Previsão automática de evasão estudantil: um estudo de caso na UFCG.

Abstract

Students’ dropout is a major concern of the Brazilian higher education institutions as it may cause waste of resources. The early detection of students with high probability of dropping out, as well as understanding the underlying causes, are crucial for defining more effective actions toward preventing this problem. In this paper, we cast the dropout detection problem as a supervised learning problem. We use a large sample of academic records of students across 76 courses from a public university in Brazil in order to derive and select informative features for the employed classifiers. We create two classification models that either consider the course to which the target student is formally committed or not consider it, respectively. We contrast both models and show that not considering the course leads to better results.