Personalized precision education and intimate data analytics

Ben Williamson

Precision_Pascal Volk

The word ‘precision’ has become a synonym for the application of data to the analysis and treatment of a wide range of phenomena. ‘Precision medicine’ describes the use of detailed patient information to individualize treatment and prevention based on genes, environment and lifestyle, while ‘precision agriculture’ has become an entire field of R&D focused on ‘engineering technology, sensor systems, computational techniques, positioning systems and control systems for site-specific application’ in the farming sector.

Precision medicine and precision farming approaches share a commitment to the collection and analysis of diverse data and scientific expertise for the purposes of highly targeted intervention. This may seem to make sense when it comes to medical diagnosis or optimizing crop production. But the production of precision may have more worrying consequences in other domains. Cambridge Analytica’s involvement in voter microtargeting through psychographic profiles, for example, has been termed ‘precision electioneering.’ Data-driven precision is therefore both a source of scientific certainty and of controversy and contestation.

Emerging interests in ‘precision education’ foresee the concerted use of learner data for purposes of implementing individualized educational practices and ‘targeted learning.’ As precision education has been described on the Blog on Learning and Development (BOLD):

Scientists who investigate the genetic, brain-based, psychological, or environmental components of learning … aim to find out as much as possible about learning, in order to accommodate successful learning tailored to an individual’s needs.

As this indicates, precision education is based on enormous ambitions. It assumes that the sciences of genes, neurology, behaviour and psychology can be combined in order to provide insights into learning processes, and to define how learning inputs and materials can be organized in ways best suited to each individual student. Advocates of precision education also suggest that complex computer programmes may be required to process these vast troves of data in order to personalize the learning experience for the individual.

The task of precision education requires the generation of ‘intimate’ data from individuals, and the constant processing of genetic, psychological, and neurological information about the interior details of their bodies and minds.

Unpacking precision education
It’s worth trying to think through what is involved in precision education, what it might look like in practice, and its implications for education policy.

In some ways, precision education looks a lot like a raft of other personalized learning practices and platform developments that have taken shape over the past few years. Driven by developments in learning analytics and adaptive learning technologies, personalized learning has become the dominant focus of the educational technology industry and the main priority for philanthropic funders such as Bill Gates and Mark Zuckerberg.

For example, the private non-profit National University (which runs concentrated online courses) has a ‘Precision Institute’ dedicated to precision education through ‘adaptive, machine learning instruction’ and ‘individualized course navigation’ using ‘real-time data generated from multiple sources of assessment tools.’ It is creating a Precision Education Platform for Personalized Learning to gather data from students in order to analyse relationships between ‘student characteristics and learning outcomes.’

A particularly important aspect of precision education as it is being advocated by others, however, is its scientific basis. Whereas most personalized learning platforms tend to focus on analysing student progress and assessment outcomes, precision education requires much more intimate data to be collected from students. Precision education represents a shift from the collection of assessment-type data about educational outcomes, to the generation of data about the intimate interior details of students’ genetic make-up, their psychological characteristics, and their neural functioning.

A key example is the Precision Learning Center, a partnership established 2017 between a number of labs across the University of California and Stanford Graduate School of Education, which is dedicated to ‘improving the science of learning and education using cognitive, psychological, biomedical and environmental information.’ One of the key partners is Neuroscape, a lab at UCSF with a stated mission to ‘use modern technology … to harness the brain’s inherent plasticity to enhance our cognition, refine our behavior, and ultimately to improve our minds.’ Another key Precision Learning Center partner, BrainLENS, also based at UCSF, integrates ‘the latest brain imaging techniques, genetic analysis, and computational approaches to examine processes of learning.’ BrainLENS focuses especially on the neurobiological underpinnings of ‘grit,’ ‘growth mindset’ and ‘social-emotional processing,’, the neural inheritance of cognitive and character traits, the genetics of cognition, and ‘personalized education’ based on predictive learner profiling.

As such, precision education is part of a surge in interest in educational neuroscience and educational genomics to ‘enable educational organisations to create tailor-made curriculum programmes based on a pupil’s DNA profile.’ Researchers are already undertaking studies of the links between genes and attainment, and proposed DNA analysis devices such as ‘learning chips‘ to make reliable genetic predictions of heritable differences between children in terms of their cognitive ability and academic achievement. Cheap DNA kits for IQ testing in schools may not be far away, driven by the ‘new genetics of intelligence.’ Psychology, too, has begun ‘advancing the science and practice of precision education to enhance student outcomes.’

Two articles on the BOLD blog have made a particularly strong case for a scientific approach to precision education and personalized learning. BOLD is itself an initiative funded by the Jacobs Foundation. It is ‘dedicated to spreading the word about how children and young people develop and learn’, with a pronounced emphasis on ‘the science of learning,’ neuroscience, developmental psychology and genetic factors in learning, along with considerations of the technologies and programs required to ‘tailor education to children’s individual needs, taking into account biological, social and economic differences as well as differences in their upbringing. … A wide variety of disciplines – psychology, neurobiology, evolutionary biology, pediatrics, education, behavioral genetics, computer science and human-computer interaction – need to be involved.’ It is in this context that BOLD has begun to address the potential and challenges of precision education.

The precision education articles by Annie Brookman-Byrne are thoughtful and cautious, but also clearly angled towards the development of an interdisciplinary field of research. In the first post, Brookman-Byrne acknowledges that ‘We are currently a long way off from having the kinds of information needed to realise precision education’ but argues that ‘the groundwork has started’:

  • Educational neuroscience is building an understanding of the science behind learning and teaching through the convergence of multiple disciplines and collaborations with educators.
  • Evidence is being gathered from a diverse set of fields, which will eventually lead to a deeper understanding of the mechanisms involved in learning.
  • The study of genetics is part of this investigation. Rather than something to be feared, our understanding of genes is simply another part of the puzzle in the science of learning.
  • As the appetite for evidence-based practice increases, the future of teaching and learning may well be personalised education that takes into account a host of factors about the individual.

In the follow-up post, Brookman-Byrne in particular highlights how it will be ‘necessary to gather vast amounts of data’ to make precision education possible:

  • This process of data collection has already begun, in the form of the many studies that aim to uncover the psychological and neurological processes that underpin learning.
  • If precision education is to come to fruition, each individual learner will need to provide their own data in order to establish which type of learning materials best suit them.
  • Precision education would draw on the best available evidence from a host of factors which might include test scores, genetic data, the learner’s own interests, and environmental factors.
  • Precision education may also lead to greater choice for the learner – in particular, adolescents choosing which subjects to focus on later in school.
  • A very strong scientific understanding of the mechanisms that influence learning will be the first step towards the realisation of precision education.

Brookman-Byrne acknowledges that it is too early to say how precision education will appear in practice, if at all. But BOLD itself has already begun to propose that neuroscience provides ‘ever-advancing technologies that allow us to image the thinking brain,’ thus enabling educational neuroscientists to ‘know more than ever before about how students learn,’ although it also cautions that ‘it’s not easy to translate these findings to the classroom.’ It has also supported researchers examining the links between genetics and educational success.

Regardless of the cautions and caveats, the sciences of the brain and the gene, as well as psychology and behavioural science, are already becoming lodged in education policy. It is easy to see the potential appeal of precision education to policymakers eager to find ‘scientific’ evidence-based solutions to educational problems. A new combination of education policy and the human sciences is currently emerging in the context of policy preoccupations with ‘what works.’ As Kalervo Gulson and P. Taylor Webb have argued, new kinds of ‘bio-edu-policy-science actors’ may be emerging as authorities in educational policy, ‘not only experts on intervening on social bodies such as a school, but also in intervening in human bodies.’

Critical approaches to precision education
Many people will find the ideas behind precision education seriously concerning. For a start, there appear to be some alarming symmetries between the logics of targeted learning and targeted advertising that have generated heated public and media attention already in 2018. Data protection and privacy are obvious risks when data are collected about people’s private, intimate and interior lives, bodies and brains. The ethical stakes in using genetics, neural information and psychological profiles to target students with differentiated learning inputs are significant too. Such concerns will be especially acute as politics press for greater emphasis on the biological determinants of learning, or as precision education approaches are developed by startup companies with dubious credentials.

Precision education also needs to be examined in considerable detail to understand the feasibility of its promises and claims.

The technical machinery alone required for precision education would be vast. It would have to include neurotechnologies for gathering brain data, such as neuroheadsets for EEG monitoring. It would require new kinds of tests, such as those of personality and noncognitive skills, as well as real-time analytics programs of the kind promoted by personalized-learning enthusiasts. Gathering intimate data might also require genetics testing technologies, and perhaps wearable-enhanced learning devices for capturing real-time psychophysiological data from students’ bodies as proxy psychometric measures of their responses to learning inputs and materials. By combining neurological, genetic, psychological, and behavioural data along with environmental factors and test scores, precision education is an outgrowth of current enthusiasms to ‘quantify the human condition’ while reducing human being to ‘databodies‘ of informational patterns.

Each of the technologies for the production of intimate data about students relies on complex combinations of scientific knowledge, technical innovation, business plans and social or political motivations. Some of them are likely not to be interoperable, either technically or intellectually. Just as software platforms do not always plug into each other effectively, there remain significant disciplinary cleavages between psychology, neuroscience and genetics which would need bridging for precision education to become possible. There are already concerns that precision medicine can reproduce bias and discrimination through its datasets and outcomes. Precision education data could be a similarly risky exercise in data collection and use.

In addition, brain science, genetics and psychology have all been subjected to considerable critique. Contemporary science often appears to treat the brain, the body and the mind as malleable and manipulable, able to be ‘recoded’ and ‘debugged’ in the same ways as software, as distinctions between the computational and the biological have begun to dissolve. Concerns have also been raised about ‘the new geneism’ and the potential for genetic data to reproduce ‘dangerous ideas about the genetic heritability of intelligence.’ Both old controversies in the use of genetics, neuroscience and psychology in the governing of bodies and behaviours, and new concerns about treating the body as if it were silicon, have potential for reproduction through precision education.

One productive way forward might be to approach precision education from a ‘biosocial‘ perspective. As Deborah Youdell  argues, learning may be best understood as the result of ‘social and biological entanglements.’ She advocates collaborative, inter-disciplinary research across social and biological sciences to understand learning processes as the dynamic outcomes of biological, genetic and neural factors combined with socially and culturally embedded interactions and meaning-making processes. A variety of biological and neuroscientific ideas are being developed in education, too, making policy and practice more bio-inspired.

Other biosocial studies also acknowledge that ‘the body bears the inscriptions of its socially and materially situated milieu,’ being  ‘influenced by power structures in society,’ and that ‘the brain is a multiply connected device profoundly shaped by social influences.’ The social gets ‘under the skin’ to impress upon the biological. As such, a biosocial approach would seek to understand precision education in both biological and social scientific terms by appreciating that the social environments in which learning takes place do in fact inscribe themselves on bodies and brains. Such an approach would view precision education as a source of power, reshaping the social environment of the school or the university in order to intervene in the biological, neurological and psychological correlates of learning.

Intimate data analytics
The intimate data analytics of precision education raise a few key themes for future interrogation:

  • The emergence of bio-evidence-based education policy, as data captured about the biological–genetic, neural and psychophysiological–details of students’ bodies are turned into policy-relevant knowledge and targets of intervention
  • The translation of students into bioinformational flows of numbers and scientific categories, bringing about new ways of understanding learning processes as biologically-centred, and erasing other perspectives
  • The accumulation of biocapital by companies that are able to market products, collect, analyse, and then exchange and sell students’ biodata, whether directly to schools and parents or by less direct means
  • The development of bioeconomies of educational data as genetic, neural and psychophysiological technologies and assessment tools become new competitive marketplaces (including scam outfits looking to exploit interest in student biodata)
  • The sculpting of new student biosubjectivities, as students are addressed and begin to address themselves in quantified, biological terms, and are incited to undertake activities to improve themselves in response to their genetic, neural and psychophysiological data

Whether or not precision education ever really takes off as an interdisciplinary field of R&D, let alone influences policy and practice, may itself matter very little if we recognize that many of the technologies and priorities captured in this emerging category already exist or are coming online. Developments in neurotechnology, psychoinformatics and genetics technologies are either already available or in the development pipeline for mining intimate data from the interior of bodies and brains. And with newer developments such as neurofeedback, gene-editing and behaviour-change apps, technologies stand poised not just to mine biology, cognition and behaviour, but to tweak and modify them too. As a biosocial perspective would see it, intimate data analytics get under the skin.

