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


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, and on Twitter he is @BenPatrickWill.


    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.


    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.

  • References

    Adams, J. M. (2014) Measuring a ‘growth mindset’ in a new school accountability system. EdSource, 5 May. Available at
    Alamuddin, R., Brown, J. and Kurzweil, M. (2016) Student Data in the Digital Era: An overview of current practices. New York: Ithaka S+R. Available at
    Alba, D. (2016) Silicon Valley’s new-age AltSchool unleashes its secrets. Wired, 18 October. Available at
    Albright, J. (2016a) How Trump’s campaign used the new data-industrial complex to win the election. LSE US Centre blog, 26 November. Available at
    Albright, J. (2016b) Data is the real post-truth. Medium, 27 November. Available at
    AltSchool (2015a) AltSchool hires top execs from Google, Uber, Rocket Fuel and Zynga to help reinvent education from the ground up. AltSchool Press Releases. Available at
    AltSchool (2015b) AltSchool raises $100 million in funding to reimagine education for U.S. students and teachers. AltSchool Press Releases. Available at
    AltSchool (2016) Our Education Approach. AltSchool. Available at
    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): 20177.
    Amoore, L. and Poitukh, V. (eds) (2016) Algorithmic Life: calculative devices in the age of big data. Abingdon: Routledge.
    Anagnostopoulos, D., Rutledge, S. A. and Jacobsen, R. (2013) Mapping the information infrastructure of accountability. In Anagnostopoulos, D., Rutledge, S. A. and Jacobsen, R. (eds), The Infrastructure of Accountability: data use and the transformation of American education. Cambridge, MA: Harvard Education Press. pp. 120.
    Andrejevic, M., Hearn, A. and Kennedy, H. (2015) Cultural studies of data mining: introduction. European Journal of Cultural Studies, 18 (4–5): 37994.
    Asdal, K. (2011) The office: the weakness of numbers and the production of non-authority. Accounting, Organizations and Society, 36: 19.
    Axline, K. (2014) The universe is programmable. We need an API for everything. Wired, 30 April. Available at
    Baker and Siemens, G. (2013) Educational data mining and learning analytics. Available at
    Ball, S. J. (2008) The Education Debate. Bristol: Policy Press.
    Ball, S. J. (2012) Global Education Inc. New policy networks and the neoliberal imaginary. Abingdon: Routledge.
    Ball, S. J. (2016) Following policy: networks, network ethnography and education policy mobilities. Journal of Education Policy, 31 (5): 54966.
    Ball, S. J. and Junemann, C. (2012) Networks, New Governance and Education. Bristol: Policy Press.
    Barber, M. with Ozga, J. (2014) Data work: Michael Barber in conversation with Jenny Ozga. In Fenwick, T., Mangez, E. and Ozga, J. (eds), Governing Knowledge: comparison, knowledge-based technologies and expertise in the regulation of education. London: Routledge. pp. 7585.
    Barnes, T. J. and Wilson, M. W. (2014) Big data, social physics, and spatial analysis: the early years. Big Data and Society, 1 (1). Available at
    Barocas, S., Hood, S. and Ziewitz, M. (2013) Governing algorithms: a provocation paper. Social Sciences Research Network. Available at
    Bartlett, J. and Miller, C. (2011) Truth, Lies and the Internet. London: Demos.
    Bayne, S. (2015) Teacherbot: interventions in automated teaching. Teaching in Higher Education, 20 (4): 45567.
    BBC (2015) Make it Digital. BBC Media Centre, 12 March. Available at
    Beer, D. (2013) Popular Culture and New Media: the politics of circulation. London: Palgrave Macmillan.
    Beer, D. (2016a) Metric Power. London: Palgrave Macmillan.
    Beer, D. (2016b) How should we do the history of big data? Big Data and Society, 3 (1). Available at
    Beer, D. (2017) The social power of algorithms. Information, Communication and Society, 20 (1): 113.
    Beer, D. & Burrows, R. (2013) Popular culture, digital archives and the new social life of data. Theory, Culture and Society, 30 (4): 4771.
    Behrens, J. (2013) Harnessing the currents of the digital ocean. Paper presented at the Annual Meeting of the American Educational Research Association, San Francisco, CA, April.
    Bell, H. (2014) Coding for civic service: what we are learning? Nesta blogs, 11 March. Available at
    Beneito-Montagut, R. (2017) Big data and educational research. In Wyse, D., Smith, E., Suter, L. E. and Selwyn, N. (eds), The BERA/Sage Handbook of Educational Research. London: Sage. pp. 91333.
    Big Brother Watch (2014) Biometrics in schools: the extent of biometrics in English secondary schools and academies. Big Brother Watch. Available at
    Big Brother Watch (2016) Classroom management systems: another brick in the wall. Big Brother Watch. Available at
    Blikstein, P., Sipitakiat, A., Goldstein, J., Wilbert, J., Johnson, M., Vranakis, S., Pedersen, Z. and Carey, W. (2016) Project Blok: designing an development platform for tangible programming for children. Project Bloks. Available at
    Blyth, T. (2012) Legacy of the BBC Micro: effecting change in the UK’s cultures of computing. London: Nesta.
    Boden, L. (2016) Going with the affective flows of digital school absence text messages. Learning, Media and Technology. Available at
    Bogost, I. (2014) Welcome to dataland: design fiction at the most magical place on Earth. Re-Form, 28 July. Available at
    Bogost, I. (2015) Programmers: stop calling yourselves engineers. The Atlantic, 5 November. Available at
    Bone, J. (2016) The nature of structure: a biosocial approach. Sociological Review Monograph Series: Biosocial Matters: Rethinking Sociology-Biology Relations in the Twenty-First Century, 64: 23855.
    Bowker, G. C. (2008) Memory Practices in the Sciences. London: MIT Press.
    Bowker, G. C. (2013) Data flakes: an afterword to ‘Raw Data’ is an Oxymoron. In Gitelman, L. (ed.), ‘Raw Data’ is an Oxymoron. London: MIT Press. pp. 16772.
    Bowker, G. C. and Star, S. L. (1999) Sorting Things Out: classification and its consequences. Cambridge, MA: MIT Press.
    Boyd, D. and Crawford, K. (2013) Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Information, Communication and Society, 15 (5): 66279.
    Bradbury, A., McGimpsey, I. and Santori, D. (2013) Revising rationality: the use of ‘nudge’ approaches in neoliberal education policy. Journal of Education Policy, 28 (2): 24767.
    Bright, J. and Margetts, H. (2016) Big data and public policy: can it succeed where e-participation has failed? Policy and Internet, 8 (3): 21824.
    Buckingham Shum, S., Baker, R. S. J., Behrens, J., Hawksey, M., Jeffery, N. and Pea, R. (2013) Educational data scientists: a scarce breed? LAK ’13, 8–12 April, Leuven, Belgium. Available at
    Bulger, M. (2016) Personalized learning: the conversations we’re not having. Data and Society, 22 July. Available at
    Burger, M. (2015) The perception of the effectiveness of ClassDojo in middle school classrooms: a transcendental phenomenological study. Unpublished doctoral dissertation, Liberty University, Lynchburg, VA.
    Burrows, R. (2012) Living with the h-index: metric assemblages in the contemporary academy. The Sociological Review, 60 (2): 35572.
