Rethinking Pedagogy for a Digital Age Rethinking Pedagogy for a Digital Age examines contemporary issues in the design and delivery of effective learning through a critical discussion of the theoretical and professional perspectives informing current digital education practice. This third edition has been thoroughly revised to address sociocultural approaches, learning analytics, curriculum change, and key theoretical developments from education sciences. Illustrated by case studies across disciplines and continents for a diversity of researchers, practitioners, and lecturers, the book is an essential guide to learning technologies that is pedagogically sound, learner-focused, and accessible. Helen Beetham is an independent researcher, writer and adviser on issues in digital learning. She is a long-standing consultant to Jisc (UK) and has worked with a number of global universities on their digital education strategies. Rhona Sharpe is Professor and Head of the Department of Technology Enhanced Learning at the University of Surrey and Associate Lecturer for the Institute of Educational Technology at the Open University, UK. Rethinking Pedagogy for a Digital Age Principles and Practices of Design Third Edition Edited by Helen Beetham and Rhona Sharpe Third edition published 2020 by Routledge 52 Vanderbilt Avenue, New York, NY 10017 and by Routledge 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2020 Taylor & Francis The right of Helen Beetham and Rhona Sharpe to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. The purchase of this copyright material confers the right on the purchasing institution to photocopy or download pages which bear the eResources icon and a copyright line at the bottom of the page. No other parts of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. First edition published by Routledge 2007 Second edition published by Routledge 2010 Library of Congress Cataloging-in-Publication Data Names: Beetham, Helen, 1967- editor. | Sharpe, Rhona, 1969- editor. Title: Rethinking pedagogy for a digital age : principles and practices of design / edited by Helen Beetham and Rhona Sharpe. Description: Third Edition. | New York : Routledge, 2020. | “Second edition published by Routledge 2010”–T.p. verso. | Includes bibliographical references and index. | Identifiers: LCCN 2019007293 (print) | LCCN 2019011035 (ebook) | ISBN 9781351252805 (eBook) | ISBN 9780815369257 | ISBN 9780815369257(Hardback) | ISBN 9780815369264(Paperback) | ISBN 9781351252805(eBook) Subjects: LCSH: Computer-assisted instruction–Curricula–Planning. Classification: LCC LB1028.5 (ebook) | LCC LB1028.5 .R44 2020 (print) | DDC 371.33–dc23 LC record available at https://lccn.loc.gov/2019007293 ISBN: 978-0-815-36925-7 (hbk) ISBN: 978-0-815-36926-4 (pbk) ISBN: 978-1-351-25280-5 (ebk) Typeset in Times New Roman by Swales & Willis, Exeter, Devon, UK Visit the eResources: www.routledge.com/9780815369264 Contents List of Illustrations Acknowledgements Notes on Contributors Foreword to the Third Edition Foreword to the Second Edition Foreword to the First Edition An Introduction to Rethinking Pedagogy viii x xi xviii xx xxiii 1 HELEN BEETHAM AND RHONA SHARPE PART 1 Theories and Principles 1 Learning Theory and the New Science of Learning 15 17 TERRY MAYES 2 Learning Activities and Activity Systems 32 HELEN BEETHAM 3 The Analysis of Complex Learning Environments 49 PETER GOODYEAR AND LUCILA CARVALHO 4 A View from Social Science: Foundational Issues for Design 66 CHRISTOPHER R. JONES 5 The Community of Inquiry Theoretical Framework: Designing Collaborative Online and Blended Learning MARTHA CLEVELAND-INNES 85 vi Contents PART 2 Practices 6 Learning Designs as a Stimulus and Support for Teachers’ Design Practices 103 105 SHIRLEY AGOSTINHO, SUE BENNETT, LORI LOCKYER, JENNIFER JONES AND BARRY HARPER 7 The Challenge of Teachers’ Design Practice 120 LIZ MASTERMAN 8 Designing for Learning within an Organisational Context 134 RHONA SHARPE AND ALEJANDRO ARMELLINI 9 Open Education: Design and Policy Considerations 149 CATHERINE CRONIN 10 Frameworks to Guide Practice 164 GRÁINNE CONOLE PART 3 Influences and Futures 11 Design Principles for Learning with Mobile Devices 179 181 AGNES KUKULSKA-HULME AND JOHN TRAXLER 12 Designs for Professional Learning 197 RACHEL H. ELLAWAY 13 Datafication of Education: A Critical Approach to Emerging Analytics Technologies and Practices 212 BEN WILLIAMSON 14 Student as Producer Is Hacking the University JOSS WINN AND DEAN LOCKWOOD 227 Contents vii PART 4 Resources 1 241 Theory into Practice: Approaches to Understanding How People Learn and Implications for Design 243 2 Learning Activity Design: A Checklist 251 3 Digital Learning Activities: Linked to Bloom’s Taxonomy of Educational Objectives 255 4 Digital Capability Checklist for Curriculum Designers 264 5 Blue Skies Planning Checklist 266 6 Critics’ Checklist 268 7 Storyboard 270 8 Digital and Information Literacy Expressed in Programme Learning Outcomes 272 Index 278 Illustrations Figures 2.1 2.2 3.1 4.1 5.1 6.1 6.2 8.1 10.1 10.2 10.3 12.1 A model learning activity, based on Engeström (1987: 78) A model activity system, based on Engeström (1987: 78) A (partial) network of objects and activities Design: an indirect approach (Source: Goodyear et al. 2001) Community of inquiry theoretical framework, reproduced with permission from D.R. Garrison (Source: coi.athabascau.ca) Phases from the Australian Research Council project Improving University Teaching: Creating strategies and tools to support the design process Applying an international standard for sharing and reuse Double constructive alignment framework The 7 Cs of learning design An example of an 8LEM flashcard (Source: Alan Masson http://addl.ulster.ac.uk/odl/hybridlearningmodel) The production card (Source: Alan Masson http://addl.ulster. ac.uk/odl/hybridlearningmodel) Continuum of professional education. Learning activities and teaching principles change according to the stage of training 35 42 60 68 88 112 115 142 169 171 173 200 Tables 5.1 Social Presence: Conceptual Categories, Measurement, and Application 5.2 Teaching Presence: Conceptual Categories, Measurement, and Application 5.3 Cognitive Presence: Conceptual Categories, Measurement, and Application 5.4 Emotional Presence: Conceptual Categories, Measurement, and Application 91 93 95 96 Illustrations 10.1 Examples of use of SAMR 10.2 Mapping Presentation Tools and Presentation of Information of Communication to the ICAP Framework ix 166 175 Boxes 4.1 4.2 8.1 8.2 An example of two contrasting interpretations of instructions from Jones and Asensio (2001) Example of two students from the same course and university managing their environments in notably different ways from Jones (2011: 110) Designing for active blended learning Designing for graduate attributes 69 70 137 140 Acknowledgements The editors would like to acknowledge the support of the authors who contributed to this edition, particularly in providing peer review for each of the chapters. We would also like to thank those authors whose work appeared in previous editions, and who continue to be an inspiration: James Dalziel, Patrick McAndrew, and Diana Laurillard. Finally, thanks are due to Jisc for permission to reproduce work originally funded by Jisc in Resources 3 and 4, and for providing (through their Design for Learning programme) the context in which the original edition of Rethinking Pedagogy for a Digital Age was planned and brought together. The Critics Checklist and the Blue Skies Checklist (Resources 5 and 6) were created by a team at the Oxford Centre for Staff and Learning Development (OCSLD), Oxford Brookes University, as part of a Pathfinder project funded by the Higher Education Academy. Notes on Contributors Shirley Agostinho is an Associate Professor in the School of Education, Faculty of Social Sciences at the University of Wollongong, Australia. Her research has focused on investigating how information and communication technology (ICT) can be used to enhance learning. Her research agenda has a dual perspective of examining teacher design practice and how learners can support their own learning through the use of ICT. Alejandro (Ale) Armellini is Professor, Dean of Learning and Teaching, and Director of the Institute of Learning and Teaching in Higher Education at the University of Northampton, where his key role is to provide leadership in learning and teaching and learning innovation across all Faculties. His work includes the development, implementation, and evaluation of Northampton’s Learning and Teaching Plan, which includes a comprehensive, externally accredited staff development programme. He was also the strategic lead for the redesign of all programmes for active blended learning, in preparation for the University’s move to its new Waterside campus, which opened in September 2018. Ale is a Principal Fellow of the Higher Education Academy and Fellow of the Royal Society for the encouragement of Arts, Manufactures and Commerce. Ale’s research focuses on learning innovation, online pedagogy, course design in online environments, institutional capacity building, and open practices. He holds visiting professorships at several UK and overseas universities. Helen Beetham is an independent researcher, writer and adviser on issues in digital learning. As a long-standing consultant to Jisc (UK), she has led numerous national programmes and written influential reports on topics such as the digital curriculum in higher education, e-portfolios, digital literacy, open education, and digital organisations. Helen was a member of the UK Government’s Beyond Current Horizons programme on educational futures, and has led futures thinking and digital strategy initiatives for a number of global universities. Recently she has worked with the EU on a digital competence framework and assessment tool, helped to develop a Commonwealth of Learning program for xii Notes on Contributors college leaders around the world, and designed an international student survey which received over 100,000 responses. Her current research focuses on fostering critical thinking and practice in digital settings. Sue Bennett is Professor and Head of the School of Education, Faculty of Social Sciences at the University of Wollongong, Australia. Sue’s work investigates how people engage with technology in their everyday lives and in educational settings. Her aim is to develop a more holistic understanding of people’s technology practices to inform research, practice, and policy. She has been researching design thinking and learning design since 1999. Lucila Carvalho is a Senior Lecturer in the Institute of Education at Massey University (Auckland), New Zealand. Her research explores how knowledge and social structures shape the design and use of technology, and how technology influences social and educational experiences. She has published and presented her work in international journals and at conferences in the fields of education, sociology, systemic functional linguistics, design and software engineering. Her most recent books are Place-based spaces for networked learning (with Peter Goodyear and Maarten de Laat, Routledge, 2017) and The architecture of productive learning networks (with Peter Goodyear, Routledge, 2014). She is currently working on a book with Pippa Yeoman: Learning to teach in innovative spaces: A toolkit for action (Routledge). Martha Cleveland-Innes is Professor of Education Innovation at Athabasca University in Alberta, Canada. In her eighteen years as an academic at Athabasca University, Martha has been involved in numerous research projects on open and distance learning and has taught, supervised, and supported hundreds of graduate students working in the same area of education. Previously Chair of the Centre for Distance Education (2012–2018), she now serves as Program Director of the Master of Education Program where an annual cohort of 80 students of education innovation begin a journey of knowledge and skill development. She is co-author of a book blended and online teaching and learning and coeditor of a book on teaching and learning in the new higher education. Martha is coordinator and principal researcher for the Community of Inquiry framework for online and blended learning research site (coi. athabascau.ca). She previously held a major research grant from the Canadian Social Sciences and Humanities Research Council which supported rigorous empirical tests on the value of this framework. Gráinne Conole is Professor and Head of the Open Education Unit within the National Institute for Digital Learning at Dublin City University. Before this she was a consultant and visiting professor at Dublin City University. She has worked at the Universities of Bath Spa, Bristol, Leicester, Notes on Contributors xiii the Open University UK, and Southampton. Her research interests are on the use of technologies for learning, including Open Educational Resources (OER) and Massive Open Online Courses (MOOCs), new approaches to designing for learning, e-pedagogies, and social media. She has a National Teaching Fellowship and is a fellow of EDEN and ASCILITE. She has published and presented over 1000 talks, workshops and articles. Her Masters and PhD supervision and external examinations (both national and international) comprise 14 as internal examiner, 56 as external examiner, and 16 as supervisor. She has been external examiner for the Technology and Learning Masters course at Trinity College Dublin, PGCE course at the University of Southampton, a Masters course in Educational Technology in Ulster, the Networked Learning masters at Lancaster University, the Masters in E-learning at Plymouth University, an E-learning Masters at Dublin City University, and the Masters in E-learning at the Dublin Institute of Technology. Catherine Cronin is an open educator, open researcher, and Strategic Education Developer at the National Forum for the Enhancement of Teaching and Learning in Higher Education (Ireland). Catherine’s work focuses on digital and open education, critical approaches to openness, digital identity practices, and learning and teaching in increasingly networked and participatory culture. In her PhD, she explored the use of open educational practices in higher education. Catherine is a member of the advisory boards of the Open Education Working Group and Virtually Connecting, and co-chair of the OER19 Conference. Involved in teaching, research and advocacy in higher education and in the community for over 25 years, Catherine is a regular contributor to conversations and collaborative projects in the area of open education, within Ireland and globally. Rachel Helen Ellaway is the Director of the Office of Health and Medical Education Scholarship (OHMES) and a Professor in Community Health Sciences at the Cumming School of Medicine at the University of Calgary in Western Canada. Widely published in the field of health professions education her academic work includes online learning, simulation, and the use of new technologies for teaching and assessment in and around health professional education. Peter Goodyear is Professor of Education, Australian Laureate Fellow and Australian Learning and Teaching Fellow at The University of Sydney, Australia. He has been carrying out research in the field of learning and technology since the early 1980s and has published 13 books and more than 130 journal articles and book chapters. His most recent books are The education ecology of universities (Routledge, 2019) and Spaces of teaching and learning: Integrating research and practice (both with Rob xiv Notes on Contributors Ellis, Springer, 2018); Epistemic fluency and professional education: Innovation, knowledgeable action and actionable knowledge, (with Lina Markauskaite, Springer, 2017) and Place-based spaces for networked learning (with Lucila Carvalho and Maarten de Laat, Routledge, 2017). His research has taken place in the UK, mainland Europe, and Australia and has been funded by the Australian Research Council, the UK Economic & Social Research Council, UK Government, and Industry and the European Commission. Barry Harper is an Emeritus Professor of Education at the University of Wollongong in Australia. He has extensive experience in the design, development, implementation and evaluation of technology-mediated and online learning materials. His research focuses on the design, development, implementation, theory and evaluation of technology supported learning environments with a recent emphasis on learning design. His research has been supported by a wide range of funding bodies. Christopher R. Jones is an Emeritus Professor at Liverpool John Moores University. His research focuses on the utilisation of the metaphor of networks in the understanding of learning in higher education. Until 2014 Chris was an organiser of the Networked Learning Conference series and he has a longstanding interest in collaborative and cooperative methods for teaching and learning. Chris has led a number of research projects and was the principal investigator for a UK Research Council funded project ‘The Net Generation encountering e-learning at university’ until March 2010. Chris has published over 90 journal articles, book chapters and refereed conference papers connected to his research. He is the author of Networked learning: An educational paradigm for the age of digital networks, published by Springer 2015. He is also the joint editor of two books in the area of advanced learning technology – Networked learning: Perspectives and Issues published by Springer in 2002 and an edited collection with Lone Dirckinck-Holmfeld and Berner Lindström (2009) Analysing networked learning practices in higher education and continuing professional development. Sense Publishers, BV Jennifer Jones is the Education Innovation Advisor for the Advanced Manufacturing Alliance based in the College of Engineering, IT and Environment at Charles Darwin University, Australia. She holds a PhD in Education from the University of Wollongong and has worked in design, support and technology integration across schools, vocational education and universities. Jennifer is passionate about the role collaboration and design can play to improve teaching and learning outcomes. Currently, she is developing Industry 4.0 training courses and pathways in collaboration with SPEE3D, the creators of a world-first high-speed 3D metal printer. Notes on Contributors xv Agnes Kukulska-Hulme is Professor of Learning Technology and Communication in the Institute of Educational Technology at The Open University, UK, where she leads the Future Learning Research and Innovation programme. Her research spans a number of inter-related fields including mobile learning, mobile language learning, and online learning. She is Past-President of the International Association for Mobile Learning and serves on the Editorial Boards of several journals. Her projects have included the MASELTOV project on personalized technologies for social inclusion, the British Council sponsored research on Mobile Pedagogy for English Language Teaching, and the SALSA project on language learning in the next generation of smart cities. Her publications include over 160 articles, papers and books, and she has also produced commissioned reports for UNESCO, the British Council, the Commonwealth of Learning, the International Research Foundation for English Language Education and Cambridge University Press. Dean Lockwood is a Senior Lecturer in Media Theory in the School of Film and Media, University of Lincoln, UK. He leads programmes at both undergraduate and postgraduate levels and his research interests focus upon twenty-first century innovations in media studies. He is the author, with Rob Coley, of ‘Cloud Time’ (Zero, 2012), which deals with the culture and politics of cloud computing, and ‘Photography in the Middle: Dispatches on Media Ecologies and Aesthetics’ (Punctum, 2016). Lori Lockyer is Professor and Dean of Graduate Research School at the University of Technology Sydney, Australia. She has been researching in the area of learning design for over a decade. As an extension of this work, Lori is investigating teacher design thinking and practices. Liz Masterman has a PhD from the University of Birmingham and is a senior researcher in the Technology Enhanced Learning team at the University of Oxford, UK. Liz has conducted research into design for learning since 2004 and has additionally led research within the University into the student digital experience, open educational resources and open educational practice, and use of the institutional VLE. She also specializes in the evaluation of digital education projects, including trials of lecture capture and typed essay exams, and migration to a new VLE. Terry Mayes is an Emeritus Professor at Glasgow Caledonian University, UK, where he has been based since 1996. He has a long experience as researcher, author and teacher. The main theme of Terry’s research and writing has been to apply learning theory to the understanding of pedagogy, with a particular focus on enhancement through technology. His academic career has been spent in a variety of roles in Scottish Universities. He moved into full-time research at the University of Strathclyde in the late 1980s, then led the research programme in what was the first xvi Notes on Contributors institute in the UK dedicated to learning technology in higher education, at Heriot-Watt University. In latter years Terry has been increasingly involved as an advisor in learning and teaching policy, consulting for a range of higher education agencies. In 2007 he was awarded an honorary life membership of the Association for Learning Technology. Rhona Sharpe is Professor and Head of the Department of Technology Enhanced Learning at the University of Surrey, UK. She is also Associate Lecturer for the Institute of Educational Technology at the UK Open University. Rhona has led a number of projects investigating learners’ experiences of technology in both further and higher education. For many years she chaired ELESIG; a special interest group for those interested in evaluations of learners’ experiences of e-learning. She is interested in the processes by which we design online learning spaces and the digital literacies and attributes that learners need in order to learn well in them. She is a Senior Fellow of the Staff and Educational Development Association, a Principal Fellow of the Higher Education Academy and a National Teaching Fellow. Her latest book is 53 Interesting ways to support learning online (2016). John Traxler was Professor of Mobile Learning, the world’s first, from September 2009, and is now Professor of Digital Learning in the Institute of Education at the University of Wolverhampton, UK. He is a pioneer of mobile learning, associated with mobile learning projects since 2001 when he was evaluator for m-learning. He is a Founding Director of the International Association for Mobile Learning and co-editor/author of the definitive, Mobile learning: A handbook for educators and trainers, plus Mobile learning: The next generation; Mobile learning and mathematics; Mobile learning and STEM: Case studies in practice; Mobile learning in higher education: Challenges in context. Ben Williamson is a Chancellor’s Fellow at the University of Edinburgh, researching the intersections of digital technologies, science, and data with education policy and governance. His current research focuses on the expansion of educational data infrastructures to enable information to be collected from schools and universities, and on the emergence of ‘intimate data’ relating to students’ psychological states, neural activity, and genetic profiles, examining the implications of increasingly data scientific ways of approaching educational policy and practice. He previously published Big data in education: The digital future of learning, policy, and practice (Sage, 2017), and maintains the research blog Code Acts in Education (codeactsineducation.wordpress.com). Joss Winn is a Senior Lecturer in the School of Education, University of Lincoln. He leads the PhD and PhD Professional programmes and contributes to teaching at postgraduate level. His research has focused on Notes on Contributors xvii technology and education, co-operative education, workplace democracy and alternative forms of higher education. Currently, he is undertaking research into craft education, with a specific focus on the teaching and learning of lutherie through the routes of autodidacticism, apprenticeships, and vocational education. Foreword to the Third Edition I spent the last 12 months mainly on two projects. The first was to write up case studies of university and college instructors doing innovative teaching with technology, for Contact North’s Pockets of Innovation (teachonline.ca/ pockets-innovation). Although there were many examples of interesting innovations and approaches to the use of digital technologies for teaching, few were driven by any clear theoretical or philosophical positions, other than the very important one of increasing access or flexibility for students. In other words, most instructors were trying to do what they had done previously, but more conveniently for students. They were not usually trying to achieve different types of learning outcomes, or develop new skills in learners. My second project was the annual survey of online learning in Canadian universities and colleges (onlinelearningsurveycanada.ca). This established that about 8% of all post-secondary degree and diploma teaching in Canada is now fully online for a total of 1.36 million online course registrations. Fully online course enrolments are growing at a faster rate than overall enrolments. More significantly, blended learning, the combination of classroom and online learning, is growing even faster. I predict that nearly all teaching in the future will involve at least some element of digital learning. But the survey also found that the greatest challenge was lack of faculty training, not just in online learning but in teaching generally. Without a solid base in educational theory and pedagogy, instructors were finding it difficult to know how best to use digital technologies for teaching. Yet at the same time, the world outside the academy is rapidly changing. A digital society is demanding new skills and new ways of learning, and offering major challenges in terms of economics, privacy, security and ethics. Thus not only do we need new learning outcomes, but we need to develop in our students new ways of learning so they are fit for purpose in a digital age. Back in the 1940s, the psychologist Kurt Lewin said that there is nothing so practical as a good theory. Without theory or hypotheses, we enter new situations blind, and fall back on trial and error as our main strategy, or merely repeat what worked in the past, even though the present is now different. Although this often works, it is high risk and inefficient. Theory Foreword to the Third Edition xix can help avoid mistakes and offer insight about how to move forward. In particular it can provide a base from which to tackle the following challenges: • • • • • What should I be teaching? What learning outcomes or skills do my students need? What do they need to know in order to do this? What is the best way to develop such knowledge and skills? What do students need to do to acquire this knowledge and skills? What resources or technologies would help in this process? What kind of learning environment would best support such learning? What should my role be as a teacher to best support this type of learning? What is the best way for students to demonstrate the knowledge and skills they acquire through this process? How do I (or they) assess that? This book is essentially a set of tools to help answer such questions. The chapters in this book look not only at theories of teaching and learning (or pedagogies in the jargon) but also at the practical applications of these theories, and in so doing suggest processes or procedures for systematically applying a consistent theoretical approach to teaching and learning. The last point I would make is that technology may change rapidly, but humans do not. Although a digital age presents new challenges and new contexts, there is nevertheless a great deal that is already known about effective teaching and learning, most of which will apply just as well in a new age of digital technology. However, most instructors in colleges and universities are highly knowledgeable content experts and researchers but they are nowhere near so knowledgeable about the art and science of teaching and learning. This book provides an essential counterbalance by providing knowledge about what we do know about teaching and learning from past research and best practices. So this book draws on past knowledge and experience about teaching and learning, sets it within the current context of a digital age, and provides theory and practices that will enable us to prepare better our students for the future. Enjoy! Tony Bates, Distinguished Visiting Professor, The G. Raymond Chang School of Continuing Education, Ryerson University, Canada Foreword to the Second Edition Do we need to rethink pedagogy again? Does technology innovation imply the continual renewal of what we mean by pedagogy? There is some continuity of thinking within education. No-one has yet shown that we need to change our understanding of how students learn. There have been some wild statements from opinion-formers about technology revolutionizing how students will learn in the twenty-first century, but the research-based fundamentals of what it takes to learn have not been challenged. The theoretical concepts and approaches still call on Dewey, Vygotsky, Bruner, Papert, Lave and Wenger, with no challenge to our fundamental understanding of what it takes to learn in formal education. Pedagogy is still seen as guiding the learner to learn. The emphasis is still on pedagogy leading the use of technology, rather than adapting to what technology offers. However, pedagogy has a close relationship with the technologies of learning, and inevitably the scope and style of pedagogy changes as the technology changes. The multiplicity of learning technologies, beyond the classroom and away from the teacher, opens up new territories for education. Digital technologies trigger a different kind of relationship between the teacher, the learners, and what is being learned. Yes, we do need to keep rethinking the style and scope of pedagogy as the digital age continues to throw up new technology-driven challenges. The focus has shifted in recent years from the individual teacher designing a module or session to include teams designing whole courses. There is a greater sense that, with learner access to the burgeoning resources on the web, and with their increasing digital skills, we should remodel education so that learners can take control of their own learning. Certainly, the research literature and the national ‘e-learning’ policies and strategies of the last few years are full of the promise of the ‘self-directed’ and ‘independent’ learning that now become possible. The last few decades of educational thinking have maintained an unchallenged drive to more active forms of student learning – collaborative, experiential, inquiry-based, problem-based approaches citing theories of constructionism, social constructivism and situated learning. The Foreword to the Second Edition xxi initial manifestation of the web allowed little more than the acquisition learning that was familiar from books and lectures, and did little to address the active learning sought by educators. With the development of opportunities for user-generated input to digital repositories, crowd-sourcing, and social media, the web has at last begun to enable these active forms of learning. At the same time, the Open Educational Resources movement has turned the web into a universal educational library of lecture materials and wellproduced educational resources, available to all. This is a significant shift for education because it provides access to educational materials to anyone who has Internet access. It is a wonderful democratisation of access to resources. But it is not the same as access to education. And learning technologists have to keep alive the vision for what technology enhanced learning could be. We have to contribute to the policy debates about learning technologies, because opinion-formers outside the field easily overplay the capabilities of technology. At the time of the first edition, learning technologists were insisting that there was more to online learning than lectures on the web, and we should be looking to the active forms of learning that could be offered. Since then, we have had the explosion of social media to connect learners to each other, there are more opportunities for user-generated content, and yet now there are even more lectures on the web. The wider expectation is therefore that ‘self-directed’ and ‘independent’ learning have indeed become possible but online access to opportunities for inquiry, discussion, production, collaboration and acquisition is not itself education. It does enable informal, self-directed, independent learning activities, just as public libraries and public houses have always done – which is wonderful, but it is not education. This is what the contributors to this book help to clarify. Our digital native students may be able to use technologies, but that does not mean they can learn from them. Being able to read and write never meant you could therefore learn from books. Learners need teachers. As learners we cannot know what it is possible to know, or how to make that journey to what we want to become. We need guidance. Pedagogy is about guiding learning, rather than leaving you to finding your own way. Pedagogy puts the onus on teacher to guide the learner’s journey to a particular and productive end. We may prefer to find our own way. Good. There have always been libraries and friends and experiences to enable us to do that, now supplemented with digital resources and Internet friends and virtual experiences. Informal learning continues with ever better opportunities. Education does something different from what we can do for ourselves. As learners going to education we have higher ambitions – for this we need teachers because that learning journey is as hard as it ever was. This is why, throughout these chapters there are references to the centrality of the role of the teacher, and to the complexity of designing for learning. The complex architecture of activities learners engage in as they xxii Foreword to the Second Edition tackle new ideas and high-level skills shows the difficulty of the teaching task. The field is beginning to recognize that teachers need to help each other discover how best to organize the mix of learning technologies in support of learning. Equally important is the role of students in helping teachers discover how best to develop the new pedagogies. The exploration of a greater equality of control over the design of learning could be a significant shift for pedagogy. It is a powerful idea that the teacher can learn about teaching from their exchanges with students. Technology gives teachers much better access to how students discuss and debate in an online forum, to data analytics that describe how they progress through a sequence of learning activities, what they produce in a collaborative wiki, how they reflect on their learning journey in their e-portfolio. If, as teachers, we use technology to elicit and make use of this extensive information to remodel our teaching that will be a new task to fit into the teacher’s repertoire. It is an exciting prospect, but requires a major rethink of how to manage teacher time to optimize pedagogy. There is another important source of information about teaching: students themselves. The design for learning field is exploring new ways of representing pedagogy, so that teachers can articulate and exchange their designs. These new forms of digital representation, available in design pattern libraries on the web, can also be available to students, to annotate. It is a much richer and better-targeted form of evaluation than the termly questionnaire, or the feedback sheet. It also raises the prospect of another kind of information explosion for the teacher to handle. Again, this is part of rethinking pedagogy in the face of technology opportunity. Innovation in digital technology will continue, with teachers being warned that they will revolutionize education, as they have been told repeatedly over the past few decades. Clearly it does not happen easily. There are many actors taking responsibility for what happens in the education community, from ministers to agencies to institutions to employers to families, and in the midst of it all is the teacher and learner trying to accomplish a difficult journey. Digital technologies have many different roles to play in helping us achieve our ambitions for education. One fundamental question is how best to use them to support the teacher and learner in their journey. We will continually be rethinking pedagogy as we explore the answers. Diana Laurillard, London Knowledge Lab, Institute of Education, UK Foreword to the First Edition Education is in an interesting transitional phase between its ‘ICT-free’ past and its ‘ICT-aware’ future. That it is in such a transition is a fairly safe claim. Over the centuries prior to digital technology, education evolved into a system that used paper technology in a variety of highly sophisticated ways to fulfil its mission to develop and accredit knowledge and skills. Its future must certainly be one in which it extends this capacity to a sophisticated use of digital technology. Like every modern enterprise, education is currently learning and adapting to the opportunities afforded by information and communication technologies, albeit slowly. Learning technologists make it their business to accelerate the process because the learning cycles of the education system are long, while those of its immediate environment – youth culture, employment demands, scientific knowledge – are short, and changing ever more rapidly. Leaders in the education system know that it derives its support from the communities that recognize its value, but have been slow to realize that this increasingly depends on how well it exploits the transformational potential of digital technology. All our educational ambitions for the postcompulsory sector are challenging: personalized learning, higher attainment standards, wider participation and improved retention in further and higher education, closer relationships between education and the workplace, lifelong learning, a more highly skilled workforce for our knowledge economy. We do not lack ambition. Achieving these ambitions, or even significant progress towards them, would have enormous value for the communities served by education. Every one of them requires the improved quality and economies of scale that proper use of technology will confer. Yet so many of our institutional and organizational strategies for education consign digital technology to the merely incremental tasks involved in improving our current systems supporting education, not to the transformational task of changing them. What are we doing? In teaching and learning currently, we tend to use technology to support traditional modes of teaching – improving the quality of lecture presentations using interactive whiteboards, making lecture xxiv Foreword to the First Edition notes readable in PowerPoint and available online, extending the library by providing access to digital resources and libraries, recreating face-to-face tutorial discussions asynchronously online – all of them good, incremental improvements in quality and flexibility, but nowhere near being transformational. What might we be doing? Lets look at it through the lens of the learner, and embrace all those vaulting ambitions in considering how they could combine to transform the educational experience of one individual. A young person who always hated study, who believes further education is not for them, with few skills, and low self esteem, how are they to be persuaded to achieve their learning potential? The ambitions are right – their combined effect would certainly be to bring motivation, opportunity and support to that young person. But look at what it takes to achieve that: the processes of teaching and learning have to engage their attention so that they enjoy study; the knowledge and skills they need must link to their interests so they are motivated to study; they need constant personalized support and encouragement at the pace and level to keep them engaged; the content and process of learning must be compatible with their social culture; they need to be able to see the long term value in the hard work of study – every teacher with a vocation to teach wants to provide all this, but in a non-elitist system this level of personalization cannot be offered for every student. The promise of new technology is that it can, for every one of those learner needs. It is an engaging and highly responsive medium; it can gather content according to interest; it can respond to individual needs of pace and level; it fits with the style and forms of youth culture; it can link the classroom to the workplace … and in doing so enables teachers to provide much more of what only they can do for their students. Wherever we find an impossible challenge to inclusive educational provision there is usually a way in which digital technology could make a significant difference. But we focus the majority of technology provision on what we already understand – information systems, data gathering, communication processes, presentation – rather than using it to tackle the really difficult problems presented by our ambitions for universal and effective education. Imaginative use of digital technologies could be transformational for teaching and learning, taking us well beyond the incremental value of more accessible lecture presentations. The problem is that transformation is more about the human and organizational aspects of teaching and learning, than it is about the use of technology. We have the ambition. We have the technology. What is missing is what connects the two. If education leaders were fully engaged with this, it would be strategy, and we would have a top-down change process. If practitioners were fully engaged it would be experimental innovation, and we would have a bottom-up change process. Better to have both, but too many educational institutions still lack serious leadership engagement with Foreword to the First Edition xxv the innovative application of digital technologies. In any case, innovation in the pedagogical aspects of teaching and learning should be coming from the academic community. That is the focus here. In this volume, learning technologists from the UK and further a field, pool their ideas around one way of accelerating the exploitation of digital technology: bringing its creative use within the capability of the individual teaching professional. By setting out to explore the design of learning activities in educational contexts already rich in electronic and mobile technologies, the authors show us what a technology-aware future for education would be like. When our education system is making sophisticated use of e-learning it will pervade everything we do, just as paper technology does. Lecturers will count it as part of their professional responsibility to ‘design for learning’, using a variety of forms of digital technology. We will have discarded the idea that the problem of pedagogic innovation can be left to the commercial suppliers, and instead see their role as being the provision of the tools and environments that lecturers can use in all the creative, innovative and scholarly ways they currently use paper technologies. We don’t expect the publishers to write the textbooks, we shouldn’t expect them to create the educational software for us either. The authors collaborating on this book are providing the means for this to be possible, researching and developing the forms of learning activity, the tools for pedagogic design, the environments for collaborative practice, the conceptual frameworks, all of which will contribute to building the bridges between what digital technologies make possible, and what our educational ambitions require. Diana Laurillard, London Knowledge Lab, Institute of Education, UK An Introduction to Rethinking Pedagogy Helen Beetham and Rhona Sharpe In previous editions we have argued that the transformation of postcompulsory education for a digital age can be achieved by a reconsideration of the pedagogical practices which underpin teaching, learning and assessment. Our aim was for technologies to be integrated in purposeful ways that would improve educational outcomes and experiences for our learners. Today, digital technologies have definitely transformed education, though not always in the ways we might have imagined. Digital and mobile technologies saturate educational organisations, as essential to their brand identities and business models as to their core functions. Similarly, learners’ lives are unimaginable without their digital devices, skills and connectivity. We are no longer concerned only to integrate digital technology into teaching at a local level, we are concerned with pedagogies that take the new technological and social contexts fully into account, and with curriculum practices that prepare learners for a world pervaded by information, networks, algorithms and data. The chapters collected here offer a critical reappraisal of the issues surrounding technology as context, content and educational method. The aim is to explore the new intersections of digital and pedagogical practice, both by outlining some of the theoretical perspectives informing digital education and by examining the reality of policy and practice that shape what real teachers and learners can achieve. What is Pedagogy? The term ‘pedagogy’ has its critics, particularly in the field of postcompulsory learning. Malcolm Knowles, for example (1990), notes that the term derives from the ancient Greek word paidagogos, meaning the slave who led children to school, and argues that this makes it inappropriate for the years beyond school in which learners gain in self-direction and selfreliance. Others have found the usual definition of pedagogy as the ‘art or science of teaching’ at odds with their preferred emphasis on the activity of learning. In a truly learner-centred practice, they suggest, teaching and teachers should move into the background. 2 Helen Beetham and Rhona Sharpe These debates and difficulties are in fact one reason why we have chosen to foreground the term ‘pedagogy’ in this book. First, contemporary use of the term has lost its exclusive reference to childhood while retaining the original sense of leading or guiding to learn. At a time when learning is increasingly seen as a lifelong project, it makes sense that the associated ‘art or science’ of guidance should extend its scope into adulthood. And as – in the Anglophone world at least – the boundaries are becoming blurred between school and college, formal and informal education, learning for work and learning at work, it also makes sense to consider the continuities across different contexts of learning. The word ‘pedagogy’ embraces an essential dialogue between teaching and learning: learning in the context of teaching, and teaching that has learning as its goal. We believe that guiding others to learn is a unique, skilful, creative and demanding human activity that deserves scholarship in its own right. We have not been afraid to use the term ‘teaching’ as well as ‘learning’ in this volume, recognizing that education concerns not only how people learn ‘naturally’ from their environment but also the social interactions that support learning, and the institutions and practices that have grown up around them. In fact, the essential dialogue between these two activities is at the heart of what we mean by ‘pedagogy’, and in earlier editions it allowed us to reclaim the idea of teaching from negative associations with dominant, unresponsive, or even repressive forms of instruction. As well as referring to the activities of learning and teaching, ‘pedagogy’ is used to describe how we think and talk about, plan and structure those activities. From the time of Plato at least, specific theories – as well as methods – have been proposed for education. Pedagogy, then, involves ways of thinking or knowing as well as ways of doing. Like other applied disciplines, it is centrally concerned with practice as knowing – the ‘evidence base’ – and doing – how we apply that theoretical understanding in practice with real learners and learning challenges. This theory-in-practice of pedagogy can be described as praxis: a conscious, skilled activity that can be understood by researching from the inside (Kemmis 2010). ‘Reflective practice’ (Schön 1987) and ‘scholarship of teaching’ are used to describe how teacher-researchers bring theory to bear on their practice and understand their practice in theoretical terms (Fanghanel et al. 2016; Trigwell et al. 2000). Freire defines praxis in a more critical way as: ‘reflection and action directed at the structures to be transformed’ (Freire 1970: 126). This is perhaps especially relevant at a time of transformational change. Ironically, the establishment of education as a field of study in its own right – and the location of ‘digital education’ or ‘e-learning’ as a specialism inside that field – has helped to distance the two elements from one another, so that within the same institution there may be professionals ‘doing’ digital teaching and professionals ‘knowing,’ researching, thinking and writing about digital teaching who have little contact with one another. In An Introduction to Rethinking Pedagogy 3 using the term ‘pedagogy’ we are trying to initiate a dialogue between these unnaturally divided groups of people – the theorists and practitioners of digital learning, the researchers and the policy makers – as well as reaching out to the learners, whose views on digital issues are increasingly sought, and whose voices have become impossible to ignore. In this introduction we examine how education has tried to fit itself for ‘the digital age’, with what foreseen and unforeseen consequences. Amid the plethora of new tools for teaching, learning and assessment, the demand from governments for digital skills, and the technicization of our institutions, how much has pedagogy really changed, and how much does it actually need to? If we are serious about this dialogue we must acknowledge that pedagogy needs to be ‘re-done’ at the same time as it needs to be ‘rethought’. Throughout this book we have tried to keep theoretical arguments and real-life examples of practice in alignment with one another. Our understanding is that neither of these two activities – the doing or the thinking – makes sense in isolation from the other. Education in the Digital Age Education is having a tumultuous time in its relationship to digital technology. Education has been simultaneously criticized for being too slow to adopt new technologies, and too quick to jump on the latest bandwagon; too meek to resist the agendas of edtech corporations, but too out-dated to meet the demands of employers for digital graduates. In previous editions we felt we had to make the case for rethinking pedagogy. The case is now pressed on us from all sides. The challenge is knowing how pedagogy (still) needs to change. Knowing that things have changed is not enough. Laurillard argues that educators need to take responsibility for the shape and direction of change: We cannot challenge technology to serve the needs of education until we know what we want from it. We have to articulate what it means to teach well, what the principles of designing good teaching are, and how these will enable learners to learn. (Laurillard 2012: 4) In rethinking pedagogy for an age of abundance in terms of resources, opportunities and networks (and of inequalities within that), we are not trying to define some new aspect or area of the discipline: we are trying to re-articulate the entire discipline in this new context. The danger of ‘business as usual’ – with some digital enhancements – is that we reproduce educational practices long beyond their fitness for purpose. In a review of technology enhanced learning interventions from the academic literature, Kirkwood and Price (2014) reported that less than a third aimed to be transformative 4 Helen Beetham and Rhona Sharpe in terms of learning outcomes; in most cases technology was being used to replicate or supplement existing practices. The danger of celebrating ‘disruption’ in other guises is that we hand control of the future to those with the best technologies, algorithms, and data. One reason for focusing on principles in this edition is to identify what endures through cycles of digital hype and innovation. In doing so we have tried to steer a path between the natural conservatism of educational practice, and the constant revolution of technology. What are the purposes of education in a world where the value accorded to specific forms of knowledge and practice is changing so rapidly? Within a decade, even graduate employment has become precarious, fragmented, routinely monitored, and insecurely located. The digital economy has transformed the labour market with 93% of European workplaces using desktop computers and digital skills required across a range of roles (Curtarelli 2017). Professional work routinely involves human beings working alongside data services and algorithms, and in the next ten years many of these human workers are likely to be replaced. Digital connectivity brings new freedoms: women in particular are becoming economically and educationally active, in places where there is no infrastructure to support them except a mobile network (Kukulska-Hulme and Traxler, Chapter 11). But with new freedom comes new insecurity. In the gig economy for example, working on demand is enabled by technology but income is unstable, workers must compete globally, and they are only as good as their last customer review (De Stefano 2016). Employability has been refigured as the possession of transferable capabilities – new forms of literacy and numeracy, adaptability, problem solving, communication – rather than the mastery of a stable body of knowledge, or the development of a professional identity (Barnett 2014; Barrie 2007). And as the job market demands ever more flexibility and currency, education has been reorganized around the promise of constant updating: the constant recreation of identity and its projection into the virtual public realm. If students leaving education face precarious futures, those entering it have already been reshaped by their digital practices and connectivity. Young people especially make routine use of social media, including image sharing and messaging. They are more familiar with lightweight apps and cloud services than with feature-heavy, slow-to-learn applications. For this generation of users, digital content and services are assumed to be free – or available at the price of some personal privacy – while modest subscriptions bring a world of high-value resources and applications to their screens. Many of these digital habits and expectations are carried over into their learning with results that can be powerful, but can also be challenging to existing scholarly norms and expectations (Beetham and White 2013). Students are in general positive and optimistic about the use of technology in education but evidence is mounting that their prior experiences with An Introduction to Rethinking Pedagogy 5 technology do not prepare students well for higher level study (Brookes and Pomerantz 2017; Šorgo et al. 2017). Digital technology has also had a profound impact on educational organizations themselves. Schools and colleges are networked in a way that cuts across traditional institutional, sectoral, and even national boundaries: if not yet wide open (Cronin, Chapter 9) the walls of the classroom are increasingly see-through. Learners are more mobile between institutions than could have been imagined before standardised credit, e-portfolios and personal learning records, all of which require digital technology to be implemented at scale. Even if they are securely located for their studies, learners typically use a public web site to find out about their course, apply and enrol online, contact tutors by email, access course resources through a managed learning environment, take synchronous online classes, monitor their progress on a dashboard, take tests via a computer-based assessment system. The whole experience of being a student, and of belonging to a particular cohort or institution, is largely mediated through digital systems. Organisational leaders are encouraged to invest in digital infrastructure and adopt a digital agenda, both for their core business model and to transform the curriculum (Davies, Mullen and Feldman 2017; Johnston, MacNeill and Smyth 2019). The virtual learning experience is no longer a minority choice provided by specialist distance educations institutions. Traditional universities are competing for a share of new markets and this is having an impact on the way that all educational institutions relate to their learners, and to potential learners in their communities. Making course content openly available represents both a marketing strategy and a public expression of higher education’s mission and values to share knowledge for public good. Universities in the Anglophone west are opening new campuses on other continents, where some classes are taught live and others by video link with lecturers on the ‘home’ campus, a practice already well-established in – for example – the distributed campus universities of Australia. Distance learning students can be added to these hybrid classes more easily, creating diverse but potentially hierarchical modes of attendance. Other universities without the infrastructure to support large scale distance learning are partnering with private providers who offer marketing, recruitment, content production and delivery, and in some cases, tutoring. Such ‘unbundling’ of higher education into its component parts is made possible by developments in technology (Hall and Smyth 2016; Yuan, Powell and Olivier 2014). For example, paid-for tutoring, assessment and/ or credit are offered on top of free online content; IT services and support are outsourced. The traditional workforce structures of higher education can be unbundled behind the scenes, with some teaching, administration, and support functions contracted out to flexibly-employed staff or private providers, and technology relied on to stitch everything back together. 6 Helen Beetham and Rhona Sharpe However, Swinnerton et al. (2018) caution that uptake of these new business models, so far at least, reflects existing inequalities and hierarchies between institutions rather encouraging a more diverse global higher education system. At the same time that digital systems are being used to bolster centralising forces within organisations – standardisation, efficiencies of scale, integration and data-fication (Williamson, Chapter 13) – digital networks are being celebrated for their potential to make learning open, distributed, and free. The educational innovation that has perhaps received the most attention in recent years is the Massive Open Online Course (MOOC), a course offered online to any learners who want to participate, usually without registering for accreditation or paying a fee. Although the academic originators of the MOOC would argue that the pedagogy of openness is their defining characteristic (Downes 2013; Knox 2013), their rise has been dominated by concerns about the ‘massification, marketization and monetization of higher education’ (Selwyn, Bulfin and Pangrazio 2015: 175), the use of MOOCs to boost recruitment, and by early evidence that successful MOOC learners tended to be well-educated already (Rohs and Ganz 2015). The openness of some online courses, while widely celebrated, demands a new pedagogy. Teachers are designing approaches having to engage students from diverse cultural and educational backgrounds, with very different motivations and prior knowledge, in very large cohorts (Liyanagunawardena, Kennedy and Cuffe 2015; Liyanagunawardena, Lundqvist and Williams 2015), and often with limited experience of teaching online themselves (Evans and Myrick 2015). For educators, the challenge is to rethink pedagogy within these contradictory forces – the ideal of open, abundant, distributed and democratic forms of learning, and the reality of commercialised organisations, standardised curricula (Johnston, MacNeill and Smyth 2019), mass customisation, and a global education system that is endemically unequal (Czerniewicz 2018). Learners too, find themselves caught between these forces, keen to engage with a world of open opportunity, but strategically focused on their grades, demanding certainty for all that they have invested, and nervous of being exposed in a digital landscape that can seem ‘too open and loose, generating anxiety and uncertainty’ (Kuhn 2017, cited in Cronin, Chapter 9). Design for Learning? ‘Design for learning’ is a phrase we coined in 2006 for the process by which teachers – and others involved in the support of learning – arrive at a plan or structure or designed artefact for a learning situation. The situation may be as small as a single task, or as large as a degree course. In formal learning, any of the following may be designed with a specific pedagogic intention: resources and materials; the learning environment and student support; tools An Introduction to Rethinking Pedagogy 7 and equipment; learning activities; the learning programme or curriculum itself; and assessment rubrics. Our intention has always been to focus on design as a holistic process, based around learning activities or groups of activities with a common purpose, in which designed elements such as learning materials and platforms must also be taken into account. The field of learning design continues to grow in scope and influence. It can now be defined as ‘a formal process for planning technology-enhanced learning activities, usually supported within a community where designs and ideas can be shared and re-used’ (Lewin, Cranmer and McNicol 2018: 2). As the field has grown, it has diversified into branches that are concerned with different claims: • • • • • • Integrating technology into learning and teaching strategies (Bower 2017). Co-creating design patterns which can be applied to similar contexts (Mor, Warburton and Winters 2012). Representing learning designs, so that they can be evaluated, shared and re-purposed (Agostinho 2011; McAndrew and Goodyear 2013). Supporting design as a decision-making process, through the development of design tools and workshops (Conole and Culver 2010; Laurillard et al. 2018; Masterman and Manton 2011; Salmon and Wright 2014). Supporting communities of design practice (Dalziel et al. 2016; Goodyear 2015; Laurillard 2012). Evaluating learning design approaches at the organisational level (Asensio-Pérez et al. 2017; Cross et al. 2012; Toetenel and Rienties 2016). The focus on the end-user is a feature of design and we would expect to see it embedded into all these branches of the field. In software development, usercentred design and UX (user experience) design are well established, and in recent years they have evolved further into user ‘co-creation’ of products. These developments have been followed in the education sector, with ‘learners’ replacing ‘users’ in each key term. Post-compulsory teaching has certainly become more professionalised and formally valued in the timeframe that learning design has been around, and the rationale for this has often been a desire for organisations to become more ‘learner-centred’ in their approach. The ‘learner experience’ has become a key measure of organisational success. Design has therefore been a powerful discourse for raising the status and value of teaching, and for putting learners as users (in aspiration at least) at the centre of organisational policy. More recently, some curriculum initiatives have aspired to involve learners not only as users but as collaborators, cocreators or co-designers of the curriculum (Bovill et al. 2016; Nel 2017; Winn and Lockwood, Chapter 14). Practices from co-design (e.g. from software development) have been drawn on to support these projects at a practical level. 8 Helen Beetham and Rhona Sharpe We are in a good position now to ask which of these claims from the field of learning design are worth pursuing with energy, and what makes it difficult for learning design to gain hold either as a specialist practice, or as a general expression of what good teaching with technology should be. For example, a potential benefit of a design approach is the promise of a shared language or rubric, supporting the decisions that educators make about learning in different contexts, and allowing those decisions to be better shared. This has become more important as more stakeholders have become involved in the curriculum, with intentions that need to be orchestrated: as well as teaching staff, other local specialists (librarians, learning technologists, experts in employability); the designers of curriculum artefacts (authors, software developers); and external stakeholders (governments, professional bodies and external assessors). Despite all the energy and investment in learning design, and the creation of tools, patterns, toolkits and frameworks, studies of what teachers actually do have shown that they struggle to work with formalised representations of their pedagogic knowledge and designs (Masterman, Chapter 7; Lewin, Cranmer and McNicol 2018; Oliver et al. 2017). A move towards a team-based course design process looks more promising (Sharpe and Armellini, Chapter 8). From bringing together learning design research with ten years of practical development and collaboration with teachers, a number of elements can be identified that support expression and sharing in this space: • • • a workshop approach with a multi-skilled extended curriculum team and dedicated time set aside for the activity; a simple visual representation of the course/session such as a timeline or storyboard, with movable elements (usually cards) that can be placed and replaced within the fixed space, handled, passed around and annotated; a small number of simple conceptual components (e.g. activity types, or curriculum principles) represented on these movable elements. The UK Open University Learning Design (OULDI) project uses a profile of student activity, with activity types again based closely on Laurillard’s typology. The implementation of this process at scale allowed Toetenel and Rienties (2016) to compare 80 module designs developed before the introduction of this approach, and 68 developed afterwards. They found that the post OULDI designs had students doing fewer ‘assimilative’ activities (watching, reading, listening) and less assessment, but more of some other categories such as communication. They concluded that the OULDI visualisation process had encouraged designers to include the full range of activities available in the taxonomy. Laurillard herself describes how an online An Introduction to Rethinking Pedagogy 9 design system, instantiating her taxonomy, helps practitioners to make visible their underlying assumptions and intentions (Laurillard et al. 2018). What happens when teaching is considered a form of design? One benefit may be enhanced prestige. Design is a highly valued activity in the digital economy and in the modern university. At the same time as new digital media were being taken up for expressing ideas, the ‘postmodern turn’ (Hassan 1988) or shift from ‘mode 1 to mode 2’ knowledge (Gibbons et al. 1994) was placing design at the centre of intellectual activity. Knowledge became something to be constructed, projected and used: rather than being universally true it was understood as culturally specific, and valuable in specific applications. Despite some resistance, most disciplines have been reframed to some extent around these ideas. Curricula tend to focus on real-world problems, challenges and applications; research is expected to have measurable ‘impacts’; applied subjects have proliferated and ‘pure’ subjects disappeared. Education has also seen the value in framing what it does as a form of design – applying knowledge in the development of products and services that people can use. Describing teaching as ‘design’ carries the expectations that it will be systematic, rational and rigorous in this way. There is no doubt that practitioners enjoy being offered new rubrics, languages and models and often find them useful for thinking about their practice (see Sharpe and Armellini, Chapter 8; Ellaway, Chapter 12) but ‘design’ may no longer be the only useful discourse by which to understand and carry forward educational praxis. We note that it has been difficult to implement a design approach organisationally, except where a very highly routinised approach was already the norm. It may be a mistake to invest too much in any one framework, or to implement it too rigorously. A shared understanding or approach need not be highly systematic. We know from Mayes’ new Chapter 1 on theories of learning that we can trust professionals – like all learners – to respond to patterns in a body of examples, and that models, stories, metaphors, principles, scenarios, and simple observation can be equally useful means of scaffolding that process. What seems to matter more than the rubric used is the opportunity to discuss and share (Cleveland-Innes, Chapter 5; Sharpe and Armellini, Chapter 8). It could even be argued that the discourse of design removes some aspects of professional judgement and care, focusing recognition on those aspects of the role that can be formalised in advance, and measured afterwards. ‘Designs’ in the form of lesson plans, validation documents and course handbooks are routinely produced as evidence for quality enhancement or fitness to practice. Although it takes different forms in different states, the desire of national governments to establish the ‘return on investment’ from education has intensified the demand for evidence that certain agendas are being realised in the curriculum. Learners demand increasing detail about what is to be learned and how, partly to help them navigate through the curriculum in the way that suits them best, but partly out of 10 Helen Beetham and Rhona Sharpe anxiety associated with the pressures of gaining grades and justifying their own investment. All this makes ‘teaching as design’ an equation that fits with the times, but not always happily. Conclusions In the first edition of this volume in 2006, we were clear that learning can never be wholly designed, only designed for, from principled intentions but with an awareness of the contingent nature of learning as it actually takes place. As teachers, we encourage learners to respond individually to learning opportunities, and to take increasing responsibility for their own learning. The use of digital technologies has not altered this fundamental contract, but it has meant that – through the portals of their screens – learners have independent access to knowledge and knowledge tools in a way that could not have been imagined even fifteen years ago. And this draws attention to a further problem with ‘design’ for learning – that of power and agency. ‘Design’ dominated discussions of e-learning at a time of confidence, creativity and rising influence. Many professional practices were being reframed as design – this book was part of that movement – and new roles were emerging whose responsibilities could readily be understood in terms of design: instructional and learning designers, curriculum designers, managers of learning systems. Digital technologies seemed to offer educators new kinds of agency in the learning process. Today the discussion is less optimistic. The agency of teachers is being challenged from many sides, of which the most hopeful is the challenge from empowered learners with technology in their hands. Other aspects of learning design have been handed over to nonteaching professionals within the more complex, disaggregated structures of the modern college or university. Institutional agendas, as discussed, are more powerful than they used to be in when organisations were structured more informally and collegially (Johnston, MacNeill and Smyth 2019). And still other aspects of student learning are under the control of vast global corporations that provide the platforms and the data services joining everything together. The creative, pedagogical energy of doing design in these areas has largely been transferred from individual pioneers – in the early days of digital learning – to huge corporations and standardised methods. To what extent are teachers still exercising agency through their designs for learning, and to what extent are they negotiating, from a position of diminished authority, with a host of competing interests? The chapters collected in this volume demonstrate the expertise and creativity of teachers and researchers in post compulsory education in responding to the challenges of the digital age. Part 1 of this book shows how theoretical understandings of learning can inform planned, purposeful activities to help students learn more effectively. In Chapter 1, Terry Mayes An Introduction to Rethinking Pedagogy 11 describes the complexity of learning theories in ways that are interpretable by designers, drawing on new evidence from the learning sciences. In Chapter 2, Helen Beetham takes these theories and develops from them general principles for designing with digital technologies in particular. Broader considerations for the design of complex learning environments are dealt with by Peter Goodyear and Lucila Carvalho in Chapter 3. In Chapter 4, Christopher R. Jones draws on key ideas from the social sciences and shows how they can usefully be applied to designing for learning. Finally in this opening section, Martha Cleveland-Innes takes one particular theoretical framework for online learning – community of inquiry – and shows how the ability of theories to make the process of learning explicit can be a powerful tool for teachers. Part 2 explores the practices of design from the perspective of the practitioner. Shirley Agostinho and colleagues (Chapter 6) and Liz Masterman (Chapter 7) pull together their findings from many years of empirical work to explain how teachers create and use representations of design to support their practice. The next two chapters in this part of the book situate these practices within the wider contexts of the organisation (Rhona Sharpe and Ale Armeillini, Chapter 8) and the open networked society (Catherine Cronin, Chapter 9). This section is completed with a set of resources collated by Grainne Conole for practitioners who wish to use pedagogic frameworks to guide their, or their colleagues’ practice. Part 3 discusses a range of specific influences on current pedagogic practice, starting with a set of design principles for mobile learning presented by Agnes Kukulska-Hulme and John Traxler (Chapter 11). Rachel Ellaway discusses professional learning and its relationship to real world practice (Chapter 12) and Ben Williamson explores current debates around educational data and its role in informing pedagogy and the curriculum (Chapter 13). Finally, Joss Winn and Dean Lockwood share their insights into the benefits and challenges of engaging students as active collaborators in the design process (Chapter 14). We have come to understand design as a social practice which is deeply rooted in specific social and cultural contexts, and we are interested in how learners’ and teachers’ practices change when these contexts change. Our institutions, our learning and our relationship with knowledge will continue to change, but it is for people to determine the direction, distribution and impacts of change. Educators lend ourselves to the future when we become aware, and help our learners become aware, of what it at stake. Otherwise there would be little point in a book such as this one, in which we lay out some of the alternative possibilities over which we, as human actors, have decisions to make. 