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. In these challenging times, teachers must be free to
respond critically, as well as creatively, to the new technologies on offer and
the new imperatives that they bring.
12
Helen Beetham and Rhona Sharpe
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.
Asensio-Pérez, J., Dimitriadis, Y., Pozzi, F., Davinia, H.-L., Prieto, L., Persico, D., &
Villagra, S. (2017). Towards teaching as design: Exploring the interplay between
full-lifecycle learning design tooling and teacher professional development, Computers & Education, 114, 92–116.
Barnett, R. (2014). Conditions of flexibility. York: Higher Education Academy.
Barrie, S.C. (2007). A conceptual framework for the teaching and learning of
generic graduate attributes, Studies in Higher Education, 32(4), 439–458.
Beetham, H., & White, D. (2013). Students’ expectations and experiences of the digital
environment. Bristol: Jisc.
Bovill, C., Cook-Sather, A., Felten, P., Millard, L., & Moore-Cherry, N. (2016).
Addressing potential challenges in co-creating learning and teaching: Overcoming
resistance, navigating institutional norms and ensuring inclusivity in student–staff
partnerships, Higher Education, 71(2), 195–208.
Bower, M. (2017). Design of technology-enhanced learning: Integrating research and
practice. Bingley, UK: Emerald Publishing Limited.
Brookes, C., & Pomerantz, J. (2017). ECAR study of undergraduate students and information technology, 2017. Louiseville, CO: Educause Centre for Applied Research.
Conole, G., & Culver, J. (2010). The design of cloudworks: Applying social networking practice to foster the exchange of learning and teaching ideas and
designs, Computers and Education, 54(3), 679–692.
Cross, S., Galley, R., Brasher, A., & Weller, M. (2012). Final project report of the
OULDI-JISC project: Challenge and change in curriculum design process, communities, visualisation and practice. Milton Keynes, UK: The Open University.
Curtarelli, M., Gualteiri, V., Shater Jannati, M., & Donlevy, V. (2017). ICT for work:
Digital skills in the workplace. A study for the European Commission. European
Union. Retrieved from https://ec.europa.eu/digital-single-market/en/news/ictwork-digital-skills-workplace
Czerniewicz, L. (2018) Inequality as higher education goes online. In N. Bonderup
Dohn, S. Cranmer, J.-A. Sime, M. de Laat & T. Ryberg (Eds.), Networked learning:
Reflections and challenges (pp. 95–106). New York: Springer.
Dalziel, J., Conole, G., Wills, S., Walker, S., Bennett, S., Dobozy, E., & Bower, M.
(2016). The Larnaca declaration on learning design, Journal of Interactive Media
in Education, 2016(1), 7.
Davies, S., Mullen, J., & Feldman, P. (2017). Rebooting learning for the digital
age: What next for technology enhanced higher education? Oxford, UK:
Higher Education Policy Institute.
De Stefano, V. (2016). The rise of the just-in-time workforce: On-demand work,
crowdwork, and labor protection in the gig-economy, Comparative Labour Law
and Policy, 37(3), 471–504.
Downes, S. (2013). MOOC - The resurgence of community in online learning [blog
post], 30 May 2013, Retrieved from http://halfanhour.blogspot.co.uk/2013/05/
mooc-resurgence-of-community-in-online.html
An Introduction to Rethinking Pedagogy
13
Evans, S., & Myrick, J.G. (2015). How MOOC instructors view the pedagogy
and purposes of massive open online courses, Distance Education, 36(3),
295–311.
Fanghanel, J., McGowan, S., Parker, P., McConnell, C., Potter, J., Locke, W., &
Healey, M. (2016). Literature review. defining and supporting the scholarship of
teaching and learning (SoTL): A sector-wide study. York: Higher Education
Academy.
Freire, P. (1970). Pedagogy of the oppressed. New York: Bloomsbury Academy.
Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M.
(1994). The new production of knowledge. London: Sage.
Goodyear, P. (2015). Teaching as design, HERDSA Review of Higher Education, 2,
27–50.
Hall, R., & Smyth, K. (2016). Dismantling the curriculum in higher education, Open
Library of Humanities, 2(1), e11, 1–28.
Hassan, I. (1988). The postmodern turn: Essays in postmodern theory and culture.
Ohio: Ohio State UP.
Johnston, B., MacNeill, S., & Smyth, K. (2019). Conceptualising the digital university:
The intersection of policy, pedagogy and practice. London: Palgrave Macmillan.
Kemmis, S. (2010). Research for praxis: Knowing doing, Pedagogy, Culture & Society,
18(1), 9–27.
Kirkwood, A., & Price, L. (2014). Technology enhanced learning and teaching in
higher education: What is ‘enhanced’ and how do we know? A critical literature
review, Learning, Media and Technology, 39(1), 6–36.
Knowles, M.S. (1990). The adult learner: A neglected species (4th ed.) Houston: Gulf
Publishing.
Knox, J. (2013). The limitations of access alone: Moving towards open processes in
education technology, Open Praxis, 5(1), 21–29.
Kuhn, C. (2017). Are students ready to (re)-design their Personal Learning Environment? The case of the E-Dynamic.Space. Journal of New Approaches in Educational
Research, 6(1), 11–19.
Laurillard, D. (2012). Teaching as a design science: Building pedagogical patterns for
learning and technology. New York and Oxford: Routledge.
Laurillard, D., Kennedy, E., Charlton, P., Wild, J., & Dimakopoulos, D. (2018). Using
technology to develop teachers as designers of TEL: Evaluating the learning
designer, British Journal of Educational Technology, 49(6), 1–15.
Lewin, C., Cranmer, S., & McNicol, S. (2018). Developing digital pedagogy
through learning design: An activity theory perspective, British Journal of
Educational Technology, 49(6), 1131–1144.
Liyanagunawardena, T., Kennedy, E., & Cuffe, P. (2015). Design patterns for
promoting peer interaction in discussion forums in MOOCs, eLearning
Papers, 42, 1–16.
Liyanagunawardena, T., Lundqvist, K., & Williams, S. (2015). Who are with us: MOOC
learners on a FutureLearn course, British Journal of Educational Technology, 46(3),
557–569.
Masterman, E., & Manton, M. (2011). Teachers’ perspectives on digital tools for
pedagogic planning and design, Technology, Pedagogy and Education, 20(2),
227–246.
14
Helen Beetham and Rhona Sharpe
McAndrew, P., & Goodyear, P. (2013) Representing practitioner experiences through
learning designs and patterns. In H. Beetham & R. Sharpe (Eds.), Rethinking
pedagogy for a digital age: Designing for 21st century learning (2nd ed.) (pp. 133–
144). London and New York: Routledge.
Mor, Y., Warburton, S., & Winters, N. (2012). Participatory pattern workshops:
A methodology for open learning design inquiry, Research in Learning Technology, Supplement, doi: 10.3402/rlt.v20i0.19197.
Nel, L. (2017). Students as collaborators in creating meaningful learning experiences
in technology-enhanced classrooms: An engaged scholarship approach, British
Journal of Educational Technology, 48(5), 1131–1142.
Oliver, M., Avramides, K., Clark, W., Hunter, J., Luckin, R., Hansen, C., &
Wasson, B. (2017). Sharing teacher knowledge at scale: Teacher inquiry, learning
design and the representation of teachers’ practice, Teacher Development, 22(4),
587–606.
Rohs, M., & Ganz, M. (2015). MOOCs and the claim of education for all:
A disillusion by empirical data, International Review of Research in Open and
Distributed Learning, 16(6), 1–19.
Salmon, G., & Wright, P. (2014). Transforming future teaching through ‘Carpe
Diem’ learning design, Education Sciences, 4(1), 52–63.
Schön, D.A. (1987). Educating the reflective practitioner. 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). Three
aspects of skill acquisition. doi:10.31234/osf.io/rh6zt
Anderson, J. R. (2007). How can the human mind occur in the physical universe?
Oxford, UK: Oxford University Press.
Anderson, J. R., Lee, H. S., & Fincham, J. M. (2014). Discovering the structure of
mathematical problem solving, NeuroImage, 97, 163–177.
Andringa, S., & Rebuschat, P. (2015). New directions in the study of implicit and
explicit learning, Studies in Second Language Acquisition, 37, 185–196.
Bandura, A. (1965). Vicarious processes: A case of no-trial learning, in L. Berkowitz
(Ed.), Advances in experimental social psychology, Vol. 2 (1–55). New York:
Academic Press.
Bayne, S. (2015). What’s the matter with ‘technology-enhanced learning?, Learning,
Media and Technology, 40(1), 5–20.
Benjamin, L. T. (1988). A history of teaching machines, American Psychologist, 43
(9), 703–712.
Learning Theory and the New Science of Learning
29
Bisra, K., Liu, Q., Nesbit, J. C., Salimi, F., & Winne, P. H. (2018). Inducing
self-explanation: A meta-analysis, Educational Psychology Review, 30, 703–725.
Boto, E., Holmes, N., Leggett, J., Roberts, G., Shah, V., Meyer, S. S., …
Brookes, M. J. (2018). Moving magnetoencephalography towards real-world
applications with a wearable system, Nature, 555, 657–661.
Bowers, J. S. (2016). The practical and principled problems with educational
neuroscience, Psychological Review, 123(5), 600–612.
Bruer, J. T. (1997). Education and the brain: A bridge too far, Educational
Researcher, 26(8), 4–16.
Burton, J. K., Moore, D. M., & Magliaro, S. G. (2003). Behaviorism and instructional technology, in D. Jonassen & M. Driscoll (Eds.), Handbook of research on
educational communications and technology (3–36). New York: Routledge.
Cepeda, N. J., Vul, E., Rohrer, D., Wixted, J. T., & Pashler, H. (2008). Spacing effects
in learning: A temporal ridgeline of optimal retention, Psychological Science, 19
(11), 1095–1102.
Chi, M. T. H. (2009). Active-constructive-interactive: A conceptual framework for
differentiating learning activities, Topics in Cognitive Science, 1(1), 73–105.
Clarà, M., & Barberà, E. (2013). Learning online: Massive open online courses
(MOOCs), connectivism, and cultural psychology, Distance Education, 34(1),
129–136.
Collins, A., & Kapur, M. (2014). Cognitive apprenticeship, in R. K. Sawyer
(Ed.), Cambridge handbook of the learning sciences (109–127). Cambridge,
UK: Cambridge University Press.
Cormier, D. (2008). Rhizomatic education: Community as curriculum, Innovate Journal of Online Education, 4(5). Retrieved from https://nsuworks.nova.edu/innovate/
Day, S. B., & Goldstone, R. L. (2012). The import of knowledge export: Connecting
findings and theories of transfer of learning, Educational Psychologist, 47(3),
153–176.
Downes, S. (2012). Connectivism and connective knowledge: Essays on meaning and
learning networks. Ottawa, ON: National Research Council. Canada.
Duckworth, A., & Gross, J. J. (2014). Self-control and grit, Current Directions in
Psychological Science, 23(5), 319–325.
Duckworth, A. L., & Seligman, M. E. P. (2017). The science and practice of
self-control, Perspectives on Psychological Science, 12(5), 715–718.
Fitts, P. M., & Posner, M. I. (1967). Human performance. Oxford: Brooks Cole.
Gagné, R. M. (1985). The conditions of learning and theory of instruction (4th ed.).
New York: Holt: Rinehart and Winston.
Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T., & Danks, D.
(2004). A theory of causal learning in children, Psychological Review, 111(1), 3–32.
Gopnik, A., O’Grady, S., Lucas, C. G., Griffiths, T. L., Wente, A., Bridgers, S., …
Dahl, R. E. (2017). Changes in cognitive flexibility and hypothesis search across
human life history from childhood to adolescence to adulthood, Proceedings of the
National Academy of Sciences of the United States of America, 114(30), 7892–7899.
Gureckis, T. M., & Markant, D. B. (2012). Self-directed learning: A cognitive and
computational perspective, Perspectives on Psychological Science, 7(5), 464–481.
Howard-Jones, P. A. (2014). Neuroscience and education: Myths and messages,
Nature Reviews: Neuroscience, 15(12), 817–824.
30
Terry Mayes
Howard-Jones, P. A., Varma, S., Ansari, D., Butterworth, B., De Smedt, B.,
Goswami, U., … Thomas, M. S. C. (2016). The principles and practices of educational neuroscience: Comment on Bowers (2016), Psychological Review, 123(5),
620–627.
Ito, T.A. (2013) Imaging the pictures in our heads: Using ERPs to inform our understanding of social categorization, in B. Derks, D. Scheepers, & N. Ellemers (Eds.)
Neuroscience of prejudice and intergroup relations (25–44), New York, NY: Psychology Press.
Jonassen, D., Mayes, T., & McAleese, R. (1993). A manifesto for a constructivist
approach to uses of technology in higher education, in T. M. Duffy, J. Lowyck,
D. H. Jonassen & T. M. Welsh (Eds.), Designing environments for constructive
learning (231–247). Heidelberg: Springer.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and
prospects, Science, 349(6245), 255–260.
Kirschner, P. A. (2017). Stop propagating the learning styles myth, Computers and
Education, 106, 166–171.
Kumaran, D., Hassabis, D., & McClelland, J. L. (2016). What learning systems do
intelligent agents need? Complementary Learning Systems Theory updated,
Trends in Cognitive Sciences, 20(7), 512–534.
Lave, J., & Wenger, E. (1991). Situated learning. Cambridge, UK: Cambridge
University Press.
LeCun, Y., Bengio Y., & Hinton, G. (2015). Deep learning, Nature, 521, 436–444.
Lee, M. J. W., Kirschner, P. A., & Kester, L. (2016). Learning analytics in massively
multi-user virtual environments and courses, Journal of Computer Assisted Learning, 32(3), 187–189.
Mace, F. C., & Critchfield, T. S. (2010). Translational research in behavior analysis:
Historical traditions and imperative for the future, Journal of the Experimental
Analysis of Behavior, 93(3), 293–312.