    Busso, D. and Pollack, C. (2015) No brain left behind: consequences of neuroscience discourse for education. Learning, Media, and Technology, 40 (2): 16886.
    Cambridge Analytica (2016) Election 2016: the data game. Cambridge Analytica news blog, 18 November. Available at
    Carvalho, L. M. (2014) The attraction of mutual surveillance of performances: PISA as a knowledge-policy instrument. In Fenwick, T. Mangez, E. and Ozga, J. (eds), Governing Knowledge: comparison, knowledge-based technologies and expertise in the regulation of education. London: Routledge. pp. 5872.
    Castells, M. (1996) The Rise of the Network Society. Oxford: Blackwell.
    Castells, M. (2009) Communication Power. Oxford: Oxford University Press.
    Castro, D. and New, J. (2016) The Promise of Artificial Intelligence. Washington, DC: Center for Data Innovation.
    Cavanagh, S. (2016) Tech giant Oracle makes billion-dollar pledge for coding education in Europe. EdWeek Market Brief, 2 December. Available at
    Cave, T. and Rowell, A. (2014) A Quiet Word: Crony capitalism and broken politics in Britain. London: Bodley Head.
    Cederstrom, C. and Spicer, A. (2015) The Wellness Syndrome. Cambridge: Polity.
    Ceglowski, M. (2016) The moral economy of tech. SASE conference, Berkeley University, 26 June. Available at
    Cerruzzi, P.E. (2012) Computing: a concise history. London: MIT Press.
    Chadwick, A. and Stromer-Galley, J. (2016) Digital media, power, and democracy in parties and election campaigns: party decline or party renewal? The International Journal of Press/Politics, 21 (3): 28393.
    Character Lab (2016) About us. Character Lab. Available at
    Cheney-Lippold, J. (2011) A new algorithmic identity: soft biopolitics and the modulation of control. Theory, Culture and Society, 28 (6): 16481.
    Chung, E., Cromby, J., Papadopoulos, D. and Tufarelli, C. (2016) Social epigenetics: a science of social science? Sociological Review Monograph Series: Biosocial Matters: Rethinking Sociology-Biology Relations in the Twenty-First Century, 64: 16885.
    Clarence, E. and Gabriel. M. (2014) People Helping People: the future of public services. London: Nesta.
    ClassDojo (2016a) ClassDojo expands from classrooms to schools. PR Newswire. Available at
    ClassDojo (2016b) Stanford PERTS lab and ClassDojo partner to bring growth mindset to every classroom. ClassDojo Press Releases. Available at
    Clow, D. (2013) An overview of learning analytics. Teaching in Higher Education, 18 (6): 68395. (2014) Overview. Available at
    Compendium of Physical Activities (2011) Home page. Compendium of Physical Activities. Available at
    Computing at School (2010) Computing at School White Paper. Computing at School. Available at
    Computing at School (2014) Computing in the national curriculum: a guide for secondary teachers. Computing at School. Available at
    Cope, B. and Kalantzis, M. (2015) Interpreting evidence-of-learning: educational research in the era of big data. Open Review of Educational Research, 2 (1): 21839.
    Cope, B. and Kalantzis, M. (2016) Big data comes to school: implications for learning, assessment and research. AERA Open, 2 (2): 119.
    Crawford, K. (2014) The anxieties of big data. The New Inquiry, 30 May. Available at
    Dalton, C. and Thatcher, J. (2014) What does a critical data studies look like, and why do we care? Seven points for a critical approach to ‘big data’. Society and Space: Available at
    Daniels, J., Gregory, K. and McMillan Cottom, T. (eds) (2016) Digital Sociologies. Bristol: Policy Press.
    Davidson, C. (2011) Now You See It: how the brain science of attention will transform the way we live, work and learn. London: Viking.
    Davies, W. (2012) The emerging neocommunitarianism. The Political Quarterly, 83 (4): 76776.
    Davies, W. (2015) The Happiness Industry: how business and government sold us well-being. London: Verso.
    Davies, W. (2016) Happiness and children. Open Democracy, 11 May. Available at
    de Abaitua, M. (2007) The Red Men. Haddenham: Snowbooks.
    Decuypere, M. (2016) Diagrams of Europeanization: European education governance in the digital age. Journal of Education Policy, 31 (6): 85172.
    Decuypere, M., Ceulemens, C. and Simons, M. (2014) Schools in the making: mapping digital spaces of evidence. Journal of Education Policy, 29 (5): 61739.
    Desrosieres, A. (2001) How real are statistics? Four possible attitudes. Social Research, 68 (2): 33955.
    DiCerbo, K. E. and Behrens, J. T. (2014) Impacts of the Digital Ocean. Austin, TX: Pearson.
    Dourish, P. and Bell, G. (2011) Divining a Digital Future: mess and mythology in ubiquitous computing. London: MIT Press.
    Dredge, S. (2014) Forcing a generation to code is unprecedented, says Codecademy chief. Guardian, 5 September. Available at
    Duckworth, A.L. and Yeager, D.S. (2015) Measurement matters: assessing personal qualities other than cognitive ability for educational purposes. Educational Researcher, 44 (4): 23751.
    Dufva, T. and Dufva, M. (2016) Metaphors of code – structuring and broadening the discussion on teaching children to code. Thinking Skills and Creativity, 22: 97110.
    Dunleavy, P. and Margetts, H. (2015) Design principles for essentially digital governance. Annual Meeting of the American Political Science Association, San Francisco, 3–6 September.
    Dweck, C. S. (2015) The secret to raising smart kids. Scientific American, 1 January. Available at
    Eassom, S. (2015) IBM Watson for education. IBM Insights on Business, 1 April. Available at
    Ecclestone, K. and Hayes, D. (2009) The Dangerous Rise of Therapeutic Education. Abingdon: Routledge.
    EdSurge (2016) The state of edtech: how edtech tools are evolving. EdSurge Research. Available at
    Edtech UK (2015) About us. Edtech UK. Available at
    Education Datalab (2014) About. Education Datalab. Available at
    Education Foundation (2015) EdTech: London capital for learning technology. London: Education Foundation. Available at
    Edwards, P. N. (1997) The Closed World: computers and the politics of discourse in cold war America. London: MIT Press.
    Edwards, P. N., Jackson, S. J., Chalmers, M. K., Bowker, G. C., Borgman, C. L., Ribes, D., Burton, M. and Calvert, S. (2013) Knowledge Infrastructures: Intellectual Frameworks and Research Challenges. Ann Arbor, MI: Deep Blue.
    Edwards, R. (2015) Software and the hidden curriculum of digital education. Pedagogy, Culture and Society, 23 (2): 26579.
    Emejula, A. and McGregor, C. (2016) Towards a radical digital citizenship in digital education. Critical Studies in Education, September. Available at
    Ensmenger, N. (2010) The Computer Boys Take Over: computers, programmers, and the politics of technical expertise. London: MIT Press.
    Epstein, R. (2016) The empty brain. Aeon, 18 May. Available at
    Evans, J. and Rich, E. (2011) Body policies and body pedagogies: every child matters in totally pedagogised schools? Journal of Education Policy, 26: 36179.
    Facer, K. (2011) Learning Futures: education, technology and social change. Abingdon: Routledge.
    Fenwick, T. and Edwards, R. (2016) Exploring the impact of digital technologies on professional responsibilities and education. European Educational Research Journal, 15 (1): 11731.
