Ben Williamson
Image by CSCW
In recent years the pace of education policy has begun to pick up speed. As new kinds of policy influencers such as international organizations, businesses, consultancies and think tanks have entered into educational debates and decision-making processes, the production of evidence and data to support policy development has become more spatially distributed across sectors and institutions and invested with more temporal urgency too. The increasing availability of digital data that can be generated in real time is now catalysing dreams of an even greater acceleration in policy analysis, decision-making and action. A fantasy of real-time policy action is being ushered into material existence, particularly through the advocacy of the global edu-business Pearson and the international organizations OECD (Organization of Economic Cooperation and Development) and WEF (World Economic Forum). At the same time, the variety of digital data available about aspects of education means that these policy influencers are focusing attention on the possible measurement of previously unrecorded activities and processes.
Fast policy
Education policy processes are undergoing a transformation. A spatial redistribution of policy processes is underway whereby government departments are becoming parts of ‘policy networks’ that also include consultants, think tanks, policy labs, businesses, and international non-governmental organizations.
In their recent book Fast Policy, policy geographers Jamie Peck and Nik Theodore argue that:
The modern policymaking process may still be focused on centers of political authority, but networks of policy advocacy and activism now exhibit a precociously transnational reach; policy decisions made in one jurisdiction increasingly echo and influence those made elsewhere; and global policy ‘models’ often exert normative power across significant distances. Today, sources, channels, and sites of policy advice encompass sprawling networks of human and nonhuman actors/actants, including consultants, web sites, practitioner communities, norm-setting models, conferences, guru performances, evaluation scientists, think tanks, blogs, global policy institutes, and best-practice peddlers, not to mention the more ‘hierarchical’ influence of multilateral agencies, international development funds, powerful trading partners, and occupying powers.
These policy networks sometimes do the job of the state through outsourced contracts, commissioned evidence-collection and analysis, and the production of policy consultancy for government. They often also act as channels for the production of policy influence, bringing new agendas, new possibilities, and new solutions to perceived problems into the view of national government departments and policymakers. Policy is, therefore, becoming more socially and spatially distributed across varied sites, across public, private and third sectors, and increasingly involves the hybridization of methods drawn from all the actors involved in it, particularly in relation to the production and circulation of evidence that might support a change in policy.
The socially and spatially networked nature of the contemporary education policy environment is leading to a temporal quickening in the production and communication of evidence. In the term ‘fast policy’, Peck and Theodore describe a new condition of accelerated policy production, circulation and translation that is characterized not just by its velocity but also ‘by the intensified and instantaneous connectivity of sites, channels, arenas, and nodes of policy development, evolution, and reproduction.’ Fast policy refers to the increasing porosity between policymaking locales; the transnationalization of policy discourses and communities; global deference to models of ‘what works’ and ‘best practices’; compressed R&D time in policy design and roll-out; new shared policy experimentality and evaluation practices; and the expansion of a ‘soft infrastructure’ of expert conferences, resource banks, learning networks, case-study manuals, and web- based materials, populated by intermediaries, advocates, and experts.
Fast policy is becoming a feature of education policy production and circulation. As Steven Lewis and Anna Hogan have argued,
actors work within complex policy networks to produce and promote evidence tailored to policymakers, meaning they orchestrate rather than produce research knowledge in order to influence policy production. These actors tend to construct simplified and definitive solutions of best practice, and their reports are generally short, easy-to-read and glossy productions.
As a consequence they claim the desire for policy solutions and new forms of evidence and expertise, is ultimately leading to the ‘speeding up’ of policy:
This ‘speeding up’ of policy, or ‘fast policy’ … is characterized not only by the codification of best practice and ‘ideas that work’ but also, significantly, by the increasing rate and reach of such policy diffusion, from sites of policy development and innovation to local sites of policy uptake and, if not adoption, translation.
In other words, policies are becoming more fast-moving, both in their production and in their translation into action, as well as more transnational in uptake and implementation, more focused on quick-fix ‘best practice’ or ‘what works’ solutions, and more pacey and attractive to read thanks to being packaged up as short glossy handbooks and reports, websites and interactive data visualizations.
