Preventing university drop outs : early detection of university drop outs using machine learning

Abstract

University drop out rates present a significant challenge for higher education institutions, leading to wasted resources and decreased graduation rates. This thesis explores the potential of machine learning to provide an early detection system for identifying students at risk of dropping out. Using academic data from SISU, a student information system used in Finland, various machine learning models were tested and optimized. Key steps included feature engineering, data balancing using SMOTE, and model evaluation to ensure reliable predictions. The results indicate that academic data alone is sufficient for creating a viable predictive system. The findings emphasize the importance of early intervention strategies and the potential for data-driven decision-making in university administration. This study demonstrates that machine learning can serve as a powerful tool for drop out prevention, enabling universities to take proactive steps toward student retention.