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Digital platforms and university strategies: tensions and synergies

by Sam Sellar

This blog is based on a presentation to the 2021 SRHE Research Conference, as part of a Symposium on Universities and Unicorns: Building Digital Assets in the Higher Education Industry organised by the project’s principal investigator, Janja Komljenovic (Lancaster). The support of the Economic and Social Research Council (ESRC) is gratefully acknowledged. The project introduces new ways to think about and examine the digitalising of the higher education sector. It investigates new forms of value creation and suggests that value in the sector increasingly lies in the creation of digital assets.

In a lecture delivered to Stanford University in 2014, which was provocatively titled Competition is for losers, Peter Thiel argued that ‘[m]onopoly is the condition of every successful business.’ Thiel’s endorsement of monopoly over competition has become business strategy orthodoxy for Big Tech firms, which, as Birch, Cochrane and Ward (2021, p6) argue, have ‘often been willing to accept low revenues in the short- to medium-term with the longer term goal of capturing markets and monopoly rents through their expected future control over data’. Assets are replacing commodities in contemporary capitalism, and an asset can be defined as ‘something that can be owned or controlled, traded, and capitalised as a revenue stream … [and] the point is to get a durable economic rent from them’ by limiting access to the asset (Birch and Muniesa, 2020, p2). We can see these assetisation dynamics emerging in EdTech markets serving UK higher education, and in this article I offer early insights into how these dynamics, driven by the growth in use of digital platforms during the Covid-19 pandemic, are shaping university strategies and practices.

This article reports on findings from the second phase of the Universities and Unicorns: building digital assets in the higher education industry project. The project is led by Dr Janja Komljenovic at Lancaster University and it aims to investigate new processes of value creation and extraction-assetisation-in the HE sector as it increasingly digitalises its operations. In Phase 2 of our project, we are conducting a series of university case studies in the UK, along with the investor and the company case studies. The university case studies are designed to help us understand how universities work with their commercial partners and what are the synergies and tensions. We are also curious about how universities view changing business models that focus on assetisation.

Importantly, we are not evaluating the use of EdTech in the context of teaching and learning or evaluating the strategies of individual institutions. Our concern is with how the HE sector is evolving in connection with EdTech markets. We are interviewing senior leaders, academic staff, directors of IT departments, IT developers and staff working in procurement, commercialisation and legal departments. We are also collecting a range of documents relating to digital strategy, business and data management plans, technical reports, financial records, and contracts with EdTech companies.

Our fieldwork with universities is a work in progress, and in this blog post I will outline three of our emerging findings, which relate to: (1) the ways that universities think about digital strategy; (2) the value of data from a university perspective; and (3) emerging processes of assetisation.

Digital strategy

None of the universities that we have studied so far have had formal and distinct digital strategies. Rather, digital strategy is embedded in IT, teaching and learning (T&L) and library strategies. In most cases, universities appear to be ‘between’ official strategy documents that cover this area. COVID-19 clearly shifted the short-term focus to tactics – working urgently to adjust and develop digital ecosystems to accommodate new demands of large-scale shifts online – and these universities are just now catching their breath and starting to update their strategies. However, despite this lack of formal strategy, some universities are very clear regarding the use of digital platforms to lead the sector and create value. In these cases, there clearly is an overarching strategy, it just isn’t described or formally presented as such.

Universities see themselves as developing institutional digital ecosystems by joining up platforms and focusing on the interoperability of their systems. Decisions about specific platforms are increasingly shaped by their potential integration into these ecosystems, and how data can be managed and integrated across platforms.

Interestingly, digital strategy is being driven by teaching and research strategy rather than shaping it. In one case, the point was made very strongly that digital is not separate, but rather a way of delivering the core business. Digital platforms are largely being used to deliver existing activity in digital form, rather than to create new forms of economic activity and new sources of value. However, questions are being raised about the relationship between IT and teaching and learning. For example, should IT departments simply support other business functions, or might they lead on digital strategy to enable new possibilities for the university?

The value of digital data

The primary value of digital data for universities appears to be reputational, and responses from our participants thus far have been remarkably consistent in this regard. Digital platforms can help to enhance the university’s brand and extend the business over a wider geographic range. This primacy of reputational, rather than financial, value is a distinctive feature of university perspectives on digital platforms, in contrast to companies.