Mayes, J. T. (2015). Still to learn from vicarious learning, E-Learning and Digital
Media, 12(3-4), 361–371.
Mayes, J. T., & de Freitas, S. (2013). Technology-enhanced learning: The role of theory,
in H. Beetham & R. Sharpe (Eds.), Rethinking pedagogy for a digital age: Designing
for 21st century learning (17–30). Abingdon, UK and New York: Routledge.
McClelland, J. L., & Rumelhart, D. E. (1999). Parallel distributed processing.
Cambridge, MA: MIT Press.
Meltzoff, A. N., Kuhl, P. K., Movellan, J., & Sejnowski, T. J. (2009). Foundations for
a new science of learning, Science, 325(5938), 284–288.
Nemirovsky, R. (2011). Episodic feelings and transfer of learning, Journal of the
Learning Sciences, 20(2), 308–337.
Nickerson, R. S., & Adams, M. J. (1979). Long-term memory for a common object,
Cognitive Psychology, 11(3), 287–307.
Nicol, D. (2012). Resituating feedback from the reactive to the proactive, in D. Boud
& E. Molloy (Eds.), Feedback in higher and professional education: Understanding
it and doing it well (39–49). Abingdon, UK: Routledge.
Ohlsson, S. (2011). Deep learning. Cambridge, UK: Cambridge University Press.
Palincsar, A. S. (1998). Social constructivist perspectives on teaching and learning,
Annual Review of Psychology, 49(1), 345–375.
Learning Theory and the New Science of Learning
31
Philip, T. M., Bang, M., & Jackson, K. (2018). Articulating the “How,” the “For
What,” the “For Whom,” and the “With Whom” in Concert: A call to broaden
the benchmarks of our scholarship, Cognition and Instruction, 36(2), 83–88.
Piaget, J. (1970). Science of education and the psychology of the child. New York:
Orion Press.
Sawyer, R. K. (2014). The new science of learning, in R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (1–20). Cambridge, UK: Cambridge
University Press.
Shafto, P., Goodman, N. D., & Frank, M. C. (2012). Learning from others: The consequences of psychological reasoning for human learning, Perspectives on Psychological Science, 7(4), 341–351.
Sfard, A. (1998). On two metaphors for learning and the dangers of choosing just
one, Educational Researcher, 27(2), 4–13.
Siemens, G. (2005). Connectivism: A learning theory for the digital age, International
Journal of Instructional Technology and Distance Learning, 2(1), 3–10.
Sörqvist, P., Dahlström, Ö, Karlsson, T., & Rönnberg, J. (2016). Concentration: The
neural underpinnings of how cognitive load shields against distraction. Frontiers
in Human Neuroscience, 10, 221, doi: 10.3389/fnhum.2016.00221
Stewart, B. (2013). Massiveness + Openness = New Literacies of Participation?,
Journal of Online Learning and Teaching, 9(2), 228–238.
Suchman, L. A. (1999). Plans and situated actions. Cambridge, UK: Cambridge
University Press.
Walsh, M. M., Gluck, K. 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.
Beetham, H., and White, D. (2013). Students’ expectations and experiences of the
digital environment. Bristol: Jisc.
46
Helen Beetham
Biggs, J., and Tang, C. (2011). Teaching for quality learning at university: What the
student does (4th ed.). Maidenhead: Open University Press.
Bloom, B.S. (Ed.) (1956). Taxonomy of educational objectives: The classification of
educational goals. New York: MacKay.
Cecile, S. (2012). Activity Theory and qualitative research in digital domains. Theory
Into Practice, 51(2), 83–90.
Chi, M.T., and Wylie, R. (2014). The ICAP framework: Linking cognitive engagement
to active learning outcomes. Educational Psychologist, 49(4), 219–243. doi:10.1080/
00461520.2014.965823.
Coffield, F., Moseley, D., Hall, E., and Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. London: Learning and
Skills Research Centre.
Department for Business, Innovation and Skills. (2016). Digital skills for the UK
economy. [Report]. HM Govt. Retrieved from https://assets.publishing.service.gov.
uk/government/uploads/system/uploads/attachment_data/file/492889/DCMSDigi
talSkillsReportJan2016.pdf.
Downes, S. (2010). New technology supporting informal learning. Journal of Emerging
Technologies in Web Intelligence, 2(1), 27–33.
Ellis, R.A., and Goodyear, P. (2010). Students’ experience of e-learning in higher
education: A sustainable ecology. New York: Routledge.
Engeström, Y. (1987). Learning by expanding: An activity-theoretical approach to
developmental research. Helsinki: Orienta-Konsultit.
Engeström, Y. (1999). Activity Theory and individual and social transformation. In
Y. Engeström, R. Miettinen and R.-L. Punamaki (Eds.), Perspectives on activity
theory (pp. 19–38). Cambridge: Cambridge University Press.
Engeström, Y. (2001). Expansive learning: Towards an activity-theoretical reconceptualization. In K. Illeris (Ed.), Contemporary theories of learning: Learning theorists in their own words (pp. 46–66). Oxford: Routledge.
Goodyear, P. (2015). Teaching as design. HERDSA Review of Higher Education
Vol. 2. Retrieved from: www.herdsa.org.au/herdsa-review-higher-education-vol2/27-50.
Goodyear, P., Banks, S., Hodgson, V., and McConnell, D. (2004). Research on networked learning: An overview. In P. Goodyear, S. Banks, V. Hodgson and
D. McConnell (Eds.), Advances in research on networked learning (pp. 1–10).
London: Kluwer Academic Publishers.
Goodyear, P., and Dimitriadis, Y. (2013). In medias res: Reframing design for
learning. Research in Learning Technologies Supplement, 21(2013). http://dx.doi.
org/10.3402/rlt.v21i0.19909.
Hall, R., and Smyth, K. (2016). Dismantling the curriculum in Higher Education.
Open Library of Humanities, 2, 1. http://doi.org/10.16995/olh.66.
Hargittai, E., and Hsieh, Y.P. (2013). Digital inequality. In William H. Dutton (Ed.),
Oxford Handbook of Internet Studies (pp. 129–150). Oxford: Oxford University
Press.
Hargittai, E., and Shaw, A. (2015). Mind the skills gap: The role of internet know-how
and gender in differentiated contributions to Wikipedia. Information, Communication and Society, 18(4), 424–442.
Learning Activities and Activity Systems
47
Hatlevik, O.E., Guðmundsdóttir, G.B., and Loi, M. (2015). Digital diversity among
upper secondary students: A multilevel analysis of the relationship between cultural capital, self-efficacy, strategic use of information and digital competence.
Computers and Education, 81, 345–353.
Helsper, E.J., and Reisdorf, B.C. (2016). The emergence of a "digital underclass" in
Great Britain and Sweden: changing reasons for digital exclusion. New Media &
Society 19(8), 1253–1270.
Issroff, K., and Scanlon, E. (2002). Activity theory and enhancing learning: Using
technology in higher education. Journal of Computers and Learning, 18(1), 77–83.
Johnston, B., MacNeill, S., and Smyth, K. (2018). The digitally distributed curriculum. In B. Johnston, S. MacNeill and K. Smyth (Eds.), Conceptualising the
digital university: The intersection of policy, pedagogy and practice (pp. 149–176).
London: Palgrave MacMillan.
Kember, D. (2009). Promoting student-centred forms of learning across an entire
university. Higher Education, 58(1), 1–13.
Kress, G., Jewitt, C., Ogborn, J., and Charalampos, T. (2006). Multimodal
teaching and learning: The rhetorics of the science classroom. London:
Continuum.
Kress, G., and Selander, S. (2012). Multimodal design, learning and cultures of
recognition. The Internet and Higher Education, 15(4), 265–268.
Laurillard, D. (2012). Teaching as a design science. Pedagogical patterns for learning
and technology. London: Routledge.
Lave, J., and Wenger, E. (2002). Legitimate peripheral participation in communities of
practice. In R. Harrison (Ed.), Supporting lifelong learning Volume 1: Perspectives
on learning (pp. 111–126). London and New York: Routledge Falmer.
Lei, J., and Yong Z. (2007). Technology uses and student achievement:
A longitudinal study. Computers and Education, 49, 284–286.
Littlejohn, A., Beetham, H., and McGill, L. (2012). Learning at the digital frontier:
A review of digital literacies in theory and practice. Journal of Computer Assisted
Learning, 28, 547–556.
Littlejohn, A., Hood, N., Milligan, C., and Mustain, P. (2016). Learning in MOOCs:
Motivations and self-regulated learning. The Internet and Higher Education, 29,
40–48.
Martínez-Cantos, J.L. (2017). Digital skills gaps: A pending subject for gender digital
inclusion in the European Union. European Journal of Communication, 32(5),
419–438.
McConnell, D. (1999). Networked learning [Guest editorial]. Journal of Computer
Assisted Learning, 15(3), 177–178.
McConnell, D., Hodgson, V., and Dirckinck-Holmfeld, L. (2012). Exploring the
theory, pedagogy and practice of networked learning. In L. Dirckinck-Holmfeld,
V. Hodgson and D. McConnell (Eds.), Networked learning: A brief history and
new trends (pp. 3–24). New York: Springer.
Neary, M., and Beetham, H. (2015). The nature of academic space. In J. Lea (Ed.),
Enhancing learning and teaching in higher education: Engaging with the dimensions
of practice (pp. 83–102). Maidenhead: Open University Press.
Newman, T., and Beetham, H. (2018). Digital experience insights survey: Findings
from students in UK Further and Higher Education. Bristol: Jisc.
48
Helen Beetham
Nistal, A., Van Dooren, W., and Verschaffel, L. (2011). What counts as a flexible representational choice? An evaluation of students’ representational choices to solve
linear function problems. Instructional Science, 39(1), 1–21.
Oliver, M. (2006). The problems with affordance. E-Learning, 2(4), 402–413.
Oliver, M., and Gourlay, L. (2018). Student engagement in the digital university:
Sociomaterial assemblages. London and New York: Routledge.
Pashler, H., McDaniel, M., Rohrer, D., and Bjork, R. (2009). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9, 105–119.
Ryan, M., Franklin, E., Galsinh, T., Potter, D., Wren, J, Kerrigan, M., Coombs, A.,
and Walker, S. (2013). Students as digital change agents. In E. Dunne and
D. Owen (Eds.), The student engagement handbook: Practice in higher education
(pp. 437–459). Bingley: Emerald.
Seale, J., Draffan, E.A., and Wald, M. (2010). Digital agility and digital decisionmaking: Conceptualising digital inclusion in the context of disabled learners in
higher education. Studies in Higher Education, 35(4), 445–461.
Sharpe, R., Beetham, H., Benfield, G., Lessner, E., and de Cicco, E. (2009). Learners
experiences of e-learning synthesis report: Explaining learner differences. Bristol:
Jisc.
Sharpe, R., and Pawlyn, J. (2008). The role of the tutor in blended e-learning: Experiences from interprofessional education. In R. Donnelly and F. McSweeney (Eds.),
Applied eLearning and eTeaching in higher education (pp. 18–34). New York:
Information Science Reference.
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International
Journal of Instructional Technology and Distance Learning, 2(1), 3–10.
Universities UK. (2015). Making the most of data: Data skills training in English universities. Universities UK. Retrieved from www.universitiesuk.ac.uk/highereduca
tion/Documents/2015/MakingTheMostOfDataDataTrainingSkillsInEnglishUni
versities.pdf.
Vygotsky, L. (1986). Thought and language. Cambridge, MA: MIT Press.
Wang, C.H., Shannon, D.M., and Ross, M.E. (2013). Students’ characteristics, selfregulated learning, technology self-efficacy, and course outcomes in online learning.
Distance Education, 34(3), 302–323.
Yoo, S.J., and Huang, D.W. (2011). Comparison of web 2.0 technology acceptance
level based on cultural differences. Journal of Educational Technology and Society,
14(4), 241–252.
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). Pedagogy, symbolic control, and identity: Theory, research,
critique (Revised ed.). Oxford: Rowan and Littlefield.
Biggs, J., and Tang, C. (2007). Teaching for quality learning at university: What the
student does (3rd ed.). Buckingham: Open University Press.
Boivin, N. (2008). Material cultures, material minds: The impact of things on human
thought, society and evolution. Cambridge, UK: Cambridge University Press.
Boys, J. (2011). Towards creative learning spaces: Re-thinking the architecture of postcompulsory education. New York: Routledge.
Carvalho, L., Dong, A., and Maton, K. (2009). Legitimating design: A sociology of
knowledge account of the field, Design Studies, 30(5), 483–502.
Carvalho, L., and Goodyear, P. (Eds.). (2014). The architecture of productive learning
networks. New York: Routledge.
Collins, H. (2010). Tacit and explicit knowledge. Chicago: The University of
Chicago Press.
Conole, G., and Dyke, M. (2004). What are the affordances of information and communication technologies? ALT-J: Research in Learning Technology, 12(2), 113–124.
Crandall, B., Klein, G., and Hoffman, R. (2006). Working minds: A practitioner’s
guide to cognitive task analysis. Cambridge, MA: MIT Press.
Dohn, N. (2009). Affordances revisited: Articulating a Merleau-Pontian view, International Journal of Computer-Supported Collaborative Learning, 4(2), 151–170.
Ellis, R., and Goodyear, P. (2010). Students’ experiences of e-learning in higher education: The ecology of sustainable innovation. New York: Routledge Falmer.
Ellis, R., and Goodyear, P. (2019). The education ecology of universities: Integrating
strategy, learning and the academy. Abingdon: Routledge.
Evans, S. K., Pearce, K. E., Vitak, J., and Treem, J. W. (2017). Explicating affordances:
A conceptual framework for understanding affordances in communication
research, Journal of Computer-Mediated Communication, 22, 35–52.
