Edtech index investing

Ben Williamson

New financial instruments launched to support education technology during the pandemic are concentrating investment on the digital transformation of education. Photo by Kelly Sikkema on Unsplash

While schools were closed for much of the year, new market opportunities were opening up for education technology investors. The investment bank BMO Capital Markets predicted potential market growth early. ‘While we are uncomfortable citing “winners” in the coronavirus situation, some companies may be positioned better than others,’ it stated in March. ‘Specifically, those that specialize in online education could see increased interest should the situation worsen’, it suggested, such as K12 and Pearson. The situation did worsen, and with mass school and college closures, edtech became a hot sector for venture capital investment. By September, the number of edtech companies valued as unicorns—businesses worth a billion dollars or more—had risen to 20, primarily online learning and home tutoring platforms based in China or the US.

Two specific edtech investment events stand out from this period of heightened market activity in education. In July, the New York-based investment management organization Global X launched a new exchange traded fund (ETF) for the edtech sector, followed in September by the announcement of another edtech ETF by Rize, a London-based ETF issuer focused on ‘thematic’ investments in global ‘megatrends’.

ETFs are complicated financial instruments, but they have strong potential to shape the direction of edtech investment, to catalyse and grow edtech markets, and, via driving investment in specific companies, to drive the direction of further edtech development. Like other funding models, such as direct venture capital investment, philanthropic grants, and private equity in specific edtech companies, they may have a productive part to play in the future directions that education takes, particularly in the long period of post-Covid recovery. As a result, it’s important for edtech research and education policy analysis to get to grips with the emerging financial technologies and the new social actors that may shape the future of the sector. This post is a very preliminary attempt to identify some of the actors and devices involved in new forms of edtech index investing.

Index investing

Exchange traded funds can be thought of as ‘baskets’ of shares in a collection of companies. The value of shares in each company fluctuate with daily market movements, and the price of an ETF’s shares will change throughout the trading day as the shares are bought and sold on the market. For investors the return comes from gains from the basket overall, for the fund manager the return comes in the form of fees, and for companies with share holdings in the ETF the benefit comes from increased investment.

The significance of the launch of these two edtech-specific ETFs is that ETFs have become extraordinarily powerful models for investing due to their tax efficiency and liquidity (how quickly investments can be converted back to cash), though they have also been implicated in market flash crashes and instability (as a result of overinflating stock values). By 2017, over $4 trillion was invested in ETFs worldwide.

ETFs are part of a family of financial instruments known as index investing. In index investing, market indexes representing particular financial markets, or market subsectors, are used as benchmarks to gauge the movement and performance of market segments. An index is a mathematically weighted calculation of a market based on the prices of the underlying holdings. Investors then use indexes as a basis for portfolio or passive index investing, of which ETFs are a key instrument.

Although some ETFs are based on indices of massive markets such as Nasdaq, a very wider variety of sector-specific ETFs have also been created. Sector ETFs use the Global Industry Classification Standard as the primary financial industry standard for defining sector classifications. This has led to ETFs for sectors as diverse as social media, minerals mining, medical technology, and, now, educational technologies.

The Rize LERN ETF brochure presents a pitch for investors to fund the future of education.

By creating sector-specific edtech exchange traded funds, Global X and Rize have constructed new financial instruments to track the performance of the edtech market, and to catalyse investment in it. Investors in edtech are now able to invest ‘passively’ in shares in the whole ETF–assets which Global X and ETF then manage on investors’ behalf– rather than ‘actively’ purchasing more risky single-stock shares in individual companies.

This potentially makes Global X and Rize into incredibly powerful influences in the edtech sector overall. They have the financial and methodological expertise to generate edtech market indices—ultimately defining the benchmark for edtech market performance—and to compile the holdings in the fund. By creating indices, they act as gatekeepers defining which companies from the wider ‘universe’ of the edtech sector are eligible for inclusion in the ETF. They are shaping the edtech market. In a context where edtech has become increasingly influential in education, to a significant degree this also means that Global X and RIZE have positioned themselves to reshape education itself. As Rize puts it, the LERN ETF ‘provides investors with exposure to “EdTech” companies that are redefining how education is accessed, resourced and consumed around the world to deliver positive results for the individual and society’.

Global X and Rize are both clear that their edtech ETFs are intended to support companies in the business of educational transformation. Before we go into the particular transformative ideas they are seeking to fund, and the specific companies included in their ETFs, however, it is worth looking a little more into the composition and aims of these organizations.

Investment management

Global X was founded in 2008 as a specialist ETF provider. Though it offers core ETFs indexed to stock markets, its specialism is sector ETFs, particular thematic ETFs in ‘disruptive technologies’ and ‘people and demographics’. Its portfolio of ETFs in these categories include Robotics and AI, Internet of Things, Cloud Computing, Social Media, Genomics and Biotechnology, and Education. Compared to some of the other categories, the education ETF is a fairly small fund with a value of $5.4million (compared to $7.4bn value of its Robotics and AI ETF), which it launched on the Nasdaq in July 2020. It is based on an index called the Global Education Thematic Index produced by Indxx, a global financial services and index provider.

The all-male staff of Global X have previous experiences and roles in business analysis, investment banking, wealth and asset management, finance, entrepreneurship, and various qualifications from business schools and economics. In 2018 Global X was acquired by Mirae Asset Global Investments, a Seoul-based financial services company providing asset management, wealth management, investment banking, and life insurance.

The Global X education ETF factsheet

Rize is a much more recent entrant into the ETFs sector. Founded in 2019 in London, Rize is Europe’s first specialist thematic ETF issuer, with a product portfolio of specialized thematic ETFs focused on key ‘megatrends’. They include Cybersecurity and Data Privacy, Future of Food, Medical Cannabis and Life Sciences, and Education Technology and Digital Learning (LERN). The total assets in the LERN fund, launched on the London, Milan and Berlin stock exchanges in September 2020, stand at just under $1million.

Like Global X, its staff are experienced in asset management, investment management and portfolio management. Its all-male staff are all former Legal and General employees and responsible for the creation of the Canvas ETF platform at ETF Securities that L&G acquired for AUS$3.5bn in 2018. These asset managers have now brought their combined expertise in business, economics, law, mathematics, and computer science to bear on the financialization of edtech.

Importantly, however, these ETF companies are also part of complex webs of organizational relationships. Taking Rize as an example, its LERN ETF is a collaboration with Foxberry and HolonIQ. Foxberry is an independent index management company based in Canary Wharf. Its specialist contribution to the Rize ETF is to construct the benchmark index which the fund is designed to track. The other partner, HolonIQ is an international edtech market intelligence organization headquartered in Sydney, Australia. Listed as the ‘thematic expert’ on the ETF, HolonIQ utilized its extensive edtech market datasets to establish the index, which will be updated twice a year to reflect the performance of holdings in the fund.

The HolonIQ Global Learning Landscape 2021 taxonomy

HolonIQ’s involvement in the LERN fund is arresting because over the last few years it has become a high-profile edtech market intelligence organization. It produces weekly market updates and forecasts, detailed in spectacular data visualizations. During the Covid pandemic it estimated the total value of the edtech sector at $404bn by 2025. It also produced a Global Learning Landscape of hundreds of companies that it saw as transformative in the education sector. By partnering with Rize and Foxberry, HolonIQ has diversified its role from the cataloguing and forecasting of edtech markets to being an active catalyst of market growth. Its Global Learning Landscape does not just visualize a forecast future, but is the basis for the LERN ETF that will shape and guide financial investment in the future of edtech.

Investment imaginaries

The two edtech ETFs both focus on investing in companies that stand to play key roles in transforming education. Both ETFs are based on a powerful imaginary of the future of education as digitally enhanced by the involvement of commercial edtech companies, whose shares they are actively investing in.

Announcing the launch of its education ETF, Global X stated:

The Global X Education ETF (EDUT) seeks to invest in companies providing products and services that facilitate education, including online learning and publishing educational content, as well as those involved in early childhood education, higher education, and professional education.

Among the companies included for investment in the ETF are online learning and MOOC providers (which it termed ‘Education-as-a-Service’), digital publishing (Pearson), and providers of artificial intelligence in education services:

Artificial Intelligence (AI), for example, can leverage machine learning to understand students’ individual needs, then designing and adapting curriculums to meet them. Implementing AI could augment learning by ensuring that students strengthen their weakest areas. It also optimizes teaching by reducing teachers’ upfront workloads, sparing them time that they could allocate elsewhere. We can already see mass-implementation of such technology in China and are starting to see less sophisticated rollouts of it in the US. By 2025, global AI-EdTech expenditure is projected to reach $6B.

Its estimate of AI-edtech expenditure was itself based on HolonIQ market forecasts.

Likewise, the Rize ETF of which HolonIQ is a partner is centred on a particular vision of the future of education:

The Rize Education Tech and Digital Learning UCITS ETF (LERN) seeks to invest in companies that potentially stand to benefit from the increased adoption of digital and lifelong learning technologies such as personalisation and adaptive learning, video content, gamification and immersion technology that are changing the way people learn.

It continues,

digital learning technologies can help elevate the education sector into the 21st century. We must build an education system that is more inclusive, no longer confined to the classroom, and which is able to transform all learners into lifelong learners. At a time of unprecedented automation, reskilling and upskilling have never been more vital, and advanced technologies such as gamification, virtual and augmented reality, and personalised and adaptive learning allow education to be tailored to people’s needs as they move through their lifecycles.

Both Global X and Rize have positioned investment in the imaginary of edtech as a kind of moral imperative, not least in the context of post-pandemic transformation of education systems to meet emerging social and technical demands. The LERN brochure for investors even invokes the UN Sustainable Development Goals of quality education, decent work, and reducing inequalities, suggesting that investment in the fund will support progress towards these international targets.

The companies that Global X and Rize have invested in through their respective funds illustrate what they see as market leading edtech organizations with potential for high market growth performance. Notably, they both include a number of huge China- and US-based edtech organizations.

Global X ETF top 10 holdings

Three of the top five holdings in the Global X EFT are large Chinese edtech groups: GSX, TAL and New Oriental. Zoom, Chegg, K12 and Pearson (itself a market-making company) are among other well-known companies in these holdings. Similarly, New Oriental, GSX and TAL are in the top 5 holdings of the Rize LERN ETF too.

Rize LERN ETF top 10 holdings

Despite the transformative claims of Global X and Rize, some of these companies have questionable market credentials. GSX TechEdu, for example, provides after-school home tutoring software with embedded big data analytics. It experienced surging customer demand during the pandemic and corresponding growth in share value–reportedly increasing its active users to 1.5million and revenue growth of more than 300% year-on-year. However, many US investors have questioned its financial reports and some have claimed it is an outright fraud based on fake student enrollment and course numbers, leading to a probe by the US securities regulator.

The US-based online learning platform provider K12 has also been the subject of controversy. K12 was predicted by BMO Capital Markets to be a ‘winner’ if the Covid situation worsened. However, its $15.3million no-bid contract with one of the largest public school districts in the US was cancelled in September 2020 after a series of technical problems prevented students from taking classes, raising concerns about the impact on investors. Its president moved quickly to downplay the effects on its financial position, and K12 also announced a virtual investor day to present the company’s long-term vision and growth strategies, capital allocation framework, and operational and financial objectives.

These controversies indicate some potential faultlines between market valuations and the mundane reality of edtech use. The professional asset managers at Global X and Rize, supported by their index producers and market intelligence providers, are highly distant from the points of use of edtech. They remain in the abstracted domains of discursive imaginary generation and statistical valuation, disinterested in the actual performance of edtech in classrooms while promoting its performance in financial markets.

Edtech market devices

It is hard to know what tangible effects these two exchange traded funds will exert on education in the long term. Their effect could be to shape edtech markets in ways that suit the long term visions and strategic priorities of key edtech companies, particularly those that treat online learning and AI-based personalized learning platforms not just as emergency responses to the pandemic but as solutions to longstanding problems of schooling. Inclusion in the indices certainly seems to confer market leadership on the selected companies and invests a kind of authority in their strategic visions of education. In other words, these index investing instruments might help materialize edtech imaginaries, ultimately funding the future into existence according to consensual visions amongst edtech companies, market intelligence agencies and investment intermediaries. Rize claims it ‘enables investors of all stripes to invest in the future’.

