Table Of ContentBig Data in Education
The digital future of learning, policy and practice
Big Data in Education
The digital future of learning, policy and
practice
Ben Williamson
SAGE Publications Ltd
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© 2017 Ben Williamson
First published 2017
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Library of Congress Control Number: 2017931037
British Library Cataloguing in Publication data
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ISBN 978-1-47394-799-3
ISBN 978-1-47394-800-6 (pbk)
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Contents
About the author
Preface
Acknowledgements
About the book
1 Introduction: Learning machines, digital data and the future of
education
2 Conceptualizing Digital Data: Data mining, analytics and
imaginaries
3 Software, Code and Algorithms: Programming, automating
and governing everyday life
4 Digital Education Governance: Political analytics,
performativity and accountability
5 The Social Life of Education Data Science: Learning analytics,
educational data mining and metrological platforms
6 The CompPsy Complex: Non-cognitive learning, psychological
enhancement and behaviour change
7 Rewiring Brains: Artificial intelligence, cognitive systems and
neuroeducation
8 Making and Coding Cultures: Digital citizens, DIY makers and
apprentice data analysts
9 Conclusion: Programmable public pedagogies of software and
big data
References
Index
About the author
Ben Williamson
is a Lecturer in the Faculty of Social Sciences at the University
of Stirling, UK. His research focuses on educational policy and
digital technology, with a particular emphasis on the involvement
of networks of technical, commercial, philanthropic and scientific
experts in data-driven educational governance. He has
previously published his research in a range of education,
sociology and policy studies journals, maintains a research blog
at https://codeactsineducation.wordpress.com/, and on Twitter
he is @BenPatrickWill.
Preface
Big data, algorithms, data mining, analytics, machine learning and AI
have become some of the most significant technical developments
and concepts of recent years. Today’s most successful companies
are those that can provide an engaging digital service while also
compelling users to provide data that can then be mined using
sophisticated analytics technologies. Google’s search algorithms
provide access to information while tracking the online habits of
users. Facebook collects data about its users to monitor and
enhance their engagement with their timelines and newsfeeds.
Amazon, Netflix and Spotify analyse user information to make
automated recommendations of media that users might like.
Meanwhile, wearable health devices gather information about
physical health and fitness and prompt their users to make healthy
lifestyle choices, and business intelligence software helps
organizations make better strategic decisions.
In our everyday lives spent living with digital hardware and software,
we are constantly generating information that can be used to identify
where we go, what we like, who we know, how we feel, what we do,
what we consume and so on. A consequence is that we can be
watched by organizations that can access our data. Government
agencies have sought to access social media networks to identify
and track people’s online activities. Police forces have been
experimenting with predictive analytics technologies that can
calculate where and when crime is most likely to take place, and who
is most likely to commit it. Facebook has been experimenting on its
users by manipulating their news feeds in order to change their
moods. Even elections are now fought through computational
propaganda that spreads through social media networks thanks to
trending algorithms and detailed profiles of users’ behaviours,
preferences and tastes.
Whether you like it or not, a data-based version of yourself exists out
there, scattered among different databases as data points in
massive torrents of big data. Data mining, algorithms and analytics
processes are increasingly being put to work to know and
understand you, and also to know and understand the wider
populations, communities and societies to which you belong. And as
technical innovation in machine learning and artificial intelligence
makes technologies smarter, new kinds of machines are emerging
that are designed to interact with you by collecting and analysing
your activities in real-time in order to learn about you, and adjust to
serve your needs and interests.
Public awareness of these activities is emerging as media coverage
about social media, government online snooping and computational
propaganda has grown. Some of these issues have become the stuff
of popular culture. The UK television show Humans, for example,
dramatizes current concerns about robotics, automation and artificial
intelligence, with its cast of ‘synthetic humans’ undergoing ‘machine
learning’ as they seek survival in the human world. The satirical
novel The Circle by Dave Eggers, about a social media company
that seeks perfect knowledge through seamless surveillance, has
been turned into a major Hollywood film. The Australian TV series
The Code depicts a shady world of governmental big data agencies
and private sector surveillance contractors. Finally, the movie Margin
Call dramatizes what happened in the financial crash when risky
computer models and algorithms developed to process huge
financial data began to operate beyond the control of their designers.
Far from being merely technical, code, data, algorithms, artificial
intelligence and machine learning are now firmly embedded in
society and culture, as well as in economics and politics.
Big data is now becoming a key part of the educational landscape
too. The same kind of learning machines that share our lives with us
on social media, on our smartphones and on the Web take on a
special importance as they begin to occupy the educational field.
According to many of the enthusiasts we will encounter in this book,
big data can help people learn more, learn faster, and learn better.