Image by Pascal Volk
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Learning from psychographic personality profiling

Ben Williamson

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Contemporary education policy and practice is increasingly influenced by developments in data analytics. The big data analytics story of the year so far concerns the alleged ‘psychographic’ profiling techniques of data analytics firm Cambridge Analytica and its use of the personal data of millions of Facebook users. A torrent of writing has appeared looking at it through various lenses. Among the best commentaries are by Jamie Barlett, who argued that the Cambridge Analytica/Facebook controversy reflects more generally how politics is drifting into a behavioural science of algorithm-based triggers and nudges which are tuned to personality and mood. From a different perspective, David Beer cast the enterprise through a cultural lens, arguing that Cambridge Analytica’s efforts reflect the wider aspirations of the data analytics industry, which ‘is aiming to turn anyone into a data analyst … to speed us up, make us smarter, allow us to see into the hidden depths of organisations, allow us to act in real-time or enable us to predict the future’.

Both these pieces resonate with developments in education that I’m currently tracking–that is, the weaving together of the sciences of the brain and psychology with data-processing educational technologies and education policy. The ways education policy is becoming a kind of behavioural science, supported by intimate data collected about psychological characteristics or even neural information about students, is the central focus of this ongoing work. Expert knowledge about students is increasingly being mediated through an edu-data analytics industry, which is bringing new powers to see into the hidden and submerged depths of students’ cognition, brains and emotions, while also allowing ed-tech companies and policymakers to act ‘smarter’, in real-time and predictively, to intervene in and shape students’ futures.

A more direct line can be drawn between the psychographic personality profiling of Cambridge Analytica and education, however. Although the science of psychographic personality profiling that Cambridge Analytica has boasted it has perfected may well be highly dubious, it is based on an underlying body of psychological knowledge about how to measure and classify people by personality that has a long history. At the core of the Facebook dataset it allegedly used for psychographic profiling and micro-targeting of US voters is a psychological model called ‘The Big Five’, and in particular instruments such as the Big Five Inventory originally created by Oliver John of the Berkeley University Personality Lab. When Cambridge Analytica contracted Aleksandr Kogan of Cambridge University for its psychographics project, it was to implement a digital survey on Facebook that would, like the Big 5 Inventory but adapted into the form of an online quiz, capture intimate personal data on users’ ‘openness’, ‘conscientiousness’, ‘extroversion’, ‘agreeableness’ and ‘neuroticism’ (OCEAN). These categories are believed by personality theorists to be suitable for capturing and classifying the full range of human personalities. OCEAN is a universal, culture-free psychological classification for assessing and categorizing human characteristics.

Having possession of a vast quantified personality database would clearly grant power to any organization wishing to find ways to engage, coerce, trigger or nudge people to think or behave in certain ways–advertisers, say, or propagandists. Whether it worked in Cambridge Analytica’s case remains open to debate–though I think Jamie Bartlett is right to understand this as just one example of a shift to new forms of behavioural government in the wider field of politics. Mark Whitehead and colleagues call it ‘neuroliberalism‘–a style of behavioural  governance that applies psychology, neuroscience and behavioural sciences methods and expertise to public policy and government action–and convincingly show how it has been installed in governments and businesses around the world. In education we have already seen how organizations such as the Behavioural Insights Team (‘Nudge Unit’) are being contracted to provide policy-relevant insights based on psychological and behavioural expertise and knowledge.

The more direct connection between the Big Five personality profiling and education, however, comes in the shape of the OECD’s planned Study on Social and Emotional Skills. A computer-based international assessment scheduled for implementation in 2019, at its core the test is a modified version of the Big Five Inventory. I previously called it ‘PISA for personality testing‘, and detailed how the OECD had drawn explicitly on the expertise of both personality psychologists and econometrists to plan and devise the test. Indeed, the architect of the Big Five Inventory, Oliver John, presented his work at the OECD meeting where its application to social-emotional skills testing was agreed. When it is implemented in 2019, the social and emotional skills test will assess 19 skills which fit into each of the Big Five categories. Moreover, it will collect metadata from test-takers which might also be used to support the assessment.

To be clear, the connection I am trying to make here is that personality profiling–the production of psychographic renderings of human characteristics–is not just confined to Cambridge Analytica, or to Facebook, or to the wider data analytics and advertising industries. Instead, the science of personality testing is slowly entering into education as a form of behavioural governance.

The OECD test is not that dissimilar to the personality quiz at the heart of the Cambridge Analytica/Facebook scandal. The same psychological assumptions and personality assessment methods underpin both. And while Cambridge Analytica appears to have been an unofficial instrument of a potential government, the OECD assessment is supposed to be a policy instrument of global governance–encouraging national departments of education to focus on calculated levels of student personality. The OECD assessment of social-emotional skills shares personality testing approaches with the Cambridge Analytica personality quiz, and its results are intended to support political decisionmaking.

That said, the OECD test itself will not produce individual psychographic profiles of students. Its emphasis is on aggregating the data in order to assess, at macro-scale, whether countries have the right stock of social-emotional skills to deliver future socio-economic outcomes. Large-scale personality data is presumed to be predictive of potential productivity.

However, the OECD is a powerful influence on national education policies at a global scale. The impact of PISA is well known–it has reshaped school curricula, assessments and whole systems in a ‘global education race‘. Could its emphasis on personality testing similarly reshape schooling practcies and education policy priorities? Already, a commercial market of ed-tech apps and products–such as ClassDojo–has emerged to support and measure the development of students’ social-emotional skills in schools, while educational ‘psycho-policies’ and government interventions have begun to focus on social-emotional categories of learning, such as grit, growth mindset and character, too. In the UK, for example, the Department for Education supports the development of character skills in schools.

While the OECD is only measuring student personality, the inevitable outcome for any countries with disappointing results is that they will want to improve students’ personalities and character to ensure their competitiveness in the global race. Just as PISA has catalysed a global market in products to support the skills tested by the assessment, the same is already occuring around social-emotional learning, character skills and personality development. While ClassDojo is currently popular as a classroom app for supporting growth mindset and character development, it is certainly conceivable that it could be used to promote and reward the Big Five (its website says it is also compatible with Positive Behavioural Interventions and Support, a US Department of Education program, for example–it’s flexible to market demands).  It’s not a huge leap to link ClassDojo to psychographic personality profiling–ClassDojo’s founders have openly described being inspired by economist James Heckman, and Heckman helped shape the OECD’s views on the links between personality and economic productivity.

Just as ClassDojo can already be used to produce visualizations and reports based on teachers’ observations of individual students’ behaviours, future iterations or other products could be used to produce psychographic educational profiles of individuals based on personality categories. It’s not hard to imagine teachers awarding ClassDojo points for behaviours that correlate with the Big Five. Educational applications of wearable biometrics, affective computing and even neuroheadsets to monitor attentional levels and emotional arousal are sitting at the edges of ed-tech implementation, ready to render students in psychographic detail.

Given current developments in personality testing, character development and social-emotional skills modification through ed-tech, maybe we can paraphrase Jamie Bartlett to suggest that not only are politics drifting to behavioural government, but education policy and practice too are beginning to embrace a behavioural science of algorithm-based triggers and nudges which are tuned to personality and mood. Education appears to be generating more intimate data from students, mining beneath the surface of their measurable knowledge to capture interior details about their personality, character and emotions. Policymakers, test developers and ed-tech producers may not openly say so, but just like Cambridge Analytica they are seeking to learn from psychographic personality profiling.

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10 definitions of datafication (in education)

Ben Williamson

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What is datafication? And how does it affect education? These questions were put to me ahead of conference discussion panel recently. While writing a few notes, it quickly became apparent I needed some categories to sort out my thinking. In simple terms, datafication can be said to refer to ways of seeing, understanding and engaging with the world through digital data. This definition draws attention to how data makes things visible, knowable, and explainable, and thus amenable to some form of action or intervention. However, to be a bit more specific, there are at least ten ways of defining datafication.

1 Historically
Datafication as we know it today has a long history, going back at least as far as the industrial revolution and efforts  then to capture statistical knowledge of the state, society and its population, and then to use that knowledge to come up with better institutions and practices of management and intervention. David Beer offers a really good historical view of the historical evolution of ‘metric power.

In terms of education, Michel Foucault of course articulated how children could be counted in terms of their development, knowledge, behaviour, progress, worth, cleanliness, age, social class and character, in order that they could then be ranked, supervised and disciplined more effectively. He called schools and classrooms ‘learning machines’. Within education policy, Martin Lawn and others have charted the historical rise of data in education systems. These authors have shown, for example, how the nineteenth century Great Expositions became carefully stage-managed presentations of different states’ educational performance rates, so allowing different national systems to be compared for their effectiveness in producing the labour required for social and economic progress. These early historical developments in the datafication of education have slowly given rise to the ‘global race’ that we still see in education policy today, driven by comparative analysis of performance in large scale assessments (LSAs).

Although there are clear continuities from the past to the present, the current version of datafication through ‘big data’ also represents a bit of a rupture with the past. The assessment data that dominates LSAs is sampled, collected at long temporal intervals, and slow to collect. New digital datafication technologies such as ‘learning analytics,’ by contrast, harvest data in real-time as students complete tasks, enable high-speed automated analysis and feedback or adaptivity, and can capture data from all participants rather than a sample. They also allow individuals to be compared against each other and with aggregated norms calculated in massive datasets, rather than the broad-brush comparison of national systems enabled by LSAs.

2 Technically
In technical terms, datafication is a process of transforming diverse processes, qualities, actions and phenomena into forms that are machine-readable by digital technologies. Datafication allows things, relationships, events, processes to be examined for patterns and insights, often today using technical processes such as data analytics and machine learning which rely on complex algorithms to join up and make sense out of thousands or millions of individual data points. The technical language of datafication can get quite bewildering, proliferating to include technical concepts and methods which are even being modelled to some degree on human processes–so-called ‘cognitive’ computing, deep ‘learning’, and ‘neural’ networks.

Thinking educationally, it’s intriguing that much of the language associated with digital datafication refers to learning, training and neural processes of cognition. Datafication relies to a significant technical degree on ‘learning machines’. Algorithms have to be ‘taught’, using ‘training sets’ of past data to determine how to act when put ‘into the wild’ to process live and less structured data. This can be done through ‘supervised learning’, which sounds rather like direct instruction, or through ‘unsupervised learning,’ which is more like autodidactic learning through experience. DeepMind’s AlphaGo Zero–a highly advanced AI program for unsupervised learning–for example, learns purely from its own experience and from a ‘self-reinforcement learning algorithm’ that rewards it for every ‘success’ it experiences. BF Skinner’s famous behaviourist ‘teaching machines’ have been encoded in algorithmic form.

Also, in the technical sense datafication relies on the material infrastructure of hardware, software, servers, cables, connectors, microprocessors—all of the ‘stuff of bits’, as Paul Dourish has argued, that has to be assembled together in order to generate data. The materialities of datafication significantly shape how data are generated and how they can be put to use.

3 Epistemologically
Thinking epistemologically about datafication concerns what we can know from data. For some, datafication rests on the assumption that the patterns and relationships contained within datasets inherently produce meaningful, objective and insightful knowledge about complex phenomena. As Rob Kitchin has shown, this empiricist epistemology assumes that through the ‘application of agnostic data analytics the data can speak for themselves free of human bias or framing.’

For critics, however, this empiricist epistemology is flawed because all data are always framed and sampled; data are not simply natural and essential elements that are abstracted from the world in neutral and objective ways to be accepted at face value. As Nathan Jurgenson has put it, data do not provide a ‘view from nowhere‘ because factors such as algorithms, databases, and venture capital pre-format data and so shape what may be seen or known. Data don’t tell the unbiased ‘truth’ because the data points captured and analysed are always affected by the choices of the original designers.  Making sense of data is also always framed–data are examined through a particular lens that influences how they are interpreted. Jose van Dijck has described an epistemological ‘data-ist’ trust in the numbers provided through datafication.