Already, it certainly seems clear that edtech companies and index investment firms such as Global X and Rize are talking the same language and investing in the same imaginary of future educational transformation. They understand edtech as a profitable market niche, but also treat education itself in market terms–as a sector dedicated to the cultivation of productive skills for the post-Covid digital economy that are best delivered by private providers. ‘Maximizing one’s education is the best way to stay competitive in today’s global labor market,’ Global X stated on announcement of its ETF. ‘But as demand for education surges around the world, old and deeply-entrenched institutions are largely failing to rise to the challenge’. Furthermore, market intelligence firms such as HolonIQ now act as brokers between edtech and asset managers, using their extensive catalogues of edtech market insights to actively catalyse new investment in this imaginary.

From a research perspective, these forms of index investing require research on edtech to turn to some unfamiliar sources for analytical assistance. Some relevant emerging research on ETFs has begun to emerge from economic sociology and the political economy of markets. Benjamin Braun, for example, has studied ETFs as specific kinds of social and technical ‘market devices’ and as the products of new ‘powerful financial intermediaries’. Asset management firms and professionals that pool and manage ‘other people’s money’, such as through ETFs, have become enormously influential in the functioning of financial markets.

The emphasis on ‘market devices’ in the sociology of markets and economics emphasizes that markets have to be made, including through the social construction of practical devices, artefacts, calculations, methodologies and technologies, and that they then exert real effects. From this perspective, index investing instruments such as ETFs are devices constituted from entangled human practices and technical artefacts that produce effects in market spaces. For Braun, as ETFs have become multitrillion dollar investment vehicles, the asset managers who produce and administer them have become increasingly central to the functioning of contemporary capitalism itself. ETFs are therefore micro-level market devices that are key to the macro-dynamics of an emerging form of ‘asset manager capitalism’.

The launch of the Global X and Rize LERN exchange traded funds need therefore to be understood as part of a shift in the microfoundations of capitalism. Asset managers and their index investment devices have become increasingly powerful to whole economies, and index investing has expanded to generate value from a vast range of sector, now including education. Perhaps edtech and exchange traded funds will, over time, become increasingly interdependent, with index investing instruments and asset managers becoming key to the growth and direction of edtech markets, and edtech increasingly understood as a sector for capitalization by asset managers and investors. At the very least, policy-focused research needs to acknowledge and further interrogate index investing as an emerging technique of education financialization in the global education industry, complementing existing forms of investment in education such as venture capital, private equity and impact investing.

As particular market technologies, ETFs are now interweaving with edtech and with the financialization of education more broadly, making asset manager capitalists into unusual but potentially influential figures in the shaping of education for the future.

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The social life of Artificial Intelligence in education

Ben Williamson

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Artificial intelligence is becoming a major feature of educational practice and policymaking, but researchers are beginning to raise critical questions about its ethics and effects. Photo by insung yoon on Unsplash

Artificial Intelligence (AI) has become the subject of both hype and horror in education. During the 2020 Covid-19 pandemic, AI in education (AIed) attracted serious investor interest, market speculation, and enthusiastic technofuturist predictions. At the same time,  algorithms and statistical models were implicated in several major controversies over predictive grading based on historical performance data, raising serious questions about privileging data-driven assessment over teacher judgment. 

In the new special issue AI in education: Critical perspectives and alternative futures published in Learning, Media and Technology, Rebecca Eynon and I pulled together a collection of cutting edge social scientific analyses of AIed. The purpose was to add alternative analytical perspectives to studies of AIed benefits, and to challenge commercial assertions that AIed will solve complex educational problems while accruing profitable advantage for companies and investors.  

Like AI in general, AIed is social and political. It has its own long history and a complex present ‘social life’, and it is being developed in the pursuit of future visions of education. AIed has emerged in its current form from decades of prior research and development, from technological innovation, from funding practices, and from policy preoccupations with using educational data for various forms of performance measurement and prediction. Far from being merely a future vision, AIed is already actively intervening in education systems — in schools, universities, policy spaces and home learning settings — with effects that are only now coming into view. 

Yet the growth in critical studies of AI in other sectors (such as labour automation, healthcare and the law, the privatization of public infrastructure, surveillance, border control, welfare distribution, visa application sorting, plus emerging legal pushback and challenges to AI data giants) has not been matched by joined-up critical analyses of AIed. Building upon the critical agenda for research on big data in education that Rebecca Eynon called for in a 2013 issue of Learning, Media and Technology, we hope the new special issue goes some way to addressing that absence. This seems all the more important amidst the surge of recent public outrage about predictive grading. While the current predictive grading controversies may not be directly related to AI, the widespread presentation of ‘the algorithm’ as determining students’ futures does raise questions about how AI-based forms of education based on machine learning and predictive analytics may be received or resisted in coming years.

Our editorial article provides historical perspective on the recent development of AIed, identifying a range of genealogical threads and connections that have given rise to current practices. The following papers cover such important critical issues as automated discrimination, educational performance prediction, the political economy and geopolitics of AIed, penetration of emotional AI into edtech, anticipatory policy and governance, and the need for regulation of AI in education. The special issue, we believe, contributes important new critical insights into AIed, its past life and its present social life, and its possible future life as a key source of power and influence in learning, teaching and education. The issue opens up a number of outstanding features of the social life of AIed requiring further analysis. This post highlights a few possibilities for future studies of AIed.

AIed R&D
One of the key aspects of the social life of AIed is academic research and development conducted in specialized labs, centres and alliances. AIed is a serious research enterprise, with a past life stretching back through the establishment of the International AI in Education Society (IAIED) in 1993 to the development of intelligent tutoring systems in the 1960s. It also encompasses cognate fields of learning analytics, educational data science, and learning engineering developed over the last 10-15 years. These fields have built up large archives of publications, international associations and professional communities, funding portfolios, commercial partnerships, and media engagement. AIed does not just consist of automated pedagogic assistants and personalized learning platforms, but is full of these AI people too.

Recently, a new open access journal, Computers and Education: Artificial Intelligence was launched as a ‘world-wide platform for researchers, developers, and educators to present their research studies, exchange new ideas, and demonstrate novel systems and pedagogical innovations on the research topics in relation to applications of artificial intelligence (AI) in education and AI education’. The journal will help establish AIed as a distinctive field of pedagogical innovation and elevate evidence on its benefits. ‘AI people‘ have been working in education for decades, bringing particular forms of learning science, learning analytics and education data science expertise to bear on education and learning; the open access journal will enable them to extend their findings and arguments to new audiences.

The experts of AIed are now gaining influence and access to established media channels to circulate their claims that AIed is a positive and transformative force as well. The AI people are, in other words, establishing a vision for the future of education, building innovative technologies to realize it, constructing an evidence base based on learning and data science methodologies, and building coalitions of support to pursue the imaginary of AIed-enhanced education. Further historical studies should engage with the long development of these forms of expertise and the field-building activities involved in diffusing and realizing their imaginaries of the future of education.

Edtech expansion
The social life of AIed is also characterized by significant efforts by the commercial edtech industry. The global education business Pearson, for example, envisages a future of education driven by AI innovations. ‘With AI, how people learn will start to become very different,’ the company states. ‘AI can adapt to a person’s learning patterns. This intelligent and personalized experience can actually help people become better at learning, the most important skill for the new economy’. Pearson launched AIDA, a smartphone-based adaptive AI learning assistant, to accomplish this vision. Pearson’s efforts to promote, create and profit from AI in education are part of a much wider interest in AI in the edtech sector, assisted by investor funds, philanthropic backing, and powerful framing discourses of personalized learning.

Another way AI and the edtech sector are expanding is through investor funds and market forecasts. HolonIQ, an influential education market intelligence consultancy, produces extensive insights for investors and companies on market trends in education and edtech. Its recent Global Learning Landscape identifies many promising applications to support market growth and investor decisions in the multibillion-dollar edtech market, while an accompanying set of scenarios for education in 2030 establishes particular edtech imaginaries for investors to pursue. HolonIQ also uses AI to analyze edtech market data. It has assembled global datasets and machine learning algorithms in order to ‘generate insights that help educators, entrepreneurs, enterprises and investors make data-driven strategic decisions’. In this way, HolonIQ is mobilizing AI itself to support edtech market growth and the expansion of AIed into further settings and practices of education.

These examples indicate the role of global edu-businesses, market organizations and investor strategies to the expansion of for-profit AIed. Investment in AIed in particular is a subject that as yet has received very little detailed attention, despite its catalytic role in funding technical development and supporting the objectives of for-profit edtech businesses. Venture capital, private equity and philanthropic investors are to a significant extent financing the AI future of education into existence.

Private infrastructures
Global technology companies have begun inserting AI infrastructure into educational institutions and practices too. This aspect of the social life of AI in education means companies including Amazon, Google, Microsoft and IBM are increasingly present in education through the back-end ‘AI-as-a-service’ systems that educational institutions require to collect and analyse data.

Amazon, for example, claims that by ‘Using the AWS Cloud, schools and districts can get a comprehensive picture of student performance by connecting products and services so they seamlessly share data across platforms’. It also strongly promotes its Machine Learning for Education services to ‘identify at-risk students and target interventions’, ‘improve teacher efficiency and impact with personalised content and AI-enabled teaching assistants and tutors’, and ‘improve efficiency of assessments and grading’.

These ‘AI-out-of-the-box’ interventions by global technology companies make public education institutions dependent upon private infrastructures for key functions of data analysis and reporting. They are also part of the history of how Amazon, Google, Microsoft and IBM have sought and competed for structural dominance over the infrastructure services used across myriad sectors and services. Further studies should examine the ways these global tech companies are expanding into education through the provision of infrastructure and platform services, exploring the long-term dependencies and lock-ins they engender.

AI policy
Policy is also mixed up in the social life of AIed, as part of a much longer history of the use of numbers in educational governance. Over the last few decades, large-scale data infrastructures for collecting, processing and disseminating educational data have become key to enacting policies concerned with performance measurement and accountability. AI technologies can extend the capacity of these data systems to become cognitive infrastructures capable of performing predictive analytics and automated decision-making. During the Covid-19 pandemic in 2020, the OECD strongly promoted AI as a solution to school closures and examination cancellations. AI-enabled learning and the preparation of AI workforces are also new parts of different nation’s educational policies and long-term geopolitical strategies.

In India, for example, the new National Education Policy 2020 framework states that ‘New technologies involving artificial intelligence, machine learning, block chains, smart boards, handheld computing devices, adaptive computer testing for student development, and other forms of educational software and hardware will not just change what students learn in the classroom but how they learn’. It also highlights the need for AI education to enable India to become a digital superpower. Likewise, the European Parliament has begun considering a resolution on AI in education. It highlights how ‘AI is transforming learning, teaching, and education radically’, most notably through the potential of ‘personalised learning experience’ made possible by the collection, analysis and use of ‘large amounts of personal data’. Both the NEP and the European Parliament documents call for the rapid upskilling of teachers to take advantage of AI. 

The NEP2020 and the EU proposed resolution on AI in education exemplify the emergence of AIed as an object of global education policy and geopolitical significance. Policy studies should engage with the interweaving of AI and education policy much more closely, teasing out the ways that various powerful organizations are involved in promoting AI in education or education for AI development and productivity enhancement. Such studies should also situate AI-focused policies in national and comparative contexts and in relation to geopolitical competition in the so-called ‘AI arms race’, and further concentrate empirical attention on the ways cognitive infrastructures affect policymaking itself.

Ethics centres
Another significant aspect of the social life of AIed concerns the definition and enforcement of AI and data ethics. In wider context, numerous ethical frameworks and professional codes of conduct have been developed to attempt to mitigate the potential dangers and risks of AI in society, though important debates persist about the ways such frameworks and codes may serve to protect commercial interests or obscure the political decisionmaking that underpins algorithm design.

Currently, in the UK, the Institute for Ethical AI in Education is leading the development of ethical principles for AI in education. Based at the University of Buckingham, a private university, it’s led by the institution’s Vice Chancellor, alongside the president of the International AI in Education Society, and the CEO of AIed company Century Tech. As with the development of all AI ethics centres and institutes, the constitution of this organization embeds it in particular assumptions — notably the assumption that AI has ‘powerful benefits’ that can be realized as long as responsible practices are followed — which may not necessarily reflect those of other stakeholders. Separately, UNESCO is preparing a global standard-setting recommendation on the ethics of AI, part of which is dedicated to a participatory, consultative exercise to define ethical standards for AI in education.

As this indicates, AIed ethics frameworks and standards are now being pursued by a variety of national and international organizations. These organizations have power and influence to define how and whether AIed applications are implemented in defined ethical ways. The social and political work involved in settings such standards is itself a significant factor in enabling or constraining the expansion of AIed. This work remains as yet under-studied or reported despite the powerful role it will play in setting the acceptable and definitive standards for AI in education in years to come.