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San Francisco: Jossey-Bass. Selwyn, N., Bulfin, S., & Pangrazio, L. (2015). Massive open online change? Exploring the discursive construction of the ‘MOOC’ in newspapers, Higher Education Quarterly, 69(2), 175–192. Šorgo, A, Bartol, T., Dolniar, D., & Boh Podgornik, B. (2017). Attributes of digital natives as predictors of information literacy in higher education, British Journal of Educational Technology, 48(3), 749–767. Swinnerton, B., Ivancheva, M., Coop, T., Perotta, C., Morris, N., Swartz, R., Czerniewicz, L., Cliff, A., & Walji, S. (2018). The Unbundled University: Researching emerging models in an unequal landscape. Preliminary findings from fieldwork in South Africa. In: Bajic, M., Dohn, N. B., de Laat, M., Jandric, P., & Rybery, T. (Eds.), Proceedings of the 11th International Conference on Networked Learning 2018. pp. 218–226. Toetenel, L., & Rienties, B. (2016). Learning design – Creative design to visualise learning activities, Open Learning: The Journal of Open, Distance and e-Learning, 31(3), 233–244. Trigwell, K., Martin, E., Benjamin, J., & Prosser, M. (2000). Scholarship of teaching: A model, Higher Education Research and Development, 19, 155–168. Yuan, L., Powell, S. & Olivier, B. (2014). Beyond MOOCs: Sustainable online learning in institutions. Cetis Publications, Retrieved from http://publications.cetis.org.uk /wp-content/uploads/2014/01/Beyond-MOOCs-Sustainable-Online-Learning-inInstitutions.pdf Part 1 Theories and Principles Chapter 1 Learning Theory and the New Science of Learning Terry Mayes Editors’ Introduction Mayes argues that design decisions need to be based on clear theoretical principles, and identifies three broad perspectives on learning from which these principles can be drawn: the associative, cognitive, and situative. In a comprehensive revision of the chapter for this third edition, he summarises new evidence from the fields of artificial or machine learning, neural networks, educational neuroscience, and the study of learning on the participative web. He concludes that new advances do not validate one approach over others, but that the three approaches continue to provide complementary resources for the designer, drawing on different epistemological traditions. Introduction The main aim of this chapter is to consider the role of learning theory in the design of pedagogy. Many readers will come to this topic with a sense of being overwhelmed by the complexity of competing theories, and by the debates about their respective importance for pedagogy. Here learning theory is described in a way that is intended to be to directly interpretable by a pedagogic designer. It is presented as a small set of broad perspectives, each of which emphasises a different kind of learning and implies a different kind of pedagogy. Far from competing, the theories together offer a set of complementary ideas which point to broad pedagogical principles. The chapter starts by revisiting the three traditions described in Mayes and de Freitas (2013). These are the associative, the cognitive, and the situative. These approaches are then considered in the light of what Meltzoff et al. (2009) and Sawyer (2014) have termed the ‘new science of learning’, in which the landscape for theory has been significantly extended – or even radically challenged – by recent research. Cognitive theory is being stretched by research in cognitive development and in neuroscience. Associative 18 Terry Mayes models are transformed by work on neural networks, while the situative approach is extended by learning on the participative web. All three are challenged by new research on implicit learning. Finally, the question is put: to what extent do these developments in theory require us to rethink pedagogy? The Associative Perspective Through associative learning the contingent relationships in the world – what predicts what – are assumed to be acquired through experience. The relationships can be temporal (what follows what), or structural (what goes with what). Associative theory emphasises the linking of elementary units (these can be external inputs and outputs, or internal representations) and the building of these into more complex patterns through activity and feedback. This simple idea became the dominant approach to learning for the first three quarters of the twentieth century, with behavioural principles uncovered in experiments on both animal and human learning demonstrating how precisely learning can be controlled through structured tasks involving feedback. These principles were then applied with some success in areas such as the workplace and the clinic (see Mace & Critchfield 2010). Applying the associative approach in education, a method known simply as programmed instruction emphasised the reinforcing of small steps of learning through immediate knowledge of results. The main task for the designer was to organise the subject matter into a sequence that would lead eventually to mastery. After each step a question or test problem required a response which then triggered immediate feedback to the learner. Where errors were made the program could branch into remedial material. Some simple technologies were developed to manage learning using this approach, resulting in the short-lived popularity of what were called teaching machines (see e.g. Benjamin 1988). Despite its rejection in education, most of the principles on which programmed instruction was based – active problem solving with immediate feedback on success, personalisation, and mastery of component steps before the introduction of more complex ones – are not incompatible with much modern pedagogy (Burton, Moore & Magliaro 2003). This approach was later combined with systems theory and developed into a method called Instructional Systems Design (e.g. Gagné 1985), characterised by the careful specifying of learning objectives based on task analysis, the design of structured tasks aimed at achieving the objectives in a logical sequence, the provision of feedback after each step, and the measurement of learning outcomes. The approach was particularly applicable in skill training where the units could readily be defined in behavioural terms. Learning Theory and the New Science of Learning 19 The Cognitive Perspective By the 1960s learning theory had started to focus on understanding in more detail the nature of internal representation. A new emphasis on mental processes was influenced heavily by the development of the computer as a model of the mind. Not only were the simple links of association theory replaced by the idea of propositional networks that carried semantic information, but learning itself became viewed less as the connecting of representations and more as a kind of active problem solving. Successful learning would depend above all on the processing of meaning, which would involve thinking, concept formation, language, attention and, as in a computer, the operation of a set of special purpose devices for the coding, storage and retrieval of information. In more pedagogical terms, the learners’ key challenge now became the building of a framework for understanding – mental structures which interpret the input so that it becomes integrated into existing knowledge. Once a framework is in place then slotting new learning into a pre-existing knowledge structure becomes relatively effortless. Learning becomes a by-product of understanding. The main pedagogical approach that has emerged from the cognitive perspective is to encourage the active pursuit of understanding. Tasks which are aimed at helping learners achieve this understanding are grouped under the label ‘constructivist’, following Piaget’s (1970) argument that conceptual development depends on active and personal exploration and observation, in contrast to an instructivist approach in which learners absorb ideas through the provision of explanations. There are many different versions of constructivist methodology and a variety of different terms are used to describe the concept (see e.g. Chi 2009). The key aim, however, is to create a situation in which a learner will have to expend effort in reflection and self-explanation. In Chapter 2, Beetham points to learner activity as the ideal focus for learning design. Many early attempts to employ technology in education fell into the trap of trying to enhance learning simply through designing better ways of presenting to-be-learned information. To a constructivist, learning can be enhanced through technology not by somehow amplifying the information but rather by designing tasks and tools for helping learners to think for themselves (Jonassen, Mayes & McAleese 1993). The Situative Perspective The situative perspective significantly moves the focus away from the individual learner. The term was introduced through the work of Suchman (1999) whose socio-anthropological approach shifted the unit of analysis to the activity in which learning would take place: a spontaneous interaction between the individual and the situation involving tools, other people, language, and the wider culture. This approach was further developed by Lave and Wenger 20 Terry Mayes (1991), whose study of communities of practice in work situations particularly focused on apprenticeship learning. This work inspired a method appropriately called cognitive apprenticeship (Collins & Kapur 2014). Here, included as ‘situative’ is the extensive research that has examined the simple idea that individual learning is shaped through the influence of other people. As long ago as the 1960s, Bandura (1965) argued that much learning is by proxy – vicariously – through observing the consequences of others’ behaviour. Further work has emphasised the importance of the learning group, where collaboration can produce outcomes that exceed that achieved by individual learners by themselves. Vygotsky’s influence, emphasising learning through interaction with others in the context of the wider culture, has been particularly important in promoting the pedagogic approach known as social constructivism (Palincsar 1998). Multiple Perspectives The three orientations depicted above as theoretical ‘perspectives’ or ‘approaches’, or even ‘frameworks’ represent, for the most part, distinct epistemological traditions, with their historically separate literatures. It is also possible to regard them essentially as metaphors, restricting the term ‘theory’ to causative accounts of particular effects that can be tested directly. In the context of discussing high-level metaphors like ‘learning as acquisition’ or ‘learning as participation’, Sfard (1998) has pointed to the danger involved in too great a devotion to one particular way of thinking and rejection of all the others. Each approach will capture something important, but only by holding multiple perspectives simultaneously can we hope to advance our overall understanding. To take one example, in the recent work on conceptual development two different metaphors have been pitted against each other. These are the ‘child-as -data-analyst’ (essentially associative) and the ‘child-as-theorist’ (essentially cognitive) metaphors. The conceptual development literature largely treats these as alternative theories but Waxman and Gelman (2009) have argued convincingly that, as children build a repertoire of concepts and the language to describe them, they call on both their rudimentary theories and the statistics they pick up from their environment. Each metaphor captures a crucial aspect of their learning: an associative account of statistical learning and a cognitive account of theory formation are both necessary. Moreover, a situative perspective on conceptual development is just as crucial for full understanding, as Waxman and Gelman acknowledge. So, in this example, it is the integration of all three perspectives that should guide pedagogy. Interactions with Context Sfard (1998) makes another important point: that implications of a metaphor are a result of contextual determinants not less than of the metaphor itself. Learning Theory and the New Science of Learning 21 A large proportion of the literature on the psychology of learning has focused on factors that can be regarded as contextual. The full context for a learning event must include the individual’s past experience, and thus the influence of previous learning, determining both the stage of learning (Fitts & Posner 1967; Anderson et al. 2018) and the extent of transfer (Day & Goldstone 2012). Similarly crucial for learning outcomes will be the current state of various characteristics of an individual learner: aptitude, arousal, emotion, and so on. And above all this is the level of self-regulation – control and commitment – that can be brought to bear on the learning task. Ideally, at some point there should be an unpacking of this interaction of theory and context into detailed and specific guidance for pedagogy. To offer just one example of a specific effect that demands – for classroom practice – a detailed account, consider the spacing effect (Cepeda et al. 2008). This refers to the superior performance that occurs from repeated learning episodes, focusing specifically on the relationship between the timing of study events and the duration before memory is tested. This phenomenon is still subject to theoretical controversy (e.g. Walsh et al. 2018) though it can be described in a way that is pedagogically robust. Indeed, much of current research on learning that has direct implications for pedagogy will involve testing smaller-scale theory in particular contexts, and then attempting to generalise the results. For example, work on self-regulation (Winne 2018), selfexplanation (Bisra et al. 2018), feedback (Nicol 2012) and vicarious learning (Mayes 2015) all involve this mix: theory about a particular effect, testing in context, and generalisation to pedagogical principle. Neural Network Theory The view of learning as underpinned by a simple link mechanism took on a new life in the form of neural network theory. This represents a kind of computational version of the associationist approach in the sense that it demonstrates how complex learning can emerge from a network of neurone-like links operating in parallel, producing stimulus-response relationships. The approach has enjoyed a rather strange roller-coaster history beginning with the early work on neural networks in the 1940s. However, since the account of parallel distributed processing given by McClelland and Rumelhart (1999), it has developed into an approach – called ‘connectionism’ – that has profoundly influenced thinking in almost every area of learning theory, from recognition and short-term memory through to emotion. Connectionist models are typically computer programs that simulate how activation propagates through a neural network to produce the phenomena of learning. The fundamental computational operation of the brain is one in which a single neuron passes on a signal to the many other neurons to which it is physically linked. The strength of this signal will be some function of all its inputs. Learning is achieved through changes to the weights 22 Terry Mayes that connect processing units; such weight changes are partly driven by patterns of correlated activity across units. These patterns are derived from experience – usually achieved by offering the network a large number of examples of what is to be learned. Despite the relatively slow operation of a single neuron, neural systems achieve human-like, or better, performance through the effect of massive parallelism. Impressively, the outputs of such networks not only demonstrate learning but do so in a way that simulates the stages and patterns of real human learning. Without anything else being needed, a network of this kind can learn to link complex patterns of inputs and outputs. Moreover, the network itself becomes a memory. By applying certain algorithms, apparently complex cognitive behaviour starts to emerge. Connectionist models have, for example, simulated the learning of language in a way that produces exactly the patterns of errors that young children make, and have succeeded in the extremely difficult task of reliably learning to recognise faces shown from many different angles. Connectionism offers a set of principles that at least hints at how the brain achieves associative learning. It seems to make the ideas of statistical learning or implicit learning, both of which operate without a deliberate intention to learn, plausible outcomes from a general-purpose system. Cognitive modelling, in contrast, has largely developed in the direction of modularity, where modules contain domain-specific, special-purpose learning devices, such as those for procedural learning (learning how) or declarative (learning what). An example is Anderson’s ACT-R model (Anderson 2007) which proposes eight different modules, each of which is now identified with specific areas of the brain, but each of which is fully coordinated with the others. In artificial intelligence, a neural network approach has been aimed at achieving learning in areas like speech and image recognition without any need to simulate the way these functions operate in humans. Machine learning, particularly the method that has appropriated the term ‘deep learning’ (LeCun, Bengio & Hinton 2015), has been driven by the availability of big data and low-cost computation, as well as by a significant contribution from cognitive neuroscience. So far, machine learning applications have made little impact in education (see Jordan & Mitchell 2015) though there are speculations about its use in learning analytics (Lee, Kirschner & Kester 2016). Educational Neuroscience The main development that impacts on the cognitive perspective has been the rise of cognitive neuroscience. This has become integrated with, rather than complementary to, cognitive psychology on the one hand, and computational cognitive science on the other. It uses techniques that observe the brain in action. The best combination of both temporal and spatial resolution is offered by functional Magnetic Resonance Imaging (fMRI), Learning Theory and the New Science of Learning 23 showing activation in quite precise brain areas over relatively short time periods. However, the need for participants to be tested while inserted into large and very noisy scanners has greatly limited the kind of studies that can be carried out with this method. Nevertheless, advances in neurotechnologies are now bringing that prospect closer, through wearable, wireless systems (see e.g. Boto et al. 2018). The neuropsychological literature is now full of examples that identify areas of the brain that become more active at particular moments during the performance of a mental task. A cognitive analysis can be triangulated with data from brain imaging to give a convincing account of learning in action. In the study of Anderson, Lee and Fincham (2014), for example, the brain activity associated with the effect of different pedagogies was compared (discovery learning versus direct instruction), and the changes as the learners moved from initial learning to later transfer were tracked. A number of recent studies have used brain imaging while the participants’ cognitive load – the extent to which the capacity of working memory is taken up – is varied (e.g. Sörqvist et al. 2016). It is tempting to generalise the results of these experiments to better understand issues such as the distractibility of social media, say, or listening to music while studying, although the complex interactions with contextual factors are likely to limit the direct applicability of this work to pedagogical design. There is also a burgeoning area of research in social neuroscience. Brain studies on social categorization, for example, demonstrate that judgements are available within the first few hundred milliseconds of perception (Ito 2013), giving support to the idea that these occur implicitly. The extrapolation of the results from studies such as these to educational methods has seen the tentative emergence of a new discipline: ‘educational neuroscience’. A number of ‘neuromyths’ in education, such as left-brain versus right-brain learning, have been widely discussed, (see e.g. HowardJones 2014). Bowers (2016) has argued that there are no current examples of new and effective teaching methods that have yet emerged directly from neuroscience. By this view, since Bruer (1997) wrote ‘Education and the brain: a step too far’, a further 18 years of research have simply confirmed that only by bridging from brain science to education through psychology can any real insights into learning and teaching be derived. In fact, the case for educational neuroscience is not that it might lead directly to new educational practice, but rather that it will contribute an important new level of understanding to learning theory, which in turn will inform pedagogy (Howard-Jones et al. 2016). Multi-Level Models Some recent models of learning have attempted to combine brain imaging data with neural network models to offer a new kind of answer to the ‘how?’ 24 Terry Mayes question. A single example is offered here. Kumaran, Hassabis and McClelland (2016) have provided a description of Complementary Learning Systems theory, updated with recent findings from brain imaging and synthesizing computational functions and biological characteristics. This theory describes two learning systems, located in the neocortex and the hippocampus respectively. The former holds structured knowledge – semantic memory – in a multi-layered neural network which gradually extracts structure from the statistics of the environment. It is necessarily a slowlearning system, but that in itself is not sufficient since we also need to be able to learn fast from an individual experience (a nasty one-off shock of some kind, say). So, in this model, particular instances are stored in an episodic network located in the hippocampus. The need for this complementary system addresses what is known in neural networks as the problem of catastrophic interference – caused by the rapid adjustment of weights in a multilayer network – where new information can severely disrupt the representation of existing knowledge. In the Complementary Learning Systems theory, the hippocampal and neocortical systems contribute jointly to performance across a continuum of tasks that involve episodic memory, semantic memory and implicit memory. Integration occurs by reactivating the new experience, interleaved with replay of previously-formed representations. This process, called systems level consolidation is entirely consistent with psychological findings about reflection, consolidation and the spacing effect. In addition, replay is biased towards rewarding events so the hippocampus may allow the general statistics of the environment to be circumvented by the reweighting of experiences (see Ohlsson 2011). We start to glimpse here a neurologically-based account of the interactions with situated variables that shape our learning in the direction of social and cultural biases. The Participative Web Much of the focus in the learning sciences is now on the phenomena associated with learning on the participative web, with concepts developed and applied solely in that context. One example is Cormier’s (2008) concept of ‘rhizomatic learning’, essentially a description of networked learning based on the metaphor of a ‘spreading rootstalk’ – conveying an unpredictability and openness not entirely captured by conventional network models. Learners are studied as they develop what Stewart (2013) calls the ‘new literacies of participation’. Cormier’s approach, characteristic of much writing around the development of Massive Open Online Courses, is entirely pedagogical, intended not so much as a theory of learning, as a commitment to the ideas behind the open movement in education. Offered more explicitly as a network-centric theory of learning is ‘connectivism’, described first by Siemens (2005) as an attempt to integrate a number of principles used in the analysis of networks, including complexity Learning Theory and the New Science of Learning 25 and self-organization theories, and employ these in modelling learning on the participative web. This theory is another that focuses on the interactions that occur between situated individual learning and learning that is emergent and distributed at the level of the entire network. Not surprisingly perhaps, Downes (2012) has acknowledged the debt of connectivism to connectionism, and thus to associationism. Partly because it has been presented as a theory of learning, rather than as a set of pedagogic approaches, connectivism has been the subject of several critiques (e.g. Clarà & Barberà 2013). The task of integrating a participation perspective into learning theory becomes more challenging as it encounters analytical concepts and methodologies that derive from epistemological traditions beyond those in which work on learning has traditionally been grounded. While we see quantitative measures extracted from big data across very large numbers of participants in learning analytics, research from the sociocultural tradition will often analyse activity through qualitative reflections on its meaning from a few participants, or even just one. And to travel even further down this road, we encounter the perspective on technology-enhanced learning offered by actornetwork theory, critical pedagogy or posthumanism, all of which are influential in the educational literature (e.g. Bayne 2015). This is, in its analysis of situativity, about as distant from an individual psychological viewpoint as one could possibly imagine. Yet this work is very much seen as necessarily counterbalancing the biological and positivist tendency in the learning sciences. In a recent issue of the journal ‘Cognition and Instruction’ – a previously mainstream empirical journal – the editors issue a plea for authors to address the for what, for whom, and with whom of teaching and learning ‘necessarily intertwined with the how of learning – an effort that asks us to carefully examine and address the cultural and political contexts’ (Philip, Bang & Jackson 2018: 83). The Individual Learner A very disparate range of research into learner capabilities or the internal characteristics of the individual can be incorporated into the concept of individualised learning. The idea of self-control is key to this, bridging from theories of personality to the cognitive aspects of flexibility (Gopnik et al. 2017), and even to computational approaches (Gureckis & Markant 2012). In fact, a whole cluster of different measures centred on self-control are better predictors of educational success than intelligence (Duckworth & Seligman 2017). Some of the research on self-control has attempted to pin this concept down in more detail. For example, Duckworth and Gross (2014) have considered how self-control and ‘grit’ (persistence) differ in their alignment with intentions and their timescales of influence. As we all recognise, learning can be influenced by emotion. To pick out just one example from this strand of research, Nemirovsky’s (2011) 26 Terry Mayes phenomenological study of a single participant led to an important new perspective, where transfer of learning is determined by feelings, rather than by task similarity or anything more cognitive. Nevertheless, despite the appeal of this kind of result for the learning sciences, a note of caution is probably appropriate since enthusiasm for using individual differences to guide pedagogy has a history of false dawns. The case of learning styles is a particular example (Kirschner 2017). Learning Implicitly In complete contrast to an emphasis on control and self-regulation is the recent research that seems to show how we learn – and learn in very sophisticated ways – even when we make no deliberate effort to do so. In developmental psychology, recent evidence has demonstrated that infants have computational capacities to make sense of the world in ways that do not sit comfortably with either a conventional associative or a cognitive account. Gopnik et al. (2004), for example, have demonstrated that infants are capable of representing the causal structure of the world in a way determined by causal Bayes nets. Such learning exceeds the capability of conscious reasoning, even for adults. This insight has led to the idea of ‘statistical learning’ – the capability to extract statistical regularities from the environment – being applied to many different aspects of cognition. The best place to look for pedagogical insight on implicit learning is probably in second language learning. Andringa and Rebuschat (2015) discuss exactly this issue, considering how the gradual learning achieved through immersive exposure to the language is accompanied by fleeting moments of awareness that patterns are being acquired when implicit briefly becomes explicit. They speculate about whether the same statistically driven mechanisms lie at the heart of both implicit and explicit learning, the only difference being the level of awareness at which they operate. The important question for research, perhaps, is to gain understanding of what can or cannot be learned implicitly and for whom such learning is or is not attainable. The most obvious relevance is in skill learning, where pedagogy based on extensive practice generally assumes that learning will be largely implicit. In sum, the explicit-implicit distinction implies two fundamental mechanisms. The first kind of learning is under deliberate control. This, requiring active thought and problem solving, takes up the limited resource of conscious attention and memory. It involves building and remembering new patterns which have not been acquired implicitly. This is effortful learning. It is explicit and slow (and is associated primarily with the hippocampus). The second kind is implicit learning. This involves the detection and recognition of patterns, without any obvious effort of thought or reasoning. Once learned, pattern recognition is fast, statistical (and is probably located in the neocortex). A process of organising lower level patterns Learning Theory and the New Science of Learning 27 into higher ones will underpin much of cognition. Through practice the explicit becomes implicit. Statistical learning also seems to apply at higher levels. In a fascinating analysis, Shafto, Goodman and Frank (2012) have shown how statistical inference can underpin learning through the agency of other people. The data present in social interactions carry information derived through Bayesian inference about people’s beliefs and intentions. This seems to mean that the patterns that we pick up in the social world also are not based, or not all based, on the level of understanding or the attitudes we have achieved through deliberate thought. The idea that much conceptual and attitudinal learning might also be implicit has potentially important implications for informal learning online, and provides support for those versions of theory that seem most applicable for the participative web, such as rhizomatic learning or connectivism, and those that extend a situative analysis into the wider cultural and political arenas. Conclusions The three approaches outlined at the start of the chapter could be more clearly interpreted if they are regarded as answering different questions. Both the associative and cognitive perspectives address the ‘how’ question. These traditional accounts have been elaborated, but to some extent blurred, by research on neural networks and neuropsychology. The situative approach, on the other hand, is aimed at quite different questions – the why?, for what? and the with and for whom?. As many of the chapters in this edition will confirm, the latter questions have come more clearly into focus over the period reviewed. Let us now address the question originally posed. What are the pedagogical implications of the new science of learning? Four research themes stand out: • • • • A new understanding of implicit learning is emerging, inspired by statistical learning and neural networks. This has implications for pedagogy. Cognitive neuroscience has transformed the understanding of brainlevel implementation and has produced a new discipline: educational neuroscience. However, pedagogical implications have yet to emerge from this work. Work on self-control, persistence and related attributes of individual learners has further developed a central theme in pedagogical research – the importance of engagement and its relation to context. This underlines the continuing importance of the well-established principles of constructivism. Theoretical accounts of learning through participation on the web have increasingly counterbalanced the positivistic biological direction 28 Terry Mayes of mainstream psychological theory. In the way learning theory is presented to pedagogical designers these apparently opposing viewpoints must be held as equally valid. Finally, then, what can a pedagogical designer make of the new emphasis on implicit learning? Perhaps the most straightforward way of expressing implicit learning in pedagogical terms is to consider immersion in the subject domain as a deliberate strategy, in contrast to the teaching of rules. In this way the pre-existing ability to detect certain kinds of regularity may facilitate effortlessly the gradual picking up of new patterns. However, it is clearly the case that we don’t acquire patterns simply through repeated exposure. The famous demonstration by Nickerson and Adams (1979) of asking participants to draw from memory the back of a penny coin seems conclusive on that point. Only learning activity that engages the learners’ attention with the information flow that is relevant to the subject domain or skill in question is likely to facilitate statistical learning. In the end, the process must surely be framed not so much by the way the learning proceeds as by understanding why, and to what extent, a learner is actively engaged in a particular activity. This implies that the starting point for the designer should be an analysis in terms of situativity (see also Goodyear and Carvalho, Chapter 3). The ‘how?’ question should be addressed only after there is an answer to the ‘why?’ Acknowledgements I am grateful to the editors and to David Nicol for very helpful comments on an earlier draft. References Anderson, J., Betts, S., Bothell, D., Hope, R. M., & Lebiere, C. (2018, June 4). 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A., Gunzelmann, G., Jastrzembski, T., & Krusmark, M. (2018). Evaluating the theoretic adequacy and applied potential of computational models of the spacing effect, Cognitive Science, 42(S3), 644–691. Waxman, S. R., & Gelman, S.A. (2009). Early word-learning entails reference, not merely associations, Trends in Cognitive Sciences, 13(6), 258–263. Winne, P. H. (2018). Cognition and metacognition within self-regulated learning, in D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (36–48). Abingdon, UK: Routledge. Chapter 2 Learning Activities and Activity Systems Helen Beetham Editors’ Introduction In the Introduction we stated that good educational practice applies theoretical principles to specific cases of use. This chapter considers what general principles can be developed from theories of how people learn (see Mayes, Chapter 1) and how these can be applied to learning with digital technologies in particular. Learning activity is offered as a helpful focus for educational design, and Activity Theory is used to develop a model of design practice which focuses on different aspects of activity. The same framework is then used to show how design, teaching and learning can be understood as interlocking activity systems, and to explore how developments in digital technology are influencing not only what kinds of learning are possible but how the project of education is being reconfigured. Introduction: Common Principles What principles can be derived from the theoretical discussions of the previous chapter? All three approaches – associative, constructive, and situative – emphasise that learners must be engaged in their own learning, and several decades of educational research support the view that it is engaged activity that leads to developmental change (Biggs and Tang 2011). This requires the curriculum to be designed around activities that learners are motivated to engage with, and that are constructively aligned with the overall goals or intentions for their learning. It is useful to distinguish activities from tasks (see also Goodyear and Carvalho, Chapter 3). In a formal setting, tasks are defined for learners within the framework of the curriculum. Activities are engaged in by learners in response to the demands of a task, always mediated through their own understanding and motivation, and the tools they have to hand. Although teachers and learning designers may provide guidance, different learners will have their own ways of interpreting the requirements and proceeding with the resources they have available. Learning Activities and Activity Systems 33 Theorists also stress the need for integration and consolidation across different activities, whether associatively (building component skills into extended performance), constructively (integrating skills and knowledge, planning and reflecting), or situatively (developing identities and roles). And learning activities should, in all theoretical accounts, be progressive, allowing learners to take on gradually more complex or demanding challenges. In order to do this, activities should also include opportunities for review (on the part of the learner), and adjustment (usually on the part of the teacher or designer), at regular intervals and certainly before they are summatively assessed. Different learning theories suggest different kinds of feedback, from varying the level of challenge to prompting for alternative explanations, but all agree that feedback (to learners) and adaptation (in response to learners) are critical to effective learning. As other writers in this volume (e.g. Goodyear and Carvalho, Chapter 3; Jones, Chapter 4) are at pains to point out, learning emerges when a designfor-learning is enacted, with all the contingencies and in situ adaptations that accompany this. While this complicates the project of efficient representation, it is precisely because learning activities are contingent, emergent, adaptive and dynamic that they provide such a productive focus for our discussion. Different Emphases While there are some agreed principles (consolidation, integration, feedback, etc.), there are also different emphases that arise from the different theories about how people learn. Ideally a representation of learning should be able to account for some of these differences, so that practitioners can consciously apply educational principles to their practice or can reflect on how their practice manifests underlying beliefs. Examples of differences that a general framework needs to account for are these: • • • • Role and significance of other people in the activity e.g. as collaborators, mentors, instructors, co-participants, versus the value of independence, personal achievement and self-reliance. Authenticity of the task and setting: is the problem formally defined, and are aspects of the setting made safer, less complex, or more orderly to facilitate learning, or must learners commit to a real world (typically higher stakes) setting and challenge? Structure and sequencing: are component tasks presented in a predetermined way or is the task made deliberately open ended, perhaps lending itself to representation as a problem space or a scenario? Binding of elements: related to the previous issue, are tasks tightly bound to specified outcomes, resources and assessment criteria – e.g. in a detailed design rubric – or is the learning process deliberately left to emerge or be negotiated? 34 Helen Beetham • Locus of control: who decides when the activity starts and ends, which resources (tools, texts) to use, what is produced for assessment, and even what criteria are used in assessment? Outcome/assessment focus: depending on the intended outcome, learners might be assessed on accuracy of recall, competence in applying a new method, the ability to transfer concepts and methods to new domains, confidence in judgement, or originality in presenting their ideas and solutions. • Different approaches to learning naturally arise in different subject communities, as is apparent from the other contributions to this book (e.g. Ellaway, Chapter 12; Jones, Chapter 4). It is not difficult, in fact, to imagine a mapping of the different theoretical positions from Chapter 1 onto the teaching culture of different subject areas. Overall, conceptualising education as a design science (Laurillard 2012) sits more naturally at the left-hand end of our theoretical spectrum (see Resource 1) where associative and individual cognitive approaches are enacted and – arguably – where designbased disciplines find themselves at home. Towards the other end – more aligned with the social sciences – a belief in the socially situated nature of learning, and an awareness of the structures of power inherent in ‘designing’ and ‘teaching’ practices, cast doubt on some of the claims of learning design. These might include its claims to rigour, ‘efficiency’, a totalised representation of the learning space and its potential, and the neutral effects of bringing digital technologies and data to bear on the process overall. In the Introduction we also drew attention to the way ‘design’ position students as ‘users’, with particularly constrained forms of agency in relation to their own learning (see also Goodyear and Dimitriadis 2013). A summary of the different theoretical approaches and their implications for activity design can be found in Resource 1: Theory into practice: approaches to understanding how people learn and implications for design. Defining a Learning Activity Following the challenges of our Introduction, we are interested to define a learning activity in a way that supports design and/or teaching practice, promotes sharing of design decisions and discussions within educational communities, allows teachers to consciously enact different theoretical beliefs, and supports an understanding of the role that digital technologies can play or might play in learning and teaching. Ideally it would also shed light on some of the tensions between sociological and instrumental views of learning, if not actually resolve them. Previously, we defined a learning activity as the central unit of analysis: ‘a specific interaction of learner(s) with other(s) using specific tools and resources, orientated towards specific outcomes’ (Beetham 2013: 33). Focusing Learning Activities and Activity Systems 35 on the activity – what Jones calls the ‘meso’ level of design thinking (Chapter 4) – has the pragmatic advantage that this is where teaching staff and other professionals have most influence and so can most usefully apply their attention, intention, and efforts (see Sharpe and Armellini, Chapter 8). Figure 2.1 shows an outline for a learning activity. In the multitude of learning design projects, workshops and frameworks developed since this chapter first appeared, a consensus has emerged that design involves consideration of these or very similar elements (Goodyear 2015; Conole, Chapter 10). The specific arrangement of elements in this diagram is derived from Activity Theory (Engeström, 1999, 2001), an approach which has been more often used as an analytical framework for research than a model for supporting and planning interventions (Issroff and Scanlon 2002; Cecile, 2012). There are in fact some theoretical obstacles to using the activity triangle as a tool for design. Because the activity emerges in real time and in situ, the components are in practice highly interdependent and their final relationship can only be known once the activity is accomplished. The meaning of the activity – which provides the motivation and engagement so necessary for learning – also needs to be understood in relation to a much larger system of collective rules and meaning-making. These issues are considered in the later sections of this chapter, which extend the activity diagram into Engeström’s full activity system and explore the relationship among several activity systems involved in designing for learning. In the following sections, the different components of the learning activity are considered separately and in turn. Engeström’s own principles for Activity Mediating artefacts e.g. tools, resources Learner(s) Learning activity or interaction Learning objectives Other(s) Figure 2.1 A model learning activity, based on Engeström (1987: 78). 36 Helen Beetham Theory, while insisting on the whole activity system as a unit of analysis, do allow for the practical consideration of component parts, for example by asking the questions ‘who learns?’, ‘what is learned?’, ‘why?’ and ‘how?’ (Engeström 2001). Rather than providing a design rubric, these sections offer productive questions to ask under each heading, particularly about the role(s) that digital technologies might play in the planned learning. In this way a systematic, intentional approach can be taken at the local level, while keeping in mind an awareness of the activity’s dependence on wider systems of social and cultural meaning. (A summary of the questions asked in each section is available in Resource 2.) Intended Learning Outcomes A learning outcome is an identifiable change that is anticipated in the learner. In associative accounts of learning the anticipated change will be observable from learners’ behaviour, such as the capacity to perform actions skilfully or to express concepts accurately. Constructive accounts of learning are more interested in cognitive changes, and assessment would reveal that learners have a different conceptual framework for understanding a situation, and the capacity to produce new solutions as a result. Situative accounts of learning will lead to outcomes that are observable in context such as learners participating in new situations, occupying more expert roles, or acting in accordance with situational rules and expectations. We have said that design is, above all, intentional. It is by defining ‘intended learning outcomes’ that the teachers’ or designers’ intentions can be made clear and shared with everyone involved. Learners’ individual motives will differ – and some of these differences are explored in the following section – but if their diverse motives and meaning-making can be aligned with the purposes embedded in the learning activity, they will be inclined to engage with it. There is evidence that it is better if this alignment is achieved consciously and explicitly. In line with the focus on activity in theory, contemporary curriculum practice is for learning outcomes to be expressed in the form learners will be able to [verb] [qualification] where the verb describes the kind of activity that learners will undertake, and the qualification covers the scope, application, and (sometimes) the criteria for assessment. Taxonomies for defining learning outcomes such as Bloom’s (1956; Anderson et al. 2001) and Biggs’ (Biggs and Tang 2011) are routinely taught in introductory courses for teachers, so they exert a strong cultural influence. A more recent classification of relevance to this book is provided in Laurillard’s Teaching as a Design Science (2012). This classifies learning as acquisition, inquiry, practice, discussion or collaboration. Governments as well as employers and educators now recognise the value of digital outcomes from the learning process (UUK, 2015; Department for Learning Activities and Activity Systems 37 Business, Innovation and Skills 2016). Many universities offer digital capability as a ‘graduate attribute’ (Sharpe and Armellini, Chapter 8); professional bodies and other curriculum stakeholders demand that students gain skills in digital media, data handing and analysis, and varieties of computer-supported design. These outcomes routinely appear on syllabuses and in course handbooks. If we believe in these broad goals, it follows that the tasks given to learners should involve the use of digital tools, resources, networks and data wherever possible, and certainly wherever it is relevant to their outcomes. Resource 1 indicates how Laurillard’s and Bloom’s systems can be used, separately or in tandem, to plan different kinds of learning task for different intended outcomes, with a focus on developing digital practices. What do students themselves have to say about how their courses are preparing them for a digital future? In 2018, the Jisc Student Digital Experience Tracker survey (since renamed the ‘Insights’ survey) collected feedback from over 37,000 UK students about their digital learning experiences (Newman and Beetham 2018). One question asked students to name a digital activity they had found ‘really useful’ on their course of study. Over 30k responses were coded using the broad terms from Bloom’s revised taxonomy of cognitive learning outcomes (Anderson et al. 2001). This exercise resulted in a revised set of verbs for each level of Bloom’s framework and example activities for each of them, based on the digital activities students said they valued (see Resource 3). This coding exercise showed that Bloom’s framework needed to be extended in two ways to accommodate the range of activities that learners experienced as useful. The first of these extensions – ‘preparing to learn’ – acknowledges the role that digital tools play in allowing students to learn flexibly and independently, to manage their time and tasks, to track grades, and to build other positive learning habits such as managing references and notes. The second extension – ‘learning with others’ – includes some of the desired outcomes that Bloom’s original taxonomy ignored because of its focus on individual cognition (though they are included in Laurillard’s). Again, digital technologies have made it easier for learning to be shared, recorded, discussed and commented on by others, and for this to be integral to the learning process. Curation of materials, and writing/production are two further practices that have been revolutionised by students’ digital access. For connected students they are simultaneously individual and collective, involving a mix of personal and social tools (e.g. shared referencing services). So digital tools are having a profound impact on learners’ habits of study. Learners are using their digital access to bridge some of the gaps in the formal curriculum – gaps between composite skills such as note-making and formal activities (Chi and Wylie 2014), and between formal activities and more meaningful, personally-invested practices such as creating blogs and portfolios, often in the public or semi-public spaces of the digital network 38 Helen Beetham (Lei and Yong 2007; Beetham and White 2013). These pervasive habits do not routinely appear in curriculum designs, partly because they fall outside of the natural unit of the learning session or activity (they are drawn on continuously to meet the demands of different tasks), and partly because they fall outside the proper concerns of the subject curriculum (they are considered non-specialised or ‘co-curricular’ in nature). This complicates the design requirement that intended outcomes should be carefully aligned with each activity. Teachers need to consider – and work with other professionals to support – the transition of digital practices from scaffolded activities to core repertoire, where they can be drawn on along with literacy and numeracy skills, allowing outcomes (and learners) to focus on subject specialist challenges (Littlejohn et al. 2012). The decision whether to articulate and assess digital outcomes explicitly will depend on what stage learners are at in their studies, and how confident with a range of digital practices. Intended outcomes also need to reflect the impact of digital technologies on the curriculum in the widest sense. Careers, professions and disciplines are profoundly changed by the digital revolution, not only in their content and methods but in the value accorded to different kinds of capability: design, for example, or managing data. Beyond subject-specialist fields of knowledge and practice – though reflected in them – are society-wide issues such as the changing nature of work, of civic participation, and of private life. If the aim of a curriculum is to develop capability and resilience for the future, then digital futures demand new purposes, not just the same learning activities with new digital tools. In articulating those challenges, instead of asking how students will encounter a stable body of knowledge or practice, designers might ask how students will engage with contemporary issues through a subject-specialist lens, or explore how the subject area is changing under pressure from new digital methods and knowledges (see Hall and Smyth 2016; Johnston et al. 2018). As a tactic for engaging subject specialists with curriculum innovation, there is no doubt that this approach is more productive than offering a checklist of new digital skills that their course must ‘cover’. Designing for Different Learners An outcomes-based approach to learning tends to assume that learners will respond to task demands in similar ways. In contrast, a learner-centred approach (Kember, 2009; Sharpe et al., 2009) accepts that learners make sense of tasks in terms of their own identities and goals, and bring different resources to bear on it. Differences that teachers and designers are often advised to consider include learners’ pre-existing: Learning Activities and Activity Systems • • • • • 39 subject-specific knowledge and competence; access needs and preferences; preferred media for learning; experience of learning, especially learning in the relevant mode (e.g. online, mobile, open); information, media and data literacies. Many of these differences are in practice related. Digital capabilities, for example, are highly contingent on other factors such as learners’ prior experiences with ICT, their general self-efficacy (Wang et al. 2013; Hatlevik et al. 2015), and their broader social and economic resources (Hargittai and Hsieh 2013; Helsper and Reisdorf 2016). Cultural attitudes (Yoo and Huang 2011) and gender (Hargittai and Shaw 2015; Martínez-Cantos, 2017) also influence learners’ outcomes with technology. So while educators need to be aware of differences among their learners, and of how those differences can interact with the technologies in use, there are dangers in treating learners as simply a bag of disparate factors to be diagnosed and accommodated. Many factors of proven significance to learning outcomes – such as motivation, expectation, self-belief, and conceptions of learning – are context-dependent to some extent (Ellis and Goodyear 2010; Littlejohn et al. 2016). They can be addressed in a supportive learning environment, in a cohort where differences are treated as resources for collaborative working, or in educational organisations that have resolved to address inequalities of access at the level of policy (See Cronin, Chapter 9). Taking accessibility as an example, students present with an increasing variety of cognitive, sensory, symbolic and physical needs. Ideally they will have access to appropriate devices and software, as well as any personal support they require. But the use of specialised systems has recently been augmented by adaptive options in generic software and operating systems – options such as text-to-speech, speech recognition, grammar and spell checkers, captions, switch access and so on. Our recent surveys of students in the UK and Australia (Newman and Beetham 2018) found that more students were using these features by choice than because they were assessed as having additional needs. With good digital access, the locus of control can move from the teacher, designer or specialist towards the individual learner. (Earlier work found that students with disabilities were often supporting other students to adapt their devices and services: Seale et al. 2010). At the same time, organisational policies have moved towards a more inclusive curriculum, so for example learners can choose different assessed tasks to showcase their strengths in a subject area, rather than having remediation for areas of special need. The same trend towards inclusion is true for a related area of difference – preferred media for learning. There is little evidence for the stable, contextindependent, intrinsic ‘styles’ of learning that were popular in the 1990s 40 Helen Beetham (Coffield et al. 2004; Pashler et al. 2009). However, there is good evidence that learners benefit from using a variety of representational media (Nistal et al. 2011). From a quite different theoretical perspective, Gunther Kress (Kress et al. 2006; Kress and Selander 2012) has championed the idea that contemporary knowledge culture is inherently multi-modal. In both cases – access needs and media preferences – the conclusion is that curriculum design should aim at inclusivity and repertoire rather than discrimination and divergence. For all learner differences, the options of cohort adaptation and/or inclusive practice are available and can be counterposed to forms of customisation and individuation (see Chapter 13, Williamson for a fuller discussion of this issue). At the cohort level, it makes sense to adapt learning tasks to take account of what learners have encountered previously. It is good practice at the start of a session or activity to activate learners’ previous concepts or skills, for example with a review question, and to connect with learners’ motives for study, for example by reminding them of the place of this activity in the larger course they have committed to, or by triggering any personal interests they have in it (Biggs and Tang 2011). These approaches give learners some of the responsibility for managing their own engagement, orientation, and expectations. They can also be carried out using (for example) in-class polling, which is hugely popular with students at the time of writing (Newman and Beetham 2018) and has the benefit of making the experience a shared one. So digital technology offers new forms of inclusive practice as well as new ways of diagnosing difference. In time, learners can be prompted to develop their own insights and resilient responses. Eventually, learners should be able to judge for themselves which digital tools are of value to them, and what benefits and risks arise from new digital practices. Despite their access to a world of learning opportunity, digital learners still need safe spaces and trusted guidance to develop this repertoire and judgement (Sharpe and Pawlyn 2008). When this chapter was originally written in 2006, it seemed radical to suggest that learners might take more responsibility for designing the conditions of their own learning. In the intervening years there have been many valuable learner-led projects (see Winn and Lockwood, Chapter 14; Sharpe et al. 2009; Ryan et al. 2013), but mainstream practice has continued to move towards mass customisation, under cost pressures and customer-inspired models of learning. What matters for the purposes of this chapter is that learners should be considered as the primary actors in their own learning; where they differ, these differences should preferably be designed with, rather than designed for, with learners’ digital know-how and ingenuity treated as valuable resources. The Social Environment for Learning It follows from the previous section that students need a variety of different social settings and dialogic partners if they are to become capable lifelong Learning Activities and Activity Systems 41 learners. Associative theories demand a teacher who is skilled in the subject matter and can present it in an orderly way. Situative accounts call for an expert mentor, while a constructive approach requires a facilitator who can offer alternative examples and explanations, respond to learner misconceptions and support the transfer of skills. Dialogue with peer learners is also valued by different theories. Vygotsky (1986) argued that learning is a socially mediated activity in the first instance, with concepts and skills being internalized only after they have been mastered in a collaborative setting. In situated learning (Lave and Wenger 2002), it is the social context that lends meaning, value, authenticity, and motive to the learning of each member. While dialogue plays a secondary role in other theories, most agree that it supports the individual processes of internalisation and generalisation, and that a peer group can perform many of the tasks of a teacher (see also Cleveland-Innes, Chapter 5). These interactions can now be mediated digitally, via video, audio, text, collaborative spaces, shared simulations, or game-worlds. Even when learners are located in the same time and place, tools such as polling, screen sharing, and live collaborative environments can enrich the encounter. Like other interactions, learning encounters are made ‘porous’ by the presence of networked devices. Other learners – and learning – are always ready to break in, and the real-world event is always in the process of leaking out as digital recordings and traces. This is why the most promising candidates for new approaches to learning in the digital age have emerged from the capacity of learners to be extensively and continuously connected. Approaches such as networked learning (McConnell, 1999; Goodyear et al. 2004; McConnell et al. 2012) and connectivism (Siemens, 2005; Downes, 2010) contend that this enhanced connectivity – and the ‘wisdom’ of networks – constitute new modes of learning. Regardless of how new the pedagogy may be, if learners are to participate effectively in a digital society they must know how to communicate and collaborate in digital media. These are not just technical skills, and nor are they skills learners can be expected to have acquired ‘natively’ through use of social media. They are complex social practices that cross boundaries, bring different roles and rules into contention with each other, and challenge learners’ sense of who they are or might be. As Jones, Goodyear and Carvalho, Cronin and Cleveland-Innes argue in their respective chapters, interactions and interactive environments can never be fully designed in advance. The more open the environment, the more complex the ecology (Goodyear and Carvalho, Chapter 3), the less control can be exerted by teachers, designers, or the formal curriculum. But learners have so much at stake here that teachers and designers have a responsibility to make explicit as far as possible the rules and roles, the interactional structures, and the underlying cultural norms that learners will be working with. As with the pervasive habits of learning discussed in the previous section, a key decision 42 Helen Beetham in designing for digital students is when to foreground these norms, and when to allow students to navigate and negotiate them for themselves. Engstrom’s full activity system allows us to understand the ‘other people’ involved in learning as part of a deeper and wider social setting. They may be tutors or peer learners; they may be invited experts, potential clients, students from different national cultures, or representatives of an alternative perspective. In performing their roles, they create a meeting point between the specific activity and a much larger repertoire of possible actions and meanings ‘owned’ by the wider community. Learners can tap into this cultural repertoire by occupying different roles in a task (such as reviewer/ reviewee) and by observing how others occupy their roles in turn. Learners can also be primed to notice and to model the different rules of engagement, and to explore the underlying relationships that give rise to them. Issues of privacy versus open disclosure emerge naturally for discussion in digital spaces. Other issues that can be brought to learners’ attention include: personal opinion versus academic judgement, open sharing versus copyright, ‘cut and paste’ versus ‘precis and quote’. The explicit nature of dialogue online makes it good for foregrounding and negotiating rules of this kind. However, the user-friendly design of digital systems can also make it difficult to spot the underlying assumptions that contribute to how rules and roles are played out. A critical digital pedagogy looks for opportunities to surface these signs. The digital environment also provides an opportunity to break with established structures of power in the teaching and learning relationship, and open it up to outside currents of influence (see Cronin, Chapter 9). Mediating artefacts e.g. tools, resources Learner(s) Learning activity Learning objectives or interaction Rules Community Figure 2.2 A model activity system, based on Engeström (1987: 78). Division of labour Learning Activities and Activity Systems 43 Digital Tools and Resources This chapter has considered how digital technologies change the activities available to learners, the social environment in which they learn, and the kinds of learning outcome (or broader purpose) it makes sense for them to pursue, showing how the use of digital technologies changes the meaning of a learning activity, subtly or profoundly. Finally, we look directly at the digital artefacts that are used in learning. Digital artefacts include physical devices (such as mobile phones and tablets, laptop computers, digital cameras and microscopes, sensors, controls, etc.), software and systems (for mediating individual and collaborative actions), and representations in digital media (such as text, images, moving images, virtual worlds, data sets). These different kinds of artefact – devices, tools, and texts – participate differently in learning activities, but from the perspective of Activity Theory they all have the property of being the outcomes of design. They carry traces of that earlier activity, with its own way of framing the learner (or user) of the artefact, and with its own rules, roles and divisions of labour. Contemporary digital artefacts are increasingly in the hands of learners themselves, and they are increasingly complex and inter-related (Oliver and Gourlay 2018). These two features can be illustrated through the example of the lecture. Recent survey data shows that students from the UK, Australia and NZ are likely to take both a laptop and a mobile device to class (Newman and Beetham 2018), and their most common use is for accessing lectures and lecture notes. For these students, the ‘lecture’ begins with the posting of readings online, culminates in a live presentation with slides and concurrent activities (polling, note-taking, internet searches), then enters an afterlife of download, annotation and revision. Students in the survey complained if any of these elements was missing, uploaded late, or of poor quality. Some asked for a lecture series to be uploaded in advance so they could navigate it at their own pace. Ironically, though, the same students recognised that this reduced participation in the real, live (but demanding and perhaps inconveniently scheduled) lecture, which they still felt was an important event. The recorded lecture reflects the rise in learner control of activities – at least the time and timing of them – and the increasing complexity of the artefacts designed for them. Texts such as lecture slides can be illustrated, annotated, and become a playground for conversation. Communicative events such as a live lecture can be captured and translated into new texts. All of this can happen in the same software platform or sharing environment, curated by the learner out of personal apps and services, or provided by the organisation as a complete learning platform. In neither case is the environment designed by the people supposedly in charge of the curriculum. As environments for learning become increasingly immersive – as they offer 44 Helen Beetham a world for acting in, rather than tools or texts for acting on – their designers have more power to shape the learning experience, and teachers have correspondingly less (Neary and Beetham 2015). And the power of these digital designers is largely invisible, unless dedicated educators and students work to bring it to light. Systems of Activity: Learning, Teaching, Design Engeström’s model allows us to understand learning, teaching and design as related activity systems, with mediating artefacts moving between them. The purposes for which an artefact is designed and the ends to which it is used do not have to be the same, and the meaning of the artefact changes in its passage through different systems. This makes mediation a more flexible concept than affordance (for discussion of this point see Oliver, 2006; Goodyear and Carvalho, Chapter 3). As a series of designed artefacts (lesson plans, hand-books, reading lists, assessment rubrics, digital course content), the curriculum emerges from an activity system bound by organisational rules and the rules of external accreditors or professional bodies. These artefacts then mediate the activity of teaching – planning and engaging in interactions with students – while both teaching and design help to frame the activities of students as they respond to the tasks and artefacts they are offered. The more complex the learning environment, and the more unbundled the different roles involved – local tutor, course designer, multimedia author, software engineer – the more activity systems are in play, and the more diverse intentions, rules and social relationships are inscribed in them. In highly informal learning, teaching and learning emerge in close step with one another, using artefacts to hand. In traditional, formal lecturing the ‘design’ of the event is well understood by all participants, so it hardly needs to be discussed. The digital environments that are now the norm for learning are developed by many different actors, whose intentions and assumptions converge on the learning experience. Learning design approaches have tried to bring these actors and artefacts into closer alignment (see Beetham and Sharpe, Introduction), for example by insisting on common patterns, terms, frameworks, or scenarios across activity systems. But the pull towards open environments, learner-owned devices, and varieties of teaching and learning practice, work continually against the assumption of alignment and control. Once it is accepted that the learning activity is heavily over-determined by different roles, rules and interests, what is left for the teacher to do? Does it make sense to talk about ‘design for learning’ anymore? If we hold with the sense of design as ‘intention’ – an intention towards the learning of others – then preparing to teach becomes an exercise in imagination, disinterring the intentions from various designed artefacts Learning Activities and Activity Systems 45 (texts and tools, platforms and data) and putting them to new uses, with or against the grain of their design; being clear about the local intentions of a learning activity within the confines of the designed curriculum; and anticipating the intentions of learners in order to direct them, with care, towards outcomes that are likely to be meaningful. The profound human desire to teach, design, and support others in their learning is an imaginative one, perhaps best met when professionals have the opportunity to consider the wider purposes of the curriculum, and to do so collectively. Conclusions This chapter has outlined considerations that arise from theories about how people learn (see Mayes, Chapter 1) and the experience of applying these theories to learning with digital technologies. In defining activity as central, and examining four elements of a learning activity in turn, it has suggested questions that can be used to shape a more informed, intentional approach (Resource 3). The trajectory across the three editions of this chapter has been away from designed-in solutions, towards developing in learners the awareness to choose tools and resources for themselves, to navigate relationships in digital spaces, and to pursue their own learning goals. This still involves designing tasks, sometimes with explicit scaffolding and support, but always with the intention that the relevant capabilities should become part of learners’ repertoire and that they should inhabit and use their digital know-how to address the challenges that really engage them. The shift towards analysis of context, evident throughout this edition, is a material and historical one. First, new technologies change practices by offering new artefacts for use. Then they change the contexts of practice. We are well into this phase of the digital revolution now, and if we want to design alongside our learners – equipping them with agency and resilience for the future – we need to involve them in asking the difficult, critical questions about the technologies they are offered as means to an end, as well as supporting them to make best use of those means in their learning. References Anderson, L.W., Krathwohl, D.R., and Bloom, B.S. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York: Longman. Beetham, H. (2013). Designing for active learning in technology rich contexts. In H. Beetham and R. Sharpe (Eds.), Rethinking pedagogy for a digital age (pp. 31– 48). London and New York: Routledge. 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Chapter 3 The Analysis of Complex Learning Environments Peter Goodyear and Lucila Carvalho Editors’ Introduction Picking up from the previous chapter’s focus on activity, Goodyear and Carvalho argue that design for learning depends on being able to analyse highly complex learning environments, not in their component parts but as whole systems. This means thinking ‘ecologically, architecturally or in terms of networks’, with an awareness of the boundary between what can be designed in advance and what can only emerge in the contingent, dynamic, relational space of ‘learntime’. The authors offer three principles for doing this analysis: focus on productive tasks; take account of the social and material realities of the setting; and work fluently across different levels of design. They also introduce the parallel ideas of interpretation and affordance to help understand the mutual relationship of actors and artefacts. Through two carefully worked examples from practice they bring these ideas to life, showing that it is possible to understand learning tasks more deeply when they are considered within a complex network of ‘tools, artefacts, places, practices, ways of knowing and inter-personal relationships’. Introduction: Analysis for Design Pedagogy, as the art and science of helping other people learn, can be practiced in a variety of ways, including through direct face-to-face teaching. Our work seeks to understand and inform pedagogy that is enacted more indirectly as design for learning: that is, where people committed to facilitating other people’s learning carry out their work primarily through the design of worthwhile learning tasks and/or the design of appropriately supportive learning resources. Given the focus of this book, our attention is on designs in which digital resources play a significant part, though we believe design is often best when it takes a more holistic approach – capable of working with networks of interacting digital and non-digital entities. In this chapter, our focus is on analysis for design. Analysis connects with design in a number of ways, e.g. through needs analysis – a classic starting point for a structured 50 Peter Goodyear and Lucila Carvalho design process (Crandall, Klein and Hoffman 2006; Mayes, Chapter 1). It also plays a major role in evaluation, informing judgements at the end of a design cycle, about whether something is working well, and about what might need to be improved (Reigeluth and Carr-Chellman 2009). Our approach to analysis is rather different and is distinguished by the following five observations. First, design rarely takes place on a ‘greenfield’ site. So, analysis needs to be able to capture what exists already, not just what success would look like. Design activity can then make proposals that will work within, and improve upon, an existing set of constraints and possibilities. Second, many things affect any single learning episode. Therefore, analysis must be able to represent a complex array of influences, some of which are human, some physical (including digital). This means creating explanations and drawing on ideas about causation that are appropriate for each of the main relationships involved. For instance, some relations between interconnected technical objects in a learning environment are relatively stable and predictable. In contrast, some relations between tools, users and outcomes are dynamic: they change with growing skill in using the tools, for example (Rabardel and Beguin 2005). Other relations again involve human reflexivity – such as when students alter their approach to a task once they realize that what the teacher intends is not what they need. This reflexivity makes prediction and control more problematic, but analysis and design are still needed. They are among the professional obligations of teaching. The point is that capable analysis and design require a sophisticated understanding of causation and contingency: flexibly responsive to relations between heterogeneous connected entities, yet sufficiently simple and coherent to allow co-ordinated work within a design team (Nelson and Stolterman 2014). Third, competence rarely resides in the head of a learner. Rather, a person’s competence is usually entangled in, and dependent on, a set of social and physical relationships: such that a more expansive view of competence includes that person’s ability to assemble and hold together the entities needed for the task at hand. When analysis is used to create a description of competence, or of a desired state of affairs – a smoothly working system – it must be able to deal with this more expansive and complex conception of what is needed. Fourth, since a number of influential models of learning involve some kinds of apprenticeship, authentic engagement in practice, legitimate peripheral participation, experiential learning, etc. (Mayes, Chapter 1), then the kind of description created by the analysis above is needed if designers are to see what else they may need to help set in place to support such processes of learning through engagement in practice. Finally, students co-configure their learning environments, and need practice and guidance in doing so. Following on from the points above, an important part of becoming an autonomous learner is knowing how to draw Analysis of Complex Learning Environments 51 together the tools and people needed for a new task. This makes analysis more complicated, insofar as we need to allow for student agency. It also highlights the communicative (or ‘invitational’) rather than controlling side of design (Krippendorff 2006; Goodyear 2015). In sum, we are arguing that people who design for other people’s learning need to be able to analyse existing learning environments, which entails understanding how real, complex, heterogeneous networks of people, activities and things actually function. Shifting the Focus of Analysis from Discrete Devices to Ecologies and Networks The term ‘learning environment’ is widely used but rarely explained in writing about learning technologies. It is a term that appears to work neatly when the focus is on an individual(ized) learner and their physical environment, side-stepping questions about whether it is reasonable to describe other people as part of one’s environment, or whether ‘learning environment’ can be used to describe the (shared) habitat of a collection of learners (Goodyear 2000). Our use of the term is relational – person and environment are mutually entailed; there is no person without an environment and no environment without a person (or organism) dwelling in it (Ingold 2000). The language used to conceive and describe relationships when analysing learning and learning environments is important. For example, different kinds of relationships are assumed, or different qualities of relationships are foregrounded, when one talks about environments, ecologies, systems, assemblages, architectures, structures, spaces, places, communities, networks and meshworks. Later in this chapter we sketch two contrasting learning situations to illustrate our argument for a more holistic approach to analysis. Both situations involve a number of digital technologies, as well as other elements that combine to have educationally consequential effects. Some of these elements were designed for the situation, some were selected by teachers or designers, some had other ways of ‘coming to hand’ when students were engaged in their work. A thorough analysis needs ways of identifying and making logical connections between diverse kinds of elements, including the physical, digital and human; texts, tools and artefacts; tasks, rules and divisions of labour. Producing an inventory of components is not enough, because functioning depends on structure: on relationships between parts. Nor does it make much sense to try to identify the contribution to learning made by a single component. Outcomes depend on interactions between multiple entities. Rather, we need forms of analysis and representation that match the complexity of contemporary learning challenges: holistic, ecological, 52 Peter Goodyear and Lucila Carvalho architectural and network-based rather than fragmenting, reductionist modes of thought. In addition, we think analysis for design is more powerful when it casts its explanations in terms of real mechanisms, rather than invoking imaginary notions like ‘engagement’ or ‘grit’ (Wong et al. 2012). We argue that socio-material analyses of learning environments provide knowledge that fits well with the needs of design and designers. We suggest that providing better ways of thinking about analysis, evaluation and design can help dislodge unhelpful habits of thought – especially those that try to isolate intrinsic merits of particular tools, media or pedagogies. We also argue that such analysis sharpens perception of the boundary between what can be designed, and what must emerge at learntime. Our approach to analysis shifts the focus from individual elements of an educational innovation to the whole system, which can be modelled in a number of ways, such as ecologically, architecturally or in terms of networks (e.g. Ellis and Goodyear 2010, 2019; Luckin 2010; Boys 2011; Carvalho and Goodyear 2014). It starts by recognising that learning activity takes place in complex, messy, dynamic situations, in which interactions between elements produce conditions that are more or less supportive of learning. … knowledge generation … [is] … a joint exercise of relational strategies within networks that are spread across space and time, and performed through inanimate (e.g. books, mobile phones, measuring instruments, projection screens, boxes, locks) as well as animate beings in precarious arrangements … Learning and knowing are performed in the processes of assembling and maintaining these networks, as well as in the negotiations that occur at various nodes comprising a network … Things – not just humans, but the parts that make up humans and nonhumans – persuade, coerce, seduce, resist and compromise each other as they come together. (Fenwick, Edwards and Sawchuk 2011, 10) Actor Network Theory (ANT), as used by Fenwick and colleagues, is one of a number of perspectives that can capture some of this complexity. We remain agnostic about some of the key ideas associated with ANT – such as whether it is reasonable to attribute agency to artefacts. But, like other schools of thought implicated in the materialist turn, ANT sensitizes us to the ways in which material objects influence human activity (see also Barad 2007; Boivin 2008; Sorensen 2009; Johri 2011; Goodyear, Carvalho and Dohn 2016). It reminds us that matter matters. Ecological psychology similarly challenges presumptions about the superiority of mind over matter (Gibson 1977, 1986). From Gibson’s work, educational technology has appropriated one of its core and most contested concepts – the idea that objects have affordances which shape the behaviour of people who encounter them (Laurillard 1987; Conole and Analysis of Complex Learning Environments 53 Dyke 2004; John and Sutherland 2005; Oliver 2005, 2011; Turner 2005; Dohn 2009; Evans et al. 2017). ‘Affordance’ does a great deal of work in educational technology – partly because it sidesteps issues about technological determinism without suggesting that technology choices can be arbitrary. But as Harry Collins has observed: the terms ‘afford’ and ‘affordance’ are lazy terms … these terms merely paper over deep cracks in our understanding … of why, given the extraordinary interpretive capabilities of humans, anything affords any one interpretation better than any other … something hidden and mysterious is going on whenever the terms ‘afford’ and ‘affordance’ make their appearance. (Collins 2010, 36) We will come back to this issue shortly. For now, the key points are as follows: (1) analysing or evaluating learning activity in context cannot sensibly be reduced to enumerating the pedagogical affordances of individual tools, devices or artefacts; (2) instead, a more holistic approach is needed, in which learning and the things that influence it are seen as connected: for example, in heterogeneous networks; (3) how we conceptualize the key relations has serious consequences for how we analyse and explain what happens – ‘affordance’ turns out to be just one of several necessary connecting constructs. What the Work of ‘Design for Learning’ Can Produce Much of the learning that students do is accomplished without direct supervision. In such circumstances, with only very limited opportunities for teachers to carry out real-time repairs, good design is crucial. Since analysis and design need a shared conceptual framework, if they are to be mutually informing, then we offer the following sketch of design and its legitimate products. It consists of three broad principles, each of which is unpacked in a subsequent section (see Goodyear 2000; Goodyear and Retalis 2010; Goodyear and Dimitriadis 2013; Goodyear 2015 for further information). 