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Learning analytics won’t close awarding gaps. Better teaching will.

by David Mather

Universities have invested heavily in understanding awarding gaps. We know more than ever about differential outcomes between student groups. We have sophisticated learning analytics platforms, increasingly accurate predictive models and growing confidence in our ability to identify students who may disengage, withdraw or underperform. This is important work, as it has helped institutions move away from reactive support towards earlier and more informed intervention.

Despite this, awarding gaps remain stubbornly persistent. Perhaps that is because we have spent the last decade using data to identify students rather than improve teaching. If we have become better at identifying students who are likely to struggle, why have we made less progress in changing the outcomes we are trying to improve?

Perhaps the answer lies in the purpose we have given our data. Learning analytics has become one of higher education’s greatest success stories. Universities now know more about their students than at any point in the sector’s history. Attendance, engagement with virtual learning environments, assessment patterns and a range of demographic indicators provide increasingly sophisticated pictures of student behaviour (Romero & Ventura, 2024). Most institutions now use this information to answer a single question: who is most likely to struggle?

It is an important question, but I am no longer convinced it is the most educational one.

Learning analytics has become remarkably good at supporting institutional decision making. It helps us identify educational risk, target student support and improve continuation. However, prediction is not pedagogy. Identifying students who may struggle is not the same as understanding how teaching might change their trajectory. Awarding gaps are not created by dashboards. They emerge through complex interactions between curriculum, assessment, expectations, recognition, belonging and students’ experiences of learning. They will not be closed by increasingly sophisticated prediction alone. Therefore, the assertion of this article is simple: better teaching closes awarding gaps. The role of data should be to improve teaching rather than improve prediction.

The distinction that I have presented became clear to me long before I joined higher education. My career began in further education, where I taught and led in sixth form colleges. Like many colleagues working in that sector, I became accustomed to using ALPS, a value added system that compares students’ outcomes with those expected from their prior attainment (ALPS Education, 2025). The numbers themselves were rarely the most interesting part of the conversation – the pedagogic conversations they created were.

Course teams did not gather simply to identify students who might miss their target grades. They asked why one assessment had produced stronger outcomes than another. They debated whether curriculum sequencing was helping students build confidence. They compared approaches to feedback and questioned whether different teaching strategies had influenced progress. Data became a catalyst for professional dialogue. It encouraged teachers to think critically about pedagogy rather than simply monitor performance.

One observation emerged repeatedly. Students at either end of the attainment spectrum were visible. Those achieving well continued to receive stretch and challenge. Those facing significant barriers rightly attracted additional support. However, those in the middle of those two poles often received much less attention. These were students who attended lessons, completed assignments and achieved respectable grades. They were progressing, but they had begun to plateau. Their potential remained largely hidden because nothing within the data suggested they required intervention.  I have become increasingly convinced that higher education contains the same group.

Earlier this year I described those between the two poles as the silent middle (Mather, 2026a). My argument then focused on belonging. Universities have become increasingly effective at identifying students who excel and students who experience significant challenge. Those in between often remain unnoticed because they continue to progress through their programmes without attracting concern. Progress, however, should never be mistaken for fulfilled potential. My earlier articles have led me to a conclusion that the silent middle is not simply a group of overlooked students – they reveal something more fundamental about the questions we ask of our data.

Learning analytics tells us who is likely to struggle. It tells us much less about where teaching is most likely to make the greatest educational difference. Those are not the same question. One helps universities manage institutional risk. The other has the potential to improve educational practice. The distinction is significant because universities cannot close awarding gaps through prediction alone. They will close them by improving teaching, strengthening relationships and creating learning environments in which more students can succeed. Learning analytics has an important role to play in that ambition, but only if we stop asking it to tell us merely who needs support and start asking what it can teach us about teaching itself. The question is not whether universities should continue to invest in learning analytics (they should). The question is whether we have become too comfortable allowing learning analytics to define the educational problem.

