ClassDojo app takes mindfulness to scale in public education

Ben Williamson

ClassDojo Messaging

A globally popular educational app used by millions of teachers and schoolchildren worldwide has begun to deliver mindfulness meditation training into classrooms. Based on a mobile app that teachers can carry in their pockets, ClassDojo is embedding positive psychology concepts in schools worldwide. In the process, it may be prototypical of new ways of enacting education policy through pocketable devices and social media platforms, while activating in children the psychological qualities that policymakers are seeking to measure.

The Beast

ClassDojo, launched just 6 years ago, is already used by over 3 million teachers and 35 million children in 180 countries—with penetration into the US K-8 sector at a staggering 90%. Originally designed as a behaviour monitoring app to allow teachers to reward ‘positive behaviour’ using a points system, more recently ClassDojo has extended into an educational content delivery platform to promote the latest ‘big ideas’ from positive psychology in the classroom.

Starting in early 2016 with a series of video animations on ‘growth mindsets,’ the ClassDojo company has since developed classroom content about ‘perseverance,’ ‘empathy’ and, in May 2017, ‘mindfulness.’ All its big ideas videos feature the cute Mojo character, a little green alien schoolchild, learning about these psychological ideas from his friend Katie while experiencing challenges, personal worries, setbacks and doubts about his learning abilities. In the mindfulness series, Mojo has to confront what Katie calls ‘The Beast’—‘your most powerful emotions, anger, fear and anxiety’—which, she tells Mojo, ‘can get out of control.’

The big ideas videos have been wildly popular with schools. ClassDojo has claimed that the growth mindset series alone has been viewed over 15 million times. The announcement of new big ideas series is accompanied by online content which is shared to its vast worldwide community of teachers via Facebook, Twitter and Instagram. To promote its new mindfulness series, ClassDojo has announced a ‘month of mindfulness’ across its social media accounts and communities.

ClassDojo’s expansion hasn’t just included video content delivery. It is also now used as a communication platform between schools and parents, to compile student portfolios, and to allow students to share their ‘stories.’ Its stated aim is to ‘connect teachers with students and parents to build amazing classroom communities’ and ‘happier classrooms.’ As a result ClassDojo is now one of the hottest educational technology companies in the world. It has raked in huge venture capital investment from Silicon Valley VC firms (about $31million in total, including $21m in 2016 alone), and is the regular subject of coverage in the educational, technology and business media.

It would not be overstating things much to suggest that ClassDojo has in fact become the default educational social media platform for a very large number of schools, functioning ‘like a social-media community where … the app creates a shared classroom experience between parents, teachers, and students. Teachers upload photos, videos, and classwork to their private classroom groups, which parents can view and “like.” They can also privately message teachers and monitor how their children are doing in their classrooms through the behavior-tracking aspect of the app.’

Many of ClassDojo’s features would be familiar to users of commercial social media such as Facebook, Snapchat and Slack. ‘If you’re an adult in the United States, you’ve got LinkedIn for work, Facebook for friends and family. This ends up being the third set of relationships, around your kids,’ one of ClassDojo’s major investors has claimed. As well as being geographically based in Silicon Valley, ClassDojo is strongly influenced by a Silicon Valley mindset of technical optimism in social media for relationships, sharing, and community-building. Like many recent education startups in Silicon Valley, ClassDojo’s founders are seeking to do good while turning a profit—specifically in their case by building a globally successful and scalable business brand on the back of building happier classroom communities through social media apps and platforms.

While social media organizations like Facebook and Twitter are now dealing with adverse issues such as fake news, political disinformation and computational propaganda on their platforms, however, ClassDojo has defined itself as a platform for diffusing positive psychology into schools. It’s aiming to achieve its ambitions directly through the mobile apps carried by millions of teachers in their pockets.

Emotions that count

The success of ClassDojo is due at least in part to the recent growth of interest in ‘social-emotional learning.’ A term that encompasses a range of concepts and ideas about the ‘non-cognitive’ aspects of learning—such as personal qualities of character, resilience, ‘grit,’ perseverance, mindfulness, and growth mindset—social-emotional learning has lately become the focus of attention among educational policymakers, international influencers and technology companies.

The OECD and the World Economic Forum have both begun promoting social-emotional learning and are seeking ways to foster it through technology and quantify it through measurement instruments. A US Department of Education report published in 2013 promoted a strong shift in policy priorities towards such qualities, and listed a then-young ClassDojo as a key resource. New accountability mechanisms have even been devised to judge schools’ performance in developing students’ non-academic personal qualities. The US Every Student Succeeds Act (ESSA) has now made it mandatory for states to assess at least one non-cognitive aspect of learning as part of updated performance measurement and accountability programs.

Notably, too, ClassDojo’s big ideas resources have been produced through partnerships with powerful US university departments. The original growth mindset series was devised with the Project for Education Research That Scales (PERTS) at Stanford University, as was its follow-up perseverance series. The empathy series late in 2016 was co-produced with the Making Caring Common Project at Harvard University’s Graduate School of Education, while the mindfulness series released in May 2017 is the result of collaboration with the Center for Emotional Intelligence at Yale University.

A concern for social-emotional learning is not just confined to dedicated educational organizations. The ed-tech researcher Audrey Watters has described social-emotional learning as a ‘trend to watch’ in 2017, and detailed some of the technology companies and investors involved in promoting it. ‘Ed-tech entrepreneurs and investors are getting in on the action, as have researchers like Angela Duckworth who’s created software to measure and track how well students perform on these “social emotional” measurements,’ she has argued. Meanwhile, ‘startups like ClassDojo,’ Watters adds, ‘promise to help teachers monitor these sorts of behaviors.’ She concludes by asking, ‘Can social emotional learning be taught? Can it be tested? Can it be profited from?’

Pocket policy platforms

ClassDojo needs to be understood as the product of a complex network of actors and activities including business interests, policy priorities, and expert psychological knowledges concerned with social-emotional learning (as I argued in earlier research published recently). With education policy increasingly influenced by the social-emotional learning agenda, ClassDojo and its academic partners and venture capital investors are increasingly part of distributed ‘policy networks.’ Although much education policy is still performed by government authorities, it is increasingly influenced by diverse sources, channels and sites of policy advice and ‘best practice’ models–of which ClassDojo is a good example

In this sense, ClassDojo is acting as an indirect best practice policy model and a diffuser of the social-emotional learning agenda into the practices of schools. In reality, it may even be prefiguring official policy. With venture capital funding from its investors driving its development and growth, ClassDojo has already distributed the vocabulary of social-emotional learning worldwide, and influenced the uptake of practices related to growth mindsets, perseverance and mindfulness among millions of teachers. It has done so through producing highly attractive content and then distributing it through its vast social media networks and communities on the Facebook, Twitter and Instagram platforms too.

‘If we can shift what happens inside and around classrooms then you can change education at a huge scale,’ ClassDojo’s CEO Sam Chaudhury has publicly stated. ‘We are looking for broad concepts really applicable to every classroom,’ its product designer has added. ‘We look for an idea that can be powerful and high-impact and is working in pockets, and work to bring it to scale more quickly … incorporated into the habits of classrooms.’

Although ‘working in pockets’ here clearly refers to potentially high-impact but small-scale startup activities, it is notable too that as a mobile app ClassDojo is already working in the pockets and palms of teachers. ClassDojo, in other words, represents a new way of doing large-scale policy through classroom apps that are already working in teachers’ pockets and hands rather than through political deliberation and direct interference. This would be an impossible task to coordinate at global scale through traditional government organs of education—although the interests of the global policy influencers OECD and WEF suggest ClassDojo could be prototypical of attempts to roll-out social-emotional learning into the habits of teachers through pocket-based policy platforms. Its method of enacting policy-by-app is being achieved by mobilizing practical classroom applications that can be carried in teachers’ pockets and enacted through their fingertips, generously funded by Silicon Valley venture capital, without the encumbrances of bureaucratic policymaking processes.


Beyond being a pocket-policy technology that prefigures official policy priorities, ClassDojo also represents another policy innovation—that of using an app to translate psychological expertise into practical techniques for teachers, and of acting as a technical relay between disciplinary knowledge and practitioner uptake.

The kind of policy that ClassDojo anticipates is already developing in other sectors. Lynne Friedli and Robert Stearn have identified the emergence of ‘psycho-policy’ as a new approach to policymaking in the area of ‘well-being.’ Techniques of psycho-policy, they argue, are characterized by being heavily influenced by psychological concepts and methods, and by the ‘coercive use of psychology’ to achieve desired governmental objectives. As such, psycho-policy initiatives emphasize the ‘surveillance of psychological characteristics’ and techniques of ‘psycho-compulsion,’ which Friedli and Stearn define as ‘interventions intended to modify attitudes, beliefs and personality, notably through the imposition of positive affect.’

Psycho-policy, then, is the use of psychology to impose well-being and activate positive feeling in individuals, and thereby to enrich social well-being at large. In this context, as the sociologist William Davies has argued, the use of mobile ‘real-time mood-monitoring’ apps is increasingly of interest to companies and governments as technologies for measuring human emotions, and then of intervening to make ‘that emotion preferable in some way.’ As a pocket policy diffuser of such positive psychological concepts as mindfulness and growth mindset into schools, the ClassDojo app and platform can therefore be seen as part of a loosely-coordinated, multi-sector psycho-policy network that is driven by aspirations to modify children’s emotions to become more preferable through imposing positive feelings in the classroom.

Viewing ClassDojo as a pocket precursor of potential educational psycho-policies and practices of social-emotional learning in schools raises some significant issues. Mindfulness itself, the subject of ClassDojo’s latest campaign, certainly has growing popular support in education. Its emphasis on focusing meditatively on the immediate present rather than the powerful emotional ‘Beast’ of ‘anger, fear and anxiety,’ however, does need to be approached with critical social scientific caution.

‘Much of the interest in “character,” “resilience” and mindfulness at school stems from the troubling evidence that depression and anxiety have risen rapidly amongst young people over the past decade,’ William Davies argues. ‘It seems obvious that teachers and health policy-makers would look around for therapies and training that might offset some of this damage,’ he continues. ‘In the age of social media, ubiquitous advertising and a turbulent global economy, children cannot be protected from the sources of depression and anxiety. The only solution is to help them build more durable psychological defences.’

According to this analysis, school-based mindfulness initiatives are based on the assumption that young people are stressed, fragile and vulnerable, and can benefit from meditative practices that focus their energies on present tasks rather than longer-term anxieties caused by uncontrollable external social processes. James Reveley has further argued that school-based mindfulness represents a ‘human enhancement strategy’ to insulate children from pathologies that stem from ‘digital capitalism.’ Mindfulness in schools, he adds, is ‘an exercise in pathology-proofing them in their capacity as the next generation of unpaid digital labourers.’ It trains young people to become responsible for augmenting their own emotional wellbeing and in doing so to secure the well-being of digital capitalism itself.

According to Davies, however, much of the stress experienced by children is actually caused more mundanely by the kinds of testing and performance measurement pressures forced on schools by current policy priorities. ‘The irony of turning schools into therapeutic institutions when they generate so much stress and anxiety seems lost on policy-makers who express concern about children’s mental health,’ he argues.

It is probably a step too far to suggest that ClassDojo may be the ideal educational technology for digital capitalism. However, it is clear that ClassDojo is acting as a psycho-policy platform and a channel for mindfulness and growth mindsets practices that is aimed at pathology-proofing children against anxious times through the imposition of positive feelings in the classroom. While taming ‘the Beast’ of his uncontrollable emotions of ‘anger, fear and anxiety’ through mindfulness meditation, ClassDojo’s Mojo mascot is both learning the lessons of positive psychology and acting as a relay of those lessons into the lives of millions of schoolchildren. Its model of pocket-based psycho-policy bypasses the kind of slow-paced bureaucracy so loathed in the fast-paced accelerationist culture of Silicon Valley, and imposes its preferred psychological techniques directly on the classroom at global scale.