For Lewis and Hogan, the development of fast policy in education is exemplified by the work of the education business Pearson and the international organization OECD. In their specific example of fast policy in action, they observe how ‘so-called best practices travel from their point of origin (to the extent that this can ever be definitively fixed) at the OECD to their uptake and development by an international edu-business (Pearson),’ and how they are from there translated into more ‘localized’ concerns with improving state-level schooling performance within national systems. In particular they show how OECD data collected as part of the global PISA testing program have been translated into Pearson’s Learning Curve Databank, itself a public data resource intended to inform ‘evidence-based’ educational policymaking around the world, and from there mobilized in the specification of local policy problems and solutions. The concern with evidence-based policymaking, they show, involves the use of best practice models and learning from ‘examples’:
We see the dominance of fast policy approaches, and hence their broad appeal across policy domains such as schooling, as directly emanating from the promotion of decontextualised best practices that can, so it is alleged, transcend the specific requirements of local contexts. This is despite ‘evidence-based’ policymaking being an inherently political and contingent process, insofar as it is always mediated by judgements, priorities and professional values specific to the people, moments and places in which such policies are to be enacted.
Additionally, in the fast policy approaches that are developing in education through the work of OECD and Pearson, quantitative data have become especially significant for evidence-based practices, as measurement, metrics, ranking and comparison all help to create new continuities and flows that can overcome physical distance in an increasingly interconnected and accelerating digital world. Numbers and examples form the evidential flow of fast policy, enabling complex social, political and economic problems to be rendered in easy-to-understand tables, diagrams and graphs, and their solutions to be narrated and marketed through exemplar best practice case studies.
Real-time policy action
Pearson and OECD are additionally seeking to develop new computer-based data analytics techniques that can be used to generate evidence to inform education policy. Pearson, for example, has proposed a ‘renaissance in assessment’ that will involve a shift to new computer-based assessment systems for the continuous tracking and monitoring of ‘streaming data’ through real-time analytics, rather than the collection of data through discrete temporal assessment events. Its report promotes 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.’ Much of the data analytic and adaptive technology required of this vision is in development at Pearson’s own Center for Data Analytics and Adaptive Learning, its in-house centre for educational big data research and development.
Moreover, the authors of the renaissance in assessment report argue 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.’ The report clearly represents an emerging educational imaginary where policy is to concentrate on the real-time tracking of the individual rather than the planned and sequenced longitudinal measurement of the institution or system. Along these lines, its authors note that the OECD itself is moving towards new forms of machine learning in its international assessments technologies, with a proposal to assess collaborative problem solving through ‘a fully computer-based assessment in which a student interacts with a simulated collaborator or “avatar” in order to solve a complex problem.’ Such systems, for Pearson and OECD, can speed up the process of providing feedback to students, but are, importantly, also adaptive, meaning that the content adapts to the progress of the student in real time.
The potential promise of such computer-based adaptive systems, for the experts of Pearson and OECD, is a further acceleration in policy development to real-time speed. Instead of policy based on the long time-scales of temporally discrete assessment events, data analytics platforms appear to make it possible to perform a constant automated analysis of the digital timestream of student activities and tasks. Such systems can then adapt to the student in ways that are synchronized with their learning processes. This process appears to make it feasible to squeeze out conventional standardized assessments and tests, with their association with bureaucratic processes of data collection by governmental centres of political authority, and replace them with computer-adaptive systems.
These proposals imagine a super-fast policy process that is at least partly automated, and certainly accelerated beyond the temporal threshold of human capacities of data analysis and expert professional judgment. Heather Roberts-Mahoney and colleagues have analysed US documents advocating the use of real-time data analytics for personalized learning, and conclude that they transform teachers into ‘data collectors’ who ‘no longer have to make pedagogical decisions, but rather manage the technology that will make instructional decisions for them,’ since ‘curriculum decisions, as well as instructional practices, are reduced to algorithms and determined by adaptive computer-based systems that create ‘personalized learning,’ thereby allowing decision-making to take place externally to the classroom.’ The role of policymakers is changed by such systems too, turning them into awarders of contracts to data processing companies and technological vendors of adaptive personalized learning products. It is through such technical platforms and the instructions coded in to them that decisions about intervention will be made at the individual level, rather than bureaucratic decision-making at national or state system scale.