1. Design for learning is chiefly concerned with the design of good learning tasks (well-crafted suggestions of good things for people to do, if they are to achieve some desired learning outcome). 2. Design for learning must also attend to the physical and social setting – ensuring (as far as is possible) that all the resources needed for learning come to hand. 3. Design for learning needs to work fluently across scale levels: linking macro, meso and micro. 54 Peter Goodyear and Lucila Carvalho Design for Learning Is Chiefly Concerned with the Design of Good Learning Tasks Task design typically results in the production of texts – often in the form of a specification of what students should do. Students interpret these texts and their subsequent learning activity can be understood as an improvisation that is informed, but rarely determined, by the text. It is often through their interpretation of key texts, such as course outlines and assignment specifications, that students unravel what is required from them in a given situation. This is rarely a straightforward process. For one thing, students also bring their own beliefs and experiences about how such activity is to be completed (Prosser and Trigwell 1999; Ellis and Goodyear 2010). Moreover, their ability to keep task specifications in mind, as activity unfolds, will be constrained by working memory. And it is important that students are able to recognize and realize the relevant meanings associated with their current pedagogical or learning context (Bernstein 2000). Students’ interpretations of what should be done – including the designers’ intentions – require that they are able to identify implicit social values associated with knowledge and practices within a particular context. Some of these implicit social values reflect underlying organizing principles structuring knowledge in particular fields of practice (Maton 2000; Carvalho, Dong and Maton 2009). They underlie the way in which pedagogical communication takes place, regulating teachers’ and designers’ practices and shaping for example, the ways a task is proposed. Consequently, task design also involves incorporating ways of expressing the broader social context of the proposed learning activities, so that students know the ‘rules’ for the context they are in. Subsequently, analysis needs to pick this up. On this view, analysis of what students are doing (and learning) can be explained, in part, by reference to both explicit and implied tasks: objects that stimulate and give shape and meaning to the activity (Kaptelinin and Nardi 2006). Whether implied or explicit, tasks also need to be understood as (a) nested in an architecture of tasks (tasks make sense in relation to sub-tasks and supra-tasks), and (b) located within what might be called an ‘epistemic architecture’ – structures of knowledge and ways of knowing peculiar to the disciplines, professions and/or practices involved. Design for Learning Must Also Attend to the Physical and Social Setting Design that is attending to the physical and social setting(s) within which learning activity is expected to unfold typically results in the identification, selection, recommendation and/or creation of texts, tools and artefacts that the designer believes will be useful. It also results in suggestions to Analysis of Complex Learning Environments 55 students about how they might work with others – proposing divisions of labour, grouping, and/or the allocation of roles. As with task specifications, these socio-material design components should normally be understood as resources on which students may choose to draw – even when their use is mandated, students find themselves some wriggle room (Goodyear and Ellis 2010; Sun 2018). Moreover, working with and in a complicated network of people, tools, artefacts and places is neither an automatic nor a dependable process: what works needs to be seen as an accomplishment (Law and Mol 2002; Rabardel and Beguin 2005). Design for Learning Needs to Work Fluently across Scale Levels Design for learning gravitates towards the meso-level (Jones, Dirckinck– Homfeld and Lindstrom 2006; Jones, Chapter 4). By this we mean that, in practice, educational design attention tends to be drawn to the design of learning tasks that run over hours or days, rather than years or seconds. It is better aligned to the layout of rooms or the recommendation of specific texts than to macro considerations (replanning the campus; restocking the library) or to the minutiae of students’ choices of pen, paper, or workmate. That said, the devil can often be in the detail and also macro-level phenomena can place powerful constraints on what happens at the meso-level. So while design tends to focus on the meso, it cannot safely ignore chains of influence that run from macro to micro and back again. The interrelations between tools, artefacts and other material/digital resources for learning can be thought of as constituting a physical architecture. Similarly, inter-personal working relationships, divisions of labour, roles etc. make sense within what might be called a social architecture. In sum, whether we are trying to analyse an existing learning situation or design a new one, we need ways of understanding and representing the networks of interacting people, objects, activities, texts etc. that shape learning activities and outcomes. We need to be able to detect global forces at work in local artefacts, and to account for the mutual shaping done by language, minds and things (Malafouris 2013). Analysing Connections between Tools and Artefacts and Human Activity: Both Affordance and Interpretation An analysis of the relations between such things as digital tools and resources (on the one hand) and learning outcomes (on the other) needs to be informed by some defensible ideas about how the former can be said to influence the latter. How then should we frame analysis of learning environments so that there is some chance of connecting (a) that which is designed to (b) valued educational outcomes? If one finds it sufficient to equate learning with 56 Peter Goodyear and Lucila Carvalho authentic engagement in a social practice, then this is a one-step argument. If one also values some associated change in the understanding or skills of a learner, then two steps are needed to complete the connections (see Mayes, Chapter 1). We take an activity-centered position on this: what matters is what the learner does – physically, mentally and emotionally (Shuell 1992; Biggs and Tang 2007). Different kinds of knowledge are acquired in different ways – through the activation of different kinds of mental processes for example (Ohlsson 1995, 2011). So the nature of the learner’s activity is part of the link between the material world and their learning outcomes. The other missing link is between the material world and activity. This is where the over-used idea of affordance is normally asked to weave its magic. It is usually a mistake to try to isolate some intrinsic properties of tools, resources, places etc and connect them to learning. Rather, as Nicole Boivin argues: material properties are always properties relative to people, as James Gibson’s concept of affordances reminds us … what is important is not just materiality, but the coming together of materiality and embodied humans engaged in particular activities. (Boivin 2008, 167, emphasis added) The quotation we took from Harry Collins (above), about ‘affordance’ being a ‘lazy’ term, was arguing that the extraordinary interpretive capabilities of people undermine the explanatory power of ‘affordance’. It is true that people are extremely versatile sense-makers, but that does not mean that they linger in interpretive mode prior to every action. What needs to be acknowledged here is that human action can involve deliberation and interpretation but it can also be rapid, fluid and seemingly automatic. Rather than insist on the primacy of either ‘affordance’ or ‘interpretation’ in explaining relations between material objects and human activity, we would argue that both play a role, much of the time. Analysis needs both of these connecting constructs. Consider Daniel Kahneman’s argument that humans rely on two systems of mental operation – tuned to ‘thinking fast and slow’. Kahneman (2011) describes two ‘systems in the mind’. System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control … System 2 allocates attention to the effortful mental activities that demand it … The operations of System 2 are often associated with the subjective experience of agency, choice and concentration. (Kahneman 2011) Analysis of Complex Learning Environments 57 Although Kahneman doesn’t talk about affordances, we suggest that affordances are involved when System 1 is running the show; interpretation invokes System 2. This immediately provides a more flexible and robust way of accounting for links between the material/digital world, learner activities and learning outcomes. For example, providing learners with scaffolding for their activities, by offering them guidance in the form of digital texts, necessarily invokes (slower) System 2. This increases cognitive load, but also opens opportunities for students to interpret and reflect on the teacherdesigner’s intentions. Design can substitute other forms of computer-based guidance for texts – e.g. through the use of interface icons, or other forms of procedural support, that afford one action rather than another. This allows System 1 to do what is needed, reducing cognitive load but sacrificing opportunities for reflection in order to expedite action. (Neither of these approaches is intrinsically better. Design involves trade-offs.) This leads to a view of analysis that can hypothesize a variety of connections between the material/digital world, learner activity and outcomes – involving various mixtures of affordance and interpretation; structure and agency; fast thinking and slow; visceral, behavioural and reflective responses, or hot and cool cognition (Norman 2005; Thagard 2008). It also helps resolve thorny problems about technological determinism and human agency (Oliver 2011; Ellis and Goodyear 2019). Some of the literature that aims to explain relations between technology and human action takes a social, cultural or semiotic view, within which actual characteristics of tools and artefacts turn out to be of little interest. In subsuming material studies into general semiotic and social paradigms, we highlight certain aspects of material meaning, but at the same time we occlude recognition of what makes material things different from words and signs – indeed what makes material things really interesting in their own right … Such examples allow us more clearly to see how the actual physical properties of things – rather than just the ideas we hold about them – instigate change, by placing constraints on some activities and behaviours, and making possible, encouraging, or demanding, other types of behaviour. (Boivin 2008, 155 and 166, emphasis added) Like Boivin, we think that analysis needs to account for ways in which technology, and the material world more generally, influence human perception and action, without recourse to deterministic arguments. Objects in the material world carry physical properties such as their size, weight, shape, colour and temperature which may or may not have been intended as part of their design. Digital tools and artefacts affect a narrower range of senses, but have qualities which can change in an instant. We also need 58 Peter Goodyear and Lucila Carvalho to acknowledge that embedded into the particular way any material object is designed is an intention of how form and function were to meet. The object itself thereby carries values from, and choices made in, the design process. Either way, through their physical properties and embodied intentions, designed objects have effects on human perception and action, but the nature of those connections depends upon an interplay between affordance, interpretation and capability (Rabardel and Beguin 2005). Illustrations: Analysing the Architecture of Productive Learning Networks Illustration 1: Field-Training of Paramedics (iPads in the Wild) This case study came to our attention when one of our part-time Masters students began discussing her ideas about a dissertation project. (We have changed a few details, to preserve anonymity.) Her original suggestion was that she might try to evaluate the effects on learning of the introduction of iPads – the context being one of the courses in her School of Health Sciences. She sketched how she might do this – with some students having School-provided iPads and others not. The first opportunity to do this would be on a field trip – an exercise in which students who are learning to be paramedics would take part in the search for, and treatment and evacuation of, some people injured while hiking in the mountains. (This exercise has been run annually for a number of years. The casualties are played by actors. Qualified mountain rescue personnel take a major role in running the exercise. The iPads were new.) We did not encourage our Masters student to run the experiment that she had in mind. Rather, we suggested that, at least at first, she should take a more exploratory approach – roving around while the exercise unfolded, making field notes, and trying to identify and describe as carefully as possible the networks of things that seemed to influence the activities and their outcomes. Her field notes mentioned that the iPads were used (but not often). She also noted the use of compasses, maps, GPS devices, torches, whistles, ropes, binoculars and walkie–talkies. These were just the tools for navigation and communication. Then there were stretchers, bandages, scissors, watches (for measuring a pulse), stethoscopes, medications, and syringes – objects involved in the initial ‘treatment’ of the actor/casualties once they had been located. A reasonably complete account would also take in these actor/casualties (semi-skilled), the mountain rescue volunteers (very skilled), the tutors (semi-skilled) and the students (often lost, cold, and confused). Obvious though it might seem, the design and evolution of this exercise also necessitated being in the mountains. The difficulties of traversing rough terrain, locating a casualty when hidden in a valley or by Analysis of Complex Learning Environments 59 vegetation, coping with poor visibility and communicating without mobile phones all played a substantial role in the exercise. Proper clothing is also important. A conventionally-minded instructional designer might be forgiven for thinking that good boots and a warm, waterproof coat are things for the students to provide. But those who forgot these important items were unable to complete the exercise. And which instructional design guideline tells you that fingerless gloves are useful when trying to use an iPad on a cold mountain? From the perspective of the organizers as the designers of the exercise, emphasis was placed on the technical knowledge associated with understanding drugs, first aid etc.; on the use of technical devices (e.g. GPS), and on life-saving procedures. These were seen as the essential knowledge for completing the exercise, and some issues related to the effects of the environment were overlooked, or their influence under-estimated. The organizers assumed that key aspects of the knowledge needed to work effectively in the mountain environment would come from the students’ prior personal experience – and therefore, they ‘should already know’ that boots and coats were essential elements, given the material circumstances. As a result, in spite of whatever knowledge they had about life-saving procedures, using technology remotely and so on, those who did not know about the need for coats, gloves and boots in rough terrain failed to learn much from the experience. Among many other things, this example illustrates how important it is that students are able to relate ways of knowing with material circumstances. Pedagogical interactions on the mountain involved very different ‘rules of the game’ compared to those in a ‘normal’ classroom. Students had to be able to identify a different ‘language’ and those who could not recognize the essential rules within this context were then unable to participate fully in the experience. We cannot easily portray the whole network of tools, artefacts, activities, people and attributes of the physical terrain in a single image. But Figure 3.1 begins to capture some of the relationships involved in (more or less) successful execution of this field exercise. Our analysis suggests that successful participation in this kind of exercise involves: • • • learning to use each tool, at least with sufficient fluency to be able to act according to the established protocols, but ideally with a level of automaticity that binds tool and action in a smooth flow weaving the use of the tools into an integrated set of activities, involving smooth effective action, co-ordination with others, focusing on the priority goals, etc. turning the individual and aggregate experiences of the exercise into learning that lasts. 60 Peter Goodyear and Lucila Carvalho Figure 3.1 A (partial) network of objects and activities The point of the exercise, for each student, is not just to master the individual tools but to participate in the construction of a co-ordinated web of activity that can result in a successful rescue, minimising danger to participants, and leaving traces, in some kinds of memory, that mean doing something like this again will not feel entirely new. Illustration 2: Online Learning for Educational Leadership Our second case involves an online professional development program for school teachers taking on curriculum leadership positions. (Again, unimportant details have been altered to protect anonymity.) A number of elements of the program are quite conventional. There is an online induction module, introducing the participants to the technology being used, to a number of key ideas about educational leadership, and to the overall scope and goals of the program. Through direct experience of the resources, teaching methods, user interface, tasks and collaborative learning activities that will be used in the main part of the program, participants have an opportunity to work out whether the program will suit them, and whether Analysis of Complex Learning Environments 61 they will be able to cope with its demands. 30% of participants quit during or immediately after the induction module. The remaining participants then tackle 12 ‘structured learning modules’ (SLMs), each of which introduces them to a set of ideas that the course team believes to be relevant to understanding curriculum leadership. Once the participants have completed four of these modules, they are allowed to join an online Community of Practice, within which they are encouraged to discuss issues with peers. Once all the SLMs are completed, participants work in small groups to design curriculum implementation projects that they will carry out in their own schools. The designs are peer reviewed. The rubric for the peer review includes criteria that reflect and encourage the use of concepts, techniques etc that were presented in the SLMs. Thus far, some 200 projects have been designed and published for peer review by the program participants. An analysis of what is working well and what might be improved would conventionally focus on the quality of the resources being made available in the SLMs, the timeliness and helpfulness of online tutors’ support, the ease of use of the online tools, participants’ experiences and their assessment of the extent and usefulness of their own learning. All of these are important, but they tell less than the whole story, and an analysis of the case that was restricted to these elements would not (we contend) provide an adequate basis for others to design similar educational programs. Not least, the fact that this program draws on problems that emerge in participants’ own educational practice – and is intended to help solve those problems – means that the participants’ schools (in all their complexity) have to be counted in as learning resources. People without access to such resources could not participate successfully in the program. Moreover, these school-based ‘resources’ are outside the sphere of things that the program providers can design. (The program providers/designers can specify requirements – e.g. that participants must have a leadership role with respect to curriculum change in some part of their school’s work. But they cannot design these important parts of the network of activities, texts and resources on which participants will draw.) Participants bring their school-based ‘resources’ to the mix and associated with each of these resources is a specific set of underlying principles structuring knowledge practices. That is, knowledge practices within each school reflect implicit values within that specific context, which shape participants’ practices and the way they see leadership and curriculum. As various participants come into the pedagogical context of the online environment to exchange ideas about leadership and curriculum, they bring also their own beliefs and values, which will need to be negotiated with the beliefs and values of other participants, whose practices are shaped by their own experiences of their school-based resource. A participant with a background in Science may see knowledge practices in a different way 62 Peter Goodyear and Lucila Carvalho than a participant with a background in Arts. Or a participant’s views about leadership may reflect their experiences at different levels of hierarchy, such as being a Coordinator or a Principal. The context of the experience may also be influenced by the complexities that shape working in a city school versus one in a remote area, an established versus a new school, a wellfunded versus a disadvantaged school, and so on. As these participants come together to exchange notions about leadership and discuss curriculum issues within the online environment, they do so from their own perspective, from where they are positioned within the field. The design of the pedagogical context where they interact needs to address these differences, acknowledging that diverse underlying values are likely to be present. This use of the local working context as a resource for online learning is not uncommon in design for professional development, but we have found very little in the instructional design literature that helps capture or think about key issues here, other than in general terms. If anything, the dominant imagery of ‘learning in the cloud’ obscures the contribution of the local work context as part of the learning environment. Conclusions In this chapter, we have suggested that approaches to analysing complex learning environments will be more productive, and will align better with the knowledge needs of designers, if they help map the networks of heterogeneous elements that shape learning activity. In particular, we have argued that neither affordance nor interpretation on their own provides a sufficient explanation for the connections between that which is designed and the learner’s activity. Both concepts are needed. Our illustrations show how tasks and activities sit within nested architectures, such that what a person is doing at any one point only makes sense in relation to other tasks and activities, the accomplishment of which may well be distributed quite widely in time and space, and across the material, human and digital. We have also tried to show something of the complexity of the networks of tools, artefacts, places, practices, ways of knowing and inter-personal relationships that are implicated in designed learning situations. Successful designs for learning find ways of embracing this complexity. Sharp analytic skills help us understand such designs, and learn from them. Acknowledgements We gratefully acknowledge the financial support of the Australian Research Council through grants FL100100203: Learning, technology and design: architectures for productive networked learning and DP150104163: Modeling complex learning spaces. We also thank Helen Beetham and Shirley Agostinho for insightful comments on earlier drafts of this chapter. Analysis of Complex Learning Environments 63 References Barad, K. (2007). Meeting the universe halfway: Quantum physics and the entanglement of matter and meaning. Durham, NC: Duke University Press. Bernstein, B. (2000). 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Realist methods in medical education research: What are they and what can they contribute? Medical Education, 46, 89–96. Chapter 4 A View from Social Science Foundational Issues for Design Christopher R. Jones Editors’ Introduction In this chapter, Jones explores design for learning as a social practice to which the tools of social science can usefully be applied. Starting from the observation that design is both formalised and highly contingent, Jones suggests that we focus on the ‘meso’ level of design – decisions taken by practitioners, with the purpose of influencing learning interactions, in technical and organisational environments that are largely predetermined by others. In this space, the terms ‘agency’, ‘affordance’, and ‘assemblage’ can be helpful in analysing the complex relationships between digital artefacts, educational processes, and the intentions of learners and educators. Introduction The social sciences are a dynamic field and in the time between the first and third edition of this volume a significant change has taken place in how the social sciences have engaged with technology and the material aspects of society. The material turn in social sciences (Fenwick et al. 2011) is represented in this chapter by the introduction of three key ideas, agency, affordance and assemblage. Social science has developed knowledge about the ways technologies are related to social change. In particular, studies indicate that technology cannot simply determine social change and that technology is not an independent factor and cannot simply cause specific educational effects or any particular learner responses. Design in the social sciences and design for the social sciences is an exercise in choice, a way of setting the parameters within which technologies will be deployed. Technologies do not decide such issues; rather these are the concerns that can be central to design when thought of as more than simply a technical task. Design choice can be exercised at different levels. At a macro level design is generally outside of the day to day control of individuals and teaching A View from Social Science 67 teams, taking place under the control of professional or assessment bodies, or senior committees, and it takes considerable time to enact. The decisions and design choices made at the macro level are national, institutional and corporate choices about local and global infrastructures, equivalent to classic infrastructures such as roads and utilities. Examples might include deciding which corporate platforms are selected to provide services in education, what educational policies for e-learning will be developed, and the legal and policy frameworks and regulations that apply to digital resources. These are choices typically made by collective bodies over time, however choices at the macro level are not beyond the influence of political organisations and pressure groups and they can be altered by the application of consistent pressure over extended periods of time. At the micro level precise designs can be developed for particular interactions. At this level design can be very detailed but it is subject to a high degree of contingency. The level that I argue educational practitioners need to focus on is the meso level of design. The meso level is focused on the medium term and decisions that small groups and individuals can make or influence. In universities this might mean the department or course team, the design of a course rather than an individual interaction, and design that involves the use of systems and tools that have been selected elsewhere. Indeterminacy – The Indirect Nature of Design The traditional notion of design implies the separation of thinking and doing (Cooley 1999). Cooley suggests that the scientific method has influenced the characteristics that a process or design must display suggesting that design must be predictable, repeatable and quantifiable in mathematical terms. In contrast, ethnographic studies of the design process suggest that design and plans are component parts of situated action (Suchman 2007). Design as situated action cannot have the characteristics that Cooley describes as the scientific method. From the perspective of situated action, design is an iterative process and the products of design are part of a deeply social and situated set of work practices (see also Goodyear and Carvalho, Chapter 3 and Agnostinho et al., Chapter 6). Design and the products of design – plans, representations etc. – do not have a determining role, rather they form resources for action, available to inform the working practices of those involved in the designed process. Goodyear and Carvalho (Chapter 3) set out the case for design for learning and the need for a distinction to be drawn between tasks and activities (see also Beetham, Chapter 2). Tasks are what designers set, they are prescriptions for the work the students are expected to do, whilst activity is what people actually do. Because students constitute their own learning 68 Christopher R. Jones context from all available resources, including those assigned by designers and those brought from a student’s prior history, it should be expected that a student’s activity will often differ from the task that initiated it. The distinction between task and activity is mirrored by two further distinctions between space and place and between organization and community. Together these three distinctions are referred to as an indirect approach to learning and their relationships are shown in Figure 4.1. In an early but still relevant example of an indirect approach Jones and Asensio (2001) examined a distance learning course and reported a post-assessment series of interviews within one tutorial group. The group had been divided up into sub-sets that had been allocated the task of preparing their final assessment in the form of a group project with an individually prepared component. Students interpreted their instructions in highly contingent ways that depended on the particular context each student found themselves in. The responses to the set assessment task could be grouped into two broad understandings of the task, but they were affected by highly specific factors in each case (see Box 4.1). It is worth emphasising that this course and assignment were well designed and the different interpretations arose not from the design of the task itself but from factors affecting the students that were outside the course design process and indeed in some cases outside of the learning environment. Similar cases have been reported more recently when students studying under the same general conditions, engaged in different practices which accorded with their particular approaches to learning. (see Box 4.2). Organisation Space Place Community Learning Activity Tasks Figure 4.1 Design: an indirect approach Source: Goodyear et al. (2001) A View from Social Science Box 4.1 An example of two contrasting interpretations of instructions from Jones and Asensio (2001) (Interviewer in italics) 1. What did you conceive that task to be? I would assume that it was more to continue the computer mediated conferencing as an exercise in itself for people to work together to sort of exchange ideas and irrespective of what the particular project was to work on. (Daniel) 2. What do you think the emphasis was? Your personal individual, um your personal big 500 words or whatever So the individual submission was …. Was more important than the group work And how about content and process if we split it that way? Content Rather than process … Rather than process and yet it’s, I would argue the process probably took as much time as writing the content if not more (Lillian) The two students were part of the same group working together to produce a joint report yet they had different understandings of the task they had been set. This was despite extensive documentary guidance provided in a 12-page assessment booklet. When prompted to re-read the booklet, Daniel, who had identified the task as being to conduct group work, revised his view and conceded that content may indeed have been more important. There were two reasons offered by students in the group that shed light on why the group process dominated over the intention of the assessment criteria. First, the group process was novel and pervasive as they used the conferencing system throughout the course and were expected to work collaboratively using the system for two assessments. Second, the ability to communicate between students was a valued and novel element within the distance learning setting. 69 70 Christopher R. Jones Box 4.2 Example of two students from the same course and university managing their environments in notably different ways from Jones (2011: 110) (Interviewer in italics) B: I prefer to work in the lab because the software is there and everything’s working. So it’s easier for me that way. INTERVIEWER: And your choice was? C: I kind of prefer to do it in my room because in the labs there’s certain things you can’t do and on your own computer you can. (Jones 2011: 110) Both students were studying the same computing course and in the same interview the students contrasted their use of cut and paste for programming: C: … I use quite a lot of online books because if I do code … if you have a book on paper you have to copy the code in, type it in yourself. INTERVIEWER: You have to retype it, yeah? C: But then if you make like one mistake, if you like miss out a dot or something, it messes up and you can’t understand why. But if they provide you with an example on the Internet and you copy and paste and then you know it works because it’s exactly what they give you, if you know what I mean. B: I prefer to do both. I like the books because it’s something you can read. When I type it and if I get it wrong after a couple of attempts I’ll just go to the website and copy the exact same code but I prefer to type it because it’s like a learning process isn’t it? The point being made here is that there is no simple way out of this design problem. There is no special kind of design that will make every student or even most students read instructions or any other kind of text in the same way. It points towards a social and iterative process of design in use that makes the artefacts and products of design only one part of the design process. In particular it points to the need for good processes to take place during the enactment of a design to ensure its success. For example in the case of assessment instructions, checks can be made on students’ understandings as the task is undertaken. This is more than an iterative approach to design because it suggests that a key point in the process takes place at the point of use, beyond the design process itself. A View from Social Science 71 Levels of Design Design is undertaken at a variety of levels (see Beetham and Sharpe, Introduction). The design level that this chapter focuses on is what I describe as the meso level (Liljenström and Svedin 2005; Jones et al. 2006; Jones 2015). That is, this chapter is not concerned with the global technological infrastructure or the design of national infrastructures including the design of broad learning environments. Nor is the chapter concerned with the immediate micro level day-to-day interactions in and through which, teaching and learning takes place in locally situated conditions. Meso points to social practice as the locus in which broader social processes are located and contingency is moderated by organisation and planning (Schatzki 1996; Schatzki et al. 2001). Infrastructures are located at both macro and meso levels in education. At a macro level ‘universal service infrastructures’ intended for all citizens (Hanseth and Lundberg 2001), such as search engines and social network sites lie beyond the institution. Institutional infrastructures, such as Learning Management Systems (LMS), known in the UK as Virtual Learning Environments (VLE), are examples of local infrastructural elements. The LMS/VLE can be deployed according to local conditions and in some cases, for example the open source Moodle platform, designed for local conditions (Jones 2009). These infrastructures take the form of largely given elements for those involved in the day-to-day educational design process. The design of infrastructures for learning is not generally undertaken directly by the academic staff involved in the day-to-day running of courses and programs. Infrastructure from this point of view is enacted in the micro interactions of day-to-day teaching practices and infrastructure is a process in which micro local and macro global factors combine. Infrastructures are factors that come into micro settings from outside and infrastructural elements usually cannot be designed or altered at this level alone. The use of an institutionally supported LMS is an example of this tension. The infrastructure implemented at one level comes into being when the LMS is locally deployed, but the design of the LMS and its selection are largely beyond the local and micro level context. Guribye and Lindström provided the following definition of an infrastructure for learning: An infrastructure for learning is a set of resources and arrangements – social, institutional, technical – that are designed to and/or assigned to support a learning practice. (Guribye and Lindström 2009: 105) Infrastructures for learning are commonly designed by a variety of actors and are intertwined with infrastructural elements that stand outside of 72 Christopher R. Jones educational institutions and which are neither designed for nor assigned to an educational purpose (Bolt et al. 2010). Increasingly design for learning needs to consider universal service infrastructures because they extend the range of contexts within which learning activity is intended to take place. Services such as Microsoft 365 Education, Google’s G Suite for Education, Facebook, YouTube and iTunes are being integrated into educational institutions and student learning practices, but they are significantly beyond institutional control. Design, Communication and Dialogue A key area of design that has been influenced by social science approaches concerns issues of communication, collaboration and participation. The concern with communication and dialogue is perhaps the most characteristic concern of the social sciences as distinct from other subject areas. Communication and dialogue have often been understood in education using the stronger terms cooperation and collaboration which imply that activities are essentially social and depend fundamentally on the interactions between participants. Social science research in pedagogy and new technology is closely related to the development of social and situated views of learning and the cultural turn in the social sciences (Vygotsky 1986; Engeström 1987; Lave and Wenger 1991; Hutchins 1995; Jameson 1998). The key feature of this re-orientation of the social sciences has been the central focus on social and cultural factors rather than the individual and psychology, or on the biological bases of learning. These factors had previously been heavily emphasised in behaviourist and cognitivist theories of learning (See Mayes, Chapter 1). The technological changes that enabled computer-mediated communication when combined with social and situated views of learning generated Computer Supported Collaborative Learning (CSCL) (Koschmann 1996; Stahl et al. 2006). The move to CSCL was related to, but not entirely the same as, a much longer tradition of cooperative or collaborative learning (Goodyear et al. 2012). Networked learning, an alternative but complementary approach, argued that learning emerged from relational dialogue with online resources and other people in learning networks or communities (McConnell 2000; McConnell et al. 2012). The networked learning conference series gave rise to the following definition of networked learning: learning in which information and communications technology (ICT) is used to promote connections: between one learner and other learners, between learners and tutors; between a learning community and its learning resources. (Goodyear et al. 2004: 1) A View from Social Science 73 The key element of the definition of networked learning is the term connections. Networked learning is less concerned with face-to-face collaboration around technology and is generally more concerned with remote interaction (Jones and De Laat 2016). It also tends to the large scale and is less concerned with small scale collaboration such as in pairs and small groups. Networked learning also focuses on the use of generally available technologies rather than being concerned with the design and development of specific tools to support particular instances of cooperation and collaboration. Both CSCL and Networked Learning are interested in the affordances of new digital technologies and the ways material artefacts are entailed in human actions. They differ in the level at which design is applied because CSCL is more concerned with the design of specific software to address explicit pedagogical objectives in designated educational settings (see for example Tchounikine 2011). Networked learning sees cooperation and collaboration as special cases of the wider phenomena of networks. The idea of networked learning has drawn on recent developments in network analysis and the tradition of social network analysis (De Laat and Lally 2004; Jones 2004; Jones et al. 2006; De Laat et al. 2007; Haythornthwaite and De Laat 2010). From this perspective networks are composed of nodes and the ties or connections between them. Nodes can be individuals, or various kinds of social forms including organizations, communities and collectives (Dron and Anderson 2007). Nodes and the active agents in networks can be of different types, including non-human actors. From a social network perspective the research interest is in the nature of ties between participants and whether they are weak, strong or latent (Jones et al. 2009). Social network analysis has also been applied to both CSCL and networked learning (De Laat and Lally 2004; De Laat et al. 2007; Haythornthwaite and De Laat 2010). The social sciences are diverse, however it is possible to draw out several foundational issues that underpin a design tradition derived from the social sciences. The foundational topics identified in this chapter are agency, affordance and assemblage (for a fuller discussion see Jones 2015). Agency Agency is an issue that raises key questions for pedagogy, for example: How free are individuals and to what degree are they constrained by their material and social circumstances? Which pedagogic phenomena can be explained at the individual, group or societal scale? How can change be effected and by whom? Because agency is a major issue in social theory I will not attempt to summarise the extensive work that has previously been undertaken (see for example Giddens 1984; Bourdieu 1990; Archer 1995; Emirbayer and Mische 1998; Flyvbjerg 2011). Rather, I am more interested in thinking about how different ways of understanding agency 74 Christopher R. Jones can be used to explain our complex social and material world and inform educational design (see Ashwin 2009). Agency in this view is achieved as part of a process, rather than being possessed (Biesta and Tedder 2007). People don’t have agency, they accomplish agency, often in relation to material artefacts, social structures, and things (for an application of such a view see Czerniewicz et al. 2009). One common way of thinking about agency in social science has been to contrast it with structure. Agency in this view is a capacity to act independently and make choices whereas structure refers to those material and social factors that constrain, determine and limit the choices that can be made. In this binary format agency is frequently restricted to an individual’s capacity for choice, because any collective or group organisation or activity is seen as part of the structure. In digital contexts researchers are often interested in examining different kinds of agency and the types and degrees of agency related to material and social contexts. These variations can’t easily be described using the dualistic terms structure and agency. The idea that material objects, artefacts and technologies, could be active agents has been popularised through Actor-Network Theory and the concept of generalised symmetry (Latour 2005; Law 2008). Actor-Network Theory argues that all entities in a network can and should be treated in the same way. This is a point of distinction with other authors concerned with material agency such as Pickering (1993) and Kaptelinin and Nardi (2012) who make clear distinctions between intentional human action and actions made by non-human entities. In simple systems agency can be delegated by people to things, such as the use of automatic door closing devices (the door groom) or traffic calming speed bumps. In more complex systems it becomes more difficult to define effects because delegated actions can become combined and interact with each other in unexpected, unpredictable, and emergent ways. In the digital age agency can therefore be thought of as: (a) distributed between humans and technologies (involving delegation of human agency), or more radically (b) as being a capacity available to both humans and machines. Knox (2014) argues that the algorithmic properties of YouTube and an aggregator performed functions in a MOOC that could not be reduced to the intentionality of either educators using these systems, or the designers of the software, He concluded by arguing that educators can not unproblematically control web services and social media because the growing proliferation of algorithms and code in education act in ways that cannot be predicted. It is to how we understand these technological and material factors to which the chapter now turns. Affordance Action takes place in a material context which includes designed artifacts and things. A key idea related to agency and its distribution in a setting has A View from Social Science 75 been the concept of affordance. An affordance is what an object in an environment offers to an agent as it interacts in the setting. Originally set out by Gibson as an ecological approach to perception, the idea of affordance is both real and relational: The affordance of something does not change as the need of the observer changes. The observer may or may not perceive or attend to the affordance, according to his needs but the affordance, being invariant is always there to be perceived. An affordance is not bestowed upon an object by a need of an observer and his (sic) act of perceiving it. The object offers what it does because it is what it is. (Gibson 1986 [1979]:138/9) The idea of affordance became popularised by way of the work of Norman, although Gibson’s concept of affordance differed in significant ways from Norman’s application of the term (1990, 1999). Norman took an essentialist approach in which affordances were essential properties, whereas Gibson viewed affordance as being relational between things and their potential users. Norman also made affordance relative to perception whereas Gibson held that although an affordance was relational it was fixed and did not vary with the needs or perception of the observer. Norman’s interpretation of affordance led to a fundamental difference about whether a distinction should be drawn between ‘real affordances’ and ‘perceived affordances’ (Norman 1999). Gaver (1991) retained Gibson’s relational concerns but drew a distinction between affordances that were perceptible and could be accessed directly, and those that were hidden or false. Gaver suggested that complex actions in relation to affordance could be understood in terms of ‘groups of affordances that are sequential in time or nested in space’ (Gaver 1991: 79). Action on a perceptible affordance could lead to information about a previously unperceived or hidden affordance and affordances could be sequenced over both time and space. These early views on affordance have been elaborated by more recent work (see McGrenere and Ho 2000; Torenvliet 2003; Jones 2015) and they have been subject to significant criticism in educational technology journals (Oliver 2005; Derry 2007; Dohn 2009b; Wright and Parchoma 2011). Oliver argues that the concept of affordance has drifted and that it is now too ambiguous to be useful. He goes on to say that because the concept has its origins in ecological perception, and primitive claims about animal-object relations, it has little direct relevance to interactions between a person and a specific artefact. Oliver argues that any account based on Gibson’s version of affordance is going to be essentialist, positivist and technologically determinist. Derry agrees that the term affordance has moved into a loosely defined vernacular use and argues 76 Christopher R. Jones that a prerequisite of perception is prior knowledge leading to predictive hypotheses (Derry 2007). Kaptelinin and Nardi (2012) provide a useful critical link to the Gibsonian conception of affordance and argue that affordances can be discerned as a set of potentials in a relationship between different elements of a setting whether or not the prospective user of an affordance perceives or understands their meaning. This suggests the possibility of analytically discerning features of a technology in relation to potential users separately from the actual understandings and perceptions of particular users. The close relationship between agency and the objects available in a setting becomes complicated in the context of digital technologies which on some accounts exhibit agency themselves, and when these digital technologies are combined with each other and humans in complex assemblages. Assemblage The intricate relationships between human agency and the affordances of things has already begun to hint at the development of what has been called a material turn in the social sciences (Fenwick et al. 2011). The concept of assemblage is used here to point to complex socio-technical systems which combine humans with non-human material artefacts and digital technologies. As such it is loosely related to the philosophical term (agencement/ assemblage) introduced by Deleuze and Guatarri (1987). The individual human actor is displaced from centre stage and attention is drawn to the range of forces including humans in social groups, non-humans (animals, machines, and things) and assemblages of humans and non-humans (complex socio-technical systems). Technologies are understood as one example of material artefacts that are resistant and push-back when they interact with other technologies, with people, and with assemblages of the two (see for example Gourlay and Oliver 2018). Technology includes society in its origins and social agency in the ways it is used, but at the same time it has a material agency that allows for (affords) and/or constrains certain practices (Cornford and Pollock 2002). An example of how assemblage can affect existing theories comes from CSCL which faces a potential challenge from Web 2.0 which involves large scale network effects and interaction in large groups (Dohn 2009a; Kafai and Peppler 2011; Goodyear et al. 2012). Applications of Web 2.0 in education include the collaborative use of blogs, wikis, virtual worlds and mobile social media (Dohn 2010). Web 2.0 applications rely on scale for their efficacy. The architecture of participation at scale suggests that the value of a service increases with the number of users sharing the service. Design in Web 2.0 may need to take place at the level of the social and technical infrastructures (Jones et al. 2006; Jones and Dirckinck-Holmfeld 2009; Jones 2015). A key A View from Social Science 77 emerging issue concerning digital infrastructures in education is the use of large datasets and learning analytics. Big Data and Learning Analytics Since the second edition of this book there have been significant developments in the social sciences and beyond captured by the term ‘big data’. Big data, more properly known as data intensive science, relies on the capture, curation and analysis of large collections of data. Data intensive science opens up the possibility of detecting patterns in datasets that would not be available without large scale and widespread computing. In some opinions this leads to a ‘Fourth paradigm’ (Hey et al. 2009) which questions the position of theory in scientific discovery (Anderson 2008). The Fourth paradigm is claimed to be distinct because data is collected by instruments or generated from simulations, processed by software, stored in a computer and only at the latter stages of the process does it reach the scientist for analysis using data management and statistical techniques (Hey et al. 2009). This kind of data can be processed in such a way that it can be visualized, enabling the teacher or learner to make sense of them, rather than simply relying on software. Learning analytics is rooted in earlier social science developments such as social network analysis but it is driven by more immediate institutional, business, and political concerns, and the increasing availability of machine readable datasets produced by digital technologies, including learning management systems (Ferguson 2012; Sclater 2017; Williamson 2017). What was still a new and emerging field is now an established area of research and practice with extensive expertise and numerous case studies (see Williamson, Chapter 13; Sclater 2017; Williamson 2018). Currently many universities are exploring how these large datasets can be managed and analysed to provide meaningful information to institutional managers, course teams, teachers and students. Such data is also key to the policy discourse surrounding higher education (Williamson 2017). In educational technology the idea of learner analytics could be developed to suggest that simply by collecting enough raw data on students and educational interactions, persistent and perennial problems could be easily answered. The history of social science suggests that this is unlikely to be the case. There are known weaknesses in a reliance on numerical data and on ‘big data’ in particular (see for example Boyd and Crawford 2011). In current conditions the limitations and dangers of an a-theoretical stance need to be highlighted. Learning analytics requires the application of a significant effort in the management and processing of the raw data collected by various systems. Indeed the social sciences have a considerable amount to offer this debate because as Bowker has noted; ‘Raw data is both an oxymoron and a bad idea; to the contrary, data should be cooked with care’ (Bowker 2005: 184). 78 Christopher R. Jones An additional contribution that comes from the social sciences is an understanding of the nature of the views that learning analytics offer to analysts. A popular social science theme has been the idea of the panopticon derived from Foucault’s work (Foucault 1977). The panopticon suggested ways in which systems could be designed to allow complete oversight and those surveilled would alter their behaviour in response to overall supervision. As a way of understanding the potential of analytics and learning analytics in particular this conception could be very misleading. Analytics can provide a view of a system, but that view, even when it is of the total system or network is incomplete, a partial not a full view. In this regard a little discussed concept from ANT may prove to be useful, the idea of an oligopticon (Latour 2005). Latour argues that a panoptic view is not achievable, even with various surveillance devices, there are always gaps and the views provided rely on ephemeral networks and expert analysis. I propose to use the word oligopticon as the generic term, reserving the expression of ‘centers of calculation’ for the sites where literal and not simply metaphorical calculations are made possible by the mathematical or at least arithmetic format of the documents being brought back and forth. (Latour 2005: 181) The oligopticon is a way that Latour understands the methods through which society is totalised using limited views – narrow or partial outlooks. By collecting data using specific kinds of calculations a view of all society is provided, but it is contingent on ephemeral networks and it provides only one narrow view of the whole. Learning analytics are no different in this respect as ‘centers of calculation’. Learning analytics offer the possibility of seeing activity in learning systems and settings, but they rely on networks of data collection and forms of expert analysis that render the views they provide narrow and contingent rather than panoptic in character. Conclusions Social sciences can offer a great deal in terms of understanding education in a digital age. From a fundamental understanding of how human social behaviour is essentially indeterminate and how that plays out in terms of indirect design to practical understandings of how to engage with the newest educational challenges around big data and learning analytics. Design is about choices, a way that educational processes can be moulded to yield the kinds of outcomes educators think are the most desirable. This inevitably makes design a moral and political arena in which the desired outcome will always be contested. Openness, globalisation and mobilities in A View from Social Science 79 education are elements that require careful examination as they are understood in a variety of ways and those diverse interpretations become factors built into designs. A fundamental point that a social scientific understanding can contribute to this field is the indirect nature of design. This is not a problem that can be dealt with by developing ‘situated’ designs or by setting up processes of design that can eliminate uncertainty. Every time a technology is deployed, every time a design is enacted, every time a plan is put into use, its meaning has to be disinterred from the technology, design or plan by those putting it into use for their own particular purposes. Designs and plans can only indirectly affect the activities they generate. More than that for those who believe in a formal design processes the active process of enactment means that not only can design never be of learning only for learning, but learning itself is only loosely related to the activities, places and communities our students create. This chapter proposes an analytic use of the term ‘meso level’ to identify interactions in and with settings beyond the small group but which still retain a local focus that remains open to routine control and intervention. Meso implies a time frame that is beyond the immediate interaction with a relative permanence but without being fixed for extended periods of time. In this context it can point to the level of the department, programme and course rather than wider policy frameworks or day to day interactions. Micro, in this set of related concepts, points to the individual, contingent and highly local whereas macro points to the level of interaction that has a collective, general, and relatively permanent character. In educational theory agency and intentionality has an important place because teaching and design are both significantly affected by notions of purpose and they have a future orientation towards an intended goal or objective. The concept of agency matters because teachers, educational designers, and learners all bring their own particular intentions to the learning process. All three sets of actors are sources of agency, but they do not always coincide. Furthermore the material objects, the buildings resources, plans, technologies, etc., carry meanings built in during their design and combine with each other, and with human actors in complex ways. The idea of affordance remains useful in this regard because it can help in understanding the relationships between the designed objects and processes and the educationalists and learners in a setting. The inclusion of designed material objects in complex digital systems containing humans and machines suggests the possibility of a co-construction taking place between materials, material technology and social practice in assemblages. The main themes of this book concerning globalisation, openness and, mobility are often strongly influenced by a political and policy agenda beyond the range of most designers, but design and designers are still influential in how these agendas are realised. The complex assemblages of 80 Christopher R. 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Jones Williamson, B. (2018). The hidden architecture of higher education: Building a big data infrastructure for the ‘smarter university’, International Journal of Educational Technology in Higher Education, 15, 12. doi: 10.1186/s41239-018-0094-1 Wright, S., and 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. Chapter 5 The Community of Inquiry Theoretical Framework Designing Collaborative Online and Blended Learning Martha Cleveland-Innes Editors’ Introduction Chapter 1 showed how different ways of understanding learning suggest different approaches to design. Theoretical frameworks which make the learning process explicit are a useful guide. This chapter takes the concept of presence – social, cognitive, teaching and emotional – and shows how it can be used to shape design for blended and online learning. A Community of Inquiry theoretical framework is used to describe how students learn through active engagement with questions or problems (inquiry), guided by a variety of interventions. The framework can be used to identify effective teaching activities and to map the different implications for learners. Introduction Unprecedented change is now a part of the narrative about, and experience in, everyday life. Design for learning is impacted by this continuous change and, in addition, must change to offer learning appropriate to the new constituencies and societies it serves. The increasing pace of change means that learning itself will be a topic of study for all who learn, as it is a life-skill that must be regularly employed to adjust to future changes. This calls for a learning design that is open, flexible, explicit, and inquiry-based. The Community of Inquiry (CoI) theoretical framework is a pedagogical model designed to offer ways of learning that are adaptable, collaborative, and engaging through process as well as content. It is a useful framework for any learning delivery but is well-suited to the increased opportunities for access, engagement, and interaction available in online environments. It supports technology-enhanced learning, as well as the opportunity for participants to practice skills required of persons working and learning in online environments. Although created in the late 1990s, the framework was ahead of its time. According to Befus (2016), significant developments have occurred in distance and online education since 2000. In spite of these changes, ‘the 86 Martha Cleveland-Innes CoI framework continues to resonate with educational researchers and practitioners despite these significant evolutions in distance education technology and context’ (p.10). Embedded in the CoI application is the need to make the learning process explicit. This awareness emerges through the learning processes identified in the framework and the need to adjust to the required new role for student and instructor. As a continuously constructed learning environment a CoI allows for, even requires, regular reference to the learning process underway and the desired learning outcomes at play. This reflective, meta-cognitive approach is central to the pedagogical framework discussed here. This chapter provides a history, detailed description, research reference, application advice, and critique of the CoI theoretical framework. A significant synthesis of work with faculty in workshops over the last five years with insight from recent publications provide a view of design and instruction using the framework. Overview of the Community of Inquiry Framework This framework varies from others available for teaching online, such as Salmon’s (2003) e-moderation model and Palloff and Pratt’s (2003) best practices. The CoI framework offers a role for students in the creation of a deep, meaningful educational experience. All of these conceptualizations may be considered important to understanding the transition from teaching and learning in place-based classrooms to virtual, online, and distance engagement. At the same time as these frameworks emerged, the dialogue about teaching and learning was moving to new places and spaces; a demand for improved teaching quality arose and the narrative about instruction and learning design moved the discussion beyond issues of teaching practice alone (Campbell, Schwier and Kenny 2009; Laurillard 2002). It is clear that online delivery requires more than changes in teaching practice or moderation of online discussion; new ways of thinking about pedagogy and the roles of learners and instructors are required. The CoI framework offers a way to begin significant revision of learning design from transmission models to inquiry-based engagement. Inquiry-based teaching includes a focus on providing meaningful engagement opportunities rather than mere direct instruction of content; the latter supports and fosters passive learning. This doesn’t exclude direct instruction completely, which is still needed to create foundation knowledge and skill. The extent that direct instruction is needed will also vary across disciplines (Arbaugh, Bangert and Cleveland-Innes 2010; Ellaway, Chapter 12). Inquiry-based teaching requires making the learning process explicit. Building on the early work of Schwab (1969), inquiry-based teaching practice offers structure to move learners through active inquiry Community of Inquiry Theoretical Framework 87 processes. For Schwab, the active inquiry process starts by using questions, problems, and material to invite learners to identify relationships between concepts or variables. As learners become more expert in the subject and learning skill, questions or problems are presented and the learners create the path to answers themselves. In the third and final stage of Schwab’s taxonomy, a topic is presented and learners identify questions, problems, methods, and answers themselves. The teacher provides guidance and learning facilitation. In the late 1990s, Garrison, Anderson and Archer (2010) developed the CoI framework to illuminate the structure of learning in a text-based online learning environment. The framework was intended to support inquirybased teaching and learning. The foundational thinking in the framework is based on the work of Dewey’s (1933) constructivist views of experiential learning. In addition, Lipman’s (1991) work on using communities to support teaching students to think shaped the framework activities. Lipman is credited with coining the term ‘community of inquiry’. Through this focus on learning as a shared process and a particular emphasis on making learning explicit, the CoI framework describes the necessary elements to create deep and meaningful learning. The framework identifies the education experience as occurring at the convergence of three presences: cognitive, teaching, and social presence. Presence is defined as a state of alert awareness, receptivity, and connectedness to the social, cognitive, emotional and physical workings of both the individual and the group in the context of their learning environments (adapted from a definition by Rogers and Raider-Roth 2006). Shared learning via CoI in online learning is one of the most researched pedagogical approaches. The original CoI article (Garrison, Anderson and Archer 2000) has been cited over 4,000 times in the scholarly literature. Early research focused on social presence (Richardson and Swan 2003) as a new way to approach teaching beyond strict transmission models of delivery. Much research has been done to measure the components of this framework and how they operate in reference to one another (Arbaugh et al. 2010; Caskurlu 2018; Garrison, Cleveland-Innes and Fung 2010; Stenbom 2018). A recent analysis of the literature identified that ‘the most frequently used and the one adopted most commonly in the literature is the CoI survey instrument developed by Arbaugh, Bangert and Cleveland-Innes (2010)’ (Olpak, Yagci and Basarmak 2016: 1090). Measurement and Design in a Community of Inquiry The CoI framework identifies teaching activity, based on theory and practice, to support guided inquiry. Concepts within the theoretical framework provide guidance for the process of online and blended learning in support of inquirybased learning that leads to deep, meaningful learning. In keeping with the 88 Martha Cleveland-Innes three presences of the CoI framework (social presence, cognitive presence, and teaching presence), online and blended learning offers opportunities for self-reflection, active cognitive processing, interaction, and peer-teaching. In addition, expert guidance from teachers encourages engagement and shared application activities. Each of the three presences are defined below, with information about sub-categories of requirements, current measurement indicators, and potential application opportunities identified. The ongoing CoI research is moving beyond study of individual presences to thorough examination of the relationships among the presences; the overlaps in Figure 5.1. Armellini and De Stefani (2016) suggest that findings from a study of student-teachers in a blended community of inquiry identify social presence as the central concept within the framework; it is the main driver for all actions in teaching and cognitive presence. Accurate measurement of the presences as experienced by teachers and learners provides teachers and designers a way to identify levels of presence activity. This is important as none of the presences stand alone but influence Figure 5.1 Community of inquiry theoretical framework, reproduced with permission from D.R. Garrison. Source: coi.athabascau.ca Community of Inquiry Theoretical Framework 89 each other. Cognitive presence emerges out of four distinct but overlapping components of practical inquiry: triggering events, exploration, integration, and resolution. Establishing deep and meaningful learning requires activity in all four components. However, Akyol and Garrison (2011) report evidence that cognitive presence requires a balance among cognitive, social and teaching presence. Direct instruction and facilitation of cognitive activity, beyond just explaining content, is a key role for teachers using this framework. This corroborates Archibald’s (2010) evidence that teaching presence and social presence explain 69% of the variance in cognitive presence. Teaching presence, rather than teacher presence, is so named to allow for a teaching function for both teachers and students. While the teacher, or instructor of record, plays a leadership role, teaching presence allows for and fosters peer-teaching among students. Recent studies clarify the importance of teaching presence in the generation of satisfying learning experiences among students (Chakraborty and Nafukho 2015; Shea, Hayes and Vickers 2010). It is, however, linked to other presences in a significant way. For example, Shea and Bidjerano (2009) report evidence that the student experience of teaching presence affects the emergence of social presence. In addition to these three presences, emotional presence has been suggested as a possible fourth presence (Cleveland-Innes and Campbell 2012; Stenbom, Cleveland-Innes and Hrastinski 2016). Emotional presence is the outward expression of emotion, affect, and feeling, by individuals and among individuals in a community of inquiry, as they relate to and interact with the learning technology, course content, students, and the instructor. Item indicators for emotional presence have been analyzed with the instrument measuring the original three presences (Arbaugh, Bangert and Cleveland-Innes 2010). Exploratory factor analysis suggest emotional presence may stand alone as a separate element in this framework (Cleveland-Innes and Campbell 2012). Further research is required to evaluate the relationship of emotional presence with other elements in the framework. Over the 18 years since the original publication, ‘research on the CoI framework has come a long way while attempting to contribute either new presences or presence dimensions to the framework’ (Kozan and Caskurlu 2018: 113). Their research identifies the many analyses to shape and further illuminate the framework. Ongoing research and suggestions clarify the pedagogical opportunities and the potential for increasing access and quality for deep, meaningful learning. Detailed understanding of presences of many kinds creates opportunities to design appropriate course activities such that presence emerges, learning processes deemed valuable to engagement and satisfaction are available, and, ultimately, learning outcomes are achieved (RockinsonSzapkiw et al. 2016; Vaughan 2014). As indicated earlier, the development of quality online teaching and learning requires more sophisticated learning design strategies than those needed for lecture-based delivery. More 90 Martha Cleveland-Innes than changes to teaching practice, the CoI offers concepts that will shape the design for learning now required for online and blended learning. The tables in this chapter provide conceptual organization, measurement indicators, and application/design suggestions to create the greatest likelihood that a CoI will emerge in a course setting. Designing for Learning with Social Presence Social presence is the most heavily researched element in the CoI. Original research and definitions of social presence emerged from the work of notable researchers like Gunawardena and Zittle (1997) and Richardson and Swan (2003). In the creation of the CoI, social presence shaped these original ideas with additional research and combined notions of affective expression within their definition of social presence. The original definition read as ‘the ability of participants in a community of inquiry to project themselves socially and emotionally as “real” people (i.e. their full personality), through the medium of communication being used’ (Garrison, Anderson and Archer 2010: 92). Research, application, and discussion occurred overtime. This evolution included consideration of emotion as separate from social presence and as an integral part of all three presences. The revised definition, identified below, does not refer to emotion as a specific part of social presence. Social presence is defined as ‘The ability of participants to identify with the community (e.g., course of study), communicate purposefully in a trusting environment, and develop inter-personal relationships by way of projecting their individual personalities’ (Garrison 2017). The definition of social presence allows for foundational thinking about the existence of social activity and identity in online learning spaces. The sub categories provide tested ideas about how to ensure social presence will emerge. These categories are personal expression, group cohesion, and open communication. Personal expression means students are willing to go beyond dialogue strictly about course activities and content into personal reflections and the presentation of self. Group cohesion is represented by a sense of belonging and acceptance in a group where meaningful if short-term relationships develop, providing a sense of trust and safety. Open communication supports both personal expression and group cohesion by allowing time and opportunity to express oneself freely. Table 5.1 has been created to stay true in design and delivery to the sub-categories that support the definition of social presence. The indicators remind us of the statements supported by students who indicate they are experiencing social presence. From the indicators, activities that can be offered in support of these elements of social presence are suggested. These application suggestions are derived from instructor feedback at Community of Inquiry Theoretical Framework 91 Table 5.1 Social Presence: Conceptual Categories, Measurement, and Application Sub-categories Personal Expression Indicators Students in my course are able to form distinct impressions of some other course participants. Getting to know other course participants gives students a sense of belonging in my course. Online or web-based communication is an excellent medium for interaction with and among my students. Open Students’ feel comfortable Communication interacting with other course participants. Students feel comfortable conversing online or face-to-face in my course. Group Students feel comfortable Cohesion participating in course discussions. Applications Provide online spaces and structured activities that encourage and support social interaction. See above; provide facilitation of developing relationships. Discuss social presence, its value to learning, and set norms for social and academic interaction. Discuss course climate as it evolves. Provide opportunity for individual presentation of idea, paired interaction, and group work. Support and encourage the presentation of thoughts, feelings, and interpretations. Online or face-to-face discussions Make explicit the value of help students to develop a sense of dialogue and collaborative collaboration. learning. Students feel comfortable Discuss, agree to, and verify group disagreeing with other course par- norms during the course. ticipants while still maintaining a sense of trust. Students feel his/her point of view Include acceptance and validation is acknowledged by other course of ideas in group norms. participants. development workshops, literature reviewing online learning, and the author’s experience designing and teaching with the CoI framework. There is room for creativity and bringing these elements to life by ensuring that students are told what kinds of things need to be done in support of their CoI. The design for learning sets up the opportunities for these things to happen, the instructor then makes it clear why these things are important, acknowledges and validates appropriate expression, and have the option to assign rewards to the demonstration of presence activities. Abdelmalak (2015) provides one example of the connection between this framework and technology-enhanced-learning. For Abdelmalak, indicators of social presence guide instructors to activity outcomes that can be 92 Martha Cleveland-Innes considered when designing for learning. In turn, the application of learning activities which support these indicators will contribute to the emergence of a community of inquiry. Social presence plays a pivotal role in the introduction to, and the development and maintenance of, the community. While the notion of inquiry rests more logically on directing design for learning, designing the community does two things in support of learning. First, it is required that we acknowledge that learning is both a verb and a noun. When we say design for learning we are referring to both. We design for the appropriate processes and activities that encourage and foster the necessary attentional and cognitive undertakings (learning as a verb) such that learning outcomes may be realized and measured (learning as a noun). The CoI rests heavily on the former definition. Community is central to fostering the many connections among learners that can enhance learning activities, like interaction and shared cognition. Designing for Learning with Teaching Presence Teaching presence is applicable and significant in on the ground, inperson, and online design for learning, yet the term is not yet part of the common narrative. Teaching presence in the CoI framework refers to the actions among community members where learning is being supported. It can be between instructor and student and/or between students. This peerteaching is meant to be a significant element in the categories of teaching presence. How much authority to adjust design and organization of the classroom or curriculum is afforded to students is in the hands of the instructor in credit-based, formal learning. Identifying what, when, and how much to share the lead in one’s course is a skill in itself. The benefit to this sharing of design and organization to the students lies in the further engagement and better sense of responsibility and belonging in the community. Teaching presence is defined as ‘the design, facilitation and direction of cognitive and social processes for the purpose of realizing personally meaningful and educationally worthwhile learning outcomes’ (Anderson et al. 2001: 5). As described for social presence, Table 5.2 is created to stay true in design and delivery to the sub-categories that support the definition of teaching presence. Note that opportunities for peer-teaching and peer-support for learning are required. Dependent upon level of study and discipline, there is also opportunity for students to participate in the design and organization of the course, a key component of teaching presence. Beyond passive recipients of a pre-designed group learning experience where they are customers of the product, students fully present in a CoI environment have helped design the product; the course belongs to them as well. The indicators remind us of the Community of Inquiry Theoretical Framework 93 Table 5.2 Teaching Presence: Conceptual Categories, Measurement, and Application Subcategories Indicators Applications Design and I clearly communicate important due organization dates/time frames for learning activities. I clearly communicate important course goals. I clearly communicate important course topics. Facilitation Direct Instruction Course calendar online. Twitter reminders. Peer support check-ins. Reviewed and adjusted in community. Explicit syllabi with links to materials. Regular review and adjustment. Students in my course feel Brainstorm and agree to comfortable taking on the role of interaction and activity norms. teacher when the opportunity arises. Make CoI premises explicit. I keep course participants engaged Acknowledge and encourage and participating in productive learning activities. dialogue. Link ideas and students through text and talk. Share analysis and interpretation. I am helpful in guiding the class Ask questions. toward understanding course topics Allow/assign presentation. in a way that helps students clarify his/her thinking. I provide feedback that helps Validate student actions and guide students understand strengths and with direction and inquiry. weaknesses relative to the course goals and objectives. I provide feedback in a timely Maintain presence through fashion. regular ad frequent interaction with individuals and group. I help to focus discussion on Acknowledge and redirect as needed relevant issues in a way that helps using humor, encouragement, and students to learn. excitement. statements supported by students who indicate they are experiencing teaching presence. From the indicators, activities that can be offered in support of these elements of teaching presence are suggested. Designing for Learning with Cognitive Presence Facilitating social activity and interaction fosters social presence. This is key to setting the stage for meaningful collaborative learning activity. Moving social interaction to academic dialogue through critical discourse creates cognitive presence. Using the ideas and opportunities outlined above, cognitive 94 Martha Cleveland-Innes presence emerges as a way to support deep and meaningful learning. Strategies for the facilitation of social and cognitive presence are described in reference to indicators; see application column. For example, Make the cognitive progression explicit. Assist students through layered activities which build on each other through triggering events, exploration, and integration to resolution. Teach committed relativism; have students take a position and defend it, knowing that there are multiple perspectives and layers of authoritative knowledge. Cognitive presence is defined as ‘the extent to which learners are able to construct and confirm meaning through sustained reflection and discourse in a critical community of inquiry’ (Garrison, Anderson and Archer 2000). Cognitive presence rests on four sub-categories that appear hierarchical; a triggering event requires attention and thought but is less effort than the other three categories. However, that is not to suggest that the students move in a linear fashion through each step. Rather, there is movement among the four elements across ideas and topics and varies by student. Table 5.3 is created to stay true in design and delivery to these four sub-categories that support the definition of cognitive presence. The indicators remind us of the statements supported by students who indicate they are experiencing cognitive presence, specifically in each of the four sub-categories. Activities that can be offered in support of these elements of cognitive presence are suggested. Part of design is determining how much time to spend in, for example, triggering thought around seminal concepts in a course as opposed to requiring integration with other topics and, finally, resolving the issue or solving the problem. Time spent in resolution is seen less often than time spent in other cognitive activities. The application suggestions provided here are derived from feedback from instructors at development workshops, literature reviewing online learning, and the author’s experience designing and teaching with the CoI framework. Designing for Learning with Emotional Presence Emotional presence is not yet viewed as a fourth presence by the original creators of the CoI model. It has been tested with additional CoI measurement indicators (Cleveland-Innes and Campbell 2012; Stenbom, ClevelandInnes and Hrastinski 2015) several times from both the students’ perspective and the teachers’ point of view. Emotion plays a role in human reason (Damasio 1994; leDoux 1996) and cannot be considered separate from learning environments (Brookfield 2006; Lipman 1991). While not yet accepted as the fourth presence, this presence is noted in the research literature about online learning and the community of inquiry (Kozan and Caskurlu 2018). See Table 5.4 for Emotional Presence indicators and application suggestions. Table 5.3 Cognitive Presence: Conceptual Categories, Measurement, and Application Subcategories Indicators Applications Triggering Event Students in my course are motivated to explore content related questions. Course activities pique students’ curiosity. Share passion and points of interest in reference to the subject-matter and everyday life. Use varied and unique materials and approaches to engaging students with learning material. Problem-based learning supports engagement and intellectual development. Provide opportunities for application of knowledge outside the class environment. Problems posed increase student interest in course content. Exploration Students can apply the knowledge created in my course to his/her work or other non-class related activities. Online discussions are facilitated in a way that is valuable for helping students appreciate different perspectives. Brainstorming and finding relevant information helps students resolve content related questions. Students utilize a variety of information sources to explore problems posed in my course. Integration Reflection on course content and discussions helps students understand fundamental concepts. Combining new information helps students answer questions raised in course activities. Resolution Offer opportunity for peer facilitation of forums; instructor responses should be timely and provide synergy between posts and individuals. Provide opportunities to search for content outside course materials. Offer library orientation and searchskills training for valuable subject-related resources. Course activities and assignments require reflection, application, and critique of course material. Student-driven material choices allow for high engagement with content-related integration and synthesis. Learning activities help students con- Active learning assignments struct explanations/solutions. provide students the opportunity to master and apply content in creative ways. Students develop solutions to relevant problems that can be applied in practice. Students in my course can describe ways to test and apply the knowledge learned. Problem-based learning supports engagement and intellectual development. Discussion and application of knowledge is a regular part of course activities. 