    Fenwick, T., Mangez, E. and Ozga, J. (eds) (2014) Governing Knowledge: comparison, knowledge-based technologies and expertise in the regulation of education. London: Routledge.
    Finn, M. (2016) Atmospheres of progress in a data-based school. Cultural Geographies, 23 (1): 2949.
    FitnessGram (2016) What is FitnessGram? FitnessGram. Available at
    Fitzgerald, D. and Callard, F. (2014) Social science and neuroscience beyond interdisciplinarity: experimental entanglements. Theory, Culture and Society, 32 (1): 332.
    Fitzgerald, D., Rose, N. and Singh, I. (2016) Revitalizing sociology: urban life and mental illness between history and the present. British Journal of Sociology, 67 (1): 13860.
    Floridi, L. (2016) Should we be afraid of AI? Aeon, 9 May. Available at
    Fogg, B. J. (2002) Persuasive technology: using computers to change what we think and do. Ubiquity, December: 89120.
    Fontaine, C. (2016) The myth of accountability: how data (mis)use is reinforcing the problems of public education. Data and Society, 8 August. Available at
    Ford, P. (2015) What is code? Business Week, 11 June. Available at
    Foresight (2008) Mental Capital and Wellbeing: making the most of ourselves in the 21st century. Executive Summary. London: Government Office for Science. Available at
    Foucault, M. (1990) The History of Sexuality, Volume I: The will to knowledge (trans. R. Hurley). London: Penguin.
    Foucault, M. (1991) Discipline and Punish: the birth of the prison (trans. A. Sheridan). Harmondsworth: Penguin.
    Foucault, M. (2007) Security, Territory and Population: lectures at the College de France 1977–1978 (trans. G. Burchell). New York: Palgrave Macmillan.
    Foucault, M. (2008) The Birth of Biopolitics: lectures at the College de France, 1978–1979 (trans. G. Burchell). New York: Palgrave Macmillan.
    Frenkel, S. (2016) Renegade Facebook employees form task force to battle fake news. BuzzFeed, 14 November. Available at
    Friedli, L. and Stearn, R. (2015) Positive affect as coercive strategy: conditionality, activation and the role of psychology in UK government workfare programmes. Medical Humanities, 41: 4047.
    Fuller, M. (2003) Behind the Blip: essays on the culture of software. Brooklyn, NY: Autonomedia.
    Fuller, M. (2008) Introduction. In Fuller, M. (ed.), Software Studies: a lexicon. London: MIT Press.
    Furedi, F. (2009) Wasted: why education isn’t educating. London: Continuum.
    Gaysina, D. (2016) How genetics could help future learners unlock hidden potential. Conversation, 15 November. Available at
    Gehl, R. (2015) Sharing, knowledge management and big data: a partial genealogy of the data scientist. European Journal of Cultural Studies, 18 (4–5): 41328.
    Gillespie, T. (2014a) The relevance of algorithms. In Gillespie, T., Boczkowski, P. J. and Foot, K. A. (eds), Media Technologies: essays on communication, materiality, and society. London: MIT Press. pp. 16793.
    Gillespie, T. (2014b) Algorithm. Culture Digitally, 25 June. Available at
    Gillespie, T. (2016) Algorithms, clickworkers, and the befuddled fury around Facebook trends. Social Media Collective, 18 May. Available at
    Gitelman, L. and Jackson, V. (2013) Introduction. In Gitelman. L. (ed.), ‘Raw Data’ is an Oxymoron. London: MIT Press. pp. 114.
    Gomes, P. (2015) Your guide to a nation of Edtech accelerators. EdSurge, 21 October. Available at–10–21-your-guide-to-a-nation-of-edtech-accelerators
    Gorur, R. (2015) The performative politics of NAPLAN and My School. Working paper, June. Available at (2016) Games workshop founder and entrepreneur to open 2 free schools. Available at
    Gregory, K., McMillan Cottom, T. and Daniels, J. (2017) Introduction. In Daniels, J., Gregory, K. and McMillan Cottom, T. (eds), Digital Sociologies. Bristol: Policy Press. pp. xviixxx.
    Grek, S. (2009) Governing by numbers: the PISA ‘effect’ in Europe. Journal of Education Policy, 24 (1): 2337.
    Grek, S. (2016) The life and work of the killer chart: on the art of visually assembling education comparisons. European Consortium for Political Research, Prague, September.
    Grosvenor, I. and Roberts, S. (2013) Systems and subjects: ordering, differentiating and institutionalising the modern child. In Lawn, M. (ed.), The Rise of Data in Education Systems: collection, visualization and use. Oxford: Symposium. pp. 7996.
    Gulson, K., Sellar, S. and Webb, T. (forthcoming) Emerging Biological Rationalities for Policy: (Molecular) Biopolitics and the New Authorities in Education.
    Hacking, I. (1990) The Taming of Chance. Cambridge: Cambridge University Press.
    Hacking, I. (2007) Kinds of people: moving targets. Proceedings of the British Academy, 151: 285318.
    Haggerty, K. D. and Ericson, R. V. (2001) The surveillant assemblage. British Journal of Sociology, 51 (4): 60522.
    Halford, S., Pope, C. and Weal, M. (2013) Digital futures? Sociological challenges and opportunities in the emergent semantic web. Sociology, 47 (1): 17389.
    Halverson, E. R. and Sheridan, K. (2014) The maker movement in education. Harvard Educational Review, 84 (4). Available at
    Hardy, I. and Lewis, S. (2016) The ‘doublethink’ of data: educational performativity and the field of schooling practices. British Journal of Sociology of Education. Available at
    Hartong, S. (2016) Between assessments, digital technologies and big data: the growing influence of ‘hidden’ data mediators in education. European Educational Research Journal, 15 (5): 52336.
    Hayes, B. (2015) Cultures of code. American Scientist, 103 (1).
    Hayles, N. K. (2013) How We Think: digital media and contemporary technogenesis. London: University of Chicago Press.
    Hayles, N. K. (2014) Cognition Everywhere: the rise of the cognitive nonconscious and the costs of consciousness. New Literary History, 45 (2): 199220.
    Hercher, J. (2016) Trump did have a paid media strategy, and it focused on Facebook. AdExchanger, 15 November. Available at
    Hilbert, M. (2016) Big data for development: a review of promises and challenges. Development Policy Review, 34 (1): 13574.
    Hill, P. and Barber, M. (2014) Preparing for a Renaissance in Assessment. London: Pearson.
    HM Treasury (2013) 100,000 young people to become ‘digital makers’. Available at
    Hogan, A., Sellar, S. and Lingard, B. (2015) Network restructuring of global edu-business: the case of Pearson’s Efficacy Framework. In Au, W. and Ferrare, J. J. (eds), Mapping Corporate Education Reform: power and policy networks in the neoliberal state. London: Routledge. pp. 4364.
    Holbein, J. (2016) Left behind? Citizen responsiveness to government performance information. American Political Science Review, 110 (2): 35368.
    Housley, W. (2015) The emerging contours of data science. Discover Society, 23. Available at
    Howard, P. (2016) Is social media killing democracy? Culture Digitally, 14 November. Available at
    Howard-Jones, P., Ott, M., van Leeuwen, T. and De Smedt, B. (2015) The potential relevance of cognitive neuroscience for the development and use of technology-enhanced learning. Learning, Media and Technology, 40 (2): 13151.
