用静态数据提前预测学生风险，第2周准确率达97%。
AI-Driven Early Warning Systems for Student Success: Discovering Static Feature Dominance in Temporal Prediction Models
- 用静态人口特征主导预测，无需课程数据即可早期预警
- 第2周LSTM模型召回率达97%，第20周精确率达90%
- 不同干预阶段需匹配不同模型，早中期选决策树，后期选LSTM
在线学习中早期识别高风险学生对有效干预至关重要。本研究将时间预测分析扩展至课程进行到第20周（50%时长），比较决策树与长短期记忆（LSTM）模型在六个时间点的表现。结果表明：早期干预（第2-4周）需高召回率，中期资源分配（第8-16周）需平衡精确率与召回率，后期（第20周）则以高精确率为重。分析显示，静态人口特征占预测重要性68%，可实现无课程数据的早期预测。LSTM在第2周达到97%召回率，适合早期干预；决策树在中期保持78%稳定准确率。至第20周，两模型召回率均达68%，但LSTM精确率更高（90% vs 86%）。研究建议模型选择应依干预时机而定，且第2-4周的早期信号已足够用于可靠初始预测。
原文摘要 · Abstract (English)
Early identification of at-risk students is critical for effective intervention in online learning environments. This study extends temporal prediction analysis to Week 20 (50% of course duration), comparing Decision Tree and Long Short- Term Memory (LSTM) models across six temporal snapshots. Our analysis reveals that different performance metrics matter at different intervention stages: high recall is critical for early intervention (Weeks 2-4), while balanced precision-recall is important for mid-course resource allocation (Weeks 8-16), and high precision becomes paramount in later stages (Week 20). We demonstrate that static demographic features dominate predictions (68% importance), enabling assessment-free early prediction. The LSTM model achieves 97% recall at Week 2, making it ideal for early intervention, while Decision Tree provides stable balanced performance (78% accuracy) during mid-course. By Week 20, both models converge to similar recall (68%), but LSTM achieves higher precision (90% vs 86%). Our findings also suggest that model selection should depend on intervention timing, and that early signals (Weeks 2-4) are sufficient for reliable initial prediction using primarily demographic and pre-enrollment information.
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