Edtech sci-fi

Ben Williamson

Artistic sci-fi depiction of a futuristic classroom. Image by Josan Gonzales.

Before making a career out of studying education technology, I was a student of literature. As an undergraduate student of English Lit at Cardiff University, we were taught it was possible to critique the canon, analyze cultural objects as mundane as cereal packets, and engage with ‘genre’ fiction such as crime, horror and sci-fi. Later, as a part-time PhD literature student working full-time for an edtech ‘futurelab’, I read Neal Stephenson’s 1995 sci-fi novel The Diamond Age; among many elements, it features an edtech device called the Primer. It was a strange moment as my PhD, partly about Stephenson’s novels, came into contact with my edtech day-job.

The idea of exploring edtech in sci-fi has remained in the background of my work ever since, but I’ve never properly figured out what to do about it or even if it was too niche an area of literary interest.

Doing something about edtech sci-fi came up again during a recent workshop to develop a new taught course. Might edtech sci-fi open up students to critical perspectives on current edtech issues such as datafication, inequalities, commercialization and so forth?

As a way of finding out, on Twitter, I asked “Anyone got good examples of education technologies in sci-fi, text or film? Got the Primer in the Diamond Age, roboteachers in Class of 1999, but what else? Possibly for a course #edtechscifi”. Below I’m listing all the responses I received, partly for my own benefit but hopefully in case others are interested too. But first a quick discussion of why studying edtech in sci-fi may be a useful way of approaching a range of critical current issues in research on education.

Science fiction has, for well over a century, provided authors with a way of speculating about the future from current trends, and by doing so to explore major concerns, tensions and anxieties characterizing its historical, social and political context. From fears of nuclear destruction–such as A Canticle for Liebowitz by Walter Miller in the 1950s–and anxieties over neural implants–in Gibson’s Neuromancer and Stephenson’s Snow Crash in the 80s/90s–today, much contemporary sci-fi is grappling with the consequences of social media, data profiling, surveillance, automation and inequalities.

Recent favourites are Zed by Joanna Kavenna, the novella collection Radicalized by Cory Doctorow, and Burn-In: a novel of the real robotic revolution by PW Singer and August Cole. The latter is a heavily endnoted, research-based novel about the dangers of automation and right-wing extremism authored by two intelligence analysts. They’ve termed it “fiction intelligence” that blends narrative with nonfiction. I’ve also got Kim Stanley Robinson’s The Ministry for the Future on my shelf, a near-future fiction about environmental destruction. In the book How to Run a City like Amazon, and other fables, a group of academic social scientists and geographers even produced a collection of social science fiction stories and poetry about corporate digital urbanism.

Fiction may even animate social theory: as David Beer argues, “fiction has been used to encounter and interrogate far-reaching and vital questions about the social world, some of which are deeply political and global in their scope”.

So fiction in general and sci-fi specifically can speak to urgent contemporary social, technical, political and environmental concerns. As academic geographer (and fiction writer) Rob Kitchin points out,

science fiction employs the tactics of estrangement (pushing a reader outside of what they comfortably know) and defamiliarisation (making the familiar strange) as a way of creating a distancing mirror and prompting critical reflection on society, now and to come. Perhaps unsurprisingly, there is a long history of academics drawing on the imaginaries of science fiction in their analyses, and also science fiction writers using academic ideas in their stories.

I’d suggest this should prompt more engagement with edtech in sci-fi – not to treat as sci-fi as a model for the future of education, but as a way of exploring the far-reaching personal, social, political and envionmental impacts of edtech development from recent trends.

Artist vision of the future of education: the Edu Ocunet by Tim Beckhardt

One suggestion to my Twitter query from several people was the 2002 dystopian novel Feed by MT Anderson, a fabulous near-future novel featuring neural interfaces and the complete handover of state responsibility to corporations. We don’t have to think too hard to come up with examples of individual tech entrepreneurs and corporations already pursuing the development of brain-computer interfaces that could bring the dystopia of Feed to fruition.

Feed also features a very ominous depiction of education in the shape of “SchoolTM“, a completely corporatized education system that teaches students, through their direct-to-brain feed, to value rampant consumerism and environmental destruction over history, politics and civic participation. The novel explores the consequences of such an technology-centred, corporate education system for its teenaged protagonists and, moreover, for democracy itself.

David Golumbia and Frank Pasquale were kind enough to send me a copy of a recent chapter in which they analyze Feed as a way in to understanding a current “corporate-political world” characterized by the “primacy of the corporate form”. It’s a brilliant chapter, and offers a compelling justification for focusing analytical attention on fiction as a way of studying contemporary social, technical, economic and political problems.

Fiction, they argue,

frees authors to extrapolate from current trends to thick descriptions of the futures they portend. Corporations and governments often use scenario analysis to understand a range of possible futures to prepare for, but such analyses tend to eschew the visceral, subjective, and psychological insights that good fiction embodies. A novelist can imagine the ways in which the minds of individuals both reflect and reinforce their social environment. These considerations are just as worthy of policy-makers’ attention as the economic and political models that now dominate discussions of corporate rights.

Beyond the depiction of the interior lives of characters, novels engage with the complex social, political, economic and and environmental crises of our time.

The depiction of education in Anderson’s novel, they go on, “forms the critical backdrop for the world depicted in Feed, since so much of the novel turns out to depend on the characters’ lack of critical thinking skills and ignorance of fundamental issues of history and politics.” This, for me, offers a rich opening for the further examination of edtech in sci-fi, or, indeed, “social science fiction” writing as critical academic practice in edtech research. I’m interested to explore further how to engage with edtech sci-fi in possible future research and teaching.

In the meantime, however, here’s a list the lovely people on Twitter suggested of edtech sci-fi texts, TV, and film. Three were even suggestions of existing compilations of edtech sci-fi: a 2015 piece by Audrey Watters on Education in Science Fiction, a collection by Stephen Heppell, and an entry on Education in SF at the Encyclopedia of Science Fiction. Check those out too. I’ve alphabetized the list but nothing more. Some people added short descriptions, which I’ve paraphrased, and others links, which you’ll have to mine the replies to find, I’m afraid.

A Clockwork Orange, novel by Anthony Burgess, film by Stanley Kubrick – technologized socialization

AI film by Steven Spielberg – Dr Know, a holographic answer engine

Anathem by Neal Stephenson – anti-tech monasteries

And Madly Teach by Biggle

An Enterprising Man by Joe Frank

A.R.T.H.U.R. poem by Laurence Learner – “metal people / And movers” who “make what they call mistakes”

Beyond Freedom by BF Skinner – behaviourist utopia

Brave New World by Aldous Huxley – hypnopaedia and audio conditioning

Chronopolis by JG Ballard – education after civilization has tried to forget measuring time

Class of 1999 – robot teachers

Computer Friendly by Eileen Gunn

Copying Toast – memory-printed bread

Cypher – psychedelic brainwashing

Cyteen by CJ Cherryh – muscle memory and hypnopaedia through AV/nerve stimulation input

Deep Space 9 – future classroom and school

Die Fernschule (The Distance Learning School) by Kurd Lasswitz

Doomsday and others by Connie Willis – Oxford uni students educated for time travel

Doraemon – 18th generation robot academy

Electric Dreams – safe and sound episode

Ender’s Game by Orson Scott Card – novel and film – 50% about edtech

Erewhon by Samuel Butler – intelligent machines and futuristic university

ET – Speak & Spell

Firefly Srenity – futuristic classroom scenes

Futuretrack 5

Hitch Hiker’s Guide – Babel Fish

Hunger Games – training simulations

Idiocracy – testing

Jetsons – robot teacher

Knight Rider – KTT helps with Hoff with planning and problem solving

Limitless – NZT bio-stimulant

Never Let Me Go by Kazuo Ishiguro – boarding school for student clones raised and educated for body organ donation

