for disruptive and malleable positions to occur. New sources of authority and new constructs exist in this hybrid space. In his interview with Rutherford, Bhabha says: The importance of hybridity is not to be able to trace two original moments from which the third emerges, rather hybridity to me is the ‘third space’, which enables other positions to emerge. This third space displaces the histories that constitute it, and sets up new structures of authority, new political initiatives … The process of cultural hybridity gives rise to something different, something new and unrecognizable, a new area of negotiation of meaning and representation. (Rutherford, 1990, p. 211)

Similar constructs of third space were developed by Soja, a cultural geographer. Soja (1996) suggested that in this space everything comes together… subjectivity and objectivity, the abstract and the concrete, the real and the imagined, the knowable and the unimaginable, the repetitive and the differential,

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structure and agency, mind and body, consciousness and the unconscious, the disciplined and the transdisciplinary, everyday life and unending history. (p. 57)

From Soja’s work, we derive the constructs of boundary crossing: a third space in which the two ends of continua are brought together to provide new understandings. While there are many other authors who have discussed the construct of third space in non-educational settings, most of their understandings have been informed by Bhabha and Soja. Another concept related to that of third space is the idea of third place, as presented by Oldenburg and Brissett (1982). Many theorists distinguish between the third space and third place by suggesting the third space is a more metaphysical concept, dealing as it does with abstract concepts such as time, sociality, identity and space. In contrast, third place is thought to represent more geographical constructs. For the purposes of this book, however, some of the discussion by Oldenburg and Brissett regarding the third place has been appropriated as it aligns well with the notions of third space related to m-learning. These authors suggested third places as the in-between places, where the first place is home and the second place is the workplace. They characterise third places as ones “where people gather primarily to enjoy each other’s company” (p. 269). They note that third places have the following characteristics: • they are public places that are “accessible to [their] inhabitants and appropriated by them as their own” (p. 270) allowing for inhabitants to freely socialise; • they are democratic places where interaction is not dictated by hierarchies but rather by acceptance of inhabitants as equals; • conversation is not purposeful but is characterised by a sense of joyfulness and playfulness; • they are “populated by a shifting diversity of inhabitants who are granted involvement by virtue of their presence at a particular place at a particular time” (p.274), leading to an anticipation of new experiences by these inhabitants. It can be seen from the articulation of these characteristics that for Oldenburg and Brissett, the third place is located outside of the workplace and the home and exhibits an atmosphere of openness, flexibility and collegiality. It is seen as a gathering place for community activities. These characteristics align well with our construct of third spaces for m-learning, as will be discussed in a later section of this chapter. In the next section, we consider ideas about the third space located in educational settings. The influences of Bhabha, Soja and Oldenburg are apparent in these educational constructs of third space. Ideas of hybridity, of in-between spaces, of negotiation and fluidity underpin the ideas in this section and ideas of geographical locations that are informal, playful and joyful align well with m-learning.

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4.3 The Third Space in Educational Settings One of the seminal writers on third space in educational settings is Gutiérrez, who studies language learning, participation and socio-cultural practices. With colleagues (Gutiérrez, Rymes, & Larson, 1995), Gutiérrez indicates how creating a third space in which students and teachers can interact and negotiate meanings can prevent a teacher script from being subverted by counterscripts offered by students. The teacher script indicates the official and accepted version of knowledge sanctioned by the teacher; the counterscripts are the oppositional and subversive scripts that the students operating in a different cultural setting offer as their resistance to the teacher script. Gutiérrez et al. (1995) suggest that the third space provides a framework for redefining what counts as effective classroom practice. Effective practice, in this sense, exists in contexts in which various cultures, discourses, and knowledges are made available to all classroom participants, and thus become resources for mediating learning (p. 458).

The third space acts as a bridge between the dominant culture and the student cultures. It allows all inhabitants of the space to redefine accepted knowledge and provides opportunity for all to disrupt traditional pedagogical practices and scripts and create new ones. Gutiérrez, Baquedano-López, and Tejeda (1999) have suggested that it is in the third space that “local practice, knowledge, and beliefs of both the local community and of the classroom and school community [are] brought to bear in everyday classroom practice” (p. 291). In her later work, Gutiérrez (2008) discussed her understanding of the third space as “a transformative space where the potential for an expanded form of learning and the development of new knowledge are heightened” (p.152). She goes on to discuss how the third space can be seen as a zone of proximal development. She presents the notion of a collective third space, one in which different activity systems and new shared visions of education can develop while acknowledging the different trajectories and cultures of the participants in this space. Gutiérrez’s work in educational settings is useful in identifying the conflicts and varying trajectories that different groups bring to their learning. Her notion of a third space for learning takes this complexity into account and suggests a need for renegotiation of knowledge and authority in learning. Moje et al. (2004) cited the works of Bhabha, Gutiérrez and Soja as important in their development of third space constructs in education. Working in the field of content area literacy, Moje et al. considered the knowledge and understandings that adolescents bring to particular content areas, and how these knowledge interplay with the classroom knowledge that is often the privileged version of knowledge. For these authors, the third space is the bridge between the first space of the students’ homes, networks and discourses and the second space of the formal classroom learning. It provides a space for a third kind of knowledge that can be negotiated and constructed, a hybrid of the first and second knowledge. As will be seen in our discussion of the third space for m–learning, the acknowledgement of the importance of the students’

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out-of-school interactions and networks in creating knowledge and forging practice is fundamental to the ideas of m–learning. These two examples of third space in education focus on literacy and language learning, and consider knowledge of non-dominant groups and their interactions in the third space with the dominant culture and privileged knowledge. Moje et al. (2004) outlined three types of third space: (1) a space where bridges are built from marginalised communities to accepted academic learning; (2) a way of crossing boundaries and navigating across different knowledge; and (3) a place of transformation where competing versions of knowledge and understanding are brought into the space and challenge the existing knowledge of participants in the space. We suggest that it is the third type outlined here that relates most closely to the third space for m–learning. We now move to a different discussion in which teacher education is the focus of the research on third spaces. A number of authors have conducted thought pieces and research on this topic (Flessner, 2009, 2014; Martin, Snow, & Franklin Torrez, 2011; Zeicher, 2010), and we now discuss their work. Flessner (2009) discusses how, as a teacher, he utilised third space theory to support the learning of students who were struggling with mathematics. He found that the more he planned, the less success he had with using a third space and that when activities happened organically they were most successful. He notes how he used the knowledge funds of the multicultural students to help them understand some mathematical concepts and suggests this activity was an example of third space learning. In later work, Flessner (2014) examined how third spaces can facilitate the practices of teacher educators to ensure that they are navigating the space between their academic approaches and the realities of the school classroom. He suggests that avoiding the binaries of theory/practice to re-create and re-imagine a hybrid of the two is a useful way to augment teacher education and ensure it transcends the boundaries of academia and enhances classroom practice. Like Flessner (2014), other authors have examined the theory/practice gaps and sought to find ways that the third space can enhance teacher education practices. Martin et al. (2011), for example, sought to remove the oppositionality that often exists among the three educational stakeholders: teacher educators, pre-service teachers (PSTs) and practising teachers. They suggested that by moving into the third space, binary oppositions can be rejected in favour of more useful boundary crossings, negotiation of new understandings and hybridity. Both Flessner (2014) and Martin et al. (2011) have been influenced by the thinking of Zeichner (2010), who focused on the education of PSTs. In his article, Zeichner noted the gap between what students are taught in the university component of their teacher education programs and what they experience in the classroom component. He suggested that discussions about the third space could lead to a new paradigm for PSTs’ learning. In this paradigm, PSTs would learn in the third space in which binaries of academic discourse and practitioner knowledge are rejected and a new hybrid space is created. In such a hybrid space, teachers, teacher educators and PSTs interact in less hierarchical and more egalitarian ways than exist when we separate the learning provided to PSTs into first spaces of academic discourse and second spaces

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of practitioner knowledge. In the new third space, learning is more democratic and one type of knowledge is not privileged over another; rather it is a place for discussion, negotiation and learning for all stakeholders. The preceding examples are of educational third spaces that vary in context but are nonetheless hybrid spaces in which all stakeholders conduct their interactions in an egalitarian way and power is shifted and shared. What they have in common is a rejection of binaries such as practitioner and academic knowledge and theory and practice and involve the integration of what are often seen as competing discourses in new ways—an either/or perspective is transformed into a both/also point of view. (Zeichner, 2010, p. 92)

This discussion of educational third spaces is also linked to ideas of the third space that have been used in online learning. In the next section, we introduce third space learning with digital technologies.

4.4 Third Space Learning with Digital Technologies We now examine those studies which investigated third space learning with digital technologies. This is a precursor to investigating how m–learning is aligned to learning in the third space. Authors who have discussed the use of third space theory in learning with educational technologies, including learning online, have noted the suitability of this construct due to its identifying characteristics across all the literature on third spaces. In particular, characteristics of online learning and learning with digital technologies that align well with third space constructs include the democratic nature of the space, the rejection of binaries, the hybridity of the space and the opportunities for negotiation and collaboration. We expand on these alignments as we consider articles that have been written on this topic. Howell, Sheffield, Shelton, and Vujaklija (2017) address the context that Zeichner (2010) had discussed, of supporting PST learning. They consider how an online space could provide a third space for PSTs to benefit from both the theory provided by their teacher educators and the experiences they were having in classroom settings. While their setting is similar to that put forward by Zeicher, that is, a setting evolved to support PSTs to bridge the gap between theory and practice, third space constructs are not strongly evident in their study. They created a backchannel online platform to enable PSTs to work with their teacher education facilitators and gain their support as they strove to make sense of their practical experiences in the classroom while on a teaching practicum. While these PSTs were being provided with a safe space in which to negotiate understandings with the support of their faculty instructors, the space illustrated limited hybridity. For example, PSTs worked with their colleagues and instructors to understand practice but the authority structures between the instructors and the PSTs did not appear to be under negotiation and the instructors did not appear to be learning while in the online space. Identities were not being challenged in this study of third space. Nevertheless, the backchannel did provide a useful and effective space for PSTs to merge their learnings about theory and practice and to consider the

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meaning of their experiences. There was fruitful dialogue among the inhabitants of this space about learning and practice. A valuable suggestion of the study is that an online backchannel could serve productive purposes in providing opportunities for a safe and supportive third space for analysing teacher practices and understanding the practices in the classroom. A similar online space is discussed in Lawson’s (2004) article on third places and online interactions. Lawson considers the position of virtual learning in a discussion of libraries as third place sites. In this article, the geographical notion of third places is invoked, with Lawson proposing that libraries are third places, in the way that Oldenburg and Brissett (1982) conceptualised this construct: homes as first places and schools or workplaces as second places indicate a need for informal and relaxed third places to gather socially. While Oldenburg and Brissett suggested that these would take the form of coffee shops, cafes and community centres, Lawson (2004) argues that the public library also serves as a third place, a place for community interactions in relaxed and accessible ways that “enrich public life and democracy” (p. 125). They do this by being available to all members of the community and promoting a sense of collegiality and welcome. Having argued for the public library as a third place in the sense discussed here, Lawson then moves to examine the nature of the virtual library to evaluate its role as a third place. The existence of virtual communities has flourished since the time of Lawson’s article and many of these do indeed provide a sense of belonging and a place for people to exchange ideas, relax and interact. In this way, virtual places can become third places that are faithful to Oldenburg’s (1989) ideas. Virtual library sites are no exception, and with their ability to generate discussion forums they too can be third places that serve the community. It does need to be noted, however, that not all online communities are third places, just as not all coffee shops are third places. Specific social and environmental characteristics need to be in place (Wright, 2012). Accessibility, democracy and an ability to have exchanges and interactions are the defining features that make the place become a third place fitting the description presented by Oldenburg and Brissett (1982). However, the emphasis that these authors place on a common geographical site is not a key characteristic of virtual third places, and perhaps the term third space is more appropriate for virtual characterisations of the third place (Wright, 2012). Wright argues that in third spaces it is the common link shared between participants that is central, rather than the common location at which interactions take place. However, he goes on to suggest that the distinction “between place and space, real and virtual, is unhelpful” (p. 11). As new digital technologies evolve, the distinction between place and space starts to blur, with both geographical and non-geographical communities potentially part of third spaces. The above discussion suggests that third spaces can exist both physically and virtually and that their locations are less important than other defining characteristics concerning hybridity, rejection of binaries, shared issues, accessibility and inclusivity. We now consider third spaces in relation to m–learning, and we reference our early work on this topic in which we proposed that this construct is particularly useful in discussions about m–learning (Schuck, Kearney & Burden, 2017).

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4.5 M-Learning in the Third Space As discussed in Chapter 3, m–learning has a number of distinctive characteristics. Firstly, the learning can take place in any location and is not restricted to formal institutions of learning or to the home. Learning in a coffee shop, on the train or in an airport lounge are all possible due to our increased connectivity and to the portability of our devices. Secondly, as indicated in all discussions of m–learning, this learning can take place at a time of convenience to the learner and does not have to be restricted to institutionalised timetable periods; the expression ‘learning anytime, any place’ is frequently used to describe m–learning. Further, as we articulate in more detail in Chaps. 5 and 7, m–learning can promote traits of personalisation, collaboration and authenticity in the learning tasks. Finally, m–learning allows users to access information from experts around the world and to hold positions of expertise themselves, and it has the potential to encourage and promote democratic and egalitarian interactions. Mobile devices are almost ubiquitous in many countries, and they allow their users to do new and different activities that would be impossible without them. These characteristics and affordances of learning with mobile devices suggest a need to expand the language regarding m–learning and to consider a broadened understanding of learning that is facilitated by mobile devices. The construct of the third space enables this expansion of understanding of m–learning. As noted in the discussion above regarding third space and third place, the characteristics of these two concepts contribute a notion of egalitarian and democratic interaction in a hybrid space. Oldenburg’s third place (1989) is an accessible, comfortable and inclusive place for participants to interact and can be in the form of a coffee shop or meeting place in which all are welcome. Bhabha, Soja, Zeichner and Gutiérrez discuss third spaces as spaces in which different cultures can meet and negotiate shared understandings, a space in which egalitarian sharing of cultural knowledge and resources is valued by all in the space. The third space includes a rejection of binaries, and the third place opens the learning to new geographical locations, accessible to all. These locations can be virtual or physical. We suggested in a previous article (Schuck et al., 2017) that the characteristics of third spaces align well with the nature of m–learning, and so we introduced this metaphor to describe the new phenomena that are promoted by m–learning. Firstly, when we consider the rejection of binaries that operates in third spaces, it is clear that this rejection operates in m–learning: the distinctions between formal and informal learning are becoming blurred, asynchronous and synchronous learning are fusing, and expertise is being broadened to include the cultural capital of more learners. Secondly, the hybridity of the space in which m–learning takes place is obvious, for example, through the blending of online and physical learning. The rejection of binaries ensures that learning takes place with hybrid features. M–learning can allow all to express their views, and this creates a democratic discourse—a characteristic of third spaces. Students, teachers and external participants can interact in ways that are respectful of all that the actors bring to the space.

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If we turn to the characteristics of third places identified by Oldenburg and Brissett (1982), we can see that many of the characteristics articulated earlier are present in m– learning interactions: they often take place in virtual public spaces accessible to all; they are often playful; interactions between participants are egalitarian; the virtual spaces are generally easy to access; and often for users, participation in these mlearning interactions can lead to an anticipation of new experiences and opportunities to come together purely to socialise. We therefore suggest that the metaphor of the third space is a useful one for conceptualising m–learning. As Schuck et al. (2017) note: The metaphor of the third space helps us move away from current notions of 21st Century learning that are still bounded by 20th Century concepts of school learning. It allows us to consider learning that includes but is not restricted to classrooms, set curricula, and authoritative teachers. We argue that to develop the flexibility, autonomy, collaboration and resilience required in 21st Century learning, we need to extend current views of learning, views that anchor learning to classrooms, rooted in traditional concepts of time and place. We need to acknowledge the choices that learners have today for the nature, time and place of their learning, choices that far exceed what is offered in contemporary schooling. (p. 125)

The third space learning discussed in this quotation is facilitated by m–learning. The use of mobile devices is often noted as allowing learning to be untethered from the classroom (Schuck & Maher, 2018), to provide opportunities for seamless learning (Hedberg & Stevenson, 2014; Rushby, 2012; Toh, So, Seow, Chen & Looi, 2013) as students cross boundaries between various sites of learning and allow opportunities for both personalisation and collaboration (see Chaps. 5 and 7). The metaphor provides us with a vehicle for reconceptualising the nature of m-learning and for identifying central characteristics of such learning (Schuck et al., 2017). It clearly indicates that we need to consider m-learning in a different way to traditional notions of learning that might not fit as well with these notions of hybridity. As a result of viewing m-learning in this way, questions arise as to the implications of such learning for the roles of teachers and students and for teacher educators. The next section considers these implications.

4.6 Implications of M-Learning in the Third Space for Teaching and Learning In this section, we intend to be provocative. We do not believe that schools are going to be redundant and obsolete structures in the near future, nor do we believe that there will be radical changes in the way schooling occurs. Education is notoriously reactive and averse to change (Kearney, Burden, & Schuck, 2019). However, we put forward the following controversial suggestions to encourage and provoke debate on the nature of learning and teaching in an epoch in which learning can take place at anytime, anywhere, at any pace and about any content, supported by connected and portable learning devices that are becoming ‘smarter’ with the passage of time.

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There are a number of areas in which the advent of m-learning as a third space activity challenges the status quo and suggests the need for debate and possible reconceptualisation of learning. These areas include teacher roles, student roles, the need for schools, the nature of curriculum, high-stakes testing and examinations, and entry requirements for university (Schuck et al., 2017). We will briefly discuss each of these in turn. Teachers’ roles in this third space will very likely need to change from them being the authorities in the classroom to becoming more egalitarian. Given that the need for a teacher to be the source of knowledge will diminish as connectivity allows students to source their information and their skills from the Internet via podcasts, vodcasts and MOOCs, it may be that teachers should have a different role to play. This role might be to help students develop critical skills so that they can discern whether or not offerings are validated and reliable. Teachers might become guides supporting students in seeking the necessary knowledge to enact projects that interest them (see Chap. 3). They might engage in learning in the third space alongside their students and with global learning communities that have valuable expertise. A corollary to the change in teacher roles is that student roles will change too. Students will become more independent, and their cultural knowledge will contribute as much to the teachers’ knowledge as to peer learning. Students will have opportunities to choose where, when and what to learn in these third spaces. This will have implications for school classes, timetables and curricula. New roles might be needed for schools—rather than being in their current forms, with set classrooms, timetables, schedules and goals, schools might need to change in nature. Currently, some of these changes are already occurring. For example, newly built schools often are much more open in design, with some not having formal classrooms, structures and timetables. Students are encouraged to investigate topics that interest them and teachers support them to do so. Curriculum reviews are also in progress in educational jurisdictions, with projectbased assessments becoming popular. One of the biggest obstacles to change in assessment processes is the universities, which still require high-stakes assessments to convey information about the abilities of students entering them. Until these forms of assessment change, the changes in schooling suggested in this section are unlikely.

4.7 Conclusion This chapter has used third space theory to propose transformational ways of conceptualising m–learning. Third space theory suggests that the third space is a hybrid area in which participants from the first and second space can interact, discuss and negotiate (Bhabha, 1996; Gutiérrez, 2008; Soja, 1996). The idea of democratic and egalitarian interactions is crucial to this theory. Zeichner (2010) discusses a hybrid space in which educators from different spaces can meet in a spirit of collegiality. These key ideas about the third space make it a useful metaphor for m-learning. The advent of mobile devices and their increasing ubiquity allow learning to cross

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boundaries and provide more democratic, accessible and egalitarian experiences. Mlearning promotes out-of-school interactions and allows existing knowledge to be challenged and negotiated. It is incumbent on educators to consider how to leverage these opportunities to enhance learning for students. The use of the metaphor of third space for m-learning will help to realise these opportunities. While changes may not be as radical as those suggested here, there is ample opportunity to utilise m-learning in a third space in ways that expand and optimise learning experiences. This chapter concludes the part on context. The next part of the book considers frameworks for m-learning, introduces our Mobile Pedagogical Framework and then traces its development and transition to become the iPAC model.

References Bhabha, H. K. (1988). The commitment to theory. New Formations, 5, 5–23. Retrieved from http:// banmarchive.org.uk/collections/newformations/05_05.pdf Bhabha, H. K. (1994). Frontlines/Borderposts. In A. Bammer (Ed.), Displacements: Cultural Identities in question (pp. 269–272). Bloomington, IN: Indiana University Press. Bhabha, H. K. (1996). Culture’s in-between. In S. Hall & P. Du Gay (Eds.), Questions of cultural identity (pp. 53–60). London: Sage. Flessner, R. (2009). Working toward a third space in the teaching of elementary mathematics. Educational Action Research, 17(3), 425–446. Flessner, R. (2014). Revisiting reflection: Utilizing third spaces in teacher education. The Educational Forum, 78(3), 231–247. Gutiérrez, K. D. (2008). Developing a sociocritical literacy in the third space. Reading Research Quarterly, 43(2), 148–164. Gutiérrez, K. D., Baquedano-López, P., & Tejeda, C. (1999). Rethinking diversity: Hybridity and hybrid language practices in the third space. Mind, Culture, and Activity, 6(4), 286–303. Gutierrez, K., Rymes, B., & Larson, J. (1995). Script, counterscript, and underlife in the classroom: James Brown versus Brown v. Board of Education. Harvard Educational Review, 65(3), 445–471. Hedberg, J. G., & Stevenson, M. (2014). Breaking away from text, time and place. In M. G. D. Ifenthaler (Ed.), Curriculum Models for the 21st Century (pp. 17–33). New York: Springer. Howell, P. B., Sheffield, C. C., Shelton, A. L., & Vujaklija, A. R. (2017). Backchannel discussions during classroom observations: Connecting theory and practice in real time. Middle School Journal, 48(2), 24–30. Kearney, M., Burden, K., & Schuck, S. (2019). Disrupting education using smart mobile pedagogies. In L. Daniela (Ed.), Didactics of smart pedagogy: Smart pedagogy for technology enhanced learning (pp. 139–157). Cham, Switzerland: Springer. Lawson, K. (2004). Libraries in the USA as traditional and virtual “third places”. New Library World, 105(3/4), 125–130. Martin, S. D., Snow, J. L., & Franklin Torrez, C. A. (2011). Navigating the terrain of third space: Tensions with/in relationships in school-university partnerships. Journal of Teacher Education, 62(3), 299–311. Moje, E. B., Ciechanowski, K. M., Kramer, K., Ellis, L., Carrillo, R., & Collazo, T. (2004). Working toward third space in content area literacy: An examination of everyday funds of knowledge and discourse. Reading Research Quarterly, 39(1), 38–70. Oldenburg, R. (1989). The great good place: Cafés, coffee shops, community centers, beauty parlors, general stores, bars, hangouts, and how they get you through the day. New York, NY: Paragon House. Oldenburg, R., & Brisset, D. (1982). The third place. Qualitative Sociology, 5(4), 265–284.

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Rushby, N. (2012). Editorial: An agenda for mobile learning. British Journal of Educational Technology, 43(3), 355–356. Rutherford, J. (1990). The third space: Interview with Homi Bhabha. In J. Rutherford (Ed.), Identity: Community, culture, difference (pp. 207–221). London: Lawrence & Wishart. Retrieved from http://s3.amazonaws.com/arena-attachments/90186/444c4a43b13aec9 2039a31bef35c4945.pdf?1364059011 Schuck, S., Kearney, M., & Burden, K. (2017). Exploring mobile learning in the third space. Technology, Pedagogy and Education, 26(2), 121–137. Schuck, S., & Maher, D. (2018). Creating opportunities for untethered learning. Technology, Pedagogy and Education, 27(4), 473–484. https://doi.org/10.1080/1475939X.2018.1510788. Soja, E. W. (1996). Thirdspace. Malden, MA: Blackwell. Steinkuehler, C. A., & Williams, D. (2006). Where everybody knows your (screen) name: Online games as “third places”. Journal of Computer-Mediated Communication, 11(4), 885–909. Toh, Y., So, H. J., Seow, P., Chen, W., & Looi, C. K. (2013). Seamless learning in the mobile age: A theoretical and methodological discussion on using cooperative inquiry to study digital kids on-the-move. Learning, Media and Technology, 38(3), 301–318. Wright, S. (2012). From “third place” to “third space”: Everyday political talk in non-political online spaces. Javnost - The Public, 19(3), 5–20. https://doi.org/10.1080/13183222.2012.11009088. Zeichner, K. (2010). Rethinking the connections between campus courses and field experiences in college-and university-based teacher education. Journal of Teacher Education, 61(1–2), 89–99.

Part II

Frameworks for Understanding Mobile Learning

Chapter 5

Rationale for a Mobile Pedagogical Framework

Abstract This chapter introduces the mobile pedagogical framework that is the central focus of this book. The Framework is underpinned by socio-cultural theory. It comprises a pedagogical perspective of mobile learning which highlights three central features of mobile learning: authenticity, collaboration and personalisation, embedded in the unique time-space contexts of mobile learning. The pedagogical framework was developed and tested through activities in two mobile learning projects located in teacher education communities: Mobagogy, a project in which faculty staff in an Australian university developed understanding of mobile learning; and The Bird in the Hand Project, which explored the use of smartphones by student teachers and their mentors in the United Kingdom. The theoretical underpinnings of the Framework are described, and the dimensions comprising the Framework are articulated, together with their sub-dimensions. Once the Framework is explicated in this chapter, it is used to examine the pedagogies in a selection of reported mobile learning scenarios, enabling an assessment of mobile activities and pedagogical approaches, and consideration of their contributions to learning from a socio-cultural perspective. Keywords Mobile learning · Pedagogy · Socio-Cultural theory · Pedagogical features · iPAC · M-Learning framework · Digital pedagogy

5.1 Introduction Part I of this book has considered the potential contribution of mobile learning (mlearning) for school-aged students. It has articulated some of the key characteristics of mobile devices that support particular types of learning. One of the characteristics concerns the mobility of the device, which allows the user to learn in any place and at any time. Another characteristic is the way that the learning experience can be personalised and customised to learners’ needs. Interactions with others are easily This chapter is adapted from: Kearney, M., Schuck, S., Burden, K., & Aubusson, P. (2012). Viewing mobile learning from a pedagogical perspective, Research in Learning Technology, 20(1). https:// doi.org/10.3402/rlt.v20i0.14406. Creative Commons Attribution 4.0 International (CC BY) license. © Springer Nature Singapore Pte Ltd. 2020 M. Kearney et al., Theorising and Implementing Mobile Learning, https://doi.org/10.1007/978-981-15-8277-6_5

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done using mobile devices, and this highlights a third characteristic, that of collaboration. These characteristics are central to the development of our Mobile Pedagogical Framework (MPF) that underpins the research in this book. This chapter outlines how we went about developing the MPF. In it, we consider what a pedagogical framework for m-learning may look like from a socio-cultural perspective. This theoretical perspective emphasises the historical, cultural and societal experience, and considers not just the tools available for use (which, in the context of m-learning, are mobile devices and educational apps), but also the impact of these tools on society. It suggests a two-way relationship between tools and their users: learning is affected and modified by the use of technologies, and reciprocally these learning tools are modified by the ways that they are used for learning (Glassman, 2001; Salomon & Perkins, 1998). Central to this position is the notion that learning is a situated, social endeavour. The context in which the learning takes place is important, as is the social embeddedness of the tools. Learning is developed through social interactions and conversations between people (Vygotsky, 1978), and mediated through tool use (Wertsch, 1991). Chapter 6 considers one of the key dimensions of MPF: the construct of authenticity. Chapter 7 tracks the development of the MPF into the better-known iPAC Framework, and subsequent chapters outline research and impact of iPAC on mlearning. Chapter 8 provides an analysis of various m-learning frameworks that have been developed since 2000. At the time of the development of the MPF in 2012, most of these frameworks were based on what the mobile technology enabled. What was often missing was how such frameworks aligned with socio-cultural theory, a theory that underpins much of modern learning and teaching today. The emphasis on the affordances of the technology rather than on the learning needs of the students and the pedagogical directions of m-learning seemed to indicate that a new direction for m-learning frameworks was needed. We therefore developed and disseminated the MPF to address the shortcomings of frameworks that were focused more on the design of the tools than on the learner and teacher. We sought to address an ongoing need to examine the pedagogies that are suitable for m-learning, and to conceptualise m-learning from the perspective of learners’ experiences rather than the affordances of the technology tools (Traxler, 2007). In Chap. 8, we also investigate frameworks developed post-MPF and analyse their contributions and limitations in contrast to those of the MPF. Identifying specific current features of m-learning and m-teaching from a sociocultural perspective offers critical insights into the design of m-learning materials and provides a potentially useful lens for researchers to analyse pedagogical approaches and for teachers to critique and reflect on their teaching activities. Our Framework offers an examination of m-learning which foregrounds pedagogy rather than technology; a perspective in which the pedagogy is central and the technology is under investigation only for what may be distinctive about the learning afforded by it. Although sophisticated theoretical models have been developed (Laurillard, 2007; Pachler, Bachmair, & Cook, 2009; Sharples, Taylor, & Vavoula, 2007), as discussed in Chap. 8, the distinctive features of m-learning are evolving as devices and associated technologies mature.

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Accordingly, informed by both m-learning theory as it was in 2012 and sociocultural theory, this chapter reprises our article on the development of a mobile pedagogical framework (Kearney, Schuck, Burden, & Aubusson, 2012). It identifies three distinctive features of m-learning through our Framework: authenticity, collaboration and personalisation. The pedagogical framework was developed and extensively tested through a range of activities in two m-learning projects located in teacher education communities. ‘Mobagogy’ was a professional learning community of eight academics in an Australian university, formed to investigate how to use mobile technologies in their own learning and teaching (Schuck, 2015; Schuck, Aubusson, Kearney, & Burden, 2010). This community met regularly over a period of 18 months to discuss emerging relevant teaching issues and applications. The ‘Bird in the Hand Project’ was a UK-sponsored initiative supported by the Teacher Development Agency, and it examined the experiences of a group of PSTs and newly qualified teachers who were provided with smartphones (iPhones) to use in their placement and first teaching schools. It explored how this group and their mentors used these smartphones to support and enhance their professional practice. Extensive descriptions of activities within both these projects are available in Kearney, Schuck, & Burden (2010). In developing this Framework, we acknowledge the work preceding ours that had an influence on the development of the MPF. For example, other researchers have provided insights into different social aspects of m-learning. Traxler (2009) described m-learning as ‘noisy’ and problematic, featuring three essential elements: the personal, contextual and situated (p. 30); while Klopfer, Squire, & Jenkins (2002) identified five features: portability, social interactivity, context sensitivity, connectivity and individuality. Pachler, Cook, & Bachmair (2010) analysed the interrelationship of learners with the structures, agency and cultural practices of what the authors call the “mobile complex” (p. 1). The identification of these sets of characteristics and relationships established core features that we had to ensure were addressed in the development of our Framework. Larger scale, more complex conceptual frameworks for m-learning design and evaluation had been proposed at the time of development of the MPF. Parsons, Ryu, and Cranshaw (2007) proposed a framework for m-learning with four perspectives: generic mobile environment issues, learning contexts, learning experiences and learning objectives. Vavoula and Sharples (2009) proposed a three-level framework for evaluating m-learning that comprised a micro-level concerned with usability, a meso-level focusing on the learning experience (especially on communication in context) and a macro-level dealing with integration within existing organisational contexts. Our Framework aimed to further interrogate this meso-level of learners’ experience. Numerous frameworks have been proposed in the literature, ranging from the complex, multi-level examples just mentioned to smaller frameworks that often omit important socio-cultural characteristics of learning or pedagogy. Common themes include portability of m-learning devices and mobility of learners, interactivity, control and communication. These frameworks acknowledge the prime importance of context, including spatial and temporal considerations, for analysing m-learning

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experiences; however, they typically attempt to merge affordances of mobile devices or characteristics of applications with features of the learners’ experience. While acknowledging that the features identified in other frameworks are important in characterising technology-mediated learning by mobile users, we proposed a succinct framework highlighting a unique combination of distinctive characteristics of current mobile pedagogy to bring socio-cultural insights to the literature on m-learning.

5.2 Development of the Mobile Pedagogical Framework Formal learning is traditionally characterised by two constants or boundaries: time and space. Learning places occupy fixed, physical spaces which are defined by relatively impermeable boundary objects such as walls, classrooms and school buildings. Similarly, traditional learning is situated in permanent temporal slots such as teaching periods (timetables or semesters) which are relatively immutable (Traxler, 2009). Mlearning has the potential to transcend these spatial and temporal restrictions, overcoming “the need to tie particular activities to particular places or particular times” (p. 7). We have discussed this time-space phenomenon in more detail in Chap. 4 on the third space. We expand on some of this here. With ‘space’, m-learning offers a variety of alternatives including virtual or nongeographical spaces, such as virtual world environments created for mobile devices. In temporal terms, the requirements to learn in fixed, scheduled time-spaces (which characterise current schooling) are also relaxed, enabling the individual to be more flexible about when they learn. Previously fixed engagements or appointments can now be readily rescheduled, and fixed notions of linear time are increasingly making way for a softer version of what some authors have termed ‘socially negotiated time’ in which each party to an event is able to create and rearrange their schedules without excessive detrimental effect to either side (Ling & Donner, 2009). The implications of these two vectors in m-learning have been thoroughly discussed in Chap. 4 and therefore will not be repeated here. However, it is clear that taken together they create what we term ‘malleable spatial-temporal’ contexts for learning. In blurring the physical and scheduled formats of institutional-based learning, time-space implications of m-learning open up opportunities for a wide variety of pedagogical patterns. Mobile technologies thus enable learning to occur in a multiplicity of more informal (physical and virtual) settings situated in the context where the learning is occurring. These informal scenarios range from structured, teacher-mediated experiences in semi-formal places like museums and libraries, to more self-regulated experiences in learner-generated contexts such as coffee shops and public transport settings (Luckin 2010). We are not attempting to identify specific causal links between the levels of formality of ‘time-space’ and m-learning experiences. However, in order to discuss distinctive features of mobile pedagogy, we must first acknowledge that the organisation of time-space in any learning environment profoundly affects m-learning experiences (Ling & Donner 2009). From a socio-cultural standpoint, insight into

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Fig. 5.1 A two-way relationship between the organisation of time-space and mobile learning experiences (socio-cultural perspective) (Kearney, Schuck, Burden, & Aubusson, 2012)

the organisation of time-space in a given learning environment is an essential part of understanding the nature of an m-learning experience, as depicted in Fig. 5.1. The MPF was developed through an iterative design-test-analyse-refine cycle, akin to that suggested by Kemmis and McTaggart (1988), to address our key question: What does a pedagogical framework for mobile learning look like from the perspective of socio-cultural theory? Activities in both projects noted above fed into this cycle and leveraged numerous opportunities to test and refine the framework and its representation. Project activities contributing to the framework development included exploring the socio-cultural characteristics underpinning m-learning, interrogating the literature on m-learning, investigating best practice approaches by interviewing global experts in the field, and initiating and testing selected m-learning pedagogies in the context of our own higher education subjects. A variety of strategies were used to promote collaborative critical reflection (Ghaye & Ghaye, 1998) throughout the cycle, taking into account a range of perspectives from discipline, pedagogical and e-learning experts in our group. The Framework was validated through four methods. Firstly, inter-researcher validation was gained using feedback from m-learning researchers after presenting versions of the framework at four scholarly meetings: one internal teaching and learning university conference, an m-learning working group with scholars from around Australia and beyond, an internal faculty presentation, and an international m-learning conference (Kearney et al., 2010). Secondly, intra-researcher validation was achieved through discussions among the designers of the Framework. These discussions critiqued the Framework from a pedagogical perspective and interrogated how well it aligned with the underlying socio-cultural theory. Thirdly, each iteration of the framework was tested by using it to analyse existing m-learning initiatives in both the Mobagogy and the Bird in the Hand projects, and also using it to guide the design of further m-learning experiences. Fourthly, a critical friend—an expert in pedagogy from within the group—was invited to critique final iterations of the framework. His feedback contributed to the current framework presented here. These methods involving the users’ perspective in the design process follow general design guidelines based on constructivist theory (Willis, 2000).

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Informed by these processes and mindful of our quest to use socio-cultural theory to capture central pedagogical features of m-learning environments, a framework prototype was designed using four dimensions: place, connection, immediacy and activity. This early version of the framework integrated temporal and spatial considerations. This version was tested by using it to critique our student teachers’ use of mobile devices to vote on a controversial issue in a mass lecture (see upward thick arrows on each of the four scales in Fig. 5.2). In another example from our project trials, this version of the framework was used to critique the lack of interactivity in a group member’s trial of student teachers’ instructional use of podcasts. Similar trials took place in the UK where versions of the framework were used, for example, to gauge the extent to which pre-service teachers could sustain the vibrant sense of community that had characterised their face-to-face elements whilst away from the university on their first teaching placements. Further iterations of the framework emerged from our design and development cycle as we tried to capture more succinctly the distinctive features of mobile learners’ experiences. A well-developed framework incorporating five scales and numerous sub-scales was presented at our university teaching and learning conference (see Fig. 5.3). A more succinct, penultimate version of the framework (see Fig. 5.4) was subsequently presented at mLearn2010 (Kearney et al., 2010). Apart from being more succinct, a major development here was our treatment of time-space as a separate entity in the framework: Further feedback from m-learning researchers at the conference and from our critical friend was valuable and informed refinement of the framework in light of other data from the project activities. For example, one conference reviewer suggested we more closely examine critical features of game-based m-learning scenarios to help us further clarify the customisation section of our framework. Descriptions of this scale and other sections of the framework were subsequently refined. Our critical friend critiqued our use of a ‘third space’ theme (Kearney et al., 2010) and suggested that this might be a distraction to the main focus of presenting the three pedagogical constructs. Also, two subsidiary sub-scales were developed for each section to more accurately pinpoint critical features of m-learning. As part of this final development, the customisation scale was changed to personalisation in the published 2012 Framework, with sub-scales of agency and customisation. Similarly, the social interactivity scale was changed to collaboration, with sub-scales of communication and data sharing (see next section). Also, it became evident that the three-circle representation (see Fig. 5.4) caused confusion, especially with the intersecting sections, and consequently, the three scales were separated in the 2012 visual representation (see Fig. 5.5).

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Fig. 5.2 Use of a prototype framework to analyse one of our project teaching trials (Kearney, Schuck, Burden, & Aubusson, 2012)

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Fig. 5.3 Another prototype framework presented at a university teaching conference, 2009 (Kearney, Schuck, Burden, & Aubusson, 2012) Fig. 5.4 Penultimate framework presented at mLearn 2010 (Kearney, Schuck, & Burden, 2010)

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Fig. 5.5 The 2012 Framework comprising three distinctive characteristics of mobile learning experiences, with sub-scales (Kearney, Schuck, Burden, & Aubusson, 2012)

5.3 Sub-dimensions of the MPF In this section, we describe a rationale for including personalisation, authenticity and collaboration as the three distinctive features of m-learning that formed the basis of the 2012 Framework, working within our previously discussed conception of time and space. We also formulated two sub-scales for each of these three constructs, as depicted in Fig. 5.5 and described in the subsequent subsections. We present the graphical representation of the Framework as developed for the 2012 article (Kearney et al., 2012). Chapter 7 will indicate the amended graphical representation which built on this version. The 2012 graphical representation consisted of circular layers, to show the close, connected relationship between the three constructs depicted in the inner layer and the six sub-scales in the outer layer. The bi-directional arrows in the representation depict the previously discussed symbiotic relationship between time-space and m-learning features.

5.3.1 Personalisation Personalisation, drawing on motivational theory (Pintrich & Schunk, 1996) and socio-cultural theory (Vygotsky, 1978), is a cornerstone of e-learning. Key features associated with personalisation include learner choice, agency and self-regulation, as well as customisation (McLoughlin & Lee, 2008). Learners can enjoy a high degree of agency in appropriately designed m-learning experiences (Pachler et al., 2009).

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Table 5.1 Two sub-scales of the personalisation dimension used in our Framework SCALE

SUB-SCALE

LOW Activity is:

HIGH Activity is:

Personalisation

Agency (Pachler, Bachmair, & Cook, 2009)

Externally controlled

Negotiated learning choices, e.g. content, goals

Customisation (McLoughlin & Lee, 2008)

Uniformly structured, just-in-case

Tailored, Just-enough, just-in-time, just-for-me

They may have control over the place (physical or virtual), pace and time they learn, and they can enjoy autonomy over their learning content. Goals are typically set by learners and their peers (e.g. some games). Furthermore, the ‘just-enough, just-intime, just-for-me’ nature of some m-learning activities can create a personalised, tailored learning journey. M-learning experiences can be customised at both a tool and an activity level. Users enjoy a sense of intimacy and convenience with their personal devices and the flexible, autonomous, often individually tailored activities lead to a strong sense of ownership of one’s learning (Traxler 2007). In this sense, activities are customised for the learner to meet their different learning styles and approaches. Hence, we developed two sub-scales (agency and customisation) in our analysis of personalisation, as shown in Table 5.1. Mobile users can use tools to record, organise and reflect on their customised mlearning experiences over time (Naismith, Lonsdale, Vavoula, & Sharples, 2004). Emerging context-aware capabilities allow devices to acquire information about the user and their immediate environment (e.g. time, location, nearby people and objects), presenting unique opportunities to personalise learning experiences. Also, augmented reality applications and customised interactions with The Internet of Things (Sundmaeker, Guillemin, Friess, & Woelfflé, 2010) offer promising ways for learners to select, manipulate and apply information to their own unique needs in a pervasive learning environment (Laine, Sedano, Vinni, & Joy, 2009).

5.3.2 Authenticity There is general agreement that authentic tasks provide real-world relevance and personal meaning to the learner (Radinsky, Bouillion, Lento, & Gomez, 2001), although ultimately, authenticity “lies in the learner-perceived relations between the practices they are carrying out and the use value of these practices” (Barab, Squire, & Dueber, 2000, p. 38). CTGV (1990) delineate task, factual and process levels of authenticity. Task authenticity refers to the extent to which tasks are realistic and offer problems encountered by real-world practitioners. Factual authenticity refers to how particular details of a task (such as characters, instruments, etc.) are similar to the real world, while a process level of authenticity refers to how learner practices

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Table 5.2 Two sub-scales of the authenticity dimension used in our Framework SCALE

SUB-SCALE

LOW Activity is:

HIGH Activity is:

Authenticity

Contextualisation (e.g. CTGV, 1990)

Contrived

Realistic/relevant to learner

Situatedness (e.g. Radinsky et al.,. 2001)

Simulated

Participatory/embedded in real community of practice

are similar to those practices carried out in the community or real world of practice. Radinsky et al. (2001) espoused two models of authentic learning environments: a simulation model and participation model. Tasks that fit a simulation model of authenticity use the learning space (e.g. classroom) as a ‘practice field’ (separate from the real community) but still provide contexts where learners can practise the kinds of activities they might encounter outside of formal learning settings. Alternatively, under a participation model of authenticity, students participate in the actual work of a professional community, engaging directly in the target community itself. Hence, we used two sub-scales (contextualisation and situatedness) in our analysis of authenticity, as shown in Table 5.2. Our understanding of simulated activities as ‘low’, as shown in Table 5.2, derived from the fact that a simulation does not take place in a real setting. For example, mathematics learners measuring floor areas in a simulation are not developing authentic measuring skills, and therefore this experience has low situatedness. However, our further work as described in Chaps. 6 and 7 indicates that simulation activities can provide a relatively authentic experience for learners. For example, Google Trips or many flight simulations provide a realistic experience for learners. So in later chapters, low situatedness is characterised by an absence of realistic settings. M-learning episodes potentially involve high degrees of task and process authenticity as learners participate in rich, contextual tasks (setting, characters, tools), involving real-life practices. Learners can generate their own rich contexts (Pachler et al., 2009) with or through their mobile devices. The deeper contextualisation of tasks in these physical or virtual spaces can be supported by geolocation and data capture facilities (Brown, 2010).

5.3.3 Collaboration Collaboration in socio-cultural theory is often emphasised in terms of learning interactions with more capable peers or adults and a pedagogical emphasis on scaffolding (Trudge, 1990). More broadly, social interaction, conversation and dialogue are fundamental to learning from a socio-cultural perspective as people engage in negotiating meaning (Vygotsky, 1978). Many pedagogical frameworks foreground the importance of these conversations in teaching and learning (e.g. Laurillard, 2007;

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Table 5.3 Two sub-scales of the collaboration dimension used in our Framework SCALE

SUB-SCALE

LOW Activity is

HIGH Activity is

Collaboration

Conversation (e.g. Laurillard, 2007; Sharples, Taylor, & Vavoula 2007)

Unconnected/solitary

Rich/involves deep, dynamic dialogue

Data sharing (e.g. Traxler, 2010)

Isolated/emphasis on content acquisition and transmission

Networked/includes learner-generated content

Sharples et al., 2007), building on well-accepted Vygotskian theory. Shared conversational spaces mediated by mobile devices are conducive to timely, personally tailored feedback from instructors, as well as rich peer interactions (e.g. multi-user mobile gaming environments). Mobile learners can enjoy a high degree of collaboration by making rich connections to other people and resources mediated by a mobile device. This oft-reported high level of networking creates shared, socially interactive environments so that mobile learners can readily communicate multi-modally with peers, teachers and other experts, and exchange information. Learners consume, produce and exchange an array of digital content, sharing information and artefacts across time and place. Exchanged data files are often ‘just-in-existence’, enhancing the immediacy of the m-learning experience. Indeed, the spontaneity of these communications and the currency of exchanged data are made possible by the accessibility and expectation of users being reachable at any time. We used two sub-scales (conversation and data sharing) in our analysis of collaboration, as shown in Table 5.3.

5.4 Next Steps As noted above, once we had developed the three constructs and the two sub-scales belonging to each construct, we tested the Framework on a number of learning scenarios to assess whether it had a value in identifying the presence of various socio-cultural characteristics, as delineated in our MPF. The purpose of this analysis was to demonstrate how the Framework could highlight important aspects of learning and pedagogy, as distinct from other analyses in the literature that typically focus on technical issues surrounding the affordances of mobile devices. We applied the Framework to 30 scenarios chosen from recent m-learning conferences and other publications to capture the most innovative, contemporary activities flagged in the current literature. Activities were analysed using the six sub-scales to rate the critical features of these m-learning activities. See Kearney et al. (2012) for full details of these analyses.

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Despite the rhetoric around m-learning virtually guaranteeing contextualised learning, very few of these scenarios rated highly in the scales for authenticity. Most activities involved either some form of contrived context or activities that were merely providing a simulation of reality (such as a game). Interestingly, an example using Twitter at a professional learning conference rated highly in authenticity, despite being in a formal professional learning setting. The activity was relevant (task, process, etc.) to participants who chose to contribute to the Twitter feed. Delegates were certainly engaged directly in the professional community—including networking with colleagues who were not physically at the conference—and in this way they were following a participation model of authenticity. Indeed, the process of Tweeting has an increasing level of factual authenticity, as teachers take up this activity as a normal everyday part of their professional networking practices. Also found surprising were the generally low ratings in the personalisation scales. An exception was a game design scenario that allowed learners to enjoy high degrees of customisation and self-control over the learning process. In contrast, like most school-based tasks restrained by curriculum and learning space constraints, most school examples lacked agency and customisation. The analysis highlighted a marked difference in the nature of collaboration in these scenarios. Scenarios such as an augmented reality application in a museum indicated a solitary activity that lacked social interactivity. Only the Twitter and Games scenarios rated highly on these scales, due to the large network involved in the conference ‘Twitterverse’ (including ‘lurker’ colleagues in cyberspace) and the multi-player nature of the game. These two m-learning experiences also elicited indepth conversations in supplementary activities. Given the text constraints of Twitter, the face-to-face and virtual conversations elicited from the Twitter display became a crucial part of the experience, at least from the perspective of the delegates present at the conference. Indeed, a point of interest was the way that teachers used hybrid, integrated approaches (Dillenbourg, 2006) to enhance pedagogically ‘weaker aspects’ of an m-learning scenario. For example, the supplementary, post-activity face-toface class discussions used by teachers in some examples elicited further learning conversations. Use of the Framework to interrogate m-learning scenarios identified a potential problem with collaboration and authenticity in augmented reality scenarios in informal settings such as museums and science centres. Cook (2010) addresses the problem of collaboration in a similar location-based, augmented learning museum activity by supplementing this experience with students working in pairs. In Cook’s example, students were also asked to create a collaborative video blog emerging from their discussions in the museum. These activities initiated further collaboration through both the collaborative nature of the video blog production and also the stimulus it provided for further verbal and blog-based conversation. Similarly, to address a potential problem with authenticity, Laine et al. (2009) describe a system called LieksaMyst that enhances the authenticity of the museum experience by creating a role-play scenario whereby users interact with the museum artefacts (focused on Finland’s history and culture). Authentic, albeit fictional, characters are introduced through the system through a story-based, role-play game, and users interact with

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these characters through the device. Although this is done through the technology in this example, role-play could be introduced as a face-to-face teaching strategy to enhance the authenticity of these museum-based m-learning scenarios. In summary, the Framework provided a renewed focus on important aspects of socio-cultural theory for educators and researchers working in and examining mlearning contexts. Use of the 2012 Framework as a lens to analyse more than 30 scenarios from m-learning literature of the time indicated that it was scalable for examining and critiquing the pedagogical impact of a wide range of m-learning contexts. Some scenarios typically promoted in a positive light in this recent literature base did not necessarily rate highly in our three scales. For example, while listening to instructional podcasts on public transport may sound novel in terms of the informal context and control of task pacing, under closer inspection it mimics a transmission pedagogy with its roots in didactic teaching traditions of formal learning settings. A second use of the Framework is as a guide for practitioners to interrogate their own m-learning designs. We recently examined our students’ use of handheld devices to complete class-based polls. Although the task elicited rich learning conversations and involved some networking activity, it was a relatively contrived, structured task with minimal flexibility. The insights gained from the use of the Framework contributed to development and enhancement of our practice. These contributions arose from using the Framework to make the relationships among elements of the learning explicit. In this way, the Framework also served as a developmental tool by focusing on the essential constructs of learning from a socio-cultural perspective. Importantly, the Framework itself will continue to be revised and refined to enable it to represent the many varied manifestations of m-learning. A detailed discussion of amendments to date is provided in Chap. 7.