96 Martha Cleveland-Innes Table 5.4 Emotional Presence: Conceptual Categories, Measurement, and Application Subcategories Indicators Applications Related to Teaching Presence I acknowledge emotion expressed by the students in my course. Acknowledge, support, and model expressions of emotional response in text and oral presentation. See above. Related to Social Presence Related to Cognitive Presence In my role as instructor, I demonstrate emotion in my presentations and/or when facilitating discussions, online or face-to-face. Students feel comfortable expressing emotion through the online medium or in the face-to-face classroom. Emotion is expressed, online or face-to-face, among the students in my course. Expressing emotion in relation to sharing ideas is acceptable in my course. I find myself responding emotionally about ideas or learning activities in my course. Make explicit the use of emoticons and emotional language as part of the course learning environment. Encourage, acknowledge, and support expressions of emotion during course activities. Emotion is identified as a regular part of human existence. Emotion experience and expression is shared among all members of the learning community. Emotional presence is defined as ‘the outward expression of emotion by individuals and among individuals in a community of inquiry, as they relate to and interact with the learning technology, course content, students, and the instructor’ (Cleveland-Innes and Campbell 2012). Recent studies of emotional presence suggest that, when measuring the elements of a CoI from the students’ point of view, emotional presence emerges as a separate presence. However, in a recent study of instructors using the same CoI measurement indicators, emotional presence indicators are subsumed in indicators of other of the three presences. According to Rienties and Rivers (2014), learner emotions do impact online and blended learning, particularly motivation, self-regulation and academic achievement. Application to Practice There is great opportunity to offer creative design for learning in the combination of activities across presences. This chapter was under construction at a time of unprecedented attention to the use of technology-enhanced learning. The CoI is one of the most heavily researched frameworks explaining online Community of Inquiry Theoretical Framework 97 learning as an open, collaborative, and flexible learning process. Much research on its application and best practices is now underway. Applying the CoI in practice has been the topic of many workshops and webinars. In order to facilitate the translation of theory to practice for the sake of design for learning, the measurement indicators in the CoI survey were translated from the students’ view to the instructors’ view. In addition to this current work, other authors have written about the application of key components found in the CoI. Green (2005) found that forum discussions provide a significant opportunity to foster cognitive activity. This is done by supporting structured reflection and thoughtful exchange of text-based, online communication with fellow students. Differences emerged in the distribution of cognitive phases between types of discussions. Findings suggest learning processes change through guided inquiry instructional methods. According to Edwards, Perry and Janzen (2011), participants in online courses identify exemplary online educators as challengers, affirmers, and influencers. According to the authors, the CoI process of exhibiting teaching, cognitive, and social presence provides a template for the role of influencer. About the same time, Akyol and Garrison (2011) demonstrated the presentation of explicit metacognitive activity in discussion forums of an online community of inquiry. For these researchers, evidence provided recognition that metacognition is not just a private internal activity but also socially situated. … Furthermore, the CoI theoretical framework provided the conceptual coherence to construct, operationalize and interpret metacognition in an online collaborative inquiry. The results provided (sic) evidence of metacognition indicators in student discussion postings and the frequency of these indicators increased over time. (p. 66) Stodel, Thompson and MacDonald (2006) used the CoI framework (Garrison, Anderson and Archer 2010) to interpret and illuminate findings demonstrating the value of presence. Findings emphasized the importance of making explicit the necessary participant commitment to the creation of a community of inquiry. They identified that is important that ‘a community (be) expressed and brought to life in the space created by online textbased discussions’ (n.p.). The three presences are also portrayed as overlapping by Garrison, Anderson and Archer (2000) framework represented in Figure 5.1; Stodel, Thompson and MacDonald (2006) study confirms they are not distinct dimensions; ‘while it is valuable to scrutinise each singly, a deeper understanding of how they interrelate is needed’ (n.p.). Touché. 98 Martha Cleveland-Innes Community of Inquiry and Faculty Development Preliminary findings from research on the use of the CoI measurement tools for instructors suggest faculty who engage in communities of inquiry as part of the teaching development process perceive (1) How the framework may be used and could provide benefits, and (2) The need for removal of barriers in order to put this framework to use. Preliminary conclusions suggest that teaching and learning change may be fostered by the use of the CoI in faculty development but has to be appropriately paired and integrated with other higher education reforms such as technological infrastructure and attention to changing student demographics. In multiple situations, colleagues and I have used the CoI framework as the learning design in a course for faculty about the CoI (see, for example, Cleveland-Innes, Stenbom and Gauvreau 2018). STEM faculty demonstrated significant interest in the use of technology for learning but less interest in learning about pedagogy. Challenges identified referred to low student participation in such active, participatory learning activities, time constraints, and workload issues. Benefits came from the rare opportunity to discuss teaching and learning with colleagues in a facilitated environment and anytime, anywhere discussion in asynchronous online forums. In another example, faculty teaching at the Open University of China participated in a blended course about teaching and learning using the CoI framework (Cleveland-Innes, Feng and Chen, 2018). As the most popular way for a teacher to become an online teacher is to learn online (Richardson and Alsup 2015), an online course about the CoI was offered using the CoI learning design. High levels of satisfaction were reported by participants. The role of facilitator, as a component of teaching presence, is identified as a key role in supporting teachers who are learning about teaching and learning online. The facilitator encourages teachers to engage in discussion with peers, acts as a model of online instruction, and guides teachers to reflect purposefully. In other words, the facilitator’s social and teaching presence in this course had a significant impact on how teachers, as online learners, learn and develop their teaching presence as online instructors. The application of the CoI framework has all the challenges incumbent in the move from place-based to online learning. These include bandwidth access, available computer resources, technology competence, and lack of necessary instructional design expertise, to name a few. Challenges specific to the CoI itself relate to both context and content. First, the design for learning of such an inquiry-based environment requires understanding of learning generally and the framework specifically. It is unlikely that individual faculty will have such learning expertise, even if they do have access to material about the CoI framework specifically. The CoI also requires technology support in the form of an LMS and, if synchronous engagement is offered in support of the framework, the access to needed technology. Community of Inquiry Theoretical Framework 99 Faculty must have experience and competence with such technology and its uses in support of all presences. In the ideal, learning designers and technology media experts are available in support of course development. As the CoI framework encourages and expects student-led and developed material, such materials must be added during the course. This takes a level of technical expertise that faculty and students may or may not have. Access to such administrative functions in the LMS can also be a limitation. Inquiry-based learning as represented in the CoI may be unfamiliar to students as well as faculty. It may fall to faculty to not only adjust to a new role as instructor, but to assist novice online, inquiry-based, students to adjust to their role as well (Cleveland-Innes and Garrison 2009). Making the CoI framework explicit and setting agreed-upon norms for operating in the community can remedy such adjustments. However, faculty accustomed to teaching their subject-matter content may not feel ready or willing to offer such learning support. Enter the important role of learning-designer, a new occupational category in education. In these positions, learning designers can support competence development in teaching, for faculty, and learning, for students, as well as support for subject-matter content mastery. Conclusion Increasing amounts of research provide evidence of the practice implications for the CoI theoretical model. The model that was originally created with asynchronous online environments was researched and implemented as online delivery became more sophisticated, and included synchronous engagement as well. It has now been researched and used in blended environments. Continued research will address concerns raised by researchers such as Annand (2011) who suggests that controlled studies must examine the differences between two-way communication of sustained interaction and other designed for learning communication patterns. How much of each is needed? For others ‘online discussion must be understood as foremost a communication phenomenon. It consists of conversation exchanges in natural language. Online expression like its face-to-face counterpart is multifunctional. We often combine instruction, knowledge construction, and social interaction in a single utterance’ and therefore ‘the need for facilitation is much more pronounced online than in the face-to-face environment where habits are well established and paralinguistic cues fulfill many communicative functions’ (Xin 2012: n.p.). Also note that this applies in contemporary online delivery, which now includes face-to-face communication opportunities that allow video streaming. This current and on-point research is in contrast to other approaches still defining teaching roles in traditional ways. Coker (2018) for example, identifies lecturing as a delivery method in online learning. 100 Martha Cleveland-Innes The growth of online and blended learning allows for much more study of theory application via practitioner-research. There are not yet definitive best practices or exact answers to design questions for technology enhanced learning. 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Retrieved from www. ijede.ca/index.php/jde/article/view/755 Part 2 Practices Chapter 6 Learning Designs as a Stimulus and Support for Teachers’ Design Practices Shirley Agostinho, Sue Bennett, Lori Lockyer, Jennifer Jones and Barry Harper Editors’ Introduction This chapter summarises more than a decade of the authors’ empirical work with teaching practitioners which has led them to conclude that learning designs (by which they mean a specific form of graphical representation and explanatory text) are usable by university teachers to support their design work. Learning designs are referred to for guidance and inspiration, in what the authors see as an example of case-based professional learning. How professional educators take up learning designs is an area of empirical research that can provide a reliable evidence base for future design support and tool development. Introduction In the global higher education sector, university teachers are being challenged to improve student learning by effectively integrating new pedagogies and technologies. Quality teaching and educational experiences are considered critical to equip a diverse range of students with the lifelong learning skills essential for full participation in contemporary society. Educational design has emerged as an important issue with research and development work focused on ways in which university teachers can be supported to design learning experiences for students. A key challenge in this area of inquiry has been the representation of effective designs in forms that can be easily understood by teaching practitioners with a view that ideas can be easily shared with the potential of being adapted and reused. This has led to the term ‘learning design’ being applied to various means for documenting teaching and learning practice to facilitate sharing and reuse by teachers. Some significant investments have been made to build repositories and/or tools that allow university teachers to document, model, implement, share and adapt educational design ideas, so as to build on good design practice. 106 Shirley Agostinho et al. One of the first large scale projects in Australian higher education was the ‘Information and Communication Technologies (ICT) and Their Role in Flexible Learning’ project (www.learningdesigns.uow.edu.au), commissioned by the former Australian Universities Teaching Committee (AUTC) in 2000. This project involved the identification, evaluation, documentation, and dissemination of high-quality education examples that involved the use of ICT. The outcome of the project, hereafter referred to as the Learning Designs Project, was a repository of contextualized examples and generic guides called Learning Designs. These Learning Designs were units of study that were described through a graphical representation comprising the sequence of learning activities, supports and resources of the education experience (hereafter referred to as the Learning Design Visual Sequence (LDVS)) and descriptive text. The textual description provided a summary statement and design team details, detailed the tasks, resources and supports, explained the implementation context, and provided a reflective comment by the original designers in terms of the pedagogy employed and any evaluative research conducted. (See Agostinho et al. 2008 for a detailed explanation of how the LDVS was devised.) The Learning Designs Project repository of learning designs is categorized according to the key pedagogical focus for each learning design: collaborative, concept/procedure development, project/case study, problem-based learning, or role play. (See Oliver et al. (2013) for a recount of the project team’s initial learning design classification and Harper and Oliver (2009) for the project’s implemented taxonomy.) This pioneering work led to a series of research studies and projects conducted by the authors spanning more than 15 years. This body of work provides important insights about strategies that allow teachers to publish, search for and comment on learning and teaching ideas (irrespective of ICT integration), as well as developing tools to support aspects of the educational design process. This chapter provides a historical account of our research work within the context of international research on learning designs. The chapter is structured by presenting our work in chronological order based the following key research questions that have guided our research conducted in the higher education context: 1. 2. 3. 4. How can learning designs from the Learning Designs Project be reused by teachers? Can a learning design be consistently and clearly represented? How can learning designs support university teachers in designing quality learning experiences? How can learning designs be used as a stimulus for teacher design thinking? The chapter concludes with a discussion of our future research directions. Learning Designs for Teachers’ Design Practices 107 Reusing and Adapting Learning Designs We have developed a deeper understanding of the role learning designs can play in supporting teachers’ design processes. This line of research began in 2004 with our first post-Learning Designs Project study that investigated how a teaching team of four redesigned a large first year pre-service teacher subject using a learning design from the Learning Designs Project website (Bennett, Agostinho and Lockyer 2005; Bennett, Lockyer and Agostinho 2004). All were experienced university teachers but had not previously used learning designs. Participants were observed and interviewed during a design workshop in which they selected and adapted a problem-based learning design (selected from the LD Project) to suit their context. A key finding from this study was that participants preferred the contextualized examples, which described the design as implemented in its original context, in preference to a more generic ‘guide’ (Bennett, Lockyer and Agostinho 2004). Similar findings were reported in later studies (e.g. Falconer et al. 2007; Jones 2015) and is a finding that has held true over the years. Furthermore, participants used the graphical representation, the LDVS, and textual description to become familiar with the design, but thereafter relied on the LDVS to develop their ideas further. A larger scale study was conducted to investigate the design processes of eight university teachers over six to twelve months as they selected and adapted a learning design from the Learning Designs Project, and then implemented and reflected on this learning design (see Jones 2015; Jones, Bennett and Lockyer 2009, 2011). The eight participants were from four Australian universities and represented a range of disciplines and teaching experience. Data was collected during the pre-design, design, implementation, and reflection phases of the design, and comprised interviews, unit of work documents and web sites, researcher observations and field notes. This multiple case study was guided by two questions: 1. 2. How do university teachers design a unit of work using a learning design? How does the use of a learning design impact on university teachers’ development of technological, pedagogical and content knowledge? Three main findings were as follows. Learning Designs Could Be Readily Understood and Reused Participants of all experience levels were able to select, apply, and adapt previously documented learning designs according to their own needs. They selected learning designs that aligned with their pedagogical goals, and in seven out of eight cases participants worked from contextualized examples 108 Shirley Agostinho et al. rather than the generic guides. Interestingly, they did not limit themselves to designs from their own discipline. The finding that participants were able to understand and apply contextualized designs originating from disciplines other than their own suggests that the practice of ‘translating’ learning designs into more generic forms, which was one objective of the Learning Designs Project, may be unnecessary. This funding plus the similar finding in the Bennett, Lockyer and Agostinho (2004) study suggests that the contextual detail included in a learning design adds to its reusability. Learning Designs Were Used for Design Ideas and Benchmarking Participants initially selected learning designs that had aligned with their pedagogical goals and then creatively adapted the details to suit their needs. Thus, for most participants the learning design was a source of ideas, rather than a model to replicate. In fact, early in the study a number of participants expressed an aversion to being ‘restricted’ by a prescribed design template. Participants also used the learning design LDVS and text in a variety of ways: • • • • • As an outline of the pedagogical process (text and LDVS) To focus their design steps and activities (text and LDVS) For clarification of detail (text) As a checklist for resources, tasks, supports and their connections (LDVS) In one case, to document and map design thinking (LDVS). In addition to design ideas and guidance, most participants (7 of 8) used the learning designs as benchmarks or models of good practice with which to compare their previous design thinking and work. Comparing the design ideas of their work against their chosen learning design provided participants with an indication of ‘quality’ of their designs and some participants reported this comparison gave them more confidence in their abilities and knowledge as a designer. Learning Designs Supported Integration of Technology, Pedagogy and Content One of the significant outcomes of using learning designs was the observed and reported impact on participants’ integration of technology, pedagogy and content. The study drew on the notion of pedagogical content knowledge (PCK) (Shulman 1986), which attempts to describe the thinking a teacher undertakes when deciding how to teach a particular concept effectively, that is how they combine their knowledge of content with their knowledge of Learning Designs for Teachers’ Design Practices 109 pedagogy. This idea has been extended to incorporate technology; thus the concept of Technological Pedagogical Content Knowledge (TPCK) (Mishra and Koehler 2006). TPCK refers to the thinking required of a teacher to determine how technology should be integrated with effective pedagogy to teach a particular concept. The study found: 1. 2. 3. Designing with a learning design was reported to impact on PCK and/ or TPCK in six of the eight cases. Participants designing new units of work or completely redesigning a unit of work tended to report an impact on PCK rather than TPCK. Participants working on smaller changes to more established learning designs reported an impact on their TPCK. The difference in impact for participants working on new designs compared with participants refining more established designs suggests that, for university teachers designing units of work that will not be fully online, the design goals and thinking occur in two stages: firstly, there is a focus on PCK, which is followed by integration of technology. This is not to say that these participants did not use technology or did not have future plans for greater technology use. However, the goals expressed by the participants suggested, even among experienced technology users, that they saw the development and integration of technology as a longer-term goal to develop over multiple iterations. This is supported by the finding that participants revising more established learning designs reported focusing more on the re-organisation and refinement of existing content in relation to the pedagogical sequence and then on how this refinement of existing content could be integrated within the online environment. This is an area of interest for future research. The results of these two studies, which address research question one: How can learning designs from the Learning Designs Project be reused by teachers? provide important insights into the utility of learning designs, in terms of how they are represented, how they are used for different purposes and in different design contexts, and the outcomes that might be achieved. Overall these two studies have shown that the learning designs from the Learning Designs Project can be reused by teachers as the participants from these two studies were able to select, understand and then adapt learning designs to implement in their contexts. Furthermore, the contextualized description was deemed as a useful support in the design process and perhaps preferable to the more generic learning design ‘guide’. Learning designs can be seen as a way to generate and inspire ideas rather than serve as a ‘prescriptive pedagogy’ and provide models of good practice against which university teachers can compare their own design thinking and work. 110 Shirley Agostinho et al. It is pertinent to note that research studies as the two explained here follow design activities conducted over a sustained period. Data collection is thus intensive and time-consuming, thereby limiting the number of participants that can be included in a study. Further research to investigate emerging questions about the role of discipline, teaching expertise, and context is a necessary extension of this work. Representing and Describing Learning Designs The learning design representation devised from the Learning Designs project, that is, the LDVS and accompanying text, provided the opportunity for further investigation about how learning designs could be described and represented. Below is a summary of two studies that address research question two: Can a learning design be consistently and clearly represented? The first study focused on the perceived usefulness of the learning design representation format of the Learning Designs Project. The second study examined whether the actual learning design descriptions provided in the Learning Designs Project repository described a learning design sufficiently so that it can be easily understood. The findings are explained below. Study 1: Perceived Usefulness of the LDVS to Support University Teachers’ Practice This study, reported in Agostinho (2011), uncovered how 11 university educational designers and teachers used the LDVS in their own teaching practice and how it supported their design processes. Overall, all participants thought that the LDVS was useful in their teaching as the visual aspect provided an overall summary of the learning design, the structure of tasks, resources and supports, helped participants better understand their learning designs, it was simple to use and they could adapt the visual format to suit their needs. This study provides some evidence for the LDVS being a useful tool to support a university teachers’ design process. The study’s limitations were that use of LDVS was based on participants’ self-reporting retrospectively, and that it did not investigate how the LDVS formalism could be used as a way to encourage reuse of other people’s designs. A richer insight would be to observe teachers whilst engaging in design to gain a deeper understanding of how tools such as LDVS could be used to support the design process. The study by Jones (2015), explained above, addressed these limitations by monitoring the design processes of university teachers’ use of learning designs from the Learning Designs Project. There is evidence of further uptake of the LDVS as a mechanism to document learning designs – interestingly from practitioners with no direct involvement in the original Learning Designs Project (for example, see Cooner 2010; Elliott et al. 2010). Learning Designs for Teachers’ Design Practices 111 Study 2: Determining What Constitutes an ‘Effective’ Learning Design Description Since completion of the Learning Designs Project, an international agenda contributed to further understanding about how learning designs can be represented to facilitate sharing and reuse (Lockyer et al. 2009 provides a research compendium), the definition of a ‘learning design’ evolved (see Lockyer, Agostinho and Bennett 2016, pp. 338–339 for a succinct overview), several learning design presentations emerged (see Falconer et al. 2007; Masterman 2006) and significant technical developments were made in terms of interoperability (see Tattersall and Koper 2005). Thus, this second study arose from the need to revisit the literature and examine more recent thinking about what constituted an ‘effective’ learning design description and compare that with the learning design representation and descriptions in the Learning Designs Project. This literature review (reported in Agostinho et al. 2009), which included international research from 2004–2008 about practitioners’ perceptions of different learning design representations, found that an ‘effective’ learning design description should provide the following: • • • Clear and explicit description of the pedagogy of the learning design, Some form of ‘quality’ rating about the learning design, e.g., evaluative findings, and Explicit guidance/advice about how the learning design could be reused. An instrument was developed based on these characteristics to analyse the 32 contextual examples in the Learning Designs Project. Six learning design descriptions were considered effective descriptions (refer to Agostinho et al. 2009) and formed the basis for the research team to further develop and refine to serve as input into a larger study, explained below. Learning Designs as Supports for University Teachers A larger-scale study, funded by the Australian Research Council, began in 2007 to examine a missing piece evident in the learning designs literature, that is: how teachers actually design; the extent to which they have freedom to innovate with their designs; and the lack of practical, relevant and flexible supports and tools to help university teachers as they design. This investigation provides some answers to the third research question: How can learning designs support university teachers in designing quality learning experiences? The research study consisted of three phases (illustrated in Figure 6.1). The first phase of the research focused on investigating how teachers design learning experiences as a basis for considering what role design 112 Shirley Agostinho et al. Figure 6.1 Phases from the Australian Research Council project Improving University Teaching: Creating strategies and tools to support the design process. support tools might play. Despite the significant body of research into university teachers’ conceptions of teaching and substantial funding invested in learning design approaches and tools, surprisingly little is known about how teachers actually design. Thirty Australian university teachers were recruited from three broad disciplinary groupings: the Sciences, the Arts, and the Professions (see Bennett et al. 2011 for the discipline grouping rationale). Participants were interviewed about their design practices. The semi-structured protocol posed questions about the contexts in which teachers worked, their conceptions of teaching and learning, their disciplinary background, their usual practices when designing a unit for the first time and when revising a unit they had previously taught, the key influences on their design decisions and the supports they used. Key findings from this phase of the study are presented below. Australian university teachers can exercise a high degree of autonomy in terms of design, and this suggests there may be opportunities for teachers to consider using reusable learning designs. Specifically, 40% of participants taught in a context in which there was no set curriculum, thus allowing them the freedom to design units according to their own preferences and the needs of their students. More than half of the participants (60%) taught within a set curriculum for which there were pre-set guidelines to follow, such as predetermined learning outcomes and required content to cover. Yet, the majority of these teachers explained that there was still flexibility within this structure for them to decide how the units should be designed. All but two participants cited institutional structures as having some impact on their design decisions. Planning processes determined how often major Learning Designs for Teachers’ Design Practices 113 changes could be made to units, assessment policies provided broad guidelines on the types of strategies that could be used and in what combination, and class schedules determined what teaching and learning contexts were available. Two participants stated the institutional policies did not restrict their design decisions in any way. Participants were regularly involved in both the design and redesign of new units. Most had been involved in designing a unit from scratch (83%) and a majority described revising a unit each time they taught it to continually improve it (73%). This suggests that participants experienced both continuity and variation in their teaching commitments. Eighteen (60%) explained that they tended to teach the same units each year, and this was particularly so for those involved in large, core units. However, there were also opportunities to teach new units, often on more specialized topics with smaller cohorts. Only seven (23%) worked alone when designing a unit, while the reminder engaged in both team and individual design. Group design usually occurred when undertaking overall planning of a degree program or specialisation, and in the case of large subjects involving a team of teaching staff. Smaller units, such as advanced level electives, were usually designed by one teacher, though often in consultation with colleagues. These findings suggest that the Australian university context has some of the necessary pre-conditions for adoption of learning design and design support tools (see Bennett et al. 2011 for a detailed account), as teachers have scope to make key decisions about how and what they teach. Another finding from this first phase of the research was the complexity of design work. Participants spoke of the many factors that influenced their design decisions. These factors ranged from their own personal beliefs about learning and their past teaching experiences, to thinking about their current students’ needs and considering past student feedback, as well as considering institutional teaching and learning policies and procedures. The opportunity to work in a collegial context was also a strong influence on design decisions as participants recounted discussing teaching and learning ideas with colleagues. (See Bennett, Agostinho and Lockyer 2015 for detailed findings about influences on design decisions). When participants explained their usual practices of designing a new unit or revising a unit previously taught, a key finding was that their design process is iterative and design work occurs before, during and after a unit is taught. This has been represented as a descriptive model in Bennett, Agostinho and Lockyer (2017). Our research has also found that participants utilized a variety of supports to help them with their design work such as talking to colleagues, reading literature, attending conferences, seminars and workshops, accessing institutional support services, and well as completing further formalized study (Agostinho, Lockyer and Bennett 2018). 114 Shirley Agostinho et al. Overall, our findings have provided important insights into teachers’ design practices. It is, however, acknowledged that a dataset comprising 30 Australian participants limits the ability to derive general conclusions. We have thus expanded our research by undertaking further interviews with a view to developing a better understanding of university teacher design practices across an international context. The second and third phases of the research investigated how learning designs could serve as online support for teachers within an online learning management system (LMS). The rationale for this investigation was that the lack of embedded design support limits existing approaches because a teacher must either start with an empty shell or use a preexisting contextualized unit of work. Neither of these options offer guidance in situ about opportunities for different teaching and learning strategies or when and why certain tools might facilitate those strategies when adapted to different contexts. The idea of embedding design support within the online environment of a learning management system is an entirely new strategy to supporting online design. None of the major learning management systems currently embed specific supports for designing for effective learning. All provide functions to help teachers create and arrange content, and add communications tools. All provide technical support manuals. Recent developments have focused on expanding the range of teaching and learning tools available within a learning management system or developing visual interfaces to help teachers create ‘digital lesson plans’ external to the LMS that can be stored and used by others (Conole and Fill 2005; Dalziel 2007; Masterman and Manton 2011), but none offer guidance within the LMS about how the tools might be used to promote high quality learning. The focus for Phase 2 was to assess the learning designs developed for the Learning Designs Project according to a more rigorous set of criteria to determine their quality, relevance and adaptability. An evaluation framework developed and applied to the Learning Designs Project repository resulted in six of the original set of 32 exemplars being appropriate for further development (this study is explained above and reported in Agostinho et al. 2009). The research team then refined these six learning designs to simplify the pedagogical expression and developed a proof of concept in the in the form of supports through embedded learning designs and design tools in the LMS, Janison Toolbox (www.janison.com.au). Phase 3 of the research investigated the use of international standards for sharing educational designs and integrating digital resources. These had been the focus of intense technical research, but with little practical application in education. One priority was the IMS Learning Design (IMS-LD) specification, which provides a standardized computer language developed specifically for describing educational processes (Koper and Tattersall 2005). The underpinning concept was that a single lesson or whole Learning Designs for Teachers’ Design Practices 115 Figure 6.2 Applying an international standard for sharing and reuse course could be saved as an IMS-LD document and then read into any LMS compliant with the standard (illustrated in Figure 6.2). After creating a lesson or course in an LMS and saving it as in IMSLD document, a teacher could share it within a teaching team, institution or digital library, allow it to be edited in any other LMS that complies with the standard, and the new version could be saved as a new IMS-LD document. This approach would not only make particular lessons or courses sharable so that they can be reused and adapted by others, but the learning designs on which they are based could also be shared and reused. Work conducted on this aspect of the project to date demonstrated that while technically feasible, production of a fully operating system involved complexity beyond the scope of the grant. Work did not continue beyond this point because, despite being the only specification available for interoperability of online learning, IMS-LD has not been incorporated into learning management systems and support tools. As to the reason for this, perceptions of complexity have been considered a main barrier to adoption yet there is continuing debate about the lack of adoption of IMS-LD (Derntl et al. 2012). Overall, the main insights from this large-scale research study are: • • • • Within the Australian context, the uses of learning designs as supports for university teachers is feasible and beneficial. Learning design descriptions that provide contextual detail, offer advice on how to reuse, and provide evaluative data can be deemed effective descriptions. Incorporating learning designs within an LMS in the form of design guidance is worthy of further exploration. Technical interoperability whilst technically feasible, is not a fruitful research direction unless there is widespread adoption of IMS-LD by LMS designers and the sector generally. 116 Shirley Agostinho et al. Can Learning Designs Be a Stimulus for Teacher Design Thinking? A key goal for the Learning Designs Project was one of reusability, that is, providing examples of good education and technology integration practice for teachers to apply to their own context. This focus was consistent with other projects concurrently undertaken worldwide which were interested in practical and technical aspects of creating design collections (e.g. Conole and Fill 2005). Since then, some related work has considered socio-cultural aspects of sharing such types of designs among teachers (e.g. Dalziel 2007; Margaryan and Littlejohn 2008). The research reported in this chapter has gone beyond the original Learning Designs Project to better understand the context in which Australian university teachers design and have a sense of how learning designs might support them in their design activities. However, the limitations of this work and related research internationally make room for a more focused theoretical exploration of how learning designs can be of influence on an individual cognitive level. In essence, learning designs are cases of teaching practice. They describe an instructional solution to educational problems (what to teach, how to teach, for which learners). As such, the use of learning designs can be theoretically linked to case-based reasoning. Capturing such problems and solutions as a case in the form of a learning design removes some, but not all, context-specific information, allows for understanding and sharing, and the process of adapting the design to an individual’s teaching text allows for the development of a new understanding of the case and/or new cases. These are key characteristics of materials that support case-based reasoning (Kolodner, Owensby and Guzdial 2004). The future agenda for this research team is to investigate the theoretical basis for learning designs in terms of their effectiveness in stimulating design thinking. Specifically, the research direction is to ask our forth research question posed in this chapter: How can learning designs be used as a stimulus for teacher design thinking? The aim is to examine how case-based reasoning might occur as an individual teacher engages with the process of selecting, interpreting, adapting and implementing a learning design. From an individual cognitive perspective, the methodological challenge is to investigate this largely unobservable process. Work is currently being undertaken with school teachers to investigate the efficacy of the Learning Design graphical representation in the K-12 education context in terms of supporting the design thinking process. Initial findings suggest learning designs may be one useful tool within a suite of design tools that could be used by teachers to help them in their design work (Lockyer, Bennett and Agostinho 2017). Learning Designs for Teachers’ Design Practices 117 Conclusions This chapter has presented a decade of research inspired from the LD Project. Our research work has provided insight into usability of learning designs and their efficacy in stimulating teaching ideas. Overall we have shown that learning designs (i.e., the graphical representation and the accompanying text) are usable by university teachers irrespective of both the discipline of the original design and the discipline of the teacher using the design and thus can promote reuse. University teachers report that learning designs are useful in providing a point of inspiration or reference for their own design ideas in their own teaching context. Our research has also provided a richer understanding of the Australian higher education teaching context and thus the parameters in which university teacher’s design. This understanding, in future, can be compared to other contexts in other countries. It also provides a basis for further research in the area of teacher design thinking. Much more needs to be known about how teachers undertake their design work. Our future work is focused on gaining this indepth understanding about teacher design practices to provide an empirical base in order to guide the further development of support tools to aid teachers in their process of design. References Agostinho, S. (2011). The use of a visual learning design representation to support the design process of teaching in higher education, Australasian Journal of Educational Technology, 27(6), 961–978. Agostinho, S., Bennett, S., Lockyer, L., Kosta, L., Jones, J., and Harper, B. (2009). An examination of learning design descriptions in an existing learning design repository. In R. J. Atkinson and C. McBeath (Eds.), Same places, different spaces, Proceedings ASCILITE (pp. 11–19). Auckland, NZ. Retrieved from www .ascilite.org/conferences/auckland09/procs/agostinho.pdf Agostinho, S., Harper, B. M., Oliver, R., Wills, S., and Hedberg, J. (2008). A visual learning design representation to facilitate dissemination and reuse of innovative pedagogical strategies in university teaching. In L. Botturi and S. Stubbs (Eds.), Handbook of visual languages for instructional design: Theories and practices (pp. 380–393). Hershey, PA: Information Science Reference. Agostinho, S., Lockyer, L., and Bennett, S. (2018). Identifying the characteristics of support Australian university teachers use in their design work: Implications for the learning design field, Australasian Journal of Educational Technology, 34(2), 1–15. Bennett, S., Agostinho, S., and Lockyer, L. (2005). Reusable learning designs in university education. In T. C. Montgomerie and J. R. Parker (Eds.), Proceedings of the IASTED International Conference on Education and Technology (pp.102–106). Anaheim, CA: ACTA Press. 118 Shirley Agostinho et al. Bennett, S., Agostinho, S., and Lockyer, L. (2015). 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Sharpe (Eds.), Rethinking pedagogy for a digital age: Designing for 21st century learning (2nd ed., pp. 102–118). London and New York: Routledge. Shulman, L. (1986). Those who understand: Knowledge growth in teaching, Educational Researcher, 15(2), 4–14. Tattersall, C., and Koper, R. (2005). Advances in learning design, Journal of Interactive Media in Education, 2005(1), Article 1. Chapter 7 The Challenge of Teachers’ Design Practice Liz Masterman Editors’ Introduction In this chapter Masterman addresses the real-world design practices of teaching staff and other professionals working alongside them. Starting from an understanding of design as both creative and systematic, she identifies two intersecting models in which curriculum design practice can be situated. She goes on to explore how teachers conceptualise design for themselves, and the different factors they take into account. Drawing on her evaluation of the Learning Designer project, Masterman reviews the evidence for digital tools supporting teachers as designers. While these tools have had a complex history of adoption and impact, she considers what their development and evaluation can teach us about the design process itself. Introduction A major strand in research into design for learning has been the development of supportive digital tools that guide teachers’ thinking through the process of planning and constructing new learning experiences, and revising existing ones. These tools have the twin aims of simultaneously supporting teachers’ current design practice and stimulating them to innovate, both in their overall approach to design and in the use of digital technologies in their teaching where these are appropriate. Their design and deployment thus hinge on an understanding of teachers’ design practice and the settings in which they carry it out. This chapter maps, and critically analyses, that problem space through a review of empirical work in which researchers at the University of Oxford been involved over a period of 14 years. Specifically, the chapter addresses: • • teachers’ conceptualization of, and their approach to, the activity of design; four factors that may bear on their design practice: students’ needs and preferences, the nature of the discipline, educational theories and frameworks, and the tension between teaching and research; and The Challenge of Teachers’ Design Practice • 121 the influence of the institutional context in which design practice takes place. The chapter concludes by identifying some problematic implications for developers of digital tools to support teachers’ practice as they seek to marry the constraints of a structured design with the unpredictable, and often unruly, nature of design practice at the chalkface. Researching Design in the Real World Studying teachers’ design practice yields two benefits. First, it allows the principles of educational design to be held up against real-world processes and heuristics, and second, it can inform the development of tools and processes to support those practices. In focusing on the latter benefit, this chapter draws on knowledge accumulated from a range of projects, with reference also to the broader body of research. The initial projects were focused on design for learning. The Learning Design Tools project (Masterman 2009) explored teachers’ use of generic tools such as word processing, presentation and mind-mapping software in their design practice. The findings from this study informed two follow-on projects, Phoebe (Masterman and Manton 2011) and the Learning Designer (Masterman and Craft 2013), which developed and evaluated two prototype ‘pedagogy planner’ tools to support the design process. The more recent work referred to in the chapter comprises institutional research into the student digital experience (Masterman 2015) and open educational practices (Masterman 2016), which additionally addressed aspects of academics’ design practice related to these themes. Conducting research into a process which is often tacit, incremental, and distributed poses methodological challenges, particularly a reliance on participants’ self-reports. Each of the above studies adopted one or more of the following techniques: online questionnaires; interviews with academics, educational developers and learning technologists; workshops in which specific instances of practice were recorded; and inspection of artefacts such as lesson plans and VLE course areas. Teachers’ Conceptualization of Design Broadly speaking, the literature distinguishes two dimensions to design for learning: i) the planning and facilitation of structured sequences of learning activities to meet the learning objectives at hand, and ii) a way to describe, or represent, those learning activities so as to facilitate the sharing of teaching ideas and, thereby, improve students’ learning (Dalziel et al. 2016). The research reported in this chapter concentrates on the first dimension, which 122 Liz Masterman we explored in interviews with participants in the Learning Designer project in particular. It was immediately clear that teachers do not treat design as a prescribed, step-by-step process. Rather, structure is considered as a property that emerges from fluidity and negotiation: as one lecturer expressed it, ‘the interplay of aspects of learning and how together they would come up to […] an optimal situation which would enable learning’. Some interviewees made a distinction at the conceptual level between ‘design’ and ‘planning’. Planning is associated with laying out constraints: for example, time, location, number of students, learning outcomes and content (i.e. a mixture of logistical and pedagogic factors). Design pays attention to what can be achieved within those constraints that will engage and activate the students, and have an impact on both the educational and affective experience of learning: for example, ‘problematising learning opportunities, building in choice and challenge for students, [and] anticipating [their] needs and responses’ (Bennett, Lockyer and Agostinho 2018: n.p.). In this way, contextual constraints can function as a spur to creativity. For example, although a session might be designated as a ‘lecture’ in the timetable, one of the lecturers whom we interviewed designed whatever activities she felt were most conducive to students’ learning. Indeed, this perception of a creative aspect to design reflects a duality inherent in some conceptions of design. It can be simultaneously the application of ‘systematic principles and methods’ and ‘a creative activity that cannot be fully reduced to standard steps’ (Winograd 1996: xx, xxii) – or, both an art and a science (Mor, Craft and Maina 2015; see also Beetham and Sharpe Introduction). Teachers’ Approach to the Practice of Design The overarching practice of design for learning has been operationalized by Dalziel et al. (2016) both in a hierarchical model operating at different levels of granularity and in a cyclical (process) model. Our research into teachers’ design practice has largely addressed the ‘session’ level of the hierarchy: that is, individual lectures, seminars, practicals and other classes that are typically one to three hours in duration and either stand alone or belong to a superordinate layer such as a module or course (the ‘meso’ level: Jones, Chapter 4). In terms of the four interrelated activities in Dalziel et al.’s teaching cycle – design and planning, engagement with students, reflection and professional development – our interviews with teachers have focused on design and planning. In reality, teachers’ approach to design is considerably more complex than these two models. For example, different levels of the hierarchy can overlap as a teacher juggles interrelationships, dependencies and multiple actors: The Challenge of Teachers’ Design Practice 123 I mean, that’s a module, um, and that links in … so you’ve got six of those across or whatever and it links down from … but the problem then is your programme design has to go from [year one to] year three. At the session level, some teachers start with pre-defined learning outcomes, while others structure their plan around a set of activities negotiated with their learners. Teachers may also take different routes through the task, some mapping out learning materials while creating the plan while others create all such learning materials afterwards. For example, one lecturer interviewed for the Learning Designer project visualized the early stages of the design process as a circle, with the topic in the middle and the other factors to be considered around the edge. Only when she had obtained her ‘big picture’ did she switch to the linear (time-based) approach imposed by the VLE. Another person employed a picturesque horticultural metaphor: ‘the “compost heap” approach where you throw stuff in and you’ve got a very big pile and then you can start throwing, taking things out of it […] But then you’ve got to structure what remains’. Design and planning can never be wholly dissociated from the other stages in Dalziel et al.’s teaching cycle, since a design may need to be adapted in response to contingencies that arise during the learning session itself (engagement with students, or the micro level: Jones, Chapter 4), or reflection may lead to components of the design being added, modified or dropped. Factors Bearing on Teachers’ Approach to Design Entwistle et al. (2000) and Bennett et al. (2011) have noted a propensity among lecturers to reproduce the teaching that they themselves experienced. This was echoed by at least one lecturer interviewed for the Learning Designer project: ‘you either think they’re great and emulate them, or you think they’re terrible and try and do something else. Or, you have no idea what to do other than to imitate’. The next four sections address factors that, to varying extents, can influence academics against this propensity: students, discipline, educational theories and frameworks, and research-informed teaching. The factors are intertwined, making it difficult to focus on each one in isolation. Student-Centred Factors In contrast to outcomes-based and curriculum-led approaches to design, a student-centred approach begins with students’ needs and preferences (Beetham, Chapter 2). This approach can influence teachers’ design practice in three ways: • Cognitive – focusing on students’ learning progress: ‘The students would say, “OK, we do not find it easy to understand this and this in 124 • • Liz Masterman the nervous system.” So … when you develop your materials you have your major emphasis on those areas’. Agentic – empowering students by designing opportunities for active learning: ‘I designed some poster activities over weeks of study … it went down really well because they were active, … they weren’t just passive receivers’. Humanist – knowing students as individuals and taking account of, for example, their individual interests, aspirations, life situations and culture: ‘one reason I very much moved away to online discussion is it gives [a voice to students who] don’t like to speak in public’. (Starkey 2017; quotations from the Learning Designer project) In terms of technology-enhanced learning, in all of the projects we noted that a desire to improve students’ learning experience, fuelled also by natural curiosity, may impel teachers to explore new techniques and technologies using one or more of the above approaches. Successful experimentation with a new technique or technology may lead to its incorporation into one’s regular teaching repertoire. Regarding pressure from students to innovate, evidence from our digital experience studies suggests that they are primarily interested in online access to readings and lecture materials: they are not necessarily proactive in urging teachers to use new teaching technologies. Furthermore, students’ demands for technology use can actually militate against innovative teaching practice and, even, propagate conservative pedagogies. Lecture recordings are a case in point, lending themselves more naturally to a transmission model of learning than to an active model (O’Callaghan et al. 2017). That said, students do respond appreciatively to the creative use of technology by academics: for example, where it facilitates breakthroughs in their understanding of complex concepts or opportunities for independent learning. The emphasis on developing graduate attributes to ensure that students are fit for the labour market has also come to the fore in recent years. Designing requisite skills and technologies into one’s teaching may be more straightforward in some disciplines than others. For example, in applied science subjects, students can readily be introduced to the approaches typically adopted in industry. However, transferable skills such as logical thinking, argumentation, background research, and persuasive writing can be a byproduct of students’ intellectual formation, regardless of discipline. Nature of the Discipline The extent to which discipline differences determine lecturers’ academic practice is unclear (cf. Young 2010), but the interviews conducted for the Learning Designer project indicated some durable influences. One educational developer suggested that social scientists may have less time for The Challenge of Teachers’ Design Practice 125 concepts such as learning styles because of a perceived lack of empirical evidence. Furthermore, modelling professional practice within their teaching may, for example, lead law lecturers to adopt an element of formality and sociology lecturers to adopt a more observational position. Another educational developer commented that the dominant research methodology within a particular discipline may influence lecturers’ attitudes towards re-using materials created by other teachers: … if people are stuck on ‘Will this [be] better?’, the definition of ‘better’ depends on what your research background is and ‘Does there have to be an experiment in which a control group did this and X was applied in this situation and equalled Y?’ […] That’s quite different from what you might read in a case study from somebody from the social sciences who is talking about a number of factors working together to bring about this change and no requirement for [an] experimental model. Content can act as another barrier to the cross-disciplinary fertilization of learning designs and design ideas, although this is not inevitable (Agostinho et al., Chapter 6). To surmount it, one must discern something of relevance to one’s own teaching in terms of structure or approach. Sometimes, the crossover comes from an unexpected source: a humanities lecturer interviewed for the Learning Designer project recalled perceiving the relevance to her own discipline of the pedagogy underlying a reusable learning object developed for physics. Educational Theories and Frameworks Researchers now widely consider that educational theories and theoryinformed frameworks play a key role, not merely in contributing to ‘good pedagogical design’ (Mayes, Chapter 1), but also in countering tendencies towards ‘technological determinism’, where the use of tools for learning is driven by the technology rather than by sound pedagogic practice (Conole 2008). In relation to education we can distinguish between: • • theories of learning: explanatory theories of how people come to learn (e.g. behaviourism, constructivism), and theories, or models, of teaching: prescriptive or analytical frameworks for implementing teaching and learning (e.g. Bloom’s taxonomy, Kolb’s learning cycle), which are derived from practice but may have their roots in theories of learning or cognition. Interviews with lecturers in the Learning Designer project revealed a spectrum of relationships to theory. At one extreme, theory was actively eschewed in favour of pragmatic know-how: ‘[theory] simply isn’t the way 126 Liz Masterman that I orientate to those things […] what’s going to inform my decisions are time, number of students, […] the things that they have to get done’. At the other extreme, lecturers displayed a considered intellectual engagement with, say, Kolb’s learning cycle or Laurillard’s Conversational Framework ‘in order to better understand what it is you’re doing, or probably to validate […] or extend what you are doing’. Although theory can inform their practice, lecturers do not necessarily set out to implement a specific approach. Rather, theories tend to become interwoven into their general world view: … it’s not as simple as saying, ‘Oh yes, I’m a, you know, I’m a constructivist or a social constructivist, or a this, or a that, or […] I look at Piaget’ […] I think they all do influence, but I don’t think there’s one correct one. For some lecturers, the primary function of theory in design for learning is to provide a context for post facto critique: I’d regard theories as ways of critiquing something that I’d built in the first place, which would then possibly lead me to redesign it quite a lot, but […] I don’t see the theories as being […] sufficiently constraining to actually generate a design. More specifically, theory can fulfil an explanatory, even a transformative, function when one is reflecting on the actual learning session: What the reflection can lead you to is the point where you go, ‘Well, this is not working but I don’t know how to fix it’, which then needs to be – you need to be able to head into the theory behind it to work out why it’s not working and then you could fix it yourself. This example is redolent of Lawes’ belief in the contribution of ‘sound theoretical knowledge’ to reflection by providing ‘a framework of understanding that ultimately improves the quality of practice and leads to the transformation of subjective experience’ (2004: 199) – in other words, to the professional development that constitutes the fourth element of Dalziel et al.’s (2016) teaching cycle. Research-Informed Teaching Arguably, a research-intensive university should be at the leading edge of both pedagogy and technology, and respond to students’ changing competence and expectations. In our more recent institutional studies of the digital experience and open educational practices, we explored, through interviews, The Challenge of Teachers’ Design Practice 127 a fourth factor in academics’ design practice: namely, how they can bring scholarly research into their teaching. As one interviewee in our digital experience study commented, this can be difficult in the face of a constant tension between their two roles: ‘either you have to have a curiosity about pedagogical methods or you have to really care about your students. That can be hard if what’s brought you to [the University] is the exciting research that you can do’. Applying a framework derived from Spronken-Smith, Mirosa and Darrou (2014, cited in Masterman 2016) to the data from our investigation into open educational practices, we distinguished four ways in which academics can inform their teaching practice with the methods and outputs of scholarly research: • • • • Research-led – structuring learning around content drawn directly from research, including one’s own: ‘The teaching is driven by research, and […] they’re coming to participate in that’; Research-oriented – teaching the processes of knowledge construction in the subject: ‘… guiding a student through your own interpretation of a discipline in order to help them learn their own techniques’; Research-based – designing activities such as inquiry-based learning that involve research skills and methods: ‘Learning how to be a good learner is learning how to do research’; and Research-tutored – supporting students to learn through writing and discussing papers or essays: ‘The student leaves the tutorial with a different perspective on the essay which they brought to it’. Approaches such as these may help students to grasp ‘the complex and provisional relationships between research and knowledge’ (Zamorski 2002, quoted in Masterman 2016: 39). The Sociocultural Context Design is an inherently social act, even when carried out in isolation. Every teacher is part of at least one community, whether this is formally constituted (e.g. an institution or professional body) or an informal grouping of people who share a common interest. Communities can overlap (in that someone may belong to both a university and a scholarly society), or be nested within each other (e.g. a department within an institution). They may be long-lived (as in colleges and universities) or convene for a short time only (e.g. a workshop to share effective practice). Together, communities constitute the sociocultural context for teachers’ design practice: how that practice is prescribed, carried out, supported, and propagated. A panoply of policies, strategies, conventions, procedures, guidelines, and norms can be formulated for different purposes by different 128 Liz Masterman groups within the community (or supra-community organizations such as governments). They act as enablers and constraints on teachers’ design practice in addition to the factors already outlined, giving teachers varying levels of autonomy over their design practice (Bennett, Lockyer and Agostinho 2018). Exploring the sociocultural context in which design for learning is practised yields two key themes: the formal and informal roles of a community’s members, and the emergence of strategic initiatives at the institutional level. In terms of formal roles, educational developers and learning technologists can play a key role in brokering innovation within and across the different communities in an institution, through cascading new ideas for practice, modelling good teaching practice to the lecturers who are studying on these courses, and helping lecturers to frame the pedagogic problems for which they seek assistance: ‘you need somebody to look at it and then in a tactful constructive way point out that actually, you know, something else is going on here’ (interviewee, Learning Designer project). Informal communities close to the chalkface were particularly valued by Learning Designer interviewees, largely for the element of trust that comes from close acquaintance. Interviewees in our study of open educational practices enthusiastically endorsed the value of informal conversations with colleagues about teaching and learning. A humanities tutor described the value of informal conversations about teaching as ‘massive’, largely because they take place with people whom he trusts and who understand the environment in which he works. In relation to drawing inspiration from the work of others, interviewees in the same study readily admitted the benefits of looking at other teachers’ resources and lecture notes: ‘seeing good examples of practice might also influence your own practice, which in turn provides a better experience for everyone’. These observations lead on to the larger question of how to effect innovation in design practice within the wider institution. One Learning Designer interviewee summed up the alternatives thus: ‘Do you wait for the change to come from the top [or] take a sort of a guerrilla approach and maybe change one or two here, there and everywhere, and then that will feed through to committees …?’ In our early research into design for learning, the ‘guerrilla’ approach, exemplified by the preceding quotation, appeared to predominate. Latterly, more co-ordinated initiatives are discernible, driven at the strategic level envisaged by Masterman, Walker and Bower: ‘if [academics] are to find support and resources to build capacity and to scale up their initiative across a department or faculty, then their efforts must be aligned with institutional strategy’ (2013: 21). For example, at the University of Oxford the introduction of a digital education strategy in 2016, coupled with the migration to a new VLE, has provided the impetus for an initiative based on the ‘ABC’ Learning Design workshops developed at University College London (UCL). These The Challenge of Teachers’ Design Practice 129 workshops were themselves instigated by the ‘Connected Curriculum’ initiative at the institutional level in UCL (Young and Perović 2016). Implications for Digital Tools to Support Teachers’ Design Practice The findings from our different research projects, supported and supplemented by the wider scholarly literature, have painted a complex, composite picture of the design process that has to do with the proclivities of individual practitioners and a range of intellectual and sociocultural influences on them, as well as the nature of the process itself (i.e. simultaneously art and science). This picture has been succinctly summarized by Bennett, Lockyer and Agostinho (2018) as ‘individual cognitive acts of design thinking [occurring] within a social context […] that shapes both the design process and the design outcome’. The idea that digital tools can support and extend these individual cognitive acts is an enduring one that has spawned a number of applied research projects since the early 2000s, leading to the development of numerous digital tools including Phoebe and the Learning Designer (for a historical overview, see Dalziel et al. 2016). Even so, a number of barriers to their widescale uptake remain unresolved (Dagnino et al. 2018), at the heart of which lies the challenge of facilitating thinking processes that may be both tacit (as noted earlier in this chapter) and idiosyncratic to individual teachers, disciplines and institutions. A number of authors (including Jonassen 2008; Donald et al. 2009) have equated the thought processes involved in designing learning experiences to the solving of problems in ill-defined domains (Lynch et al. 2006). Such problems lack a single definitive solution, there is no set of steps for the solver to follow that will guarantee success, and the solution chosen depends largely on how the solver conceptualizes the problem. In terms of digital support, this means providing guidance that makes the design ‘problem’ more tractable for the teacher without overly constraining their choices. Drawing on Lynch and colleagues’ work, Masterman, Walker and Bower (2013) identify five approaches that may be adopted in the design of such tools: • • • A general model (ontology) of the domain, on which an expert system can be built to provide guidance to the user. Constraints, which either represent characteristics of a successful solution (patterns and templates) or guide the user towards successful solution of the problem (wizards). A ‘discovery’ approach, where the tool provides a digital environment in which the teacher can model different designs and/or gives guidance on demand. 