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Code Acts in Education: Psychodata

‘Social and emotional learning’ (SEL) has become one of the most active topics in education policy and practice over the last few years. At an international scale, the OECD is about to run its new Study on Social and Emotional Skills for the first time this month, in its bid to produce policy-relevant comparative data on different nations’ ‘non-cognitive’ capacity. Nationally and regionally, government education departments have begun to endorse SEL as a key priority. At classroom level, teachers are using SEL-based edtech devices like ClassDojo, Panorama and HeroK12 to observe students’ social-emotional learning, twinned with tasks such as ‘emotion diaries’, ‘managing your emotions’ posters, and self-assessment scales for children to rate their emotions.

How should we understand this SEL explosion? In a new research article entitled ‘Psychodata‘, just published in Journal of Education Policy, I argue that SEL is a good case of a ‘policy infrastructure’ that is currently in-the-making, and that its main objective is the construction of ‘data infrastructure’ for the measurement of students’ social-emotional skills. The article presents my attempt to ‘disassemble’ the statistical, psychological and economic infrastructure of social-emotional learning into some of its main constituent parts.

Policy & data infrastructures
By policy infrastructure I mean all the various organizations, forms of expert knowledge, concepts, techniques and technologies that all have to be brought together to make any policy area operational. Psychology, economics and statistics–which include people, knowledge, devices, practices and techniques–are key aspects of SEL policy infrastructure. And by data infrastructure I mean the technologies, modes of quantification, actors and desires that have to be assembled together for large-scale measurement–the system of data collection, analysis and presentation. In fact, I argue that the construction of data infrastructure is making social-emotional learning possible to conceive and enact as a key policy area. A policy infrastructure, in this sense, to a large extent is its data system.

Social-emotional learning sounds like a progressive, child-centred agenda, but behind the scenes it’s primarily concerned with new forms of child measurement. As the OECD noted in a 2015 report proposing its study on social-emotional skills, ‘While everyone acknowledges the importance of social and emotional skills, there is insufficient awareness of “what works” to enhance these skills and efforts to measure and foster them.’ Many other SEL advocates talk of the importance of building a ‘psychometric evidence base’ to truly demonstrate the polict-relevance of social-emotional learning, and to consolidate SEL as a coherent ‘policy field’. As a result, the construction of data infrastructure has become the central focus of many SEL organizations, from transnational governance organizations like OECD to edtech companies, philanthropies, think tanks, campaign coalitions, edu-businesses, and many others. The enumeration of student emotions as evidence for policymaking is the central agenda of SEL advocates.

This is not to suggest that we necessarily see a coherent data infrastructure for the quantification of SEL. That perhaps is the ultimate objective but actually SEL measurement is being done in myriad ways, involving multiple different conceptualizations of SEL, different political positions, and different sectoral interests. The OECD’s study is clearly an attempt to create a global measurement standard for SEL—but its use of personality theory and the Big Five personality testing method in the test is not entirely consistent with SEL frameworks derived from positive psychology and youth development literatures deployed by other SEL organizations and coalitions. The article is an attempt to identify continuities and relations across the diverse SEL field, as well as to highlight inconsistencies and incoherence.

Psycho-economic expertise
I make six main points in the paper. First, SEL needs to be understood as the product of a ‘psycho-economic’ fusion of psychological and economics expertise. Long-standing collaboration between the positive psychologist Angela (‘Grit’) Duckworth and the economist James Heckman in the measurement of social-emotional learning and related ‘non-cognitive’ qualities illustrates this interdisciplinary combination. These psycho-economic experts have attained remarkable transnational promiscuity as authorities on social-emotional learning and its measurement.

But this psycho-economic fusion also illustrates a wider political context where psychology and economics have become dominant forms of expertise in contemporary governance. This is not necessarily novel, but as big data have become available it has become increasingly possible to gather behavioural and other psychological data from populations, which may be embraced by authorities (governmental or otherwise) in economic forecasting and political management. Heckman, Duckworth and other SEL authorities embody a political economy in which human psychological qualities are translated into psychometric data as quantitative measures of potential economic value, and behavioural data has become a source for governmental ‘nudging’ and control.

Policy mobility
The second key point is about ‘policy mobility’ and the sets of moving relations among think tanks, philanthropies and campaigning coalitions which have been central to establishing SEL as an emerging policy field. Big players in the US include CASEL, the Aspen Institute and the Templeton Foundation. They, like the OECD, are forming relations with experts and packaging up SEL in glossy brochures, meta-analyses, evidence digests, and summaries of existing psychometric data, in order to attract policy commitment. They are, in other words, involved in the painstaking work of assembling diverse sources and resources into actionable policy-relevant knowledge.

Rather than a project of central governments, then, SEL is the product of networked governance involving organizations from across sectors and working from diverse perspectives and interests. Yet despite considerable heterogeneity, these organizations are slowly translating their different interests into shared objectives, forming coalitions, and producing ‘consensus’ statements that seek to stabilize social-emotional learning as a coherent area of policy development.