Old Man’s War – Brainpal

Orbital Resonance by John Barnes

Otherland by Williams

Pern and Pegasus series by Anne McCaffrey – AIVAS system and online learning

Profession by Isaac Asimov – students educated for specific professions by direct brain-computer interfaces (“Taping”)

Quantum Logic series by Greg Bear – plot about universities and privatized education

Rainbow’s End by Vernor Vinge – high school immersive environments

Raised by Wolves – the teacher is the tech

Ready Player One – Oasis, school in VR

Robot Revolt by Nicholas Fisk – robot tutor

Star Trek – the Holodeck, Kobayashi simulation, Vulcan learning sphere

Star Wars – lightsabre training, robot lecturer, clone training centre

Starship Troopers – 3D bug training models

Stranger in a Strange Land by Robert Heinlein – teaching via Martian telepathy

2000AD – Tharg’s Future Shock

TeleAbsence by Michael Burstein

The Child Garden by Geoff Ryman – learning about Derrida from viral injections

The Diamond Age by Neal Stephenson – personalized learning Primer

The Dispossessed by Ursula LeGuin – interstellar communication

The Fun They Had by Isaac Asimov

The Last Book in the Universe by Rodman Philbrick

The Machine Stops by EM Forster – predicts online education by a hundred years

The Matrix – “I know Kung Fu”

The Prisoner – ‘The General’ episode – mind-altering edtech called Speed Learn

The Simpsons – ‘Miseducation of Lisa Simpson’ episode

The Thing Under the Glacier by Brian Aldiss – student wearable brain-controlled ‘miniputer’

The Veldt by Ray Bradbury

Thirty Days Had September by Robert F Young – second-hand robot teacher

Time in Thy Flight by Ray Bradbury

To Live Again by Silverberg

Ulysses 31 – the Cortex

Venture Brothers – learning beds

Walden Two by BF Skinner – intersection of sci-fi, imaginaries and edtech

WarGames – Joshua and machine learning

Years and Years – cyborg training technologies

If you come across any others, please do tag #edtechscifi and @BenPatrickWill on Twitter and I’ll keep adding.

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Pandemic privatization and digitalization in higher education

Ben Williamson and Anna Hogan

The state of emergency in higher education systems around the world during the Covid-19 pandemic has opened up the sector to an expanding range of education technologies, commercial companies, and private sector ambitions. In our new report commissioned by Education International (the global federation of teacher unions), entitled ‘Pandemic Privatisation in Higher Education: Edtech and University Reform’, we examine various ways commercialization and privatization of higher education have been pursued and advanced through the promotion of edtech and ‘digital transformation’ agendas during campus closures and disruptions over the last year. Although we recognize that digital technologies and private or commercial organizations can bring many benefits to HE, they also raise significant challenges with long-term implications for HE staff, students and institutions. Many of these challenges are long-term political and economic matters as much as they are short-term practical matters of online teaching.

The report is detailed and long enough, but even since we finished it in late 2020, the developments we identified have accelerated and expanded. These include investors seeking to capitalize on new visions of teaching and learning, and multisector coalitions coming together to reimagine the future of HE through digital infrastructure and platform-based transformations — ultimately ‘re-infrastructuring’ and ‘platformizing’ universities to operate according to design principles imported by the digital tech industry. These are profoundly political issues about control, power, influence and governance in HE, mirrored by similar shifts of control to technology in the health sector.

Maybe most of the proposed changes associated with so-called digital transformation won’t work out in practice. That may be for several reasons: large-scale transformative proposals are rarely realized in their ideal form, and technologies can always be resisted, subverted, ignored, or simply mobilized in much more mundane ways than their architects intended. But we hope the report at least raises awareness of the changes that many powerful organizations are imagining and seeking to materialize in the very near future. The form, role and functions of higher education may be profoundly reimagined and reconstructed during post-pandemic recovery, and all stakeholders in the sector need to be involved in debates over the sector’s future.

Here is the summary from our full report as a starter for such debates:

  • Pandemic privatisation through multi-sector policy. Emergencies produce catalytic opportunities for market-oriented privatisation policies and commercial reforms in education. The COVID-19 pandemic has been used as an exceptional opportunity for expanding privatisation and commercialisation in HE, particularly through the promotion of educational technologies (edtech) as short-term solutions to campus closures and the positioning of private sector actors as catalysts and engineers of post-pandemic HE reform and transformation. The pandemic privatisation and commercialisation of HE during the COVID-19 emergency is a multi-sector process involving diverse actors that criss-cross fields of government, business, consultancy, finance, and international governance, with transnational reach and various effects across geographical, social, political, and economic contexts. It exemplifies how ‘disaster techno-capitalism’ has sought to exploit the pandemic for private sector and commercial advantage.
  • Higher education reimagined as digital and data-intensive. Diverse organisations from multiple sectors translated the public health crisis into an opportunity to reimagine HE for the long term as a digitally innovative and data-intensive sector of post-pandemic societies and economies. While face to face teaching constituted an urgent global public health threat, it was also constructed by organisations including education technology businesses, consultancies, international bodies and investors as a longer-term problem and threat to student ‘upskilling’, ‘employability’, and global post-coronavirus economic recovery. Framed as a form of ‘emergency relief’ during campus closures, education technologies were also presented as an opportunity for investment and profit-making, with the growing market of edtech framed as a catalytic enabler of long-term HE reconstruction and reform.
  • Transformation through technology solutionism. Education technologies and companies became highly influential actors in HE during the pandemic. Private organisations and commercial technologies have begun to reform colleges and universities from the inside, working as a social and technical infrastructure that shapes institutional behaviours and, as programmed pedagogical environments, determines the possible organisation of teaching and learning. In the absence of the physical infrastructure of campuses and classrooms during the pandemic, institutions were required to develop digital infrastructure to host online teaching. This opened up new and lucrative market opportunities for vendors of online learning technologies, many of which have actively sought to establish positions as partners in long-term transformations to the daily operations of colleges and universities. New kinds of technical arrangements, introduced as temporary emergency solutions but positioned as persistent transformations, have affected how teaching is enacted, and established private and commercial providers as essential infrastructural intermediaries between educators and students. These technologies are enacting significant changes to the teaching and learning operations and practices of HE institutions, representing a form of solutionism that treats all problems as if they can be fixed with digital technologies.
  • New public-private partnerships and competition. New public-private partnerships developed during the pandemic blur the boundaries between academic and industry sectors. Partnerships between academic institutions and the education and technology industries have begun to proliferate with the development of business models for the provision of online teaching and learning platforms. Global technology companies including Amazon, Google, Alibaba and Microsoft have sought to extend their cloud and data infrastructure services to an increasing number of university partners. Colleges and universities are also facing increasing competition from private ‘challenger’ institutions, new industry-facing ‘digital credential’ initiatives, and employment-based ‘education as a benefit’ schemes offering students the convenience of flexible, affordable, online learning. These developments enhance the business logics of the private sector in HE, privileging education programs that are tightly coupled to workplace demands, and expand the role of for-profit organisations and technologies in the provision of education.
  • Increasing penetration of AI and surveillance. Edtech companies and their promoters have increased the deployment of data analytics, machine learning and artificial intelligence in HE, and emphasised the language and practices of ‘personalised learning’ and ‘data-driven decision-making’. Organisations from across the sectoral spectrum have highlighted the importance of ‘upskilling’ students for a post-pandemic economy allegedly dominated by AI and automation and demanding new technical competencies. AI has also been enhanced through the deployment of large-scale data monitoring tools embedded in online learning management software, surveillance technologies such as distance examination proctoring systems, and campus safety systems such as student location and contact tracing apps. In imaginaries of the AI-enabled future of HE, next-generation learning experiences will be ‘hyperindividualised’ and scaled with algorithms, coupled with digital credentialing and data-driven alignment of education with work.
  • Challenges to academic labour, freedom and autonomy. The professional work of academic educators has been affected by the increasing penetration of the private sector and commercial technology into HE during the pandemic. Staff have had little choice over the technologies they are required to employ for their teaching, resulting in high-profile contests over the use, in particular, of intrusive surveillance products or concerns over the potential long-term storage and re-use of recorded course materials and lectures. Academic educators have been required to double up their preparation and delivery of classes for both in-person and online formats. Classes and events featuring ‘controversial’ speakers or critical perspectives have been cancelled due to the commercial terms of service of providers of online video streaming platforms. The expansion of data analytics, AI and predictive technologies also challenges the autonomy of staff to make professionally informed judgments about student engagement and performance, by delegating assessment and evaluation to proprietorial software that can then prescribe ‘personalised learning’ recommendations on their behalf. Finally, academic freedom is at risk when online teaching and learning conducted in an international context runs counter to the politics of certain state regimes, leading to concerns over censorship and the suppression of critical inquiry in remote education.
  • Alternative imaginaries of post-pandemic HE. Online teaching and learning is neither inevitably transformative nor necessarily deleterious to the purpose of universities, the working conditions of staff, or the experience of students. However, the current reimagining of HE by private organisations, and its instantiation in commercial technologies, should be countered with robust, critical and research-informed alternative imaginaries centred on recognising the purpose of higher education as a social and public good. The appearance of manifestos and networks dedicated to this task demonstrates a widespread sense of unease about the ways emergency measures are being translated into demands to establish a new ‘digital normalcy’ in HE. Educators, students, and the unions representing them should dedicate themselves to identifying effective practices and approaches, countering the imposition of commercial models that primarily focus on profit margins or pedagogically questionable practices, and developing alternative imaginaries that might be realised through collective deliberation and action. 

