'Edtech in Higher Education: Empirical Findings from the Project ‘Universities and Unicorns: Building Digital Assets in the Higher Education Industry’ by Janja Komljenovic, Morten Hansen, Sam Sellar and Kean Birch (published by the Centre for Global Higher Education, Department of Education, University of Oxford comments
Higher education (HE) is by now thoroughly digitalised. Universities use a variety of digital products and services to support their operations. The educational technology (EdTech) industry has been expanding in the past decade, while investors have become important actors in the field. This report offers findings from the ESRC-funded research project ‘Universities and Unicorns: Building Digital Assets in the Higher Education Industry’ (UU), which investigated new forms of value in digitalised HE as the sector engages with EdTech providers. ...
The project was conducted between 1 January 2021 and 30 June 2023. It investigated new forms of value in digital and digitalised higher education (HE) as the sector engages with educational technology (EdTech) providers. The project was especially interested in digital user data and data operations. We followed three groups of actors: universities, EdTech start-up companies, and investors in EdTech.
Our study of universities focused on understanding their: digitalisation strategies and practices; digital ecosystems and collaborations with EdTech companies; attitudes towards and experiences with EdTech companies; user data operations and data outputs; and key challenges with digitalisation.
Our study of EdTech start-up companies focused on understanding: development of products and services; business models and strategies; how products are datafied and their data operations; how user data is made valuable; experiences and relations with universities; experiences and relations with investors; and challenges they are facing in their work and growth.
Our study of investors focused on understanding: their views of HE and the future of the sector; the role that EdTech should play in this future; their beliefs about the value of user data; their investment theses, strategies and activities; and their experiences and relations with the EdTech and HE sectors. xx Understanding EdTech relationally, and bringing these groups together, allowed us to gain particular insights into the digitalisation of HE and its political economy. We aimed to trace the flow of ideas, strategies, and actions between these actors and to understand how and why the EdTech industry is developing as it is.
Our conceptual approach centred on rentiership and assetisation. The global economy is increasingly characterized by rentiership: the move from creating value via producing and selling commodities in the market to extracting value via controlling access to assets. In the digital economy, rentiership is often exercised by controlling digital platforms and pursuing revenues associated with platforms, such as collecting and monetising digital data extracted via these platforms. Users became valuable through their engagement with the platform and are made visible through various user metrics. Emerging work on assetisation in education argues that this is a productive way to understand the impact of the privatisation, financialisation, and digitalisation of public education. However, the rise of assetisation does not mean that HE is no longer a public good or subject to commodification. Instead, it adds new complex forms of value creation and governance to the sector. We should note that this research project was conducted before the release of ChatGPT into public use. Therefore, this report does not make reference to the turbulent discussions about generative AI and its potential usage and impacts in HE. Finally, we note that this report offers an empirical description of key themes and dynamics identified in our study. More in-depth and theorised analyses of project findings are being published in journal articles and book chapters, all of which are openly accessible. The Appendix includes a list of publications. ...
In this section, we briefly summarise key overall findings, which are analysed in more detail in academic publications, i.e. journal articles and book chapters (see Appendix). The following findings are relevant to our case studies and might be different in other contexts.
Takeaway #1: Big Tech and legacy software are prominent in digitalising higher education
Big Tech infrastructure and platforms, legacy software, and EdTech incumbents dominate university digital ecosystems. It is challenging for the EdTech start-up industry to enter HE markets. Digital products and services offered by new companies represent a small proportion of digitalisation work at universities. EdTech companies primarily target individuals as customers, enterprises for staff development and training, and lower levels of education (i.e. schooling rather than HE).
Takeaway #2: EdTech in HE is less advanced than imagined
There is a discrepancy between the promises of the EdTech industry regarding the quality and impact of digital products and services and the perception of university customers. Many university actors, as well as a few EdTech companies, argued that the current quality of EdTech products is generally low compared to other sectors.
Takeaway #3: Making user data valuable is difficult
Collecting, cleaning, sorting, processing, and analysing digital user data demands significant human, technological, and financial resources. It is difficult to make user data analysis useful and valuable, such that universities are willing to pay higher fees for data-driven products. Most EdTech companies that we analysed struggle with monetising user data. There is also less user data analysis currently in the sector than imagined by the EdTech industry in its public discourse. The omnipresent belief in the value of user data among all actors is disjunctive with the realities of data practices, which are mostly simple or non-existent. Most university users are sceptical about learning analytics.
Takeaway #4: User data analytics in HE are not well-developed
EdTech companies attempt to make their digital products valuable by incorporating user data analytics into their core products. However, currently, these analytics are simple and remain at the level of basic descriptive feedback loops for the user. Nevertheless, there is a clear trend in which EdTech companies are continuing their attempts to construct new metrics, scores, and analytics to monetise data, with efforts to convince customers of the value of these analytics.
Takeaway #5: Datafication in HE happens at universities
Universities are in the driving seat of their institutional datafication. Universities are establishing data warehouses, and many aim to collect all user data produced by external digital platforms in order to organise and analyse it for pedagogical and business purposes. However, universities currently lack the capacity to analyse, interpret and act on data. Universities need to establish frameworks for action based on data and acquire the requisite personnel and skills to do so. Universities should ensure that data outputs (e.g. analytics, metrics, scores) are truly representative of what is measured and build confidence in their communities regarding data-driven decision-making.
Takeaway #6: Digitalisation and datafication create work and costs for universities
Digitalisation and EdTech promise to bring efficiency and cost savings for universities, but in reality, university actors feel that digitalisation and data operations create more work and higher costs. In addition, new staff profiles and skills are needed, including data scientists, vendor managers, cloud engineers, as well as more learning technologists.
Takeaway #7: Good EdTech does not challenge core university values and practices
University actors find technology useful in general and are interested in technological innovation in relation to their work. However, there are two instances where university actors are sceptical towards EdTech. First, when companies' business models are exploitative and extractive. Second, when digital products interfere with the university's core values and practices, such as by challenging professional judgement or academic freedom. Intentions to automate the teaching process or provide behavioural nudges are often received with scepticism. Most university actors feel that user data collection should be limited, and data outputs, including analytics, should be restricted and carefully evaluated.
Takeaway #8: The aims of EdTech require greater clarity
The key aims of EdTech are understood to be personalisation, automation, enhanced student engagement, and greater institutional efficiency. However, there are discrepancies between university, EdTech, and investor actors in terms of how they understand these objectives and, consequently, how they will be achieved. Each of these aims needs clarification, including recognising the plurality of dimensions to each objective.
Takeaway #9: Future imaginaries of tech companies and universities
The future imaginaries of HE and EdTech are constructed by the EdTech industry and policy actors. There are discrepancies between investors, EdTech companies, and universities in relation to what EdTech should do and how it should shape the future of HE. Universities should drive these discussions and determine their futures and the role of technology in creating these futures.
Takeaway #10: Democratic data governance
Universities should do more to inform students and staff about the digital products and services they routinely use. Universities should also continuously provide transparent information to students and staff about user data collected from them and what is being done with this data within their universities and externally. Students and staff should have the choice to participate or not in user data collection and processing. Students and staff should be included in the governance of EdTech and user data at their institutions.
Takeaway #11: There is a plurality of assetisation processes in EdTech
EdTech companies establish a variety of processes to control and charge for access to their assets. These include mediating content, organising and mediating teaching interventions, and digitalising and mediating credentials. Typical moats that EdTech companies build are lock-in, network effects, and integration of products into everyday individual practices.