Detoxing education policy

To its credit, the ClassDojo organization is seeking to expand the focus of schools to the non-cognitive aspects of learning rather than concentrate narrowly on teaching to the tests demanded by existing policy. Paradoxically, however, it is advancing the kinds of social and emotional qualities in children for which schools may in the near future be held accountable, and that may be measured, tested and quantified. Its accelerated Silicon Valley business model depends on increasing the scale and penetration of the app into schools, and by doing so is actively enabling schools to future-proof themselves in the event they are held responsible for children’s measurable social-emotional learning and development.

ClassDojo has also hit on the contemporary perception of child fragility and vulnerability among educational practitioners and policymakers as a market opportunity, one its investors have generously funded with millions of dollars in the hope of profitable future returns. It is designed to activate, reward and condition particular preferred emotions that have been defined by the experts of mindfulness, character and growth mindset, and that are increasingly coming to define educational policy discourse. The psycho-policy ideas ClassDojo has embedded in teachers’ pockets and habits across public education, through Silicon Valley venture capital support, are already prefiguring the imperatives of policymakers who are anxious about resolving the toxic effect of children’s negative emotions on school performance.

ClassDojo is simultaneously intoxicating teachers worldwide while seeking to detoxify the worst effects of education policy on children. In the process it—and the accelerated Silicon Valley mindset it represents—may be redefining what counts as a valuable measure of a good student or teacher in a ‘happier classroom community,’ and building a business plan to profit from their feelings.

Image credit: ClassDojo product shots
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Brain data, neurotechnology and education

Ben Williamson

Brains Neil ConwayImage by Neil Conway

The brain sciences are playing an increasingly powerful role in the development of the digital technologies that may augment everyday life in future years. ‘Neurotechnology’ is a broad field of brain-centred technical R&D. It includes advanced imaging systems for real-time brain monitoring and mining the mind via the collection of brain data, but also new and emerging brain stimulator systems that may have the capacity to influence brain activity. The possibilities associated with brain-machine interaction have begun to attract educational interest, raising significant concerns about how young people’s mental states may in the future be governed through neurotechnology.

Brain data

The human brain has become the focus of intense interest across scientific, technical R&D, governmental, and commercial domains in recent years. Neuroscientific research into the brain itself has advanced significantly with the development and refinement of brain imaging neurotechnologies. Driven by massive research grants and private partnerships, huge teams of neuroscience experts associated with international projects—such as the US-led BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative and European Human Brain Project—have begun to visualize and build ‘wiring diagrams’ and computational models of the cells and neural circuits of the brain at a highly granular, neuromolecular level of detail and fidelity, all based on the collection and analysis of massive records of brain data.

This knowledge of the brain developed by neuroscience is being applied to the design of new brain-machine interface technologies such as neuroprosthetic devices that can be implanted in the brain—with algorithms that can translate ‘thought’ into movement—and noninvasive neurostimulators that might modify cognition and emotions. In the last few months, technology entrepreneurs from some of Silicon Valley’s most successful companies have also begun to concentrate R&D resources on Brain-Computer Interfaces (BCI) and brain-signalled remote control of devices–as well as more speculative attempts to hybridize the human brain with artificial intelligence implants. Tesla boss Elon Musk, for instance, has established Neuralink to use brain implants to directly link human minds to computers and ‘augment the slow, imprecise communication of our voices with a direct brain-to-computer linkup.’ Facebook, meanwhile, has announced it is pursuing the development of a new kind of noninvasive brain-machine interface—possibly a cap or headband—that lets people text and ‘share’ their thoughts by simply thinking rather than typing. Its intention is to use optical technologies to use light, like LEDs or lasers, to sense neural signals emanating from the cerebral cortex.

At the same time, the brain is being treated as an inspiration for the design of neurocomputing systems. These complex cognitive computing, neural networks and AI systems are designed to emulate some of the brain’s capacities, especially for efficient low-energy information storage, processing, retrieval and learning, in order to maximize the efficiency and speed of big data processing and machine learning algorithms. Neural-network research, for example, focuses simultaneously on improving understanding of the human brain and nervous system, and on using that knowledge to ‘find inspiration’ to ‘construct information processing systems inspired by natural, biological functions and thus gain the advantages of these systems.’ The development of ‘bio-inspired’ or ‘bio-mimetic’ systems in neural-network research, and neurocomputing more generally, is already being applied in many settings, notably through companies like IBM. IBM’s recent advances in cognitive computing, such as Watson, take inspiration from neuroscience for the design of brain-like neural networks algorithms and neurocomputational devices that are now being deployed in healthcare, business and educational settings.

A huge field has developed around Brain-Computer Interface research and development too. BCI, or sometimes Brain-Machine Interface R&D, depends on signal processing of brain data to allow brain activities to control external devices or even computers through electrodes–‘the enabling technologies that allow brain information to be encoded by different techniques and algorithms providing input to control devices.’ Although previously largely confined to clinical and laboratory research, the possibilities of brain-machine mental control have begun to attract significant research grant funding along with commercial interest in recent years. The growth in interest at least partly stems from advances in BCI R&D which have seen the invasive implantation of microelectrodes within the brain itself being displaced by increasingly noninvasive techniques. Noninvasive BCI does not involve penetration of the scalp or skull with electrode implants but still holds the potential for mental control over devices through the real-time capture of brain activity data using portable EEG neuroimaging technologies.

Various portable and wearable EEG headbands that allow easy attachment of electrodes to the skull have become commercially and clinically available, with brand-names including Emotiv, Neurosky, BrainBand, Myndwave and BrainControl. Mental control videogaming is a major commercial application of BCI. Further out in R&D terms, other neuroscience inspired brain interface proposals include ‘neural dust’ consisting of microscopic free-floating sensors that could be spread around the brain.

The policy implications of neuroscientific and neurotechnological development have been articulated by, among others, the Potomac Institute for Policy Studies, a policy institute with its own Center for Neurotechnology Studies. Its report on ‘enhancing the brain and reshaping society’  has called for collaborative efforts between policymakers, scientists and the private sector to develop novel neurotechnologies that can improve individuals’ cognitive abilities and behaviours as well as the ‘social order,’ and thereby ‘ensure neuroenhancement of the individual will result in enrichment of our society as a whole.’

As with all technical development, neurotechnology is not merely technical. It is imprinted with powerful social visions of a future in which brain data can be used to know and monitor populations, and to enhance the mental states of individuals to meet certain objectives and aspirations for society at large.


Neurotechnological development and application of neuroenhancement techniques may seem far removed from education. However, neuroscience itself is currently enjoying fast growth within educational research and practice, with new research centres in educational neuroscience appearing, with support from grant awarding bodies, and research results and applications increasingly being shared by global community using the Twitter hashtag #edneuro. The journal Learning, Media and Technology ran a special issue in 2015 on neuroscience and educational technology.

Various neurotechnologies such as brain imaging are being used by ‘ed-neuro’ researchers in ways which are intended to generate insights for educational policymakers and practitioners. One ed-neuro study has made use of mobile, wearable EEG headbands to study students’ ‘brain-to-brain synchrony’ within the classroom context. EEG neuroimaging has even been used to visualize the brain ‘lighting up’ when students have adopted a ‘growth mindset.’  Attempts have also been made to use brain imaging technologies to analyse the possible biological mechanisms by which socio-economic status influences and effects brain and cognitive development in children. Studies have used neuroimaging to examine whether socioeconomic status correlates with differences in brain structure, and measured the electrical activity in the brains of children from lower SES groups to detect deficits in their selective attention. Such studies and conclusions have begun to influence policymakers, who can interpret the results to specify remedial interventions such as early years education provision. In these ways, neurotechnologies are becoming integral parts of new policy science approaches, the instruments that enable policymakers to see policy problems visualized in the neurobiological detail provided by highly persuasive brain images.

Neurotechnology-based cognitive computing systems developed by commercial organizations have also appeared in the educational landscape. The edu-business Pearson has partnered with IBM to bring IBM’s Watson system into the learning process, as previously detailed. For at least the last decade, IBM has been engaged in an extensive program of brain-based computing R&D, involving neurocomputing, neural-network research and the development of specific neurosynaptic and neuromorphic hardware and software. For IBM, as detailed in its white paper on ‘Computing, cognition and the future of knowing,’ cognitive tools are ‘natural systems’ with ‘human qualities’ which are inspiring the ‘next generation of human cognition, in which we think and reason in new and powerful ways’:

It’s true that cognitive systems are machines that are inspired by the human brain. But it’s also true that these machines will inspire the human brain, increase our capacity for reason and rewire the ways in which we learn.

Pearson has itself articulated a vision of AI teaching assistants and cognitive tutors using technologies based on advances in educational neuroscience and psychology. For both Pearson and IBM cognitive computing does not just mean smarter computing systems, but cognitively optimized individuals whose very brain circuitry has been rewired through interfacing and interacting with machine cognition.

Political support for commercial educational neurotechnology has also emerged. Recently-appointed head of the US Department of Education, the private-education advocate Betsy DeVos, is a major investor and former board member of Neurocore, a brain-training treatment company that specializes in ‘neurofeedback’ technology. The company uses real-time EEG with electrodes attached to the scalp to diagnose individuals’ symptoms by comparing their brainwaves to a massive database of others’ brainwaves. Its proprietorial neurofeedback software can then be applied to run a game that rewards the desired brain activity. Over time, Neurocore claims, the brain starts to learn to produce activity that was rewarded by the increase in stimulation. One of Neurocore’s targets is children with ADHD (Attention Deficit Hyperactivity Disorder); its ‘natural treatments’ with drug-free neurofeedback ‘work with a child’s natural ability to learn, helping them reach their full potential.’

From a more speculative perspective the Center for Neurotechnology Studies at the Potomac Institute has issued a report on ‘neurotechnology futures’ with some key implications for education. It describes how brain interface technologies could become applications for ‘augmented cognition’, including ‘non-invasive devices that complement or supplement human capabilities, such as tools for learning and training augmentation.’ It has detailed how ‘greater understanding of the neural mechanisms of learning and memory is needed to provide the appropriate theoretical basis for neurotechnologically enhancing learning’ and enabling the educational system ‘to significantly improve teaching techniques for iteratively more complex knowledge.’ It even suggests the ‘provocative possibility of technology that could “down-load” experience and facilitate learning in a time-compressed manner.’

The Potomac Institute provides advice to the US military. And the US military Defense Advanced Research Projects Agency (DARPA) has itself begun exploring the potential to boost the acquisition of skills and learning through its Targeted Neuroplasticity Training (TNT) program, itself part of the BRAIN Initiative. The program aims to develop safe, noninvasive neurostimulation methods for activating synaptic plasticity–the ability of the brain to connect neurons which is understood to be the neural requirement for learning. According to a press release from the TNT program manager,

Targeted Neuroplasticity Training (TNT) seeks to advance the pace and effectiveness of a specific kind of learning—cognitive skills training—through the precise activation of peripheral nerves that can in turn promote and strengthen neuronal connections in the brain. TNT will pursue development of a platform technology to enhance learning of a wide range of cognitive skills…. The TNT program seeks to use peripheral nerve stimulation to speed up learning processes in the brain by boosting release of brain chemicals, such as acetylcholine, dopamine, serotonin, and norepinephrine. These so-called neuromodulators play a role in regulating synaptic plasticity, the process by which connections between neurons change to improve brain function during learning. By combining peripheral neurostimulation with conventional training practices, the TNT program seeks to leverage endogenous neural circuitry to enhance learning by facilitating tuning of neural networks responsible for cognitive functions.