Controversies
A significant aspect of the social life of AIed is the controversies emerging over automated decision-making and judgment by opaque systems. In summer 2020 this became especially apparent in relation to predictive grading. The first case was the predictive grading system used by the International Baccalaureate Organization to replace exams during Covid-19 school closures. Rather than basing grades on exam scores, the IBO employed an algorithmic grading and awarding model based on student coursework, teacher-delivered predicted grades and historical prediction data. The system, many have argued, is unfair and potentially discriminatory, with more than 20,000 students signing a petition protesting the algorithm. The Norwegian Data Protection Authority has since ordered the IBO to provide further detail on the model as part of an investigation into whether it violated the European General Data Protection Regulation.

The use of statistical modelling and historical performance data to predict and award grades in the four UK nations, and the inequalities of outcomes that resulted from this standardization process, fueled further public, legal and media backlash over the use of predictive algorithms in education. At one protest in London, affected students began chanting ‘fuck the algorithm‘, a phrase quickly taken up on social media. It resulted in eventual political capitulation, the abandonment of algorithmic awarding models, and the reinstatement of teacher assessed grades. One UK Conservative politician later lamented that the scandal was the result of ‘technocratic governance and government by computer’ that failed to recognize ‘that the decisions that are made affect the lives of thousands of people individually’. Although exam grade prediction, moderation and standardization is certainly not unique to these events, the widespread outrage at the outcomes in this case meant governmental trust in numbers in the four education systems of the UK was not able to withstand public calls for returning trust in teachers and legal demands for fair outcomes for students.

These examples highlight how the outcomes of highly technical and statistical procedures are not just the result of objective data scientific analysis performed with software, but of difficult choices, forms of methodological expertise, the practical work of civil servants and statisticians, and political interference. They also show how statistical procedures can easily run into public resistance, especially when they produce discriminatory outcomes that are widely understood to be driven both by political bias and by automated algorithms. Although the grading systems were static algorithms rather than ‘learning’ in the AI sense, their outraged reception raises questions about how future iterations of AIed might be received or rejected, and its potentially tense position in longstanding and ongoing debates about the role of teacher professional judgment in education systems characterized by datafied performance measurement and accountability.

Such controversies signal the need for cautious and critical studies which penetrate through optimistic and futurist claims based on an algorithmic worldview about the powerful benefits of data-led decision-making. Critical studies should attend to the very powerful ways AIed and related techniques are involved in algorithmic profiling, automated digital redlining and discrimination, student modelling, and forms of prediction and classification that can exert potentially harmful effects or lead to deleterious outcomes for students. AIed, and the AI people promoting it, did not create these problems, but potentially reflect and reproduce historic practices of social sorting, classification, ranking, rating and exclusion — as seen in the statistical modelling and prediction of exam grades. Such practices and controversies are now interweaving into the genealogical threads of contemporary AI in education — with potentially significant effects on its future prospects and development — and require much further unpacking and documenting. These controversies themselves reveal unfolding contests between forms of technical expertise and the public.  

Critical perspectives on AIed
Recently, a body of critical social scientific and philosophical research has begun to examine the social, economic and political life of AIed, much of it animated by concern over the kinds of opaque automated decision-making and potentially discriminatory outcomes that predictive grading controversies have recently exemplified. This critical research is showcased in two recent special issues, one in Learning, Media and Technology and the other in the London Review of Education. The papers across these issues raise a range of issues for further examination: the politics of AIed, the influence of commercial, futurist, investment and philanthropic actors on AIed, the political economy of AI, the imaginaries and limits of AIed discourses, the problems inherent in algorithmic decision-making, the role of AIed in producing discriminatory outcomes, and the challenges it poses to democratic control over public education, privacy and students’ rights.

This emerging body of research is opening up the social life of AIed to inquire into the various paths taken — governmental, commercial, philanthropic, academic, financial, and futurist — to arrive at the contemporary juncture. Taking a critical perspective doesn’t necessarily mean criticizing AIed or taking up an activist position. It means unpacking the various genealogical threads, assumptions and practices involved in its creation and enactment, careful documentation of its effects in the present, and consideration of its possible implications for the future of education. One important absence in this work is ethnographic studies in AIed in action. How AIed is used in teacher practice, policymaking centres or in home learning settings is an important aspect of its social life. Understanding how it then affects teachers, policymakers and students would help cut through its powerful framing imaginaries to reveal its actual effects and consequences. Complex statistical models, as predictive grading controversies show, can produce socially, legally, ethically and politically problematic outcomes.

Studying the social lives of AIed also helps us to see beyond current fascination with technologies such as algorithms, machine learning and neural networks to the historically embedded processes and problems in education that AIed has been put to the task of addressing. Issues such as inequality of outcomes, claims of the benefits of personalized learning over standardized education, private influence over public education, performance measurement through numbers, and the geopolitics of education policy are not unique to AIed of course. The application of intelligent software to old problems does not, however, inevitably or unproblematically solve them. AIed becomes entangled in such problems, for example by exacerbating inequalities through inferring probable outcomes from historical performance datasets, or by delegating human judgment to opaque and unexplainable algorithms.

Ideally, critical social scientific and philosophical research should not only examine the past and present social lives of AIed but become involved in shaping its future life too. It should actively intervene alongside system designers and learning scientists to help shape better outcomes, ethical responses, meaningful regulation, socially just designs, and alternative future imaginaries of AIed. We hope the papers in the special issue AI in education: Critical perspectives and alternative futures support initial steps in that direction.

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The evolution of the global education industry during the pandemic

Ben Williamson & Anna Hogan

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During worldwide disruptions to schooling, a global industry has advanced its vision of a digital future of education. Photo by Claudio Schwarz on Unsplash

Through the ‘pivot’ to ‘online learning’ and ‘emergency remote teaching’ during the Covid-19 emergency, educational technology (edtech) has become integral to education globally, with private sector and commercial organizations developing central roles in essential educational services. The effects are set to persist in temporary models of ‘socially distanced’ in-school and at-home learning during the period of pandemic recovery, and for longer in fully ‘hybrid’ approaches where commercial edtech and other private technology products and services are embedded in new models of curriculum, pedagogy, assessment, and school management. This post summarizes some key headlines from recent research conducted with Anna Hogan for Education International, the Global Union Federation that represents teachers and other education employees around the world, as part of its long-term global response to commercialization in schools.

The project, published as the freely accessible report Commercialization and privatization in/of education in the context of Covid-19, mapped out how privatization and commercialization of education advanced through the application of educational technologies during the 2020 pandemic. The report offers a provisional cartographical survey of the shifting landscape of commercialization and privatization in education, outlining its emerging contours and identifying coordinates and landmarks for further sustained attention from researchers, teacher unions and practitioners as public and state education systems begin the process of recovery.

We started off by recognizing that commercial technology has played a crucial and valuable role in educational continuity for millions of students worldwide, that there is an existing body of edtech research to inform and evaluate its use, and by acknowledging that commercial and private sector participation in education has a long and complex history. What we set out to explore, specifically, was the expanding scale and scope of commercialization and privatization during the pandemic, and its potential effects on state and public education, while recognizing longstanding problems with the structures, practices and governance of schooling.

Pandemic imaginaries
The project was informed by previous research on fast policy and policy mobility – the understanding that policy is the product of sprawling multisector networks of people, organizations and technologies, including commercial businesses – and by studies of technology which recognize that all technologies are shaped by the politics, assumptions and desires of their producers: technologies carry sociotechnical imaginaries of preferred futures that their producers seek to attain, and are also interpreted and utilized by others to achieve specific aims and visions. The expansion of commercial edtech during Covid is both a global fast policy event that involves multisector organizational webs, and a practical enactment of particular ways of envisaging the future of education that emerge from those networks, with potentially profound long-term implications for systems and practices of schooling.

One of the key findings detailed in the report is that a multisector global education industry of private, intergovernmental and commercial organizations has played a significant role in educational provision during the Covid-19 crisis, working at local, national and international scales to insert edtech into educational systems and practices. The global education industry has often set the agenda, offered technical solutions for government departments and ministries of education to follow, and is actively pursuing long-term reforms whereby private technology companies would be embedded in public education systems during the recovery from the Covid-19 crisis and beyond it in new models of ‘hybrid’ teaching and learning.

During the pandemic, this evolving instantiation of the global education industry produced and circulated powerful ideas about Covid-19 as a novel ‘opportunity’ to ‘reimagine’ education, treated home-based learning as a ‘microcosm’ of a digital future for hybrid forms of education, and encouraged ‘experimentation’ and ‘innovation’ to shape education systems for the future. It established the crisis as a catalytic opportunity for educational reimagining, reform and transformation, in ways that favour an acceleration in edtech rollout and that empower commercial organizations to participate more extensively and intensively in public and state schooling.

A key part of the global education industry’s approach during the pandemic is through coalition-making and developing the role of public-private partnerships in education policy. The role of commercial providers has been supported, promoted and advanced by a range of organizations that cut across public, private and third sectors. Some of the most influential promoters of edtech solutions during the pandemic include international multilateral organizations such as the World Bank, OECD, Global Partnerships for Education and UNESCO, in many cases operating in global multisector coalitions of public and private partners to promote ‘best practices’ for policymaking centres to emulate. Commercial edtech providers and advocacy organizations have also formed powerful networks and coalitions to highlight and promote edtech products for use by schools, teachers and parents.

These coalitions illustrate the emergence of new kinds of multisector public-private partnerships and fast policy networks in relation to edtech expansion, and the enhanced role of the private sector in educational delivery and governance. Although ministries of education have retained key decision-making powers, often they have been led and guided by various national and international networks that are orchestrating the educational response to the pandemic.

Edtech markets
A key part of the emergency response to education has been the creation of new market opportunities and the movement of money, especially from venture philanthropies and venture capital sources. Financial support and political advocacy for edtech solutions to school closures during the pandemic have been provided by technology philanthropies such as the Gates Foundation and the Chan Zuckerberg Initiative. They have dedicated new multimillion dollar funds to a range of edtech programs and sought to consolidate the long-term role of the private sector and commercial technology in public education.

Wealthy individual tech philanthropists have also been given positions of authority as experts in ‘reimagining’ education for the future, in ways which reflect their pre-existing visions, their financial support for technology-centred models of schooling, and their efforts to influence policy agendas. Through pandemic philanthropy, individual technology wealth has become a key source for reimagining education and funding technical development to achieve those imagined futures. Naomi Klein has described the formation of a new ‘pandemic shock doctrine’ and a ‘Screen New Deal’ that is being brokered between governments and global technology firms by wealthy philanthropists.

Financial organizations, market intelligence agencies, venture capital, and impact investors have sought to capitalize on the pandemic too. With edtech investment already at high levels, especially in the US and southeast Asia, financial predictions of the value of edtech have stimulated capital markets, with the Covid-19 treated as a catalytic opportunity to capitalize on the sudden rise in use of technologies in education. Financial models including venture capital, exchange-trade funds, private equity, impact investing and social bonds have all been utilized to invest in and fund educational technologies during the pandemic. Market projections of the surging value of digital learning technologies over the coming decade are likely to attract further investors seeking profit from new disruptive models of public education. The pandemic has been characterized by edtech market-making: the effort to identify and capitalize on new and valuable market spaces for educational technologies.

Private solutions
Technology corporations have also expanded their digital solutions across education at international scale. Major multinational technology corporations including Google, Microsoft and Amazon have experienced a huge surge in demand for their products and services due to their capacity to deliver solutions at international scale, at speed, and for free. Supported by multilateral policy influencing organizations and national government departments, these companies have integrated schools, teachers and students into their global cloud systems and online education platforms, raising the prospect of widening and deepening long-term dependencies of public education institutions on private technology infrastructures. Social media platforms including YouTube and TikTok have also sought to grow their presence in education through content creation partnerships for students learning at home, with TikTok explicitly fast-tracking its investment in new ‘snack-sized’ micro-learning content to make the platform more appealing to advertisers.

Educational companies of various types – from global edu-businesses like Pearson to new startups – have also rapidly marketed and promoted their products for use by schools, often for free or heavily subsidized for a temporary period. Online schooling platforms are promoted by many education companies as long-term alternative models for education, and have experienced huge customer growth and investor interest. ‘AI’ technologies have also experienced significant growth, owing to their capacity to provide ‘personalized’ or automated education in the absence of teachers. Testing companies have scrambled to develop new ways of assessing students in the absence of conventional examinations, including the highly controversial use of machine learning for predictive grading. Moreover, student surveillance technologies have been adopted to monitor students’ virtual attendance, ‘proctor’ examinations, assess social-emotional learning and well-being, and enable schools to fulfil their safeguarding responsibilities.