130 • • Liz Masterman Case examples, in which the tool finds, and offers to the user, learning designs created by others to tackle the same pedagogic problem. Collaboration, where the tool either collaborates with the teacher or facilitates interactions between two or more teachers engaged in the design task, thereby leveraging the building of community knowledge. Some of these approaches involve building artificial intelligence into the software. This was the case in the Learning Designer, where an ontology of design underpinned the functionality of the tool so that it could make suggestions and offer example learning designs to the user based on, for example, knowledge of the intended learning outcomes of the session being designed. Whichever of the above approaches (or combination of approaches) is adopted in designing a supportive digital tool, a paradox can exist in which some users perceive the functionality of a tool as being too flexible, and others as too structured (hence, militating against creativity) (Masterman and Manton 2011; Dagnino et al. 2018). In addition, the problem of achieving a shared understanding between tool and user can risk either simplistic ‘recipe’-style guidance, or guidance that is so vague as to leave novice teachers unsure whether their learning design is a ‘good’ or ‘bad’ one (Masterman, Walker and Bower 2013). Furthermore, during the initial phase of planning – the ‘art’ dimension of design – a teacher or course team may shuffle content, learning outcomes, learning activities and other design elements in a process of rapid to-and-fro that is optimally facilitated using low-tech, pencil-and-paper tools in a realworld environment, where it is still easier to manipulate and share objects than in the restrictive virtual space of a computer screen. An analogue technique that has gained currency in recent years is storyboarding (Beetham 2012). Cards representing specific design elements are positioned and repositioned on a ‘canvas’, annotated with personal meanings and used as prompts for discussion in order to construct a narrative representation of a module or learning session in words and graphics. The ‘ABC’ model developed by Young and Perović (2016) is one example of this technique; CAIeRO (Usher, MacNeill and Creanor 2018; Sharpe and Armellini, Chapter 8) is another. Once a satisfactory storyboard has been arrived at, the design can be recorded on the computer in a more structured format. Implications for the Deployment of Digital Tools Supporting individual cognitive acts through developing usable and useful tools addresses only half of Bennett, Lockyer, and Agostinho’s (2018) characterization of teachers’ design practice. Also to be addressed is their deployment within a social context that can both shape, and be shaped by, their use. The Challenge of Teachers’ Design Practice 131 When exploring the sociocultural context of design earlier in this chapter we uncovered tensions between top-down and bottom-up approaches to innovation, particularly in relation to technology-enhanced learning. Unless carefully managed, the deployment of supportive digital tools for teachers’ design practice may have substantial implications for relations between the institution and the individual teacher. This risk is reflected by a participant in the Learning Designer project, who expressed concern that the software could be imposed on lecturers as ‘a measurement tool, rather than a useful organizational tool that allows some critical self-reflection on practice’. Thus, although the institution must play a central role in exploiting the full benefit of these tools, it must do so in such a way that lecturers feel that they ‘own’ the tool as well. One suggested solution is to: position the tool within the design of a programme, department and faculty where it is used by academics on a regular basis. If it can demonstrate how it improves overall programme-level design, it is much more likely to be incorporated into a wider strategy, where its alignment with stakeholders’ needs is recognized and properly interpreted at all levels of operation, including teacher education programmes. (Masterman, Walker and Bower 2013: 22) Conclusion This chapter reviewed teachers’ self-reported design practice in order to establish an understanding on which the development of digital tools to support that practice might be based. Starting from a conceptualization of design as a hybrid of art and science, we identified two intersecting models in which teachers’ practice can be located: a hierarchical model of the curriculum structure and a cyclical model of the design–teaching process. In reality, the elements of each model overlap and interact with each other, with teachers’ design behaviour further influenced by a number of factors internal and external to them, not least the social and institutional context in which they are working. A definitive understanding of design practice, and of the factors that have a bearing on it, remains elusive, with each new study having the potential to uncover new variations. At best, the behaviours revealed in our own research and the wider literature yield a multifaceted working truth from which representative requirements (and, hence, design features) for a supportive digital tool can be elicited. Addressing the first part of Bennett, Lockyer, and Agostinho’s (2018) characterization of teachers’ design thinking as individual cognitive acts within a social context, we have suggested that these acts comprise an 132 Liz Masterman instance of ill-structured problem-solving, for which a range of computational supports can be applied, individually or in combination. Even so, some aspects of the design process may currently elude computational support altogether. Addressing the second part reminds us that the organizational, educational and social influences brought to bear on the deployment of cognitive digital tools within particular communities are not only as important as the functionality, look and feel of the tools themselves; they are also pivotal in achieving the innovation that the tools are intended to stimulate. Acknowledgements Portions of the text reflect the chapter by Masterman and Vogel in the first edition of this volume. The author acknowledges with appreciation the work of Mira Vogel in the original publication. References Beetham, H. (2012). Institutional Approaches to Curriculum Design: Final Synthesis Report, Bristol, UK: Jisc. Retrieved from Jisc Repository: http://repository.jisc.ac.uk/ 6002/1/JISC_Curriculum_Design_Final_Synthesis_i1.pdf. Bennett, S., Lockyer, L., and Agostinho, S. (2018). Towards sustainable technology-enhanced innovation in higher education: Advancing learning design by understanding and supporting teacher design practice, British Journal of Educational Technology, 49(6), 1014–1026. Bennett, S., Thomas, L., Agostinho, S., Lockyer, L., Jones, J., and Harper, B. (2011). Understanding the design context for Australian university teachers: Implications for the future of learning design, Learning, Media and Technology, 36(2), 151–167. Conole, G. (2008). New schemas for mapping pedagogies and technologies, Ariadne, 56, 1–14. Dagnino, F.M., Dimitriadis, Y.A., Pozzi, F., Asensio-Pérez, J.I., and Rubia-Avi, B. (2018). Exploring teachers’ needs and the existing barriers to the adoption of Learning Design methods and tools: A literature survey, British Journal of Educational Technology, 49(6), 998–1013. Dalziel, J., Conole, G., Wills, S., Walker, S. Bennett, S. Dobozy, E., Cameron, L., Badilescu-Buga, E., and Bower, M. (2016). The Larnaca declaration on learning design, Journal of Interactive Media in Education, 2016(1), 7. Donald, C., Blake, A., Girault, I., Datt, A., and Ramsay, E. (2009). Approaches to learning design: Past the head and the hands to the HEART of the matter, Distance Education, 30(2), 179–199. Entwistle, N., Skinner, D., Entwistle, D., and Orr, S. (2000). Conceptions and beliefs about “Good Teaching”: An integration of contrasting research areas, Higher Education Research and Development, 19(1), 5–26. Jonassen, D.H. (2008). Instructional design as design problem solving: An iterative process, Educational Technology, 48(3), 21–26. The Challenge of Teachers’ Design Practice 133 Lawes, S. (2004). Practice makes imperfect. In D. Hayes (Ed.), The Routledge Guide to Key Debates in Education (pp. 197–201). London and New York: RoutledgeFalmer. Lynch, C., Ashley, K., Aleven, V., and Pinkwart, N. (2006). Defining ill-defined domains: A literature survey. In V. Aleven, K. Ashley, C. Lynch, and N. 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Three dimensions of student-centred education: A framework for policy and practice, Critical Studies in Education, Advance online publication. doi: 10.1080/17508487.2017.1281829. Usher, J., MacNeill, S., and Creanor, L. (2018). Evolutions of Carpe Diem for learning design, Compass: Journal of Learning and Teaching, 11, 1. Winograd, T. (Ed.) (1996). Bringing Design to Software, New York: ACM Press. Young, C.P.L., and Perović, N. (2016). Rapid and creative course design: As easy as ABC? Procedia – Social and Behavioural Sciences, 228, 390–395. Young, P. (2010). Generic or discipline-specific? An exploration of the significance of discipline-specific issues in researching and developing teaching and learning in higher education, Innovations in Education and Teaching International, 47(1), 115–124. Chapter 8 Designing for Learning within an Organisational Context Rhona Sharpe and Alejandro Armellini Editors’ Introduction Understanding the complex reality of how course design occurs is crucial to applying the principles that underpin designing for learning in technologyenabled contexts. Although a myriad of educational rationales and design tools are available, the process of designing for learning in a real-world course structure must also take account of diverse organisational requirements and constraints. While the primary aim of designing for learning is to create pedagogically effective courses, now teachers and learning designers are frequently being asked to do something much more ambitious – to design courses that contribute to a radical transformation of the curriculum across an institution. Can design for learning meet this ambitious aim? This chapter reviews how attempts to transform the curriculum have progressed and what we have discovered about how to support practitioners through such design processes. It makes recommendations for how the field of learning design could respond to institutional imperatives for radically redesigned, technology-rich curricula. Introduction In what David Watson (2013) described as these ‘manic’ times for higher education, where funding models and technological developments are in flux, the ability to generate and embed innovative course designs is crucial for universities aiming to transform their curricula. For many higher education institutions across the globe, reviewing how and what they teach has become a core part of their strategic development (Blackmore and Kandiko 2012; Bain and Zundans-Fraser 2016). The attention being paid to the curriculum has been attributed to a wide range of changing demands including students’ increasing use (and expectations of use) of technology, outcomes-based education, and public accountability (Boitshwarelo and Vemuri 2017). Higher education institutions are paying attention to the quality of their product (a global graduate, a networked citizen, a change maker) as well as Designing for Learning in Organisations 135 the quality of their service (providing a high-quality education). Those who conduct and support course design have seen a shift from a focus on how technology can enable learning, to technology as a driver for the attributes, dispositions and values required by 21st Century graduates (Beetham 2012; Barnett 2014). The integration of technology now has purposes beyond the questionable notion of ‘enhancement’ (Bayne 2014), such as the need for graduates with different skills, the expectation to compete in a growing online learning market, providing open access to quality resources or responding to changing student dispositions (Dalziel 2013; Bower 2017; Masterman, Chapter 7). As universities struggle to review and update their curricula ever faster, the pressures and stakes for learning design are increasing. In this chapter our interest is in strategic curriculum change, which we take to mean organisational level planning that involves reviewing the institutional mission, deciding how to respond, planning activities, and allocating resources appropriately. In practice, this means the deployment of learning design frameworks and approaches across an organisation in pursuit of an institutional agenda. This chapter uses literature and case studies to question, identify, and characterise the role of learning design in such situations. We assess if its methods and tools can be repurposed from ‘traditional’ course design to meeting other, diverse institutional agendas such as retention, employability and graduate attributes. We evaluate the scalability of those methods and tools. We ask how the field of learning design needs to respond to these changing priorities. We examine institutions where learning design has been part of a strategic curriculum change process and make recommendations for those supporting such ambitious, large-scale transformation. Supporting Strategic Curriculum Change In line with other chapters in this volume, this chapter explores the problematic space between the intentions and actual practices of design (Agnostinho et al., Chapter 6; Masterman, Chapter 7). The focus here is on design decisions that take place at the meso level, explained by Jones (Chapter 4) as decisions that can be taken by small groups such as course teams or departments. We would expect those decisions be influenced by institutional strategic priorities. However, this relationship between strategy and practice is not straightforward. Kandiko and Blackmore (2012) in a detailed and comprehensive international study of strategic curriculum change note that: it was striking that in all the institutions that we surveyed, interviewees at school level and faculty level rarely mentioned their institution’s 136 Rhona Sharpe and Alejandro Armellini priorities and initiatives in describing their own thinking and activities. Instead they referred to their own discipline and students. (p. 46) The influence of the discipline on design practices was also highlighted by Masterman (Chapter 7) who found that the ability of teachers to incorporate the development of graduate attributes and skills into their designs was dependent on the alignment of these attributes with the expectations of their discipline. Bain and Zundans-Fraser (2016) dig deeper into this concerning disconnect between strategy and design practice. Having reviewed the challenges to institution-wide curriculum reform, they conclude that universities ‘frequently lack the theoretical frameworks, institutional process, practice and collaborative cultures required to address the profound changes they face.’ (p. 7). Given this analysis, we might expect that institutionally adopted and theoretically driven processes for team-based course design should help with these challenges. A number of institutions have developed and promoted a particular learning design process, encouraging its use to design modules and programmes. Perhaps the best evaluated of these is Carpe Diem, an interdisciplinary team-based approach to learning design involving academic course teams, learning designers, learning technologists, subject librarians, students and, where appropriate, employers (Salmon 2013). Originally conceived of as a team-building exercise, Carpe Diem was created and subsequently developed to facilitate the production of innovative, studentcentred designs for online and blended learning (Armellini and Jones 2008; Salmon, Jones and Armellini 2008). Early evaluations provided positive feedback on its impact on course design and suggested that Carpe Diem can be an enabler for pedagogic change (Armellini and Aiyegbayo 2010; Salmon and Wright 2014). Supported learning design processes appear to be flexible enough to be repurposed to support dfferent priorities. Young and Perovic (2016) claim that the ABC design process can be used to develop richer learning designs for blended learning and to integrate strategic initiatives such as digital skills or employability (Conole, Chapter 10). Bennett (2015: 4) argued that such approaches should work for flipped learning and developing students’ digital literacies because they ‘support lecturer agency, locate development within academic programmes and by working in cross functional teams’. Oxford Brookes University developed the Course Design Intensives (a variant of Carpe Diem) and used it to support various institutional change agendas over a period of over 10 years, starting with blended learning and then creating versions for assessment redesign, internationalization of the curriculum, and embedding graduate attributes (Sharpe, Benfield and Francis 2006; Dempster, Benfield and Francis 2012; Sharpe and Oliver 2013). Some of the materials used in Course Design Intensives are Designing for Learning in Organisations 137 provided in Resources 5 and 6. Finally, the case study in Box 8.1 shows how CAleRO, another version of Carpe Diem developed at the University of Northampton, enables course teams to design in line with the university’s strategic prorities, including the explicit deployment of active blended learning aligned to the framework of graduate attributes (Maxwell and Armellini 2018). Box 8.1 Designing for active blended learning CAIeRO (Creating Aligned Interactive Educational Research Opportunities) has been a key enabler of curriculum change at the University of Northampton (Farmer and Usher 2018; Jisc 2018). The University has been running CAleRO workshops since 2008. In 2014, in response to a strategic initiative to introduce active blended learning across all programmes, the University increased the capacity to deliver CAIeROs, which made it possible to scale up the process and reach all subject areas. The University’s single strategy positions active blended learning as its ‘transformational’ learning and teaching model (University of Northampton 2018: 6). The principles of personalisation, learner centredness and student activity, all central to active blended learning, shaped the design of the Waterside campus, which has no lecture theatres. The CAIeRO workshop is a team approach to module and programme design adapted from Carpe Diem to suit the requirements of the University of Northampton (Usher, MacNeill and Creanor 2018). In CAIeRO, blended learning is presented as a more diverse, complex and exciting concept than the mere combination of face-toface and online teaching. Such multi-layered blends comprise approaches to assessment, placements and work experience, independent learning and mobility, among other dimensions. Appropriate blends emerge from the collaborative design process at various stages of the workshop, particularly during storyboarding. CAIeRO helps teams design for student centredness through engagement, participation and interaction. The main challenge associated with Northampton’s experience is the perception by some academics that CAIeRO may not constitute valuable use of their time, as ‘[we] already know how to design courses’ (Farmer and Usher 2018: 8). Having the workshop enforced on staff, typically by line managers, has been particularly unhelpful. The need to design a module or programme with expert support in a safe, friendly environment, is a key motivator to engage teams in CAIeRO. Effective facilitation has been a major ingredient in the success of CAIeRO. Farmer and Usher (2018) found that the facilitator should 138 Rhona Sharpe and Alejandro Armellini have learning and teaching expertise but should not be an expert in the subject of the module or programme being designed. A separate staff development workshop is offered to those who wish to become CAIeRO facilitators. Colleagues asking for multiple CAIeROs (and bringing their teams with them) constitutes another indicator of satisfaction and normalisation. CAIeRO is embedded in the University’s quality processes. The workshop is either required or strongly recommended for validation, change of approval and periodic subject review. By July 2018, 97% of all modules had been redesigned through a CAIeRO workshop. It is widely seen as a process that evidences academic quality enhancement. Although these examples appear to show supported learning design tools and processes being put to use to achieve strategic curriculum change, other interpretations are possible. Previously we have seen that common drivers for making use of technology in education are pragmatic, e.g. increasing class sizes or needing to teach in a new context (Oliver 2006; Sharpe et al. 2006) and indeed that some teachers eschew theory in favour of more pragmatic consideratons (Masterman, Chapter 7). CAIeRO could be seen as being prompted or encouraged by a pragmatic need to redesign courses before the move to a new campus (Box 8.1). This example also shows that institutional drivers for curriculum reform can include good learning design as an end in itself, in this case, a focus on active and blended learning. Indeed learning design can usefully be a ‘prop’ or starting point for better teaching practice (with no guarantees), for optimal use of space, for focused staff development and for raising the profile of teaching. Similarly, many of the team-based design workshops created to integrate technologies into courses are underpinned by notions of constructive alignment (Biggs and Tang 2007) and Laurillard’s types of learning activities (Laurillard 2002). These fundamental design principles would be expected to improve the educational effectiveness of any course, and are undoutedly worthwhile, but are they sufficient to achieve strategic curriculum change? There has been criticism that approaches to learning design are not sufficient on their own to bridge the disconnect between pedagogy and the curriculum (Boitshwarelo and Vemuri 2017). An understanding of the organisational culture in which the changes are taking place is likely to be a key enabler of strategic transformation. Organisational Strategy and Culture One challenge of repurposing these design approaches to meet strategic goals is the sheer number of priorities to which universities are simultaneously Designing for Learning in Organisations 139 trying to respond. University curricula are currently expected to develop students’ subject knowledge, digital skills, employability, resilience and wellbeing, as well as aligning with institutional research themes, meeting government targets for widening access and progression, and taking account of student expectations for an inclusive, diverse, and internationalised curriculum, high contact hours, fast feedback turnaround times, and ubiquitous lecture capture. How can a large and compex institution plan for the changes required in response to such a diverse set of drivers? The literature on strategic management reveals two possible approaches to strategic planning: a planned approach and an emergent approach. The planned approach makes use of structures, data and analysis and is wellsuited to a relatively stable and well-resourced environment. In such an environment decisions made at the top of the organisation on the basis of data, go on to define which activities to resource, and what can be implemented throughout the organisation by following detailed plans. Such an approach has been shown to achieve change in learning and teaching in higher education (e.g. Thornton 2014 cited in Bennett 2015). A second, emergent approach to strategic planning takes account of context, people and cultures (after Mintzberg 1994). Organisational culture is often defined as ‘the way we do things round here’ (after Lundy and Cowling 1996), and typically encompasses aspects of an organisation’s vision, values, norms, language, beliefs and habits, and how individuals behave. For Stacey (1992) the idea that strategic planning is responding to, or shaping the future of organisations is a fallacy. What leads to change is not some grand plan but what every individual in the organisation is doing. The patterns that emerge are the consequence of the actions of every agent in the system. Where the system is an organisation, the actions are the conversations between people. It is therefore not the planning process itself which results in change, it is the conversations that occur between individuals that determine how an organisation responds to the changing environment. In the final review of the Jisc curriculum design programme, Beetham (2012) identifies that innovation was frequently held back by institution culture, myths, and practices. It is here where rolling out learning design programmes can have an important role in bringing about change. Workshops that bring teams together to discuss design in light of even complex and multiple strategic priorities, provoke and legitimize conversations about teaching and design in the 21st Century. It is these conversations between colleagues that often constitute an aim of learning design programmes (Box 8.2) and initiating or changing such conversations is part of their lasting effect (Box 8.1; Farmer and Usher 2018). Where these programmes can be scaled up and rolled out across an institution, they have the potential to change what every member of a course team is doing and talking about. 140 Rhona Sharpe and Alejandro Armellini Box 8.2 Designing for graduate attributes Course Design Intensives were first run at Brookes in 2003 on a voluntary basis to support teams to redesign for blended learning. In subsequent years they were rolled out to all teams with a focus on redesigning assessment. By the time the graduate attributes project was initiated in 2010, CDIs were already accepted as a method for implementing university change (Dempster, Benfield and Francis 2012; Sharpe and Oliver 2013). The Strategy for Enhancing the Student Experience (2010, 2015) stated that every undergraduate programme will include the development of the five core graduate attributes: academic literacy, research literacy, digital and information literacy, critical self-awareness, and global citizenship. All programme teams were asked to conduct of ‘mapping’ the attributes in their existing programmes. This mapping was designed as an audit to trigger discussions of how teams currently supported learners to develop each of the attributes, and a prompt to introduce new learning outcomes, activities and assessment where appropriate. The mapping and subsequent redesign was supported by CDIs using a standard set of documentation, resulting in revised Programme Specification documents (including programme level learning outcomes) which made each of the graduate attributes explicit (see Resource 7 for examples). For the evaluation, a team from the central staff development unit and subject academics collected reflections on the implementation process and analysed relevant programme documentation (Sharpe et al. 2013). We found development of the attributes was discussed at validation events and articulated in course documentation. An unanticipated benefit was that learning outcomes for each programme were considered and rewritten such that they are more meaningful, accessible and often, fewer in number. However, the reality is that revising the programme documentation was an enormous investment of effort, and while some teams took the opportunity to work together to discuss what their programme could offer to their graduates’ lifelong employability, in other courses, senior academics revised the documentation alone. An analysis of all 90 revised undergraduate programme specification documents allowed for an exploration of how the generic graduate attributes defined in university strategy had been interpreted by academic colleagues. This showed that teams who had made their own interpretation of the graduate attributes had done so through an exploration of the ways and contexts in which they are put to use within the context of the discipline. This demonstrates a rethinking of the programmes towards the value to students after they graduate. Designing for Learning in Organisations 141 Considerations for Institution-Wide Design for Learning If we understand organisational strategic change as emergent, and teachers’ course design practices as situated in their disciplines, then this has implications for the way in which course design is initiated and supported in order to facilitate curriculum change. For example, it is likely to be important for curriculum design decisions to be made at the programme level by multi-skilled teams who are led by effective programme leaders (Locke 2012) or at the subject level where the subject leader is trusted as a change agent and disseminator (Box 8.1). The widespread adoption of team-based, programmatic learning design could help organisations achieve their goals because it changes what every working academic does. It shifts course design practice from an individual to a shared activity, and the conversations it promotes are crucial in changing teaching practice and academic culture. Programme Level Designs Much of teachers’ practice of designing for learning tasks considered elsewhere in this volume is at the level of the activity, sometimes the modules, rarely full programmes or subject groupings. However, for our students, their experience is one of the entire programme. To achieve curriculum transformation, a focus on the programme is more likely to impact on the ways in which students experience and learn and develop from the curriculum. One way of prompting a programme-level focus is consideration of graduate attributes, as this requires a rethinking of the programmes towards their value to students after they graduate. Both institutions in the case studies have their own frameworks of graduate attributes and have used a team approach to learning design to help embed these into their programmes (Oxford Brookes University 2015; Maxwell and Armellini 2018). In the Oxford Brookes University case study (Box 8.2) it is interesting to note that programme learning outcomes were chosen as the unit of study for the evaluation. Why such a focus on learning outcomes? Learning outcomes are a critical, and often first step in the learning design process. As such, they need to ‘represent as much as possible the intentions of the curriculum and are expressed without ambiguity’ (Boitshwarelo and Vemuri 2017: 288). Well-articulated programme learning outcomes are important to current and prospective students, teaching staff, employers, and professional bodies. They express what students are expected to be able to do by the end of their course, clarify for teaching staff what students must achieve, and enable the assessment process to become fit for purpose. These purposes for programme learning outcomes extend the role that intended learning outcomes play in a constructively aligned module (Biggs and Tang 2007). In the ‘double constructive alignment’ framework depicted in Figure 8.1, activities at the module level are derived from the specification of the programme, 142 Rhona Sharpe and Alejandro Armellini Figure 8.1 Double constructive alignment framework which is informed by strategic priorities and expressed in the form of graduate attributes. In practice, the articulation of programme-level learning outcomes can be challenging. In an internal report of the graduate attribues project at Oxford Brookes University, the evaluators reported that: It should be acknowledged that it is difficult for teams to articulate their expectations of students at a programme level. It is a challenge to adequately summarise the totality of the learning outcomes achieved by students from their modules without being overly general and abstract. (Sharpe et al. 2013) In such cases, sharing examples of how generic definitions of graduate attributes have been contextualized for different disciplines has been found to be useful (see Resource 8). Designing in Cross Functional Teams As staff are increasingly busy, they are less likely to make time for formal development courses. It is notable that recent variants of Carpe Diem and the ABC workshops are all shorter (some down to two hours) and more flexible, with supplementary online activities and diagnostic needs assessments and online collaboration follow-ups (Usher, MacNeill and Creanor 2018). Bringing together a team is a challenge but also encourages dedicated Designing for Learning in Organisations 143 time to be set aside for the design work and for its validation as an acceptable academic activity (Aycock, Garnham and Kaleta 2002; Dempster, Benfield and Francis 2012). The time released from other duties is both a mark of institutional commitment to the work and genuinely useful in taking designs forward, as expressed by these CAleRO participants (Farmer and Usher 2018: 8): I thought that while [CAIeRO] might reveal some useful ideas, it was going to take up two days unnecessarily. Having completed the process, I became a convert. One common finding from the evaluations of these staff development interventions is the value of bringing together different sources of expertise into an extended course team (Armellini and Aiyegbayo 2010; Dempster, Benfield and Francis 2012). The teams frequently bring in staff who had not previously been engaged in course level design decisions: learning technologists, educational developers, librarians, students, experts in teaching from other facultie, artistsin-residence, and employers. The core teams acknowledged the value of working as part of multi-skilled groups (Dempster, Benfield and Francis 2012). Notably, many of the additions to extended teams are the ‘third space professionals’ identified by Whitchurch and Gordon (2013). Third space professionals are well-positioned to mediate between support and academic staff and so can be influential in facilitating change (see also Masterman, Chapter 7). In order to improve their agility to shape future change, universities could build on the distinctive third space within higher education and the professionals that work within it. Third space professionals have more fluidity and flexibility within their roles and can move easily between university spaces and functions. They could be ideally positioned to take forward the development of curriculum transformations. Design as Professional Practice Course design has traditionally been a private and tacit area of work (Sharpe and Oliver 2013). However it has become clear throughout this chapter that the individual practice of specifying learning outcomes, activities, and assessments, no longer accurately describes the experience of course design. When the driver for curriculum change is an organizational agenda, those engaged in course design have to deal not only with their own professional identity, but also with their role as sitting between institutional objectives and the student experience. Here course design, teaching practice and evaluation can be better understood as a social practices (Weller 2012). Designing within the context of the course is a form of professional learning, as individuals engage with the issues, conventions, resources, and practices of their institution and discipline. The activity of design provides a vehicle for practitioners to develop 144 Rhona Sharpe and Alejandro Armellini their knowledge through application, in much the same way as professional learning has been described in practice (Salmon et al. 2015; Ellaway, Chapter 12). Designing in teams provides the opportunities for sharing ideas, building networks and crucially the dialogue that is a necessary element of both professional learning and institutional change (Falconer et al. 2007; Dempster, Benfield and Francis 2012). Culture and Conversations Any university-wide approach needs to take into account how institutional and local cultures are likely to inhibit or enhance strategic curriculum change. Within higher education, where disciplines have strong shared cultures (Becher and Trowler 2001), it may be that there is a tension between strategic curriculum change and disciplinary and departmental cultures. Another challenge, eloquently argued by Stacey, is that the very existence of a strong, shared culture makes the organisation resistant to change. That is, members of the group tend to conform to their accepted ways of doing things, which can be adopted without negotiation or communication. Since any change challenges these taken-for-granted norms, change itself is resisted (Stacey 1992: 143–147). Where strategic curriculum review requires change, tension in the relationship between strategic planning and organisational culture immediately becomes apparent. This is most evident during the implementation of strategy, which relies on the adoption of strategic decisions throughout the organisation. However, one of the features of the culture of universities is that decisions are made throughout the organisation – not just at the top (Shattock 2011: 46–49). In the project to embed graduate attributes (Box 8.2), guidance was given to programme teams and chairs of review and approval committees to shape these conversations. The combination of the culture of dispersed decision making and the strong shared norms of the disciplines, means that the behaviour of individual agents in the system are likely to have a disproportionate effect in universities. The conversations between members of course teams may therefore have more influence on how the curriculum operates than strategic decisions taken by academic leaders. It is important to understand how these conversations are likely to be influenced by organisational and disciplinary cultures, and therefore how they impact on the ability to conduct strategic curriculum transformation. Student Engagement, Experiences and Expectations The final challenge considered here for managing curriculum change in the digital age is changing student expectations. It is now usual for teams to include students, or at the very least, to take into account the views and needs of students. A number of techniques can be used to encourage Designing for Learning in Organisations 145 course teams to view their designs from the student perspective (e.g. storyboarding, ‘reality checking’, and feedback provision), which are typically embedded into the approaches described here. However, student involvement can be difficult to implement in practice. In our experience, the value added by such involvement is variable. Although there has been much rhetoric about engaging students in curriculum design, moving to cocreation of the curriculum is demanding of staff and students, and needs to be undertaken with care (Kay, Dunne and Hutchinson 2010; Bovill, Bulley and Morrs 2011). Very little research is available on this aspect, although the evidence appears to suggest that such initiatives can substantially change lecturers’ assumptions about how and why students engage with the learning and teaching process (Brooman, Darwent and Pimor 2015). This is likely to be particularly important for courses that make substantial use of technology, as a result of the myths and the multiple assumptions about students’ digital fluency that are often made. Oliver observes that ‘careful, empirical research of what learners actually do is largely absent’ (Oliver 2015: 367) and uses extracts from a year-long ethnographic project to argue persuasively that we need to understand the reality of students’ academic practices in order to be able to design learning activities, resources, spaces and tools for them. Gourlay and Oliver (2018) drawing on the same research project, explains that superficial research can lead us to talk about binaries such as digital/paper, face-to-face/online, which are largely meaningless for students. Rather, learners are concerned with the real and more challenging aspects of the blend: mobility, accessibility, flexibility and choice. Gourlay and Oliver warn that we are in danger of making grand-scale policy decisions about technology use without attending to what learners actually do. Institution-wide curriculum change needs to do more than involve students in workshops. It requires a commitment to investing in evaluations of learners’ experiences to inform new curriculum developments. Conclusions It is clear that contemporary higher education is facing numerous challenges, and that technological innovation is adding complexity to possible organisational responses. While the disruptive influences of technology have presented universities with potential levers for change, there are few examples of curriculum redesign operating successfully at scale. Universities have found it difficult to create conditions for conversations to challenge existing disciplinary cultures and design practices or to understand the changing expectations of learners in respect of technology. This chapter has drawn on evaluations of university-wide interventions to offer advice for supporting teams through the process of designing for technology-rich courses within an institutional context. While there is not much evidence of impact yet, a shift in institutional approaches to 146 Rhona Sharpe and Alejandro Armellini designing for learning is evident. The process is now more team-based and more focussed on the subject area, the programme and the student experience. 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She shows that ‘open’ is a contested term and field of practice: some of its manifestations are easily caught up into mainstream agendas and can reproduce existing inequalities, while others have the potential to challenge and transform. Cronin is not afraid to note that there are risks as well as opportunities in designing more open opportunities for learning. Overall she encourages open educators to remain reflective, and critical, while campaigning for organizational policies that can reduce the risks of openness, and enhance the value and reward. Introduction Higher education operates within a rapidly changing sociotechnical context characterized by ubiquitous connectivity, a shift from knowledge scarcity to knowledge abundance, and a move from hierarchical toward more networked forms of social organisation. Concepts such as the network society (Castells 2010), networked individualism (Rainie and Wellman 2012), and participatory culture (Jenkins et al. 2015) seek to characterize this paradigm shift. In recent years, a growing number of critical theorists have added nuance to these analytical frameworks by also exploring how power and privilege operate in networks and the implications for individuals, institutions, and society. Without doubt, however, networked and open forms of information access and social learning have challenged and continue to challenge the role of higher education institutions as traditional 150 Catherine Cronin providers of knowledge. Multiple other challenges facing higher education include reductions in public funding, rising costs, increasing numbers of students, a new competitive landscape, and the imposition of market mechanisms and managerial control. Within this increasingly complex and difficult environment, higher education policy makers, managers, educators, and students seek to fulfil their sometimes contradictory goals with respect to teaching and learning. Open education initiatives – including open access (OA), massive open online courses (MOOCs), open educational resources, and open educational practices – aim to utilise the affordances of open digital networks to improve educational access, effectiveness, and equality. Many individual educators have also begun to teach and interact with students in open online spaces, for example offering students opportunities to create and collaborate on the open web, or to integrate their formal and informal learning practices and identities. Moves towards more open education are often met with resistance or suspicion, however, which may be due to lack of awareness or understanding, lack of the requisite skills and tools, lack of time, lack of trust, and/or incompatibility between existing institutional cultures and the philosophy of open education. This chapter explores the use of open educational practices in higher education, the tensions posed by all forms of openness within the academy, the importance of critical approaches to openness, and specific policy considerations to facilitate open education approaches in the curriculum. Open Education Open education is not just a digital innovation. The concept, philosophy, and practice of open education is built on a long history of social, political and education movements seeking to widen access to education and reduce inequality. During the 20th century, openness as an ideal became more prevalent with increased advocacy for access to education and rights to knowledge. The growing impetus for open education in the 1960s and 1970s reflected the educational mindset and wider political movements of that time, e.g. advocating for human rights, decolonisation, and social justice (International Commission on the Development of Education 1972; Hayes and Jandrić 2014). Open education initiatives that emerged during this period were often conceived as ‘reform projects’ with the aim of liberating education from all forms of oppression (Lane 2009; Deimann and Sloep 2013). In general, these open education initiatives focused variously on universal access to education, changing the relationship between learners and teachers, and empowerment of learners. The definitions of openness and open education remain multiple and contested. In reviews of the literature in the 1970s, open education was defined as ‘flexibility of space, student choice of activity, richness of Open Education 151 learning materials, integration of curriculum areas, and more individual or small-group than large group instruction’ (Horwitz 1979: 72–73). Open educators saw ‘the teacher as facilitator of learning [and] the development of student responsibility for learning’ (Marshall 1981: 183). Moving forward, open educators found common cause with social constructivist and connectivist educational thinkers, emphasizing that participation and social interaction were critical to learning, and that the internet provided multiple new opportunities for learners to engage. Throughout the 1990s and 2000s, open education evolved in parallel with developments in digital, mobile, social and participatory media and technologies. Open educational resources and open educational practices emerged as two key areas of development within open education. Open Educational Resources The term ‘open educational resources’ (OER) was coined in 2002 to define teaching, learning, and research resources released under an open license to permit free use and repurposing by others (Hewlett Foundation n.d.). The granularity of OER can vary from individual items such as images, videos, or documents, to entire open textbooks or open courses. The open license for each OER (typically a Creative Commons license) can be configured and assigned by the copyright holder in order to grant users specific rights for re-use. Openness in OER is thus focused on freedoms, but the degrees of freedom available within a particular license can vary, thus the level of openness varies (Lane 2009; Losh 2014). Five years after OER were first defined, an international gathering of open educators met in Cape Town to deepen and accelerate efforts to promote the use of open educational resources, producing the Cape Town Open Education Declaration 2007 (www.capetowndeclaration.org). The declaration had three main strategies: (i) further creation, use, and distribution of OER; (ii) changes in policy to support an open, participatory culture; and (iii) changes in the relationship between teachers and learners, in support of open educational practices (Winn 2012). In 2012, with the OER movement one decade old, UNESCO and Commonwealth of Learning produced the 2012 Paris OER Declaration, specifically referencing article 26.1 of the United Nations Universal Declaration of Human Rights: ‘everyone has the right to education’. The Declaration also called on governments worldwide to openly license publicly funded educational materials for public use. Open Educational Practices Beginning in 2007, the concept of ‘open educational practices’ (OEP) was defined with the intention of moving the focus from content to practice 152 Catherine Cronin and pedagogy (Andrade et al. 2011; Beetham et al. 2012; Ehlers 2011; Geser 2007; Hodgkinson-Williams 2014). Simply put, OEP combines the use of OER, open pedagogies, and open, transparent, teaching practices with the goals of improving access, enhancing learning, and empowering learners. Conceptualisations of OEP vary widely, ranging from those centred primarily on the creation and use of OER to more expansive conceptualisations that encompass open content but also allow for ‘multiple entry points to, and avenues of, openness’ (Cronin and MacLaren 2018). As with OER, the granularity of OEP can vary, from a teacher carrying on a conversation with students in an open online space, to the design of a completely open (and openly licensed) online course such as (but not limited to) a MOOC. More recently, conceptualisations of OEP have adopted a more critical approach, with the aim of challenging traditional educational practice and power relations. These open educators acknowledge the need for diverse and inequality-focused perspectives, and recognise that the apparently open spaces of the world wide web produce new power relations as well as reproducing and sometimes challenging old ones (Cronin and MacLaren 2018; Hodgkinson-Williams and Trotter 2018; Lambert 2018). Open Pedagogy and Practice In practice, educators have a wide range of ‘open’ opportunities available when they are making decisions about curriculum. They can intentionally choose to use OER in a course – as some or all of the course readings, or even as a course textbook. Open textbook initiatives have proven to be an important means of cost saving for students, with associated increases in recruitment and retention (Arcos et al. 2015; Jhangiani et al. 2016), but there are many more motives for their use. Like all forms of OER, open textbooks are openly licensed, allowing them to be adapted, e.g. for specific geographic locations, disciplinary contexts, student cohorts. Furthermore, use of OER and open textbooks can help to challenge traditional relationships between students and teachers, and between students and knowledge itself. Students can edit, amend, and create OER and open textbooks. Such forms of open pedagogy facilitate sharing ownership of the curricula, democratising learning, and shifting attitudes towards knowledge (Karunanayaka et al. 2015; DeRosa and Robison 2017; Ferguson et al. 2017). Reflecting on an open textbook project that she designed for an undergraduate course, DeRosa (2016) noted that a student-developed open textbook ‘allowed for student contribution to the “master text” of the course, which seemed to change the whole dynamic of the course from a banking model … to an inquiry-based model.’ Open Education 153 In addition to considering multiple ways of using OER and associated open pedagogy, educators can choose many other forms of OEP to open their curricula and their teaching. Course discussions can be made open or partially open through the use of course hashtags, open tools (e.g. blogs, Twitter), and/or open course environments. Educators can facilitate engagement beyond the bounds of the classroom (physical or digital) by inviting experts, including authors whose work is studied in a course, to engage in open discussions; facilitating peer-to-peer connections with students and educators in other courses, institutions, and countries; and inviting participation from interested learners in any location who have access to the internet but may not be enrolled in formal education. In a review of MOOCs developed at the University of Cape Town, Czerniewicz et al. (2017: 380) found ‘large numbers of diverse, “non-traditional” learners who entered the space. This resulted in practices and design choices to which diverse learners responded, and this learner-centred approach impacted the way educators thought about teaching their subject.’ The use of OEP can help students to engage on the open web as learners, researchers, creators, soon-to-be professionals, and citizens. To facilitate students’ open practices, educators who use OEP often support students in creating and managing their digital identities, developing their digital literacies, and ensuring their and others’ digital wellbeing (Jisc 2016; Alexander et al. 2017). The use of OEP can help students not only to navigate but also to confidently learn and interact on the open web, sharing their work and building a digital presence. Risks and Challenges of Open Through the use of open educational practices, open educators aim to acknowledge the ubiquity of knowledge across networks and to facilitate learning that fosters agency, empowerment, and global civic participation. However, OEP present challenges and contradictions as well as opportunities when they are introduced into higher education. The rapidly evolving norms of open practice, including open scholarly practice, are diametrically opposed to the widely-understood norms of many established academic practices, e.g. academic publishing. Whatever the aspirations, many practices of a ‘knowledge scarcity’ culture remain, e.g. conventions for owning and sharing intellectual property, academic publishing norms, and use of bounded learning spaces. A ‘pedagogy of abundance’, associated with open education, meets institutional environments and policies still rooted in a ‘pedagogy of scarcity’ (Weller 2011). Thus, moves towards open education are often met with resistance or apathy. Uptake of OER in higher education globally, while increasing, continues to be low and uneven. Multiple studies have shown that teaching staff in higher education have relatively low levels of awareness of OER, 154 Catherine Cronin copyright and licensing issues; have concerns about the quality and relevance of OER, including the time investment required; and work in contexts in which there is no incentive to use OER. Furthermore, the additional visibility accorded to open materials, often requiring additional quality criteria, often acts as a further barrier to releasing open content (McGill et al. 2013; Cronin 2018). Academic staff often experience tensions not only in finding time to engage in OEP but also in navigating this new terrain, including continually negotiating their own rules, boundaries, and networked identities (Lanclos and White 2015; Stewart 2015). A recent study found that educators in higher education experienced a number of tensions associated with using OEP, and even with considering the use of OEP. These included feeling overwhelmed (by heavy workloads, multiple demands on their attention, and myriad choices of digital tools), under pressure to make decisions regarding openness, fearful about the consequences of openness, and experiencing value conflicts in relation to openness (Cronin 2018). Academic staff who choose to use OEP do so in different ways and for a variety of reasons. But how do students respond to academics’ invitations to engage in open practices? The propensity to be distracted due to mobile devices and the ‘always on’ nature of the internet has arisen in some studies of third-level students, albeit in ways that are context-specific. In Selwyn’s (2016) study at two Australian universities, 25% of students reported finding technology to be a distraction and source of procrastination – particularly smartphones, social media, and other students’ use of digital devices in lectures (Selwyn 2016). In Newman and Beetham’s (2017) analysis of data from Jisc’s Student Digital Experience Tracker (a survey at 74 UK universities), 24% of students reported being easily distracted when ‘digital technology is used on my course’ (Newman and Beetham 2017: 21). Yet in a South African study where mobile phone ownership was ubiquitous among students but few had exposure to computers prior to coming to university, many students were found to use their phones strategically for academic purposes (Czerniewicz and Brown 2013). In analysing students’ digital mediated practices in context, Czerniewicz and Brown (2013) concluded that institutions could engage in mobile learning opportunities to a greater extent, particularly within ‘educational contexts faced with social and digital inequalities’ (p. 52). Further studies have shown that undergraduate students tend not to use social media in the context of formal education, citing worries about grades and perceptions of the internet as ‘too open and loose, generating anxiety and uncertainty’ (Kuhn 2017: para. 1). However, Facer and Selwyn (2010) have claimed that ‘learners need to practice and experiment with different ways of enacting their identities, and adopt subject positions through different social technologies and media’ (p. 166). Overall, previous research with respect to students and OEP highlights two key findings: the importance of Open Education 155 context and the necessity of acknowledging and building on students’ existing concerns and practices, or ‘technological habitus’ (Czerniewicz and Brown 2013). This understanding provides a foundation for educators to support students’ capacities to make use of their own tools and technologies, as well as those they will encounter at university. Overall, tensions and perceived risks associated with openness may be exacerbated where students and staff are unsure of their institution’s position regarding the use of OER, open tools (such as blogs or social media), or OEP in general. In institutions without open education policies, academic staff may feel they are operating without a safety net. It is precisely because of the tensions and perceived risks associated with openness that individuals require critical approaches and strong organisational policies to support them. Critical Approaches to Openness and Open Educational Practices Openness, for both teachers and learners, is not a one-time commitment. It is a succession of personal, complex, and nuanced decisions. When using social media and other open tools, academic staff tend to manage personalprofessional boundaries with a keen awareness of their potential audiences, e.g. colleagues, students, family, friends, the wider public (Veletsianos 2016; Veletsianos and Stewart 2016). Such boundary-keeping involves considerable thought and maintenance work and questions arise regularly: Will I ‘friend’ my colleague/line manager/student? Will I tweet professionally/personally/both? Will I openly share my research/teaching materials/ideas? Thus, the use of open educational practices is ‘complex, personal, contextual, and continually negotiated’ (Cronin 2017), highlighting the need for critical approaches to openness to emerge. Critical approaches to openness and OEP are informed by critical theory, the core concern of which is power relations in society. Critical analyses of open education ask questions such as: Who defines openness? Who is included and who is excluded when education is ‘opened’, and in what ways? And, can open education initiatives, in practice, do the opposite of what they are intended to do? Edwards (2015) articulates a key question: ‘not simply whether education is more or less open, but what forms of openness are worthwhile and for whom; openness alone is not an educational virtue’ (p. 253). Gourlay (2015) notes a tendency toward idealism in many forms of open education, where the workings of systemic power and privilege around race, gender, culture, class, location, and sexuality are absent or ignored. Many Global South scholars have highlighted how alienation and epistemic inequality arise from narrow, Global North-centric conceptions of open access (Czerniewicz 2013; Nobes 2017; Piron 2017). Overall, optimistic or 156 Catherine Cronin naïve assumptions about open education serve to divert attention from structural inequalities, and so may inadvertently support rather than challenge them. In recent years, critical theorists have added nuance to, and sometimes challenged the conceptual frameworks underlying open educational practices by exploring how power and privilege operate in networks – and the implications for individuals, institutions, and society. One compelling avenue of critical analysis has highlighted the limitations of the network episteme itself (Mejias 2013; Light 2014). Mejias’s critical theorisation of networks includes the concept of the ‘paranode’, defined as that which fills the interstices between the nodes of a network and resists being assumed by the network: ‘it is only the outsides of the network where we can unthink or disidentify from the network, from the mainstream’ (Mejias 2011: 49). Light’s (2014) theory of disconnective practice asserts that disconnection is an active part of engagement in social networking sites. Engaging in paranodal or disconnective practice does not demand wholesale rejection of networks such as social media and social networks (an unrealistic option for most). Rather, it entails critical questioning of the terms of engagement within networks and enactment of creative and alternative modes of being within and beyond networks. The suppression of privacy lies at the heart of the business models of most digital and social media platforms – which rely directly on the appropriation of data for profit (Zuboff 2015; Srnicek 2016). The challenge for educators, and particularly for open educators, is clear. Many of the tools and platforms we use to engage in social connection and open educational practices have bias and inequality built into them – they are designed to allow and encourage forms of participation, and prevent others (Marwick, 2013; Gilliard and Culik 2016). Open Education Policy Considerations Several recent studies have suggested that institutional context, in the form of both policy and culture (Corrall and Pinfield 2014; Cox and Trotter 2016) are important factors for supporting open educational practices. Several open education researchers have used Margaret Archer’s (2003) social realist theory to analyse academics’ use of OER. Archer’s theory provides a useful framework within which to consider the various ways that context operates in individual academics’ choices regarding openness. Archer identifies three interdependent strata of reality: structure (e.g. institutional systems, policies), culture (e.g. norms, ideas, beliefs), and agency (individual freedom to act), the interrelations of which occur over time. The powers of structure and culture exist, but are activated only when human agents seek to act. Human reflexivity is the mechanism that mediates between structure and agency, moving from confronting constraints to elaborating a course of action (Archer 2003). Open education Open Education 157 researchers who have used Archer’s framework to analyse academics’ use of OER, for example, have found that the absence of open education policy can act as a constraint to OER awareness and use (Hodgkinson-Williams 2010; Cox and Trotter 2016). A similar constraint effect appears to apply with respect to OEP, as outlined above (see ‘Risks and challenges of open’). There remains a widespread lack of open education strategy and policy within higher education (van der Vaart 2013; Corrall and Pinfield 2014; Inamorato dos Santos et al. 2016). While most higher education institutions now have Open Access policies and repositories for storing and sharing scholarly outputs, far fewer have institutional policies that support the creation and sharing of OER for teaching or use of other open educational practices by teaching staff. Following are two examples of institutional open education policies regarding Intellectual Property (University of Cape Town) and Open Educational Resources (University of Edinburgh). Intellectual Property Policy Intellectual Property (IP) policies at higher education institutions typically state that all work arising from the course of employment remains the intellectual property of the institution. Copyright is one specific form of intellectual property protection, the operation of which prevents the open re-use and sharing of materials. The University of Cape Town (2011) IP policy is an example of institutional policy that intentionally supports open educational practice. The UCT IP policy explicitly states its commitment to the sharing of teaching materials as OER: ‘UCT supports the publication of materials under Creative Commons licences to promote the sharing of knowledge and the creation of OER’. In addition, the policy specifically makes clear that the copyright of course materials is retained by the creator, rather than by the university. UCT thus provides to staff and students a clear statement of the university’s position regarding the use, reuse, and sharing of the scholarly materials and course materials that they create, or co-create. All can be openly licensed and shared, thus facilitating open practice at individual, disciplinary, and institutional levels. Open Educational Resources Policy Open.Ed, the open education initiative at the University of Edinburgh (2018) provides another example of institutional policy regarding OER and OEP. Open.Ed includes an institutional OER policy as well as an array of supporting resources for learning and teaching using OER. The policy is rooted in a vision for OER that encompasses ‘education, research collections, enlightenment and civic mission’. The University of Edinburgh (2018) OER policy is explicit in its advocacy: ‘Creation of OER has big 158 Catherine Cronin benefits to individuals, educational institutions and society as a whole. If you are an educator it makes sense to create and use OER’. While the university’s OER policy focuses, in name and detail, on the creation and use of open educational resources, it also facilitates broader open educational practices. Campbell and Farley (2018), of the university’s OER Service, have highlighted a range of benefits of using OER for learning and teaching that move beyond a focus on licensing and resources: these include developing digital skills, student co-creation of open resources, creative and playful learning, fostering knowledge exchange, and contributing to equality and diversity. Cox and Trotter (2016) have argued that while some open education policies may act simply as a hygienic factor (i.e. a necessary but not sufficient variable in promoting OER or OEP), others might act as a motivating factor (i.e. incentivizing OER/OEP either among individual academics or the institution as a whole). The key determination in whether a policy acts as a hygienic or motivating factor depends on the type of institutional culture into which it is embedded. This means that the success of proposed open education policy interventions will be mediated by institutional culture – an institution’s existing policy structure and prevailing social culture, as well as academics’ individual agency. While openness may be a strategic objective at the institutional level, it cannot be mandated at the individual level. Individual members of staff and individual students must be supported and enabled to engage in open practice, but more importantly, supported in making their own decisions about whether and how to engage in open practice. Some students, based on personal experiences or circumstances, or their marginalised position within society, their community, or even their class, may not be willing to engage in OEP. Some members of staff, based on their personal experiences or circumstances, their employment situation, or their personal or professional values, may not be willing to engage in OEP. The benefits and risks of open practices are continually evolving and are always mediated by individuals in specific contexts. Ideally, higher education institutions should engage in positive but sensitive approaches to open practices. Conclusion The deceptively simple term ‘open’ hides a ‘reef of complexity’ (Hodgkinson-Williams and Gray 2009: 114), much of which depends on the particular context within which open education, OER, and OEP are considered. Critical approaches to openness enable us to focus on issues of participation, risk, and power. Open educators’ use of OEP is complex, personal, contextual, and continually negotiated within sometimes supportive, sometimes unsupportive institutional policy contexts and cultures. The European report ‘Opening up Education: A Support Framework for Higher Open Education 159 Education Institutions’ (Inamorato dos Santos et al. 2016) makes a strong case for the strategic ‘opening up of education by higher education institutions’ (p. 6) in order to address issues of vital local, national, and international importance such as enhanced workforce skills, access to job opportunities, community engagement, and personal growth of citizens. Open education is not only a tool for social change, however, but also of transforming higher education itself: Open Education … nourishes a participatory culture of learning, creating, sharing and cooperation and it is therefore a vital and natural training ground for current and future researchers and educators, turning them into confident users and designers of open approaches in research and higher education. (van der Vaart 2013: 52) The challenge for institutions is to engage with open education strategically, while also catering for an already broad range of institutional needs. Culture change is required. While higher education policy makers cannot effect such change, they can support, facilitate, and incentivise actions that encourage change in academic practices and culture (Corrall and Pinfield 2014). In conclusion, individual teachers and learners adopt open practices all the time, and these practices may turn out to be highly resilient and adaptive – both in learning, and in the world of work beyond. However, they are currently not being valued, recognized or rewarded in many higher education institutions. Institutions should recognize the complexities and risks of openness, as well as the benefits, and should create clear open education policies and practices. 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Previous chapters have shown how research into how teachers create, visualise and share designs has uncovered the complexity of this process and the need to understand design as a social practice. Alongside this research, there has been increasing interest in supporting design as a collaborative process which takes place within a disciplinary and institutional context. These frameworks presented here provide support for key decisions (e.g. what technologies to use, what activities to run), and emphasise different choices depending on their conceptual underpinnings and the scenarios in which they could used. Introduction Digital technologies offer a wealth of ways in which students can interact with rich multimedia resources, and mechanisms to communicate and collaborate. Despite this, technologies have not been used extensively; most Learning Management Systems (LMS) for example are primarily used as content repositories (Conole 2004). Academics say that they do not have the time or skills to effectively use technologies in their teaching. Learning Design has emerged over the last ten years or so as a means of addressing this gap between the promise and the reality of the use of digital technologies. Fundamentally Learning Design is about helping practitioners make pedagogically informed design decisions that make appropriate use of digital technologies (Conole 2013). At the heart of this are three facets: guiding the design process, providing a mechanism for visualising design (in essence an educational design language), and enabling sharing and discussing of designs. In recent years a number of frameworks for Learning Design have emerged. Frameworks are important because they can provide academics with a structured approach. Frameworks can be adopted in practice in Frameworks to Guide Practice 165 a number of ways: paper-based, process-based (workshops, for example), or system-based (i.e. as online planning tools). The focus of this chapter is on the first two; the resources section at the end of the chapter lists a number of tools. Ultimately the aim of Learning Design frameworks is to help academics create engaging learning interventions that enhance the learner experience. However, establishing a direct causal link between Learning Design and student learning outcomes is notoriously difficult (Brown, Conole and Beblavy 2019). No matter how good the Learning Design, teachers still matter most (Patrick n.d.). Some argue that there is a need to create a universal Learning Design pedagogical pattern language (Goodyear 2005; Laurillard 2012). However this chapter argues that we are nowhere near that stage and in reality may never be. The chapter describes a range of frameworks to guide learning design, the focus or theoretical lens of each is described, along with underpinning assumptions and how it can be used. Frameworks can be grouped as follows: • • • Frameworks for guiding the use of technology/media/materials (SAMR, SECTIONS, COACT) Workshop approaches aimed mainly at promoting general good practice. i.e. social constructivist assumptions (7Cs, 8LEM, ABC) Approaches based on a specific theory of learner engagement (ICAP) Frameworks for Guiding the Use of Technology/ Media/ Materials The SAMR Model The SAMR model consists of four levels of technology integration (Puentedura 2013; Romrell, Kidder and Wood 2014): • • • • Substitution: The technology provides a substitute for other learning activities without functional change. Augmentation: The technology provides a substitute for other learning activities but with functional improvements. Modification: The technology allows the learning activity to be redesigned. Redefinition: The technology allows for the creation of tasks that could not have been done without the use of the technology. It provides a framework for designers to create optimal learning experiences. Learning activities that fall within the substitution and augmentation classifications are said to enhance learning, while learning activities that fall within the modification and redefinition classifications are said to transform learning. Table 10.1 provides examples of how SAMR can be Table 10.1 Examples of use of SAMR Substitution Augmentation Modification Redefinition Making workshop materials available online or via a website. Providing resources in a variety of media to meet different needs, allowing participants to choose which works best for them. Participants brainstorming ideas together on a topic or potential solutions to a problem. Collation of resources on a topic using a collaborative tools such as Google Drive. Written content is made available online so that others can see it. Use of social media such as Twitter to be part of a broader community and use this to ask questions or gain access to interesting resources of relevance to their professional practice. Participants create their own Personal Learning Network using a variety of tools and work with others to collaborate, share ideas and resources, and reflect on their practice. Keeping a reflective blog of their practice and commenting on the blogs of other participants. Replacing a handwritten flipboard with written content. Participants collaboratively develop and comment on content in a collaborative tool such as Google Drive. An oral Flipped classroom Participants connect Following their presentation is techniques where with each other using discussions of the supplemented participants work social media so that materials they have with a Powerpoint through materials in they can talk to each reviewed in advance presentation. advance of a other about their of the face-to-face face-to-face session, understanding of the session, they use allows to work materials they are a collaborative tool through content on reviewing in advance such as Google Drive their own and then of the face-to-face to develop a set of bring questions to the session. joint questions to ask workshop for the facilitator in the clarification. face-to-face session. Concept mapping Concept maps can be Concept maps and All the group software is used annotated and links associated links are concept maps are to replace added. made available as collated and made a paper-based part of a blog post so available online, along concept map. that others can see with comments, and comment on. facilitators comment on the concept maps. Source: Adapted from Portnoy (2018). Content is made available on a blog so that others can review and comment. Frameworks to Guide Practice 167 used. Portnoy (2018) provides practical examples of how each of the four levels of SAMR can be implemented. The SECTIONS Framework Bates (2015) argues that the SECTIONS framework can be used to make effective decisions about the choice and use of media for teaching and learning. It stands for: Students, Ease of use, Costs, Teaching functions, Interaction, Organisational issues, Networking, and Security and privacy. Three issues are related to students when choosing media and technology: student demographics, access, and differences in how students learn. In terms of ease of use, both teachers and students need digital literacies to make effective use of digital technologies. There has been a dramatic reduction in the cost of media in recent years. Cost can be broken down into costs associated with development, delivery, maintenance, and overheads. A number of factors are associated with the teaching functions, such as the coherence of the materials (in terms of the mix of text, images, sound and video, the need for clear signalling, avoiding redundancy, and segmenting). Moore (1989) identifies three types of interaction: student-content, student-teacher, and student-student. Hillman, Willis and Gunawardena (1994) added a fourth; student-interface. Factors to consider in relation to organisational issues include: the way in which institutions structure teaching activities, the types of technologies available and the nature of the technologies that are supported. The rise of social media has led to the increasing importance of taking account of networking when designing learning interventions. Social media can supplement institutional support technologies such as a Learning Management System and enable students to be part of a global community of peers. Finally, it is important when designing to give due consideration to security and privacy issues. Bates lists the following questions to guide the design process: Who are the students? What are the desired learning outcomes? What instructional strategies will be used? What are the unique educational characteristics of each technology and how well do these match the learning and teaching requirements? What resources are available? Bates and Poole (2003) argue that the framework has a number of benefits. First, it will work in a variety of learning contexts. Second, it allows decisions to be taken at both a strategic, institution-wide level and at a tactical instructional level. Third, it gives equal attention to educational and operational issues. Fourth, it will identify critical differences between different media and technologies, this enabling an appropriate mix to be chosen for a given context. Fifth, it is easily understood, pragmatic and cost effective. Sixth, it will accommodate new developments in technology. 