Although TNT is primarily aimed at military training, it clearly indicates how the scientific and technical possibilities of neurotechnology are being taken up in relation to education and learning.

At least one educational entrepreneur has leapt upon the potential of ‘frictionless’ brain-computer interfaces of the kind imagined by DARPA, Silicon Valley entrepreneurs like Elon Musk and the vision of neurotechnologically-enhanced learning promoted by the Potomac Institute. Donald Clark, the founder of the AI-based online learning company Wildfire Learning, the ‘world’s first AI content creation service’ for education, has imagined that invisible, frictionless and seamless interfaces between human brains and AI will have massive implications for education:

The implications for learning are obvious. When we know what you think, we know whether you are learning, optimise that learning, provide relevant feedback and also reliably assess. To read the mind is to read the learning process…. We are augmenting the brain by making it part of a larger network … ready to interface directly with knowledge and skills, at first with deviceless natural interfaces using voice, gesture and looks, then frictionless brain communications and finally seamless brain links. Clumsy interfaces inhibit learning, clean smooth, deviceless, frictionless and seamless interfaces enhance and accelerate learning. This all plays to enhancing the weaknesses of the evolved biological brain … and [to] think at levels beyond the current limitations of our flawed brains.

These aspirations for the future of education merge the scientific R&D of the emerging ‘ed-neuro’ field with the kind of techno-optimism often found in educational technology, or ‘ed-tech,’ development and marketing, to suggest the emergence of a new hybrid field of ‘ed-neurotech.’

Like the plans of Musk and Facebook, the ed-neurotech imaginary of a deviceless, frictionless and seamless neurotechnological future of education is likely to be highly controversial and contested. Part of this resistance will be on primarily technical and scientific grounds–neurotechnologies of brain imaging are one thing, and seamless neuroenhancement of the so-called flawed brain quite another. But another part of the resistance will be animated by concerns over the aspirations of either governments or commercial companies to engage in mental interference and cognitive modification of young people.

Neuroenhancement may not be quite as scientifically and technically feasible yet as its advocates hope, but the fact remains that certain powerful individuals and organizations want it to happen. They have attached their technical aspirations to particular visions of social order and progress that appear to be attainable through the application of neurotechnologies to brain analytics and even neuro-optimization. As STS researchers of neuroscience Simon Williams, Stephen Katz & Paul Martin have argued, the prospects of cognitive enhancement are part of a ‘neurofuture’ in-the-making that needs as much critical scrutiny as the alleged ‘brain facts’ produced by brain scanning technologies.

Neurotechnological governance

In a new article on neuroscience, neurotechnology and human rights, the bioethicists Marcello Ienca and Roberto Andorno have mapped out some of the challenges raised by these emerging ‘brain-society-computer entanglements.’ The ‘neurotechnology revolution’ in ‘neuroimaging’, they argue, highlights how the ‘possibility of mining the mind (or at least informationally rich structural aspects of the mind) can be potentially used not only to infer mental preferences, but also to prime, imprint or trigger those preferences.’ They note how brain imaging techniques have been taken up in ‘pervasive neurotechnology applications’ such as BCIs that ‘use EEG recordings to monitor electrical activity in the brain for a variety of purposes including neuromonitoring (real time evaluation of brain functioning), neurocognitive training (using certain frequency bands to enhance neurocognitive functions), and noninvasive brain device control.’

In addition to neuroimaging and brain-computer interface and device control, however, Ienca and Andorno also note the emergence of ‘brain stimulators’ or ‘neurostimulators.’ Unlike neuroimaging tools, these ‘are not primarily used for recording or decoding brain activity but rather for stimulating or modulating brain activity electrically.’ Available neurostimulators include portable, easy-to-use, consumer-based transcranial direct current stimulation (tDCS) devices aimed at optimizing brain performance on a variety of cognitive tasks, and applications based on transcranial magnetic stimulation (TMS), a magnetic method used to briefly stimulate small regions of the brain for both diagnostic and therapeutic purposes, which has also evolved into portable devices. ‘In sum,’ they state,

if in the past decades neurotechnology has unlocked the human brain and made it readable under scientific lenses, the upcoming decades will see neurotechnology becoming pervasive and embedded in numerous aspects of our lives and increasingly effective in modulating the neural correlates of our psychology and behaviour.

The emergence of neuroimaging, neuromodulation of behaviours, and cognition-stimulating neurotechnologies therefore raises considerable challenges, as Ienca and Androno articulate them:

  • the use of pervasive neurotechnology for malicious ‘brain-hacking’ (or ‘brainjacking’–the unauthorized modification of emotions and cognition)
  • third party eavesdropping on the mind
  • illicit memory-engineering
  • technology-induced personality change
  • the neuromodulation of behaviours
  • illegitimate access to and use of brain data generated by consumer-grade brain-computer interface applications.

These concerns reflect the emergence of what some social scientific critics of the brain sciences have begun to term ‘neurogovernance’ or ‘neuropower.’ As Victoria Pitts-Taylor puts it in her recent book The Brain’s Body, neuroscience-based programs designed to mould and modulate behaviour through targeting the brain for modification represent strategies of ‘preemptive neurogovernance’ that are intended to promote the economic and political optimization of the population. She notes how neuroscience concepts like ‘brain plasticity’ have been taken up by developers of ‘cognitive exercises, brain-machine interfaces, drugs, supplements, electric stimulators, and brain mapping technologies,’ in order to ‘target the brain for modification and rewiring.’ These technical advances clearly amplify the possibilities of preemptive neurogovernance, and the shaping of society and the social order through the modification of the mental states, affects and thoughts of individuals. The plasticity of the brain has become the basis for technoscientific ambitions to monitor, control and transform processes of life for political and commercial purposes, Pitts Taylor argues. And Nikolas Rose and Joelle Abi-Rached, in their book Neuro, have argued that the plastic brain is now the focus for attempts to ‘govern the future’–as is especially the case with interventions into the developing brains and hence future lives of children.

As a consequence, Ienca and Andorno suggest that neurotechnologies raise significant challenges for human rights. In particular they highlight recent debates about the right to  ‘cognitive liberty,’ or the right to alter one’s mental states with the help of neurotools, and the associated right to refuse to do so. Ultimately, cognitive liberty is a conceptual update of the right to ‘freedom of thought’ that takes into account the power available to states and companies to use neurotechnology coercively to manipulate the embrained mental states of citizens. They also add the right to ‘mental privacy,’ defined as a ‘neuro-specific privacy right which protects private or sensitive information in a person’s mind from unauthorized collection, storage, use or even deletion in digital form or otherwise.’  Cognitive liberty and mental privacy, in other words, constitute new rights to take control of one’s own mental life in the face of creeping techniques of neurogovernance in spheres of life including social media, government, consumption, and education.

Educational neuropower

The application of neurotechnology to education that we are just beginning to detect needs to be undertaken in ways which are sensitive to issues of neurogovernance, cognitive liberty and mental privacy. As parts of an educational neurofuture in-the-making, optimistic aspirations towards neuroenhancement and cognitive modification of ‘flawed brains’ through neurotechnologically enhanced education need to be countered not just with technical and scientific scepticism. Greater awareness of the political, military and commercial interests involved in new and developing neurotechnology markets and interventions are required, as well as theoretically engaged studies of the sociotechnical processes involved in producing neurotechnologies and of their uptake and effects in education. Deeply social questions also need to be asked about the use of brain data to exercise neuropower over young people’s mental states, and about how to safeguard their cognitive liberty and mental privacy amid persuasive and coercive promises about neuroenhancement in the direction of personal cognitive improvement.

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Imaginaries and materialities of education data science

Future educationImage: The .edu Ocunet, by Tim Beckhardt

Ben Williamson

This is a talk I presented at the Nordic Educational Research Association conference at Aalborg University, Copenhagen, on 23 March 2017.

Education is currently being reimagined for the future. In 2016, the online educational technology  magazine Bright featured a series of artistic visions of the future of education. One of them, by the artist Tim Beckhardt, imagined a vast new ‘Ocunet’ system.

The Ocunet is imagined as a decentralized educational virtual-reality streaming network using state­-of-­the-­art Panoptic headsets to deliver a universal knowledge experience. The infrastructure of public education has been repurposed as housing for the Ocunet’s vast server network. Teachers have been relieved of the stress of child-behavior management, and instead focus their skills on managing the Ocunet—editing our vast database to keep our students fully immersed in the latest curriculum—while principals process incoming student data at all times.

The Ocunet is an artistic and imaginative vision of the future of education. I use it as an example to start here because it illustrates a current fascination with reimagining education. The future it envisages is one where education has been thoroughly digitized and datafied—the educational experience has been completely embedded in digital technology systems, and every aspect of student performance is captured and processed as digital data.

This may all sound like speculative educational science fiction. But some similar imaginative visions of the future of education are now actually catalysing real-world technical innovations, which have the potential to change education in quite radical ways.

In this talk, I want to show you how education is being imagined by advocates of a field of research and development becoming known as ‘education data science.’ And I’ll explore how the social and technical future of education it imagines—one that is digitized and datafied much like the Ocunet—is also being materialized through the design of digital data-processing programs.

The social consequences for the field of education in general are significant:

  • Education data science is beginning to impact on how schools are imagined and managed.
  • It’s influencing how learning is thought about, both cognitively and emotionally, and introducing new vocabularies for talking about learning processes.
  • Its technologies and methods, many developed in the commercial sector, are being used in educational research and to produce new knowledge about education.
  • And education data science is also seeking to influence policy, by making educational big data seem an authoritative source for accelerated evidence collection.

Big data imaginaries and algorithmic governance

Just to set the scene here, education is not the only sector of society where big data and data science are being imagined as new ways of building the future. Big data are at the centre of future visions of social media, business, shopping, government, and much more. Gernot Rieder and Judith Simon have characterized a ‘big data imaginary’ as an attempt to apply ‘mechanized objectivity to the colonization of the future’:

  • Extending the reach of automation, from data collection to storage, curation, analysis, and decision-making processes
  • Capturing massive amounts of data and focusing on correlations rather than causes, thus reducing the need for theory, models, and human expertise
  • Expanding the realm of what can be measured, in order to trace and gauge movements, actions, and behaviours in ways that were previously unimaginable
  • Aspiring to calculate what is yet to come, using smart, fast, and cheap predictive techniques to support decision making and optimize resource allocation

And here the figure of the computer algorithm is especially significant. While in computer science terms algorithms are simply step-by-step processes for getting a computer program to do something, when these algorithms start to intervene in everyday life and the social world they can be understood as part of a process of governing—or ‘algorithmic governance.’

By governing here we are working with ideas broadly inspired by Michel Foucault. This is the argument that every society is organized and managed by interconnected systems of thinking, institutions, techniques and activities that are undertaken to control, shape and regulate human conduct and action—captured in phrases such as ‘conduct of conduct’ or ‘acting upon action.’