Engagement with digital platforms was also seen to be valuable insofar as it generates market intelligence, supports student recruitment, changes perceptions of teaching and learning (eg blended approaches); and change perceptions of students (eg enabling particular cohorts to engage in new ways with benefits for their learning outcomes). Most interviewees are not thinking about the data generated by digital platforms as an asset, but it is clear that digital content (eg recorded lectures) are being seen in these terms insofar as they can be controlled by intellectual property rights and re-used over time.

Interestingly, our participants clearly hold the view that there is more potential for universities to make use of the digital data generated by platforms they use. However, in the case of learning analytics there is also scepticism regarding what it promises and its true value at this time. Despite a number of trials and experiments, many in UK universities are yet to see the benefits beyond what can be achieved using more prosaic approaches to data analytics.

Assetisation

The universities that we have studied so far do not appear to be using data to develop new products or services that generate value through economic rents; this kind of activity appears limited to commercial providers of digital platforms. However, universities increasingly understand the potential value of the data generated by their staff and students, and they are actively pursuing access to these data in their contractual negotiations with partners.

This is where we are seeing the emergence of assetisation dynamics in EdTech markets, which reflect the business strategies that Thiel promotes in his celebration of monopolies. Even if universities are able to negotiate favourable terms in individual contracts, providing rights to access and use data generated by university users on a given platform, they do not have access to aggregated data collected by companies through the use of this platform by other universities.

There is thus concern about the assetisation of data by commercial providers, for example, in relation to the use of aggregated data sets to develop new products and services that automate aspects of academic work (eg assessment). Turnitin is a primary example that came up in many of our discussions. The monopoly created by Turnitin leaves universities with little choice but to use their platform and pay whatever is asked, and relationships with Turnitin have become strained in many cases. The value of Turnitin is based on the data they have collected, and this data could be used to develop new services that automate, and thus substitute for, aspects of teaching currently delivered by lecturers. Work is being pursued through industry bodies to negotiate fairer distribution of the potential value generated by digital platforms in such cases.

Conclusion

While our university case studies are a work in progress, these three themes are already emerging quite consistently across our research sites. The value of data for universities is primarily reputational, extending the reach of teaching and learning functions, enhancing recruitment and supporting innovation in teaching and learning. Universities see digital strategy and the use of digital platforms as a way to extend their core business, not as a means to create new kinds of economic activity. In this respect, tech sector business strategies focused on creating value from data as an asset are not yet evident in the strategies of universities. However, we are seeing early signs that data is being assetised by EdTech companies, in an effort to extract monopoly rents by locking-in users through subscriptions to digital platforms. In this sense, we are curious to see whether monopoly will be a condition of every successful business in the burgeoning HE EdTech space.

Sam Sellar is Dean of Research (Education Futures) and Professor of Education Policy at the University of South Australia. Sam’s research focuses on education policy, large-scale assessments and the datafication of education. Sam also works closely with teacher organisations around the world to understand the impact of digitalisation on teachers’ work. His most recent book is titled Algorithms of education: How datafication and artificial intelligence shape policy (University of Minnesota Press), co-authored with Kalervo N Gulson and P Taylor Webb. Contact here: sam.sellar@unisa.edu.au

References

Birch, K, Cochrane, DT, and Ward, C (2021) ‘Data as asset? The measurement, governance, and valuation of digital personal data by Big Tech’ Big Data & Society, 8(1), 20539517211017308.

Birch, K, and Muniesa, F (eds) (2020). Assetization: turning things into assets in technoscientific capitalism Boston: MIT Press

Thiel, P (2014) ‘Competition is for losers’ The Wall Street Journal Available from: https://www.wsj.com/articles/peter-thiel-competition-is-for-losers-1410535536


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Critically analysing EdTech investors’ logic in business discourse

by Javier Mármol Queraltó

This blog is based on a presentation to the 2021 SRHE Research Conference, as part of a Symposium on Universities and Unicorns: Building Digital Assets in the Higher Education Industry organised by the project’s principal investigator, Janja Komljenovic (Lancaster). The support of the Economic and Social Research Council (ESRC) is gratefully acknowledged. The project introduces new ways to think about and examine the digitalising of the higher education sector. It investigates new forms of value creation and suggests that value in the sector increasingly lies in the creation of digital assets.