For the past decade, learning analytics has largely been framed as an exercise in prediction. Institutions seek to identify students who may withdraw, fail or disengage so that support can be targeted before those outcomes become inevitable (Romero & Ventura, 2024). This represents an advance over retrospective approaches that identified concerns only after students had already fallen behind. Early identification and timely-bound support are crucial. The sector should not lose either.

However, prediction is not an educational strategy. Prediction tells us which students are most likely to experience difficulty. It tells us very little about what educators should do differently. A dashboard cannot redesign a curriculum, improve formative feedback, strengthen relationships between students and staff or create cultivate belonging. All of those are fundamentally pedagogical acts.

Awarding gaps emerge through pedagogy as much as they emerge through student characteristics. Curriculum design, assessment practices, expectations, feedback, representation and relationships all influence whether students flourish or merely progress. All of this means that learning analytics should be judged not simply by its ability to predict outcomes, but by its ability to improve teaching.

The distinction between prediction and teaching quality has profound implications for educational leadership. Every university leader understands that resources are finite. Personal tutors, learning developers, academic skills teams and lecturers cannot provide intensive support to every student throughout the academic year. Decisions therefore have to be made about where educational effort should be concentrated. Those decisions are often informed by indicators of risk. Students who stop attending, miss assessments or disengage understandably receive attention. That is appropriate and necessary, but I wonder whether we have overlooked another question: where is teaching most likely to change a student’s trajectory?

That question shifts the purpose of learning analytics completely. Instead of asking who is most likely to fail, we begin asking where educational opportunity exists. Instead of identifying institutional risk, we begin identifying pedagogical opportunity. Those are not competing priorities – they are different ways of thinking about educational gain.

My experience in further education suggests that this distinction is crucial. ALPS data did not conclude conversations about attainment – it started them. Teachers debated why one class had responded differently from another, why particular assessment approaches appeared more effective and which students seemed to benefit most from timely feedback or increased challenge. Data did not replace professional judgement – it sharpened it. Higher education has an opportunity to claim that mindset.

Freire (1970/2005) argued that education is transformative because it encourages dialogue, reflection and action rather than passive acceptance. His critique was directed at pedagogy, yet it also offers a useful way of thinking about educational data. Learning analytics should not become another form of educational banking in which institutions accumulate information without fundamentally changing practice. Data has educational value only when it changes the conversations educators have about teaching.

Addressing data for the purpose of informing pedagogy and practice should intersect with the conversation about awarding gaps. Universities have invested considerable effort in understanding differential outcomes across student groups. That work has transformed institutional awareness of structural inequalities and rightly challenged assumptions that degree classifications simply reflect individual effort or ability. Research has consistently demonstrated that awarding gaps emerge through complex interactions between curriculum, assessment, belonging, institutional culture and wider social structures rather than any single deficit within students themselves (Mountford-Zimdars et al, 2015). Addressing those gaps therefore requires sustained institutional commitment.

The institutional commitment I refer to is where the silent middle re-enters the conversation. Those in the silent middle are rarely identified as requiring support because they continue to engage, submit work and progress through their programmes (Mather, 2026a). They do not generate alerts because they are doing precisely what institutional systems expect them to do. Many achieve marks in the middle classifications throughout their degrees. They are neither failing nor flourishing – they simply continue. My question is: do our data encourage us to notice them? 

I suspect they do not. As such, the consequence is that some of the students with the greatest capacity for educational gain receive the least pedagogical attention. Universities rightly identify students whose continuation is at risk. We devote much less effort to identifying students whose attainment could be transformed through relatively modest changes to teaching, feedback or academic relationships.

Educational gain is not distributed evenly across a student population. Some learners require substantial pastoral support simply to remain engaged, whilst others may experience significant academic development following one timely conversation, carefully designed formative feedback or an increased sense of belonging within their discipline. If learning analytics cannot help us distinguish between those different forms of educational opportunity, then we are asking increasingly sophisticated systems to answer significantly limited questions. Perhaps the challenge facing higher education is not to build better dashboards – perhaps it is to ask more educational questions of the data we already possess.

