Education and the big data imaginary

Ben Williamson

Big data and ‘smarter’ software systems are beginning to impact on education, particularly within the schools sector. In a paper prepared for the Big Data–Social Data conference at the University of Warwick, I have sought to trace the emergence of a ‘big data imaginary,’ a vision of a desirable future of education that its advocates believe is attainable through the application of big data technologies and practices.

The full paper, ‘Smarter learning software: education and the big data imaginary,’ provides a series of examples of how such an imaginary is emerging. It draws on previous material and publications produced during the Code Acts in Education project to offer a synthesis of educational big data developments, and engages with a recent paper by Rob Kitchin and Gavin McArdle on the diverse nature of big data to suggest that a certain ‘species’ of big data is emerging in education.

Firstly, it identifies a ‘first wave of big data’ in nineteenth-century education exhibitions and its continuities with the visualization of large-scale educational data today. The ‘exhibitionary practices’ associated with the display of educational data in the past are now being continued in far more large-scale and joined-up databanks such as Pearson’s Learning Curve, which make multiple global datasets into interactive data-visualized exhibits for the manipulation of users.

Secondly, it details the emergence of ‘educational data science’ as an exemplar of how ‘second wave big data’ has entered the imagination of many actors within education. Educational data science combines approaches such as learning analytics and educational data mining, and demands new computational skills of educational researchers, such as machine learning, algorithmic playlisting, and data visualization, and is increasingly seen as the the community dealing with big data in education. Commercial companies such as Pearson, as well as academics from major universities, are now propelling this nascent field into practice, with significant consequences for how educational data are generated and used to construct new insights into learning–even to animate new theories.

Thirdly, it then demonstrates how education is being reimagined in relation to ‘smart cities’ that depend on big data for their functioning.  Major smart cities software vendors such as IBM are now proposing to design ‘smarter schools’  in which many aspects of administration, leadership, spatial organization, student management, communication and even pedagogy itself are to be governed through big data practices and processes. At the same time, other smart cities initiatives focus on educating ‘smart citizens’ with the data literacies and programming skills to carry out data analyses and produce new digital urban services and solutions on behalf of their cities.

Finally, it details the recent appearance of ‘startup schools’ that are being established by Silicon Valley entrepreneurs to run as testbeds of smarter learning software systems. Designed as scalable technical platforms and underpinned by software engineering expertise, they are staffed and managed by entrepreneurs, executives and engineers from some of Silicon Valley’s most successful startups and web companies; and proposed to reinvent, reimagine and rebuild education in the mould of Silicon Valley itself. In particular, they depend on a particularly enthusiastic big data imaginary associated with the culture of the technology entrepreneurship sector that sees digital data analytics as the source for the solutions required to fix the ‘broken’ system of state schooling.

A concluding section discusses how the future of education–animated by this big data imaginary–may be governed by the production and circulation of the ‘data and algorithms of the powerful.’ Although educational sociology has long grappled with the idea that schools are dominated by the ‘knowledge of the powerful’–the selections from culture that are specified for study in schools, and that reflect the dominant interests of governments and commercial education providers–in the paper I suggest that the data and algorithms of powerful groups and organizations are becoming increasingly consequential to the organization of the social institution of education. The big data imaginary of the future of education is, in this respect, part of a ‘species’ of big data–a species with characteristics that are seen as desirable and attainable by advocates of a smarter, big-data-driven education system.