用行为突变识别辍学风险，提升早期预警准确率15%。
Predicting Student Dropout Risk With A Dual-Modal Abrupt Behavioral Changes Approach
- 结合学业与行为数据，动态捕捉学生行为突变模式
- 相比传统方法预测准确率提升15%，支持早期干预
- 适合教育机构用于学生留任预警与个性化支持
及时预测高辍学风险学生对早期干预和改善教育成果至关重要。然而，在离线教育环境中，数据质量差、规模小、异质性强常制约先进机器学习模型的应用。尽管教育理论为辍学现象提供重要洞见，但关键指标缺乏可量化度量，限制了其在数据驱动建模中的使用。通过数据分析与文献综述，我们识别出学生行为的骤变是辍学风险的关键早期信号。为此，提出双模态多尺度滑动窗口（DMSW）模型，融合学业表现与行为数据，以最少数据动态捕捉行为模式。该模型相较传统方法预测准确率提升15%，使教育者能更早识别高风险学生，及时提供支持，营造更具包容性的学习环境。分析揭示了关键行为模式，为预防策略与个性化支持提供实践指导。研究弥合了理论与实践在辍学预测中的鸿沟，为教育者提供了创新工具以提升学生留存率与学业成果。
原文摘要 · Abstract (English)
Timely prediction of students at high risk of dropout is critical for early intervention and improving educational outcomes. However, in offline educational settings, poor data quality, limited scale, and high heterogeneity often hinder the application of advanced machine learning models. Furthermore, while educational theories provide valuable insights into dropout phenomena, the lack of quantifiable metrics for key indicators limits their use in data-driven modeling. Through data analysis and a review of educational literature, we identified abrupt changes in student behavior as key early signals of dropout risk. To address this, we propose the Dual-Modal Multiscale Sliding Window (DMSW) Model, which integrates academic performance and behavioral data to dynamically capture behavior patterns using minimal data. The DMSW model improves prediction accuracy by 15% compared to traditional methods, enabling educators to identify high-risk students earlier, provide timely support, and foster a more inclusive learning environment. Our analysis highlights key behavior patterns, offering practical insights for preventive strategies and tailored support. These findings bridge the gap between theory and practice in dropout prediction, giving educators an innovative tool to enhance student retention and outcomes.
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