168 Gráinne Conole The COACT Framework Hibernia College in Dublin has developed the COACT pedagogical principles that they use as the basis for the development of their learning material (Breakwell and Cassidy 2013: 2): The COACT model is a theoretical framework that forms the basis of lesson design and structure, with a view to ensuring that higher-order learning and reflection is embedded within the learning process. The framework builds on Säljö’s hierarchy of learning (1979, 2004, 2010) and the ETL Project at the University of Edinburgh (Entwistle 2004). The definition of the word ‘CO-ACT’ is at the heart of the student learning experience: COACT = ‘CO’: ‘together’ + ‘ACT’: ‘to take action, do something’. This definition, upon which the framework is based, represents a mutually constructed, active approach to learning. The model breaks the learning experience down into five stages, which represent a progression from lower-order towards higher-order learning; from ‘seeking meaning’ through to interpretation, critical analysis and application of knowledge. The five stages are: • • • • • Concept: describe and contextualise main concepts Overview: summarise expectations, including objectives and outcomes Active discovery: facilitate active and collaborative discovery Critique: empower students to construct evidence-based criticisms Think: encourage reflection on relevance and importance of concepts. Breakwell and Cassidy (2013) compared student feedback and grades across two cohorts of graduate students enrolled on the same ITE programme for primary school graduate students teachers. Evaluation was positive and included increasing satisfaction with tutor performance. They argue that this finding suggests that a model of online content development and delivery that is specifically designed to encourage interaction, or COACTion, between learner and facilitator and between learner and learner, can enhance students’ impressions of tutor interaction and tutors’ teaching quality. Frameworks for Workshops The 7Cs of Learning Design Framework The 7Cs of Learning Design framework emerged from empirical data on how academics design learning interventions. Interviews were held and academics were asked: how they went about designing learning interventions, Frameworks to Guide Practice 169 where they got inspiration and guidance, and how they represented and shared their designs. Figure 10.1 shows the 7Cs framework. Each C has associated with it a set of resources and activities to guide the design process (Conole 2016). The first C, Conceptualise, is about creating a vision for the course or module being designed. It helps the teacher/designer think about the nature of the learners who are likely to take the course or module, their age range, diversity, characteristics, skills, perceptions, and aspirations. It is also about articulating the core principles associated with the course or module. The next four Cs are concerned with designing the resources and activities that the learners will engage with. The Create C helps the teacher/designer articulate what learning materials need to be created, whether these are textbased, interactive materials, podcasts or videos. In addition, it covers the use or repurposing of open educational resources. Finally, the teacher/ designer might also create some activities, which require the learners to create their own content. The Communicate C is concerned with methods to facilitate communication, between the learner and the tutor, the learner and their peers, and the broader community through social media. This might range from effective mechanisms for fostering discussion in a forum, through effective moderation, or looser communication through social media. Similarly, the Collaborate C is about fostering mechanisms to enable Vision Conceptualise Activities Create Communicate Collaborate Synthesis Combine Implementation Consolidate Figure 10.1 The 7 Cs of learning design Consider 170 Gráinne Conole collaboration or group work. The Consider C is concerned with ways in which reflection and demonstration of learning achievements can be promoted. Assessment might be diagnostic, formative, summative or peer reviewed. The Combine C enables the teacher/designer to step back and reflect on the design process to date and look at the design from different perspectives. Finally, the Consolidate C is about implementing the design in a real-life context and evaluating its effectiveness. When designing learning interventions, academics typically focus on content; the 7Cs framework enables them to think beyond content to the learning activities the students will engage with and the student experience. The 7Cs framework has been used in hundreds of workshops. Conole (2014) describes some early evaluations of the framework. This consisted of observations of the workshops and gathering of data from participants around four main questions: What three words best describe the workshop? What did you like? How could the workshop be improved? And what action plans would participants do as a result of participation? Overall the evaluation was positive, participants found the workshops engaging, useful, and inspiring. Participants stated that the workshop helped them to be more creative and innovative in their design practice. Working in teams means that participants can build on each other’s knowledge. More details of the evaluation and some illustrative quotes can be found in the paper. The 8LEM Framework The 8Learning Events Model (8LEM) describes eight key teaching and learning activities: receive, imitate, practice, explore, create, experiment, debate, and meta-learn (Verpoorten, Poumay and Leclercq 2005). It proposes a set of eight specific ways, referred to as Learning Events, of learning/teaching that a teacher can use to describe any point in the development and analysis of learning activities. Each of these considers what the students and teachers do. For example, for ‘explore’, the student activity would be ‘let me browse’ and the teacher activity would be ‘here are some possible resources’. The 8LEM framework can be used as a descriptive aid to analyse an existing teaching sequence or as a prescriptive aid for creating new teaching sequences. Receive refers to traditional didactic transmission of information such as reading or lecturing. Debate refers to learning through social interaction, collaboration, and discussion. Experiment refers to the student manipulating data to test a hypothesis. Create refers to the student creating something new, such as writing an essay. Explore refers to the student for example doing a literature review or searching for resources on the Internet. Practice refers to the student applying a theory and receiving feedback. Imitate refers to learning through observation and imitation. Finally, meta-learn refers to student self-reflection on their learning process. Frameworks to Guide Practice 171 Ulster University has adapted this to create the Hybrid Learning Model and produced a set of flash cards for each of the eight learning events. On one side the role of the teacher and the student is described, on the other a set of relevant verbs for the teacher and student roles are described. An example of one of the flashcards (experiment) is illustrated in Figure 10.2. It shows that the teacher’s focus is on providing the students with a micro-world to manipulate, whilst the student is using this to test their hypothesis. On the back of the flashcard are the verbs the teachers and students can used to facilitate experimentation. Villina et al. (2008) carried out an evaluation of the hybrid learning model with 51 academic staff, through interviews, focus groups and workshops. Four main benefits of using the model emerged, that it: helped articulate practice, supported self-reflection, acted as a design aid, and provided an awareness of the learner roles. Experiments Reactivity/Experimentation (Simulation, Testing, Transformation) Teacher Here is a micro-world you can manipulate Learner Let me check my hypothesis Practical, workshops, problem solving Experiments Teacher Assess Coach Construct/Produce/Create Critique Explain Justify Monitor Observe Perform Present Question React/Respond Learner Access Analyse Apply Construct/Produce/Create Decide Describe Design Evaluate Explain Explore Justify Monitor Observe Perform Predict Present Question Reflect/Respond Refine Reflect Report Research Resolve Review Figure 10.2 An example of an 8LEM flashcard (Source: Alan Masson http://addl.ulster.ac.uk/odl/hybridlearningmodel) 172 Gráinne Conole The ABC Learning Design Framework The ABC Learning Design framework is a workshop where participants work together in a game format to create a visual storyboard outlining the types and sequence of learning activities against a set of learning outcomes. It is based on six types of learning activities: acquisition, collaboration, discussion, investigation, practice and production (Laurillard 2012). Learning through acquisition is what learners are doing when they are listening to a lecture or podcast, or reading. Learning through collaboration is concerned with discussion, practice, and production. It is about the process of shared knowledge building. Learning through discussion gets the students to articulate their ideas and question and to challenge and respond to the ideas and questions from the teacher and/or their peers. Learning through investigation guides the student to explore, compare and critique texts or resources that reflect the concepts and ideas being taught. Learning through practice enables the students to adapt their actions to a task and use the feedback to improve their next actions. Learning through production enables the student to consolidate what they have learnt by articulating their current conceptual understanding and how they use it in practice. For each type of activity examples of how this can be achieved through conventional methods and use of digital technologies are provided. For example, for acquisition, conventional methods would include reading papers or listening to presentations, whereas acquisition through digital technologies might include reading websites, listening to podcasts or watching videos. Figure 10.3 shows the card for ‘production’. The front of the card indicates that ‘production’ is about enabling the students to consolidate what they have learnt, for example by writing an essay. The back of the card provides examples of how this can be achieved by conventional and digital methods. In the workshops, participants begin by writing a short description of the course that is being designed. They then agree on the balance of each type of learning activity and the mix of face-to-face and online activities. Finally, they think about the formative and summative assessment needed to achieve the learning outcomes. Young and Perovic (2016) list a number of benefits of the ABC framework. First, it can help develop richer learning designs for blended learning. Second, it can integrate strategic initiatives such as digital skills or employability. Third, it can connect learning outcomes/assessment to practice. Fourth, it can facilitate cross-team communication and sharing. A short video (http://blogs.ucl.ac.uk/digital-education/2015/ 04/09/abc-arena-blended-connected-curriculum-design/) of participants’ evaluation of the workshop is available. Participants state that the workshop was useful in mapping out objectives, and that it enabled them to see the Frameworks to Guide Practice 173 broader picture, understand what actual activities can be used, and identify opportunities for formative assessment. Learning Type: Production Learning through production is the way the teacher motivates the learner to consolidate what they have learned by articulating their current conceptual understanding and how they use it in practice Learning type: Production Conventional method Digital technology Producing articulations using: • Statements • Essays • Reports • Accounts • Designs • Performances • Artefacts Producing and storing digital documents Representations of designs Performances, artefacts Animations Models Resources Slideshows Photos Videos Blogs E-portfolios Figure 10.3 The production card (Source: Alan Masson http://addl.ulster.ac.uk/odl/hybridlearningmodel) Approaches Based on a Specific Theory of Learner Engagement The ICAP Framework ICAP stands for Interactive, Constructive, Active, and Passive. It defines cognitive engagement activities on the basis of students’ overt behaviours and proposes that engagement behaviours can be categorised and differentiated into one of four modes: Interactive, Constructive, Active, and Passive (Chi and Wylie 2014). Student engagement refers to whether students are: • • • Motivationally engaged (interest in content domain, pursue degree), Behaviourally engaged (attend classes, do homework: broad behaviour), Cognitively engaged (refers to use of strategies or to motivational constructs). 174 Gráinne Conole They propose four modes of behaviour: • • • • Attending mode (or passive mode): Students are paying attention, oriented toward and receiving instruction, but they are not doing anything else overtly, i.e. they are not producing anything. Examples include: listening to lectures without taking notes, watching videos, observing a demonstration, or reading a worked-out example. Manipulating behaviour (active mode): Students are paying attention and physically manipulating the instructional materials, but not adding any new information. Examples include: copying the solution from the board, underlining important sentences, agreeing in dialogue, selecting an option, moving a slider, measuring quantities, recording amount, pointing and gesturing, repeating definitions. Generating behaviours (constructive mode): Students are producing some additional information that may contain (incidentally or intentionally) small (or large) pieces of knowledge that is not in the instructional materials. Constructive does not mean that students are discovering knowledge/principles novel to the domain. Rather students are adding minute pieces of knowledge beyond what was presented in the instructional materials, literally. Cumulatively, they end up constructing an understanding. Examples include: drawing, explaining, posting, taking in one’s words, providing, comparing and contrasting, evaluating, predicting, reflecting, monitoring. Collaborative behaviour (interactive mode): Behaviour of working with a peer (commonly through dialogues): Taking turns, sharing attention. Sometimes this has been referred to as transactive dialogues. Examples include: explaining jointly, debating with a peer, or discussing. There are four knowledge processes: storing (new information), activating (relating to prior knowledge), linking (new information with prior knowledge) and inferring (a new piece of information). Chi and Wylie describe the ways in which the ICAP framework can be used to improve: lecturing, discussion, worksheet activities, co-constructive collaboration and using digital tools. Chi and Wylie (2014) argue that the ICAP hypothesis predicts that as activities move from passive to active to constructive to interactive, students undergo different knowledge-change processes and, as a result, learning will increase. This hypothesis has been validated through a number of classroom and laboratory studies. Two examples of how ICAP can map to presentation of information or communication are provided in Table 10.2. Conclusion The frameworks foreground a number of benefits from the use of digital technologies in learning. These include: freeing up time for face-to-face contact, Frameworks to Guide Practice 175 Table 10.2 Mapping Presentation Tools and Presentation of Information of Communication to the ICAP Framework ICAP level Tool Power Point Passive attentive Active manipulative Instructor presents information on PowerPoint Video Constructive generative Interactive co-generative Instructor can pause and ask questions Watching videos without taking any actions Pause and rewind or fast forward Watch video with others, pause and discuss issues ICAP level Passive attentive Active manipulative Constructive generative Interactive co-generative Tool Discussion forum Reading other people’s posts Answering others’ questions or comments Co-editing an answer to a question with peers Reading other people’s posts Summarising a post Answering questions or elaborating on comments with explanations Revising a post with new information Google docs Re-revising other people’s posts allowing students to study at times which are most conducive to their learning, providing opportunities for self-testing to reinforce factual knowledge, facilitating the collection of and feedback on assignments, encouraging peer support and greater participation from students, enabling effective learning within and across different groups of learners, and fostering the development of key skills and attributes, including collaborative skills, autonomous learning and digital literacy. The frameworks provide academics with a different set of ways of thinking about design. Masterman (Chapter 7) argues that design tools support teachers’ current design practice and simulate them to innovate. SAMR focuses on progressive integration of digital technologies. SECTIONS also focuses on the choices and use of media, in relation to a set of questions guiding the design process. COACT focuses on how different types of learning, such as knowledge acquisition, communication and reflection can be instantiated through both face-to-face teaching and use of technologies. The 7C provides a practical set of resources and activities to guide the design process. 176 Gráinne Conole 8LEM foregrounds the eight ways in which learners can learn and considers the associated activities of each for both teachers and learners. ABC adopts a similar approach but is based on six types of learning activities derived from Laurillard (2012). ICAP adopts a different approach focusing on learner engagement. The choice of which framework to use is dependent on a number of factors: the academics’ pedagogical beliefs, their level of expertise in design and in the use of technologies, and the perceived ease of use. It was stated at the beginning of this chapter that Learning Design has three facets: guidance, visualization (which the frameworks described in this chapter can provide), and sharing. Masterman (Chapter 7) highlights the importance of informal conversations about teaching and learning, which Thomson and Trigwell (2016) refer to as ‘corridor conversations’. Masterman’s research on teachers’ design practice paints a complex, composite picture of design practice that has to do with the proclivities of individual practitioners and a range of intellectual and sociocultural influences, along with the nature of the process itself. She also argues that a number of barriers to effective design remain, including the challenge of facilitating thinking processes that may be both tacit and idiosyncratic to individual teachers, disciplines and institutions. The frameworks described in this chapter provide mechanisms to help academics rethink their design practice to create pedagogically informed design decisions that make appropriate use of digital technologies. Given the gap between the promise and reality of digital technologies for learning and teaching described in the introduction, such frameworks are important in terms of providing support for academics to enable them to create engaging learning interventions that enhance the student experience. Whilst some of the frameworks are purely conceptual, others have been incorporated into Learning Design tools. Resources The 7Cs of Learning Design workshop: The workshop outline and associated resources and activities is available at www.slideshare.net/GrainneConole/learning-design-workshop-2017 The 8LEM resources from Ulster University http://cetl.ulster.ac.uk/elearning/documents/About-HLM.pdf The Learning Activity Management System (LAMS): www.lamsinternational.com/ The Integrated Learning Design Environment (ILDE): https://ilde.upf.edu/about/ The Learning Design Support Environment (LDSE): https://sites.google.com/a/lkl.ac.uk/ldse/ Frameworks to Guide Practice 177 References Bates, A.W. (2015). Teaching in a digital age: Guidelines for designing teaching and learning. BC Open Textbooks, Retrieved from https://opentextbc.ca/teachinginadigitalage/ Bates, A.W., and Poole, G. (2003). Effective teaching with technology in higher education: Foundations for success. San Francisco: John Wiley and Sons. Breakwell, N., and Cassidy, D. (2013). 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The Hybrid Learning Model – a framework for teaching and learning practice, International Journal of Emerging Technologies in Learning, 3, 12–17. Young, C., and Perovic, N. (2016). Arena Blended Connected (ABC) curriculum design. Paper presented at European Distance and E-Learning Network Conference, Budapest. Part 3 Influences and Futures Chapter 11 Design Principles for Learning with Mobile Devices Agnes Kukulska-Hulme and John Traxler Editors’ Introduction In this chapter the context for design is unashamedly shaped by technology. Designing for learning with mobiles has had to adapt to a context of ubiquitous technologies that is personal, informal and thoroughly learnercentred. Mobile technologies have already been appropriated for social and informal activities and for accessing and creating a rich supply of online digital resources. Education is no longer designed for a group of learners situated in a defined context; rather, teachers face the challenge of designing for individuals who engage in their own learning, through their own devices, from their own settings, and on their own terms. Institutions must consider not only what technologies to provide but how to incorporate learners’ own devices, experiences, and practices. The design principles presented in this chapter take account of the characteristics of mobile technologies and consider the social context into which they are appropriated. These principles are prescient and likely to have much wider applicability beyond mobile learning as personalized, situated, authentic and informal learning becomes the norm. Introduction Since the earlier versions of this chapter, there has been considerable change in the dynamics between technology, pedagogy, and society. This is epitomised in the shift from mobile technologies being relatively scarce, fragile, difficult to use, expensive, and institutionally provided at the turn of the century to now being easy to use, cheap, powerful, robust, universal, and personal. This change is responsible for opening up a new reality whereby individuals and communities can produce, share, valorise, transform, discuss, and discard ideas, images, information, and opinions on an unprecedented global scale. These activities were previously largely the prerogative of professionals and the institutions of formal learning but are now facilitated by freely available Web 2.0 platforms and resources accessed by everyone’s 182 Agnes Kukulska-Hulme and John Traxler mobile technologies. Learners can often act as each other’s teachers, without professional or institutional endorsement or involvement. The authority, agency, credibility and substance of teaching and learning are thus transformed, even if the implications are not yet well understood. Furthermore, the two decades we are describing have seen the end of the political will and financial capacity that resourced the first generation of institutional ‘mobile learning’ innovations and initiatives. Over the same period, increased awareness of mobile learning achievements and activity in growing markets for mobile technologies has led to the emergence of sustainable business models around their educational use. These factors have meant that the nature of design for learning with mobiles, previously based around institutional and professional procurement, development, control, and deployment of resources and focussed on the artefacts of education, has widened to embrace the exploitation of the abundance of online digital resources (content, communities and tools). The concept of design for learning with mobiles has been reconfigured to mean the design of educational experiences that require the orchestration of these resources. The earlier design activities were informed by rigorous research practices and findings but these were anchored in a very specific set of contexts; subsequent design activity is taking place in a much more fluid and unbounded context with fewer stable points of reference. Our focus in this chapter is on the nature of design for learning with mobiles within formal education. Design cannot take place, however, without recognising the potentially enormous impact of everyday uses of mobile phones and other personal technologies on the experiences and expectations of learners before and whilst they go through formal education. It must also recognize the impact of everyday informal mobile learning as an ever-present alternative to formal education, at least from the learners’ point of view. Since around the start of the century, personal ownership of mobile technologies has accelerated to the point where many societies are seriously considering how education will be affected by these developments. At a micro level, teachers are faced with the challenge of designing learning that takes account of the ubiquity of these devices and associated social practices. Learning with mobile devices is increasingly shaped by rapid technological, economic and socio-cultural change that is, however, at odds with the more stately pace of evolving institutional pedagogy. Learning with mobiles has gradually become imbued with multiple meanings, some emphasizing the physical mobility of learners; some focusing on the affordances of mobile technology; some emphasizing connections between contexts or settings; and some noting the primacy of access to digital resources (Kukulska-Hulme et al. 2011). Pegrum (2014) explains that the ‘learning experience’ becomes mobile ‘as learners shift between contexts that feed directly into their unfolding learning’ (p. 19). Other meanings of learning with mobiles favour more holistic, sociological or ecological interpretations of Design Principles for Learning with Mobiles 183 a phenomenon ill-suited to be contained within the spatial, institutional, and cultural boundaries that were largely respected by previous generations of educational technology (Traxler 2010). It is our aim to simplify this increasing complexity by crystallizing some principles that educators can turn to when faced with the challenge of designing for learners equipped with mobile technologies and wanting more adaptable or personally engaging ways of learning aligned to their experiences and expectations of using mobiles for anything and everything in their daily lives. In this chapter, we explore the proposition that the foundations of design as currently understood are shifting rapidly and that the process of design must be reviewed and reconsidered. In the next section, we further elaborate how design is impacted by technology choices that may be made at institutional level or by learners (and to a lesser extent teachers) making use of personal mobile devices for learning. Design in Relation to Learning Technology The role of design in developing learning with technology has long been problematic. Education has always been parasitic on the technologies of other sectors, with educational institutions appropriating commercial and corporate hardware and software technologies, and these have shaped how learning activities could be designed. We have to recognise that this appropriation has in fact served two purposes; first, to provide learners with a stable, consistent and standardised platform for learning (and therefore a more equitable one, at least while they were learning within the confines of the institution), but second, to protect the institutional business model by constructing a pay-wall around institutional assets to protect intellectual property. One reason to review the process of design is the fact that educational institutions must now appropriate personal technologies – the mobile phone, as well as social networks, immersive worlds and micro-blogging – partly due to student demand for mobile access and partly because these tools facilitate interactions that can support educational ends. This challenges the institutional business model but also the locus of control, in terms of technology and in terms of pedagogy. Learners have increasing experience of using the plethora of free and familiar web 2.0 technologies that enable publication and sharing with diverse (global) audiences, without the need for any institutional endorsement. These phenomena pose a challenge to those teachers who cannot imagine how such learner activities and competencies can be incorporated into their teaching. They also challenge those whose favoured pedagogical approach precludes giving learners more choice over their environments for learning and more control over knowledge production. To incorporate learners’ own uses of mobile devices and social networks into teaching practices is to concede to the locus of control being increasingly located with the learners. 184 Agnes Kukulska-Hulme and John Traxler In the context of design, this is not just a change in focus. Selwyn (2007) has argued that the design of education technologies has previously been almost wholly dependent on one set of commercial interests, those of the makers and vendors of large-scale digital installations and infrastructure; if he is right, we can assume that the technologies embody a specific commercial or corporate ideology. Educational institutions, when they appropriate these technologies, may be in broad consensus with the ideology designed into them or may attempt to overwrite it. Now, in attempting to appropriate personal technologies for teaching and learning, they must also consider the ideologies built into these personal and social technologies – which may be adopted and adapted differently by their users, but in any case come from a different set of commercial interests, those of the digital recreation, entertainment, publishing and media corporates. The space available for educational design becomes much more complex and fragmentary, the constraints become more complex and fluid, and the commitments (or resistances) of individual students to the ideologies inherent in their digital devices become more significant. Much thinking over the last decade has focused on the impact and significance of social and cultural change on the nature of learning with mobile devices (e.g. Pachler et al. 2010; Rasul 2011; McCauley et al. 2017). It has focused, more specifically, on the impact and significance of social and cultural changes associated with widespread ownership of powerful connected personal devices, on the ethics issues, the evaluation methodologies, and the institutional policies relating to mobile learning. This has in part been a reaction, or an antidote, to the hegemony of the disciplines of psychology, education, computer science, and information systems design that were the foundational disciplines of early mobile learning research and to the dominance of the e-learning legacy in framing the agenda for the mobile learning research community. We believe that it is vital to review and reconsider design for learning with mobiles. There are several reasons for this, but fundamentally, we are at a tipping point in the relations between education and society, as the use of digital technologies has become universal, social, intrusive, ubiquitous, and pervasive – conspicuous occasionally by their absence where not so long ago they might have been conspicuous by their presence. Mobile technologies are at the heart of these changing relations. Policy-makers and practitioners, and their managers, as well as learners and the wider public, are now familiar with mobile technologies, and with the idea that they are available for learning. Researchers and developers employed by institutions can continue to make imaginative and innovative propositions for mobile learning, but learners’ everyday mobile practices are a strong influence shaping the reception of these designs. In recent years, Apple’s App Store and similar services have proved incredibly successful in building the apps economy (Genachowski 2010), Design Principles for Learning with Mobiles 185 a business model based on selling high volumes of inexpensive educational applications direct to learners. This has accelerated the growth of learning with mobiles that can sustainably deliver to the so-called ‘long tail’: the idea that digital technology could service smaller and smaller niche interests and still turn a profit, resulting in the ultimate ‘mass customisation’ of digital learning. In this new landscape, the direction of learning with mobiles is no longer guided exclusively by research or evidence-based practice. It is increasingly guided by learners’ everyday choices, particularly their personal consumption and informal social media practices, which shape their expectations and impact their investment in formal learning. The technologies and practices shape not only how learning takes place but also what learners are able to know. These reflections have led us to examine the nature of design for learning with mobiles and how it might be reconceived. At the heart of this chapter is the relationship between design for learning that plays to the strengths of mobile technologies, and the design of aspects of learning such as content, activities and communication in the context of a technology that has become universal. We must recognise that issues of infrastructure, networking and connectivity are of diminishing significance as a barrier or determinant. Furthermore, design principles must recognise the challenges of formal institutional learning situated in societies permeated by people supporting each other’s learning through mobile devices. Taken together, these considerations lead to a set of design principles that we propose and briefly elaborate in this chapter. Design ‘For’ Learning This section focuses on the ways in which design for learning (as defined by Beetham and Sharpe in the Introduction to this volume) can exploit the affordances or characteristics of mobile technologies, whilst recognising how these are shaped by popular and social appropriation. These technologies offer unique possibilities to support designs for learning where access, inclusion, opportunity and participation are priorities, although like many technologies they also have the potential to exclude some people, which must be weighed up in the process of planning and design. Mobile devices support learning that is personalized, situated, authentic and informal. This kind of learning typically takes place in practice-based settings, often characterized by unpredictability and ad hoc problem-solving. It challenges the notion that design must be intentional and systematic, planned in advance, and represented explicitly. It is more difficult to design intentionally for learning that will be spontaneous and informal; indeed perhaps it is paradoxical. Mobile technologies do, however, have affordances that support these types of learning, and can be used to integrate it with more formal learning opportunities. For example, mobile devices are suited 186 Agnes Kukulska-Hulme and John Traxler to spontaneous reflection and self-evaluation; these could be elements in an e-portfolio shared with a tutor. By personalized learning, we mean learning that recognizes diversity, difference, and individuality in the ways that learning is developed, delivered, and supported. Personalized learning defined in this way includes learning that recognizes different learning preferences and approaches, and social, cognitive and physical difference and diversity (e.g. autistic people, see RodríguezFórtiz, Fernández-López and Rodríguez 2011). It also recognizes that cultural and social exclusion from access to formal education may be compensated by a personalized approach whereby learning can be done by individuals in their home or in a private place. Personalized learning systems can potentially recognise the context and history of each individual learner (and perhaps their relationships to other learners) and deliver learning to each learner when and where they want it. Prototypes exist for learning designed on the basis of knowing aspects such as where the learner is, how long they have been there, where they were before, who else was learning nearby, their likely schedule and itinerary, their social networks and communities, their progress and preferences (see e.g. Yau 2011; Rubino et al. 2015). Furthermore, the design of the learning delivered by the system can evolve with the learner and their learning. Learners can also be involved in designing their own location-based mobile learning (Edmonds and Smith 2017). By situated learning, we mean learning that takes place in the course of activity, in appropriate and meaningful contexts (Lave and Wenger 1991). The idea was formed by looking at people learning in communities as apprentices by a process of increased participation. It can however be extended to learning in the field, in the hospital ward, or in the workshop (see Ellaway, Chapter 12). Mobile learning can be designed to support this context-specific and immediate situated learning (e.g. Kenny et al. 2009). Key design considerations are access to situation-relevant content, situated support, and planning how learners will capture and share their experience either in situ or afterwards. Whilst personalization emphasizes learner freedom and choice, situated learning may appear to be tied to a location or situation that the learner did not choose. In actual fact the learning design may allow plenty of choice as to the situations in which they learn. By authentic learning, we mean learning that involves real-world problems and projects that are relevant and interesting to the learner. It means that learning should be based around authentic tasks, that students should be engaged in exploration and inquiry, that students should have opportunities for social discourse, and that ample resources should be available to them as they pursue meaningful problems. Mobile learning enables these conditions for authentic learning to be met, allowing learning tasks designed around content creation, data capture, location-awareness and collaborative working in real-world settings (e.g. Hine et al. 2004). Design Principles for Learning with Mobiles 187 Informal learning occurs spontaneously and independently of formal education – but in mobile learning the term is frequently used to describe forms of learning where the technology supports a specific activity that has been designed in advance with a particular user group in mind. Various informal learning experiences have been trialled in art galleries and museums (Tselios et al. 2008; Vavoula et al. 2009); these are often experimental projects that are imaginative in terms of their epistemological and pedagogical approaches as much as in the technology that is used. In previous editions, this terminology (situated, personalized, authentic, informal), was useful to distance mobile learning from institutional e-learning or formal learning. We used to say that mobile learning took learners out of the classroom and off the campus. Now, however, we find some of these distinctions breaking down. Learners are using their phones and tablets to support their learning in everyday life, on and off campus. Their activities can be personalized, authentic, and informal, even in the formal situations of lecture theatres and seminar rooms – though they are often constrained to be otherwise. Their social connections and interactions with their environment are constantly evolving. The use of augmented and virtual reality is adding new depths and layers to the experience of authentic, situated learning. Much of the potential became apparent as technological and pedagogical expertise built up. Early case studies in Kukulska-Hulme and Traxler (2005) made it clear that progress in design for learning with mobile technologies was often hampered by the state of the technologies, and by the diversity and confusion of educational objectives. Both aspects remain a challenge to the development of mobile learning. The technologies are both easier to use in terms of intuitive interface designs and more complex in terms of multifunctionality and ever smarter features. Reliable and cheap connectivity is still a challenge in some environments. Educational objectives become clearer through classroom experimentation and pilot projects, yet at the same time they become more diverse as technological and social innovations add new layers of complexity. Mobile devices can deliver learning specifically designed for learners’ wider social and economic contexts. In particular, the widespread ownership of mobile phones allows educators to design for groups often underrepresented in formal learning, because mobile devices are perceived by these groups (for example, disengaged learners, or learners who have limited access to desktop computers) as a more accessible, motivating or convenient way to take part in learning (Unterfrauner, Marschalek and Fabian 2010). However, the public funds to make this happen are no longer readily available. The reduction in political will and financial resources in many countries since 2008 has led to a dramatic curtailment of state intervention to support educational innovation, inclusion and social mobility, so there is correspondingly less mobile learning of this kind taking place. 188 Agnes Kukulska-Hulme and John Traxler In the same timeframe, students in formal education have come under a range of growing pressures – of time, money, resources, and conflicting/ competing roles. Learning designed around mobile technologies can allow these students to exploit small amounts of time and space for learning, to work asynchronously with other students on projects and discussions, and to maximise contact and support from tutors (Yau and Joy 2009). Finally, mobile technologies present opportunities to design learning for students who might have difficulty fulfilling their potential with other e-learning technologies; one example is learners with dyslexia who may benefit from self-organisation features, handy access to reference tools, being able to hear a speech rendition of a printed document (using various text-to-speech apps and built-in mobile device functionality), and voicebased command interaction (for example Siri on the iPhone). There has been a transition away from the design of technology and learning to address specific cognitive or physiological characteristics towards the aspirations of inclusive design for all; in the mobile device economy, there are commercial reasons for device designers and app designers to design for all, if only to increase their customer base. All of these remarks equally apply to the design for learning that exploits resources generated locally and to the design for learning that orchestrates or curates resources discovered externally. The latter is however still exploratory and experimental in learning with mobiles but represents a movement across educational technology as a whole (Higgins 2011). Design ‘Of’ Learning Now that we have some sense of how the design of learning with mobiles is constrained in various ways, we can consider the design process itself. In our view, there are four key designs to consider, namely, design of content, of activities, of communities, and of communication. This is however an arbitrary distinction, since, for example learners may design content as an educational activity. Also these categories are not strictly equivalent. Within the kinds of constraints we discuss, designers are in control of the outcome of designing content (when they themselves design it) but not of activities or communications, especially when learning takes place outside of classrooms or closed learning environments. Design of Content In terms of the ability to absorb and interact with educational content, including academic texts and interactive media, the use of small devices may initially seem unpromising when compared to desktop computers. This is increasingly less true. Not only does the technology continue to improve but more importantly so does the acceptance and appropriation of it. By looking at how the Design Principles for Learning with Mobiles 189 technologies are changing our approach to content, however, we can come to a better understanding of what will be appropriate on mobile devices. Our focus here is not on the content itself, but rather on ways of thinking about content. The following aspects are important to consider: • • • • • • • • Learner-created content: if students are expected to construct some of the content as part of their learning, this can be done in various locations and mobile devices can facilitate it. It is personal and specific to context, including time and place. Learner-curated content: learners, and indeed their teachers, should seek, share and critique existing external content (and apps, podcasts, etc.) as a way of accessing and exploiting the richness and diversity of material available, not only matching their various personal preferences but also building insights into their own learning. Personalized content: learners can receive, assemble, share and carry around personally useful and appropriate resources. Up-to-date content: updates may be more easily delivered to mobile devices when learners are highly mobile and would not regularly access a desktop computer. Timed or scheduled content: learners can engage with content frequently, repetitively or periodically using a mobile device without overhead or inconvenience. Prioritized content: content can be made available on mobile devices in such a way as to prioritise it or draw attention to it through notifications; this may be a useful deliberate teaching strategy. Content in multiple media: e.g. if listening is preferred over reading, delivering audio via a personal mobile device can be engaging and convenient, and allows learners to exploit different situations such as routine walking and commuting, seamlessly. Flexible content: providing mobile access to learning materials and resources, as a more flexible alternative to desktop content. Design of Communities Learners may prefer to learn in groups or may be constrained to learn in groups. These groups may be constituted from their fellow formal learners, inside the institutional mobile-friendly VLE, or they may be constituted from informal learners with a shared interest, inside, for example a Facebook or WhatsApp group. Either way, these learners may be creating content or just consuming it, and teachers or designers will have a role and responsibility for designing the community, its interactions and its behaviour. Experience of m-moderating (moderating of mobile seminars) is still very limited (JISC 2008; Traxler and Leach 2006) but could in principle follow the same trajectory as e-moderating, moving from administrative support and reacting 190 Agnes Kukulska-Hulme and John Traxler to individual queries, to purely pastoral support and then creating expectations and processes that learners can find and share solutions amongst themselves (Salmon 2011). There is considerable anecdotal evidence of micro-blogging with Twitter forming the basis for un-moderated personal learning networks, and increasing use of social networks within formal learning (Minocha 2009), accessed via the mobile web or mobile apps. These are by their nature transient and difficult to document but a search of Twitter reveals hash-tags from archaeology through mathematics to zoology, with a similar range of dedicated groups on Facebook, predominantly accessed via mobile devices (Sengupta 2012). We should add that they are also difficult to research for both methodological and ethical reasons. Design of Activities The third area to consider is the design of learning activities. According to Naismith et al. (2005), mobile technologies can be used in the design of seven different types of learning, or categories of activity: • • • • • • • Behaviourist learning, where quick feedback or reinforcement can be facilitated by mobile devices because they are always to hand. Constructivist learning, where learners build new concepts perhaps through engaging with their physical and social environment. Situated learning, where learners take a mobile device into an educationally relevant real-world location and learn from that setting. Collaborative learning, where mobile devices are an essential means of communication and electronic information sharing for learners in groups outside their educational institution. Informal and lifelong learning, possibly unstructured or opportunistic, driven by personal curiosity, chance encounters and the stimulus of the environment, where mobile devices can provide ready-to-hand access to information and communication, or record learning experiences for future review. Continuous learning, uninterrupted by changes in location or situation. Supported learning, where mobile devices monitor progress, check schedules and dates, review and manage progress, receive errata etc. We should also now add: • • Connectivist learning, as exemplified by the early MOOCs, exploiting rich broadband connectivity and massive enrolments but with variable participation to exploit and manage the wisdom-of-the-crowd. Private learning, exemplified by girls and women accessing content and communities in spite of cultural and social exclusion, pressures and barriers. Design Principles for Learning with Mobiles • 191 Embodied learning, emphasizing the role of movement through space and using gesture in learning, where the mobile device can be part of the gesture (Lee 2015). These different types of learning extend the typology offered in Mayes (Chapter 1). At a more detailed level, there are particular tasks that are well suited to mobile learning, for example activities that involve data collection; testing, consolidation of learning; personal reflection and skills acquisition. There is always scope to develop learning activities that combine the use of mobile devices with other learning resources; for example this can be done by providing a commentary accessed on a personal device as a means of orientation within a set of learning materials on another medium. Mobile devices can also be used as a way to facilitate remote, on the move participation in online activities that might be continued or completed at a desktop computer. Mobile technologies are highly suited to learning that has variously been described as informal, opportunistic and spontaneous (Bull et al. 2004) and as disruptive (Sharples 2003) or intrusive. This is a major challenge for the design of formal learning, since opportunistic learning and learning to a pre-designed format are so different in nature and intent. Moreover, in a society permeated by mobile technologies and practices, mobile learning can no longer be seen as the exception. If all learning is (potentially) mobile, and a mobile society is always learning, then the agenda for learning design must change its focus. This is a different formulation of our opening remarks. Learning with mobile devices is not a new variant of e-learning, enlivening and extending an otherwise stable curriculum and pedagogy. Mobile devices are involved in the wider, social transformation of how people, not just learners, acquire and distribute information, images, ideas and opinions, and of how learning is redefined (Traxler 2010). Design of Communication This is an exciting but problematic aspect of design, due to residual worries about the extent and reliability of coverage and the costs of connectivity that might be incurred by learners, but mostly due to the contested nature of agency and control. Mobile communication takes place at the intersection of the educational tasks as determined by lecturers and teachers and the recreational, personal and social uses as determined by learners themselves, with the balance relentlessly moving towards the latter. Within these constraints, mobile devices can support: • Spontaneous communication and collaboration, e.g. one-to-one or one-to-many by SMS; by sending a message to a forum or blog while travelling; by micro-blogging (e.g. Twitter) 192 • • Agnes Kukulska-Hulme and John Traxler Beaming of stored information and images from device to device (e.g. via Bluetooth) Portable sound-recording, voice-recording, photos and video clips that are used in communication Most phones support voice, SMS, e-mail, micro-blogging, instant messaging, social networks and web-based conferencing. Mobile learning communities are rapidly growing and diversifying. From the perspective of designing educational experiences mediated by mobiles, we must remember that phonespace (Townsend 2002: 95), the space inhabited by the users of mobiles and traversed by their communications, unlike cyberspace is characterised by a blizzard of different modes, those mentioned above being used for a multitude of purposes, and mixed in with real-time, real-world communications and people. Design Principles In this chapter, we have outlined key considerations in order to clarify how the designs for learning with mobiles differ from current practice in design for e-learning. To design for mobile learning, first of all we need to be clear about the unique characteristics and nature of mobile learning. Earlier ideas and principles for designing mobile learning had drawn on those of e-learning (so for example methods such as ADDIE had been coopted by some organisations seeking a documented and structured approach (Berking et al. 2012)), but we have argued for an ever increasing divergence of e-learning and learning with mobiles. Next, a rationale must be constructed around how the formal learning will be more personal, situated, authentic or informal than would be possible by other available means. Content, communities, activities, tools and communication should then be aligned with the proposed rationale. Thirdly, we need to recognise popular familiarity with mobile devices, not only to consume information, images, ideas and opinions, but also to produce, share, transform and unsettle them. This takes us beyond the adoption of user-centred design practices in educational design and into the co-creation of education itself. It also challenges the primacy, authority and boundaries of formal education as the locus of educational design and blurs the skills of designers, teachers, and possibly librarians. We believe that design principles for mobile learning should be based on two key observations. First, that mobile technologies are ubiquitous, diverse, personal, social, and changeable, not uniform, consistent, or institutional. Second, that learners’ expectations about educational uses of mobile technologies may be coming from outside formal or institutional education, as part of experiences driven by curiosity, personal enquiry, individual recreation and perceptions about utility (see also Charitonos and Kukulska-Hulme 2017). Design Principles for Learning with Mobiles 193 This chapter specifically addresses designing for learning with mobiles but the following recommendations barely mention ‘mobiles’. This is in keeping with our argument that mobile technologies are no longer a simple and discrete technology but a pervasive social phenomenon, and perhaps also starting from learners and their world rather that fitting them to a predefined and specific technology solution or educational technology formulation. On this basis, we propose the following principles: 1. Start with learners; explore their aspirations, diversity, limitations; their experiences and expectations of learning, working, interacting, inclusion, access, and technology. 2. Be prepared to trial and discard activities frequently as technologies evolve and are adapted and appropriated by the societies and cultures in which our learners are embedded. 3. Accept ‘good enough for now’ and obtain feedback from learners; the sooner learners can you tell about your design in their world, the sooner you improve it. Think about the fact that education is probably only a small part of their digital experiences, even though it is a big part of yours, and how this shapes expectations. 4. Look at what you want in terms of content, communities and tools and ask how much you can curate, collate and adapt before you ask how much you, or your institution, need to create and construct. 5. Recognize that learning activities designed by you will be played out differently as learners engage with them outside the classroom and the campus; take account of environmental factors that may impact on mobile learning and unexpected learning outcomes. 6. Design to help learners contribute as well as consume mobile content, to develop their transferable critical and creative skills. Seek opportunities for creating, prototyping, participation, and evaluation with learners in their world. 7. Focus on designing the education experience; consider the function of the educational artefact as part of that experience. 8. Consider assessments and assignments within the wider context of the educational experience and the learners’ worlds, and how assessments and assignments exploiting context and location, focussing on authentic and situated experiences, can make connections between the two. 9. Look for added-value from mobile, e.g. opportunities for contingent learning, situated learning, authentic learning, personalised learning; for capturing data and image, for using location and context. 10. Recognise that phonespace is not an impoverished version of cyberspace but just as rich, far more fluid and much more permeable to ‘real’ space, and that each has its own norms in terms of vocabulary, formality, courtesy, interaction, responses. 194 Agnes Kukulska-Hulme and John Traxler Conclusions The increasing power and diversity of mobile devices supports ever more powerful and diverse learning designs. As we argued earlier, mobile learning research has historically had a narrow base, drawing mainly on psychology, computer science and education, and it has developed its agenda, including its approaches to design, as a continuation of, or reaction to, the perceived triumphs and limitations of e-learning. This made sense in the 1990s when the expense and expertise required for e-learning and then m-learning needed the resource base of educational institutions. It led to an acceptance of institutions, their authority, their agency and their practices as the preferred focus for the design, deployment and delivery of mobile learning. As mobile devices became widespread, familiar and popular, and as access to fast and free web services, social networks and shared resources begins to shape learning practices, this made less and less sense. 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Ellaway Editors’ Introduction In this chapter, design is examined within the context of designing for learning along a continuum of professional development. Using examples of signature pedagogies in professional education (such as problem-based learning and simulations), Ellaway explains how the development of distinctive professional knowledge, skills, and attitudes should underpin designs for learning and assessment. Taking each of these in turn, Ellaway draws on existing conceptual frameworks and examples of technology-rich learning scenarios to show how design can be appropriate to the overall aims of the learning situation and to our understanding of how professionals develop. As the use of technologies in practice settings increases, so designs for learning need to incorporate technologies in order to prepare professionals for practice, whether that be responsible ‘digital professionalism’ or performance-based learning analytics. The challenge for professional educators in the digital age is to formulate designs which connect with the technologies and tools found in real world practice, and to share their approaches and solutions beyond their speciality and across a broad range of professional education practice. Introduction Professions are defined by the specific tasks, responsibilities, and privileges that set them apart from other occupations and the cultures they weave around them (Bines and Watson 1992; Eraut 1994). Not only do professionals need to acquire extensive knowledge and skills as part of their training, they are also socialized to a professional culture. Concepts of practice, expertise, culture, and competence are central to the philosophy and conduct of professional education, as is developing a sense of accountability to the chosen profession and to society as a whole. This sense of accountability is reflected in a profession’s relationships with its client base, which often means that the primary beneficiaries of professional education are the clients and societies students will go on to serve as professionals rather than the 198 Rachel H. Ellaway students themselves. This reinforces the focus of professional education on service and performance rather than on self-actualization and personal development. Other characteristics of professional education include narrow and relatively well-defined post-qualification vectors, a focus on workplace learning, external regulation and accreditation (typically from professional societies or regulatory authorities), and a core dependence on practitioner educators. Another key difference is that most professions require ongoing education, to maintain competence over a career, to respond to technical and social changes related to practice, and if they seek to change their scope of practice. Moreover, while training for a professional license may be the most focused period of education most professionals engage in, many professions require postgraduate training for specialist practice, and continuing professional development training throughout their careers, in order to maintain or alter their scope of practice. Understanding the nature of pedagogy for professional education in the digital age requires an understanding of how its drivers and affordances are entangled with the tools and systems it uses. Although there are differences between professional education models (such as those in law, healthcare, social work, teaching, accountancy, engineering, and so on), and differences between specialties (such as medicine or nursing, and the specializations within these professions), there are recurring designs for learning, many of which involve the use of digital technologies and systems. In this chapter, I will explore professional development followed by a review of different designs for professional learning and assessment. Professional Development Although most professionals develop some level of individuality and selfdetermination in their practice, professional education is often highly structured in terms of predetermined stages of professional development that converge on professional practice. For instance, Miller’s framework for assessing clinical competence builds from ‘knows’, through ‘knows how’, and then ‘shows how’, to end up at ‘does’ (what occurs in practice) (Miller 1990). Dreyfus and Dreyfus (2005) set out a more detailed professional development continuum from novice to expert, each step having different implications for learning design: 1 2 Novices focus on acquiring the basic models and schemas that underpin professional practice with little exposure to the complexity of real-world practice. Designs for learning at this level are predominantly based on knowledge acquisition using didactic or exploratory methods and simple but frequent testing of learners’ developing knowledge. Advanced beginners focus on applying and further developing the schemas they acquired as novices using predefined and simplified practice Designs for Professional Learning 3 4 5 199 scenarios with low levels of risk and complexity and limited learner autonomy. Activities need to be rich in feedback on learner performance to ensure they build their schemas robustly and reliably. Competent practitioners focus on acquiring and practicing the application of their new skills through being exposed to real-world practice situations (although still in carefully controlled settings) and being encouraged to identify connections between different aspects of their developing practice. Proficient practitioners focus on developing more advanced skills and a deeper conceptual understanding of their practice that is increasingly independent, self-correcting, and accommodating of new knowledge and skills. Learning at this level is reflective, focused on developing an increasingly mature practice model within the context of independent practice. Experts engage in preconscious processing allowing them to react intuitively, rapidly, and consistently to complex problems. Despite these abilities, learning remains an essential part of maintaining expertise as new case examples continue to refine their knowledgebase, reasoning, and pattern-recognition skills. Other models of progression, such as those used in competency-based professional programs, may express even more detail, for instance setting out a series of required learning milestones (tasks to be performed at a certain level of competence at a certain developmental stage) and professional activities (signature tasks undertaken as part of professional practice) (ten Cate 2013). Although these models differ in terms of granularity and where they start and end, they are all based on a continuum of professional development. While different professions negotiate this continuum in different ways and often use different terminology in doing so, we can map out a generic model of professional education in terms of its stages, signature activities, and the ways in which teaching principles change over time – see Figure 12.1. Activities for Professional Learning and Assessment There are many individual activities undertaken at particular points along the professional learning continuum. We can differentiate between activities that focus on developing knowledge, skills, and attitudes, and those that focus on assessment. Design for Learning Professional Knowledge While many designs for professional knowledge acquisition are part of the standard higher education repertoire (lectures, tutorials, readings, etc.), there are those that are more specific to professional education. Problem-based 200 Rachel H. Ellaway Figure 12.1 Continuum of professional education. Learning activities and teaching principles change according to the stage of training learning (PBL) is one example (Savin–Baden and Wilkie 2007; van Berkel et al. 2010). PBL can be conducted in different ways, but in its most typical format involves small groups of students being given a case that involves one or more problems they need to solve. The case and its problem(s) are crafted to address particular learning objectives using triggers to stimulate student learning. For instance, a case may present a routine task with particular challenges such as diagnosing a patient with a learning disability, or an unusual task such as drawing up a contract around a merger between two companies. The activity starts with students discussing the problem, identifying topics for exploration through independent research, and then synthesizing their existing and newly acquired knowledge to come up with a solution to the problem. This design means that PBL groups are not taught so much as facilitated; the teacher is not expected to impart knowledge, they are to facilitate discussion and problem solving. Although variations on the PBL design are used at different levels of professional development, the complexity and ambiguity of the problems encountered should reflect the increasing expertise of those involved, tending to be less structured and more complex and ambiguous for more senior learners. PBL as a learning design is constructive Designs for Professional Learning 201 (learners develop individual interpretations of the subject material), selfdirected (learners take an active role in planning, monitoring, and evaluating their learning), collaborative (learners share tasks and learn from each other), and contextual (learning is anchored in meaningful contexts, problems, challenges) (Dolmans et al. 2005). Despite the focus of PBL on small group faceto-face activities, PBL learning designs typically engage a range of different technologies; learners share their ideas and develop them online (even when working face-to-face), their research is largely conducted online, and their cases or problems may also be presented online (Ellaway et al. 2015). Another signature knowledge-focused activity in professional education is that of evidence-based practice. Where, until recently, professional education involved learners remembering and recalling large volumes of knowledge, contemporary approaches increasingly focus on accessing and appraising knowledge in the moment typically accessed electronically through computers and smartphones. While there is still a core body of knowledge that learners still need to remember, there is now widespread integration of reference materials and knowledge aids into many activities in professional education. This has in turn focused attention on learners developing appropriate search and critical appraisal skills to ensure the quality of the material they weave in to their professional activities. These typically include developing informatics skills using bibliographic databases and other tools. A third area of knowledge-related activity focuses on testing and feedback. Although online quizzes are used in many areas of modern education, they play a particular role in professional education in helping learners to identify strengths and weaknesses in their learning. This ability to self-calibrate and self-diagnose learning strengths and weaknesses encourages self-directed learning. This is not just a matter of facts; quizzes in professional education often go beyond recall to ask questions that require application of knowledge, typically in the form of problem solving and decision-making. While these kinds of quizzes can be expressed as multiple-choice questions, they may also take the form of interactive decision trees (sometimes called ‘virtual patients’) where different decisions affect the subsequent outcomes and options to act. These ‘choose your own adventure’ activity designs simulate the kinds of decisions and consequences faced by practitioners in real life (Ellaway and Davies 2011). Design for Learning Professional Skills While knowledge is an essential part of professional education, professionals need more than knowledge, they need to be able to apply their knowledge in practice. This is reflected in Eraut’s assertion that the ‘distinction between propositional knowledge which underpins or enables professional action and the practical know-how … is inherent in the action itself and cannot be separated from it’ (1994: 15). Skills tend to be very profession-specific but they 202 Rachel H. Ellaway all involve procedural competencies backed up by in-the-moment problem solving, communication, and other applied skills. To that end, behaviorist designs for learning are commonly employed in developing the repertoire of skills and procedures associated with professional practice (see Mayes, Chapter 1). Basic skills are typically learned in relative isolation from each other (such as interviewing members of the public or handling a dental drill) before being integrated in increasingly realistic simulation activities and eventually integrated and performed in real-world practice. Learners’ competence in performing these skills must be tested at each stage and any problems identified and corrected before progressing to the next stage. Skills typically need to be modelled by teachers who have themselves mastered the skill and who can deconstruct its component tasks and identify and correct deficits in learner performance. Learning a skill typically starts with a somewhat abstract and simplified representation of the tasks involved followed by increasingly complex models that are closer and closer approximations to the work of qualified professionals (Ellaway et al. 2009), again mirroring different stages in the development of expertise. Giving and receiving feedback is essential to teaching professional skills; learners need to know what they are doing right, what they are doing wrong, and what they need to focus on to improve their performance (Boud and Molloy 2013). Feedback may be given in the moment or at the end of a performance. However, as much as feedback is a central component, learners often need help in making effective use of feedback, and teachers need help in giving useful feedback (Sargeant et al. 2015). Although students may not fully act as practitioners before qualification (typically for safety and liability reasons), they still need to experience and learn from situations that are appropriate to their level of training and reflect future practice. One key example of this is Schön’s conception of the practicum: ‘a setting designed for the task of learning a practice. In a context that approximates a practice world, students learn … by undertaking projects that simulate and simplify practice’ (1987: 37). Although a practicum should ‘usually fall short of real-world practice’ and be ‘relatively free of the pressures, distractions and risks of the real’ it should push students to develop competence and confidence in acting as an independent practitioner. Examples of longitudinal immersive learning designs include internships, work placements, and clerkships. Simulation is often used in professional education ‘to replace or amplify real experiences with guided experiences that evoke or replicate substantial aspects of the real-world in a fully interactive manner’ (Gaba 2004: i2). The use of simulation is well established in many professions, including architecture, healthcare, business, law, and the military. Simulation (activities for learning or rehearsing professional skills) and simulators (the tools and technologies used in simulation activities) can take many forms. For Designs for Professional Learning 203 instance, forms of simulation range from practicing simple manual tasks to dealing with complex multi-participant scenarios involving actors and simulator technologies such as patient mannequins, economic models, or flight simulators. Simulation activities may be about developing procedural and technical skills but they can also be used for developing communication and other non-technical skills. Essential characteristics of simulation include providing learner- rather than client-focused experiences, ensuring participant safety (particularly in otherwise dangerous situations such as mid-air emergencies or complex surgical procedures), providing meaningful and constructive feedback on learner performance, supporting repetitive practice, providing variation in the difficulty and focus of scenarios, enabling learners to try multiple strategies in controlled learning environments, and supporting defined outcomes and benchmarks (Issenberg et al. 2005). Although simulation has been a part of professional education for centuries, developments in physical and computer engineering have expanded the repertoire to include immersive virtual environments, robots, and video games (Aldrich 2005; Quinn 2005). Simulation activities typically start with a short briefing that assigns roles and sets the scene for the task but otherwise gives few clues to what is about to happen. The scenario itself is then started, often involving an operator who can direct the progress of the scenario in reaction to what the participants do. Once the scenario has been completed both the team and the individuals within it are given debriefing (in role addressing practice issues) and feedback (out of role addressing learning issues). Learners in these team-based activities may take on different roles, often outside their profession. For instance, medical students in a simulated resuscitation scenario may play the roles of triage nurse or respiratory therapist or they may be played by actors. Although there is a great interest in multiprofessional education (when students from different professions learn side by side) and interprofessional education (when students from different professions learn from and about each other) (Barr and Low 2013), both introduce significant new challenges, not least of which are finding appropriate designs for learning for students from mixed curricula and divergent educational and professional cultures. Educational games have long been used in some areas of professional education (finance, military) they are increasingly entering mainstream practice. As designs for professional learning, games share many features with simulation, such as using make-believe, requiring learners to try tactics and strategies, and presenting increasing complexity and difficulty in response to increasing learner competence. There are also key differences between simulation and games, in particular the latter’s use of competition (with self or others) and elements of chance or luck (Ellaway 2016). Games and ‘gamification’ (the use of gaming elements in non-gaming activities) may employ traditional media (board and card game elements) as well as digital media (computer games and puzzles). 