In the context of the current SARS-COVID-19 pandemic, the ongoing process of digitalisation of education has become a prominent area for social, financial and, increasingly, (critical) educational research. Higher education, as a pivotal social, economic, technological and educational domain, has seen its activities drastically affected, and Universities and the multitude of people involved in them have been forced to adapt to the unfolding crisis. HE researchers agree both on the unpreparedness of countries and institutions faced by the pandemic, and on its potential lasting impact on the educational sector (Goedegebuure and Meek, 2021). In as much as educational technologies (EdTech) have been brought to the fore due to their pivotal role in the enablement and continuation of educational practices across the globe, EdTech companies and investors have also become primary financial beneficiaries of these necessary processes of digitalisation. The extensive use and adoption of EdTech to bridge the gap between HE professionals and students due to the application of strict social distancing measures has been welcomed by investors as an opportunity for EdTech to establish themselves as key players within an educational landscape under a process of assetisation (Komljenovic, 2020, 2021). Investors and EdTech are scaffolding new digital markets in HE, reshaping the conceptualisation of universities, HE and the sector itself more generally (Williamson, 2021; Komljenovic and Robertson, 2016). In this brief entry, I focus on EdTech investors’ discourses, owing to the potential of such discourses to shape the future of educational practices broadly speaking.

Within the ‘Universities and Unicorns’ ESRC-funded project, this exploratory research (see full report) aimed at unveiling the ideological uses of linguistic, visual and multimodal devices (eg texts and charts) deployed by EdTech investors in a variety of texts that have the potential, due to their circulation and goals, to shape public understandings of the role of Educational Technologies in the unfolding crisis. The research was conducted deploying a framework anchored in Linguistics, specifically cognitive-based approaches to Critical Discourse Studies (CL-CDS; eg Mármol Queraltó, 2021b). A central assumption in this approach is that language encodes construal: the same event/situation can be alternatively linguistically formulated, and these can have diverse cognitive effects in readers (Hart, 2011). From a CL-CDS perspective, then, texts can potentially shape the way that the public think (and subsequently act) about social topics (cf Watters, 2015).

In order to extract the ideologies underlying discourse practices carried out by HE investors, we examined qualitatively a variety of texts disseminated in the public and semi-private domains. We investigated, for example, HolonIQ’s explanatory charts, interviews with professionals and blog entries (eg Charles MacIntyre, Alex Latsis, Jan Lynn-Matern), and global financial reports by IBIS Capital, BrightEye Ventures, and EdTechX, among several others. Our main goal was to better understand how EdTech investors operationalised discourse to shape the imageries of the future in the relationship between HE institutions, EdTech and governance. In line with CDS approaches, we examined the representations of social actors in context using van Leeuwen’s (2008) framework, and more in line with CL-CDS, we also operationalised the analysis of metaphorical expressions indexing Conceptual Metaphors, and Force dynamics. Force-dynamics is an essential tool deployed to examine how the tensions between actors and processes within business discourse are constructed (see Oakley, 2005).

Our study yielded important findings for the critical examination of discourse processes within the EdTech-HE-governance triangle of influences. In terms of social actor representation (whose examination also included metaphor), the main findings are:

  • EdTech investors and companies are rendered as opaque, abstract collectives, and are positively represented as ‘enablers’ and ‘disruptors’ of educational processes.
  • Governments are rendered as generic, collective entities, and depicted as necessary funders of process of digital transformation.
  • Universities or HE institutions are mainly negatively represented as potential ‘blockers’ of processes of digital transformation, and they are depicted as failing their students due to their lack of scalability and flexibility.
  • Individuals within HE institutions are identified as numbers and increasing percentages within unified collectives, students routinely cast as beneficiaries in ‘consumer’ and ‘user’ roles, while educators are activated as ‘content providers’.
  • Metaphorically, the EdTech sector is conceptualised as a ‘ship’ on a ‘journey’ towards profit, where HE institutions can be ‘obstacles along a path’ and the global pandemic and other push factors are conceptualised as ‘tailwinds’.
  • The EdTech market is conceptualised as a ‘living organism’ that grows and evolves independent of the actors involved in it. The visual representations observed reinforce these patterns and emphasise the growth of the EdTech market in very positive terms.

The formulation of ‘push’ and ‘pull’ factors is also essential to understand the discursively constructed ‘internal tensions’ within the sector. In order to examine these factors, we operationalised Force-dynamics analysis and metaphor, which allowed us to arrive to the following findings:

  • Push factors identified by investors driving the EdTech sector include the SARS-COVID19 global pandemic, the digital acceleration being experienced in the sector prior to the pandemic, the increasing number of students requiring access to HE, and investors’ actions aimed at disrupting the EdTech market.
  • Pull factors encouraging investment in the sector are conceptualised in the shape of financial predictions. The visions put forward by EdTech investors become instrumental in the achievement of those predictions.
  • The representation of the global pandemic is ambivalent and it is rendered both as a negative factor affecting societies and as a positive factor for the EdTech sector. The primary focus is on the positive outcomes of the disruption brought about by the pandemic.
  • Educational platforms are foregrounded in their enabling role and replace HE institutions as site for educational practice, de-localising educational practices from physical universities.
  • Students and educators are found to be increasingly reframed as ‘users’ and ‘content providers’, respectively. This discursive shift is potentially indicative of the new processes of assetisation of HE.

