A Mathematical Framework for Temporal Modeling and Counterfactual Policy Simulation of Student Dropout
This work addresses student retention for educational institutions, but it is incremental as it builds on existing methods without causal identification.
The study tackled student dropout prediction in higher education by developing a temporal modeling framework with counterfactual policy simulation, achieving test AUCs around 0.84 and showing scenario-specific survival contrasts such as 0.0102 to 0.0819 for positive cases.
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 $ÎS(T)$ 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 ($ÎS_{\rm mech}(18)=-0.0078$, $ÎS_{\rm mech}(38)=-0.0134$). 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.