用大模型分析互动课程数据，预测并召回可能退课的学生。
Handling Students Dropouts in an LLM-driven Interactive Online Course Using Language Models
- 基于文本交互模式构建自适应退课预测框架
- 预测准确率达95.4%，可识别高风险学生
- 通过个性化邮件代理实现有效召回，适合在线教育研究者
以大规模人工智能赋能课程（MAIC）为代表的交互式在线学习环境，利用大语言模型驱动的多智能体系统，将被动的MOOC转变为以文本为基础的动态平台。本文针对一个具体的MAIC课程开展实证研究，探讨三个问题：（1）导致退课的因素有哪些？（2）能否预测退课？（3）能否减少退课？通过分析交互日志定义退课行为并识别影响因素，发现退课与文本交互模式存在强关联。提出一种课程进度自适应的退课预测框架（CPADP），可实现最高95.4%的预测准确率。基于此，设计个性化邮件召回代理以重新吸引高风险学生。在部署于超过3,000名学生的MAIC系统中验证了该方法的可行性与有效性，适用于多样化背景的学习者。
原文摘要 · Abstract (English)
Interactive online learning environments, represented by Massive AI-empowered Courses (MAIC), leverage LLM-driven multi-agent systems to transform passive MOOCs into dynamic, text-based platforms, enhancing interactivity through LLMs. This paper conducts an empirical study on a specific MAIC course to explore three research questions about dropouts in these interactive online courses: (1) What factors might lead to dropouts? (2) Can we predict dropouts? (3) Can we reduce dropouts? We analyze interaction logs to define dropouts and identify contributing factors. Our findings reveal strong links between dropout behaviors and textual interaction patterns. We then propose a course-progress-adaptive dropout prediction framework (CPADP) to predict dropouts with at most 95.4% accuracy. Based on this, we design a personalized email recall agent to re-engage at-risk students. Applied in the deployed MAIC system with over 3,000 students, the feasibility and effectiveness of our approach have been validated on students with diverse backgrounds.
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