We hope educators, unions, leaders and others will engage with some of these issues in the months to come. The full report is available to view or download here, or you can access PDF versions of the summary in English, French and Spanish.

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New financial actors and valuation platforms in education technology markets

Ben Williamson

New financial and investment organizations have become important influences in the education technology sector. Photo by Andreas Klassen on Unsplash

Prepared for the Education/Globalization/Marketization virtual workshop hosted by Malmo University, 9-10 December 2020

Investment in educational technologies has grown fast over the last ten years or so. Investors annually inject billions of dollars into edtech companies, helping fund the technologies that will shape the future practices of schools and universities. However, little is known about these financial and investment actors, the practicalities and materialities of their work, and their potential power to exert influence over education systems and practices.

This post describes some initial digital fieldwork on one new financial actor in the edtech sector. HolonIQ is an education market intelligence agency with a very considerable role in edtech investment, as well as a key source of edtech market information which is cited extensively in the media and published research. I’ve been collecting data from watching HolonIQ online webinars and YouTube presentations; gathering weekly newsletters, its reports and research notes; collecting website content, social media updates on Twitter, Facebook and Pinterest, and staff details from LinkedIn; mapping its organizational relations, and tracing external citations and social media @mentions. This all gives us some glimpses into the professional, technical and practical work of HolonIQ. There is also a lot of potential research data ‘hidden’ behind the annual subscription costs that HolonIQ charges for access to its proprietary platform and other client-only services.

What I want to suggest here is that HolonIQ acts as a ‘meta-edtech’ platform—an educational technology whose central concern is processing data about edtech, or edtech about edtech. As a meta-edtech platform and an emerging financial actor in education, HolonIQ not only catalogues edtech market movements but actively catalyzes future edtech market dynamics. It exemplifies the growing power of new kinds of market and finance actors to influence education, particularly as the edtech sector and its investors seek to capitalize on the ‘catalytic effects’ of the Covid-19 pandemic in 2020.

New financial actors

The role of financial actors has been the subject of previous studies of the ‘global education industry’, and edtech investment is now the subject of emerging political economy studies that have mapped the sprawling networks of edtech investment. Besides the wider political economy of edtech investment, very little research has examined the specific social, economic and technical practices of this new domain of financial work in education. As Janja Komljenovic notes in her research agenda on ‘assetization’ in digitalized education, ‘The opportunity recognised by investors and entrepreneurs lies in calculating the digital share in the global spending on education,’ and edtech’s ‘asset value is constructed in the light of expectations about future returns on investments’. As such, financial and investment actors are significant because they are seeking to shape the direction of edtech development and stimulate market growth, with a view to generating financial returns.

Making sense of edtech investment actors therefore requires some engagement with economic sociology, particularly its emphasis on the practices and devices of all economic activity. To simplify greatly, economic sociology insists, for example, that markets have to be actively made and maintained through specific micropractices and sociotechnical devices. Capitalization is produced through operations that include practices of assetization and valuation—how things get valued for investment based on calculations about their prospective future income. The recognition of market-making and valuation as operations and processes means any inquiry requires description of the actors, relations, settings, and actions of such operations, as well as the databases, technical literature, methodologies, disciplinary standards, and more, that are all involved in turning things into capitalizable assets with future value for investors.

From this perspective, we can approach HolonIQ as an agent of edtech market-making and valuation. It actively prompts edtech markets, is deeply involved in making edtech into objects of investment, and produces valuations that attract investors to the future income available from their investment in edtech. But this approach requires getting up close to those operations—which this post can only begin to identify for future detailed examination.

Capitalization professionals

The first way in to studying HolonIQ is to view it in terms of its organizational history, the professionals who work there, their wider relationships, and their specialized technoeconomic practices and methodologies. As Fabian Muniesa and colleagues argue, capitalization is a kind of job, performed by ‘capitalization professionals’ in particular kinds of organizations within a wider system of professions and geopolitical locations, using specialized techno-economic practices and methodologies.

The history of HolonIQ is very short, only being founded in May 2018, and yet in those few years it has expanded from an office in Sydney to London, San Francisco, New York and Beijing, and a larger network of international research partners. The website appears in two languages, English and standard Chinese, reflecting the geopolitical importance of edtech in China and HolonIQ’s attempt to embed itself in that context. Its founders are on LinkedIn, so we can begin getting some sense of their personal and professional backgrounds too. One co-founder, for example, has an MBA in corporate strategy, finance and management, an online degree in machine learning, and a background in maths, computer science, military strategy and leadership. Another has a background in enterprise education, as well as an MBA and a prestigious ‘global entrepreneur-in-residence’ position at a leading US university. Both worked at a global education services company before co-founding HolonIQ. HolonIQ also runs a virtual Global Innovation Internship Program to train new edtech market professionals.

But HolonIQ is not just a company of human capitalization professionals or embodied technoeconomic practice. A page on its website describes HolonIQ as a ‘trusted global source of market intelligence’. It connects ‘people, ideas and capital’ to support ‘the future of education’ through a ‘global market intelligence platform’ that ‘provides data and analysis’ of ‘global markets’. The platform, it claims, powers ‘governments, institutions, companies and investors by connecting billions of data points’ and by applying ‘machine learning to analyse, evaluate and identify patterns’ for ‘data-driven decisions’. The market professionalism of HolonIQ is partly constituted by its platform algorithms, and by the machine learning techniques it has enrolled to the task of edtech market analysis.

As a platform company, what HolonIQ primarily does is make predictions about the future of education and edtech’s role (and potential share) in it—as its detailed report Education in 2030: Global scenarios indicates. Published just a month after the company’s launch as a statement of its ambition and analytics capacity, the five scenarios were built using natural language processing algorithms and cluster analysis to identify patterns from a very large quantity of texts about the future of education, cross-checked against data and reports from the World Bank, OECD, and UNESCO. As such, the scenarios are the result of a complex methodology of algorithmic futurism.

