Adaptive Navigational Support and Explainable Recommendations in a Personalized Programming Practice System
34TH ACM CONFERENCE ON HYPERTEXT AND SOCIAL MEDIA, HT 2023(2023)
Abstract
We present the results of a study where we provided students with textual explanations for learning content recommendations along with adaptive navigational support, in the context of a personalized system for practicing Java programming. We evaluated how varying the modality of access (no access vs. on-mouseover vs. on-click) can influence how students interact with the learning platform and work with both recommended and non-recommended content. We found that the persistence of students when solving recommended coding problems is correlated with their learning gain and that specific student-engagement metrics can be supported by the design of adequate navigational support and access to recommendations' explanations.
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Key words
adaptive navigation support,educational recommender systems,explainability,transparency
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