    Hunckler, M. (2015) ‘How this hackathon is inspiring students to better education’. Forbes, 17 August. Available at:
    Hunsinger, J. and Schrock, A. (2016) The democratization of hacking and making. New Media and Society, 18 (4): 5358.
    Huxley, M. (2007) Geographies of governmentality. In Crampton, J. and Elden, S. (eds), Space, Knowledge and Power: Foucault and geography. Aldershot: Ashgate. pp. 185204.
    IBM (2016) IBM Watson Education and Pearson to drive cognitive learning experiences for college students. IBM Press Release. Available at
    IBM Research (2011) IBM’s first cognitive computing chips mimic functions of the brain. IBM Research News, 18 August. Available at
    IBM Watson Education (2016a) Education in the cognitive era. Watson Education POV. Available at
    IBM Watson Education (2016b) Transform education with Watson. IBM Watson. Available at
    Iliadis, A. and Russo, F. (2016) Critical data studies: an introduction. Big Data and Society, 3 (2). Available at
    Imagine K12 (2015) Our program. Imagine K12. Available at
    Introna, L. (2016) Algorithms, governance, and governmentality: on governing academic writing. Science, Technology, and Human Values, 41 (1): 1749.
    Irani, L. (2015) Hackathons and the making of entrepreneurial citizenship. Science, Technology, and Human Values, 40 (5): 799824.
    Isin, E. and Ruppert, E. (2015) Being Digital Citizens. London: Rowman and Littlefield International.
    Ito, M. (2009) Engineering Play: a cultural history of children’s software. London: MIT Press.
    Jasanoff, S. (2015) Future imperfect: science, technology, and the imaginations of modernity. In Jasanoff, S. and Kim, S-H. (eds), Dreamscapes of Modernity: sociotechnical imaginaries and the fabrication of power. Chicago, IL: University of Chicago Press. pp. 133.
    Jones, R., Pykett, J. and Whitehead, M. (2013) Changing Behaviours: on the rise of the psychological state. Cheltenham: Edward Elgar.
    Jurgenson, N. (2014) View from nowhere: on the cultural ideology of big data. The New Inquiry, 9 October. Available at
    Keller, E. F. (2016) Thinking about biology and culture: can the natural and human sciences be integrated? Sociological Review Monograph Series: Biosocial Matters: Rethinking Sociology-Biology Relations in the Twenty-First Century, 64: 2641.
    Kelly III, J. E. (2015) Computing, Cognition and the Future of Knowing: how humans and machines are forging a new age of understanding. Somers, NY: IBM Corporation.
    Kelly III, J. E. and Hamm, S. (2014) Smart Machines: IBM’s Watson and the era of cognitive computing. New York: Columbia University Press.
    Kennedy, H. (2016) Post. Mine. Repeat. Social media data mining becomes ordinary. London: Palgrave Macmillan.
    Kennedy, H. and Moss, G. (2015) Known or knowing publics? Social media data mining and the question of public agency. Big Data and Society, 2 (2). Available at
    King, M., Cave, R., Foden, M. and Stent, M. (2016) Personalised Education: from curriculum to career with cognitive systems. Portsmouth: IBM Corporation.
    Kitchin, R. (2014a) The Data Revolution: big data, open data, data infrastructures and their consequences. London: Sage.
    Kitchin, R. (2014b) Big data, new epistemologies and paradigm shifts. Big Data and Society, 1 (1). Available at
    Kitchin, R. (2017) Thinking critically about and researching algorithms. Information, Communication and Society, 20 (1): 1429.
    Kitchin, R. and Dodge, M. (2011) Code/Space: software and everyday life. London: MIT Press.
    Kitchin, R. and Lauriault, T. (2014) Towards critical data studies: charting and unpacking data assemblages and their work. The Programmable City Working Paper 2. Available at
    Kitchin, R. and McArdle, G. (2015) What makes big data, big data? Exploring the ontological characteristics of 26 datasets. Big Data and Society, 3 (1). Available at
    Kitchin, R., Lauriault, T. and McArdle, G. (2015) Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards. Regional Studies, Regional Science, 2 (1): 628.
    Knewton (2011) Pearson and Knewton partner to advance next generation of digital education. Knewton in the News, 1 November. Available at
    Knewton (2013) Knewton Adaptive Learning: building the world’s most powerful education recommendation engine. Knewton. Available at
    Knox, J. (2016) Posthumanism and the Massive Open Online Course: contaminating the subject of global education. Abingdon: Routledge.
    Kolodny, L. (2016) ClassDojo raises $21 million for app to make parent-teacher meetings obsolete. TechCrunch, 15 April. Available at
    Krasodomski-Jones, A. (2016) What does the alt-right do now that ‘God Emperor’ Trump won? CNN, 15 November. Available at
    Lane, J. E. (2014) Building a Smarter University: big data, analytics and innovation. New York: SUNY Press.
    Langley, P. and Leyshon, A. (2016) Platform capitalism: the intermediation and capitalisation of digital economic circulation. Finance and Society: Available at
    Lapowsky, I. (2015) Inside the school Silicon Valley thinks will save education. Wired, 4 May. Available at
    Lapowsky, I. (2016) The 2016 election exposes the very, very dark side of tech. Wired, 7 November. Available at
    Lascoumes, P. and le Gales, P. (2007) Introduction: understanding public policy through its instruments – from the nature of instruments to the sociology of public policy instrumentation. Governance, 20 (1): 121.
    Latour, B. (1986) Visualization and cognition: thinking with eyes and hands. Knowledge and Society, 6: 140.
    Latour, B. (2016) Two bubbles of unrealism: learning from the tragedy of Trump. Los Angeles Review of Books, 17 November. Available at
    Lavecchia, A. M., Liu, H. and Oreopoulos, P. (2014) Behavioral economics of education: progress and possibilities. National Bureau of Economic Research, Working paper. Available at
    Law, J., Ruppert, E. and Savage, M. (2011) The double social life of methods. CRESC Working Paper No. 95. Open University.
    Lawn, M. (2013) The rise of data in education. In Lawn, M. (ed.), The Rise of Data in Education Systems: collection, visualization and use. Oxford: Symposium. pp. 710.
    Lawn, M. and Grek, S. (2012) Europeanizing Education: governing a new policy space. Oxford: Symposium.
    Lemke, T. (2011) Biopolitics: an advanced introduction. London: New York University Press.
    Lessig, L. (2000) Code v.2. New York: Basic Books.
    Levy, H. O. (2016) How a Trump administration could back revolutionary education technology. Fox News Opinion, 12 December. Available at
    Lewis, S. and Hardy, I. (2016) Tracking the topological: the effects of standardised data upon teachers’ practice. British Journal of Education Studies. Available at
    Lewis, S. and Hogan, A. (2016) Reform first and ask questions later? The implications of (fast) schooling policy and ‘silver bullet’ solutions. Critical Studies in Education. Available at
    Lindh, M. and Nolin, J. (2016) Information we collect: surveillance and privacy in the implementation of Google apps for education. European Educational Research Journal, 15 (6): 64463. Available at
    Lindtner, S. (2015) Hackerspaces and the internet of things in China: how makers are reinventing industrial production, innovation, and the self. China Information, 28 (2): 14567.
