用技能解释提升意外课程推荐的吸引力与信心
Skill-based Explanations for Serendipitous Course Recommendation
- 通过深度学习提取课程描述中的关键概念，构建技能映射
- 在伯克利系统中测试，显著提升高意外性课程的兴趣度
- 适合教育推荐系统开发者和关注学生决策辅助的研究者
美国本科教育中，学术选择至关重要，但学生面临信息不足、指导有限和课程选择过多等问题，尤其受时间约束和热门课程竞争影响。尽管有职业顾问，但数量不足；现有推荐系统虽个性化，却缺乏对学生认知的理解与解释能力。本文提出一种基于深度学习的概念提取模型，从课程描述中高效提取相关概念，用于改进推荐流程。研究在加州大学伯克利分校的AskOski系统中，测试了技能型解释在意外推荐框架中的效果。结果表明，此类解释不仅提升了用户对高意外性课程的兴趣，还增强了决策信心，凸显将技能数据与解释融入教育推荐系统的重要性。
原文摘要 · Abstract (English)
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.
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