Previsão da evasão escolar através da análise de dados e aprendizagem de máquina: um estudo de caso

Abstract

School dropout is a significant global challenge, which compromises the educational, economic and social development of different communities. This work proposes the use of automatic methods based on machine learning to predict and monitor students at risk of dropping out of school, focusing on students at the Instituto Federal de Pernambuco - Campus Jaboatão dos Guararapes. The assessed risk refers to the possibility of classes interrupting their studies before completion, due to academic and sociodemographic factors identified in the data collected. Several algorithms were tested, and Random Forest presented the best performance, surpassing the other models based on the analyzes used. The results show that it is feasible to use predictive models to support decisions and develop policies to reduce school dropout rates.