A Mathematical Framework for Temporal Modeling and Counterfactual Policy Simulation of Student Dropout

• URL: http://arxiv.org/abs/2604.08874v1
• Date: Fri, 10 Apr 2026 02:26:23 GMT
• Title: A Mathematical Framework for Temporal Modeling and Counterfactual Policy Simulation of Student Dropout
• Abstract summary: Dropout is operationalized as a time-to-event outcome at the enrollment level.<n>Weekly risk is modeled in discrete time via penalized, class-balanced logistic regression over person--period rows.<n>Under a late-event temporal holdout, the model attains row-level AUCs of 0.8350 (train) and 0.8405 (test)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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