Predicting Student Dropout from Pre-Enrollment Data in Mexican Higher Education: A Theoretically Grounded and Calibrated Machine Learning Approach

Student dropout remains a persistent challenge in higher education, with significant academic, social and institutional implications. Although machine learning (ML) models for dropout prediction have proliferated, most select input variables based on data availability rather than theory and rarely report psychometric validation of the constructs used. This study addresses both gaps by developing and validating an early warning system at a Mexican university using exclusively pre-enrollment data from more than 46,000 student records from 2014 to 2025. Following the CRISP-DM methodology, eight theoretically grounded constructs, anchored in Tinto’s integration model, Bean’s attrition model, and Cabrera et al.’s persistence model, were operationalized from institutional intake questionnaires and assessed for internal consistency using Cronbach’s alpha prior to model training. Eight supervised ML algorithms were benchmarked across distance-based (Logistic Regression, SVM, AdaBoost, ANN) and tree-based (Random Forest, XGBoost, LightGBM, CatBoost) families. A tuned and isotonically calibrated Random Forest achieved the best overall performance (recall = 0.743, F1 = 0.524, ROC-AUC = 0.734, PR-AUC = 0.478) on a strictly held-out test set that was not used at any stage of model development. The 2023–2025 cohorts, whose dropout labels were not yet observable under the institutional definition, were scored prospectively to generate operational risk profiles. SHAP analysis identified high-school GPA, parental education, and household asset indices as dominant predictors, which mapped directly onto the three theoretical frameworks. These findings demonstrate that psychometrically grounded pre-enrollment data alone can support an operationally deployable dropout detection system, enabling proactive, evidence-based retention interventions from the first day of enrollment.