Fenwick, T., Edwards, R., and Sawchuk, P. (2011). Emerging approaches to educational research: Tracing the sociomaterial. Abingdon: Routledge.
Gibson, J. (1977). The theory of affordances. In R. Shaw and J. Bransford (Eds.),
Perceiving, acting, and knowing: Toward an ecological psychology (pp. 67–82).
Hillsdale, NJ: Lawrence Erlbaum Associates.
Gibson, J. (1986). The ecological approach to visual perception. Hillsdale, NJ: Lawrence
Erlbaum Associates.
Goodyear, P. (2000). Environments for lifelong learning: Ergonomics, architecture and
educational design. In J. M. Spector and T. Anderson (Eds.), Integrated and holistic
perspectives on learning, instruction and technology: Understanding complexity
(pp. 1–18). Dordrecht: Kluwer Academic Publishers.
Goodyear, P. (2015). Teaching as design, HERDSA Review of Higher Education, 2,
27–50.
Goodyear, P., Carvalho, L., and Dohn, N. (2016). Artefacts and activities in the
analysis of learning networks. In T. Ryberg, C. Sinclair, S. Bayne, and M. de
Laat (Eds.), Research, boundaries and policy in networked learning (pp. 93–110).
New York: Springer.
64
Peter Goodyear and Lucila Carvalho
Goodyear, P., and Dimitriadis, Y. (2013). In medias res: Reframing design for
learning, Research in Learning Technology Supplement, 21, 19909. doi:10.3402/rlt.
v21i0.19909
Goodyear, P., and Ellis, R. (2010). Expanding conceptions of study, context and
educational design. In R. Sharpe, H. Beetham, and S. de Freitas (Eds.), Rethinking learning for a digital age: How learners are shaping their own experiences (pp.
100–113). New York: Routledge.
Goodyear, P., and Retalis, S. (2010). Learning, technology and design. In
P. Goodyear and S. Retalis (Eds.), Technology-enhanced learning: Design patterns
and pattern languages (pp. 1–28). Rotterdam: Sense Publishers.
Ingold, T. (2000). The Perception of the environment: Essays in livelihood, dwelling
and skill. Abingdon: Routledge.
John, P., and Sutherland, R. (2005). Affordance, opportunity and the pedagogical
implications of ICT, Educational Review, 57(4), 405–413.
Johri, A. (2011). The socio-materiality of learning practices and implications for the
field of learning technology, Research in Learning Technology, 19(3), 207–217.
Jones, C., Dirckinck–Homfeld, L., and Lindstrom, B. (2006). A relational, indirect,
meso-level approach to CSCL design in the next decade, International Journal
of Computer-Supported Collaborative Learning, 2(2–3), 35–56.
Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar: Straus and Giroux.
Kaptelinin, V., and Nardi, B. (2006). Acting with technology: Activity theory and
interaction design. Cambridge, MA: MIT Press.
Krippendorff, K. (2006). The semantic turn: A new foundation for design. Boca
Raton, FL: CRC Press.
Laurillard, D. (1987). The different forms of learning in psychology and education. In
J. Richardson, M. Eysenck, and D. Warren Piper (Eds.), Student learning: Research
in education and cognitive psychology (pp. 198–207). Buckingham: Open University
Press.
Law, J., and Mol, A. (Eds.). (2002). Complexities: Social studies of knowledge practices.
Durham: Duke University Press.
Luckin, R. (2010). Re-designing learning contexts: Technology-rich, learner-centred
ecologies. New York: Routledge.
Malafouris, L. (2013). How things shape the mind: A theory of material engagement.
Cambridge, MA: MIT Press.
Maton, K. (2000). Languages of legitimation: The structuring significance for intellectual fields of strategic knowledge claims, British Journal of Sociology of Education, 21(2), 147–167.
Nelson, H., and Stolterman, E. (2014). The design way: Intentional change in an
unpredictable world (2nd ed.). Cambridge, MA: MIT Press.
Norman, D. (2005). Emotional design: Why we love (or hate) everyday things.
New York: Basic Books.
Ohlsson, S. (1995). Learning to do and learning to understand: A lesson and
a challenge for cognitive modelling. In P. Reimann and H. Spada (Eds.), Learning
in humans and machines: Towards an interdisciplinary learning science (pp. 37–62).
London: Pergamon.
Ohlsson, S. (2011). Deep learning: How the mind overrides experience. Cambridge,
UK: Cambridge University Press.
Analysis of Complex Learning Environments
65
Oliver, M. (2005). The problem with affordance, E-Learning, 2(4), 402–413.
Oliver, M. (2011). Technological determinism in educational technology research:
Some alternative ways of thinking about the relationship between learning and
technology, Journal of Computer Assisted Learning, 27, 373–384.
Prosser, M., and Trigwell, K. (1999). Understanding learning and teaching: The
experience in higher education. Buckingham: SRHE/Open University Press.
Rabardel, P., and Beguin, P. (2005). Instrument mediated activity: From subject development to anthropocentric design, Theoretical Issues in Ergonomic Science, 6(5),
429–461.
Reigeluth, C., and Carr-Chellman, A. (Eds.). (2009). Instructional design theories and
models. Vol. 3. New York: Routledge.
Shuell, T. (1992). Designing instructional computing systems for meaningful learning.
In M. Jones and P. Winne (Eds.), Adaptive learning environments (pp 19–54).
New York: Springer Verlag.
Sorensen, E. (2009). The materiality of learning: Technology and knowledge in
educational practice. Cambridge, UK: Cambridge University Press.
Sun, S. Y. H. (2018). Student configuration and space-making in fully online
language learning, Computer Assisted Language Learning, 31(8), 932–959.
Thagard, P. (2008). Hot thought: Mechanisms and applications of emotional cognition.
Cambridge, MA: MIT Press.
Turner, P. (2005). Affordance as context, Interacting with Computers, 17, 787–800.
Wong, G., Greenhalgh, T., Westhorp, G., and Pawson, R. (2012). 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. Jones
people and machines in which design now takes place are a challenging
but achievable context to work in. The digital world in which designers
work is one in which their influence can be still be felt indirectly through
the affordances of designed artifacts, algorithms, pedagogies, and the planning that goes into all educational activities.
References
Anderson, C. (2008, June). The end of theory, will the data deluge make the scientific
method obsolete? In Edge: The third culture. Retrieved from www.edge.org
/3rd_culture/anderson08/anderson08_index.html
Archer, M. (1995). Realist social theory: The morphogenetic approach. Cambridge,
UK: Cambridge University Press.
Ashwin, P. (2009). Analysing teaching-learning interactions in higher education:
Accounting for structure and agency. London, UK: Continuum.
Biesta, G., and Tedder, M. (2007). Lifelong learning and the ecology of agency:
Towards a lifecourse perspective. Studies in the Education of Adults. 39(2), 132–149.
Bolt, B., Fitzgerald, M., and Jessen, S. (2010). Implementing Google Apps for faculty and
staff. Research Bulletin 21. Boulder, CO: Educause Centre for Applied Research.
Bourdieu, P. (1990). The logic of practice. Trans. R. Nice. Cambridge, UK: Polity
Press.
Bowker, G. C. (2005). Memory practices in the sciences. Cambridge, MA: MIT Press.
Boyd, D., and Crawford, K. (2011, September). Six provocations for big data.
A decade in Internet Time. In Symposium on Dynamics of the Internet and Society.
doi: 10.2139/ssrn.1926431.
Cooley, M. (1999). Human-centred design. In R. Jacobson (Ed.), Information design
(pp. 59–83). Cambridge, MA: MIT Press.
Cornford, J., and Pollock, N. (2002). The university as a ‘resourceful constraint’: Process and practice in the construction of the virtual university. In M. R. Lea and
K. Nicoll (Eds.), Distributed learning: Social and cultural approaches to practice
(pp. 170–181). London: RoutledgeFalmer.
Czerniewicz, L., Williams, K., and Brown, C. (2009). Students make a plan: Understanding student agency in constraining conditions, ALT-J: Research in Learning
Technology, 17, 75–88.
De Laat, M. F., and Lally, V. (2004). It’s not so easy: Researching the complexity of
emergent participant roles and awareness in asynchronous networked learning
discussion, Journal of Computer Assisted Learning, 20(3), 165–171.
De Laat, M. F., Lally, V., Lipponen, L., and Simons, P. R. J. (2007). Patterns of interaction in a networked learning community: Squaring the circle, International
Journal of Computer-Supported Collaborative Learning, 2(1), 87–104.
Deleuze, G., and Guatarri, F. (1987). A thousand plateaus: Capitalism and schizophrenia. Trans. B. Massumi. Minneapolis: MN: University of Minnesota Press.
Derry, J. (2007). Epistemology and conceptual resources for the development of
learning technologies. Journal of Computer Assisted Learning, 23(6), 503–510.
Dohn, N. (2009a). Web 2.0: Inherent tensions and evident challenges for education,
International Journal of Computer-Supported Collaborative Learning, 4(3), 343–363.
A View from Social Science
81
Dohn, N. (2009b). Affordances revisited: Articulating a Merleau-Pontian view, International Journal of Computer-Supported Collaborative Learning, 4(2), 151–170.
Dohn, N. (2010, May). Teaching with wikis and blogs: Potentials and pitfalls. In
L. Dirckinck-Holmfeld, V. Hodgson, C. Jones, D. McConnell and T. Ryberg
(Eds.), Proceedings of the 7th international conference on networked learning (pp.
142–150). Aalborg, Denmark: University Lancaster.
Dron, J., and Anderson, T. (2007). Collectives, networks and groups in social software
for e-learning. In T. Bastiaens and S. Carliner (Eds.), Proceedings of world conference on E-Learning in corporate, government, healthcare, and higher education 2007
(pp. 2460–2467). Chesapeake, VA: AACE.
Emirbayer, M., and Mische, A. (1998). What is agency? The American Journal of
Sociology, 103(4), 962–1023.
Engeström, Y. (1987). Learning by expanding: An activity theoretical approach to
developmental research. Helsinki: Orienta-Konsultit Oy. Retrieved from http://
lchc.ucsd.edu/mca/Paper/Engestrom/expanding/toc.htm
Fenwick, T., Edwards, R., and Sawchuk, P. (2011). Emerging approaches to educational research: Tracing the sociomaterial. London, UK: Routledge.
Ferguson, R. (2012). The state of learning analytics in 2012: A review and future challenges. Technical Report KMI-12-01. Milton Keynes, UK: Knowledge Media
Institute, The Open University. Retrieved from http://kmi.open.ac.uk/publica
tions/techreport/kmi-12-01
Flyvbjerg, B. (2011). Making social science matter: Why social inquiry fails and how
it can succeed again. In G. Papanagnou (Ed.), Social science and policy challenges
democracy, values and capacities. Paris: United Nations Educational Scientific
and Cultural Organisation. Retrieved from http://unesdoc.unesco.org/images/
0021/002117/211705e.pdf.
Foucault, M. (1977). Discipline and punish: The birth of the prison. London: Allen
Lane.
Gaver, W. W. (1991). Technology affordances. Proceedings of CHI'91 (New Orleans,
Lousiana, April 28 – May 2, 1991). New York: ACM, 79–84.
Gibson, J. J. (1986 [1979]). The ecological approach to visual perception. Mahwah,
NJ: Lawrence Erlbaum Associates.
Giddens, A. (1984). The constitution of society: Outline of the theory of structuration.
Cambridge, MA: Polity Press.
Goodyear, P., Banks, S., Hodgson, V., and McConnell, D. (Eds.). (2004). Advances in
research on networked learning. Dordrecht: Kluwer Academic Publishers.
Goodyear, P., Jones, C., Asensio, M., Hodgson, V., and Steeples, C. (2001). Effective
networked learning in higher education: Notes and guidelines. Lancaster: CSALT,
Lancaster University. Retrieved from http://csalt.lancs.ac.uk/jisc/ (accessed
04 February 2019).
Goodyear, P., Jones, C., and Thompson, K. (2012). Computer-supported collaborative
learning: Instructional approaches, group processes and educational designs. In
J. M. Spector, M. D. Merrill, J. Elen, and M. J. Bishop (Eds.), Handbook of research
on educational communications and technology (4th ed.) (439–451). New York:
Springer.
Gourlay, L., and Oliver, M. (2018). Student engagement in the digital university:
Sociomaterial assemblages. London, UK: Routledge.
82
Christopher R. Jones
Guribye, F., and Lindström, B. (2009). Infrastructures for learning and networked
tools – The introduction of a new tool in an inter-organisational network. In
L. Dirckinck-Holmfeld, C. Jones, and B. Lindström (Eds.), Analysing networked
learning practices in higher education and continuing professional development
(pp. 103–116). Rotterdam, The Netherlands: Sense Publishers.
Hanseth, O., and Lundberg, N. (2001). Designing work oriented infrastructures,
Computer Supported Cooperative Work, 10, 347–372.
Haythornthwaite, C., and De Laat, M. (2010). Social networks and learning networks:
Using social network perspectives to understand social learning. In L. DirckinckHolmfeld, V. Hodgson, C. Jones, D. McConnell, and T. Ryberg (Eds.), Proceedings
of the 7th international conference on networked learning (pp. 183–190). Aalborg,
Denmark: Lancaster University.
Hey, T., Tansley, S., and Tolle, K. (Eds.). (2009). The fourth paradigm data-intensive
scientific discovery. Redmond, WA: Microsoft Research. Retrieved from http://
research.microsoft.com/en-us/collaboration/fourthparadigm/
Hutchins, E. (1995). Cognition in the wild. Cambridge, MA: The MIT Press.
Jameson, F. (1998). The cultural turn: Selected writings on the postmodern 1983–1998.
London, UK: Verso.
Jones, C. (2004). Networks and learning: Communities, practices and the metaphor of
networks, ALT-J, Research in Learning Technology, 12(1), 82–93.
