Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role?

Abstract

University dropout rates are a problem that presents many negative consequences. It is an academic issue and carries an unfavorable economic impact. In recent years, significant efforts have been devoted to the early detection of students likely to drop out. This paper uses data corresponding to dropout candidates after their first year in the third largest face-to-face university in Europe, with the goal of predicting likely dropout either at the beginning of the course of study or at the end of the first semester. In this prediction, we considered the five major program areas. Different techniques have been used: first, a Feature Selection Process in order to identify the variables more correlated with dropout; then, some Machine Learning Models (Support Vector Machines, Decision Trees and Artificial Neural Networks) as well as a Logistic Regression. The results show that dropout detection does not work only with enrollment variables, but it improves after the first semester results. Academic performance is always a relevant variable, but there are others, such as the level of preference that the student had over the course that he or she was finally able to study. The success of the techniques depends on the program areas. Machine Learning obtains the best results, but a simple Logistic Regression model can be used as a reasonable baseline.