Hybrid Approach Based on Supervised Learning and Synthetic Data Generation for University Student Dropout Risk Prediction

Abstract

: Student dropout in higher education constitutes a structural problem that affects academic quality and institutional sustainability. In Colombia, between 30% and 50% of students left their studies, highlighting the need to strengthen early detection systems. However, the performance of supervised models is often affected by class imbalance, data scarcity, and constraints associated with the use of sensitive information. This study proposes a hybrid methodological approach structured under the CRISP-DM framework that integrates a supervised learning model with synthetic data generated by generative artificial intelligence. The workflow incorporates exploratory data analysis, business rules embedded in the generative process, statistical validation of synthetic data, and comparative evaluation under a no-data-leakage setting. The results show that incorporating synthetic data exclusively into the training set improves predictive performance, particularly for minority classes, reflected in gains in precision, recall, and F1-score, while preserving evaluation on untouched real test data. The proposed approach offers a reproducible, transferable methodological alternative to mitigate class imbalance in university student dropout prediction.