The argument I have made in this article is not that universities should invest less in learning analytics. Institutions should continue to develop their analytical capability because understanding students’ experiences is crucial if they are to be enhanced. However, my concern is that we have become so focused on prediction that we have paid insufficient attention to pedagogy. Learning analytics has become exceptionally good at telling us which students are most likely to struggle. It has been much less successful in helping us understand how teaching itself should change. If universities genuinely want to reduce awarding gaps, I contend that we should ask different questions of our data.

First, what do the data tell us about our teaching rather than our students? Much of our current use of learning analytics encourages us to examine student behaviour. Attendance, engagement and submission patterns become indicators of individual risk. Those measures have value, but they tell us little about the effectiveness of our curriculum, assessment design or feedback practices. Every dashboard should prompt course teams to ask whether the learning environment itself requires attention before concluding that the student does.

Second, where is the greatest opportunity for educational gain? Universities have become highly effective at identifying educational risk. We should become equally interested in identifying educational opportunity. Some students require substantial pastoral intervention to remain engaged. Others may transform their academic performance following timely feedback, clearer expectations or a stronger sense of belonging. Educational gain is not distributed evenly across a student population. Learning analytics should help us understand where relatively modest pedagogical changes are likely to have the greatest impact.

Third, who have we stopped noticing because they continue to pass? The silent middle is not simply a descriptive label. It represents a challenge to institutional assumptions about success (Mather, 2026a). Students who consistently achieve marks in the middle classifications rarely appear within institutional dashboards because they generate little concern. Their continued progression can disguise unrealised potential. Universities that are serious about improving outcomes should become just as interested in students who have plateaued as those whose continuation is at risk.

Fourth, what should we teach differently tomorrow because of what the data tell us today? This is the most important question of all. Learning analytics should not conclude a conversation, it should start one. If programme teams leave meetings with a list of students to contact but no discussion about teaching, assessment or curriculum, then the data has served an administrative purpose rather than an educational one. Professional judgement remains the most powerful tool available to educators. Analytics should strengthen that judgement, not replace it.

Finally, how will we know whether our teaching has changed outcomes? Universities devote considerable resources to measuring continuation, progression and degree classifications. Those metrics are important, but they should not become proxies for educational quality. We should also ask whether students have become more confident learners, whether they participate more actively, whether they experience a greater sense of belonging (on their terms) and whether teaching has enabled them to exceed the trajectories that seemed likely at the beginning of their studies. Those are educational outcomes. They deserve to be measured with the same seriousness as institutional performance indicators.

None of the questions I have posed requires higher education to adopt a system such as ALPS. Schools, sixth form colleges and universities pursue different purposes, educate different learners and operate within different frameworks. The lesson is not the system itself – it is the educational philosophy that underpins it. Freire (1970/2005) argued that education becomes transformative through dialogue, reflection and action. The same principle should apply to learning analytics. Data should not become another mechanism for classifying students or managing institutional performance – it should encourage educators to reflect critically on their practice, challenge assumptions about learning and act in ways that improve students’ educational experiences.

My own work over the past year has explored transition, belonging, good teaching and the silent middle (Mather, 2026a, 2026b). This article has helped me realise that these are not separate conversations; they point towards the same conclusion: students succeed because teaching matters. Relationships matter, recognition matters, belonging matters and curriculum matters. Learning analytics only become educationally significant when they help us improve all of those things.

David Mather is Associate Head of School (Students) and Senior Lecturer in Educational Leadership and Management at the University of Portsmouth. His research explores identity, recognition, and transition across educational and professional contexts, with a particular interest in improving educational practice and the student experience. He is the founder of We See You, an initiative supporting Armed Forces, veteran, and Blue Light communities in higher education.


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Institutional constraints to higher education datafication: an English case study

by Rachel Brooks

‘Intractable’ datafication?

