Big Data in Education: The digital future of learning, policy and practice

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Ben Williamson

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    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. Applications and services that process big data can help students in schools, colleges and universities by providing feedback on their measured progress, and recommendations on what to do to improve. They can help policymakers learn about institutional and system performance, and generate insights for future policy intervention. They can help teachers review and evaluate courses as they can track students’ engagement and achievement, and enable school and university leaders to review and evaluate institutional and staff performance at the same time. As machines are designed to get ever smarter and more responsive and adaptable as they learn from data, it is claimed, they will become embedded in education all the way from policymaking to practice.

    ‘Big data’ has become a contested term that is used diversely by different groups. The simple technical definition is that big data consists of information collected in huge volume, of highly diverse variety, which is collected at extreme velocity. Rather than working with a strict technical definition of big data, big data can be better understood as an emerging social phenomenon and as a powerful concept that has attained critical importance in recent years. Big data is also inseparable from the software programs, algorithms and analytics required to collect and manage it, all of which requires diverse forms of specialist expert practice to be performed. A whole industry of textbooks, manuals, conferences and training courses is available for those wishing to specialize in data mining, analytics and machine learning. There are people behind big data – not just data scientists, but software developers and algorithm designers, as well as the political, scientific and economic actors who seek to develop and utilize big data systems for their diverse purposes. And big data is also about the people who constitute it, whose lives are recorded both individually and at massive population scale. Big data, in other words, is simultaneously technical and social. Technical in that it is the product of software programs and processes; social because it is produced and used by human operatives working in specific organizational settings, and is generated from the everyday lives of people around the globe. As a source of knowledge, big data also has the power to change how and what we know about society and the people and institutions that occupy it.

    Big data has prompted much future-gazing. With access to huge quantities of information, it seems, comes the potential of better knowledge of behaviours, institutions, even whole societies. Better data-driven knowledge might then be used to catalyse new innovations or interventions in everything from business and entertainment to government and public services. The field of education has emerged as a key site for the production of visions of the data-driven future. This book illustrates how future visions of education have been animated by key ideas related to big data.

    As a concept and as a set of technical possibilities, big data has captured the imagination of businesses, think tanks, non-profit philanthropic foundations, politicians and policymakers alike, which have seen big data as a potentially limitless new reserve of insights into how education systems and institutions function, how teachers perform, and how learners achieve. For its supporters, the potential insights available from big data might be used as intelligence to be used in the design of new courses, new resources, new policies and new practices. Smart learning machines based on artificial intelligence might even become available that can act as digital assistants to teachers and students alike, intelligently spotting patterns in any educational activity and then providing real-time feedback to improve it, or perhaps even to automate it.

    Big data in education is as much an imaginative resource to be mined for future possibilities as it is an emerging technical reality. As this book demonstrates, however, imagining the future of education with big data is catalysing real developments that are set to impact on educational processes worldwide.

    Acknowledgements

    This book first emerged from a seminar series funded by the Economic and Social Research Council (grant reference ES/L001160/1) that I led and organized from 2013–2015. ‘Code Acts in Education: Learning through code, learning to code’ was intended to generate a critical interdisciplinary conversation about the role of software, code, algorithms and digital data between educational researchers, educators, social scientists and various organizations with an interest in technologies in education. The series proved to be the catalyst for a programme of research and writing that has finally culminated in this book. I would like to acknowledge and thank several colleagues for their collaboration on Code Acts in Education: Richard Edwards, Tara Fenwick, Sian Bayne, Jeremy Knox, Sarah Doyle, Lyndsay Grant and Alison Oldfield. Many of the brilliant speakers we invited to present their work at Code Acts helped to set the stage for the material developed in the book – thanks to all who took part. Some of the material in the book has also benefited from direct collaboration with Carlo Perrotta, Deborah Lupton, Jessica Pykett, Selena Nemorin, Jen Ross, John Morgan and Bethan Mitchell.