Because the focus of much big data analysis—and especially in education—is on measuring and predicting human activity (that most data are people), then we might say we are now living under conditions of algorithmic governance where algorithms play a role in directing or shaping human acts. Antoinette Rouvroy and Thomas Berns have conceptualized algorithmic governance as ‘the automated collection, aggregation and analysis of big data, using algorithms to model, anticipate and pre-emptively affect and govern possible behaviours.’ They claim it consists of three major techniques:

  • Digital behaviourism: behavioural observation stripped of context and reduced to data
  • Automated knowledge production: data mining and algorithmic processing to identify correlations with minimal human intervention
  • Action on behaviours: application of automated knowledge to profile individuals, infer probabilistic predictions, and then anticipate or even pre-empt possible behaviours

For my purposes, what I’m trying to suggest here is that new ways of imagining education through big data appear to mean that such practices of algorithmic governance could emerge, with various actions of schools, teachers and students all subjected to data-based forms of surveillance acted upon via computer systems.

Schools, teachers and students alike would become the objects of surveillant observation and transformation into data; their behaviours would be recorded as knowledge generated automatically from analysing those data; and those known behaviours could then become the target for invention and modification.

Importantly too, imaginaries don’t always remain imaginary. Sheila Jasanoff has described ‘sociotechnical imaginaries’ as models of the social and technical future that might be realized and materialized through technical invention.Imaginaries can originate in the visions of single individuals or small groups, she argues, but gather momentum through exercises of power to enter into the material conditions and practices of social life. So in this sense, sociotechnical imaginaries can be understood as catalysts for the material conditions in which we may live and learn.

The birth of education data science

One of the key things I want to stress here is that the field of education data science is imagining and seeking to materialize a ‘big data infrastructure’ for automated, algorithmic and anticipatory knowledge production, practical intervention and policy influence in education. By ‘infrastructure’ here I’m referring to the interlocking systems of people, skills, knowledge and expertise along with technologies, processes, methods and techniques required to perform big data analysis. It is such a big data infrastructure that education data science is seeking to build.

Now, education data science has, of course, to have come from somewhere. There is a history to its future gaze. We could go back well over a century, to the nineteenth century Great Expositions where national education departments exhibited great displays of educational performance data. And we could certainly say that education data science has evolved from the emphasis on large-scale educational data and comparison made possible by international testing in recent years. Organizations like the OECD and Pearson have made a huge industry out of global performance data, and reframed education as a measurable matter of balancing efficient inputs with effective outputs.

But these large-scale data are different from the big data that are the focus for education data science. Educational big data can be generated continuously within the pedagogic routines of a course or the classroom, rather than through national censuses or tests, and are understood to lead to insights into learning processes that may be generated in ‘real-time.’

In terms of its current emphasis on big data, the social origins of education data science actually lie in academic research and development going back a decade or so, particularly at sites like Stanford University. It’s actually from one of Stanford’s reports that I take the term ‘big data infrastructure for education.’

The technical origins of such an infrastructure lie in advances in educational data mining and learning analytics. Educational data mining can be understood as the use of algorithmic techniques to find patterns and generate insights from existing large datasets. Learning analytics, on the other hand, makes the data analysis process into a more ‘real-time’ event, where the data is automatically processed to generate insights and feedback synchronously with whatever learning task is being performed. Some learning analytics applications are even described as ‘adaptive learning platforms’ because they automatically adapt—or ‘personalize’—in accordance with calculations about students’ past and predicted future progress.

What’s really significant is how education data science has escaped the academic lab and travelled to the commercial sector. So, for example, Pearson, the world’s largest ‘edu-business,’ set up its own Center for Digital Data, Analytics and Adaptive Learning a few years ago to focus on big data analysis and product development. Other technology companies have jumped into the field. Facebook’s Mark Zuckerberg has begun dedicating huge funds to the task of ‘personalizing learning’ through technology. IBM has begun to promote its Watson supercomputing technologies to the same purposes.

And education data science approaches are also being popularized through various publications. Learning with Big Data by Viktor Mayer-Schonberger and Kenneth Cukier, for example, makes a case for ‘datafying the learning process’ in three overlapping ways:

  • Feedback: applications that can ‘learn’ from use and ‘talk back’ to the student and teacher
  • Personalization: adaptive-learning software where materials change and adapt as data is collected, analysed and transformed into feedback in real-time; and the generation of algorithmically personalized ‘playlists’
  • Probabilistic prediction: predictive learning analytics to improve how we teach and optimize student learning

The book reimagines school as a ‘data platform,’ the ‘cornerstone of a big-data ecosystem,’ in which ‘educational materials will be algorithmically customized’ and ‘constantly improved.’

This text is perhaps a paradigmatic statement of the imaginary and ambitions of education data science, with its emphasis on feedback, systems that can ‘learn,’ ‘personalization’ through ‘adaptive’ software, predictive ‘optimization,’ and the appeal to the power of algorithms to make measurable sense of the mess of education.

Smarter, semi-automated startup schools

The imaginary of education data science is now taking material form through a range of innovations in real settings. A significant materialization of education data science is in new data-driven schools, or what I call smarter, semi-automated startup schools.

Max Ventilla is perhaps the most prominent architect of data-driven startup schools. Max’s first job was at the World Bank, before he became a successful technology entrepreneur in Silicon Valley. He eventually moved to Google, where he became head of ‘personalization’ and launched the Google+ platform. But in 2013, Max left Google to set up AltSchool. Originally established as a fee-paying chain of ‘lab schools’ in San Francisco, it now has schools dotted around Silicon Valley and across to New York. Most of its startup costs were funded by venture capital firms, with Mark Zuckerberg from Facebook investing $100million in 2015.

Notably, only about half of AltSchool’s staff are teachers. It also employs software engineers and business staff, many recruited from Google, Uber and other successful tech companies. In fact, AltSchool is not just a private school chain, but describes itself as a ‘full-stack education company’ that provides day-to-day schooling while also engaging in serious software engineering and data analytics. The ‘full-stack’ model is much the same as Uber in the data analytics taxi business, or Airbnb in hospitality.

The two major products of AltSchool are called Progression and Playlist. In combination, Max Ventilla calls these ‘a new operating system for education.’ Progression is a data analytics ‘teacher tool’ for tracking and monitoring student progress, academically, socially and emotionally. It’s basically a ‘data dashboard’ for teachers to visualize individual student performance information. The ‘student tool’ Playlist then provides a ‘customized to-do list’ for learners, and is used for managing and documenting work completed. So, while Progression acts as the ‘learning analytics’ platform to help teachers track patterns of learning, Playlist is the ‘adaptive learning platform’ that ‘personalizes’ what happens next in the classroom for each individual student.

Recently, AltSchool began sharing its ‘operating system’ with other partner schools, and has clearly stated ambitions to move from being a startup to scaling-up across the US and beyond. It also has ambitious technical plans.

Looking forward, AltSchool’s future ambitions include fitting cameras that run constantly in the classroom, capturing each child’s every facial expression, fidget, and social interaction, as well as documenting the objects that every student touches throughout the day; microphones to record every word that each person utters; and wearable devices to track children’s movements and moods through skin sensors. This is so its in-house data scientists can then search for patterns in each student’s engagement level, moods, use of classroom resources, social habits, language and vocabulary use, attention span, academic performance, and more.

The AltSchool model is illustrative of how the imaginary of education data science is being materialized in new startup schools. Others include:

  • Summit Schools, which have received substantial Facebook backing, including the production of a personalized learning platform allegedly now being used by over 20,000 students across the US
  • The Primary School, set up by Mark Zuckerberg’s wife Priscilla Chan
  • The Khan Lab School founded by Salman Khan of the online Khan Academy.

All of these schools are basically experiments in how to imagine and manage a school by using continuous big data collection and analysis.

So, as AltSchool was described in a recent piece in the Financial Times, while ‘parents pay fees, hoping their kids will get a better education as guinea pigs, venture capitalists fund the R&D, hoping for financial returns from the technologies it develops.’

And these smarter, semi-automated startup schools are ambitiously seeking to expand the realm of what is measurable, not just test scores but also student movements, speech, emotions, and other indicators of learning.

Optimizing emotions

As indicated by AltSchool, education data science is seeking new ways to know, understand and improve both the cognitive and the social-emotional aspects of learning processes.

Roy Pea is one of the leading academic voices in education data science. Formerly the founding director of the Learning Analytics Lab at Stanford University, Pea has described techniques for measuring the ‘emotional state’ of learners. These include collecting ‘proximal indicators’ that relate to ‘non-cognitive factors’ in learning, such as academic persistence and perseverance, self-regulation, and engagement or motivation, all of which are seen to be improvable with the help of data analytics feedback.

Now, academic education data scientists and those who work in places like AltSchool are not the only people interested in data scientific ways of knowing and improving students’ social and emotional learning. The OECD has established a ‘Skills for Social Progress’ project to focus on ‘the power of social and emotional skills.’ It assumes that social and emotional skills can be measured meaningfully, and its ambition is to generate evidence about children’s emotional lives for ‘policy-makers, school administrators, practitioners and parents to help children achieve their full potential, improve their life prospects and contribute to societal progress.’

The World Economic Forum has its own New Vision for Education report which involves ‘fostering social and emotional learning through technology.’ Its vision is that social and emotional proficiency will equip students to succeed in a swiftly evolving digital economy, and that digital technologies could be used to build ‘character qualities’ and enable students to master important social and emotional skills. These are ‘valuable’ skills in quite narrowly economic terms.

Both the OECD and World Economic Forum are also seeking to make the language of social and emotional learning into a new global policy vocabulary—and there is certainly evidence of this in the UK and US already. The US Department of Education has been endorsing the measurement of non-cognitive learning for a few years, and the UK Department for Education has funded policy research in this area.

So how might education data science make measurable sense of students’ emotions? Well, according to education data scientists, it is possible to measure the emotional state of the student using webcams, facial vision technologies, speech analysis, and even wearable biometric devices.

Future Classroom_Josan GonzalesImage: Automated teachers & augmented reality classrooms by Josan Gonzalez

These are the kinds of ideas that have been taken up and endorsed very enthusiastically by the World Economic Forum, which strongly promotes the use of ‘affective computing’ techniques in its imaginary vision. Affective computing is the term for systems that can interpret, emulate and perhaps even influence human emotion. The WEF idea is that affective computing innovations will allow systems to recognize, interpret and simulate human emotions, using webcams, eye-tracking, databases of expressions and algorithms to capture, identify and analyse human emotions and reactions to external stimuli. ‘This technology holds great promise for developing social and emotional intelligence,’ it claims.

And it specifically identifies Affectiva as an example. Originating from R&D at MIT Media Lab, Affectiva has built what it claims to be the world’s largest emotion database, which it’s compiled by analysing the ‘micro-expressions’ of nearly 5 million faces. Affectiva uses psychological emotion scales and physiological facial metrics to measure seven categories of emotions, then utilizes algorithms trained on massive amounts of data to accurately analyse emotion from facial expressions. ‘In education,’ claims Affectiva, ‘emotion analytics can be an early indicator of student engagement, driving better learning outcomes.’

Such systems, then, would involve facial vision algorithms determining student engagement from facial expressions, and then adapting to respond to their mood. Similarly, the Silicon Valley magazine for educational technology, EdSurge, recently produced a promotional article for the role of ‘emotive computing in the classroom.’

‘Emotionally intelligent robots,’ its author claimed, ‘may actually be more useful than human [teachers] … as they are not clouded by emotion, instead using intelligent technology to detect hidden responses. … Emotionally intelligent computing systems can analyse sentiment and respond with appropriate expressions … to deliver highly-personalized content that motivates children.’

Both the World Economic Forum and EdSurge also promote a variety of wearable biometric devices to measure mood in the blood and the body of a seemingly ‘transparent child’:

  • Transdermal Optical Imaging, using cameras to measure facial blood flow information and determine student emotions where visual face cues are not obvious
  • Wearable social-emotional intelligence prosthetics which use a small camera and analyzes facial expressions and head movements to detect affects in children in real-time
  • Glove-like devices full of sensors to trace students’ arousal

This imaginary of affective or emotive computing in the classroom taps into the idea that automated, algorithmic systems are able to produce objective accounts of students’ emotional state. They can then personalize education by providing mood-optimized outputs which might actually nudge students towards more positive feelings.