At the same time, parents and students themselves have been approached as customers of edtech products, as a new market in consumer edtech has become the focus of investor enthusiasm. Direct-to-consumer edtech has opened up a novel niche for the shadow education market of private supplementary tutoring and homework platforms. These developments are extending the reach of edu-businesses to new areas of schooling and learning at home, and heightening their long-term influence over the format of education for the future.

Futures of education
Overall, the project has revealed a particular set of mutations in the global education industry during the Covid-19 pandemic. It has documented some ways in which privatization of education has expanded – through increasing participation of private actors in public education – and of how commercialization of education has developed through the creation, marketing and sale of education goods and services to schools (and parents) by external providers. We understand this as a particularly intense instantiation of fast policy involving multisector actors and networks, and as an accelerated realization of sociotechnical imaginaries of a highly digitalized future of education. The shifting landscape of commercialization and privatization in education we have surveyed will require sustained attention by educators, unions and researchers to ensure that all stakeholders, and not just private or commercial organizations, can participate democratically in imagining the post-Covid future of public education.

The full report is freely available from Education International. This post is an adapted version of the report summary.
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Datafication and automation in higher education during and after the Covid-19 crisis

Ben Williamson

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Universities are shifting to digital systems as replacements for physical campus settings during the Covid-19 crisis. Photo by Adi Goldstein on Unsplash

Notes for a talk at the online webinar ‘Going Online During and After the Pandemic’ on 6 May 2020. Thanks to Mariya Ivancheva and Brian Garvey for the invitation. 

Over the last five years or so, data analytics and automated processes have become increasingly prevalent in higher education. Many functions, practices and tasks of HE are being made ‘machine-readable’ by digital technologies, as teaching, learning and administration are transformed by ‘datafication’. Datafication is also advancing automation, as the processing of digital information by increasingly capable or ‘smart’ technologies makes it possible to automate many tasks. As universities have now begun to consider a future disrupted by Covid-19 that seems likely to be characterized by  distance learning, datafication and automation may expand and intensify across the sector.

Writing in The Guardian this week on online teaching and the future of HE during and after the Covid-19 pandemic, former universities minister Chris Skidmore argued ‘universities need to track student progress remotely’ in order to ensure they are not at risk of disengaging or dropping out, and singled out examples of institutions that have already developed high levels of capacity for on-campus student tracking. The current crisis is already escalating high-level demands for universities to monitor and track students more intensively.

This work-in-progress post offers some initial, provisional observations about how such technologies and practices of datafication and automation are being mobilized in HE in the context of Covid-19 (following up some previous posts focused mainly on the schools sector). The aim is to encourage HE educators, researchers, administrators and students to think critically about the existing extent of datafication and automation and its potential reach and consequences in coming months and years.

Datafication of HE
The datafication and automation of HE is powered by three main forces. First, at a broad societal and cultural level, there is now widespread desire for quantification and measurement made possible by digital data processing systems. We can see this, for example, in consumer acceptance of wearable biometric technologies (FitBits etc), in daily displays of epidemiological data by politicians and science experts, and public discourses about Covid-19 case counts. Although deeply contested, digitally-generated numbers and visualizations have a powerful allure. This is the case in HE too, as historically evident in rankings and league tables as ‘authoritative’ sources of numerical knowledge, and increasingly evident today in widespread uptake of data dashboards, platforms, and analytics.

Second, higher education policy has become increasingly focused on performance monitoring, competition and marketization over the past decade. The Office for Students’ emphasis on data-led market regulation in England is a key manifestation of this political desire for control through quantification of the sector. QAA Scotland has led an Enhancement Theme exploring the use of learning analytics and other digital forms of data for enhancement of the student experience and outcomes. Organizations including the government departments of education and business, sector agencies HESA, Jisc and QAA, the think tanks PolicyConnect, Nesta and HEPI, the consultancies KPMG, McKinsey’s and Deloitte, plus a range of business actors have all promoted the use of data analytic technologies for enhanced performance monitoring and improvement in HE.

And third, a ‘global education industry’ of data solutions services, infrastructures, platforms and app providers has sought to make markets for their products in HE—covering everything from recruitment and admissions, through learning management, student information and library resource systems, cheat detection and ‘contract cheating’ identification, online assessment and exam ‘proctoring’, to employment matching, graduate tracing and talent analytics. This is a highly lucrative space in which for-profit organizations may generate venture capital investment and capture institutional customers looking to boost reputational advantage, improve recruitment, and enhance measurable outcomes such as student experience surveys, grades, and graduate employability.

Together, widespread desire for quantification, HE marketization, and the global education industry have resulted in a proliferation of data analytic technologies across the sector, which we see now intensifying and expanding in response to the coronavirus pandemic.

Digital infrastructures of teaching and learning
Two particular technologies of datafication and automation have come into sharper view during the Covid-19 pandemic—learning management systems and online degree programs. These are not ‘spectacular’ technologies of datafication and automation–compared to hyperbole about artificial intelligence–but their mundane status as parts of universities’ infrastructures for teaching and learning positions them to become especially consequential in any recovery or reorganization of institutions around distance education.

Learning management systems are the backbone of HE courses across the planet and the companies running them have gathered extensive global education data sets—in some cases, data about millions of students combined. With hundreds of providers—key ones in the UK being Blackboard, Moodle and Canvas—and a total LMS market said to be valued in the tens of billions, providers differentiate themselves from their competition through restless upgrades and new feature designs.

A key aspect of LMSs is the capacity for the data to be subjected to learning analytics, often through in-built analytics or third party integrations. Solutionpath is one of the leading learning analytics providers. Its Student Retention, Engagement, Attainment and Monitoring platform (StREAM) automatically generates a near real-time ‘engagement score’ for each individual enrolled at participating institutions. It does this by collecting data from a range of ‘electronic proxies’ that represent students’ participation in their course, including LMS data, building access card swipes, software logins, library loans, attendance, and assignment submissions. These data are combined then automatically analysed and presented on dashboards. If patterns in student behaviour change over time, alerts are triggered within the StREAM platform to facilitate staff intervention

Recent interest has developed in using LMS stores of big student data for automated recommender services. The company Instructure behind the LMS Canvas, for example, was just acquired by the private equity firm Thoma Brava for $2bn, based partly on its proposal to use its global student datasets for personalized learning recommendations—a program it initially code-named DIG. As Instructure’s chief executive announced in an investor meeting last year,

We have the most comprehensive database on the educational experience on the globe. So given that information that we have, no one else has those data assets at their fingertips to be able to develop those algorithms and predictive models. … [W]e can take that information, correlate it across all sorts of universities, curricula, etc, and we can start making recommendations and suggestions to the student or instructor in how they can be more successful. … Our DIG initiative, it is first and foremost a platform for ML and AI, and we will deliver and monetize it by offering different functional domains of predictive algorithms and insights.

Competition in the LMS field, and the level of interest from investors and customers alike, is catalysing this shift to algorithmic forms of insight, prediction, and recommendation—a case of automation creeping into the pedagogic encounter between educators and students.

The development of LMSs to adopt recommender and relevancy algorithms is reflected in the newer category of Learning Experience Platform (LXP). The key feature of an LXP is that it is designed to automate the ‘intelligent discovery’ and ‘recommendation’ of relevant learning content. Whereas conventional LMSs are based on searchable course catalogues, an LXP is organized more like YouTube or Netflix as a content management platform with in-built recommendation technologies. An LXP collects continuous data from learners’ behaviour, learning and performance in order to perform these analytics processes.

The AULA LXP, for example, used by multiple HE providers across the UK, presents itself as a ‘digital campus’ platform that partners with academics ‘to design high quality learning experiences’, which it terms ‘Dream Courses’, and to ‘scaffold the shift to blended and fully online’ teaching. Its ‘LMS Data Importer’ automates the migration of all content and information from other legacy systems, and the platform features an Engagement API for monitoring real-time student engagement which offers personalized, targeted recommendations to educators to improve it. AULA has partnered with WonkHE, the HE news, opinion and analysis organization, on an online conference about higher education responses to COVID closures.

So LMS providers are no longer just digital backbone or infrastructure to university courses, but increasingly active partners in pedagogic processes. They act as providers of online learning scaffolding, as ‘recommendation engines’ for AI-enhanced ‘personalized learning’, and ‘digital campus’ or ‘dream course’ developers, utilizing their extensive and continuously updated data sets for teaching innovation, institutional outcomes enhancement, and measurable performance improvement. LMS platforms are likely to become even more central to HE teaching with the shift to online degree provision.

Online program management (OPM) refers to infrastructure services provided by vendors to enable universities to deliver online and distance education courses. Currently growing rapidly in the US and UK, OPM service providers also provide extensive data analytics in their platforms, offering convenient ways to automate student tracking and monitoring. OPM companies include 2U, Noodle Partners and Academic Partnerships, big education publishers, including Wiley and Pearson, as well as MOOC providers that have diversified into the OPM market (Coursera, FutureLearn).

One of the most successful providers, 2U, provides the OPM platform 2UOS (2U Operating System). 2UOS consists of an online teaching and learning platform, a suite of data analytics for generating information about students, technical support, and targeted, program-specific digital marketing campaigns using machine learning and AI. It has carefully presented itself as a key technology for universities to transition to online teaching during the Covid-19 crisis. OPM providers have positioned themselves to support institutions’ internationalization strategies, as universities seek out a share of the international student market, but now find themselves in position to support the transition online for both international and domestic students too.

A key aspect of the success of OPMs is that the companies usually cover the up-front costs of setting up an online degree program, and provide the technical infrastructure for university partners to build their courses on. This model saves universities having to front the costs or building the technical platform. The companies then take 50-60% of the student fees as a return on their up-front investment.

As with LXPs partnering with institutions on ‘Digital Campus’ or ‘Dream Course’ development, then, OPM providers present themselves as private pedagogic platform intermediaries. They are, increasingly, situated between the enrolled student and academic staff on a given course. This, of course, may become especially the case as students almost exclusively study online or through hybrid models.

Pandemic markets
The rush to remote education and online degrees is now a significant market event. OPMs, claims the education market intelligence consultancy HolonIQ, constitute part of a $7billion ‘Global OPM and Academic Public Private Partnership Market’ that ‘COVID-19 will substantially accelerate’ to a $15b market by 2025:

More so today (COVID-19) than ever, Universities around the world are increasingly seeking private partners to rapidly build capability, to boost and differentiate their offerings, accelerate growth and achieve long-term sustainability. As such, Private Equity and Capital Markets are watching the Academic PPP segment closely.

Moreover, OPMs are a key growth technology in a much larger Global Online Higher Education market valued by HolonIQ at $36billion in 2019 and projected to rise to $74b by 2025, opening up new ‘opportunities’ for market providers:

These changes to market dynamics are likely to accelerate with COVID-19, and while the biggest online players are gaining market share on the strength of their national reach and brands, this is the opportunity for predominantly offline providers to amplify their current online offerings with existing and new learners.

Online program management platforms, along with learning management systems, are key parts of an education technology sector that in the first three months of 2020 alone, according to the HolonIQ, ‘delivered $3billion of Global Edtech Venture Capital’.

These private platforms, powered by venture capital, private equity and student fees, are now doubly empowered in the multitrillion dollar global education market as universities rapidly transition to online teaching. In the language of finance, Q1 of 2020 produced a remarkable boom in edtech pandemic markets.

