Handling Students Dropouts in an LLM-driven Interactive Online Course Using Language Models

Yuanchun Wang; Yiyang Fu; Jifan Yu; Daniel Zhang-Li; Zheyuan Zhang; Joy Lim Jia Yin; Yucheng Wang; Peng Zhou; Jing Zhang; Huiqin Liu

arXiv · 2508.17310

Handling Students Dropouts in an LLM-driven Interactive Online Course Using Language Models

Yuanchun Wang·Yiyang Fu·Jifan Yu·Daniel Zhang-Li·Zheyuan Zhang·Joy Lim Jia Yin·Yucheng Wang·Peng Zhou·Jing Zhang·Huiqin Liu

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Abstract

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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Yuanchun Wang, Yiyang Fu, Jifan Yu, Daniel Zhang-Li, Zheyuan Zhang, Joy Lim Jia Yin, Yucheng Wang, Peng Zhou, Jing Zhang, Huiqin Liu. 2025-08-24. Handling Students Dropouts in an LLM-driven Interactive Online Course Using Language Models. https://arxiv.org/abs/2508.17310

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