In this last sense, affective computing is not just about making the emotions measurable, but about using automated systems to manipulate mood in the classroom, to make it more positive and preferable. Given that powerful organizations like the World Economic Forum and OECD are now seeking to make the language of social-emotional learning into the language of education policy, this appears to make it possible that politically preferred emotions could be engineered by the use of affective computing in education.

Cognizing systems

Not only are the non-cognitive aspects of learning being targeted by education data science however. One of its other targets is cognition itself. In the last couple of years, IBM has begun to promote its ‘cognitive computing’ systems for use in a variety of sectors—finance, business, healthcare but also education. These have been described as ‘cognitive technologies that can think like a human,’ based on neuroscientific insights into the human brain, technical developments in brain-inspired computing, and artificial ‘neural networks’ algorithms. So IBM is claiming that it can, to some extent, ‘emulate the human brain’s abilities for perception, action and cognition.’

To put it simply, cognitive systems are really advanced big data processing machines that employ machine learning processes modelled on those of embrained cognition, but then far exceed human capacities. These kind of super-advanced forms of real-time big data processing and machine learning are often called artificial intelligence these days.

The promise of IBM for education is to bring these brain-inspired technologies into the classroom, and to ‘bring education into the cognitive era.’ And it is seeking to do so through a partnership with Pearson announced late in 2016, which will embed ‘cognitive tutoring capabilities’ into Pearson’s digital courseware. Though this is only going to happen in limited college courses for now, both organizations have made it quite clear they see potential to take cognitive tutoring to scale across Pearson’s e-learning catalogue of courses.

Pearson itself has produced its own report on the possibilities of artificial intelligence in education, including the creation of ‘AI teaching assistants.’ Pearson claims to be ‘leveraging new insights in disciplines such as psychology and educational neuroscience to better understand the learning process, and build more accurate models that are better able to predict—and influence—a learner’s progress.’

Neuroscience is the important influence here. In recent years brain scientists have popularized the idea of ‘neuroplasticity,’ the idea that the brain modifies itself in response to experience and the environment. The brain, then, is in a lifelong state of transformation as synaptic pathways ‘wire together’.

But the idea of brain plasticity has taken on other meanings as it has entered into popular knowledge. According to a critical social scientific book by Victoria Pitts-Taylor, the idea of neuroplasticity resonates with ideas about flexibility, multitasking and self-alteration in late capitalism. And it also underpins interventions aimed as cognitive modification and enhancement, which target the brain for ‘re-wiring.’

Tapping into the popular understanding plasticity as the biological result of learning and experience, both IBM and Pearson view cognitive computing and artificial intelligence technologies as being based on the plastic brain. IBM’s own engineers have done a lot of R&D with ‘neuromorphic’ computing and ‘neurosynaptic chips,’ and have hired vast collaborative teams of neuroscientists, hardware engineers and algorithm designers to do so. But, they claim, cognitive and AI systems can also be potentially brain-boosting and cognition-enhancing, because they can interact with the plastic brain and ‘re-wire’ it.

The ambitions of IBM and Pearson to make classrooms into engines of cognitive enhancement are clearly put in a recent IBM white paper titled Computing, cognition and the future of knowing. The report’s author claims that:

  • Cognitive computing consists of ‘natural systems’ with ‘human qualities’
  • They learn and reason from their interactions with us and from their experiences with their environment
  • Cognitive systems are machines inspired by the human brain that will also inspire the human brain, increase our capacity for reason and rewire the ways we learn

So, Pearson and IBM are aiming to inhabit classrooms with artificial intelligences and cognitive systems that have been built to act like the brain and then act upon the brain to extend and magnify human cognition. Brain-inspired, but also brain-inspiring.

In some ways we shouldn’t see this as controversial. As computers get smarter, of course they might help us think differently, and act as cognitive extensions or add-ons. Just to anticipate one of our other keynotes at the conference, Katherine Hayles has written about how ‘cognitive systems’ are now becoming so embedded in our environments that we can say there is ‘cognition everywhere.’ We live and learn in extended cognitive networks. So, says Hayles, cognitive computing devices can employ learning processes that are modelled on those of embodied biological organisms, using their experiences to learn, achieve skills and interact with people. Therefore, when  cognitive devices penetrate into human systems, they can potentially modify human cognitive functioning and behaviours through manipulating and changing the plastic brain.

As IBM and Pearson push such systems into colleges and schools, maybe they will make students cognitively smarter by re-wiring their brains. But the question here is smarter how? My concern is that students may be treated as if they too can be reduced to ‘machine learning’ processes. The history of cognitive psychology for the past half-century has been dogged with criticisms that it treats cognition like the functions of a computer. The brain has been viewed as hardware; the mind as software; memory as data retrieval; cognition as information-processing.

With this new turn to brain-enhancing cognitive systems, maybe cognition is being viewed as big data processing; the brain as neuromorphic hardware; mind as neural network algorithms and so on. As IBM’s report indicates, ‘where code and data go, cognition can now follow.’

Owning the means of knowledge production

So we’ve seen how education data science is seeking to embed its systems into schools, and how its aims are to modify students’ non-cognitive learning and embrained cognition alike. I want now to raise a couple of issues that I think will be relevant and important for all researchers of education, not just the few of us looking at this big data business.

The first is the issue of knowledge production. As I showed at the start, big data systems are making knowledge production into a more automated process. That doesn’t mean there are no engineers and analysts involved—clearly education data science involves education data scientists. But what it does mean is that knowledge is now being produced about education through the kinds of advanced technical systems that only a very few specialist education data science researchers can access or use.

What’s more, as many of my examples have shown, education data science is primarily being practiced outside of academic education research. It’s being done inside of AltSchool and by Pearson and IBM. And these organizations have business plans, investors and bank accounts to look after. AltSchool’s ‘operating system for education,’ as we saw, is being turned into a commercial offering, while IBM and Pearson are seeking to make cognitive tutoring into marketable products for schools and colleges to buy.

These products are also proprietorial, wrapped up in intellectual property and patents law. So education data science is now producing knowledge about education through proprietorial systems designed, managed and marketed by commercial for-profit organizations. These companies have the means for knowledge production in data-driven educational research. We could say they ‘own’ educational big data, since companies that own the data and the tools to mine it—the data infrastructure—possess great power to understand and predict the world.

De-politicized real-time policy analytics

And finally, there are policy implications here too, with big data being positioned as an accelerated and efficient source of evidence about education. One of these implications is spelled out clearly by Pearson, in its artificial intelligence report. It states that:

  • AIEd will be able to provide analysis about teaching & learning at every level, whether a subject, class, college, district, or country
  • Evidence about country performance will be available from AIEd analysis, calling into question the need for international testing

So in this imaginary, AI is seen as a way of measuring school system performance via automated, real-time data mining of students rather than discrete testing at long temporal intervals.

This cuts out the need for either national or international testing. And since much national education policymaking has been decided on the basis of test-based systems in recent decades, then we can see how policy processes might be short-circuited or even circumvented altogether. When you have real-time data systems tracking, predicting and pre-empting students, then you don’t need cumbersome policy processes.

These technologies also appear de-politicized, because they generate knowledge about education from seemingly objective data, without the bias of the researcher or the policymaker. The decisions these technologies make are not based on politicized debates or ideologies, it is claimed anyway, but on algorithmic calculations.

A few years ago Dorothea Anagnostopoulos and colleagues edited an excellent book about the data infrastructure of test-based performance measurement in education. They made the key claim that test-based performance data was not just the product of governments but of a complex network of technology companies, technical experts, policies, computer systems, databases and software programs. They therefore argued that education is subject to a form of ‘informatic power.’

Informatic power, they argued, depends on the knowledge, use, production of, and control over measurement and computing technologies to produce performance measures that appear as transparent and accurate representations of the complex processes of teaching, learning, and schooling. And as they define who counts as ‘good’ teachers, students, and schools, these performance metrics shape how we practice, value and think about education.

If test-based data gives way to real-time big data, then perhaps we can say that informatic power is now mutating into algorithmic power. This new form of algorithmic power in education:

  • Relies on a big data infrastructure of real-time surveillance, predictive and prescriptive technologies
  • Depends on control over knowledge, expertise and technologies to monitor, measure, know and intervene in possible behaviours
  • Changing ways cognitive & non-cognitive aspects of learning may be understood & acted upon in policy & practice
  • Is concentrated in a limited number of well-resourced academic education data science labs, and in commercial settings where it is protected by IP, patents and proprietorial systems.

This form of algorithmic power, or algorithmic governance as we encountered it earlier, is not just about performance measurement, but about active performance management of possible behaviours–and opens up possibilities for more predictive and pre-emptive education policy.


Although many applications of big data in education may still be quite imaginary, the examples I’ve shown you today hopefully indicate something of the direction of travel. We’re not teaching and learning in the Ocunet just yet, but its imaginary of greater digitization and datafication is already being materialized.

As educators and researchers of education, we do urgently need to understand how a big data imaginary is animating new developments, and how this may be leading to new forms of algorithmic governance in education.

We need more studies of the sites where education data science is being developed and deployed, of the psychological and neuroscientific assumptions they rely on, of the power of education data science to define how education is known and understood, and of its emerging influence in educational policy.

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Psychological surveillance and psycho-informatics in the classroom

Ben Williamson


Psychology has long played a role in education by providing the expert knowledge and survey instruments required to monitor students’ attitudes, dispositions and habits of mind. Today, though, psychology is coming to play an increasingly prevalent role in schools through intertwined developments in digital technology and education policy. New technologies of psychological surveillance, affective computing, and big data-driven psycho-informatics are being developed to conduct new forms of mood-monitoring and psychological experimentation within the classroom, supported by policy agendas that emphasize the emotional aspects of schooling.

A significant emerging area of education policy development focuses on the measurement and management of students’ ‘social-emotional learning.’ A number of related terms and psychological concepts have been used to describe social-emotional learning, such as non-cognitive learning, non-academic learning, character development, personal qualities, self-control, resilience, growth mindsets, mindfulness and grit. In the US an influential report entitled ‘Promoting Grit, Tenacity and Perseverance’ was published in 2013 by the Department of Education, followed in 2015 by the Every Student Succeeds Act, a federal law requiring all states to collect information on at least one ‘non-cognitive’ or ‘non-academic’ aspect of learning.

Major international organizations have begun to promote the development and measurement of social and emotional skills, particularly through technological means. The World Economic Forum published its report ‘New Vision for Education: Fostering Social and Emotional Learning through Technology’ in 2016. Likewise, the international organization the Organization of Economic Cooperation and Development (OECD) has established its Education and Social Progress project to develop specific measurement instruments for social and emotional skills and ‘better understand how school-aged children’s skills progressively develop overtime through investments from families, schools and communities.’ The project is intended to generate evidence about children’s emotional lives ‘for policy-makers, school administrators, practitioners and parents to help children achieve their full potential, improve their life prospects and contribute to societal progress.’

As a result of such developments, it has been suggested (controversially) that instruments to measure social-emotional learning, including national standardized tests, could even become new accountability mechanisms, used to judge schools’ performance in how effectively they have developed students’ non-academic personal qualities.

In a new article just published in Learning, Media and Technology, I have argued that the emerging social-emotional policy agenda is being introduced into schools indirectly through popular classroom apps such as ClassDojo. ClassDojo is a free mobile app that allows teachers to award ‘positive points’ for individual children’s behaviour and participation in the classroom. According to its website, by 2016 it was being used by over 3 million teachers and 35 million children in 180 countries, primarily in elementary and primary schools, with the stated aim to create happier students and develop qualities such as character, perseverance and grit.

