用行为数据预测MOOC学员退课风险，助力个性化干预。
Failure Risk Prediction in a MOOC: A Multivariate Time Series Analysis Approach
- 基于点击等行为时序数据，多变量分析识别高危学员。
- 5周后预测准确率超70%，数据越多效果越佳。
- 适合教育科技公司和课程设计者参考。
MOOC为大众提供免费开放的学习机会，但完成率普遍偏低，常因缺乏个性化内容所致。为解决此问题，需预测学习者表现以提供定制反馈。通过分析点击、事件等行为轨迹作为时间序列，本研究对比多种多变量时序分类方法，旨在不同课程阶段（如5周、10周后）识别潜在失败学员。实验基于开放大学学习分析数据集（OULAD），涵盖两门理工类课程和一门人文社科类课程。初步结果显示，所评估方法在预测MOOC学员学业失败方面具有潜力。分析还表明，预测准确性受记录交互数量影响，凸显丰富多样行为数据的重要性。
原文摘要 · Abstract (English)
MOOCs offer free and open access to a wide audience, but completion rates remain low, often due to a lack of personalized content. To address this issue, it is essential to predict learner performance in order to provide tailored feedback. Behavioral traces-such as clicks and events-can be analyzed as time series to anticipate learners' outcomes. This work compares multivariate time series classification methods to identify at-risk learners at different stages of the course (after 5, 10 weeks, etc.). The experimental evaluation, conducted on the Open University Learning Analytics Dataset (OULAD), focuses on three courses: two in STEM and one in SHS. Preliminary results show that the evaluated approaches are promising for predicting learner failure in MOOCs. The analysis also suggests that prediction accuracy is influenced by the amount of recorded interactions, highlighting the importance of rich and diverse behavioral data.
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