Epistemology in this sense extends to include a methodological definition of datafication. Datafication is a process of employing certain data scientific methods to produce, analyse and circulate data. These methods have their own social origins–or ‘social lives’ as John Law claims–and derive from and reproduce the particular epistemological assumptions of the expert groups that created them. Datafication, in other words, is epistemological and methodological.

4 Ontologically
Datafication also raises ontological questions about what data really are. One view is that data are simply ‘out there’ waiting for collection, as supposed in the term ‘raw data’ and in the view that ‘data speak for themselves’. The other, more common in contemporary social science, is that data are inseparable from the software and knowledge employed to produce them. Data do not simply represent the reality of the world independent from human thought but are constructions about the world. These insights into the ontology of data are often associated with sociological theories of science, technology, statistics and economics–Sheila Jasanoff pulls these strands together in a recent article on ‘data assemblages’.

Moreover, however, data have consequences and shape individual actions, experiences, decisions and choices. In that sense, they shape and change reality; they have ontological consequences and partake in making up reality. So, ontologically, datafication is a product of the social world and of specific practices, but it also acts upon the world and on other practices, changing them in various ways. For example, Marion Fourcade has  shown how the statistical practices of economists, of ‘ever-finer precision in measurement and mathematics … have constructed a wholly separate and artificial reality,’ a ‘make believe substitution’ that is entirely made out of historical and disciplinary conventions, ‘nothing more’. Yet, as Fourcade adds, if you change the statistical convention, ‘the picture of economic reality changes too’, sometimes with dramatic real-world results. As such, datafication is ontological because it has the potential to produce or perform different versions of reality–what actor-network theorists call ‘ontological politics’.

5 Socially
Datafication is accomplished by social actors, organizations, institutions and practices. So today we have data scientists, data analysts, algorithm designers, analytics engineers and so on all bringing their expertise to the examination of data of all kinds. These people or experts are housed in businesses, governments, philanthropies, social media firms, financial institutions, which have their own objectives, business plans, projects and so on, which frame how and why digital data are captured and processed. In this sense, datafication can be defined socially because it is always socially situated in specific settings and framed by socially-located viewpoints.

In education, we have ‘education data scientists’ and learning analytics practitioners, engineers and vendors of personalized learning platforms, even entrepreneurs of artificial intelligence in education, all now bringing their own particular forms of expertise to the examination and understanding of learning processes, teaching practices, schools, universities and educational systems. They are supported by funding streams from venture capital firms, philanthropic donations from wealthy technology entrepreneurs, impact investment programs which all direct financial resources to the datafication of education. Putting it super-simply, datafication exists because people and institutions of society make it so.

Moreover, datafication needs to be defined socially because much data is captured from the social world—people, institutions, behaviours and the full range of societal phenomena are the stuff of data. As Geoffrey Bowker has memorably put it, ‘if you are not data, you do not exist’! People are data; societies are the data. Even more consequentially, these social data can be used to reshape social behaviours. Bowker adds that as data about people are stored in thousands of virtual locations, reworked and processed by algorithms, their ‘possibilities for action are being shaped’.

6 Politically
The new actors undertaking datafication are invested with a certain form of data power. Expert authority, as William Davies argues, increasingly resides with those who can work with complex data systems to generate analyses, and then narrate the results to the public, the media and policymakers. This is why governments are increasingly interested in capturing the digital traces and datastreams of citizens’ activities. By knowing much more about what people do, how they behave, how they respond to events or to policies, it becomes possible to generate predictions and forecasts about best possible courses of action, and then to intervene to either pre-empt how people behave or prompt them to behave in a certain way. For example, there’s a whole ‘Data for Policy’ movement and new funding streams for ‘GovTech’ applications in the UK to realize the potential of ‘Government by Algorithm’. Evelyn Ruppert and colleagues have termed this ‘data politics’ and note that power over data no longer only belongs to bureaucracies of state, but to a constellation of new actors in different sectoral positions.

Something of an arms race is underway by those organizations that want to attain data power in education. Education businesses like Pearson are putting large financial, material and human resources into technologies of datafication, and are seeking both to make it commercially profitable and also attractive to policymakers as a source of intelligence into learning processes. Dorothea Anagnostopoulos and colleagues have written about the ‘informatic power’ possessed by the organizations and technologies involved in processing test-based data. But some of that power is now being assumed by those actors, organizations and analytics technologies that process digital learning data and turn it into actionable intelligence and adaptive, personalized prescriptions for pedagogic intervention.

7 Culturally
This definition draws attention to datafication as a cultural phenomenon and as a concept that has attained a privileged position in the view of the public, businesses, governments and the media. Increasingly, it seems, data and algorithms are invested with promises of objectivity and impartiality, at a time when human experts are not necessarily to be trusted because they’re too clouded by subjective opinion, bias and partiality. An article in the Silicon Valley ed-tech magazine EdSurge effectively represented how the objectivity of data has been culturally adopted and accepted in some parts of the education sector. It claimed teachers are unable to recognize how well students are engaging with their own learning because the teachers are too subjectively biased. This speaks to a cultural narrative which frames datafication in terms of mechanical objectivity, certainty, impartiality.

But the cultural acceptance or otherwise of datafication is of course context-specific. In some European countries such as Germany the cultural narrative of datafication and algorithms is more contested, and perhaps legally and politically inflected. It would be interesting to tease out how datafication in general and datafication of education in particular becomes culturally embedded or not in different geographical, political and social locations. So for example, datafication in education may appear to be a largely Anglophone phenomenon. Recently, however, a new report on ‘Learning Analytics for the Global South’ appeared which considered ‘how the collection, analysis, and use of data about learners and their contexts have the potential to broaden access to quality education and improve the efficiency of educational processes and systems in developing countries around the world’. Datafication of education is becoming culturally sensitive.

8 Imaginatively
Datafication is the subject of breathless utopian fantasies of real-time responsive smart cities, global Internet of Things, human-machine symbiosis, algorithmic certainty, hyperpersonalized services, driverless cars and so on—a world plastered with a new shiny surface of machine-readable data, which acts as a fuel for an automated, responsive, personalized environment which constantly moulds itself around us.

Education is affected by the same fantasies and utopian imaginaries. At last year’s British Science Festival, Sir Anthony Seldon, Master of Wellington College and VC of the University of Buckingham, presented a picture of a robotized future of schools, with ‘extraordinarily inspirational’ machines completely personalizing the education journey, ‘adaptive machines that adapt to the individual,’ that ‘listen to the voices of the learners, read their faces and study them in the way gifted teachers study their students,’ know what ‘excites’ learners and can ‘light up the brain’ through ‘intellectual excitement.’

Datafication is in this sense the subject of imagination–but imaginary visions can sometimes catalyse real-world applications, with powerful visionaries gathering coalitions of support to make reality conform with their utopian ideals. SIlicon Valley entrepreneurs have animated their visions of data-driven education through the capture or donation of funding and engineering teams, for example.

9 Dystopically
In contrast to the utopian imagination, datafication is also a great source of anxiety. Concerns circulate about gross privacy invasion, panoptic dataveillance, data bias against ethnic groups, the manipulation of behaviours through persuasive design, viral spread of computational propaganda powered by data-driven profiling and targeting, information war, data breaches, hacking and cyberterrorism, and that datafication reduces people to their data points—as if we are our data, perfectly knowable through our digital traces.

In education, the children’s writer Michael Rosen recently posted a tweet along these lines, writing that: ‘First they said they needed data about the children to find out what they’re learning. Then they said they needed data about the children to make sure they are learning. Then the children only learnt what could be turned into data. Then the children became data.’

Recently a lot of commentary has emerged about the social and emotional anxiety experienced by students in both schools and universities. Many of these psychological frailties are at least partly blamed on social media and other technologies that harvest up data from young people and then target and manipulate them for commercial profit. Richard Freed calls it the ‘tech industry’s psychological war on kids’. These kinds of stories are now part of a kind of cultural narrative about the dystopian, nightmarish effects of datafication on children, which is happening both in their own time and in an increasingly data-driven education.

10 Legally & ethically
Finally, there are legal, ethical and regulatory mechanisms shaping datafication. Europe is much more privacy-focused than the US, for example, as the incoming EU General Data Protection Regulation shows. So how datafication plays out—what datafication is—is itself shaped by law, ethics and politics.

In the US, for example, specific federal acts such as COPPA and FERPA exist to protect children’s privacy, and organizations like the Internet Keep Safe Coalition enforce them. Other organizations such as the Future of Privacy Forum exist to produce ‘policy guidance and scholarship about finding the balance between protecting student privacy and allowing for the important use of data and technology in education’. The US also has the 2015 Every Student Succeeds Act (ESSA), which has made it possible for states and schools to apply for additional funding for personalized learning technologies. So there’s a new federal act in place which performs the double task of stimulating market growth in adaptive personalized learning software and incentivizing schools to invest in such technologies in the absence (or at least shortage) of public funding for state schooling.

Of course, the ethical issues of datafication of education are considerable and fairly well rehearsed. An interesting one is the ethics of data quality–a topic discussed by Neil Selwyn at the recent Learning Analytics and Knowledge (LAK) conference. There are significant potential consequences of poor data in learning analytics platforms. In other spaces, such as healthcare and military drones, the consequences of poor data quality can lead to disastrous, even fatal, effects. Poor quality datafication of education may not be quite so drastic, but it has the potential to significantly disrupt students’ education by leading to mismeasurement of their progress, misdiagnosis of their problems, or by diverting them on to the ‘wrong’ personalized pathways.

I’m sure datafication could be cut in different ways. But hopefully these categories capture some of its complexity.

Image by Kevin Steinhardt
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Globalizing education standards with ISO 21001

Ben Williamson

Compass Vivek Raj large

Standards are everywhere but largely invisible. They define the production of things, from the size of a brick or the dimensions of a credit card to the programming languages used to code software, and act as rules for specific processes and practices. The creation of a new global standard for management processes in education may not at first seem terribly significant. This post interrogates ISO 21001, a standard for ‘Educational Organizations Management Systems’ due for release by the International Organization for Standardization (ISO) later in 2018. While few will have heard of it, it could define priorities, products, processes and practices in the education sector at global scale for years to come.

Standard definitions
A lively academic literature has grown around standards over the last two decades. In the influential Sorting Things Out Geoffrey Bowker and Susan Star defined standards as ‘any agreed-upon rules for the production of (textual or material) objects’ which are ‘deployed in making things work together.’ Standards, they added, persist over time and different places, are often enforced, difficult to change, and define certain ways of organizing things and actions.

That may sound a little abstract. In the tangible world, standards define almost everything. There are standards for the dimensions of kitchen goods and furniture, standard measures, standard fonts and paper sizes, standard economic models, standards for food products, standard business practices, standard forms to fill in, standard formats for cataloguing and indexing, governmental standards, standard classifications of illness and healthiness, standards for ensuring software can operate on computer hardware and that data are interoperable across systems, and much more.

People are standardized too. Standard measures of personality or citizenship, standards of dress and behaviour, standards for credit-scoring and social media profiling, and standards that define social class, socio-economic status, gender, nationality and ethnicity all affect people’s everyday lives. Standard linguistic definitions help us make sense of ourselves and the world we inhabit.

Standards are, as Lawrence Busch memorably called them, ‘recipes by which we create realities,’ and they are ‘about the ways we order ourselves, other people, things, processes, numbers, even language itself.’ And as Susan Star and Martha Lampland have noted, they are not innocent or neutral, but ‘codify, embody or prescribe ethics and values, often with great consequences for individuals (consider standardized testing in schools for example).’

In short, standards may seem invisible, but they matter—they are consequential to how the world is organized, how people and their behaviour are regulated, and how processes and objects are defined and measured. Those who control standards therefore hold great power to coordinate and organize social, economic, cultural, ethical and political life. Standards constitute societies.