5.5 Conclusion A succinct Framework highlighting distinctive, current socio-cultural features of mobile pedagogy emerged from our design and development procedures, leveraged by our project activities. Three constructs characterising the pedagogy of m-learning emerged: authenticity, collaboration and personalisation. The authenticity feature highlighted opportunities for contextualised, participatory, situated learning; the collaboration feature captured the oft-reported conversational, connected aspects of m-learning; while the personalisation feature had strong implications for ownership, agency and autonomous learning. How learners ultimately experience these distinctive characteristics is strongly influenced by the organisation of spatial and temporal aspects of the m-learning environment, including face-to-face and virtual teaching strategies. The Framework discussed in this chapter is by no means prescriptive— while such a pedagogical framework provides a spotlight to illuminate and examine m-learning experiences, account still needs to be taken of learners’ specific characteristics and needs, the environments in which the learning could potentially take place,

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and the preferences and characteristics of teachers, including their epistemological beliefs. Teacher roles and the learning task design are further crucial factors. This chapter did not set out to examine causal links between the use of timespace and m-learning experiences. However, we do advocate a need for researchers to explore in more detail the time-space continuum and how it might be organised to optimise learning mediated by mobile technologies. Central to the idea of m-learning is that learning contexts can be generated by students, occurring in different places and at different times and not confined to formal learning settings in institutions. Informal learning environments characterised by fluid geographical boundaries and malleable, socially negotiated time frames need further investigation with these goals in mind. Our MPF specifying critical attributes of m-learning experiences provide a useful lens for this research agenda. As mobile technologies develop, our challenge as educational researchers is to probe new pedagogical opportunities that honour principles of authentic, collaborative, personalised learning, drawing on well-researched socio-cultural tenets. The Framework presented here is dynamic and ever-changing but its key aim is to assist practitioners’ understanding and analysis of unique teaching challenges in emerging mobile learning environments and facilitate critical insights supporting their design of m-learning experiences and resources. The remainder of this book considers amendments to the Framework and its use by teachers and researchers.

References Barab, S. A., Squire, K. D., & Dueber, W. (2000). A co-evolutionary model for supporting the emergence of authenticity. Educational Technology Research and Development, 48(2), 37–62. https://doi.org/10.1007/BF02313400. Brown, E. (2010). Introduction to location-based mobile learning. In E. Brown (Ed.), Education in the wild: Contextual and location-based mobile learning in action (pp. 7–9). Nottingham, UK: University of Nottingham Learning Sciences Research Institute. Cook, J. (2010). Mobile phones as mediating tools within augmented contexts for development. In E. Brown (Ed.), Education in the wild: Contextual and location-based mobile learning in action (pp. 23–26). Nottingham, UK: University of Nottingham Learning Sciences Research Institute. CTGV (Cognition and Technology Group at Vanderbilt). (1990). Technology and the design of generative learning environments. Educational Technology, 31(5), 34–40. Dillenbourg, P. (2006). Orchestrating integrated learning scenarios. In L. Markauskaite, P. Goodyear, & P. Reimann (Eds.), Proceedings of the 23rd Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education: Who’s Learning? Whose Technology? (p. 955). Sydney: Sydney University Press. Ghaye, A., & Ghaye, K. (1998). Teaching and learning through critically reflective practice. London: David Fulton. Glassman, M. (2001). Dewy and Vygotsky: Society, experience and inquiry in education practice. Educational Researcher, 30(4), 3–14. Kearney, M., Schuck, S., Burden, K., & Aubusson, P. (2012). Viewing mobile learning from a pedagogical perspective, Research in Learning Technology, 20(1). https://doi.org/10.3402/rlt. v20i0.14406

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Kearney, M., Schuck, S., & Burden, K. (2010). Locating mobile learning in the third space. In M. Montebello, V. Camilleri, & A. Dingli (Eds.), Proceedings of mlearn2010: 10th World Conference on Mobile and Contextual Learning (pp. 108–115). Valetta, Malta: University of Malta. Kemmis, S., & McTaggart, R. (1988). The action research planner. Deakin University Press. Klopfer, E., Squire, K., & Jenkins, H. (2002). Environmental detectives: PDAs as a window into a virtual simulated world. In Proceedings of IEEE International Workshop on Wireless and Mobile Technologies in Education (pp. 95–98). Vaxjo, Sweden: IEEE Computer Society. Laurillard, D. (2007). Pedagogical forms of mobile learning: Framing research questions. In N. Pachler (Ed.), Mobile learning: Towards a research agenda (pp. 153–176). London: WLE Centre Institute of Education. Laine, T., Sedano, C., Vinni, M., & Joy, M. (2009). Characteristics of pervasive learning environments in museum contexts. In D. Metcalf, A. Hamilton, & C. Graffeo (Eds.), Proceedings of 8th World Conference on Mobile and Contextual Learning (pp. 26–34). Uni. of Central Florida. Ling, R., & Donner J. (2009). Mobile communications. London: Polity. Luckin, R. (2010). Re-designing learning contexts. London: Routledge. McLoughlin, C., & Lee, M. (2008). The 3 p’s of pedagogy for the networked society: Personalization, participation, and productivity. International Journal of Teaching and Learning in Higher Education, 20(1), 10–27. Retrieved from https://files.eric.ed.gov/fulltext/EJ895221.pdf Naismith, L., Lonsdale, P., Vavoula, G., & M. Sharples. (2004). Report 11: Literature review of mobile technologies in learning: Futurelab series. Retrieved from https://telearn.archives-ouv ertes.fr/hal-00190143 Pachler, N., Cook, J., & Bachmair, B. (2010). Appropriation of mobile cultural resources for learning. International Journal of Mobile and Blended Learning, 2(1), 1–21. Retrieved from https://uwe-repository.worktribe.com/preview/987410/cook_IJMBL%202%281%29%202 010.pdf Pachler, N., Bachmair, B., & Cook, J. (2009). Mobile learning: Structures, agency, practices. New York: Springer. Parsons, D., Ryu, H., & Cranshaw, M. (2007). A design requirements framework for mobile learning environments. Journal of Computers, 2(4), 1–8. Pintrich, P., & Schunk, D. (1996). Motivation in education: Theory, research, and application. Upper Saddle River, NJ: Merrill. Radinsky, J., Bouillion, L., Lento, E., & Gomez, L. (2001). Mutual benefit partnership: A curricular design for authenticity. Journal of Curriculum Studies, 33(4), 405–430. https://doi.org/10.1080/ 00220270118862. Salomon, G., & Perkins, D. (1998). Individual and social aspects of learning. Review of Research in Education, 23, 1–24. Schuck, S. R. (2015). Mobile Learning in Higher education: Mobilizing staff to use technologies in their teaching, eLearn Magazine. https://doi.org/10.1145/2749476.2749226 Schuck, S., Aubusson, P., Kearney, M., & Burden, K. (2010). Mobagogy: Mobile learning for a higher education community. In I. Sánchez & P. Isaías (Eds.), Proceedings of the IADIS Mobile Learning, 2010 Conference (pp. 69–76). Porto, Portugal: IADIS Press. Sharples, M., Taylor, J., & Vavoula, G. (2007). A theory of learning for the mobile age. In R. Andrews & C. Haythornthwaite (Eds.), The SAGE handbook of e-learning research (pp. 221–224). London: Sage. Sundmaeker, H., Guillemin, P., Friess, P., & Woelfflé, S. (2010). Vision and challenges for realising the Internet of Things. Luxembourg: CERP-IoT, European Commission. Traxler, J. (2007). Current state of mobile learning. In M. Ally (Ed.), Mobile learning: Transforming the delivery of education and training (pp. 9–24). Athabasca, AB: Athabasca University Press. Traxler, J. (2009). Learning in a mobile age. International Journal of Mobile and Blended Learning, 1(1), 1–12. Traxler, J. (2010). The ‘learner experience’ of mobiles, mobility and connectedness. Background paper to presentation given at ELESIG Symposium: Digital Futures: 21 Jan 2010. iLab,

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Chapter 6

Unpacking Authenticity

Abstract Authenticity is defined as one of the ‘signature pedagogies’ of mobile learning in the iPAC Framework but existing accounts of authentic learning focus largely on contextual factors: tasks, processes, how situated the learning is and the extent to which learners engage in simulated or participative real-world activities. This chapter theorises how ubiquitous mobile technologies are fracturing the boundaries that demarcate traditional accounts of authentic learning, affording new opportunities to reconceptualise what authenticity means for learners when they use a boundary object such as a mobile device. While some of this has been captured previously with terms like ‘seamless’, ‘contextualised’ and ‘agile’ learning, this chapter argues that the concept of authentic mobile learning is a fluid construct which will continue to change as the technologies develop and as the pedagogical affordances become better understood by educators and end-users. The chapter scrutinises the authenticity dimension of our Mobile Pedagogical Framework, offering a three-dimensional model as a lens to consider authentic mobile learning. It argues that further empirical research is required to understand authentic mobile learning from the perspective of learners. Keywords Authentic learning · Mobile learning · Situated learning · Authenticity · Context · Task authenticity

6.1 Introduction Authenticity remains a concept that is referred to by many, yet poorly defined (Barab & Dueber, 2000, p. 38)

Our Mobile Pedagogical Framework was developed in response to a need to view mobile learning (m-learning) from a socio-cultural perspective, as outlined in Chap. 5. This chapter uses the same perspective to interrogate one of the more This chapter is an adaptation of the following published chapter: Burden, K., & Kearney, M. (2016). Conceptualising authentic mobile learning. In D. Churchill, J. Lu, T. Chiu & B. Fox (Eds), Mobile Learning Design: Theories and Application (pp. 27–42). Singapore: Springer. © Springer Nature Singapore Pte Ltd. 2020 M. Kearney et al., Theorising and Implementing Mobile Learning, https://doi.org/10.1007/978-981-15-8277-6_6

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problematic and often misunderstood dimensions of our Framework, authenticity. This chapter is a revised version of Burden & Kearney (2016) and represents a snapshot of our thinking about the construct of authenticity at that time. It serves as an important lens for us to further consider this complex aspect of m-learning and played an important part in subsequent developments of the iPAC Framework, as discussed in Chap. 7. Contemporary endeavours to understand and define m-learning draw attention to the situated and seamless nature of activities that are mediated through the affordances of mobile technologies, describing these activities as authentic learning (Herrington & Kervin, 2007; Herrington, Mantei, Herrington, Olney, & Ferry, 2008). Learners are considered to be more engaged in contexts which offer high levels of personal significance and cultural relevance. In terms of personal significance, such contexts act as a bridge linking new information and theories to learners’ life worlds outside of formal education and in terms of cultural relevance they enculturate the learner into the practices of the community helping them to think like a member of the discipline (Lombardi, 2007; Meyers & Nulty, 2009; Stein, Isaacs, & Andrews, 2004). Despite considerable research associated with authentic learning (Browns, Collins, & Duguid, 1989; CTVG, 1990; Petraglia, 1998; Radinsky, Bouillion, Lento, & Gomez, 2001), there are to date relatively few studies which have analysed how mobile technologies support and enhance authentic learning and reciprocally how far authenticity is an inherent characteristic of m-learning itself (Herrington & Kervin, 2007; Herrington, et al 2008; Kearney, Schuck, Burden, & Aubusson, 2012; Kearney, Burden, & Rai, 2015). Data collected by the authors from an international survey of educators using mobile technologies in their teaching and learning (Kearney et al., 2015), highlight one of many confusions associated with the twin concepts of authenticity and mlearning. Participants consistently ranked the construct of authenticity as ‘high’, with a mean average of 2.4 on a scale of 1 (low) to 3 (high), when describing a learning scenario where they had used mobile technologies for pedagogical purposes. This high ranking of authenticity by the teachers was despite the fact that 82% of their selfreported scenarios were situated in formal institutional settings such as schools and universities which might normally be considered rather inauthentic settings (Kearney et al., 2015). This paradox forms the focus for this chapter which seeks to theorise the concept of authentic learning with mobile technologies. Although authenticity and the learning theories associated with it are often described alongside m-learning, many of the underlying concepts and approaches which have been adopted to enact them as pedagogy are based on a range of assumptions about learning which are rarely articulated or fully explained (Radinsky, Bouillion, Lento, & Gomez, 2001; Selwyn, 2014). The chapter is structured as follows. The next section outlines the background of the chapter by exploring why authentic learning is considered important. We then present a section which seeks to define the term ‘authentic learning’ identifying two interpretations which are evident in authentic m-learning. The main body of the chapter brings together existing research about authentic learning to facilitate and support m-learning. In so doing, it identifies three distinct and recurring definitions.

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These are subsequently presented as vectors in a three-dimensional orthogonal model which is offered as an original way to conceptualise authentic m-learning. In the final section, we discuss the implications of these theorisations and consider the utility of the proposed model for better understanding the phenomena of m-learning and authenticity.

6.2 Why Is Authentic Learning Important? The concept of authentic learning is not new and may have reached its zenith in Europe during the Middle Ages when it functioned as the primary mode of instruction in the craft guilds where apprentices honed their skills vicariously alongside a master craftsman (Lombardi, 2007, p. 6). The advent of industrialisation brought about the need to train a mass labour force meaning the apprenticeship model of learning declined and was supplanted by less direct but more cost-efficient institutional systems of mass education (Klopfer, Yoon, & Rivas, 2004). Only in recent years has interest in more authentic, real-world learning resurfaced alongside theories of situated learning (Brown, Collins, & Duguid, 1989) and cognitive apprenticeships (Collins, 1988; Collins, Brown, & Newman, 1989). Much of this renewed interest can be traced to economic and technological imperatives which have combined to make authentic learning both economically viable and pedagogically appealing. The economic drivers stem from the structural shifts in post-Fordist capitalism which have seen the decline in traditional labour-intensive industries and the emergence of new forms of production which are largely ‘immaterial’ in nature, based on the manipulation of networked knowledge and ideas (Lazzarato, 1996; Selwyn, 2014). These structural shifts demand a new set of skills and dispositions for a largely immaterial workforce, which include creativity, networking, cooperation and autonomy (Selwyn, 2014). Technology is also an important driver in the renewed popularity of authentic learning since personal computers (PCs), and more lately mobile technologies, have matured to the point at which previously inefficient models of learning are once again feasible. Mobile technologies are relatively ubiquitous, small and discrete making them ideal for many work-based learning tasks such as capturing images, notes and reflections in situ (Burden, Schuck, & Aubusson, 2010). Today’s mobile devices are invariably networked which allow learners to participate in real communities of practice such as Science Citizen and Citizen Inquiry projects where they are supported by genuine professionals, akin to the traditional apprenticeship model, although at a greatly reduced cost (Herodotou, Sharples, & Scanlon, 2017). Given this resurgence of interest in models of authentic learning and the worldwide technological shift to post PC devices (PPD) such as mobile phones and tablet computers, it is timely and important to better understand the assumptions which underpin the concepts of authenticity and m-learning. Therefore, this chapter addresses the following research questions:

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• what assumptions underpin the concept of authentic learning with mobile technologies? • what functional value do these conceptualisations serve for educators and the wider academic community seeking to further exploit the potential of mobile technologies?

6.3 Defining Authenticity The Oxford dictionary definition of the term authentic reveals two etymological strands upon which similar but significantly different interpretations of the phrase have gradually emerged. In its original form, deriving from the Greek term ‘authentikos’, authentic is defined as meaning of ‘undisputed origin’, ‘not a copy’ or ‘replica’ and this interpretation has been appropriated into the legal lexicon where synonyms like ‘genuineness’, ‘bona fide’ and ‘veritable’ are used to imply the integrity and originality of a person, object or act. The second etymological derivation, which has become the more commonly used (at least since the eighteenth century) stems from a more representative understanding of the term associated with secondary rather than direct experience. An account of an eye witness is described as authentic if it is accurate in its representation of the facts. Authenticity, in this second sense of the term, is a measure of reliability and correspondence between the original artefact (e.g. an accident in the street) and its secondary representation (e.g. by an eyewitness). In this secondary interpretation, various proxies such as trustworthiness and authoritative certification replace the certainty afforded by direct sensory first-hand presence (Russell, 1959), and in this sense authenticity is a measure of fidelity and correspondence between the primary account and its second-hand representation. When the term authentic is used in association with learning, both the direct and representative etymological definitions are invoked but until recently with the emergence of ubiquitous ownership of mobile devices authenticity has most commonly referred to the representative interpretation whereby students tackle real-world problems and challenges through a simulated, rather than a direct participatory interface. Technology and the affordances of mobile technologies challenge these traditions as will be discussed later in the chapter.

6.4 Authentic Learning and Mobile Technologies The term authentic learning is used in a variety of ways in the field of educational technology, and this section explores three different descriptions based on studies of mobile technology use reported in the research literature. In the first of these, authenticity describes the context of the learning activity and the extent to which this is participative or simulated. In these descriptions, authenticity

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is judged by the extent to which students engage in activities and tasks like those undertaken by professional communities of practice in so-called ‘real-world’ settings. The second definition relates more to the nature of the tasks and activities undertaken. In these cases, authenticity is a measure of the degree of agency granted to students which is also correlated with the extent to which the learning activity is predefined or emergent, planned or unplanned. The third definition of authenticity is embedded within the student’s personal goal structures and emotional engagement with the learning activity. From this perspective, authenticity is a measure of how far learning activities “engage students’ lived experience, enabling students to find meaningful connections with their current views, understandings and experiences” (Stein et al., 2004, p. 240).

6.5 Unpacking Authentic Learning It is generally agreed that authentic learning ideally requires students to tackle realworld problems located in contexts that mimic the work of professionals and discipline experts (Collins, 1988; Herrington et al., 2008; Lombardi, 2007; Maina, 2004; Renzulli, Gentry, & Reis, 2004): In general, learning environments are considered authentic when there is a similarity between the structured learning activities and some meaningful context for that activity (Barab et al., 2000 p. 38)

In traditional educational paradigms, participative authenticity requires learners to be physically located in the community of practice or professional setting itself as in the apprenticeship model, whereas simulated authenticity allows learners to be located in their normal spaces and contexts where the conditions of the real-world contexts are replicated. Technology blurs these distinctions, and mobile technologies are causing them to fracture in ways which are not yet fully understood or appreciated.

6.5.1 Participatory Contexts In participative authentic contexts, learners participate in genuine real-life communities as ‘legitimate peripheral’ members (Lave & Wenger, 1991) gradually learning the practices, stories and languages of the community or what has been described as “the ordinary practices of th[at] culture” (Brown, Collins, & Duguid, 1989, p. 34). In effect learning is a socio-cultural process of identity formation as novices are enculturated into the dominant practices of the community, gradually gaining status as experts. Learning is considered to be highly authentic because it is situated in the same context in which it will be used, more likely making it personally meaningful for the learner.

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A practical example using mobile technologies would be use of the sense-it ® app which supports learners in measuring and investigating real-world phenomena. It is based on the principles of Citizen Science, whereby members of the public use the app on their mobile device to collaborate with professional scientists, contributing to observation and measurement data such as species identification and air/water pollution monitoring (Henerodotou, Villasclaras-Fernández, & Sharples, 2014). A similar participative project using mobile devices was reported by Scanlon, Woods and Clow (2014) who explain how users of the iSpot application were able to participate in location-based science activities based on the local environment, sharing their findings and data with professional scientists and other activists in an online community of practice. A simple but highly effective example of participative authenticity is reported by Ebner (2009) who undertook a study of academics using Twitter on their mobile phones as a backchannel at an academic conference. Delegates tweeted their responses and impressions of each presentation, and these tweets were simultaneously projected on a large screen behind the presenter. In this respect, delegates were physically situated in a highly authentic context (the conference) and were also participating in a genuine community of academic practice, as were those lurkers who could not attend the conference directly but could follow and participate online. In these examples of participative authenticity, mobile technologies mediate how learners work alongside professionals, gradually acquiring the habits and cultural trappings of the community as in a traditional apprenticeship model. However, in many of these examples, the learner does not need to be physically located in the actual community since this can now be achieved through virtual participation even from within a formal setting such as a classroom or conference venue. In this sense, use of mobile technologies is blurring the boundaries or seams between formal and informal learning contexts enabling learners to work in ways which are often described as seamless and unbounded (Looi, Seow, Zhang, So, Chen, & Wong, 2010).

6.5.2 Simulated Contexts Previously most authentic learning activities have been simulated in a ‘practice field’ (Brown et al., 1989; Collins et al., 1989) such as the classroom due to the logistical problems associated with direct participation including costs, time and concerns about personal safety. In these benign spaces, learners simulate the tasks and processes of real-world contexts. Many apps and tools are now available which mimic the tools and processes used by professionals in the real world such as measurement tools (e.g. virtual wind tunnels, oscilloscopes and laminators) in science. An example articulated by Foley & Reveles (2014) used real-world online resources to engage students in authentic but simulated science inquiry. In this example, students used handheld devices within the classroom to share data from their own experiments with other students and schools allowing them to compare and analyse across larger data sets and collaboratively identify trends as a community of science learners.

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In a similar case study (Jones, Scanlon, & Clough 2013) discussed how their nQuire software tool was used on mobile devices to enable science students to take greater responsibility for their own inquiries without adult help. These inquiries were engaging and personally relevant and allowed students to continue their inquiry seamlessly across different contexts such as an after school club and home. These tools and apps have the potential to support highly authentic forms of simulated learning both in formal and hybrid spaces (see below) but empirical research to date suggests they are often used by teachers for low level, unrealistic tasks which bear few resemblances to authentic practices (Kearney et al., 2012, 2015).

6.5.3 Hybrid Contexts Current advances in mobile technologies have fractured the traditional boundaries between participative and simulated contexts. In some cases, this has seen students participating virtually from within formal contexts in genuine and real communities such as the nQuire project described above (Jones et al., 2013). In these contexts, learning takes on a hybrid complexion which combines features of both a direct, participative and indirect simulated model of authentic learning, and many of the technology projects which have explored these spaces report that they combine all of the best qualities of simulations with the additional benefits of high ecological validity acquired through participation in a genuine community. The combination of augmented reality (AR) applications and mobile devices frequently results in hybrid models of authenticity referred to as ‘participatory simulations’ (Barab, & Dede, 2007). Wong & Looi (2011), for example, documented a series of games played in a physical environment but augmented by virtual artefacts through the mediation of a mobile device (they called this ‘mixed reality learning’). Mobile devices with location-based sensors allowed users in the study to interact with explorations, experiments and challenges for inquiry and games-based learning. Lui, Kuhn, Acosta, Niño-Soto, Quintana, & Slotta (2014) described an immersive, cavelike rainforest simulation (called EvoRoom) and a mobile inquiry platform (called Zyeco) that enabled users to collect and share data. Students were co-located in an immersive and physical-digital space, collecting observational data from both the classroom itself (Evoroom) and out-of-class settings (such as parks or museums), and exploring peers’ data using large visualisations displayed at front of room.

6.6 Is Authentic Mobile Learning Predefined or Emergent? Despite advances in mobile technologies which have afforded learners greater agency in how they access information, where they situate their learning and how they present the outcomes of this as assessment artefacts, some authors have noted the reluctance of educators to cede significant control of learning to students (Kearney

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et al., 2015). This is reflected in the extent to which learning is predefined or is left more open-ended and emergent in design. Williams, Karousou & Mackness (2011) define emergent learning as “learning which arises out of the interaction between a number of people and resources, in which the learners organise and determine both the process and to some extent the learning destinations, both of which are unpredictable” (p. 3). There is an implicit assumption in many of the studies on authenticity that learning is likely to be more unplanned and emergent than predefined or prescribed when students tackle illdefined problems that defy simplistic or quick solutions. Overprescription and unnecessary intervention by educators is included as one of Herrington et al.’s list of inauthentic strategies for m-learning (Herrington et al., 2008). Some researchers have identified the creation of opportunities for learners to generate their own contexts as significant for making learning more authentic (Toh, So, Seow, Chen, & Looi, 2013). These studies show how students spontaneously used their mobile devices to capture and share images or video clips related to a personal interest or hobby (e.g. bird watching) without the direction or prescription of a teacher or adult (Jones et al., 2013). These examples often occur in informal settings outside of institutional control but there is no reason to suppose this kind of incidental learning with mobile technologies, could not, and is not taking place within formal settings in the form of serendipitous learning (e.g. where a learner uses their mobile device to capture an idea or inspirational thought) (Toh et al., 2014; Williams et al., 2013). One area where emergent learning is more evident is in mobile game-based applications where players can engage in highly realistic simulations and problem-solving exercises that mimic many of the tasks undertaken by real professionals. Gwee, Chee & Tan (2010) reported one such mobile simulation which featured Year 9 social studies students using the game Statecraft X on their iPhones to learn about the concept of governance through role-play. What distinguishes the game is the amount of spontaneity and lack of planning. Students worked largely at their own pace without interventions or schedules to regulate them. These discussions then invite questions as to the extent to which authenticity can or should be designed into the learning experiences of students when they use mobile technologies (Barab, Squire, & Dueber, 2000; Petraglia, 1998). This raises an obvious tension as it is difficult to visualise how instructors can design learning activities that are entirely emergent since the very act itself assumes a degree of deliberate intent. For some researchers, the solution is to ‘deny the legitimacy of preauthentication’ altogether by which they mean they reject the notion that designers or teachers can construct predefined authentic tasks, even if these have real and practical use to a genuine community of practice (Barab et al., 2000). They argue that these elements of authentic learning cannot be predefined because they do not guarantee ‘buy in’ from learners. If the learner does not personally perceive the context to be authentic, it cannot be ‘preauthenticated’ or designed by some other person. In this sense authenticity “is manifest in the flow itself, and is not an objective feature of any one component in isolation” (Barab et al., 2000, p. 38).

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6.6.1 Personal Commitment of Learners In considering the nature of authentic learning, it is important to identify for whom the learning will be authentic (Barab & Duffy, 2000). Most descriptions of authentic learning describe it from the privileged perspective of the instructor or designer, and it is difficult to appreciate to what extent learners themselves perceive a learning practice to authentic, or what indeed learners think authentic means. However, ultimately authenticity “lies in the learner perceived relations between the practices they are carrying out and the use value of these practices” (Barab et al., 2000, p. 38). This is partly a methodological concern, and there is an urgent need for researchers to design more authentic methods and tools which will gain access to this largely missing learner perspective. For this reason, we developed student survey tools, as discussed in Chap. 9. This is a genuine concern since designing realistic, real-world tasks or contexts and processes that mimic or place learners in actual professional communities may count for little if the learner does not perceive these artefacts to have personal significance and meaning in relation to their desired learning objective. Indeed there is a concern among some that what constitute genuine real-world communities of practice for adults may be far from authentic from the perspective of learners who may speak an entirely separate discourse based on the ‘curricular language’ with which they are familiar (Heath & McLaughlin, 1994). These critics argue that teachers should attempt to locate authentic learning in what they term ‘institutions of curricular authenticity’ where familiar curricular practices, languages, norms and traditions are the Lingua franca. This position is further supported by Hiebert, Carpenter, Fennema, Fuson, Human, Murray, Olivier, & Wearne (1996) who argue that students can be engaged in deeply contextualised and authentic tasks within the curriculum as long as they are personally challenged to engage with the underlying concepts and deep structures of the discipline itself. This definition of authenticity correlates how well a learning activity matches a student’s personal goal structures (Heath & McLaughlin, 1994) or the extent to which learners themselves problematize the elements that make up the context. (Stein et al., 2004, p. 240)

These considerations therefore foreground a critical third constituent in authentic learning which is the emotional and extra-rational dimension of learning and the commitment of the learner while also highlighting one of the more substantial epistemological challenges in the field of authentic learning: how can we capture and understand the learner’s emotional sense of engagement and commitment? In many of the case studies reported in this chapter, we can infer that learners were highly motivated and engaged in the m-learning activities which are described but meaningfulness is a difficult construct to capture and few of the studies detail to what extent the mobile activity enabled learners to develop personal meanings, or indeed why. One exception is the pilot study for the Ecomobile project (Kamarainen, Metcalf, Grotzer, Browne, Mazzuca, Tutwiler, & Dede, 2013). This project explored how the use of a mobile AR application (FreshAIR) could be combined with probeware tools and software to enable students to understand the ecosystem of a pond in ways which resembled real scientific practice. Feedback and video evidence

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from students undertaking the project indicate that it was highly engaging and had considerable personal significance for students working in their local environment. They appear to have engaged with the topic on a highly personal level despite the fact it did not feature a genuine professional community of scientists as such.

6.7 Discussion and Implications Derived from the above definitions and examples, we propose the following orthogonal model as a means of further conceptualising authentic m-learning (see Fig. 6.1). We identify Context as a critical vector in understanding how and where the learning activity is situated and use the terms ‘simulated’ and ‘participative’ to describe the two distinctive parts of this vector. Contrary to our original suggestion in Table 5.2 in Chap. 5, these are not proposed as normative labels since there is no implication here that either form of authenticity is necessarily more desirable than the other. Chapter 5 reflected our initial thoughts and ideas about authenticity in which we understood simulation to be an artificial or contrived form of realistic situations and therefore low in authenticity. The discussion in this chapter understands simulation differently. It recognises simulation and participation as two distinctive aspects of the context variable.

Fig. 6.1 A conceptual model of authentic mobile learning

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The second axis called Planning Design considers the extent to which the learning activity is planned or unplanned in a similar way to the model developed by Toh et al. (2013). However, given the emerging affordances of mobile technologies, we place greater emphasis on the agency of the learner in co-negotiating and designing these contexts. Hence, this vector is used to measure both the degree of agency granted to the learner and the extent to which the learning activity as a whole is preplanned or emergent. Thirdly, we include a vector capturing the Personal Relevance and consequent engagement of the learner since this has emerged across many studies as a highly significant but often neglected element of authentic learning. Unlike the other two vectors which are not normative, this vector is more judgemental since it is recognised that learners will elect to disengage from learning that holds little or no personal significance or meaning for them.

6.7.1 How Does the Model Work? To illustrate how this three-dimensional model might further support the conceptualisation of authentic m-learning, we have populated it with the three m-learning scenarios described earlier in the paper represented by the letters A, B and C (see Fig. 6.2 and Table 6.1). In terms of the context vector, only the Twitter example (C) was classed as participative since it was set in a genuine real-world context in this professional learning scenario (an academic conference) accessible in both a physical and virtual manner through the mobile device. In the Ecomobile example (A), students participated in real-world tasks and processes using tools in a real-life way and in relevant informal settings but they did not engage with a real community of practice, even though this might have been feasible with the mediation of mobile technology. Therefore, the context was identified as a hybrid. The mobile game example (C) was entirely simulated in terms of context since there was little attempt to involve students in a genuine governance community. Both examples B and C were classed towards the emergent end of the Planning Design spectrum since neither was heavily predefined or structured. In the case of the mobile game (B), students were not restricted by fixed schedules and could engage at their own pace. This was also true in the case of the Twitter example where participants were left to determine how and when they would structure their responses (if at all). The Ecomobile example (A) was more predefined by the teacher who had devised many of the tasks in advance even though most of it occurred in an informal setting outside of school. Finally, although students were not directly questioned about their levels of personal engagement in any of these three exemplars, we might infer that motivation and engagement were high judging by the amount of activity which occurred, often

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Fig. 6.2 Authentic mobile learning examples

Table 6.1 Characteristics of exemplar authentic mobile learning scenarios Exemplar

Context

Planning design

Personal relevance

A

Ecomobile project (Kamarainen et al., 2013)

Hybrid

Predefined

High

B

Statecraft X mobile learning game (Gwee, Chee, & Tan, 2010)

Simulated

Emergent

High

C

Twitter backchannel in an academic conference (Ebner, 2009)

Participative

Emergent

High

unsolicited as in the mobile games example, and this suggests that all three examples had high personal meaning and significance from the perspective of learners themselves.

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6.7.2 Returning to Research Questions As shown in even these few examples, understanding what is authentic about mlearning is not straightforward or unproblematic. Therefore, the model presented in this chapter offers a novel way of conceptualising these issues, rejecting simplistic solutions that typically frame authentic m-learning in terms of mutually exclusive binaries. This rejection of binaries is aligned with third space notions of m-learning, as described in Chap. 4. Traditionally, authentic learning is epitomised by the duality between first-hand direct experience which equates with the participatory model of authentic learning, and indirect, second-hand experience which equates to the simulated model of authenticity. This chapter has argued that this traditional duality is no longer valid when students have access to and use mobile devices, blurring the boundaries between simulated and participative forms of real-world learning, between predefined and emergent models of learning and between high or low levels of personal engagement and meaning-making. The concepts of boundary-crossing and boundary objects which are inherent features of Activity Theory (Engeström, Engeström, & Kärkkäinen, 1995) are useful ways of thinking about authenticity and m-learning because they focus on learning which transcends conventional boundaries such as home/school, formal/informal and physical/virtual using mobile devices as cultural objects which mediate these crossings. Here, “boundaries are understood as a social cultural difference between systems, practices, or social worlds, leading to a discontinuity in action or interaction between these systems” (Snoek, 2013, p. 309). In effect, use of mobile devices fulfils a bridging action since it enables learners to cross traditional boundaries. For example, a student may join an authentic community of scientists on Twitter, posting and following tweets as a legitimate member of the community, even while located within a formal classroom setting. Formerly, this setting would have been bounded both physically and culturally in such a manner that this participation would not have been feasible. While the mobile device acts as a boundary-crossing object in these cases, it does so within culturally defined boundaries and practices of the traditional classroom setting. If the teacher, and indeed the institution, prohibit the use of technology across contexts in this seamless fashion (Jones et al., 2013; Wong, Milrad, & Specht, 2015), or if they attempt to pre-authenticate or overly predefine the learning outcomes, it is unlikely that these opportunities to cross boundaries will be seized upon, or alternatively they might become a form of subversive activity undertaken by students looking to escape the rigidity and sterility of this type of learning experience. What this chapter has also attempted to highlight is the primacy of affective factors such as perceptions of personal relevance on the part of the learner which is so critical in understanding authentic learning. Research in the pre-mobile era already suggested that authenticity was not a commodity which could be objectified and designed into the context or tasks itself (Barab, et al., 2000) but rather it was highly ephemeral and closely associated with the personal perceptions of the individual learner. Current research into authentic m-learning has identified a significant list of characteristics that are deemed to make learning more authentic (Herrington et al., 2008) but there is

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little empirical evidence of what these factors mean from the perspective of learners themselves. There is an urgent need, therefore, for the m-learning research community to better understand how this kind of data might be elicited and how it would then be used to support the design of more meaningful and engaging authentic m-learning scenarios. In this respect, we still face the same epistemological and methodological challenges that were highlighted by researchers investigating the potential of first-generation computers to enhance authentic learning.

6.8 Conclusion At the beginning of this chapter, we identified a conundrum which questioned why educators associate m-learning so closely with authenticity given that most of their learning tasks are situated in formal settings such as schools and universities. The chapter has posited that no single criteria or characteristic makes a learning activity authentic (Banas, & York, 2014), and it has also argued that traditional definitions of authenticity are in need of revision and upgrade to better reflect the contextual boundary-crossing mediated by mobile devices. Although formal settings such as schools and universities might once have been considered contrived contexts for learning compared to genuine real-world settings such as work placements or apprenticeship, this definition is rooted in pre-mobile notions of space and time (Traxler, 2009) which are no longer as applicable as they were previously. The conceptual model proposed in this chapter (see Fig. 6.1) has a practical orientation for learning design in mobile environments since it highlights three critical vectors that need to be considered carefully in order to consider the authenticity of any m-learning experience. Further research is also required to investigate to what extent educators and learners are reconceptualising their thinking about authentic learning when mobile devices are used seamlessly cross the traditional boundaries between formal and informal contexts, virtual and physical worlds and planned and emergent spaces. This chapter offers a model to initiate and support this process. In Chap. 7, we describe how our original Mobile Pedagogical Framework was further developed in light of these fresh understandings of authenticity and through other activities in two of our major m-learning projects.

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Wong, L. H., & Looi, C. K. (2011). What seams do we remove in mobile assisted seamless learning? A critical review of the literature. Computers and Education, 57(4), 2364–2381. Wong, L. H., Milrad, M., & Specht, M. (Eds.). (2015). Seamless learning in the age of mobile connectivity. Singapore: Springer.

Chapter 7

Evolution of the iPAC Mobile Pedagogical Framework

Abstract This chapter traces the conceptual development of our Mobile Pedagogical Framework, from the original MPF proposed in 2012 to the current format known as iPAC. It traces two development cycles coinciding with mobile learning research projects in Australia and Europe. It includes a description of how the Framework’s visual representation has evolved, informed by a range of stakeholders, including teacher educators, school leaders and school teachers in several countries. Keywords Mobile pedagogical framework · Visual representation · iPAC · MPF · M-learning · Digital pedagogies · Socio-cultural · School education · Teacher education · Personalisation · Authenticity · Collaboration

7.1 Introduction The benefits of mobile devices to support teaching and learning include their ability to be used anywhere at any time; their ability to be customised to reflect the user’s needs; their potential to support collaboration; and their support of students’ co-creation of digital artefacts and knowledge (Burden, Kearney, Schuck, & Hall, 2019). However, twenty-first-century educators face major challenges in using a growing repertoire of emerging mobile learning (m-learning) technologies in pedagogically effective and potentially transformational ways. Current teachers and teacher educators have been slow to exploit the full range of m-learning pedagogies (Kearney, Burden, & Rai, 2015; Royle, Stager, & Traxler, 2014). Our Mobile Pedagogical Framework and initiatives underpinned by it, such as our m-learning toolkit (Burden & Kearney, 2018), are our response to these challenges. In this second part of the book, we have so far introduced our Mobile Pedagogical Framework underpinned by socio-cultural theory (Chap. 5) and interrogated the complex and often misunderstood notion of authentic m-learning (Chap. 6). This chapter traces the iterative development of our Framework, its various representations and how it has developed into the better-known iPAC Framework since its launch in 2012.

© Springer Nature Singapore Pte Ltd. 2020 M. Kearney et al., Theorising and Implementing Mobile Learning, https://doi.org/10.1007/978-981-15-8277-6_7

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7.2 Revisiting the Original Mobile Pedagogical Framework As discussed in Chap. 5, our original Mobile Pedagogical Framework (MPF) aimed to bring a pedagogical perspective to an area largely underpinned by techno-centric considerations (Kearney, Schuck, Burden, & Aubusson, 2012). Informed by sociocultural theory (Wertsch, 1991), it highlights three central and distinctive mobile pedagogical features: personalisation, authenticity and collaboration (or ‘PAC’). How learners experience these distinctive characteristics is influenced by how they leverage a more flexible ‘time-space’ (or context) when learning with mobile devices, as depicted in Fig. 7.1, which shows the original Framework from our 2012 paper. The personalisation dimension consists of the sub-dimensions of agency and customisation. High levels of personalisation would mean the learner is able to enjoy an enhanced degree of agency (Pachler, Bachmair, & Cook, 2009) and the flexibility to tailor both tools and activities, interacting with a strong sense of ownership of both the device and the learning processes. The authenticity dimension privileges opportunities for in situ, participatory learning (Radinsky, Bouillion, Lento, & Gomez, 2001). The original sub-dimensions of task, tool and setting focused on learners’ involvement in rich, contextualised tasks, making use of tools in realistic ways, and driven by relevant real-life practices and processes. The collaboration dimension captures the conversational, networked features of m-learning. The original subdimensions consisting of conversation and data sharing, focused on how learners engage in negotiated meaning-making, forge connections and interact with peers, experts and the environment (Wang & Shen, 2012). Our MPF provided a lens to explore how mobile technologies can leverage new and emerging digital pedagogies in a range of formal and informal learning settings. Hence, it was used to provide Fig. 7.1 The original representation of our Mobile Pedagogical Framework (MPF) comprising three distinctive features of mobile learning experiences (from Kearney et al., 2012, p. 8)

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the foundational theoretical underpinning of two major funded projects focusing on m-learning in Australia and Europe. During these projects, the Framework itself was further developed, as described in the following sections.

7.3 Evolution of the iPAC 1.0 Framework The MPF (Kearney et al., 2012) was explicitly adopted as the theoretical underpinning for the EU funded, Erasmus Plus project (2014–2017) titled the Mobilising and Transforming Teacher Educators’ Pedagogies project (or MTTEP). This transnational project involved nine partners from universities and schools in the UK, Europe and Australia, as discussed in Chap. 1. The aim of this project was to design and create an m-learning toolkit to support teachers’ and teacher educators’ design and implementation of m-learning activities (the final toolkit is accessible via http://www. mobilelearningtoolkit.com). The MPF was the core theoretical framework for the mlearning toolkit, informing the design of its elements, including survey instruments and tools, video vignettes and an online course that educators and institutions can freely use and adapt (Burden & Kearney, 2018). As part of the initial assessment of use by practitioners, project leaders identified the needs of end users of the toolkit by creating an online needs analysis tool, interviewing a number of teacher educators in project members’ institutions and carrying out a global survey to identify how teachers and teacher educators use mobile devices (Burden & Kearney, 2017; Kearney et al., 2015). As part of this process, a series of focus group meetings in different parts of the world was organised, including at the Hong Kong International Festival of Learning (Burden & Kearney, 2015, 2016a) and two large-scale project workshops for teachers, teacher educators and policymakers in Bergen, Norway, in 2015 and Karlsruhe, Germany, in 2016. Feedback from over 200 participants attending these workshops enabled us to have confidence in the initial prototypes and designs for the toolkit. Further feedback from project members and stakeholders during this process indicated that parts of the original MPF (see Fig. 7.1) were overly complicated, and some dimensions were difficult to understand, especially the authenticity dimension (see Chap. 6). Subsequently, modifications to the MPF were made. At a representational level, a new triangular-shaped visual representation highlighted the three pedagogical dimensions (personalisation, authenticity and collaboration), colour coded at each apex of the triangle (see Fig. 7.2). The decision to change the representation from a circle to a triangle was purely aesthetic—it seemed to resonate more with practising teachers in the MTTEP project. At a conceptual level, the central time-space part of the Framework remained at the core of the representation, to indicate the fundamental socio-cultural influence on how learners experience these three distinctive pedagogies. However, stakeholders’ feedback on the original authenticity dimension indicated that there was a commonly perceived blurred boundary between the original situatedness and contextualisation sub-dimensions. This feedback, and our own

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Fig. 7.2 iPAC 1.0. An updated representation of the MPF, which became known as the ‘iPAC’ Framework (reprinted with permission from Burden & Kearney, 2018, p. 92)

further intellectual work interrogating the nature of authentic mobile pedagogy (see Chap. 6, also Burden & Kearney, 2016b), suggested that three new authenticity sub-dimensions (settings, task and tool) would provide clarity and more accurately capture the nuances of this distinctive mobile pedagogy (Burden & Kearney, 2018). Settings can be both physical and virtual in the mobile world, enabling learners to experience what it is like to learn in situ. Task authenticity refers to the extent to which tasks are realistic and offer problems encountered by real-world practitioners. Tool authenticity relates to the apps and tools students are using and how far they replicate those of real-world practitioners. Finally, to distinguish this updated Framework from the original MPF, the name ‘iPAC’ was chosen to describe it, PAC being the acronym for the three dimensions: personalisation, authenticity and collaboration. To address some of the problems with the original MPF, auxiliary tools were developed during the MTTEP project to provide clarity on this iPAC 1.0 Framework. A series of brief screencasts were created to introduce the updated Framework and to help explain each dimension (see http://www.mobilelearningtoolkit.com/ipac-fra mework.html). Using the same colour codes as the triangular iPAC 1.0 representation shown in Fig. 7.2, a set of six continua were developed around each sub-dimension to help educators to understand the binary extremes of each pedagogical sub-dimension (another screencast was created to explain this auxiliary tool: see http://www.mob ilelearningtoolkit.com/the-ipac-sub-constructs.html). Each continuum used binary terms to describe ‘weak’ and ‘strong’ levels of these mobile pedagogical experiences, as shown in Fig. 7.3.

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Fig. 7.3 Six continua were developed to help educators’ interpretation of each sub-dimension of the iPAC 1.0 Framework

In summary, the principal challenge in developing the m-learning toolkit during the MTTEP project was to make our original Mobile Pedagogical Framework accessible both technically and pedagogically for educators across different settings. We approached this challenge by using a design-based approach involving multiple iterations and accompanying tests in each partner country prior to the full launch of the toolkit in 2017. The Framework subsequently changed to a triangular representation and became known as iPAC. To improve the clarity and utility of the Framework, conceptual modifications were made to some sub-dimensions, particularly in the authenticity dimension.

7.4 Appropriation of the MPF and iPAC 1.0 Framework The original MPF was appropriated by users in various ways. Two examples follow. The first and earliest example is from Bartlett-Bragg and Dellow (2012), who appropriated our Framework for the purposes of a business education webinar on planning and designing mobile apps (see Fig. 7.4). As shown in Fig. 7.4, the authenticity dimension from our MPF was re-named Relevance in this appropriation. This was the first signal to us, as authors of

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Fig. 7.4 An early appropriation of the MPF by Bartlett-Bragg and Dellow (2012) for business education purposes

the MPF, that there was a usability issue with the authenticity dimension. As previously discussed, further feedback resulted in a refinement of the authenticity sub-dimensions in subsequent versions of the Framework. The second example of appropriation of our original MPF is from Townsend (2017), who used the MPF in his study of Aboriginal and Torres Strait Islander preservice teachers (PSTs) in community-based initial teacher education (ITE) programs in remote communities using mobile devices in their tertiary study. Building on our MPF, and informed by Arbon’s (2008) “metaphor of Yalka, a small onion that has layers which can be peeled to metaphorically reveal ontological foundations of what it is to be, know and do” (p. 26), he developed an expansive framework to align perspectives of Australian indigenous cultural philosophies with elements of m-learning: If a model of mobile learning is to be applicable to Aboriginal and Torres Strait Islander people, it needs to align with the worldview they already hold. Hence, the mobile learning framework is inserted within the Yalka onion layers of cultural philosophies. (Townsend, 2017, p. 214)

Townsend’s model juxtaposes our MPF with epistemological and ontological perspectives, as shown in Fig. 7.5. Townsend (2017) then used his model to examine the three areas of authenticity, collaboration and personalisation, considering Aboriginal and Torres Strait Islander

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Fig. 7.5 An appropriation of the MPF from Townsend (2017, p. 215, with permission). Juxtaposition of Aboriginal and Torres Strait Islander cultural philosophies and a pedagogic framework of mobile learning

cultural philosophies in relation to the experience of being a PST enrolled in a community-based ITE program. For example, in his discussion of the collaboration dimension, he writes: “The sharing of personal encouragement through the use of mobile devices means that a person can be ‘present’ with another, in a different place to share their joy or sorrow; and this can happen asynchronously” (p. 222). And in his analysis of the conversation sub-dimension, he reports: Participants identified numerous areas of conversation. A general purpose was expressed in the following terms: to share, share life, keep in touch, keep in contact and check up on what others were up to. Participants spoke about their online community, the network and friendships they had, and which they wanted to continue. They checked each other’s physical location, offered and requested emotional support in times of illness and grief over the death of someone, as well as sharing jokes and sending congratulations on achievements. These facets of personal encouragement through mobile devices align with aspects of Aboriginal and Torres Strait Islander cultural philosophies. (p. 222)

In this way, our MPF was appropriated by Townsend (2017) to probe deeper philosophical issues linked with PSTs and Australian Indigenous culture. Considerable autonomy was afforded to MTTEP project participants in terms of how they used and appropriated the iPAC Framework in project-related activities. One initiative from MTTEP members towards the end of the project was to re-introduce the original circular MPF representation merged with the three updated authenticity sub-dimensions, using the same colour codes from the iPAC Framework, as shown in Fig. 7.6. This circular iPAC representation was featured in other areas of the final mlearning toolkit. For example, it was used in the online rubric for evaluating apps as discussed further in Chap. 10 (see http://www.mobilelearningtoolkit.com/app-rub ric1.html). It was also used in the presentation of video exemplars created by project

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Fig. 7.6 The circular representation of iPAC 1.0 by German MTTEP project members

members to demonstrate and contextualise aspects of the iPAC Framework across various disciplines (see http://www.mobilelearningtoolkit.com/video.html).

