Handling Students Dropouts in an LLM-driven Interactive Online Course Using Language Models
This addresses dropout issues for students in AI-enhanced online courses, presenting an incremental improvement with a specific application.
The paper tackled student dropouts in an LLM-driven interactive online course by analyzing interaction logs to identify factors, predicting dropouts with up to 95.4% accuracy using a course-progress-adaptive framework, and reducing dropouts through a personalized email recall agent validated on over 3,000 students.
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.