Predicting dropout at master level using educational data mining: A case of public health students in Saudi Arabia

Abstract

Student dropout and its economic and social consequences are significant issues in developing countries. Students who drop out experience reduced employment prospects and encounter social stigma. While early dropout prediction can assist in mitigating the consequences, it remains a considerable challenge. The present research employed a data mining approach to predict dropout of public health master-level students in Saudi Arabia, a developing nation that has invested considerable resources to promote higher education. The research model focused on three fundamental determinants of students’ dropout: individual, institutional, and academic. The study analysis on a dataset of 150 students revealed that all three determinants predicted student dropout. The results indicated that students with low academic performance who received an academic warning were likelier to drop out. Freshmen with poor academic achievement were particularly at risk of dropping out of college. Students between 31 and 36 years old who attended technical courses as a subject specialization could also dropout. The research contributes to the literature by suggesting that universities should consider these individual, institutional, and academic determinants to develop their dropout prevention strategies. This study has ramifications for university administrators in developing nations, such as Saudi Arabia, who can establish dropout prevention programs based on the determinants revealed in this study.