Early Student Dropout Prediction Using Machine Learning

Abstract

Student dropout is a significant issue in higher education, affecting both students and institutions. Early identification of at-risk students can help universities improve retention. This study addresses student dropout prediction as a binary classification problem using 4,424 student records. To support realistic early prediction, only features available during the early stages of academic study were used. Three machine learning models were evaluated: Logistic Regression, Random Forest, and XGBoost. Class imbalance was handled through class weighting and parameter adjustment, while stratified 10-fold cross-validation ensured result stability. Experimental results showed that Random Forest achieved the highest accuracy (0.866), whereas XGBoost obtained the best recall (0.81) and ROC-AUC (0.913), making it particularly effective for identifying at-risk students. Logistic Regression provided a reliable and interpretable baseline. Feature importance analysis revealed that academic performance and financial factors were among the strongest predictors of dropout. The findings demonstrate the potential of machine learning as an early warning system for educational decision-making. However, the study is limited to a single dataset and does not include temporal data. Future work may incorporate additional features and evaluate model performance across different institutions.