Standardizing education
The field of education is awash with standards. Although standards have always played a significant part in education—in standard class sizes, teacher standards, standards of achievements, standard codes of behaviour, standard school uniforms and so on—today the most common references are to standardized curricula, learning standards, and standardized tests. Rules specifying consistent approaches to defining the content, objectives, and levels of achievement expected from students of a given course underpin them.

Dorothea Anagnostopoulos and coauthors have argued that quantitative scores from standardized tests based on defined learning standards and courses of study in standardized curricula have become the major measure of student, teacher and school performance, and thus of what ‘counts’ as a ‘good’ student, teacher or school. Standardized tests, standardized curricula and learning standards play significant roles in the complex of technology, people and policies that make up ‘data infrastructures of test-based accountability.’

With the expansion of international large-scale assessments such as the OECD’s PISA, researchers such as Sam Sellar and Bob Lingard have drawn attention to how these ‘products’ are now standardizing how education is approached in countries around the globe. Globalized data infrastructures based on standardized assessment products exert powerful effects on national education systems, schools, staff and students alike. Data infrastructure may even reshape education policy to become more globally standardized, supported by a global education industry of products, technologies and services. One such service provider is the International Standardization Organization, currently preparing a standard for educational management processes and practices.

Global standards
The ISO is a global organization based in Geneva and made up of over 160 national standards organizations working in partnership. It has published 22,047 International Standards, described as ‘documents that provide requirements, specifications, guidelines or characteristics that can be used consistently to ensure that materials, products, processes and services are fit for their purpose.’

The ISO states that ‘standards touch almost every aspect of our lives,’ and its mission is therefore ‘successfully sharing the best ideas and methods’ through its standards. In this sense, although it describes itself as a ‘neutral environment,’ the ISO is profoundly normative, seeking to promote its standards to achieve specific objectives as agreed by expert participants in its technical committees. It sees itself as a ‘global platform for creating consensus.’ A promotional ISO video explains how its standards can help promote growth, open markets, make trade fairer, tackle global challenges, maintain connectivity, keep us entertained, enhance productivity, promote innovation, and keep us safe and healthy. Keller Easterling, in a chapter of Extrastatecraft, defines the ISO’s standards as a ‘global operating system’ that ‘formats and calibrates’ many aspects of the contemporary world.

ISO 21001
Later in 2018, the ISO intends to release a new international standard for education. It has the potential to become part of the global operating system for the education sector.

ISO 21001 ‘Educational Organizations Management Systems’ has been in development for nearly 5 years, having been first approved as a proposal in early 2014. Development of the standard has been undertaken by Technical Committee 288, which itself consists of 140 expert members from 44 participating countries, plus 14 ‘observer’ countries, led by the Korean Agency for Technology and Standards. A map of participation shows involvement of the UK and much of mainland Europe, Australia, Canada, and many South American and Asian countries.

Keller Easterling claims that the ISO is hard to examine because of its opaque technical language, labyrinthine website, and inaccessibility of its standards documentation. Because the ISO’s standards are only available if you purchase them, it is hard to tell exactly what requirements, guidance or characteristics they promote. This is true of ISO 21001, not helped by the fact that it is at present still in ‘final draft’ form. Although  notionally open for consultation, even access to the draft requires the user to purchase it.

As Easterling notes, while the ISO ‘strives for universal impact’ it operates as a ‘secretive institution with no truly public dimension—no appeal to a citizen who is not also a consumer.’ But a published ‘Briefing Note’ and publicly available PowerPoint slides on the planned implementation and implications of ISO 21001 can help us get closer.

Standardizing educational organizations
The standard applies to management systems used by educational organizations, by which it means ‘what the organization does to manage its processes or activities in order that its products or services meet the organization’s objectives.’ These include ‘satisfying learners’ requirements’ and ‘meeting educational objectives’ as well as ‘balancing requirements from other stakeholders’ and ‘complying to regulations.’ In slightly different terms, ISO 21001 ‘concerns how an organization goes about its work,’ and ‘provides a common management tool for organizations providing educational products and services capable of meeting learner and other customer requirements and needs.’

So ISO 21001 is primarily a standard of management process for administering large and complicated educational organizations. Notably, the ISO claims the standard is making available a ‘comprehensive set’ of ‘successful practices’ that is for ‘applicable to all educational organizations that provide, share and facilitate the construction of knowledge to learners through teaching, training or research, regardless of type, size and product and service provided.’ It is not confined to either the schools or HE sectors, but ‘applies to the management system of any organization which utilizes a curriculum to provide, share and transfer knowledge.’ In other words, it appears to take a highly standardizing approach to educational organizations, treating them as universally comparable and manageable through the same best practices.

Standard benefits
Additionally, ‘Although learners and educational organizations worldwide are the main beneficiaries from this new management system standard, all stakeholders (everyone) will benefit from the output of standardized management systems in educational organization.’ The explanatory PowerPoint details that while learners have requirements of educational organizations, so too do the labour market and government, which, like learners, expect ‘satisfaction’ from a well-managed service.

The benefits listed in the briefing note are: a) better alignment of educational mission, vision, objectives and action plans, b) inclusive and equitable quality education for all, c) promotion of self-learning and lifelong learning opportunities, d) more personalized learning and effective response to special educational needs, e) consistent processes and evaluation tools to demonstrate and increase effectiveness and efficiency, f) increased credibility of the educational organization, g) recognized means to enable organizations to demonstrate commitment to education management practices in the most effective manner, h) a model for improvement, i) harmonization of national standards within an international framework, j) widened participation of interested parties, and k) stimulation of excellence and innovation.

Some of these need unpacking, because they are full of assumptions about the ways educational organizations should operate.

Standard language of learning
The first thing to note is the language of learning it operationalizes. The slides and briefing booklet refer to ‘self-learning,’ ‘personalized learning’ and related terms. The available PowerPoint notes that the ‘first principle’ of the standard is that ‘Educational Organizations should actively engage learners in their own learning’ and that ‘teaching is defined as working with learners to assist and support them with learning.’

Along with references to learners’ ‘construction of knowledge’ these sound laudable principles for those with a slightly ‘progressive’ view of education, but they do clearly presuppose that such approaches to education are widely agreed-upon. Given that educational research and practice remains philosophically divided between those who (broadly speaking) take a progressive ‘student-centred’ view and those who emphasize more liberal values regarding the transmission of powerful knowledge through subject-based curricula, the ISO’s standard-setting of student-centredness in educational organizations is controversial.

Even more controversial, perhaps, is that the ISO’s endorsement of ‘personalized learning’ resonates strongly with a particular (contested) view of education that has become closely associated with the entry of big data and artificial intelligence into education. In the slides promoting the standard, one of the stated aims is standardizing processes for the ‘identification and traceability of learners throughout the organization.’ Making students trackable through their data traces is therefore being put forward as a requirement of an effective, ‘personalized’ educational organization.

Standard harmonization
The standard’s benefits also include harmonization of national standards with international frameworks. This kind of harmonization has of course been the long-term goal of other international organizations such as the OECD, which has used evidence from its international large-scale assessments to compare national systems and derive ‘policy-relevant’ findings that might shape national-level decision-making, intervention and reform.

In this sense, the new ISO standard is supposed to participate in the contested practice of ‘commensuration’ whereby different qualities of educational systems and institutions are rendered equivalent through quantitative methods. As Steven Lewis points out, it ‘involves comparing how different national schooling systems are positioned within a commensurate global space of measurement’ and, coupled with the production of ‘examples’ of ‘best practice,’ influences how schooling is understood and practised. ISO 21001 is a commensuration instrument for easing the harmonization of school measurement at national and international scales.

Management standards
Another aspect of the standard is its references to the language of effectiveness, efficiency, evaluation, and improvement. As a management standard, it’s inevitable perhaps that it would appear managerialist. Indeed, ISO 21001 is in fact an adaptation for the education sector of its existing ISO 9001 quality management standard.

Easterling has documented how the ISO 9000 family of standards have become the ISO’s most popular products, thereby imposing uniform management and quality assurance processes on organizations worldwide. They are based on existing management theories pertaining to ‘the process of production, the procedures and practices of a company’ and their ‘social architecture,’ and have catalysed the development of a global consultancy industry regarding standards compliance. Compliance with ISO 9000 standards involves an organization evaluating itself in terms of its objectives, such as customer satisfaction, and often, Easterling argues, lead to ‘obsessive data gathering and metrics  … to quantify or prove that deliberate objectives have been met.’

Indeed, the ISO 21001 PowerPoint itself says it contains over 50 references to documented information and records that must be maintained or retained, and ‘an entire informative annexe gives examples of measures and tools which can be used in collecting and managing information.’ Compliance with the standard will involve mandatory internal auditing, review of programmes and annual reviews of the organization’s management systems ‘to address deficiencies.’

Data standards
As a further requirement, institutions must communicate publicly ‘learners’ performance data’ and ‘learner and other feedback, including satisfaction surveys and complaints.’ As these examples indicate, compliance with the standards will require data infrastructure to enable collection, analysis and presentation to the public of the required information. Moreover, making such measures public is likely to configure organizational behaviours. Geoffrey Bowker has argued that ‘It is not only the bits and bytes that get hustled into standard form in order for the technical infrastructure to work. People’s discursive and work practices get hustled into standard form as well. Working infrastructures standardize both people and machines.’

As an adaptation of ISO 9000 standards, then, ISO 21001 makes data gathering on customer satisfaction into an essential requirement of educational organizations. Of course, there is nothing especially unique about this. In the UK, a new Higher Education infrastructure for student data collection is already being built that requires universities comply with ISO 9001 quality management standards. As with ISO 21001 prioritizing students’ requirements and satisfaction, the UK data infrastructure starts from the principle of putting ‘students at the centre of the system.’ It will enable data to flow from HE institutions for centralized analysis to produce competitor rankings on many metrics, and is supposed to make HE data more usable for customers (students) as well as for policymakers and institutions themselves.

In this sense, compliance with ISO 9001 or its ISO 21001 outgrowth will reshape people through standardizing their practices. It will reshape academic staff as providers of ‘satisfying’ services, and students as consumers with rights to feed back and complain.

Market standards
What should not be overlooked here is how that data infrastructure is being developed to participate in greater marketization of HE in the UK. Janja Komljenovic and Susan Robertson have persuasively shown how HE markets are being ‘made’ through ‘ideological and political changes in the governance of higher education to make it a more globally competitive producer of knowledge, and a services sector.’ They also, importantly, point out that these macro-objectives of marketization are only possible through micro-processes such as the ‘development and deployment of policies, technologies, instruments and other “formatting devices”.’

Although Komljenovic and Robertson do not mention standards specifically, it is possible to view ISO 21001 as a kind of ‘formatting device’ to ensure that educational organizations fulfil metric requirements of data collection and reporting. As Komljenovic and Robertson note:

New technologies are constantly being invested in and deployed ‘to create efficiencies’ in methods of working, as a means to gather more and more information on the institution and its processes, to monitor staff and to measure a wide range of forms of satisfaction—most particularly student satisfaction. … At the same time, these technologies are also part of governing tools as its results influence university structures, policies and actions. Besides student surveys, there are also national and international rankings, benchmarks, indicators and so on.

The main achievement of ISO 21001, arguably, will be to create a new global standard for educational organizations to be measured and monitored as a market, according to metrics formulated from a strange hybrid of management theory and ‘student-centred’ educational discourse. Educational organizations will be ‘formatted’ by the standard to produce the data required to evidence how well they are achieving their objectives. Those objectives themselves, however, appear to be pre-formatted by ISO assumptions about what counts as ‘good’ teaching and learning.