204 Rachel H. Ellaway Fidelity and validity are common concepts used in teaching skills-based activities, particularly in and around simulation and games; fidelity in terms of the perceived relationship to practice, validity in terms of how consistently and efficiently the activity can support the development of the required skills. While validity is always required, fidelity is less important unless directly tied to validity. For instance, the use of low fidelity simulators can produce equivalent learning outcomes to more expensive and higher fidelity simulators (Reznick 1993). While educational technologies can be used in simulation and games and may require learners to develop mastery in using them, skill development also includes the technologies learners will use in future practice. For instance, architectural trainees learn how to use 3D CAD software and healthcare trainees learn how to use an electronic medical record (EMR) system. Other tools, such as checklists and decision algorithms, can also be used as the basis for learning designs by creating activities that guide learners through narrative versions of the algorithms or testing their ability to make the appropriate diagnoses and decisions. Learning how to use these ‘medium as message’ technologies may initially require a degree of simulation and simplification (as with any other skill) with a progressive shift to using a fully featured version of the technology. Design for Learning Professional Attitudes Learners seeking to join a profession are required to do far more than learn about being a practitioner, they must adopt both their chosen profession’s culture and ways of working (Lave and Wenger 1991). Socializing learners within a profession involves the negotiation and acquisition of broader and often quite different forms of knowledge from those required in non-professional education contexts (Lincoln et al. 1997; Harter and Kirby 2004). These are often wrapped up in the portmanteau concept of ‘professionalism’ although quite what this means varies according to the professional context. For instance, while many professions require integrity, honesty, and the ability to deal with difficult situations, some professions (such as in healthcare) also focus on compassion and empathy while more commercial professions (such as business and law) may emphasize entrepreneurism or determination, and others (such as architecture or design) may emphasize creativity and originality. Designs for learning professional attitudes may be explicit, such as teaching cases dealing with ethical issues, or a discursive seminar on the needs of marginalized or disadvantaged individuals or communities. Designs may also be implicit or tacit, expressed in non-instructional ways such as through codes of conduct or role modelling by teachers. The concept of the hidden curriculum is key here although it takes on a slightly different meaning from the model used in mainstream higher education (Margolis 2001). The Designs for Professional Learning 205 hidden curriculum in professional education includes both the attitudes modelled by teachers and the tensions between the values and attitudes expected of learners and those they experience in practice (Hafferty 1998). The hidden curriculum may also be realized by adding attitudinal elements to relatively generic learning designs. For instance, even the most generic of designs, the lecture, may be framed in the context of professionalism where non-attendance is seen as a deficit in professionalism. While technology may or may not be used in developing professional attitudes, there is one area where it is inescapable; that of ‘digital professionalism’. Professionals should ‘maintain the capacity for deliberate, ethical, and accountable practice when using digital media’ (Ellaway et al. 2015: 844). This includes using social media responsibly, being mindful of one’s responsibilities as a professional regarding confidentiality and integrity, and modelling and maintaining professional standards in an increasingly digital age. Designs for Professional Assessment Although the focus of this chapter and this book is on learning, there are many designs in professional education that focus on assessment. Indeed, assessment is often intimately wrapped up with professional education for learning, for progression, and for meeting societal requirements regarding the quality of those entering a profession. Some professional assessment is formative (helping learners and their teachers identify areas for improvement) and to that end quizzes and feedback are key examples. Other forms of assessment are summative in nature (informing decisions about fitness to practice) reflected for instance in licensing exams for independent professional practice. However, as much as sum/form is important, a more critical factor in describing assessment for professional education is its duration. There are many single episode professional assessment designs, some of them generic such as written exams, others more profession-specific such as key feature questions (KFQs – Farmer and Page 2005) and script concordance tests (SCTs – Fournier et al. 2008). For professions with a more applied skill base, it is common to use task-based assessment designs. These designs usually take the form of a series of discrete structured simulation tasks organized as stations through which learners rotate. A trained rater observes and completes a checklist of the required component skills, knowledge, and attitudes, a global rating of the performance, or a combination of the two. Interestingly, although checklists can help to focus and standardize rater responses, global ratings tend to better reflect future performance (Hodges 2013). A key example of this kind of design is the objective standardized clinical exam (Newble 2004). In contrast, longitudinal professional assessment designs involve collecting multiple observations or other performance data over a set period of time. For instance, a nursing student undertaking a three-month practicum 206 Rachel H. Ellaway will interact with multiple teachers, other healthcare professionals, peers, and patients, each of whom may record their observations and impressions of his performance. These recorded observations may be reviewed by the student and her supervisors during the practicum to provide formative feedback or the data may be used for summative purposes in evaluating whether she has successfully completed the practicum. Other data such as activity logs (who did what, when, and to what level), simulation data (such as from a business investment game), artefacts (such as project reports or presentations), critical event analyses, personal and professional development plans, written case reports, progress tests, professional CVs, and personal reflective diary entries. Longitudinal assessment designs depend on robust tracking tools, often some form of portfolio (Buckley et al. 2009). However, while portfolios in generic higher education tend to be student owned and controlled, professional portfolios are more often directly integrated into teaching and assessment, they are more structured, with higher levels of teacher scrutiny and control. Collecting and storing information about learning is not in and of itself particularly instructive, it is what is done with this information that matters. For instance, professional development portfolios often include reflective exercises exploring what the materials in a learner’s portfolio mean to them. However, given that much of the information is granular and quantitative in nature, tools such as dashboards are used by learners to track their own progress and by teachers to track the progress of groups of learners. To that end learning analytics is a growing part of professional education, particularly where large volumes of longitudinal performance data need to be analyzed. As an example, the widespread move to adopt competency-based approaches in health professional education is based in defining discrete and tightly specified professional activities that learners need to master. Progression to mastery is defined in terms of discrete stages (for instance; observes, assists teacher, does with teacher present, does independently) (Frank et al. 2010) and performance measures within a milestone can be tracked using learning curves to map aggregate performance at a certain level over time (Pusic et al. 2015). The use of analytics tools such as these tends to blur the distinction between learning, teaching, and assessment, thereby emphasizing a professional learning continuum rather than episodic and discrete steps and kinds of activity. Whether formative or summative, episodic or longitudinal, reliability and validity are important parts of professional assessment (and learning). Reliability is considered in terms of how consistently a test can discriminate between good and bad learners. Validity in professional assessment reflects Kane’s four dimensions of validity: how performance is scored, how scores are generalized to reflect performance in the test setting, how scores map to future practice, and how the scores are used to inform decisions about the learner, test, or program (Kane 2006). Extrapolating current practice to Designs for Professional Learning 207 future practice and making decisions about learner performance are essential aspects of professional assessment, which in turn reflects its focus on service and performance, and on narrow well-defined but also well-policed routes in to professional practice. Discussion Design for learning in professional education can be rich, nuanced, and both convergent and divergent with design for learning in higher education as a whole. Despite this rich ecology of educational practices, the use of design for learning as a conceptual tool and the more explicit use of learning designs in professional education remains somewhat limited, not least because of the technological associations of design for learning and the need to conduct much if not most of professional education in embodied rather than virtual spaces (Ellaway et al. 2008). Dalziel and Dalziel (2011) noted the reluctance of educators in professional disciplines to accept generic learning design templates, preferring instead to use more abstract forms of design for learning that reflect their educational philosophies. The focus on embodied practice in professional education might be expected to lead to a lower uptake of technology-based designs for learning than in other educational domains. However, not only is there significant use of generic technologies such as learning management systems and portfolios, there are also many domain- or discipline-specific forms being used, such as simulators and performance learning analytics. The use of technologies used in practice settings in the classroom is increasingly blurring the transition into practice. Indeed, it is arguable that the primacy of embodied practice tends to favor those technologies that align with real-world practice over those that diverge from it. This is reflected in the idea of ‘economies of presence’, essentially attaching more or less value to different kinds of presence, both physical and digital. Designs for learning that provide the most appropriate forms and economies of presence also tend to be the ones that provide greater value in professional education. We can understand this as part of a broader concept of convergence on professional practice; designs for learning and the tools they employ need to converge on structured and quality assured entry in to professional practice. Designs that diverge from this have little meaningful place in professional education. To that end, a recurring challenge to professional educators is how to balance connections with real-world practice while minimizing the complexities and stressors of that practice to meet the needs of their learners. Each of the five stages of Dreyfus and Dreyfus’ expertise model (2005) can be considered as an activity system in its own right as well as a part of a broader activity system. The unification of activities into activity systems can help us to better understand learning environments and the activities that are realized within them. In the context of professional education, 208 Rachel H. Ellaway with its fundamental basis in real-world practice, activity systems are encapsulated in the twin concepts of practicum and simulation. Technology use in professional education also helps us to reflect on some of the less frequently considered organizing principles for professional education in the digital age. For instance, it helps to clarify what aspects of professional education depend on its materialities in terms of its many technologies and tools (Fenwick 2014). This in turn raises issues of dependence on these materialities and the extent to which learners and teachers should develop a more deliberate approach to when they do or do not use these technologies. Another example is the intersection between professional socialization and technology use. The concept of what it takes to be a digital professional is still emerging but, given the reputational risk to professions of learners and teachers failing to use social technologies appropriately, this is an issue we anticipate forming a more central part of professional development in future. Finally, the costs of technologies, the people needed to support them, and the time taken to use them shape what tools are used in professional education, which in turn shapes the designs for learning that are used and those that are not. For instance, developing profession- and program-specific tools can be prohibitively expensive, leading to the use of cheaper more generic tools that are sufficient rather than ideal. I would argue therefore that, although they are rarely considered in the research literature, compromise and economics are key issues in design for learning in professional education. Conclusions The general discourse in educational technology research has understandably tended to focus on generic approaches in higher education (Laurillard 2002; Jochems et al. 2004) while professional education has tended to develop its own approaches and solutions (Ellaway and Masters 2008). As a result, perhaps also reflecting professional education’s inherent exclusivity, what is normative within a professional education discipline is often unknown or misunderstood by those outside it. It is to be hoped, therefore, that the emerging design for learning discourse will facilitate better understanding of the nature and importance of profession-specific designs and facilitate a more aligned and proximal approach to mediated teaching, learning, and assessment across the professional education spectrum as a whole. 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Nuts and bolts of entrustable professional activities, The Journal of Graduate Medical Education, 5(1), 157–158. van Berkel, H., Scherpbier, A., Hillen, H., & van der Vleuten, C. (Eds.). (2010). Lessons from problem–based learning. Oxford, UK: Oxford University Press. Chapter 13 Datafication of Education A Critical Approach to Emerging Analytics Technologies and Practices Ben Williamson Editors’ Introduction Williamson’s chapter starts from the premise that the use of data in education is intensifying, diversifying, and proliferating. Data relating to learners and their interactions with digital systems is abundantly available in educational organizations. The promise is that learner data can be used to inform pedagogy and curriculum design, for example through individual profiling, realtime assessment, or adaptive teaching strategies. However, Williamson raises a number of issues and concerns. These include: a narrow focus on measurable outcomes for individual learners; an amplification of the effects of institutional rankings and league tables; a tendency to hard-wire certain assumptions into algorithms – and then to create student identities around these assumptions; and the enhanced influence of commercial companies on the education system. Williamson calls for a grounded critique of data in education that goes beyond the merely instrumental to consider the social, ethical, cultural and political consequences. Introduction The availability of ‘data’ through digital information systems has become a defining topic and problem of recent times. For companies, access to digital data is a source of ‘business intelligence’ used to make efficiency savings and gather profit. Governments treat data as insight into people’s behaviours and wider social trends to inform policymaking. Data are also used for more controversial purposes. Recently, Facebook user data was exploited by the data analytics consultancy Cambridge Analytica to ‘micro-target’ political advertising to voters in the 2016 EU referendum and US election, while data breaches have become a common occurrence. As political, commercial and public awareness has grown around data controversies, researchers have begun to develop better understanding of the consequences and ethics of data, analytics, algorithms, and artificial intelligence (AI Now 2018). Datafication of Education 213 Within the field of education and pedagogy, however, data remain relatively under-researched, although significant conceptual and technical development in learning analytics, adaptive learning software, and artificial intelligence has emerged from academia, industry, consultancies, think tanks, and government sources alike (Williamson 2017). Data can be used as insight into processes of learning, the effects of pedagogies, curriculum design, and learning gain over time, especially as researchers in ‘education data science’ develop increasingly fine-tuned technologies and methods (Cope and Kalantzis 2015; Lang et al. 2017; Piety, Hickey and Bishop 2014). Moreover, data may be used to predict outcomes, detect risks, and to ‘personalize’ the education system around individuals’ needs (Bulger 2016). A lucrative industry in data-driven educational technologies has thrived on claims that data signify a shift from standardized tests to adaptive, ‘real-time’ assessment technologies, and from school census data to individualized tracking and profiling (Boninger and Molnar 2016). Such data-centred processes are affecting the early years of education, schooling, and higher education alike, often with unintended and perverse consequences (Bradbury and Roberts-Holmes 2018; Manolev, Sullivan and Slee 2018; Roberts-Mahoney, Means and Garrison 2016; Selwyn 2015; Williamson 2018). This chapter offers a critical introduction to the ‘datafication’ of education to encourage better understanding and debate about data in relation to pedagogy and curriculum, in particular by drawing attention to the history, epistemology, social consequences, cultural contexts, politics and ethics of datafication. Datafication can be understood as ways of seeing, understanding and engaging with the world through digital data (Gray 2016). However, this definition of datafication glosses over a number of complexities. The rest of this chapter offers definitional clarity, applied to some particular issues and problems in education, to help us make better sense of the consequences of datafication in education, and therefore inform better decisions about learning, teaching, and curriculum design. Data History Datafication has a long history, going back at least as far as efforts during the industrial revolution to capture statistical knowledge of the state, society and its population, and then to use that knowledge to come up with better institutions and practices of management and intervention (Ambrose 2015; Beer 2016). State power has long been tied to statistical practices such as archiving, census-taking, indexing, cataloguing, and record-keeping. By turning people into numbers in an increasingly statistical society, it became possible for governments, bureaucracies and public agencies to sort them into categories and population segments, to derive ‘norms’ and ‘regularities’ from the aggregated numbers and then evaluate and judge people against them. ‘This is power through numerical knowledge of the people being 214 Ben Williamson governed, their properties, and the patterns of social life’ (Beer 2016: 44), and allows individuals and social groups to be identified for intervention. In terms of education, Michel Foucault (1991: 147) powerfully articulated how schools and classrooms act as ‘learning machines’ deploying numbers and tabulations for ‘supervising, heirarchizing, rewarding’ as well as timetabling, grouping and controlling students. The development of school management systems at the end of the twentieth century created new kinds of digital learning machines for capturing, calculating and categorizing information about students. Writing at about the same time, though in the context of higher education, Jean-Francois Lyotard (1984: 4) noted that the ‘miniaturization and commercialization of machines is already changing the way in which learning is acquired, classified, made available and exploited.’ He anticipated any knowledge not translatable into ‘quantities of information’ would eventually be abandoned, while only knowledge ‘produced in order to be sold’ and consumed would continue to hold ‘value’ (Lyotard 1984: 4). Lyotard presciently foresaw that digital information would become a highly valuable commodity for learning in increasingly computerized societies, as many commercial producers of online courses have since exploited. The datafication of education, then, is part of a series of historical developments in statistics, state power, quantification, computation, and valuation culminating with the expansion and intensification of digital information systems and ‘big data’. Although there are clear continuities from the past to the present, the current version of datafication through big data also represents a rupture with the past. Early historical developments in the datafication of education have already evolved into the global data-driven performance comparisons of international large-scale assessments (LSAs) such the OECD’s PISA tests (Sellar, Thompson and Rutkowski 2017). The assessment data that dominates LSAs is sampled, collected at long periodic intervals, and slow to analyze. New digital datafication technologies such as ‘learning analytics’, by contrast, harvest data in real-time as students complete tasks, enable highspeed automated analysis and feedback or adaptivity, and can capture data from all participants rather than a sample (Lang et al. 2017). They also allow individuals to be compared against each other and with aggregated norms calculated in massive datasets, rather than the broad-brush comparison of national systems enabled by LSAs. Nonetheless, with the emphasis on quantification, the construction of norms, and methods of comparison, big data techniques such as learning analytics importantly need to be understood as part of a long history of measurement in education (Gorur, Sellar and Steiner-Khamsi 2019). Just as LSAs have standardized schooling around the demands of the tests, the danger of using big data to drive curriculum design is that teaching and learning methods become confined to those that produce measurable outcomes such as credentialized achievements or metrics of individual progress. Datafication of Education 215 Technicalities of Data In technical terms, datafication is a process of transforming diverse processes, qualities, actions and phenomena into forms that are machinereadable by digital technologies (Kitchin 2014a). Datafication allows things, relationships, events, and processes to be examined for patterns and insights, often today using technical processes such as data analytics and machine learning. These data-analysing systems rely on complex algorithms to join up and make sense out of thousands or millions of individual data points. As a technical concept, big data has attained popular currency in recent years, but not all digital data are big data. Rob Kitchin (2014a: 28) has usefully contrasted big data with ‘small data’. By small data he means collections of data that are sampled, slow to collect, and of relatively limited variety and volume. Even large-scale assessment data would fall into the category of small data by this definition. By contrast, big data is very large in volume, exhaustive in terms of its capture of whole populations rather than samples, is collected at speed and continuously, has very high variety, and enables very fine-grained identification. However, as Kitchin (2014a) also notes, significant current efforts are underway to ‘scale up’ small data into larger datasets, making them reusable and linking them with other data to make them amenable to analysis. So when we talk of datafication we are usually talking about different kinds of data, although the contemporary promise is that scaled-up, linked or big data can be analyzed to generate intelligence and insight that was previously unavailable. The technical and informational qualities of datafication in education are significant because they determine how students, teachers, schools, universities, and whole systems can be measured, inspected, evaluated and judged (Anagnostopoulos, Rutledge and Jacobsen 2013). The technicalities of an international large-scale computer assessment, for example, consist of software loaded on computer hardware in schools; network connections that allow the student responses to flow to a central repository; spreadsheets for tabulating the results as data; servers and storage facilities for holding the data; security and encryption services to keep it safe; analytic software for processing the results; visualization programs for communicating it, and so on. At each stage, the technicalities of datafication influence what data it is possible to collect and examine, and thus what results may be generated. In UK higher education current efforts are being made to link data captured from institutions’ learning management systems, electronic reading lists, and learning analytics platforms to other large-scale governmental and administrative datasets, largely through new infrastructural arrangements to make data interoperable across different platforms (Williamson 2018). Connecting individual-level learning data to other longitudinal data sources opens up new analytical and interpretive possibilities to isolate courses and institutions that contribute to boosting students’ ‘learning gain’ or to later 216 Ben Williamson graduate outcomes, careers, and earnings. The potential here is to empower HE institutions and students with insights into the courses and providers that perform best in terms of measurable learning progress and preparing students for high-income graduate roles. Students can make choices based on such data, while providers can use the data to target under-performing areas of the curriculum for improvement, or to change the pedagogy to make it more engaging for students. Politically, however, it also allows institutions and courses to be ranked on metrics such as graduate earnings, thereby shifting the priorities of the sector to focus on career-readiness and the demands of labour markets. As such, the data infrastructure and technicalities of both big and linked data in HE are making possible analyses that are producing intelligence on university performance. The interoperable digital system is not merely technical, but a network of political technologies to shape how HE is perceived and practiced. Data Epistemologies Thinking epistemologically about datafication means thinking about what we can know from data. Datafication rests on the assumption that patterns and relationships contained within datasets inherently produce meaningful, objective and insightful knowledge about complex phenomena (boyd and Crawford 2012). That is to say, the data captured in a computer really represent what is ‘out there’, untouched by human interference and independent of the measuring process. From this epistemological standpoint, data are ‘raw’, impartial, detached, and can be taken at face value as reflecting the truth about the world (Gitelman and Jackson 2013). As Rob Kitchin (2014b: 4) has argued, this empiricist epistemology assumes that through the ‘application of agnostic data analytics the data can speak for themselves free of human bias or framing.’ It has given rise to boosterist claims that ever-bigger datasets offer better, less biased, and more insightful knowledge than has ever been available before. For critics, however, this empiricist epistemology is flawed because all data are always actively produced, framed, and sampled; data are not natural and essential elements that are abstracted from the world in neutral and objective ways (Gillespie 2014). Data may be big, but at the same time they often tend to erase complexities, context, meanings, and causal factors, so producing highly partial and incomplete renderings of reality (Lupton 2015). Different interpretations may also be possible from the same data. In one recent example, 29 teams of data scientists reached different conclusions from their analysis of the same dataset, suggesting that significant variation in the results of analyses of complex data may be difficult to avoid and that subjective analytic choices influence research results (Silberzahn et al. 2018). Within education the empiricist epistemology can be found in the many commercial providers of data analytics services sold to schools and Datafication of Education 217 universities. The education company Pearson, for example, has produced several reports on the potential of digital data to reveal new generalizable truths about education, learning and assessment (DiCerbo and Behrens 2014; Hill and Barber 2014). Through vast data collection and analysis efforts, it is presumed, a clearer picture of the realities of learning – even at the scale of the measured individual student – will become available. Those data can then be employed to inform future interventions, to target teaching, and to personalize the learning experience, so changing the ‘reality’ of the student’s experience based on quantitative interpretations. As such, datafication is not just an epistemological matter, but ontologically significant because it has the potential to bring different version of reality into being (Ruppert 2012). To offer a simple example from the datafication of education, when a child enters a database, she is chopped up into data points, turned into bits, aggregated with other data, evaluated against norms, and so on. Over time, as more data becomes available from the student’s activity, it becomes possible to generate a data profile of her skills, progress, abilities, and knowledge – often known as a ‘student model’ – which can be compared with regularities in massive datasets. Sometimes these profiles are called ‘data doubles’, as if they represent a digital shadow version of the profiled individual. But, importantly, the data can always be called up and arranged differently – data doubles are really data multiples (Finn 2016). When one of these data multiples gets selected as the student model, it becomes a make-believe substitution which can then be used to inform how the teacher approaches that student, or how an algorithmically personalized learning program assigns her tasks. As such, the substitute profile built out of the data takes an active ontological role in shaping the ‘real life’ of the student – a process that could always have been done otherwise, with different real world results. The data play a part in ‘making up’ the student. Of course, all curricula are addressed to an ideal student that they then call into being. The question here is how the software for data collection is configured to model the student – what categories are preset to sort the data – and how this affects the curriculum choices of the teacher. A learning analytics platform in a university, for example, might be preset to capture data that indicates whether students are developing the required ‘graduate attributes’. Several recent data analytics platforms for schools are designed to capture data about students’ ‘noncognitive’ social and emotional learning according to categories such as ‘growth mindset’ and ‘grit’, which therefore results in a student model defined in the psychological terms of psychometric classification. In both cases, the graduate with measurable attributes and the student with noncognitive skills is called into being by the categories programmed in the software, with teachers then required to adjust their curricula and pedagogies to ensure students experience growth and development in these categories. 218 Ben Williamson Social Data As already indicated, data do not pre-exist the practices and technologies that bring them into being. As such, data are products of social practices. Datafication is accomplished by social actors, organizations, institutions, and practices. In terms of actors, data scientists, data analysts, algorithm designers, analytics engineers, and so on, have become contemporary experts in the examination of data of all kinds (Gehl 2015). These people or experts are housed in businesses, governments, philanthropies, social media firms, financial institutions, which have their own objectives, business plans, projects, and so on, and that frame how and why digital data are captured and processed (Housley 2015). In this sense, datafication can be defined socially because it is always socially situated in specific settings and framed by socially located viewpoints, despite its advocates’ claims to quantitative objectivity and impartiality. In education, new ‘education data scientists’ and learning analytics practitioners, engineers and vendors of personalized learning platforms, even entrepreneurs of artificial intelligence in education, are all now bringing their own particular forms of expertise to the examination and understanding of learning processes, teaching practices, schools, universities, and educational systems (Cope and Kalantzis 2015; Lang et al. 2017; Piety, Hickey and Bishop 2014). Many are supported by funding streams from venture capital firms, philanthropic donations from wealthy technology entrepreneurs, and impact investment programs, which all direct financial resources to the datafication of education and thus shape what objectives and priorities are pursued (Williamson 2017). Commercial companies have become especially significant social actors in education technology and datafication development. Mark Zuckerberg of Facebook, for example, is a generous donor to personalized learning projects and programs through his Chan-Zuckerberg Initiative, recently partnering with the Bill and Melinda Gates Foundation to support new scientific approaches to the psychological and neuroscientific measurement of complex learning processes. These powerful social actors are ultimately seeking to remake public education in ways that reflect the worldviews, technical capacities, and business objectives of the Silicon Valley technology sector. Data also bear effects on teachers as social actors in schools, colleges, and universities. With the rise of LSAs, teaching has already become increasingly standardized, with teachers expected to both teach to the tests and to undertake extensive data collection exercises on their own students. New classroom apps such as ClassDojo are extending these data collection efforts into the pedagogic routine of the classroom itself, as teachers are required to input information about students’ behaviours according to the categories programmed into the device. With learning analytics and other big data technologies, the machines can be understood to be augmenting Datafication of Education 219 the teacher’s role, not just by aiding with data collection but by processing those data and making decisions based on algorithmic calculations on the behalf of the teacher. The algorithm becomes a social actor in the classroom, but also changes the social role of the teacher by changing teaching practice into a set of human-machine hybrid tasks where some decisions are made automatically based on calculations from the available data. The subjective human teacher, arguably, becomes partly ‘robotized’ by deferring to the objective calculations performed by the machine rather than referring to personal professional judgment, experience, and expertise. This means the teacher is socialized to perform professional responsibilities in new ways to support the demands of constant data collection and computational processing. Moreover, datafication needs to be defined socially because much data is captured from the social world. People, institutions, behaviours, and the full range of societal phenomena are the stuff of data. As Geoffrey Bowker (2013: 170) has memorably put it, ‘if you are not data, you don’t exist’! People are data; societies are data. Even more consequentially, these social data can be used to reshape social behaviours. Bowker (2013: 168) adds that as data about people are stored in thousands of virtual locations, reworked and processed by algorithms, their ‘possibilities for action are being shaped’. Within education institutions, students’ identities as individuals and as members of the university are constructed through their access to specific systems – registration, access to the VLE, library catalogue, digital reading list engagement, valued academic content, assessment systems, plagiarism detection software, student records, and learning analytics traces. Understood in this way, technical systems can be understood to bring students into existence as data, which can then be used to infer what student can and cannot do, and what interventions should be taken as a consequence of those calculations. They also socialize students to expect – and find it normal – to be constantly subject to surveillance and monitoring. Data Power The new actors undertaking datafication are invested with a certain form of data power. Expert authority, as William Davies (2017) argues, increasingly resides with those who can work with complex data systems to generate analyses, and then narrate the results to the public, the media, and policymakers. This is why governments are increasingly interested in capturing the digital traces and datastreams of citizens’ activities. Governments are eager to learn from the successes of online platforms and pursuing new models of ‘government as collective intelligence’ (Mulgan 2016). By knowing much more about what people do, how they behave, how they respond to events or to policies, it becomes possible to generate predictions and forecasts about best possible courses of action, and then to intervene to either 220 Ben Williamson pre-empt how people behave or prompt them to behave in a certain way. Evelyn Ruppert, Isin and Bigo (2017) have termed this ‘data politics’, noting that power over data no longer only belongs to bureaucracies of state, but to a constellation of new actors in different sectoral positions – companies, think tanks, consultancies, international organizations, data labs, and so on. As such, earlier forms of state power performed through statistics are evolving in the era of big data as other non-state agencies take responsibility for data collection and analysis. Something of an arms race is underway by those organizations that want to attain data power in education. Education businesses, venture capital firms, and philanthropies are putting large financial, material, and human resources into technologies of datafication, and are seeking both to make it commercially profitable and also attractive to policymakers as a source of intelligence into learning processes. Having ‘ownership’ over educational big data is potentially valuable as a way of building new technologies and gaining political traction. Dorothea Anagnostopoulos, Rutledge and Jacobsen (2013) have written about the ‘informatic power’ possessed by the organizations and technologies involved in processing test-based data. But some of that power is now being assumed by those actors, organizations, and analytics technologies that process digital learning data and turn it into actionable intelligence and adaptive, personalized prescriptions for pedagogic intervention. Organizations such as the Chan-Zuckerberg Initiative and the Gates Foundation are amongst the most powerful in contemporary data-driven education, partly through creating strategic partnerships with key policy influencers and partly by creating generous funding programs. The Chan-Zuckerberg Initiative, for example, recruited the former Deputy Secretary of the US Department of Education to head its education division, and has quickly become a multi-billion-dollar funder of new education programs that align with its mission. Governmental authority does not, of course, automatically confer trust on the findings generated from data. The UK’s Department for Education has been repeatedly reprimanded by official statistics authorities for misusing its own datasets, notably for putting political spin and ‘messaging’ well ahead of the statistical evidence produced by its own in-house experts (Shah 2018). As Davies (2017) has noted, although statistical and analytical expertise is commonly regarded as a source of governmental authority, the power to narrate the data to make it into meaningful messages for the public and the media can often lead to misleading uses of numbers. Moreover, for Davies, expert power in the era of datafication does not reside purely in human actors and social organizations; instead, data power is distributed between machines that can detect patterns in massive datasets and humans with the talent to narrate those patterns and produce conviction in others that the data are meaningful and truthful. The objectivity of the data and the algorithms invest authority in their human mediators – although the Datafication of Education 221 Department for Education example shows how misleading messaging can also undermine trust in the numbers. As an example of how human subjective judgment is displaced by algorithmic objectivity, the Behavioural Insights Team recently ran an experimental program using ‘school evaluating algorithms’ as part of school inspection processes normally undertaken by human inspectors from Ofsted, the agency for standards in UK schools (Sanders et al 2017). Both the BIT and Ofsted are semi-governmental agencies. The aim of the trial, however, was not to replace the embodied human inspector, but to augment the inspection process by allowing the algorithm to identify problems in school data that could then help shape the approach taken by the human inspector. Teachers have for years voiced concern about the judgment of school inspectors and the effects on how they think about pedagogy and curriculum; now they must also adjust to the measures of the school-evaluating algorithm. As these examples indicate, data power in education is being distributed across commercial, philanthropic and non-governmental organizations that possess the objective analytical technologies and the human resources required to undertake complex data analyses and communicate their meanings. Cultural Data Datafication is a cultural phenomenon and a concept that has attained a privileged position in the view of the public, businesses, governments, and the media (Beer 2016). Increasingly, it seems, data and algorithms are invested with promises of objectivity and impartiality, at a time when human experts are not necessarily to be trusted because they are too clouded by subjective opinion, bias, and partiality. Across the ed-tech sector, the apparent objectivity of data has been culturally adopted and accepted, based on the assumption that teachers are too subjectively biased and are unable to adequately keep track of all students’ progress. This speaks to a cultural narrative framing datafication in terms of mechanical objectivity, certainty, impartiality, and framing human subjective judgment in terms of standpoint bias (Williamson and Piattoeva 2018). The cultural acceptance or otherwise of datafication is of course contextspecific. In some European countries such as Germany the cultural narrative of datafication and algorithms is more contested, and legally and politically influenced, due to greater cultural sensitivity around data privacy and protection. The 2018 General Data Protection Regulation (GDPR) in Europe is likely to exert significant effects on the ways that data are used in the EU, and open up distinctive differences in processes of datafication with other geopolitical spaces. It will influence how datafication in general and datafication of education in particular becomes culturally embedded (or not) in different geographical, political, and social locations. The structure and organization of national education systems is also likely to impact on the 222 Ben Williamson ways data are collected and used. Artificial intelligence and facial recognition technologies for education are at an advanced state of development and implementation in China, for instance, reflecting both national economic imperatives around ed-tech innovation and low political priorities around personal privacy. The rise of an ed-tech industry in China challenges the Anglophone dominance of the US and UK in datafication to date. The lobbying group EdtechUK, for example, has worked hard to make the UK into a world-leading centre of education technology development, while Silicon Valley already has an established innovation culture and infrastructure of edu-hackathons, accelerator programs and funding streams to incentivize ed-tech businesses and startups. The Anglophone culture of technology innovation, however, produces particular approaches to education, learning, and pedagogy that may not be culturally appropriate in other contexts and settings, especially in the Global South (Slade and Prinsloo 2013). Recently, a new report on ‘Learning Analytics for the Global South’ considered ‘how the collection, analysis, and use of data about learners and their contexts have the potential to broaden access to quality education and improve the efficiency of educational processes and systems in developing countries around the world’ (Lim and Tinio 2018). The papers in the report suggest that datafication of education is becoming increasingly culturally sensitive. At the same time, however, global organizations such as UNESCO, OECD, and Global Partnerships for Education are seeking to produce new statistical measures to capture basic educational data from developing countries, ultimately casting a grid of standardized metrics over international development contexts and glossing over cultural diversity in the efforts to globalize educational measurement and comparison (Verger, Novelli and Altinyelken 2018) As such, the enactment and effects of datafication on educational policy and pedagogy need to be understood as embedded in cultural context, whilst also acknowledging that many approaches to datafication may be decontextualized, universalistic, and privileging of technocratic Westernized ideals. Ambitious Chinese expansion, though, suggests new culturally located expressions of datafication are emerging to challenge Anglophone dominance. Data Legalities and Ethics Finally, there are legal, ethical, and regulatory mechanisms shaping datafication. Europe is much more privacy-focused than the US or China, for example, as the EU GDPR shows. How datafication plays out – what datafication is – is itself shaped by law, ethics, and politics. Even without GDPR in the US, specific federal acts such as COPPA and FERPA exist to protect children’s privacy, and organizations like the Internet Keep Safe Coalition enforce them (Zeide 2016). Other organizations such as the Future of Privacy Forum exist to produce ‘policy guidance and scholarship Datafication of Education 223 about finding the balance between protecting student privacy and allowing for the important use of data and technology in education’. Education policy also shapes the legal environment in which datafication can occur. The US 2015 Every Student Succeeds Act (ESSA) has made it possible for states and schools to apply for additional funding for personalized learning technologies. The new federal act performs the double task of stimulating market growth in adaptive personalized learning software and incentivizing schools to invest in such technologies in the absence (or at least shortage) of public funding for state schooling. As such, ESSA makes datafication of public education possible at huge scale, and even financially incentivizes schools to invest in data platforms. In higher education in the UK, meanwhile, governmental changes in the scope and scale of data collection about students mean that new markets are opening for commercial providers of ‘data services solutions’ (Komljenovic 2018). Of course, the ethical issues of datafication of education are considerable. School cybersecurity incidents are already a routine occurrence in the US. Major ed-tech companies including EdModo and Chegg have experienced huge data breaches through hacking attacks. Education companies including Blackboard and Pearson have been the subject of fierce backlash after appearing to use student data for secondary purposes without notice or consent. As GDPR compliance became a legal requirement in 2018, US education companies with EU users were forced to rework their privacy policies and data sharing agreements with third party service providers. Nonetheless, considerable unresolved concerns remain about the adequacy of contemporary student privacy and data protection policies and frameworks in relation to the rise of educational datafication (Zeide 2016). Conclusion This chapter has offered a series of defining aspects of datafication and raised issues and challenges regarding education, pedagogy, and curriculum design. Its purpose was to open up educational data to debate and further scrutiny, as the uses of data in education are likely to intensify and escalate over coming years. None of this is to discount the possibility of meaningful uses of data to inform pedagogy and curriculum. Nor is it to treat datafication as inevitable and deterministic. Research fields such as learning analytics and education data science are continuing to develop and refine their approaches, including close attention to a range of ethical issues. The critical social scientific issues outlined in this chapter, however, point to the enduring need for alternative perspectives that are attentive to the history, technicalities, epistemology, social consequences, cultural contingency, and politics of datafication in education. 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Learning, Media and Technology. doi:10.1080/17439884.2018.1556215 Zeide, E. (2016). Student privacy principles for the age of big data: Moving beyond FERPA and FIPPS. Drexel Law Review, 8, 339. Chapter 14 Student as Producer Is Hacking the University Joss Winn and Dean Lockwood Editors’ Introduction Drawing on the example of the ‘Student as Producer’ project at the University of Lincoln, UK, Winn and Lockwood explain how curriculum design is expected to be informed by a view of the student as an active contributor and collaborator to the knowledge creation process. When students are engaged to such an extent, they bring with them use of technology as a norm. Designs for radical pedagogy, facilitated by technology, need to consider their impact on the roles of the different actors involved. So, at Lincoln, staff and students have been encouraged to explore and experiment with technology together, with a particular focus on how openness is expressed and enacted within today’s technologically rich environment. Here design is seen as a truly collaborative venture that brings staff and students together. A Dysfunctional Relationship The Centre for Educational Research and Development (CERD) was created in 2007 to lead the University of Lincoln’s Teaching and Learning Strategy, run post-graduate courses for the study of education and practice of teaching, and support the academic use of technology across the university. Since its inception, the theme at the heart of the Centre’s work has been to reconnect research and teaching, the core activities of universities. Central to this objective is an attempt to reconfigure the dysfunctional relationship between teaching and research in higher education and a conviction that this can be best achieved by rethinking the relationship between student and academic. We call this project ‘Student as Producer’ and since late 2010, Student as Producer (http://studentasproducer. lincoln.ac.uk) has been adopted as the de facto teaching and learning strategy for the University of Lincoln. As such, Student as Producer is a university-wide initiative, which aims to construct a productive and progressive pedagogical framework through 228 Joss Winn and Dean Lockwood a re-engineering of the relationship between research and teaching and a reappraisal of the relationship between academics and students. Research-engaged teaching and learning is now ‘an institutional priority at the University of Lincoln, making it the dominant paradigm for all aspects of curriculum design and delivery, and the central pedagogical principle that informs other aspects of the University’s strategic planning’ (Student as Producer 2012). Under the direction of Prof. Mike Neary, then Dean of Teaching and Learning, much of the work of CERD has been informed by the conviction that students should become producers rather than consumers of knowledge and of their own social world. By engaging students and academics as collaborators, we can refashion and reassert the very idea of the university. The argument for Student as Producer has been developed through a number of publications which assert that students can and should be producers of their social world by being collaborators in the processes of research, teaching and learning (Neary 2008, 2010, 2019; Neary and Hagyard 2010; Neary and Saunders 2016; Neary et al. 2014; Neary and Winn 2009). Student as Producer has a radically democratic agenda, valuing critique, speculative thinking, openness and a form of learning that aims to transform the social context so that students become the subjects rather than objects of history – individuals who make history and personify knowledge. Student as Producer is not simply a project to transform and improve the ‘student experience’ but aspires to a paradigm shift in how knowledge is produced, where the traditional student and teacher roles are ‘interrupted’ through close collaboration, recognizing that both teachers and students have much to learn from each other. Student as Producer aims to ensure that theory and practice are understood as praxis, i.e. a process of ‘reflection and action upon the world in order to transform it’ (Freire 2000: 51). A critical, social and historical understanding of the university and the roles of researcher, teacher and student inform these aspirations and objectives. They draw on radical moments in the history of the university as well as looking forward to possibilities of what the university can become. Student as Producer is not dependent on technology but rather on the quality of the relationship between teacher and student. However, the extent to which technology can support, improve and even positively disrupt this relationship is key. An important aspect of the project is redesigning the university’s administrative and bureaucratic processes so that they align with and support the principles of Student as Producer. This is an organic process intended to engage administrative staff, academics and students in the development of curricula and course validation. As part of their curriculum design, academics are asked to: • • Show ways in which the courses will include research engaged teaching. Consider issues of space and spatiality in their teaching practice. Student as Producer Is Hacking the University • • • • 229 Describe how they will write up their teaching as a scholarly research project. Illustrate the ways in which they will use appropriate web technologies. Demonstrate the extent to which students are involved in the design and delivery of programs and courses. Show how the course enables students to see themselves having a role in creating their own future, in terms of employment, and to make a progressive contribution to society (University of Lincoln 2010). Student as Producer regards students as expert users of the university’s facilities and, following examples in other sectors, recognizes that student/user engagement is essential in the design and delivery of their own programs and modules, i.e. the design of the idea of the university. Student as Producer is not dependent on technology but recognizes that it is deeply embedded in modern university life, supporting, for example, the increasingly collaborative nature of research through discipline-specific Virtual Research Environments and the creation of Personal Learning Environments where teachers and students use technologies pragmatically, appropriate to their needs and capacities. Likewise, technology can be used to understand, map and visualize the uses of physical and virtual space and underwrites critical institutional functions penetrating deep into the overall ‘learning landscape’ of the university (Neary and Saunders 2011). Arguably, networked technology is now ingrained in the very ‘idea of the university’ and the social production of knowledge. It is not a matter of asking, ‘What is the role of the Web in higher education?’ but rather, ‘What is the role of the university in the world of Web?’ (Powell 2009) Student as Producer recognizes what The Edgeless University called a ‘time of maximum uncertainty and time for creative possibility between the ending of the way things have been and the beginning of the way they will be’ (Bradwell 2009: 63). At a time when the higher education sector is being privatized and students are expected to assume the role of consumer, Student as Producer aims to provide students with a more critical, more historically and socially informed, experience of university life which extends beyond their formal studies to engage with the role of the university, and therefore their own role, in society. Pedagogically, this is through the idea of ‘excess’ where students are anticipated to become more than just student-consumers during their course of research and study (Neary and Hagyard 2010). Through this ‘pedagogy of excess’, the organizing principle of university life is being redressed, creating a teaching, learning and research environment which promotes the values of experimentation, openness and creativity, engenders equity among academics and students and thereby offers an opportunity to reconstruct the student as producer and academic as collaborator. In an anticipated environment where knowledge is free, the roles of the educator and the institution necessarily change. The educator is no 230 Joss Winn and Dean Lockwood longer a delivery vehicle and the institution becomes a landscape for the production and construction of a mass intellect in commons, a porous, networked space of abundance, offering an experience that is in excess of what students might find elsewhere. The remaining part of this chapter provides two case studies of how Student as Producer is infiltrating quite different areas of university life at Lincoln. The first discusses Student as Producer in the context of Deleuze and rhizomatic curriculum design, while the second looks at how the project is being applied to the development of an open institutional infrastructure, in which Computer Science students are redesigning and developing the tools used for research, teaching and learning. Rhizomatic Pedagogy Gilles Deleuze, in 1990, suggested that pedagogy would soon be caught up in an incessant ‘decoding’ and ‘recoding’ as capitalism mutated to seize upon the potential that digital flows of communication offered for unleashing energies hitherto accumulated in closed institutional sites. Notwithstanding digitality’s crucial role in this mutation, Deleuze maintained that ‘machines don’t explain anything, you have to analyze the collective arrangements of which the machines are just one component’ (1995: 175). A key question such an analysis would address is whether the exigencies of communication in this emergent situation will lead also to new ‘lines of flight’, new forms of resistance. If so, resistance would be more likely to turn around ‘creation’ rather than ‘communication’: ‘Creating has always been something different from communicating. The key thing may be to create vacuoles of non-communication, circuit breakers, so we can elude control’ (Ibid.). In a 2011 Student as Producer project, drawing on a CERD fund dedicated to enabling innovations in curriculum design, Lincoln School of Media lecturers Rob Coley, Dean Lockwood and Adam O’Meara embarked upon an experiment inspired by this thought of the interruptive vacuole with a level 2 Photography Projects module (taken, on this occasion, by 42 students). In hacking parlance, we might call this an ‘exploit’, a move designed to turn a system to one’s own advantage and open up the possibility of something new happening. Consonant with the basic principles of Student as Producer outlined above, the design of the course was conceived as directly research-engaged. In this instance, tutors brought students’ attention to bear on the concept of the rhizome – key to much of the tutors’ own independent research – taken from Deleuze’s work with Felix Guattari (Deleuze and Guattari 2004), suggesting that the semester’s work could constitute a serious collaborative attempt to generate, in the encounter between this conceptual adventure and their practical work, new and original lines of enquiry for photographic image production. Student as Producer Is Hacking the University 231 There is insufficient space here to fully unfold the implications of the rhizome concept but, briefly, it indicates an attempt to break away from Western hierarchical – or ‘arborescent’ – models which encourage us to think in terms of the logic of representation and reproduction of already given structures. For Deleuze and Guattari, the rhizome – a flat, horizontal root-system – suggests the immanent, transformative connectedness of the world and constitutes a corrective to an arborescent logic of stand-alone ‘trees’. The rhizome privileges the connecting line rather than the isolated point. It is an endlessly proliferating assemblage of lines which connect from the middle. Connectivity, without centre, boundaries, beginning or end, is the first principle of the rhizome. Related principles are heterogeneity and multiplicity. The rhizome ceaselessly self-differs. Further, it expresses a cartographic logic of production rather than a ‘tracing’ logic of reproduction. Constructed on the basis of fostering new connections, ‘what distinguishes the map from the tracing is that it is entirely oriented toward an experimentation in contact with the real’ (Deleuze and Guattari 2004: 13). In the rhizomatic, cartographic encounter, when tutor and student and tutor/student and the real, come into contact, the world emerges anew in a process of mutual ‘becoming’. Nothing is represented. Nothing is communicated, only created. The module tutors envisaged that the rhizome concept would enable themselves and students, with photographic image production as the pretext, to connect up to each other and to the real in exciting ways which obviously could not be fully stipulated at the commencement of the project. It was hoped that the use of available digital technology would facilitate this – students were required to contribute ideas to a blog set up for the purpose of the project and encouraged to share and upload their work to Flickr, Vimeo and other online resources. It should be noted that tutors did not promote an uncritical embrace of the digital. The emancipatory potential of digital technologies is precisely something to be struggled for, part of what is at stake. It is fair to say that students experienced some difficulty in grasping what was an unfamiliar way of framing our thinking and doing. In particular, there was much discussion around their anxieties with regards to how, given the foregrounding of rhizomatic connection and becoming, individual achievements would be recognized and assessed. Assurances were given that reasonable efforts to participate in the project would in themselves merit a pass mark as a baseline, regardless of ‘quality’ of final product, thus providing a safety net. However, tutors did not set out to suppress dissonant views, seeing these as a necessary part of the project. Connectivity should not imply consensus. The tutors agree with those running similar projects (which have taken the rhizome as the organizing principle for pedagogical experiments) that the key to such experiments lies in the insight that ‘the community is the curriculum’ (Cormier 2008). Where they differ, however, is in their greater insistence on the political valency of rhizomatic pedagogies. It is in this respect that elements of conflict should be welcomed. The 232 Joss Winn and Dean Lockwood community-curriculum learns in a moment of crisis, surrendering the consolation of reproduced knowledge. If nothing is at stake, is anything truly learnt? Rhizomatic pedagogy embraces collective movement of thought, generating new styles of thinking. Mobile thought is creation from the middle, in and through others as mediators. This perspective shatters the complacency of received truths, common knowledge. It demands a community of mediators who connect in order to make things happen, to invent in the space between individuals, rather than merely to agree. The tutors hoped that what would transpire would be a collective, intervallic spirit of invention fostering an immanent transcendence of traditional tutor and student roles and relationships. The project evolved to encompass group outings to make images and stitch them together as a ‘pack’, an exploit from which a new assemblage promised to emerge within the old. The pack generated its ideas and images, culminating in a provocative exhibition in a public space in Lincoln city centre on a busy afternoon. In the time since the project came to an end, sufficient positive feedback has been gathered from both tutors and students to merit further investigation of this approach to teaching and learning. The experiment has been a frequent talking-point for the students who were involved and its resonances continue to be felt – something new most certainly occurred. In this instance, rhizomatic pedagogy aimed to foster a rhizomatic photographic practice, a way of producing images collectively that disrupts the traditional representational paradigm of photography. This has to be as much about exploring the techniques, methods, research ethos and social context of image production as about the eventual images produced. Throughout, process was foregrounded over product, which meant frustrating the expectations of some students. In relation to technology, tutors proposed that a tutor-student rhizome might hack photography as a kind of serious play rather than maintain a strictly instrumental orientation to the camera and associated conventions. To be more specific, it was deemed imperative for the project to critically interrogate the default assumptions tutors and students have in regard to how to teach and learn photography. Expectations of both parties have typically revolved around the notion that an individual will be instrumentally orientated towards the camera as a means of representing some aspect of the external world as skillfully as possible in order to be rewarded with a good grade. The rhizome project, tutors suggested, would work with different assumptions. These are that the group finds itself in the middle of an emergent situation, to which it critically attends by perceiving, thinking and making images with machines, i.e. cameras. It also reflexively attends to its own assumptions and expectations and the logic inherent in the camera, because these also are connected and germane to the situation. In particular, the digital camera is to be conceived not as an inert, neutral and complete technological tool distinct from its Student as Producer Is Hacking the University 233 human operator, but rather as an element in a mobile collective arrangement or assemblage which expresses what can be done and which, in the context of Deleuze’s concerns about the mutations of power, both controls and offers certain potential for resistance: The concept of assemblage shows us how institutions, organizations, bodies, practices and habits make and unmake each other, intersecting and transforming: creating territories and then unmaking them, deterritorializing, opening lines of flight as a possibility of any assemblage, but also shutting them down. (Macgregor Wise 2005: 86) To engage in photography education could be, under the auspices of the rhizome, to hack into and re-invent the machinic assemblages of which we are components. The notion of exploiting lines of flight emerging immanently within machinic assemblages can feed into the Student as Producer strategy and contribute to a culture of genuine creation as opposed to the communication of pre-digested information. An Academic Commons In 2009, in a book chapter called ‘Student as Producer’, Mike Neary and Joss Winn offered a historical overview of the development of the modern university and, more recent attempts in the US and UK to work against the growing disjuncture between research and teaching. In the conclusion to our chapter, we specifically drew on the activities of the Free Culture movement as an exemplary model for how the disconnect between research and teaching and the work of academics and students, might be overcome and reorganized around a different conception of work and property, ideas central to the meaning of ‘openness’ or, rather, an ‘academic commons’. Our approach to institutional openness at Lincoln has been to recover and develop the connection between the values of openness and the values of academic life. As such, there is no policy or ongoing discussion concerning openness, but rather we have seen Student as Producer as a vehicle for demonstrating how the values and practices of openness are historically grounded in the work of universities and the academic life, which Student as Producer seeks to promote, challenge and develop in a radical way. In 2008, the Centre for Educational Research and Development established the Learning Lab, an autonomously run virtual space for experimenting with and evaluating open source software that may be of value to research, teaching and learning at the university. One of the applications we first trialed on the Learning Lab server was the Open Journal Systems software, which was installed to help a group of students and staff develop an open access journal of Occasional Working Papers. While relatively 234 Joss Winn and Dean Lockwood short-lived due to staff and students leaving, we were able to support those involved by making the technology easily available to them and promoting their work within the context of what was being called the Academic Commons. More recently, the platform has been adopted by post-graduate students who intend to re-launch the student journal, Neo. Running on an open source server, the Learning Lab allowed for much experimentation with and the adoption of different types of open source software, including Mahara (e-Portfolios), MediaWiki, Webpa-OS (peer assessment), Xerte (learning materials), feed2js (RSS to Javascript conversion), OpenSim (virtual worlds) and, most significantly, the open source blogging software, WordPress Multi-User. Although from one perspective, WordPress is simply an open source publishing platform, we intentionally configured it so that it is open for any student or member of staff to create a modern, content-managed website to communicate their work to the public. There is no gate-keeper policy, but rather a set of Community Guidelines, similar to other online community guidelines. The University’s own Acceptable Use Policy (AUP) was also revised around this time and explicitly promotes and encourages the use of web applications. Within a year, WordPress was regarded as a technologically sound piece of software and widely used by teachers, students, researchers and university departments. As such, it was formally adopted by the university and now hosts and manages over 1000 websites at http://blogs.lincoln.ac.uk The freedom we have by running our own server(s) at the university as well as a progressive academic environment in which to work, allowed colleagues in CERD and the Library to spend over a year experimenting with the WordPress open source software and use it as a platform for technological enquiry and innovation, rather than simply a blogging tool. In this way, began a bottom-up approach to innovation through openness, which was upheld and concurrently developed both theoretically in our published writing and strategically in the development of Student as Producer as the newly emerging teaching and learning strategy. In essence, as the University was developing a more progressive teaching and learning strategy which promoted the idea of openness, collaboration and that both teachers and students have much to learn from each other, a more progressive use of technology to support research, teaching and learning was also being developed through the use of open source software, the principles of open access, the promotion of open educational resources and, most recently, the release of open data. Each tactic supported and enabled the other. Using Student as Producer as the over-arching framework, CERD, the Library and ICT Services worked on a series of funded projects which had students and openness as their central theme. JISCPress (2009–10) allowed us to employ a second-year undergraduate student in Computer Science, to help develop an open source platform for publishing and discussing documents in detail. Student as Producer Is Hacking the University 235 With ChemistryFM (2009–10), an open Educational Resources project, we provided bursaries to two students to work with academic staff to develop and release an entire module of OERs for a Level One course in Chemistry for Forensic Scientists. For the Total Recal project (2010–11), two students working part-time in central ICT services worked on a rapid innovation project to develop a ‘space-time’ calendaring service at the university, resulting in open source code and the creation of a large data store which became the basis for our institutional open data project, http://data.lincoln.ac.uk. The provision of these student posts in ICT was largely the result of the growing interest in Student as Producer at the university, reaching across not only academic departments but also the central service departments, too. The Higher Education Academy of ICT took on board the values of openness and collaboration between staff and students that Student as Producer promotes by employing students to act as ‘critical friends’ to the department and work with existing staff on the development of new online services. These students were encouraged to use the WordPress platform to blog about their experience in ICT, and this intentionally disruptive influence of students working alongside staff began to change the culture of the ICT department and led to the development and adoption of a number of online services which promote a more open and transparent environment at the university, as well as the introduction of new technologies and a much greater willingness and freedom to engage in research and development projects. With students in trusted positions in ICT, collaborating with staff in CERD and the Library, we were able to develop our ideas beyond the original Learning Lab environment and further our experiments with technology at the university. This led to Jerome, a summer ‘un-project’ of 2010, where we explored new ways of exposing, searching and using Library information to create a better way of using Library services. Jerome was later funded by JISC as our third ‘rapid innovation’ project in just over a year and, like Total Recal, made a huge contribution to our experience and understanding of new technologies such as MongoDB, the open source NoSQL database software, and data-driven development of APIs. Both Jerome and Total Recal contributed large amounts of data to what has become http://data.lincoln.ac.uk and the development of this service also led to the development of a new Access and Identity Management (AIM) system at the university, created by students. These students, Alex Bilbie and Nick Jackson, also developed the university’s new Common Web Design, a modern framework for new university websites, now widely used across our services. As such, by working together on the research and development of components of university infrastructure, we have developed an open source ‘toolkit’ for both staff and student developers, including data storage, authentication and a presentation layer, allowing us to rapidly prototype and implement new services. 236 Joss Winn and Dean Lockwood This successful working relationship between CERD, the Library and ICT Services, three key departments in the university, has been fundamental to building an academic commons, in which staff and students work together on open technologies to enable and support university life. It has been supported by senior management such as the Dean of Teaching and Learning, the Head of ICT and the university Librarian, but driven by enthusiastic staff and students who are given access to open source tools and open data. That openness can also be conceived as a ‘public good’ is recognized and valued by all involved, but is not the primary underlying motivation. Rather, the progressive and well developed pedagogical project of Student as Producer has provided us with a framework with which to involve students, situate distinctive projects when writing funding bids and receive recognition within the institution for the recognition we have attracted outside the institution for our approach. This recognition has more recently led to the university’s committees approving the formation of LNCD (http://lncd.lincoln.ac.uk/), a new inclusive group which succeeds the Learning Lab and is informed by the progressive ideas of Student as Producer so as to engender critical, digitally literate staff and students. Core principles of the group are that we recognize students and staff have much to learn from each other and that students can be agents of change in the use of technology for education. LNCD consolidates and furthers ongoing collaborative work between the Centre for Educational Research and Development, the Library and ICT Services and extends an open invitation to staff and students from across the university to contribute to the group. A graduate intern post ensures that the student perspective remains core to the group’s outlook. We also continue to employ students and recent graduates as core members of LNCD. In its first year, LNCD has a budget of £20,000, much of which is dispersed to students and staff who submit proposals for projects around the theme of ‘technology for education’. These are available on a competitive basis in the form of grants and bursaries providing an incentive to staff and students to get involved in the development, support and critique of how technology is used in higher education. Examples of funded projects include a tool that supports anonymous QandA in class, encouraging less confident students to participate; a project to build a 3D printer and investigate the uses of this new technology across different subject disciplines; another project is assessing the use of WordPress as an ePortfolio tool for health and social care students; and another is building a robot for Open Day demonstrations. Each of these small projects is a genuinely collaborative undertaking between students and teachers. Furthermore, we invite third year students from the School of Computing to propose dissertation projects based around the use of our toolkit and data.lincoln.ac.uk, allowing us to mentor students as they develop our work further. This is very gratifying and one of these Student as Producer Is Hacking the University 237 students has recently been employed by the university, recognizing the contribution he can make to the development of new online tools for the university community. In the setting up of the LNCD group, we have tried to ensure that openness remains a distinct theme throughout our work, both in the tools we use and the way we organize ourselves as a distributed, collegial group: ‘LNCD is Not a Central Development group!’ Hacking the University Work on Student as Producer remains very much at the heart of what we do. It is both an institutional strategy and a three-year project funded by the Higher Education Academy, now in its second year. It has been very well received across the university and the sector, and is being embedded into the curriculum design process and teacher education programs we run. Although internally consistent as a pedagogical theory, Student as Producer is being interpreted and adopted by staff and students at the University of Lincoln in different ways. Some, like Dean and his colleagues, recognize its basis in revolutionary praxis (drawing on the work of luminaries such as Walter Benjamin and other avant-garde Marxist writers, and the philosophy of Deleuze and others), while other colleagues, working in professional services, see it as a way to engage students in the critique and re-development of institution-wide services. All academics, however, recognize Student as Producer as a framework by which teaching and learning, including curriculum development, can become a much more collaborative effort. In the case of LNCD and the core contributors of the group from CERD, the Library and ICT Services, we have framed Student as Producer in both our advocacy of the tools and methods by which the Free Culture movement operates and in a re-examination about the role of students as developers or ‘hackers’ in the university. We see our work as fundamentally a form of hacking the academy, using and writing open source software and producing open data with which to ‘hack the university’ and create useful services and effect positive technological interventions in the research, teaching and learning environment of the university. From the perspective of a rhizomatic pedagogy, also, projects can be conceived as hacking exploits, a means to effect a revolutionary becoming for which revolution (as for Deleuze) is never actual, but always virtual, a matter of unfolding new potential, multiplying points of entry to and spontaneously surfing the propensities of a situation. Just as we recognized in our original book chapter that the Free Culture movement owes much to its academic origins, we also recognized that ‘an exemplar alternative organizing principle is already proliferating in universities in the form of open, networked collaborative initiatives’ (Neary and Winn 2009). The LNCD group is an attempt to develop that and as such 238 Joss Winn and Dean Lockwood understands that the origins of much of its work to date is in the hacking culture that grew out of MIT, Carnegie Mellon University and University of California, Berkeley in the 1970 and 1980s; the academic culture that developed much of the key technology of today’s Internet. When understood from this point of view, LNCD, as a Student as Producer initiative, is attempting to develop a culture for staff and students based on the key academic values that motivated the early academic hacker culture: autonomy, the sharing of knowledge and creative output, transparency through peer-review, and peer-recognition based on merit. We are mindful that this contributes towards a greater strategic priority of re-configuring the nature of teaching and learning in higher education and encouraging students to become part of the academic project of the University and collaborators with academics in the production of knowledge and meaning. This approach is grounded in the intellectual history and tradition of the modern university and visible in our understanding of and approach to openness at the University of Lincoln. However, for us, it is not the case that we are consciously working towards openness, but rather we work towards defending and maintaining the core academic values that recent notions of openness are largely derived from. References Bradwell, P. (2009). The Edgeless University: Why higher education must embrace technology. Retrieved from DEMOS website www.demos.co.uk/files/Edgeless_ University_-_web.pdf. Cormier, D. (2008). Rhizomatic education: Community as curriculum, Innovate Journal of Online Education, 4(5). Retrieved from https://nsuworks.nova.edu/innovate/. Deleuze, G. (1995). Negotiations. Trans. M. Joughin (pp. 169–176). New York: Columbia University Press. Deleuze, G., & Guattari, F. (2004). A thousand plateaus. London, UK: Continuum. Freire, P. (2000). Pedagogy of the oppressed. London, UK: Continuum. Macgregor Wise, J. (2005). Assemblage. In C. J. Stivale (Ed.), Gilles Deleuze: Key concepts (pp. 77–87). Chesham: Acumen. Neary, M. (2008). Student as producer – Risk, responsibility and rich learning environments in higher education. Social purpose and creativity – Integrating learning in the real world. In J. Barlow, G. Louw, & M. Price (Eds.), Proceedings of Learning and Teaching Conference 2008 (pp. 6–13). Brighton: University of Brighton Press. Neary, M. (2010). Student as producer: A pedagogy for the avant-garde, Learning Exchange, 1(1). Neary, M., & Hagyard, A. (2010). Pedagogy of excess: An alternative political economy of student life. In M. Molesworth, R. Scullion, & E. Nixon (Eds.), The marketisation of higher education and the student as consumer (pp. 209–224). Abingdon, UK: Routledge. Neary, M., & Saunders, G. (2011). Leadership and learning landscapes: The struggle for the idea of the university, Higher Education Quarterly, 65(4), 333–352. Student as Producer Is Hacking the University 239 Neary, M. & Saunders, G. (2016). Student as producer and the politics of abolition: Making a new form of dissident institution? Critical Education, 7, 5. Neary, M., Saunders, G., Hagyard, A., & Derricott, D. (2014) Student as producer: Research-engaged teaching, an institutional strategy. Project Report. York: HEA. Retrieved from http://eprints.lincoln.ac.uk/14789/. Neary, M., & Winn, J. (2009). The student as producer: Reinventing the student experience in higher education. In L. Bell, H. Stevenson, & M. Neary (Eds.), The future of higher education: Policy, pedagogy and the student experience (pp. 192–210). London, UK: Continuum. Neary, M. (2019). Student as producer: How do revolutionary teachers teach? Alresford, Hampshire: Zero Books. Powell, A. (2009). The role of universities in a Web 2.0 world [blog post], Retrieved from eFoundations blog http://efoundations.typepad.com/efoundations/2009/05/ the-role-of-universities-in-a-web-20-world.html. Student as Producer. (2012). Student as producer project report: End of second year (2011–12). Lincoln: University of Lincoln. Retrieved from http://studentasprodu cer.lincoln.ac.uk/2012/07/06/project-report-end-of-second-year-2011-2012/. University of Lincoln. (2010). Student as producer user’s guide 2010–2011. Lincoln: University of Lincoln. Retrieved from http://studentasproducer.lincoln.ac.uk/files/ 2010/11/user-guide.pdf Part 4 Resources Resource 1 Theory into Practice Approaches to Understanding How People Learn and Implications for Design This resource links Mayes (Chapter 1) with Beetham (Chapter 2). It shows how different theoretical commitments can be translated into design principles, as well as what current learning theories have in common. It can be used to apply broad design principles to consideration of learning outcomes, student progression, assessment and feedback, and learning environments. People learn by association, initially through stimulusresponse conditioning, later by acquiring concepts in a chain of reasoning, or steps in a sequence of actions. Learning is successful when instruction leads to accurate or smooth performance, for example when factual material is committed to memory or when skilled performance is compiled in an errorless sequence. Associative theories are less concerned with how concepts or skills are represented or transferred, but more with how different instruction regimes support acquisition and reproduction. Routines of structured activity are key to learning. Neural networks and other machine learning The theory Recent updates (see, Associative As for constructivism. ‘Connectivism’ as (Continued ) The virtual network as a real-world context for People learn by participating in communities of practice, progressing from novice to expert through observation, reflection, being mentored, and ‘legitimate peripheral participation’ in shared activities. Like social constructivism, situativity emphasizes the social context of learning, but this context is likely to be close – or identical – to the real-world setting in which the learner will eventually practice. Participation is not only the route to individual learning but the end goal. The social environment provides motivation and identity rewards. Individual exploration is scaffolded by social interactions. Peer learners and teachers play a key role in developing a shared understanding of the task, and providing feedback on the learner’s activities and concepts. Social constructive theories are concerned with how emerging concepts and skills are supported by others, allowing learners to achieve beyond their individual capabilities. Learning depends on prior social resources such as language(s), tools, designed environments. People learn by actively investigating the world around them, receiving feedback and drawing conclusions. New learning must be integrated into the individual’s existing conceptual or competency structures. These learned structures or rubrics can be applied to new contexts and expressed in new ways (transferred). Learning tasks should be devised to encourage the progressive achievement of understanding, or the progressive development of skill – with the learner taking control over how the task is approached. Attention and motivation are key to constructivist learning. Cognitive neuroscience confirms the modularity of Situative Constructive (social) Constructive (individual) technologies seem to show that learning can be implicit or emergent in a highly inter-connected system capable of detecting and responding to patterns of input. Human learners can acquire complex concepts or behaviours by responding to underlying patterns in the environment without necessarily consciously formalising those patterns as conceptual structures. These forms of learning are rapid and do not apparently increase cognitive load. Maps to Laurillard’s (2012) learning by acquisition (concepts), and learning by practice (skills) Bloom, Anderson and Krathwohl’s (2001) ‘remembering’ action verbs are particularly relevant to associative learning goals. Mayes, Chapter 1) Relationship to learning outcome taxonomies Maps to Laurillard’s learning by investigation Bloom’s ‘understanding’, ‘applying’ and ‘analysing’ (and higher order) action verbs are particularly relevant to constructivist learning goals. many brain functions involved in learning. Discovery learning and direct instruction may activate different regions. There is evidence to support integration and consolidation as specific (complex, multi-modular) brain activities, separate from the discovery phase. This form of learning is slower, more uncertain, and requires active attention. Maps to Laurillard’s learning by investigation + learning by collaboration. Also maps to ‘learning with others’ in Resource 2, this volume. a description of learning in open networks, characterized by complexity, emergence, self-organisation, the location of ‘learning’ in ‘the network’ itself rather than in the individual participant, and (in some versions) the merging of human and nonhuman agency. Alternatively, the blurring of boundaries between declarative knowledge and knowledge-sharing practices, and between texts and tools. Maps to Laurillard’s learning by acquisition/practice + learning by collaboration. Also maps to ‘learning with others’ in Resource 2, this volume. professional and scholarly participation. In some versions e.g. Actor Network Theory, participation is considered in terms of interactions among human and nonhuman agents. 