🤖 AI Summary
This study proposes an integrated framework combining time-series modeling with counterfactual policy simulation to predict weekly-granularity dropout risk among higher education students and evaluate intervention efficacy. Leveraging learning management system logs and administrative withdrawal records, the authors construct a person-period model using discrete-time survival analysis and penalized class-balanced logistic regression, achieving a test-set AUC of 0.8405. A counterfactual policy layer incorporating trigger mechanisms and scheduling contracts enables structured scenario comparisons. Bootstrap subgroup analyses reveal that only shock-type interventions significantly improve student survival rates (ΔS = 0.0819), whereas mechanism-aware interventions exhibit negative effects. Although gender-based survival gaps remain directionally consistent, their magnitude is minimal, highlighting substantial heterogeneity in intervention effectiveness across subpopulations.
📝 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 $\Delta S(T)$ 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 ($\Delta S_{\rm mech}(18)=-0.0078$, $\Delta S_{\rm mech}(38)=-0.0134$). 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.