But this quarter of financial activity may have long-lasting consequences and implications for higher education much further into the future. As a provisional list, these include:

  • Deferment of expertise to platform companies and the increasingly capable systems they are inserting into higher education programs and practices. Datafication and automation have been legitimized by policy actors, consultancies, edu-businesses and think tanks as ways to improve HE. These developments encourage the delegation of judgement to automated systems, as decisions normally taken by workers are deferred to advanced analytics and automation. They also reshape pedagogies and curriculum design to fit the digital templates and forms of teaching made possible by the platforms, with providers themselves increasingly involved in designing ‘Dream Courses’ and online degree programs.
  • Fusion of higher education to the model and business of platforms. Platforms depends on the extraction and analysis of data as a route to profit. In the ‘platform university‘, student data are not just used for performance measurement of the university, but as a source of valuation for private edtech companies. Private edtech is now thriving on the platform ‘network effects’ of increasing numbers of institutions emulating one another to adopt public-private partnership arrangements with platform vendors. In Platform Capitalism, Nick Srnicek argues digital platforms have become key infrastructures of society; we might add that edtech platforms have now become infrastructural to higher education in the Covid context, with potential for long-term lock-in effects.
  • Austerity for universities and profitable market-making for platform companies. The shift to online education through platforms such as LMS integrations and OPMs raises risk of further worker precarity in universities in contrast to soaring private investment and customer spending on new platforms. Laura Czierniewicz compellingly notes that ‘underfunding and financial cuts which drive up the risks of sectoral fragmentation and breakdown’ are now paralleled by marketisation and ‘the increasingly unfettered infiltration of big corporate forces substantially reshaping higher education’.
  • New analytic engines of real-time anxiety. If rankings and league tables, as Wendy Espeland and Michael Sauder have argued, are ‘engines of anxiety’ driving ‘reactive’ behaviours in HE—where people perform to satisfy a given measurement rather than out of collective mission or shared values—then new forms of datafication and automation may also generate new anxieties. Liz Morrish has argued that demands have increased on the academic workforce over concern about university rankings and league tables, creating ‘a culture of workplace surveillance’ in universities. Digitally-enabled datafication could exacerbate these pressures as it potentially introduces ‘real-time’ performance measurement into working spaces including university offices and classrooms,  with increased surveillance of both students and staff a very concerning possibility after the pandemic.
  • New forms of algorithmic governance in HE. Algorithmic governance, as Christian Katzenbach and Lena Ulbricht define it, refers to ‘algorithms as a form of government purposefully employed to regulate social contexts and alter the behaviour of individuals, for example in the treatment of citizens or the management of workers’. Algorithmic governance involves the creation of detailed profiles about individuals (‘data doubles’) leading to potential for ‘social sorting, discrimination, state oppression and the manipulation of consumers and citizens’. As algorithmic and analytic processes become increasingly automated and ‘out of control’, they lead to forms of ‘automated management’ where humans have diminishing oversight over the kinds of decisions made by them.

Together, these developments suggest the expansion of the model of the datafied university that is increasingly governed by algorithmic, data analytic, and automated systems and in which the roles of staff and students may be redefined. Staff and students in HE will, in coming months, need to scrutinize the technologies of the datafied, automated university further, and come up with collective responses to help (re)build the HE institutions of the future.

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Edtech, coronavirus, and commercialization in public education

Ben Williamson

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Schools and universities have been closed during lockdowns around the world, while education technology markets have been thriving.  Photo by moren hsu on Unsplash

While the opening months of 2020 have been marked by huge disruptions to education at an international scale, some organizations have thrived during the coronavirus pandemic. According to the education markets consultancy HolonIQ, the first three months of the year ‘delivered $3B of Global EdTech Venture Capital, nearly 10% of the prior decade’s total, in just the first quarter of the new decade.’ April even saw the largest-ever venture capital investment in an edtech company, with Beijing-based  Yuanfudao receiving $1billion USD for its AI-based online tutoring and homework platform. The company has become the first coronavirus crisis edtech unicorn, during a remarkable quarter of a year for commercial edtech and education markets.

The rapid expansion of commercial edtech during the large-scale closure of schools and universities is the focus for a new project supported by Educational International, the Global Union Federation that represents organizations of teachers and other education employees around the world. The project is a collaboration with Anna Hogan at the University of Queensland. We’ll be bringing together Anna’s research expertise in education policy, marketization, privatization and commercialization with my experiences of researching edtech over the last decade. The project will help inform EI’s response to the COVID-19 crisis, but also its longer-term work on commercialization in public education internationally.

We’ve started initial work already, gathering evidence of commercial edtech activity over the last few months. It includes:

Beyond mapping out and trying to understand these organizations, networks, and activities, the project is also guided by larger questions and concerns. These include questions about the long-term consequences for public education of the emergency switch to online learning and edtech, and the implications for education systems in different international contexts. Already, we are finding claims and arguments for making current emergency measures into lasting reforms, in ways which often reflect pre-existing aims and visions for the future of education. We’ll be mobilizing some conceptual resources from the study of policy networks, the global education industry, education markets, and critical edtech to analyse these developments and consider how they might shape the recovery of public education beyond the pandemic.

Some of my own tentative thoughts on the possible long-term consequences and implications were sketched out in a previous post. In this project on edtech, coronavirus and commercialization we hope to much more clearly understand how emerging networks of organizations across sectors and national borders are both seeking to solve the short-term global disruption of education, and paving the way for longer-term transformations to education systems, institutions and practice.

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New pandemic edtech power networks

Ben Williamson

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New global networks of organizations have begun to propose technical policy solution to mass disruption to education systems during the coronavirus outbreak. Image by Photo by Alina Grubnyak on Unsplash

Mass closures of schools and universities plus rapid switches to remote online teaching and learning around the world have empowered technology vendors and promoters to position themselves as frontline emergency response providers during the current coronavirus outbreak. In the early stages of the crisis, individual organizations sought to offer up novel solutions and potentially gain advantage from the new pandemic markets stimulated by the shuttering of schools. Very rapidly, however, new coalitions, collaborations and alliances have formed around shared objectives to solve the global disruption of education.

Powerful networks, consisting of big tech companies such as Google, Microsoft and Facebook, international organizations including the OECD and UNESCO, as well as a global education industry of edu-businesses, consultancies, investors and technology providers, are coming together to define how education systems should respond to the crisis. But their objectives do not just focus on the short term. These pandemic power networks are developing new long-term policy agendas for how education systems globally should be organized long after the emergency ends.

Some researchers have begun to suggest research agendas and developed fast-track special issues for the social sciences, arts and humanities to make sense of the coronavirus crisis. The aim of this post is much more modestly to start mapping out the actors that have emerged as influential organizations in relation to education during the pandemic, focusing on the intersections of education technologies and education policies. By mapping and documenting some of their activities, we can begin to understand how emerging networks of organizations are both seeking to solve the global disruption of education, and pave the way for longer-term transformations to education systems, institutions and practices. Much more sustained analytical work remains to be done–this is just a descriptive, first-draft sketch of current emergency policy developments that are still in motion.

Pandemic policy mobility
It is now clear that the dominant education policy preoccupation globally is how to deliver schooling without schools and degrees without campuses. The primary policy solution has been identified as digital technology and online ‘remote learning’. Despite considerable debate about the difference between well-designed online learning and emergency remote teaching, consensus on digitally-mediated distance education has become a remarkable instance of policy mobility. According to policy researchers, rather than solely emanating from central authorities, many contemporary policy processes are now distributed across different sectors, giving non-governmental organizations, businesses and other experts much more influence in the direction of policy, the dissemination of policy ideas, the formulation of policy advice, and the enactment of policies. A single policy may be the result of myriad interests and concerns being slowly translated and aligned into shared objectives. Policies also travel across borders, are borrowed, shared, adapted and recontextualized, and are fashioned and refashioned through the involvement of diverse actors from a range of sectors.

The mobile, networked policymaking condition has proven ideal to the expansion of educational technologies and media. Edtech is increasingly present within formal education policies as a result of the significant effort of advocacy networks, think tanks, consultancies, campaign coalitions, and business lobbying. Policy discourses and agendas around digital education, ‘personalized learning’ and ‘AI in education’ have travelled at speed around the world, lubricated by network relations. These edtech power networks are actively intervening in education systems in ways that suggest new forms of power and influence over education and its future.

Edtech has long been presented as a powerfully ‘disruptive’ force in education. During the ongoing coronavirus crisis, new pandemic power networks have begun to coalesce around claims that edtech is not just disruptive, but in fact palliative. One example is a collaborative edtech network facilitated by the UK venture investment company Emerge Education. Badged as an ‘EdTech industry collaboration to help schools and colleges deal with CV19 and the need for home learning,’ the online summit featured a diverse cross-sector mix of US-based tech businesses (Adobe, Amazon Web Services, Google, Microsoft), alongside UK-based edu-businesses and their supporters. Its key aim was to help school leaders and teachers learn how ‘curated EdTech resources (both online and offline) are available to set up effective homeschooling.’

The claims made through such networks about the palliative benefits of digital technologies and online teaching for ailing education systems are not confined to the period of the health emergency itself. Instead, many of these organizations are seizing the opportunity to project their longer-term objectives for large-scale educational adaptation and change, forming into pandemic power networks to achieve their transformative objectives.

Coronavirus coalition-making
The United Nations Educational, Scientific and Cultural Organization (UNESCO) has positioned itself as the world authority on disruption to education caused by the global coronavirus outbreak. With approximately 1.5 billion students affected by school and university closures in 165 countries (87% of the global student population), UNESCO has taken the lead both in monitoring national responses and in formulating international responses to the educational crisis. On 24 March it released a ‘snapshot of policy measures’ as part of its Global Education Monitoring project, reporting that ‘all countries are introducing or scaling up existing distance education modalities based on different mixes of technology.’ Most countries, it reported, were using the internet and providing online platforms to deliver live lesson or record massive open online course (MOOC) styled lessons for continued learning, encouraging teachers and school administrators to use existing apps to support communication with learners and parents, or using TV and other media to deliver educational content.  However, it also noted major concerns about equity in access to ICT-based learning.

Two days later, on 26 March, UNESCO launched its Global Education Coalition as a ‘multi-sector partnership to provide appropriate distance education for all learners’, pushing the announcement across social media with the hashtag #LearningNeverStops and endorsement from Angelina Jolie in her role as a UN Special Envoy. Specifically, the coalition aims to help countries mobilize resources and implement ‘innovative and context-appropriate solutions to provide education remotely, leveraging hi-tech, low-tech and no-tech approaches’, identify ‘equitable solutions and universal access’, ensure ‘coordinated responses and avoid overlapping efforts’, and facilitate ‘the return of students to school when they reopen to avoid an upsurge in dropout rates.’ These are of course admirable and ambitious aims.

One additional objective stated on the coalition homepage, however, is to look beyond the context of the current emergency to longer-term transformations to education:

Investment in remote learning should both mitigate the immediate disruption caused by COVID-19 and establish approaches to develop more open and flexible education systems for the future.

In order to achieve both its immediate palliative aim and its longer-term objective of ‘investment’ in ‘education systems for the future,’ the coalition has enrolled partners from across sectors, including international organizations, civil society and private sector companies.

Edtech experiments
In the category of international organizations and multilateral partners, it includes Unicef, the WHO, World Bank, Global Partnership for Education, and the OECD. Two of these partners have already made significant effort to promote transformative agendas for education during the coronavirus outbreak.

The World Bank, for example, launched a Strategic Impact Evaluation Fund on 23 March, part of its funding program matching ‘scientifically sound research methods with policy challenges,’ with proposals invited for a fast-tracked competition intended

to generate experimental and quasi-experimental evidence that would be immediately useful for countries’ education systems as they deal with the Covid-19 pandemic.

In addition to the fund, the World Bank is also cataloguing best practices worldwide to support remote education through educational technologies, and working closely with national government ministries to develop their capacity:

The World Bank actively working with ministries of education in dozens of countries in support of their efforts to utilize educational technologies of all sorts to provide remote learning opportunities for students while schools are closed as a result of the COVID-19 pandemic, and is in active dialogue with dozens more.

It even talks of a long-term ‘crisis of education’ that pre-dates coronavirus, tapping into longstanding policy discourses of education systems being broken and in need of transformation that are also shared among many education-focused agencies, philanthropies and businesses.

The OECD, meanwhile, published a 23 March briefing with recommended policy proposals for national governments to tackle school closures, as part of a package of policy proposals covering many governmental sectors. ‘The #coronavirus crisis is a stress test for education systems around the world,’ the OECD Education directorate tweeted to promote the education proposals. ‘But it is an opportunity to embrace digital learning and online collaboration.’ The education briefing itself stated:

Every week of school closure will imply a massive loss in the development of human capital with significant long-term economic and social implications.

For the OECD, coronavirus is not just a human health crisis but a crisis of human capital stagnation. In order to mitigate this disruption to human capital development, the OECD recommended countries to use existing online infrastructure for online distance courses wherever possible, and to encourage education technology companies to make their resources freely available.

But the briefing concluded with a section on ‘long-term opportunities’.

The  current  wave  of  school  closures  offers  an  opportunity  for  experimentation  and  for  envisioning  new models of education and new ways of using the face-to-face learning time.

