Machine Learning Models for Predicting Student Enrollment Decisions in Higher Education

An increasing number of higher education institutions in the Republic of Serbia are experiencing a decline in first-year enrollment, posing a significant challenge to their sustainability and effective resource planning. Timely identification of factors influencing candidates’ enrollment decisions, as well as those at risk of not enrolling, is crucial for implementing appropriate institutional measures. This study aims to build and evaluate a machine learning model to predict candidates’ decisions to enroll in a higher education institution based on relevant educational, administrative, demographic, social, and geographic characteristics. Various classification models, including ensemble approaches, were applied and compared in this study. Experimental results indicate that the Stacking Ensemble model achieved slightly higher values of the evaluated imbalance-sensitive metrics compared to the other evaluated models, with an Area Under the ROC Curve (AUC) of 0.756 and a Matthews Correlation Coefficient (MCC) of 0.364, indicating moderately balanced predictive performance in the context of imbalanced data. However, the statistical analysis conducted between the Logistic Regression and Stacking Ensemble models did not indicate a statistically significant difference in performance. The results suggest that ensemble methods may provide certain advantages over individual models, particularly for complex classification problems involving imbalanced data. The application of the proposed model may contribute to improving the decision-making process at higher education institutions, enabling more efficient enrollment policy planning and more optimal resource management.