This is what making objects suitable for investment requires—the identification of trends and circumstances in which an investment made today might multiply in years to come. For HolonIQ this is about forecasting future scenarios and predicting financial returns available from the actualization of those algorithmically-identified futures. So in a sense the HolonIQ 2030 global scenarios are an attempt to ‘de-risk’ investment, by claiming a limited selection of possible futures—in all of which edtech plays a major role–based partly on machine learning analysis. It then enables venture capital firms ‘to easily discover companies that are a strategic fit, ready for funding or primed for acquisition’ in order to ‘price your investments with confidence’.

As an organization constituted of both capitalization professionals and a machine learning platform, then, HolonIQ has positioned itself as a powerful new financial intermediary and source of technoeconomic expertise in education. It is seeking to catalogue the edtech market and its dynamics, but also to catalyze investment and procurement in ways that might realize certain future scenarios of education that promise high return on investment. It also organizes events including global innovation summits, ‘fast-paced and data-packed’ webinars and client-only executive roundtables around the world to create market encounters between edtech founders and investors, where such future prospects can be discussed and investment deals brokered.

Valuation claims and devices

One of HolonIQ’s most significant roles is the production of valuation claims that lubricate these relations. Through its platform, it performs technoeconomic work to calculate the value of edtech markets and their growth as a way of making edtech suitable for investment. It makes these valuation claims through a variety of narratives and representations, such as its 20 year graph of global education stocks. This valuation representation depicts near relentless growth, projecting prospective returns that ascend, literally, off the chart.  

Some of its valuation claims are packaged up in research note narratives featuring very large numbers and supported by eye-catching illustrative charts that ‘explain the Global Education Technology Market’. These include its year-end calculations about $16bn venture capital investment in edtech in 2020 alone–described as ‘funding backing a vision to transform the way the world learns’–and its prediction the global edtech market will reach $404bn by 2025—itself predicted from its in-house economic model and ‘tens of thousands’ of ‘machine learning revenue estimates’. Overall, HolonIQ predicts, the entire ‘Global Education Market’ will be valued at $10trillion by 2030, and argues for greater share of this spending on edtech. And these glossy, persuasive valuation claims travel through its weekly email newsletter to tens of thousands of inboxes each week.

These newsletters and images—as material transmitters of valuation claims—can be considered important market and valuation devices that make the edtech market legible and describable in terms of past, present, and future prospective value. The infographics and interactives HolonIQ produces are especially powerful market valuation devices designed to incite investment interest.

For example, one of its major outputs is the Global Learning Landscape 2021, an infographic and associated report and website, described as ‘An open-source taxonomy for the future of education’. In order to produce the Global Learning Landscape HolonIQ examined 60,000 edtech providers to come up with a global taxonomy of edtech by core functions and individual providers—1,250 are included on this one graphic. This taxonomy is a really catalytic market device—directing both the investor’s gaze and the purchasing decisions of institutions to a selection of the market that HolonIQ has determined to be of most value. Users can easily copy its images to social media too—these are easily tweetable valuation claims designed to incite edtech market excitement and optimism.

The research methodology behind the device is based on data-driven machine learning and artificial intelligence, identifying ‘natural patterns’ and clusters and segmentations in the data that are ‘not biased’ as other established taxonomies of education are. In other words, HolonIQ claims it has found the ‘natural’ shape of the edtech market based on ‘unbiased’ objective AI analysis. Its algorithms perform a significant role in organizing and ordering education into an intelligible shape to which investors as well as customers might then react.

Valuation platforms

So this is where HolonIQ’s proprietary platform comes in. It’s an advanced AI-based valuation platform made up of both human experts from the company’s Intelligence Unit and nonhuman expertise from its Intelligence Platform, which together function as a new form of technoeconomic expertise in the valuation of education. As its homepage indicates, the HolonIQ platform is made up of ‘human and machine learning smarts’ that combine to produce ‘predictive intelligence’. Kean Birch and Fabian Muniesa argue that ‘things become assets’ by being constructed through sociotechnical entanglements of human valuation practices and technoeconomic devices; the HolonIQ platform turns edtech into assets for investment by ascribing them prospective value through predictive technoeconomic machine learning analysis.

An important feature here is that not only is HolonIQ turning edtech into objects for investment with future value. As a for-profit company, the platform and the billions of data points it has indexed are also assets with high valuation potential for HolonIQ itself. It invites clients to ‘rent’ access to the data ‘on demand’ and subscribe to the platform in order to make use of its Analytics Studio, Power Tools, and data visualization studio, for annual costs ranging from $10,000 per year for limited functionality, to $120,000 per year for its full stack of services and support. Subscribing clients get access to interactive tools to model market segments by sector, region, and so on, perform competitor analysis, market mapping, generate market trends, predict VC deals, and more, but only as a paying customer. So HolonIQ also invites its users to share its market intelligence gaze, and to see education in terms of segments, products and valuations that are themselves represented in HolonIQ’s database as millions of data points.

The value of HolonIQ’s valuation platform, then, derives from the prospective value it ascribes to other edtech companies based on its extensive datasets. It even maps the world in edtech. This year HolonIQ has produced top 50 or top 100 compilations of edtech in every global region. It has made education markets internationally intelligible in these graphics, performing a key task of making edtech visible to investors either as single vendors or market clusters with high projected worth. Many of the companies selected for inclusion on the edtech regional maps take to social media to celebrate, or are invited to present their success story at HolonIQ webinars—this isn’t just cataloguing or indexing the world of edtech, but catalysing investment in and reconfiguring the world of edtech.

The maps are produced through a particular apparatus of valuation that HolonIQ calls its Scoring Fingerprint—a methodology that weights the market ‘attractiveness’ of specific edtech segments, rates product quality and team expertise, assesses a company’s financial health and ‘ability to generate or secure funding’, and its momentum, size and market velocity over time, that is, its future prospects for investors. If an edtech company wants to feature on HolonIQ’s maps, it has to ensure its organizational and market fingerprint is strong enough to be measured, scored, and ranked. This is a valuation methodology for prospective market-making as much as retrospective valuation.

Finally, HolonIQ has become a direct edtech investment partner too, mobilizing its valuation platform as an investment device itself. In September it announced a partnership with Rize, a London-based investment company, and the index investing firm Foxberry, to launch an exchange-traded fund dedicated to edtech. Exchange traded funds are like ‘baskets’ of shares that investors then invest in as a whole, rather than in individual companies, with the fund administered by asset management firms. The Rize ETF holds about $5m of edtech assets in 34 companies. HolonIQ’s particular role in this partnership is to translate its global datasets and valuations into the portfolio of companies that the fund invests in, seeking to capitalize on the rapid growth in the value of edtech companies during the Covid-19 pandemic.

Again, the fund is underscored by complex technoeconomic valuation devices. HolonIQ’s Scoring Fingerprint methodology is used to value companies included in the basket, ‘determined using publicly available data provided by the company through its published financial statements, company presentations and/or official earnings conference call transcripts’. The company fingerprint determines whether a company can be included in the ‘Global Education Stock Universe’ first launched by HolonIQ in 2018 (by 2020 it included 250+ edtech companies), from which the basket of companies to be included in the ETF basket is then selected. The result was the production of an Education Technology and Digital Learning Index. It then used one of its ‘core computational engines’, named HUM, to calculate the performance and reach of the top performing edtech organizations in the index. By mapping, indexing and valuing the (future) educational world in these ways, HolonIQ has defined both the ‘stock universe’ and the selected basket that promises the best return on investment. In other words, besides analyzing edtech markets, HolonIQ is itself actively intervening in the world of edtech investment as a new kind of asset-managing financial intermediary in private capital markets.


