mlsanityData to 14 Oct 2026

Skill-based Explanations for Serendipitous Course Recommendation

arXiv 2508.19569 ↗First posted 27 Aug 2025cs.AIcited by 0

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Abstract (arXiv)

Academic choice is crucial in U.S. undergraduate education, allowing students significant freedom in course selection. However, navigating the complex academic environment is challenging due to limited information, guidance, and an overwhelming number of choices, compounded by time restrictions and the high demand for popular courses. Although career counselors exist, their numbers are insufficient, and course recommendation systems, though personalized, often lack insight into student perceptions and explanations to assess course relevance. In this paper, a deep learning-based concept extraction model is developed to efficiently extract relevant concepts from course descriptions to improve the recommendation process. Using this model, the study examines the effects of skill-based explanations within a serendipitous recommendation framework, tested through the AskOski system at the University of California, Berkeley. The findings indicate that these explanations not only increase user interest, particularly in courses with high unexpectedness, but also bolster decision-making confidence. This underscores the importance of integrating skill-related data and explanations into educational recommendation systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 126 references that are in the map. First posted on arXiv 27 Aug 2025.

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32 of 126 references.

References · 32

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20 more
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