7.5 Evolution of the iPAC 2.0 Framework A second major development of the iPAC Framework occurred during empirical work in our funded Australian Research Council project titled: OptimisingTeaching and Learning With Mobile Intensive Pedagogies (2015–2019). As discussed in Chap. 1, this project investigated mobile technology use in secondary schools, particularly in maths and science education. The project involved the rigorous development of two versions of a teacher survey that were designed to probe teachers’ perceptions of their use of iPAC pedagogical dimensions. Results from both these surveys are discussed elsewhere in this book (Chaps. 11 and 12). The surveys’ contribution to the evolution of iPAC is discussed below. Items on the first version of the teacher survey were designed to capture teachers’ perceptions of their typical use of mobile pedagogies over the past year, based on the three dimensions of the iPAC Framework. A range of forums informed their development, including formal and informal discussions about mobile pedagogies with the four researchers from the original 2012 Framework, as well as feedback from other researchers and experts in m-learning, pre-service and in-service teachers. This first version of the survey was then implemented with ten Australian in-service teachers. These results showed that discriminant and construct validity could be improved, thereby motivating development of the second version of the survey. To begin the process of developing a second survey version, the original survey items were re-written to eliminate potential ambiguities and then reviewed by a team of five experts (the same four researchers from the original 2012 Framework and

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an expert in survey design) to ensure that items referred to only one of the three dimensions of the m-learning pedagogical framework. A subsequent classification exercise involving 79 pre-service teachers was undertaken, as described in Chap. 11. The results of the final survey development were based on 349 completed responses from practising teachers in Australia. Overall, a more robust survey instrument was developed in which validity was improved, as discussed in further detail in Chap. 11. Throughout these survey development procedures, researchers regularly scrutinised the sub-dimensions of the iPAC Framework to monitor their ‘fit’ with the emerging clusters of items that were yielding improved construct validity. To be faithful to the mobile pedagogies described by items in the final validated survey, the following changes were made to the sub-dimensions of the iPAC 1.0 Framework (see Fig. 7.2). The sub-dimension data sharing was replaced with the term co-creation to emphasise the critical collaborative, generative aspect of this sub-dimension. The three items in Table 7.1 are from the final survey and aim to capture key elements of this co-creation sub-dimension, interrogating the extent to which learners use a mobile device to co-create digital content and share information, data and artefacts. Additionally, the authenticity sub-dimensions of task, tool and setting were replaced with task and context to provide further clarity and avoid ongoing issues with blurred boundaries between these groupings (these sub-dimensions had evolved from the contextualisation and situatedness labels in the original MPF). Task authenticity refers to the extent to which m-learning activities are realistic and offer activities relevant to the real world; and the extent to which the tasks and associated processes require use of apps and tools that replicate those of real-world practitioners. The four items in Table 7.2 are from the final survey and focus on the task authenticity sub-dimension. The context sub-dimension of authenticity focuses on the extent to which learners’ m-learning experiences are enhanced by realistic, meaningful contexts, such as through in situ learning in relevant physical and/or virtual settings. The three items from the final survey focus on the context sub-dimension, as shown in Table 7.3. These developments to the sub-dimensions of collaboration and authenticity from this empirical work subsequently informed an updated visual representation of the iPAC Framework (Kearney, Burke, & Schuck, 2019), known as iPAC 2.0, as shown in Fig. 7.7. Table 7.1 Survey items relating to the co-creation sub-dimension (collaboration)

Final validated items Students work together to create a digital product, e.g. co-created a video, podcast, photo, iBook, document Students share digital content, e.g. shared a video, podcast, photo, document Students contribute to existing digital content, e.g. tagged a photo, commented on a blog post, played a multi-player game

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Table 7.2 Survey items relating to the task sub-dimension (authenticity) Final validated items Students work like an expert, e.g. collected data using GPS like a geographer; measured using an inclinometer app like a scientist; composed music or lyrics to a song like a musician. Students participate in real-world activities that benefit society, e.g. citizen science project that included real-life experts; environmental task on waste Students learn serendipitously in an unplanned way, e.g. during a game, research prompted by an unexpected query Students engaged in activities related to everyday life, e.g. developing a budget Table 7.3 Survey items relating to the context sub-dimension (authenticity) Final validated items Students learn in a place suggested by the topic, e.g. learned about stars under the night sky; pollution at a local stream; history at the site of an ancient battle Students learn in a realistic, virtual space, e.g. use of augmented (AR) or virtual reality (VR) apps, science simulation Students learn at a time suggested by the topic, e.g. night-time observation of stars; weekend analysis of sporting performance

Fig. 7.7 iPAC 2.0 Framework (reproduced with permission from Kearney et al., 2019, p. 754)

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In summary, iPAC 2.0 is the current version of our mobile pedagogical Framework at the time of writing. From Chap. 8 onwards, we will simply refer to this latest version as the ‘iPAC Framework’. As in previous versions, designers of the original 2012 Framework were closely involved in the development of this latest iteration. The current version emerged from empirical survey development work (Kearney et al., 2019). Revision to the authenticity dimension of iPAC 1.0 resulted in a more symmetrical structure to the overall representation, as authenticity now has two subdimensions as opposed to three in the previous version. New sub-dimension terms also were introduced that more accurately depict the signature pedagogies of mlearning, for example, co-creation instead of data sharing to describe one of the sub-dimensions of collaboration. Future appropriations of iPAC 2.0 by users will be reported at the iPAC website via https://www.ipacmobilepedagogy.com.

7.6 Conclusion Our socio-cultural pedagogical framework has remained robust for almost a decade. The core of the Framework (time-space) has remained unchanged since its inception, continuing to suggest a profound impact on learners’ experiences of mobile pedagogies from their m-learning environment. The three main (first-level) pedagogical dimensions, personalisation, authenticity and collaboration, have remained unchanged, as have the personalisation sub-dimensions of customisation and agency. However, other (second-level) sub-dimensions have evolved since the original development of the Framework, to improve clarity and usability among education stakeholders. One sub-dimension in the collaboration dimension and all sub-dimensions of the authenticity dimension were refined through analysis of qualitative and quantitative data collected in two large funded projects, as described in this chapter. The iPAC Framework aims to foreground teachers’ distinctively mobile pedagogies, rather than specific technologies or perceptions of m-learning drivers and constraints. At the time of writing, it has been used in a range of teaching and research projects around the world and evidently resonates with both practitioners and researchers interested in optimising digital pedagogies that exploit new and emerging mobile technologies (see Chap. 13). The potential role of the iPAC Framework as a lens to explore innovative and possibly disruptive digital practices is also being explored, as discussed in Chap. 14. These explicit foci on effective and transformational mobile digital pedagogies are timely pursuits, considering the contemporary movement to limit school students’ access to mobile devices for learning purposes (Burden, Schuck, & Kearney, 2019). The utility of the iPAC Framework for users will remain paramount, and any future conceptual or representational refinements will continue to be shaped by feedback from education stakeholders invested in pushing the boundaries of contemporary teaching and learning with technologies.

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References Arbon, V. (2008). ArlathirndaNgurkarndaItyirnda: Being-knowing-doing: De-colonising Indigenous tertiary education. Teneriffe, QLD: Post Pressed. Bartlett-Bragg, A., & Dellow, J. (2012).Planning and designing mobile apps forbusiness webinar diagram pack. Headshift Asia Pacific. Retrieved from https://www.slideshare.net/headshiftoz/ mobile-apps-webinar-diagram-pack?from_action=save Burden, K., & Kearney, M. (2015).Conceptualising authentic mobile learning. In D. Churchill, T. Chiu, & N. Gu (Eds.), Proceedings of the international mobile learning festival (pp. 373–398). Hong Kong, SAR China. Burden, K., & Kearney, M. (2016a). A snapshot of teacher educators’ mobile learning practices. Proceedings of the International Mobile Learning Festival 2016 (pp. 59–77). Bangkok, Thailand. Burden, K., & Kearney, M. (2016b). Conceptualising authentic mobile learning. In D. Churchill, J. Lu, T. Chiu, & B. Fox (Eds.),Mobile learning design: Theories and application (pp. 27–42). Singapore: Springer. Burden, K., & Kearney, M. (2017). Investigating and critiquing teacher educators’ mobile learning practices. Interactive Technology and Smart Education, 14(2), 110–125.https://doi.org/10.1108/ ITSE-05-2017-0027 Burden, K.,& Kearney, M. (2018).Designing an educator toolkit for the mobile learning age.International Journal of Mobile and Blended Learning (IJMBL), 10(2), 88–99.https://doi. org/10.4018/ijmbl.2018040108 Burden, K., Kearney, M., Schuck, S., & Hall, T. (2019). Investigating the use of innovative mobile pedagogies for school-aged students: A systematic literature review.Computers and Education, 138, 83–100.https://doi.org/10.1016/j.compedu.2019.04.008 Burden, K., Schuck, S., & Kearney, M. (2019). Should we be concerned about mobile devices in the classroom: What does the evidence say? Impact.Journal of the Chartered College of Teachers. Retrieved from https://impact.chartered.college/article/mobile-devices-schools-reallyinnovative-what-does-evidence-say/ Kearney, M., Burden, K., & Rai, T. (2015).Investigating teachers’ adoption of signature mobile pedagogies.Computers and Education, 80, 48–57.https://doi.org/10.1016/j.compedu.2014.08.009 Kearney, M., Burke, P., & Schuck, S. (2019). The iPAC scale: A survey to measure distinctive mobile pedagogies. TechTrends, 63, 751–764. https://doi.org/10.1007/s11528-019-00414-1. Kearney, M., Schuck, S., Burden, K., & Aubusson, P. (2012). Viewing mobile learning from a pedagogical perspective.Research in Learning Technology, 20(1).https://doi.org/10.3402/rlt. v20i0.14406 Pachler, N., Bachmair, B., & Cook, J. (2009). Mobile learning: Structures, agency, practices. New York, NY: Springer. Radinsky, J., Bouillion, L., Lento, E. M., & Gomez, L. M. (2001). Mutual benefit partnership: A curricular design for authenticity. Journal of Curriculum Studies, 33(4), 405–430. https://doi.org/ 10.1080/00220270118862. Royle, K., Stager, S., & Traxler, J. (2014). Teacher development with mobiles: Comparative critical factors. Prospects, 44(1), 29–42. https://doi.org/10.1007/s11125-013-9292-8. Townsend, P. (2017). Travelling together and sitting alongside: How might the use of mobile devices enhance the professional learning of Aboriginal and Torres Strait Islander preservice teachers in remote communities? Doctoral Thesis, Flinders University of South Australia.Retrieved from http://flex.flinders.edu.au/file/7a690838-1ce2-4a3e-bc1c-510289161 e3c/1/ThesisTownsend%202017.pdf Wang, M., & Shen, R. (2012). Message design for mobile learning: Learning theories, human cognition and design principles. British Journal of Educational Technology, 43(4), 561–575. Wertsch, J. V. (1991). Voices of the mind: A socio-cultural approach to mediated action. Cambridge, MA: Harvard University Press.

Chapter 8

Differentiating Mobile Learning Frameworks

Abstract This chapter examines a range of theoretical frameworks and models that have been developed to understand and underpin the adoption and application of mobile technologies in educational contexts. Many of these predate the widespread adoption and use of mobile technologies in schools and other educational settings, which became common after the launch of the iPad in 2010. The chapter identifies changing trends and foci that characterise these frameworks and models, such as the shift from a concentration on technology to a focus on pedagogy. The first part of the chapter examines a selection of mobile learning frameworks and models that predate the publication of our Mobile Pedagogical Framework (MPF) in 2012 that is the focus of this book. It explores how each of these frameworks influenced and shaped the MPF’s development. The second half of the chapter traces the influence of the MPF, now referred to as the iPAC Framework, on a selection of subsequent academic frameworks and models. In doing so, this chapter sets the context within which our Framework was conceived and developed. Keywords Mobile pedagogical framework · iPAC · Frameworks · Models · Mobile learning · M-learning theories · Digital pedagogy

8.1 Introduction The term mobile learning, or m-learning as it is referred to in its abbreviated format, first appeared in 2005, long before the widespread pedagogical adoption and use of mobile devices or the development of substantive theories, concepts, frameworks and models to explain or understand this phenomenon (Koole, Buck, Anderson, & Laj, 2018). However, the field of educational technology has been widely criticised for its lack of criticality, reflective practice and theoretical underpinning, and m-learning is particularly vulnerable to this criticism (Baran, 2014; Brown & Mbati, 2015; Hsu, Ching, & Snelson, 2014). It has long been acknowledged that a nascent field of Parts of this chapter are adapted from: Kearney, M., Schuck, S., Burden, K., & Aubusson, P. (2012). Viewing mobile learning from a pedagogical perspective, Research in Learning Technology, 20(1). https://doi.org/10.3402/rlt.v20i0.14406. Creative Commons Attribution 4.0 International (CC BY) license. © Springer Nature Singapore Pte Ltd. 2020 M. Kearney et al., Theorising and Implementing Mobile Learning, https://doi.org/10.1007/978-981-15-8277-6_8

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research such as m-learning faces the danger of falling into disrespect if it is not able to underpin its claims with robust and credible conceptual frameworks and theoretical models (Koole et al., 2018). And yet at the time of the development of our Mobile Pedagogical Framework (MPF) (see Chap. 5 for details of this development) between 2009 and 2012, only a small number of such frameworks and theories existed, with most focusing on the actual technology rather than the learning associated with mobile devices. This chapter charts the development of selected theoretical frameworks and models that are significant for m-learning, including some that have influenced our own thinking and the development and subsequent use of the MPF (see Chap. 5), now referred to as the iPAC Framework (see Chap. 7). It does not aim to be a definitive or comprehensive review of all m-learning frameworks or models, since this is neither practicable nor desirable in a chapter of this length, given the focus of the book on the iPAC Framework. Rather, this chapter concentrates on a specific selection of theoretical frameworks and models that capture the evolving nature and characteristics of m-learning from its beginnings, where the focus tended towards the technology and its affordances, through to times when the emphasis tended to be on the learner (learner centredness) and into more recent thinking and debates that acknowledge that human beings are embodied and situated and the tools and resources they use play an important part in how they interact with their surroundings. It concentrates initially on the theoretical models and frameworks that influenced and informed our MPF, building on the work set out in an earlier article by the authors of this book (see Kearney et al., 2012). It then examines a selected number of theoretical frameworks that have developed since 2012.

8.2 What Are Theoretical Frameworks and Models? At their most basic level, frameworks and models assist in translating academic theory into operational practice, and are therefore extremely valuable for practitioners (Bower & Vlachopoulos, 2018). But what are frameworks and how do they differ from models? According to Hsu & Ching (2015), “Frameworks delineate the conceptual relationships among components and hypotheses grounded in related theories, while models provide descriptive or prescriptive representation of relationships among components in a framework based on analysis of empirical evidence” (p. 2). This is an important but esoteric distinction and accordingly, for the purposes of this chapter, the terms ‘framework’ and ‘model’ are used interchangeably except where there is a need to specifically delineate between them. Lewin (1943) famously claimed there was ‘nothing so practical as a good theory’, and the most practical theoretical frameworks and models explain the constituent elements, variables and key factors that are the focus of the phenomenon. But they do more than identify the factors; they set out different permutations or potentialities for the relationships between these elements (Miles & Huberman, 1994). In this respect, theoretical frameworks and models are cognitive tools that enable users to

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see beyond the specific details of a particular setting or issue in either graphical or narrative form. Some frameworks are more conceptual in nature and are primarily concerned to describe or articulate phenomena. This may make them difficult for practitioners to instantiate or operationalise (Bower & Vlachopoulos, 2018). On the other hand, some are more procedural and practical in nature, while others lean more towards evaluation and are used in either formative or summative mode to make judgements about the phenomenon. Some include all of these features. In a recent publication analysing the various characteristics of technologyenhanced learning frameworks and models, Bower & Vlachopoulos (2018) noted how these were more often conceptual rather than procedural in nature. They also pointed out how many frameworks and models fail to provide a context or the contextual questions that should be asked, in particular, how “models rarely provide explicit and substantial consideration of the interactions between students and teachers, and quite often do not provide examples to illustrate how to apply the model” (pp. 989– 990). The frameworks and models that are outlined below, including our own MPF, exhibit these issues or shortcomings to varying degrees, highlighting the lack of consistency that continues to be a feature of how academics and researchers develop and share technology-enhanced learning conceptual designs.

8.3 Ways of Conceptualising Mobile Learning (Pre-2012) Numerous characteristics of m-learning have been identified in the literature. At the turn of the century, Klopfer, Squire, & Jenkins (2002) identified five distinct features of m-learning: portability, social interactivity, context sensitivity, connectivity and individuality. Danaher, Gururajan, & Hafeez-Baig (2009) later proposed a framework based on three key principles: engagement, presence and flexibility. Presence refers to the “simultaneous awareness and locatedness of self and others … encompassing the emotional element of being human” (p. 26). They further breakdown presence into three “interaction types” (p. 26): cognitive (student-content), social (peer) and teaching (student-teacher). Inherent in this model is an implicit discussion of pedagogy. Pachler, Bachmair, & Cook (2010) analysed the interrelationship of learners with the structures, agency and cultural practices of what they call the “mobile complex” (p. 175). Numerous m-learning frameworks have been proposed in the literature, ranging from complex multi-level models (e.g. Parsons, Ryu, & Cranshaw, 2007) to simpler frameworks that often omit important socio-cultural characteristics of learning or pedagogy. Common themes include portability of m-learning devices and mobility of learners, interactivity, control and communication. These descriptions acknowledge the prime importance of context, including spatial and temporal considerations, for analysing m-learning experiences. However, they typically attempt to merge affordances of mobile devices or characteristics of applications with features of the learners’ experience.

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Winters (2006) identified three principal ways of conceptualising m-learning, and to varying degrees the frameworks described in this chapter illustrate many of these perspectives. The first feature focuses on learning that is supported by mobile devices where the primary emphasis is on the technology itself. This characteristic was typical of some of the earliest m-learning frameworks when the technology was still a novelty in and of itself. A second perspective summarised mobile devices as technologies to support or augment learning in formal contexts such as classrooms. This perspective resonates with those frameworks that focus on traditional notions of education in which the device makes learning more efficient without transforming it. The third perspective concentrates more on the learner and the ways in which mobility (in terms of the use of both space and time) generates individual and unique contexts. The Framework for the Rational Analysis of Mobile Learning (FRAME), originally developed in 2006 by Koole as a masters dissertation, falls into the conceptual mode of m-learning frameworks (Bower & Vlachopoulos, 2018), and can be considered a sociomaterial perspective on m-learning because it attempts to illustrate how humans are intertwined with their material surroundings and cultural artefacts, which include the tools they use, such as mobile technologies. The FRAME model defines m-learning as an ‘apparatus’ consisting of three elements or ‘aspects’— learners, society and the device—represented in graphical form as a Venn diagram (see Fig. 8.1). In using an interlocking Venn diagram, the FRAME framework attempts to address some of the criticisms of previous m-learning models, most notably those that were judged to be overly techno-deterministic and focused on the features or affordances of the device itself. Equally, however, the FRAME model attempts to balance these different elements: “Similar to a jigsaw puzzle, all the pieces are necessary to create Fig. 8.1 The FRAME framework: From Koole et al., (2018), p. 3 (Creative Commons licensed. See https://www.mdpi.com/ 2227-7102/8/3/114)

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the finished picture, but no piece is more important than any other. The whole is just as important as the parts” (Koole et al., 2018, p. 3). The three different aspects of the model are fluid in their characteristics, but they are designed to help users think about the agencies of each and also how they interact with each other. It is tempting to view the three interlocking circles of the FRAME model as discrete entities, but as Koole has since stated, this is simply a limitation of a representational model (Koole et al., 2018). In focusing on three aspects of m-learning (Device—Learner—Social), the FRAME model captures the interplay between each aspect and thereby avoids the criticism of techno-determinism levelled at some of the earlier m-learning models. However, in doing so the FRAME model does not explicitly acknowledge the potential for seamless learning (Looi, Seow, Zhang, So, Chen, & Wong, 2010) or the unique potential of mobility that sets mobile devices apart from other digital technologies (see Wishart, 2015). Like many other theoretical frameworks, FRAME also fails to provide direct guidance or practical advice on how to design m-learning experiences, particularly in the domain of collaborative learning, which has been highlighted in previous research as a key affordance of m-learning (Chan, Roschelle, Hsi, Kinshuk, Sharples, Brown, & Soloway, 2006). Nonetheless, the influence and impact of Koole’s FRAME model on our thinking and subsequent development of our MPF (now termed iPAC) is clear. The FRAME model is situated within a sociocultural view of learning that takes into consideration both the technical characteristics of mobile devices as well as social and personal learning processes. Koole refers especially to enhanced collaboration, access to information and deeper contextualisation of learning. These are also important elements of our iPAC Framework, which extend Koole’s model by drawing upon and expanding the socio-cultural understandings presented in her model and foregrounding the two vectors of space and time which together constitute ‘mobility’. The 3 M Framework was originally developed in by Vavoula & Sharples (2009) as part of an art project to analyse the potential role of mobile devices in bridging the spaces between formal classroom learning and learning that takes place in less formal contexts such as during a visit to a local museum. In order to understand the learning that occurs in these spaces, the model is structured to work at three different levels: the micro, the meso and the macro. The micro-level examines the interactions, activities and behaviours of the individual learners, or actors, involved in an m-learning situation. The meso-level focuses on critical incidents to explore patterns in the learning experiences across and between individuals. The macro-level deals with integration within existing organisational contexts. Unlike the FRAME model, which is relatively loose and agnostic in how it is operationalised, the 3 M Framework is highly structured in nature, including three levels of evaluation that sit above the three stages just described. These three levels cover what is expected of a particular mobile activity (Level 1) collected through interviews with users; data such as observations to record what actually happened (Level 2), and reflective interviews conducted with users to explain any discrepancies between what was expected and what actually occurred (Level 3). Unlike the FRAME

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model, which is essentially conceptual in its focus, the 3 M Framework is designed to be an evaluative tool and is structured to guide such evaluations. Although some have noted 3 M’s lack of criteria for utility as a tool for evaluation (Harpur & de Villiers, 2015), and heavy emphasis on the social aspects of m-learning that sometimes underplays the significance of the technology itself, it has played an important role in our own conceptualisations of m-learning, and many aspects of the 3 M Framework have shaped our thinking around m-learning and informed our development of the iPAC Framework. However, although the micro-level concerned with usability and the macro-level dealing with integration within existing organisational contexts are important for us, it is the meso-level, focusing on the learning experience (especially on communication in context) that has had most impact on our thinking; this is mostly evident in the collaboration sub-dimensions of our iPAC Framework. The models and framework illustrated up to this point predate and therefore influenced our own thinking and attitudes about m-learning, leading in 2012 to the publication of our own, distinctive m-learning framework which we initially described as the Mobile Pedagogical Framework. This was a deliberate attempt to build on these earlier theorisations about m-learning in a manner that foregrounded and extended the more socio-cultural elements of previous works. The MPF was developed in 2012 (see Chap. 5), and its subsequent development as the iPAC Framework is explained in Chap. 7. The remaining sections of this chapter consider the development of selected m-learning models and frameworks since 2012.

8.4 Mobile Learning Frameworks and Models (Post-2012) As reported in Chap. 5, the MPF was published in 2012 as a response to the lack of literature about socio-cultural models of m-learning that foregrounded the distinctive characteristics or pedagogies associated with m-learning. Since its publication in 2012, the MPF has undergone a number of developments and redesigns and is now referred to as the iPAC Framework. These changes and evolutions are described and explained more fully in Chap. 7. The iPAC Framework has evolved and grown in recognition around the world since 2012, and in doing so it has influenced the design and development of several other m-learning models, frameworks and theories. A recent review of m-learning publications published after 2012, undertaken by University of Hull doctoral student Rebecca Kelly, identified at least 50 research studies that referenced one or more of the iPAC dimensions (personalisation, authenticity and collaboration) and at least seven studies that made explicit reference to the entire iPAC Framework. In the following section, we focus on and review selected research studies that make explicit use of, and cite, the iPAC Framework or substantial elements within it. This is not intended to be a comprehensive review of such studies but rather a summary of the frameworks and models that have drawn attention and interest among the academic community and how they have utilised or adapted the original iPAC Framework and its elements.

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8.4.1 The M-COPE Framework One of the first frameworks to explicitly reference and use the iPAC Framework is the M-COPE framework, published in 2014 and designed to support educators to reflect upon the critical factors that are pertinent to m-learning (Dennen & Hao, 2014). These are listed as ‘Mobile affordances’, ‘Conditions’, ‘Outcomes’, ‘Pedagogy’ and ‘Ethics’. Unlike our iPAC Framework, which defines distinctive pedagogical features of m-learning, the M-COPE framework does not attempt to establish a new model as such but rather a greater consideration of the processes that are required to design and evaluate the phenomena of m-learning. In this sense, it is not essentially a pedagogical model, but rather a set of processes that are designed to be used alongside any instructional design model, ADDIE being the example most often cited by the authors (Branch, 2009). The intention of the M-COPE framework, also implicit in the iPAC Framework, is to encourage educators to be more thoughtful and principled in the way they set about designing m-learning activities, in either formal or informal settings: Our purpose in developing a new framework was to address a different issue: to help instructors, instructional designers and technology designers view the systemic interplay of critical components of the mobile learning context, thereby enabling sound decision making in each step of the design process. (Dennen & Hao, 2014, n.p.)

Like the iPAC Framework, the main focus in this process is pedagogical, not technical. However, pedagogy is only one of the five considerations the authors urge educators to design around, and in this respect the M-COPE framework is relatively agnostic, enabling educators to adopt a wide range of learning theories and pedagogical practices. The authors of the M-COPE framework do concede that more constructivist learning pedagogies that take account of the learning environment, the learner, the topic to be studied, and the situational constraints, are more likely to be effective as pedagogical models. The area in which the M-COPE framework and iPAC most overlap is in consideration of what constitutes mobile. Many theories and frameworks that purport to be ‘mobile’ actually fail to highlight or even consider what is distinctive about learning with mobile devices and fail to identify how to exploit the use of these devices in ways not characteristic of using other technologies. Mobility is an explicit part of the M-COPE design process, which encourages educators to reflect upon and consider how use of mobile devices is adding value to the learning context. This mobility construct is also central to the iPAC Framework, which invites users to explore how both spatial and temporal factors challenge conventional modes of learning and to also explore the extent to which the pedagogical activity is genuinely mobile. Despite these similarities with our Framework, the authors of the M-COPE framework contended at the time of writing that the advent of mobile devices does not fundamentally challenge how learning is conceptualised, and therefore the framework does not require a distinct m-learning theory as such: We believe that our existing models, which guide the activities or processes of design, continue to be sufficient. The mobile platform is a delivery medium, and although it may

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incorporate different technological affordances than other instructional media it does not alter the nature of learning and instruction. (Dennen & Hao, 2014, n.p.)

We dispute this view and argue that the iPAC Framework challenges existing conceptualisations of learning, at least in some respects. These we refer to as the ‘signature pedagogies’ of m-learning, consisting of the dimensions of personalisation, authenticity and collaboration, as introduced in Chap. 5. In fairness to the authors of the M-COPE framework, they do concede that in future, the use of mobile devices may challenge and even transform existing paradigms of learning, a theme we return to in Chap. 14, which explores a recent Erasmus + project (DEIMP) that specifically focuses on exploring the transformational and innovative potential of m-learning.

8.4.2 The Mobile Learning Ecology Framework (2015) In response to challenges similar to those addressed by the M-COPE authors, Khaddage, Christensen, Lai, Knezek, Norris, & Soloway, (2015) developed a set of guidelines and what they refer to as a ‘mobile learning ecology’ to support educators in integrating the use of mobile devices into education. These authors identified four barriers that have restricted the effective implementation and wider adoption of what they refer to as ‘best practices’ in m-learning: pedagogical challenges, technological challenges, policy challenges and research challenges. Their guidelines and ecology are targeted at the systems level (e.g. local authorities and districts) and are intended to be used by educators and educational leaders to support them in planning and designing the optimal learning environment that will enable mobile technologies to be most effectively utilised. Like the M-COPE framework authors, they adopt a process orientation rather than a purely pedagogical one as in our iPAC Framework. The Mobile Learning Ecology framework differs significantly from M-COPE since it claims in a somewhat deterministic fashion that ‘new technologies lead to new pedagogies, new policy and new research’ (p. 633) and, by implication, the advent of mobile devices has enabled the development of new ways of teaching and learning. The Mobile Learning Ecology framework is essentially a guide or tool to assist in the effective implementation of large-scale m-learning deployments, and it can also be used post hoc to evaluate how effectively an implementation took place. So, for example, the controversial large-scale deployment of iPads in the Los Angeles Unified School District initiative (Norris & Soloway, 2013) would be judged to be strong in terms of the technology and policy aspects but weak in terms of the pedagogical and research aspects, where there was little attempt to match pedagogical activities with learners’ needs, or to research and evaluate what worked and what needed to be refined. In contrast, a university-wide deployment of mobile devices, such as that at Abilene University in the USA, would be judged to be strong in terms of pedagogy and research but weak in terms of policy since the deployment included a year-long pilot project during which staff were trained in how to use the devices

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for teaching and learning (pedagogy and research) but little or no attempt was made to align the initiative with state or local standards and policies (Young, 2011). In contrast to our iPAC Framework, which focuses on the specific or unique affordances of mobile technologies for learning, pedagogy is only one of the four constructs in the Mobile Learning Ecology framework. Other than collaborative methods, which their framework highlights as an important pedagogical consideration, there is no other explicit reference to any particular learning theory or approach, and in this respect the Mobile Learning Ecology framework is pedagogically agnostic.

8.4.3 A Framework for Designing Transformative Mobile Learning In categorising the development of m-learning, Winters (2006) identified four distinct phases or stages, starting with more technology-focused theorisations (see the first section of this chapter), moving through to more constructivist and socio-cultural ways of thinking. One of the more recent frameworks for m-learning is located within Winters’ fourth category, since it focuses on how learners are able to construct personal meaning and their own contexts for learning, rather than simply sharing or even creating content on and through a mobile device. This is Cochrane, Antonczak, Guinibert, Mulrennan, Rive, & Withell’s (2017) Design for Transformative Mobile Learning framework, which, as the title makes clear, is focused explicitly on a radical, transformative agenda for education that goes far beyond making learning more efficient. This framework has many features in common with the iPAC Framework, including its explicit support for socio-cultural pedagogies, learner-centred collaboration and the support of authentic, community-based learning experiences in which the use of the mobile device encourages learners to actively participate in genuine communities of practices, using authentic tools to complete real-world tasks. These elements are highly congruent with the authenticity dimension within the iPAC Framework, which also speaks of how mobile devices enable learners to undertake real-world, meaningful tasks, using professional tools, all set within realistic settings or contexts (see Chap. 6 for a full description of authenticity). While a transformational agenda may be implicit in the various dimensions and their relationships with the space-time continuum in the iPAC Framework, this is overtly and explicitly the intention and purpose of Cochrane et al.’s framework. In order for these transformations to occur, the Design for Transformative Mobile Learning framework advocates a shift from teacher-directed to student-directed pedagogies and frames m-learning as a context for the construction of authentic learning communities. We have also advocated this directly, and, like Cochrane, we recommend that educators use mobile technologies to mediate more direct participation by learners in actual communities of practice that become both opportunities for, and evidence of, deep learning and understanding (see Burden & Kearney, 2016).

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In more practical terms, the Design for Transformative Mobile Learning framework identifies six critical factors for successful mobile deployments: 1. 2. 3. 4. 5. 6.

The pedagogical integration of the technology into the course and assessment; Lecturer modelling of the pedagogical use of the tools; Creating a supportive learning community; Appropriate choice of mobile devices and Web 2.0 social software; Technological and pedagogical support; Creating sustained interaction that facilitates the development of ontological shifts, for both the lecturers and the students (Cochrane et al., 2017).

As a theory for the management of change, the Design for Transformative Learning framework utilises Luckin, Clark, Garnett, Whitworth, Akass, Cook, et al. (2010) continuum for learning which describes the constituent elements moving from teacher-centred pedagogy, through to adult-centred and finally, learner-directed heutagogy. These three stages of learning development are used in a matrix that covers a range of factors associated with m-learning, including the type of activity, the locus of control associated with the activity, the levels of cognition achievable by the learners, the degree of creativity demonstrated, the nature of knowledge production, the degree to which the learning represents an ontological shift and the self-perceptions of the learner. This matrix therefore enables educators to design m-learning episodes at different levels of learning maturity and to evaluate or measure them against these criteria. As the authors themselves acknowledge, this is a complex, multi-layered framework that “is effectively a pragmatic mashup of several interrelated models of learning, including the [Luckin’s]PAH continuum, the SAMR framework, the concept of three levels of creativity, and ontological pedagogies” (Cochrane et al., 2017, p. 17), The Design for Transformative Mobile Learning framework is still emerging, with the need for further case studies and empirical trials to validate its accuracy and establish its value and uniqueness in what is already a crowded field. Initial trials of the framework within Cochrane et al.’s own institution demonstrate how it can be applied across a range of different discipline areas to evaluate the extent to which the use of mobile devices is actually transforming the ecology of learning and helping to shift the locus of control toward the creation of student-generated contexts, which is its ultimate purpose.

8.5 Discussion In reviewing the frameworks and models that have been developed to understand and explain the phenomenon of m-learning, it is interesting to reflect on the high volume of those published prior to our own MPF in 2012, compared to those since. In the first decade of the twentieth century, when m-learning was still an emerging and niche field of research and practice, frameworks and models proliferated and tended to be holistic

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and embracing. In contrast, after 2012 when the MPF was first published, the volume of frameworks has diminished quite noticeably and those that have been published are more specific and bespoke in scope, often linked to particular discipline areas or domains. This may reflect the academic research community’s general need to codify and capture the somewhat chaotic and diverse practices of the many individuals and early adopters who characterise the early days of any digital innovation. In the case of m-learning, it may also reflect the diversity and experimentalism that captured the sense of freedom and excitement that typified the original m-learning field before the arrival in 2010 of tablet devices that have become more associated with traditional classroom practices and habitats. Similarly, there is a shift in emphasis between those frameworks produced before 2012, and those produced since. While some of the earliest frameworks and models associated with m-learning focus on the technical aspects of emerging mobile technologies, a few attempted to identify the new pedagogical opportunities afforded by use of devices that are untethered in both time and space. For example, Koole’s FRAME model and the 3 M Framework make an explicit attempt to chart the different pedagogical opportunities presented in the mobile world, although neither of these make a deliberate attempt to explore the interrelationships between the technology and the pedagogy, which is where we situate our socio-cultural Framework. The subtle interplay and nuanced relationship between technology and pedagogy have been at the heart of our own attempts to develop a framework that acknowledges and recognises the new set of possibilities and potentialities mediated by the malleable qualities of time and space associated with use of mobile devices (see Chap. 4 for a further discussion of how time and space have special characteristics in m-learning). This socio-cultural perspective suggests that learning is affected and modified by the tools used for learning, and that reciprocally the learning tools are modified by the ways that they are used for learning (see Chaps. 5 and 7 for further details). Since 2012, the pedagogical features of mobile pedagogical frameworks have remained important but there has been a marked shift to examining the processes and mechanisms that facilitate this in practice. This is vividly illustrated in a model such as the M-COPE framework (Dennen & Hao, 2014) and to a lesser extent in the Mobile Learning Ecology framework (Khaddage et al., 2015). These models are less concerned to identify the unique pedagogical features associated with m-learning, perhaps reflecting the saturated state of research associated with this particular line of mobile inquiry, and are instead more motivated by the processes that are effective in making it a reality. This raises the issue of context and the extent to which frameworks of this nature need to be more or less prescriptive in terms of the context for which they are designed. In the case of our own Framework, we made it clear when it was first published in 2012 that it was not intended to be prescriptive and account still needs to be taken of learners’ specific characteristics and needs, the environments in which the learning could potentially take place and the preferences and characteristics of teachers, including their epistemological beliefs. Teacher roles and the learning task design are further crucial factors (Kearney, Schuck, Burden, & Aubusson, 2012, p. 14).

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We did not set out to examine the casual links between the use of mobile technologies and particular pedagogical outcomes, but rather to highlight those ‘signature pedagogies’ that might be interesting and worth exploring in the new landscape afforded by mobile technologies. Other authors have adopted different approaches, and some of the post-2012 frameworks reviewed in this chapter make a much greater effort to define the contexts and boundaries within which their models might be applicable. Rather than design strict contextual parameters or guidelines for our Framework, our approach has been less constrained or deterministic and we have relied on case studies of its actual use in the field to guide our judgements about its reach and applicability. Some of these case studies and precedents are set out in Sect. 4 of this book, where we examine the use of the iPAC Framework in practice.

8.6 Conclusion This chapter has explored a selection of theoretical frameworks and models that have been used by academics and practitioners to underpin the understanding and use of mobile technologies in school settings. It has explained how many of the earliest frameworks and models that emerged as the first mobile devices came into use in educational settings focused heavily on the characteristics or affordances of the technology itself. This excessive technical focus on the device was the primary motivation behind the development of our own Mobile Pedagogical framework which subsequently evolved into the iPAC Framework, the focus of this book. Although the focus of the chapter has not been the iPAC Framework itself, we have explored how a socio-cultural theory like iPAC fits into prevailing patterns and shifts in understanding the phenomenon of mobile learning. This shift characterises more recent theories and models of m-learning which foreground the pedagogy associated with the technology rather than the technology itself. M-learning has now come of age as a fully fledged learning theory, and this chapter has charted some of the most important and influential landmarks in the journey towards our current position.

References Baran, E. (2014). A review of research on mobile learning in teacher education. Journal of Educational Technology and Society, 17(4), 17–32. Bower, M., & Vlachopoulos, P. (2018). A critical analysis of technology-enhanced learning design frameworks. British Journal of Educational Technology, 49, 981–997. https://doi.org/10.1111/ bjet.12668. Branch, R. M. (2009). Instructional design: The ADDIE approach. Springer Science & Business Media. Brown, T., & Mbati, L. (2015). Mobile learning: Moving past the myths and embracing the opportunities. International Review of Research in Open and Distance Learning, 16(2), 115–135. Retrieved from http://www.irrodl.org.

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Burden, K., & Kearney, M. (2016). Conceptualising authentic mobile learning. In Mobile learning design (pp. 27–42). Singapore: Springer. Chan, T. W., Roschelle, J., Hsi, S., Kinshuk, Sharples, M., Brown, T., Soloway, E. (2006). One-toone technology-enhanced learning: An opportunity for global research collaboration. Research and Practice in Technology Enhanced Learning, 1(1), 3–29. Cochrane, T., Antonczak, L., Guinibert, M., Mulrennan, D., Rive, V., & Withell, A. (2017). A framework for designing transformative mobile learning. In Mobile Learning in Higher Education in the Asia-Pacific Region (pp. 25–43). Singapore: Springer. Danaher, P., Gururajan, R., & Hafeez-Baig, A. (2009). Transforming the practice of mobile learning: promoting pedagogical innovation through educational principles and strategies that work. In H. Ryu & D. Parsons (Eds.), Innovative mobile learning: Techniques and technologies (pp. 21–46). Hershey: IGI Global. Dennen, V., & Hao, S. (2014). Intentionally mobile pedagogy: The M-COPE framework for mobile learning in higher education. Technology, Pedagogy and Education, 23(3), 397–419. https://doi. org/10.1080/1475939X.2014.943278. Harpur, P., & De Villiers, M. R. (2015). MUUX-E, a framework of criteria for evaluating the usability, user experience and educational features of m-learning environments. South African Computer Journal, 56(1), 1–21. Hsu, Y. C., Ching, Y. H., & Snelson, C. (2014). Research priorities in mobile learning: An international Delphi study/Les priorités de recherche en matière d’apprentissage mobile: Une étude de Delphes internationale. Canadian Journal of Learning and Technology/La revue canadienne de l’apprentissage et de la technologie, 40(2). Hsu, Y. -C., & Ching, Y. -H. (2015). A review of models and frameworks for designing mobile learning experiences and environments. Canadian Journal of Learning Technology, 41(3). Retrieved from https://scholarworks.boisestate.edu/cgi/viewcontent.cgi?article=1127&context= edtech_facpubs Kearney, M., Schuck, S., Burden, K., & Aubusson, P. (2012). Viewing mobile learning from a pedagogical perspective. Alt-J-Research In Learning Technology, 20(1). https://doi.org/10.3402/ rlt.v20i0.14406. Khaddage, F., Christensen, R., Lai, W., Knezek, G., Norris, C., & Soloway, E. (2015). A model driven framework to address challenges in a mobile learning environment. Education and Information Technologies, 20(4), 625–640. Koole, Buck, Anderson, & Laj. (2018). A comparison of the uptake of two research models in mobile learning: The FRAME model and the 3-Level Evaluation Framework, Education Sciences, 8, 114. https://doi.org/10.3390/educsci8030114. Klopfer, E., Squire, K., & Jenkins, H. (2002). Environmental detectives: PDAs as a window into a virtual simulated world. In Proceedings. IEEE international workshop on wireless and mobile technologies in education (pp. 95–98). IEEE. Lewin, K. (1943). Psychology and the process of group living. Journal of Social Psychology , 17, 113–131. Reprinted in The complete social scientist: A Kurt Lewin reader, (M. Gold Ed.) (1999) (pp. 333–345). Looi, C. K., Seow, P., Zhang, B., So, H. J., Chen, W., & Wong, L. H. (2010). Leveraging mobile technology for sustainable seamless learning: A research agenda. British Journal of Educational Technology, 41(2), 154–169. Luckin, R., Clark, W., Garnett, F., Whitworth, A., Akass, J., Cook, J., et al. (2010). Learner-generated contexts: A Framework to support the effective use of technology for learning. In M. Lee & C. McLoughlin (Eds.), Web 2.0-based E-learning: Applying social informatics for tertiary teaching (pp. 70–84). Hershey, PA: IGI Global. Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Thousand Oaks: Sage. Norris, C., & Soloway, E. (2013). Los Angeles’ 640,000 iPad purchase: Too big to fail. THE Journal. Retrieved from https://thejournal.com/articles/2013/09/17/too-big-to-fail.aspx?=THE21

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Pachler N., Bachmair B., & Cook J. (2010) Analysing the mobile complex for education: Key concepts. In Mobile learning: Structures, agency, practices (pp. 175–184). Springer, Boston, MA. https://doi-org.ezproxy.lib.uts.edu.au/10.1007/978-1-4419-0585-7_6. Parsons, D., Ryu, H., & Cranshaw, M. (2007). A design requirements framework for mobile learning environments. Journal of Computers, 2(4), 1–8. Vavoula, G., & Sharples, M. (2009). Meeting the challenges in evaluating mobile learning: A 3level evaluation framework. International Journal of Mobile and Blended Learning (IJMBL), 1(2), 54–75. Winters, N. (2006). What is mobile learning? In M. Sharples (Ed.), Big Issues in mobile learning. Report of a workshop by thekaleidoscope network of excellence mobile learning initiative. Nottingham: University of Nottingham. Wishart, J. (2015). Assimilate or accommodate? The need to rethink current use of the term ‘mobile learning’. In Proceedings of the international conference on mobile and contextual learning (pp. 229–238). Cham: Springer. Young, J. R. (2011). Smartphones on campus: The search for ‘killer’ apps. The Chronicle of Higher Education, 57(36), B6–B8.

Part III

Tools for Investigating Mobile Learning

Chapter 9

The Development and Use of the Mobile Learning Toolkit

Abstract Educators who wish to exploit the affordances of mobile technologies for learning need more extensive professional development and support to achieve this. The mobile learning toolkit, underpinned by the iPAC Framework, was launched in 2016 as part of an EU-funded transnational project, Mobilising and Transforming Teacher Educators’ Pedagogies (MTTEP), involving nine partners from universities and schools. It consists of media assets explaining the iPAC Framework, several instruments and tools, video vignettes and an online course that individuals and institutions can freely use and adapt. Data demonstrate that users of the toolkit are more likely to underpin their mobile learning activities with sound theoretical principles, based on the Framework, use a wider range of instruments to design mobile learning activities and employ a greater range of m-learning pedagogical approaches inspired by the toolkit. The initiative is now continuing through several related funded projects in Europe, Asia and Central America, including the development of an app and a new online course in another Erasmus project to ensure the toolkit is scalable and sustainable. Keywords Mobile learning toolkit · Pedagogic toolkits · iPAC · M-Learning resources · Online teaching resources · Digital pedagogy

9.1 Introduction One of the central themes underpinning this book is the potential value of mobile technologies in educational settings and the importance of underpinning this practice with sound theoretical principles such as those advocated in the iPAC Framework. Various studies from around the world, including work reported throughout this book, have demonstrated how, when used effectively, mobile technologies can support, enhance and transform teaching and learning, bringing about significant gains for students (Bano, Zowghi, Kearney, Schuck & Aubusson, 2018; Burden, Kearney, Schuck & Hall, 2019). These technologies can be used anywhere at any time and can be customised to reflect the user’s needs. Their potential to enhance collaboration and their other affordances support the creation and co-creation of new learning objects and knowledge (Burden et al., 2019). However, despite these demonstrable benefits, © Springer Nature Singapore Pte Ltd. 2020 M. Kearney et al., Theorising and Implementing Mobile Learning, https://doi.org/10.1007/978-981-15-8277-6_9

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teachers and teacher educators preparing the next generation of teachers have been slow to exploit mobile or m-learning (Kearney, Burden, & Rai, 2015; Royle, Stager, & Traxler, 2014). This is partially fueled by moral panics about issues and concerns such as cyberbullying, privacy and security, which have resulted in partial or full bans of devices by schools (Burden, Schuck, & Kearney, 2019). However, our own research indicates that the problem is more complex than this and at its heart is a lack of effective professional development and resources to convince educators that mlearning is worth investing in. The m-learning toolkit and its impact is our response to this challenge and the focus of this chapter.

9.2 The Mobilising and Transforming Teacher Educators’ Pedagogies (MTTEP) Project As mentioned in Chap. 1, in 2014 the European Union partially funded an Erasmus + Key Action 2 Strategic Partnership project (see www.mttep.eu), featuring four Initial Teacher Education (ITE) institutions from the UK, Norway, Germany and Australia, along with partner schools in each country. The project was focused on ITE since this was identified as the education sector most likely to leverage the greatest pedagogical change in schools. The objective of the project was to develop resources and examples that would enable teacher educators to use mobile technologies more effectively in teaching and learning. In turn they would then support their pre-service teachers (PSTs) in using these technologies in and beyond their classrooms. The project was underpinned by the establishment of an international m-learning network for teacher educators (www.imolente.com) and various other support structures. At the heart of the project was the creation of an m-learning toolkit to provide researchbased theoretical and practical guidance for educators in the principled use of mobile technologies. Initially, the university partners adopted the iPAC Framework to inform the development of a suite of resources, including a teacher survey and rubric tools—these instruments are elaborated on in subsequent chapters. University partners worked in combination with the school partners to also co-create a series of eBooks. At the end of each year, these prototype resources were piloted, and the feedback was used to improve their design and user-friendliness. All partners created at least one video vignette to illustrate the exemplary use of mobile technologies in practice. In the final year of the project (2016–2017), these separate resources were combined into the toolkit using an online1 web platform called Weebly. Auxiliary video resources were produced to explain the different elements of the toolkit and to make it more user-friendly, in line with feedback collected at the earlier public events. As opensource resources, all elements of the toolkit can be adapted or disaggregated by an institution to suit their local needs. 1 See

http://www.mobilelearningtoolkit.com/.

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9.3 Toolkits for Learning Toolkits for learning—often referred to as ‘pedagogic toolkits’—have long been advocated as an approach to support and engage educators in new or challenging areas of teaching and learning, and the integration of mobile or indeed any digital technologies meets this criterion (Oliver & Conole, 2000). Pedagogic toolkits are a form of professional development, and they typically include both resources such as tools and evaluations, along with strategies and advice about implementation. There is, however, a distinction to be made between professional development centred around a pedagogic toolkit and more traditionally structured professional development that tends to be ‘delivered’ to teachers in the form of course or prescribed chunks of learning. Toolkits are not intended to be prescriptive and are sometimes referred to as ‘just-in-time’ learning resources since teachers select those elements of the toolkit that are relevant at the point in time they are required (Conole & Oliver, 2002). Toolkits are also designed to move away from ‘tips for teachers’ type strategies which are seen as short-term solutions that are often only surface level in their impact. Crucially, therefore, toolkits are underpinned by sound theoretical principles or frameworks. This is important because they provide a lens by which to judge what constitutes ‘good or best practice’ rather than off the shelf solutions which may be only quick fixes. The theoretical framework underpinning this toolkit was the iPAC Framework (Kearney, Schuck, Burden & Aubusson, 2012).