    Lippert, I. (2015) Environment as datascape: enacting emission realities in corporate carbon accounting. GeoForum, 66: 12635.
    Livingstone Aspirations (2016) Home. Livingstone Aspirations. Available at
    Livingstone, I. (2012) The quest to release the ICT curriculum from the jaws of the dragon of dullness. Wired, 23 January. Available at–01/23/ict-curriculum-ian-livingstone
    Livingstone, I. and Hope, A. (2011) Next Gen. London: Nesta.
    Losh, L. (2014) The War on Learning: gaining ground in the digital university. London: MIT Press.
    Luckin, R. and Holmes, W. (2016) Intelligence Unleashed: an argument for AI in education. London: Pearson.
    Lupton, D. (2015a) Digital Sociology. London: Routledge.
    Lupton, D. (2015b) Lively data, social fitness and biovalue: the intersections of health self-tracking and social media. Social Sciences Research Network. Available at
    Lupton, D. (2016) The Quantified Self: a sociology of self-tracking. Cambridge: Polity Press.
    Lynch, T.L. (2015) The Hidden Role of Software in Education: policy to practice. New York: Routledge.
    Lyon, D. (2014) Surveillance, Snowden and big data: capacities, consequences, critique. Big Data and Society, 1 (1). Available at
    Lytics Lab (2016) About Lytics Lab: Available at
    MacCormick, J. (2012) 9 Algorithms that Changed the Future: the ingenious ideas that drive today’s computers. Oxford: Princeton University Press.
    Mackenzie, A. (2006) Cutting Code: Software and sociality. Oxford: Peter Lang.
    Mackenzie, A. (2012) More parts than elements: how databases multiply. Environment and Planning D: Society and Space, 30: 33550.
    Mackenzie, A. (2013) Programming subjects in the regime of anticipation: software studies and subjectivity. Subjectivity, 6 (4): 391405.
    Mackenzie, A. (2015) The production of prediction: what does machine learning want? European Journal of Cultural Studies, 18 (4–5): 42945.
    Mackenzie, A. and Vurdubakis, T. (2011) Codes and codings in crisis: performativity, signification and excess. Theory, Culture and Society, 28 (6): 323.
    Mager, A. (2012) Algorithmic ideology: how capitalist society shapes search engines. Information, Communication and Society, 15 (5): 76987.
    Mager, A. (2015) Glocal search: search technology at the intersection of global capitalism and local socio-political cultures. Institute of Technology Assessment (ITA), Austrian Academy of Sciences. Available at
    Mager, A. (2016) Search engine imaginary: visions and values in the co-production of search technology and Europe. Social Studies of Science. Available at
    Maltby, P. (2015) A new operating model for government. Open Policy Making, 17 March. Available at
    Mandinach, E. B. and Gummer, E. S. (2016) Every teacher should succeed with data literacy. Phi Delta Kappan, 97 (8): 434.
    Manovich, L. (2013) Software Takes Command: extending the language of new media. London: Bloomsbury.
    Margetts, H. and Dunleavy, P. (2013) The second wave of digital-era governance: a quasi-paradigm for government on the Web. Philosophical Transactions of the Royal Society A, 371 (1987).
    Margetts, H. and Sutcliffe, D. (2013) Addressing the policy challenges and opportunities of ‘big data’. Policy and Internet, 5 (2): 13946.
    Margetts, H., John, P., Hale, S. and Yasseri, T. (2016) Political Turbulence: how social media shape collective action. Oxford: Princeton University Press.
    Markham, A. (2013) The algorithmic self: layered accounts of life and identity in the 21st century. Internet Research 14.0, 2326 October.
    Markowetz, A., Błaszkiewicz, K., Montag, C., Switala C. and Schlaepfer, T. E. (2014) Psycho-informatics: big data shaping modern psychometrics. Medical Hypotheses, 82 (4): 40511.
    Marres, N. (2012) The redistribution of methods: on intervention in digital social research, broadly conceived. Sociological Review, 60 (S1): 13965.
    Martens, K., Niemann, D. and Teltemann, J. (2016) Effects of international assessments in education – a multidisciplinary review. European Educational Research Journal, 15 (5): 51622.
    Mateos-Garcia, J. Bakhshi, H. and Windsor, G. (2015) Skills of the Datavores: talent and the data revolution. London: Nesta. Available at
    Mayer-Schönberger, V. and Cukier, K. (2013) Big Data: a revolution that will change how we live, work and think. London: John Murray.
    Mayer-Schönberger, V. and Cukier, K. (2014) Learning from Big Data: the future of education. New York: Houghton Mifflin Harcourt.
    McGimpsey, I., Bradbury, A. and Santori, D. (2016) Revisions to rationality: the translation of ‘new knowledges’ into policy under the coalition government. British Journal of Sociology of Education. Available at
    Mead, R. (2016) Learn different: Silicon Valley disrupts education. The New Yorker, 7 March. Available at
    Mead, S. (2013) Profile of ClassDojo founders Sam Chaudhury and Liam Don. Education Week, 11 June. Available at
    Meloni, M. (2014) Remaking local biologies in an epigenetic time. Somatosphere, 8 August. Available at
    Meloni, M., Williams, S. and Martin, P. (2016) The biosocial: sociological themes and issues. Sociological Review Monograph Series: Biosocial Matters: Rethinking Sociology-Biology Relations in the Twenty-First Century, 64 (1): 725.
    Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., Jackson, B. L., Imam, N., Guo, C., Nakamura, Y., Brezzo, B., Vo, I., Esser, S. K., Appuswamy, R., Taba, B., Amir, A., Flickner, M. D., Risk, W. P., Manohar, R. and Modha, D. S. (2014) A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345 (6197): 66873.
    Meyers, M. (2015) Connecting the classroom with the internet of things. EdSurge, 28 March. Available at–03–28-connecting-the-classroom-with-the-internet-of-things
    Michael, M. and Lupton, D. (2015) Toward a manifesto for the ‘public understanding of big data’. Public Understanding of Science, 25 (1): 10416.
    Miller, C. (2014) The promise of social media. Demos Quarterly, Winter 2013/14. Available at
    Miller, P. and Rose, N. (2008) Governing the Present: administering economic, social and personal life. Cambridge: Polity.
    Mittelstadt, B. D., Allo, P., Taddeo, M. Wachter, S. and Floridi, L. (2016) The ethics of algorithms: mapping the debate. Big Data and Society, 3 (2): Available at
    Modha, D. (2013) Systems that perceive, think and act. The Atlantic, June. Available at
    Modha, D. (2014) Introducing a brain-inspired computer: TrueNorth’s neurons to revolutionize system architecture. IBM Research. Available at
    Monahan, T. (2005) Globalization, Technological Change, and Public Education. London: Routledge.
    Montero, C. S. and Suhonen, J. (2014) Emotion analysis meets learning analytics: online learner profiling beyond numerical data. Proceedings of the 14th Koli Calling International Conference on Computing Education Research, 1659, Koli, Finland: ACM Press.
    Morozov, E. (2013a) To Save Everything, Click Here: technology, solutionism and the urge to fix problems that don’t exist. London: Allen Lane.
    Morozov, E. (2013b) The real privacy problem. MIT Technology Review, 22 October. Available at
    Morris, P. (2016) Education policy, cross-national tests of pupil achievement, and the pursuit of world-class schooling. London: UCL Institute of Education Press.
