Predicting university dropout using machine learning: Applications based on multidisciplinary databases
Abstract Student dropout constitutes a challenge for higher education institutions due to, among other reasons, the diversity of students’ social, educational, and economic backgrounds, as well as the disciplines and academic degrees in which they enrol, resulting in a wide variability in educational trajectories. The study aimed to develop a predictive model of university dropout among students enrolled in various programs, utilising machine learning. An online questionnaire and academic records were used to obtain ten variables previously identified as relevant to the studied phenomenon, and classifiers were trained through supervised machine learning procedures. The sample consisted of 946 students from seven different academic disciplines at a university in Chile. The primary outcomes indicate that a model based on the RUSboosted Trees classifier presented the best performance in predicting dropout in higher education, successfully recognising 90% of dropout students. Several previous studies have predicted student dropout using machine learning, but typically on samples of students belonging to the same programmes. This study presents a predictive model that identifies future dropouts even under multidisciplinary conditions within their original programmes.
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