Over recent years, both policymakers and university leaders have extolled the virtues of moving to a more metricised higher education sector: statistics about student satisfaction with their degree programme are held to improve the decision-making processes of prospective students, while data analytics are purported to help the shift to more personalised learning, for example. Moreover, academic studies have contended that datafication has become an ‘intractable’ part of higher education institutions (HEIs) across the world.

Nevertheless, our research (conducted in ten English HEIs, funded by TASO) – of data use with respect to widening participation to undergraduate ‘sandwich’ courses (where students spend a year on a work placement, typically during the third year of a four-year degree programme) – indicates that, despite the strong claims about the advantages of making more and better use of data, in this particular area of activity at least, significant constraints operate, limiting the advantages that can accrue through datafication.

Little evidence of widespread data use

Our interviewees were those responsible for sandwich course provision in their HEI. While most thought that data could offer useful insights into the effectiveness of their area of activity, there was little evidence of ‘intractable’ data use. This was for three main reasons. First, in some cases, interviewees explained that no relevant data were collected – in relation to access to sandwich courses and/or the outcomes of such courses. Second, in some HEIs, relevant data were collected but not analysed. Such evidence tends to support the contention that ‘data lakes’ are emerging, as HEIs collect more and more data that often remain untapped. Third, in other cases, appropriate data were collected and analysed, but in a very limited manner. For example, one interviewee explained how data were collected and analysed in relation to the participation of students from under-represented ethnic groups, but not with respect to any other widening participation categories. This limited form of datafication, in which only some social characteristics were datafied, was not, therefore, able to inform any action with respect to the participation of widening participation students generally. Indeed, across all ten HEIs, there was only one example of where data were used in a systematic fashion to help analyse who was accessing sandwich courses within the institution, and the extent to which they were representative of the wider student population.

Constraints on data use

Lack of institutional capacity

In explaining this absence of data use, the most commonly identified constraint was the lack of institutional capacity to collect and/or analyse appropriate data. For example, one interviewee commented that they did not have a very good data system for placements – ‘we are still quite Excel- based’. Excel spreadsheets were viewed as limited as they could not be easily shared or updated, and data were relatively hard to manipulate. This, according to the interviewee, made collection of appropriate data laborious, and systematic analysis of the data difficult. Interviewees also pointed to the limited time staff had available to analyse data that the institution had collected.

Prioritisation of ‘externally-facing’ data

Several interviewees described how ‘externally-facing data’ – i.e. that required by regulatory bodies and/or that fed into national and international league tables – was commonly prioritised, leaving little time for information officers to devote to generating and/or analysing data for internal purposes. One interviewee, for example, was unclear about what data, if any, were collected about equity gaps but believed that they were generally only pulled together for high-level reports ‘such as for the TEF’.

Institutional cultures

A further barrier to using data to analyse access to and outcomes of sandwich courses was perceived to be the wider culture of the institution, including its attitude to risk. An interviewee explained that the data collected in their institution was limited to two main variables – subject of study and fee status (home or international) – because of ‘ongoing cautiousness at the university about how some of that data is used and how it’s shared with different teams’.

In addition, many participants outlined the struggles they had faced in gaining access to relevant data, and in influencing decisions about what should be collected and what analyses should be run. Several spoke of having to ‘request’ particular analyses to be run (which could be turned down), leading to a fairly ad hoc and inefficient way of proceeding, and illustrating the relative lack of agency accorded to staff – typically occupying mid-level organisational roles – in accessing and manipulating data.

Reflections

Examining a discrete set of activities within the UK higher education sector – those relating to sandwich courses – provides a useful lens to examine quotidian practices with respect to the availability and use of data. Despite the strong emphasis on data by government bodies and HEI senior management teams, as well as the claims made about the ‘intractability’ of HEI data use in the academic literature, our research suggests that datafication is perhaps not as widespread as some have claimed. Indeed, it indicates that some areas of activity – even those linked to high profile political and institutional priorities (in this case, employability and widening participation) – have remained largely untouched by ‘intractable’ datafication, with relevant data either not being collected or, where it is collected, not being made available to staff working in pertinent areas.

