Abstract
A common recommendation in critiques of datafication in education is for greater conversation between the two sides of the (critical) divide – what might be characterised as sceptical social scientists and (supposedly) more technically-minded and enthusiastic data scientists. This article takes the form of a dialogue between two academics representing these different perspectives – Dragan Gasevic (a data scientist who is one of the founders of the ‘learning analytics’ field) and Neil Selwyn (a social scientist and long-time critic of educational technology). Over the course of a six-month email exchange, we explore various arguments for and against the datafication of higher education. Thus, we are able to add a computational dimension to well-worn social criticisms of data representativeness, reductionism and injustice, as well as exploring social tensions inherent in technical claims to data-based precision, clarity and predictability. Crucially, these exchanges allow us to explore ways in which our two viewpoints converge, and therefore highlight opportunities for productive inter-disciplinary exchange and collaboration.
Original language | English |
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Pages (from-to) | 527-540 |
Number of pages | 14 |
Journal | Teaching in Higher Education |
Volume | 25 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2020 |
Keywords
- datafication
- learning analytics
- data
- platforms