In that respect, ClassDojo reinforces emerging governmental ambitions around the measurement and modification of children’s social and emotional learning in schools. It enacts these ambitions by facilitating psychological surveillance, that is, by requiring teachers to monitor and collect student data that relate to new measurable psychological concepts such as character development, growth mindsets and  other personal qualities.

The developers of ClassDojo claim they have been inspired by prominent psychologists such as Angela Duckworth, director of The Character Lab which aims to ‘advance the science and practice of character development’; James Heckman, the behavioural economist best known for his work on the economic benefits of ‘investing in the early and equal development of human potential’; and Carol Dweck, the psychologist responsible for the theory of growth mindsets. ClassDojo has even entered into partnership with Carol Dweck, and was strongly supported in the US ‘grit’ report.

ClassDojo therefore demonstrates how emerging policies about promoting and measuring social-emotional learning are being indirectly ushered into schools via new technologies designed to capture information about the non-academic aspects of learning, as defined by contemporary psychological expertise. It represents the introduction of ‘psycho-policies’ into schools. In a study of  governmental adoptions of psychology and behavioural economics in other aspects of public policy, Lynne Friedli and Robert Stearn have documented the emergence of state strategies of ‘psycho-compulsion, defined as the imposition of psychological explanations … together with mandatory activities intended to modify beliefs, attitude, disposition or personality.’

In this sense, ClassDojo exemplifies the rise of behavioural psycho-policies in schools that focus on both the surveillance of psychological characteristics and on the design of psycho-compulsion interventions intended to modify behaviours and emotions to meet specific measurable goals, particularly through the imposition of positive emotions and behavioural qualities.

Affective computing
But ClassDojo is just one early sign of much more intensive psychological surveillance in schools that will be enabled by the development of ‘mood-monitoring’ apps and even sophisticated forms of ‘affective computing.’

The World Economic Forum report on using technologies to foster social-emotional skills is indicative of future directions. One of the devices it promotes is the ‘Empathy watch,’ a wearable ‘engagement pedometer’ that can be used to measure students’ affective responses to learning situations. ‘The Embrace watch,’ the report claims, ‘is a wearable device that tracks physiological stress and activity. It can be programmed to vibrate when stress reaches a specific level, giving someone time to switch to a more positive response before stress gets out of control.  Combining the functionality of the Embrace watch with coaching from parents and teachers may further enhance opportunities to build a child’s social and emotional intelligence.’

The WEF report also advocates the use of wearable biometric sensor devices to track physical responses to learning situations, such as fluctuations in stress and emotion, and to ‘provide a minute-by-minute record  of someone’s emotional state, potentially helping to build self-awareness and even empathy, both of which are critical components of social and emotional skills.’

Even more recently, the educational technology site EdSurge has published a piece on ‘emotive computing’ in the classroom. The claims made in the piece are that emotive computing involves teaching computer-based robots ‘to recognize human emotions, based on signals, and then react appropriately based on an evaluation of how the person is feeling. Robots may actually be more useful than humans in this role, as they are not clouded by emotion, instead using intelligent technology to detect hidden responses.’

Some of the technologies profiled include facial recognition systems that use cameras to capture student responses, algorithms to identify their attention levels, and by measuring smiles, frowns and audio to classify student engagement. The article describes new psychological studies that have identified more than 5,000 facial movements to help identify human emotions. These findings are now powering a range of new technical innovations, ‘each using a combination of psychology and data-mining to detect micro expressions and classify human reactions.’

In addition, the EdSurge article profiles a number of innovations in ‘affective computing’ that are being applied to education. These include:

  • Transdermal Optical Imaging, with a camera that is able to measure facial blood flow information and determine student emotions where visual cues are not obvious
  • Electroencephalogram (EEG) electrical brain activity tests to measure students’ emotional arousal, task performance and provide computer mediation to individuals
  • Wearable affective technology such as a social-emotional intelligence prosthetic to detect human affects in children in real-time, which uses a small camera and analyzes facial expressions and head movements to infer the cognitive-affective state of the child
  • A glove-like device that maps students’ physiological arousal and measures the wearer’s skin conductivity, to deduce how excited a person it
  • Emotionally intelligent computing systems that can analyze sentiment and respond with appropriate expressions, enabling educators to deliver highly-personalized content that motivates children

As noted in the article, many of these innovations in emotive or affective computing originate in academic R&D settings, though commercial companies are taking them increasingly seriously and seeking to develop new tools to promote in the education technology market.

Real-time emotional feedback
All of these developments are part of ongoing attempts to make ‘real-time mood-monitoring’ and ‘real-time emotional feedback’ devices into key technologies for knowing, measuring, representing and governing human emotions in contemporary societies, as the sociologist William Davies has argued in a brilliant new article. Davies argues that positive emotions have attained a particularly privileged position in recent years, making the science of happiness, well-being indicators and ‘mood-tracking’ critical to contemporary forms of management and public policy.

‘The spread of “smart,” mobile, wearable technologies,’ Davies argues, ‘potentially allows humans to dwell in a purely “real-time” cognitive state (feeling, experiencing, responding and liking) and allowing machines to perform acts of judgment, evaluation and decision-making, at least as an ideal.’ Such forms of ‘affective capture,’ he suggests, represent new ways of ‘valuing’ the emotions, where the emotions become the object of assessment and judgment, and from there the targeted object of modification. Real-time mood-tracking devices are intended ‘to achieve a form of emotional augmentation,’ to ‘transform it’ and ‘render that emotion preferable in some way (be it more positive, more acceptable, simpler etc.), turning it into a different emotion.’

As Davies notes, psychological scales and categories pertaining to the measurement of emotions, such as the Positive and Negative Affect Schedule (PANAS), are integral to how many mood-monitoring devices operate. When wearable mood-monitoring devices are used for political purposes, such as in education and other areas of public policy, psychological scales can be used to define politically preferable forms of emotional conduct, and to impose interventions if individuals’ emotions are deemed to be at a negative deficit. In the process, students’ emotions would be changed to become more preferable, positive and acceptable.

Psycho-informatics in school
The forms of affective capture, mood-tracking and emotional augmentation and modification via psychological categories documented by Davies are indicative of future directions in the digitization and datafication of social-emotional learning. As both the WEF and EdSurge documents show, the alleged potential of new forms of affective computing in education is to data mine students according to psychological signals detected from the skin, face and brain. The capture and assessment of affective data can then be used to inform interventions that are ideally intended to impose socially desirable forms of positive affect on students, as defined by psychological expertise and legitimized by policy documents from both national governments and international policy influencers such as the OECD and WEF. In this way, affective computing does not just data-mine the emotions from body signals via psychological categories, but ideally attaches new emotional ways of being and conducting oneself to the body of the student.

The combination of real-time data mining with psychological experimentation is now even inspiring a new psychological subdiscipline of ‘psycho-informatics,’ which has been presented as an epochal shift in the science of psychological measurement. Psycho-informatics, it has been claimed, is ‘about to induce the single biggest methodological shift since the beginning of psychology or psychiatry. … Indeed, transferring techniques from computer science to psychiatry and psychology is about to establish Psycho-Informatics, an entire research direction of its own.’ Based on ‘the vision of a transparent human,’  psychological experimentation in psycho-informatics makes use of wearable sensors that can track movements and smartphones to trace online activities, and then deploys data mining and machine learning in order to detect, characterize and classify behavioural patterns and trends as they change over time.

The term psycho-informatics accurately captures current ambitions to apply psychology, data-mining and affective computing to the measurement and assessment of students’ social-emotional learning. It could become a key technique of government, allowing students’ emotions to be data-mined and assessed in real-time for the purposes of continuous, automated school performance measurement.

ClassDojo is prototypical of how wearable mood monitoring devices and psychological surveillance apps inspired by psycho-informatics might roll out to schools. New psycho-informatic techniques are being designed to enact and enforce new social-emotional learning policies and practices of affective measurement that value certain politically preferred forms of emotional conduct in classrooms. Psycho-informatics could become a key technique of psycho-compulsion by which schools can promote the student behaviours according to which they may in future be measured and governed. Even more technically advanced forms of psycho-informatic mood-tracking and affective computing devices driven by psychological insights into the emotional aspects of learning—and how to detect affect from the body—could become increasingly in-demand as social-emotional learning policy makes positive affect and psychological compulsion into key policy priorities, and even potentially into accountability mechanisms by which schools may be measured, assessed and judged.

Image credit: Scott Brown
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Algorithms in the news–why digital media literacy matters

Ben Williamson

Much of our work on Code Acts in Education over the past few years has focused on the work that algorithms do (and what they are made to do and by who) in relation to learning, policy and practice. But the work of algorithms extends far beyond education of course. The sociologist David Beer–a notable scholar of the ‘social power of algorithms’–has pointed out that there has been a startling increase in the amount of media coverage on algorithms over the last year. I thought it might be interesting to see whether different newspapers take distinctive editorial perspectives on the calculative devices that now play such a significant role in our lives. Below I have captured some results of simple Google searches using the ‘inurl:’ search feature to search the content of the websites of some of the UK’s best-known newspapers, just really as a test to see if any distinctive obvious patterns emerge. And I’m fully aware that Google results are highly contingent on the searcher and the date, time and location of the search. I even did a few of these searches more than once and got different results–which says a lot about how information is curated algorithmically, and why new digital media literacy approaches to news consumption and information access are going to be crucial in coming years.

The Guardian
Of all the newspapers I’ve searched, The Guardian returns the most results. Although Google reports over 10,000 search results, only 70 entries are actually displayed.


Notably, of those results the top return is an article by Cathy O’Neill, author of the recent book Weapons of Math Destruction and a fierce critic of how algorithm-driven big data is affecting everyday life. A review of that book is the third result. Other results seem to indicate a largely critical take on algorithms from The Guardian, with coverage on calls for greater scrutiny of algorithms and their role in spreading false information. The Guardian certainly appears to take the most critical editorial line, with an emphasis on the connections between algorithms and politics. If we wanted to get categorical, perhaps we could say The Guardian‘s editorial line is to treat the algorithm as a governor.

The Telegraph
Switching to The Telegraph, we can see quite a different set of results.


The top result here is about the use of algorithms in cosmetics production, followed by a story about inserting algorithmic techniques into marketing strategy. Some of the other pieces focus on lie-detection algorithms, ‘anti-elitism’ school selection algorithms, and a quantum computer that can solve algorithms. The search apparently returned over 5000 results, though only 56 were displayed. Just from these results, it looks as though The Telegraph treats the algorithm as a useful scientist whose expertise is helping society.

The Sun
The Sun returned far fewer results, at around 500 (of which 50 displayed), though the vast majority of these were associated with its Striker cartoon strip.


However, scanning through the first few pages of the results, The Sun has covered Angela Merkel’s critical comments about algorithms, and the use of Instagram data to identify indicators of depression among users, and algorithmic surveillance techniques used by the CIA. Of Google results for algorithms in The Sun, only 4 news stories appeared. In short, The Sun is largely disinterested in algorithms in terms of newsworthiness.

The Mirror
Like The Sun, The Mirror has very limited coverage of algorithms. Of 500 results, only 63 displayed but many of these were not actually content from The Mirror site. Still, it had more news about algorithms than The Sun.


The Mirror seems at first glance to focus on algorithms as scientifically reliable techniques. One of the results returned refers to any reader interested in the maths of algorithms as a ‘brain-box’–so perhaps we could say the editorial line of The Mirror is to treat algorithms in terms of brainy expertise.