Overall, as a new financial intermediary and source of technoeconomic expertise in education, HolonIQ itself plays a kind of governing role in the edtech investment ecosystem—shaping and channelling investment towards certain selected futures and associated companies. Its platform may even be considered one of the most powerful educational technologies in the edtech sector. HolonIQ is a meta-edtech platform—edtech about edtech—that taxonomizes the entire global edtech market by various segments, hierarchies and valuations through the deployment of machine learning, and directs funding towards a future vision of education.

Through complex technoeconomic practices and platform algorithms, it makes the edtech field intelligible and attractive to investors, whose investments then shape the fortunes of individual companies and products, subtly shaping practices in the schools where they are used. As a meta-edtech platform with ‘predictive intelligence’ it steers the edtech market towards particular futures that HolonIQ’s experts and algorithms have ascertained to offer high return on investment prospects. In this respect, the HolonIQ meta-edtech platform is itself a highly significant educational technology that is likely to shape educational realities, albeit at a distance from schools or classrooms, to fit prospective market trends that have been predicted with algorithms and machine learning.

These fragments of digital fieldwork surface a number of issues for further study of financial and investment actors in edtech. New studies should seek to examine the practical technoeconomic work of a range of edtech investors and financial intermediaries–from VC firms to portfolio fund asset managers–and the significant market analysis and valuation efforts involved in the edtech industry. Research should more carefully conceptualize how edtech is capitalized and assetized, and how future prospective value is calculated to attract investment by a range of financial professionals, market analysts, asset managers, investors and other intermediaries. Studies might also follow specific investment actions through to the edtech developments they fund–ultimately following the money through to the materialization of edtech products, and on out into concrete practices in schools and universities. Such studies would help to reveal the significant role of financial and market-making organizations to the functioning and fortunes of the edtech sector, and their effects on educational settings and practices.

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The rise of data-intensive biology in education – a new project!

The combination of biology and data science is leading to the production of new knowledge about learning and education. Photo by Louis Reed on Unsplash

Advanced technologies that can process complex biological data have transformed the human sciences, and are now being used to conduct studies and generate new knowledge in the field of education. The Leverhulme Trust has just awarded a research project grant to study the rise of data-intensive, computational biology in education to Ben Williamson (University of Edinburgh), Jessica Pykett (Birmingham) and Martyn Pickersgill (Edinburgh). We’re thrilled to be collaborating as a team on this project, which builds on previous work we have separately completed on the application of biology in education, including epigenetics, brain-based teaching, neurotechnologies, and bioinformatics-based polygenic scoring. The project also represents an exciting opportunity to build interdisciplinary connections across our respective fields of education technology and governance, social and political geography, and sociology of science and medicine.

As a way of initially characterizing the developments we will study, we have begun to define the emergence of ‘precision education’. As we use the term, precision education refers to the novel combination of data science, biology and the learning sciences. It involves the use of advanced computer technologies to measure and assess the biological aspects of student learning and educational outcomes, in some cases in order to develop targeted educational interventions. We see precision education emerging from three core developments.

First, advanced computer technologies are transforming the biological sciences and leading to new ways of understanding and treating human bodies, such as in the biomedical field of ‘precision medicine’. Second, biological understandings of learning are returning to educational debates as new scientific knowledge about the biological underpinnings of learning and educational outcomes are produced by scientists working in fields of neuroscience, psychology, and genomics. And third, learning sciences and analytics experts increasingly use advanced computer techniques such as biosensors and brain scanners to assess the biological aspects of learning.

Precision education builds on these developments to propose that diverse forms of biological data produced with computers may be fused together and analysed to develop insights into learning and educational outcomes. For some precision education promoters, biological data may even be used to predict individual outcomes, and to create ‘personalized learning’ interventions that are tailored and targeted to the individual student’s unique biological profile.

But we expect to find considerable contestation among different research groups working on data-intensive biology projects in education. Current controversies over the use of genetic samples and methods in research on education, for example, point to an unsettled field of scientific development which is also caught up in political disagreements. As such, we think it’s important to examine both data-intensive biology as a science-in-the-making and its positioning as a potentially policy-relevant science with significant practical and political implications in education.

The project is grounded in previous research that has studied such developments as data-centric biology, precision medicine, post-genomics, digital psychometrics, emotion analytics, neurotechnologies, and bioinformatics. Such work points to the considerable impact that data science and computation have exerted on biological discovery and knowledge production, and the scientific and ethical problems accompanying them. The project will also explore the longer historical uses of biological knowledge in education, including significant ethical issues. We will be asking questions about whether or how data-intensive biology in education constructs new knowledge about ‘plastic’, ‘networked’ or ‘calculable’ learning bodies and brains, and whether novel biological conceptions of learners and learning produced with computers are being deployed as forms of policy or practice intervention. This means studying the apparatus of knowledge production among precision education scientists–biosensors, neurotechnologies, and bioinformatics, plus associated forms of expertise and practice–and tracing the uptake and use of such knowledge.

The overarching objective of the study is to identify and interrogate the organizations, expertise, laboratory practices, and technological machinery that make precision education possible. This empirical objective will address the question of how and why precision education is being developed, and specifically enable us to understand the methodological and technical processes that underpin its knowledge claims. The second key objective is to examine how new biological understandings and knowledge of learning produced by precision education scientists are positioned to inform education policy and practice. This objective will enable us to address the question of how precision education might transform educational research, policy, practice, and public understandings of learning, and to identify the practical, political and ethical consequences of these new ways of thinking about biology in education.

We’re delighted the Leverhulme Trust has awarded us a research project grant to start this program of work, including funding for a full-time postdoctoral research fellow for two years from September 2021. It will be a really exciting post for someone interested in the empirical social scientific study of data-intensive biology, and its implications for domains of public policy such as education. We see the project opening up data-intensive biology in education to longer-term empirical analysis, conceptualization and critical examination too. In the new year we’ll begin getting up to speed with preliminary work and recruitment, and we expect to be posting updates on project progress throughout.

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Higher education platforms and cloud infrastructures in the ‘dataist state’

Ben Williamson

Higher education is being reimagined by companies and agencies as being integrated with digital platforms and cloud infrastructures. Photo by Bahador on Unsplash

Prepared for the Centre for Global Higher Education research seminar Student learning in the midst of the pandemic: the case of the UK, 10 November 2020

Privately-owned digital platforms have become integral to societies and public life, and are advancing into the provision of higher education too. As Jose van Dijck, Thomas Poell and Martijn de Waal argue in The Platform Society, two types of platforms are especially significant for HE. First, infrastructural platforms, such as those of Microsoft, Google and Amazon, undergird other platforms used in universities. They enable and manage global data flow, storage and analysis. And second, sectoral platforms have become integrated into public services and the public sector, significantly so in HE, while depending on infrastructural platforms for their functioning.

The expansion of privately-owned sectoral platforms and infrastructural platforms into HE has accelerated as COVID-19 and the declaration of states of emergency worldwide enabled the global technology sector to seize new market opportunities and carve out advantageous public-private partnerships across both the health and education sectors. Educational technologies have become key sites of private sector and commercial influence, initially by offering short term ‘relief’ from the crisis in the shape of online platforms for remote teaching and learning, and then by promoting them as a model for long-term ‘reconstruction’ involving private platform providers as fully-embedded partners in HE delivery. What does this increase in the participation of private platform actors signify for teaching and learning in universities, for the public role of higher education, and for the lives of students?

Intelligent networks

During the pandemic, higher education in the UK and internationally became a focus of intense ‘reimagining‘ by sprawling webs of think tanks, consultancies, sector agencies, edu-businesses, financial organisations and technology companies, as part of both a longer history of multisector HE reform efforts and recent projections of the ‘digital transformation‘ of HE. The Jisc Learning and Teaching Reimagined Initiative launched in October 2020 is a good example. It is a multisector programme of HE reconstruction involving the organisations Jisc (UK’s digital learning agency for HE), Universities UK (the sector’s representative body), Emerge Education (an edtch startup investment company), and Salesforce (a Silicon Valley cloud-based software company).

