Design of a Machine Learning Model to Predict Student Attrition
Tibor Fauszt, Katalin Erdélyi, Dóra Dobák, László Bognár, Endre Kovács
Abstract
Higher education institutions are facing a major issue with student dropout rates, which is a global phenomenon that affects a significant portion of enrolled students, particularly those in their first year. The challenge is how to retain students who do not meet requirements during their first year and are at high risk of dropping out, which can have significant economic and social consequences as well as personal ramifications for the students themselves. Universities must prioritize identifying at-risk students and providing targeted assistance to prevent them from leaving the system. Machine learning (ML) models have proven effective in identifying students at risk of dropping out with a high degree of accuracy. In this study, we aim to construct a machine learning model using data extracted from the administration system (Neptun) to predict student dropout rates in the Business Informatics BSc course at the Faculty of Finance and Accounting of Budapest Business School.