9.4 Principles for Developing a Pedagogical Toolkit According to Conole and Oliver (2002), the design of a pedagogical toolkit should be undertaken in a systematic and interactive fashion which includes the following: • Assessment of need by practitioners—Designers should assess the needs of their potential end users and involve these users in the ongoing design of the toolkit. • An explicit theoretical underpinning—Toolkits need to espouse an explicit theoretical position or set of principles that end users can understand and apply themselves. • Toolkit specification—An important element of a toolkit should be flexibility and transferability. It should be designed in a way that is not context-specific but also in a manner that allows the end user or institution to adapt and modify its various elements. • Toolkit refinements and inclusion of user-defined features informed by user trials—Toolkits should be developed and tested iteratively with end users over a period of time to test their value and validity. • Building of shared resources—Where possible a toolkit should include elements and examples designed by previous users and these should be made available and accessible to future users for repurposing.

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The m-learning toolkit was designed and developed based on the principles outlined above. It is a free, open-source, online resource that has been demonstrated to have a significant impact on educators and students, as discussed later in this chapter. Its objectives are to • Encourage educators to underpin their m-learning designs with sound, theoretical principles; • Provide educators with a set of tools to design and evaluate engaging m-learning activities; • Encourage educators to use a broader range of m-learning pedagogies to exploit the benefits of m-learning.

9.5 How Does the Mobile Learning Toolkit Work? The m-learning toolkit was carefully designed to achieve the objectives outlined above. To encourage educators to design m-learning activities based on sound theoretical principles, the toolkit features a rigorous and validated theoretical model. The updated version of the iPAC Framework (Fig. 9.1) is discussed in Chap. 7. The Framework identifies three distinctive dimensions around which to design m-learning activities (personalisation, authenticity and collaboration), which are referred to as signature pedagogies of m-learning.

Fig. 9.1 The updated iPAC Framework (Reproduced with permission from Kearney, Burke & Schuck, 2019, p.754)

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Fig. 9.2 Visual polar charts of teacher and student results are generated in reports from the toolkit’s survey instruments (Reproduced with permission from Burden & Kearney, 2018, p. 93)

The toolkit contains numerous tools, instruments and resources to support educators in designing and evaluating engaging m-learning activities (Objective 2). All elements are tagged and matched to the iPAC Framework, enabling users to interrogate the toolkit with respect to the signature pedagogies of m-learning. These include validated online survey tools that can be used by teacher educators, practising teachers and PSTs to measure and evaluate their m-learning practices. As explained further in Chap. 11, the teacher version of the survey can be used to evaluate educators’ own tasks, particularly their use of the distinctive iPAC pedagogies. The student version of the survey provides student voice to the evaluation process, thereby triangulating the data with the teacher survey. The results generate a personalised report for teachers, allowing them to compare their own responses (Fig. 9.2a) with their students’ responses (Fig. 9.2b), and providing them with an m-learning profile. Additionally, the survey report provides educators with guidance and suggestions for further professional development in m-learning. Educators are frequently confused and frustrated by the huge number of educational apps available to them, and the toolkit contains an entirely original app evaluation rubric to help them assess, select and use both discipline-specific and more generic educational apps. The focus of this rubric is on the iPAC pedagogical framework, rather than technical features emphasised in other existing rubrics. ‘Pop-up’ notes accompanying the online rubric, as shown in Fig. 9.3, suggest to users relevant examples of app features to facilitate more informed and confident responses. The rubric instrument is, as far as we are aware, the only app evaluation rubric based entirely on pedagogical criteria. Further details about this instrument are presented in Chap. 10. The toolkit includes a series of three exemplar eBooks available via Apple’s iBooks Store. These illustrate the use of mobile technologies in teacher education and school education, providing guidance on how to design and generate m-learning scenarios. For more detail of these eBooks, refer to Burden and Kearney (2018).

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Fig. 9.3 The collaboration items in the online rubric tool

The toolkit is pre-populated with a selection of innovative m-learning video vignettes from across a range of teacher education and school-based contexts (see http://www.mobilelearningtoolkit.com/). These videos illustrate and contextualise various dimensions of the iPAC Framework, and aim to stimulate interest and pedagogical discussion among educators. For each scenario, recommendations are included for particular tools and apps to support the depicted activity. For consistency purposes, each scenario is set out in a standard template, and further development of the toolkit will enable users to create and upload their own video vignettes, thereby helping to build a sense of community. For a detailed discussion of these templates and vignettes, see Burden and Kearney (2018). The toolkit also includes a free online course with approximately 30 hours of learning activities. The online course2 contains core and elective modules and brings together all of the various toolkit resources and exemplars, enabling teacher educators and teachers to learn as part of an international network. It can be undertaken as a self-paced course or it can be adopted as a tutored course by institutions. Finally, the toolkit has its own network or community of users. An aim of the MTTEP project was to create a sustainable International Mobile Learning Network for Teacher Educators (www.imolente.com), which, along with the other elements of the toolkit, is a legacy of this project. The network was officially launched at the Mobile Learning in Teacher Education (MITE) conference in Galway, Ireland on the 19 January 2018, where the election of officers for the network was also decided upon.3

2 http://www.mobilelearningtoolkit.com/online-course.html. 3 https://imolente.weebly.com/.

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9.6 Initial Reception by Users and Subsequent Modifications The toolkit was launched in 2016 and has been accessed over 110,000 times4 across 40 countries. It has been used in at least 20 institutions around the world to support professional development. In the University of Hull, University of Technology Sydney, Education University Karlsruhe and the National University of Ireland, Galway, the toolkit is used by all PSTs and many local teachers in placement schools or those attending in-service training programs. It forms part of the curriculum for PSTs in the University of the West of Norway (Bergen), Eindhoven University of Technology (The Netherlands), University of Western Australia (Perth) and Universidad Del Norte, Colombia. Teacher educators at Hanoi University of Education, Da Nang University of Education, Vietnam and Kuon University, Thailand have also used it to train their PSTs. The Education University, Karlsruhe, has opted to customise the entire site, making it accessible for a German-speaking audience.5 The toolkit is also used by many schools to support practising teachers’ professional development, including schools in the UK, Germany, Norway, Australia, Netherlands, Belgium, Sweden, Colombia and Cyprus.6 Feedback demonstrates that this adoption of the toolkit has made a significant impact on teachers’ practices, resulting in changed attitudes and teaching behaviours (see next sub-section). More general feedback about the Framework is discussed in Chap. 13. During and since the MTTEP project, we have collected extensive data from users to gauge their reception to the toolkit, and we have used this to modify and improve it as explained below: • Polar charts like the examples above (Fig. 9.2) were preferred in the report over traditional bar charts which users found more difficult to interpret. • We developed a template to support end users in designing and uploading their own video vignettes in response to feedback asking for this and in order to maintain a house style. • Several iterations of the app rubric were developed during the project based on feedback. This informed a range of actions addressing user-friendliness issues, such as provision of a link to a YouTube video to explain our chosen app categorisation system. The rubric is discussed in more detail in Chap. 10. • The development of the three interactive eBooks was informed by focus groups at both multiplier events. Examples of modifications include the addition of advanced organisers, shortening of text and uploading of all videos to YouTube to decrease the eBooks’ download time;

4 Weebly

statistics for website: 30.3.2020.

5 http://mtteptoolkit.weebly.com/. 6A

full list of schools and teacher education institutions can be found at http://www.mobilelearni ngtoolkit.com/impact.html.

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• Sources of feedback on the online course included (internal) project meetings and peer feedback from colleagues, again via the project’s online communication platform. Apart from technical issues relating to the two delivery platforms, feedback relating to coherency issues helped with standardisation across the course. • Finally, it was clear from the pilot phase (2016–2017) that users wanted a manual to help them disseminate the toolkit to their staff. We produced an iBook guide7 and a number of explanatory videos to accompany different elements of the toolkit. Metrics on toolkit elements so far reveal pleasing engagement by users. For instance, the video8 introducing the iPAC Framework has been viewed 1549 times. There have been 509 educational app reviews to date. 44% of teachers have evaluated a discipline-specific app (spanning 11 disciplines); 56% of users have evaluated a generic app. Further development of the online database of reviews will provide more robust evaluative data from teachers on their views of educational apps.

9.7 Impact on Users We evaluated the impact of the m-learning toolkit on learners using the project objectives set out above as the criteria. The evaluation was undertaken by project partners during the lifetime of the project, and more summatively by an external evaluator (Global Learning Ltd) at the end of the project. During the lifetime of the project impact data were collected using a variety of different methods which included evaluations built into the toolkit resources themselves (e.g. widgets built into the eBooks), questionnaires and surveys administered to attendees of project events and toolkit users, and by interviews and focus groups. The external evaluator collected impact data through a series of online questionnaires and a selection of in-depth interviews with teachers and teacher educators who have used the toolkit. The data presented below provides a representative selection of this data, structured around the three project objectives used as evaluation criteria.

9.7.1 Objective 1: Evidence that Teachers and Teacher Educators Now Underpin Their Use of M-Learning with Sound Theoretical Principles, Derived from the Toolkit The toolkit is constructed around a bespoke m-learning theoretical framework (iPAC) which helps educators identify specific pedagogical affordances of mobile technologies they wish to exploit. The Framework has proven to be extremely popular with 7 http://www.mobilelearningtoolkit.com/the-toolkit-manual.html. 8 https://www.youtube.com/watch?v=WXxp3saPeXQ.

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educators because of its accessibility and practicality, and has been widely used by both academics and practitioners around the world.9 Teachers are therefore more inclined to design m-learning activities that are underpinned by sound theoretical principles: “It [iPAC] engaged learners and was really useful as a set of manageable criteria to focus upon when designing personalised, authentic and collaborative learning tasks” (Science headteacher, secondary girls school in Sydney, Australia). The data we collected from interviews with teachers and teacher educators also revealed how the Framework has changed their attitudes about the value of mobile technologies and about learning itself, often in quite profound and deep ways. More detail is provided in Chap. 13. Many educators have described how the iPAC Framework has proved to be a practical tool that they use for both professional development purposes with other teachers and their own professional development. In the National University of Ireland (Galway), all PSTs are required to learn about the use of mobile technologies in learning, and the iPAC Framework is a compulsory element of their course. A teacher educator from NUI, Galway explained how it “augments the use of other frameworks and its subdimensions are particularly useful in developing pedagogies”. At Eindhoven University, in the Netherlands, a teacher educator said that practising teachers in her Master’s program: reflect upon the use of ICT in the educational design by using the IPAC model. We give them the link of the mobile learning toolkit. In the assessment we look on how clearly they can describe the use of mobile learning in their educational design related to the functions described in the iPAC model. This way we hope to make them aware of the functions of mobile learning, and to take them into consideration when adapting their education.

In addition to teacher educators and teachers, who have used iPAC as part of their professional development work with PSTs and teachers, the Framework has also been endorsed by Apple UK to support the professional training of their Apple Distinguished Educators (ADEs) who work with teachers in schools across Europe, the Middle East and Africa (EMEA) to educate them about the value and importance of m-learning. The iPAC Framework figures prominently in the Apple training program and has been embedded into various resources produced by Apple, such as the ‘Research iPad’ book and iTunesU course which is used extensively to help teachers research their own practice with mobile technologies.10

9 http://www.mobilelearningtoolkit.com/impact.html. 10 https://itunes.apple.com/us/course/researching-and-evaluating-ipad-in-learning/id1157447590.

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9.7.2 Objective 2: Teachers Find the Tools and Instruments Provided by the Toolkit Help Them to Be More Effective in Designing Lessons that Exploit the Unique Affordances of M-Learning Evidence collected from schools and teachers who have used the toolkit over a period of time reveals how useful it has been for planning and designing lesson activities that incorporate mobile technologies more effectively. Rennbuckel school in Karlsruhe, Germany, De Ferrers school in Burton-uponTrent, UK, Metis Academy in Bergen, Norway and Sandringham School in St. Albans, UK are examples of schools who have adopted the toolkit and its underpinning Framework at a wide level and who use it as professional development for all of their teachers and trainee teachers. They have created their own bespoke resources and training courses based on the Framework and used these to provide their own bespoke professional development training programs about m-learning across Germany, Norway and the UK, respectively.11 In many instances, schools, colleges and other professional training providers from across Europe have adopted and adapted either the full toolkit or elements of it, to provide their own bespoke m-learning training courses informed by the Framework. In the case of Metis Academy, Norway, for instance, they now run a network funded as part of a new Erasmus + project with partners in Germany and Spain, in which the iPAC Framework is used as the basis of a course on assessment and m-learning.12 In Germany, one of the original project partners (Rennbuckel school) now uses the m-learning toolkit as the basis of a professional development program called ‘Teach the Teachers’. The program is delivered to teachers and schools across Germany. In the state of Baden-Wurttemberg, where Rennbuckel is located, the Ministry of Education incorporates the iPAC Framework as one of the theoretical frameworks they recommend to schools interested in the adoption of m-learning.13

9.7.3 Objective 3: Teachers and Teacher Educators Use a Wider Range of M-Learning Pedagogies One primary purpose for developing the m-learning toolkit was a growing awareness of and concern with the narrow and instrumentalist tendencies of many educators when they adopt mobile technologies in their teaching (e.g. Kearney et al., 2015). 11 Teach

the Teachers’ (See https://itunes.apple.com/gb/book/e-teach-the-teacher/id1457656589? mt=13) and Tablet Teachers https://tablet-teachers.com/fortbildung-am-zentrum-fuer-schulqual itaet-und-lehrerbildung-zsl-aussenstelle-bad-wildbadselbstaendigkeit-im-lernen-foerdern-unddabei-digitale-medien-sinnvoll-und-zielgerichtet-nutzen/. 12 https://www.metis.no/vgs/english/erasmus. 13 https://rennbuckel.de/medienprofil/e-teach-the-teachers/ and https://tablet-teachers.com/.

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To counter this tendency, we populated the toolkit with a number of inspirational video vignettes, exemplars and eBooks to illustrate the full range of pedagogical possibilities for m-learning, informed by iPAC. Our data demonstrate that the elements of the toolkit have succeeded in encouraging teachers to explore and adopt a wider range of m-learning pedagogies. An example would be use of new practices such as the creation of eBooks on mobile devices. The learner-generated eBook is an innovative practice that is highly authentic and collaborative (Burden & Hopkins, 2016). Many educators have not previously considered setting students tasks such as constructing eBooks. In using the toolkit, this has changed, with highly encouraging results as testified in the following quote from a teacher in Colombia, who emphasised both authentic and personalised learning in her remarks: The creation of… this eBook made me reflect on how engaged our students can be in activities related to their surroundings through technology, crossing the boundaries of the space and time because they can submit their answers wherever they are. It takes learning out of the classroom. More than a tool, we can consider it a teaching methodology because it contributes to a more personalised and motivated learning, leading students to participate more actively in EFL acquisition. (MFL teacher in Barranquilla, Colombia).

By adopting a wider range of pedagogical approaches, supported and mediated through a mobile device, teachers who have used the toolkit also refer to the motivational benefits for their learners which illustrate why it is important to encourage educators to use a broader range of m-learning strategies. “Teachers using what I might consider boring or outmoded tools can generate disinterest”, recounts another MFL teacher in Colombia, “while teachers with all kinds of interesting ideas and different tools catch their students’ attention and help them to adapt to experiences they may be unfamiliar with”. As mobile devices become more ubiquitous and affordable for students, it makes sense for teachers to utilise them as tools for learning. The various resources which constitute the m-learning toolkit have proved very effective in helping teachers to see new possibilities. These include the ability to co-create and co-author digital content such as an eBook or a video narrative. These generative approaches represent a significant pedagogical shift for educators, from a curriculum model dominated by the consumption of content to one which sees students as active constructors of knowledge. This kind of shift can be easily overlooked, and we should not underestimate the power and impact that using mobile devices as tools for learning can engender. In 2019, the mobile learning toolkit was awarded first prize at the annual e-Learning Excellence Awards in Copenhagen, Denmark, and it was noted that its impact was both deep and broad.14

14 See

https://www.academic-conferences.org/conferences/ecel/ecel-excellence-awards/.

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9.8 Conclusion This chapter has discussed how the Mobile Learning Toolkit, which is underpinned at a theoretical level by the iPAC Framework, was designed and implemented as part of a larger European Erasmus + project called the Mobilising and Transforming Teacher Educators’ Pedagogies project. It explains how the toolkit has been used by both teacher educators in universities and by teachers in schools and the high impact this has had on their respective practices. In this respect, the chapter demonstrates how the use of the Mobile Learning Toolkit has encouraged educators to underpin their use of mobile technologies with stronger principles and sound theoretical underpinnings. It also reveals how useful the toolkit has been in helping educators to design effective learning activities associated with mobile technologies and how, in turn, this has led these educators to adopt a wider range of mobile learning pedagogies.

References Bano, M., Zowghi, D., Kearney, M., Schuck, S., & Aubusson, P. (2018). Mobile learning for science and mathematics school education: A systematic review of empirical evidence. Computers & Education, 121, 30–58. Burden, K., & Kearney, M. (2018). Designing an educator toolkit for the mobile learning age. International Journal of Mobile and Blended Learning, 10(2), 88–99. https://doi.org/10.4018/ IJMBL.2018040108. Burden, K., Kearney, M., Schuck, S., & Hall, T. (2019a). Investigating the use of innovative mobile pedagogies for school-aged students: A systematic literature review. Computers & Education, 138, 83–100. https://doi.org/10.1016/j.compedu.2019.04.008. Burden, K., Schuck, S. & Kearney, M. (2019). Should we be concerned about mobile devices in the classroom: What does the evidence say? Impact. Journal of the Chartered College of Teachers. UK. Jan, 2019. Burden, K., & Hopkins, P. (2016). Barriers and challenges facing pre-service teachers’ use of mobile technologies for teaching and learning. International Journal of Mobile and Blended Learning, 8(2), 1–20. https://doi.org/10.4018/IJMBL.2016040101. Conole, G., & Oliver, M. (2002). Embedding theory into learning technology practice with toolkits. Journal of Interactive Media in Education, 2002(2). Kearney, M., Schuck, S., Burden, K., & Aubusson, P. (2012). Viewing mobile learning from a pedagogical perspective. Research in Learning Technology, 20, 14406. https://doi.org/10.3402/ rlt.v20i0.14406. Kearney, M., Burden, K., & Rai, T. (2015). Investigating teachers’ adoption of signature mobile pedagogies. Computers & Education, 80, 48–57. Kearney, M., Burke, P., & Schuck, S. (2019). The iPAC scale: A survey to measure distinctive mobile pedagogies. TechTrends, 63(6), 751–764. https://doi.org/10.1007/s11528-019-00414-1. Oliver, M., & Conole, G. (2000). Assessing and enhancing quality using toolkits. Quality Assurance in Education, 8(1), 32–37. https://doi.org/10.1108/09684880010312677. Royle, K., Stager, S., & Traxler, J. (2014). Teacher development with mobiles: Comparative critical factors. Prospects, 44(1), 29–42.

Chapter 10

Evaluating Education Apps from a Sociocultural Perspective

Abstract The evaluation of education applications (‘apps’) is an increasing challenge for educators interested in exploiting opportunities for students’ learning with mobile devices. The number of education apps has exploded over the past decade, and their designs are typically underpinned by traditional didactic pedagogical assumptions. This chapter focuses on two initiatives from our projects that aim to address this problem and assist educators in selecting quality apps that potentially exploit the unique affordances of m-learning. Firstly, we describe the design and development of an app evaluation rubric for educators, based on the iPAC Framework. The instrument goes beyond the traditional focus on usability and techno-centric criteria to facilitate examination of the mobile pedagogic potential of apps. Secondly, a new technique for evaluating apps using feature-based sentiment analysis is discussed. Keywords App evaluation · Evaluation rubrics · Feature-based sentiment analysis · iPAC · Socio-cultural theory · M-learning · Mobile pedagogy

10.1 Introduction Chapter 9 focused on use of the iPAC Framework to inform the development of a mobile learning toolkit to support educators’ designs of m-learning activities. This chapter focuses on use of iPAC in the development of two initiatives for evaluating the pedagogic potential of education apps. We use the term ‘education apps’ to describe apps used by students to support their learning, ranging from interactive books, tutorials, games and simulations, to media creation and authoring resources. For the purpose of this chapter, education apps do not refer to teacher productivity resources such as apps for lesson planning, marking and management of student attendance. Unlike most other existing strategies for evaluating education apps, the two initiatives Description of the sentiment analysis study in the second half of this chapter is adapted from: Bano, M., Zowghi, D., and Kearney, M. (2018a). Feature-based sentiment analysis for evaluating the mobile pedagogical affordances of apps. World Conference on Computers in Education (WCCE) 2017—Dublin, Ireland. 2017. In A. Tatnall & M. Webb, Eds., Tomorrow’s Learning: Involving Everyone—Learning with and about technologies and computing. IFIP AICT. Heidelberg, Germany, Springer. © Springer Nature Singapore Pte Ltd. 2020 M. Kearney et al., Theorising and Implementing Mobile Learning, https://doi.org/10.1007/978-981-15-8277-6_10

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discussed in this chapter are research-informed and facilitate a more nuanced consideration of potential mobile pedagogical issues. Firstly, an online rubric was developed as part of the Erasmus + project titled Mobilising and Transforming Teacher Educators’ Pedagogies(MTTEP), introduced in Chap. 1. This rubric is currently available as part of the m-learning toolkit described in Chap. 9. Secondly, a new technique was developed as part of the Australian Research Council (ARC) project titled Optimising Teaching and Learning with Mobile-Intensive Pedagogies (subsequently abbreviated to Optimising Mobile Pedagogies), also introduced in Chap. 1. This procedure involves examining feedback from past app users using a sentiment analysis tool in order to assess their opinions through the lens of iPAC. A recent investigation has provided initial confirmation of the powerful utility of this feature-based sentiment analysis technique (Bano, Zowghi & Kearney, 2018a). We begin this chapter by addressing a possible conflict between the socio-cultural view of learning underpinning the iPAC Framework, and our two app evaluation strategies that may be perceived as labelling technologies with inherent pedagogical qualities. We recognise the risk of deterministic views of emerging technologies (Selwyn, 2010), and we emphasise that work described in this chapter is not advocating a ‘one-size-fits-all’ approach to selecting and using education apps. Informed by a socio-cultural perspective, we recognise that apps may be modified according to the ways they are used, and in turn, their design may influence how educators and students use them (Glassman, 2001; Salomon & Perkins, 1998). Indeed, we accept that there are numerous factors that contribute to the effective use of apps for learning, such as teachers’ beliefs (Ertmer, Ottenbreit-Leftwich, Sadik, Sendurur, & Sendurur, 2012), teachers’ expertise and confidence levels, students’ characteristics such as their prior knowledge, and provision of technical support. We also recognise that learning does not occur in a vacuum and that contextual information about where, when and how apps are used is important to consider in the evaluation process. Despite these concerns, we believe there is value in teachers using procedures such as the two outlined in this chapter, to critically examine app features for their potential to leverage mobile pedagogies in and beyond the classroom. Where possible, both evaluation strategies described in this chapter strive to remain faithful to a socio-cultural perspective.

10.2 Background 10.2.1 Current Challenges with Evaluating Education Apps As outlined throughout this book, a significant amount of research has been conducted to investigate m-learning in school education (Bano, Zowghi, Kearney, Schuck, & Aubusson, 2018b; Burden, Kearney, Schuck, & Hall, 2019; Kearney, Burden, & Rai 2015; Sung, Chang, & Liu, 2016). Commensurate with this increased interest in the use of mobile devices in education has been an expanding number of education apps

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and other tools for supporting children’s and teenagers’ learning. The total number of apps has increased exponentially in the last decade, and there are now literally thousands of education apps available for educators, parents and students. The Education category is the third most popular category on the Apple Store (Statista, 2019), and the large majority of these apps target pre-school and primary school-age children (Shuler, 2012). However, with this saturated market and overload of choice, it has become challenging and overwhelming for teachers to efficiently select an app that is best suited to their specific pedagogical purposes (Cherner, Dix, & Lee, 2014; Stevenson, Hedberg, Highfield, & Diao, 2015). To add to this challenge, the majority of apps in the education sections of repositories such as Apple’s App Store are information provision and drill and practice in nature, underpinned by traditional behaviourist principles—essentially replicating transmission-driven approaches to learning (El-Hussein & Cronje, 2010; Goodwin, 2012; Murray & Olcese, 2011; Stevenson & Hedberg, 2016). For example, Highfield and Goodwin (2013) examined 53 apps from the iTunes Store that focussed on mathematics learning. The large majority of these apps (n = 47) were underpinned by a transmissionist pedagogical design, involving mostly drill-and-practice activities. Other reseachers have made similar findings in their studies (Bano et al., 2018b; Bos and Lee, 2013). Highfield and Goodwin (2013) speculated that the prevalence of behaviourist apps may be due to parental preferences, curriculum goals, or the attitudes and skills of app developers. Another influential factor contributing to this imbalance may be the commonly held reductionist view of mobile learning (mlearning) as a way to merely ‘deliver information’ or to simply access content and resources (El-Hussein & Cronje, 2010). The plethora of these types of rote learning apps in repositories like the Apple App and Google Play stores makes it difficult for educators to find more creative apps that support participative, socially interactive approaches and potentially leverage more meaningful learning. Indeed, despite an increase in access to technology in society, the Office of Educational Technology (2017) warns of a new ‘digital use divide’ between “students who use technology to create, design, build, explore and collaborate, and those who simply use technology to consume media passively” (p. 18). Compounding the complexity of this landscape, students and teachers are engaging with an increasing variety of apps in a given learning task. Adopting Young’s (2014) metaphor of ‘app smashing’, Stevenson and Hedberg (2016) discuss the purposeful use of several apps (e.g. for project-based learning) that together “support a range of interfaces, modalities and literacies, while not specifying a clear sequence” (p. 21). Seen in this way, a set of apps can be ‘cognitive stepping-stones’ that allow teachers “to incorporate a wider range of skills and modalities into the learning task” (Stevenson et al. 2015, p. 374). Hence, teachers are faced with the common challenge of carefully selecting and sequencing several education apps for use in a specific learning task.

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10.2.2 The Education App Landscape Goodwin and Highfield (2013) developed a framework that was initially used for examining apps for early mathematical learning, but they also suggested it could be applied to older learners and other disciplines. Their framework consists of three main categories, arranged along a continuum from more prescriptive ‘instructive apps’ used for drill-and-practice and reinforcement tasks; through to more openended, generative ‘constructive apps’ used for creative or communicative purposes. In the middle of this spectrum, they proposed ‘manipulable apps’, used to mediate learners’ (guided) discovery and experimentation processes and to facilitate their manipulation of digital elements (e.g. augmented reality apps). From their research, Goodwin and Highfield inferred that the use of instructive apps alone is unlikely to optimise students’ mathematical learning. They found that use of constructive apps allowed students to create multimedia artefacts and supported critical thinking as well as reflection. They reported that use of these more creative apps leveraged student-generated representations that required more “cognitive investment” (p. 219) than might be required by the use of instructive or manipulable apps. In the context of ubiquitous learning, Shroff, Keyes, and Linger (2015) endorsed the classifications of Goodwin and Highfield (2013) in their discussion of app design and learning theories, while Handal, El-Khoury, Campbell, and Cavanagh (2013) also drew on Goodwin and Highfield’s (2012) work to classify mathematics education apps into three main clusters of tools: explorative, productive and instructive. Cherner et al. (2014) suggested a more precise app classification system to use when choosing education apps, also drawing on Goodwin and Highfield’s work. They emphasised an app’s purpose, content and value, and proposed three similar categories of education apps. Firstly, ‘skill-based’ apps are typically used for recall, rote memorisation and ‘skill-and-drill’ instructional strategies to build students’ literacy abilities, numeracy skills, standardised test readiness and subject area knowledge. Secondly, ‘content-based’ apps give students access to vast amounts of information and data, and facilitate application, analysis and exploration of pre-programmed content. Finally, ‘function-based’ apps focus on evaluation and creation to assist students in transforming learned information into usable forms. Domingo and Gargante (2016) also suggested three categories of apps used by primary school students in classrooms: ‘learning skills’ apps, which enable students to create their own knowledge and build their learning; ‘informational management’ apps, which are context-specific and can allow for more informal learning; and ‘content learning’ apps, which allow students to rehearse, reinforce and assess curricular content. Their study revealed that content learning apps were the most frequently used in the classroom. However, they found that the less frequently used learning skills apps more effectively supported students’ learning with mobile technology. Our own research has uncovered patterns of app use by educators in both school and teacher education contexts. Two of the items in our surveys (see Chaps. 11 and 12) asked participants to name the app(s) they used in their nominated m-learning task, and how the apps were used. Most participants listed several apps that were

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used in their m-learning task, akin the previously mentioned notion of ‘app smashing’ (Stevenson & Hedberg, 2016). Fifty-one percent of the apps mentioned by the 107 practising teacher participants in a baseline survey (Kearney, Burden, & Rai, 2015) were used in a way that would be classified under Goodwin and Highfield’s (2013) ‘constructive’ category: leveraging learners’ creation of their own digital content. The following apps were most frequently mentioned: eBook creation apps; video production apps such as iMovie, audio production apps such as Garageband, and mindmapping apps (Kearney et al., 2015). A greater proportion of apps (78%) mentioned by 46 teacher educators in this survey was classified as constructive (Burden & Kearney, 2017). Frequently mentioned apps were social media apps such as Twitter, video production apps such as iMovie, as well as note-taking apps such as Evernote. These results suggest that teachers and teacher educators are more inclined to use open-ended, content-free apps to support more design-based pedagogies (Koehler et al., 2011), in line with the growing popularity of knowledge-building activities and recent initiatives such as the digital maker movement (Niemeyer & Gerber, 2015). This emphasis by educators on choosing more open-ended, generative apps was noteworthy and somewhat surprising, given the before-mentioned dominance of instructive apps in online repositories, and previously documented trends of educators’ over-use of drill-and-practice apps in education (e.g. Goodwin, 2012; Murray & Olcese, 2011).

10.2.3 Rubrics for Evaluating Apps Several rubrics1 have been developed to assist teachers in their selection and evaluation of apps. These rubrics focus mainly on technical criteria, including app characteristics relating to usability and aesthetics. However, they typically do not guide teachers’ pedagogical decisions in regard to how an app could be used to support pedagogy, particularly from a socio-cultural view of teaching and learning. Most rubrics are not linked to research, their terminologies are limited, and their scales often do not allow sufficient nuance. Therefore, they are generally not effective for facilitating pedagogical planning and implementation (Burden & Kearney, 2018; Cherner et al. 2014; Lee and Cherner, 2015). Some research-informed rubrics have attempted to address this problem. Walker’s (2011) pioneering Evaluation Rubric for Mobile Applications (ERMA) has been influential on the development of other rubrics,2 although its motivation dimension has been criticised for adopting a reductionist view of this important and complex concept (Lee & Cherner, 2015). ERMA has six dimensions: curriculum connections, authenticity, feedback, differentiation, user-friendliness and motivation. Green,

1 For

a comprehensive list, see K. Schrock’s site https://www.schrockguide.net/ipads.html.

2 E.g. Tony Vincent’s popular rubric was based on Walker’s rubric dimensions (see https://learningi

nhand.com/blog/ways-to-evaluate-educational-apps.html).

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Hechter, Tysinger and Chassereau (2014) developed and validated a more disciplinespecific instrument for science teachers—the Mobile App Selection for Science (MASS) rubric. The dimensions of this rubric, and the language used to describe its criteria, are specifically tailored to science education. For example, one dimension of MASS is ‘scientific inquiry and practice’, while the accuracy dimension requires teachers to consider the integrity and correctness of any science content presented by the app. Other MASS dimensions include relevance of content, sharing of findings, feedback and navigation. Finally, Lee and Cherner (2015) developed a comprehensive rubric for educators to assess education apps using 24 classifications covering three domains: instruction, design and engagement. Some of these dimensions focus on pedagogy, for example, their cooperative learning dimension. These rubrics (ERMA, MASS and Lee and Cherner’s instrument) encourage users to partially focus on pedagogical issues, as well technical and usability criteria. However, the pedagogical dimensions in these rubrics are somewhat limited in their examination of the distinctive mobile pedagogies described by iPAC. For example, aspects of the collaboration dimension from our iPAC Framework are missing from ERMA and are only partially examined in the ‘shared findings’ dimension of MASS and the ‘cooperative learning’ dimension of Lee and Cherner’s rubric. Furthermore, these rubrics are silent on important contextual information, and the language used to describe criteria does not fully capture the unique nature of m-learning environments, for example, in situ learning. For these reasons, there is a pressing need for a new rubric that facilitates a more in-depth, layered analysis of mobile pedagogical affordances of education apps for specific contexts. Following a brief introduction to the use of sentiment analysis, we present the development of a new rubric that meets these criteria.

10.2.4 Use of Sentiment Analysis for Evaluating Apps App stores often provide facilities for user feedback (comments and ratings) in order to help teachers select apps and for app developers to improve their designs. These customer ratings and reviews play a critical role in the mobile app market and directly influence app downloads. User feedback is used by practitioners and app developers as a source of information in activities such as selection of apps, customer satisfaction, versioning and bug reports (Bano & Zowghi, 2014). However, it has been challenging to harness and synthesise large volumes of feedback into more useful information. Given the large review volumes in these spaces, analysing every review manually is laborious and time-consuming. Sentiment analysis is an automated approach that aims to determine the polarity of sentiments and emotions within large textual datasets (Pang & Lee, 2008). This approach is used to develop tools for calculating and monitoring the attitude and behaviour of app users from their feedback, comments and reviews in online social media and app review sites (Guzman & Maalej, 2014). Although sentiment analysis tools are a powerful utility for app ranking and selection, they have so far been

10.2 Background

135

underutilized in the field of education app evaluation. The procedure described later in this chapter essentially mines ‘big data’ to provide further information to app users (Bano et al., 2018a).

10.3 Initiative One: Development of an Online App Evaluation Rubric Previously, we identified the need for an app evaluation rubric that would facilitate a more nuanced examination of the mobile pedagogic potential of apps. Accordingly, we developed an online rubric based on criteria linked to the iPAC pedagogies that we examine in this book. Also, although sections of the literature indicate educators are looking for advice and guidance on how to select and use discipline-specific apps (Buckler, 2012; Churchill & Wang, 2014; Green, et al., 2014; Walker, 2011), our analysis of teachers’ background needs and previous research (Kearney et al., 2015) suggest that educators tend to use more generic, content-free constructive apps, as previously discussed. This more creative genre of apps is typically used by learners to generate their own digital content in a wide variety of ways across all disciplines. It was concluded that an app evaluation instrument that caters for both discipline-specific and generic apps was more useful and scalable in the longer term. Therefore, as part of the MTTEP project, we developed an app evaluation rubric to help teachers assess, select and use any type of education app, with emphasis on the iPAC Framework. For example, we developed three items that help teachers examine the potential of their selected app’s design features to support collaborative learning. Faithful to the socio-cultural perspective of our Framework, the rubric is designed to collect specific contextual information from users linked to the actual use of apps under evaluation. Our rubric is not designed to be a comprehensive instrument that attempts to evaluate all aspects of an education app. In recognition of users being busy teachers with minimal time for these endeavours, our rubric deliberately omits aspects already covered in other rubrics, such as human interactive design (e.g. aesthetics, navigation) and usability dimensions (e.g. user interface, menus, buttons,), content accuracy, cultural bias, technical attributes and accessibility. If users want to evaluate these important dimensions, we advocate use of our rubric in conjunction with one of the other previously discussed research-inspired rubrics, depending on the purpose (e.g. use of MASS instrument by science teachers to evaluate science apps). Several iterations of our online rubric were developed during the MTTEP project, informed by feedback from expert teachers, academics in teacher education and software engineering, and m-learning researchers. Details of this development process are presented in Burden and Kearney (2018).

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10.3.1 Current Structure Our app evaluation rubric is freely available online via the m-learning toolkit site described in Chap. 9 (www.mobilelearningtoolkit.com), and reviews are recorded (anonymously) into an online database. Teachers should ideally use this evaluation instrument after thoroughly exploring their selected app, and if possible, after using the app for teaching. There are two sections in the current instrument. Part A contains 11 items asking for important contextual information about past or intended use of the app, for example, information about the discipline, age of students and setting. Part B contains the nine-item rubric (three items for each of the iPAC dimensions) exploring the potential use of the app to facilitate students’ m-learning experiences. All nine items in the rubric are shown in Appendix 1. As a response to feedback relating to techno-determinism concerns during the development of the instrument, we now include the following disclaimer below the rubric: “Any ratings emerging from use of this instrument should be considered as a guide only. An app’s effectiveness as a learning tool is ultimately a function of the context of its use and the way it is used”. We were also careful to avoid techno-determinist language, deliberately adopting the term ‘potential’ (or ‘likely’ or ‘might’) throughout the nine-item rubric section. For instance, the stem of all three columns in the rubric is: “The features of this app have the potential to enable …”. Additionally, a preliminary instruction emphasised the intended use of an app’s design features: “Use the following rubric to examine how use of the app’s design features might facilitate students’ experiences” (see Appendix 1). To help teachers think about their app’s design features in relation to the iPAC pedagogies, sample features relevant to each cell of the rubric are presented to users, as shown in Appendix 2. In the online version of the rubric, these sample design features appear in a pop-up box ‘upon mouse-over’ (see Fig. 10.1, showing a sample box in the personalisation section of the rubric).

Fig. 10.1 Screenshot of the personalisation items in the online rubric (See http://www.mobilelea rningtoolkit.com/app-rubric1.html)

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10.4 Initiative Two: Use of Feature-Based Sentiment Analysis for App Evaluation In this section, results are presented from a recent investigation exploring the utility of a new sentiment analysis technique for evaluating education apps (Bano et al., 2018a). The feedback and comments of app users are assessed for their alignment against evaluation criteria comprising the three distinctive mobile pedagogies of the iPAC Framework: personalisation, authenticity and collaboration.

10.4.1 Study Design The investigation was carried out as part of our Optimising Mobile Pedagogies project, in the context of school-based mathematics and science education, a focus for this project. See Chap. 1 for details of this project. The team conducting this study comprised academics from software engineering and m-learning (Bano et al., 2018a). The steps in this investigation were as follows: (1) Ten popular discipline-specific education apps (five science and five mathematics) suitable for school students were selected, as described in Table 10.1. The apps were chosen based on popularity in various forums and blogs; Table 10.1 Selected apps for investigation (chosen based on the popularity in various forums and blogs) Mathematics

Science

App

Store

Weblink

Mathletics Student

iOS

http://au.mathletics.com/

Myscript Calculator

iOS

http://www.myscript.com/calcul ator/

GeoGebra

iOS

https://www.geogebra.org/

MalMath

Google Play

http://www.malmath.com/

Math: Mental Math Games

Google Play

https://play.google.com/store/ apps/details?id=com.astepanov. mobile.mindmathtricks&hl=en

Anatomy 4D

iOS

http://anatomy4d.daqri.com/

Little Alchemy

Google Play

https://littlealchemy.com/

NASA

Google Play

https://play.google.com/store/ apps/details?id=gov.nasa&hl=en

Skeptical Science

Google Play

https://www.skepticalscience. com/

Star walk

iOS

http://vitotechnology.com/starwalk.html

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Fig. 10.2 Word clouds relating to the three dimensions of the iPAC Framework

(2) A commercial sentiment analysis tool, Appbot (https://appbot.co/), was chosen to extract user reviews and ratings from app stores and to provide full utility of qualitative and quantitative analysis. Other similar sentiment analysis tools are able to provide the same functionality but Appbot was chosen because it provides functions to search within reviews for specific words (i.e. feature extraction) and also allows filtering of relevant reviews that match particular concepts or words; (3) A word bank based on words in the literature associated with the three main dimensions of the iPAC Framework was developed by authors of the original iPAC Framework. Figure 10.2 shows sample words from these word banks; (4) After using the word bank for feature extraction on the ten selected apps, Appbot was used to analyse the extracted reviews for the polarity of the sentiments. Data were collected covering reviews for the period of 1 year (from January 2016 to January 2017), to limit the scope of the investigation.

10.5 Results Table 10.2 shows the total number of reviews, the percentage of positive sentiments, average of star ratings and the scores. These scores ranged from D- to A+ and are calculated based on the trends in the review sentiments, review volume and star ratings, as expressed by the app users. In the domain of mathematics, MalMath and Myscript Calculator were on top of the list, whereas for science, Little Alchemy and NASA received the highest number of reviews. MalMath and Little Alchemy received a significant number of reviews from the users and above 90% of these reviews contained positive sentiments. Next, data based on the aforementioned word bank were extracted. Table 10.3 shows results for the number of reviews that matched the iPAC word bank. Table 10.4 shows the breakdown of the sentiments for the extracted reviews for the apps.

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139

Table 10.2 Results from sentiment analysis of selected mathematics and science apps

Mathematics

Science

App

Review count

Positive sentiment

Avg review stars

Score

1

MalMath

10572

91.2%

4.5

A–

2

Myscript calculator

1488

89.3%

4.3

B

3

Math: mental math games

47

88.6%

4.4

C–

4

GeoGebra

97

55.6%

3.1

D–

5

Mathletics student

122

21.2%

2.0

D–

1

Little alchemy

7929

92.3%

4.5

A

2

NASA

4161

88.0%

4.4

A–

3

Star walk™

584

94.1%

4.7

B–

4

Skeptical science

5

80.0%

4.0

D+

5

Anatomy 4D

30

58.6%

3.3

D–

Table 10.3 Feature-based sentiment analysis Apps MalMath

Total reviews

Authenticity

Collaboration

Personalisation

Reviews

%

Reviews

%

Reviews

%

10572

22

0.21

4

0.04

40

0.38

Mathletics

122

8

6.56

1

0.82

4

3.28

GeoGebra

97

2

2.06

0

0.00

3

3.09

664

4

0.60

1

0.15

6

0.90

Myscript calculator

1488

19

1.28

3

0.20

31

2.08

Little alchemy

7929

13

0.16

25

0.32

88

1.11

5

0

0.00

0

0.00

0

0.00

30

1

3.33

0

0.00

1

3.33

Math: mental math games

Skeptical science Anatomy 4D NASA Star walk

4161

8

0.19

6

0.14

32

0.77

584

12

2.05

3

0.51

22

3.77

Figure 10.3 provides the graphical representation of the feature-based sentiment analysis, delineating positive, negative and neutral sentiments for both mathematics and science apps. Overall, a nuanced picture of pedagogical affordances emerged for these ten sample apps (Bano et al., 2018a). MalMath produced considerably more positive

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10 Evaluating Education Apps from a Sociocultural Perspective

Table 10.4 Breakdown of feature-based sentiment analysis (+ = positive, 0 = Neutral, – = Negative) Apps

Authenticity +

0

Collaboration –

MalMath

22

Mathletics

1

7

GeoGebra

1

1

Math: mental math games

4

Myscript calculator Little alchemy

+

0

Personalisation –

+

0

–

4

37

1

2

1

1

3

2

17

1

1

2

6

3

4

25

1

1

5

1

1

23

3

5

78

4

6

1

2

Skeptical science Anatomy 4D

1

NASA

5

3

4

1

Star walk

9

3

1

1

1

29

2

11

3

Fig. 10.3 Feature-based sentiment analysis results

sentiments in personalisation and authenticity aspects; however, there weren’t many reviews about this app relating to collaboration. Myscript Calculator generated positive sentiments for the personalisation and authenticity dimensions, but received only negative reviews for collaboration. In the case of Little Alchemy, it generated significantly positive reviews for app features relating to personalisation and some positive reviews in collaboration; however, it received a low volume of reviews and

10.5 Results

141

a mixed range of sentiments on authenticity. NASA received positive sentiments for personalisation but not so favourable sentiments on collaboration and authenticity. The results provide preliminary evidence of the mobile pedagogical features of apps that users are choosing to comment on in their reviews, without any prompting from a rubric or other more formal evaluation instrument. There was a trend in the results showing higher frequencies of positive sentiments relating to the personalisation aspect of the selected apps. It is too early to draw conclusions about ‘low volumes’ of comments; other than that users were not choosing to comment on such features. For example, just because users were evidently not frequently commenting on (or not noticing) app features relating to collaboration doesn’t mean that these features are absent. Further research is needed to clarify the exact implications of low and high frequencies of sentiments (Bano et al., 2018a).

10.6 Contributions and Future Directions This chapter presents two initiatives that use the iPAC Framework to address the growing challenges faced by educators to effectively and efficiently select and evaluate education apps for their m-learning activity designs. These initiatives emerged from two of our m-learning projects: the MTTEP and Optimising Mobile Pedagogies projects introduced in Chap. 1. The first initiative involved the design and development of an online rubric for educators. It was developed by members of the MTTEP project, to help educators consider the design features of an education app for their potential support of specific mobile pedagogies. This rubric instrument addresses the need for a more nuanced examination of potential mobile pedagogical affordances of apps, and also the need for an accessible, online rubric that collects anonymous responses from educators. These user-generated reviews populate an app review database that will ultimately highlight design features of apps that are likely to support one or more of the three mobile pedagogies of the iPAC Framework. This database will also provide a rich layer of contextual information about how teachers are using apps. For example, relationships will be explored between specific apps, mobile pedagogies (such as co-creation) and other information elicited in Part A of the online instrument, such as disciplines, settings and teacher roles. As flagged in the discussion around the m-learning toolkit in Chap. 9, a future development direction is to supplement the rubric with rich online exemplars and case studies that illustrate and explain how specific apps might be used in various contexts. A student version of the rubric is also planned. It is critical for evaluation procedures to consider the learner voice, in this case to consider students’ views of app design features in relation to their own m-learning experiences. With more schools adopting less locked down ‘bring your own device’ programs, some students now have increased agency over their choice of apps (Stevenson & Hedberg, 2016). If this type of practice becomes widespread, student rubrics will become more important

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for both evaluating and selecting quality resources, and for triangulating data from teacher evaluations. A second initiative was presented in this chapter that used the iPAC Framework to inform the mining of big data using sentiment analysis. A study was implemented that investigated the utility of this new technique for evaluating the pedagogical affordances of education apps. It was developed during the Optimising Mobile Pedagogies project to explore the use of feature-based sentiment analysis to extract the feedback and comments of past app users, before assessing their alignment with the iPAC Framework. The results of this investigation provide preliminary evidence that using sentiment analysis is an effective way for evaluating apps in education (Bano et al., 2018a). There were some limitations to the sentiment analysis study. The precision of results was impacted by the accuracy of the word bank and the limitations of the sentiment analysis tool (Appbot). For instance, the words may not have matched well against those words used in the reviews even though they may have been synonyms. Future research in this area will include deeper analysis of the textual content of the reviews by using cutting-edge natural language processing technologies as well as newly developed algorithms for opinion mining (Haering, Bano, Zowghi, Kearney, & Maalej, in press). Feature-based sentiment analysis is proposed as a potential supplement to other more traditional app evaluation procedures, such as use of rubrics. A future research direction is to investigate a hybrid approach to evaluating apps using the iPAC Framework, such as the integration of the sentiment analysis technique with our online rubric instrument. Indeed, a design goal is to develop a software tool that would seamlessly extract the sentiments of past users of an app from our online database of reviews, to provide additional information about the app. This type of innovative, two-tiered evaluation procedure will ultimately help educators (and app designers) to more accurately evaluate the pedagogical potential and value of education apps. Other sources that could be considered to triangulate data include interviews or surveys with app users, qualitative analysis of the text of the reviews and feedback from app stores and social media. Ultimately, we hope the two app evaluation initiatives described in this chapter will influence the work of app designers. As discussed earlier in the chapter, there is a dominant instructive, transmissionist approach underpinning many education app designs. We hope that emerging reviews based on the iPAC dimensions of our rubric will help designers to consider a socio-cultural perspective in their refinements and to re-focus on important pedagogical dimensions that exploit the malleable nature of m-learning environments.

10.7 Conclusion

143

10.7 Conclusion As mobile apps develop and proliferate, the challenge for educators attempting to select quality resources is to wade through ‘the mess’ (Cherner et al., 2014) comprising the burgeoning numbers of Education apps in repositories that merely promote passive, rote learning and information provision. These typically glitzy apps (Hopper & Palmer, 2012), underpinned by behaviourist learning assumptions, are alluring to students and parents and in some cases their popularity promotes faddism (Lee, 2009). Furthermore, some schools are mandating sets of ‘core apps’ for common purposes across the curriculum, and too many apps in these selections, especially in disciplines such as mathematics, are designed to leverage more traditional, didactic pedagogical approaches. Indeed, we have argued more broadly that educational technologies conforming to notions of authority and traditional teaching models, and more generally supporting schools as regulated learning environments, are more easily accepted and endorsed by school systems (Schuck, Kearney, & Aubusson, 2012). To address these deeply entrenched problems, the two app evaluation strategies described in this chapter use the iPAC Framework to bring a muchneeded socio-cultural perspective to app evaluation. These initiatives, as well as other tools described in Chaps. 9 and 11, aim to provide a catalyst for educators to exploit new m-learning opportunities underpinned by more generative and socially interactive approaches. The next chapter focuses on use of the iPAC Framework to inform the development of validated online surveys for educators. These survey instruments are designed to assist educators’ scrutiny of their adopted mobile pedagogies, either in a specific m-learning task or more general use in m-learning tasks over a longer period of time.

