Datafication and automation in higher education during and after the Covid-19 crisis

Ben Williamson

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Edtech, coronavirus, and commercialization in public education

Ben Williamson

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

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

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

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

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

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

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New pandemic edtech power networks

Ben Williamson

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Emergency edtech

Ben Williamson

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Ben Williamson

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Platform teachers

Ben Williamson

containers by Guillaume Bolduc

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Psychodata

Ben Williamson

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Ben Williamson

Prepared for EdTech KnowHow conference, Stavanger, Norway, 26 September 2019

One month ago I set a Twitter mob against a team of young researchers working on a new education technology prototype at a major university in the United States.

Here’s how I did it.

One of my current research interests is in how technical advances in brain science and human genetics are leading to new ways of understanding learning and education. So I’m gathering a lot of material together from companies and from research labs to scope out the state of the art in the science of neurotechnology and bioinformatics.

MIT AttentivU

The MIT Media Lab project AttentivU

That’s how I came across this prototype MIT Media Lab project called AttentivU. It’s building a pair of wearable, ‘socially acceptable’ glasses with in-built electroencepholagram (EEG) detectors that can ‘sense’ from brainwave signals escaping the skull when a student is losing attention. The glasses then emit ‘bone-conducted sound’ to ‘nudge’ the student to pay attention.

Having written at length about the potential effects of neurotechnology in education before, personally I thought these seemed  potentially very concerning, and definitely worth posting on to Twitter.

MIT AttentivU tweet

Tweet on AttentivU triggered accusations of ‘eugenics’ and ‘torture’

‘Check out these cool brain-reading glasses from MIT Media Lab’, I tweeted. In retrospect I should have put scare quotes around ‘cool’, even though I thought they self-evidently were not–I mean, look at them!

By that evening my Twitter notifications were buzzing constantly with outrage. These glasses were not ‘cool’, but a ‘torture device’ from Stanley Kubrick’s film of A Clockwork Orange, especially for neurodiverse populations and young people labelled with attention deficit and hyperactivity disorder.

By the next morning, I was being called a ‘eugenicist’ and ‘human garbage’. Some people thought it was my project; most thought I was amplifying it. Doubtless the sense of outrage was pumped high because of the Media Lab’s association with Jeffrey Epstein.

Others recognized it was in fact the project of a team of young postdoctoral researchers. Two days after I posted the original tweet I started seeing a steady stream of tweets from them clarifying its aims and scope. Twitter outrage had found them and demanded they shut down the project.

techlash books

The ‘techlash’ is reflected in critical books on the social and political consequences of technology

The ‘techlash’
Now I still don’t like these brain goggles very much—the criticisms on Twitter reflected my own critical views about targeting students for automated ‘nudges’ to the skull based on simplified brainwave readings. I don’t much like the way Twitter turned this into a  ‘torture device’ either–I think we need to read these innovations more closely and develop more careful critiques.

But it has been educational for me to be on the harsh end of what I see as a recent and emerging trend—edtech resistance and pushback. Twitter outrage is its most extreme expression–but there are also good reasons to pay attention to edtech pushback.

Edtech pushback is our sector’s symptom of a much wider public backlash against ‘big tech’—or a ‘techlash’ as some are calling it.

By now we recognize how exploitation of user data for targeted advertising, online misinformation, social media bots, ‘deepfakes’ and so on, have got us to a situation where, some argue, democracy has been hacked and modern capitalism has come to depend on surveillance and behaviour control.

The techlash is a response from the public, the media, the charity sector, even some government ministers and policymakers to these data controversies. In some cases is even leading to large commercial fines, government committee summonses, and calls for much greater tech regulation.

edtechlash

News media has begun to report critically on edtech

Edtech resistance, or perhaps an ‘edtechlash’, is also gathering strength. Anyone developing, researching, or teaching with educational technologies should be paying attention to it–not least because news journalists are increasingly reporting on controversial edtech-related stories.

There are some common resistance themes emerging—such as edtech privacy, security, and data protection; concerns over artificial intelligence in schools; and the role of multinational commercial companies.

In this talk I want to raise some specific edtech resistance examples and take from these a few key lessons. As a university researcher I’m just trying to document the steady build-up of edtech pushback. For those in the edtech industry this resistance should be informing your thinking as you look to the next decade of product development, and for educators or decision-makers, these tensions should be in mind when thinking about the kinds of education systems and practices you want to develop for the future.

EdTech activists
First up, I think anyone involved in making or using edtech needs to be paying close attention to a growing number of ‘anti-edtech activists’—journalists, educators, parents, or simply concerned members of the public who feel moved to challenge the current direction of edtech development.

These activists are doing their own forensic research into edtech, its links to commercial interests, and the critical issues it raises regarding privacy, private sector influence over public education, and the challenges that are emerging for educators and students. The work of Audrey Watters at Hack Education is exemplary on these points.

Hack Education

Audrey Watters’ Hack Education site is a popular source of critical edtech commentary

These anti-edtech activists are actively disseminating their arguments via blogging and social media, and gaining public attention. Charitable groups focused on children’s digital rights are moving to a more activist mode regarding edtech too. DefendDigitalMe and the 5Rights group in the UK are already exploring the legal, ethical and regulatory challenges of technologies that collect and process student data.

