Ben Williamson
Digital technologies facilitate the generation, calculation and circulation of the data required to govern education. Seemingly objective statistical data are now being integrated into much educational policymaking, with schools and classrooms configured as ‘data platforms’ linked to vast global data collection programmes, and the ‘reality’ of education rearticulated in numerical practices that are enacted by new software developments, data companies, and data analysis instruments. In a new article published in the Journal of Education Policy, I have argued that the influence of digital technologies in such practices is complementing existing uses of data with methods of digital education governance.
Image: Gareth Halfacree
In the article I have suggested that education governance is now being enacted through new kinds of digital policy instruments that allow educational policy to be made operational. It is taking place in a context in which ‘datafication’—the objective quantification of all kinds of human behaviour to enable real-time tracking, monitoring and predictive analysis—has become a new paradigm in science and society. This is apparent with the growing interest in using big data for policymaking (and reflected in concerns over policy by algorithm), although education policy research has long been concerned with the social and political processes involved in the production of educational data, and in their socially and politically productive effects. By focusing on digital policy instruments, and the data infrastructures in which they operate, I have tried to suggest that education policy is now being influenced to a significant degree by the design of the digital instruments through which educational data are collected, calculated, analysed, interpreted and visualized.
Looking at educational data in this way means going beyond the ‘policy numbers‘ to acknowledge the infrastructural apparatus of technologies, human actors, institutions, methodologies, and social and political contexts that frame and shape their production and use. This requires us to consider the specific instruments, the human hands, eyes and minds, the companies and agencies, and the wider contexts that constitute such an infrastructure for educational data production and give it its productive power. To illustrate this, I have specifically looked at a number of examples of digital education governance at work.
Governing through databases
The first example is a fairly well-known database. The National Pupil Database was established in 2002 by the UK government under the supervision of Michael Barber, then head of the Prime Minister’s Delivery Unit. The NPD features extensive datasets on the educational progress of children and young people from the early years through to higher education and contains detailed information on over 7 million pupils currently matched over a period of 12 years. The NPD captures information on their progress through the educational system as traces of data that can be standardized, joined together and aggregated with a national population dataset. The NPD pages on the gov.uk website enable interested parties to request access to the data, which is presented in Excel spreadsheet files as thousands upon thousands of rows of numbers that can be searched and analyzed in myriad ways, and used to generate graphical displays such as charts, tables, plots, and graphs.
Spreadsheets are a highly mundane form of database technology, but have also become, since their invention at the end of the 1970s, highly influential in the organization and presentation of data across commercial and governmental sectors. The spreadsheet enables a particular view of reality as enumerable and calculable by its in-built statistical formulas and models.
As a data source that is enacted through Excel spreadsheets, the NPD has become a major policy instrument of educational governance in the UK. For example, in 2015 the Education DataLab was launched as ‘the UK’s centre of excellence for quantitative research in education, providing independent, cutting-edge research to support those leading education policy and practice,’ a task largely to be accomplished by conducting secondary analyses of the NPD in order to ‘improve education policy by analysing large education datasets.’ The Education DataLab is indicative of how education governance is being displaced to new centres of technical expertise, such as ‘policy labs,’ that are able to translate the massive data resources of the NPD into actionable policy insights through advanced digital methods of data analysis and presentation.
A raft of other database-enabled digital policy instruments has followed. These include the Department for Education’s school performance tables, Ofsted’s School Data Dashboard, and the OECD’s Education GPS application. These digital policy instruments all conform to a realist view that education can be presented as ‘visualized facts,’ rendering visible particular representations of the data whilst rendering invisible the underlying statistical and algorithmic techniques performed on it, by new kinds of technical data experts, to make it intelligible. The structure of the software interface of the policy instrument in this sense structures the data, and is intended to structure the user’s interaction with that data as a means to facilitate social action.