The Daily Mail
Finally, for now anyway, The Daily Mail. Over 57,000 search results, of which 66 displayed.


First look suggests that, other than in its piece about algorithmic trading, generally favourable coverage of algorithms, such as in crowdsourcing maps for natural disasters, stopping suicide, and anticipating terrorism. Algorithms as problem-solvers might be one way of categorizing its editorial line.

However, when I repeated the same search about an hour later, the top results were rather different.


Now I could learn that algorithms are making us small-minded, that AI can predict the future, and that algorithms can detect prejudice from body language. The Daily Mail is certainly not disinterested in algorithms–the result returns are pretty high compared to the tabloids, and the Mail does frequently re-post scientific content from sources like The Conversation–but by no means does it adopt the kind of critical line found in The Guardian.

Critical digital media literacy
Certainly this quick scan of the newspaper coverage on algorithms indicates that a deeper study would be worthwhile. It perhaps also gives us some clues about diverse perspectives in relation to algorithms from different parts of the news media landscape. Algorithms have been a pretty big news event in relation to Brexit and the US elections–but this social and political power of algorithms doesn’t appear very well covered except by The Guardian. Contrast that with the New York Times:


Here, there is much more emphasis on making algorithms accountable, the involvement of algorithms in fake news and clickbait. That The Guardian and the NY Times are on to these kinds of stories is maybe not surprising given their political leanings and target audience. However, if we genuinely are concerned that algorithms are involved in political life by filtering and curating how we access information, then it’s perhaps concerning that these issues are much less well covered in papers from alternative political perspectives. Even what we know about filter bubbles and algorithmic curation is itself filtered and curated. The Daily Mail has covered this issue, but it didn’t show up in the Google results until I conducted a second search. Why? The mutability of theses search results also indicates that any careful study along these lines would have to pay close attention to methods in order to achieve  stable results.

From an educational perspective, the diverse ways in which algorithms are presented in the news is interesting because it suggests very different ways in which people might learn about algorithms in their everyday access to news. Many educators have long been committed to forms of digital and media literacy (part of our problem today is that the alt-right have colonized critical media literacy approaches to mainstream media). Developing forms of digital media literacy that attend to the role and power of algorithms in political and cultural life now appears to be real priority that will require dedicated attention in 2017.

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Fast policy networks in the construction of the computing curriculum

Ben Williamson


Computing has replaced ICT in the English National Curriculum, bringing an emphasis on Computer Science, learning to code, and computational thinking into schools. For the last couple of months,  Bethan Mitchell and I have been conducting a small-scale research project to detail how government policy on the computing curriculum was supported by a messy network of non-governmental organizations, actors and material objects, which is now also supporting its subsequent enactment. This has built on an earlier documentary study, published in Critical Policy Studies, which mapped parts of the policy network behind the curriculum and its public statements. In our new work, we’ve been conducting interviews with key people that occupied the network and sought to  get insiders’ perspectives on the development and enactment of computing curriculum policy. In this post I outline a few observations emerging from the interviews, which we will be analysing more thoroughly in the new year.

Speeding up policy
Perhaps one of the most notable things about the introduction of computing in education policy is the speed with which it became part of the official prescribed curriculum. A fairly niche concern in 2010 among a diverse range of organizations, but with little governmental support, computing had by 2013 been translated into new programmes of study for schools that were published on the site. In our analysis, then, we are hoping to understand the policy process around the computing curriculum both as the product of a distributed cross-sector ‘policy network’ and an accelerated ‘fast policy’ event. Here we’re drawing on Jamie Peck and Nik Theodore’s conceptualization of both the spatial distribution and temporal speed-up of policymaking in Fast Policy:

The modern policymaking process may still be focused on centers of political authority, but … sources, channels, and sites of policy advice encompass sprawling networks of human and nonhuman actors/actants, including consultants, web sites, practitioner communities, norm-­setting models, conferences, guru per­formances, evaluation scientists, think tanks, blogs, global policy institutes, and best-­practice peddlers…

They further articulate how fast policy has been marked by ‘shortening of policy development cycles, fast-tracking decision-making and rapid programme roll-out,’ all features that can be seen in the development and diffusion of computing curriculum policy. Not only has the timescale of its development, implementation and enactment been highly compressed, but has also involved advice and influence from across different sectors and positions.

Network nodes
The network of organizations that has combined to influence and enact the computing curriculum consists of a diverse range of public, private and third sector actors. Some of the first advocacy for computing in the curriculum came from very different perspectives. The campaigning organization Computing at School, for example, produced a white paper back in 2010. The innovation charity Nesta produced a report in 2011 about the needs of the videogames and visual effects industry that emphasized computing in school as a solution to a skills gap. The Royal Society followed in 2012 with a report intended to protect academic Computer Science.

2012 was a key year. The Secretary of State for Education at that time, Michael Gove, gave a key speech that highlighted government ambitions to replace ICT with computing. The Department for Education then formed a working group to design draft programmes of study for the new subject. The working group was led by the British Computing Society, the Royal Society of Engineering, and Computing at School, with membership that encompassed interests from industry, education and academia.

At around the same time, organizations to support children to learn how to programme computer code started emerging—such as Code Club, Raspberry Pi, and the Festival of Code event run by Young Rewired State. In the years since, a large number of coding initiatives have sprung up in support of the curriculum, including, most notably, the BBC’s flagship Make it Digital campaign and its distribution of a million ‘Micro-Bit’ programmable computers to children across the UK.

Beyond the UK, a more distributed network exists. These include the US Hour of Code initiative (a UK version now exists too) and the Computer Science for All campaign supported by President Barack Obama. Commercial support from global technology companies such as Google, Microsoft and Oracle has also helped solidify computing in schools. Oracle, for example, has spent hundreds of millions of US dollars supporting Computer Science for All, and in 2016 announced over a billion dollars of funding for computing education in European Union member states.

Policy actor positions
Within the cross-sector, interorganizational network that supports computing curriculum policy, key individuals have been able to take up different positions. From our interviews, we have begun to build up a rough typology of these positions:

  • Guru figureheads—influential individuals, often with industry background in major global tech companies, who use their position to make persuasive public statements and galvanize political and public support
  • Relationship brokers—actors who are able to build connections between seemingly diverse organizations, sectors, discourses and individual actors; who capture good ideas and propel them forwards through building and coordinating collaborations between others
  • Lobbyists—specific campaigners who advocate the interests of the groups they represent through the production of key campaigning messages, fixing meetings, organizing events, and generating public visibility
  • Practical experts—mostly former or present educators committed to the educational benefits of computing, they take a pragmatic view of the opportunities available to drive forward their agenda through work with other educators
  • Troublemakers—network insiders who feel their own interests are not being heard or acted upon, and publicly resist and critique the dominant network activities—sometimes risking being marginalized from key events and actions
  • Geek insiders—activist programmers and technology experts, usually affiliated to voluntary groups, who are seen by government officials  as trusted sources of technical know-how and inside-knowledge about technology development and its implications for education
  • Venture entrepreneurs—influential and wealthy venture capital actors from the technology and innovation sector, some enjoy a revolving-door relationship with government departments and senior politicians, and represent major global VC firms

Other positions in the network include those for volunteer programmers who teach young people how to code outside of school, and computing teachers who enact the computing curriculum itself through their pedagogies.

Many of these actors have collaborated on working groups, campaign alliances, lobbying associations, cross-sector collaborations, and the shared production of future visions and practical strategies.

Policy materiality
A policy network never merely consists of people and organizations, but a vast material tapestry of objects, technical hardware, software and texts. The computing curriculum network can operate in a distributed and accelerated way because it encompasses a sprawling network of nonhuman stuff. Websites for all the organizations involved in the network function as key sources of information, advocacy and advice. Most of these organizations are also accomplished users of social media, with Twitter accounts and Facebook pages used to attract followers and diffuse ideas, events and key messages.

Much of the original support for computing in schools became possible because of a growing mass of reports, white papers, manifestos, working papers and draft curriculum proposals. Some of the early documents produced by Computing at School and Nesta, for example, only caught public attention months or years after initial publication, as these organizations acted opportunistically to insert their expertise into emerging political openings. Newspaper and magazine coverage in both educational and mainstream media has helped propel these ideas into public visibility.

In terms of the practical enactment of computing curriculum policy, this has also been supported in very material ways, such as through the provision of printed curriculum guidance, the supply of online teacher training materials, and the easy availability of free coding software for use in schools. Physical computing devices such as the Raspberry Pi and the Micro-Bit instantiate the computing curriculum in hardware.

Policy speak
What the network says is as important as how it works. According to our interviewees, the computing curriculum has been relatively successful because it encompasses a range of quite diverse interests and agendas. The interests of disciplinary computer science can be accommodated alongside the more practical agenda of coding and programming usually associated with the field of software engineering. Some advocates of computing talk more of computational thinking and the capacity of young people to solve real-world problems by thinking like a computer, while others talk of the urgent need to develop digital citizenship and critical digital literacy to cope in a world of massive social and technical complexity.

Indeed, some of our interviewees talked of strategic ambiguity—evident even at specific high level meetings—where computer science, programming and notions of digital literacy were treated as one and the same thing. This is despite others’ protestations that over-emphasizing computer science risks turning computing into a high stakes scientific subject for academic high achievers, or that prioritizing coding risks treating computing as a talent pipeline for the commercial software development industry.

A fast policy infrastructure
There remains much analysis to be done of the specific interviews we have conducted, and of the webs of materials and technologies that have proliferated since the computing curriculum fully came into force in English schools. We also need to make better sense of the implications for computing of the wider current context of education policymaking. Notably, for example, Computer Science is now available as a GCSE qualification in the English Baccalaureate, making it a high stakes exam subject by which school performance might be measured. At the same time, however, mass academization means secondary schools are under no obligation to even teach computing at all. Primary schools, we are told, have probably done more with computing than secondary schools, which raises real concern about transition and progress. And funding and teacher shortages remain significant problems which, despite the extensive network activity roughed-out above, shows no sign of being resolved.

However, what we think we can see emerging is a kind of ad hoc architecture of relationships, practices and materials, or a fast policy infrastructure, which has orchestrated the ways in which the computing curriculum has been diffused and is continuing to influence the ways in which it is being enacted, extended to new sites, and expanded to encompass diverse interests and agendas. This infrastructure consists of diverse organizations from the public, private and third sectors; of individual actors and mutating relationships; of technologies, texts and web materials; and of discourses, good ideas and strategically-deployed ambiguities. As an unfolding policy event, the computing curriculum is typical of fast policy and policy network enactment among networks of cross-sector human and nonhuman networks of people and materials, and their translation into delicate but mutable affiliations and strategically combined interests that have been mobilized to achieve varied aims and goals.

Image credit: Les Pounder
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Social media and public pedagogies of political mis-education

Ben Williamson


Over the past few months the close knit relationship of education with software and data has become a defining feature of political life in democratic societies. In a year that has seen ‘post-truth‘ named as word of the year by Oxford Dictionaries, social media fueled by big data has been blamed for creating deep political polarization. At the same time, the organization of formal education has itself been accused of increasing inequalities and widening a gap in the worldviews between young people who leave education with high-status qualification and those who do not. What is the link?