The lesson we can take here is that activists are increasingly expressing outrage over private exploitation of public education and students’ personal data. Look what happened when data privacy activists got organized against the Gates Foundation’s $100million inBloom platform for educational data-sharing, learning apps and curricula in 2013–it collapsed within a year of launch amid growing public alarm over personal data exploitation and misuse.  Monica Bulger and colleagues commented,

The beginnings of a national awareness of the volume of personal data generated by everyday use of credit cards, digital devices, and the internet were coupled with emerging fears and uncertainty. The inBloom initiative also contended with a history of school data used as punitive measures of education reform rather than constructive resources for teachers and students. InBloom therefore served as an unfortunate test case for emerging concerns about data privacy coupled with entrenched suspicion of education data and reform.

Diversity challenges
Then there’s the FemEdTech movement, mostly consisting of academics, edtech software developers, and STEM education ambassadors who, inspired by feminist theory and activism, are pushing greater representation and involvement of women and other excluded and disadvantaged groups in both the development of and critical scholarship on educational technologies.

femedtech_white-1024x341

The FemEdTech network challenges the lack of diversity in the edtech sector

The FemEdTech network  is:

alive to the specific ways that technology and education are gendered, and to how injustices and inequalities play out in these spaces (which are also industries, corporations, and institutions). We also want to celebrate and extend the opportunities offered by education in/and/with technology – to women, and to all people who might otherwise be disadvantaged or excluded.

The lesson I take from FemEdTech is that industry needs to act on the lack of diversity in the edtech sector, and educators need to be more aware of the potentially ‘gendered’ and ‘racialized’ nature of edtech software. We already know that education serves to reproduce inequalities and disadvantages of many kinds–the risk is that edtech worsens it. It might be claimed, for example, that the model of ‘personalized learning’ favoured by the edtech sector reflects  the mythology of the self-taught white male programmer. The introduction of computer science and programming in the National Curriculum in England has failed to appeal to girls or children from poorer backgrounds, with the result that England now has fewer girls than ever studying a computer-based subject–not a great way to build up diversity in STEM areas or in the technology workforce.

Student protests
Probably one of the most publicized acts of edtech resistance in the last year or so were the series of student walkouts and parent protests at the Mark Zuckerberg-funded Summit Schools charter chain in the US last year. Personalized learning through adaptive technology is at the core of the Summit approach, using a platform built with engineering assistance from Facebook.

As students from New York wrote in a public letter to Zuckerberg, they were deeply concerned about exploitation of their personal data, and the possibility of it being shared with third parties, but also rejected the model of computer-based, individualized learning which, they claimed, was boring, easy to cheat, failed to prepare them for assessments, and eliminated the ‘human interaction, teacher support, and discussion and debate with our peers that we need in order to improve our critical thinking’.

Summit news coverage

Student and parent protests about Summit Schools generated newspaper headlines

There were controversies too about the curriculum content in the Summit Personalized Learning Platform—students in some cases were being pointed to the UK tabloid the Daily Mail that reportedly ‘showed racy ads with bikini-clad women’. Reports surfaced of Summit curriculum developers working at such speed to create content for the platform that they barely had time to check the adequacy of the sources.

Our lesson from this is about students’ distrust in engineering solutions to schooling.  Personalized learning appears as an ‘efficiency’ model of education, using opaque technologies to streamline students’ progress through school while shaving off all the interactions and space for thinking that students need to engage meaningfully with knowledge and develop lasting understanding. Zuckerberg is now providing the funding to enable the Summit platform to roll out across US schools, through the new non-profit Teachers, Learning & Partners in Education. For educators this raises important questions about whether we want technology-based models like this at the centre of our curricula and pedagogies–because this is what’s coming, and it’s being pushed hard by the tech sector with huge financial resources to help it succeed.

Investor scepticism
Edtech resistance comes not only from activists and students, but sometimes from within its own industry.

Many of you will know AltSchool, the ‘startup charter school chain’ launched by ex-Googler Max Ventilla which quickly attracted almost $174million investment in venture capital funding and then almost as quickly ‘pivoted’ to reveal its main business model was not running schools after all but product testing a personalized learning platform for release to the wider schools market.

There has been strong resistance to AltSchool throughout its short lifecycle. It’s been seen as a template for ‘surveillance schooling’, treating its young student as ‘guinea pigs’ in a live personalized learning experiment. It even called its key sites ‘lab schools’.

Altschool tweet

A critical tweet triggered a venture capitalist backlash

Earlier this summer, though,  resistance came from venture capitalist edtech investor Jason Palmer, who claimed AltSchool had always been a terrible idea especially as, he tweeted, ‘edtech  is all about partnering w/existing districts, schools and educators (not just “product”)’.

And that tweet, in turn, attracted a torrent of criticism from other technology investors who accused Palmer of ‘toxic behaviour’ and made fairly aggressive threats about his future prospects in edtech investment.

When the New York Times ran a piece on this a couple of weeks ago, it focused on the ‘Silicon Valley positivity machine’—a kind of secret code of upbeat marketing that refuses to publicly engage with failure or even in critical debates about the social consequences of technical innovation. AltSchool has now announced it is rebranding as Altitude and will sell to schools the personalized learning product it’s been engineering and testing in its experimental lab school settings for several years.

If there is any lesson to learn here, it’s not just that edtech is about partnerships rather than product. It’s that the edtech industry needs to wake up to critical debate about its ideas and products, and that educators and activists push back against bad ideas and evidence from failed experiments–otherwise they’ll just happen again, under a different brand name. As Audrey Watters commented:

Jason Palmer was absolutely right. AltSchool was a terrible idea. It was obviously a bad investment. Its founder had no idea how to design or run a school. He had no experience in education — just connections to a powerful network of investors who similarly had no damn clue and wouldn’t have known the right questions to ask if someone printed them out in cheery, bubble-balloon lettering. It’s offensive that AltSchool raised almost $175 million.