On the whole, framing business within the ‘journey’ metaphor entails that any entities or processes affecting business are potentially conceptualised as ‘obstacles along the path’, and therefore attributed negative connotations. In our case, those entities (eg governments and HE institutions) or processes (eg lack of funding) that metaphorically ‘stand in the way of business’ are automatically framed in a negative light, potentially affording a negative reception by the audience and therefore legitimising actions designed to remove those ‘obstacles’ (eg ‘disruptions’). EdTech companies and investors are represented very positively as ‘enablers’ of educational practices disrupted by the SARS-COVID19 pandemic, but also as ‘push factors’ in processes of digital acceleration within the ‘speed of action is speed of motion’ metaphor. In the premised, ever-growing EdTech sector, those actors and processes that ‘slow down’ access to profits (or processes providing access to profit) are similarly negatively represented. The conceptualisation of the SARS-COVID-19 global pandemic in this context reflects ‘calculated ambivalence’. This ambivalence was expected, as portraying the pandemic solely as a relatively positive factor for the HE sector would be in extreme detriment to EdTech investors’ activities. Our findings reflect that, while the global pandemic is initially represented as a very negative factor greatly disrupting societies and businesses, those negative impacts tend to be presented in rather vague ways and in most occasions the result of the disruption brought about by the pandemic is reduced to changes in the modality of education experienced by learners (from in-person to online education). We have found no significant mention of social or personal impacts of the pandemic (eg deaths and scenarios affecting underrepresented social groups), where the focus has been mainly on the market and the activities within it. Conversely, while the initial framing of the pandemic is inherently negative, we have seen in several examples above that the pandemic is subtly instrumentalised as a ‘push factor’, which serves to accelerate digital transformation and is hence a positive factor for the EdTech sector. In a global context of restrictions, containment measures and vaccine rollouts, it is especially ideologically relevant to find the pandemic instrumentalised as a ‘catalyst’, or as an important player in a ‘experiment of global proportions’. Framing the pandemic in such ways detaches the audience from its negative connotations, and serves to depict EdTech companies and investors as involved in high-level, complex processes that abstract the millions of diverse victims to the pandemic. Ultimately, in the ‘journey’ towards profit, the SARS-COVID-19 is a desired push factor, also realised as a ‘tailwind’, which facilitates the desired digital acceleration.

On the whole, our research demonstrated that social actor representation and the distinction between push/pull factors are crucial sites for the analysis of EdTech discourse. EdTech’s primary focus is on the positive outcomes of the disruption brought about by the pandemic. In this context, educational platforms are foregrounded in their enabling role and replace HE institutions as site for educational practice, de-localising educational practices from physical universities. Subsequently, students and educators are found to be increasingly reframed as ‘users’ and ‘content providers’ respectively. We argue that this subtle discursive shift is potentially indicative of the new processes of assetization of HE and reflects more broadly a neoliberal logic.

Javier Mármol Queraltó is a PhD candidate in Linguistics in Lancaster University. His current research deals with the multimodal representations of discourses of migration in the British and Spanish online press. He advocates a Cognitive Linguistic Approach to Critical Discourse Studies (CL-CDS), and is working on a methodology that can shed light on how public perceptions of social issues might be influenced by both the multimodal constraints of online newspaper discourse and our shared cognitive capacities. He is also interested in the multimodal and cognitive dimensions of discourses of Brexit outside the UK, news discourses of social unrest, and the marketisation/assetisation processes of HE.


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Understanding the value of EdTech in higher education

by Morten Hansen

This blog is a re-post of an article first published on universityworldnews.com. It is based on a presentation to the 2021 SRHE Research Conference, as part of a Symposium on Universities and Unicorns: Building Digital Assets in the Higher Education Industry organised by the project’s principal investigator, Janja Komljenovic (Lancaster). The support of the Economic and Social Research Council (ESRC) is gratefully acknowledged. The project introduces new ways to think about and examine the digitalising of the higher education sector. It investigates new forms of value creation and suggests that value in the sector increasingly lies in the creation of digital assets.

