Which Data Sets Are Preferred by University Students in Learning Analytics Dashboards? A Situated Learning Theory Perspective

Paul Joseph-Richard () and James Uhomoibhi ()
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Paul Joseph-Richard: Department of Management, Leadership and Marketing, Ulster University Business School, Belfast BT15 1ED, United Kingdom
James Uhomoibhi: School of Engineering, Ulster University–Belfast Campus, Belfast BT15 1ED, United Kingdom

INFORMS Transactions on Education, 2024, vol. 24, issue 3, 220-237

Abstract: Scholarly interests in developing personalized learning analytics dashboards (LADs) in universities have been increasing. LADs are data visualization tools for both teachers and learners that allow them to support student success and improve teaching and learning. In most LADs, however, a teacher-centric, institutional view drives their designs, treating students only as passive end-users, which results in LADs being less useful to students. To address this limitation, we used a card-sorting technique and asked 42 students at a university in Northern Ireland to construct dashboards that reflect their priorities. Using a situated theory of learning as a lens and with the help of multiple qualitative methods, we collected data on what constitutes useful dashboards. Findings suggest that situated learning data sets, such as information on how students learn by talking and listening to others in their communities, need to be integrated into LADs. Students preferred to see the inclusion of qualitative narratives, self-directed learning data and financial information (money spent versus resources utilized) in LADs. As well as raising new questions on how such LADs could be designed, this study challenges institutional overreliance on measurable digital footprints as proxies for academic success. We call for recognizing the wider social learning that happens in landscapes of practice so that LADs become more useful to students.

Keywords: personalization; student-led design; student engagement; customized design; learning analytics dashboards; situated theory of learning (search for similar items in EconPapers)
Date: 2024
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