Without this kind of critical engagement and proper reflective engagement with failure and bad ideas, the danger is that even more intrusive forms of surveillance and monitoring–powered by the techno-optimism and hype of the tech sector positivity machine–become normalized and rolled out across schools and colleges.

Regulation
And of course data-based surveillance has become perhaps the most critical issue in contemporary education technology. One high-profile case is the high school in Sweden that was fined under GDPR rules just last month for the unlawful introduction of facial detection to document student attendance.

The high school board claimed that the data was consensually collected, but the Swedish Data Protection Authority found that it was still unlawful to gather and process the students’ biometric data ‘given the clear imbalance between the data subject and the controller’.

Sweden facial recognition ban

Sweden has issued a major GDPR fine for trials of facial recognition in a school

Sweden has now moved to ban facial recognition in education outright, and the case is catalyzing efforts within the European Union to impose ‘strict limits on the use of facial recognition technology in an attempt to stamp out creeping public surveillance of European citizens … as part of an overhaul in the way Europe regulates artificial intelligence’.

This example shows us growing legal and regulatory resistance to intrusive and invasive surveillance. In fact, with its core emphasis on power imbalances regarding ‘consent’ the case could raise wider debates about students’ rights to ‘opt-out’ of the very technological systems that their schools and colleges now depend on. It also raises the issue that schools themselves might bear the financial burden of GDPR fines if the technologies they buy breach its rules.

Flawed algorithms
Students’, educators’ and regulators’ critical resistance to edtech is likely to grow as we learn more about the ways it works, how it treats data, and in come cases how dysfunctional it is.

Just this summer, an investigation of automated essay-grading technology found it disproportionately discriminates against certain groups of students. This is because:

Essay-scoring engines don’t actually analyze the quality of writing. They’re trained on sets of hundreds of example essays to recognize patterns that correlate with higher or lower human-assigned grades. They then predict what score a human would assign an essay, based on those patterns.

The developers of the software in question openly acknowledged that this was a problem going back 20 years of product development. Each time they tweak it, different groups of students end up disadvantaged. There is systematic and irremediable bias in the essay scoring software.

The examination of essay scoring engines also included the finding that these technologies would give good grades to ‘well-structured gibberish’. The algorithms can’t tell between genuine student insight and meaningless sentences strung together in ways that resembled well-written English.

Increasingly, journalists are on to edtech, and are feeding into the growing sense of frustration and resistance by demonstrating these technologies don’t even fairly do what they claim to do. These investigations teach us to be dubious of claims of algorithmic accuracy used to promote new AI-based edtech products. We shouldn’t presume algorithms do a better job than educators, but insist on forensic, independent and impartial studies of their intended outcomes and unintended effects. Cases like this force educators to confront new technologies with scepticism. In the name of educational innovation, or efficiency, are we ceding responsibility to algorithms that neither care nor even do their job effectively?

Political algorithms
But edtech flaws and resistance can get even more serious.

Five years ago, the UK government Home Office launched an investigation into claims of systematic cheating in English language tests for international students. The assessment developer, English Testing Services (ETS), were called in to do a biometric voice-matching analysis of 66,500 spoken test recordings to determine if candidates had cheated in the test by getting someone else to take it for them.

Its finding was that 58% had cheated by employing a proxy test-taker, and a further 39% were questionable. Over 33,000 students had their visas revoked. More than 2,500 have  been forcibly deported, while another 7,000 left voluntarily after being told they faced detention and removal if they stayed. In all, it is believed that over 10,000 students left the country as a result of the test.

But a later investigation found the voice-matching algorithm may have been wrong in up to 20% of cases. Thousands of international students were wrongly accused of cheating, wrongly had their visas revoked, and were wrongly ordered to leave the country. Multiple news outlets picked up the story as evidence of problematic governmental reliance on algorithmic systems.

In response to the emerging scandal, the UK’s official investigative organization, the National Audit Office, conducted an investigation earlier this year, and the whole fiasco has become a political scandal–result now is that literally thousands of court cases are proceeding against the Home Office. 12,500 appeals have already been heard and over 3000 have won. According to the National Audit Office investigation:

It is difficult to estimate accurately how many innocent people may have been wrongly identified as cheating. Voice recognition technology is new, and it had not been used before with TOEIC tests. The degree of error is difficult to determine accurately because there was no piloting or control group established for TOEIC tests.

Since then a parliamentary inquiry was even launched. One properly shocking part of this is that the Home Office has spent over £21million dealing with the fallout, while ETS has made an estimated £11.4million, and only £1.6million has been reclaimed for the taxpayer. More shocking than that, individual students themselves are reported to be paying many thousands of pounds to have their appeal heard–with many others unable to afford it. The inquiry reported its findings last week, heavily criticizing the Home Office for rushing ‘to penalise students without establishing whether ETS was involved in fraud or if it had reliable evidence of people cheating’.

What lessons can we draw from this? This not just a case of resistance to educational technologies. It is a shocking example of how untested software can have huge consequences for people’s lives. It’s about how those consequences can lead to court cases with massive cost implications for individuals. It’s about the cost to the public of government outsourcing to private contractors. It’s about the outsourcing of human expertise and sensitivity to the mechanical efficiency of algorithms.

