用可解释机器学习检测在线学习中学生的脱节行为
Detection of Disengagement from Voluntary Quizzes: An Explainable Machine Learning Approach in Higher Distance Education
- 基于Moodle日志数据，筛选关键特征构建预测模型
- 平衡准确率达91%，85%脱节学生被正确识别
- 结合SHAP方法实现决策可解释，适合教育干预设计
学生在远程教育中脱离任务可能带来学业辍学等严重后果。本文针对一所远程大学42门课程、四个学期的非强制性测验数据，分析学生参与行为以检测脱节情况。从Moodle系统中提取并处理最相关的日志数据，对比八种机器学习算法，最终获得最高预测性能。通过SHAP方法构建可解释框架，帮助教育者理解模型决策。实验结果显示，平衡准确率为91%，约85%的脱节学生被正确识别。研究还探讨了如何设计及时干预措施，减少在线学习中对自愿任务的脱节。
原文摘要 · Abstract (English)
Students disengaging from their tasks can have serious long-term consequences, including academic drop-out. This is particularly relevant for students in distance education. One way to measure the level of disengagement in distance education is to observe participation in non-mandatory exercises in different online courses. In this paper, we detect student disengagement in the non-mandatory quizzes of 42 courses in four semesters from a distance-based university. We carefully identified the most informative student log data that could be extracted and processed from Moodle. Then, eight machine learning algorithms were trained and compared to obtain the highest possible prediction accuracy. Using the SHAP method, we developed an explainable machine learning framework that allows practitioners to better understand the decisions of the trained algorithm. The experimental results show a balanced accuracy of 91\%, where about 85\% of disengaged students were correctly detected. On top of the highly predictive performance and explainable framework, we provide a discussion on how to design a timely intervention to minimise disengagement from voluntary tasks in online learning.
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