Skill-based Explanations for Serendipitous Course Recommendation

TL;DR

This paper introduces a deep learning model to extract skills from course descriptions, enhancing serendipitous recommendations by providing skill-based explanations that increase student interest and confidence.

Contribution

It presents a novel deep learning approach for extracting skills from course descriptions and demonstrates how skill-based explanations improve course recommendation relevance and user trust.

Findings

Skill-based explanations increase student interest in recommended courses.

Explanations boost decision-making confidence among students.

Skill extraction improves the relevance of serendipitous course recommendations.

Abstract

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.