A global educational operating system
Time will tell what difference ISO 21001 makes to educational organizations, national systems, or even global education policy. In many ways, it represents little that’s new since standardization, quantification and marketization are already well entrenched in many education systems around the world. But it does raise a few headline points:

  • To a significant extent, the ISO is joining a policy network of international organizations that have increasingly participated in shaping global education policy over the last two decades—among them the OECD, UNESCO, the World Bank, and the World Economic Forum
  • ISO 21001 is a potential key instrument for the expansion of global education policy and a global education industry, a standards-based means for identifying policy problems and a specification tool for providers of policy solutions
  • ISO 21001 is intended as part of a global operating system for educational organizations, a kind of ‘formatting device’ to ensure institutions follow uniform management processes which require them to evidence their achievements according to ‘quality’ measures such as students’ and other stakeholders’/customers’ service ‘satisfaction’
  • The standard constitutes the basic set of rules for educational infrastructures–it defines how educational organizations should be organized, managed, monitored, recorded, compared, and acts as a coordinating device to align all actors in the system around the same shared standard
  • It is supposed to stimulate and further enable greater harmonization and commensuration between national standards and international frameworks, leading to national education systems being reconfigured around standards derived in secret by the ‘consensus’ of ISO expert members
  • The ISO endorsement of personalized self-learning and student-centredness enshrines a disputed philosophy of education in standardized form as a ‘principle’ of education systems which  organizations would then be expected to adopt as an objective, and against which they would monitor themselves, and be monitored
  • The standard configures educational organizations as service providers and students as customers whose aggregated levels of consumer satisfaction may be read as economic indicators of an institution’s market competitiveness and positioning, and data on which must be made publicly available
  • The ISO is further entrenching the language and practices of management theory in education systems at global scale, reshaping the organizational practices and social architecture of the education sector around managerialist demands of performance measurement, audit, evaluation, accountability and marketization
  • Compliance with the standard, as with ISO 9000 standards, will bring more consultancies and companies into the education sector able to offer third-party certification and compliance services, further displacing governance of education from the public sector to the private sector

Even if these are not novel or unique, it remains concerning that a secretive international organization is now seeking to stamp a global standard on such practices, effectively creating a uniform international template by which to manage and judge educational organizations and all who inhabit them.

Although it is not yet clear how ISO 21001 will be enacted, the fact that 44 member countries are participating in development of the standard suggests it is likely to have wide international impact in years to come. It could become a ‘recipe for reality’ in education that will shape organizations and practices to conform to its rules and prescriptions. It could have the effect of further standardizing schools, universities and even education ministries. It could configure people into standard form, with practices and behaviours shaped as much as possible to ‘deliver’ standardized services and standardized learning outcomes. As such, it is an important example of why invisible standards need to be brought into public visibility.

Image credit: Vivek Raj

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PISA for personality testing – the OECD and the psychometric science of social-emotional skills

Ben Williamson

OECD SEL survey

The Organization for Economic Cooperation and Development (OECD) has published details of its new Study on Social and Emotional Skills (SSES). While the OECD has been administering international large-scale assessments on ‘cognitive skills’ and ‘competencies’ with both children and adults for many years, the new SSES survey represents a significant shift in focus to ‘non-cognitive’ aspects of learning and skills. While details of the science behind its cognitive skills and competencies tests are relatively well known, it is now becoming clear that the OECD’s social-emotional skills programme will emphasize the psychometric science of ‘personality’ measurement.

As part of ongoing research on social-emotional learning and skills (SELS) policies, practices and technologies, this (lengthy) post summarizes some of the key aspects of SSES, detailing its policy context, the ways it will generate and use student data, its conceptual basis in psychometrics, and the ways the OECD frames it as an objective ‘policy-relevant’ science programme with positive social and economic outcomes for participating countries.

From PISA to SSES
In recent years, the OECD’s PISA (Programme for International Student Assessment) and PIAAC (Programme for the International Assessment of Adult Competencies) tests have been the subject of extensive debate and research. New tests, such as the PISA-based Test for Schools to help schools compare themselves to international standards, as well as the expansion of its tests to include factors like problem-solving and well-being, have become available as the OECD has gradually extended its logic of measurement and comparison into policymaking systems globally.

The OECD first began signalling its interest in measuring and assessing social and emotional skills in 2014. That year, it published Fostering and Measuring Skills: Improving cognitive and non-cognitive skills to promote lifetime success, followed in 2015 by its report Skills for Social Progress: The power of social and emotional skills. In 2017 the OECD published Personality Matters: Relevance and assessment of personality characteristics, an extensive review of the scientific literature on personality theory and the measurement of personality factors. Although Personality Matters was developed as part of the PIAAC survey of adult skills, it has been deployed as the scientific rationale for the Study of Social and Emotional Skills announced in its 2017 ‘brochure’ Social and Emotional Skills: Well-being, connectedness and success and the accompanying SSES website.

Before going into some of the detail of SSES, the OECD’s focus in this area needs to be seen in a larger policy context. Over the last five years, as I’ve documented elsewhere, social-emotional learning and skills (SELS) have become a significant education policy priority and a key focus for education technology development and investment. Organisations including the global education business Pearson and the Nudge Unit have produced research summaries and guidance on developing SELS. The core idea behind many social-emotional learning and skills approaches is that the ‘non-cognitive’ aspects of learning are fundamentally linked to academic progress and to a range of social and economic outcomes, such as productivity, labour market behaviours and overall well-being.

Moreover, many advocates maintain, SELS are malleable and can be improved through direct teaching intervention. Improving SE skills is, therefore, seen as an important prerequisite for raising attainment, achieving social and economic progress, and improving individuals’ success, and an attractive prospect for policymakers seeking new ways to boost student achievement and employability.

Major lobbying groups based in the US have produced scientific justifications for focusing on SE learning and skills. The Collaborative for Academic, Social, and Emotional Learning (CASEL) has produced its own meta-analyses on social-emotional learning research and evidence. Similarly, the National Commission on Social, Emotional, and Academic Development (NCSEAD) at the Aspen Institute has published a research consensus drawing from evidence in brain science, medicine, economics, psychology, and education research. It claims to demonstrate that ‘the success of young people in school and beyond is inextricably linked to healthy social and emotional development, such as the ability to pay attention, understand and manage emotions, and work effectively in a team.’

Although CASEL and NCSEAD appear to have identified a consensus about what constitutes social-emotional learning and skills, the terminology remains confusing. Terms used for SELS including ‘character,’ ‘growth mindset,’ ‘grit,’ ‘resilience,’ and other ‘non-cognitive’ or ‘non-academic’ ‘personal qualities’ are often used interchangeably and gain traction with different academic, practitioner and policymaking communities. ‘Character’ has become the policy focus for the Department for Education in the UK following the 2014 publication of a cross-party Character and Resilience Manifesto,  while ‘grit’ has been favoured by the US Department of Education, as in its 2013 report Promoting Grit, Tenacity, and Perseverance—Critical Factors for Success in the 21st Century. Emerging education policies in the European Union appear to emphasize ‘soft skills’ as a category that encompasses SELS.

The OECD itself has adopted ‘social and emotional skills,’ or ‘socio-emotional skills,’ in its own publications and projects. This choice is not just a minor issue of nomenclature. It also references how the OECD has established itself as an authoritative global organization focused specifically on cross-cutting, learnable skills and competencies with international, cross-cultural applicability and measurability rather than on country-specific subject achievement or locally-grounded policy agendas.

Social-emotional datafication
The SSES programme was launched in 2017 with a timetable toward delivery of the first results in 2020. According to a published project schedule, during 2018 the OECD is developing the instruments and test items, before field testing in participating countries later in the year. The first formal round of the survey will take place in 2019, with final results released to the public late in 2020. The OECD also plans to administer SSES repeatedly in order to generate longitudinal data.

The expected outputs from the project include:

  • a set of validated international instruments to measure social and emotional skills of school-aged children
  • a dataset with information on the level and spread of social and emotional skills of children at ages 10 and 15, obtained from multiple sources, and accompanied with a wide scope of background and contextual variables
  • an improved understanding amongst policy-makers, education leaders, teachers, parents and other stakeholders on the critical role of social and emotional skills and the types of policies and practices that support the development of these skills
  • an improved understanding of whole child development, specifically as it relates to the development of social and emotional skills of children and youth

Although it is anticipated that only 10-12 countries and cities will take part in the first study, a huge quantity of data will be collected from the participants to deliver these outputs. The student survey of 10 and 15 year-olds itself consists of two assessments.

The first is a direct assessment. This is to be administered as a self-report questionnaire, which will be completed online as a computer-based survey. Students will respond to questions that are designed to assess behaviours considered indicative of selected SE skills. Indirect assessment will add to the dataset, including reports from parents and teachers based on similar questions on the typical behaviours of individual participating students.

In addition to the core assessment instruments will be contextual questionnaires for completion by children, their parents, teachers and principals. The contextual questionnaire for students will gather data on demographics, family culture, subjective health and well-being, academic expectations, and perception of their own SE skills.

Parents will provide information about their children’s SE skills, family background, child’s performance, home learning environment, parent-child-relationship, parental styles, learning activities, and parents’ own attitudes and opinion. As this list indicates, SSES is not just focused on school factors involved in developing SELS, but on distinctive family factors too, including learning activities undertaken out of school.

Schools themselves will provide contextual information from teachers in the form of reports on students’ SE skills, teachers’ own backgrounds, school characteristics, teaching practices, and teachers’ values and expectations about SE skills. Principals will add data on school background, school management, principles and rules, school climate, and the role of SE skills in curriculum and school agenda, as well as further administrative data for calculating other behavioural correlates and outcomes.

The data production expectations on schools, students and their families are, as the list demonstrates, extensive and extend well beyond the normal jurisdiction of the education sector into the extraction of information about homes, family relationships and parenting practices.

A further OECD document suggested it was also considering ‘exploring ways to link social and emotional skill measurement of the proposed study with other OECD measurement instruments such as those used in PISA and PIAAC, as well as with local measurement instruments such as standardised achievement tests.’

The direct assessment will be delivered online using a centralized software platform for assessment of children’s SE skills. Notably, the OECD claims it will use log file data obtained during the test as additional indicators of SE skills.

Log file information collected during computer-based international assessments has been described by Bryan Maddox as ‘process data’ collected about such things as  response times and key strokes, which can be studied with ‘micro-analytic precision’ in the analysis of larger-scale assessment data. These log file data are increasingly used in assessment software platforms as an extension of the test and can be conceptualized as ‘the mechanisms that underlie what people do, think, or feel, when interacting with, and responding to, the item or task.’

It’s not entirely clear how SSES will use log file data, but other projects have sought to correlate process metadata such as keystroke and response times to SELS. For example, the winner of CASEL’s 2017 design challenge on technologies to assess social-emotional learning was designed to capture the metadata generated as students took a computer-based test. It claimed its ‘measure quantifies how often students respond extremely quickly over the course of a test, which is strongly correlated with scores from measures of social-emotional learning constructs like self-regulation and self-management.’

This project exemplifies a form of stealth assessment whereby students are being assessed on criteria they know nothing about, and which rely on micro-analytics of their gestures across interfaces and keyboards. It appears likely that SSES, too, will involve correlating such process metadata with the OECD’s own SELS constructs to produce stealth assessments for quantifying student skills.

As the range of its data collection activities demonstrate, the OECD has designed SSES to include not only direct survey assessments, but extensive contextual information, school-level data on students behaviours and outcomes, and log file information that can be analysed as digital signals of SE skills. Importantly, though, these data all rely on specific conceptualizations of socio-emotional skills that the OECD has invested significant institutional effort in researching and defining.

Personality measurement
Behind the OECD SSES survey lies a set of psychological knowledge about the measurable qualities and characteristics of socio-emotional skills. The SSES brochure gives an overview of how the OECD defines SE skills. It claims socio-emotional skills constructs can be classified into five broad domains, which it refers to as a well-known framework called the ‘Big Five model’: emotional regulation (emotional stability); engaging with others (extroversion); collaboration (agreeableness); task performance (conscientiousness); open-mindedness (openness). The SSES survey itself will be administered to assess 19 skills which fit into each of these five categories.

The brochure notes the ‘five-factor structure of personality characteristics’ has been extensively researched and empirically validated in multiple studies, leading to ‘widespread acceptance of the model.’ It further adds that there is ‘extensive evidence that the Big Five domains and sub-domains can be generalised across cultures and nations’ and is suitable for describing socio-emotional skills in both children and adults.

OECD SELS categories

A much fuller account of the Big Five model is provided in Personality Matters, which the SSES brochure references directly. Written by an OECD policy analyst with academic experience in educational psychology, international social policy and cross-cultural survey methodology, Personality Matters is an extensive review of psychological and psychometric research on the conceptualization and measurement of human personality.