In the joint report Digital at the Core, a strategy for ‘digital transformation’ of UK HE, this multisector group projects a shared imaginary of digital, data-intensive HE:

The technology now exists to connect the variety of applications used within the university, where the IT landscape tends to be more fragmented than in the enterprise. Replacing these siloed ‘information systems’ with intelligent information networks will enable highly personalised engagement with students and staff, individualised experiences, and actionable strategic intelligence. … Now, advanced analytics with augmented intelligence have the capacity to predict what will happen and prescribe actions which can be taken to cause an outcome.

The report supports the idea that aspiring ‘data-empowered universities’ should emulate the data-driven approach of Silicon Valley platform companies:

Consumer market leaders such as Netflix, Apple, or Uber apply data-driven decisions and provide dynamic experiences based on an individual consumer’s information. Applying these same design principles to higher education can transform the way that our stakeholders experience learning, teaching, research, and professional services.

The Jisc digital strategy anticipates HE being reconstructed to match the logics of the platform society. It involves the datafication of all activities and data-driven decision making; the commodification of education as ‘unbundled’ components provided by market suppliers; and the selection of ‘personalised’ educational content based on individualised data analytics. Being ‘digital at the core’ also means universities being connected to interoperable cloud and data systems provided by giant infrastructure partners in new public-private partnership configurations.

The partnership with Salesforce on the strategy is suggestive of how such a vision of HE could appear in practice. It has pushed its Education Cloud for HE as a key solution for universities during the pandemic. On the launch of the Jisc strategy, the general manager and senior vice-president of Salesforce.org Education Cloud said:

Higher education is at a turning point globally. And there is a tremendous opportunity to reimagine how the sector in the UK should evolve using technology, a pivotal step required to thrive in the next normal. Salesforce.org is proud to work alongside industry peers to develop a framework for digital transformation that will inevitably shape and contribute to future learner and institution success.

Salesforce enacts this vision through its Education Data Architecture offer to institutions. The EDA is designed ‘to configure Salesforce for education’ as a ‘360-degree’ infrastructure for viewing and using student data. It combines student information, learning management, and other institution systems, as well as third-party plug-in apps available through the Salesforce AppExchange, into one interoperable system.

The salesforce Education Cloud is also ‘infused with Einstein,’ the company’s machine learning and predictive ‘artificial intelligence.’ As another Salesforce executive put it:

Now, campuses of all sizes are expected to operate more like nimble software organizations—innovating quickly, scaling up virtual service centers, and putting infrastructure in place to support the always-on digital engagement needs of students. … Picture using the underlying technologies in Amazon one-click, Spotify recommendations, or the Apple Watch’s health tracking for higher education.

The promise of the Education Cloud is to compile all student data into one interoperable intelligent network for 360-degree observation, analysis and prediction, while offloading or outsourcing the technical demands to the Salesforce AI, cloud and data architecture.

Cloudsourced education

Salesforce is not the only infrastructure provider for student data cloud hosting and analytics, as other companies including Google, Microsoft and Amazon Web Services have entered into a global competition for infrastructural dominance over universities’ information and learning management systems. They offer both sectoral platforms (e.g. Google G Suite, MS Education tools, AWS Educate programs) and infrastructure services to enable a host of other platform providers. These infrastructures are important aspects of online learning platforms that have proliferated across HE during the pandemic.

The for-profit MOOC provider Coursera, for example, is a sectoral platform that relies on third-party cloud infrastructure provided by Amazon Web Services. AWS has become a major infrastructure provider in HE, providing back-end services to Coursera, as well as leading management systems providers Blackboard and Canvas too. Powered by AWS, Coursera offered Coursera for Campus free degree content during the pandemic. Institutions could sign up for ‘bundles’ of ‘job-relevant’ degree content in the absence of existing institutional online learning arrangements. Coursera also announced a new business opportunity, with plans to sell the courses it has developed as ‘courseware’ to institutions. This enables the outsourcing of key teaching functions to ready-made content and the AI software for ‘personalised learning’ that Coursera has also built into the platform.

In this sense, as a sectoral platform serving universities, Coursera anticipates the future cloud-sourcing of higher education through public-private partnerships on both the demand and supply sides. Subscribing institutions purchase the unbundled components of a degree and AI support from Coursera, which acts as a platform hosting content provided by its partners network, all of it enabled by back-end infrastructure provided by Amazon Web Services. According to a senior Coursera software engineer, ‘Quite simply Coursera could not exist without AWS. We rely on AWS every single day for the speed, service, agility and scale required to serve our students worldwide’ and to ‘provide deeper insights into student learning that would not be possible without AWS.’ AWS also runs its own popular public-facing training courses on cloud computing and machine learning on Coursera, as it both provides infrastructure for the platform and upskills new experts to work in its cloud services at the same time.

To a significant extent, then, the ‘reimagined’ HE of Jisc’s UK digital strategy would be increasingly integrated into the commercial ecosystem of the global cloud and data infrastructure providers. Jose van Dijck and colleagues argue that MOOC platforms such as Coursera are entangled in a ‘political agenda where formerly defined public and government functions are administered towards yielding private profits,’ and public funding is increasingly lured toward platforms that capitalise on ‘data-based, technology-intensive forms of teaching and learning, at the expense of investments in human-based, labor-intensive pedagogical and didactic skills.’

The dataist state

These shifts to embed HE in global computing infrastructures and public-private partnerships might be understood as a manifestation of a new form of ‘digital statecraft’ as Marion Fourcade and Jeff Gordon have conceived it. ‘By “statecraft,”’, they argue, ‘we mean the state’s mode of learning about society and intervening in it.’ In a dataist state, the state learns from tracing ‘discrete slices of people and things’ as data, and then seeks to intervene to change, ‘nudge’ or optimise their behaviours, for example through machine learning-based personalised learning technologies.

But the dataist state lacks the in-house technical capacity to undertake this analysis and intervention at huge scale. So it must outsource data collection and analysis to private platforms and the infrastructure providers on which they depend, particularly in public sectors such as healthcare and education. In the dataist state, machines take over significant parts of the state’s operations through asymmetrical public-private partnerships that raise private challenges to the state provision of services. And this, Fourcade and Gordon argue, anticipates ‘the private appropriation of public data, the downgrading of the state as the legitimate producer of informational truth, and the takeover of traditional state functions by a small corporate elite.’ The dataist state is not just digitalised but hugely privatised too, reshaping the infrastructures of the public sectors and the welfare state.

Their tentative solution is to seek to adapt big data technologies to an alternative, citizen-led political rationality. ‘Given our view that the fundamental technologies of the digital age (distributed systems, machine learning) are not going anywhere,’ Fourcade and Gordon conclude, ‘the question is whether we can put them to more solidaristic and less extractive uses.’ They suggest a mode of statecraft that identifies social problems ‘from the perspective of those affected,’ such as reorganising certain digital infrastructures as public utilities and decommodifying a number of essential internet-based services. Education could be a key sector for such experiments in alternative statecraft.

These provocations on the privatised dataist state and how to reorganise it raise questions about the kinds of values and purposes of HE promoted as parts of post-pandemic recovery and reconstruction. Public values of education as a common good and as a bedrock of democracy are now in tension with private values of individually-targeted, hyperpersonalized, job-relevant education. These are challenges to raise with sectoral bodies such as Jisc and the Office for Students, which lean towards the public-private partnership paradigm of the dataist state, as signified by political appetite for flexible and remote learning focused on economic and skills needs. They are ceding the authority of higher education institutions and the state to ‘learn’ about students, institutions and the sector to global private platform and infrastructure providers that currently only see them in economic and job-relevant terms. We need alternative ways of deploying platforms and infrastructures that are led from the perspectives of those affected within education itself.