It also teaches us that technology is not neutral. The deployment of this voice matching software was loaded with politics—the voice matching algorithm reproduced UK government ‘hostile environment’ policy by efficiently optimizing the deportation process.

Body contact
So finally, what can we learn from the edtech pushback I experienced first-hand on Twitter in relation to the Media Lab’s brain glasses?

Here we can see how proposed experiments on students’ bodies and brains can generate extremely strong reactions. In the last few years, interest in brain science, wearable biometrics and even genetic testing in education has grown substantially.

Experiments are underway with wearable neural interfaces to detect brainwave signals of student attention, and studies are being conducted in behavioural genetics that could in coming years bring about the possibility of DNA testing young children for future achievement, attainment and intelligence.

The potential here, according to behavioural geneticists, is to personalize education around a student’s genetic scores and associated predictions. Maybe consumer genetics companies, like 23andMe, will move to create a bio-edtech market, just as educational neuroscience companies  are already creating a new neuro-edtech market. One educational neurotechnology company, BrainCo, just announced a partnership with the edtech company Progrentis on a ‘fully neuro-optimized education platform’ combining brainwave reading with personalized, adaptive learning technologies.

BrainCo Progrentis

BrainCo and Progrentis have partnered to create a ‘neuro-optimised education platform’

We’re moving into deeply controversial and ethically grey area here. No wonder Twitter exploded on me with accusations of eugenics and forcible mental manipulation when I shared MIT Media Lab’s brain glasses.

These new educational developments in brain technologies and genetics raise huge ethical challenges which must be resolved before these innovations are rolled out—if not stop them in their tracks, as Sweden has moved to do in relation to facial recognition in education. Bioethicists and scientists themselves are increasingly calling for new human rights amendments to protect the human body and the brain from intrusion and extraction in all but necessary medical cases. The UK’s Royal Society just launched a report on the need for regulation of neurotechnology as developments in neural interfaces accelerate, with unknown consequences for human life itself. Yet in education we’re not having these discussions at all–and the result is more and more projects like MIT’s brain glasses, which treat education as an experimental playground for all sorts of potentially outrageous technological innovations.

Conclusion
So, there is a rising wave of edtech resistance from a wide variety of perspectives—from activists to students, journalists to regulators, and legal experts to ethicists.

If these are signals of an emerging edtechlash, then educators, decision-makers and the edtech industry would benefit from being engaged in the key issues that are now emerging, namely that:

  • private sector influence and outsourcing is perceived to be detrimental to public education
  • lack of edtech diversity may reproduce the pedagogic assumptions of engineers
  • student distrust of engineering solutions and continuing trust in human interactions as central to education
  • there may be bad science behind positive industry and investor PR
  • new data protection regulations question how easily student ‘consent’ can be assumed when the balance of power is unequal
  • algorithmic ‘accuracy’ is being exposed as deeply flawed and full of biases
  • algorithmic flaws can lead to devastating consequences at huge costs to individuals, the public, and institutions
  • increasingly invasive surveillance proposals raise new ethical and human rights issues that are likely to be acted upon in coming years.

We should not and cannot ignore these tensions and challenges. They are early signals of resistance ahead for edtech which need to be engaged with before they turn to public outrage. By paying attention to and acting on edtech resistances it may be possible to create education systems, curricula and practices that are fair and trustworthy. It is important not to allow edtech resistance to metamorphose into resistance to education itself.

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Automating mistrust

Ben Williamson

Exam by XaviTurnitin can now analyse students’ individual writing styles to tackle ‘contract cheating’. Image by Xavi

The acquisition of plagiarism detection company Turnitin for US$1.75 billion, due to be completed later this year, demonstrates how higher education has become a profitable market for education technology companies. As concern grows about student plagiarism and ‘contract cheating’, Turnitin is making ‘academic fraud’ into a market opportunity to extend its automated detection software further. It is monetizing students’ writing while manufacturing mistrust between universities and students, and is generating some perverse side effects.

Cheating software
Turnitin’s acquisition is one of the biggest deals ever signed in the edtech field. Its new owner, Advance Publications, is a global media conglomerate with a portfolio including the Conde Nast company. With traditional media forms losing audiences, the deal indicates how technology and media businesses have begun to view education as a potentially valuable investment market.

The profitability of Turnitin, and attraction to Advance, derives from the assignments that students provide for free to its platform. Its plagiarism detection algorithm is constantly fine-tuned as millions of essays are added, analysed and cross-checked against each other and other sources. The ‘world’s largest comparison database’ of student writing, it consists of 600+ million student papers, 155,000+ published works and 60+ billion web pages. Similar to social media companies profiting from user-generated content, value for Turnitin comes from analysing students’ uploaded essays against that database, and securing purchases from universities based on the analysis.

Students can even pay to upload their essays prior to submission to Turnitin’s WriteCheck service, in order to check for similar sentences and phrases, missing or inaccurate citations, and spelling or grammatical inaccuracies. WriteCheck uses the same techniques as common standardized English language tests, and offers an online Professional Tutor Service through a partnership with Pearson.

The company has had years to grow and finesse its services and its ‘Similarity Score’ algorithm. In the UK, the original version of Turnitin, then known as iParadigms, was first paid for on behalf of the HE sector by Jisc (the digital learning agency) from 2002 to 2005, giving it an inbuilt cost advantage over competitors. It also gave it an inbuilt data advantage to train its plagiarism detection algorithm on a very large population of students’ assignments. Nonetheless, studies have repeatedly shown its plagiarism detection software is inaccurate. It both mistakenly brands some students as cheats while completely missing other clear instances of plagiarism, with an error rate that suggests its automated plagiarism reports should be trusted  less than its commercial valuation and market penetration indicates.

