Student dropout in higher education: the case of UFRN.
Dropout. Higher Education. Machine Learning.
The movement to expand access to higher education is being accompanied by a concern with the permanence of these students and with the obtaining of their undergraduate degrees, considering that dropout represents prejudice in the most varied aspects. In this perspective, the objective of this paper is to analyze the dropout of higher education students and to develop a predictive model of the risk of dropout of UFRN undergraduate students. To achieve this, an explanatory research will be conducted with a quantitative approach, based on data provided by the “Superintendência de Tecnologia da Informação” of this university. For the development of the model Supervised Machine Learning Algorithms will be used.