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Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalBritish Journal of Educational Technology
Early online date9 Jul 2019
Publication statusE-pub ahead of print - 9 Jul 2019


Digital data are transforming higher education (HE) to be more student-focused and metrics-centred. In the UK, capturing detailed data about students has become a government priority, with an emphasis on using student data to measure, compare and assess university performance. The purpose of this paper is to examine the governmental and commercial drivers of current large-scale technological efforts to collect and analyse student data in UK HE. The result is an expanding data infrastructure which includes large-scale and longitudinal datasets, learning analytics services, student apps, data dashboards and digital learning platforms powered by artificial intelligence (AI). Education data scientists have built positive pedagogic cases for student data analysis, learning analytics and AI. The politicization and commercialization of the wider HE data infrastructure is translating them into performance metrics in an increasingly market-driven sector, raising the need for policy frameworks for ethical, pedagogically valuable uses of student data in HE. Practitioner Notes What is already known about this topic Learning analytics, education data science and artificial intelligence are opening up new ways of collecting and analysing student data in higher education. UK government policies emphasize the use of student data for improvements to teaching and learning. What this paper adds A conceptual framework from “infrastructure studies” demonstrates how political objectives and commercial aims are fused to HE data systems, with data infrastructure becoming a key tool of government reform. A critical infrastructure analysis shows that student data processing technologies are being developed and deployed to measure university performance through student data. Implications for practice and/or policy Educators and managers in universities need to prepare robust institutional frameworks to govern their use of student data. Learning analytics practitioners, data scientists, learning scientists and social science researchers need to collaborate with the policy community and education technology developers on new policy frameworks to challenge narrow uses of student data as performance metrics.

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