Understanding the value of edtech in higher educationvaluations. 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 golden 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.
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?
We have, when it comes to the application of personal technology, 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.