Skill-based Explanations for Serendipitous Course Recommendation
This addresses the challenge of personalized course selection for students in complex academic environments, though it is incremental as it builds on existing recommendation systems by adding skill-based explanations.
The paper tackled the problem of improving course recommendations for undergraduate students by developing a deep learning-based concept extraction model to extract relevant concepts from course descriptions and testing skill-based explanations within a serendipitous recommendation framework using the AskOski system at UC Berkeley, finding that these explanations increased user interest in high-unexpectedness courses and boosted decision-making confidence.
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.