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

• URL: http://arxiv.org/abs/2508.17310v1
• Date: Sun, 24 Aug 2025 11:40:16 GMT
• Title: Handling Students Dropouts in an LLM-driven Interactive Online Course Using Language Models
• Authors: Yuanchun Wang, Yiyang Fu, Jifan Yu, Daniel Zhang-Li, Zheyuan Zhang, Joy Lim Jia Yin, Yucheng Wang, Peng Zhou, Jing Zhang, Huiqin Liu,
• Abstract summary: This paper conducts an empirical study on a specific Massive AI-empowered Courses (MAIC) course to explore three research questions about dropouts.<n>We analyze interaction logs to define dropouts and identify contributing factors.<n>We propose a course-progress-adaptive dropout prediction framework to predict dropouts with at most 95.4% accuracy.
• Score: 30.847725742817982
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning [52.07170679746533]
  Large Language Models (LLMs) are increasingly used to simulate human users in interactive settings such as therapy, education, and social role-play.<n>We introduce a unified framework for evaluating and improving persona consistency in LLM-generated dialogue.<n>We define three automatic metrics: prompt-to-line consistency, line-to-line consistency, and Q&A consistency, that capture different types of persona drift and validate each against human annotations.
  arXiv  Detail & Related papers  (2025-10-31T19:40:41Z)
• CoDAE: Adapting Large Language Models for Education via Chain-of-Thought Data Augmentation [8.901227918730562]
  Large Language Models (LLMs) are increasingly employed as AI tutors due to their scalability and potential for personalized instruction.<n>We introduce CoDAE, a framework that adapts LLMs for educational use through Chain-of-Thought data augmentation.<n>We collect real-world dialogues between students and a ChatGPT-based tutor and enrich them using CoT prompting to promote step-by-step reasoning and pedagogically aligned guidance.
  arXiv  Detail & Related papers  (2025-08-11T18:13:31Z)
• Beyond classical and contemporary models: a transformative AI framework for student dropout prediction in distance learning using RAG, Prompt engineering, and Cross-modal fusion [0.4369550829556578]
  This paper introduces a transformative AI framework that redefines dropout prediction.<n>The framework achieves 89% accuracy and an F1-score of 0.88, outperforming conventional models by 7% and reducing false negatives by 21%.
  arXiv  Detail & Related papers  (2025-07-04T21:41:43Z)
• From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning [76.09281171131941]
  Large language models (LLMs) can transform education, but their optimization for direct question-answering often undermines effective pedagogy.<n>We propose an online reinforcement learning (RL)-based alignment framework that can quickly adapt LLMs into effective tutors.
  arXiv  Detail & Related papers  (2025-05-21T15:00:07Z)
• Alignment Drift in CEFR-prompted LLMs for Interactive Spanish Tutoring [0.0]
  This paper investigates the potentials of Large Language Models (LLMs) as adaptive tutors in the context of second-language learning.<n>We simulate full teacher-student dialogues in Spanish using instruction-tuned, open-source LLMs ranging in size from 7B to 12B parameters.<n>The output from the tutor model is then used to evaluate the effectiveness of CEFR-based prompting to control text difficulty across three proficiency levels.
  arXiv  Detail & Related papers  (2025-05-13T08:50:57Z)
• Playpen: An Environment for Exploring Learning Through Conversational Interaction [81.67330926729015]
  We investigate whether Dialogue Games can also serve as a source of feedback signals for learning.<n>We introduce Playpen, an environment for off- and online learning through Dialogue Game self-play.<n>We find that imitation learning through SFT improves performance on unseen instances, but negatively impacts other skills.
  arXiv  Detail & Related papers  (2025-04-11T14:49:33Z)
• The StudyChat Dataset: Student Dialogues With ChatGPT in an Artificial Intelligence Course [1.9116784879310025]
  Large language models (LLMs) have significantly impacted education, raising both opportunities and challenges.<n>We introduce StudyChat, a dataset capturing real-world student interactions with an LLM.<n>We deploy a web application that replicates ChatGPTs core functionalities, and use it to log student interactions with the LLM.<n>We analyze these interactions, highlight usage trends, and analyze how specific student behavior correlates with their course outcome.
  arXiv  Detail & Related papers  (2025-03-11T00:17:07Z)
• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks.<n>However, they still struggle with problems requiring multi-step decision-making and environmental feedback.<n>We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models [15.825663946923289]
  Large language models (LLMs) excel at answering questions but remain passive learners-absorbing static data without the ability to question and refine knowledge.<n>This paper explores how LLMs can transition to interactive, question-driven learning through student-teacher dialogues.
  arXiv  Detail & Related papers  (2024-12-16T02:28:53Z)
• Modulating Language Model Experiences through Frictions [56.17593192325438]
  Over-consumption of language model outputs risks propagating unchecked errors in the short-term and damaging human capabilities for critical thinking in the long-term. We propose selective frictions for language model experiences, inspired by behavioral science interventions, to dampen misuse.
  arXiv  Detail & Related papers  (2024-06-24T16:31:11Z)
• Keeping Teams in the Game: Predicting Dropouts in Online Problem-Based Learning Competition [0.4779196219827506]
  The study employs an online longitudinal problem-based learning (PBL) collaborative robotics competition as the testbed. The study aims to predict dropout behavior via the contributions of Discourse discussion forum 'activities' of participating teams, along with a self-reported Online Learning Strategies Questionnaire (OSLQ) The findings demonstrate the reliability of OSLQ with our substantial sample size and reveal promising results for predicting the dropout rate in online competition.
  arXiv  Detail & Related papers  (2023-12-27T00:08:19Z)
• Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network [56.62345811216183]
  We propose a novel approach using Graph Neural Networks (GNNs) to achieve better student performance prediction in interactive online question pools. Specifically, we model the relationship between students and questions using student interactions to construct the student-interaction-question network. We evaluate the effectiveness of our approach on a real-world dataset consisting of 104,113 mouse trajectories generated in the problem-solving process of over 4000 students on 1631 questions.
  arXiv  Detail & Related papers  (2020-08-04T14:55:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.