The use of real-time systems in education is therefore part of ‘a reconfiguring of intensities, or “speeds”, of institutional life’ as it is ‘now “plugged into” information networks,’ as Greg Thompson has argued. It makes the collection, analysis and feedback from student data into a synchronous loop that functions at extreme velocity through systems that are hosted by organizations external to the school but are also networked into the pedagogic routines of the adaptive, personalized classroom. In short, real-time data-driven systems are ideal fast policy technologies.
Affective policy
Importantly, these fast policy influencers are also pursuing the possibility of measuring non-academic aspects of learning such as social and emotional skills. The OECD has launched its Education and Social Progress project to develop specific measurement instruments for ‘social and emotional skills such as perseverance, resilience and agreeableness,’ ‘using the evidence collected, for policy-makers, school administrators, practitioners and parents to help children achieve their full potential, improve their life prospects and contribute to societal progress.’
The World Economic Forum, another major international organization that works in policy networks to influence education policy, has similarly produced a report on fostering social and emotional learning through technology. It promotes the development of biosensor technologies, wearable devices and other applications that can be used to ‘provide a minute-by-minute record of someone’s emotional state’ and ‘to help students manage their emotions.’ It even advocates educational applications of ‘affective computing’:
Affective computing comprises an emerging set of innovations that allow systems to recognize, interpret and simulate human emotions. While current applications mainly focus on capturing and analysing emotional reactions to improve the efficiency and effectiveness of product or media testing, this technology holds great promise for developing social and emotional skills such as greater empathy, improved self-awareness and stronger relationships.
The affective analytics of education being proposed by both the OECD and WEF make the emotional life of the school child into the subject of fast policy experimentation. They are seeking to synchronize children’s emotional state, measured as a ‘minute-by-minute record,’ with societal progress, rendering students’ emotions as real-time digital timestreams of data that can be monitored and then used as evidence in the evaluation of various practices and policies. Timestreams of data about how students feel are being positioned by policy influencers the OECD and WEF as a new form of evidence at a time of accelerating policy experimentation. These proposals are making sentiment analysis into a key fast policy technology, enabling policy interventions and associated practices to be evaluated in terms of the feelings they generate–a way of measuring not just the effects of policy action but its production of affect too.
Following super-fast policy prototypes
Writing about fast policy in an earlier paper prefacing their recent book, Jamie Peck and Nik Theodore have described ‘policy prototypes that are moving through mutating policy networks’ and which connect ‘distant policy-making sites in complex webs of experimentation-emulation-evolution.’ They describe the methodological challenges of ‘following the policy’ in the context of spatially distributed policy networks and temporally accelerated modes of policy development where spefific policies are in a constant state of movement, translation and transformation. For them:
Policy designs, technologies, and frames are … complex and evolving social constructions rather than as concretely fixed objects. In fact, these are very often the means and the media through which relations between distant policy-making sites are actively made and remade.
A research focus on the kind of super-fast policy prototypes being developed by Pearson, the WEF and the OECD would likewise need to focus, methodologically, on the technologies and the designs of computer-based approaches as socially created devices. It would need to follow these policy prototypes through processes of experimentation, emulation and mutation, as they are diversely developed, taken up or resisted, and modified and amended through interaction with other organizations, actors, discourses and agendas. As with Peck and Theodore’s focus on fast policy, researching the super-fast policy prototypes proposed for education by the OECD, WEF and Pearson would investigate the ‘social life’ of the production of new technologies of computer-adaptive assessment, personalized learning, affective computing and so on, but also attend to their social productivity as they change the ways in which education systems, institutions, and the individuals within them perform.
code acts in education 