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Digital data and the post-pandemic university

Ben Williamson

Higher education was reimagined by the education, technology and financial industries during campus disruptions. Photo by Nathan Dumlao on Unsplash

Prepared for the QAA Scotland Enhancement Themes 2020 conference on 3 November 2020

Digital data technologies are at the centre of many current controversies. From Airbnb driving up over-tourism and property prices, to Twitter taking measures to reduce misinformation in the US election, digital data technologies are not merely tools enabling people to do things. They change what we do—how we travel, socialise, communicate, consume, and how democratic societies function.

Similar changes are already playing out in higher education, as part a long history of using measurement and rating techniques to change how universities operate, and the more recent history of datafication and surveillance in the sector. For at least ten years, educational technologies and increased data production has been affecting teaching, learning and decision making in universities—but today more so than ever. Digital technologies and data can productively inform decision making, at institutional, faculty or even individual levels, but should be informed by shared values and purposes of higher education. This is an important point, because digital data technologies may prioritise other values, purposes and interests—such as calculating the value of a degree as a ‘return on investment’ in labour markets, making ‘performance’ measurement the key indicator of teaching ‘quality’, or even by treating HE as a sector to be ‘disrupted’ for purposes of commercial gain.

In this context, important discussions need to be had about the kind of values and purposes we want to support as we begin the difficult process of sectoral recovery from the current crisis.

University 4.0?

Higher education has been the focus of a great deal of ‘reimagining’ in recent years. Magazine articles such as the ‘Vanishing University‘ series in Quartz highlight a trend to envision HE systems where institutions, textbooks, and teaching practices are all transformed. Various experts and authorities speak of ‘smart campuses’, ‘unbundling’, ‘outsourced universities’ or the ‘University 4.0’ that is aligned to the ‘Fourth Industrial Revolution’ of advanced technologies, artificial intelligence, and automation.   

These imagined visions anticipate a high-tech, digitally-driven, data-intensive, and partly automated university in which as many functions as possible are delegated to or augmented by digital tech, and all processes are recorded and analysed as data to inform decision making. The decision maker in the University 4.0 may not even be entirely human, as these visions highlight how data analytics and dashboards can augment institutional decisions; make decisions on behalf of students, such as ‘recommending’ course texts; or make pedagogical decisions for staff, such as evaluating student performance and assessing assignments.

Imagining and materialising high-tech HE visions

The new ‘visionaries’ of HE can be found everywhere from international consultancies McKinsey, KPMG, Deloitte and Ernst Young, and global organisations such as the World Economic Forum, to national sector agencies and government departments. Their reports stabilise a particular vision of the future of HE, aiming to produce consensus and conviction in others that this data-led digital transformation is desirable and attainable. They provide ‘actionable’ insights into realising such transformations in practice too.

They’re also full of assumptions about the value and purpose of higher education. Those include alignment of the university to the data economy as a producer of the technical talent required to boost innovation and productivity. A higher education becomes centred on hyperspecialised careers development in the science, technology, engineering and maths disciplines, with the related downgrading of the arts, humanities and social sciences.

It’s right to be sceptical of the grand idealised claims of such visions. Their full realisation is doubtful. But, in more limited form they are already materialising in policy proposals, governmental interventions, and on-the-ground technical developments. In the UK, various reports and requirements produced by Jisc, the Office for Students, the Higher Education Commission, KPMG, Universities UK, and the Department for Education have pushed the data agenda into actual developments and uptake. There is considerable multi-sector consensus about the directions higher education should take to ‘satisfy’ digitally-savvy students—innovating the ‘learning experience’—and to deliver economic renewal through innovation in teaching and learning.

High-tech HE visions have materialised most spectacularly in the rapid growth of the educational technology industry and its financial sponsors. The education market intelligence agency HolonIQ recently identified and taxonomised hundreds of edtech suppliers serving every conceivable task or activity of higher education in a soc-called Global Learning Landscape, and estimated edtech to be worth hundreds of billions of dollars.

A market intel agency like this does two things. It attracts customer attention, like an online recommendation engine for ‘what works’ in higher education ‘digital capability’. And it attracts venture capital and private equity in products that can deliver profitable return on investment. In other words, market intel makes edtech markets by convincing customers that digital solutions will solve their problems, and by building up the financial power of the industry that provides technological solutions to them.

Programmed visions

We can understand the educational technologies supported by these market-making activities as the computer-programmed instantiation of the vision of a high-tech, digitally-enhanced and data-intensive HE sector. That is to say, the imagined HE of University 4.0 or the smart campus gets operationalised by being written in computer code as the software to be used by organisations.

The learning management system Canvas, for example, was acquired this year by a private equity firm for 2 billion US dollars. This followed its chief executive’s claims that Canvas had the most extensive database on the student experience on the planet, which it would monetise with predictive algorithms and study recommendations. The online learning company 2U claims it provides an ‘operating system’ for hybrid teaching and learning, bringing both the language and the specific mechanisms of computation to education.

There are also new product types that use emerging sources of data. Spotter and others now offer student location monitoring through smartphone Bluetooth or wifi identification. Library system providers like Ex Libris have expanded to become cloud-based library services platforms running granular analytics. They don’t just support library managers to monitor usage, but feed back into course leaders’ decisions about what texts to assign, or even automatically anticipate and recommend what students should read next, based on performance comparison of items included on lecturers’ reading lists.

Computer programming ‘executes’ and operationalises a particular vision of what HE is, can or should be. Once programmed according to these ideals and the business plans of companies, edtech gets into the hands and practices of university managers, educators and students—with the aim of changing processes of teaching and learning, and of affecting organisational behaviours and decision-making practices.

Emergency edtech

What these examples show is that a vision of the smarter data-intensive University 4.0 is being pursued into being, sponsored by consultancies, put into policy proposals and projects, financed by investors and paid for by institutional customers, and operationalised through programming. The problem, according to many promoters of this vision, is that the HE sector itself has been resistant to such change. Until, of course, Covid-19 struck earlier this year. In the educational emergency of mass campus closures, opportunities for ‘long overdue’ revolution were identified, and higher education entered into a historically unique period of edtech experimentation.  

A key question surfaced by the edtech emergency is what values and purposes were pursued in this experiment? Yes, values of equality, access, fairness and quality education for all became key issues, particularly to address digital inequalities and ensure educational continuity for millions. But this was coupled with private interests such as securing market share, competitive advantage, and return on investment. The issue is whether these private interests support the public values, ideals and common goods of education by offering temporary ‘relief’ from the catastrophe, or whether they are actually motivated to ‘reconstruct’ higher education for the long term in ways that reflect private agendas.

The stakes here are high: long-term commercial reconstruction around the visions and programmed templates provided by digital data technologies could fundamentally alter how HE operates, potentially raising major and damaging disputes. The so-called ‘global education industry’ is highly controversial for positioning the private sector advantageously to deliver an increasing array of tasks in universities.

Recently, too, academic researchers have begun documenting the issues arising from digital surveillance during the pandemic—such as governmental dependence on global technology companies or consultancies for epidemiological and contact tracing. HE is at the centre of these two issues—increasing privatisation and increasing monitoring through data.

Pandemic markets

One indication of the increasing privatisation is the surge in edtech markets. Large quantities of investment have been made over recent months, both as direct venture capital in companies and their products, and as investments in new portfolio edtech funds dedicated to ‘digital transformation’. The wealth and asset management company Credit Suisse ran partner content in the Financial Times on the potential of the ‘Netflix moment’ for education, and attracted investors to its ‘Edutainment Equity Fund’.

