Urgent questions for edtech stakeholders and policy-makers
Digital platforms in higher education
Although the debates over the past two years have helped to educate those who previously knew little about the role(s) of digital platforms in higher education, they have failed to adequately account for the tremendous diversity of digital platforms that exist in and around higher education.
However, we must understand this diversity in order to truly grasp the potential long-term implications of the ‘digitalisation’ of higher education across the world.
Broadly conceived, there are three categories of digital platforms that can be found in the higher education sector.
First, there are platforms that target individual students directly, running in parallel to the institutionalised and regulated higher education system (for example, apps that automate note-taking or allow for group annotation of course materials). Such platforms collect content and aggregate user data, while the platform owner makes pedagogic decisions, structures the learning process and innovates (if desired) with the collected user data.
Second, there are platforms that almost serve as educational ‘institutions’ in their own right (for example, apps that allow self-employed teachers to offer micro- and other courses directly to prospective students).
Such platforms can also benefit from user data, for example, by offering personalised suggestions to learners for particular classes, deciding on teacher payment based on user behaviour, etc.
Finally, there are platforms that are integrated directly into the work of a university via contractual arrangements. Generally, universities pay a subscription or fees for the use of such platforms. A university might integrate such external proprietary platforms into its digital ecosystem, allow certain data flows and even use proprietary analytics operations (that is, receiving intelligence about teachers and students as part of the platform functionality).
In such cases, the university is the personal data controller and is responsible for making sure that personal data is collected, accessed, stored and processed legally. Nevertheless, there are ways in which personal data might be shared with the proprietary platform owner to aggregate, analyse and create new data about particular users.
Generally, it is very difficult to change such arrangements, given contractual implications and also the scale of integration that occurs.
Understanding digital platforms as assets
The three categories outlined here have different business models and client foci. The first is a direct-to-consumer service, the second involves intermediation between individual users and the third is a business-to-business model.
There are many implications to this, which must be better understood by higher education institutions around the world. There are three key points that are particularly relevant for policy and practice, namely the implications for value, for control and for user data.
First, the fact that edtech platforms operate as assets in terms of their financial models has important implications. Universities do not pay once for ownership rights over a particular platform. Rather, they generally pay annual subscriptions for access and use. There are similar ongoing payment models in place for platforms that directly target students.
These arrangements ensure that students, staff and higher education institutions are locked into ongoing relations with platform owners as it becomes increasingly technologically, legally or pragmatically difficult to sever ties. As a result, the platform owner has significant power to increase the cost of accessing and using the platform.
A second implication relates to control. With commodities, ownership rights are exchanged when products and services are bought and sold. However, in the case of accessing assets, all ownership, follow-through and control rights stay with the asset owners. They decide about issues of access to the platform, how users interact and what they can or cannot do.
Individual and institutional users have little say about how things are run on the platform, including algorithms that make predictions and have a consequential impact on their learning paths. In addition, due to commercial sensitivity, users often have little awareness of which operations exist in the platforms and how they are designed.
At the moment, the discourse in edtech and education more generally puts a lot of emphasis on data-rich processes and how they support personalisation and automation to bring greater efficiencies and effectiveness. In reality, we notice the early stages of such operations in higher education. There is lots of experimentation and innovation going on with user data when it comes to how various analytics and other intelligence are integrated into a platform offer.
Data privacy regulations do not tackle the issue of data-rich operations and statistical calculations. When user data is aggregated, individuals are always put in groups and in relation to each other in the search for potential trends. New information is produced about individuals with looping back used to target their behaviour.
But, as users, students and staff do not have a say in how their data is processed for producing analytics and predictions in the products they use for their studies and work. It is, therefore, of key importance who gets access to the aggregated user data, who has an opportunity to innovate in edtech and who can benefit from its potential future economic value.
There is much to say about edtech in higher education. Clearly, edtech has enormous potential to bring benefits to students, staff and higher education at large, but it matters how it is rolled out and how it is governed.
We need to think much more carefully about how we can make proprietary edtech platform owners accountable to higher education stakeholders and the public at large.
We also need to do more to control potential predatory lock-in and monopoly exploitation. If edtech becomes dominated by a few giants, as has happened in other industries, what will that mean for the future of our sector?
Finally, we need to find ways to ensure more democratic governance of user data. Should currently private data assets be made publicly available, for example, so that aggregated user data can be used by everyone for ethical and socially just innovation? These are key questions that policy-makers and stakeholders should urgently address.
Janja Komljenovic is senior lecturer at Lancaster University, United Kingdom. E-mail: email@example.com. This article was first published in the current issue of International Higher Education.