用AI推荐数学题，提升学习系统互动与效果。
Enhancing Math Learning in an LMS Using AI-Driven Question Recommendations
- 用Llama-3生成题目嵌入，通过三种算法找相似题
- SOM推荐使用户满意度最高，但过度变化会降低效果
- 基于用户行为数据评估，适合改进在线数学教学
本文提出一种基于AI的数学学习增强方法，通过在现代学习管理系统（LMS）中推荐相似数学题来提升学习效果。利用Meta的Llama-3.2-11B-Vision-Instruct模型生成题目深度嵌入，并采用余弦相似度、自组织映射（SOM）和高斯混合模型（GMM）三种方法进行题目推荐。通过用户交互数据（包括会话时长、作答时间和正确率）评估各方法表现。结果表明，余弦相似度能产生几乎完全相同的题目匹配，而SOM在用户满意度上表现更优；相比之下，GMM整体表现较差。数据表明，适度引入题目多样性可提升学习参与度和潜在学习成效，但若多样性失衡则适得其反。
原文摘要 · Abstract (English)
This paper presents an AI-driven approach to enhance math learning in a modern Learning Management System (LMS) by recommending similar math questions. Deep embeddings for math questions are generated using Meta's Llama-3.2-11B-Vision-Instruct model, and three recommendation methods-cosine similarity, Self-Organizing Maps (SOM), and Gaussian Mixture Models (GMM)-are applied to identify similar questions. User interaction data, including session durations, response times, and correctness, are used to evaluate the methods. Our findings suggest that while cosine similarity produces nearly identical question matches, SOM yields higher user satisfaction whereas GMM generally underperforms, indicating that introducing variety to a certain degree may enhance engagement and thereby potential learning outcomes until variety is no longer balanced reasonably, which our data about the implementations of all three methods demonstrate.
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