Análise discriminante e probabilidade de evasão dos alunos de engenharia de produção na UFSJ

Abstract

Student dropout in higher education is a problem that affects several educational institutions, and can cause academic, social and economic waste. In this context, the present study aimed to identify the characteristics that most discriminate the group of evasers from the group of non evasers in the Production Engineering course at the Federal University of São João del-Rei (UFSJ). To achieve such results, information regarding the students who entered the mentioned course between the years 2014 and 2018 was extracted from the institution's database. Subsequently, these were exported to the Statistical Package for the Social Sciences (SPSS) software and the discriminant analysis technique was used. The results of the research showed that there is a considerable amount of dropouts happening in the analyzed course. It was also possible to find a discriminant function with a real accuracy of over 90%, which allows the preemptive identification of students more likely to drop out of the course and consequently facilitate the work of management or coordination so that they can act differently with such students in order to avoid dropouts.