Predictive Model for Taking Decision to Prevent University Dropout

Abstract

Dropout is an educational phenomenon studied for decades due to the diversity of its causes, whose effects fall on society's development. This document presents an experimental study to obtain a predictive model that allows anticipating a university dropout. The study uses 51,497 instances with 26 attributes obtained from social sciences, administrative sciences, and engineering collected from 2010 to 2019. Artificial neural networks and decision trees were implemented as classification algorithms, and also, algorithms of attribute selection and resampling methods were used to balance the main class. The results show that the best performing model was that of Random Forest with a Matthew correlation coefficient of 87.43% against 53.39% obtained by artificial neural networks and 94.34% accuracy by Random Forest. The model has allowed predicting an approximate number of possible dropouts per period, contributing to the involved instances in preventing or reducing dropout in higher education.