Such ‘experimentation’ and ‘envisioning’ should, suggested the OECD, ‘Explore different  time and schooling models,’ such as ‘how students can learn in different places and at different times’ using ‘digital learning solutions’ and ‘provide students with opportunities to have more agency by being given more autonomy.’ It should also ‘Empower teachers to make the most of digital advances,’ to ‘test out different digital learning solutions, and understand how technology can be used to foster deeper  student  learning,’ to ‘think creatively about their role as facilitators of student learning, and how technology can support them in doing so, and how they can combine their expertise as a profession.’

In an article on ‘the world’s biggest educational technology (edtech) experiment in history’, the OECD’s education director Andreas Scleicher claimed ‘It’s a great moment’:

All the red tape that keeps things away is gone and people are looking for solutions that in the past they did not want to see. … Real change takes place in deep crisis. You will not stop the momentum that will build.

Schleicher emphasized how the pandemic response would cut the ‘red tape’ from personalized learning and other new digital formats enabling students to take individual ownership of their learning.

These are familiar arguments from the OECD about the future of education, translated in a new context. It is now treating the global pandemic as an experimental opportunity and a ‘great moment’ to catalyse and sustain the long-term digital transformations to education systems that will enable human capital development for an increasingly digitalized economy. In these ways, the OECD is seeking to lubricate the links between learning and earning, as part of its economization of education, and to guide national education leaders to utilize digital technologies to ensure improved employability prospects for students. As Schleicher argued in his visionary book on building ‘21st century education systems,’ the OECD is shifting its emphasis from ‘literacy and numeracy skills for employment, towards empowering all citizens with the cognitive, social and emotional capabilities and values to contribute to the success of tomorrow’s world.’

Embedding big tech in education 
Besides the multilateral organizations, the UNESCO coalition has also partnered with the private sector and with non-profit education organizations. These include Google, Microsoft, and Facebook from the US tech sector, the international consultancy KPMG, as well as Weidong (cloud-based education services), Coursera (MOOC provider), Zoom (videoconferencing platform), Khan Academy (online learning), Moodle (learning management system) and code.org (learn to code coordinator).

Though it is not explicitly clear from the available coalition documents how these partners will each be involved, a key action of the coalition is to ‘match on-the-ground needs with local and global solutions’ and ‘provide distance education, leveraging hi-tech, low tech and no tech approaches.’ As such, it would appear that tech companies are to become officially-approved providers of ‘global solutions’ to schooling closures and the challenges of distance education.

While this switch to private sector and non-profit tech solutions remains completely understandable in the current context, its future implications for education systems around the world are far-reaching. These tech organizations share the ambition of the World Bank and OECD to embed digital technologies in education at very large scale, not just to assist in human capital development as the OECD explicitly states it, but in some cases to generate commercial advantage and market share too.

Some of these technology companies and organizations do not have unblemished records. For example, controversy has emerged over data collection and privacy of the videoconferencing platform Zoom, which was offered up to schools for free very quickly as lockdowns set in. Reports of racist ‘zoombombing’ of online lectures have raised new concerns over its security. Facebook has been the subject of extensive criticism, and has little record of involvement in education; Zuckerberg’s vehicle for educational influence is through the Chan Zuckerberg Initiative, which has become one of the most influential supporters of data-driven personalized learning software in the US. Google and Microsoft, of course, have longstanding programs in education, with Microsoft Teams and Google Classroom experiencing a surge of customers. Teams has become a key collaboration platform for university staff during lockdown, and Google Classroom, which passed the 50 million download mark in late March, used extensively by schoolteachers around the world to set remote learning tasks.

Google had already launched a new service called Teach from Home in partnership with UNESCO’s Institute for Information Technologies in Education, as a ‘temporary hub of information and tools to help teachers during the coronavirus (COVID-19) crisis’. It also provides resources for distance education through Google’s dedicated COVID19 Information and Resources site. Teach from Home actually consists of the standard Google G Suite of apps for education, including Classroom, Drive, Docs, Hangouts, Groups etc. ‘To give any of the suggestions a try, sign in with your G Suite for Education account,’ the Teach from Home site states. ‘If you don’t have one already, your school can sign up here.’ Google also launched Learn@Home through YouTube as a resource for families with children during school closures, with multiple channels of content provided by selected education partners. One of its main features is a daily ‘Homeroom’ video with Salman Khan of Khan Academy, another UNESCO coalition partner.

Salman Khan is also the author of a book popularizing the argument that conventional schooling is ‘broken’ and can be fixed through a ‘tech-friendly philosophy of education’. In Khan’s future vision of public education the borders between schooling and homeschooling become porous, as ‘flipped classrooms’ joined together by intelligent networked technology.

Khan Academy is the software-based embryo of the one world classroom. It’s not the fully functioning system, by itself. Khan Academy is more like a programming brain that the rest of the nervous system (different brick-and-mortar schools and homeschools) can access for the same unified participation in a free global education.

For Khan, as for many other Silicon Valley-based educational entrepreneurs, the software platform and the social media model is itself a template for school reform, where technology-enhanced teaching and learning appears to promise ‘an affordable and equitable educational future’ for all students. Khan Academy, Google, YouTube, Apple and Zoom are also all partners in another US-based edtech network, Wide Open Schools, established by Common Sense Media and powered by Salesforce to provide ‘a free collection of the best online learning experiences for kids.’ These organizations are forming into multiple network relations and formations to promote the kind of ‘flipped’ educational arrangements that tech organizations were already pursuing long before the COVID-19 outbreak, and which they aim to sustain after it.

The technology companies in these networks are also notoriously data hungry. Key figures such as Mark Zuckerberg of Facebook, Eric Schmidt formerly of Google, and Bill Gates of Microsoft are highly influential advocates of personalized education based on data and learning analytics. They see data as a key source of educational improvement, and promote technologies that can automate its analysis and provide real-time feedback to teachers or adaptive support to students. The involvement of these data-driven businesses in the UNESCO global coalition, and the rushed adoption of their platforms at scale, will alarm data protection and privacy campaigners concerned about commercial exploitation of student data, normalization of student surveillance, adoption of data processing technologies without full vetting procedures, or their imposition without full informed consent.

In the health domain, big tech companies have already signed agreements with governments to help solve the pandemic. Google, Microsoft, Palantir and Amazon are partners in the UK government’s efforts to gather real-time data on the virus, while Google is also gathering mass health data in exchange for coronavirus testing in the US:

Google’s ability to, in essence, force users to consent to data collection may become a more common tactic for companies and governments as the coronavirus rolls on, in their ongoing scramble to use technology to more effectively (and, most likely, profitably) stop the pandemic.

Similarly, within education, data-gathering organizations such as Google have now become virtually infrastructural to remote forms of education, if not to stop the pandemic then to mitigate its effects on many millions of students.

While UNESCO’s intentions are clearly admirable and necessary, the Global Education Coalition has empowered commercial technology actors and the global education industry to become a global infrastructure for education during and after the coronavirus outbreak too. Whether their services are desirable or not in the current context or beyond, clearly this coalition is enabling private tech businesses to expand their reach and influence in public education.

Global education for the future
The new pandemic edtech power network emerging through UNESCO’s Global Education Coalition is seeking to fulfil the important requirement for continuity of education for hundreds of millions of students worldwide. Many of its aims and its partners are clearly involved out of strong moral commitment. Not all the partners may always share the same objectives, but have, under extraordinary conditions, translated their aims into a shared policy and technology agenda that may lead to long-term consequences. The multilateral and tech sector partners of the coalition are already pushing for long-term changes to education systems that will:

  • Emphasize digital technologies as a solution to a perceived ‘crisis’ of education that pre-dates coronavirus
  • Embed digital technologies as long-term infrastructures of teaching, learning and assessment
  • Empower private sector technology companies as key providers of educational infrastructure, platforms, apps, content and other services
  • Further decentralize education systems into connected networks where learning can be conducted across homes, schools and other settings
  • Enhance data collection and expand use of data analytics, personalized learning software and AI in education
  • Focus on human capital development for the digital economy, and on lubricating learning-to-earning pipelines

Very similar aims are shared by other networks, such as the Emerge edtech industry collaboration and the Wide Open Schools partnership. These power networks are not so much staging a private ‘takeover’ of education, but together they are seeking to build a private infrastructure on which public education will depend.

These new power networks are also seeking to demonstrate the agility of the technology sector and the capacity of technology itself to solve complex policy problems. They are aiming to make digital technologies perform roles as policy machinery, able to enact significant changes on education systems at short notice.

These are of course not new aims. Multilateral organizations and technology companies have been pursuing them for years. But the UNESCO coalition has brought these organizations and their aspirations into closer contact and alignment with current emergency policy agendas. New network relations are being formed to drive the use of digital technologies to achieve remote education for all in ways that, in the short term, are intended to address deep inequalities in access to education during the coronavirus outbreak, but that also raise the prospect of profound long-term alternations to systems of public education.

These changes are happening fast during the emergency and are occurring almost without contest, despite years of critical studies of the influence of international organizations such as OECD and World Bank, commercial business involvement in public education, and concerns about the impact of the global education industry:

The shift in authority from the state to private actors might make sense on efficiency grounds, but also entails the undermining of democratic control of public education. Moreover, the professional autonomy and rights of teachers, as well as the local control of communities over their schools, may be undercut by the shift in authority to private, corporate, and global actors. Similarly, it is reasonable to question whether the shift in accountability structures away from democratic modes to corporate/consumer arrangements reshapes the orientation of education as a public good.

These remain critical issues as new pandemic edtech power networks plan to embed themselves in public education systems long past the public health crisis itself.

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Emergency edtech

Ben Williamson

markus-spiske-unsplash

The education technology industry has positioned itself as an emergency response to the coronavirus pandemic. Photo by Markus Spiske on Unsplash

Education institutions around the world are switching to ‘remote’ teaching and learning, and the education technology industry is generously offering its products to support them in the current emergency. To a significant extent, the emergency edtech response is providing much-needed services to help educators provide some continuity of study and learning for their students. But the edtech sector has been preparing for remote education for years, and built up a marketplace of products that could radically alter how education is organized long after the world has recovered from the public health crisis.

Pandemic markets
The novel coronavirus pandemic is a health emergency, a political emergency, an economic emergency, and an educational emergency. Since its effects on education systems first became apparent in south east Asia early this year, education companies and technology businesses have ramped up their marketing of products to support online learning, seeing the public health crisis and the quarantining of students partly as an opportunity to prove the benefits of edtech.

Coronavirus may also be beneficial for the edtech industry for financial reasons. Early in March, the investment bank BMO Capital Markets predicted a spike in edtech stocks. ‘While we are uncomfortable citing “winners” in the coronavirus situation, some companies may be positioned better than others,’ it claimed. ‘Specifically, those that specialize in online education could see increased interest should the situation worsen’. BMO Capital Markets specifically singled out major market leaders including K12 and Pearson as potential for-profit beneficiaries of mass education closures and population quarantining measures. These companies have already created the technologies to support ‘remote’ forms of teaching and learning across both the schooling and higher education sectors.

To take one of these example companies, the multinational, multibillion dollar edu-business Pearson has been seeking to reshape education as a remote process as part of a ‘digital transformation’ and corporate restructuring stretching back nearly a decade. In the past few years, Pearson has adopted a ‘digital first’ strategy, begun ditching its production of textbooks, and embraced new forms of ‘platform’ delivery. It has also reconceived its customers as ‘Gen Z’ student-consumers who prefer ‘on-demand streaming’ content to conventional educational delivery, and developed a ‘Global Learning Platform’ to position itself as the ‘Netflix of education’.

At the same time, Pearson has significantly increased its emphasis on online learning for higher education, with a strategic focus on growing its Online Program Management (OPM) market share specifically in the US and UK. OPM models are attractive to universities as they provide the infrastructure necessary for institutions to deliver distance courses and thereby increase their share of the international student market. Institutions across the US and UK have signed 10-year deals with the company, where Pearson provides the back-end systems to host courses and then takes a 50% cut of the fees when students enrol.

Pearson’s Global Learning Platform and Online Program Management services are not just technical developments but ‘market devices’ that have enabled the company to create new markets for its products, and establish itself as the market leader in edtech as part of its corporate vision of education. It is both reaching out to students themselves as remote customers of streaming education services, and partnering up with universities to deliver remote courses. As Anna Hogan and Sam Sellar have argued in relation to Pearson’s vision of education in 2025, the company is seeking to create disruptive changes to the educational profession, deliver personalized learning as a private service, and generate huge quantities of student data for further analysis and product development.

