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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 NotesWhat 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. [ABSTRACT FROM AUTHOR]

The increased availability and development of immersive technologies have given students growing opportunities to engage in different educational subjects. However, there is a lack of empirical research exploring the educational influence of using Immersive Virtual Reality (IVR) in science classrooms. To address this gap, this study developed a series of IVR‐based science lessons for middle‐school students and further examined these lessons' effects on learning performance. Our quasi‐experimental approach employed a pretest and posttest to measure academic achievement and questionnaires to measure engagement and technology acceptance. A total of 90 sixth‐grade students from two classes were randomly assigned to the experimental and control groups. The experimental group engaged with the science lessons using Head‐Mounted Displays, whereas the control group learned the same material through traditional teaching methods. The results revealed that the experimental group obtained significantly higher academic achievement and engagement scores (cognitive, behavioral, emotional and social) than the control group. Moreover, the experimental group had a high level of technology acceptance for IVR usage in classrooms. Our study provides empirical evidence for IVR's use in science education. Furthermore, it also sheds light on how to develop and implement an IVR‐based classroom for formal educational purposes. [ABSTRACT FROM AUTHOR]