EdTech companies are, on average, priced modestly, although some have earned strong valuations. We know that valuation practices normally reflect investors’ belief in a company’s ability to make money in the future. We are, however, still learning about how EdTech generates value for users, and how to take account of such value in the grand scheme of things.


Valuation and deployment of user-generated data

EdTech companies are not competing with the likes of Google and Facebook for advertisement revenue. That is why phrases such as ‘you are the product’ and ‘data is the new oil’ yield little insight when applied to EdTech. For EdTech companies, strong valuations hinge on the idea that technology can bring use value to learners, teachers and organisations – and that they will eventually be willing to pay for such benefits, ideally in the form of a subscription. EdTech companies try to deliver use value in multiple ways, such as deploying user-generated data to improve their services. User-generated data are the digital traces we leave when engaging with a platform: keyboard strokes and mouse movements, clicks and inactivity.


The value of user-generated data in higher education

The gold standard for unlocking the ‘value’ of user-generated data is to bring about an activity that could otherwise not have arisen. Change is brought about through data feedback loops. Loops consist of five stages: data generation, capture, anonymisation, computation and intervention. Loops can be long and short.


For example, imagine that a group of students is assigned three readings for class. Texts are accessed and read on an online platform. Engagement data indicate that all students spent time reading text 1 and text 2, but nobody read text 3. As a result of this insight, come next semester, text 3 is replaced by a more ‘engaging’ text. That is a long feedback loop.


Now, imagine that one student is reading one text. The platform’s machine learning programme generates a rudimentary quiz to test comprehension. Based on the students’ answers, further readings are suggested or the student is encouraged to re-read specific sections of the text. That is a short feedback loop.


In reality, most feedback loops do not bring about activity that could not have happened otherwise. It is not like a professor could not learn, through conversation, which texts are better liked by students, what points are comprehended, and so on. What is true, though, is that the basis and quality of such judgments shifts. Most importantly, so does the cost structure that underpins judgment.


The more automated feedback loops are, the greater the economy of scale. ‘Automation’ refers to the decoupling of additional feedback loops from additional labour inputs. ‘Economies of scale’ means that the average cost of delivering feedback loops decreases as the company grows.


Proponents of machine learning and other artificial intelligence approaches argue that the use value of feedback loops improves with scale: the more users engage in the back-and-forth between generating data, receiving intervention and generating new data, the more precise the underlying learning algorithms become in predicting what interventions will ‘improve learning’.


The platform learns and grows with us

EdTech platforms proliferate because they are seen to deliver better value for money than the human-centred alternative. Cloud-based platforms are accessed through subscriptions without transfer of ownership. The economic relationship is underwritten by law and continued payment is legitimated through the feedback loops between humans and machines: the platform learns and grows with us, as we feed it.


Machine learning techniques certainly have the potential to improve the efficiency with which we organise certain learning activities, such as particular types of student assessment and monitoring. However, we do not know which values to mobilise when judging intervention efficacy: ‘value’ and ‘values’ are different things.


In everyday talk, we speak about ‘value’ when we want to justify or critique a state of affairs that has a price: is the price right, too low, or too high? We may disagree on the price, but we do agree that something is for sale. At other times we reject the idea that a thing should be for sale, like a family heirloom, love or education. If people tell us otherwise, we question their values. This is because values are about relationships and politics.


When we ask about the values of EdTech in higher education, we are really asking: what type of relations do we think are virtuous and appropriate for the institution? What relationships are we forging and replacing between machines and people, and between people and people?


When it comes to the application of personal technology we have valued convenience, personalisation and seamlessness by forging very intimate but easily forgettable machine-human relations. This could happen in the EdTech space as well. Speech-to-text recognition, natural language processing and machine vision are examples of how bonds can be built between humans and computers, aiding feedback loops by making worlds of learning computable.


Deciding on which learning relations to make computable, I argue, should be driven by values. Instead of seeing EdTech as a silver bullet that simply drives learning outcomes, it is more useful to think of it as technology that mediates learning relations and processes: what relationships do we value as important for students and when is technology helpful and unhelpful in establishing those? In this way, values can help us guide the way we account for the value of edtech.

Morten Hansen is a research associate on the Universities and Unicorns project at Lancaster University, and a PhD student at the Faculty of Education, University of Cambridge, United Kingdom. Hansen specialises in education markets and has previously worked as a researcher at the Saïd Business School in Oxford.