    In the three years that this work has been ongoing, my children, Cormac and Carys, have both started primary school. Though schools have of course been capturing children in numerical form for a very long time, it has been fascinating as a parent to witness the extent of data work that goes on in schools today. Much of it remains in pencil and paper format, but Cormac and Carys have entered school as data collection, analysis and presentation is becoming increasingly digitized. As parents we can now monitor from home how they’re getting on at school – academically, socially, emotionally, behaviourally – through new glossy web-based tools. Data are even becoming the source of pride for young children, but they are also becoming parts of big databases distantly hosted on powerful cloud servers. Young people are being inserted into sprawling networks of hardware and software that are managed by commercial technology companies and promoted by the latest political priorities. Cormac and Carys, this book is for you, and it is animated by my disquiet about the digital shadow versions of you that are being produced through data as you set off on your educational journeys. That’s why mummy and daddy love taking you away to the woods, lochs and mountains of Scotland so much. Thanks, as ever, to Vanessa for getting us everywhere – you drive, I’ll read the map.

    About the book

    In the introductory first chapter, I describe how education is becoming increasingly ‘digitized’ and ‘datafied’. Through digitization, more and more aspects of education – from the early years through school, higher education and on into lifelong learning – are being conducted through software programs that have been written in code, and that rely on algorithms for their functioning. The process of datafication has involved diverse forms of information about education being rendered in machine-readable digital data, which can then be subjected to sophisticated forms of processing, calculation, analysis, interpretation, visualization and circulation. The chapter details how digitization and datafication reinforce each other, and how they have begun to animate the imaginations and visions of powerful social actors. Adopting the concept of ‘sociotechnical imaginaries’, in the chapter I explore how ‘desirable’ future visions for education based on digital data are now being projected and enacted.

    Chapter 2 provides a historical and conceptual map for an understanding of big data. It demonstrates how a concern with data has become central to the activities of businesses, cities, governments, think tanks, and social scientific practice itself. The central contention of the chapter is that big data needs to be understood critically. It is not just an accurate statistical reflection of the social world as some data scientists contend, but a key source of social power that, through the technical experts that collect, clean and calculate it, is actively intervening in how social worlds are known, seen and then acted upon. But big data cannot exist on its own; it requires a massive complex of software, code, algorithms and infrastructures for its collection and analysis. Chapter 3 will detail the work that software does in the organization of big data to illuminate how code and algorithms are implicated in the organization of data-driven institutions, spaces and everyday life.

    Subsequent chapters provide detailed examinations of how sociotechnical imaginaries of digital data in education are being developed and diffused into real-world contexts. Chapter 4 focuses on the ways that education policy is increasingly accomplished through digital policy instruments that enable policy data to flow across the system in real-time and continuously. Learning analytics and educational data mining tools, the focus of Chapter 5, enable the tracking, monitoring and real-time prediction of student activities, behaviours and sentiments within the pedagogic apparatus of the classroom. Focusing on the emerging field of ‘educational data science’, the chapter will detail the different forms that educational data mining and analytics now take – from administrative and academic analytics at the institutional level to granular, individualized learning analytics that offer feedback within the physical classroom itself.

    Chapter 6 then focuses on educational technologies that have been designed to collect data about learners’ bodies, emotions and behaviours. It highlights how new kinds of ‘affective computing’ devices have been produced to generate a constant stream of information about students’ movements and feelings, and how new behaviour management devices are being used to collect and visualize information about individuals’ conduct. Current developments in ‘cognitive-based systems’, ‘artificial intelligence’, ‘machine learning’ and ‘neural networks’ promise to produce increasingly ‘intelligent’ educational technologies, able to adapt and respond to the learner – the subject of Chapter 7. The specific contribution of this chapter will be to understand the interdependencies of big data technologies and neuroscience in plans for the future design of cognitive classrooms where humans and machines act together as symbiotic systems.

    In Chapter 8 the focus shifts on to the idea that young people themselves can learn to write computer code and conduct digital data analyses. This chapter documents how ‘learning to code’ and ‘digital making’ initiatives are part of a concerted effort shared across government, the commercial technology sector, and civil society to mould citizens as productive participants for a digitized and data-driven future. The conclusion highlights an important context for further work in this area: the increasing phenomenon that people learn about the world through social media, and the consequences this is exerting for collective and political life as social media filter, curate and personalize access to information based on users’ data profiles.

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