Centres of visualization
A key techniques of digital education governance is data visualization. Visualization is now a major topic in social science studies of big data. It’s important, again, to acknowledge that a data visualization is an expertly crafted accomplishment, not simply a visual reproduction of some underlying reality. Any visualization produced using software and digital data is an ‘interfacial site’ created through networks of human bodies at work with various kinds of software and hardware, facilitated by vast repositories of code and databases of fine-grained information, and possesses productive power to shape people’s engagement and interaction with the world itself.
A notable producer of data visualizations in education is the global educational publisher Pearson Education. Pearson’s Learning Curve Data Bank combines 60 global datasets in order to ‘enable researchers and policymakers to correlate education outcomes with wider social and economic outcomes.’ The Learning Curve includes national performance data (sourced from, for example, the National Pupil Database) along with global data sources, in order to produce a ‘Global Index’ of nations that is ranked in terms of ‘educational attainment’ and ‘cognitive skills’. The Learning Curve is highly relational, enabling the conjoining of multiple datasets, as well as scalable in that it can expand rapidly as new datasets become available.
It features a suite of dynamic and user-friendly mapping and time series tools that allow countries to be compared and evaluated both spatially and temporally. Countries’ educational performance in terms of educational attainment and cognitive skills are represented on the site as semantically resonant ‘heat maps.’ It also permits the user to generate ‘country profiles’ that visually compare multiple ‘education input indicators’ (such as public educational expenditure, pupil:teacher ratio, educational ‘life expectancy’) with ‘education output indicators’ (PISA scores, graduation rates, labour market productivity), as well as ‘socio-economic indicators’ (such as GDP and crime statistics). The Learning Curve is a powerful technique of political visualization for envisioning the educational landscape, operationalizing the presentation and re-presentation of numbers for a variety of purposes, users and audiences. Michael Barber, the Chief Education Adviser to Pearson who launched the Learning Curve (and formerly the leading government adviser behind the National Pupil Database), has described it as allowing the public to ‘connect those bits together’ in a way that is more ‘fun’ and ‘co-creative’ than preformatted policy reports.
Even so, as an interactive and co-creative policy instrument, the Learning Curve is no neutral device. The choice of the instrumentation materializes the forms of analysis that are possible. Users’ own analyses are in effect preformatted by the design of the interface as a form of user-generated comparative analysis, inciting users to compare country performance according to in-built tools constructed according to the assumptions and preferences of its technical and methodological producers. At the same time, the Learning Curve is structured according to the social media logic of ‘prosumption,’ where users are seen not simply as consumers of data but as its producers too. The Learning Curve therefore reconfigures education governance as a form of ‘play’ and ‘fun’ that is consonant with the logics of social media participation and audience democracy in the popular domain, but at the same time preformats the possible results of such activities through the methodological preferences built-in to its interface. It incites the wider publics of education to see themselves as comparative analysts, and as participatory actors in the flow of comparative data, but subtly configures and delimits what users can do with the data and what can be said about them.
As such, the global ‘centres of calculation‘ such as Pearson that manage the global flow of educational data are now increasingly becoming centres of visualization with the technologies and techniques to render dynamic educational data visualizations and to mobilize the interactivity of users to secure their consensus. Their visualizations act as surfaces on which millions of educational performances and measurements are inscribed and made visible for inspection, analysis, evaluation and comparison. As policy instruments, these visualizations act as ‘interfacial sites’ through which different views and visions of education are constantly being composed and compared, altered and modified, developed and designed in order to render certain kinds of meanings and arguments possible.
Centres of anticipation
The emergence of big data in education means that data can now, increasingly, be collected and analysed in real-time and automatically. Pearson, for example, has established a Center for Digital Data, Analytics, and Adaptive Learning, intended to ‘make sense of learning in the digital age,’ which has produced a report on the impacts of big data on education. It envisions education systems where ‘teaching and learning becomes digital’ and ‘data will be available not just from once-a-year tests, but also from the wide-ranging daily activities of individual students.’ The report highlights the possibilities of data tracking, learner profiling, real-time feedback, individualization and personalization of the educational experience, and probabilistic predictions to optimize what students learn. Consonant with the wider potentials of data analytics, these approaches combine real-time data tracking of the individual with synchronous feedback and pedagogic recommendation.