    Moss, P., Dahlberg, G., Grieshaber, S., Mantovani,, S., May, H., Pence, A., Rayna, S., Blue Swadener, B. and Vandenbroeck, M. (2016) The Organisation for Economic Co-operation and Development’s International Early Learning Study: opening for debate and contestation. Contemporary Issues in Early Childhood, 17 (3): 34351.
    Mulgan, G. (2016a) A machine intelligence commission for the UK: how to grow informed public trust and maximise the positive impact of smart machines. Nesta, 7 February. Available at
    Mulgan, G. (2016b) Government as collective intelligence. Oxford Government Review, 1 (August): 4446.
    Nafus, D. (2016) Introduction. In Nafus, D. (ed.), Quantified: biosensing technologies in everyday life. London: MIT Press. pp. ixxxxi.
    Nature Neuroscience (2014) Focus on big data. Nature Neuroscience, 17 (11): 1429.
    Naughton, J. (2012) A manifesto for teaching computer science in the 21st century. Observer, 31 March. Available at
    Naughton, J. (2014) Year of Code already needs a reboot. The Guardian, 15 February. Available at
    Nesta (2015) Analytic Britain: securing the right skills for the data-driven economy. London: Nesta. Available at
    New Zealand Ministry of Education (2016) Establishing a regulatory framework for online learning., 25 August. Available at
    New, J. (2016) Building a data-driven education system in the United States. Center for Data Innovation. Available at
    Neyland, D. (2015) On organizing algorithms. Theory, Culture and Society, 32 (1): 11932.
    Nielsen, M. (2015) Who owns big data? Change: 19 Key Essays on How the Internet Is Changing Our Lives. Open Mind. Available at
    Nitta, S. (2014) Cognitive learning content: a vision for how to make learning deeply engaging as well as intuitive. IBM Insights on Business, 14 May. Available at
    Nominet Trust (2013) Digital making activities to expand opportunities for UK young people. Nominet Trust. Available at
    Novoa, A. and Yariv-Mashal, T. (2014) Comparative research in education: a mode of governance or a historical journey? In Fenwick, T., Mangez, E. and Ozga, J. (eds), Governing Knowledge: comparison, knowledge-based technologies and expertise in the regulation of education. London: Routledge. pp. 1330.
    O’Keeffe, C. (2016) Producing data through e-assessment: a trace ethnographic investigation into e-assessment events. European Educational Research Journal, 15 (1): 99116.
    O’Reilly, T. (2016) Media in the age of algorithms. Medium, 11 November. Available at
    OECD (Organization for Economic Cooperation and Development) (2015) Skills for Social Progress: the power of social and emotional skills. OECD Skills Studies. Paris: OECD.
    Open Glasgow (2014) Engagement and literacy programme. Open Glasgow. Available at
    Orton-Johnson, K. and Prior, N. (eds) (2013) Digital Sociology: critical perspectives. Houndmills: Palgrave Macmillan.
    Orton-Johnson, K., Prior, N. and Gregory, K. (2015) Sociological imagination: digital sociology and the future of the discipline. The Sociological Review blog, 17 December. Available at
    Ozga J. (2009) Governing education through data in England: from regulation to self-evaluation. Journal of Education Policy, 24 (2): 14963.
    Ozga, J. (2016) Trust in numbers? Digital education governance and the inspection process. European Educational Research Journal, 15 (1): 6981.
    Ozga, J., Dahler-Larsen, P., Segerholm, C. and Simola, H. (eds) (2011) Fabricating Quality in Education: data and governance in Europe. London: Routledge.
    Pace, L. (2016) How the president-elect can scale personalized learning. Getting Smart, 12 December. Available at
    Paglen, T. (2016) Invisible images (your pictures are looking at you). The New Inquiry, 8 December. Available at
    Papert, S. (1980) Mindstorms: children, computers and powerful ideas. New York: Basic Books.
    Pariser, E. (2015) Did Facebook’s big new study kill my filter bubble thesis? Backchannel, 7 May. Available at
    Partnership on AI (2016) About the partnership. Partnership on AI. Available at
    Pasquale, F. (2015) The Black Box Society: the secret algorithms that control money and information. Cambridge: Harvard University Press.
    Patton, B. (2016) The trouble with taking biometric technology into schools. Conversation, 6 January. Available at
    Pea, R. (2014) A Report on Building the Field of Learning Analytics for Personalized Learning at Scale. Stanford: Stanford University.
    Pearson (2016) IBM Watson Education and Pearson to drive cognitive learning experiences for college students. Pearson News. Available at
    Peck, J. and Theodore, N. (2015) Fast Policy: experimental statecraft at the thresholds of neoliberalism. Minneapolis, MN: University of Minnesota Press.
    Perrotta, C. and Williamson, B. (2016) The social life of learning analytics: cluster analysis and the performance of algorithmic education. Learning, Media and Technology. Available at
    Persson, J. (2016) School census changes add concerns to the richest education database in the world. Parenting for a Digital Future, 19 July. Available at
    Peyton Jones, S., Mitchell, B. and Humphreys, S. (2013) Computing at school in the UK. Microsoft Research Papers. Available at
    Piattoeva, N. (2015) Elastic numbers: national examinations data as a technology of government. Journal of Education Policy, 30 (3): 31634.
    Picard, R. W. (2016) What happened to the Q sensor? MIT Media Lab. Available at
    Pickersgill M. (2013) The social life of the brain: neuroscience in society. Current Sociology, 61 (3): 32240.
    Piety, P. J., Behrens, J. and Pea, R. (2013) Educational data sciences and the need for interpretive skills. American Educational Research Association, 27 April–1 May.
    Piety, P. J., Hickey, D. T. and Bishop, M. J. (2014) Educational data sciences – framing emergent practices for analytics of learning, organizations and systems. LAK ’ 14, 2428 March, Indianapolis.
    Pluim, C. and Gard, M. (2016) Physical education’s grand convergence: FitnessGram, big-data and the digital commerce of children’s bodies. Critical Studies in Education. Available at
    Popkewitz, T. S. (2012) Numbers in grids of intelligibility: making sense of how educational truth is told. In Lauder, H., Young, M., Daniels, H., Balarin, M. and Lowe, J. (eds), Educating for the Knowledge Economy? Critical perspectives. Abingdon: Routledge. pp. 16991.
    Pykett, J. (2012) The pedagogical state: education, citizenship, governing. In Pykett, J. (ed.), Governing Through Pedagogy: re-educating citizens. London: Routledge. pp. 120.
    Pykett, J. (2013) Neurocapitalism and the new neuros: using neuroeconomics, behavioural economics and picoeconomics for public policy. Journal of Economic Geography, 13: 84569.
    Pykett, J. (2015) Brain Culture: shaping policy through neuroscience. Bristol: Policy Press.
    Pykett, J. and Disney, T. (2015) Brain-targeted teaching and the biopolitical child. Politics, Citizenship and Rights: Available at–981–4585–94–1_22–1
    Quinlan, O. (2015) Young digital makers: surveying attitudes and opportunities for digital creativity across the UK. Nesta. Available at
    Rabinow, P. and Rose, N. (2006) Biopower today. BioSocieties, 1: 195217.
    Raley, R. (2013) Dataveillance and counterveillance. In Gitelman, L. (ed.), ‘Raw Data’ is an Oxymoron. London: MIT Press. pp. 12146.