Jones, C. (2009). A context for collaboration: The institutional selection of an infrastructure for learning. In C. O’Malley, D. Suthers, P. Reimann and
A. Dimitracopoulou (Eds.), Proceedings of the 9th international conference on computer supported collaborative learning: CSCL2009: CSCL Practices (pp. 292–296).
Rhodes, Greece: University of the Aegean.
Jones, C. (2011). Networked learning environments. In M. Keppell, K. Souter, and
M. Riddle (Eds.), Physical and virtual learning spaces in higher education: Concepts for the modern learning environment (pp. 102–118). Hershey, PA: IGI
Global.
Jones, C. (2015). Networked learning: An educational paradigm for the age of digital
networks. Heidelberg and London: Springer.
Jones, C., and Asensio, M. (2001). Experiences of assessment: Using phenomenography for evaluation, Journal of Computer Assisted Learning, 17(3), 314–321.
Jones, C., and De Laat, M. (2016). Networked Learning. In C. Haythornthwaite,
R. Andrews, J. Fransman, and E. M. Meyers (Eds.), The SAGE handbook of
e-learning research (2nd ed., pp. 43–62). London: Sage.
Jones, C., and Dirckinck-Holmfeld, L. (2009). Analysing networked learning practices:
An introduction. In L. Dirckinck-Holmfeld, C. Jones, and B. Lindström (Eds.),
Analysing networked learning practices in higher education and continuing professional development (pp. 1–27). Rotterdam: Sense Publishers.
Jones, C., Dirckinck-Holmfeld L., and Lindström, B. (2006). A relational, indirect,
meso-level approach to CSCL design in the next decade, International Journal of
Computer-Supported Collaborative Learning, 1(1), 35–56.
Kafai, Y. B., and Peppler, K. A. (2011). Beyond small groups: New opportunities for
research in computer-supported collective learning. In H. Spada, G. Stahl,
N. Miyake, and N. Law (Eds.), Connecting computer-supported collaborative
A View from Social Science
83
learning to policy and practice: CSCL2011 conference proceedings. Volume I –
Long Papers (pp. 17–24). Hong Kong, China: The University of Hong Kong.
Knox, J. K. (2014). Active algorithms: Sociomaterial spaces in the e-learning and
digital cultures MOOC, Campus Virtuales, 3(1), 42–55.
Koschmann, T. (Ed.). (1996). CSCL: Theory and practice of an emerging paradigm.
Mahwah, NJ: Lawrence Erlbaum Associates.
Latour, B. (2005). Reassembling the social: An introduction to actor-network theory.
London: Routledge.
Lave, J., and Wenger, E. (1991). Situated learning: Legitimate peripheral participation.
Cambridge, UK: Cambridge University Press.
Law, J. (2008). Actor network theory and material semiotics. In B. S. Taylor (Ed.),
The new Blackwell companion to social theory (pp. 141–158). Chichester, UK:
Wiley-Blackwell.
Liljenström, H., and Svedin, U. (Eds.). (2005). Micro meso macro: Addressing complex systems coupling. Hackensack, NJ: World Scientific Publishers.
McConnell, D. (2000). Implementing computer supported cooperative earning (2nd
ed.). London, UK: Kogan Page.
McConnell, D., Hodgson, V., and Dirckinck-Holmfeld, L. (2012). Networked learning: A brief history and new trends. In L. Dirckinck-Holmfeld, V. Hodgson, and
D. McConnell (Eds.), Exploring the theory, pedagogy and practice of networked
learning (pp. 3–24). New York: Springer.
McGrenere, J., and Ho, W. (2000). Affordances: Clarifying and evolving a concept.
Proceedings of graphics interface 2000. New York: ACM, 179–186.
Norman, D. A. (1990). The design of everyday things. New York: Doubleday.
Norman, D. (1999). Affordance, conventions, and design. Interactions, 6(3), 38–42.
Oliver, M. (2005). The problem with affordance. E–Learning, 2(4), 402–413.
Pickering, A. (1993). The mangle of practice: Agency and emergence in the sociology
of science, American Journal of Sociology, 99, 559–589.
Schatzki, T. R. (1996). Social practices: A Wittgensteinian approach to human activity
and the social. Cambridge, UK: Cambridge University Press.
Schatzki, T. R., Cetina, K., and von Savigny, E. (Eds.). (2001). The practice turn in
contemporary theory. London, UK: Routledge.
Sclater, N. (2017). Learning analytics explained. New York and London: Routledge.
Stahl, G., Koschmann, T., and Suthers, D. (2006). Computer-supported collaborative
learning: An historical perspective. In R. K. Sawyer (Ed.), Cambridge handbook of
the learning sciences (pp. 409–426). Cambridge, UK: Cambridge University Press.
Suchman, L. (2007). Human-machine reconfigurations: Plans and situated actions
(2nd ed.). New York and Cambridge, UK: Cambridge University Press.
Tchounikine, P. (2011). Computer science and educational software design: Resources
for multidisciplinary work in technology enhanced learning. Heidelberg: Springer.
Torenvliet, G. (2003). We can’t afford it! The devaluation of a usability term. Interactions, 10, 12–17.
Vygotsky, L. S. (1986). Thought and language. Trans. A. Kozulin. Cambridge, MA:
MIT Press.
Williamson, B. (2017). Big data in education: The digital future of learning, policy and
practice. London, UK: Sage.
84
Christopher R. 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. There may never be such templates of directed practice given the subjectivity and variation in the online and blended classroom. What we do have in the
CoI is a theoretically sound, principle-based, framework for learning with evidence-based practice emerging as those who use it do so with careful measurement and documentation of results. We invite you to join the adventure.
References
Abdelmalak, M. (2015). Web 2.0 technologies and building online learning communities: Students’ perspectives, Online Learning, 19(2), 87–106.
Akyol, Z., and Garrison, D. (2011). Assessing metacognition in an online community of
inquiry, The Internet and Higher Education, 14(3), 183–190.
Anderson, T., Rourke, L., Garrison, D., and Archer, W. (2001). Assessing teaching
presence in a computer conference context, Journal of Asynchronous Learning
Networks, 5(2), 1–17.
Annand, D. (2011). Social presence within the community of inquiry framework, The
International Review of Research in Open and Distributed Learning, 12(5), 40–56.
Arbaugh, J., Bangert, A., and Cleveland-Innes, M. (2010). Subject matter effects and
the community of inquiry (CoI) framework: An exploratory study, The Internet
and Higher Education, 13(1–2), 37–44.
Arbaugh, J.B., Cleveland-Innes, M., Diaz, S., Garrison, D.R., Ice, P., Richardson, J.
C., Shea, P., and Swan, K. (2009). Developing a community of inquiry instrument: Testing a measure of the community of inquiry framework using a
multi-institutional sample, The Internet and Higher Education, 11(3–4), 133–136.
Archibald, D. (2010). Fostering the development of cognitive presence: Initial findings using the community of inquiry survey instrument, The Internet and Higher
Education, 13(1–2), 73–74.
Armellini, A., and De Stefani, M. (2016). Social presence in the 21st century: An
adjustment to the community of inquiry framework, British Journal of Educational Technology, 47(6), 1202–1216.
Befus, M. (2016). Conducting a multivocal thematic synthesis on an extensive body
of literature, Canadian Journal of Learning and Technology, 42(2). Retrieved from
www.cjlt.ca/index.php/cjlt/article/view/27463/20215
Brookfield, S.D. (2006). The skillful teacher: On technique, trust and responsiveness in
the classroom. San Francisco, CA: Jossey-Bass Inc.
Campbell, K., Schwier, R., and Kenny, R. (2009). The critical, relational practice of instructional design in higher education: An emerging model of
change agency, Educational Technology Research and Development, 57(5),
645–663.
Caskurlu, S. (2018). Confirming the subdimensions of teaching, social, and cognitive
presences: A construct validity study, The Internet and Higher Education, 39, 1–12.
Chakraborty, M., and Nafukho, F.M. (2015). Strategies for virtual learning environments: Focusing on teaching presence and teaching immediacy, Internet Learning
Journal, 4(1).
Community of Inquiry Theoretical Framework
101
Cleveland-Innes, M., and Campbell, P. (2012). Emotional presence, learning, and the
online learning environment, The International Review of Research in Open and
Distributed Learning, 13(4), 269–292.
Cleveland-Innes, M., Feng, X., and Chen, P. (October, 2018). Novice online instructors
and the Community of Inquiry: A tool for instructors in China. Paper presentation
at BNU-AU Research Symposium, Beijing Normal University, Beijing, China.
Cleveland-Innes, M., and Garrison, D. (2009). The role of learner in an online community of inquiry: Instructor support for first time online learners. In
N. Karacapilidis (Ed.), Solutions and innovations in web-based technologies for
augmented learning: Improved platforms, tools and applications (pp. 167–184).
Hershey, PA: IGI Global.
Cleveland-Innes, M., Stenbom, S., and Gauvreau, S. (June, 2018). Technology and
teaching in engineering education : A blended course for faculty, Proceedings of
the 13th International CDIO Conference, University of Calgary, Calgary, Alberta.
Coker, H. (2018). Purpose, pedagogy and philosophy: ‘Being’ an online lecturer, The
International Review of Research in Open and Distributed Learning, 19(5), 128–144.
Damasio, A.R. (1994). Descartes’ error and the future of human life, Scientific
American, October 1994, 144.
Dewey, J. (1933). How we think. New York: Dover Publications.
Edwards, M., Perry, B., and Janzen, K. (2011). The making of an exemplary online
educator, Distance Education, 32(1), 101–118.
Garrison, D. (2017). E-learning in the 21st Century: A framework for research and
practice. New York: Routledge.
Garrison, D., Anderson, T., and Archer, W. (2000). Critical inquiry in a text-based
environment: Computer conferencing in higher education. The Internet and
Higher Education, 2(2-3), 87–105.
Garrison, D., Anderson, T., and Archer, W. (2010). The first decade of the community of
inquiry framework: A retrospective, The Internet and Higher Education, 13(1–2), 5–9.
Garrison, D., Cleveland-Innes, M., and Fung, T. (2010). Exploring causal relationships
among cognitive, social and teaching presence: Student perceptions of the community
of inquiry framework, The Internet and Higher Education, 13(1–2), 31–36.
Green, M. (2005). Factors influencing the process of critical thinking among health
professional during computer conferencing: A case study (Unpublished master
thesis). Athabasca University, Athabasca, Canada.
Gunawardena, C., and Zittle, F. (1997). Social presence as a predictor of satisfaction
within a computer-mediated conferencing environment, American Journal of Distance Education, 11(3), 8–26.
LeDoux, J. (1996). The emotional brain: The mysterious underpinnings of emotional
life. New York: Touchstone: Simon & Schuster.
Kozan, K., and Caskurlu, S. (2018). On the Nth presence for the community of
inquiry framework, Computers and Education, 122, 104–118.
Laurillard, D. (2002). Rethinking university teaching: A conversational framework for
the effective use of learning technologies. London: Routledge.
Lipman, M. (1991). Thinking in education. Cambridge, UK: Cambridge University
Press.
Olpak, Y.Z., Yagci, M., and Basarmak, U. (2016). Determination of perception of
community of inquiry, Educational Research and Reviews, 11(12), 1085–1092.
102
Martha Cleveland-Innes
Palloff, R., and Pratt, K. (2003). The virtual student: A profile and guide to working
with online learners. San Francisco, CA: Jossey-Bass.
Richardson, C., and Alsup, J. (2015). From the classroom to the keyboard: How
seven teachers created their online teacher identities, International Review of
Research in Open and Distance Learning, 16(1), 142–167.
Richardson, J., and Swan, K. (2003). Examining social presence in online courses in
relation to students’ perceived learning and satisfaction, Journal of Asynchronous
Learning Networks, 7, 1.
Rienties, B., and Rivers, B.A. (2014). Measuring and understanding learner emotions:
Evidence and prospects, Learning Analytics Review, 1, 1–28. Retrieved from: www
.laceproject.eu/publications/learning-analytics-and-emotions.pdf.
Rockinson-Szapkiw, A., Wendt, J., Whighting, M., and Nisbet, D. (2016). The predictive relationship among the community of inquiry framework, perceived learning
and online, and graduate students’ course grades in online synchronous and asynchronous courses, The International Review of Research in Open and Distributed
Learning, 17(3), 18–35.
Rodgers, C.R., and Raider-Roth, M.B. (2006). Presence in teaching, Teachers and
Teaching,12(3), 265–287, DOI: 10.1080/13450600500467548
Salmon, G. (2003). E-Moderating: The key to teaching and learning online (2nd ed.).
London: Taylor and Francis.
Schwab, J. (1969). The practical: A language for curriculum, The School Review, 78
(1), 1–23.
Shea, P., and Bidjerano, T. (2009). Community of inquiry as a theoretical framework to foster “epistemic engagement” and “cognitive presence” in online education, Computers & Education, 52(3), 543–553.
Shea, P., Hayes, S., and Vickers, J. (2010). Online instructional effort measured
through the lens of teaching presence in the community of inquiry framework: A
re-examination of measures and approach, The International Review of Research
in Open and Distributed Learning, 11(3), 127–154.
Stenbom, S. (2018). A systematic review of the community of inquiry survey, The
Internet and Higher Education, 39(3), 22–32.
Stenbom, S., Cleveland-Innes, M., and Hrastinski, S. (2015). Emotional presence in
a relationship of inquiry: The case of one-to-one online math coaching, Journal of
Online Learning, 20(1), 41–56.
Stenbom, S., Cleveland-Innes, M., and Hrastinski, S. (2016). Online coaching as
teacher training: Using a relationship of inquiry framework. In N. Ostashewski,
M. Cleveland-Innes, and J. Howell (Eds.), Handbook of research on optimizing K-12
education through online and blended learning. Hershey, PA: IGI Global.