These and other funds reposition higher education as a fast-moving market opportunity, and as an ‘edutainment’ sector with the disruptive potential of Netflix and other platforms. One of the main market segments of interest in these funds is consumer edtech—selling products and streaming content to students with all the convenience and subscription fees of streaming platforms.

One of the other main market segments to grow in interest and activity was online degrees and program management services. HolonIQ calculated that the online degree market would be worth 74 billion dollars in 2025. Online program managers have begun diversifying from the provision of graduate programs for distance international students to undergraduate programs, and shifting to being enablers of hybrid programs who partner with institutions.  

Outsourced universities

This model of public-private partnership reconfigures HE as a hybrid between universities and private industry, and is itself of serious interest to investors, companies and institutions alike—for investors, for the ROI; for companies, for the revenue-sharing agreements, where they often take around 50-60% of student fees; and for universities seeking income from enrolments. The edtech commentator Phil Hill recently wrote about the ‘instant global campus’ as the outcome of such deals.

These public-private partnerships anticipate a greater degree of unbundling of core university operations to outsourced private providers. Take Noodle, for example, which offers a full stack of services:  marketing and recruitment; business model; data flow between the student info system, learning management system, and third-party integrations; and learning design support for online and hybrid education, as part of its promise to produce an ‘agile campus’ for its partners. Noodle is not just an online learning platform provider, but a kind of outsourced shadow campus that augments many of the tasks of its public partners, right down to the organisation and development of taught courses. It offers a platform template for pedagogy that constrains how HE partners can develop and deliver courses.

Data lakes and cloud campuses

A key feature of online learning is how private providers have configured higher education in terms of ‘job-relevance’. Massive open online courses returned this year as data-driven platforms for online and hybrid education. Coursera for Campus extended the MOOC into formal degree programs, providing back-up for students and staff lacking access to either classrooms or online learning facilities. But Coursera was also all about ‘job-relevant, credit-ready’ online education, including new ‘industry credentials’ offered by partners including Google, Amazon and IBM that might even short-circuit the need for a formal higher education. Google’s own offer on the platform was a 6 month online credential that, it claimed, would count as much as a four year degree for posts in the company.

Coursera even released reports detailing its own ‘impact’ and ‘quality‘, using ‘eight years of learner data and nearly 200 million course enrolments to provide actionable, data-driven insights into how instructors and learners can optimize their digital learning experience’. This is the realisation of the data-driven university that is barely possible on a campus location, with mere thousands of enrolments to study. Coursera has 200 million enrolments to analyse, giving it unprecedented power to quantify and identify ‘what works’ in digital teaching and learning. It’s a kind of cloud university constantly producing outputs and recycling these as data inputs to improve its product.

Coursera indicates the truly global scale of many of the commercial digital suppliers advancing across higher education. The data they extract and analyse requires significant back-end infrastructure for data storage and processing too. Coursera itself uses Amazon Web Services to ‘handle half a petabyte of traffic each month and scale to deliver courses’ to its millions of learners from around the globe. The learning management systems Blackboard and Canvas, too, are institutional AWS partners, meaning a significant proportion of the world’s universities are now plugged in to the Amazon ecosystem of cloud and data infrastructure facilities.

In September AWS announced a price discount scheme for universities to develop ‘data lakes’ of very large volumes of heterogeneous information. The process of ‘architecting a data lake‘ involves tying together multiple AWS programs for data storage, interoperability, management, analytics and machine learning with institutions’ own student info systems and their third-party learning management providers.

The longer-term implications of this remain unclear, but higher education now depends to a substantial degree on AWS for back-end data services. We might even say that the production of data lakes condenses into the formation of cloud campuses that exist in Amazon’s giant data centres—cloud campuses that overshadow the physical spaces of the university while siphoning off their data lakes, and performing the programming actions required to run the partner institution in its idealised University 4.0 format.


The result of the formation of such cloud campuses is the possibility of using artificial intelligence for ‘personalised’ education. Personalisation is the central objective of the University 4.0 imaginary. This is a university that runs constant data analytics, in real time, to diagnose students’ strengths and weaknesses, their progress and problems, in order to prescribe automated interventions or direct educators’ attention.

The University of Buckingham’s proposed ‘Education 4.0 trailblazer degree’, for example, will use AI ‘to create personalised and adaptive course content tailored to each student’s specific abilities and learning methods’. Microsoft’s Power Platform offers similar promises of utilising machine learning for personalised learning. As does Coursera’s recent upgrades for ‘tailored study suggestions’, ‘smart material’ recommendations, and personalised advice towards ‘career-specific skills’.

The University 4.0 vision may have been relatively long in the making, and slow in the uptake, but it is now materialising at speed. It represents the hybridisation of the public role of universities and the private interests of technology providers: promises of institutional relief and recovery from the current crisis are tied to the enrolment of universities on to corporate cloud infrastructures and AI systems at truly huge scale. It anticipates the reconstruction of HE by the tech and data industry.

Reimagining the post-pandemic university

The purpose of detailing these transformations is not necessarily to critique them as negative developments. They demand much more careful empirical study for their implications and effects. And nor is it to dismiss online teaching and learning, which can be and often is done creatively and purposively by educators in a range of contexts. But these developments do of course raise many critical issues. Articles in the Chronicle of HE in the US, and the UK edition of Wired magazine, have questioned the ‘edtech mania’ of ‘utopian-minded tech gurus’, and the use of data analytics as ‘surveillance’ technologies to spy on students through their course ‘engagement’ traces. Even Teen Vogue magazine ran a piece on the student anxieties caused by course surveillance.

In the wake of scandals over the Higher and A level exams this summer, the media is already interogating the other algorithms that might be deciding students’ futures. If we turn over the sector to commercial companies, AI, data analytics and cloud centres—and absorb the problematic language of technological solutions, data-driven decision-making and personalised learning—we can expect this critical scrutiny, and perhaps more drastic actions, to grow in intensity.

A modest solution, perhaps, is to open up a much richer discussion about the possibilities of the post-pandemic university, by drawing on the sector’s own research and expertise. Paul Ashwin’s Transforming Higher Education: A Manifesto returns us to questions about the values and purposes of higher education. He resists the dominant framing of HE as an economic investment, even in countries that do not charge fees, and the valuation of a degree in terms of preparing the future workforce for the benefit of individuals and the economy. The purpose of a higher education, he argues, is ‘bringing students into a transformational relationship with disciplinary and/or professional knowledge’. The book asks readers to consider this transformational relationship to knowledge – rather than transforming education into data-driven career skills training for employability – as the central purpose of a higher education.

The Manifesto for Teaching Online, by Sian Bayne and colleagues, questions the ‘impoverished techno-corporate futures’ for higher education promoted by commercial and government edtech. It asks that we take into account the values and experiences of teachers and students in universities instead. The book embraces the creative, critical and innovative potential of online education driven by teaching values and purposes rather than driven by data, arguing that ‘those of us actually teaching using technology … need to take active control and ownership of digital education’ rather than allowing it to be driven by data-led technological solutionism or commercial business plans.

In these manifestos, digital data still plays a part in decision making, but those decisions are informed by truly educational purposes, by acknowledgement of the social and economic contexts of education, and by a recognition of the public value of a higher education to individual lives and to the wider society. 

One model for dialogic negotiation over the shape of higher education to come is the Post-Pandemic University Network—a series of online events and a collective blog aimed at stimulating and sustaining discussions about the purposes of higher education and how to practice them. Working collegially in such ways, the sector needs to take the lead in restating its values and purposes, and making private providers work to advance explicitly educational aims, rather than edtech and even Big Tech making decisions that make higher education work for them.  

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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


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.

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


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


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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