With the announcement of its acquisition by Advance, critics say the $1.75bn deal also amounts to the exploitation of students’ intellectual property. ‘This is a pretty common end game for tech companies, especially ones that traffic in human data’, commented Jesse Stommel of the University of Mary Washington. Turnitin’s business model, he added, is to ‘create a large base of users, collect their data, monetize that data in ways that help assess its value, [and] leverage that valuation in an acquisition deal’.

The tension between students’ intellectual property and Turnitin’s profit-making is not new. In many universities, it is compulsory for all student assignments to be submitted to Turnitin, with their intellectual effort then contributing to its growing commercial valuation without their informed knowledge. Ten years ago, four US college students tried to sue Turnitin for taking their assignments against their will and then profiting from them.

Manufacturing mistrust
Beyond its monetization strategy, Turnitin is also reshaping relationships between universities and students. Students are treated by default as potential essay cheats by its plagiarism detection algorithm. This is not a new concern. Ten years ago Sean Zwagerman argued that  plagiarism detection software is a ‘surveillance technology’ that ‘treats writing as a product, grounds the student-teacher relationship in mistrust, and requires students to actively comply with a system that marks them as untrustworthy’. Turnitin’s continued profitability depends on manufacturing and maintaining mistrust between students and academic staff, while also foregrounding its automated algorithm over teachers’ professional expertise.

In the book Why They Can’t Write, John Warner argues that students’ writing abilities have been eroded by decades of standardized curriculum and assessment reforms. Turnitin is yet another technology that treats writing as a rule-based game. ‘It signals to students that the writing is a game meant to please an algorithm rather than an attempt to convey an idea to an interested audience’, Warner has noted. ‘It incentivizes assignments which can be checked by the algorithm, which harms motivation’.

Turnitin also changes how students practice academic writing. One of the leading critical researchers of Turnitin, Lucas Introna, argues it results in the ‘algorithmic governance’ of students’ academic writing practices. Moreover, he suggests that ‘what the algorithms often detect is the difference between skilful copiers and unskilful copiers’, and as a result that it privileges students ‘who conceive of “good” writing practice as the composition of undetectable texts’.

The new deal will open opportunities for Turnitin to develop and promote new features that will further intervene in students’ writing. One is its new service to scan essays to detect an individual’s unique writing style, launched to the HE market in March just a week after announcing its acquisition. This could then be used to identify ‘ghostwriting’—when students hire someone else to write their essays or purchase made-to-order assignments.

Turnitin contract cheatingTurnitin has published expert guidance for universities to identify and combat contract cheating

The new Authorship Investigate service extends Turnitin from the analysis of plagiarism to students’ writing ability, using students’ past assignments, document metadata, forensic linguistic analysis, machine learning algorithms and Natural Language Processing to identify if a student has submitted work written by someone else. It reinforces the idea that the originality, value and quality of student writing should first be assessed according to the criteria of the detection algorithm, and treats all student writing as potential academic piracy. It is also likely to require students to submit extensive writing samples to train the algorithm to make reliable assessments of their writing style, thereby further enhancing the monopoly hold of Turnitin over data about student writing.

Turnitin has bred suspicion and mistrust between students and academics, while affecting how students value and practice academic writing. Yet this mistrust is itself a market opportunity, as the company seeks to offer more solutions services to the perceived problem of increased student plagiarism and contract cheating. As suspicions about student cheating have continued to grow since it was launched nearly 20 years ago, Turnitin has been able to capitalize to dramatically profitable results. Its ghostwriter detection service, of course, is a solution to one of the very problems Turnitin created–because plagiarism has become so detectable, the huge essay mills industry has emerged to produce original on-demand content for students to order. As a result, Turnitin is automating mistrust as it erodes relationships between students and universities, devalues teacher judgment, and reduces student motivation.

Plagiarism police
However damaging and inaccurate it may be, the Advance acquisition will enable Turnitin to further expand its market share and product portfolio. For Turnitin, the timing is ideal, as universities and HE policymakers are collectively beginning to address the rise of online ‘essay mills’ and their erosion of ‘academic integrity’. Government education departments in the UK and Australia have begun to tackle contract cheating more seriously, including through advocating increased use of innovative plagiarism detection software.

In a speech to the Universities UK International higher education forum in March, universities minister Chris Skidmore identified essay mills as one of the issues that needed to be tackled to protect and improve the quality of higher education in England and ensure that it retained its reputation for excellence.

UK academic leaders, HE agencies and ministers have already asked PayPal to stop processing payments to essay mills, and Google and YouTube to block online ads, in an effort to close down the $1 billion annual market in made-to-order assignments. These moves to prevent contract cheating also affect university students and graduates in Kenya, a ‘hotspot‘ for essay mill companies and writers, who rely on contract academic writing as a major source of income. So while Turnitin is set to profit from the detection of contract cheating in Global North contexts, it is disrupting a significant source of employment in specific Global South contexts. In Kenya, for example, where unemployment is high, ‘participants think of their jobs as providing a service of value, not as helping people to cheat. They see themselves as working as academic writers.’

Turnitin’s website now prominently markets its ghostwriter detection service along with a series of free-to-download ebooks to help universities identify contract cheating and develop strategies and tactics to combat it. It’s positioning itself not just as a technical solutions vendor, but as an expert source of insight and authority on ‘upholding academic integrity’. At the same time, Authorship Investigate will allow Turnitin to become the market leader in the fight against essay mills.

