用学习行为数据预测学生退学风险，支持政策模拟与可审计分析。
An Auditable Policy-Simulation Framework for Student Dropout in Intervention-Free Data
- 基于学习系统数据建模每周退学风险，采用带惩罚的平衡逻辑回归。
- 测试集行级AUC达0.8405，高风险群体校准不足但趋势可信。
- 可模拟不同干预策略效果，适合教育决策者评估干预时机与影响。
本研究提出一种时间建模框架，结合反事实政策模拟层，用于高等教育中学生退学的预测。退学被定义为以入学为单位的时间至事件结果，通过在个体-周期数据上使用带惩罚的类别平衡逻辑回归，对每周风险进行离散时间建模。在延迟事件时间留出验证下，模型训练集与测试集的行级AUC分别为0.8350和0.8405，整体校准尚可，但在最高风险区间支持稀疏。消融分析表明性能对特征组成敏感，凸显时序参与信号的重要性。情景索引的政策层生成在明确触发/时间表合约下的生存率差异ΔS(T)：正向差异仅出现在冲击分支（T=18时为0.0102、0.0260、0.0819），而机制感知分支为负值（ΔS_mech(18)=−0.0078，ΔS_mech(38)=−0.0134）。按性别分组的子群分析通过自助法量化情景引发的生存差距，方向稳定但幅度小。结果未实现因果识别，但展示了在观测数据限制下对内部结构情景对比的分析能力。
原文摘要 · Abstract (English)
This study proposes a temporal modeling framework with a counterfactual policy-simulation layer for student dropout in higher education, using LMS engagement data and administrative withdrawal records. Dropout is operationalized as a time-to-event outcome at the enrollment level; weekly risk is modeled in discrete time via penalized, class-balanced logistic regression over person--period rows. Under a late-event temporal holdout, the model attains row-level AUCs of 0.8350 (train) and 0.8405 (test), with aggregate calibration acceptable but sparsely supported in the highest-risk bins. Ablation analyses indicate performance is sensitive to feature set composition, underscoring the role of temporal engagement signals. A scenario-indexed policy layer produces survival contrasts $ΔS(T)$ under an explicit trigger/schedule contract: positive contrasts are confined to the shock branch ($T_{\rm policy}=18$: 0.0102, 0.0260, 0.0819), while the mechanism-aware branch is negative ($ΔS_{\rm mech}(18)=-0.0078$, $ΔS_{\rm mech}(38)=-0.0134$). A subgroup analysis by gender quantifies scenario-induced survival gaps via bootstrap; contrasts are directionally stable but small. Results are not causally identified; they demonstrate the framework's capacity for internal structural scenario comparison under observational data constraints.
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