Interpretable machine learning approaches for student dropout prediction in higher education

Abstract

Student dropout remains one of the most significant challenges in higher education, affecting academic performance, financial sustainability, and strategic planning within universities. This study presents an approach to predicting student dropout risk using machine learning methods and educational analytics. The research is based on an open-access dataset from the UCI Machine Learning Repository containing 4,424 records and 36 attributes without missing values. Three experimental datasets were constructed using different preprocessing strategies, including class balancing, logarithmic feature transformation, and processing of the Enrolled category. Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), k-Nearest Neighbours (k-NN), and Neural Network (NN) models were implemented and compared. The results showed that Logistic Regression and Random Forest achieved the highest performance, with accuracy above 90% and ROC AUC values of up to 0.96. The most significant risk factors included academic performance during the first and second semesters, tuition fee payment status, outstanding debt, scholarship status, age at enrolment, and study programme. The findings indicate that machine learning methods can effectively support early dropout prediction and decision-support systems in higher education institutions.