Evaluating student-facing learning dashboards of affective states

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Abstract

Detection and visualizations of affective states of students in computer based learning environments have been proposed to support student awareness and improve learning. However, the evaluation of such visualizations with students in real life settings is an open issue. This research reports on our experiences from the use of four different types of dashboard visualizations in two user studies (n = 115). Students who participated in the studies were bachelor and master level students from two different study programs at two universities. The results indicate that usability, measured by interpretability, perceived usefulness and insight, is overall acceptable. However, the findings also suggest that interpretability of some visualizations, in terms of the capability to support emotion awareness, still needs to be improved. The level of students awareness about their emotions during learning activities based on the visualization interpretation varied depending on previous knowledge on visualization techniques. Furthermore, simpler visualizations resulted in better outcomes than more complex techniques.

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Sedrakyan, G., Leony, D., Muñoz-Merino, P. J., Kloos, C. D., & Verbert, K. (2017). Evaluating student-facing learning dashboards of affective states. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10474 LNCS, pp. 224–237). Springer Verlag. https://doi.org/10.1007/978-3-319-66610-5_17

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