Stodel, E., Thompson, T., and MacDonald, C. (2006). Learners’ perspectives on what
is missing from online learning: Interpretations through the community of inquiry
framework, The International Review of Research in Open and Distributed Learning,
7(3), 1–24.
Vaughan, N. (2014). Student engagement and blended learning: Making the assessment connection, Education Sciences, 4(4), 247–264.
Xin, C. (2012). A critique of the community of inquiry framework, International
Journal of E-Learning and Distance Education, 26(1). 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). Technology tools to support
learning design: Implications derived from an investigation of university teachers’
design practices, Computers and Education, 81, 211–220.
Bennett, S., Agostinho, S., and Lockyer, L. (2017). The process of designing for
learning: Understanding university teachers’ design work, Educational Technology
Research and Development, 65, 125–145.
Bennett, S., Lockyer, L., and Agostinho, S. (2004). Investigating how learning
designs can be used as a framework to incorporate learning objects. In
R. Atkinson, C. McBeath, D. Jonas-Dwyer and R. Phillips (Eds.), Beyond the
comfort zone: Proceedings of the 21st ASCILITE Conference (pp. 116–122).
Perth, Australia, Retrieved from www.ascilite.org/conferences/perth04/procs/ben
nett.html
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., and Fill, K. (2005). A learning design toolkit to create pedagogically
effective learning activities, Journal of Interactive Media in Education, 2005(1),
Article 9.
Cooner, T. S. (2010). Creating opportunities for students in large cohorts to reflect in
and on practice: Lessons learnt from a formative evaluation of students’ experiences of a technology-enhanced blended learning design, British Journal of Educational Technology, 41(2), 271–286.
Dalziel, J. (2007). Building communities of designers. In H. Beetham and R. Sharpe
(Eds.), Rethinking pedagogy for a digital age: Designing and delivering e-learning
(pp. 193–206). London and New York: Routledge.
Derntl, M., Neumann, S., Griffiths, D., and Oberhuemer, P. (2012). The conceptual
structure of IMS learning design does not impede its use for authoring, IEEE
Transactions on Learning Technologies, 5(1), 74–86.
Elliott, K., Boin, A., Irving, H., Johnson, E., and Galea, V. (2010). Teaching scientific
inquiry skills: A handbook for bioscience educators in Australian universities. NSW:
Australian Teaching and Learning Council. Retrieved from https://trove.nla.gov.au
/work/38825890?qandversionId=51558248
Falconer, I., Beetham, H., Oliver, R., Lockyer, L., and Littlejohn, A. (2007). Mod4L
final report: Representing learning designs. Glasow, UK: Glasgow Caledonian University. Retrieved from Open University repository http://oro.open.ac.uk/53036/
Harper, B., and Oliver, R. (2009). Developing a taxonomy for learning designs. In
L. Lockyer, S. Bennett, S. Agostinho and B. Harper (Eds.), Handbook of research
on learning design and learning objects: Issues, applications and technologies (pp.
228–242). Hershey, NY: IGI Global.
Jones, J. (2015). Investigating the role and impact of learning designs within university
teachers’ design work (Doctoral dissertation), University of Wollongong, Australia.
Retrieved from http://ro.uow.edu.au/theses/4709
Jones, J., Bennett, S., and Lockyer, L. (2009). Investigating lecturers’ use of learning
designs to support technology enhanced course design. In T. Bastiaens et al.
(Eds.), Proceedings of World Conference on E-Learning in Corporate, Government,
Healthcare, and Higher Education (pp. 2719–2725). Chesapeake, VA: AACE.
Retrieved from www.editlib.org/p/32870
Learning Designs for Teachers’ Design Practices
119
Jones, J., Bennett, S., and Lockyer, L. (2011). Applying a learning design to the
design of a university unit: A single case study. In T. Bastiaens and M. Ebner
(Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia
and Telecommunications (pp. 3340–3349). Chesapeake, VA: AACE. Retrieved
from www.editlib.org/p/38334
Kolodner, J. L., Owensby, J. N., and Guzdial, M. (2004). Case-based learning aids. In
D. H. Jonassen (Ed.), Handbook of research on educational communications and
technology (pp. 829–861). Mahwah, NJ: L. Erlbaum Associates.
Koper, R., and Tattersall, C. (Eds.). (2005). Learning design: A handbook on modelling
and delivering networked education and training. Berlin, Germany: Springer.
Lockyer, L., Agostinho, S., and Bennett, S. (2016). Design for e-learning. In
C. Haythornthwaite, R. Andrews, J. Fransman, and E. M. Meyers (Eds.), The
SAGE handbook of e-learning research (2nd ed., pp. 336–353). London: SAGE
Publications.
Lockyer, L., Bennett, S., and Agostinho, S. (2017, September). Design thinking:
Teachers’ approaches to creating learning experiences for their students. Poster
presented at 17th Biennial EARLI Conference for Research on Learning and
Instruction, University of Tampere, Finland.
Lockyer, L., Bennett, S., Agostinho, S., and Harper, B. (Eds.). (2009). Handbook of
research of learning design and learning objects: Issues, applications, and technologies.
Hershey, NY: IGI Global.
Margaryan, A., and Littlejohn, A. (2008). Repositories and communities at
cross-purposes: Issues in sharing and reuse of digital learning resources, Journal of
Computer Assisted Learning, 24(4), 333–347.
Masterman, E., and Manton, M. (2011). Teachers’ perspectives on digital tools for
pedagogic planning and design, Technology, Pedagogy and Education, 20(2),
227–246.
Masterman, L. (2006). The learning design tools project: An evaluation of generic
tools used in design for learning. Bristol: JISC.
Mishra, P., and Koehler, M. (2006). Technological pedagogical content knowledge:
A framework for teacher knowledge, Teachers College Record, 108(6), 1017–1054.
Oliver, R., Harper, B., Wills, S., Agostinho, S., and Hedberg, J. (2013). Describing
ICT-based learning designs that promote quality learning outcomes. In
H. Beetham and R. 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. Pinkwart
(Eds.), Proceedings of the Workshop on Intelligent Tutoring Systems for Ill-Defined
Domains at the 8th International Conference on Intelligent Tutoring Systems (pp. 1–
10). Jhongli (Taiwan): National Central University.
Masterman, E. (2009). Activity theory and the design of pedagogic planning tools.
In L. Lockyer, S. Bennett, S. Agostinho, and B. Harper (Eds.), Handbook of
Research on Learning Design and Learning Objects: Issues, Applications, and
Technologies (pp. 209–227). Hershey, New York: IGI Global.
Masterman, E. (2016). Bringing open educational practice to a research-intensive
university: Prospects and challenges, The Electronic Journal of e-Learning, 14(1),
31–42.
Masterman, E., and Craft, B. (2013). Designing and evaluating representations to
model pedagogy, Research in Learning Technology, 21 supplement.
Masterman, E., and Manton, M. (2011). Teachers’ perspectives on digital tools
for pedagogic planning and design, Technology, Pedagogy and Education,
20(2), 227–246.
Masterman, E., Walker, S., and Bower, M. (2013). Computational support for
teachers’ design thinking: Its feasibility and acceptability to practitioners and
institutions, Educational Media International, 50(1), 12–23.
Masterman, L. (2015). DIGE 2: Student Digital Experience Project 2: Research
Report, Oxford: University of Oxford.
Mor, Y., Craft, B., and Maina, M. (2015). Introduction. In M. Maina, B. Craft, and
Y. Mor (Eds.), The Art and Science of Learning Design (pp. vii–xxvi). Rottterdam:
Sense Publishers.
O’Callaghan, F.V., Neumann, D.L., Jones, L., and Cred, P.A. (2017). The use of
lecture recordings in higher education: A review of institutional, student, and
lecturer issues, Education and Information Technologies, 22(1), 399–415.
Starkey, L. (2017). 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. We have argued that such a direction for learning design is a good fit
with an emergent view of strategic planning that sees change as occurring
through conversations between individuals. In order to achieve transformational curriculum redesign, the implementation processes need to take
account not just of institutional strategy, but also of culture, and an understanding of design as social practice.
References
Armellini, A., and Aiyegbayo, O. (2010). Learning design and assessment with
e-tivities, British Journal of Educational Technology, 41(6), 922–935.
Armellini, A., and Jones, S. (2008). Carpe Diem: Seizing each day to foster change in
e-learning design, Reflecting Education, 4(1), 17–29.
Aycock, A., Garnham, C., and Kaleta, R. (2002). Lessons learned from the
hybrid course project, Teaching Scholars Forum, 8(6), Retrieved from: https://
hccelearning.files.wordpress.com/2010/09/lessons-learned-from-the-hybridcourse-project.pdf.
Bain, A., and Zundans-Fraser, L. (2016). Rising to the challenge of transforming
higher education: Designing universities for learning and teaching. Springer Singapore: Springer Briefs in Education.
Barnett, R. (2014). Conditions of flexibility: Securing a more responsive higher education
system. York, UK: Higher Education Academy.
Bayne, S. (2014). What’s the matter with ‘technology enhanced learning’? Learning,
Media and Technology, 40(1), 5–20.
Becher, T., and Trowler, P.R. (2001). Academic tribes and territories (2nd ed.). Milton
Keynes, UK: The Society for Research into Higher Education and Open University Press.
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, E. (2015, December). The case for a curriculum development approach to
developing students’ digital literacies. Paper presented Society for Research in
Higher Education Annual Research Conference, Newport, UK.
Biggs, J., and Tang, C. (2007). Teaching for quality learning at university (3rd ed.).
Buckingham, UK: The Society for Research into Higher Education and Open
University Press.
Blackmore, P., and Kandiko, C. (Eds.). (2012). Strategic curriculum change: Global
trends in universities. London and New York: Routledge.
Boitshwarelo, B., and Vemuri, S. (2017). Conceptualising strategic alignment
between curriculum and pedagogy through a learning design framework, International Journal for Academic Development, 22(4), 279–292.
Bovill, C., Bulley, C.J., and Morrs, K. (2011). Engaging and empowering first year
students through curriculum design: Perspectives from the literature, Teaching in
Higher Education, 16(2), 197–209.
Designing for Learning in Organisations
147
Bower, M. (2017). Design of technology-enhanced learning: Integrating research and
practice. Bingley, UK: Emerald Publishing Limited.
Brooman, S., Darwent, S., and Pimor, A. (2015). The student voice in higher education curriculum design: Is there value in listening? Innovations in Education and
Teaching International, 52(6), 663–674.
Dalziel, J. (2013) Larnaca Declaration on Learning Design. Retrieved from: https://lar
nacadeclaration.wordpress.com/.
Dempster, J., Benfield, G., and Francis, R. (2012). An academic development model
for fostering innovation and sharing in curriculum design, Innovations in Education and Teaching International, 49(2), 145–147.
Falconer, I., Beetham, H., Oliver, R., Lockyer, L., and Littlejohn, A. (2007). Mod4L
final report: Representing learning designs. Glasow, UK: Glasgow Caledonian University. Retrieved from Open University repository: http://oro.open.ac.uk/53036/
Farmer, R., and Usher, J. (2018). Why CAIeRO? Perceptions and impact of ten years of
CAIeRO at the University of Northampton. Northampton, UK: University of Northampton. Retrieved from Univeristy of Northampton: www.northampton.ac.uk/ilt/
wp-content/uploads/sites/2/2018/08/Why-CAleRO-July-2018.pdf
Gourlay, L., and Oliver, M. (2018). Student engagement in the digital university:
Sociomaterial assemblage. London and New York: Routledge.
Jisc. (2018). Active blended learning: Transforming teaching and learning at
university, Retrieved from Jisc repository: http://repository.jisc.ac.uk/7129/10/uniof-northampton-active-blended-learning-case-study.pdf
Kandiko, C., and Blackmore, P. (2012). Curriculum organisation and outcomes. In
P. Blackmore and C. Kandiko (Eds.), Strategic curriculum change (pp. 43–61).
London and New York: Routledge.
Kay, J., Dunne, E., and Hutchinson, J. (2010). Rethinking the values of higher education – students as change agents? QAA, Retrieved from UCL Digital Education
Resource Archive: https://dera.ioe.ac.uk/1193/
Laurillard, D. (2002). Rethinking university teaching (2nd ed.). London: Routledge.
Locke, W. (2012). The dislocation of teaching and research and the reconfiguring of
academic work, London Review of Education, 10(3), 261–274.
Lundy, O., and Cowling, A. (1996). Strategic human resource management. London,
UK: Routledge.
Maxwell, R., and Armellini, A. (2018). Identity, employability and entrepreneurship:
The ChANGE framework of graduate attributes, Higher Education, Skills and WorkBased Learning, Advance online publication. doi: 10.1108/HESWBL-02-2018-0016.
Mintzberg, H. (1994). The rise and fall of strategic planning. London, UK: Prentice
Hall.
Oliver, M. (2015). From openness to permeability: Reframing open education in
terms of positive liberty in the enactment of academic practices, Learning, Media
and Technology, 40(3), 365–384.
Oliver, R. (2006). Exploring a technology-facilitated solution to cater for advanced
students in large undergraduate classes, Journal of Computer Assisted Learnin, 22
(1), 1–12.
Oxford Brookes University. (2015). Strategy for enhancing the student experience.
Oxford: Oxford Brookes University. Retrieved from: www.brookesac.uk/aboutbrookes/strategy/strategy-for-enhancing-the-student-experience/
148
Rhona Sharpe and Alejandro Armellini
Salmon, G. (2013). E-tivities: The key to active online learning (2nd ed.). London and
New York: Routledge.
Salmon, G., Gregory, J., Lokuge, K., and Ross, B. (2015). Experiential online development for educators: The example of the Carpe Diem MOOC, British Journal of
Educational Technology, 46(3), 542–556.
Salmon, G., Jones, S., and Armellini, A. (2008). Building institutional capability in
e-learning design, Research in Learning Technology, 1(2), 95–109.
Salmon, G., and Wright, P. (2014). Transforming future teaching through ‘Carpe
Diem’ learning design, Education Sciences, 4(1), 52–63.