Appendix 1: EVALUATION RUBRIC: Education Apps The purpose of this instrument is to assist school teachers in evaluating educational applications (‘apps’) for mobile devices, particularly the potential of the app to support mobile pedagogies. This instrument could also be used for teacher education purposes. NB. This abridged version contains the rubric (only). A longer online version of the instrument contains 11 preliminary questions and can be found at: http://www. mobilelearningtoolkit.com/app-rubric1.html. Directions: Use the following rubric to examine how use of the app’s design features might facilitate students’ experiences. [Circle one option per row]. NB. To help with your evaluation, further notes, including examples of design features, are presented in Appendix 2.

144

Collaboration

Personalisation

Authenticity

10 Evaluating Education Apps from a Sociocultural Perspective 3

2

1

The features of this app have the potential to support:

The features of this app The features of this have the potential to app have the support: potential to support:

Learners talking with peers online

Limited online peer discussion

Learners working together to create/modify digital content

Limited opportunities for No learners to work together creation/modification to create/modify content of content together

Learners sharing/exchanging digital content online

Limited opportunities for learners to share/exchange digital content online

No opportunities for learners to share/exchange digital content

Learner choice/control over the activity

Restricted learner choice/control over the activity

No learner choice/control. External control only

Learner customisation of the app

Restricted access to app settings or preferences

No possibilities for learner to modify/personalise the app. ‘Once size fits all’.

No online peer discussion.

Learner access to unique Similar/identical information tailored to information provided to them all learners

No access to personalised information for learners

Learners’ participation in real-life activities

Restricted realism and relevancy in activities

Artificial activities only

Realistic use of the mobile device by learners, similar to real-world experts

Restricted real-world use of mobile device by learners; only similar to experts in a small way

Contrived use of the mobile device by learners, unrelated to discipline/real life

Opportunities for students to learn in a realistic learning space, relevant to the topic/real-life

Restricted opportunities for learning in a realistic learning space, relevant to the topic/real-life

Learning in a decontextualised learning space, unrelated to the topic/real-life

Appendix 1: EVALUATION RUBRIC: Education …

145

Disclaimer: Any ratings emerging from use of this instrument should be considered as a guide only. An app’s ‘effectiveness’ as a learning tool is ultimately a function of the context of its use and the way it is used. Therefore, the language used in many of the items in this instrument attempts to avoid ‘techno-determinism’ by using words such as ‘potentially’ and ‘likely’. This instrument mainly focuses on pedagogical aspects of mobile learning. Therefore, it does not contain items focusing on aspects such as age appropriateness, content accuracy, curriculum ‘fit’, cultural bias, language, technical attributes, navigation, user-friendly design (menus, buttons, user interface, etc.), aesthetics, use of sound, graphics and accessibility. It also does not contain items on motivation, engagement, assessment, reporting or reflection. If you would like to evaluate other aspects of your app, we suggest you also use an instrument that suits your needs from https://www.schrockguide.net/ipads.html.

Appendix 2: EVALUATION RUBRIC: Notes/Sample Features of Apps (to Assist with Rubric Responses)

Collaboration

3

2

1

Pedagogical features of the app design that may promote online peer learning conversations, e.g. role-play design encourages communication; or technical features such as extensive, networked chat or discussion facilities, e.g. in social media or multi-player game apps

Pedagogical features of the app design that promote online peer learning conversations in a limited way; or technical features such as SMS, texting and message boards; and access to camera and microphone to support small group video conferencing

Pedagogical or technical features promoting online peer learning conversations are absent

Pedagogical features of the app design promote co-creation of digital artefacts; or technical features such as co-editing facilities, e.g. in a wiki or multi-player simulation app

Pedagogical features of the app design promote limited ways of co-creating digital artefacts; or technical features such as single-user editing features, e.g. in iMovie app or Kahoot app

Pedagogical or technical features promoting co-creating digital artefacts are absent

(continued)

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10 Evaluating Education Apps from a Sociocultural Perspective

(continued) 3

2

1

Pedagogical features of the app design that may promote online sharing of digital artefacts with others, e.g. multi-player game suggests learner sharing; or technical features such as in-built links to social media or online communities; or screen sharing facilities, e.g. in multi-player game apps

Pedagogical features of the app design that may promote online sharing of digital artefacts with others in a limited way; or technical facilities to share content on a small scale, such as use of email or screen sharing, e.g. in Skype or Google Hangout apps

No opportunities for learners to share/exchange digital content Pedagogical or technical features promoting sharing of digital artefacts are absent

(More likely) Pedagogical features of the app design that may promote restricted learner autonomy, such as allowing learners to adjust limited parts of the activity. Also, technical features allowing learners to make minor activity adjustments such as challenge/difficulty levels, grade/age levels or time limits/rate of progress

Pedagogical or technical features promoting learner autonomy are absent, e.g. Features suggest teacher control, e.g. ‘remote presentation’ apps like Nearpod

Personalisation (More likely) Pedagogical features of the app design that may promote learner autonomy, such as allowing learners to choose a question or problem to explore. Also, technical features such as access to a range of ways to work/express (write, draw, narrate, animate, etc.)

Pedagogical features or (more likely) technical features of the app design that allow learners to customise the app or user interface, such as access to numerous app settings or preferences for learners to tailor to their individual liking, e.g. background images/music, building personal profile using motifor avatars

Pedagogical features or Pedagogical or technical (more likely) technical features promoting app features of the app customisation are absent design that allow learners to customise the app in a restricted way, such as turning location settings on/off

(continued)

Appendix 2: EVALUATION RUBRIC: Notes/Sample …

147

(continued)

Authenticity

3

2

1

Pedagogical features of the app design that promote personalised information to learners informed by their past use (e.g. adaptive feedback), or technical facilities presenting personal information to learners based on their location, such as real-time weather data based on the user’s geographical position; or facilities collecting and showing user’s heart rate or personal travel information (e.g. activity tracker apps)

Pedagogical features such as limited choice of pathways/feedback based on past use; or technical features of the app design that allow learners access to personalised information in a restricted way, e.g. facility to trigger information based on learner’s location, or an image/QR code

Pedagogical or technical features promoting personally tailored information are absent

Pedagogical features of the app design that may promote meaningful, relevant activities for the learner, e.g. community projects; or technical features such as facilities to collect/access ‘real data’ for/from experts, e.g. citizen science apps; in-built links to real-life ‘online communities’/experts

Pedagogical features of the app design that promote meaningful, relevant activities in a limited way, e.g. prompts to record a ‘selfie’ or publish work to a real audience beyond the class; or technical features such as simulations resembling a real-world activity; or learners’ adoption of realistic avatar profiles

Pedagogical or technical features promoting meaningful, relevant activities are absent

(continued)

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(continued) 3

2

1

Pedagogical features of the app design that promote realistic use of the device in a similar way to experts (e.g. inquiry approach encourages collection of real data); or technical features such as links to ‘professionally relevant’, discipline-specific tools, e.g. the camera facility to support observation process (like real scientist); or the microphone to take audio notes in the field (like real historian) or translate speech to text (like journalist)

Pedagogical features of the app design promote use of device in only a minor realistic way; or technical features such as limited in-built links to ‘real-life’ tools such as Google Maps, Calculator and clock, e.g. ‘timestamping’ student-generated reports

Pedagogical or technical features promoting realistic use of the device are absent

Pedagogical features of the app design that promote numerous opportunities for situated learning (e.g. astronomy apps that suggest learners go outside at night to analyse the stars); or technical features such as augmented reality (AR) facilities to enhance relevance of physical setting

Pedagogical features of the app design promote limited opportunities for use in an authentic learning space; or technical features such as virtual reality (VR) facilities create a relevant, albeit simulated, virtual space, such as in Google Cardboard apps

Pedagogical or technical features promote irrelevant setting to topic/learners, e.g. Features suggest use in a classroom or contrived online space, such as a LMS

References Bano, M., & Zowghi, D. (2014). Users’ voice and service selection: An empirical study. In IEEE Fourth International Workshop in Empirical Requirements Engineering (EmpiRE 2014), Karlskrona, Sweden. Retrieved from https://opus.lib.uts.edu.au/bitstream/10453/32300/4/PublishedMB’s%2Bpreprint.pdf. Bano, M., Zowghi, D., & Kearney, M. (2018a). Feature based sentiment analysis for evaluating the mobile pedagogical affordances of apps. In A. Tatnall & M. Webb (Eds.), Tomorrow’s learning: Involving everyone. Learning with and about technologies and computing. WCCE 2017 (Vol. 515, pp. 281–291). IFIP Advances in Information and Communication Technology. Cham: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-74310-3_30.

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Chapter 11

iPAC Survey Development: Capturing Mobile Pedagogical Practices

Abstract This chapter focuses on the design of iPAC survey instruments developed during two of our major m-learning research projects. The first instrument focuses on the evaluation of teachers’ general (or typical) mobile pedagogical approaches reported as being used over the past year, while the second instrument facilitates the evaluation of approaches adopted in one specific m-learning task. Both instruments have teacher and student versions. Their validity was further improved in a recent study described later in the chapter. Implications for teacher education are examined, and validated scales are presented. Keywords Mobile pedagogies · iPAC · M-learning practices · Survey method · Scale validation · Survey development · Teacher survey · Student survey · Typical use · M-learning task

11.1 Introduction This chapter describes the development of survey instruments that allow researchers and practitioners to scrutinise the distinctive mobile pedagogies highlighted in the iPAC Framework, drawing on established socio-cultural tenets. These mobile pedagogical approaches privilege personalised, authentic and collaborative learning. The instruments were developed over several years through a series of studies that aimed to critically examine contemporary m-learning practices in a range of school and teacher education contexts by capturing teachers’ and students’ perceptions of adopted mobile pedagogies (see Chap. 1 for details of these studies). In the most recent study, carried out as part of our Optimising Teaching and Learning with Mobile-Intensive Pedagogies project, robust scales were developed to capture and measure the three mobile pedagogical dimensions described in the Framework. The study validated and established the measurement properties of these scales to provide tools that other scholars are able to use to investigate mobile pedagogies in a range of settings. This chapter is the last of three chapters in this part of the book that details This chapter is an adaptation of the following published article: Kearney, M., Burke, P., & Schuck, S. (2019). The iPAC scale: A survey to measure distinctive mobile pedagogies. TechTrends, 63(6), 751–764. Creative Commons Attribution 4.0 International (CC BY) license. © Springer Nature Singapore Pte Ltd. 2020 M. Kearney et al., Theorising and Implementing Mobile Learning, https://doi.org/10.1007/978-981-15-8277-6_11

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methods and tools for investigating mobile learning (m-learning) using iPAC. The previous two chapters have discussed a toolkit designed to help teachers and teacher educators plan and implement effective m-learning activities (Chap. 9) and methods to evaluate the pedagogic potential of mobile applications (Chap. 10).

11.2 Background M-learning has been defined elsewhere in this book but can be broadly considered as learning that takes place with the help of portable digital devices (Quinn 2000). Numerous instruments have been developed to measure a variety of aspects of m-learning. Based on the literature on technology readiness, online learning (or e-learning) readiness and mobile computer anxiety, Lin, Lin, Yeh, Wang, and Jansen (2016) developed and validated an m-learning readiness (MLR) scale which can be used to assess learners’ psychological readiness to embrace m-learning in numerous contexts, such as school, university and the workplace. They reported on the scale development of this 19-item instrument with three dimensions: m-learning self-efficacy, optimism and self-directed learning. It was validated using responses from a diverse range of 319 participants, including students and workplace learners. In an earlier study, Wang (2007) reported on the development and validation of another scale targeting the affective domain: the ‘mobile computer anxiety scale’. This 38-item instrument supplemented similar instruments that measure computer and Internet anxiety. The author used responses from 287 participants to validate their instrument. Participants were adults from 19 to 60 years old from a range of organisations in Taiwan. Another instrument called the ‘mobile learning scale’ has been used in higher education (Knezek & Khaddage, 2012) to measure university students’ attitudes towards m-learning in both informal and formal learning environments. Other studies have developed survey instruments to investigate m-learning in school contexts. Domingo and Garganté (2016) developed a survey instrument to collect data about teachers’ individual information, teachers’ perceptions on the impact of mobile technology in learning, and use of apps in the classroom. Their study gathered data from 102 teachers from 12 different primary schools in Spain. Findings suggested that teachers’ perception of how mobile devices support learning is a major influence on their choice of educational apps for children. Facilitating access to information and increasing engagement were the two main perceived impacts of mobile technology in the classroom. Lai, Hwang, Liang, and Tsai (2016) developed a ‘mobile learning environmental preference survey’ (MLEPS) consisting of eight factors: ease of use, continuity, relevance, adaptive content, multiple sources, timely guidance, student negotiation and inquiry learning. They used this instrument to investigate differences between m-learning environmental preferences of 429 high school teachers and 1239 students in Taiwan. They found that the teachers tended to focus more on technical issues, while the students cared more about the richness and usefulness of the learning content. Uzunboylu and Ozdamli (2011) reported on the scale development of an instrument designed to elicit school teachers’ perceptions of m-learning in three areas: mobile technologies’ fit, appropriateness and forms of

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155

applications. Their study elicited responses from 467 teachers from 32 schools in Northern Cyprus, and scale validity was established using factor analysis. In a later study focusing on university students, Uzunboylu, Hursen, Ozuturk, and Demirok (2015) developed a reliable and valid scale to determine students’ attitudes towards mobile-enabled language learning. Their ‘English Language Learning via Mobile Technologies Attitude Scale’ (ELLMTAS) was prepared by incorporating experts’ (n = 15) views and contained 37 items, composed of sub-dimensions probing beliefs about how use of mobile technologies might facilitate English-language learning and motivation, including development of English vocabulary, writing skills and high-level thinking skills. Many m-learning studies have the goal of measuring the effectiveness of mlearning, particularly in higher education (Hung & Zhang, 2012; Wu et al., 2012), or aim to measure attitudes to m-learning, or to identify affordances and barriers to adoption. Although the literature provides examples of m-learning practices and developments (e.g. Valk, Rashid, & Elder, 2010) and their measurement, there is often an emphasis on technical aspects of these activities. Many studies (e.g. Stockwell, 2007) rely on self-reported scales or students’ performance measures (such as literacy tests) when using mobile devices and make comparisons to results obtained without use of technology. In some cases, the comparison is made with learning or enjoyment that occurred over a period of time in which the technology had not been used (e.g. Cavus & Ibrahim, 2009; Wang, Shen, Novak, & Pan, 2009). Other studies describe how researchers undertook a controlled experiment to evaluate outcomes across both time and technology dimensions (e.g. Basoglu & Akdemir, 2010). Missing in such evaluations is the nature of the adopted pedagogies. Indeed, the pedagogical characteristics of m-learning often have been neglected in the design of survey instruments in the literature. In particular, there is a need for instruments that can be used by researchers to fully interrogate socio-cultural characteristics of m-learning approaches, as indicated in the iPAC Framework. Learning mediated by mobile devices is becoming more commonplace. As a result, mobile pedagogies are becoming of interest to both researchers and practitioners. To our knowledge, however, a survey instrument to measure the nature of mobile pedagogies that are being used by practitioners has not previously been developed, which is surprising given the need to better understand practice and pedagogies in this new learning paradigm. Our iPAC surveys described in this chapter aim to foreground teachers’ mobile pedagogies, rather than specific technologies or perceptions of m-learning drivers and constraints. They do so by interrogating teachers’ adoption of signature m-learning practices. Use of these instruments provides unique data in specifically targeting the three iPAC dimensions (personalisation, authenticity and collaboration) to help researchers confidently investigate how teachers are orchestrating mobile pedagogies in their task designs. The remaining sections of this chapter describe the development and use of earlier versions of survey instruments during our m-learning research agenda over the past several years, before introducing our recent empirical study that developed a robust scale to measure m-learning practices, based on our socio-cultural m-learning framework (iPAC).

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Fig. 11.1 Current representation of the iPAC framework (reproduced with permission from Kearney, Burke & Schuck, 2019, p. 754)

11.3 The iPAC Framework The iPAC Framework originated as the Mobile Pedagogical Framework (Kearney, Schuck, Burden, & Aubusson, 2012) and subsequently became known to practitioners and users as the iPAC Framework (Burden & Kearney, 2018). Its key dimensions are personalisation, authenticity and collaboration, each with two subdimensions. How learners experience these distinctive characteristics of m-learning is influenced by their exploitation of spatial and temporal boundaries (or ‘time-space’), as depicted in Fig. 11.1. For a full discussion of the iPAC dimensions and ensuing research that resulted in amendments to some of the sub-dimensions, see Chaps. 5 and 7.

11.4 Earlier Versions of iPAC Surveys Survey instruments have been developed over several years, primarily through research activities in two of our major m-learning projects first introduced in Chap. 1: an Erasmus + project titled Mobilising and Transforming Teacher Educators’ Pedagogies (MTTEP); and an Australian Research Council (ARC) funded project titled Optimising Teaching and Learning with Mobile-Intensive Pedagogies (subsequently abbreviated to Optimising Mobile Pedagogies). Two survey formats evolved during

11.4 Earlier Versions of iPAC Surveys

157

these projects. The task-specific survey was developed to interrogate teachers’ adoption of mobile pedagogies in a recently implemented task or activity where students used mobile devices to support their learning. As mentioned in Chap. 7, the general use survey was developed to probe typically adopted mobile pedagogies over a longer period (e.g. 1–3 years). Both teacher and student versions of these surveys were developed to triangulate findings.

11.4.1 Investigating Mobile Pedagogies Adopted in a Specific Task The earliest version of the task-specific survey was developed over several iterations in an international study investigating school teachers’ and teacher educators’ m-learning practices from around the world (Kearney, Burden, & Rai, 2015). Intra-researcher validation was achieved through regular discussions among the researchers, and with regular feedback from the other co-designers of the original Mobile Pedagogical Framework (Kearney et al., 2012). These discussions critiqued each version of the survey and how well items aligned with the Framework’s dimensions and its underpinning socio-cultural tenets. A penultimate version of the survey instrument was trialled as part of a pilot study with 20 external teacher educators and school teachers. Feedback from participants, including four specialist m-learning researchers, helped us to make final refinements before using the instrument to collect data. For example, a distinction was made in two items between face-toface and online conversations in the collaboration category. Also, items relating to ‘data sharing’ (a collaboration dimension at the time the survey was designed—see Chap. 7) were divided up into ‘generativity’ (the extent to which learners shared learner-generated content) and ‘networking’ (the extent to which they shared data in networked collaborations). Subsequent revisions were made to the task-specific iPAC survey during the MTTEP project. One of the aims of this project was to produce an m-learning toolkit for school teachers and teacher educators (Burden & Kearney, 2018), as described in Chap. 7. Key components of the toolkit are online teacher and student versions of the task-specific iPAC survey. The teacher version allows educators to evaluate mobile pedagogical aspects of their own recently implemented m-learning task. Teachers receive an automatically generated report at the end of the survey, showing how specific iPAC mobile pedagogies have been adopted. The report also includes links to pertinent professional learning resources. If the student version of the survey is completed, a supplementary chart is generated allowing teachers to compare their own perceptions with their students’ perceptions. The development of these online, freely accessible survey instruments was informed by trials at international MTTEP project workshops, project multiplier events and (internal) project meetings. Feedback was received in relation to userfriendliness and basic aesthetics of the instruments and the format of the teacher’s

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11 iPAC Survey Development: Capturing …

report. For example, polar charts were preferred in the report over traditional bar charts, which users found more difficult to interpret. Additional advice for teachers about professional development opportunities was requested in the report, especially for teachers with lower task ratings in one or more iPAC categories, or ratings that were significantly different from their students’ scores. These task-specific online surveys are freely available on the m-learning toolkit website (mobilelearningtoolkit.com) and have since been updated to reflect the current scales described later in this chapter.

11.4.2 Investigating Mobile Pedagogical Approaches Typically Adopted in Tasks The earliest version of the general use survey was developed during the ARC project, Optimising Mobile Pedagogies. In this project, the survey was designed to examine Australian teachers’ typical use of mobile pedagogies in m-learning tasks over the past 3 years, rather than in a specific activity. The aim of this general use survey was to gather foundational data for the project by capturing secondary maths and science teachers’ perceptions of their typical m-learning practices. A well-developed version of the survey instrument was trialled as part of a pilot study with 10 practising teachers. Ensuing team discussions focused on how well items elicited data relevant to the three iPAC dimensions and also considered the internal consistency of the results. These discussions and feedback from pilot survey participants informed final refinements. The survey was disseminated to Australian teachers, and results are discussed in Chap. 12. The student version of the general use survey was developed later in the Optimising Mobile Pedagogies project to ascertain secondary school students’ perceptions of maths and science m-learning practices in four mobile-intensive case schools participating in the project. The research team used items from the teacher survey (described above) as a starting point and modified the language in these items to make it more teenager-friendly. In summary, development of these earlier versions of the iPAC survey was undertaken in a range of forums in two of our major m-learning projects, including formal and informal discussions about mobile pedagogies with other researchers and experts in m-learning, as well as trials with pre-service teachers (PSTs) and in-service teachers. Results showed that item reliability and discriminant validity were satisfactory for most items but could be improved in some items. For example, teachers using the typical survey were asked to report on m-learning activities involving communicating with experts. Data analysis showed that responses to this item were capturing both sub-dimensions of using mobile technology to collaborate, as well as elements of authenticity. As such, there were some concerns regarding cross-factor loadings to the point that an improved instrument was desirable, thereby motivating the empirical study described in the next section.

11.5 Scale Development

159

11.5 Scale Development To address issues of discriminant validity, the original items from earlier surveys were reviewed by a team of five m-learning experts to ensure that each of these items referred to only one of the three dimensions of the iPAC Framework. Given the eventual task of measurement would rely on the comprehension and perceptions of teachers, a subsequent classification exercise involving 79 PSTs was undertaken.

11.5.1 Classification Exercise PSTs were first provided with a small description of what personalisation, authenticity and collaboration mean in the iPAC context. Respondents could refer to these descriptions at any time during the exercise. The classification task presented each proposed survey item to participants. Respondents then classified each item to indicate if it was describing personalisation, authenticity or collaboration (PAC). They also had the option to indicate if they felt that an activity description was not describing any of these three dimensions by selecting ‘neither’ or if they were unsure, to indicate ‘unsure’. As such, respondents used a five-point scale (authenticity, collaboration, personalisation, neither, unsure) to classify 24 randomised statements. The statements initially comprised statements related to the three PAC dimensions of personalisation (six statements), authenticity (seven statements) or collaboration (seven statements). Further four statements were included as distractors to demonstrate the engagement of participants in this exercise. The results demonstrated that 17 of the 20 PAC items were classified as intended by at least 50% of PSTs against the focal dimension. For example, 93% of respondents classified that the activity described as “Students talk about the work displayed on the screen with others around them” was reflective of collaborative m-learning activity. Overall, the average level of agreement in classification of these 17 items was 78% of respondents. With respect to misclassification, 40% of respondents indicated that “Students annotate existing digital content e.g. creating a media mash, tagging a photo, editing a video” referred to personalisation rather than online collaboration as intended. Similarly, only 39% of respondents agreed that activities in which “Students learn through a community activity/project e.g. Platypus census using platypusSPOT app; environment projects such as bush regeneration or water quality” represented an authentic activity with another 38% indicating that this item represented collaboration. Overall, the results served to indicate the typical statements and words that teachers would use to identify whether the activity was collaborative, authentic or involved personalisation. This is reported in Table 11.1. The final list of items was further developed based on another review of the statements by the team of experts. Where items had shown high rates of misclassification, they were re-worded to enhance the likelihood of reliability and of increased discrimination from other sub-dimensions. The final survey that emerged from this exercise

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Table 11.1 Results of item-classification exercise Item

N

U

M

Students talk about the work displayed on the C screen with others around them, e.g. talking with a friend nearby about homework shown on the screen

Target

Resultˆ C

A 1

C 93

P 0

3

0

3

Students discuss the work online with their friends, e.g. discussing ideas with peers via email, SMS, Skype, Facebook, etc.

C

C

1

90

0

0

0

9

Students work together and create a digital product, e.g. video, podcast, photo, iBook, document

C

C

1

90

0

0

1

7

Students share digital content, e.g. video, podcast, photo, document

C

C

1

76

3

7

1

10

Students annotate existing digital content, e.g. creating a media ‘mash-up’, tagging a photo, editing a video

C

–

16

10

40

7

7

18

Students discuss the work online with people they don’t know, e.g. a student gamer from another school, tweet a NASA scientist, ask a question to a Brainpop mathematician

C

C

7

64

3

1

1

22

Students communicate with others using a variety of text, image or video modes, e.g. SMS, Instagram and Skype

C

C

0

82

3

3

0

12

Students choose the place to do the activity, e.g. P bus, home, playground

P

10

3

76

3

0

7

Students determine the pace at which they do the activity

P

P

3

0

88

0

0

9

Students decide what they want to learn, e.g. choosing their own question or problem or project to explore

P

P

3

3

85

0

0

9

Students choose how to express their thinking, e.g. text, diagram, annotated image, narrated animation

P

P

10

0

82

1

0

6

The app guides them, based on their past use, e.g. game challenge level, YouTube recommendations

P

P

14

2

67

8

5

6

Students tailor the app settings to their preferences, e.g. location on/off, camera/microphone access; time limits

P

P

3

0

91

0

1

4

The app gives them context-specific A information, e.g. about an artefact in a museum, number of steps they walked, earthquake tremors nearby

A

70

1

16

1

3

7

(continued)

11.5 Scale Development

161

Table 11.1 (continued) Item

Resultˆ

A

N

U

M

Students learn in a place suggested by the topic, A e.g. learning about stars under the night sky, learning about pollution at a local stream, learning about History at the site of an ancient battle

Target

A

78

C 4

P 6

4

1

6

Students learn in a realistic, virtual space, e.g. use of augmented (AR) or virtual reality (VR) apps, science simulation

A

A

73

1

9

1

1

13

Students work like an expert, e.g. collect data A using GPS like a Geographer; measure using an inclinometer app like a scientist; compose music or lyrics to a song like a musician.

A

78

6

6

0

0

10

Students learn through a community activity/project, e.g. Platypus census using platypus SPOT app; environment projects such as bush regeneration or water quality

A

–

39

37

1

0

0

22

Students experience unplanned, incidental learning events, e.g. an unexpected visitor, an unprompted tweet or Facebook message, or spontaneous sharing of a photo

A

–

40

15

1

25

9

9

Students link new information and concepts to their ‘real lives’, e.g. family history project, healthy eating project

A

A

51

1

25

3

3

16

Students repeat a practice exercise until rewarded for correct answer

D

–

21

3

40

27

3

6

Students do an online quiz to test their knowledge

D

–

31

0

22

36

4

6

Students listen to a teacher podcast, e.g. on YouTube

D

–

19

10

25

27

13

4

Students draw a diagram to summarise understanding of a topic, e.g. using the iPad pencil/ stylus

D

P

20

0

61

8

6

6

Note Numbers in each cell represent proportion of pre-service teachers (%) ˆClassification result based on maximum number of pre-service teachers (bolded percentage) A = Authenticity; C = Collaboration; P = Personalisation; N = Neither; U = Unsure; M = multiple selections made

was subsequently tested (see next sub-section) and consisted of 24 items to measure collaboration in the form of conversations (4 items) and co-creation (3), personalisation in the form of agency (4) and customisation (5), and authenticity in the form of context (4) and task (4). A five-item measure was also developed to gauge overall m-learning outcomes.

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11.5.2 Testing the iPAC Scale The survey instrument emerging from the classification exercise was then tested using an online platform. Teacher participants were initially asked for their main discipline area out of 21 areas (e.g. English, Mathematics, Science, Agriculture, Food Technology) in which mobile devices were used in their teaching activities. Teachers were also asked to nominate the main class level over the last year in which they had implemented m-learning activities. As our Optimising Mobile Pedagogies project was funded to focus on Years 7–12 cohorts, only secondary school teachers were retained for analysis. The information regarding subject area and cohort was then piped into each question by the survey platform to contextualise the items, that is, teachers were asked to consider certain questions about the behaviour of their students with mobile devices in the previously nominated cohort and subject area. Specifically, each set of iPAC items began with the stem: “When my students in Year used mobile devices to learn in activity, …”. Participant teachers were then asked to nominate which option best described their response to each statement on a scale of 1–5, where ‘1’ meant ‘Strongly disagree’ and ‘5’ meant ‘Strongly agree’. Four different versions of the survey were tested. Firstly, the stem of the iPAC items either referred to typical use with mobile devices over the past year with a given cohort and subject; or to one recently implemented, chosen activity with mobile devices for a given cohort and subject area. Secondly, each item was varied to either be presented with examples or without examples. For instance, an item relating to the conversation sub-dimension was presented as “Discussed the work online with their friends/peers e.g. discussed ideas via email, SMS, Skype, Facebook etc.”, whereas in the other condition this was presented simply as “Discussed the work online with their friends/peers”. Respondents were randomly allocated to each of these four survey versions: A: General use over past year, with examples; B: General use over past year, without examples; C: Specific task, with examples; and D: Specific task, without examples. Respondents consisted of Australian teachers who were either invited to participate via social media, previously listed on a database registering interest in being contacted for the study or provided access via a survey panel provider.

11.6 Results The results of the scale development are based on 349 completed responses. Exploratory and confirmatory factor analysis was first undertaken to assess the measurement properties of the developed scale. Below we present the details of the final model that emerged from that analysis with respect to each of the main and sub-dimensions of mobile pedagogical practice.

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11.6.1 Personalisation Personalisation captures the extent to which m-learning involves students choosing the parameters of their learning activities with respect to time, pace and location (i.e. agency) as well as the tailoring of the m-learning activity based on learning preferences and needs of the student (i.e. customisation). With respect to agency, in all four survey versions the inclusion of the four items to measure this sub-dimension resulted in an average variance extracted (AVE) below 0.5. However, the removal of item 4 (‘[students] chose how to express their thinking’) resulted in an acceptable measure overall (see agency section of Table 11.2). It is noted that the version involving teachers reporting on a specific m-learning activity without any examples for the item (i.e. version D) resulted in a measure of Table 11.2 Measurement properties of personalisation dimension Agency

A

B

C

D

Combined

Chose the place to do the activity, e.g. chose to work on the bus, at home, in the playground (1)

0.725 0.791 0.807 0.610 0.739

Determined the pace at which they did the activity (2) 0.888 0.714 0.761 0.527 0.724 Decided what they wanted to learn, e.g. chose their own question, problem or project to explore (3)

0.439 0.640 0.723 0.891 0.650

Chose how to express their thinking, e.g. chose text, diagram, annotated image, narrated animation (4)

–

–

–

–

–

AVE

0.50

0.51

0.58

0.48

0.50

CR

0.74

0.76

0.81

0.73

0.75

Customisation

A

B

C

D

Combined

Were guided by the app(s) based on their past use, e.g. 0.790 0.715 0.688 0.823 0.750 by previous game challenge levels, YouTube recommendations prompted by their previous views (1) Tailored app(s) settings to their preferences, e.g. customised location on/off, camera/microphone access, time limit settings (2)

0.845 0.854 0.685 0.666 0.754

Received individualised information through the 0.759 0.848 0.719 0.685 0.756 app(s) about themselves, e.g. information about the number of steps walked, calories eaten, hours slept (3) Customised feeds and links for their learning needs, e.g. tailored social media or news feeds (4)

0.711 0.754 0.856 0.604 0.733

Customised the learning to their requirements (5)

–

–

–

–

–

AVE

0.61

0.62

0.55

0.49

0.56

CR

0.89

0.89

0.87

0.84

0.87

Entries against each item refer to standardise factor loadings; AVE = Average Variance Extracted; CR = Composite reliability Versions: A = past year with examples; B = past year without examples; C = last time with examples; D = last time without examples

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agency that did not meet the requirements for convergent validity (see Table 11.2). Overall, however, the agency items reflect the extent to which students choose the time, pace and place to undertake their m-learning activities. Personalisation was also considered in terms of the extent to which teachers agreed that their students experienced customised learning as guided by their mobile devices. A four-item measure was supported based on appropriate factor loadings and reliability statistics. The inclusion of item 5 (‘[students] customised the learning to their requirements’) was found to slightly lower the AVE and composite reliability (CR) in all cases. However, its inclusion had negligible effect except for version D (specific task, no examples). Taken together, these customisation items capture the extent to which students’ device use and app preferences resulted in learning experiences tailored to their individual learning needs.

11.6.2 Authenticity The authenticity of m-learning was conceptualised with respect to two underlying dimensions: context and task. With respect to context, a three-item measure was supported, as shown in Table 11.3. Item 3 (“[Students] engaged in learning content that was relevant to them”) was shown to decrease the reliability so that the AVE was unacceptable in three of the four versions and in the combined sample cases. Hence, item 3 was excluded. Taken collectively, the context items capture the extent to which the time and place of the m-learning activities are suggested by the topic and create meaning for learners. The factor loadings and reliability measures of the final three-item measure of context are presented in Table 11.3. Authenticity with respect to task was captured by four proposed items. These items are reflective of m-learning that involves students’ working like an expert, participating in real-world activities and engaging in activities related to everyday life. The latent dimension ‘task’ is about the learning that is relevant to students’ everyday life.

11.6.3 Collaboration The collaboration dimension was considered with respect to two underlying subdimensions: conversation and co-creation. With respect to conversations, the scale which included the first item (“Talked about the work displayed on the screen with others around them”) resulted in an AVE of less than 0.5 in all versions of the survey and also in the analysis combining all sample data. For this reason, convergent validity could not be established (Fornell & Larker, 1981). When this item was dropped, however, the AVEs increased to be all above 0.5 and an acceptable level of convergent validity. In addition, the CR was above 0.7 in all cases, confirming the reliability of the items in relationship to the individual dimension (Raykov, 1997). In retrospect,

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Table 11.3 Measurement properties of authenticity dimension Context

A

B

C

D

Combined

Learned in a place suggested by the topic, e.g. learned about stars under the night sky; pollution at a local stream; History at the site of an ancient battle (1)

0.744

0.995

0.781

0.821

0.820

Learned in a realistic, virtual space, e.g. use of augmented (AR) or virtual reality (VR) apps, science simulation (2)

0.762

0.671

0.483

0.818

0.682

Engaged in learning content that was relevant to them, e.g. healthy eating (3)

–

–

–

–

–

Learned at a time suggested by the topic, e.g. 0.894 night-time observation of stars; weekend analysis of sporting performance (4)

0.666

0.876

0.711

0.811

AVE

0.64

0.63

0.54

0.62

0.60

CR

0.84

0.83

0.77

0.83

0.82

Task

A

B

C

D

Combined

Worked like an expert, e.g. collected data using 0.759 GPS like a geographer; measured using an inclinometer app like a scientist; composed music or lyrics to a song like a musician. (1)

0.562

0.769

0.852

0.752

Participated in real-world activities that benefit society, e.g. citizen science project that included real-life experts; environmental task on waste (2)

0.848

0.820

0.859

0.769

0.823

Learned serendipitously in an unplanned way, e.g. during a game, research prompted by an unexpected query (3)

0.608

0.569

0.565

0.673

0.615

Engaged in activities related to everyday life, e.g. 0.643 developing a budget (4)

0.682

0.680

0.648

0.663

AVE

0.52

0.44

0.53

0.55

0.55

CR

0.81

0.76

0.81

0.83

0.83

Entries against each item refer to standardise factor loadings; AVE = Average Variance Extracted; CR = Composite reliability Versions: A = past year with examples; B = past year without examples; C = last time with examples; D = last time without examples

the first item referred to conversing with others around the mobile device, while the three other items referred to conversing online. As such the finalised sub-dimension as measured by the three items in Table 11.4 is reflective of m-learning activities involving online conversations ‘through’ the device. The co-creation sub-dimension was measured by three items and refers to learners’ use of the mobile devices to collaboratively create digital content. The factor loadings and associated measures of reliability are presented in Table 11.4. The co-creation scale captures m-learning in terms of students working together to create, contribute and share digital content. There was sufficient convergent validity and reliability at

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Table 11.4 Measurement properties of collaboration dimension Conversations

A

B

C

D

Combined

Talked about the work displayed on the screen with others around them, e.g. talked with a friend nearby about homework shown on the screen (1)

–

–

–

–

–

Discussed the work online with their friends/peers, 0.800 0.757 0.860 0.786 0.812 e.g. discussed ideas via email, SMS, Skype, Facebook, etc. (2) Discussed the work online with people they don’t 0.890 0.635 0.666 0.720 0.664 know, e.g. discussed with a student gamer from another school, tweeted a NASA scientist, asked a question to a Brainpop mathematician (3) Communicated with others using a variety of text, image or video modes, e.g. by using SMS, Instagram, Skype (4)

0.740 0.895 0.745 0.698 0.772

AVE

0.66

0.59

0.58

0.54

0.56

CR

0.85

0.81

0.80

0.78

0.79

Co-creation

A

B

C

D

Combined

Worked together to create a digital product, e.g. co-created a video, podcast, photo, iBook, document (1)

0.735 0.696 0.634 0.881 0.737

Shared digital content, e.g. shared a video, podcast, photo, document (2)

0.856 0.928 0.744 0.834 0.879

Contributed to existing digital content, e.g. tagged a photo, commented on a blog post, played a multi-player game (3)

0.642 0.784 0.456 0.661 0.622

AVE

0.56

0.65

0.39

0.64

0.57

CR

0.79

0.85

0.65

0.84

0.79

Entries against each item refer to standardise factor loadings; AVE = Average Variance Extracted; CR = Composite reliability Versions: A = past year with examples; B = past year without examples; C = last time with examples; D = last time without examples

the aggregate level. Version C of the survey (specific task; with examples), however, showed that item 3 in this sub-dimension (‘[students] contributed to existing digital content’) was less satisfactory in being a reflective measure of co-creation.

11.6.4 Overall M-Learning Experiences An overarching measure of m-learning activities was devised to reflect teachers’ views of the experiences for students with respect to learning, enjoyment and understanding of subject material. The original measure proposed use of an item to capture difficulty in learning a subject using the mobile devices which when reversed

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Table 11.5 Overall m-learning experience Overall experience

A

B

C

D

Combined

Using mobile devices improved my Year __ students’ 0.826 0.829 0.770 0.838 0.819 learning in school (1) My Year __ students enjoyed using mobile devices to 0.730 0.736 0.603 0.737 0.704 learn about school (2) My Year __ students found it difficult learning school – using mobile devices (3) (R)

–

–

–

–

Using mobile devices helped my Year __ students to 0.892 0.875 0.887 0.717 0.839 understand concepts in school (4) Using mobile devices helped my Year __ students to practise school skills (5)

0.847 0.810 0.805 0.676 0.779

AVE

0.68

0.66

0.60

0.55

0.62

CR

0.90

0.89

0.85

0.83

0.87

(R)—Item 3 was reverse coded for the analysis. Entries against each item refer to standardise factor loadings; AVE = Average Variance Extracted; CR = Composite reliability Versions: A = past year with examples; B = past year without examples; C = last time with examples; D = last time without examples

coded would be reflective of the same overarching dimension as the other measures. However, the item did not work as intended, based on its lack of inter-item correlation with the four other items and overall dimension. In turn, the measure of overall mlearning experiences was best captured using the four items in Table 11.5, reflecting the extent to which teachers agreed that mobile devices help students learn, practise and improve their understanding of the given subject in which they had been employing mobile technologies. This forms the dependent variable later considered in the structural model of mobile pedagogy. The factor scores and measurement properties of the finalised scale for m-learning experience are presented in Table 11.5.

11.6.5 Discriminant Validity Discriminant validity is the extent to which each dimension is sufficiently different from other dimensions. While this arose as a concern in earlier forms of the scale development process, discriminant validity of the items was established using the outlined measures. This validity was confirmed as the squared correlation between any two variables and was shown to be less than their respective AVEs (Fornell & Larker, 1981) as reported in Table 11.6.

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Table 11.6 Assessment of discriminant validity Construct

CONV

Conversations (CONV)

(0.75)

COCR

AGEN

CUST

CONT

TASK

Co-creation (COCR)

0.57

(0.75)

Agency (AGEN)

0.39

0.35

(0.71)

Customisation (CUST)

0.55

0.46

0.45

(0.75)

Context (CONT)

0.50

0.46

0.46

0.62

(0.77)

Task (TASK)

0.49

0.49

0.47

0.56

0.62

(0.74)

Overall experience (OVERNEW)

0.32

0.39

0.48

0.33

0.30

0.47

OVERNEW

(0.81)

Note Diagonal entries represent sqrt(AVE); off-diagonal elements represent correlations between latent constructs

11.6.6 Differences Across Survey Versions Overall, based on establishing that construct validity and discriminant validity were acceptable for all six sub-dimensions, the final 20-item scale performed adequately in all cases when teachers were prompted to consider their general practices over the prior year (versions A and B) regardless of whether each item was presented with examples (version A) or without (version B). Further, the average AVE and CR were higher in these two versions over the versions that asked teachers to consider practices for a specific task only (versions C and D). The measure of co-creation (collaboration dimension) failed for one item (item 3 in Table 11.4) using version C based on the AVE being less than 0.05 (AVE = 0.39) and CR being less than 0.70 (CR = 0.65). Version D on the other hand passed the benchmarks for CR on all six sub-dimension scales, but marginally failed with respect to AVE in relation to agency (personalisation dimension) due to Item 2 (AVE = 0.48) and Item 4 for customisation (AVE = 0.49), as shown in the personalisation results (Table 11.2). Taken together, the recommendation in using the iPAC scales would be to use this scale with or without examples if asking teachers about their general pedagogical practices over the last year. If only asking about practices regarding a specific mlearning task, the recommendation is to use either the long or short forms (with or without examples) for gauging m-learning in terms of conversation, context or task; to offer examples when measuring agency and customisation; and offer no examples when measuring contributions to existing digital content to gauge levels of co-creation. The final validated iPAC scales, including these recommendations, are presented in the Appendix.

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11.6.7 Structural Model of M-Learning Practice and Experience A structural model was developed to consider the relationships between latent constructs with respect to each overarching dimension and to develop a measure of overall mobile pedagogical practice. The model was used to confirm that extensive use of mobile pedagogical practices by teachers would result in more significant levels of enjoyable and rewarding m-learning experiences for students. The measure of pedagogy is also considered with respect to how practices may vary by subject area and by student year. The model was estimated using a covariance-variance-based approach based on all 349 observations. The comparative fit index, CFI, was 0.935 (Bentler, 1990; Bentler & Bonett, 1980) and the Tucker-Lewis Index (TLI) was 0.923 (Tucker & Lewis, 1973) indicating acceptable levels of incremental fit (Hu & Bentler, 1999). The root-mean-square error of approximation (RMSEA) was estimated to be below 0.05 (p < 0.01 level) (Steiger & Lind, 1980), again indicating acceptable model fit (Browne & Cudeck, 1993). All parameter estimates were significant at the 0.01 level. The model results and standardised path coefficients are presented in Fig. 11.2. The results show that collaboration in m-learning activities is reflected by conversation and co-creation. On the other hand, personalisation in m-learning is slightly more

Fig. 11.2 Structural model and estimates

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reflected by elements of customisation than agency, although both dimensions are significant. Authenticity is reflected by context and task, with some dominance of context in reflecting this overarching dimension. With respect to the main overarching construct, mobile pedagogical practice (labelled ‘m-learning pedagogy’ in Fig. 11.2), it is most reflected by teachers encouraging students to undertake personalised m-learning practice, authentic m-learning practice, followed by collaborative m-learning practice. The model predicts that those who adopt such mobile pedagogical practices are likely to report positive m-learning experiences (β = 0.57; p < 0.001).

11.7 Current Use of the Surveys and Future Directions As discussed throughout this book, the iPAC Framework is designed to describe m-learning from a pedagogical perspective, with socio-cultural theory underpinning it (Kearney et al., 2012). The Framework has been embraced by practitioners and adopted for use in numerous countries throughout the world (Burden & Kearney, 2018). Earlier versions of the surveys were developed to measure teachers’ mobile pedagogies during two of our major m-learning projects (MTTEP and Optimising Mobile Pedagogies), based on the dimensions of the Framework. The empirical study reported in this chapter further developed these surveys, resulting in an iPAC scale with enhanced validity. These surveys in their current form have been found to be robust in their measurement of the key dimensions of the iPAC Framework and its sub-dimensions. Scale development indicated that the survey had strong construct validity when the versions (both using examples, and the shorter version, without examples) evaluating general use over a year are used. With the versions evaluating a specific task, some minor modifications are recommended. These comprise provision of examples for the sub-dimensions of agency and customisation, and removing examples from the sub-dimension of co-creation. The scale is presented in Appendix.

11.7.1 Current Online Surveys The authors provide validated versions of these scales in freely available online surveys for teachers and researchers to use, via the iPAC website (www.ipacmobil epedagogy.com) or the m-learning toolkit (www.mobilelearningtoolkit.com). With support of these measurement instruments, practitioners both in schools and in teacher education programs can examine and improve their m-learning designs and practices. The current online surveys contain four sections. Sections A and D collect background data (e.g. experience integrating mobile devices into teaching) and information about m-learning task(s) (e.g. discipline, age group, task location, device ownership and applications used). To reduce the length of the student survey, there

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are fewer items in these background sections. Section D contains an open-ended question asking participants to describe how apps were used. Section B is the core of the online survey and contains the validated iPAC scales: 20 Likert-scale items in the teacher version and 21 items in the student version. Items are chunked over six screens to allow participants to consider each sub-dimension of the iPAC Framework, with three or four items per screen (the agency section of the student survey has a fifth item). Figures 11.3 and 11.4 show screenshots of sample teacher and student screens of items in the online survey relevant to the authenticity dimension. Section C of the online survey consists of additional Likert-scale items developed from the literature on m-learning and innovation. The first set of five items, introduced earlier in the chapter when describing the empirical study, was designed to probe participants’ views of overall learning and enjoyment. The second set of five items was developed in our DEIMP project (introduced in Chap. 1) to elicit participants’ views of innovation, as described in Chap. 14. Figures 11.5 and 11.6

Fig. 11.3 Screenshot of iPAC items from teacher survey (specific task). Authenticity dimension. Nominated year group and subject area is Year 7 English

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Fig. 11.4 Screenshot of iPAC items from student survey (specific task). Authenticity dimension. Nominated subject area is English

show screenshots of sample teacher and student screens of items in the online survey relevant to innovation. Like earlier versions of the online survey, teachers receive an automatically generated report that presents a unique m-learning profile upon survey completion. This report contains polar charts based on responses to the iPAC scale items, indicating their level of adoption of the three mobile pedagogical dimensions, as shown in Fig. 11.7. An innovation score is also presented in the report (see Fig. 11.8), based on responses to the previously mentioned innovation items. If teachers completing the specific task survey choose to implement the corresponding student survey with their class, they receive a class report based on average scores across all students in the class. This additional report provides a perspective based on student voice (Campbell & Groundwater-Smith, 2007), thereby triangulating the data from the teacher survey and allowing teachers to compare their own perceptions with their students’ perceptions of the specific task being examined. Hence, the polar chart provided in the class report can be used alongside the chart in the original teacher report to prompt such comparisons. Finally, reflective prompts and links to resources are provided in the class report to facilitate teachers’ considerations of how they might improve their m-learning designs and practices.

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Fig. 11.5 Screenshot of innovation items from teacher survey (specific task). Nominated year group and subject area is Year 7 English

11.7.2 Plans for Further Survey Development The language and examples used in both teacher and student versions of the current surveys are tailored more towards secondary and upper primary mainstream school education contexts, reflecting the predominant contextual foci of our m-learning projects from which the instruments emerged. Further work is needed to modify the surveys to be suitable for other educational contexts, for example, teachers in early childhood and lower primary school education, and teachers of students with disabilities in both mainstream and specialist school settings. Although the survey had mostly strong construct validity when examples were used in the iPAC items, another goal is to have more age- and discipline-appropriate examples appear in these items. After participants record the specific contexts they are considering in the opening section of the surveys (e.g. age, discipline), future versions of the online survey software could draw on an extensive bank of examples and automatically insert suitable ones in the main iPAC items to match the nominated context. So, if a teacher indicates they are considering mobile pedagogies adopted in

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Fig. 11.6 Screenshot of innovation items from student survey (specific task). Nominated subject area is English Fig. 11.7 Sample screenshot of polar chart provided in the Teacher Report from (specific task) survey

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Fig. 11.8 Screenshot of sample Teacher Report from (specific task) survey, including an innovation score

a Year 4 music activity, suitable music examples will appear in the iPAC items that are relevant to Year 4 students.