    Ratto, M. and Boler, M. (eds) (2014) DIY Citizenship: critical making and social media. London: MIT Press.
    Reveley, J. (2015) School-based mindfulness training and the economisation of attention: a Stieglerian view. Educational Philosophy and Theory, 47 (8): 80421.
    Reynolds, L. and Birdwell, J. (2015) Mind Over Matter. London: Demos.
    Rich, E. and Miah, A. (2014) Understanding digital health as public pedagogy: a critical framework. Societies, 4: 296315.
    Rieder, G. and Simon, J. (2016) Datatrust: or, the political quest for numerical evidence and the epistemologies of big data. Big Data and Society, 3 (1). Available at
    Rienties, B. and Rivers, B. A. (2014) Measuring and Understanding Learner Emotions: evidence and prospects. Bolton: University of Bolton.
    Rizvi, F. and Lingard, B. (2010) Globalizing Education Policy. Abingdon: Routledge.
    Roberts-Holmes, G. (2015) The ‘datafication’ of early years pedagogy: ‘If the teaching is good, the data should be good and if there’s bad teaching, there is bad data’. Journal of Education Policy, 30 (3): 30215.
    Roberts-Mahoney, H., Means, A. J. and Garrison, M. J. (2016) Netflixing human capital development: personalized learning technology and the corporatization of K12 education. Journal of Education Policy, 31 (4). Available at
    Robertson, H. and Travaglia, J. (2015) Big data problems we face today can be traced to the social ordering practices of the 19th century. LSE Impact of Social Sciences, 13 October. Available at
    Rogers, R. (2013) Digital Methods. London: MIT Press.
    Rose, G., Degen, M. and Melhuish, C. (2014) Networks, interfaces and computer-generated images: learning from digital visualizations of urban regeneration projects. Environment and Planning D: Society and Space, 32: 386403.
    Rose, N. (1996) Inventing Our Selves: psychology, power and personhood. Cambridge: Cambridge University Press.
    Rose, N. (1999a) Powers of Freedom: reframing political thought. Cambridge: Cambridge University Press.
    Rose, N. (1999b) Governing the Soul: the shaping of the private self (
    edn). London: Free Association Books.
    Rose, N. (2016) Reading the human brain: how the mind became legible. Body and Society, 22 (2). Available at
    Rose, N. and Abi-Rached, J. (2013) Neuro: the new brain sciences and the management of the mind. Oxford: Princeton University Press.
    Rose, N. and Abi-Rached, J. (2014) Governing through the brain: neuropolitics, neuroscience and subjectivity. Cambridge Anthropology, 32 (1): 323.
    Rose, N., Aicardi, C. and Reinsborough, M. (2016) Future Computing and Robotics: a Foresight report from the Human Brain Project lab. London: King’s College London.
    Rose, S. (2013) Beware ‘brain-based learning’. Times Higher Education, 12 December. Available at
    Rosenberg, D. (2013) Data before the fact. In Gitelman, L. (ed.), ‘Raw Data’ is an Oxymoron. London: MIT Press. pp. 1540.
    Royal Society (2011) Brain Waves II: neuroscience implications for education and lifelong learning. London: The Royal Society.
    Royal Society (2012) Shut Down or Restart? The way forward for computing in UK schools. London: The Royal Society.
    Runciman, D. (2016) How the education gap is tearing politics apart. Guardian, 5 October. Available at
    Ruppert, E. (2012) The governmental topologies of database devices. Theory, Culture and Society, 29 (4–5): 11636.
    Ruppert, E. (2015) Who owns big data? Discover Society, 30 July. Available at
    Ruppert, E., Harvey, P., Lury, C., Mackenzie, A., McNally, R., Baker, S. A., Kallianos, Y. and Lewis, C. (2015) Socialising big data: from concept to practice. CRESC Working Paper no. 138. Available at
    Ruppert, E., Law, J. and Savage, M. (2013) Reassembling social science methods: the challenge of digital devices. Theory, Culture and Society, 30 (4): 2246.
    Rushkoff, D. (2010) Program or Be Programmed: ten commends for a digital age. New York: OR Books.
    Sahlberg, P. and Hasak, J. (2016) Data was supposed to fix education. Washington Post, 9 May. Available at
    Saltman, K. (2016) Corporate schooling meets corporate media: standards, testing, and technophilia. Review of Education, Pedagogy, and Cultural Studies, 38 (2): 105123.
    Sandlin, J. A., O’Malley, M. P. and Burdick, J. (2011) Mapping the complexity of public pedagogy scholarship, 1894–2010. Review of Educational Research, 81 (3): 33875.
    Sandvik, L. (2014) Resignation. GitHub, 26 August. Available at
    Savage, M. (2013) The ‘social life of methods’: a critical introduction. Theory, Culture and Society, 30 (4): 321.
    Savage, M. and Cunningham, N. (2016) Why inequality matters: the lessons of Brexit. Items: Insights from the Social Sciences, 20 September. Available at
    Schechtman, N., DeBarger, A. H., Dornsife, C., Rosier, S. and Yarnall, L. (2013) Promoting Grit, Tenacity and Perseverance: critical factors for success in the 21st century. Washington, DC: US Department of Education, Office of Educational Technology.
    Schiller, D. (2015) Digital capitalism: stagnation and contention? Open Democracy, 13 October. Available at
    Sclater, N., Peasgood, A. and Mullan, J. (2016) Learning Analytics in Higher Education: a review of UK and international practice. Bristol: Jisc.
    Scottish Government (2016) Delivering Excellence and Equity in Scottish Education: a delivery plan for Scotland. Edinburgh: Scottish Government.
    Sefton-Green, J. (2013) Mapping Digital Makers. Oxford: Nominet Trust.
    Sellar, S. (2015a) Data infrastructure: a review of expanding accountability systems and large-scale assessments in education. Discourse: Studies in the Cultural Politics of Education, 36 (5): 76577.
    Sellar, S. (2015b) A feel for numbers: affect, data and education policy. Critical Studies in Education, 56 (1): 13146.
    Selwyn, N. (2011) Schools and Schooling in the Digital Age: a critical analysis. Abingdon: Routledge.
    Selwyn, N. (2015) Data entry: towards the critical study of digital data and education. Learning, Media and Technology, 40 (1): 6482.
    Selwyn, N. (2016) Is Technology Good for Education? Cambridge: Polity Press.
    Selwyn, N., Nemorin, S., Bulfin, S. and Johnson, N. F. (2017) Toward a digital sociology of school. In Daniels, J., Gregory, K. and McMillan Cottom, T. (eds), Digital Sociologies. Bristol: Policy Press. pp. 14762.
    Shapiro, J. (2016) President Obama wants every kid to learn coding – for all the wrong reasons. Forbes, 31 January. Available at
    Sharples, J. and Kelley, P. (2015) Introduction to learning, media and technology: neuroscience and education special edition. Learning, Media and Technology, 40 (2): 12730.
    Shepard, M. (2011) Toward the sentient city. In Shepard, M. (ed.), Sentient City: ubiquitous computing, architecture, and the future of urban space. Cambridge, MA: MIT Press. pp. 1035.
    Siemens, G. (2013) Learning analytics: the emergence of a discipline. American Behavioral Scientist, 57 (10): 1380400.