The document reviews research on a variety of potential measures of social-emotional learning and skills, including ‘grit,’ ‘character skills’ and other socio-emotional competencies (though it makes no reference to ‘growth mindset,’ a current popular psycho-policy concept). The review favours the five factor model of personality consisting of openness, conscientiousness, extroversion, agreeableness and neuroticism (OCEAN). It acknowledges that psychologists have developed tests and assessments such as the Big Five Inventory (BFI), the Neuroticism-Extraversion-Openness Personality Inventory (NEO-PI) the International Personality Item Pool (IPIP) and Trait Descriptive Adjectives (TDA) to measure these personality factors.

It is of course clear that the OECD’s SSES categories map exactly on to the five factor personality categories, with ‘emotional stability’ standing in for ‘neuroticism.’ The model was approved at an OECD meeting in 2015, following a presentation by Oliver John, a psychologist at the Berkeley University Personality Lab and original author of the Big Five Inventory personality test. Likewise, the SSES survey has been designed to assess 19 skills associated with its Big Five model, in ways which emulate the structure of many of the personality tests cited in its review. As this indicates, the way the OECD has formulated social and emotional skills is a direct translation of the OCEAN categories used by psychologists for personality testing. In this sense, SSES appears to represent a therapeutic shift in OECD focus, with its target being the development of emotionally stable individuals who can cope with intellectual challenge and real-world problems.

Crucially, the review also notes strong correlations between high scores in the Big Five and other outcomes such as academic achievement, job performance, and standardized test scores. Notably, it emphasizes the ‘policy relevance’ of the insight that many personality characteristics—or socio-emotional skills as the SSES describes them—are malleable and can therefore become a ‘potential target for policy intervention.’  Arguably, policy interventions that would be relevant to remedying identified personality problems would be forms of therapeutic provision, such as remedial classes in socio-emotional skills, which schools would then be responsible for delivering. ‘Therapeutic education,’ as Kathryn Ecclestone and Denis Hayes characterized it a decade ago, includes activities and underlying assumptions ‘paving the way for coaching “appropriate” emotions’ and ‘replace education with the social engineering of emotionally literate citizens who are also coached to experience emotional wellbeing.’

The combination of the human sciences with public policymaking has a long history, enabling governments to act upon the capacities of those subjects they govern. The promise of scientific objectivity regarding human behaviour and emotion is very attractive to policymakers wishing to manage or social-engineer the ‘appropriate’ behaviours of populations more effectively. However, as Sheila Jasanoff has argued, the objectivity of ‘policy-relevant knowledge’ is always achieved through hard work, argument, and the strategic deployment of persuasive, authoritative claims. The ‘objectivity-making’ practices of psychologists of personality underpin the recent policy shift to SELS embodied by the OECD, and the forms of therapeutic education that are likely to proliferate as schools realize they are to be measured and assessed according to SELS categories.

The OECD has made a science itself out of crafting objective policy-relevant knowledge from larger, contested bodies of scientific evidence about human competencies and personalities. With SSES, it either dismisses or omits the ‘grit,’ ‘growth mindset’ and ‘character skills’ literature—which in the Personality Matters report it suggests imply ‘moral connotations that many researchers and policy advisers would like to avoid’—and instead translates the concepts and practices of psychometric personality testing into policy-relevant approaches to measuring and assessing the social and emotional worlds of children.

Socio-emotional indicators and socio-economic outcomes
Beyond the presumed scientific objectivity of personality testing, interest in SELS among government departments and policymakers is also due at least in part to the economic arguments made by its advocates.

In the US, SELS are a lucrative investment opportunity under the banner of ‘impact investing.’ These ‘pay for success’ schemes allow investment banks and wealthy philanthropies to invest in educational services and programs and then collect public money with additional interest as profits if they meet agreed outcomes metrics.  The metrics for calculating the social benefit and monetary value of SELS schemes have  been published as a cost-benefit analysis with the title The economic value of social and emotional learning.

Beyond direct profitability of SELS programs for investors, however, the OECD makes a strong argument to governments that its assessment of socio-emotional skills can produce indicators of socio-economic outcomes. As such, it makes the case that government investment in SELS through departments of education will generate a substantial return in the shape of productive human capital. This is an argument the OECD has refined through years of PISA and PIAAC testing and analysis.

The Nobel laureate of economics James Heckman has advised the OECD on its social-emotional learning program through co-authoring its 2014 report on measuring non-cognitive skills. The report claimed that some programmes to support non-cognitive skills development ‘have annual rates of return that are comparable to those from investments in the stock market.’ Based on  extensive economics analysis twinned with developmental psychology and the neuroscience of ‘human capability formation,’ Heckman has influentially argued for over a decade that non-cognitive social-emotional skills and ‘personality factors are also powerfully predictive of socioeconomic success and are as powerful as cognitive abilities in producing many adult outcomes.’ Making ‘personality investments’ in young people, he claims, leads to high returns in labour market outcomes.

It’s notable that the organization contracted to lead SSES is the Center for Human Resource Research (CHRR) at The Ohio State University. The CHRR’s mission is to provide ‘substantive analyses of economic, social, and psychological aspects of individual labor market behavior to examining the impact of government programs and policies.’ According to the CHRR, the SSES project will identify ‘those social and emotional skills that are cross-cultural, malleable, measurable, and that contribute to the success and well-being of both the youth and their society.’ The assessment of SELS is therefore to be undertaken through the logic of human resource management and the analysis of labour market behaviours.

As the OECD itself phrases it, the purpose of SSES is to ‘provide participating cities and countries with robust and reliable information on the social and emotional skills of their students,’ and also to ‘have policy relevance’ by identifying ‘the policies, practices and other conditions that help or hinder the development of these critical skills.’ As a ‘policy-relevant project,’ the OECD claims, ‘study findings can also be used by policy makers to devise better policy instruments geared towards promoting these types of skills in students.’ Its brochure gives examples of ‘critical life outcomes’ that correlate with socio-emotional skills, including school achievement, college graduation, job performance, health and well-being, behavioural problems, and citizenship participation. These, it claims, can be improved because social and emotional skills are learnable and personality is malleable.

Psycho-economic policymaking & personality modification
This is necessarily a very partial overview of some of the key features of SSES. However, it does raise a few headline points:

  • SSES extends international-large scale assessment beyond cognitive skills to the measurement of personality and social-emotional skills as a way of predicting future economic and labour market outcomes
  • SSES will deliver a direct assessment instrument modelled on psychological personality tests
  • SSES enacts a psychological five-factor model of personality traits for the assessment of students, adopting a psychometric realist assumption that personality test data capture the whole range of cross-cultural human behaviour and emotions in discrete quantifiable categories
  • SSES extends the reach of datafication of education beyond school walls into the surveillance of home contexts and family life, treating them as a ‘home learning environment’ to be assessed on how it enables or impedes students’ development of valuable socio-emotional skills
  • SSES normalizes computer-based assessment in schools, with students required to produce direct survey data while also being measured through indirect assessments provided by teachers, parents and leaders
  • SSES produces increasingly fine-grained, detailed data on students’ behaviours and activities at school and at home that can be used for targeted intervention based on analyses performed at a distance by an international contractor
  • SSES involves linking data across different datasets, with direct assessment data, indirect assessments, school admninistrative data, and process metadata generated during assessment as multiple sources for both large-scale macro-analysis and fine-grained micro-analytics–with potential for linking data from other OECD assessments such as PISA
  • SSES uses digital signals such as response times and keystrokes, captured as process metadata in software log files, as sources for stealth assessment based on assumptions about their correlation with specific social-emotional skills
  • SSES promotes a therapeutic role for education systems and schools, by identifying ‘success’ factors in SELS provision and encouraging policymakers to develop targeted intervention where such success factors are not evident
  • SSES treats students’ personalities as malleable, and social-emotional skills as learnable, seeking to produce policy-relevant psychometric knowledge for policymakers to design interventions to target student personalities
  • SSES exemplifies how policy-relevant knowledge is produced by networks of influential international organizations, connected discursively and organizationally to think tanks, government departments and outsourced contractors
  • SSES represents a psycho-economic hybridization of psychological and psychometric concepts and personality measurement practices with economic logics relating to the management of labour market behaviours and human resources

There is likely to be additional concern that the OECD will use SSES to conduct large-scale international comparison of children’s social-emotional learning and skills. At present the first stage study appears too limited for that, with only an estimated 10-12 participating cities and countries.

However, over time SSES could experience function creep. PISA testing has itself evolved considerably and gradually been taken up in more and more countries over different iterations of the test. The new PISA-based Test for Schools was produced in response to demand from schools. Organizations like CASEL are already lobbying hard for social-emotional learning to be used as an accountability measure in US education—and has produced a State-Scan Scorecard to assess each of the 50 states on SEL goals and standards. Even if the OECD resists ranking and comparing countries by SELS, national governments and the media are likely to interpret the data comparatively anyway.

If these developments are taken as indicators, it is possible that over time the OECD may generate international comparisons, accountability metrics and league tables of education systems based on intimate assessments of students’ personalities.

Image credits: OECD
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Mapping the data infrastructure of market reform in higher education

Ben Williamson

Cables_Thomas Williams

A new regulator for Higher Education in England came into legal existence on 1st January 2018. Announced as part of the 2017 Higher Education and Research Act (HERA), the Office for Students was controversial before even formally opening for business. The appointment to its board of Toby Young, the free schools champion and journalist, appalled critics who vocally called for his sacking over previous misogynistic comments in the press and on social media. Despite Conservative Party ministers including Jo Johnson, Boris Johnson and Michael Gove defending his selection, Young resigned within 10 days.

The Toby Young storm, however, distracted attention from one of the most significant aspects of the HE reforms the Office for Students will preside over. That is the escalation and acceleration in the collection, analysis and use of student data, and the building of a new HE data infrastructure to enact that task. Under the Office for Students, student data is to become a significant source for regulating the HE sector, as universities are put under increasing pressures of market reform, metrics and competition by HERA.

As with all data infrastructure, mapping the HE data infrastructure is a complex task. In this initial attempt to document it (part of a longer published paper), I am following Rob Kitchin’s call for case studies that trace out the ‘sociotechnical arrangements’ of people, organizations, policies, discourses and technologies involved in the development, evolution, influence, dead-ends and failures of data infrastructures. It is necessarily a very partial account of a much larger project to follow the development, rollout and upkeep of a new data infrastructure in UK HE, and to chart how big data, learning analytics and adaptive learning technologies are being positioned as part of this program to deliver a reformed ‘smart’ sector for the future.

Metrics, markets and HE reform
As will be familiar to many working within UK HE, the Higher Education and Research Act (HERA) came into effect in 2017. It is the result of governmental reforms to the sector that have been underway since the beginning of the decade, as detailed in government papers produced by the Department for Business, Innovation and Skills (BIS)—notably 2011’s Students at the Heart of the System and 2016’s Success as a Knowledge Economy. These reforms, as others have documented and debated, have unleashed a ‘metric tide’ of performance measurement across the sector in an effort to create a marketized system of mass higher education.

The creation of the Office for Students (OfS) as a new regulator for HE in England has now consolidated the metricization and marketization of the sector as demanded by HERA. Described by BIS as a ‘non-departmental public body’ operating ‘at arm’s length from Government,’ the OfS is intended as an ‘explicitly pro-competition and pro-student choice’ organization, as well as a ‘consumer focused market regulator’ much like Ofcom in the media sector and Ofwat in water services. Its chair is Sir Michael Barber, formerly the ‘deliverology’ champion under Tony Blair’s government, a partner at management consultancy McKinsey, and more recently the chief education adviser at the global education business Pearson. At Pearson Barber oversaw its attempted transformation into a ‘digital-first’ company focused on digital data analytics and adaptive learning technologies for both the schools and universities sectors.

Barber was also the co-author of a 2013 report on the future of HE in the UK with the IPPR think tank, which argued:

With a massive diversification in the range of providers, methods and technologies delivering tertiary education worldwide, the assumptions underlying the traditional relationship between universities, students and local and national economies are increasingly under great pressure – a revolution is coming.