The launch of Authorship Investigate has coincided with a Times Higher Education report on the ‘surprising level of support’ among academics for contract cheating services to be made illegal and for ‘the criminalising of student use of these services’. This would appear to raise the prospect of algorithmic identification of students for criminal prosecution. Though there’s nothing to indicate quite such a hard punitive line being taken, the UK Department for Education has urged universities to address the problem, commenting to the THE, ‘universities should also be taking steps to tackle this issue, by investing in detection software and educating students on the severe consequences they face if caught cheating’.

Turnitin is the clear market-leader to solve the essay mills problem that the department has now called on universities to tackle. Its technical solution, however, does not address the wider reasons—social, institutional, psychological, financial or pedagogic—for student cheating, or encourage universities to work proactively with students to resolve them. Instead, it acts as a kind of automated ‘plagiarism police force’ to enforce academic integrity, which at the same time is also set to further disadvantage young people in countries such as Kenya where preparing academic texts for UK and US students is seen as a legitimate and lucrative service by students and graduates.

Robotizing higher education
Like many other technology organizations in education, Turnitin is increasing automation in the sector. Despite huge financial pressures, universities are investing in Turnitin to automate plagiarism and ghostwriting detection as a way of combating academic fraud. The problem of essay mills that politicians are now fixated upon is the ideal market opportunity for Turnitin to grow its business and its authority over student writing even further. In so doing, it also risks standardizing students’ writing practices to conform to the rules of the algorithm–ultimately contributing to the algorithmic governance, and even ‘robotization’, of academic writing.

The real problem is that universities are being motivated to invest in these robotized, data-crunching edtech products for multiple complex reasons. As universities have to seek larger student enrolments for their financial security, algorithmic services become efficient ways of handling huge numbers of student assignments. They satisfy government demands for action to be taken to raise standards, boost student performance, and preserve academic integrity. But automated software is a weak, robotic, and error-prone substitute for the long-term development of trusting pedagogic relationships between teachers and students.

A version of this post was previously published on Research Professional with the title ‘Manufacturing mistrust‘ on 12 June 2019.
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Learning from surveillance capitalism

Ben Williamson

Fraction collectorSurveillance capitalism combines data analytics, business strategy, and human behavioural experimentation. Image: “Fraction collector” by proteinbiochemist

‘Surveillance capitalism’ has become a defining concept for the current era of smart machines and Silicon Valley expansionism. With educational institutions and practices increasingly focused on data collection and outsourcing to technology providers, key points from Shoshana Zuboff’s The Age of Surveillance Capitalism can help explore the consequences the field of education. Mindful of the need for much more careful studies of the intersections of education with commercially-driven data-analytic strategies of ‘rendition’ and ‘behavioural modification’, here I simply outline a few implications of surveillance capitalism for how we think about education policy and about learning.

Data, science and surveillance
Zuboff’s core argument is that tech businesses such as Google, Microsoft, Facebook and so on have attained unprecedented power to monitor, predict, and control human behaviour through the mass-scale extraction and use of personal data. These aren’t especially novel insights—Evgeny Morozov has a 16,000 word essay on the book’s analytical and stylistic shortcomings—but Zuboff’s strengths are in the careful conceptualization and documentation of some of the key dynamics that have made surveillance capitalism possible and practical. As James Bridle argued in his review of the book, ‘Zuboff has written what may prove to be the first definitive account of the economic – and thus social and political – condition of our age’.

Terms such as ‘behavioural surplus’, ‘prediction products’, ‘behavioural futures markets’, and ‘instrumentarian power’ provide a useful critical language for decoding what surveillance capitalism is, what it does, and at what cost. Some of the most interesting documentary material Zuboff presents include precedents such as the radical behaviourism of BF Skinner and the ‘social physics’ of MIT Media Lab pioneer Sandy Pentland. For Pentland, quoted by Zuboff, ‘a mathematical, predictive science of society … has the potential to dramatically change the way government officials, industry managers, and citizens think and act’ (Zuboff, 2019, 433) through ‘tuning the network’ (435). Surveillance capitalism is not and was never simply a commercial and technical task, but deeply rooted in human psychological research and social experimentation and engineering. This combination of tech, science and business has enabled digital companies to create ‘new machine processes for the rendition of all aspects of human experience into behavioural data … and guarantee behavioural outcomes’ (339).

Zuboff has nothing to say about education specifically, but it’s tempting straight away to see a whole range of educational platforms and apps as condensed forms of surveillance capitalism (though we might just as easily invoke ‘platform capitalism’). The classroom behaviour monitoring app ClassDojo, for example, is a paradigmatic example of a successful Silicon Valley edtech business, with vast collections of student behavioural data that it is monetizing by selling premium features for use at home and offering behaviour reports to subscribing parents. With its emphasis on positive behavioural reinforcement through reward points, it represents a marriage of Silicon Valley design with Skinner’s aspiration to create ‘technologies of behaviour’. ClassDojo amply illustrates the combination of behavioural data extraction, behaviourist psychology and monetization strategies that underpin surveillance capitalism as Zuboff presents it.

Perhaps more pressingly from the perspective of education, however, Zuboff makes a number of interesting observations about ‘learning’ that are worth unpacking and exploring.