    Siemens, G. (2016) Reflecting on learning analytics and SoLAR. Elearnspace, 28 April. Available at
    Simon, S. (2012) Biosensors to monitor US students’ attentiveness. Reuters, 13 June. Available at
    Sleeman, C. (2016) The state of interactive data visualisation. Nesta, 12 May. Available at
    Sobe, N. (2013) Educational data at late nineteenth- and early twentieth-century international expositions: ‘accomplished results’ and ‘instruments and apparatuses’. In Lawn, M. (ed.), The Rise of Data in Education Systems: collection, visualization and use. Oxford: Symposium. pp. 4156.
    Soep, E. (2014) Participatory Politics: next-generation tactics to remake public spheres. London: MIT Press.
    Solove, D. J. (2006) A taxonomy of privacy. University of Pennsylvania Law Review, 154 (3): 477564.
    Soroko, A. (2016) No child left alone? The ClassDojo app. Our Schools/Our Selves, 25 (3): 6374.
    Sqord (2016) About Sqord. Available at
    Summit Basecamp (2016) Explore base camp. Available at
    Summit Learning (2016) Bring personalized learning to your students. Summit Personalized Learning. Available at
    Suoto-Otero, M. and Beneito-Montagut, R. (2016) From governing through data to governmentality through data: artefacts, strategies and the digital turn. European Educational Research Journal, 15 (1): 1433.
    Taylor, E. (2013) Surveillance Schools: security, disciplines and control in contemporary education. Basingstoke: Palgrave Macmillan.
    TeacherMatch (2015) About TeacherMatch. TeacherMatch. Available at
    Thompson, G. (2016) Computer adaptive testing, big data and algorithmic approaches to education. British Journal of Sociology of Education. Available at
    Thompson, G. and Cook, I. (2016) The logic of data-sense: thinking through learning personalisation. Discourse: Studies in the Cultural Politics of Education. Available at
    Thrift, N. (2005) Knowing Capitalism. London: Sage.
    Thrift, N. (2014) The promise of urban informatics: some speculations. Environment and Planning A, 46: 12636.
    Townsend, A. M. (2013) Smart Cities: big data, civic hackers and the quest for a new utopia. London: Norton.
    Tufekci, Z. (2014) Engineering the public: big data, surveillance and computational politics. First Monday, 19 (7). Available at
    Uber (2015) Uber + HackingEDU: gaining momentum in education. Uber Newsroom. Available at
    UNICEF Kid Power (2016) Home. UNICEF Kid Power. Available at
    Urban Data School (2015) Aims. Urban Data School. Available at
    van Dijck, J. (2013) The Culture of Connectivity: a critical history of social media. Oxford: Oxford University Press.
    van Dijck, J. (2014) Datafication, dataism and dataveillance: big data between scientific paradigm and ideology. Surveillance and Society, 12 (2): 197208.
    van Dijck, J. and Poell, T. (2013) Understanding social media logic. Media and Communication, 1 (1): 214.
    van Dijk, P. E. E. (2016) ClassDojo and PERTS launch growth mindset toolkit. Stanford Daily, 17 February. Available at
    Vander Schee, C. (2009) Fruit, vegetables, fatness, and Foucault: governing students and their families through school health policy. Journal of Education Policy, 24 (5): 55774.
    Viner, C. (2016) How technology disrupted the truth. Guardian, 12 July. Available at
    Wakeford, J. (2016) Fake news detector plug-in developed. BBC News, 2 December. Available at
    Watters, A. (2016) Ed-tech patents: prior art and learning theories. Hack Education, 12 January. Available at
    WEF (World Economic Forum) (2016) New Vision for Education: fostering social and emotional learning through technology. Cologny/Geneva: World Economic Forum.
    Weisenthal, J. (2016) Donald Trump, the first President of our post-literate age. Bloomberg, 29 November. Available at–11–29/donald-trump-the-first-president-of-our-post-literate-age
    White House (2016a) The people’s code – now on White House blog, 3 November. Available at
    White House (2016b) Preparing for the future of artificial intelligence. Executive Office of the President National Science and Technology Council Committee on Technology. Available at
    Wilkins, A. (2015) Professionalizing school governance: the disciplinary effects of school autonomy and inspection on the changing role of school governors. Journal of Education Policy, 30 (2): 182200.
    Williams, S., Katz, S. and Martin, P. (2011) The neuro-complex: some comments and convergences. Media Tropes, 3 (1): 13546.
    Williamson, B. (2015a) Governing methods: policy innovation labs, design and data science in the digital governance of education. Journal of Educational Administration and History, 47 (3): 25171.
    Williamson, B. (2015b) Algorithmic skin: health tracking technologies, personal analytics and the biopedagogies of digitized health and physical education. Sport, Education and Society, 20 (1): 13351.
    Williamson, B. (2015c) Educating the smart city: schooling smart citizens through computational urbanism. Big Data and Society, 2 (2). Available at http://dx/
    Williamson, B. (2016a) Digital education governance: data visualization, predictive analytics and ‘real-time’ policy instruments. Journal of Education Policy, 31 (2): 12341.
    Williamson, B. (2016b) Digital methodologies of education governance: Pearson plc and the remediation of methods. European Educational Research Journal, 15 (1): 3453.
    Williamson, B. (2016c) Coding the biodigital child: the biopolitics and pedagogic strategies of educational data science. Pedagogy, Culture and Society, 24 (3): 40116.
    Williamson, B. (2016d) Political computational thinking: policy networks, digital governance, and ‘learning to code’. Critical Policy Studies, 10 (1): 3958.
    Williamson, B. (2017) Computing brains: learning algorithms and neurocomputation in the smart city. Information, Communication and Society, 20 (1): 8199.
    Williamson, B. (forthcoming) Who owns educational theory? Big data, algorithms and the expert power of education data science. E-learning and Digital Media.
    Willson, M. (2017) Algorithms (and the) everyday. Information, Communication and Society, 20 (1): 13750.
    Wilson, K. and Nichols, Z. (2015) The Knewton platform: a general-purpose adaptive learning infrastructure. Knewton. Available at
    Wolf, M., Taimurty, M., Patel, M. and Meteyer, J. (2016) The Dean’s information challenge: from data to dashboard. EduCause Review, 28 November. Available at
    Woolgar, S. (1991) Configuring the user: the case of usability trials. In Law, J. (ed.), A Sociology of Monsters: essays on power, technology and domination. London: Routledge. pp. 5799.
    Woolley, S.J. (2016) Automating power: social bot interference in global politics. First Monday, 21 (4). Available at
    Year of Code (2014) What is year of code? Available at
    Yeung, K. (2017) Hypernudge: big data as a mode of regulation by design. Information, Communication and Society, 20 (1): 11836.
    Youdell, D. (2016a) A biosocial education future? Research in Education, 96 (1). Available at
    Youdell, D. (2016b) New biological sciences, sociology and education. British Journal of Sociology of Education, 37 (5): 788800.
    Young, J. R. (2016) What clicks from 70,000 courses reveal about student learning. Chronicle of Higher Education, 7 September. Available at
    Zamzee (2016) Zamzee home page. Available at
    Zeide, E. (2016) Student privacy principles for the age of big data: moving beyond FERPA and FIPPS. Drexel Law Review, 8: 339.
    Zernike, K. (2016) Testing for joy and grit? Schools’ nationwide push to measure students’ emotional skills. New York Times, 29 February. Available at

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