11.7.3 Future Research Directions Future research will involve collection and analysis of responses from the current online surveys to further examine m-learning practices and trends across different contexts (e.g. regions, education sectors, disciplines, ages, gender, etc.). Differences between student and teacher perceptions will be explored as part of these investigations. Longitudinal studies may use the surveys to monitor changes in pedagogies over time, for example, as part of evaluations of whole school initiatives aiming to effectively adopt new and emerging mobile technologies. Data from several background items on the current surveys will allow more nuanced explorations of m-learning practices. For instance, the open-ended items about apps and their use can be linked to responses to the main iPAC items to explore relationships between technological affordances and pedagogy from a socio-cultural perspective (Wertsch, 1991). It will be interesting to ascertain how facilities offered by specific apps are used in different contexts to facilitate one or more of the signature mobile pedagogies; or indeed how adopted pedagogies might influence use of the technologies. The background survey item about use of learning spaces will allow a more extensive mapping of m-learning activities and mobile pedagogies across contexts. The ways that teachers are exploiting learning in and across physical and virtual learning spaces will also be of interest, including analysis of survey data to explore seamless learning scenarios, especially tasks that privilege learner-generated spaces, and how they intersect with their adoption of iPAC pedagogies. Investigations

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will triangulate survey data with teacher and student interview and observation data, and student-generated artefacts. Another point of interest for the research team is analysis of responses to the innovation items in the survey. These data will be useful in exploring interactions between the iPAC pedagogies and teachers’ and students’ perceptions of digital pedagogical innovation. Analysis of this data will help identify iPAC pedagogies and other contextual information about m-learning tasks that are associated with different levels of innovation—from low, sustained innovation to more radical disruption. Studies in teacher education contexts will also be important, for example, using survey data to help measure the impact of professional learning initiatives for inservice teachers, or to investigate the development of PSTs’ mobile pedagogical practices from campus-based micro-teaching scenarios, or during their school-based practicums. We envisage PSTs’ use of the task surveys to be useful in prompting postlesson reflections and discussions with supervisors, mentors and school students. Use of the survey tools to stimulate these professional learning conversations will be explored. Finally, further instruments are needed to collect other data to triangulate with survey data in future investigations of mobile pedagogies. Development will include the design of apps facilitating ‘on-the-fly’, spontaneous multimodal artefact collection from research participants such as teachers, students or parents. For example, audio recordings of ‘ah-ha’ moments from teacher or peer learning conversations, screenshots of digital artefacts created with peers and video recordings of students’ interactions with resources or with other experts. Learner-generated artefacts are needed, especially where teachers or other experts are absent from m-learning contexts. Photographs or screenshots of students’ self-generated, in situ physical and/or virtual learning contexts will also be useful. Use of journal apps could facilitate more spontaneous, in situ teacher or student reflective note-taking. Such instruments would be scaffolded around the iPAC Framework to guide observations and artefact collection.

11.8 Conclusion Most m-learning studies reveal insights into m-learning from a technical perspective or an understanding of related outcomes (e.g. learning, enjoyment, performance, etc.). However, more research is needed to provide insights into mobile pedagogical elements, and rigorous instruments are needed to facilitate these studies. Measuring m-learning from a pedagogical perspective is often overlooked in the literature, and a rigorous, formal mechanism to capture this perspective has been elusive. Our iPAC survey instruments have been developed over several years to address this problem. These validated instruments are likely to be helpful to teachers who wish to develop and design mobile activities, or are already using mobile activities and wish to evaluate where their mobile pedagogies are located in a socio-cultural learning paradigm.

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They will also be useful for educational researchers wanting to use a robust instrument to confidently interrogate m-learning phenomena. This chapter concludes the part on tools for investigating m-learning. The next part of the book examines case studies of teachers and teacher educators using the iPAC Framework.

Appendix: Final Validated iPAC Scales (The Authors Provide These Scales in Freely Available Online Surveys for Teachers and Researchers to Use, at the iPAC Website via www.ipacmobilepedagogy.com) Stem (General version): In the m-learning activities in over the past year, my students typically … Stem (Specific version): When my students in used mobile devices to learn in this activity, they … Personalisation > Agency 1. Chose the place to do the activity, e.g. chose to work on the bus, at home, in the playground 2. Determined the pace at which they did the activity 3. Decided what they wanted to learn, e.g. chose their own question, problem or project to explore Personalisation > Customisation 4. Were guided by the app(s) based on their past use, e.g. by previous game challenge levels, YouTube recommendations prompted by their previous views 5. Tailored app(s) settings to their preferences, e.g. customised location on/off, camera/microphone access, time limit settings 6. Received individualised information through the app(s) about themselves, e.g. information about the number of steps walked, calories eaten, hours slept 7. Customised feeds and links for their learning needs, e.g. tailored social media or news feeds Authenticity > Context 8. Learned in a place suggested by the topic, e.g. learned about stars under the night sky; pollution at a local stream; History at the site of an ancient battle 9. Learned in a realistic, virtual space, e.g. use of augmented (AR) or virtual reality (VR) apps, science simulation 10. Learned at a time suggested by the topic, e.g. night-time observation of stars; weekend analysis of sporting performance Authenticity > Task 11. Worked like an expert, e.g. collected data using GPS like a geographer; measured using an inclinometer app like a scientist; composed music or lyrics to a song like a musician. 12. Participated in real-world activities that benefit society, e.g. citizen science project that included real-life experts; environmental task on waste 13. Learned serendipitously in an unplanned way, e.g. during a game, research prompted by an unexpected query (continued)

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(continued) 14. Engaged in activities related to everyday life, e.g. developing a budget Collaboration > Conversation 15. Discussed the work online with their friends/peers, e.g. discussed ideas via email, SMS, Skype, Facebook, etc. 16. Discussed the work online with people they don’t know, e.g. discussed with a student gamer from another school, tweeted a NASA scientist, asked a question to a Brainpop mathematician 17. Communicated with others using a variety of text, image or video modes, e.g. by using SMS, Instagram, Skype Collaboration > Co-creation 18. Worked together to create a digital product, e.g. co-created a video, podcast, photo, iBook, document 19. Shared digital content, e.g. shared a video, podcast, photo, document 20a (General) Contributed to existing digital content, e.g. tagged a photo, commented on a blog post, played a multi-player game 20b (Specific) Contributed to existing digital content

References Basoglu, E. B., & Akdemir, O. (2010). A comparison of undergraduate students’ English vocabulary learning: Using mobile phones and flash cards. Turkish Online Journal of Educational Technology, 9(3), 1–7. Retrieved from https://eric.ed.gov/?id=EJ898010. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246. https://doi.org/10.1037/0033-2909.107.2.238. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606. https://doi.org/10.1037/0033-2909. 88.3.588. Browne, M. W., & Cudeck, R. (1993).Alternative ways of assessing model fit.Sage Focus Editions. Burden, K., & Kearney, M. (2018). Designing an educator toolkit for the mobile learning age. International Journal of Mobile and Blended Learning (IJMBL), 10(2), 88–99. https://doi.org/ 10.4018/ijmbl.2018040108. Campbell, A., & Groundwater-Smith, S. (Eds.). (2007). An ethical approach to practitioner research: Dealing with issues and dilemmas in action research. Routledge. Cavus, N., & Ibrahim, D. (2009). m-Learning: An experiment in using SMS to support learning new English language words. British Journal of Educational Technology, 40(1), 78–91. https:// doi.org/10.1111/j.1467-8535.2007.00801.x. Domingo, M., & Garganté, A. (2016). Exploring the use of educational technology in primary education: Teachers’ perception of mobile technology learning impacts and applications’ use in the classroom. Computers in Human Behavaviour, 56(1), 21–28. https://doi.org/10.1016/j.chb. 2015.11.023. Fornell, C., & Larker, D. (1981). Structural equation modeling and regression: Guidelines for research practice. Journal of Marketing Research, 18(1), 39–50. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. https://doi. org/10.1080/10705519909540118.

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Hung, J.- L., & Zhang, K. (2012). Examining mobile learning trends 2003–2008: a categorical meta-trend analysis using text mining techniques. Journal of Computer Higher Education, 24(1), 1–17. https://doi.org/10.1007/s12528-011-9044-9. Kearney, M., Burden, K., & Rai, T. (2015). Investigating teachers’ adoption of signature mobile pedagogies. Computers & Education, 80, 48–57. https://doi.org/10.1016/j.compedu.2014.08.009. Kearney, M., Burke, P., & Schuck, S. (2019). The iPAC scale: A survey to measure distinctive mobile pedagogies. TechTrends, 63(6), 751–764. Kearney, M., Schuck, S., Burden, K., & Aubusson, P. (2012). Viewing mobile learning from a pedagogical perspective. Alt-J-Research in Learning Technology, 20(1). https://doi.org/10.3402/ rlt.v20i0/14406. Knezek, G., & Khaddage, F. (2012). Bridging formal and informal learning: A mobile learning attitude scale for higher education. British Journal of Social Sciences, 1(2), 101–116. Lai, C., Hwang, G., Liang, J., & Tsai, C. C. (2016). Differences between mobile learning environmental preferences of high school teachers and students in Taiwan: A structural equation model analysis. Educational Technology Research and Development, 64, 533–554. https://doi.org/10. 1007/s11423-016-9432-y. Lin, H.-H., Lin, S., Yeh, C.-H., Wang, Y.-S., & Jansen, J. (2016). Measuring mobile learning readiness: Scale development and validation. Internet Research, 26(1), 265–287. https://doi.org/ 10.1108/intr-10-2014-0241. Quinn, C. (2000). mLearning: Mobile, wireless, in-yourpocket learning. LineZine, Fall 2000. Retrieved from http://www.linezine.com/2.1/features/cqmmwiyp.htm. Raykov, T. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement, 21(2), 173–184. https://doi.org/10.1177/01466216970212006. Steiger, J. H., & Lind, J. C. (1980, May). Statistically-based tests for the number of common factors. Handout for a presentation delivered at the meeting of the Psychometric Society, Iowa City, IA. Stockwell, G. (2007). Vocabulary on the move: Investigating an intelligent mobile phone-based vocabulary tutor. Computer Assisted Language Learning, 20, 365–383. https://doi.org/10.1080/ 09588220701745817. Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10. https://doi.org/10.1007/bf02291170. Uzunboylu, H., & Ozdamli, F. (2011). Teacher perception for m-learning: Scale development and teachers’ perceptions. Journal of Computer Assisted Learning, 27(6), 544–556. https://doi.org/ 10.1111/j.1365-2729.2011.00415.x. Uzunboylu, H., Hursen, C., Ozuturk, G., & Demirok, M. (2015). Determination of Turkish university students’ attitudes for mobile integrated EFL classrooms in North Cyprus and scale development: ELLMTAS. Journal of Universal Computer Science, 22(10), 1283–1296. Valk, J. H., Rashid, A. T., & Elder, L. (2010). Using mobile phones to improve educational outcomes: An analysis of evidence from Asia. The International Review of Research in Open and Distributed Learning, 11(1), 117–140. https://doi.org/10.19173/irrodl.v11i1.794. Wang, Y.-S. (2007). Development and validation of a mobile computer anxiety scale. British Journal of Educational Technology, 38(6), 990–1009. https://doi.org/10.1111/j.1467-8535.2006.00687.x. Wang, M., Shen, R., Novak, D., & Pan, X. (2009). The impact of mobile learning on students’ learning behaviours and performance: Report from a large blended classroom. British Journal of Educational Technology, 40(4), 673–695. https://doi.org/10.1111/j.1467-8535.2008.00846.x. Wertsch, J. V. (1991). Voices of the mind: A sociocultural approach to mediated action. Cambridge: Harvard University Press. Wu, W. H., Wu, Y. C. J., Chen, C. Y., Kao, H. Y., Lin, C. H., & Huang, S. H. (2012). Review of trends from mobile learning studies: A meta-analysis. Computers & Education, 59(2), 817–827. https://doi.org/10.1016/j.compedu.2012.03.016.

Part IV

Case Studies and Projects

Chapter 12

Mobile Pedagogies in Mathematics and Science Education

Abstract This chapter interrogates the views of teachers about their use of mobile devices for student learning. It focuses on a data sample collected from Australian teacher participants in a project funded by the Australian Research Council. Data were collected using an early version of the ‘typical/general mobile pedagogies usage’ survey instrument introduced in Chap. 11. Results from the survey suggested current trends and patterns in mobile learning and teaching in the area of mathematics and science education, and these are examined through the lens of the iPAC Framework. We present insights into how teachers implemented the different signature pedagogies of iPAC and discuss implications for enhancing mobile pedagogies. Keywords Mobile pedagogies · iPAC · M-Learning · Survey method · Mathematics education · Science education · Mathematics learning · Science learning · Mobile learning

12.1 Introduction The previous part of the book described the development of tools and instruments for educators and researchers interested in using the iPAC Framework. This part considers the impact of the Framework on practitioners from schools and teacher education. This chapter investigates Australian mathematics and science teaching through the lens of the Framework, and Chap. 13 considers how the Framework has been used internationally by school teachers and teacher educators. We consider innovative m-learning practices in Chap. 14. Much of the research literature suggests that mobile learning (m-learning) is on the increase, as mobile devices (m-devices) become more affordable and accessible for personal use and connectivity improves (Bano, Zowghi, Kearney, Schuck, & Aubusson, 2018). Yet, m-learning penetration of schools is at best patchy. Reasons for this are discussed in Chap. 3, and include lack of access, equity challenges, fears that use of m-devices will be a distraction to learning; teacher lack of confidence in Paul Burke and Peter Aubusson of the University of Technology Sydney are guest co-authors of this chapter. © Springer Nature Singapore Pte Ltd. 2020 M. Kearney et al., Theorising and Implementing Mobile Learning, https://doi.org/10.1007/978-981-15-8277-6_12

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educational uses; and teacher beliefs about the value of such use. Many concerns about these issues are not based on data but are often driven by the media (Burden, Schuck, & Kearney, 2019). The study reported in this chapter was the first stage of our major project, funded by the Australian Research Council (ARC), titled Optimising Teaching and Learning with Mobile-Intensive Pedagogies (subsequently abbreviated to Optimising Mobile Pedagogies). The project was introduced in Chap. 1 and investigated mobile technology use in secondary schools, particularly in mathematics and science education. The focus on mathematics and science education is due to the current emphasis in most developed countries on the need for more mathematics and science teachers, a desire to increase uptake of mathematics and science at higher levels of education, and aspirations to engage students in these areas (Prescott, Coupland, Angelini, & Schuck, 2020). This first stage of the project investigated how mathematics and science teachers are exploiting the affordances of m-devices for their teaching.

12.2 Background 12.2.1 Mobile-Intensive Pedagogies Studies that consider m-learning discuss several factors that are thought to enhance engagement and learning (Cristol & Gimbert, 2014). These factors include an emphasis on student agency in choosing the topic of interest, the mode of learning and the way the student chooses to conduct the learning. Another factor that is considered important is the relevance or authenticity of the task, including the setting of the task and its requirements. Authentic learning tasks have the characteristics of being seamless, coherent and integrated, and not restricted to taking place in a classroom (Schuck, Kearney, & Burden, 2017). Self-regulation also plays an important role in learning, as does meaningfulness to students of the learning activity (Burden & Kearney, 2016). As discussed in Chap. 3, m-learning is well suited to learning tasks that seek to include these factors (Cristol & Gimbert, 2014; Ng & Nicholas, 2013; Royle, Stager, & Traxler, 2014; Traxler, 2010). Indeed, the ability to personalise, collaborate and create authentic tasks underlies the mobile pedagogical framework (iPAC) developed by the authors (Kearney, Schuck, Burden & Aubusson, 2012) and discussed further in Chaps. 5 and 7. In looking at the place of mobile pedagogies in learning, this chapter focuses on the learning rather than the technology—a perspective supported by other authors in technology-enhanced learning (Nouri, Spikol, & Cerratto-Pargman, 2016; Wright & Parchoma, 2011). For example, Nouri et al. (2016) note that the emphasis on designbased learning and research should not be on the technologies but on the learning tasks themselves. The argument supporting the value of mobile-intensive pedagogies is that the drivers of effective learning that will engage students and produce valuable learning outcomes for them are activities that are personalised, relevant and offer

12.2 Background

185

opportunities for collaboration. We suggest that while use of m-devices is not the only way to facilitate such opportunities, the use of these technologies in nuanced ways can support heightened m-learning experiences for students (Kearney et al., 2012).

12.2.2 Mobile Pedagogies in Mathematics and Science We now turn our attention to mobile pedagogies in mathematics and science. Our rationale for doing so derives from the ARC project mentioned above (Optimising Mobile Pedagogies), in which our focus was on m-learning in mathematics and science. However, this focus also has support from other literature. As mobile technologies have become more prevalent in society and in education, several reviews of research on m-learning have recently been published. A systematic review of research conducted on m-learning in P/K–12 education between 2010 and 2015 revealed that science was the subject most often researched (56%), followed by literacy (21%) and mathematics (10%) (Crompton, Burke, & Gregory, 2017). Previous reviews also reported that science was the dominant domain represented in m-learning studies (Lui, Kuhn, Acosta, Niño-Soto, Quintana, & Slotta, 2014; Wu, Wu, Chen, Kao, Lin, & Huang, 2012). This may be because the field of science is based on inquiry, and m-devices provide tools that make inquiry more accessible (Crompton, Burke, Gregory, & Gräbe, 2016). Several reviews have focused solely on m-learning in mathematics or science education. In a systematic literature review of m-learning in science and mathematics in school education, Bano et al. (2018) confirmed the findings mentioned above that indicate that there are more empirical studies investigating the use of mobile technologies in science than in mathematics. Other reviews (Fu & Hwang, 2018; Suárez, Specht, Prinsen, Kalz, & Ternier, 2018) have focused on inquiry-based learning and collaborative learning. These two pedagogical approaches are among the three principal approaches (inquiry-based learning, collaboration and realistic learning) identified in the mathematics and science studies reviewed by Bano et al. (2018).

12.3 Study Design The study under discussion in this chapter aimed to provide a contemporary snapshot of Australian secondary teachers’ mobile pedagogies. An online survey was developed for this purpose, as described in the next section, with a focus on the three distinct pedagogies associated with m-learning that were identified in our Framework (see Chap. 5): personalisation, authenticity and collaboration (Kearney et al., 2012). Secondary teacher participants taught a variety of subjects but we grouped the data so that we could understand what is happening in science and mathematics teachers’

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practices, in contrast to non-STEM teachers’ practices from other disciplines (i.e. subjects not related to science, technology, engineering and mathematics). We chose to consider non-STEM respondents as opposed to non-science and non-mathematics respondents as the balance of respondents from the group of STEM teachers that did not teach mathematics and/or science was small and comprised teachers of computer science, technological and applied studies (TAS), and other related disciplines, all of which tended to be more technology-oriented. The results from this small group were unlikely to inform our investigation of how m-devices are typically used in non-technology subjects. The study discussed in this chapter uncovers understandings of current mobileintensive practices in secondary mathematics and science education in Australia, exploring the following questions: • How are Australian secondary mathematics and science teachers using mobile devices in their teaching? • What pedagogical emphases are preferred by mathematics and science teachers? • How do the pedagogies and preferences in approach compare with those of nonSTEM teachers?

12.3.1 Development of the Survey An online survey was developed to determine general patterns of mobile technology usage associated with secondary school learning. Like the development of the taskspecific survey (see Chap. 9), this general use survey was developed over several iterations. Intra-researcher validation was achieved through frequent discussions among the ARC project researchers, and with a critical friend of the ARC project (Burden). These discussions critiqued each version of the survey and considered how well items aligned with the Framework’s dimensions and the underlying socio-cultural theory. A well-developed version of the survey instrument was trialled as part of a pilot study with ten teachers. Ensuing team discussions focused on how well items elicited data relevant to the three dimensions and also considered the internal consistency of the results. These discussions and feedback from pilot survey participants informed final refinements. Final versions of the items are shown in Tables 12.1, 12.2 and 12.3 in the Findings section. The general use survey under discussion required teacher participants to consider their typical use of m-learning tasks in their teaching over the past 3 years. Like the task-specific survey, teacher participants were initially asked to select a discipline area and a cohort (e.g. Year 9 science) to consider as their chosen context when responding to items. This context was subsequently piped through by the survey software system to be included in the core survey items focusing on iPAC. For example, the stem for the core iPAC items was: “In the m-learning activities in over the past three years, my students typically: …”. The final survey comprised five items probing Australian teachers’ overall pedagogical preferences; five items about settings; and core items relating to iPAC, consisting of seven items on each of the

12.3 Study Design

187

iPAC dimensions. The survey was disseminated nationally via social media. This general use survey for teachers is freely available on the iPAC website (www.ipa cmobilepedagogy.com) and has since been updated to reflect the current scales, as described in Chap. 11.

12.3.2 Analysis Data from the core iPAC items were analysed according to the three themes of personalisation, authenticity and collaboration. Participants used an 11-point scale for these items, ranging from never (0) to every time (10), with a score of 5 being a neutral point. The mean for each of these core items was compared with the neutral point to note teachers’ perceptions of their students’ experiences as either infrequent on average (i.e. statistically significantly below 5) and denoted by ‘~’ in Tables 12.1, 12.2 and 12.3 in the Findings section; or as frequent on average (i.e. statistically significantly above 5) and denoted by ‘ˆ’ in these tables. A comparison of three teacher groups (mathematics, science and non-STEM teachers) was made across a number of measures. Teachers’ reports of their mlearning activities were considered with respect to the frequency with which they included aspects of personalisation, authenticity and collaboration. For this purpose, three sets of averages for each group of teachers were calculated for each of these three dimensions (iPAC), as reported in the top rows of Tables 12.1, 12.2 and 12.3 in the Findings section. Also, to determine whether the mean frequency of items significantly differed over the three teacher groups (mathematics, science and nonSTEM teachers), a one-way ANOVA between groups was performed on each item, as reported in the right-hand columns of Tables 12.1, 12.2 and 12.3 in the Findings. In the case of significant differences, post hoc comparisons using the Tukey-Kramer honest significant difference (HSD) test (Tukey, 1949) were also performed for each item.

12.3.3 Demographic Data The responses of 385 Australian teachers were considered in the analysis. These teachers worked in a range of school contexts: state/government schools (47.4%), independent Catholic or Catholic systemic schools (30.1%); independent religious non-Catholic schools (18.9%); and other independent schools (3.5%). These schools were located in capital cities (57%), country cities and towns (21.5%) and coastal cities and towns (20.3%) around Australia. The majority of teachers worked in coeducational schools (79%), while 8% worked in boys-only schools and 13% in girlsonly schools.

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Fig. 12.1 Quality of Wi-Fi access at teachers’ schools

The average teaching experience of teachers in the survey was 17.3 years. The majority of teachers (67%) were working in a school environment that had good (48%) or excellent (19%) access to Wi-Fi, as shown in Fig. 12.1. Most teachers (63%) were working in schools that encouraged students to bring their own m-devices, as shown in Fig. 12.2. Thirty-five percent of teachers worked under school policies that supported school ownership of devices for use either at school only (17%), or in and beyond school (18%). The results were analysed to examine differences among those teachers implementing m-learning activities in the areas of mathematics (n = 111), science (n = 115) and non-STEM areas (n = 159). The subjects that the 159 non-STEM teachers identified as the main discipline area in which they implemented m-learning activities

Fig. 12.2 Ownership of m-devices as reported by teachers

12.3 Study Design

189

Fig. 12.3 Nominated cohorts by teacher participants

were English (45%), followed by History (14%), PHDPE (11%), Languages (5%) and Religious Studies (5%). As well as a discipline, teachers were asked to choose a cohort to focus on as a context for the survey items, as previously mentioned. A breakdown of the cohorts chosen by all survey participants is shown in Fig. 12.3. Years 9 and 10 were the most frequently chosen contexts, followed by Years 11 and 12, then Years 7 and 8. Teachers were also asked five questions related to their confidence with using mobile technologies using a 7-point scale ranging from strongly disagree (1) to strongly agree (7). On average across these five measures, all three groups of teachers reported strong levels of confidence (mathematics teachers, 5.62; science teachers, 5.59; non-STEM teachers, 5.37).

12.4 Findings Our findings draw on data from closed and open-ended survey items and are presented under each of the three iPAC dimension themes. We then present data on settings used by teachers in their m-learning practices over the past 3 years, followed by their adopted broad pedagogical approaches.

12.4.1 Collaboration Co-creation (working together to create and share digital content) was a stronger feature of science and non-STEM teachers’ self-reported teaching approaches relative to mathematics teachers, as shown in Table 12.1. This is particularly true of nonSTEM teachers in relation to the sharing of digital content (item C4) and working together to create a digital product (item C3). The respective mean scores for these two items on a 7-point scale for both non-STEM teachers (5.45, 6.27) and science

2.52

3.30

2.26

2.57

C3. Work together and 2.03~ create a digital product, e.g. video, podcast, photo, iBook, document

3.72~

C4. Share digital content, e.g. video, podcast, photo, document

C5. Change or add to 1.35~ existing digital content, e.g. tagging a photo, adding a comment to a blog, creating a ‘mash-up’, and re-tweeting

C6. Network with other people, e.g. exchanges through a multi-player game or Facebook

1.94~

2.16

1.66~

C2. Participate in peer online discussions, e.g. via email, SMS, Skype, social media

3.01

3.01~

C1. Participate in peer face-to-face discussions, e.g. around an iPad screen

1.71

2.12~

1.78~

2.10~

5.30

4.45~

1.95~

3.77~

2.98~

Est.

Average of all measures referring to aspects of Collaboration

Science

Est.

SD

Mathematics

Table 12.1 Comparison of collaboration frequencies

2.65

2.58

2.77

2.81

2.57

3.22

1.85

SD

2.52~

2.90~

6.27ˆ

5.45

2.88~

3.87~

3.70~

Est.

Non-STEM

2.90

2.93

2.69

2.99

2.86

3.21

1.53

SD

2.79

11.33

25.28

49.52

8.39

2.70

23.60

F-statistic

0.06

0.00

0.00

0.00

0.00

0.07

0.00

Sig.

Difference in means

**

**

**

**

**

(continued)

c

abc

abc

abc

bc

b

abc

H.S.D

190 12 Mobile Pedagogies in Mathematics …

1.89

1.53~

Est.

1.12~

Science

Est.

SD

Mathematics 1.95

SD 2.01~

Est.

Non-STEM 2.58

SD 5.43

F-statistic 0.00

Sig.

Difference in means **

b

H.S.D

Notes: Items measured on 11-point scale from never (0) to every time (10) Activity is denoted infrequent (~) or frequent (ˆ) relative to neutral level of frequency (M = 5) Significant difference in means of (a) mathematics and science; (b) mathematics and non-STEM and (c) science and non-STEM (Tukey HSD; p < 0.05) ** p < 0.01 and * p < 0.05

C7. Interact with people they don’t normally work with, e.g. students in other schools, outside experts

Table 12.1 (continued)

12.4 Findings 191

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12 Mobile Pedagogies in Mathematics …

teachers (4.45, 5.30) were significantly higher, on average, compared to the frequency reported by mathematics teachers (2.03, 3.72). There was a much stronger emphasis by science and non-STEM teachers on their students using m-devices to collaboratively create digital products, and an even stronger emphasis on sharing these artefacts. Examples of science m-learning tasks featuring these aspects of collaboration were self-reported by teachers in some of the open-ended survey items. For example, a science teacher explained: “Students created their own Stopmotion animation using their iPad. Students also worked in groups and collaborated on Google Docs to develop their reports and ideas for the task”. Non-STEM teachers also emphasised co-creation in the open-ended survey items. For example, a visual arts teacher described features of collaboration in an m-learning task: We use Pinterest to create case studies in Visual Arts. Students can share and connect with each other easily and access resources across a variety of devices. We also have students designing lessons for their peers, posting them on YouTube and blogging about their learning.

All three groups of teachers reported that their students were infrequently using their m-devices to support online discussions and networked exchanges, as indicated in responses to items C2 (participation in peer online discussions) and C6 (networking with other people). Mathematics and science teachers’ averages were ‘significantly below neutral’ for C2 (Mmaths = 1.66; Mscience = 1.95) and C6 (Mmaths = 1.94; Mscience = 1.78). Furthermore, all three teacher groups reported in item C7 that m-learning activities involving students interacting with people they would not normally work with (e.g. students in other schools, outside experts) were significantly infrequent when compared to the neutral position. Finally, the averages across all seven measures referring to aspects of collaboration were also compared, as displayed in the top row of Table 12.1. The results show that average frequency in collaboration activities significantly differs across the three teacher groups (F(2,384) = 23.60; p < 0.0001). Both science and non-STEM teachers were found to be significantly higher in their use of collaborative m-learning activities compared to mathematics teachers (p < 0.001).

12.4.2 Personalisation Students’ self-pacing through m-learning tasks was the strongest feature of the personalisation dimension, as shown in results of item P2 in Table 12.2. Indeed, the means for item P2 for all three groups of teachers were the highest across all three dimensions (personalisation, authenticity and collaboration) in the survey. The results for both mathematics (Mmaths = 6.10) and non-STEM teachers (Mnon-STEM = 5.55) for this item indicated that students in these disciplines frequently controlled the pace of their m-learning tasks. Mathematics teachers reported a significantly higher frequency of activities in which students determined their own pace through m-learning activities as compared to science teachers (Mscience = 5.30; SD = 2.54; p < 0.05). Mathematics teachers emphasised the benefits of students’ self-pacing in

2.58

3.09

2.85

3.15

P3. They decided what they 2.97~ wanted to learn, e.g. choosing their own question or problem or project to explore

3.45~

P4. They chose how to express their thinking using the mobile device, e.g. text, diagram, annotated image, narrated animation

P5. They chose and 2.23~ customized their own apps on the mobile device to support their learning

P6. The m-learning activity was typically tailored and adapted to their individual preferences

3.93~

2.74

6.10ˆ

P2. They determined their own pacing through the m-learning activity

2.81

3.84~

P1. They chose and controlled when and where they did the m-learning activity

2.00

3.73~

3.81~

2.61~

4.86

3.49~

5.30

3.84~

3.87~

Est.

Average of all measures referring to aspects of Personalisation

Science

Est.

SD

Mathematics

Table 12.2 Comparison of personalisation frequencies

2.65

2.80

2.86

2.52

2.54

2.60

1.85

SD

4.71

3.06~

5.47ˆ

4.56~

5.55ˆ

4.25~

4.42~

Est.

Non-STEM

2.94

3.26

2.80

2.63

2.51

2.62

1.52

SD

3.94

2.53

16.07

13.40

2.87

1.11

4.62

F-statistic

0.02

0.08

0.00

0.00

0.06

0.33

0.01

Sig.

Difference in means

*

**

**

*

(continued)

bc

b

ab

bc

a

bc

H.S.D

12.4 Findings 193

3.39

3.20~

Est.

3.60~

Science

Est.

SD

Mathematics 2.95

SD 3.31~

Est.

Non-STEM 3.22

SD 0.48

F-statistic 0.62

Sig.

Difference in means H.S.D

Notes: Items measured on 11-point scale from never (0) to every time (10) Activity is denoted infrequent (~) or frequent (ˆ) relative to neutral level of frequency (M = 5) Significant difference in means of (a) mathematics and science; (b) mathematics and non-STEM and (c) science and non-STEM (Tukey HSD; p < 0.05) ** p < 0.01 and * p < 0.05

P7. They used the device to receive instant feedback adapted to their context. e.g. real-time weather; fitness ‘apps’; QR codes in a museum

Table 12.2 (continued)

194 12 Mobile Pedagogies in Mathematics …

12.4 Findings

195

their open-ended responses. Two sample responses were: “They can go at the own pace and be provided with choices that better suit their learning style”; and “It allows students to work at their own pace through the material”. There was also a significant difference between mathematics and science means for item P4 relating to students’ choice of how to express their thinking in their m-learning activities. Mathematics teachers reported that their students infrequently chose how to express their thinking using the m-device (Mmaths = 3.45; SD = 3.09) as compared to both science (Mscience = 4.86; SD = 2.86; p < 0.001) and nonSTEM teachers (Mnon-STEM = 5.47; SD = 2.80; p < 0.0001). Two science teachers commented on the benefits of this type of student agency: “The fact that students are able to work at their own pace and choose how they wish to show what they know [is a benefit]”; and“[Students can] control their work and choose how to present tasks”. There was a surprisingly large significant difference between the non-STEM teachers and the mathematics and science teachers’ perceptions for item P3 relating to students’ choice over what they learn (e.g. choosing their own question or problem to investigate). The means for this item were significantly higher for the non-STEM teachers (Mnon-STEM = 4.56), compared to the mathematics (Mmaths = 2.97) and science (Mscience = 3.49) teachers. The oft-cited ‘anywhere, anytime’ phenomenon in m-learning, as discussed in Chap. 3, informed the design of item P1, where teachers were asked about the level of student control over ‘where and when’ they participated in their m-learning tasks. The low means for all three groups indicated students’ infrequent control over the context of their learning. Another point of interest was the generally low mathematics and science means for item P5 (Mmaths = 2.23; and Mscience = 2.61), which probed the level of student control over their choice and use of apps. Finally, the average across all seven measures referring to aspects of personalisation was compared, as displayed in the top row of Table 12.2. All three groups were deemed as infrequently engaged in these personalised m-learning activities, although the overall averages for the personalisation dimension were higher than the overall averages for the other two dimensions (collaboration and authenticity). Of the three groups, non-STEM teachers reported significantly higher levels of engagement in activities that captured some form of personalisation (Mnon-STEM = 4.42; SD = 1.52) as compared to mathematics teachers (Mmaths = 3.73; SD = 2.00; p < 0.001) and science teachers (Mscience = 3.87; SD = 1.85; p < 0.01).

12.4.3 Authenticity There were numerous significant differences between mathematics and science teachers’ mobile pedagogical practices in the authenticity theme. Students’ use of m-devices to work in similar ways to real practitioners was a strong feature of science and non-STEM teachers’ reported pedagogical approaches, as indicated in the results for item A3 (see Table 12.3). The science teachers’ mean frequency of m-learning

2.32

2.98 2.45

1.95

1.74~

A3. Work in a similar way to 3.98~ a practitioner

A4. Communicate with 1.71~ others using a variety of text, image or video modes

1.23~

1.49~

A2. Learn in a realistic, virtual space beyond the school’s formal online spaces

A5. Participate in genuine, real-world community activities

A6. Experience unplanned, incidental learning events

2.23

2.59

2.16~

A1. Learn in a real-world, physical setting outside the classroom

1.73

2.13~

2.34~

2.30~

3.01~

4.81

3.34~

3.73~

3.37~

Est.

Average of all measures referring to aspects of Authenticity:

Science

Est.

SD

Mathematics

Table 12.3 Comparison of authenticity frequencies

2.71

2.45

2.81

2.84

2.85

2.78

1.91

SD

2.45~

2.64~

3.57~

4.69

3.62~

3.55~

3.63~

Est.

Non-STEM

2.69

2.80

3.14

2.86

2.93

3.14

1.55

SD

5.10

11.08

13.97

2.76

16.61

10.28

20.00

F-statistic

0.01

0.00

0.00

0.06

0.00

0.00

0.00

Sig.

Difference in means

*

**

**

**

**

**

(continued)

ab

ab

ab

a

ab

ab

ab

H.S.D

196 12 Mobile Pedagogies in Mathematics …

2.80

4.10~

Est.

2.58~

Science

Est.

SD

Mathematics 2.83

SD 4.87

Est.

Non-STEM 2.90

SD 21.29

F-statistic 0.00

Sig.

Difference in means **

abc

H.S.D

Notes: Items measured on 11-point scale from never (0) to every time (10) Activity is denoted infrequent (~) or frequent (ˆ) relative to neutral level of frequency (M = 5) Significant difference in means of (a) mathematics and science; (b) mathematics and non-STEM and (c) science and non-STEM (Tukey HSD; p < 0.05) ** p < 0.01 and * p < 0.05

A7. Link new information and concepts to their ‘real lives’

Table 12.3 (continued)

12.4 Findings 197

198

12 Mobile Pedagogies in Mathematics …

activities of these types (Mscience = 4.81) was significantly higher than the mathematics teachers’ mean (Mmaths = 3.98; p < 0.0001). There was evidently a strong emphasis by science teachers on students using their m-devices to ‘work like a scientist’. In the open-ended responses, science teachers gave illustrations of this aspect of authenticity in their m-learning task designs. One science teacher said the students used “free apps … bridge builder and meters and data loggers, making dropout lenses to turn smartphones into digital microscopes”, while another mentioned that students used “iPhones to photograph cells dividing through a microscope. Then printed out the picture and found out how many cells are in each stage of cell division”. When asked about the benefits of m-learning for their students, science teachers also emphasised aspects of authentic learning, including making links with real scientists, the ability to conveniently analyse data, and simulation of real-world applications. Examples of benefits reported by science teachers were: “Access to real world data and simulations not possible to perform in a classroom”; “Better simulations of real world applications”; “Access to current theories and breaking news in the topics … linking up with everyday working scientists”; and “The ability to capture (photo and video) real events/experiences that then can be analysed”. Another significant difference between science and mathematics teachers occurred with respect to the frequencies with which their students linked new information and concepts to their ‘real lives’, as indicated in the results for item A7. Science teachers (Mscience = 4.10; SD = 2.83) reported a significantly higher frequency than mathematics teachers (Mmaths = 2.58; SD = 2.80; p < 0.0001) for item A7. Additionally, the non-STEM teachers’ mean for this item (Mnon-STEM = 4.87) was also significantly higher than the mathematics teachers’ mean. In this way, science and non-STEM teachers perceived their m-learning tasks were generating more relevance and meaning for their students. Two science teachers described how students learned in sample tasks: Students had installed a seismograph app on their phone and placed it on top of a sand bed. When the sand was hit by a weight, the students could see the waves picked up on the app. Students were able to relate to worldwide earthquake monitoring systems. My students used Minecraft to create houses with all lights operated by a light switch and also a safety switch. … students had to use their knowledge of series and parallel circuits to also create the right circuit. It really made students think about the logistics of wiring a house and the constraints of space when designing something.

Also of note were the low means for all three groups’ responses to item A5, which probed to what extent the students’ m-learning practices were part of genuine, realworld community activities (Mmaths = 1.23; Mscience = 2.30; Mnon-STEM = 2.64). The mathematics teachers’ mean for this item was the second lowest in the whole survey. Finally, the three sets of averages for the authenticity dimension are reported in the top row of Table 12.3. The results from all three groups indicate that students infrequently experienced aspects of authentic m-learning. In particular, mathematics teachers reported significantly lower frequencies of students engaging in aspects of authentic learning (Mmaths = 2.13; SD = 1.73) as compared to science (Mscience = 3.37; SD = 1.91; p < . 0001) and non-STEM teachers (Mnon-STEM = 3.63; SD = 1.55; p < . 0001).

12.4 Findings

199

12.4.4 Location of M-Learning Activities Teachers were asked to report on the frequency with which they implemented mlearning activities with respect to various locations. Frequency was captured on a 7-point scale ranging from very infrequently (1) to very frequently (7). As compared to a neutral score of 4, all three groups of teachers were significant in the frequency with which their m-learning activities were reported to occur in the classroom and at home (see Table 12.4). Responses to items S1 (classroom) and S4 (home) produced the highest means for this set of questions for both mathematics (MS1 = 5.01; MS4 = 4.90) and science teachers (MS1 = 5.25 and MS4 = 4.96), with these means all ‘significantly above neutral’. In contrast, the means for item S5, asking about the use of more learner-generated ‘informal settings’ (bus, coffee shop, etc.), were the lowest in this set of questions for both mathematics (MS5 = 1.67) and science (MS5 = 1.58) teachers. In this way, traditional school-based classrooms and homework settings were clearly dominating mathematics and science teachers’ task designs in terms of where their m-learning tasks were enacted. Learner-controlled, learner-generated informal settings were generally not part of their practices. Table 12.4 indicates that the frequency of implementation is significantly high in the classroom and at home. However, Tables 12.1, 12.2 and 12.3 show that levels of personalisation, authenticity and collaboration are significantly lower. Hence, while in-class and home use are high, distinctive mobile pedagogical occurrences are the exception not the norm.

12.4.5 Overarching Approaches Teachers were also asked to report on the frequency of implementing m-learning activities with respect to their adopted broad pedagogical approach, on a 7-point scale (see Table 12.5). As reported in Table 12.5, non-STEM teachers’ scores were significant in their average frequency of using inquiry-based (Mnon-STEM = 5.66; SD = 1.79) and projectbased learning approaches (Mnon-STEM = 5.66; SD = 1.53) as compared to both science and math teachers (p < 0.0001). Science teachers were similarly frequent users of inquiry (Mscience = 5.21; SD = 1.54) and project-based learning (Mscience = 4.50; SD = 1.91) in their m-learning activities and recorded significant differences to mathematics teachers for these items (O1 and O2). Mathematics teachers were found to be more significant users of drill-and-practice approaches (Mmaths = 4.78; SD = 2.02) as well as direct instruction (Mmaths = 4.41; SD = 2.06) in their m-learning activities. In the case of drill and practice, science and non-STEM teachers were significantly less likely to use this approach relative to mathematics teachers and their use was deemed infrequent based on their average reported uses. This tendency of mathematics teachers towards adopting directed approaches was also evidenced in their open-ended responses. For example, one

1.57

2.12

2.33~

Semi-formal setting 1.86~ outside school, e.g. excursion site, museum

4.90ˆ

1.67~

At home

Informal setting, e.g. on bus, in coffee shop, etc.

1.58~

4.96ˆ

2.20~

2.44~

5.25ˆ

1.21

1.71

1.59

1.61

1.64

SD

1.72~

5.02ˆ

2.58~

2.75~

5.54ˆ

Est.

Non-STEM

0.11

0.14

0.14

0.15

0.12

SD

0.33

0.13

6.30

2.07

3.46

F-stat.

0.72

0.87

0.00

0.13

0.03

Sig.

Difference in means

**

*

b

b

H.S.D

Notes: Items measured on 7-point scale ranging from very infrequently (1) to very frequently (7) Activity is denoted as infrequent (~) or frequent (ˆ) relative to neutral level of frequency (M = 4) Significant difference in means of (a) Mathematics and Science; (b) Mathematics and non-STEM and (c) Science and non-STEM (Tukey HSD; p < 0.05) ** p < 0.01 and * p < 0.05

1.42

1.87

5.01ˆ

Out of the classroom but at school, e.g. hall, playground

1.78

Est.

SD

Est.

In the classroom

How frequently do you implement mobile learning activities:

Science

Mathematics

Table 12.4 Comparison of frequency of settings for mobile learning activities

200 12 Mobile Pedagogies in Mathematics …

12.4 Findings

201

Table 12.5 Comparison of frequency of mobile learning approaches Mathematics

Science

Est.

SD

Est.

SD

Non-STEM

Difference in means

Est.

SD

F-stat. Sig.

H.S.D

Inquiry

4.23

1.94

5.21ˆ

1.54 5.66ˆ

1.53

24.25

0.00 **

abc

Project-based learning

3.31~

2.03

4.50ˆ

1.91 5.14ˆ

1.79

30.88

0.00 **

abc

Problem-based learning

3.98

2.03

4.36ˆ

1.70 4.25

1.92

1.19

Drill and practice

4.78ˆ

2.02

3.43~ 1.96 3.00~

1.96

27.51

Direct instruction

4.41ˆ

2.06

3.92

1.91 4.03

2.06

1.91

Student reflection

3.14~

1.94

3.38~ 1.85 4.25

1.94

12.83

0.00 **

bc

Blogging

1.47~

1.25

1.64~ 1.25 2.26~

1.66

11.73

0.00 **

bc

Game-based learning

3.33~

2.15

2.86~ 1.79 2.76~

2.08

2.81

0.06

b

Formative assessment

3.87

2.05

4.17

2.00 4.52ˆ

1.94

3.47

0.03 *

b

Summative assessment

3.06~

2.01

3.37~ 2.18 4.54ˆ

2.06

19.37

0.00 **

bc

Students’ self-assessment

3.29~

2.09

3.14~ 1.92 3.86

2.01

4.95

0.01 **

bc

Activities frequency-Type:

0.30 0.00 **

ab

0.15

Notes: Items measured on 7-point scale ranging from very infrequently (1) to very frequently (7) Activity is denoted as infrequent (~) or frequent (ˆ) relative to neutral level of frequency (M = 4) Significant difference in means of (a) Mathematics and Science; (b) Mathematics and non-STEM and (c) Science and non-STEM (Tukey HSD; p < 0.05) ** p < 0.01 and * p < 0.05

mathematics teacher said that the main benefit of m-learning for mathematics students was ‘receiving self-paced direct instruction for mathematics’.

12.4.6 Summary of Findings A synopsis of results showing similarities and differences between mathematics and science teachers’ m-learning practices is presented in Tables 12.6 and 12.7. The main similarity between the two groups’ m-learning practices was their choice of traditional settings for their students’ learning, with evidently little consideration given to

202

12 Mobile Pedagogies in Mathematics …

Tables 12.6 Featured similarities between mathematics (n = 111) and science teachers’ (n = 115) m-learning practices Dimensions

Similarities in mathematics and science teachers’ m-learning practices

Collaboration

Infrequent use of online conversations and networked exchanges

Personalisation

Infrequent content control and app choice/customisation

Authenticity

–

Settings

Frequent use of classroom/home settings Infrequent use of semi-formal/informal settings

Tables 12.7 Featured differences between mathematics (n = 111) and science teachers’ (n = 115) m-learning practices Dimensions

Differences in Mathematics and Science teachers’ m-learning practices

Collaboration

Higher co-creation (in Science) Higher sharing of content (in Science)

Personalisation

Higher self-pacing (in Mathematics) Higher choice for expressing thinking (in Science)

Authenticity

Higher similarity to practitioners (in Science) Higher linking of concepts to real-life (in Science) Higher multimodal communication (in Science) Higher use of realistic physical or virtual settings (in Science) Higher use of real-world community activities (in Science) Higher incidental learning (in Science)

Overarching approaches

Higher use of Drill and Practice (in Mathematics) Higher use of Inquiry and Project-based learning (in Science)

semi-formal and informal learning spaces, as shown in Table 12.6. Comparing quantitative survey data relevant to all three mobile pedagogies (personalisation, authenticity and collaboration), the biggest range of differences between mathematics and science teachers occurred in the authenticity theme, as shown in Table 12.7. There were also major differences in choice of broader pedagogical approaches. Mathematics teachers revealed a preference for didactic, drill and practice approaches, while science teachers preferred inquiry and project-based learning approaches.

12.5 Discussion All three groups of teachers generally restricted students’ use of m-devices to communicate and exchange information online with peers, either inside or outside of their class groups. There were also low levels of communication with people unknown to students. A contributing factor to these results may be the duty-of-care and cybersafety concerns of schools, for instance, apprehension about online ‘stranger danger’,

12.5 Discussion

203

as well as the varying quality of Wi-Fi access in schools, as shown in the demographic data (see Fig. 12.1). Students’ self-pacing was a feature of all three teacher group results, perhaps indicating that it is one of the easier (or more feasible) mobile pedagogies to enact, or at least to adapt from more conventional pedagogies. Other aspects of mobile technology-mediated personalisation were less evident. Students evidently had little control over their choice of apps and lacked opportunities to customise settings to support their own learning. This may be related to stringent, inflexible school policies reported by teachers in the open-ended survey items, especially guidelines that encourage schools to ‘lock down’ apps and laptop software, even in BYO schools. The lack of mathematics and science students’ control over task content was noteworthy, including a lack of autonomy over what they learn, for instance, in choosing their own question or problem to investigate. This finding was in contrast to the significantly greater control over task content afforded to non-STEM students. This result was most unexpected for science teachers, given their preference for inquiry approaches in their m-learning task designs (see Table 12.5) and the emphasis in science education on student autonomy associated with these approaches, especially guided and open inquiry (Banchi & Bell, 2008). Further research is needed to investigate these anomalies in science teachers’ inquiry-based m-learning practices. Despite the rhetoric in the m-learning literature around learner-generated contexts, findings indicate a lack of student control (for all three groups) over their context for learning (see Table 12.2). Indeed, there was a tendency for all three groups of teachers to predominantly use traditional school-based classrooms to enact their m-learning tasks. Given that teacher participants in this survey were confident and probably enthusiastic about m-learning, one might expect these teachers to be placing a greater emphasis on exploiting a wider range of learning environments in their mlearning practices. However, perhaps this trend was somewhat unsurprising, given the scheduled nature of school timetables and conventional formal classroom settings that typically influence the contexts of students’ and teachers’ work environments. The low levels of genuine community activities evident for all three teacher groups could be linked to the lack of external communication and networked learning discussed previously in the collaboration theme. These low ratings were again particularly surprising in science education, given science teachers’ preferences for inquiry and project-based learning approaches (as evidenced in Table 12.5) that might otherwise easily articulate into real-world community engagement. Open-ended responses suggested some possible drivers of teachers’ m-learning practices, although these would need to be confirmed in future studies. The first such driver is the teachers’ overarching pedagogical preferences. Mathematics teachers were emphasising more directed approaches in their task designs (see Table 12.5), affecting their students’ tendencies to use drill-and-practice apps, with less emphasis on creative and collaborative initiatives. Or as one teacher said: Until mathematics moves away from the current philosophy of drill and practice and more toward problem solving and allowing our students to create their own learning, I think using mobile devices in the classroom will be restricted to online textbooks and programs like ‘hot maths’.