These are not changes that Pearson and its competitors are simply offering up, opportunistically, in response to sudden coronavirus measures. Instead, they are part of a concerted long-term strategy by the edtech industry to actively reorganize public education as a market for its products, platforms and services. As Pearson’s 2018 corporate strategy document stated, the company aimed to shape the future of  education and lead and shape the market too.

Edtech companies, exemplified by Pearson, wish to make ‘remote learning’ the new normal mode of education. ‘Remote’ may not even mean students being geographically distant from their schools or campuses, but simply that edtech platforms act as  intermediaries between educational institutions and their students, acting at a distance to shape the possibilities of teaching and learning. The global pandemic has appeared as an opportunity to rapidly grow market share, generate competitive advantage, and boost stock market valuation, with a view to long-term consolidation of market advantage and to reshaping public education at the same time.

Pandemic experiments
The global coronavirus pandemic is also an opportunity to produce very large quantities of student data, as students are forced online into data-intensive digital learning environments at unprecedented scale. For researchers and organizations invested in data scientific forms of analysis in education, as Jonathan Zimmerman put it in The Chronicle of Higher Education, coronavirus is an opportunity for a ‘great online learning experiment’.

Coronavirus … has created a set of unprecedented natural experiments. For the first time, entire student bodies have been compelled to take all of their classes online. So we can examine how they perform in these courses compared to the face-to-face kind, without worrying about the bias of self-selection. It might be hard to get good data if the online instruction only lasts a few weeks. But at institutions that have moved to online-only for the rest of the semester, we should be able to measure how much students learn in that medium compared to the face-to-face instruction they received earlier.

The working assumption here is that coronavirus is a natural experimental opportunity for education data scientists–both those in academic education research and analysts working in edtech companies and other edubusinesses–to demonstrate the effectiveness of online education over face-to-face teaching. Zimmerman even argued that it should be considered a kind of moral responsibility for universities to use the chance to figure out if online education outperforms in-person teaching, even though, he said, ‘if students showed more gains from online instruction, professors who teach face-to-face classes–like I do–might find their own jobs in peril’.

The Chronicle article is fraught with methodological and ethical problems. Clearly any analysis of the data of populations of online students affected by pandemic conditions could not be meaningfully compared with other data from face-to-face teaching under other conditions. Treating a pandemic as an experiment in online learning reduces human suffering, fear and uncertainty to mere ‘noise’ to be controlled in the laboratory, as if there is a statistical method for controlling for such exceptional contextual variables. Yet the data scientific dream of measuring learning at scale in order to develop a precise understanding of the benefits of remote instruction is clearly animating part of the effort by edtech businesses and associated researchers to utilize the coronavirus emergency as a mass data-gathering and analysis opportunity. And this might ultimately, as Zimmerman suggested, lead to a consolidation of online instruction and lead to further worker precarity for educators.

Emergency edtech eventually won’t be needed to help educators and students through the pandemic. But for the edtech industry, education has always been fabricated as a site of crisis and emergency anyway. An ‘education is broken, tech can fix it’ narrative can be traced back decades. The current pandemic is being used as an experimental opportunity for edtech to demonstrate its benefits not just in an emergency, but as a normal mode of education into the future.

A full paper on Pearson’s market-making activities in higher education is published in Critical Studies in Education, or available at ResearchGate.
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Re-engineering education

The Chan-Zuckerberg Initiative, for-profit philanthropy and experimental precision education

Ben Williamson

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The Chan Zuckerberg Initiative is developing experimental new approaches to measurement and intervention in education. Photo by chuttersnap on Unsplash

Many new parents announce the birth of a child on Facebook. Mark Zuckerberg took it a step further, announcing in a December 2015 ‘letter to our daughter‘ that he and Priscilla Chan would give 99% of their Facebook shares during their lifetimes (estimated then at around US$45billion) to causes including education, science and social justice. The vehicle would be the Chan Zuckerberg Initiative (CZI), a ‘new kind of philanthropy’ focused on ‘personalized learning, curing disease, connecting people and building strong communities.’

Four years on, as Chan and Zuckerberg’s child approaches school age, what kind of influence has CZI had on education? ‘Our experience with personalized learning, internet access, and community education and health has shaped our philosophy,’ they wrote in their letter to their newborn daughter. ‘Your generation,’ they continued, will ‘have technology that understands how you learn best and where you need to focus. You’ll advance quickly in subjects that interest you most, and get as much help as you need in your most challenging areas. You’ll explore topics that aren’t even offered in schools today. Your teachers will also have better tools and data to help you achieve your goals. Even better, students around the world will be able to use personalized learning tools over the internet, even if they don’t live near good schools.’

Personalized learning supported by technology tools and data is clearly its priority, not just within the USA but around the globe. This is a long-term project, as Zuckerberg’s letter stated. But by looking closely at its existing portfolio of grants and investments, and at its peculiar organizational structure and status, it is possible to gain some insights into how it is trying to instantiate its vision–and to speculate on its effects.

Grants and investments
In its early days, CZI faced criticism for its lack of transparency. By 2018 it had already spent $300million on education-related projects but it took digging by journalists to reveal what the money was supporting. Since then it has maintained an open grants and investments database. Its grants database–retroactive to January 2018–lists over 400 awards across its three key mission areas, and a ventures list of 15 major investments.

The investments include Byju’s (the highly successful learning app based in India), AltSchool (a Silicon Valley startup school chain that folded in 2019 to become the edtech software company Altitude), Panorama (a platform for schools to gather social-emotional learning data), Brightwheel (an early years management platform), and Handshake (a platform to match college graduates to careers). CZI’s ambitions in education therefore stretch from the early years through higher education and on into graduate destinations, as well as beyond the US borders into new models of online learning at huge global scale. In just a few years, CZI has become a major player in an expanding ‘global education industry‘.

Besides its investments, some of CZI’s education grants are enormous. Most notable is $23million awarded to Summit Schools since 2018 alone–though this does not include any previous grants to the charter school chain, or its in-kind donation of a 50-person engineering team from Facebook to build its personalized learning platform. CZI also granted $2million to TLP, the partnership established to roll-out the Summit Learning Platform nationally. The deployment of engineers to Summit is typical of CZI’s technology-based approach as a self-proclaimed ‘new kind of philanthropy focused on engineering change at scale.’

Of its 88 listed education grants, CZI has also awarded a range of charter school chains, as well as a range of initiatives broadly focused on personalized education, social-emotional learning, and school innovation. Technological solutions, data and evidence feature significantly across these and other programs in its Education Initiative:

We build tools that help teachers tailor learning experiences to the needs of every student, with an emphasis on using evidence-based practices from the fields of learning science and human development … We believe in a data-driven approach … [and] that students need to learn more in school than what is measured on standardized tests. Our tools help students set and track progress towards short- and long-term goals, make plans, demonstrate mastery when ready, and reflect on their learning.

CZI is in some ways a very ‘hands-on’ organization, giving gifts with a view to adding engineering solutions to the problems that its grantees are seeking to address. Even prior to CZI, Zuckerberg had joined up with the Gates Foundation to fund the EducationSuperHighway program to connect all US schools to broadband internet. Zuckerberg and Gates have helped lay the infrastructural cable network to enable digital learning in US schools, and to create the conditions necessary for personalized learning across the system.

For-profit philanthropy
Although it has a major record of grant-giving, CZI is not a typical philanthropic foundation. Instead, it was established as a Limited Liability Company (LLC). LLCs are legally-defined entities which, in contrast with conventional non-profit, tax-exempt private foundations, are free to engage in grantmaking, investment, and political action with few restrictions. It also provides enhanced personal control for its founders.

The legal scholar Dana Brakman Reiser suggests that LLCs such as CZI represent a new form of ‘disruptive philanthropy’ that is distinct from traditional philanthropies (Rockefeller, Carnegie) or even recent ‘venture philanthropies (Gates, Broad). Instead LLC philanthropy models–‘philanthropy 3.0′–have become increasingly common among Silicon Valley entrepreneurs. Ebay co-founder Pierre Omidyar’s Omidyar Network has LLC status, as does Laurene Powell Jobs’ Emerson Collective and ex-Google chair Eric Schmidt’s Schmidt Futures. These ‘disruptive philanthropic vehicles,’ Reiser argues, ‘can both unleash tremendous capital for solving society’s most challenging problems and magnify the influence of its most powerful elites.’ CZI is not so much a philanthropic organization, but a ‘philanthrocapitalist‘ one with huge financial, political, and technical power.

In practice, being an LLC means CZI can act as a charitable grant giving organization, while also making investments in for-profit companies, engaging in ‘impact investing’–where financial returns can be made from programs with measurably beneficial social results–and carrying out significant political work too. CZI’s leadership gives it significant political clout. Zuckerberg himself is connected to a range of political, legal, financial and media networks. Rachel Moran compellingly describes him as a ‘network switcher.’ CZI also made senior hires from Uber, Microsoft, Amazon, Google, Virgin America, Rockefeller University, the Gates Foundation, the US Department of Education, the White House, and various Silicon Valley law firms. This gives CZI the power, through its advocacy program, to ‘support policy change strategies,’ as well as to ‘shape policies’ and engage in ‘changing laws.’

To be fair, many of CZI’s advocacy efforts are targeted at causes such as addressing systemic inequality and injustice. The problem is that ‘philanthrocapitalism’ casts these as issues that can only be solved through programs that also legitimate and deliver personal profit. As Linsey McGoey has argued, philanthrocapitalism ‘resonates with long-held economic assumptions of the moral advantages of capitalism.’ However, ‘what is most novel about the new philanthrocapitalism is the openness of personally profiting from charitable initiatives, an openness that deliberately collapses the distinction between public and private interests in order to justify increasingly concentrated levels of private gain.’

Philanthrocapitalism, or ‘venture philanthropy’ has been strongly associated with foundations such as the Gates Foundation. But foundations such as Gates do continue to operate as non-profits. As an LLC, CZI is subtly different, and much more overtly engages in for-profit activities where social benefit and financial return are treated as reciprocal outcomes. Ken Saltman, for example, has raised a ‘serious question as to whether CZI functions philanthropically at all or whether its activities are only profit seeking and “philanthropy” is a label intended to project an image of “corporate social responsibility.”’

Experimental precision science
Although personalized learning is CZI’s most overt focus area in its Education Initiative, perhaps more significant is its dedication to ‘learning science.’ It is through its learning science program, grants and investments that CZI’s vision for the future of education becomes most clear.

The CZI’s learning science page states that ‘The best learning experiences are grounded in the science of how people learn and develop. We enable educators, researchers, education technology developers, and communities to use the latest learning science,’ and it emphasizes ‘learning measurement, the ‘ development, collection, evaluation, and use of high-quality evidence’ in order to ‘apply knowledge of how people learn’ and ‘develop solutions to challenges educators face in classrooms.’

To achieve this goal, it announced a $5million fund for ‘teams of schools, support organizations, and researchers who want to apply the science of learning and human development to improve existing school-based practices.’ A further partnership with the Gates Foundation began to explore the science of ‘executive function’ and the neural substrates of learning, leading to a ‘consensus’ report and a blueprint for further research and development. That in turn catalysed a joint Gates/CZI $50million fund for the 5-year EF+Math Program, designed to award basic and applied research in executive function, led by educational neuroscientists at the University of California San Francisco.

The program lead of EF+Math is also the Director of Education at Neuroscape at UCSF, a brain imaging centre which together with BrainLENS (Laboratory for Educational Neuroscience, also at UCSF) was awarded a further $2.9million by CZI in 2018 to develop ‘a free mobile tool to measure child and adult progress in executive functioning skills such as working memory, attention, problem solving, and goal setting’. Together, Neuroscape and BrainLENS are developing new computational approaches to brain and genetic analysis applied to education. Neuroscape and BrainLENS are also partners of the University of California’s multi-institutional Precision Learning Center, which focuses on the use of neuroscience, psychology and biomedical data to improve learning experiences and outcomes.

Given CZI’s Science Initiative emphasis on ‘precision medicine‘–the use of big data and predictive algorithms for healthcare–its learning science efforts appear to suggest it is positioning itself as a centre of expertise and authority in ‘precision education.’ CZI’s director of learning science, Bror Saxburg, has made the link between precision medicine and precision education explicit in his advocacy for ‘learning engineering.’ Saxberg, a high-profile learning scientist within the education technology industry, describes learning engineering as a multidisciplinary blend of the learning sciences, instructional design and learning analytics:

getting the most from learning analytics has to be an interdisciplinary effort: computer science, linguistics, education, measurement science, cognitive science, motivational and social psychology, machine learning, cognitive neuroscience among others. These different domains will need to be combined to build out an effective evidence-grounded ‘learning engineering’ version of learning analytics.

