Artificial Intelligence for detection of University dropout

Abstract

The university dropout is an unresolved problem in Latin America [1], and it is urgent to understand its causes, which will allow the development of retention policies both in the university and government. In this context, Artificial Intelligence may play an important role in finding hidden patterns and relationships in data, which will allow us to recognize the true causes and make predictions that lead to more effective decisions. The general objective of this work is to generate models that allow us to discover those students who have a higher probability of dropping out of a university studies, allowing retention actions. Data mining and machine learning techniques were used, using data provided by the Academic Management System of the Catholic University of Santiago del Estero.