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12 Mobile Pedagogies in Mathematics …

In contrast, science and non-STEM teachers had a much stronger preference for inquiry and problem-based approaches, so to some extent it was expected that they would be using more creative and collaborative approaches. Although as previously noted, it was somewhat contradictory that science teachers with a strong preference for inquiry approaches were granting limited student control over the learning content, the learning context and even the apps being used. Other possible factors driving teachers’ m-learning practices are school-based policies and curriculum guidelines. Teachers commented on how policies affected the level of autonomy afforded to students (and teachers) over the types of apps that students had access to. These policies can indirectly affect students’ learning experiences, such as creativity and collaboration. Regarding school policies, a teacher responded: “A number of mobile devices are simply set up to be consumers of content rather than creators of content”. Additionally, many mathematics teachers commented on the over-emphasis on curriculum content and testing regimes and the undesirable influence of these factors on their m-learning practices: The curriculum is a constraint. There is a lot of maths content to fit in and students are in general underprepared for the pace that is required – therefore needing more instruction/assistance and hence less time to do deep application tasks, to data collect and analyse, etc. A big pity! There is an expectation of high student study scores in end of year exams and the constant emphasis on the improvement and growth of these scores means that focusing on the delivery of content and preparation for exam overrides the use of mobile learning devices being used proactively.

These findings have implications for teachers’ professional development. More work is needed to support the change of mindset needed for teachers to design tasks that exploit more malleable learning contexts, including activities that use students’ ability to seamlessly learn with their devices across multiple formal and informal boundaries (Schuck et al., 2017). Teachers also need assistance to help address other areas of concern identified in this survey, including the design of more collaborative, networked online learning tasks; and m-learning activities that leverage students’ agency over content and app choice and facilitate ways to express their thinking. In particular, mathematics teachers evidently need more specific support with designing and enacting m-learning tasks. Although mathematics teachers emphasised students’ self-pacing in their mlearning practices, they might be able to leverage the use of m-devices to consider other signature mobile approaches, especially more generative, mobile technologymediated collaborative practices involving learners’ co-creation and sharing of digital content. Mathematics teachers should be encouraged to consider ways to improve their students’ authentic learning experiences, including the design of more meaningful tasks for students that leverage more realistic, discipline-specific ways of working. New diagnostic and survey tools for teachers and students, as discussed in Chap. 11, will assist teachers in accurately evaluating trials of new initiatives that address these areas of concern. These tools can be accessed via https://www.ipacmo bilepedagogy.com/

12.6 Conclusion

205

12.6 Conclusion Teachers are making daily choices about what digital technologies they employ in teaching and how their students use these tools (Aubusson, Burke, Schuck, Kearney, & Frischknecht, 2014; Burke, Schuck, Aubusson, Kearney, & Frischknecht, 2018). These decisions need to be based on sound empirical research that accounts for changing contexts, and new national curricula and pedagogies. This chapter has discussed research that contributes to the evidence base informing mobile pedagogical decisions in this complex and changing landscape. The study captures a snapshot of Australian teachers’ contemporary use of mobile pedagogies and highlights gaps between the rhetoric around m-learning and current practices. It also highlights nuanced similarities and differences between mathematics and science teachers’ current practices, and points to specific areas for their professional development. The next chapter will examine specific cases showing how the iPAC Framework has been used by educators in schools and teacher education.

References Aubusson, P., Burke, P., Schuck, S., Kearney, M., & Frischknecht, B. (2014). Teachers choosing rich tasks: The moderating impact of technology on student learning, enjoyment, and preparation. Educational Researcher, 43(5), 219–229. Banchi, H., & Bell, R. (2008). The many levels of inquiry. Science & Children, 46(2), 26–29. Bano, M., Zowghi, D., Kearney, M., Schuck, S., & Aubusson, P. (2018). Mobile learning for science and mathematics school education: A systematic review of empirical evidence. Computers & Education, 121, 30–58. https://doi.org/10.1016/j.compedu.2018.02.006. Burden, K., & Kearney, M. (2016). Conceptualising authentic mobile learning. In D. Churchill, J. Lu, T. Chiu, & B. Fox (Eds.), Mobile learning design: Theories and application (pp. 27–42). Singapore: Springer. Burden, K., Schuck, S., & Kearney, M. (2019, January). Should we be concerned about mobile devices in the classroom: What does the evidence say? Impact. Journal of the Chartered College of Teachers, Special Issue. Retrieved from https://impact.chartered.college/article/mobile-devicesschools-really-innovative-what-does-evidence-say/. Burke, P., Schuck, S., Aubusson, P., Kearney, M., & Frischknecht, B. (2018). Exploring teacher pedagogy, stages of concern and accessibility as determinants of technology adoption. Technology, Pedagogy & Education., 27(2), 149–163. https://doi.org/10.1080/1475939X.2017.1387602. Cristol, D., & Gimbert, B. (2014).Academic achievement in BYOD classrooms.Journal of Applied Learning Technology,4(1), 24–30.https://doi.org/10.5339/qproc.2013.mlearn.15. Crompton, H., Burke, D., Gregory, K., & Gräbe, C. (2016). The use of mobile learning in science: A systematic review. Journal of Science Education and Technology, 25(2), 149–160. https://doi. org/10.1007/s10956-015-9597-x. Crompton, H., Burke, D., & Gregory, K. H. (2017). The use of mobile learning in PK–12 education: A systematic review. Computers & Education, 110, 51–63. https://doi.org/10.1016/j.compedu. 2017.03.013. Fu, Q.-K., & Hwang, G.-J. (2018). Trends in mobile technology-supported collaborative learning: A systematic review of journal publications from 2007 to 2016. Computers & Education, 119, 129–143. https://doi.org/10.1016/j.compedu.2018.01.004.

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Kearney, M., Schuck, S., Burden, K., & Aubusson, P. (2012). Viewing mobile learning from a pedagogical perspective. Research in Learning Technology, 20(1). https://doi.org/10.3402/rlt. v20i0/14406. Lui, M., Kuhn, A., Acosta, A., Niño-Soto, M. I., Quintana, C., & Slotta, J. D. (2014). Using mobile tools in immersive environments to support science inquiry. Paper presented at the CHI’14 Extended Abstracts on Human Factors in Computing Systems. Ng, W., & Nicholas, H. (2013). A framework for sustainable mobile learning in schools. British Journal of Educational Technology, 44(5), 695–715. https://doi.org/10.1111/j.1467-8535.2012. 01359.x. Nouri, J., Spikol, D., & Cerratto-Pargman, T. (2016). A Learning activity design framework for supporting mobile learning. Designs for Learning, 8(1), 1–12. Prescott, A., Coupland, M., Angelini, M., & Schuck, S. (2020, in press). Making school maths real: The story of the maths inside project. Ch 2. Dordrecht: Springer. Royle, K., Stager, S., & Traxler, T. (2014). Teacher development with mobiles: Comparative critical factors. Prospect, 44, 29–42. Schuck, S., Kearney, M., & Burden, K. (2017). Exploring mobile learning in the Third Space. Technology, Pedagogy and Education, 26(2), 121–137. https://doi.org/10.1080/1475939X.2016. 1230555. Suárez, Á., Specht, M., Prinsen, F., Kalz, M., & Ternier, S. (2018). A review of the types of mobile activities in mobile inquiry-based learning. Computers & Education, 118, 38–55. Traxler, J. (2010). Students and mobile devices. Alt-J, 18(2), 149–160. Tukey, J. W. (1949). Comparing individual means in the analysis of variance. Biometrics, 5(2), 99–114. Wright, S., & Parchoma, G. (2011). Technologies for learning? An actor-network theory critique of ‘affordances’ in research on mobile learning. Research in Learning Technology, 19(3). https:// doi.org/10.3402/rlt.v19i3.17113. Wu, W.-H., Wu, Y.-C. J., Chen, C.-Y., Kao, H.-Y., Lin, C.-H., & Huang, S.-H. (2012). Review of trends from mobile learning studies: A meta-analysis. Computers & Education, 59(2), 817–827. https://doi.org/10.1016/j.compedu.2012.03.016.

Chapter 13

Use of the iPAC Framework in Schools and Teacher Education

Abstract This chapter draws on the experiences of teacher educators, teachers and school leaders in schools and universities in Australia, Norway, Germany, UK and Colombia, as they learned about and began to implement the iPAC Framework in their practice. Apart from the teachers in Colombia, all the practitioners whose experiences are discussed in this chapter were initially participants in the Mobilising and Transforming Teacher Educators’ Pedagogies (MTTEP) project, which was discussed earlier in this book. Therefore, this chapter is a case study of the impact of iPAC on educators’ thinking, attitudes and behaviours related to mobile learning in schools and in institutions of initial teacher education (ITE). Keywords Case studies · MTTEP · Impact · iPAC · Teachers · Teacher educators · M-learning

13.1 Introduction In Chap. 9, we explored how the iPAC Framework was operationalised in the Mobilising and Transforming Teacher Educators’ Pedagogies project (MTTEP) to develop and provide a mobile learning toolkit for educators. Building on the original iPAC theoretical research by Kearney, Schuck, Burden, and Aubusson (2012) reprised in Chap. 5, the MTTEP project was underpinned by subsequent research undertaken by the authors of this book that revealed how infrequently mobile technologies were used both in schools and in institutions of initial teacher education (ITE) to support and enhance the quality of teaching and learning. This lack of effective mobile learning (m-learning) in schools and teacher education occurred despite the emergence of convincing research-based evidence demonstrating the benefits of mlearning in these sectors (Burden & Kearney, 2016, 2017). Our own empirical studies undertaken between 2012 and 2019 indicate this deficit to be a multifaceted challenge and highlight the need for interventions to be undertaken at multiple levels that include the individual, the institution and the system. At the level of the individual, our research identified that teachers and teacher educators:

© Springer Nature Singapore Pte Ltd. 2020 M. Kearney et al., Theorising and Implementing Mobile Learning, https://doi.org/10.1007/978-981-15-8277-6_13

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• continue to think and conceptualise the use of mobile technologies in a limited or negative manner that focuses on a narrow range of pedagogical practices (Kearney, Burden, & Rai, 2015; Burden & Kearney, 2016); • lack confidence and the necessary skills to use mobile technologies to enhance or change their pedagogical practices (Burden & Kearney, 2017); • use mobile technologies infrequently in their practice and only for a limited range of pedagogical activities (Burden, Kearney, Schuck, & Hall, 2019). At the institutional level, our research (Burden & Kearney, 2017; Bano, Zowghi, Kearney, Schuck, & Aubusson, 2018) suggests that many schools and institutions of ITE • do not support the use of mobile technologies or support their use in very limited ways; • lack the necessary resources and understanding to support the professional learning of their staff in the effective use of mobile technologies. And at the systemic level, our research (Kearney, Burden, & Rai, 2015; Burden & Kearney, 2017) and that of others around the world indicates that decision and policymakers are often ignorant or unsure about the genuine benefits of using mobile technologies and are therefore reluctant or hesitant to sanction their wider deployment and implementation. The MTTEP project was designed to address many of the problems and challenges identified in this emerging body of research, and it resulted in the design and development of a bespoke m-learning toolkit (see Chap. 9 for full details) for teacher educators and teachers. It was developed over a period of 3 years (2014–2017), and along with other practical instantiations of the iPAC Framework it has been used extensively by educators in and beyond Europe, where it was originally tested and refined. This chapter focuses on the experiences and learning of both teachers in schools and teacher educators in institutions of ITE who have used the iPAC Framework and the m-learning toolkit primarily for their own professional development (for which it was developed) and also with their respective students, who include pupils in schools and pre-service teachers (PSTs).

13.2 The Nature of Impact We are aware that the iPAC Framework and its various operationalised representations, such as the m-learning toolkit (see www.mobilelearningtoolkit.com), have been used extensively by educators and institutions of ITE around the world. User data collected from the project website indicates that the m-learning toolkit has been accessed in over 40 countries, with at least 20 institutions (schools and universities) having used it, or parts of it, to support the professional development and education of PSTs and in-service teachers. Most of the users are teachers and teacher educators,

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so this chapter focuses on these two particular groups in order to explain the impact that the iPAC Framework and its various instantiations has had on their practices. The word ‘impact’ is something of a weasel term: easy to state but difficult to define. As academic researchers, we are all under pressure to demonstrate the benefits and worth of our research, and in countries such as the UK and Australia this has generated considerable policy discussion and debate to the extent that impact beyond academia is now an integral element of the respective research evaluation exercises in these countries (for the UK see https://www.ref.ac.uk; for Australia see https://www. education.gov.au/assessing-engagement-and-impact-university-research). However, there is still considerable misunderstanding about what constitutes or defines impact, even within academia itself, and it is often confused with dissemination events such as conferences, website usage, social media outputs or public engagements such as lectures and community workshops. Each of these events may eventually result in impact beyond academia (a definition of research impact used in the UK by REF21) but they are not impacts in their own right; rather they are pathways to impact. Professor Mark Reed (2018), an international expert in research impact, puts this more eloquently: We still have to draw people close enough to our work to actually see the insights, appreciate their relevance, and turn them into knowledge they can use. Typically, this means we have to cradle the flame and carry it to people, rather than just hope that people will be drawn to the light. (p.4)

This notion of ‘carrying’ the knowledge and findings our research has uncovered to a wider public audience was at the heart of MTTEP project and its successor, the Designing and Evaluating Innovative Mobile Pedagogies project (DEIMP—see Chap. 14). It was also central to our Australian Research Council (ARC) Discovery Project titled Optimising Teaching and Learning with Mobile-Intensive Pedagogies (subsequently abbreviated to Optimising Mobile Pedagogies) that investigated the complex factors that promote or inhibit quality teaching and learning with mobile technologies in secondary schools in Australia. All three of these projects were introduced in Chap. 1 and were underpinned by the iPAC Framework. They focused on teacher educators in institutions of ITE and on teachers in schools. These were the primary target consumers and beneficiaries of our research on m-learning, although PSTs were the ultimate target in the MTTEP and DEIMP projects, based on the assumption that enhancing and transforming the attitudes and behaviours of teacher educators towards m-learning would in turn impact on their PSTs in a similar fashion. Our approach to impact, therefore, is one that can best be described as ‘relational’ (Reed, 2018) since it requires that we engage with those that are interested and are able to apply or ‘relate’ our research to their practice. To achieve this aim, we need to build relationships and trust with potential beneficiaries, rather than simply sharing our research with them, by involving practitioners throughout each stage of our research, not simply at the dissemination point. This implies that impact is complex and likely to take many forms requiring multiple measurements and benchmarks. For the purposes of this chapter, which focuses on the practical applications

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of the iPAC Framework for teachers and teacher educators, we have highlighted the following characteristics of non-academic impact adapted from a longer impact typology developed by Reed (2018, pp. 20–21). • Level 1: Understanding and awareness: evidence to indicate that beneficiaries are better informed about m-learning as a result of the research and/or its various instantiations; • Level 2: Attitudes and thinking: evidence to indicate that beneficiaries have changed their attitudes and/or thinking as a result of the research and/or its various instantiations; • Level 3: Capacity or preparedness: evidence to indicate that the research and/or its various instantiations have enhanced the capacity of the beneficiaries (e.g. development of new skills and abilities to use m-learning); • Level 4: Behaviours and practices: evidence to indicate that beneficiaries have altered their practices (e.g. how they teach) or changed how they behave (e.g. how they communicate).

13.3 The Participants Although iPAC is an intuitive, stand-alone theoretical framework that can be accessed and used by anybody, this chapter focuses on a particular subset of participants who have worked closely with the original iPAC authors and have therefore enjoyed greater exposure and immersion to the research than some other users. The majority of participants described in this chapter were involved in the development of the m-learning toolkit as MTTEP project partners. As well, we have included a case of teachers in Colombia, who have taken part in intensive Continuing Professional Development (CPD) sessions featuring the iPAC Framework. While this relationship with the participants featured in this chapter has granted us privileged access and insights to and about these end users of the iPAC research, it also challenges the objectivity of these insights, and for this reason we have attempted to triangulate our research sources wherever possible so as to present a variety of different perspectives. Going forward, this also raises questions about the likely impact of the iPAC Framework and its various instantiations in settings and contexts where this kind of close mediation from the research authors is not available or viable, although this is beyond the scope of this chapter. It is, however, a theme we return to in Chap. 15, the conclusion of the book. The teacher educators reported in this chapter were drawn from five ITE institutions, four of whom were project members in the MTTEP project. They include teacher educators from a variety of different discipline areas: the Humanities, STEM disciplines, Mathematics, ICT, English, and Modern Foreign Languages. In the case of the MTTEP project members, the ITE institutions reported in this chapter include the University of Hull (UoH), UK, Western University of Norway Applied Science (Norway), Karlsruhe University of Education (Germany) and the University of Technology Sydney (Australia), the only non-European MTTEP project member. Outside

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of these project member institutions, data are also reported from the Universidad del Norte in Barranquilla, Colombia, where academics and postgraduates in the Faculty of Modern Languages have used the iPAC Framework with one of the authors for professional development purposes. Further to the above data from teacher education institutions, we have collected data from practising school teachers. These data were collected from schools who had participated as project partners in one of our projects. These consisted of one secondary school (11 to 18 years of age) in the UK (DEIMP), a middle school in Germany (11 to 16) (MTTEP), a senior college in Norway (16 to 19) (MTTEP) and two secondary schools in Australia (11 to 18) (Optimising Mobile Pedagogies project). Each of these institutions had used the m-learning toolkit, or elements of it, as part of professional development work for their staff, and in some cases they extended this to more extensive forms of professional development for staff beyond their own school, often funded through subsequent Erasmus + projects that are explained below.

13.4 The Impact of the iPAC Framework in Schools and in Teacher Education This section of the chapter examines the different types of impact that are discernable from teachers and teacher educators who have studied and used the iPAC Framework for at least 6 months. It is organised around the previously described typology developed by Mark Reed in the UK. This includes • • • •

Level 1: Enhanced levels of understanding and awareness; Level 2: Changed attitudes and thinking; Level 3: Increased capacity or preparedness; Level 4: Modified behaviours and practices.

13.4.1 Level 1: Raising Awareness and Understanding About Mobile Learning Initially, some educators upon encountering the iPAC Framework reported finding it somewhat confusing as it was not designed to be used to explicitly instruct educators on how to engage with mobile technologies. Rather it highlights distinctive mobile pedagogical approaches—referred to as ‘signature pedagogies’—that users are invited to explore and apply themselves. Adapting and adopting these digital pedagogies may take time. This is confirmed by the lead teacher educator at Karlsruhe PH, Germany, where the iPAC Framework has been integrated into the ITE curriculum for over 500PSTs annually:

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Initially they are very confused by it. Later it helps them deepen their knowledge. They like a framework in the background that helps them to focus (German teacher educator, Karlsruhe PH).

At the core of the iPAC Framework is the ‘time/space’ concept that explains how m-learning is uniquely mediated by the malleability of both temporal and spatial constructs that conventionally constrain learning to specific places and times. This is a concept that researchers and teachers alike sometimes overlook, and a number of the participants in the MTTEP study noted how the iPAC Framework had clarified their understanding of what the term ‘mobile’ actually means: it has sharpened my thinking. It has helped me come back to this idea of mobiles and to say okay that fundamental question of how does this use of mobiles inform our sense of teaching and learning either in a discipline sense or in a more general sense. (UK teacher educator, UoH)

The iPAC Framework was deliberately designed to fill an identified gap and meet a need for a socio-cultural perspective of m-learning that privileges a pedagogical approach to the use of mobile technologies over a technological one (see Chaps. 5 to 7 for a full explanation of how the iPAC Framework was developed and evolved). This perspective is recognised as one of the Framework’s principal strengths and is perceived as a distinctive feature by many educators who feel it has deepened their understanding of how to use mobile devices in ways that are pedagogically sound. Indeed, for many users, the Framework has had a profound impact by providing an educational rationale for using mobile technologies that was previously unavailable or poorly articulated. The iPAC Framework is currently used by a number of organisations and institutions to support teachers in better understanding the nuances of m-learning, including Apple UK, who use it as one of their preferred frameworks for the training of their Apple Distinguished Educators (ADEs) in the UK. In Germany, the Framework is also used to underpin professional development activities provided by teachers from Rennbuckel School in Karlsruhe, one of the MTTEP project schools. One of the lead teachers in this German school described how: When I do training with teachers [workshops or keynotes] I have to explain [justify] why I am using mobile devices…when I show iPAC then immediately the people are silent because they realise there is a method behind this….and it is deeper than just gaming or playing with apps. (German school teacher and professional development leader)

The iPAC Framework and the resulting MTTEP toolkit have been adapted by teachers at Rennbuckel School as the basis of a new Erasmus + project that they are leading: the Tablet Teacher project (https://tablet-teachers.com/). Their new project is having a significant impact on teachers in developing a deeper understanding of how mobile technologies can make learning more authentic, personal and collaborative. Their project website illustrates the iPAC Framework with 12 short video vignettes made by participant teachers and schools demonstrating how lessons can be designed to maximise the affordances of m-learning. One of the resources they developed, based on the iPAC Framework, is shown below (see Fig. 13.1). This observation template is used extensively in the schools where they offer professional development to help teachers evaluate the effectiveness of their m-learning activities.

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Fig. 13.1 Lesson observation tool developed as part of the European Tablet Teacher project in 2019 (Used with permission from Stoller, Hughes & Wadsworth, 2019, p. 18)

Anecdotal evidence provided by the lead German teachers who run the Tablet Teacher initiative suggests that the iPAC Framework has had a profound impact on practitioners because it gives them confidence that the m-learning pedagogies they are encouraged to adopt are well researched and underpinned by a rigorous theoretical framework, which is very important to them. To conclude this section, educators who have spent time thinking about and using the iPAC Framework believe it has made a major contribution to their understanding of m-learning by helping them to structure and organise their thinking. One teacher educator in the UK described the Framework as “something which you can hang pedagogic questions around”, noting how it foregrounds pedagogical rather than technological questions and issues and encourages teachers and students to think from a learner’s perspective about how and why they are using technology. This view is endorsed by comments from teachers who find the Framework helps them to better understand how to implement the use of technology in their teaching in ways that are effective and support their subject-specific curriculum objectives. This is particularly the case for language teachers, who are often aware of the importance of integrating the use of technology into their lessons but are also concerned that doing so will detract from their discipline-specific objectives, as shown in the following comment from a language teacher in Colombia: Learning about the iPAC Framework was the best thing about this process. The iPAC Framework disclosed another way of blending technology and pedagogy. It showed me how to engage learners in the content while using their built-in skills for technology, motivating them to create, participate, and even share what they make. (School teacher, Barranquilla, Colombia)

These comments reveal how exposure to the iPAC Framework from both theoretical and practical perspectives impacts the deepening understanding and awareness about the nature of m-learning and its effect on learners. However, enhanced understanding and awareness do not necessarily translate directly into changes in

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behaviours and practices: it is often necessary to challenge attitudes and thinking, which is illustrated in the next sub-section.

13.4.2 Level 2: Changes in Attitudes and Thinking About Mobile Learning In raising awareness and understanding of how to integrate the use of mobile technologies in teaching and learning, evidence collected from teachers who have used the iPAC Framework reveals that it often challenges them to think differently about learning and how they organise their classrooms: It [the iPAC Framework] has influenced my thinking in how I see more and more now a set of tablet devices as a pretty essential tool to have in a classroom because of the flexibility it allows me to do a number of things. (Teacher educator, University of Western Norway).

Another area of thinking that was frequently challenged is that of content knowledge and how this is accessed and represented in a mobile world where learners have almost limitless and instant access to unbounded knowledge through the Internet. In schools like Rennbuckel in Germany, where teachers have used mobile technologies like the iPad for some time, thinking and attitudes about knowledge and what students need to remember in their heads have changed quite dramatically. Teachers in this school who have used the iPAC Framework no longer believe they need to overtly teach content matter, as they used to, and are more relaxed in their general approach to learning, believing that mobile technologies have relieved them of some pressures and provided time for more authentic and personalised learning activities structured around the iPAC constructs. Such changes in attitudes and thinking about subject content are also reflected in the growth in popularity and interest in learner-generated eBooks, which were themselves a focus of the MTTEP project and final m-learning toolkit. Software tools and apps like Apple’s iBook Author and the online version of Book Creator make the production of eBooks more manageable for both teachers and students and exemplify in practice many of the sub-constructs of the iPAC Framework such as customisation (each book can be tailored for a specific audience), agency (students demonstrate considerable choice when constructing eBooks) and data sharing (now re-named ‘cocreation’ in the latest version of the iPAC Framework), since eBooks can be authored by students collaboratively and their data shared with audiences near and far. The actual practice of learners co-designing and co-constructing eBooks as a pedagogical strategy is explained in more detail below in the section on behavioural changes, but noted here is how this form of knowledge creation also represents a significant change in how teachers think about knowledge and learning more generally. A teacher in Colombia who was immersed in such an experience for professional development purposes said:

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First, I have to admit that changing my traditional mindset was not an easy task for me. After many years of relying on traditional textbooks, I believe that certain features from my eBook were biased. I tried to be more “crazy” at the moment I organised my layout, the pictures, texts, and everything, but after reviewing them they seemed untidy for me. Then, I had to put them in a way that was appealing to me. Though, I think this way of thinking is attached to my personality. (School teacher, Barranquilla, Colombia)

The learner-generated creation of eBooks, based on the principles embedded in the iPAC Framework, has also had a significant impact on teacher educators and their thinking about what constitutes authentic learning: It [the iPAC Framework] has also influenced my thinking around the use of electronic books. It helps us to ask the question of how do we take quite a traditional tool as the textbook and turn it into something quite different. (UK teacher educator)

Arguably, one of the most profound impacts on the thinking and attitudes of those who have used the iPAC Framework concerns the roles and identities of teachers and students. In traditional settings, the role of teachers and students is clearly demarcated, and at the risk of caricaturing them, one is associated predominantly with participative knowledge dissemination and the other with passive knowledge consumption. In schools where mobile technology is embraced and used effectively, this demarcation line appears to be diminishing and the boundaries that mark the traditional role of teachers and students appear to be blurring. These changes go beyond attitudinal impacts, as shown below in the section on behaviours, and it would be disingenuous to associate them all with mobile technologies, let alone exposure to the iPAC Framework. Nonetheless, there does appear to be a persistent and significant association between the adoption of mobile technologies, a specific focus on the three iPAC constructs of personalisation, authenticity and collaboration, and changes in the way educators perceive their roles and identities, vis-à-vis their students. This is most evident with respect to the personalisation construct and the sub-construct of agency that emphasises the opportunities that mobile technologies afford in terms of student choice and autonomy. More than any other element of the iPAC Framework, it is the sub-construct of agency that teachers refer to when they are asked to illustrate its impact on their attitudes and thinking. This often goes beyond simply enabling more choice for students since it often challenges how teachers think about learning itself and the role of students in the learning process. The example below captures this complexity: The iPAC Framework showed me that students also play an important role in their learning process. Being able to choose what they want to do, how they want to do it, and where they want to do it is part of their process. Thus, the personalisation construct made me realise that learning can be self-directed and self-regulated by students, where teachers play the role of facilitators. It means that classrooms should be more learner-centred rather than teachercentred, allowing students to make their own choices for deeper understanding. (School teacher, Barranquilla, Colombia)

This example is typical of how many teachers begin to recognise a change in their own thinking when they have used the iPAC Framework, in relation to potential emancipative autonomous roles of students in the learning process. If learners are

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granted more agency to make choices about their own learning, mediated by the affordances of a mobile device, this will inevitably lead teachers to think about and question their own roles and ultimately their sense of professional identity. This is a potentially transformational impact for educators, particularly in those instances where this kind of realisation can be traced through to behavioural changes such as new teaching practices involving more student-centred learning. Some of these examples are examined in the following section.

13.4.3 Level 3: Changes in Capacity and Preparedness to Use Technology in the Classroom Educators who have encountered the iPAC Framework, particularly in its operationalised format such as the m-learning toolkit, which includes tools and instruments to plan and evaluate m-learning activities, report how it has impacted on their capability and preparedness to use mobile devices. This is especially evident in terms of developing new skill sets, rising levels of confidence and willingness to overcome many of the barriers, and dealing with challenges educators had previously perceived to be associated with m-learning. Increased confidence in using mobile technology in the classroom was frequently mentioned by teachers who found the video exemplars and case studies in the m-learning toolkit to be useful features. These resources were designed specifically to illustrate what each of the iPAC constructs and subconstructs looks like in practice, and each can be filtered by construct and discipline area, helping users to understand how they can implement similar strategies in their own classrooms. For teachers new to or hesitant about the concept of m-learning, these resources underpinned by the iPAC Framework appear to have been instrumental in encouraging them to experiment and adapt similar ideas for their own classrooms: This was an amazing experience that made me grow professionally, learning about the iPAC Framework, the different mediational tools for learning and all the pedagogical links to mlearning approaches, have made a great impact in my teaching practice. Now I feel more confident using technology in my classes. (School teacher, Barranquilla, Colombia)

13.4.4 Level 4: Changes in Behaviours and Practices Exposure to the concepts and ideas that underpin the iPAC Framework, along with practical examples and resources, such as the m-learning toolkit, appear to have raised awareness of the potential value of m-learning among teachers and teacher educators, as evidenced in the previous sections. In doing so, this has challenged and changed the existing attitudes and thinking of many of these educators in terms of m-learning. Important as these impacts may be for the individual, however, they would have limited significance and reach if they were not accompanied by any

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discernible changes in behaviours such as teaching approaches and strategies that have further impact. Fortunately, there is considerable evidence available from both teachers and teacher educators to conclude that the iPAC Framework has led to significant changes in teaching approaches and strategies that are correlated with the elements of the Framework. One of the most obvious impacts and changes is linked to the sub-construct of agency, part of the personalisation construct. In the theoretical discussion of the iPAC Framework, it was postulated that mobile technologies are likely to be game changers for educators because they are personal and personalised devices, often owned by the individual and not the organisation, and enable learners to take control of what, where, when and how they learn (Kearney et al., 2012). Subsequent research has reinforced these hypotheses while also revealing how teachers and teacher educators who are unfamiliar with the iPAC Framework are often reluctant to cede control of learning in this way and thus limit the opportunities granted to students to use their mobile devices to make meaningful choices and decisions of this nature (Kearney et al., 2015). However, as educators become more interested in, and aware of, the iPAC Framework, and especially the exemplars and tools that have been created to illustrate it, they start to think differently about student agency (see Sects. 13.4.1 and 13.4.2 above) and change their teaching approaches to become more student-centred: My teaching is different … my lessons are more project oriented with a lot more self-regulated learning scenarios where students have to keep doing something for themselves….I show them some apps and leave them alone to learn independently for 1–2 h. Then I show them some YouTube videos. (School teacher, Karlsruhe, Germany)

And this outcome is not an isolated example or outlier. It was highlighted, to varying extents, by most of the respondents featured in this case study, particularly those who had longer experience in using mobile technologies for teaching and learning. In Sandringham school in the UK, for example, mobile devices are part of a strategic initiative to develop greater student autonomy and self-regulated learning, and the iPAC Framework has been embraced by the senior leadership team to support and implement this. Writing for a professional teachers’ journal in 2019, Fergal Moane, a deputy head teacher in the school, is unequivocal about how the concept of student agency has led to greater student self-regulation and a changing role for the teacher in Sandringham school: Agency was encouraged by the choices of task opened up by digital technologies. No longer bound by the worksheet (or at least the worksheet links to the digital world via a QR code), students were able to stretch themselves with extension work and also ‘unstick’ themselves without waiting for their teacher. (Moane, 2019, n.p.)

Teachers who have embraced the Framework in this manner frequently talk about how their role as a teacher at the front of the classroom has changed. One teacher at Rennbuckel school in Germany remarked: “I am more like a counsellor now… It’s more motivating for students”. This is echoed by the school’s deputy head teacher,

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who claims the use of mobile technologies in this manner has brought about significant and far-reaching change to the existing spoon-feeding culture of the school, as her ‘students have more responsibility for their own learning’. Alongside this shift to a more student-centred teaching approach is the change in how teachers and teacher educators set tasks and assess learning. These changes are associated with the authenticity construct of the iPAC Framework that encourages educators to think about how the use of mobile technologies can make learning more meaningful and realistic for students by situating it in more authentic contexts and by using more realistic tools and tasks. One example features a transnational, interdisciplinary, cross-phase project between ITEs and schools in the UK and Norway, in which students participated in a variety of authentic fieldwork experiences designed to construct a series of eBooks (Naylor & Gibbs, 2018). Thornwick Bay, along the east coast of England, was selected as a realistic setting to undertake a series of scientific inquiries and investigations in which the English and Science PSTs from the UoH worked as mentors with a group of younger Norwegian school students. Mobile technologies were used extensively to capture and log real-time data about fauna and flora at the site enabling school students to deepen their understanding and appreciation of this unique ecosystem. The setting itself was highly authentic as was the m-learning task set for the school students, which mimicked the type of investigations undertaken by actual scientists. The task was also highly realistic for the PSTs who worked as mentors with the younger school students since this replicated the kind of fieldwork experience they would be expected to conduct with their own students in schools. The iPAC Framework inspired the design of a similar m-learning fieldwork visit for a group of history PSTs in the UoH who were taken to the cemeteries and battlefields of the First World War in Belgium and France to research and document the journey, and ultimately the death, of an individual soldier who bore their surname. Little or nothing was previously known about each of the individual soldiers, making this a highly original and meaningful task for the students who were creating their own piece of history. Students used their iPads to research the historical archives before leaving the UK and to capture images and video footage, and record interviews with local members of the public in Belgium and France. Much of this work was undertaken during the visit itself, often in the University minibus as students travelled between sites. At the end of the exercise, students co-produced an individual eBook to capture their individual soldier’s hitherto untold story, and these digital narratives were used again as the basis of a similar exercise by the following year’s cohort of PSTs. Both examples demonstrate how the iPAC Framework, allied with suitable mobile equipment for use in authentic settings such as a fieldwork visit, is able to support more meaningful forms of learning that are more engaging for learners because they involve realistic tasks and tools, in this case akin to those done by a historian. The authenticity construct of the iPAC Framework has encouraged many teachers to reconceptualise their thinking about what makes learning authentic for students, leading some to modify their teaching in order to exploit the affordances of mobile

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technologies. The link between this sequence of processes and the iPAC Framework is often evident in the language teachers use: Task authenticity was also supported by the blended learning program – for example, launching a Raspberry Pi computer on a weather balloon to the edge of space, and the possibilities for video were transformative in dance, drama and PE. (Moane, 2019, n.p.)

Sometimes, the iPAC terminology is not cited directly in this manner but it is possible to deduce that respondents have appropriated the principles behind the Framework, resulting in new or modified teaching approaches that would not be otherwise possible. An example of this is provided by a teacher educator from the UoH who explains how mobile technologies enable his primary PSTs to behave and use tools like real scientists: So for instance with my primary trainees we were looking at this idea of capturing data while doing an experiment and I model the activity so they are doing it as their students would. We were looking at capturing data and we videoed the experiment of them designing little jet powered engines which we then videoed while they were testing and it allowed us to video it which allowed us to revisit the moment and re-examine and to talk about the forces at play in their groups because they had that data captured immediately and that’s a very tool-orientated task. You couldn’t have done that without the short clips of video. (UK teacher educator)

This example also alludes to changes in assessment and evaluation strategies that are made possible with mobile technologies. Teachers at the Metis school in Bergen (Norway) have adapted the iPAC Framework itself for evaluation and assessment purposes, exploiting the opportunities provided when all of their students have access to a mobile device provided by the school: In developing this tool, we evaluate the work that our students are doing, using the so-called iPAC Framework, which consists of three main elements: personalisation –authenticity – collaboration. We measure the degree of these elements in the various tasks the students do. (Metis School Teacher, Bergen, Norway)

Finally in this sub-section, a number of teachers and teacher educators have consciously designed mobile pedagogies that exploit the collaborative affordances of mobile technologies, inspired by the sub-constructs of the iPAC Framework—particularly data sharing (now co-creating)—and the exemplars situated in the m-learning toolkit case studies. In Sandringham school in the UK, each faculty is encouraged to use their own Twitter feed to flag up wider reading and events of interest to students in their particular subject area. As Fergal Moane, the deputy head teacher, explained: “Collaboration was explored through a number of tools that allowed the sharing of data and conversation”. One of these was Google Sites, which is used by school students across the school to encourage collaborative learning that is not restricted to a particular time or space: Google Sites was used to construct a home page for resource-sharing for each subject, from past papers and revision videos to entire schemes of work and lesson-by-lesson resources. Whole teams of students could work on their DT projects collaboratively after school, using Google Slides, even if they weren’t physically together. (Moane, 2019, n.p.)

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Although the space and time elements of the iPAC Framework were rarely referenced explicitly, the example above indicates that educators are beginning to recognise the importance and practical value of what some are describing as third space learning (Schuck, Kearney, & Burden, 2017). In one example, this concept was referenced more overtly along with a practical example of what this looks like on the ground: I think the mobility part challenges me to think about how you can move and take tools out to not just the field in a wider sense but like when we took students away on that trip with long journeys on a bus between sites, but certainly when you have access to a tool that meant you could immediately start using the information that you gathered and that multimodal way you could have data, you could have images, you could have videos, you could have text, you could immediately begin to work with that while it was fresh in children’s minds. There was a real advantage in doing that way rather than in a traditional way of pen and paper, so when you come back that immediacy is lost. I think immediacy is a key word for me. (UK teacher educator)

13.5 Conclusion This chapter draws upon a purposive sample of teachers and teacher educators who are familiar with the iPAC Framework and have used it in their teaching. Generally speaking, the feedback from these users suggests that the Framework needs to be assimilated and fully understood by educators before they are in a position to implement it in practice, and this can vary in time from user to user. Judging from the feedback provided here, the various practical instantiations of the iPAC Framework, such as the m-learning toolkit, clearly play a significant role in bridging theory into practice for these practitioners, thereby accelerating this process of understanding. Despite the oft-quoted dictum that there is nothing so practical as a good theory, the evidence from these iPAC users indicates that the Framework is likely to be more effective when it is mediated in different ways, which may be through the actions of experienced coaches and mentors, through focused training and professional development programs, or virtually, through online resources, tools and materials such as the m-learning toolkit. It is also evident from the examples in this chapter that some elements and features of the iPAC Framework are more readily adopted and have had a greater impact on practitioners than others. Suprisingly, given the discussion in Chap. 6, the authenticity construct and its various sub-constructs were incorporated frequently in respondents’ practices. As noted in Chap. 6, previous research undertaken by the authors investigated how teacher educators and teachers use mobile technologies in teaching and learning (Kearney et al., 2015) and found very few examples of activities situated outside the classroom. This finding led to the recommendation that the context subconstruct of authenticity should be highlighted and used more often, and accordingly the authenticity construct was highlighted in many of the MTTEP case studies and

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video vignettes. Since many of the examples in this chapter are based on work undertaken by the original partners in the MTTEP project, this may explain why authenticity features so prominently in this chapter’s examples. Agency, a sub-construct of personalisation, and data sharing, part of the collaboration construct, were also referred to frequently. However, the customisation and conversation sub-constructs were mentioned less frequently, if at all. Since the iPAC Framework has been thoroughly piloted and validated in many different settings (Kearney et al., 2019), this is unlikely to be a weakness in the Framework itself. It may simply reflect the size or composition of the sample that this chapter is based upon, since it makes no claim to be representative of all teachers and teacher educators, nor is it overly extensive in size. Alternatively, it may point to deeper issues such as the shortage of clear and replicable exemplars for sub-constructs such as customisation, and some of the privacy concerns that teachers have voiced about involving external experts, part of the conversation sub-construct. The other mediating tools that have added form and substance to the Framework are the surveys that have been developed to help educators evaluate the impact of their m-learning pedagogies on both their own practice and the experiences of their students (see Chap. 11). Although these surveys were not overtly referenced in the data reported in this chapter, they have been used extensively by teachers and teacher educators, and they help to illustrate what the different elements of the iPAC Framework look like in practice. The next chapter is the penultimate chapter in this book and examines innovative mobile pedagogies for school-aged learners, and how principles of innovation are aligned to the iPAC Framework.

References Bano, M., Zowghi, D., Kearney, M., Schuck, S., & Aubusson, P. (2018). Mobile learning for science and mathematics school education: A systematic review of empirical evidence. Computers & Education, 121, 30–58. https://doi.org/10.1016/j.compedu.2018.02.006. Burden, K., & Kearney, M. (2016). Future scenarios for mobile science learning. Research in Science Education, 46(2), 287–308. Burden, K., & Kearney, M. (2017). Investigating and critiquing teacher educators’ mobile learning practices. Interactive Technology and Smart Education, 14(2), 110–125. Burden, K., Kearney, M., Schuck, S., & Hall, T. (2019). Investigating the use of innovative mobile pedagogies for school-aged students: A systematic literature review. Computers & Education, 138, 83–100. https://doi.org/10.1016/j.compedu.2019.04.008. Kearney, M., Burden, K., & Rai, T. (2015). Investigating teachers’ adoption of signature pedagogies. Computers & Education, 80, 48–57. https://doi.org/10.1016/j.compedu.2014.08.009. Kearney, M., Burke, P., & Schuck, S. (2019). The iPAC scale: A survey to measure distinctive mobile pedagogies. TechTrends, 63, 751–764. https://doi.org/10.1007/s11528-019-00414-1. Kearney, M., Schuck, S., Burden, K., & Aubusson, P. (2012). Viewing mobile learning from a pedagogical perspective, Research in Learning Technology, 20(1). https://doi.org/10.3402/rlt. v20i0.14406 Moane, F. (2019). Interweaving traditional and digital approaches: The development of blended learning at Sandringham School. Impact, Journal of the Chartered College of Teaching.Retrieved

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from https://impact.chartered.college/article/interweaving-traditional-digital-approaches-develo pment-blended-learning-sandringham-school/ Naylor, A., & Gibbs, J. (2018). Deep learning: Enriching teacher training through mobile technology and international collaboration. International Journal of Mobile and Blended Learning (IJMBL), 10(1), 62–77. Reed, M. S. (2018). The research impact handbook. Fast Track Impact. Schuck, S., Kearney, M., & Burden, K. (2017). Exploring mobile learning in the Third Space. Technology, Pedagogy and Education, 26(2), 121–137. https://doi.org/10.1080/1475939X.2016. 1230555. Stoller, M., Hughes, G., & Wadsworth, I. (2019). E-teach the teacher. The de Ferrers Academy. Retrieved 31st March 2020 from https://books.apple.com/au/book/e-teach-the-teacher/id1457 656589

Chapter 14

Innovative Mobile Pedagogies with School-Aged Learners

Abstract This chapter examines notions of innovation and disruption and their intersection with mobile pedagogies. We introduce 21 principles underpinning innovative mobile pedagogies for school-aged learners, as ascertained from a rigorous systematic literature review conducted by the authors as part of a transnational project on innovative mobile learning. We then explore the alignment of these principles with the dimensions of the iPAC Framework to demonstrate its utility. Almost half of the 21 innovation principles could be directly linked to the authenticity mobile pedagogical dimension. Implications for school and teacher education are presented. Keywords Mobile pedagogies · iPAC · M-Learning · Innovation · Disruption · Pedagogical principles · DEIMP · Digital pedagogies

14.1 Introduction A growing body of evidence shows that traditional pedagogies still dominate the school educational field and are misaligned with the diverse learning opportunities offered by the use of mobile technologies (Kearney, Burden, & Rai, 2015). There is an imperative to question these conservative approaches in school education, including how, where and when teaching and learning are enacted; and to explore how new and emerging mobile pedagogies might disrupt contemporary understandings of digital pedagogy. This chapter firstly deconstructs the notion of innovation and disruption in the context of digital pedagogies in school education. We then explore the concept of innovative mobile pedagogies for school-aged learners by presenting 21 principles that emerged from a rigorous and extensive systematic literature review study recently undertaken as part of the Erasmus + project introduced in Chap. 1 titled Designing and Evaluating Innovative Mobile Pedagogies (DEIMP). We investigated how these principles aligned with the iPAC Framework to consider how the Framework could contribute to studies of m-learning innovation. The chapter goes on to discuss future Part of this chapter is based on the article: Kearney, M., Burden, K., & Schuck, S. (2019). Disrupting education using smart mobile pedagogies. In L. Daniela (Ed.), Didactics of smart pedagogy: Smart pedagogy for technology-enhanced learning (pp. 139–157). Cham, Switzerland: Springer. © Springer Nature Singapore Pte Ltd. 2020 M. Kearney et al., Theorising and Implementing Mobile Learning, https://doi.org/10.1007/978-981-15-8277-6_14

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directions for innovative mobile learning (m-learning) that are feasible for school education contexts.