Learning divided
The first point is about the ‘division of learning in society’ (the subject of chapter 6, and drawing on her earlier work on the digital transformation of work practices). By this term Zuboff means to demarcate a shift in the ‘ordering principles’ of the workplace from the ‘division of labour’ to a ‘division of learning’ as workers are forced to adapt to an ‘information-rich environment’. Only those workers able to develop their intellectual skills are able to thrive in the new digitally-mediated workplace. Some workers are enabled (and are able) to learn to adapt to changing roles, tasks and responsibilities, while others are not. The division of learning, Zuboff argues, raises questions about (1) the distribution of knowledge and whether one is included or excluded from the opportunity to learn; (2) about which people, institutions or processes have the authority to determine who is included in learning, what they are able to learn, and how they are able to act on their knowledge; and (3) about what is the source of power that undergirds the authority to share or withhold knowledge (181).

But this division of learning, according to Zuboff, has now spilled out of the workplace to society at large. The elite experts of surveillance capitalism have given themselves authority to know and learn about society through data. Because surveillance capitalism has access to both the ‘material infrastructure and expert brainpower’ (187) to transform human experience into data and wealth, it has created huge asymmetries in knowledge, learning and power. A narrow band of ‘privately employed computational specialists, their privately owned machines, and the economic interests for who sake they learn’ (190) has ultimately been authorized as the key source of knowledge over human affairs, and empowered to learn from the data in order to intervene in society in new ways.

Sociology of education researchers have, of course, asked these kinds of questions for decades. They are ultimately questions about the reproduction of knowledge and power. But in the context of surveillance capitalism such questions may need readdressing, as authority over what constitutes valuable and worthwhile knowledge for learning passes to elite computational specialists, the commercial companies they work for, and even to smart machines. As data-driven knowledge about individuals grows in predictive power, decisions about what kinds of knowledge an individual learner should receive may even be largely decided by ‘personalized learning platforms’–as current developments in learning analytics and adaptive learning already illustrate. The prospect of smart machines as educational engines of social reproduction should be the subject of serious future interrogation.

Learning collectives
The second key point is about the ‘policies’ of smart machines as a model for human learning (detailed in chapter 14). Here Zuboff draws on a speech by a senior Microsoft executive talking about the power of combined cloud and Internet of Things technologies for advanced manufacturing and construction. In this context, Zuboff explains, ‘human and machine behaviours are tuned to pre-established parameters determined by superiors and referred to as “policies”’ (409). These ‘policies’ are algorithmic rules that

substitute for social functions such as supervision, negotiation, communication and problem solving. Each person and piece of equipment takes a place among an equivalence of objects, each one “recognizable” to the “system” through the AI devices distributed across the site. (409)

In this example, the ‘policy’ is then a set of algorithmic rules and a template for collective action between people and machines to operate in unison to achieve maximum efficiency and optimal outcomes. Those ‘superiors’ with the authority to determine the policies, of course, are those same computational experts and machines that have benefitted from the division of learning. This gives them unprecedented powers to ‘apply policies’ to people, objects, processes and activities alike, resulting in a ‘grand confluence in which machines and humans are united as objects in the cloud, all instrumented and orchestrated in accordance with the “policies” … that appear on the scene as guaranteed outcomes to be automatically imposed, monitored and maintained by the “system”’ (410). These new human-machine learning collectives represent the future for many forms of work and labour under surveillance capitalism, according to Zuboff.

Zuboff then goes beyond human-machine confluences in the workplace to consider the instrumentation and orchestration of other types of human behaviour. Drawing parallels with the behaviourism of Skinner, she argues that digitally-enforced forms of ‘behavioral modification’ can operate ‘just beyond the threshold of human awareness to induce, reward, goad, punish, and reinforce behaviour consistent with “correct policies”’, where ‘corporate objectives define the “policies” toward which confluent behaviour harmoniously streams’ (413). Under conditions of surveillance capitalism, Skinner’s behaviourism and Pentland’s social physics spill out of the lab into homes, workplaces, and all the public and private space of everyday life–ultimately turning the world into a gigantic data science lab for social and behavioural experimentation, tuning and engineering.

And the final point she makes here is that humans need to become more machine-like to maximize such confluences. This is because machines connected to the IoT and the cloud work through collective action by each learning what they all learn, sharing the same understanding and ‘operating in unison with maximum efficiency to achieve the same outcomes’ (413). This model of collective learning, according to surveillance capitalists, can learn faster than people, and ‘empower us to better learn from the experiences of others’:

The machine world and the social world operate in harmony within and across ‘species’ as humans emulate the superior learning processes of the smart machines. … [H]uman interaction mirrors the relations of the smart machines as individuals learn to think and act by emulating one another…. In this way, the machine hive becomes the role model for a new human hive in which we march in peaceful unison toward the same direction based on the same ‘correct’ understanding in order to construct a world free of mistakes, accidents, and random messes. (414)

For surveillance capitalists human learning is inferior to machine learning, and urgently needs to be improved by gathering together humans and machines into symbiotic systems of behavioural control and management.

Learning in, from, or for surveillance capitalism?
These key points from The Age of Surveillance Capitalism offer some provocative starting places for further investigations into the future shape of education and learning amid the smart machines and their smart computational operatives. Three key points stand out.

1) Cultures of computational learning. One line of inquiry might be into the cultures of learning of those computational experts who have gained from the division of learning. And I mean this in two ways. How are they educated? How are they selected into the right programs? What kinds of ongoing training provides the kinds of privilege to learn about society through mass-scale behavioural data? These are questions about new and elite forms of workforce preparation and professional education. How, in short, are these experts educated, qualified and socialized to do data analytics and behaviour modification—if that is indeed what they do? In other words, how is one educated to become a surveillance capitalist?