Late in 2014 Pearson Education also published a report (co-authored by Michael Barber) calling for an ‘educational revolution’ using ‘intelligent software and a range of devices that facilitate unobtrusive classroom data collection in real time,’ and to ‘track learning and teaching at the individual student and lesson level every day in order to personalise and thus optimise learning.’ In particular, the report promotes ‘the application of data analytics and the adoption of new metrics to generate deeper insights into and richer information on learning and teaching,’ as well as ‘online intelligent learning systems,’ and the use of data analytics and automated artificial intelligence systems to provide ‘ongoing feedback to personalise instruction and improve learning and teaching.’ Moreover, it argues for a revolution in education policy, shifting the focus from the governance of education through the institution of the school to ‘the student as the focus of educational policy and concerted attention to personalising learning.’ In the report, intelligent analytics are taken to be key policy instruments that concentrate policy on the real-time tracking of the individual rather than the planned and sequenced longitudinal measurement of the institution or system, and that ultimately possess the ‘algorithmic power‘ to determine classroom pedagogy itself. Pearson’s own Center for Digital Data, Analytics, and Adaptive Learning is intended as the organizational setting for the development and advancement of such instruments.
Ultimately, the data analytics being developed Pearson anticipate a new form of ‘up-close’ and ‘future-tense’ educational governance. Its analytics makes every individual learner into a micro-centre of anticipation—the focus for a constant and recursive accumulation, analysis and presentation of data, real-time feedback, probabilistic predictions, and future-tense prescriptions for pedagogic action. These analytics capacities complement existing large-scale database techniques of governance. But they also, to some extent, short-circuit those techniques. The deployment of big data practices in schools is intended to accelerate the temporalities of governing by numbers, making the collection of enumerable educational data, its processes of calculation, and its consequences into an automated, real-time and recursive process materialized and operationalized ‘up close’ from within the classroom and regulated ‘at a distance’ by new centres of calculation that house expertise in digital methods of automated data analytics.
As big data developments increasingly join disparate datasets, it is feasible to speculate that the linking of global international assesssment data with individualized learning analytics data by data companies such as Pearson would produce a vast and powerful data infrastructure in which student data could be collected continuously, analysed in real-time, and fed back not just into national profiles, global league tables and data dashboards, but directly into the pedagogic instrumentation of the classroom.
Data experts
The collection and digitization of massive educational datasets has a relatively long history, and data collection in education goes back well over a century. However, emerging digital data practices of data visualization and data analytics enabled by emerging public policy instruments—many based on functional principles derived from social media and big data science—are becoming powerful sources of contemporary digital educational governance. Digitally rendered as a vast surface of machine-readable data traces, education is increasingly amenable to being effortlessly and endlessly crawled, scraped and mined for insights. While this is not all new, then, it does indicate the emergence of a relatively distinctive style of digital education governance in which data-based policy instruments are employed to perform a constant audit of student actions in order to make them visible and thus amenable to pedagogic intervention. As a consequence, to examine educational governance increasingly requires exploration of the data infrastructures framing it, the digital policy instruments making it operational, and the experts that analyse, visualize, and prepare it for the interaction and interpretation of others.
The new managers of the virtual world of educational data are the technical, statistical, methodological and graphical experts—both human and non-human—that inscribe schools and the learners within them in enumerable, visible and anticipatory data, and address their audiences as particular kinds of users. New kinds of data careers have been made possible, both for leading policy advisers such as Michael Barber, but also for the educational data scientists, experts and algorithmists required to do the data work, construct the database architectures, and design the analytics that now make education policy operational and productive. The techniques produced and promoted by such data experts appear to respatialize education governance to new centres of expertise beyond central government, and to accelerate the temporalities of digital data collection and use in education. They complement the massive, longitudinal datasets such as those held by national governments or by massive international organizations with more dynamic, automated, and recursive systems that are intended to sculpt learners’ performances in real-time through the pedagogic instruments of the classroom.
code acts in education 
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