14.2 Background 14.2.1 Innovation and Disruption This section examines notions of innovation in education, particularly in relation to digital pedagogies. The word ‘innovation’ is used liberally across education literature, policies and reports (Moyle, 2010) to describe new ideas, products, approaches or processes (Fenwick, 2016). Innovations can be small or large scale but need to go beyond superficial change to introduce new ideas or practices that are impactful and valuable to individuals or communities (Denning, 2004; Fenwick, 2016; Lindfors & Hilmola, 2016). In an education context, innovation could mean new curricula, pedagogies or assessment solutions to improve student outcomes (Danaher, Gururajan,& Hafeez-Baig, 2009). Interpretations of ‘innovation’, or the extent to which an idea or process is new or impactful, will ultimately depend on one’s perception and context (Caldwell, 2018). Subsequently, some writers emphasise a ‘frame of reference’ (Moyle, 2010, p. 11) as critical to the discussion of innovation. Tornatzky and Fleischer (1990) suggest that innovation needs to be impactful, at least to the people or organisation carrying out the innovation; while Potgieter (2004) suggests that innovation in education needs to be “an idea, practice, object or combination of these that is perceived as new by staff” (p. 271). The literature suggests that innovation can be best understood as a continuum. At the more conservative end are sustaining innovations, described as an adaption of existing approaches (Christensen, Horn, & Johnson, 2008; Fenwick, 2016) and a trade-off with established practices and paradigms (Christensen, 1997). At the radical end of the continuum, disruptive innovations are extremely different from the status quo and can initiate a paradigm shift (Christensen, 1997), transforming existing dominant practices. In education, disruptive innovations create new practices, purposes and processes (e.g. of learning), new relationships between students and teachers, and potentially a change in the nature of school and its relationship with the community “the innovation as a whole can be considered a ‘disruption’ to prevalent practices” (Law, 2008, p. 428). These new practices may demand re-imagining of schooling, for example, allowing school students “to personalise education to fit their needs” (Christensen et al., 2008, p. 243). In the case of innovative pedagogies supported by technology (or digital pedagogies), sustaining innovation would support existing, prevalent technology-enhanced practices to achieve existing curriculum goals, while disruptive innovative digital pedagogies would promote new technology-mediated practices to achieve new goals and replace traditional approaches (Hedberg, 2006; Law, 2008). Or as Selwyn (2017, p. 32) explains, disruptive innovation “is not

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about using technology to do the same things differently, but using technology to do fundamentally different things”. Most of the education literature tends to discuss sustaining innovations, designed to simply improve the quality of established practices rather than supplant them. However, there is debate over which end of the continuum is ideal for school contexts. Sustaining innovations set out to adapt existing approaches (critics label such changes as mere ‘renovations’) but ensure enhanced quality of existing practices (Fenwick, 2016). In contrast, disruptive innovations are radically different from the status quo, and their implementation can initially be less successful than the traditional practices they are attempting to replace. Another issue is the pace and scope of implementation of innovations. A gradual, incremental approach is often advocated in schools— institutions that are well known for their conservatism and resistance to change (Law, 2003; Zhao, Pugh, Sheldon, & Byers, 2002). For instance, Zhao et al. (2002) studied a number of innovations in schools and found that an ‘evolutionary rather than revolutionary’ approach to innovations was more likely to succeed and “innovations that were the most distant from the teachers’ existing practices and school culture were less likely to succeed” (p. 512). Innovation in schools is typically discussed with a focus on pedagogical innovation. There is general consensus that pedagogical innovations in schools attempt to change ‘traditional teaching’ that is assumed to be isolated, knowledge-focused and teacher-centred (Hedberg, 2006; Law, 2008). For example, in their discussion of innovative teaching, Zhu, Wang, and Engels (2013) argue for less traditional approaches, drawing on social constructivist learning theory to emphasise active participation, collaboration in real learning situations, and authentic learning tasks. There is an increasingly well-accepted argument for schools to supplement or replace traditional pedagogical approaches with more innovative approaches to help students develop the diverse knowledge, skills and attitudes needed for living and working in the twenty-first century (OECD, 2018; Voogt, Erstad, Dede, & Mishra, 2013; Zhu et al., 2013) and to prepare them for careers that may not yet exist (Dede, 2011). Voogt et al. (2013) argue specifically for curriculum change, including an emphasis on new literacies, linking learning between formal and informal settings, and new assessment frameworks, while Law (2008) makes similar arguments for pedagogical renewal: “Pedagogical innovation is becoming increasingly important in the 21C when the focus in education shifts toward lifelong learning and knowledge creation, demanding changes in educational goals, as well as curriculum and pedagogical processes” (p. 427). Zhu et al. (2013) explain the need for innovative teaching: “It seems that innovative teaching is necessary for the present and future of education to help students reach their potential” (p. 9). Innovative digital pedagogical approaches, or what Law (2008) calls ‘ICT-using pedagogical innovations’, typically explore the use of learning technologies to support new strategies that might change or replace traditional teaching approaches. Hedberg (2006) advocates the use of innovative digital pedagogies that facilitate a shift towards constructivist pedagogical approaches adopting student-centred learning strategies. He argues that these approaches give students control over choice of learning topics and sequences and typically encompass an emphasis on their

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creation, evaluation and synthesis processes. Social interaction and social construction of knowledge are also emphasised, as well as a shift in focus “from assessment of the end product to assessment of the learning journey” (p. 179). He suggests that disruptive digital pedagogies need learning spaces that support a shift from the learner as “a passive participant toward an active engaged constructor of their own experience” (p. 181). For example, when discussing ‘online learning’, Hedberg suggests a revolutionary move away from simply replicating traditional (classroom-based) teaching practices in an online environment. In a similar way, in their discussion of innovative mobile digital pedagogies, Schuck, Kearney, and Burden (2017) discuss third space learning as disruptive to existing practices: “The ways that portable, multifunctional mobile devices can untether the learner from formal institutional learning give scope for learning to be conceptualised in an expanded variety of places, times and ways” (p. 121). In Law’s (2003) study of innovative classroom practices, she used six dimensions of innovation/change in the context of digital pedagogies: intended learning objectives/curriculum goals; pedagogical roles of teacher; roles of learners; nature and sophistication of technology used; connectedness of classroom (i.e. collaboration with people outside of the school); and learning outcomes exhibited by the learner. These dimensions were used by Law (2003) to explore the digital pedagogical innovations from each case in her study and to gauge the extent of change. She also highlighted the overall extent of innovations using categories ranging from ‘traditional’ to ‘some new elements’ to ‘emergent’ to ‘innovative’ to ‘most innovative’ (p. 174). More recently, a team at The Open University in the UK has issued an annual report on ‘new forms of teaching, learning and assessment for an interactive world’ (Ferguson et al., 2017), focusing on “novel or changing theories and practices of teaching, learning and assessment for the modern technology-enabled world” (p. 6). This group defined digital pedagogical innovation as “new pedagogies making use of technologies to go further, to open up new possibilities” (p. 8). For example, they discuss ‘crowd learning’ pedagogy as allowing learners to “update and revise knowledge, offering a more personal and local perspective than centrally published media” (p.8) and they describe “citizen inquiry” approaches as learners using technology to “explore new areas of knowledge and to investigate together” (p. 8). However, Ferguson et al. (2017) deliberately avoid techno-centric discussion in their focus on new pedagogies: “By examining innovative pedagogies, we aim to ride the roller coaster of technology adoption, highlighting ways of teaching, learning and assessing that can be successful both now and in the future” (p. 9). Law (2008) also warns that innovative digital pedagogies do not depend on the technology per se but rather on the intended use of the technology and the educational context. She argues that technology is not a catalyst but a ‘lever’ for changed practices: “Technology is leveraged by teachers as a disruptive force (or resource) in realising pedagogical innovations” (p. 428).

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14.2.2 Innovation with Mobile Technologies The discussion above has alluded in several instances to the potential disruption that mobile pedagogies might pose for traditional educational structures and practices. Mlearning is predicated on the personal ownership of agile, mobile devices. This challenges and disrupts the traditional model of technology ownership, which is bound by corporate influences and designed to control how learners access and use technology within, but not beyond an institution. Given their growing ubiquity and pervasiveness, mobile devices also challenge existing models of classroom organisation, even raising fundamental questions around the future utility and necessity of traditional classrooms, and therefore of schools themselves (Schuck, Kearney, & Burden, 2017). If learners are capable of independently accessing learning resources and the allied services that potentially make deep learning a reality without direct intervention and control from teachers, m-learning begins to challenge the notion of ‘delivering’ learning which underpins most formal models of learning in schools. M-learning potentially disrupts this content-and-transmission model of learning, which situates learners as passive consumers of pre-packaged learning content, predominantly of a text-based nature. M-learning promises to realise many of the aspirations behind learners as knowledge constructors and co-authors (Bereiter & Scardamalia, 2014) as user-generated content is accessed and repurposed rather than simply consumed. Hence, m-learning and mobile pedagogies have the potential to be disruptive and challenge many of the norms and mores that education traditionally holds in high esteem.

14.3 Investigating Mobile Pedagogical Innovation and Disruption This section of the chapter details the process and selected findings of a systematic literature review (SLR) conducted by the authors (Burden, Kearney, Schuck, & Hall, 2019), exploring innovative mobile digital pedagogies in school education. An SLR comprises more than an ad hoc search of literature. It uses a set of criteria and well-defined procedures to scan various databases for articles that fit the criteria. As noted in previous studies, “It is a methodical and meticulous process of collecting and collating the published empirical studies of acceptable quality with systematic criteria for selection to reduce researcher bias and provide transparency to the process” (Bano, Zowghi, Kearney, Schuck, & Aubusson, 2018, p.33). We initiated an SLR with a focus on the following research question: How does the use of mobile technologies support innovative teaching and learning practices for school-aged learners? Three major search terms were derived: ‘mobile learning’, ‘innovation’ and ‘school-aged learners’. From these major search terms, synonyms and alternative terms were identified. For example, informed by the literature on digital pedagogical innovation (see previous section), the ‘innovation’ component of the search string

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included words such as ‘disrupt’, ‘renew’, ‘redefine’, as well as phrases such as ‘new practice’, ‘new teaching approach’ and ‘emerging learning strategy’. The search string was applied to a range of databases to ensure that relevant studies were not missed. This initial search and selection process yielded 208 papers. A further selection process was then carried out by the team of three researchers and a research assistant. Pairs of researchers applied the following selection criteria to all 208 papers included in the search results: the paper was published in English between 2010 and 2017; the SCImago journal ranking (SJR) of the paper was in the top two quartiles; the study targeted school-aged learners (5 to 18 years old); the study adopted a rigorous methodology and compelling evidence was presented; the paper focused on innovative mobile pedagogies (as defined in the previous section); and strategies and approaches were identified (e.g. as interventions). If these criteria were not met, the paper was excluded. Issues related to the possible exclusion of papers were resolved through inter-researcher discussion at team meetings, and any remaining questions were resolved by reading the full text of papers. Different team members randomly checked among the results to reduce selection bias. At the conclusion of this process, there were 72 papers selected as being suitable for inclusion in this SLR. After this initial work, pairs of researchers then read the full text of papers to further assess the rigour of methodology, the evidence-based benefits for learners, teaching interventions and pedagogical innovations. At the conclusion of this process, there were 57 articles selected as being suitable for inclusion in the systematic review because they met the rigour and enhanced learning criteria. The research team then scrutinised this final selection of papers to differentiate the papers based on the level of mobile pedagogical innovation. Consequently, we started the process of scoring each paper according to four criteria for innovation using mobile pedagogies that were adapted from the literature on digital pedagogical innovation (Law, Chow, & Yuen, 2005). These four criteria concerned the nature of the task, its context, the relationship between student and teacher, and student agency. For each factor, a score of 1 to 3 was given: 1 for low innovation on that factor, 2 for medium innovation and 3 for high innovation. These scores were agreed upon by mutual consent after discussion between pairs of researchers. A total score for each article comprised a sum of the scores the article had achieved on each of the four factors. Given that all of the final set of 57 articles displayed some innovation, papers scored at least one for each factor. Therefore, the expected total score for each article across all four factors ranged from 4 to 12. Using these total scores, we were able to classify each paper as follows: Low Innovation: (total score of) 4–6, Medium Innovation: 7-9 and High Innovation: 10-12. For a more detailed account of this scoring procedure and strategies adopted to improve inter-researcher reliability, see Burden et al. (2019). Based on the total scores for each of the final set of papers, a large proportion (29) described ‘sustaining innovations’, i.e. activities that were low on the innovation spectrum. Twenty-five papers were identified as describing more disruptive innovations (labelled as medium on the innovation continuum) and just three described

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Fig. 14.1 Innovation Continuum—breakdown of final set of papers according to the level of innovation (Reproduced with permission from Burden, Kearney, Schuck & Hall, 2019, p. 92)

radically disruptive innovations (high on the innovation continuum), as shown in Fig. 14.1. The average score for all 57 papers was 6.5, located on the borderline between the low and medium innovation categories. An elaboration of these three categories and lists of associated papers are outlined in Burden et al. (2019). Of the final set of papers, only three (Akom, Shah, Nakai, & Cruz, 2016; Barak & Ziv, 2013; Toh, So, Seow, & Chen, 2017) focused on mobile pedagogical practices that were assessed by the research team as demonstrating high levels of innovation, containing pedagogical elements that could potentially disrupt traditional practices. All three papers report how students undertook tasks and activities that would have been demanding or even impossible without mobile technologies. We revisit two of these papers later in this chapter as illustrative examples.

14.4 Emerging Innovation Principles and Links to the iPAC Framework As indicated above, our SLR identified a set of 57 papers that discussed innovative or disruptive use of mobile pedagogies for school-aged learners. One of the tasks that the authors implemented after identifying innovative and disruptive pedagogies in this review was to identify a set of pedagogical principles that were apparent in the selected papers. In this section of the chapter, we outline the set of principles identified before considering their alignment with the iPAC Framework.

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The set of principles identified from the SLR originally comprised a group of 42 distinct principles (Burden, Kearney, Schuck, & Burke, 2019). By grouping like principles together, the list was narrowed down by the researchers to a group of 21 principles after a prolonged period of analysis and codification. See Table 14.1 for details of these principles and their descriptions. These 21 innovation principles are firmly grounded in, and extracted from, the high-quality and rigorous research studies identified by the SLR. We therefore argue that these 21 principles, or variations of these, underpin innovative and effective activities that are discussed in the m-learning literature. Despite these principles emerging from this entirely independent and rigorous SLR study examining innovative m-learning for school-aged learners, it can be seen that they are also closely aligned to the iPAC Framework that is the focus of this book. These links are outlined in Table 14.2. Table 14.1 Principles underpinning innovative mobile pedagogies for students 5 to 18 years’ old (from Kearney et al., 2019, p. 148) 1. Seamless learning: Activity occurs across a variety of physical and/or virtual settings 2. Digital play: Activity involves explorations without an explicit curriculum goal 3. Student agency: Students have choice of how to do activity 4. Student autonomy: Students determine the activity 5. Gamification: Applies elements of games such as competitions, random events, scoring 6. Customisation: Learning pathways are adapted to individual input 7. Authentic environment: Activity occurs in situ (that is, it occurs in its original or natural location) 8. Simulation: Conducting realistic virtual task, e.g. Google expedition 9. Context-awareness: Activity adapts to environmental stimuli, e.g. new vocabulary is determined by external items 10. Data sharing: Learners share digital artefacts with peers 11. Artefact construction: Learners make digital object, e.g. video, music, game 12. Co-construction: Learners use collaborative authoring tools, e.g. Google docs 13. Reflection: Learners reflect in multimodal ways, e.g. with vlogs, colours, sound 14. Real-world processes: Learners engage in activities similar to those done by practitioners, e.g. testing aero-dynamics of object with app 15. Real-world tools: Activity uses app as tool, e.g. to compose music or paint a picture 16. Role-play: Learners assemble tools and methods and enact roles, e.g. citizen journalist 17. Peer review: Learners review each other’s contributions, e.g. via blogs 18. Co-design for mobile learning: Students and teachers ‘mobilise activities’, i.e. transform them into ones with mobile features 19. Intergenerational learning: Learners across different generations work together, e.g. capturing an oral history 20. Bridging: Learners work across formal and informal contexts 21. Community-based: Learners conduct a community activity or project, e.g. monitoring litter

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Table 14.2 Linking 21 innovation principles to the iPAC Framework Innovation Principles (Number from Table 14.1)

iPAC dimension (and sub-dimension)

Student agency (3); Student autonomy (4)

Personalisation: Agency

Customisation (6); Context-awareness (9) Weaker links: Gamification (5)

Personalisation: Customisation

Digital play (2); Real-world processes (14); Real-world tools (15); Role-play (16); Community-based (21)

Authenticity: Task

Seamless learning (1); Authentic environment (7); Simulation (8); Bridging (20)

Authenticity: Context

Peer review (17); Intergenerational learning (19) Weaker links: (multimodal) Reflection (13)

Collaboration: Conversation

Data sharing (10); Artefact construction (11); Co-construction (12); Co-design for mobile learning (18)

Collaboration: Co-creation

Nineteen of the 21 principles emerging from the SLR could be directly linked to the iPAC Framework. With the exception of gamification and reflection, all innovation principles were strongly aligned to one of the dimensions of the iPAC Framework, showing its utility in going beyond effective or distinctive instances of m-learning (Kearney, Schuck, Burden, & Aubusson, 2012) to ostensibly account for this increasingly important area of innovation and disruption. Almost 50% of the innovation principles had strong links to authentic mobile pedagogies. Nine of the principles could be categorised under the authenticity dimension of the iPAC Framework, with five of these nine fitting into the task authenticity sub-dimension. Six innovation principles could be directly linked to the collaboration dimension (four principles linked to the co-creation sub-dimension), while four principles could be directly linked to the personalisation dimension of our Framework. Given personalisation is arguably the signature pedagogy for m-learning (Kearney, Burden, & Rai, 2017), it was somewhat surprising that less than 20% of the innovation principles could be categorised under personalised learning.

14.5 Illustrative Examples To make this discussion more concrete, we revisit two of the three papers from our SLR—Akom et al. (2016) and Toh et al. (2017)—that were found to focus on practices that contained pedagogical elements that could potentially disrupt traditional practices. We use these two examples to discuss the benefits and constraints of implementing such disruptive activities, with respect to the 21 principles for innovative m-learning identified above, and their feasibility for implementation by most teachers engaged in m-learning. Finally, we discuss these examples with reference to the iPAC Framework to again show its utility.

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The first illustration comes from the article by Akom et al. (2016). These authors describe how they utilised a digital platform called Streetwyse as part of an activity which allowed young people to co-develop and participate in a community health promotion. They found that participation in this activity promoted the young people’s self-esteem and supported the development of their leadership skills, environmental awareness and academic engagement. The authors worked with 90 young people in a particular community and focused on food availability. They mapped locations and information on retail food/drink stores in the urban area in which they lived. They found that the majority of products sold were either liquor or foods with high salt and sugar content such as chips, soda drinks and confectionaries. They used available data to make recommendations about healthy food that should be stocked in the stores. If the article is analysed for the innovative pedagogical principles that are present, it can be seen that there are a number of such pedagogical principles built into the scenario: seamless learning occurred as the students moved throughout the community and educational institution; student agency and autonomy were clearly present; the activity took place in an authentic environment; data sharing occurred between the young people; they used real-world processes to analyse and map the locality; and they co-designed the Streetwyse app they worked with. The activity is also disruptive as it called for a different way of working with young people, one that promoted their activism and authority. The second illustration discusses practices emerging from the Toh et al. (2017) study from our SLR. Their paper describes how children (aged 9 to 10 years old) in their study used mobile devices across a range of informal and formal learning contexts to support their science learning through their daily lives via two case studies. Both cases described activities that were underpinned by an inquiry-based learning approach where the children’s devices were promoted by their teachers as a “cultural tool and learning hub” (p. 305), helping them to exploit their mobility across time and space. The first case study described a child who was learning about marine studies by drawing upon his real-life experiences in a range of family fishing and wildlife field trips. The child used his device to link up with family members, peers and teachers as social resources in his learning, for example, asking questions for additional help, as needed. His mother was a significant figure in his learning, helping him to value the process of finding solutions to questions independently. He also captured, created and archived numerous multimodal resources ‘on the fly’ (e.g. accessing real-time information and capturing photos and videos) and framed questions for inquiry using these resources. The second case study explained a variety of mainly self-initiated m-learning activities enacted by another child. This study participant used his device to blog for self-expression, to create resources such as imaginary worlds for role-playing, and to create animations to extend his knowledge on science topics. A number of innovation principles from Table 14.1 are evident in this illustration. For example, seamless learning is clearly present in the first case study as the child’s science activities spanned a range of informal and formal learning spaces. Elements of authentic learning (authentic environment and real-world tools) are also present,

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with the activities situated in real-life contexts (such as fishing) and use of tools such as the camera to enhance observation. There is also a strong sense of student agency as the child’s inquiry-based learning was self-directed, and the traditional ‘controlling’ role of the teacher was diminished. In the second case study, the principle of role-play is apparent in the activities, as is peer review (use of blogs), artefact construction (learner-generated animation) and to a lesser degree, data sharing. Student autonomy is also a feature, with researchers in the study expressing amazement at some of the self-initiated, digitised materials created by the participant in the second case (p. 310). In this way, this illustration describes mobile pedagogical practices that contain numerous elements of disruptive innovation. Both illustrations feature high levels of student autonomy, such as how, where and when they undertook a task and how they demonstrated their learning. The relationship between students and teachers was more democratic than normal, including tasks such as the co-authoring of an m-learning activity. Learning occurred across multiple contexts such as the classroom and the local environment, and the mobile device bridged the boundaries between these contexts, making learning more authentic and meaningful. Using Table 14.2 as a guide, the principles evident in both of these illustrations can be linked to the adoption of all three iPAC dimensions. Principles linked to the authenticity dimension of the iPAC Framework occurred slightly more frequently, though this is unsurprising given that almost half of the innovation principles can be directly linked into this category. Overall, both illustrations show the utility of the iPAC Framework in the innovation space.

14.6 Implications for School and Teacher Education In this section, we consider how much disruption is feasible, desirable and able to be implemented within the constraints of a teacher’s role. We will also discuss implications for other stakeholders involved in disruptive m-learning. Implications for teacher education are also examined. Given the lag in take-up of disruptive pedagogies by school teachers, we recommend the following: that the type of innovation we should be encouraging in the education of school-aged students incorporates changes that involve more than small increments in innovation, or what we have called sustaining innovations. Rather, the innovation should incorporate changes that have some elements of disruption in them. Indeed, we should not expect all teachers to embrace radical innovation, as this would likely to lead to a low take-up of innovation of any kind, given that it will be too challenging for most to adopt such disruptive practices. Rather, we view disruption as being on a continuum and encourage innovation somewhere between conservative and radical. An important aspect of disruption, we argue, is that it is feasible. The discussion above indicates the principles that have been identified as central to innovative and effective m-learning activities. We noted that their deployment may disrupt current practice, and we argue for ‘feasible disruption’ as the most desirable

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of the innovative digital pedagogies. We now turn to the implications of promoting such pedagogies for teacher education. Teacher education faces a number of challenges in the preparation of pre-service teachers (PSTs). Teacher educators need to keep abreast of emerging technologies and ensure that the pedagogies they suggest are current and in alignment with the needs and practices of contemporary schools and societies (Royle, Stager, & Traxler, 2014). They have the dual challenge of both keeping themselves current and also inspiring their PSTs to be competent and confident users of new technologies and new pedagogies, with the aim of improving school student outcomes (Burke & Foulger, 2014). Sadly, teacher education appears to lag behind in these endeavours (McClanahan, 2017). Indeed, education in general does not seem to have kept up with the innovations in technology use that industry and society are enjoying (McClanahan, 2017; Papert, 2004). Often the reason given for this state of affairs is that the teachers and teacher educators have not received sufficient preparation in using mobile pedagogies and other emerging digital pedagogies during their teacher education courses (Burden & Kearney, 2017). While this explanation is debatable, the fact remains that teacher educators in general do not seem to employ innovative or disruptive pedagogies in their preparation of PSTs. The question that then becomes critical is: How do teacher education institutions encourage their teaching staff to embrace innovations, and support their PSTs to do likewise? Given that the PSTs are likely to be teaching school students long into the future (possibly for as long as 35 years into the future), what skills and competencies do teacher educators need to support them (Schuck, Aubusson, Burden,& Brindley, 2018)? It is likely that the 21 principles identified above will underpin most pedagogies. As shown in the previous section, the iPAC Framework can also be appropriated to analyse and discuss innovative mobile pedagogies. It seems that there are several factors that support innovation and mobile pedagogies occurring in teacher education institutions. These include support at an institutional level; the expectation that all faculty members participate in the innovation (Burke & Foulger, 2014); addressing the values and beliefs of staff (Law, 2008); and importantly that there is some driver that leads to long-term implementation of the innovation (Bereiter, 2002). Our recommendations, therefore, are that teacher educators include the iPAC Framework and the principles underpinning innovative mobile pedagogies in their activity designs, and that their institutions offer them time and opportunity to develop their own skills in implementing these approaches. It is anticipated that once the teacher educators are comfortable with the role of innovator, they are more likely to be able to inspire their PSTs in this regard. Mindful of the research on innovation, there needs to be some driver that will encourage sustained implementation. This driver might be policy from government or accreditation bodies, demand from PSTs (Schuck et al., 2018) or even a global pandemic. It is likely that all will become critical forces for change.

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14.7 Conclusion Mobile pedagogies have been predicted as a game changers for some time now, predicted to engender disruptive innovation and bring school education into an ‘Age of Mobilism’ (Norris & Soloway, 2011). Despite these forecasts, the use of mobile devices in schools has so far not been disruptive, with adopted mobile pedagogies predominantly replicating traditional transmissionist approaches (Kearney et al., 2015). This phenomenon follows a familiar historical pattern of the past four decades: teachers tend to use increasingly sophisticated educational technologies in culturally familiar ways (Zhang, 2010), adopting traditional digital pedagogies that too often align with existing and sometimes antiquated school structures and practices that were originally designed for the industrial age, similar to ‘attaching a jet engine to a stage coach’ (Papert, 2004). This chapter offers a way forward for breaking out of this cycle and optimising the impact of mobile technology use on learning by children and teenagers. In particular, it offers 21 evidence-based principles underpinning innovative mobile pedagogies that have emerged from a rigorous SLR study. Furthermore, although these innovation principles were developed independently of the iPAC Framework, they clearly align with the Framework’s dimensions, showing its robustness and utility. We therefore argue that these innovation principles may be distilled and adopted through the lens of the iPAC Framework. We hope that they are applied to new mobile practices that go beyond sustaining innovations but at the same time are ‘feasibly innovative’ for teachers and teacher educators to implement within the contextual realities of conservative and often bureaucratic institutions that are resistant to change. In light of these goals and contextual realities, we explore future possibilities for m-learning in the final chapter of the book.

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Part V

Future Possibilities for Mobile Learning

Chapter 15

Considering iPAC in a Mobile-Intensive Future

Abstract The focus of this book was on the development and use of our iPAC Framework for theorising and evaluating mobile learning. This concluding chapter synthesises the main themes and content areas covered in the book and then focuses on a series of questions and agendas that stem from it. These include the applicability and usefulness of the iPAC Framework in different geographical and cultural settings and a discussion around the importance and necessity of mediation to use and implement the framework. The chapter concludes with recommendations that might benefit stakeholders such as teachers, policymakers, school leaders and teacher educators. It outlines a series of challenges and areas for further research in the area of m-learning with the iPAC Framework. Keywords Future research directions · Mediation · Impact · iPAC framework · M-Learning · Digital pedagogies · Mobile pedagogical framework

15.1 Introduction Over the previous 14 chapters, we have introduced, explained and illustrated the research that led to, and continues to underpin a socio-cultural framework of mobile learning (m-learning), the iPAC Framework. At the core of the Framework and central to our position throughout this book has been the contention that learning is essentially a social and situated activity, mediated through social interactions (e.g. conversations), and tools, in this instance, digital mobile technologies (Kearney, Schuck, Burden, & Aubusson, 2012; Vygotsky, 1978; Wertsch, 1991). While m-learning is a subset of digital learning, it has very distinctive features or affordances which have been highlighted in previous chapters. We have highlighted and illustrated its particular characteristics in their many forms throughout the book. In Sect. 1, we examined the broader context of technology in education and then considered how m-learning has evolved over the course of the early twenty-first century. We considered the challenges faced by educators in working with educational technologies, including mobile devices, and explored the particular characteristics of m-learning that distinguish it from other forms of learning. How m-learning can shape and be shaped by the twin vectors of space and time was an important feature of our © Springer Nature Singapore Pte Ltd. 2020 M. Kearney et al., Theorising and Implementing Mobile Learning, https://doi.org/10.1007/978-981-15-8277-6_15

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discussion. In Sect. 2, we explained the evolution of a new socio-cultural theory for m-learning, initially referred to as the Mobile Pedagogical Framework (MPF), which was designed to fit a gap in the theoretical literature around the unique pedagogies associated with m-learning. Informed by a robust research agenda over a decade, this Framework was amended and updated to become the version we now refer to as the iPAC Framework with its three distinctive dimensions of personalisation, authenticity and collaboration. The original version of the Framework was distinctive and particularly well aligned to the challenges and opportunities afforded in the ‘Mobile Age’ (Traxler, 2009). However, it needed to be operationalised and made accessible for practitioners, and in Sect. 3 we outlined a variety of different approaches and resources pertaining to the iPAC Framework that has been developed to address this challenge. Some of these resources include individual tools and instruments to support the design and evaluation of mobile pedagogies, for example, the iPAC scale and app rubric, while other developments involve more holistic and large-scale initiatives to make the Framework more appealing to particular audiences and stakeholders. For example, the Mobilising and Transforming Teacher Educators’ Pedagogies (MTTEP) project in Europe and Australia, targeted teacher educators and pre-service teachers and the Optimising Teaching and Learning with Mobile-Intensive Pedagogies project in Australia focused on m-learning with mathematics and science secondary teachers (see Chap. 1 for more details of these projects). We completed the book by examining the impact of the iPAC Framework across a range of different contexts and demographics to illustrate the wide variety of uses and settings to which the iPAC Framework has been applied. The breadth and international scope of these case studies in Sect. 4 serves to demonstrate the growing popularity, applicability and transferability of the Framework but it also identifies areas for further development and investigation, and these are explored more fully below.

15.2 The Impact and Achievements of the iPAC Framework to Date 15.2.1 Who Might Benefit from iPAC? The iPAC Framework is less than a decade old but in this relatively short period of time it has attracted interest from across both the research and practitioner communities as illustrated in the case studies that comprise Sect. 4. These case studies suggest that the Framework has both theoretical and practical values, challenging and changing both ideas and behaviours. It has clearly guided the practice and thinking of many educators who are using the Framework and its various operationalised components to gain more value from their investment in mobile technologies. For some practitioners, particularly those working in formal settings such as schools or colleges, it

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has provided a much welcomed educationally defensible rationale for the use and promotion of mobile technologies at a time when many feel besieged and pressurised to adopt the more Luddite or cautious practices advocated by some in the media and also some in positions of authority (Kearney, Burden & Schuck, 2019). iPAC is perceived by many practitioners to be intuitive since it appeals to pedagogical norms and practices that are well known and familiar to educators such as the importance of making learning relevant and meaningful for students (e.g. authenticity), the need to engage learners in active, rather than passive processes of learning (e.g. agency) and the emphasis on tailoring learning experiences to the needs of the individual (e.g. personalisation). There is little that is unorthodox or alien in the iPAC Framework and, at least on a superficial level, it can be interpreted by educators as ‘business as usual’ because it does not impose strict guidelines or specific pedagogical practices. Indeed the flexibility and adaptability of our research-inspired Framework is often cited as one of its greatest strengths by practitioners who use it alongside existing popular frameworks such as TPACK (Koeller & Mishra, 2009) and SAMR (Rommrel, Kidder & Wood, 2014). iPAC is not a technologically focused framework of m-learning. Its socio-cultural orientation encourages users to consider the unique time-space contexts of m-learning and how these contexts might influence learners’ experiences. This orientation which emphasises pedagogy over the technology is at the heart of its appeal and popularity with practitioners. However, it would be overly simplistic and naive to conclude that the flexibility and adaptability of the Framework also implies that there is nothing new or innovative about it or that it supports an entirely traditional model of mobile learning: ‘business as usual’. This is part of the dilemma we currently face as authors of the iPAC Framework. We are delighted by the popularity and adoption of the Framework by practitioners (and researchers) and acknowledge this may be partly based on its familiarity and commonsense terminology. But equally we are also aware of the limitations this mindset can generate and of the necessity to explore how the Framework pushes against existing boundaries and mindsets around m-learning, enabling educators to see new opportunities of which they had not been aware. The other principal beneficiary of the iPAC Framework, and indeed this book, is the research community associated with the use of digital technologies and mobile technologies in particular. This community has grown from a highly specialised group focused on what was essentially a niche activity in schools, into a worldwide movement that embraces the use of mobile technologies in a multitude of different settings and contexts. As a research community the importance of a sound, validated theoretical framework can never be over-estimated, and the volume of academic citations associated with the original Framework publication in 2012 testifies to the importance and value associated with it among this community. iPAC has been used, applied and adapted by researchers and doctoral students around the world working in many different contexts. The growing popularity of the iPAC Framework with the research community underpins the shift in focus for this group from a technological perspective to one which is concerned with the kind of pedagogies we refer to as socio-cultural.

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In the next section of this chapter, we focus on these challenges and frame them as research agendas since they are likely to be driven by the research community as it seeks to extend current understandings and paradigms of learning that are mediated by mobile technologies.

15.3 Future Research Agendas 15.3.1 Mediation One of the most salient issues and questions that permeates the entire book is the question of mediation or the extent to which the iPAC Framework can be used by practitioners without overt support and facilitation by experts or by artefacts such as the many video vignettes we have created to explain the different iPAC dimensions and sub-dimensions in the mobile learning toolkit discussed in Chap. 9 (see www. mobilelearningtoolkit.com). The first version of the iPAC Framework appeared in 2012 as an academic, theoretical article, theorising how the signature pedagogies of m-learning—personalisation, authenticity and collaboration—might play out from a socio-cultural pedagogical perspective. Although the Framework was underpinned by robust and rigorous data collected from an extensive review of the m-learning research literature, it was largely theoretical in nature and offered little in the way of support or direct guidance to mediate theory into practice. Since that point in time we, and many other researchers and practitioners, have explored the impact of different practical strategies and approaches to instantiate the iPAC Framework in practice. Mediation can take multiple forms, and many of these have been reported in the chapters of this book. The most immediate and direct form of mediation is through the support and expertise of a teacher, coach or facilitator, and examples of such mediation practices are documented in Chaps. 12 and 13 which explore how teachers and teacher educators learned to use the Framework as part of large-scale national and international research projects. Less immediate or synchronous is mediation through a resource that has been recorded, such as a ‘talking head’ instructional video or a video vignette illustrating how the Framework has been used in practice. An example of this is described in Chap. 9 which explores the mobile learning toolkit. Some other less immediate, asynchronous forms of mediation include the app rubrics and survey tools that are described in Chaps. 10, 11 and 12. Additionally, the iPAC Framework has been mediated through remote online courses such as the MOOC developed as part of the MTTEP project which was designed for independent use without a tutor (see Chap. 9) and for an online course designed for use with a tutor as part of the DEIMP project discussed in Chaps. 1 and 14. Drawing upon the data that have been collected from these many different interventions, we would conclude that while the iPAC Framework can, and has been used without any direct mediation that we are aware of, it is more likely to be effective

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and sustainable when it is mediated in one form or another. We would also conclude that some forms of mediation have proved to be more popular than others, although it would be more difficult to gauge if any are more effective than others. The most direct and immediate form of mediation has been through face-to-face communication, taking many forms that include formal training programs (see the E-Teach the Teacher program in Germany, Chap. 13), school-based coaching and mentoring schemes (see, for example, Moane, 2019), self-help support groups and one-to-one individual support and mentoring schemes. In all of these cases, the iPAC Framework has been mediated or scaffolded through the insights and experiences of more knowledgeable others who have implemented the theoretical aspects of the model in their own practice or, in some cases, with ourselves, working to distil and share our ideas and aspirations with practitioners. Data collected from some of these examples indicate that this is a vital and sometimes neglected stage in enabling practitioners to understand the Framework at a deep rather than superficial level. A good example involves the dimension of authenticity which originally consisted of three separate components (task, tool and setting) and which has now been consolidated into two sub-dimensions (task and context). In the various face-to-face workshops and tutorials we have organised to share the Framework with practitioners, we are frequently faced by what we would term ‘teacherly’ interpretations of authenticity, such as language teachers who describe worksheets they have created as authentic because they contain images of real places or people from the country or culture they are teaching about. Through careful explanation and selected examples, we have been able to overcome this kind of misconception of what we mean by authenticity in the context of m-learning, and this suggests that face-to-face mediation is a powerful and necessary ingredient in enabling practitioners to move beyond superficial or everyday understandings of what constitutes m-learning. This example also reveals the iterative process that underpins the ongoing design of the iPAC Framework since it was feedback collected in these workshops, which informed our redesign of the authenticity dimension as explained elsewhere in this book (see, for example, Chaps. 6 and 7). Online mediation, both synchronous and asynchronous, has also proved to be a popular method to support the transfer of theory into practice with the Framework. We have explored a variety of different online formats including synchronous webinars, video conferencing tutorials and seminars and asynchronous ‘talking heads’ videos and online courses. In some cases, such as the online course created to explain and illustrate the iPAC Framework (see http://www.mobilelearningtoolkit.com/onlinecourse.html), the impact has been quantified and measured. For example, we are aware that the Karlsruhe University of Education requires all pre-service teachers (PSTs) (over 500 per year) to complete the course as part of their preparation to use digital technologies effectively in their role as educators. Feedback from these PSTs reveals how a combination of the digital artefacts (e.g. video tutorials) which they can study asynchronously, and synchronous online tuition from their university tutor is perceived to be essential in enabling them to understand and put into practice the principles of the Framework. We are less certain, however, about the value and effect of using the online course alone without direct mediation from a tutor. As with all of

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these mediation artefacts, there is a need to conduct more research to ascertain the most effective strategies to implement the iPAC Framework. This includes but is not limited to the following research questions: • What differences in understanding and awareness of the iPAC Framework occur when the framework is explained by a tutor or expert or studied independently? • What kind of digital artefacts (e.g. instructional videos compared to eBooks) are most effective in helping educators understand and use the iPAC Framework effectively? • Are some strategies more efficacious than others in supporting educators to actually implement and use the iPAC Framework in their practice? There is an urgent need to understand better the efficacy, and indeed the need, for different mediation strategies to support educators in their use of the iPAC Framework and this is an important area for future research as we have suggested above. Nonetheless the research we have conducted already and the experiences of practitioners who have used the iPAC Framework suggests to us that although it is not essential, mediation makes a significant difference in accelerating the process by which educators come to understand and subsequently apply the Framework.

15.4 Applicability and Usefulness of the Framework A second and important issue arising from the issues covered in this book is the applicability and transferability of the iPAC Framework across different phases and sectors of education and indeed, across different cultural settings. Initially, the Framework was developed without a specific phase or context of education in mind although it was rooted in the m-learning literature which tended to draw more upon formal, rather than informal contexts of learning. Much of our subsequent research and field trials with the Framework have focused on the applicability of the iPAC Framework in teacher education, our own domain, and in formal education, mainly in schools and colleges (Burden et al., 2019; Burden & Kearney, 2017; Kearney, Burden & Rai, 2015). This includes both primary and secondary phases of education although there is a noticeable lack of research in pre-school and early years’ settings, a phase in which further research studies are urgently required (see Drigas & Kokkalia, 2016). The research we have conducted in these formal education contexts illustrate the widespread adoption of the Framework across large geographical areas of the world. iPAC has been used extensively across Europe, encouraged in part by the multiple Erasmus + projects we have initiated; in many different parts of Asia including China, Hong Kong, Vietnam, Thailand and Singapore; in Australasia where it has been used in many schools and institutions of teachers education and in some parts of North America, and more recently, Latin America. There is less tangible evidence to suggest it has been used in Africa to any significant extent. The adoption and use of iPAC seems, therefore, to transcend geographical boundaries, at least in formal educational contexts, but many questions remain regarding its cultural applicability

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and transferability. Are, for example, the socio-cultural assumptions or indeed biases that explicitly underpin the theoretical foundations of iPAC, universal pedagogical principles? Given the wide diversity of educational models and approaches adopted across the world, it would be unwise to assume they are, or that they are even core pedagogical approaches within the countries, regions and institutions where iPAC is used. Similarly, can we assume that the core dimensions that form the basis of iPAC—the signature pedagogies of mobile learning as we referred to them in 2012 (Kearney, Schuck, Burden & Aubusson, 2012)—are universally recognised as such and for how long will they remain ‘signature pedagogies’ when m-learning becomes the norm rather than a niche field of research and practice which it has tended to be until now? By default, most of the existing m-learning literature which we drew upon in designing iPAC had a western, ‘developed world’ orientation and therefore referenced educational norms and conventions prevalent in those societies up to that point in time. There was, for example, a noticeable lack of m-learning research undertaken in Asian contexts before 2012 and although this has changed rapidly in the intervening period, it is still necessary to test the cultural assumptions upon which the Framework was developed across more diverse cultural settings, including those in developing countries—an urgent area for further research—and in a variety of different cultural contexts including different religious and political contexts.

15.5 Formal Versus Informal Settings One variable that affects the applicability and adoption of the iPAC Framework is its use in either formal or informal educational settings, and the data collected so far point to a growing disparity between the two. As a lens for designing and evaluating effective m-learning episodes, iPAC tends to be implemented more holistically in informal rather than formal educational settings where there is a noticeable reluctance to exploit the proven value of mobile technologies or to design for the unique spatial and temporal opportunities they present. This has been a constant feature of the research we have undertaken over the past 10 years in formal educational settings, and it is comparable in both school and initial teacher education (Kearney, Burden & Rai, 2015; Schuck, Kearney and Burden, 2017). In 2016 when we asked a large number of school teachers and teacher educators around the world to share their m-learning pedagogies with us, we were surprised to find how many of these were situated in formal settings such as the classroom or lecture theatre, despite being described as authentic by their creators, and how infrequently they exploited the setting or flexibility of mobile devices to make learning more realistic and meaningful for their learners (Burden & Kearney, 2016). Agency and the freedom for learners to make greater choices about how, where and when they complete a task, is also noticeably different across formal and informal sites of learning. In formal contexts, teachers and teacher educators alike are less likely to grant students as many opportunities to exercise personal agency as in informal settings. This claim is substantiated by recent research exploring what

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constitutes innovation in mobile learning (Burden, Kearney, Schuck & Hall, 2019) in which the authors identified only three research studies from 57 that were deemed to be radically innovative. Tellingly, all three studies were located in non-formal settings, and there were multiple facilitators of the learning, including teachers, parents and community experts. In these rare instances, students demonstrated considerable agency and freedom to make choices about how they utilised a mobile device to undertake learning that they identified as personally meaningful and realistic. See Chap. 14 for more detail of the pedagogical principles underpinning these studies of innovation. It seems that although the iPAC Framework is used extensively in formal educational settings, it is not always used as fully or as effectively as it is in informal settings, and this is clearly an area for further research. It also raises questions about the boundaries between formal and informal learning which are generally held up as binaries in such discussions, even though mobile technologies are a significant factor in blurring and crossing such boundaries (Schuck et al., 2017; Viberg, Andersson & Wiklund, 2018). This is another area for further research.

15.6 Looking Forward: What Next? As the iPAC Framework continues to be used more widely by educators, we are presented with more opportunities to explore how it is being used and to better understand its impact. This has in turn encouraged us to re-examine our own assumptions and understandings about m-learning, which is generating several interesting fields for further research, by both ourselves and the wider research community. One area we see as important is to examine the three central dimensions more thoroughly to help gain a more nuanced understanding of their meaning and how they can be operationalised. In 2016, we started this process by revisiting the authenticity dimension (see Chap. 6) to theorise at a more granular level about the sub-constructs and their value. It would be appropriate to undertake this exercise for all three of the principal iPAC dimensions at regular intervals as the Framework evolves and more data become available to analyse. The customisation sub-dimension of personalisation would be a particularly timely starting point for this exercise. In 2012 when the Framework was first published, the customisation sub-dimension of personalisation was more theoretical than most of the other sub-dimensions because researchers in the field of m-learning were only just beginning to identify how the use of a mobile device could personalise a learning experience, sometimes at a rather superficial level (e.g. everybody can have their own home screen) but occasionally at a more profound level. A small number of apps such as Duolingo and Beluga Maths hinted at the way in which the power of algorithms, artificial intelligence (AI) and learning analytics might be combined to anticipate the cognitive needs of learners at a more granular level than was otherwise possible. This was an early indication that mobile devices might have the potential to be a portal for adaptive learning that could genuinely meet the needs

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of every individual and in a manner that was meaningful rather than purely novel. At the time of writing in 2020, we are beginning to perceive what this might look like as ever more powerful devices combine with smarter algorithms, context-aware apps that significantly enhance customised learning. Given these emerging software and hardware developments, it is timely to revisit the sub-dimension of customisation in order to better understand what this looks like when mediated through a mobile device. There is also a need to extend the scope of our research in formal settings beyond schools and teacher education to explore the value of the iPAC Framework in tertiary settings such as universities, post-compulsory colleges and in learning venues situated in workplace and commercial contexts. Although the use of mobile technologies has expanded rapidly in these settings (see for example Compton & Burke, 2018), we currently have very limited data or case studies that have applied and studied the iPAC Framework in these phases of education and it would be very informative to explore the value of the Framework in these settings and to compare these with the much greater volume of data collected in schools and teacher education. For instance, are students able to exercise greater agency in these post-compulsory settings than their counterparts in schools (and indeed teacher education) or do university lecturers still impose constraints on the choices their students are able to make as we have seen in many school studies? Similarly, are the opportunities for more authentic learning, using real-world tasks and different physical, virtual and hybrid settings, more likely to be exploited in post-compulsory settings such as work-based learning where the potential gains from doing so are obvious? These questions and more are important parts of the research agenda that need to be further explored and understood if we are to gain a more holistic and complete picture of iPAC’s value and applicability.

15.6.1 iPAC in a Post-Pandemic World At the time of writing in 2020, the world is experiencing an unprecedented challenge to traditional teaching and learning in schools and tertiary education brought about by a global pandemic. With traditional physical learning spaces quarantined or off-limits, educators have been forced to explore the use of online learning for their students with video conferencing and online webinar systems hastily requisitioned for use in ways many were not designed for. Given the scale and ongoing duration of the current pandemic, it is unlikely school education will return to its previous format anytime soon and indeed it is more than likely that many of the old certainties will never return. The current crisis has necessitated significant changes to the way teaching and learning are usually organised and in turn these changes have led educators and observers to question many of the taken for granted assumptions upon which our educational structures are constructed. These changes include both the temporal and spatial conditions that schools have traditionally operated within, such as fixed timetable-based teaching slots and routines, the organisation, size and layout

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of teaching spaces and the need for factory-like standardisation whereby all students are treated as cohorts rather than individuals. In this respect, the iPAC Framework, which has been the focus of this book, has considerable relevance for the discussions and planning that teachers, educational leaders and policymakers are currently struggling to understand and cope with. In particular, it will be challenging to return to normal practices and structures in a postnormal world. We proffer at least two alternative responses based on the evidence collated and presented in previous chapters. In the first, we draw the reader’s attention to the approaches and case studies that demonstrate possible ways in which to use mobile technologies to replicate or approximate traditional classroom practices that are currently difficult or impossible to undertake during the pandemic. In the second scenario, we offer examples and models from the book to question the notion of a return to normalcy and point out how the current pandemic is an opportunity to use mobile technologies to rethink and transform traditional educational practices. There is little doubt that mobile devices have enabled many students who would not otherwise have access to the Internet, to join in with and benefit from the alternative online approaches that schools have hastily put in place to replace face-to-face provision. Students are using their personal phones, tablet devices and laptops to access and participate in a spectrum of different online provisions that include video conferencing and virtual learning environments. In these instances, the mobile device has supported online conversations (including text-based conversations), as represented in the collaboration dimension within iPAC, and have therefore gone some way towards recreating a sense of presence that is associated with face-to-face teaching and learning. In some cases, teachers have encouraged students to use their mobile devices to collect information such as images of their location to provide learners with more opportunities for active learning, or what the iPAC Framework labels agency under the personalisation dimension. However, in many of the instances where digital technologies are highlighted for their role in supporting a return to formal schooling (albeit virtually), they appear to have been used to support rather passive and traditional pedagogies which reduce or minimise student agency. This invites further discussion around the opportunities that the current pandemic offers to structure learning differently through the mediation of mobile devices. In this second scenario, we can discern from the evidence presented in this book how the iPAC Framework can act as a template and source of inspiration to encourage educators to experiment with different mobile pedagogies that exploit affordances such as mobility, even in a time of relative lockdown. The mobile learning toolkit which was explained in some detail in Chap. 9 illustrates many different ways in which educators might use mobile technologies to overcome some of the challenges and difficulties facing both teachers and learners in the current crisis. One of these features is the co-creation of digital content (part of the collaboration dimension) and the multiple opportunities for students to demonstrate their understanding of a topic by sharing an artefact with their peers. This activity could be the development and sharing of an eBook, an app or a short video (see Chap. 9, for examples) which provide students with opportunities to demonstrate choice-making (Agency) and a more customised approach to learning than is normally the case in formal education.

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Under these circumstances, there is less need to focus on synchronous learning in which an entire class moves through an activity at the same pace and time and more opportunity for students to explore their individual interests at times and at a pace that best suits them. Perhaps more significantly, the current crisis calls out for tasks and activities that are relevant and meaningful for learners who may be locked down in isolation, away from the familiar contexts, friends and conventions they associate with their pre-pandemic world. The authenticity dimension of the iPAC Framework captures opportunities for educators to design mobile pedagogies that make tasks more realistic, often tapping into the use of realistic tools and settings that would not be available in a formal school setting. Students might be encouraged to exploit the learning opportunities still accessible in the physical environment (e.g. their backyard) by undertaking a mobile citizen science activity, or by joining a virtual tour (e.g. Google Expeditions) with a group of their peers to investigate a community challenge, such as plastics pollution in rivers. These latter mobile pedagogies exploit the features of the iPAC Framework in ways that are deeper and more meaningful for students than some of the more passive activities we are currently observing. Such activities might point towards an alternative narrative for how m-learning can be effectively adopted in the current crisis. In conclusion, the current crisis facing the world is already revealing why mlearning may finally be conceptualised and used as a mainstream approach. We hope that iPAC can help educators to think about this in a principled manner that is underpinned by a strong body of validated evidence. Important research on how mlearning has been utilised by educators during the pandemic is already underway (Hall et al., 2020), and further research would be welcome at this time of unprecedented change and uncertainty.

15.7 Conclusion The phenomenon of m-learning which started as a niche gathering of researchers and innovative practitioners in the early part of the twenty-first century (Traxler, 2009; Kukulska-Hulme, 2010) has matured into a mainstream activity in education, fueled and accelerated by the near-ubiquity and pervasiveness of mobile technologies which have increased in computational power exponentially, while decreasing in cost in real terms. Pedagogically, however, m-learning remains problematic, and its value as a learning tool is highly contested and debated not only in educational circles but in political ones as well (Scutt, 2019). Our own research indicates how educators tend to use only a small subset of the m-learning affordances that have been identified and how this is accentuated in formal settings such as schools and colleges. This book reveals a different narrative, illustrating the importance of establishing and supporting a sound theoretical model to underpin the design of m-learning experiences, which is applicable across a wide range of contexts and settings. There will come a time when it becomes redundant to refer to learning with any kind of digital prefix and at that point it may well be necessary to re-examine what was unique or signatory

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about learning mediated through a mobile device. However, this is not that time and as m-learning begins to mature and morph into mainstream education, it is even more imperative that we consider what really adds value to learning above and beyond what would be possible without a mobile device. The iPAC Framework has a proven track record in addressing this question but it needs to be applied, critiqued and modified in many more diverse settings and contexts than we have been able to illustrate within the confines of a single volume. We challenge researchers and practitioners alike to consider and use the Framework as broadly as possible and to share the findings and discoveries with a growing community of iPAC users who have done likewise. We have established a dedicated portal and website for this purpose (see https:// www.ipacmobilepedagogy.com/) where research and case studies can be reported and publicised and we invite you to contribute and join with us in this next phase of the iPAC journey.

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