Sharpe, R., Benfield, G., Corrywright, D., and Green, L. (2013). Evaluation of the
Brookes graduate attributes: Year 1 final report, internal report. Oxford: Oxford
Brookes University.
Sharpe, R., Benfield, G., and Francis, R. (2006). Implementing a university e-learning
strategy: Levers for change within academic schools, Research in Learning Technology, 14(2), 135–151.
Sharpe, R., Benfield, G., Roberts, G., and Francis, R. (2006). The undergraduate experience of blended e-learning: A review of UK literature and practice undertaken for the
Higher Education Academy. York, UK: Higher Education Academy.
Sharpe, R., and Oliver, M. (2013). Designing for learning in context. In H. Beetham
and R. Sharpe (Eds.), Rethinking pedagogy for the digital age: Designing for 21st century learning (2nd ed., pp. 163–176). London and New York: Routledge.
Shattock, M. (2011). Managing successful universities (2nd ed.). Maidenhead, UK:
Open University Press.
Stacey, R. (1992). Managing the unknowable: Strategic boundaries between order and
chaos in organization. San Francisco, CA: Jossey Bass.
Thornton, T. (2014). Professional recognition: Promoting recognition through
the Higher Education Academy in a UK higher education institution, Tertiary
Education and Management, 20(3), 225–238.
Univeristy of Northampton. (2018). Strategic plan 2018, Retrieved from: www.north
ampton.ac.uk/wp-content/uploads/2015/10/Strategic-plan-2018.pdf
Usher, J., MacNeill, S., and Creanor, L. (2018). Evolutions of Carpe Diem for learning design, Compass: Journal of Learning and Teaching, 11(1), 1–8.
Watson, D. (2013). Leading the British University today: Your fate in whose hands?
In C. Callendar and P. Scott (Eds.), Browne and beyond: <odernizing English
higher education (pp. 194–205). London, UK: Institute of Education Press.
Weller, S. (2012). Achieving curriculum coherence: Curriculum design and delivery as
social practice. In P. Blackmore and C. Kandiko (Eds.), Strategic curriculum
change (pp. 21–33). London and New York: Routledge.
Whitchurch, C., and Gordon, G. (2013). Staffing models and institutional flexibility
(Research Report Series 3–6), Retrieved from Leadership Foundation for Higher
Education website: www.lfhe.ac.uk/en/research-resources/research-hub/2013research/index.cfm
Young, C., and Perovic, N. (2016). Arena Blended Connected (ABC) curriculum
design. Paper presented at European Distance and E-Learning Network Conference, Budapest.
Chapter 9
Open Education
Design and Policy Considerations
Catherine Cronin
Editors’ Introduction
The Introduction and several earlier chapters have discussed how open
access to knowledge and opportunity are reframing the contexts of education. In this chapter, Cronin turns the lens on open practices inside the
education system. She traces the history of open education through transformations in the policy landscape and in media for learning, through
a decade or more of public funding for the release of Open Educational
Resources, to the current excitement about Open Educational Practices
and pedagogies. 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. While the ideas of open education are not new, approaches to open education are continually evolving, bringing with them new opportunities and
risks. By definition, these practices aspire to cross institutional boundaries.
Therefore as well as seeking to influence institutional strategies in this space,
open educators must build their own networks, and develop their own democratic, flexible, strategic, and critical approaches.
References
Alexander, B., Adams Becker, S., Cummins, M., and Hall Giesinger, C. (2017). Digital literacy in higher education, part II: An NMC Horizon project strategic brief.
Volume 3.4. Austin, TX: The New Media Consortium. Retrieved from https://cdn.
nmc.org/media/2017-nmc-strategic-brief-digital-literacy-in-higher-education-II.pdf
Andrade, A., Ehlers, U.-D., Caine, A., Carneiro, R., Conole, G., Kairamo, A.-K., …
Holmberg, C. (2011). Beyond OER: Shifting focus to open educational practices.
Open Education Quality Initiative (OPAL). Retrieved from https://oerknowledge
cloud.org/sites/oerknowledgecloud.org/files/OPAL2011.pdf
160
Catherine Cronin
Archer, M. S. (2003). Structure, agency and the internal conversation. Cambridge,
UK: Cambridge University Press.
Arcos, B. de los, Farrow, R., Perryman, L.-A., McAndrew, P., Pitt, B., and
Weller, M. (2015). OER evidence report 2013–2015. Retrieved from OER Research
Hub https://oerresearchhub.files.wordpress.com/2014/11/oerrh-evidence-report2014.pdf
Beetham, H., Falconer, I., McGill, L., and Littlejohn, A. (2012). Open practices.
Briefing paper. Bristol: Jisc.
Campbell, L., and Farley, C. (2018, August).Open.Ed at RepoFringe18 [blog post]
31 August 2018. Retrieved from Open World blog http://lornamcampbell.org/oer/
open-ed-at-repofringe18/
Castells, M. (2010). The rise of the network society (2nd ed., Vol. 1). Hoboken, NJ:
Wiley-Blackwell.
Corrall, S., and Pinfield, S. (2014). Coherence of “open” initiatives in higher education
and research: Framing a policy agenda. In iConference 2014 Proceedings (pp. 293–
313). Berlin, Germany. https://doi.org/10.9776/14085
Cox, G., and Trotter, H. (2016). Institutional culture and OER policy: How structure, culture, and agency mediate OER policy potential in South African
universities. The International Review of Research in Open and Distributed Learning, 17(5). doi:10.19173/irrodl.v17i5.2523
Cronin, C. (2017). Openness and praxis: Exploring the use of open educational practices in higher education. The International Review of Research in Open and Distributed Learning, 18(5). doi:10.19173/irrodl.v18i5.3096
Cronin, C. (2018). Openness and praxis: A situated study of academic staff meaningmaking and decision-making with respect to openness and use of open educational
practices in higher education (doctoral thesis). NUI Galway, Galway. Retrieved
from https://aran.library.nuigalway.ie/handle/10379/7276
Cronin, C., and MacLaren, I. (2018). Conceptualising OEP: A review of the theoretical and empirical literature in Open Educational Practices. Open Praxis, 10(2),
127–143.
Czerniewicz, L. (2013, April 29). Inequitable power dynamics of global knowledge
production and exchange must be confronted head on [blog post]. LSE Impact
blog. http://blogs.lse.ac.uk/impactofsocialsciences/2013/04/29/redrawing-the-mapfrom-access-to-participation/#more-10331
Czerniewicz, L., and Brown, C. (2013). The habitus of digital “strangers” in higher
education. British Journal of Educational Technology, 44(1), 44–53.
Czerniewicz, L., Deacon, A., Walji, S., and Glover, M. (2017). OER in and as MOOCs.
In C. Hodgkinson-Williams (Ed.), Adoption and impact of OER in the Global South
(pp. 349–386). Cape Town, South Africa: University of Cape Town.
Deimann, M., and Sloep, P. (2013). How does open education work? In A. Meiszner
and L. Squires (Eds.), Openness and education, Vol. 1 (pp. 1–23). Bingley, UK: Emerald Group Publishing.
DeRosa, R. (2016, May 18). My open textbook: Pedagogy and practice [blog]. Retrieved
from http://robinderosa.net/uncategorized/my-open-textbook-pedagogy-andpractice/
DeRosa, R., and Robison, S. (2017). From OER to open pedagogy: Harnessing the
power of open. In R. S. Jhangiani and R. Biswas-Diener (Eds.), Open: The
Open Education
161
philosophy and practices that are revolutionizing education and science (pp. 115–
124). London, UK: Ubiquity Press.
Edwards, R. (2015). Knowledge infrastructures and the inscrutability of openness in
education. Learning, Media and Technology, 40(3), 251–264.
Ehlers, U.-D. (2011). Extending the territory: From open educational resources to
open educational practices. Journal of Open, Flexible, and Distance Learning, 15
(2), 1–10.
Facer, K., and Selwyn, N. (2010). Social networking: Key messages from the research.
In R. Sharpe, H. Beetham, and S. de Freitas (Eds.), Rethinking learning for
a digital age: How learners are shaping their own experiences (pp. 31–42). London
and New York: Routledge.
Ferguson, R., Barzilai, S., Ben-Zvi, D., Chinn, C. A., Herodotou, C., Hod, Y., …
Whitelock, D. (2017). Innovating pedagogy 2017 (Open University Innovation
Report 6). Milton Keynes, UK: The Open University. Retrieved from. https://iet.
open.ac.uk/file/innovating-pedagogy-2017.pdf.
Geser, G. (2007). Open educational practices and resources: OLCOS roadmap 2012.
Salzburg, Austria: Salzburg Research/EduMedia Group. Retrieved from www.
olcos.org/cms/upload/docs/olcos_roadmap.pdf
Gilliard, C., and Culik, H. (2016, May 24). Digital redlining, access, and privacy
[blog]. Retrieved from Common Sense Education www.commonsense.org/educa
tion/privacy/blog/digital-redlining-access-privacy
Gourlay, L. (2015). Open education as a ‘heterotopia of desire’.. Learning, Media
and Technology, 40(3), 310–327.
Hayes, S., and Jandrić, P. (2014). Who is really in charge of contemporary education?
People and technologies in, against and beyond the neoliberal university. Open
Review of Educational Research, 1(1), 193–210.
Hodgkinson-Williams, C. (2010). Benefits and challenges of OER for higher education
institutions (Open Educational Resources (OER) Workshop for Heads of Commonwealth Universities). Cape Town, South Africa: Commonwealth of Learning
(COL). Retrieved from. www.researchgate.net/publication/242551671_Benefits_
and_Challenges_of_OER_for_Higher_Education_Institutions.
Hodgkinson-Williams, C. (2014, June). Degrees of ease: Adoption of OER, open
textbooks and MOOCs in the Global South. Keynote. Paper presented at OER
Asia 2014: 2nd Regional Symposium on Open Educational Resources: Beyond
Advocacy, Research and Policy. Penang, Malaysia.
Hodgkinson-Williams, C., and Gray, E. (2009). Degrees of openness: The emergence of OER at the University of Cape Town. International Journal of Education and Development Using Information and Communication Technology, 5(5),
101–116.
Hodgkinson-Williams, C. A., and Trotter, H. (2018). A social justice framework for
understanding Open Educational Resources and Practices in the Global South.
Journal of Learning for Development, 5(3). Retrieved from www.jl4d.com/index.
php/ejl4d/article/view/312
Horwitz, R. A. (1979). Psychological effects of the “open classroom.”. Review of
Educational Research, 49(1), 71–85.
Inamorato dos Santos, A., Punie, Y., and Muñoz, J. C. (2016). Opening up education:
A support framework for higher education institutions (JRC Working Paper No.
162
Catherine Cronin
JRC101436). Directorate Growth and Innovation and JRC-Seville, Joint Research
Centre. doi:10.2791/293408
International Commission on the Development of Education. (1972) Learning to be:
The world of education today and tomorrow. Paris, France: UNESCO.
Jenkins, H., Ito, M., and boyd, d. (2015). Participatory culture in a networked era:
A conversation on youth, learning, commerce, and politics. Cambridge, UK: Polity
Press.
Jhangiani, R. S., Pitt, R., Hendricks, C., Key, J., and Lalonde, C. (2016). Exploring
faculty use of open educational resources at British Columbia post-secondary institutions (No. BC Campus Research Report). Victoria, BC: BCcampus. Retrieved from
https://bccampus.ca/files/2016/01/BCFacultyUseOfOER_final.pdf
Jisc. (2016). Building digital capability. Bristol: Jisc. Retrieved from www.jisc.ac.uk
/rd/projects/building-digital-capability
Karunanayaka, S. P., Naidu, S., Rajendra, J. C. N., and Ratnayake, H. U. W. (2015).
From OER to OEP: Shifting practitioner perspectives and practices with innovative learning experience design. Open Praxis, 7(4), 339–350.
Kuhn, C. (2017). Are students ready to (re)-design their Personal Learning Environment? The case of the E-Dynamic.Space. Journal of New Approaches in Educational
Research, 6(1), 11–19.
Lambert, S. R. (2018). Changing our (dis)course: A distinctive social justice aligned
definition of open education. Journal of Learning for Development – JL4D, 5(3).
Retrieved from www.jl4d.org/index.php/ejl4d/article/view/290
Lanclos, D., and White, D. (2015). The Resident Web and its impact on the Academy
[blog post]. Hybrid Pedagogy. Retrieved from http://hybridpedagogy.org/residentweb-and-impact-on-academy/
Lane, A. (2009). The impact of openness on bridging educational digital divides. The
International Review of Research in Open and Distance Learning, 10(5).
doi:10.19173/irrodl.v10i5.637
Light, B. (2014). Disconnecting with social network sites. London, UK: Palgrave
Macmillan.
Losh, E. (2014). The war on learning: Gaining ground in the digital university. Cambridge,
MA: The MIT Press.
Marshall, H. (1981). Open classrooms: Has the term outlived its usefulness? Review
of Educational Research, 51(2), 181–192.
Marwick, A. E. (2013). Online identity. In J. Hartley, J. Burgess, and A. Bruns (Eds.),
Companion to new media dynamics (pp. 355–364). Malden, MA: Blackwell.
McGill, L., Falconer, I., Dempster, J. A., Littlejohn, A., and Beetham, H. (2013).
Journeys to open educational practice: UKOER/SCORE review final report. Bristol: Jisc.
Mejias, U. A. (2011). Towards a critique of digital networks for learning. Progressive
Librarian, 34/35, 46–49.
Mejias, U. A. (2013). Off the network: Disrupting the digital world (Vol. 41). Minneapolis,
MN: University of Minnesota Press.
Newman, T., and Beetham, H. (2017). Student digital experience tracker 2017: The
voice of 22,000 UK learners. Bristol: Jisc.