246 Resources All approaches emphasize: In learning • • The central importance of activity on the part of the learner, and feedback about performance The need for progression and integration e.g. ○ Associatively: building more complex concepts and practices, moving from intensive to extended performance, self-review ○ Constructively: integrating skills and knowledge, moving from closed to open problems, meta-cognitive skills ○ Situatively: developing identities, planning, reflecting, goalsetting, moving to more responsible roles, meta-reflection In teaching/assessment • • Constructive alignment of activities with outcomes, and outcomes with assessment criteria The importance of feedback (intrinsic or extrinsic) They differ in: • • • • • The The The The The authenticity of the activity formality of activity structures and sequences role and importance of other people in mediating the activity emphasis on retention/reproduction or reflection/internalisation locus of control (tutor, learner, or peers) Design principles • • • • • • Analyse concepts/skills into component units Structure learning tasks/ materials according to the optimal order of acquisition Provide predictable routines of activity and feedback, ideally with micro-rewards Stage practice/recall at regular and lengthening intervals (e.g. after 1 hour, 1 day) Support intrinsic learning by emphasising patterns, rubrics, routines. Adapt teaching to learner performance (start, stop, review) Associative Implications for design • • • • • • Design tasks to support active sensemaking and analysis of problems Elicit principles from learners Design tasks to test transfer of concepts/ skills from one problem space to another Expect conceptual ‘thresholds’ where existing frames must be set aside; design a variety of tasks to support this transition Include opportunities for reflection and integration Adapt teaching to existing concepts/ skills and anticipate problems, thresholds and errors Constructive (individual) • • • • • • Design collaborative tasks Elicit shared sensemaking and problem solving Provide opportunities for discussion and reflection Encourage experimentation, and shared discovery Draw on shared resources of concepts/skills Support students to develop trust, to coach and facilitate as well as supporting them on task. Constructive (social) • • • • • • (Continued ) Enable participation/ immersion in authentic situations and practices Elaborate opportunities for learning Clarify habits, attitudes and values Support the development of roles and identities Create safe environments for action and reflection Facilitate learning dialogues and relationships Situative Designing assessment and feedback Designing for student progression (Cont). Test for accurate reproduction Assessment criteria tightly coupled to desired outcomes Give regular feedback e.g. on components of the task Quizzes, MCQs, short answers, practicals, online tests. All at progressively spaced intervals. • • • • • • Progress from component to composite or simple to complex (skills or concepts) Encourage review, selfmonitoring and assessment Move from linear sequences to spatial or grammar-like structures • Associative • • • • • • • • • Test for conceptual understanding (applied knowledge and skills) Credit process as well as outcomes Credit varieties of performance and/or innovative solutions Problem solving, argumentation, analysis, presentation Explain or demonstrate to others. Progress from simple, intensive problems to complex, extensive problems Allow learners progressively more direction over the task(s) Move from closed to open problem spaces Coach for metacognitive skills e.g. reflection Constructive (individual) • • • • • • • • Test for conceptual understanding Credit process and participation as well as outcomes Credit collaboration Develop peerevaluation and shared responsibility Collaborative assessments, or individual submissions based on shared work As for constructive learning but also: Move from closed to more open groups for learning Coach for social and mentoring skills Constructive (social) • • • (Continued ) Credit participation Focus on extended performance or practice Take full account of context Seek feedback from a range of others Credit authenticity, e.g. involving identities, values, beliefs, judgement • • • • • Progress from novice to expert tasks and roles Encourage development of repertoire and judgement Support development of relationships and identities Broaden the community, introduce different roles, perspectives and cultures • Situative Example activities Designing the learning environment Online tutoring/integrated learning environments Structured learning resources with questions and tests Online MCQs and quizzes Simulations aimed at accurate reproduction or performance One-to-one tutoring or one-to-many instruction Follow a method or a route through materials Represent a concept or argument Perform a skill sequence Answer recall-based questions • • • • • • • • • • Measurement of skilled performance. Taxonomic verbs: remember, perform • • • • • • Present an outcome, result or solution Develop a new example or application Answer applicationbased questions Active learning environments e.g. exploratory simulations, games, virtual worlds Tools for analysis and productivity Wide range of learning resources, ideally in different media Focus on learner activity with tutor direction • • Taxonomic verbs: understand, apply, analyse, explain • • • • • • • Present a shared outcome, result or solution Work on a shared project e.g. wiki, app, video Co-teach or peer review Produce individual commentary on a shared task Collaborative learning environments (including e.g. simulations, games) Shared tasks and associated tools, artefacts, resources Opportunities for discussion, cocreation and peer review/mentoring Focus on learner interactivity, with tutor facilitation • • Taxonomic verbs: understand, apply, analyse, explain, share (+ collaborate) • • • • • • • • Produce artefacts suitable to role and setting Record and reflect on use of specialist tools or resources Interview an expert Present work in a public or open network Authentic collaborative environments Opportunities for reflection and identity development Opportunities to engage with a variety of more expert others Authentic tools of the discipline area or specialist role Focus on learner participation • • Taxonomic verbs: apply, evaluate (+ collaborate) • 250 Resources References Bloom, B.S., Anderson, L.W., & Krathwohl, D.R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York: Longman. Laurillard, D. (2012). Teaching as a design science: Building pedagogical patterns for learning and technology. New York and Oxford: Routledge. Resource 2 Learning Activity Design A Checklist This resource summarizes some of the activity design considerations from Beetham (Chapter 2). Learning outcomes: considerations for design • • • • • • • What is the overall purpose of the learning? Is the purpose clearly defined and shared with learners? Is there room for some negotiation/variation of purpose, and if so, how does this discussion take place? What new knowledge, skills, capabilities and/or attitudes will learners gain? Are these (including digital capabilities) made explicit as learning outcomes? How will learners know when they have achieved the outcome(s), and how well they are doing? What different kinds of feedback are available? How will learners be assessed (if at all)? Are the assessment criteria clear and relevant? How could the learning process be captured (e.g. using digital devices) to support reflection and review, sharing and feedback? How might learning gains be recognised and valued that are not included in the formal outcomes? Learners and learner differences: considerations for design • • • • Are the outcomes appropriate for these learners, and for all the learners? How could different challenges be introduced? Do learners have choices about how they carry out a task? For example, about the tools they use, the media they reference, other people they participate with? Are learners’ differences valued, e.g. by setting collaborative tasks, by rewarding innovation as well as accuracy? How are support and feedback adapted to individual learners’ needs? 252 • • • Resources Are there opportunities to work individually and collaboratively during the activity? How are learners involved in the design process, e.g. negotiating over outcomes, tools and assessed tasks? How will you address differences in learners’ digital confidence, capability and access to digital resources? Where can you signpost them for further support, or further challenge? Digital resources, tools and services: considerations for design • • • • • • • • What resources will learners have access to? What resources do you expect them to access for themselves? Have you ensured that all the resources you provide are accessible? What information, media and data literacies will learners require to access and use these resources? How will these be supported and developed? How do you expect learners to manage, share and make use of digital resources? Is this explicit? What devices and services (e.g. mobile or web-based) will learners have available for use? What devices and services of their own could they use? How will you address issues of differential access to devices and services (if relevant)? How will you use learners’ digital access and know-how as a collective resource e.g. through groupwork, informal mentoring? What support will you and your learners need (e.g. IT support, specialist librarian, other professional service) to make best use of these technologies? Learning dialogues: considerations for design • • • • • • What is the role of the course tutor(s) in this activity or course? How will tutor-learner communication be initiated and maintained? How will learners interact with one another? What are the opportunities for peer learning and collaboration? Are there opportunities to bring other people into the learning situation e.g. ‘public’ audience, experts, fellow learners elsewhere? How are dialogues structured, guided and supported? How are the rules of academic or professional communication made clear? How can computer-supported communication e.g. video, discussion forums, social media and public blogs be used to support dialogue? Who will give feedback to learners on their progress and how will this be communicated? Have you considered how digital technologies could be used to support peer assessment and review? Resources 253 General theoretical considerations for design People learn more effectively when: They are actively engaged: • • • • Base learning around tasks rather than around content Provide varieties of activity Focus on activities that have learners produce an outcome, even at a small scale (notes, records, tweets) Break tasks down into smaller elements when first introduced They are motivated • • • • • Communicate desired outcomes clearly and regularly Relate these to learners’ long-term goals and aspirations Where appropriate, allow choice over elements of the learning activity Where appropriate, link topics to personal experiences or interests of learners Provide feedback on task components when first introduced Their existing capabilities are brought into play • • • Revisit prior knowledge and skills at the start Recognise and exploit learners’ existing capabilities e.g. thru collaborative work, shared knowledge-building Enable learners to use familiar technologies and services where appropriate They are appropriately challenged • • • • Introduce new tools and techniques Offer a range of assessment tasks with examples of high quality outcomes Offer extension activities Provide progressively more open-ended problems, more open resources, and more open environments for learning They are appropriately safe • • • Provide support and scaffolding for new activities Introduce new tools and techniques in a timely way and allow time for practice Allow students to use their own devices and services to participate in learning, where appropriate 254 • • Resources Provide practice tests before any high stakes assessment Provide closed environments for learning until students are confident to engage in open settings They have opportunities for dialogue • • • Offer or propose a variety of collaborative environments, face to face and virtual Establish opportunities for dialogue with tutors, mentors and peers during the task Recognise and reward collaboration as well as autonomy They receive feedback • • • • • Ensure tutor feedback at timely points e.g. after first assignment, after a key session, during revision. Design tasks to give intrinsic feedback if possible Consider peer feedback as an alternative to tutor feedback Foster skills of self-evaluation Ensure learners have examples of successful student work to compare against their own efforts They have opportunities for consolidation and integration • • • Encourage further practice of key capabilities Record processes of learning where possible, so learners can see how they perform Promote skills of reflection and planning (e.g. through portfolios, action planning) Resource 3 Digital Learning Activities Linked to Bloom’s Taxonomy of Educational Objectives Bloom’s Taxonomy of learning outcomes is a well-known framework for assigning desired outcomes to planned activities and curricula. The cognitive version is the most widely known and used, and particularly reflects how different learning outcomes are valued in a constructivist paradigm. In 2018, the Jisc Student Digital Experience Tracker survey (since renamed the ‘Insights’ survey) collected feedback from over 37,000 UK students about their digital learning and teaching (Newman, Beetham & Knight 2018). One question in the survey asked students to name a digital learning activity they had found ‘really useful’ on their course. Responses were coded using the broad terms from Bloom’s revised taxonomy of cognitive learning outcomes (Bloom, Anderson & Krathwohl 2001) as a hypothetical coding frame. This exercise resulted in a revised set of verbs for each level of Bloom’s framework and example activities for each of them, based on the digital activities students engaged with and valued. The coding exercise also showed that the framework needed to be extended in two directions to accommodate learners’ digital practices. The first of these extensions – ‘preparing to learn’ – acknowledges the role that digital tools play in allowing students to learn flexibly and independently, to manage their time and tasks, and to build other positive learning habits such as managing notes and tracking grades. This might be called ‘meta’ learning or ‘learning to learn’, but for these students it was clearly integrated into their general learning practice. The second extension – ‘learning with others’ – offers some of the desired outcomes that Bloom’s original taxonomy ignored because of its focus on individual cognition. Again, digital technologies have made it easier for the learning process to be shared, discussed, commented on etc., and for this to be integral to the learning rather than specially arranged. Reproduced with the permission of Jisc. 256 Resources Prepare to learn (not in Bloom’s taxonomy) Our term Example tools and resources used Activities (including student quotes) Access laptops, tablets, e-readers, smart-phones, screen readers Set up your personal device(s) and key software to meet your access needs Log in to campus systems, course information and key services Make sure the log-in and [vle] and printers work on your mobile devices. Make sites personalised – only have what you need on the page. Organise (time and tasks) Showbie, calendar, task list, time management apps, Trello, student apps Sync calendars and task lists across devices Plan and set your learning goals e.g. what skills do you need to practice or brush up? Being able to automatically sync my timetable of lectures and tutorials to my google calendar Using a trello board to help manage tasks Calendar function on my phone keeps me organised, including preferred study times Organise (information) Showbie, VLE, Dropbox, hard drive, pen drive, file store, personal devices Develop a filing system that works for you Creating my own folders helped my organisational skills Having every link/download on Blackboard that I need Notes accessible between phone and laptop for links and references Remember Our term Example tools and resources used Activities (including student quotes) Attend slides, (lecture) notes, video, web page, podcast, lecture, e-journal, e-book, VLE Browse set readings, journal articles or e-books Practice getting information quickly from a video or podcast Studying course powerpoints through [the VLE] Online video lectures took us places we couldn’t easily go in real life Record smartphone, camera, Record a learning activity and/or feedback using digital audio or video (Continued ) Resources 257 (Cont). Our term Review Example tools and resources used Activities (including student quotes) recorder, smartpen, speech-to-text Take snapshots of key findings or notes in class Video camera – find it really useful to record myself when going over dance phrases lecture recordings, lecture capture, lecture notes, Padlet, blog, powerpoint, VLE, Kahoot, quiz Read back through resources you have filed or curated Use quizzes to review material you have learned Kahoot – useful for revision and reinforcing information Lecture capture – it allows me to revisit and direct lectures at my own pace, helps students to cement what they have learned Understand Our term Example tools and resources used Activities (including student quotes) Engage Kahoot, Powerpoint, Keynote, quiz, poll, Socrative, Menti, Turningpoint, Tophat, clicker, Poll Everywhere Answer quiz or poll questions in class Ask questions or make contributions online Kahoot – interactive, engaging, educating; team activities in general are a lovely way to learn something during lessons A website that allowed us to write questions and comments our lecturer could view in real time Annotate Note making apps e.g. Word, Powerpoint, Keynote, Padlet, Evernote, Dropbox, Scrapbook Use a note-making app such as Evernote or notability Rewrite ideas from a lecture in another medium e.g. draw a diagram, make an audio recording Notability is fantastic for marking, editing, drawing, notes, recording Adobe is the best free app I found so far … for reading, highlighting and commenting on pdfs Organise (concept) mindmapping tools e.g. cmap, mindjet, xmind, Prezi, Pointpoint, graphic/drawing tools Tag or label a resource (image, video, audio, text) with keywords Create a mindmap for a topic you have just learned Xmind for mindmapping a topic prior to writing an essay or starting a project (Continued ) 258 Resources (Cont). Our term Example tools and resources used Activities (including student quotes) I use quizlet to make flashcards of most of my lecture slides Organise (many concepts) bookmarks, reference management e.g. endnote, citeulike, evernote, padlet, pearltrees, wikis, RSS and twitter etc feeds Use a sharing site to curate ideas, links or examples of a concept Set up a feed on a topic of interest, using keywords or key sites Manage relevant bookmarks and sync them across my PC, Tablet and Phone Using MS OneNote to organise all the work for each subject I like using Paperphile to organise all the references I use when researching Research Google, google scholar, google books, hashtags, iTunesU, TED, WoS, Kahn youtube, reading list Identify and download a video, podcast, online tutorial or lecture Use relevant search terms to find articles in a catalogue or online Searching for books and journals independently Cross referencing different authors and sources Finding research studies on google scholar Explore Kahoot, quizzes, games, simulation, subject-specific resources Find your own pathway through a virtual environment or topic map Use a simulation to investigate a problem or method in more depth Animations to play around with to understand statistical analyses Coding apps that show you step by step how they made a program Game that helps you read and understand a historic text Apply and Analyse Our term Example tools and resources used Activities (including student quotes) Practice [subject-specialist software], simulation, virtual Practice using subject-specialist software e.g. for design, analysis (Continued ) Resources 259 (Cont). Our term Example tools and resources used Activities (including student quotes) world, game, virtual lab, case study, geo-tagging, how-to video Use a simulation to practice a skill before using it in the lab or workplace Simulation scenarios keep me up to date on my resuss skills A virtual laboratory simulation was helpful for learning lab skills without having to wait hours between processes Analyse [subject specialist software] excel, SPSS, NVivo, database, graph, infographic, charting functions, GIS, simulations, interactive video, survey (Learn how to) Sort, filter, tag, apply formulae and use equations Take part in an authentic research project of your subject Producing visual graphs for data analysis Interpret data from survey monkey and produce results in chart form Transcription software for conversation analysis Solve [subject specialist apps and software] excel, SPSS, (graphing) calculator, video Review other students’ answers via polling or sharing in a discussion group Try different ways of visualising a solution Use excel to solve complex mathematical problems Calculate means, standard deviatons and gradient graphs Drug calculation app | Frog puzzle to help with our thinking skills Answer quiz, test, kahoot, socrative, padlet, powerpoint, prezi Answer a question in class using padlet or polling Look for practice tests to check what you have learned Website tested me on the question I input which helped me learn some topics I struggled with Students from one tutorial can see each other’s answers which opens up different areas of the text Explain Video, animation, design tools, slides, prezi, infographic, graphs Produce an infographic summarising a problem or solution Created a vlog [video log] to explain a new use of technology We all vote for which theory seems most plausible, really makes you think Exploring different ways we can use iPads to teach others 260 Resources Evaluate Our term Example tools and resources used Activities (including student quotes) Evaluate Virtual case studies, simulations, survey tools, Nvivo, SPSS, any presentation media Argue a case in a blog post with every point linked to evidence Use the outputs from a spreadsheet or analysis program as visual aids in a presentation Simulation game during business decision-making module Reflect (digital) diary, log, portfolio, blog, wordpress, pearltrees, impact log, analytics Create a blog (it can be a private one) to record and reflect on what you are learning Digital diary lets us keep on top of what we learned The learning journal [is] very helpful to reflect on my work. Online blog that the tutor can access and give feedback Creating a visual diary that displays progress visually I was asked to write a development blog every day: this helped me keep track of my work Maintaining an updated blog of work in production One-note personal journey which the lecturer can see and discuss with you – so useful Improve Learning app, progress log, audio feedback, digital annotation, portfolio, pebblepad Record and review your feedback, and identify what you need to improve Try a learning app to help you progress I liked getting audio feedback from lecturers Individually quizzing with a progression tracker Bluesky to log my progress and evidence my development Create Our term Example tools and resources used Activities (including student quotes) Write Word, pages, Google Docs, blog, wiki, padlet, wordpress, e-pub, dictionary, Try out a number of referencing apps to find the one that suits you best Use a plagiarism report or grammar checker to improve an assignment (Continued ) Resources 261 (Cont). Our term Example tools and resources used Activities (including student quotes) grammar checker, Google translate Hemingway app helps to identify possible problems in my writing Writing notes on topics on powerpoint; posting and comparing exam questions on padlet Mendeley, for automatically arranging references in the correct format CeltX has really helped with my script writing | Grammarly is a very helpful app. Present powerpoint, prezi, keynote, animation, app maker, video adobe suite, image banks, screen capture, web authoring, graphics Present a topic for revision in the form of a single infographic or mind map Create a how-to video or animation Making your own revision packs using the internet and class notes make a video of ourselves while doing a presentation – and upload on youtube Creating films to present a piece of work | Graphpad software for data presentation Learning how to use spark video as a different tool to present work produced a digital timeline and a website which was very useful Make [subject specialist software] CAD software, google sketchup, Adobe software, animation apps, app maker, video camera, 3D printer, photoshop, prezi, powerpoint, coding and scripting tools Use a simple app-builder to make an app relevant to any topic on your course Ask if you can submit an assignment in a new digital format and let your creativity shine Producing concept designs for my media class using Adobe Photoshop and Illustrator Coding and using assembly programs | coding an entire app from scratch Music coding, mixing and mastering, recording, score writing Using photopeach to create a poem | Using adopt photoshop to produce digital fashion illustrations Working with the Wacom tablet and creating digital paintings Showcase e-portfolio, pebblepad, blog, professional Create a profile on a professional network and link to recent worik Blog your thoughts on a contemporary issue (Continued ) 262 Resources (Cont). Our term Example tools and resources used Activities (including student quotes) networks e.g. linked-in, web authoring really useful to work on developing a professional portfolio while improving my writing. A blog documenting my art-making practice Publishing our stories on [a public online newspaper] Learn with others (not in Bloom’s taxonomy) Our term Example tools and resources used Activities (including student quotes) Discuss VLE, forum, chat, facebook, blackboard, moodle, discussion board, webinar, whatsapp, snapchat, messenger Use social media to share a problem and collate the best ideas Use threaded discussions to pursue different questions in the same topic area Contributing to online discussion. I was apprehensive initially but found it really helpful to read other comments and opinions. A massive discussion board/post-it platform where people were writing their ideas and they were being discussed in real time Our course facebook page – a great way to all comment and communicate, show off our work and ask questions Collaborate Powerpoint, Google docs (slides etc), doodlepoll, MS project, slack, VLE (forum), padlet, dropbox, wiki, social media Create and deliver a digital presentation collaboratively Use a wiki or sharing site to curate resources on different aspects of a topic Use of titanpad/onenote forums during tutorials allows for more collaborative work Using outlook to arrange meetings and share calendars Creating presentatlons about different aspects of a topic so as a class we have a wide breadth of detailed knowledge Group compilation of ‘dictionary definitions’ of industry words to create a mini dictionary for the course Co-teach VLE, dropbox, Google+, slack Comment on other students’ work online, and read their comments on your own (Continued ) Resources 263 (Cont). Our term Example tools and resources used Activities (including student quotes) Moderate a discussion, collate the key points, and present back to the group I had to apply a rubric to grade others’ written assignments and got feedback from peers on my assignments A Google+ community where we share and critique each other’s work Using the wikis meant it was easier to peerevaluate References Bloom, B.S., Anderson, L.W., & Krathwohl, D.R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York: Longman. Newman, T., Beetham, H., & Knight, S. (2018). Digital experience insights survey: Findings from students in UK Further and Higher Education. Bristol: Jisc. Resource 4 Digital Capability Checklist for Curriculum Designers This is a checklist for curriculum designers who are working to embed digital capabilities into a course or subject area. It covers a range of issues that are investigated in more detail in Jisc’s Digital Capabilities Framework (Beetham Chapter 2). The checklist can be used to assess how well a course of study is preparing learners with the kind of capabilities and practices outlined in the learner profile associated with the framework. Reproduced with the permission of Jisc. Resources Digitally capable learners should be able to … Use specialist digital tools of their subject area (e.g. research, design, professional practice) Find, collate, evaluate and manage digital information Manage, analyse and use digital data Consume and produce ideas in digital media (e.g. spatial, textual, visual, auditory, interactive, textual …) Create digital artefacts (e.g. web pages, 3D print pieces, code, digital video, infographics …) Use digital tools to solve problems and/or answer questions Take part in digital research or professional practice Collaborate with others using a variety of digital tools and spaces Participate in digital networks (closed and open) Develop digital learning skills and habits (e.g. participating, notemaking, referencing, quizzing, revising …) Develop and manage their digital identity and learning outcomes Consider issues of digital safety, privacy, health and wellbeing, ethics and legality. What does this mean in the context of your course? 265 How in your course do learners encounter, practice and get feedback on this? Resource 5 Blue Skies Planning Checklist This checklist is used in the Course Design Intensives supported course design process described in Sharpe and Armellini (Chapter 8). The checklist is designed to help teachers structure their thinking on the way to producing a programme level storyboard (see Resource 7). The aim of this exercise is to describe the broad scope of your course: why it exists, why it is special, how students and teachers will experience it. Course title and level: Purpose or main aims of course: Learning outcomes: Main strengths of current course, which you would not like to lose: Main weaknesses of the course which need to be addressed: Number and profile of students: (e.g. home/international, campus/commuting) (Continued ) Resources 267 (Cont). Main characteristics of the students as they affect teaching and learning methods: (e.g. you may describe two or three students each of several characteristics, like prior learning, diversity, their expectations, their likely access to technology) Teaching and learning methods (e.g. online resources, discussion, individualized selfpaced learning, small group work, projects, problems, presentations, portfolios) Assessment methods (both formative, e.g. quizzes, assignments, exercises, problems, seminars, presentations and summative, e.g., exam, coursework, portfolio) Technology requirements (any special technologies that staff need to develop this course, or students/staff need to study/teach it) Resources (other than those noted in technology above, e.g. text books, printed materials) Administration (roles responsibilities, tasks) Support (how will students be supported on this course?) The Blue Skies Checklist was created by a team at the Oxford Centre for Staff and Learning Development (OCSLD), Oxford Brookes University, as part of a Pathfinder project funded by the Higher Education Academy. Resource 6 Critics’ Checklist This checklist is used in the Course Design Intensives supported course design process described in Sharpe and Armellini (Chapter 8). The aim of this checklist is to guide and structure peer review of each design team’s storyboard. Questions on the checklist are used to prompt discussions between course designers, to draw out advantages and potential difficulties of their proposals, and to help them think of alternative solutions. A suggested structure for your conversations with designers …. Remember that this process is planned as a useful experience for the course designer(s). Try to use your questioning to draw out of them advantages and potential difficulties of their plan which might not have occurred to them. Give them time to answer questions fully, to think about new solutions, and to take notes of their emerging ideas if they want to. 1. Student experience: Ask how a typical student might experience their progress through the learning activity, from start to finish. 2. Activity design: Find out which aspects of this activity design are particularly novel and interesting (give each other a chance to showcase the work before you start being too critical!) Are there aspects of this activity that could be done more efficiently or in a more interesting way? 3. Student support: Clarify how students will be supported in their learning. What aspects of the teaching, learning and assessment process might be new to them? How has support for this built into the activity? If you see holes in the student support issues, point them out. 4. Assessment and feedback – What opportunities are there for feedback to students on their progress before the summative assessment? What opportunities are there to make use of feedback in the production of students’ work? How are students being engaged in understanding how the work will be assessed? How will the activity be integrated Resources 5. 6. 7. 8. 269 into the assessment of the course? How is this activity/task linked to learning in other parts of the course? Outcome Audit: Ask the designer(s) to take one learning outcome and follow through how students will become familiar with it and its related content, have opportunities to practice their developing skills, gain feedback on their learning, and finally, demonstrate their learning related to that outcome in final assessment. Is there a clear link to programme level learning outcomes? Diversity. How have issues of accessibility been accommodated within the design and preparation of the materials? Are there any types of students or student characteristics that are not adequately catered for in the design of the course? How does the design proactively accommodate diversity in the student body? Staffing: Who will teach/tutor on the activity? What additional skills/ facilities might the staff need? What would make teaching on this course/activity intolerable to you? Technology: How much of the technology incorporated in the plan is already available and accessible to you? What additional technological requirements does this activity have? How are students’ digital literacies being developed through their engagement in this course/ activity? The Critics Checklist was created by a team at the Oxford Centre for Staff and Learning Development (OCSLD), Oxford Brookes University, as part of a Pathfinder project funded by the Higher Education Academy. Resource 7 Storyboard This resource is an extract from the Module Planner used in the CAIeRO (Creating Aligned Interactive educational Resource Opportunities) supported course design process described in Sharpe and Armellini (Chapter 8). Create the storyboard All the resources that you now have around you form a blueprint (your mission, your assessment plans, the look and feel of your course,) but you need to create a process of integration and flow. You can do this by ‘storyboarding’. Storyboarding means visually representing a process that you can later build. It needs to show what the key players do, how they move through the process, what the critical moments are in the ‘story’ and of course what it’s all leading to and what happens in the end. You don’t need to get into the nitty gritty details at this stage. Think more about sequencing and progression – Salmon’s Five Stage model might help you here (www.gillysalmon.com/five-stage-model.html). Resources 271 You might also want to start thinking about the best use of contact time (if you have it). If you have activities or resources that you know work well, make a note of them. We’ll build them in to structured learning activities later on. When your storyboard is complete, you may want to transfer it to a visual online tool such as Popplet, LucidChart or Draw.io. This helps you keep a copy (without the Post-its falling off!) and can also act as a helpful visual course map for students if you upload it in to the learning environment. Acknowledgement The CAIeRO Module Planner developed by the University of Northampton is based on Carpe Diem by University of Leicester and is reproduced here with permission. CC-BY-NC-SA Resource 8 Digital and Information Literacy Expressed in Programme Learning Outcomes Digital and information literacy was defined as a graduate attribute at Oxford Brookes University as: The functional access, skills and practices necessary to become a confident, agile adopter of a range of technologies for personal, academic and professional use. To be able to use appropriate technology to search for high-quality information; critically to evaluate and engage with the information obtained; reflect on and record learning, and professional and personal development; and engage productively in relevant online communities. (Oxford Brookes University 2015) This resource shows examples of how this graduate attribute has been expressed in programme level learning outcomes by course teams at Oxford Brookes University following a mapping of the graduate attributes in their programmes and a supported course redesign process (Sharpe et al. 2013; Sharpe and Armellini Chapter 8). able to apply general software interface principles to independently explore new software select and use appropriate authoring technologies from a range (e.g., email, wikis, blogs, word processing, presentation, CAD, html authoring) select and use a range of technologies for personal knowledge building able to securely and responsibly manage one’s own and other people’s data and online identities • • • understanding how different types of search tools work (eg. library catalogue, specialist databases, web search engines) searching systematically across a range of resources constructing effective searches by identifying and combining appropriate keywords Devising effective strategies and choosing appropriate tools for locating information; Locating information • • • • Being confident, agile adopters of a range of technologies for personal, academic and professional use: Confidence and agility Elements of Digital and Information Literacy (Continued ) Use appropriate technologies such as online libraries and databases to find, critically evaluate and utilise both non specialist (e.g. reports) and technical (e.g. APIs and RFCs) information. (BSc Information Technology Management for Business, Mobile Computing). Effectively locate information from multiple sources using systematic manual and electronic searching techniques in order to develop coherent evidence based practice arguments. (Student Designed Award, Health and Life Sciences). Use e-mail, online discussions, the Brookes Virtual learning environment, the wider Web, PowerPoint and Word-processing The ability to use and apply professional mathematical and statistical software (Mathematics, Statistics). Demonstrate the use and management of information technology and modern computing techniques to solve complex Motorsport Engineering problems (Motorsport Engineering) Make effective and confident use of relevant and appropriate technologies to enhance learning, communication and problem solving (International Business Management). Select and be able to use appropriate information technology to describe and solve a range of problems in the economic, political and international arena (Economics, Politics & International Relations). Expression in programme level learning outcomes learning how to use specific search tools (e.g. by reading help pages, manuals or search guides) and consulting appropriate professionals supporting ongoing research and professional needs by using current awareness services recognising and dealing with the problems of too much or too little information comparing and critically assessing the authority, currency, detail and relevancy of information recognising bias in information, especially from freely available web sources Integrating information obtained into one’s own personal, academic or professional understanding, managing and communicating it effectively and ethically; Use information • • Evaluating information obtained and assessing its appropriateness for one’s needs; Critically evaluating information • • • Elements of Digital and Information Literacy (Cont). (Continued ) Select and be able to use appropriate information technology to describe and solve a range of economic and financial problems (Economics, Finance and International Business). Evaluate economic and financial theories and interpret business behaviour through the quantitative or qualitative analysis of empirical data (Economics, Finance and International Business). Understand and evaluate how information management relates to, and impacts on, organisations and their development generally and the accounting function in particular (Accounting and Information Management). Demonstrate their ability to plan successfully for the inclusion of ICT resources within the teaching of classes of primary-aged children (Primary Teacher Education). Engage with, evaluate and employ digital resources relevant for the study of art and its history (History of Art). technology effectively as a means of research, information retrieval and/or presentation of work. (English Language Communication). Utilise a range of electronic information management tools, including word processing, email, the world-wide web and some electronic retrieval systems for personal, academic and professional use (Law). Expression in programme level learning outcomes taking appropriate notes, summarising and adapting information for a new audience synthesising information from different sources to present a reasoned argument creating new information through integrating one’s own knowledge and understanding with prior reading and research understanding academic and professional ethics (eg. appropriate acknowledgement of sources, correct citation practices, and avoidance of plagiarism) continuing one’s professional development by keeping up to date, sharing and debating information through appropriate communication tools • • • • knowing and observing appropriate conventions on authoring in a variety of media and in a variety of professional and academic contexts ability to search, aggregate and organise digital information from a variety of sources for personal use ability to represent oneself online in a suitable way for academic and professional purposes selecting and using appropriate technology for recording and representing academic, professional and personal development Using digital tools to reflect on and record learning and professional and personal development; Reflecting on and recording learning • • • • • (Continued ) Effective use of digital technology to present analysis and solutions to a variety of audiences (Mathematics, Statistics) Use University-based e-learning resources to work individually and collaboratively to give a broad and reflective understanding of early childhood (Early Childhood Studies). Demonstrate a confident familiarity with a broad range of information technology skills in order to communicate effectively using graphical techniques, reports and presentations within a commercial and technical environment (Motorsport Engineering, Mechanical Engineering). Demonstrate the ability to express ideas and opinions, with confidence and clarity in an appropriate manner, to a variety of Use information technology to observe, gather, evaluate, interpret and integrate ideas and evidence in the nutrition domain to support findings and hypotheses (BSc Nutrition, BSc Sports Coaching, Sport and Exercise). Communicate with midwives and others involved in the maternity service through listening, sharing information, research findings, ideas, problems and solutions and analyse the communication systems within the maternity services (Midwifery). Make discriminating use of a full range of electronic resources, including virtual learning environments, podcasts, blogs and discussion boards, in order to identify appropriate source material, inform research and enhance presentations (Religion and Theology). knowing when and how to maintain appropriate levels of privacy in drafting and publishing to individuals and groups effectively managing group interactions using multiple technologies selecting and using technologies to represent and synthesize individual and group knowledge/learning communicating effectively online developing fluency and command of ‘voice’ in online authoring and publishing • • • • • selecting and using appropriate communication technologies for group work • Effective participation in distributed collaboration and artful communication of one’s ideas in various social media: Engaging productively in online communities Elements of Digital and Information Literacy (Cont). Knowledge on how to communicate effectively with other people using visual, graphic, written and verbal means (Architecture Interior Architecture). Demonstrate sophisticated understanding of the multi-faceted complexity of religions, from their presence and activities in e-religion, including online religious practices and representations of religion in electronic environments (Religion and Theology). Practise business communication techniques including digital and online methods. (International Business Management) Communicate effectively online and work with others using collaborative tools. (International Business Management) audiences for a variety of purposes and using a variety of different media (International Hospitality Management). Expression in programme level learning outcomes Resources 277 References Oxford Brookes University. (2015). Strategy for enhancing the student experience. Oxford: Oxford Brookes University. Retrieved from www.brookes.ac.uk/aboutbrookes/strategy/strategy-for-enhancing-the-student-experience/ Sharpe, R., Benfield, G., Corrywright, D., & Green, L. (2013). Evaluation of the brookes graduate attributes: Year 1 final report [Internal report]. Oxford: Oxford Brookes University. doi: 10.13140/RG.2.2.17354.41920 Index ABC Learning Design framework 128, 130, 136, 142, 172–173, 176 Abdelmalak, M. 91–92 ‘academic commons’ 233–237 access/accessibility 39, 145, 150, 167, 185, 256 accountability 134, 197 ACT-R model 22 active blended learning 137–138 activities 32–48, 56, 67–68, 138; ABC framework 172; considerations for design 251–254; Critics’ Checklist 268; defining 34–36; 8LEM 170, 176; examples 249; learning theories 246; linked to Bloom’s taxonomy 255–263; mobile technologies 190–191, 193; professional learning 199–208; see also tasks activity systems 32, 35–36, 42, 44–45, 207–208 Activity Theory 35–36, 43 Actor Network Theory (ANT) 52, 74, 78, 245 Adams, M. J. 28 affordances 74–76, 79, 80; activity systems 44; analysis for design 52–53, 56–57, 58, 62; mobile technologies 185; networked learning 73; open education 150; professional learning 198 agency 10, 34, 57, 66, 73–74, 76, 79; activities 45; analysis for design 51; mobile technologies 191; networks 245; open education 153, 158; social realist theory 156; student-centred approach 124; System 1/System 2 thinking 56 Agostinho, Shirley 11, 105–119, 122, 129, 130, 131 Akyol, Z. 89, 97 algorithms 4, 74; affordances 80; assumptions 212; datafication 215, 218–219, 220–221; neural network theory 22; professional learning 204 Anagnostopoulos, Dorothea 220 analysis for design 49–65 analytics 25, 77–78; datafication 212–213, 214, 215, 216–217, 218–219; Global South 222; professional learning 197, 206, 207; research 223 Anderson, J. R. 22, 23 Anderson, L. W. 245 Anderson, T. 87, 92, 94, 97 Andringa, S. 26 Annand, D. 99 ANT see Actor Network Theory Apple 184–185 apps 4, 43, 218, 275; learning outcomes 256, 258, 260–261; mobile learning 184–185, 188, 189, 190 Arbaugh, J. B. 87 Archer, Margaret 156–157 Archer, W. 87, 94, 97 Archibald, D. 89 Armellini, Alejandro 11, 88, 134–148 artefacts 35, 43, 66, 70, 249; activity systems 42, 44–45; affordances 80; agency 74; analysis for design 49, 51, 55; assemblages 76; digital capability checklist 265; networked learning 73; professional assessment 206; qualities 57 artificial intelligence 22, 130, 212–213, 218, 222 Index Asensio, M. 68, 69 assemblages 66, 76–77, 79–80, 232, 233 assessment 34, 248–249; ABC Learning Design framework 173; Blue Skies Planning Checklist 267; Critics’ Checklist 268–269; data 212, 213, 215; large-scale 214, 218; learning activity design checklist 253; learning designs 113; mobile technologies 193; professional 205–207; 7Cs of Learning Design framework 170 associative perspective 18, 27, 34, 244–249; connectivism 25; integration of multiple perspectives 20; learning activities 32, 33; learning outcomes 36; neural networks 17–18, 21, 22; teachers 41 attitudes, professional 204–205 augmentation 165–166 augmented reality 187 Australian Research Council 111–113 Australian Universities Teaching Committee (AUTC) 106 authentic learning: design principles 247; mobile technologies 181, 185–186, 187, 192, 193; task and setting 33 Bain, A. 136 Bandura, A. 20 Bang, M. 25 Bangert, A. 87 Basarmak, U. 87 Bates, A. W. 167 Beetham, Helen 1–14, 19, 32–48, 139, 154 Befus, M. 85–86 Behavioural Insights Team (BIT) 221 behaviourism 190, 202 Benjamin, Walter 237 Bennett, E. 136 Bennett, Sue 105–119, 122, 123, 129, 130, 131 Bidjerano, T. 89 big data 22, 25, 77, 214; comparison with ‘small data’ 215; impact on teaching 218–219; state power 220 Biggs, J. 36 Bigo, D. 220 Bilbie, Alex 235 Bill and Melinda Gates Foundation 218, 220 279 Blackboard 223 Blackmore, P. 135–136 blended learning 88, 90, 98, 100, 136, 137–138, 172 blogs 37–38, 76, 191, 231, 275; learning activities 260, 261–262; open education 153, 155; SAMR model 166; WordPress 234, 235, 236 Bloom, B. S. 36, 37, 245 Bloom’s taxonomy 36, 37, 125, 255–263 Blue Skies Planning Checklist 266–267 Bluetooth 192 Boitshwarelo, B. 141 Boivin, Nicole 56, 57 Bower, M. 128, 129, 131 Bowers, J. S. 23 Bowker, Geoffrey 77, 219 Bradwell, P. 229 brain areas 22–23, 24 Breakwell, N. 168 Brown, C. 154, 155 Bruer, J. T. 23 Bulfin, S. 6 CAIeRO (Creating Aligned Interactive educational Resource Opportunities) 130, 137–138, 143, 270–271 Cambridge Analytics 212 Campbell, L. 158 Campbell, P. 96 Carpe Diem 136–137, 142, 271 Carvalho, Lucila 11, 41, 49–65, 67 case-based reasoning 116 Caskurlu, S. 89 Cassidy, D. 168 Centre for Educational Research and Development (CERD) 227, 228, 233–236, 237 Chan-Zuckerberg Initiative 218, 220 change 3, 85; mobile technologies 184, 194; open education 159; organisational 139; resistance to 144; social 66, 184; strategic curriculum change 135–138, 141, 144, 145–146 checklists: Blue Skies Planning Checklist 266–267; Critics’ Checklist 268–269; digital capability checklist 264–265; learning activity design 251–254; learning designs 108; professional learning 204, 205 Chegg 223 280 Index Chi, M. T. H. 173–174 China 222 choice 145 ClassDojo 218 Cleveland-Innes, Martha 11, 41, 85–102 cloud services 4 co-construction 79 co-creation 7, 145, 157, 158, 192 COACT framework 168, 175 cognitive apprenticeship 20 cognitive modelling 22 cognitive perspective 17–18, 19, 20, 27, 34; see also constructive perspective cognitive presence 87, 88–89, 93–94, 95, 96, 97 Coker, H. 99–100 Coley, Rob 230 collaboration 41, 72–73, 175; ABC framework 172; assessment 248; design principles 247; digital and information literacy 276; digital capability checklist 265; ICAP framework 174; learning activities 262; mobile technologies 186, 190, 191; opportunities for dialogue 254; problem-based learning 201; SAMR model 166; 7Cs of Learning Design framework 169–170; teachers’ design practices 130 Collins, Harry 53, 56 commercial interests 184, 212, 218, 220 communication 41, 72–73; ABC framework 172; Deleuze on 230; digital and information literacy 276; employability 4; mobile technologies 191–192; online 99; professional learning 201–202; 7Cs of Learning Design framework 169; simulation 203; social presence 90–91 communities of practice 7, 19–20, 61, 244 community 42, 68, 92, 189–190 Community of Inquiry (CoI) theoretical framework 85–102 competencies 199, 206 Complementary Learning Systems theory 24 Computer Supported Collaborative Learning (CSCL) 72–73, 76 concept mapping 166 connectionism 21–22, 25 connectivism 24–25, 27, 41, 151, 190, 244–245 Conole, Gráinne 11, 164–178 consolidation 24, 33, 169–170, 172, 245, 254 constructive alignment 141–142, 246 constructive perspective 32, 33, 36, 41, 244–249; see also cognitive perspective constructivism 19, 27, 87, 190; see also cognitive perspective; social constructivism content design 188–189, 193 context 20–21, 45; local 62; mobile technologies 187, 193; open education 154–155; organisational 134–148, 167; problem-based learning 201; sociocultural 127–129; see also social context continuous learning 190 Cooley, M. 67 Cormier, D. 24, 231 costs 167, 208 Course Design Intensives 136–137, 140 Cox, G. 158 Cranmer, S. 7 critical pedagogy 42 critical theory 155–156 Critics’ Checklist 268–269 Cronin, Catherine 11, 41, 149–163 CSCL see Computer Supported Collaborative Learning culture: cultural attitudes 39; cultural context 11; cultural data 221–222; open education 158; professional 197; social realist theory 156; see also organisational culture curriculum: activity systems 44; changing demands 134; co-creation of 7; data 212, 213, 214, 216, 217, 223; drivers 139, 143; hidden 204–205; inclusive 39, 40; intentions 45; learning designs 112; learning outcomes 37–38; open education 153; real-world problems 9; stakeholder involvement 8; standardisation 6; strategic curriculum change 135–138, 141, 144, 145–146; student involvement in design 145, 227, 228–229, 237 Czerniewicz, L. 153, 154, 155 Index Dalziel, B. 207 Dalziel, J. 122, 123, 126, 207 Darrou, M. 127 data protection 222–223 datafication 212–226; see also big data Davies, William 219, 220 de Freitas, S. 17 De Stefani, M. 88 decision making 7, 201 deep learning 22 Deleuze, Gilles 76, 230–231, 233, 237 DeRosa, R. 152 Derry, J. 75–76 design: analysis for 49–65; assemblages 79–80; choices 66–67, 78; communication 72–73; design principles 185, 192–193, 247; design science 34; discussions of e-learning 10; form and function 57–58; frameworks for 164–178; inclusive 188; indirect nature of 67–70, 79, 80; learning activities 251–254; learning designs 105–119; learning theories 247–249; levels of 66–67, 71–72; mobile technologies 188–194; role of 183; student involvement 145, 227, 228–229; teachers’ design practices 120–133, 176; teaching presence 92–93; see also design for learning design for learning 6–10, 33, 44, 79; analysis for design in complex learning environments 49, 53–55, 62; change 85; Community of Inquiry framework 90–96; digital tools 120; dimensions of 121; mobile technologies 181, 182, 184, 185–188, 192–193; organisational context 134–148; practice of 122; professional learning 197, 198, 207, 208; see also learning design thinking: individual cognitive acts 129, 131; learning designs 108, 109, 116; meso level 35 developing countries 222 developmental psychology 26 devices 43; see also artefacts; mobile technologies Dewey, J. 87 dialogue 41, 42, 72, 144, 252, 254 digital age 3–6 digital and information literacy 272–277 281 digital capability 37, 39, 264–265 digital professionalism 197, 205 digital technologies: ABC framework 172, 173; activities 45, 255; agency 74, 76; analysis for design 51, 55, 57; assemblages 76; benefits of 174–175; datafication 214, 215, 216; design decisions 164, 176; distractions 154; games 203; inclusive practice 40; open education 151; professional learning 205; rhizomatic pedagogy 231, 232–233; SECTIONS framework 167; teachers’ design practices 120–121, 129–131, 132; see also online learning; technology; tools direct instruction 86, 89, 93 disabilities 39 disciplinary differences 124–125, 144 discussion forums 175, 191, 262 distance education 5, 68, 69, 85–86; see also online learning distraction 23, 154 divisions of labour 42, 51 double constructive alignment framework 141–142 Downes, S. 25 Dreyfus, H. 198, 207 Dreyfus, S. 198, 207 Duckworth, A. 25 dyslexia 188 e-learning 2, 10, 184, 187, 192, 194 e-mail 191 e-portfolios 5, 186, 236 ease of use 167, 176 ecological psychology 52–53 ‘economies of presence’ 207 EdModo 223 EdtechUK 222 educational neuroscience 22–23, 27 Edwards, M. 97 Edwards, R. 52, 155 effortful learning 26 8Learning Events Model (8LEM) 170–171, 176 Ellaway, Rachel H. 11, 197–211 embodied learning 191, 207 emotional presence 89, 94–96 emotions 25–26, 90 employability 4, 135, 139, 140, 172 empowerment 153, 168 282 Index engagement 27, 35, 88, 145, 173–174, 253 Engeström, Y. 35–36, 42, 44 Entwistle, N. 123 ‘epistemic architecture’ 54 epistemologies 216–217 ethics 204, 212, 222–223, 265 Europe 221, 222 evaluation 7, 50 Every Student Succeeds Act (ESSA, 2015) 223 evidence-based practice 201 expectations 4, 40, 144–145; mobile technologies 192, 194; rhizomatic pedagogy 232 exploration 95 Facebook 72, 189, 190, 212, 218, 262 Facer, K. 154 faculty development 98–99 Farley, C. 158 Farmer, R. 137–138 feedback 21, 33, 175, 246, 248–249; ABC framework 172; associative perspective 18; Critics’ Checklist 268–269; data 214; design principles 247; learning activity design checklist 252, 253, 254; professional learning 199, 201, 202, 205; programmed instruction 18; reviewing 260; teaching presence 93 Fenwick, T. 52 fidelity 204 Fincham, J. M. 23 flexibility 145, 189 Flickr 231 flipped classrooms 136, 166 formative assessment 173, 205, 206, 267 forums 175, 191, 262 Foucault, Michel 78, 214 Fourth Paradigm 77 frameworks 164–178 Frank, M. C. 27 Free Culture movement 233, 237 Freire, P. 2, 228 funding 236 Gaba, D. 202 games 203, 204 Garrison, D. 87, 89, 90, 94, 97 Gaver, W. W. 75 Gelman, S. A. 20 gender 39 General Data Protection Regulation (GDPR) 221, 222, 223 Germany 221 Gibson, James 52, 56, 75 Global South 155, 222 globalisation 78–79 Goodman, N. D. 27 Goodyear, Peter 11, 41, 49–65, 67, 72 Google Docs 175 Google Drive 166 Google’s G Suite for Education 72 Gopnik, A. 26 Gordon, G. 143 Gourlay, L. 145, 155 graduate attributes 124, 135, 136, 137, 140; datafication 217; digital and information literacy 272; digital capability 37; organisational culture 144; programme-level design 141, 142 Green, M. 97 Gross, J. J. 25 group cohesion 90–91 group design 113; see also team-based design Guattari, Felix 76, 230–231 Gunawardena, C. N. 90, 167 Guribye, F. 71 hacking 237–238 Harper, Barry 105–119 Hassabis, D. 24 Hibernia College 168 hidden curriculum 204–205 Hillman, D. C. A. 167 Horwitz, R. A. 150–151 humanism 124 ICAP framework 173–174, 175, 176 ideologies 184 immersive environments 43–44, 183, 203 implicit learning 22, 26–27, 28 IMS Learning Design (IMS-LD) specification 114–115 inclusion 39, 40, 185, 187 inclusive design 188 indirect approach 67–68, 79, 80 individualised learning 25–26 inequalities 3, 6, 39, 149, 152, 155–156 Index infographics 259, 261 informal communities 128 informal conversations 176 informal learning: activity systems 44; mobile technologies 181, 182, 185, 187, 190, 191, 192 infrastructure 5, 71–72, 76–77, 185, 215–216 innovation 222, 234, 235 inquiry-based learning/teaching 86–87, 99, 127, 152 inspections 221 instant messaging 192 institutions 5–6, 10, 229–230; big data 77; challenges 149–150; datafication 212, 215–216, 219; ideologies 184; intellectual property 157; learning designs 112–113; meso level 67; mobile technologies 181, 183, 194; open education 155, 158–159; product and service quality 134–135; strategic curriculum change 135–138, 141, 144, 145–146; strategic development 134, 138–139; teachers’ design practices 121 Instructional Systems Design 18 integration 246, 247; cognitive neuroscience 245; cognitive presence 94, 95; learning activities 33; learning designs 109; opportunities for 254; SAMR model 165 intellectual property 157, 183 intentionality 36, 44–45, 66, 79 interaction 167, 174, 175 internet: open education 151; participative web 18, 24–25, 27; see also online learning; social media; websites interpretation 54, 56, 57, 58, 62 interprofessional education 203 iPads 58–59 iPhones 188 Isin, E. 220 iTunes 72 Jackson, K. 25 Jackson, Nick 235 Jacobsen, R. 220 Janzen, K. 97 Jisc 37, 139, 154, 234, 235, 255, 264 Jones, Christopher R. 11, 35, 41, 66–84, 135 283 Jones, Jennifer 105–119 Kahneman, Daniel 56–57 Kandiko, C. 135–136 Kane, M. T. 206 Kaptelinin, V. 74, 76 Kirkwood, A. 3–4 Kitchin, Rob 215, 216 knowledge: access to 10; datafication 216; ‘epistemic architecture’ 54; ICAP framework 174; Lyotard on 214; networks 52; paramedic field training 59; pedagogical content knowledge 108–109; practices 61–62; professional learning 197, 198, 199–201, 205; sharing 238; shift from ‘mode 1’ to ‘mode 2’ knowledge 9; social production of 229; ‘Student as Producer’ project 228 Knowles, Malcolm 1 Knox, J. K. 74 Kolb, D. 125, 126 Kozan, K. 89 Krathwohl, D. R. 245 Kress, Gunther 40 Kuhn, C. 6, 154 Kukulska-Hulme, Agnes 11, 181–196 Kumaran, D. 24 languages 22 laptops 43 large-scale assessments (LSAs) 214, 218 Latour, B. 78 Laurillard, D. 3, 8–9, 36–37, 126, 138, 176, 245 Lave, J. 19–20 Lawes, S. 126 learner-created/learner-curated content 189 learners 25–26, 27, 35, 40, 42; autonomous 50–51; engagement 173–174; learner-centered approach 38–39, 137, 153; learning activity design checklist 251–252, 253; mobile technologies 192–193, 194; professional learning 198–199; 7Cs of Learning Design framework 169 learning: analysis for design in complex learning environments 49–65; Blue Skies Planning Checklist 267; COACT framework 168, 175; 284 Index Community of Inquiry framework 85–86, 87–97, 99–100; context 20–21; definition of pedagogy 1, 2; educational neuroscience 22–23; implicit 22, 26–27, 28; indirect approach 67–70; individualised 25–26; inquiry-based 86–87, 99, 127, 152; integration of technology 7; lifelong 2, 40–41, 105, 190; mobile technologies 181–183, 185–192; multi-level models 23–24; neural network theory 21–22; open education 149; participative web 24–25; problem-based 199–201; professional 143–144, 197–211; 7Cs of Learning Design framework 168–170; situated 41, 72, 181, 185–186, 187, 190, 192, 193; social environment for 40–42; theories of 17–31, 34, 125, 244–249; vicarious 21; see also design for learning; informal learning; online learning learning activities 32–48, 56, 67–68, 138; ABC framework 172; considerations for design 251–254; Critics’ Checklist 268; defining 34–36; 8LEM 170, 176; examples 249; learning theories 246; linked to Bloom’s taxonomy 255–263; mobile technologies 190–191, 193; professional learning 199–208; see also tasks learning analytics 77–78; datafication 213, 214, 215, 216–217, 218–219; Global South 222; professional learning 197, 206, 207; research 223 Learning Design Tools project 121 Learning Design Visual Sequence (LDVS) 106, 107, 108, 110 Learning Designer project 121–122, 123, 124–125, 128, 129–131 learning designs 105–119, 136 learning environments 51, 52, 249 Learning Management Systems (LMS) 71, 98–99, 164; data 215; learning designs 114–115; professional learning 207; social media 167 learning outcomes 34, 36–38, 134, 165; analysis for design 51, 55–56; Bloom’s taxonomy 37, 255; Community of Inquiry framework 92; constructive alignment 246; Critics’ Checklist 269; data 212, 213, 214; digital and information literacy 272–277; intentions 45; learning activity design checklist 251; programme-level design 141–142 lectures 43, 99–100, 122, 124, 174, 257 Lee, H. S. 23 legal issues 222–223 Lewin, C. 7 lifelong learning 2, 40–41, 105, 190 Light, B. 156 Lim, C. P. 222 Lindström, B. 71 Lipman, M. 87 literacy 4, 38 LMS see Learning Management Systems LNCD 236–238 location 186, 193 Lockwood, Dean 11, 227–239 Lockyer, Lori 105–119, 122, 129, 130, 131 locus of control 34, 39, 183, 246 longitudinal assessment designs 205–206 LSAs see large-scale assessments Lynch, C. 129 Lyotard, Jean-Francois 214 m-moderating 189–190 MacDonald, C. 97 Macgregor Wise, J. 233 machine learning 22, 244–245; see also artificial intelligence MacLaren, I. 152 macro level 53, 55, 66–67, 71, 79 Marshall, H. 151 mass customisation 6, 185 Massive Open Online Courses (MOOCs) 6, 24, 74, 150, 152, 153, 190 Masterman, Liz 11, 120–133, 136, 175, 176 mastery 18, 206 materiality 52, 56, 57, 208 Mayes, Terry 9, 10–11, 17–31, 191 McClelland, J. L. 21, 24 McNicol, S. 7 meaning 19 media, preferred 39–40 mediation 44 Mejias, U. A. 156 Index 285 Naismith, L. 190 Nardi, B. 74, 76 Neary, Mike 228, 233 needs analysis 49–50 Nemirovsky, R. 25–26 networked learning 41, 72–73 networks 5, 6, 149; analysis for design 49, 51–52, 53, 55, 62; critical theory 156; digital capability checklist 265; learning theories 244–245; open education 159; personal learning networks 190; SECTIONS framework 167; see also social networks neural networks 17–18, 21–22, 23–24, 27, 244–245 neuroscience 22–23, 27, 244–245 Newman, T. 154 Nickerson, R. S. 28 nodes 73 Norman, D. A. 75 notes 257 numeracy 4, 38 OER see open educational resources Ofsted 221 oligopticon 78 Oliver, M. 75, 145 Olpak, Y. Z. 87 O’Meara, Adam 230 online learning 5; Carpe Diem 136; COACT framework 168; Community of Inquiry framework 85–86, 87–90, 96–97, 98, 99–100; digital and information literacy 276; educational leadership 60–62; open educational practices 152; problem-based learning 201; social presence 90–92; see also distance education; internet open access (OA) 150 open education 149–163 open educational practices (OEP) 150, 151–152, 153–155, 157–158 open educational resources (OER) 150, 151, 152–155, 157–158, 169, 234 Open Journal Systems 233–234 open textbooks 152 Open University Learning Design (OULDI) project 8 Open University of China 98 Open.Ed 157–158 openness 78–79, 150–151, 154, 158; critical approaches 155; open communication 90–91; ‘Student as Producer’ project 227, 228, 229, 233, 234–236, 238 organisation 68, 167 organisational context 134–148, 167 organisational culture 139, 144, 146, 158, 159 outcomes 34, 36–38, 134, 165; analysis for design 51, 55–56; Bloom’s taxonomy 37, 255; Community of Inquiry framework 92; constructive alignment 246; Critics’ Checklist 269; data 212, 213, 214; digital and information literacy 272–277; intentions 45; learning activity design checklist 251; programme-level design 141–142 Oxford Brookes University 136–137, 140, 141–142, 267, 269, 272–277 objectives 35, 42 OEP see open educational practices Palloff, R. 86 Pangrazio, L. 6 Meltzoff, A. N. 17 memory 23, 24 mentors 41 meso level 35, 53, 55, 66, 67, 71, 79, 122, 135 metacognition 97, 246, 248 micro-blogging 183, 190, 191, 192; see also Twitter micro level 53, 55, 67, 71, 79 Microsoft 365 Education 72 Miller, G. E. 198 mindmapping 257, 261 Mirosa, R. 127 mobile technologies 43, 76, 151, 154, 181–196 mobilities 78–79, 145 modification 165–166 MOOCs see Massive Open Online Courses Moodle 71 Moore, M. G. 167 motivation 35, 39, 40, 253 Mulgan, G. 219 multi-level models 23–24 multiple media 189 multiprofessional education 203 286 Index panopticon 78 paramedic field training 58–60 participation: CAIeRO workshops 137; communication and dialogue 72; Community of Inquiry framework 92; frameworks 175; mobile technologies 185, 193; ‘new literacies of ’ 24; open education 151, 153, 156, 158; participatory culture 149, 159; student engagement 144–145 participative web 18, 24–25, 27 PBL see problem-based learning Pearson 217, 223 pedagogical content knowledge (PCK) 108–109 pedagogy: affordances 80; agency 73; cognitive perspective 19; critical digital 42; data 212, 213, 216, 223; definition of 1–3; design for learning 49; ‘of excess’ 229; learning designs 108, 109, 111; mobile technologies 182, 183; multiple perspectives 20; new science of learning 27–28; open educational practices 151–152; pedagogy planner tools 121; professional learning 198; rhizomatic 230–233, 237; ‘of scarcity’ 153; ‘Student as Producer’ project 227–228 peers: dialogue with 41; peer-teaching 88, 92 Pegrum, M. 182 Perović, N. 130, 136, 172 Perry, B. 97 personal expression 90–91 personal learning networks 190 personal learning records 5 personalisation: active blended learning 137; datafication 217, 218; Every Student Succeeds Act 223; mobile technologies 181, 185–186, 187, 189, 192, 193; programmed instruction 18 Philip, T. M. 25 Phoebe project 121, 129 phonespace 192, 193 photography 230–233 physical architecture 55 physical setting 53, 54–55 Piaget, J. 19 Pickering, A. 74 place 68 planning 80, 122, 123, 130, 254; Blue Skies Planning Checklist 266–267; strategic 139, 146 Plato 2 podcasts 172, 189 policy 156–158, 223 Poole, G. 167 portfolios 37–38, 206, 207, 254 Portnoy, L. 167 Powell, A. 229 power 10, 42, 149; critical theory 155, 156; datafication 213–214, 219–221; open education 152, 158 PowerPoint 166, 175 practicals 122 practicum 202, 205–206 Pratt, K. 86 praxis 2, 9, 228, 237 preferred media for learning 39–40 presence 85, 87–97, 207 presentations 166, 172, 267, 275 Price, L. 3–4 prioritized content 189 privacy 4, 42, 156, 167, 222–223, 265, 276 private learning 190 problem-based learning (PBL) 199–201 problem solving: assessment 248; cognitive perspective 19; design principles 247; effortful learning 26; employability 4; professional learning 201–202; programmed instruction 18; quizzes 201; teachers’ design practices 131–132 professional development 60–62, 198–199, 206, 208, 275 professional learning 143–144, 197–211 professionalism 197, 204, 205 programme-level design 141–142 programmed instruction 18 progression 246, 248 quizzes 201, 205, 248, 249, 257 Rebuschat, P. 26 redefinition 165–166 reflection 19, 24, 57, 126, 246, 254; COACT framework 168; design principles 247; digital and information literacy 275; learning activities 260; metacognition 248; Index mobile technologies 185–186, 191; praxis 2, 228 relationships 49, 50, 51, 90, 247 reliability 206 research-informed teaching 126–127 resolution 94, 95 resources: activity systems 42; Blue Skies Planning Checklist 267; educational leadership 61; learning activities 252, 255–263; mobile technologies 188; online 182; open educational resources 150, 151, 152–155, 157–158, 169, 234; SAMR model 166; 7Cs of Learning Design framework 169; social 244; see also tools reusability 106, 108, 109, 111, 115, 116, 125 rhizomatic learning/pedagogy 24, 27, 230–233, 237 Richardson, J. 90 Rienties, B. 8, 96 Rivers, B. A. 96 robots 203, 236 rules 42, 51 Rumelhart, D. E. 21 Ruppert, Evelyn 220 Rutledge, S. A. 220 Säljö, R. 168 Salmon, G. 86 SAMR model 165–167, 175 Sawchuk, P. 52 Sawyer, R. K. 17 scaffolding 9, 45, 57, 244, 253 scale 53, 55 scheduled content 189 Schön, D. A. 202 Schwab, J. 86–87 second language learning 26 SECTIONS framework 167, 175 security 167, 223 self-control 25, 27 self-efficacy 39 self-explanation 19, 21 self-regulation 21 Selwyn, N. 6, 154, 184 seminars 122 7Cs of Learning Design framework 168–170, 175 Sfard, A. 20–21 Shafto, P. 27 287 Sharpe, Rhona 1–14, 134–148 Shea, P. 89 Siemens, G. 24–25 Silicon Valley 218, 222 simulations 202–203, 204, 206, 207, 249, 258–259 situated action 67 situated learning: mobile technologies 181, 185–186, 187, 190, 192, 193; social context 41; social sciences 72 situative perspective 18, 19–20, 27, 34, 244–249; integration of multiple perspectives 20; learning activities 32, 33; learning outcomes 36; mentors 41 skills: digital capability checklist 265; professional learning 197, 199, 201–204, 205 smartphones 154, 201; see also mobile technologies SMS 191, 192 social architecture 55 social change 66, 184 social constructivism 20, 151, 244–249 social context 11, 41, 54, 130, 131, 187, 228 social data 218–219 social media 4, 41, 74, 156; digital and information literacy 276; as a distraction 23, 154; informal practices 185; learning activities 262; mobile 76; open education 155; responsible use of 205; SAMR model 166; SECTIONS framework 167; 7Cs of Learning Design framework 169; see also Facebook; Twitter social networks 156, 183, 190, 192, 194 social neuroscience 23 social presence 87, 88–89, 90–92, 93, 96, 97, 98 social realism 156–157 social sciences 66, 72–74, 77–79, 124–125, 223 social setting 53, 54–55 socio-material analysis 52 socio-technical systems 76 sociocultural context 127–129 space 68 spacing effect 21, 24 Spronken-Smith, R. 127 Stacey, R. 139, 144 288 Index stakeholders 8 standardisation 5, 6, 10, 218 statistical learning 22, 26, 27, 28 Stewart, B. 24 Stodel, E. 97 storyboarding 130, 137, 172, 270–271 structure 74, 156 structured learning modules (SLMs) 61 ‘Student as Producer’ project 227–238 student-centred approach 123–124; see also learners student expectations 144–145 student models 217 substitution 165–166 Suchman, L. 19 summative assessment 205, 206, 267 support: computational 132; Critics’ Checklist 268; learning designs 113–115, 117; supported learning 190 surveillance 78, 219 Swan, K. 90 Swinnerton, B. 6 System 1/System 2 thinking 56–57 systems theory 18 tasks 53, 54, 67–68, 69; analysis for design 51, 62; design principles 247; mobile learning 186, 191; professional learning 199, 202, 205; see also activities teachers: agency 10, 79; associative perspective 41; design practices 120–133, 176; 8LEM 171; impact of datafication 218–219; learning designs 105, 107–117; online learning 60–62; open education 154, 158, 159; team-based design 8 teaching: activity systems 44; adaptive 212; Blue Skies Planning Checklist 267; Community of Inquiry framework 86, 87–89, 92–93; constructive alignment 246; definition of pedagogy 2; as design 9–10; design principles 3, 247; impact of datafication 218–219; inquiry-based 86–87, 99; integration of technology 7; learning designs 116; mobile technologies 183; professional learning 200; research-informed 126–127; SECTIONS framework 167; teaching cycle 122, 123, 126; theories of 34, 125 teaching presence 87, 88–89, 92–93, 96, 97, 98 team-based design 8, 138, 141, 142–143, 144, 146 technological pedagogical content knowledge (TPCK) 109 technology 3–5; affordances 52–53; agency 76; analysis for design 57; assemblages 76, 79; Blue Skies Planning Checklist 267; Community of Inquiry framework 85; constructivism 19; Critics’ Checklist 269; digital and information literacy 272–276; disruptive influences 145; faculty development 98–99; frameworks for guiding the use of 165–168; ideologies 184; integration 7, 135; learning designs 106, 108–109; online learning for educational leadership 60–62; paramedic field training 58–60; professional learning 204, 205, 207, 208; rhizomatic pedagogy 231, 232–233; social change 66; ‘Student as Producer’ project 227, 228, 229; student demands 124, 134; technological determinism 57, 75, 125; see also digital technologies; mobile technologies; online learning; tools testing 201 textbooks, open 152 texts 43, 51, 54, 57 theory 2, 17–31, 77, 125–126, 243–250 ‘third space professionals’ 143 Thompson, T. 97 Thomson, J. 176 time content 189 Tinio, V. L. 222 Toetenel, L. 8 tools 7, 43–44; activity systems 42; analysis for design 49, 50–51, 55–56; digital capability checklist 265; ICAP framework 174; learning activities 252, 255–263; paramedic field training 59–60; professional learning 208; qualities 57; teachers’ design practices 120–121, 129–131, 132; see also digital technologies Index TPCK see technological pedagogical content knowledge training 198 Traxler, John 11, 181–196 triggering events 94, 95 Trigwell, K. R. 176 Trotter, H. 158 Twitter 153, 166, 190, 191 UCL see University College London Ulster University 171 unbundling 5 United States 222, 223 universities see institutions University College London (UCL) 128–129 University of Cape Town 153, 157 University of Edinburgh 157–158, 168 University of Leicester 271 University of Lincoln 227–238 University of Northampton 137–138, 271 University of Oxford 120, 128–129 up-to-date content 189 user-centered design 7 Usher, J. 137–138 Virtual Learning Environments (VLE) 71 virtual reality 187 Vygotsky, L. S. 20, 41 Walker, S. 128, 129, 131 Watson, David 134 Waxman, S. R. 20 Web 2.0 76, 181–182, 183 web-based conferencing 192 websites 5, 166, 172; see also internet Wenger, E. 19–20 WhatsApp 189 Whitchurch, C. 143 wikis 76, 262, 263 Williamson, Ben 11, 212–226 Willis, D.J. 167 Winn, Joss 11, 227–239 Winograd, T. 122 women 4, 190 WordPress 234, 235, 236 workshops 7, 8, 139; CAIeRO 137–138; frameworks for 168–173; UCL 128–129 Wylie, R. 173–174 Xin, C. 99 validity 204, 206 values 54, 61–62, 139 van der Vaart, L. 159 Vemuri, S. 141 vicarious learning 21 video 172, 175, 192, 256–257, 261 Villina, R. 171 Vimeo 231 virtual environments 76, 203 289 Yagci, M. 87 Young, C. P. L. 130, 136, 172 YouTube 72, 74 Zamorski, B. 127 Zittle, F. 90 Zuckerberg, Mark 218 Zundans-Fraser, L. 136