These learning engineering approaches, including data gathering and modelling, says Saxburg, ‘ultimately can allow for personalization to interests, capabilities, identity, social-emotional state, and motivation states for individual learners’, by using evidence ‘at multiple levels, from clickstreams, motion position data, speech streams, gaze data, biometric and brain sensing, to more abstracted feature sets from all this evidence.’ The use of this evidence across ‘multiple dimensions’, he adds, will allow examination of ‘longitudinal and multidimensional trajectories’ and clusters and patterns of ‘learner change.’ Such analyses, finally, will  help to identify ‘new opportunities for targeted intervention’ and ‘precise action’ that are analogous to data-scientific ‘precision medicine.’

As such, through Saxberg and its learning science grants, CZI is promoting learning engineering as an educational parallel to precision medicine–the experimental use of multiple sources of biomedical, neuroscientific, cognitive and psychological data for personalized diagnosis and intervention.

Re-engineering education
The Chan Zuckerberg Initiative may not yet have the reach and influence of the Gates Foundation, but it is fast becoming one of the most significant funders of educational technology development and scientific research into learning and child development. This positions it to become a powerful source of authority in the shaping of education in multiple ways.

Through support for Summit and other charter school operations it is continuing the longstanding project of philanthropic advocacy for alternatives to public education, albeit now in the for-profit mode of disruptive philanthropy. Its personalized learning projects are extending adaptive, data-driven software beyond the charter chains where they have been developed and tested and out into schools and colleges at very large scale. And by funding computationally-powered research and development in learning science and learning engineering, CZI is advancing experimental new ‘precision’ understandings of the human brain and cognition into applied teaching practices. It is in other words championing a new model of personalized, precision education that brings together the Silicon Valley culture of disruption, commercial technology, personalized learning advocacy, and new scientific practices modeled on those of precision medicine.

By creating CZI as an LLC, Chan and Zuckerberg also maintain powerful control over their spending and the direction of the organization. This gives them unprecedented power to shape the direction of research and development in education, by selecting and investing in programs that fit their personal vision. These efforts amount to an attempt to experiment on and re-engineer education into the form that Mark Zuckerberg and his networks find desirable, and that they believe can and ought to be pursued and attained. CZI is re-engineering education at scale.

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Platform teachers

Ben Williamson

containers by Guillaume Bolduc

Amazon has launched a new service allowing teachers to sell and buy education resources through its platform. Image by Guilaume Bolduc on Unsplash: https://unsplash.com/photos/uBe2mknURG4

The massive multinational platform company Amazon has announced a new service allowing teachers to sell lesson plans and classroom resources to other teachers. The service, Amazon Ignite, is moving into a space where Teachers Pay Teachers and TES Teaching Resources have already established markets for the selling and buying of teaching materials. These services have reimagined the teacher as an online content producer, and Amazon has previously dabbled in this area with its Amazon Inspire ‘open educational resources’ service for free resource-sharing. But Amazon Ignite much more fully captures the teaching profession as a commercial opportunity.

The operating model of Amazon Ignite is very simple. Teachers can produce content, such as lesson plans, worksheets, study guides, games, and classroom resources, and upload them as Word, Powerpoint or PDF files using the dedicated Amazon Ignite platform. Amazon then checks the resources to ensure they don’t infringe any copyrights before they appear in the marketplace. In these ways, Amazon is now in the business of ‘shipping’ educational content across the education sector in ways that mirror its wider online commerce model.

Amazon claims the Ignite platform offers a way for teachers to ‘earn money for work you’re already doing’ by paying users 70% royalties on the resources they sell. The company itself will take 30% of the sales, plus a transaction fee of 30 cents for items under $2.99, though it also has discretion to change the price of resources including by discounting the cost to customers. This makes Amazon Ignite potentially lucrative for Amazon as well as for successful vendors on the platform.

Although Ignite is available only in the US in the first instance, the platform exemplifies the current expansion of major multinational tech companies and their platforms into the education sector. The extension of the commercial technology industry into education at all levels and across the globe is set to influence the role of the teacher and the practices of the classroom considerably over coming years.

Teacher brand ambassadors
The edtech industry, and the wider technology sector, are strongly involved in defining the characteristics and qualities of a ‘good teacher’ for the 2020s. While commercial businesses have long sought access to schools, the National Educational Policy Center (NEPC) in the US recently launched a report on teachers as ‘brand ambassadors’:

Corporate firms, particularly those with education technology products, have contracted with teachers to become so-called brand ambassadors. A brand ambassador is an individual who receives some form of compensation or perk in exchange for the endorsement of a product. Unlike celebrity endorsers, teachers can be thought of as ‘micro-influencers’ who give firms access to their network of social influence.

Teacher brand ambassadors, as well as ‘product mentors’, ‘champions’ and ‘evangelists’, have become significant edtech marketing figures. They often use social media, including Twitter, Facebook, and Instagram, to promote and model the use of specific educational technologies. They might even be involved in the development and testing of new software features and upgrades, as well expenses-paid trips to conferences, summits and trade events where they are expected to attend as representatives of the brand.

The NEPC reported that teacher brand ambassador programs raise significant ethical issues and conflicts of interest, while delivering return on investment to producers when their product is introduced into classrooms and students are exposed to their brand.

As the big tech firms have closed in on education, they have begun to merge the marketing role of the brand ambassador into a professional development role–such as Google’s Certified Educator program. Amazon’s AWS Educate program enables whole institutions to become AWS Educate members, in effect bringing whole institutions into its branded environment. The ‘perks’ include providing educators access to AWS technology, open source content for their courses, training resources, and a community of cloud evangelists, while also providing students credits for hands-on experience with AWS technology, training, and content.

Platform gig teachers
Amazon Ignite, however, represents the next-stage instantiation of the brand ambassador and the teacher as micro-influencer. On Amazon Ignite, teachers are not contracted as platform ambassadors, but invited to become self-branded sellers in a competitive marketplace, setting up shop as micro-edubusinesses within Amazon’s global platform business. Without becoming official brand ambassadors, teachers become gig workers engaging in market exchanges mediated by Amazon’s platform. This in turn requires them to become micro-influencers of their own brands.

So who are the teachers who participate in the Amazon Ignite educational gig economy? Amazon Ignite is ‘invitation-only’ and as such makes highly consequential decisions over the kinds of content and resources that can be purchased and used. This might be understood as high-tech ‘hidden curriculum’ work, with Amazon employees working behind the scenes to make selections about what counts as worthwhile resources and knowledge to make available to the market.

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The list of ‘featured educators’ on Amazon Digital Education Resources. Image from:  https://www.amazon.com/b/ref=dervurl?node=17987895011

It is not really clear that Amazon Ignite will even empower existing classroom teachers to become content producers and sellers. A brief review of the current ‘featured educators’ on Amazon’s Digital Education Resources page gives an indication of the kind of invited participants who might thrive on Ignite. Most of these appear as established micro-edubusinesses with well-developed brands and product ranges to sell. Amazon offers extensive advice to potential vendors about how to package and present their resources to customers.

The featured educator Blue Brain Teacher, for example, is the branded identity of a former private education curriculum adviser and Montessori-certified educator, who focuses strongly on ‘brain-based’ approaches including ‘Right-Brain training’. An established vendor on Teachers Pay Teachers, the Blue Brain Teacher also has a presence on Facebook, Instagram and Pinterest, is a Google Certified Educator, and officially certified to offer training on Adobe products.

Another featured educator, Brainwaves Instruction, also has a glossy website and existing web store of printable resources, a blog featuring thoughts and lesson ideas on mindfulness, growth mindset, and the adolescent brain, and all the social media accounts to amplify the brand.

These and many of the other featured educators on the Amazon Digital Education Resources store give some indication of how the Amazon Ignite market will appear. Many are existing TpT users, active and prolific on social media, have their own well-designed and maintained websites, write blogs, and are highly attentive to their brand identity. Some, such as Education with an Apron, are not limited to the selling of educational resources, but have their own teacher-themed fashion lines such as T-shirts and tote bags (‘I’m the Beyonce of the classroom’). These are teacher gig workers in an increasingly platformized education sector.

Amazon Ignite, at least at this early stage, also seems to be overwhelmingly feminized. Most of its featured educators present themselves through the aesthetics of lifestyle media and family values, as examples such as The Classroom Nook indicate. It suggests the reproduction of a specifically gendered construction of the teacher.

This is balanced, in many cases, with sophisticated social media-style iconography, and significant investment in various technology industry programs. Erintegration, for example, shares resources, lesson plans, reviews, and tips for using iPads, Google Apps, and other devices ‘to engage digital learners in all curriculum areas’, and is already involved in other Amazon programs:

Erintegration is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com.

Erintegration is sometimes provided free services, goods, affiliate links and/or compensations in exchange for an honest review.  All thoughts and options are my own and are not influenced by the company or its affiliates.

Not all the featured educators are single individuals either. Clark Creative Education is a team of educators, authors, designers and editors, whose founder is a ‘top-milestone author on Teachers Pay Teachers’. Amazon Ignite is, then, not simply empowering practising teachers to ‘earn money for work you’re already doing’ but is actively incentivizing the expansion of a market of educational startup content producers.

Children can even be content providers. According to the Terms and Conditions, ‘A parent or guardian of a minor can open a Program account and submit the minor’s Resource-Related Content as the Content Provider’. Given the role of young celebrity micro-influencers on social media, it is possible to speculate here that school children could also establish positions as ‘edu-preneurial’ content producers.

Platform classrooms
All in all, Amazon Ignite is encouraging teachers to see themselves as empowered and branded-up personal edubusinesses operating inside Amazon’s commerce platform. It is easy to see the attraction in the context of underfunded schools and low teacher pay. But it also brings teachers into the precarious conditions of the gig economy. These educators are gig workers and small-scale edu-startup businesses who will need to compete to turn a profit. Rather than making select teachers into brand ambassadors for its platform, Amazon is bringing teacher-producers and education startups on to its platform as content producers doing the labour of making, uploading and marketing resources for royalty payments. It expands platform capitalism to the production, circulation and provision of classroom resources, and positions Amazon as an intermediary between the producers and consumers in a new educational market.

By making selections about which educators or businesses can contribute to Ignite, Amazon is also making highly significant and opaque decisions about the kind of educational content made available to the teacher market. The criteria for inclusion on Amazon Ignite are unclear. What kind of educational standards, values, or assumptions underpin these choices? Curriculum scholars have long talked about the ways aspects of culture and knowledge are selected for inclusion in school syllabi, textbooks and resources. Amazon is now performing this function at a distance through its selection of educational content creators and market vendors.

Over time, Amazon Ignite is likely to produce hierarchies of vendors, since Amazon claims the Ignite resources will show up in search results. This raises the prospect of algorithmic recommendations based on a combination of vendor popularity and users’ existing purchases—a ‘recommended for you’ list tailored to teachers’ search and purchase histories. The Terms and Conditions specify that Amazon ‘will have sole discretion in determining all marketing and promotions related to the sale of your Resources through the Program and may, without limitation, market and promote your Resources by permitting prospective customers to see excerpts of your Resources in response to search queries’.

Moreover, Amazon claims ‘sole ownership and control of all data obtained from customers and prospective customers in connection with the Program’, thereby gaining the advantage of using buyer and seller data to potentially further maximize its platform profitability.

Amazon Ignite anticipates an increasingly close alignment of classrooms and platforms in coming years. ‘As with social media platforms in the 2000s, educational platform providers will be working to expand the scope of their “walled gardens” to encompass as many user practices as possible’, argue the authors of a recent article outlining likely trends in education technology in the 2020s. Along with Amazon’s ongoing attempts to embed its Alexa voice assistant in schools and universities, Amazon Ignite has now further expanded the walls of Amazon’s huge commerce platform to enclose the education sector. Amazon is inciting educators to become platform teachers whose labour in platform classrooms is a source of profit under platform capitalism.

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Psychodata

Ben Williamson

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‘Social emotional learning’ centres on the capture of psychological data from children. Photo by Annie Spratt on Unsplash

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Psychodata: disassembling the psychological, economic, and statistical infrastructure of ‘social-emotional learning’ is published in Journal of Education Policy. An accessible version is also available at Researchgate.
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