Nobes, A. (2017, December 8). Must we decolonise Open Access? Perspectives from
Francophone Africa [blog]. http://journalologik.uk/?p=149
Open Education
163
Piron, F. (2017, September 19). Qui sait? Le libre accès en Afrique et en Haïti.
Retrieved from www.laviedesidees.fr/Qui-sait.html
Rainie, L., and Wellman, B. (2012). Networked: The new social operating system.
Cambridge, MA: MIT Press.
Selwyn, N. (2016). Digital downsides: Exploring university students’ negative engagements with digital technology. Teaching in Higher Education, 21(8), 1006–1021.
Srnicek, N. (2016). Platform capitalism. Cambridge, UK: Polity Press.
Stewart, B. (2015). In abundance: Networked participatory practices as scholarship.
The International Review of Research in Open and Distributed Learning, 16(3).
doi:10.19173/irrodl.v16i3.2158
University of Cape Town. (2011, July 27). Intellectual property policy. University of
Cape Town. Retrieved from www.rci.uct.ac.za/rcips/ip/policy
University of Edinburgh. (2018, November 2). Open educational resources. Retrieved
from http://open.ed.ac.uk/about/
van der Vaart, L. (2013). e-InfraNet: “Open” as the default modus operandi for research
and higher education. European Network for co-ordination of policies and programmes on e-infrastructures (e-InfraNet Project). Retrieved from www.surf.nl/
binaries/content/assets/surf/en/knowledgebase/2013/e-InfraNet-Open-as-the-DefaultModus-Operandi-for-Research-and-Higher-Education.pdf
Veletsianos, G. (2016). Social media in academia: Networked scholars. Abingdon,
UK: Routledge.
Veletsianos, G., and Stewart, B. (2016). Discreet openness: Scholars’ selective and
intentional self-disclosures online. Social Media + Society, 2(3), 1–11.
Weller, M. (2011). A pedagogy of abundance. Revista Española de Pedagogía, 69(249),
223–235.
Winn, J. (2012). Open education: From the freedom of things to the freedom of people.
In H. Stevenson, L. Bell, and M. Neary (Eds.), Towards teaching in public: Reshaping
the modern university (pp. 133–147). London, UK: Continuum.
Zuboff, S. (2015). Big other: Surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30, 75–89.
Chapter 10
Frameworks to Guide Practice
Gráinne Conole
Editors’ Introduction
In this resource, Conole has collected together some frameworks which can
be used to guide teachers through the design process. 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). Surviving the avalanche: Improving retention
in MOOCs, Retrieved from https://linc.mit.edu/linc2013/proceedings/Session3/Ses
sion3Breakwell.pdf
Brown, M., Conole, G., and Beblavy, M. (2019). Education outcomes enhanced by the
use of digital technology: What are the conditions for success? EU-Commissioned
report.
Chi, M.T.H., and Wylie, R. (2014). The ICAP Framework: Linking cognitive engagement to active learning outcomes, Educational Psychologist, 49(4), 219–243.
Conole, G. (2004). E-learning: The hype and the reality, Journal of Interactive Media
in Education, 12, www-jime.open.ac.uk/2004/12/
Conole, G. (2013). Designing for learning in an open world. In M. Spector and S.
P. Lejoie (Eds.), Explorations in the learning sciences, instructional systems and
performance technologies, New York: Springer.
Conole, G. (2014). The 7Cs of Learning Design – a new approach to rethinking design
practice, Networked Learning Conference, Retrieved from www.networkedlearning
conference.org.uk/past/nlc2014/abstracts/pdf/conole.pdf
Conole, G. (2016). The 7Cs of Learning Design. In J. Dalziel (Ed.), Learning design:
Conceptualizing a framework for teaching and learning online (pp. 117-145).
London: Routledge.
Entwistle, N. (2004). Approaches to learning and levels of understanding: Influences
and responsibilities in enhancing learning skills at University. Dublin: University
College Dublin.
Goodyear, P. (2005). Educational design and networked learning: Patterns, pattern
languages and education design, Australasian Journal of Educational Technology,
21(1), 82–101.
Hillman, D.C.A., Willis, D.J., and Gunawardena, C.N. (1994). Learner interface
interaction in distance education: An extension of contemporary models and strategies for practitioners, The American Journal of Distance Education, 8(2), 30–42.
Laurillard, D. (2012). Teaching as a design science: Building pedagogical patterns for
learning and technology. New York and London: Routledge.
Moore, M.G. (1989). Three types of interaction, American Journal of Distance Education, 3(2), 1–6.
Patrick, S. (n.d.). Why teachers still matter most in the digital age, Retrieved from
www.amplify.com/viewpoints/why-teachers-still-matter-most-digital-age
Portnoy, L. (2018). How SAMR and Tech can help teachers truly transform
assessment, Feb. 1 2018, Retrieved from www.edsurge.com/news/2018-02-01-howsamr-and-tech-can-help-teachers-truly-transform-assessment
Puentedura, R.R. (2013). SAMR and TPCK: An introduction, Retrieved from www.
hippasus.com/rrpweblog/archives/2013/03/28/SAMRandTPCK_AnIntroduction.pdf
Romrell, D., Kidder, L.C., and Wood, E. (2014). The SAMR model as a framework
for evaluating mLearning, Journal of Asynchronous Networks, 18, 2.
178
Gráinne Conole
Säljö, R. (1979). Learning in the learner’s perspective I: Some common-sense conceptions, No. 76 In Institute of Education, University of Gothenburg.
Säljö, R. (2004). Learning and technologies, people and tools in co-ordinated
activities, International Journal of Educational Research, 41, 489–494.
Säljö, R. (2010). Digital tools and challenges to institutional traditions of learning:
Technologies, social memory and the performative nature of learning, Journal of
Computer Assisted Learning, 26(1), 53–64.
Thomson, J., and Trigwell, K.R. (2016). The role of informal conversations in developing university teaching? Studies in Higher Education, 43(9), 1536–1547.
Verpoorten, D., Poumay, M., and Leclercq, D. (2005). The 8 Learning Events Model
(8LEM): A pedagogic conceptual framework for the design of dedicated (and
Generic?) Scenarios, 503–508.
Villina, R., Murphu, C., MacNeill, A., and Masson, A. (2008). 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.
The design principles we have put forward recognize the centrality of
learners with their personal technologies and their preferences, experiences
and expectations drawn from outside, before and after their educational
institutional identity, alongside the unique nature and added value of
mobile learning, and the idea that mobile learning is synonymous with
unpredictability and constant change.
References
Berking, P., Haag, J., Archibald, T., and Birtwhistle, M. (2012, December). Mobile
learning: Not just another delivery method. In: Paper presented at the Interservice/
Industry Training, Simulation, and Education Conference, Orlando, FL.
Bull, S., Cui, Y., McEvoy, A. T., Reid, E., & Yang, W. (2004). Roles for mobile
learner models. In: The 2nd IEEE International Workshop on Wireless and
Mobile Technologies in Education, 2004. Proceedings. (pp. 124–128). IEEE.
Charitonos, K., and Kukulska-Hulme, A. (2017, June). Community-based interventions
for language learning among refugees and migrants. In: Paper presented at Refugees
and HCI Workshop: The role of HCI in responding to the refugee crisis, communities and technologies 2017, Troyes, France. Retrieved from: http://oro.open.ac.uk/
49677/
Edmonds, R., and Smith, S. (2017). From playing to designing: Enhancing educational
experiences with location-based mobile learning games, Australasian Journal of
Educational Technology, 33(6), 41–53.
Genachowski, C. J. (2010). Mobile broadband: A 21st century plan for US competitiveness, innovation and job creation. Washington, DC: Speech, New America
Foundation.
Higgins, S. (2011). Digital curation: The emergence of a new discipline, International
Journal of Digital Curation, 6(2), 78–88.
Hine, N., Rentoul, R. and Specht, M. (2004). Collaboration and roles in remote field
trips, Learning with Mobile Devices: Research and Development, 2004, 69–72.
Design Principles for Learning with Mobiles
195
JISC. (2008). MELaS: Mobiles enhancing learning and support. Final report. JISC.
www.jisc.ac.uk/media/documents/programmes/elearninginnovation/melasfinalre
port.pdf.
Kenny, R. F., Park, C. L., Van Neste-Kenny, J. M. C., Burton, P. A., and Meiers, P. A.
(2009). Using mobile learning to enhance the quality of nursing practice education.
In: M. Ally (Ed.) Mobile learning: Transforming the delivery of education and training
(pp. 75–98). Athabasca, AB: Athabasca University Press.
Kukulska-Hulme, A., Sharples, M., Milrad, M., Arnedillo-Sánchez, I., and Vavoula, G.
(2011). The genesis and development of mobile learning in Europe. In: D. Parsons
(Ed.) Combining e-learning and m-learning: New applications of blended educational
resources (pp. 151–177). Hershey, PA: Information Science Reference.
Kukulska-Hulme, A., and Traxler, J. (Eds.) (2005). Mobile learning: A handbook for
educators and trainers. London, UK: Routledge.
Lave, J., and Wenger, E. (1991). Situated learning: Legitimate peripheral participation.
Cambridge, UK: Cambridge University Press.
Lee, H. (2015, June). Language learners and multimodal interaction via mobile devices:
An exploration of online gesture. In: Paper presented at the conference on Multimodality and Cultural Change, The University of Agder, Kristiansand, Norway.
McCauley, B., Thong, L. P., Nkhoma, M., and Nguyen, N. (2017, August). Vietnam
run: An alternative approach to mobile learning. In: E. Cohen (Ed.) Proceedings of
the 20th Informing Science and Information Technology Education Conference
(pp. 159–169). Ho Chi Minh (Saigon), Vietnam.
Minocha, S. (2009). Role of social software tools in education: A literature review,
Education and Training, 51(5/6), 353–369.
Naismith, L., Lonsdale, P., Vavoula, G. & Sharples, M. (2005). Literature Review in
Mobile Technologies and Learning. Report 11, NESTA Futurelab. Bristol:
NESTA Futurelab. Also available at http://www2.futurelab.org.uk/resources/docu
ments/lit_reviews/Mobile_Review.pdf
Pachler, N., Bachmair, B., Cook, J., and Kress, G. (Eds.) (2010). Mobile learning:
Structures, agency, practices. London, UK: Springer.
Pegrum, M. (2014). Mobile learning: Languages, literacies and cultures. Basingstoke:
Palgrave Macmillan.
Rasul, A. A. (2011). Cultural factors in a mobile phone adoption and usage model:
A case of UUM postgraduate students (Unpublished masters thesis). Universiti
Utara, Malaysia.
Rodríguez-Fórtiz, M. J., Fernández-López, A., and Rodríguez, M. L. (2011). Mobile
communication and learning applications for autistic people. In: T. Williams
(Ed.) Autism spectrum disorders: From genes to environment (pp. 349–362).
London, UK: InTech Open Access Publishing.
Rubino, I., Barberis, C., Xhembulla, J., and Malnati, G. (2015). Integrating a locationbased mobile game in the museum visit: Evaluating visitors’ behaviour and learning,
ACM Journal of Computing and Cultural Heritage, 8(3), Article 15.
Salmon, G. (2011). E-Moderating: The key to online teaching and learning (3rd ed.).
London: Routledge.
Selwyn, N. (2007). The use of computer technology in university teaching and learning:
A critical perspective, Journal of computer assisted learning, 23(2), 83–94.
196
Agnes Kukulska-Hulme and John Traxler
Sengupta, S. (2012). Facebook’s prospects may rest on trove of data, New York
Times, May 14, 2012. www.nytimes.com/2012/05/15/technology/facebook-needsto-turn-data-trove-into-investor-gold.html
Sharples, M. (2003). Disruptive devices: mobile technology for conversational
learning, International Journal of Continuing Engineering Education and Lifelong
Learning, 12(5/6): 504–20.
Townsend, A. M. (2002). Mobile communications in the twenty-first century city. In:
B. Brown, N. Green, and R. Harper (Eds.) Wireless world: Social and interactional
aspects of the mobile world (pp. 62–77). London: Springer-Verlag.
Traxler, J. (2010). Students and mobile devices, Research in Learning Technology,
18(2).
Traxler, J., & Leach, J., (2006). Innovative and Sustainable Mobile Learning in
Africa. In: Proceedings of WMTE'96: Fourth IEEE International Workshop on
Wireless, Mobile and Ubiquitous Technology in Education pp. (98–102). Athens,
Greece, 16–17 November, 2006.
Tselios, N., Papadimitriou, I., Raptis, D., Yiannoutsou, N., Komis, V., and Avouris, N.
(2008). Mobile learning in museums: Design and evaluation challenges. In:
J. Lumsden (Ed.) Handbook of research on user interface design and evaluation for
mobile technology (pp. 253–259). Hershey, PA: IDEA Publishing.
Unterfrauner, E., Marschalek, I., and Fabian, C. (2010, March). Mobile learning with
marginalized young people. In: I. Arnedillo Sánchez, and P. Isaias (Eds.) Proceedings of the IADIS International Conference Mobile Learning 2010 (pp. 28–35).
Porto, Portugal: IADIS.
Vavoula, G., Sharples, M., Rudman, P., Meek, J., and Lonsdale, P. (2009). Myartspace: Design and evaluation of support for learning with multimedia phones
between classrooms and museums, Computers and Education, 53(2), 286–299.
Yau, J. Y. K., & Joy, M. (2009). A mobile context-aware framework for managing
learning schedules: data analysis from an interview study, International Journal of
Mobile and Blended Learning (IJMBL), 1(4), 29–55.
Yau, J. Y. K., & Joy, M. (2010). A context-aware personalized m-learning application
based on m-learning preferences. In: 2010 6th IEEE International Conference on
Wireless, Mobile, and Ubiquitous Technologies in Education (pp. 11–18). IEEE.