As a consequence, the extent to which students from widening participation backgrounds were accessing sandwich courses – and then succeeding on them – relative to their peers typically remained invisible. While the majority of our interviewees were able to speculate on the extent of any under-representation and/or poor experience, this was typically on the basis of anecdotal evidence and their own ‘sense’ of how inequalities were played out in this area. Although reflecting on professional experience is obviously important, many inequalities may not be visible to staff (for example, if a student chooses not to talk about their neurodiversity or first-in-family status), even if they have regular contact with those eligible to take a sandwich course. Moreover, given the status often accorded to quantitative data within the senior management teams of universities, the lack of any statistical reporting about inequalities by social characteristic, as they pertain to sandwich courses, makes it highly likely that such issues will struggle to gain the attention of senior leaders. The barriers to the effective use of metrics highlighted above may thus have a direct impact on HEIs’ capacity to recognise and address inequalities.  

The research on which this blog is based was carried out with Jill Timms (University of Surrey) and is discussed in more detail in this article Institutional constraints to higher education datafication: an English case study | Higher Education

Rachel Brooks is Professor of Higher Education at the University of Oxford and current President of the British Sociological Association. She has conducted a wide range of research on the sociology of higher education; her most recent book is Constructing the Higher Education Student: perspectives from across Europe, published (open access) with Policy Press.


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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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Mapping financial investment flows in digital higher education: a focus on data-rich operations

by Janja Komljenovic

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.

Universities worldwide are increasingly interested in digital technologies and how they can support higher education. A recent study by the European University Association found that most European universities are already using or planning to use data-rich products and services, such as artificial intelligence, machine learning, learning analytics, big data, and the internet of things (see Figure 18 on page 36). Indeed, it is precisely these data-rich operations that are central to the idea of the disruptive potential of education technology (edtech), as argued by my colleague, Javier Mármol Queraltó, in the recent UU project report. The discourse of investors and edtech companies promises thoroughly improved higher education based on personalisation, automation and efficiency. But how deliverable are these promises? Who innovates in the space of data-rich operations, for which services and for which users? Who profits? These are some of the questions we address in the Universities and Unicorns project, which aims to understand forms of value and ways of creating it in digital higher education. In this blog post, I will address three possible trends that can be identified from the interim findings of our quantitative analysis. But before proceeding to discuss these trends, I will contextualise our analysis.

We used Crunchbase to build three databases covering 2,012 edtech companies, 1,120 investors in edtech, and 1,962 edtech investment deals. We identified those relevant to the higher education sector, and our data reflects the state of the sector as of July 2021. Based on this analysis, we identified four key service models in the higher education edtech industry. First, the business to business (B2B) model includes digital platforms serving universities and companies, such as virtual learning environments. Second, the business to customer (B2C) model includes platforms targeting individuals directly. Third, the business to business to customer (B2B2C) model serves institutions that use or further develop the platform to reach individuals, such as Massive Open Online Courses (MOOC) or Online Programme Management platforms (OPM). Finally, the business to the customer to customer (B2C2C) model includes platforms that connect individuals, such as skills and knowledge sharing platforms. B2B2C and B2C2C platforms, in particular, act as the kind of infrastructural intermediaries that are so popular in other sectors of our social and economic lives.

Our analysis found that half of all investment went into B2B platforms, followed by investment into B2C, while B2C2C and B2B2C together received just under a quarter of all investment. However, platforms with the fastest pace of increasing investment are those targeting individuals directly or through intermediation, ie B2C and B2C2C models. This might indicate emerging parallel or alternative higher education products and services that compete with traditional university provision, especially in the context of lifelong learning.