WonkHE named Barber the most powerful person in HE in 2017, noting his ‘legendary fondness for metrics,’ while the OfS began recruiting high-profile positions in data analytics ‘for those who understand the latest thinking in data science practice and how data can support policy development.’

The combination of BIS, HERA and Barber’s OfS represent the most visible aspects of current HE reform. As Neil Selwyn argues, ‘neoliberal logics’ of competition, performance measurement, quality management, marketization, commercialization and privatization have been growing in HE around the globe for many years, and are part of an increasingly powerful ideal of ‘digital universities’ which use data and metrics for monitoring performance and planning.  Underlying the reforms, however, is a less acknowledged project to upgrade and rebuild the data infrastructure that will allow the necessary information to be collected, analysed and put to use. The business of marketization in HE, as Janja Komljenovic and Susan Robertson have argued, involves ‘not just people, but technologies such as software, algorithms, computers, procedures and so on, in a rich collage of people, technology and programmes … that align the work of the university with the logics of capitalist markets.’

Data Futures
The enactment of HERA and the OfS will depend on massive quantities of data from universities, and also the utilization of new digital technologies to collect and process those data. In the last couple of years, as a recent Westminster event illustrates, government has begun to take more and more interest in ‘the role of big data and learning analytics for universities, including targeted marketing of prospective students, improving retention and personalising learning experience for individuals.’

Similarly, a collaboration between the Higher Education Commission and the think tank Policy Connect has produced From Bricks to Clicks: The potential of data and analytics in Higher Education to focus on the use of ‘fluid data’ that is ‘generated through the increasingly digital way a student interacts with their university.’ It highlights the potential of learning analytics to ‘improve the student experience at university, by allowing the institution to provide targeted and personalised support and assistance to each student.’ The government’s major 2017 Industrial Strategy also committed investment in big data, AI and machine learning within digital courses.

These future aspirations are beginning to be realized through the ‘Data Futures’ program being undertaken by the Higher Education Statistics Agency (HESA). Designated the official statistics and data body for HE since 1993, HESA compiles huge quantities of data about students, staff and institutions, departments, courses and finances, as well as performance indicators used to evaluate and compare providers. It maintains the data infrastructure for HE recording and reporting first established in its current form in 1994.

Data Futures is HESA’s flagship data infrastructure upgrade program, which it initiated as part of its corporate strategy in 2016, in response to government demands, and plans to operationalize fully by 2020. Funded with £7.5million from the HE funding councils, Data Futures is intended to enhance HE data quality, reduce duplication, and make data more useful and useable by members of the public, policymakers, providers, and the media. Its first priority is upgrading the systems for student data collection and analysis, in order to satisfy government demands that prospective students, as potential consumers of HE, can receive the best possible information about courses and providers, while current students might be able to monitor their own progress and rate the value-for-money provision they receive from their chosen courses.

The data infrastructure model being developed by HESA was first proposed through a series of reports by the global consulting firms Deloitte and KPMG, as part of the Higher Education Data and Information Improvement Programme (HEDIIP) hosted by HESA. In 2013 Deloitte produced ‘a proposal for a coherent set of arrangements for the collection, sharing and dissemination of data for the higher education data and information landscape.’ KPMG followed it with a ‘blueprint’ for a ‘New Data Landscape’, envisioned as ‘a data and information landscape for Higher Education in the UK that has effective governance and leadership, promotes data standards, rationalises data flows and maximises the value of technology and enables improved data capability.’

In the KPMG blueprint, used to establish Data Futures, HESA is positioned as a central ‘data warehouse’ for all HE data collection and access. Rather than once-a-year reporting, under Data Futures all HE providers will be required to conduct ‘in-year’ reporting. This will speed up the flow of data between institutions and HESA, and enable HESA to produce analyses and make them available to the public, media and policymakers more swiftly.

Writing in the Times Higher Education, HESA’s chief executive Paul Clark has described how the environment in which HE institutions operate is becoming increasingly data-intensive, with policymakers, students, funders and regulators all seeking information for their own purposes and needs:

Good data allow students to make informed choices, allow policymakers and regulators to make better decisions, promote public trust and confidence in the system, enable institutions to be competitive and provide a lever to incentivise or penalise behaviour in the absence of public funding.

At the same time as these ‘trends are being driven by developments in higher education policy,’ added Clark in another piece, ‘changes in the worlds of data, digital service delivery, and technology’ are taking place as big data technologies and practices are embedded across sectors and industries.

In this context, Data Futures instantiates, perhaps, a tentative first move toward the use of ‘live data’ and ‘real-time metrics’ which could be used for continuous performance measurement and comparison of the competitive HE market of providers. Although a real-time data model was suggested in the KPMG blueprint, Data Futures is not going quite that far, just yet. As Clark noted in his THE article,

Further developments can build out from this – providing enhanced analytical tools for users and providers, opening up larger stores of data for analysis and innovation, linking datasets across government departments and policy areas to improve decision-making and reducing the transactional costs associated with data flows around the sector.

While its first goal is to implement in-year reporting, HESA is clearly positioning itself to introduce advanced digital data methods and large linked datasets drawn from across government departments into some aspects of HE reporting and decision-making.

Data platforms and dashboards
Although Data Futures appears rather mundane as an effort to streamline and improve student data collection and analysis, it is an immense organizational and technical undertaking. At its core is the construction of a new ‘data platform’ for data collection, and new ‘data dashboards’ and visualization technologies to analyse the data and communicate results.

With regard to the data platform, HESA released a specification document in 2016 for potential suppliers. The specification reveals the platform would include a vast number of interconnected technical components, including three ‘user interfaces’: a data collection portal, an analytics portal and a governance portal. Behind these interface portals would be a range of ‘services,’ all underpinned by ‘human and machine readable specifications,’ a ‘logical model’ and ‘physical data model,’ a ‘unique student identifier lookup service,’ and a ‘reporting engine.’ The data platform would also include cloud storage, encryption, secure file transfer services, metadata, code, rules, data files, metrics, and specifications in terms of quality, reports, and data delivery, and more.

The appointed supplier to deliver the specification, announced in 2017, is Civica, which ‘provides a wide range of software, digital solutions and technology-based outsourcing’ for ‘organisations to improve and automate the provision of efficient, high quality services, and to transform the way they work in response to a rapidly changing and increasingly digitalised environment.’ In its announcement of the contract, HESA said Civica would deliver an ‘improved data model and extended capabilities [which] will offer users of HESA data a regular flow of accessible information through an enhanced user interface and visualisation tools.’

Data dashboard development to communicate findings from the data platform is being undertaken through a collaboration between HESA itself and Jisc (Joint Information Services Committee) as part of their ‘business intelligence shared service.’ The Analytics Labs collaboration provides an agile data processing environment using ‘advanced education data analytics’ in order ‘to rapidly produce analyses, visualisations and dashboards for a wide variety of stakeholders to aid with decision making.’ It emphasizes ‘cutting-edge data manipulation and analysis,’ access to current and historic data for time series analysis of the sector, and competitor benchmarking, using the Heidi Plus software platform provided by the commercial supplier Tableau Server.

Notably, early in 2018 HESA signed an agreement with both The Guardian and The Times newspapers to use Heidi Plus to produce interactive HE dashboards of rankings and measures based on their league tables. This, claimed HESA, would ‘enable universities to accurately and rapidly compare and analyse competitor information at provider and subject level, changes in rank year on year,’ and ‘the highest climbers and the biggest “fallers.”’ It also noted that the dissemination and presentation of league table data help shape public opinion about different providers.

The data platform being built by Civica, twinned with dashboards produced using Heidi Plus, are therefore interfaces to the data infrastructure being built by HESA. Together, these software platforms enable student data to be collected, analysed, visualized and circulated to the public, the press, providers themselves, and policymakers and politicians—and in so doing, to shape opinion and influence decision-making. As such, these systems of measurement and visualization are intimately tied to political ambitions to subject HE to increased marketization and competition.

Measurement and visualization are never simply neutral representations. As David Beer has argued, ‘systems of measurement become powerful’ as ‘mechanisms of competition,’ especially when the results of those measurements are disseminated and circulated to circumscribe future possibilities. Moreover, Jamie Bartlett and Nathaniel Tkacz have described how data dashboards ‘bring about a new “ambience of performance”, whereby members of staff or the public become more attuned to how whatever is measured is performing.’ Metrics and their graphical representation shape how people and institutions think, behave, compare themselves, and act to change themselves based on those representations.

The metrics operating inside the Data Futures platform and dashboards, in this sense, are mechanisms of competition and performance measurement within the HE sector, acting to make marketization part of the ambient conditions of Higher Education. They  extend governmental ambitions around increased metricization and marketization through the computer interface and into the eyes, hands and decisions of university administrators and leaders. As HESA’s Andy Youell has written, the work to build a new HE information landscape is not so much about systems but about ‘changing behaviours’ and ‘changing attitudes to the value of data within institutions.’

A market of smart, connected universities
The utopian dream behind current efforts to reform HE, of which Data Futures provides the material infrastructure, is that marketization and competition will drive up innovation within universities. An innovative HE sector is at the forefront of the 2017 Industrial Strategy. The ‘winners’ will be those institutions able to provide measurable evidence of innovation and quality in research, teaching, and impact on society. Student data, in addition to ratings of research excellence, teaching excellence and knowledge exchange, are to become the measure of market competitiveness.

But Data Futures also signals a more long-term utopian vision of the future of the sector. In a 2016 article, HESA’s chief executive Paul Clark wrote in visionary terms of a future HE in which learning analytics are part of teaching intervention, research performance metrics are pervasive, policymakers routinely base decisions on sector data, and ‘universities are gathering and benchmarking more and more data to ensure they are operating efficiently, and competing effectively with their peers’:

In ten years’ time, it’s possible to envisage a digital HE sector, with data-driven universities operating within a smart, connected environment. In this vision, universities would routinely use data drawn from many sources and devices to design and deliver their services, allocate resources, and monitor their performance. Policy-makers would similarly pool data from across government and the public sector to design interventions, monitor progress, and gain a far better and more granular understanding of how policy should be designed and delivered in order to achieve their aims. And users of the system would be able to access critical real-time data and information on their own progress, the resources available to them, and what they can do to maximise their chances of success.

Although Data Futures alone won’t deliver this vision when it rolls out nationwide in 2020, it is building the infrastructure necessary for new data-driven universities of the imagined future.

Late in 2017, in fact, HESA ran the Data Matters conference for HE data practitioners and leaders, where it not only showcased Data Futures developments but also hosted speakers and panels on the latest developments in big data, learning analytics, data dashboards and visualization. Data Futures is establishing the necessary infrastructure to support the application of these technologies as part of a utopian vision of smart, connected universities driven by market demands and real-time metrics. It is also worth noting that there are similar ambitions to create a national student data network in the US, through the campaigning of the Postsecondary Data Collaborative and Gates Foundation funding.

Much more remains to be done to unpack and understand these new infrastructure projects and the implications of accelerating and expanding student data collection and analysis. This post simply represents a first attempt to map some aspects of the new data infrastructure that will orchestrate aspects of UK HE in years to come. It’s an infrastructure made up of policies, people, organizations and future imaginings as much as technologies, and an ideal illustration of a dynamic sociotechnical network in which politics, practices and technologies interpenetrate one another. It also highlights how governance of HE is extended across software companies, think tanks and consultancies, as well as government departments, public bodies and arms-length agencies, and of how software can act as a relay of government strategy into university planning.

As part of the regulatory apparatus for a reformed HE, controversial appointees to the board of the OfS have understandably generated concern. But they should not distract attention from current ongoing infrastructural work to establish a new digital architecture for Higher Education that universities, staff and students will inhabit for decades.

Image by Thomas Williams
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The Nudge Unit, data science and experimental education

Ben Williamson

light graph victor

The UK government’s Behavioural Insights Team has announced it has been experimenting with data science methods in school inspections. In partnership with the Office for Standards in Education (Ofsted), it has designed machine learning algorithms to predict and rate school performance.

