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Conference Papers Year : 2022

Adapting Learning Analytics Dashboards by and for University Students


Learning Analytics Dashboards (LADs) are becoming a key element in enabling learners to monitor their learning, plan and actually learn. However, LADs are sometimes not completely adapted to students, who are rarely involved in their design. Moreover, even when they are, the implemented LADs are often the same for all students, whereas previous works have shown the value of adapted LADs. Here we investigate which adaptations are requested by students, and attempt to identify which data and visualizations are suitable depending on the student's profile. More specifically, we consider dynamic profiles as students' expectations can vary over the course duration. By using LADs co-design sessions both online and on-site, we collected needs from N=386 university students from different disciplines and degree level, split in 108 groups (2 to 4 students). After a manual annotation, we identified a total of 54 types of data and indicators, divided into 12 thematics. Our first analysis confirmed some previous results, particularly on the use of peer comparisons that do not fulfill every student's needs. And we noticed other expectations according to the student's learning context or the academic period. Future work will benefit from these results to define a model of adapted LADs.
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hal-03918008 , version 1 (02-01-2023)



Katia Oliver-Quelennec, François Bouchet, Thibault Carron, Kathy Fronton Casalino, Claire Pinçon. Adapting Learning Analytics Dashboards by and for University Students. 17th European Conference on Technology Enhanced Learning, Sep 2022, Toulouse, France. pp.299-309, ⟨10.1007/978-3-031-16290-9_22⟩. ⟨hal-03918008⟩
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