Two-Level Monitoring System for Preventing Academic Failure, Based on Predictive Models and SHAP Analysis

Student dropout remains a critical challenge in higher education, requiring early detection and targeted intervention. This study aims to develop an interpretable two-level monitoring framework for identifying at-risk students—those with academic debts but not yet dismissed—across successive stages of the academic debt lifecycle. Using digital profile data and LMS digital footprints from a large public university (18,192 records covering the years 2022–2024), we trained CatBoost, XGBoost, LightGBM, and Random Forest for each of two stages: initial retakes and final commission retakes. SHapley Additive exPlanations (SHAP) were applied for post hoc interpretation. SHAP analysis identified key indicators of initial retake failure: semester, year of study, number of academic debts, GPA in the previous semester, and LMS activity in the previous and current semesters. The strongest indicator of success on commissions was the presence of a digital footprint at the beginning of the current semester, which eliminated dropout risk regardless of prior academic history. Dismissal risk increases for junior-year students and those with higher debt counts. These findings enabled student profiling into Red, Yellow, and Green risk categories for optimized allocation of administrative and tutoring resources. Utilizing the proposed framework, educators can streamline pedagogical support and enhance student retention.