The other way of approaching this concerns what is actually involved in ‘learning’ about society through its data. This is both a pedagogic and a curricular question. Pedagogically, education research would benefit from a much better understanding of the kinds of workplace education programmes underway inside the institutions of surveillance capitalism. From a curricular perspective, this would also require an engagement with the kinds of knowledge assumptions and practices that flow through such spaces. As mentioned earlier, sociology of education has long been concerned with how aspects of culture are ‘selected’ for reproduction by transmission through education. As tech companies and related academic labs become increasingly influential, they are producing new ‘social facts’ that might affect how people both within and outside those organizations come to understand the world. They are building new knowledge based on a computational, mathematical, and predictive style of thinking. What, then, are the dynamics of knowledge production that generate these new facts, and how do they circulate to affect what is taught and learnt within these organizations? As Zuboff notes, pioneers such as Sandy Pentland have built successful academic teaching programs at institutes like MIT Media Lab to reproduce knowledge practices such as ‘social physics’.

2) Human-machine learning confluences. The second key issue is what it means to be a learner working in unison with the Internet of Things. Which individuals are included in the kind of learning that is involved in becoming part of this ‘collective intelligence? When smart machines and human workers are orchestrated together into ‘confluence’, and human learning is supposed to emulate machine learning, how do our existing theories and models of human learning hold up? Machine learning and human learning are not obviously comparable, and the tech firms surveyed by Zuboff appear to hold quite robotic notions of what constitutes learning. Yet if the logic of extreme instrumentation of working environments develops as Zuboff anticipates, this still raises significant questions about how one learns to adapt to work in unison with the smart machines, who gets included in this learning, who gets excluded, how those choices and decisions are made, and what kinds of knowledge and skills are gained from inclusion. Automation is likely to lead to both further divisions in learning and more collective learning at the same time–with some individuals able to exercise considerable autonomy over the networks they’re part of, and others performing the tasks that cannot yet be automated.

In the context of concerns about the role of education in relation to automation, intergovernmental organizations such as the OECD and World Economic Forum have begun encouraging governments to focus on ‘noncognitive skills’ and ‘social-emotional learning’ in order to pair human emotional intelligence with the artificial cognitive intelligence of smart machines. Those unique human qualities, so the argument goes, cannot be quantified whereas routine cognitive tasks can. Classroom behaviour monitoring platforms such as ClassCraft have emerged to measure those noncognitive skills and offer ‘gamified’ positive reinforcement for the kind of ‘prosocial behaviours’ that may enable students to thrive in a future of increased automation. Being emotionally intelligent, by these accounts, would seem to allow students to enter into ‘confluent’ relations with smart machines. Rather than competing with automation, they would complement it as collective intelligence. ‘Human capital’ is no longer a sufficient economic goal to pursue through education—it needs to produce ‘human-computer capital’ too.

3) Programmable policies. A third line of inquiry would be into the idea of ‘policies’. Education policy studies have long engaged critically with the ways government policies circumscribe ‘correct’ forms of educational activity, progress, and behaviour. With the advance of AI-based technologies into schools and universities, policy researchers may need to start interrogating the policies encoded in the software as well as the policies inscribed in government texts. These new programmable policies potentially have a much more direct influence on  ‘correct’ behaviours and maximum outcomes by instrumenting and orchestrating activities, tasks and behaviours in educational institutions.

Moreover, researchers might shift their attention to the kind of programmable policies that are enacted in the instrumented workplaces where, increasingly, much learning happens. Tech companies have long bemoaned the adequacy of school curricula and university degrees to deliver the labour market skills they require. With the so-called ‘unbundling’ of the university in particular, higher education may be moving further towards ‘demand driven’ forms of professional learning and on-the-job industry training provided by private companies. When education moves into the smart workplace, learning becomes part of the confluence of humans and machines, where all are equally orchestrated by the policies encoded in the relevant systems. Platforms and apps using predictive analytics and talent matching algorithms are already emerging to link graduates to employers and job descriptions. The next step, if we accept the likeliness of the direction of travel of surveillance capitalism, might be to match students directly to smart machines on-demand as part of the collective human-machine intelligence required to achieve maximum efficiency and optimized outcomes for capital accumulation. In this scenario, the computer program would be the dominant policy framework for graduate employability, actively intervening in professional learning by sorting individuals into appropriate networks of collective learning and then tuning those networks to achieve best effects.

All of this raises one final question, and a caveat. First the caveat. It’s not clear that ‘surveillance capitalism’ will sustain as an adequate explanation for the current trajectories of high-tech societies. Zuboff’s account is not uncontested, and it’s in danger of becoming an explanatory shortcut for deployment anywhere that data analytics and business interests intersect (as ‘neoliberalism’ is sometimes evoked as a shortcut for privatization and deregulation). The current direction of travel and future potential described by Zuboff are certainly not desirable, and should not be accepted as inevitable. If we do accept Zuboff’s account of surveillance capitalism, though, the remaining question is whether we should be addressing the challenges of learning in surveillance capitalism, or the potential for whole education systems to learn from surveillance capitalism and adapt to fit its template. Learning in surveillance capitalism at least assumes a formal separate of education from these technological, political and economic conditions. Learning from it, however, suggests a future where education has been reformatted to fit the model of surveillance capitalism–indeed, where a key purpose of education is for surveillance capitalism.

Zuboff, S. 2019. The Age of Surveillance Capitalism: The fight for a human future at the new frontier of power. London: Profile.
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