A Data Mining Approach to Predict Key Factors Impacting University Students Dropout in a Least Developed Economy

Abstract

University students’ dropout is a complex issue with life and career ramifications, especially in least developed countries. Ethiopia, a country with one of the least developed economies, has made considerable efforts to strengthen its higher education; yet university student attrition remains a major concern. In this study, we utilized the data mining methodology to reveal the important factors that impact dropout among the Ethiopian university students. The current research results indicate that personal, institutional, and academic factors affect university student dropout. In Ethiopia, low-performing rural female students are more likely to drop out than male students, according to the findings of this study. In general, rural low- achieving students have a greater likelihood of dropping out of university. This is likely to occur during the students' first semester of study, especially if they have a poor attendance rate. This research contributes to the body of knowledge by indicating that university remedial programs may be successful in reducing the incidents of students’ dropout. The current research has implications for policymakers in the least developed nations, such as Ethiopia, to construct dropout intervention programs based on the factors identified in this research.