The education gap
Both the UK’s Brexit referendum and the US election have raised significant questions about education. One question was about why, on average, people with fewer educational qualifications had tended to vote for the UK to leave the EU, or for Trump to take the presidency despite his lack of political experience, while those with more qualifications tended to vote the other way. A new ‘education gap’ has emerged as an apparent determinant of people’s political preference. This education gap has begun to raise concerns about divisions in democracy itself, as the political scientist David Runciman has argued:

The possibility that education has become a fundamental divide in democracy—with the educated on one side and the less educated on another—is an alarming prospect. It points to a deep alienation that cuts both ways. The less educated fear they are being governed by intellectual snobs who know nothing of their lives and experiences. The educated fear their fate may be decided by know-nothings who are ignorant of how the world really works.

Of course, plenty of wealthy educated people in the UK voted out of the EU, and voted for Trump in the US. But statistics from both votes did indicate significant population differences in terms of educational qualification, in relation to a range of other social factors, in determining voting patterns.

Significantly, the statistics from the EU referendum indicate that the vote for leaving the EU was concentrated in geographical areas already most affected by growing economic, cultural and social inequalities, as well as by physical pain and mental ill-health and rising mortality rates. The sociologists Mike Savage and Niall Cunningham have vividly articulated the consequences of growing inequalities for citizens’ political participation:

There is ample evidence that political dynamics are being increasingly driven by the dramatic spiraling of escalating inequalities. To put this another way, growing economic inequalities are spilling over into all aspects of social, cultural, and political life, and that there are powerful feedback loops between these different spheres which are generating highly worrying trends.

Education, of course, is itself highly unequally distributed in terms of how well children achieve in schools, in ways that reproduce all sorts of social, cultural and economic inequalities. The increasing separation of children from more or less affluent backgrounds, and according to geographical locales and social and cultural contexts, is part of the dramatic spiralling of inequalities observed by sociologists. The kind of political polarization that materialized during both Brexit and the US election is the result of the related dynamics of education, geography, economics, and cultural and social networks, and the feedback loops between them.

It would be naive to suggest that those people with fewer qualifications are somehow to blame for not being critically aware of how their perspectives were being sculpted by populist propaganda during these campaigns. Anxiety among highly educated elites about the consequences of a lack of political awareness are far from novel. Moreover, the challenge here is to reconcile the polarizing interests of both those who are highly educated and those who are less educated. As Savage and Cunningham concluded, ‘the way that the wealthy elite are increasingly culturally and socially cocooned, and the extent to which large numbers of disadvantaged groups are outside their purview is deeply worrying.’ In their view, a kind of educated ignorance is the problem.

In the EU referendum and the US presidential election alike, neither side appeared to have any deep awareness of the other or of the deep-seated social issues that led to such distinctive and divided patterns of voting, as David Runciman explained:

Social media now enhances these patterns. Friendship groups of like-minded individuals reinforce each other’s worldviews. Facebook’s news feed is designed to deliver information that users are more inclined to ‘like’. Much of the shock that followed the Brexit result in educated circles came from the fact that few people had been exposed to arguments that did not match their preferences. Education does not provide any protection against these social media effects. It reinforces them. … [T]he gap between the educated and the less educated is going to become more entrenched over time, because it … represents a gulf in mutual understanding.

This point raises the other question, which was couched much less explicitly in terms of education. This concerned the role of social media in filtering how people learned about the issues on which they were being invited to vote.

Personalized political learning
The issue of how social media has participated in filtering people’s exposure to diverse political perspectives has become one of the defining debates in the wake of Brexit and the US election. An article in the tech culture magazine Wired on the day of the US election even asked readers, uncharacteristically, to consider the ‘dark side of tech’:

Even as the internet has made it easier to spread information and knowledge, it’s made it just as easy to undermine the truth. On the internet, all ideas appear equal, even when they’re lies. … Social media exacerbates this problem, allowing people to fall easily into echo chambers that circulate their own versions of the truth. … Both Facebook and Twitter are now grappling with how to stem the spread of disinformation on their platforms, without becoming the sole arbiters of truth on the internet.

The involvement of social media in the spread of ‘post-truth politics’ points to how it is leading citizens into informational enclaves designed to feed them news and knowledge that has been filtered to match their interests, based on data analysis of their previous online habits, what they have ‘liked’ or watched, what news sources they prefer, who they follow and what social networks they belong to.

‘Platforms like Twitter and Facebook now provide a structure for our political lives,’ Phil Howard , a sociologist of information and international affairs, has argued. He claims that social algorithms allow ‘large volumes of fake news stories, false factoids, and absurd claims’ to be ‘passed over social media networks, often by Twitter’s highly automated accounts and Facebook’s algorithms.’

Since the US election, it has been revealed that Trump’s campaign team worked closely with Facebook data to generate audience lists and targeted social media campaigns. Added to this, other more politically-activist social media sites such as Breitbart and Infowars have actively disseminated right-wing political agendas, reaching audiences that count in the tens of millions, as  Alex Krasodomski-Jones has detailed. ‘Computational propaganda’ involving automated bots spreading sensationalist political memes across social media networks have further compounded the problematic polarization of news consumption. Facebook and Twitter now accelerate the spread of fake news or sensationalized political bias through mechanisms such as trending topics and moments, which are engineered to be personalized to users’ preferences.

Clearly there are important implications here for how young people access and evaluate information. Jamie Bartlett and Carl Miller of the think tank Demos wrote a report 5 years ago that highlighted a need to teach young people critical thinking and scepticism online to ‘allow them to better identify outright lies, scams, hoaxes, selective half-truths, and mistakes.’

But the debate is not just about how to protect young people from online trolling, propagandist bias and fake news. Just as with the debate about the education gap, it’s important to note that people from across the political spectrum, whether highly educated or not, are all increasingly ‘socially and culturally cocooned’ as Mike Savage and Niel Cunningham phrased it. Education and social media are both involved in producing these cocooning effects.

The sociologist of social media Tarleton Gillespie wrote a few years ago about how big data-driven social media creates not just ‘networked publics’ who cohere together online around shared tastes and preferences, but ‘calculated publics‘: algorithmically produced snapshots of the ‘public’ around us and what most concerns it. He argued that search engines, recommendation systems, algorithms on social networking sites, and ‘trend’ identification algorithms not only help us find information, but provide a means to know what there is to know and to participate in social and political discourse.

Algorithmic calculations are now at the very centre of how people are learning to take part in political and democratic life, by filtering, curating and shaping what information and news we consume based on calculations of what most concerns and engages us — the logic of social media personalization now applies to political life. In other words, we are now living in a period of personalized political learning, whereby our existing political preferences are being reinforced by the consumption of news and information via social media and our participation in calculated, networked publics, with the consequence that  alternative perspectives are being systematically curated out of our feeds and out of our minds.

So seriously is this problem being taken that, in the fallout from the US election, it has been reported that a team of ‘renegade’ Facebook employees has established itself to deal with fake news and misinformation, although Mark Zuckerberg has denied Facebook had anything to do with it. The web entrepreneur Tim O’Reilly has suggested it would be a mistake for Facebook to reinstate human editors — whose alleged political bias was itself the centre of a major controversy not so long ago — but to design more intelligent techniques for separating information from sensationalist misinformation:

The answer is not for Facebook to put journalists to work weeding out the good from the bad. It is to understand, in the same way that they’ve so successfully divined the features that lead to higher engagement, how to build algorithms that take into account ‘truth’ as well as popularity.

Expect the quest for truth-divining algorithms to become a dominant feature of technical development in the social media field over the next years. Google in Europe, for example, has already announced support for a startup company that is developing automated, real-time fact-checking software (called RoboCheck) for online news. The appeal of apparently objective, impartial and unbiased truth-seeking algorithms in post-truth times is obvious, though as recent work in digital sociology and geography has repeatedly shown, algorithms are always dependent on the choices and decisions of their designers and engineers. The ‘social power of algorithms‘ such as those of Facebook to intervene in political life may not easily be resolved by new algorithms.

Public pedagogies of political mis-education
The post-truth spread of misinformation twinned with the magnification of political and social polarization via social media platforms and algorithms is at the core of a new public pedagogy of political mis-education. Public pedagogy is a term used to refer to the lessons that are taught outside of formal educational institutions by popular culture, informal institutions and public spaces,  dominant cultural discourses, and public intellectualism and social activism. Big data and social media are fast becoming the most successful sources of public pedagogy in the everyday lives of millions around the world. They are educating people by sealing them off into filter bubbles and echo chambers, where access to information, culture, news, and intellectual and activist discourse is being curated algorithmically.

The filter bubbles or echo chambers that calculated publics inhabit when they spend time on the web are consequential because they appear to close off access to alternative perspectives, and potentially lead people to think that everyone thinks like they do, shares their political sentiments, their aspirations, their fears. This is further related to, reproduced and exacerbated by  social inequalities in education, economics and cultural access. Doing well in formal education or not now appears to be a determinant of which kinds of social networks and calculated publics you belong to. ‘The educational divide that is opening up in our politics is not really between knowledge and ignorance,’ David Runciman argues. ‘It is a clash between one worldview and another.’

In an age where highly educated people and less educated people are being sharply divided both by social media and by their experience of education alike, serious issues are raised for the future of education as a social institution itself and the part it plays in supporting democratic processes. Existing educational inequalities and the experience of being parts of calculated publics in social media networks are now in a dynamic feedback loop. The public pedagogies of social media are becoming mis-educational in their effects, polarizing public opinion along different axes but most especially between the highly educated and the less educated.

Forms of measurement using data have long been at the core of how governments know and manage populations, as the sociologist David Beer has demonstrated in his work on ‘metric power.’ Today, the measurement of people’s interests, preferences and sentiments via social media, and the use of that information to feed-back content that people will like and that matches their existing preferences, is leading to a form of calculating governance that is exacerbating divisive politics and eroding democratic cohesion. Via social media data, people are being educated and governed according to measurements that indicate their existing worldview, and then provided access to more of the same.

As Brexit and the US election indicate, increasingly people in the UK and US are being governed as two separate publics, with many of the less-educated incited to support political campaigns that the more-educated find alien and incomprehensible, and vice versa. The philosopher Bruno Latour has described them as ‘two bubbles of unrealism,’ one clinging to an imagined future of globalization and the other retreating to the imagined ‘old countries of the past,’ or ‘a utopia of the future confronting a utopia of the past’:

For now, the utopia of the past has won out. But there’s little reason to think that the situation would be much better and more sustainable had the utopia of the future triumphed instead. … If the horizon of ‘globalization’ can no longer attract the masses, it is because everyone now understands more or less clearly that there is no real, material world in the offing corresponding to that vision of a promised land. … Nor can we count any longer on returning to the old countries of the past.

Education has long reinforced these utopias of unrealism — we’ve been teaching and learning in ‘post-truth’ times for years. Contradictory policy demands over the last two decades have pointed simultaneously towards an education for the future of a high-skills, globalized knowledge economy (as reinforced by global policy actors like the OECD), and an education of the past which emphasizes traditional values, national legacy, social order and authority. Social media algorithms and architectures have further enabled these utopias of unrealism to embed themselves across the US and Europe.

The mis-education of democratic society by the public pedagogies of big data and social media is being enabled by algorithmic techniques that are designed to optimize and personalize people’s everyday experiences in digital environments. But in the name of personalization and optimization, the same techniques are leading to post-truth forms of political mis-education and democratic polarization.

Sociologists have begun asking hard questions about the capacity of their field to address the new problems surfaced by Brexit and Trump. The field of education needs to involve itself too in this new problem space, in order to probe how young people are measured and known through traces of their data from early age; how their tastes and preferences are  formed through the dynamics between imagined utopias and social media feedback loops; how these relate to entrenched patterns of educational and other social inequalities; and how their sense of their place and their futures in democratic societies is formed as they encounter the public pedagogies of big data and social media in their everyday lives. How, in short, should we approach education in post-truth times?

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