Money moves
Third, SEL is a site of considerable movement of money. There’s a lot of investment in SEL programs, SEL-based edtech products, and philanthropic funding of SEL organizations. For example, both the Gates Foundation and the Chan-Zuckerberg Initiative have generously funded some of the key SEL organizations mentioned above. A statistical algorithm has been devised to calculate the economic value of social and emotional learning, and prediction of substantial return on investment has stimulated a very active impact investing sector. Government departments are also funding SEL through, for example, grants for schools.

As such, SEL is thoroughly entangled with financial mechanisms which show how education policy has become inseparable from market logics. Money is flowing into businesses from investors, and into schools from governments, and into classroom practices through impact investment, all of which is making SEL appear practicable while also contributing to the production of ‘evidence’ about ‘what works’ for further policy influence. The beneficial social ‘return’ of SEL is also generating lucrative return for investors, as financial investment has begun to prefigure official policy intervention.

Policy machinery
The fourth point is that a huge industry of SEL products, consultancy and technologies has emerged, which has allowed SEL practices to proliferate through schools. Edtech platforms, with reach into thousands of schools globally, may even be understood as new producers of policy-relevant knowledge, by generating large-scale SEL data in ‘real time’ and an extensive evidence base at the kind of scale and speed that bureaucratic international organizations or state departments of education cannot match. They act as practical relays of the commercial aims of SEL edtech providers into the spaces and practices of pedagogy at scales exceeding the national or local boundaries of education systems.

We might think of such edtech devices as policy machinery in their own right. SEL is building momentum through teacher resources and edtech markets, as well as through the work of consultants and in-service professional development providers. The policy infrastructure of SEL is, then, populated by people doing new kinds of policy work but also by nonhuman policy machines that are active in school practices and in the quantification of student affects.

Glocal policy
Fifth, while much SEL activity is working in mobile ways across national borders, its enactment is also contingent on local, regional and national priorities. In the UK, for example, the Department for Education has focused on ‘character education’, partly as a result of advocacy by the Templeton Foundation-funded Jubilee Centre. In California, ‘growth mindset’ measurement is being tied to school accountability mechanisms.

At the same time, however, how SEL is locally enacted is dependent upon the global markets of resources and technologies available—which allows a device such as ClassDojo to participate in classrooms globally, directly through the fingertips and observations of teachers. As such, SEL exemplifies the increasingly ‘glocal’ character of education policy, with flows of transnational influence on local practices and local priorities sometimes scaling back up to the global. Edtech SEL products emanating from Silicon Valley, for example, travel globally and bring concepts such as growth mindset–which originated at Stanford University–into schools thousands of miles distant from the culture of entrepreneurial self-improvement in the tech sector.

Global metrics
The sixth and final main point is about the OECD’s effort to create a standardized global metric for SEL. The OECD overtly brings together psychology and economics with the test positioned as a way of calculating the contribution of social-emotional skills to ‘human capital’. Directly informed by the economist James Heckman and by the personality theorist Oliver John, the OECD test uses the Big Five personality testing method and labour market calculations to connect up students’ socio-emotional qualities to quantitative socio-economic outcomes. In this way, the OECD test shows how students’ psychological qualities have been ‘economized’.

The test represents a significant shift in focus for the OECD. As the OECD’s Andreas Schleicher has argued, it is shifting its emphasis from ‘literacy and numeracy skills for employment, towards empowering all citizens with the cognitive, social and emotional capabilities and values to contribute to the success of tomorrow’s world’. It is also increasingly emphasizing the new ‘sciences of learning’ emerging from psychology, neuroscience and biomedical fields. As such, the OECD SSES test exemplifies how education policy influencers are increasingly turning to the human sciences as sources of policy-relevant insights for education. In the case of SSES specifically, it involves the use of personality testing as a way of calculating economic competitiveness, and entails that subsequent policy interventions would focus on modifying student personality characteristics for economic advantage.

Psychoeconomic governance
Overall, what I’ve tried to show in the article is that SEL is a policy field in-the-making and that it remains inchoate and in some ways incoherent. We can understand it as a policy infrastructure that is being assembled from highly diverse elements, and that is centrally focused on the production of ‘psychodata’. In fact, the potential of a SEL policy infrastructure depends to a great extent on the creation of the data infrastructure required to produce policy-relevant knowledge. In other words, the generation of psycho-economic calculations is at the very core of current international policy interest in social-emotional learning, which is already relaying into classroom practices globally, governing teachers’ practices, and shaping the priorities of education systems to be focused on the enumeration of student emotions.

Ben Williamson

Ben Williamson is a Chancellor’s Fellow at the Centre for Research in Digital Education and the Edinburgh Futures Institute at the University of Edinburgh. His research traces the connections between educational policy, digital technologies, and practices in schools and universities. He is the author of Big Data in Education: The digital future of...