Originally established as part of the Cabinet Office in 2010, the Behavioural Insights Team—or ‘Nudge Unit’ as it informally known—became a ‘social purpose company’ in 2014, jointly owned by the UK Government, the innovation charity Nesta, and its employees. Its staff have academic grounding in economics, psychology or a background in government policymaking, and it has expanded its office from London to Manchester, New York, Singapore and Sydney. It has always been closely associated with the ‘what works’ model of policy, employing randomized control trials (RCTs) to test out policy interventions. The BIT has also established a revenue-generating arm that uses ‘behavioural insights to design and build scalable products and services that have social impact.’ One of its advisory panel is Richard Thaler, the behavioural economist recently awarded a Nobel economics prize for his work on the application of behavioural science and ‘nudge’ techniques in public policy.

The BIT’s Data Science Team published details of its experiments in a new report in December 2017. The team’s defined aims are to ‘make use of publicly available data, web scraped data, and textual data, to produce better predictive models to help government’; ‘to test the implications of these models using RCTs’; and ‘to begin developing tools that would allow us to put the implications of our data into the hands of policymakers and practitioners.’

The report, Using Data Science in Policy, detailed a number of projects the Data Science Team had undertaken to apply behavioural insights to diverse areas of public policy:

over the past year we have been working to conduct rapid exemplar projects in the use of data science, in a way that produces actionable intelligence or insight that can be used not simply as a tool for understanding the world, or for monitoring performance, but also to suggest practical interventions that can be put into place by governments.

The experiments were in policy areas including health, social care and education.

School-evaluating algorithms
In its education project with Ofsted, the BIT described how it used ‘publically available datasets to predict which institutions are most likely to fail and thereby target their inspections accordingly. We showed that this data, married to machine learning techniques such as gradient boosted decision trees, can significantly outperform both random and systematic inspection targeting. … We are excited to be working with Ofsted to put the insights from this work into action.’

In order to apply data science and machine learning to school inspection, the BIT compiled publicly available data from the year before an inspection happened. These data, its report said, included workforce data, UK census and deprivation data from the local area, school type, financial data (sources of finance and spending), performance data (Key Stage 2 for primary schools and Key Stages 4 and 5 for secondary schools) and Ofsted Parent View answers to survey questions. Parent View is Ofsted’s online tool to allow parents to record their own views on their child’s school. These data are then considered in Ofsted inspections.

According to a report of the Ofsted experiment in Wired magazine, its ‘school-evaluating algorithm pulls together data from a large number of sources to decide whether a school is potentially performing inadequately.’ By matching statistical data to the Parent View data, which includes textual information that can be analysed for sentiment, BIT claims it can predict which schools are not performing well and are likely to fail an inspection. The system ‘can help to identify more schools that are inadequate, when compared to random inspections’ and may even be used to automate decisions made by Ofsted in the future.

So far, the Nudge Unit’s trial with Ofsted has not been used to inform any real-world decisions, although the two organizations plan to extend their partnership in 2018, and are considering the use of further datasets, including data that are not open to the public.

An important aspect of the experiment with Ofsted is that the BIT doesn’t want schools to know how the algorithm works, as the project’s director told Wired. ‘The process is a little bit of a black box—that’s sort of the point of it,’ he said. In other words, schools are to be kept in the dark about the school-evaluating algorithm so that they don’t have the opportunity to ‘game’ their data in advance, which would result in skewing the predictive model.

It’s not the first time the Nudge Unit has been involved in education in the UK. Earlier in 2017 it was reported that the Department for Education was recruiting a permanent behavioural insights manager and an adviser. The aim was to change the culture of the department with psychology specialists applying behavioural science in strategic policymaking processes, and to commission research, trials and interventions drawing on behavioural insights to ‘improve our education and children’s services’.

The Nudge Unit’s experiment with Ofsted, and the DfE’s recruitment of behavioural scientists, exemplify the increasing role of behavioural science agencies to produce policy-relevant science in public education in recent years, as Alice Bradbury, Ian McGimpsey and Diego Santori have previously documented. This raises a number of issues.

Data labs
The first issue is how public education is increasingly being influenced by arms-length agencies. As a co-owned entity of the Cabinet Office and Nesta, the Nudge Unit is strictly independent of government but now acting as an outsourced contractor within public policy. It was reported in Wired that if Ofsted rolls out the school evaluation algorithm developed by BIT, local authorities would be required to pay between £10,000-£100,000 to implement.

Nesta itself, the co-owner of BIT, is an often-overlooked organization in UK education. As a ‘policy innovation lab’ it has successfully campaigned for ‘coding’ to be included in the English National Curriculum and for data science to be applied in the analysis of public services. A core hub in a global network of policy labs, Nesta and similar organizations worldwide are seeking to innovate in public policy, often using technological innovations as models for government reforms.

As the US GovLab has reported, the application of data science in public policy by ‘data labs’ can help create a ‘smarter state.’ Indeed, Nesta and the Cabinet Office have previously collaborated to develop ideas about a ‘new operating system for government,’ using  data science, predictive analytics, artificial intelligence, sensors, autonomous machines, and platforms to redefine the role of government.

As such, organizations such as Nesta and the Nudge Unit, which perceive data science as a new model for enacting government, are now seeking to locate data science methods within the institutions and processes of educational policymaking and school evaluation. The Ofsted project is part of their wider ambitions around digital governance using data science to drive policymaking. They are seeking to attach arms-length ‘data labs’ to centres of public policy, bringing new forms of technical and statistical expertise—as well as economic, behavioural and psychological science—into policy processes, including education. This exemplifies what I have elsewhere described as ‘digital education governance’—the use of digital data to make education visible for inspection and intervention.

Inspecting algorithms
Second, as part of this shift, the Nudge Unit is seeking to transform the way school inspections are performed. Rather than inspection through embodied expertise, school evaluation is now to be enacted predictively, before the inspector arrives. Jenny Ozga has previously written of how digitally recorded data increasingly surrounds the inspection process. The Nudge Unit is seeking to pre-empt the inspection process through the application of machine learning algorithms which have been trained to spot patterns and make predictions from pulling together a wide range of multimodal data sources about schools and their contexts.

These deliberately ‘black-boxed’ and opaque systems, which schools would be unable to understand, could be significant actors in practices of school accountability. If, as anticipated, some of Ofsted’s tasks are automated by the Nudge Unit’s intervention, then it may be unclear how certain decisions have been made in relation to a school’s overall evaluation. Although the BIT claims it doesn’t wish to replace the professional inspector, it is clear that school inspection will become a more distributed task involving both human and nonhuman decisionmaking and judgment, with data science methods perceived as more objective and impartial means for producing evidence than professional observation. In this sense, it is entirely consistent with behavioural science claims that human decision-making is less rational and evidence-based–and more emotionally-charged, cognitively-biased and subjective–than is commonly assumed.

At a time when there is increasing political, public and legal concern about machine learning opacity and its lack of ‘explainability’ or transparency, it seems ethically questionable to create systems that are deliberately black boxed, not least as their algorithms may well contain biases and potential for statistical discrimination. The cognitive bias of the school inspector is to be combated with systems that may have their own encoded biases. If a school is predicted to be inadequate by the algorithm, its stakeholders will expect and need to know what factors and calculations produced that evaluation.

It is notable too that BIT claims ‘missing data’ is predictive of a failed inspection, presumably the consequence of human error in the data-inputting process, and that it is seeking other non-public data sources to improve its predictive models. It remains unclear how deeply BIT intends to scrape schools for data, or which additional data would be included in their calculations, raising methodological questions about reliability and commensurability of their analyses.

Behavioural government
The third issue relates to the application of behavioural science within education. Mark Whitehead and coauthors describe how ‘behavioural government’ has proliferated across public policy in many countries in recent years—especially the UK and US—through the application of ‘nudge’ strategies. Nudging involves the design of ‘choice architectures’ that can shape and condition choices, decisions and behaviours, and is deeply informed by behavioural and psychological sciences. The Nudge Unit exemplifies behavioural government.

In its project with Ofsted, the BIT is seeking to use data science as a way of constructing choice architectures for inspectors. The results of the data analyses can identify particular areas for concern, as predicted by the algorithm, that may then be targeted by the inspectors, thus creating a more efficient and cost-effective machinery of inspection. The BIT is, in effect, nudging Ofsted to make strategically informed choices about how to conduct inspection. This, claims BIT, would reduce the number of inspections required and free up Ofsted staff to work on improvement interventions with schools. (Though it might also lead to Ofsted staff reduction and cost-savings.) In these ways, the Ofsted inspector is being reimagined as a nudge operative, intervening in schools by offering them targeted improvement frameworks. At the same time, it is seeking to supplement subjective human judgment, with all the flaws that behavioural science claims comes with it, with algorithmic objectivity.

The Nudge Unit also makes extensive use of psychological insight. Perhaps the most obvious use of psychological data in the Nudge Unit’s project with Ofsted is the sentiment analysis it is performing on Parent View data, with aggregation of parents’ subjective feelings into patterns that can be used as objective indicators to supplement the statistical inspection.

More innovative sources of psychological data, however, could be used by the Nudge Unit to undertake algorithmic school inspections in the future.

Behavioural sciences have already amalgamated with data science in relation to the policy area of ‘social-emotional learning.’ The logic of the social-emotional learning movement is that ‘non-academic’ qualities are strongly linked to academic outcomes; students need to be trained to be socially and emotionally resilient if they are to succeed in school. There are close ties between many of the major voices in the social-emotional learning movement and the behavioural sciences, and educational technologies have become central to efforts to monitor and nudge students’ non-academic learning.

As I’ve documented elsewhere, a range of technical innovations to support social-emotional learning has been proposed and developed, such as behaviour monitoring apps and wearable biometric monitors, that might be able to detect indicators of student emotions such as engagement, attention, frustration and anxiety. Data from these devices could then be fed back to the teacher, who would be able to prompt the student in ways that might generate a more positive response.

Real-time student data, it is possible to speculate, could well become part of the school inspection process under a Nudge Unit style of data scientific experimentation. Student sentiment data, tracked to progress and attainment, would then become a measure, defined by behavioural economists, to be used for purposes of school accountability

Real-time psychological data, as well as more mundane user data scraped from the web or captured by mobile smartphones, argue Mark Whitehead and colleagues, now appear to present rich opportunities for behavioural scientists to both record and nudge behaviours and emotions.  They cite an article from Forbes claiming ‘the proliferation of connected devices—smartphones, wearables, thermostats, autos—combined with powerful and integrated software spells a golden age of behavioral science. Data will no longer reflect who we are—it will help determine it.’

Behavioural government is, then, informed by the testing culture of the tech sector, which constantly experiments on its users to see how they respond to small changes in design. As such, ‘behavioural design’ is the application of behavioural science to technological environments to influence and determine user behaviours. With increased behavioural science influence in education, twinned with massive escalation in data-processing ed-tech applications, the culture of testing and behavioural design could significantly impact on policy, schools and professional practitioners in years to come.

The education experiment
As with its work in other sectors, the Nudge Unit’s involvement with Ofsted and the Department for Education is bringing the methodological logic of data-driven experimentation and behavioural design into education. Increasingly, automated algorithms are being trusted to perform tasks previously undertaken by embodied professionals. Their opacity makes the decisionmaking these systems perform difficult to scrutinize. Ofsted has long been a source of concern for schools, of course. It is hard to see how transforming the inspector into an algorithm which is better at identifying more inadequate schools will reduce teachers’ worries about performance measurement. Data and the culture of performativity in education have a long history.

More generally, the application of data science to education policy is indicative of how the education sector itself is becoming the subject of increasing levels of experimentation with data science methods. The Department for Education is currently seeking to reintroduce baseline testing into pre-school settings. A previous trial of early years baseline testing in 2015 collapsed amid concerns over the methodology of the original contractor. In the tender for the second version of baseline assessment, however, the DfE has more carefully specified the testing methods it expects the contracted assessment company to use. In an experimental education sector, schools, professionals and students look increasingly like laboratory specimens, repeatedly subjected to tests and trials, inspections and interventions, as part of the pursuit of identifying ‘what works’ in education policy.

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