Big data in education: the digital future of learning, policy and practice / PDF
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Big 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 1 Oliver’s Yard 55 City Road London EC1Y 1SP SAGE Publications Inc. 2455 Teller Road Thousand Oaks, California 91320 SAGE Publications India Pvt Ltd B 1/I 1 Mohan Cooperative Industrial Area Mathura Road New Delhi 110 044 SAGE Publications Asia-Pacific Pte Ltd 3 Church Street #10-04 Samsung Hub Singapore 049483 © 2017 Ben Williamson First published 2017 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act, 1988, this publication may be reproduced, stored or transmitted in any form, or by any means, only with the prior permission in writing of the publishers, or in the case of reprographic reproduction, in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. Library of Congress Control Number: 2017931037 British Library Cataloguing in Publication data A catalogue record for this book is available from the British Library ISBN 978-1-47394-799-3 ISBN 978-1-47394-800-6 (pbk) Editor: James Clark Assistant editor: Robert Patterson Production editor: Nicola Carrier Copyeditor: Solveig Gardner Servian Proofreader: Kate Campbell Marketing manager: Dilhara Attygalle Cover design: Sheila Tong Typeset by: C&M Digitals (P) Ltd, Chennai, India Printed in the UK 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.
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