Chapter 12
Designs for Professional Learning
Rachel H. 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. There is great promise, therefore, (if, at present, largely unrealized)
for learning design to increase the quality and quantity of scrutiny and
review in professional educational practice. Furthermore, by binding design
for learning with aspects of activity theory the opportunities for innovative
approaches to the research and development of professional education
become all the more compelling.
Designs for Professional Learning
209
References
Aldrich, C. (2005). Learning by doing. San Francisco, CA: Pfeiffer.
Barr, H., & Low, H. (2013). Introducing interprofessional education. Fareham, UK:
CAIPE. Retrieved from CAIPE website www.caipe.org/download/barr-low-gray2013-introducing-interprofessional-education-1-pdf/
Bines, H., & Watson, D. (1992). Developing professional education. Milton Keynes,
UK: Open University Press.
Boud, D., & Molloy, E. (2013). Feedback in higher and professional education: Understanding it and doing it well. Abingdon, UK: Routledge.
Buckley, S., Coleman, J., Davison, I., Khan, K., Zamora, J., Malick, S., Morley, D.,
Pollard, D., Ashcroft, T., Popovic, C., & Sayers, J. (2009). The educational effects
of portfolios on undergraduate student learning: A Best Evidence Medical Education (BEME) systematic review. BEME Guide No. 11, Medical Teacher, 31(4),
282–298.
Dalziel, J., & Dalziel, B. (2011). Adoption of Learning Designs in teacher training
and medical education: Templates versus embedded content. Proceedings of the
6th International LAMS and Learning Design Conference (pp. 81–88), Sydney,
Australia. Retrieved from https://lams2011sydney.lamsfoundation.org/docs/RP/
Dalziel.pdf
Dolmans, D., De Grave, W., Wolfhagen, I., & van der Vleuten, C. (2005). Problembased learning: Future challenges for educational practice and research, Medical
Education, 39(7), 732–741.
Dreyfus, H., & Dreyfus, S. (2005). Peripheral vision: Expertise in real–world
contexts, Organization Studies, 26(5), 779–792.
Ellaway, R., & Masters, K. (2008). AMEE Guide 32: e–Learning in medical education Part 1: Learning, teaching and assessment, Medical Teacher, 30(5), 455–473.
Ellaway, R.H. (2016). A conceptual framework of game-informed principles for
health professions education, Advances in Simulation, 1, 28.
Ellaway, R.H., Corral, J., Topps, D., & Topps, M. (2015). Exploring digital
professionalism, Medical Teacher, 37(9), 844–849.
Ellaway, R.H., Dalziel, J., & Dalziel, B. (2008). Learning design in healthcare
education, Medical Teacher, 30(2), 180–184.
Ellaway, R.H., & Davies, D. (2011). Design for learning: Deconstructing virtual
patient activities, Medical Teacher, 33(4), 303–310.
Ellaway, R.H., Kneebone, R., Lachapelle, K., & Topps, D. (2009). Connecting and
combining simulation modalities for integrated teaching, learning and
assessment, Medical Teacher, 31(8), 725–731.
Ellaway, R.H., Poulton, T., & Jivram, T. (2015). Decision PBL: A 4-year retrospective
of using virtual patients in problem-based learning, Medical Teacher, 37(10),
926–934.
Eraut, M. (1994). Developing professional knowledge and competence. London, UK:
The Falmer Press.
Farmer, E., & Page, G. (2005). A practical guide to assessing clinical decision–
making skills using the key features approach, Medical Education, 39(12),
1188–1894.
Fenwick, T. (2014). Sociomateriality in medical practice and learning: Attuning to
what matters, Medical Education, 48(1), 44–52.
210
Rachel H. Ellaway
Fournier, J., Demeester, A., & Charlin, B. (2008). Script concordance tests: Guidelines for construction, BMC Medical Informatics and Decision Making, 8(18)
doi:10.1186/1472-6947-8-18.
Frank, J.R., Snell, L.S., Cate, O.T., Holmboe, E.S., Carraccio, C., Swing, S.R., …
Harris, K.A. (2010). Competency-based medical education: Theory to practice,
Medical Teacher, 32(8), 638–645.
Gaba, D. (2004). The future vision of simulation in health care, Quality and Safety in
Health Care, 13, 2–10.
Hafferty, F.W. (1998). Beyond curriculum reform: Confronting medicine’s hidden
curriculum, Academic Medicine, 73(4), 403–407.
Harter, L., & Kirby, E. (2004). Socializing medical students in an era of managed care:
The ideological significance of standardized and virtual patient, Communication
Studies, 55(1), 48–67.
Hodges, B. (2013). Assessment in the post-psychometric era: Learning to love the
subjective and collective, Medical Teacher, 35(7), 564–568.
Issenberg, S., McGaghie, W., Petrusa, E., Gordon, D., & Scalese, R. (2005). Features
and uses of high–fidelity medical simulations that lead to effective learning:
A BEME systematic review, Medical Teacher, 27(2), 10–28.
Jochems, W., van Merriënboer, J., & Koper, R. (2004). Integrated e–learning: Implications for pedagogy, technology and organization. London, UK: RoutledgeFalmer.
Kane, M.T. (2006). Validation. In: R.L. Brennan (Ed.), Educational Measurement
(4th ed., pp. 17–64). Westport, CT: Praeger.
Laurillard, D. (2002). Rethinking university teaching: A conversational framework
for the effective use of learning technologies (2nd ed.). London, UK:
RoutledgeFalmer.
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation.
Cambridge, MA: Cambridge University Press.
Lincoln, M., Carmody, D., & Maloney, D. (1997). Professional development of students and clinical educators. In L. McAllister, M. Lincoln, S. McLeod, &
D. Maloney (Eds.), Facilitating learning in clinical settings (pp. 65–98). Cheltenham: Stanley Thornes.
Margolis, E. (2001). The hidden curriculum in higher education. New York: Routledge.
Miller, G.E. (1990). The assessment of clinical skills/competence/performance,
Academic Medicine, 65(9), S63–S67.
Newble, D. (2004). Techniques for measuring clinical competence: Objective structured clinical examinations’, Medical Education, 38(2), 199–203.
Pusic, M.V., Boutis, K., Hatala, R., & Cook, D.A. (2015). Learning curves in health
professions education, Academic Medicine, 90(8), 1034–1042.
Quinn, C. (2005). Engaging learning: Designing e–learning simulation games. San
Francisco, CA: Pfeiffer.
Reznick, R. (1993). Teaching and testing technical skills, The American Journal of
Surgery, 165(3), 358–361.
Sargeant, J., Lockyer, J., Mann, K., Holmboe, E., Silver, I., Armson, H., …
Power, M. (2015). Facilitated reflective performance feedback: Developing an
evidence- and theory-based model that builds relationship, explores reactions and
content, and coaches for performance change (R2C2), Academic Medicine, 90(12),
1698–1706.
Designs for Professional Learning
211
Savin–Baden, M., & Wilkie, K. (2007). Problem–based learning online. London, UK:
Routledge.
Schön, D. (1987). Educating the reflective practitioner. San Francisco, CA: Jossey–
Bass.
ten Cate, O. (2013). 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. Without such perspectives, educational understanding of
datafication would remain limited to its practical role in teaching and neglect
its wider social, cultural, economic, and political significance.
224
Ben Williamson
References
Ambrose, M. (2015). Lessons from the avalanche of numbers: Big data in historical
perspective. I/S: A Journal of Law and Policy for the Information Society, 11(2),
201–277.
Anagnostopoulos, D., Rutledge, S.A., & Jacobsen, R. (Eds.) (2013). The infrastructure
of accountability: Data use and the transformation of American education.
Cambridge, MA: Harvard Education Press.
Beer, D. (2016). Metric power. London: Palgrave Macmillan.
Boninger, F., & Molnar, A. (2016). Learning to be watched: Surveillance culture at
school. Boulder, CO: National Education Policy Center.
Bowker, G.C. (2013). Data flakes: An afterword to ‘Raw data’ is an oxymoron. In
L. Gitelman (Ed.), ‘Raw data’ is an oxymoron (pp. 167–172). London: MIT Press.
boyd, d., & Crawford, K. (2012). Critical questions for big data: Provocations for
a cultural, technological, and scholarly phenomenon. Information, Communication &
Society, 15(5), 662–679.
Bradbury, A., & Roberts-Holmes, G. (2018). The datafication of primary and early
years education: Playing with numbers. London: Routledge.
Bulger, M. (2016). Personalized learning: The conversations we’re not having. Data
and Society, Working Paper. Retrieved from Data and Society Research Institute,
New York. www.datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf
Cope, B., & Kalantzis, M. (2015). Interpreting evidence-of-learning: Educational
research in the era of big data. Open Review of Educational Research, 2(1), 218–239.
Davies, W. (2017). Elite power under advanced neoliberalism. Theory, Culture &
Society, 34(5–6), 227–250.
DiCerbo, K.E., & Behrens, J.T. (2014). Impacts of the digital ocean. Austin, TX:
Pearson.
Finn, M. (2016). Atmospheres of progress in a data-based school. Cultural Geographies,
23(1), 29–49.
Foucault, M. (1991). Discipline and punish: The Birth of the prison. Trans.
A. Sheridan. Harmondsworth: Penguin.
Gehl, R. (2015). Sharing, knowledge management and big data: A partial genealogy
of the data scientist. European Journal of Cultural Studies, 18(4–5), 413–428.
Gillespie, T. (2014). The relevance of algorithms. In T. Gillespie, P.J. Boczkowski, &
K.A. Foot (Eds.), Media technologies: Essays on communication, materiality, and
society (pp. 167–193). London: MIT Press.
Gitelman, L., & Jackson, V. (2013). Introduction. In L. Gitelman (Ed.), ‘Raw data’ is
an oxymoron (pp. 1–14). London: MIT Press.
Gorur, R., Sellar, S., & Steiner-Khamsi, G. (Eds) (2019). Comparative methodology
in the era of big data and global networks. London: Routledge.
Gray, J. (2016). Datafication and democracy. Juncture, 23(3). www.ippr.org/juncture/
datafication-and-democracy.
Hill, P., & Barber, M. (2014). Preparing for a renaissance in assessment. London:
Pearson.
Housley, W. (2015). The emerging contours of data science. Discover Society, 23.
http://discoversociety.org/2015/08/03/focus-the-emerging-contours-of-data-science/.
Kitchin, R. (2014a). The data revolution: Big data, open data, data infrastructures and
their consequences. London: Sage.
Datafication of Education
225
Kitchin, R. (2014b). Big data, new epistemologies and paradigm shifts. Big Data &
Society, 1, 1. doi:10.1177/2053951714528481
Komljenovic, J. (2018). Making higher education markets: Trust-building strategies
of private companies to enter the public sector. Higher Education, Advance online
publication. doi:10.1007/s10734-018-0330-6.
Lang, C., Siemens, G., Wise, A., & Gasevic, D. (Eds.) (2017). Handbook of learning
analytics. Society of Learning Analytics Research.
Lim, C.P., & Tinio, V.L. (Eds.) (2018). Learning analytics for the global south.
Quezon City, Philippines: Foundation for Information Technology Education
and Development.
Lupton, D. (2015). Digital Sociology. London: Routledge.
Lyotard, J.-F. (1984). The Postmodern condition: A report on knowledge. Trans.
G. Bennington & B. Massumi. Manchester: Manchester University Press.
Manolev, J., Sullivan, A., & Slee, R. (2018). The datafication of discipline: ClassDojo, surveillance and a performative classroom culture. Learning, Media &
Technology, Advance online publication. doi: 10.1080/17439884.2018.1558237.
Mulgan, G. (2016). Government as collective intelligence. Oxford Government
Review, 1, 44–46.
AI Now. (2018). AI Now Report 2018. New York: AI Now Institute.
Piety, P.J., Hickey, D.T., & Bishop, M.J. (2014). Educational data sciences – framing
emergent practices for analytics of learning, organizations and systems. LAK '14,
Indianapolis.
Roberts-Mahoney, H., Means A.J., & Garrison, M.J. (2016). Netflixing human capital development: Personalized learning technology and the corporatization of
K-12 education. Journal of Education Policy, 31(4), 405–420.
Ruppert, E. (2012). The Governmental topologies of database devices. Theory, Culture & Society, 29(4–5), 116–136.
Ruppert, E., Isin, E., & Bigo, D. (2017). Data politics, Big Data & Society, JulyDecember, 1–7.
Sanders, M., Lawrence, J., Gibbons, D., & Calcraft, P. (2017). Using data science in
policy: A report by the Behavioural Insights Team. London: The Behavioural
Insights Team.
Sellar, S., Thompson, G., & Rutkowski, D. (2017). The global education race: Taking
the measure of PISA and international testing. Alberta, Canada: Brush Education.
Selwyn, N. (2015). Data entry: Towards the critical study of digital data and
education. Learning, Media & Technology, 40(1), 64–82.
Shah, H. (2018). The DfE’s repeated misuse of statistics is embarrassing. Schools
Week, 8 October: https://schoolsweek.co.uk/the-dfes-repeated-misuse-of-statisticsis-embarrassing/
Silberzahn, R., Uhlmann, E.L., Martin, D.P., Anselmi, P., Aust, F., Awtrey, E.,
Bahník, Š., Bai, F., Bannard, C., Bonnier, E., & Carlsson, R. (2018). Many
analysts, one data set: Making transparent how variations in analytic choices
affect results. Advances in Methods and Practices in Psychological Science, 1(3),
337–356.
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas.
American Behavioural Scientist, 57(10), 1510–1529.
226
Ben Williamson
Verger, A., Novelli, M., & Altinyelken, H.K. (Eds.) (2018). Global education policy
and international development: New agendas, issues and policies (2nd ed.). London:
Bloomsbury.
Williamson, B. (2017). Big Data in education: The digital future of learning, policy
and practice. London: Sage.
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
Williamson, B., & Piattoeva, N. (2018). Objectivity as standardization in
data-scientific education policy, technology and governance. 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