Digital platforms that say they incorporate data-rich operations in their products and services are not the priority area for investors. While we noticed an increasing investment in data-rich platforms, it was still only less than a quarter of all investment going into innovating such products. Nevertheless, we identified three possible trends that are especially worthy of our attention: (1) data-rich operations are being innovated largely in B2B platforms; (2) there is notable unevenness in terms of the location of edtech companies and investments in those platforms who innovate in data-rich operations; and (3) there might be potential for monopolies in data-rich innovation. Let’s delve into each of these possible trends.

Almost all investment in the companies developing data-rich operations in their platforms went to the B2B service model. Looking only at higher education institutions as the target customer, already half of the investment supports data-rich innovation. Most of that went into platforms that act as the institutional digital backbone, indicating that the intention might be to support all institutional functions beyond teaching with data-rich operations, such as artificial intelligence, machine learning and various kinds of analytics beyond learning analytics. There seems to be a trend towards data-rich digital ecosystems at universities that harvest all user and other data in the near future.

There is high unevenness in where the investment in data-rich platforms is allocated. Regarding the number of companies, 239 in our database declare that they offer data-rich operations on their platforms. Almost half of those (101) are based in the USA, 21 in the UK and 19 in India. Companies based in Africa are entirely missing from the list. In terms of investment amounts, 88% of all investment in companies offering data-rich services in their platforms went into companies based in the USA, 3% each to those based in Norway and the UK, and 6% to the rest of the world. The discrepancy between the number of companies and investment size indicates that investment amounts are higher in the USA than elsewhere in the world.

Finally, if we compare different indicators of investment in companies that innovate data-rich solutions for higher education institutions, we notice interesting dynamics. Looking at the money raised, half of B2B investment went into those companies with a platform that included data-rich operations. But this is only 30% of deals and 25% of companies. This indicates that the concentration of investment in data-rich operation platforms for higher education institutions goes into a smaller number of companies who get higher investments. We wonder if this signals potential for monopolies in the future. Moreover, if we compare granted patents, we notice that a higher percentage of companies offering data-rich solution platforms own patents (30%) versus those offering other kinds of service or product platforms (10%). Digital platforms are typically still protected by a licence, but that differs from a more restrictive patent protection. We wonder if such discrepancy in patent share might indicate black-boxing of data-rich operations in higher education?

Our research on digitalising higher education is showing the complex impact of digital technology and datafication on the sector. This impact includes potential positive and supportive measures, but also many potentially worrying trends. However, further research is needed into these trends and the role of different actors, particularly financial investors and edtech companies. Please follow our project in which we will share the findings from this further work as it unfolds.

Janja Komljenovic is a Senior Lecturer and co-Director of the Higher Education Research and Evaluation at Lancaster University in the UK. She is also a Research Management Committee member of the Global Centre for Higher Education with headquarters at the University of Oxford. Janja’s research focuses on the political economy of knowledge production and higher education markets. She is especially interested in the relationship between the digital economy and the higher education sector; and in digitalisation, datafication and platformisation of knowledge production and dissemination. Janja is published internationally on higher education policy, markets and education technology.


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Challenges of multilingual studies

The SRHE Blog is now read in more than 100 countries worldwide, and we have therefore decided to introduce publications in more than one language. Click on ‘Versão em Português‘ below to jump to the Portuguese language version of this post. In the next few months we hope to post blogs in French, Russian, Chinese and more. SRHE members worldwide are encouraged to forward this notification, especially to non-English-speaking colleagues.

New contributions are welcome, especially if they address topical issues of policy or practice in countries other than England and the USA. Submissions may be written either in English or in the author’s native language. Please send all contributions to the Editor, rob.cuthbert@uwe.ac.uk. 

Desafios de realizar pesquisas multilíngues Versão em Português

by Aliandra Barlete

I have been intrigued – and somehow fascinated, too – by the ethical implications of conducting international research. As an international student in the UK, ethical dilemmas have surfaced many times, in spite of preparation during the course of studies. Continue reading →