Análise de dados acadêmicos que indicam a evasão de alunos em um curso de graduação tecnológica

Abstract

Student dropouts represent one of the biggest problems of education institutions, including higher education, affecting the entire educational system. This might be why many researches are being developed to understand the main factors causing dropouts, and how it can be mitigated in face of the social resources, academic and economic factors that are wasted with this problem. The main goal of this study was to verify the possibility to identify evasion trends only by observing students' academic data. The specific objectives were: identify the reasons why students' dropout; identify academic student data that can be related to the dropout factors; and identify parameters that may signal drop out, from the analysis of academic data of a technological undergraduate course. The study was conducted with the academic data of 305 students who had enrolled in a technological course since its beginning in 2010. First, a literature review examined what factors lead students to drop out of their courses. Then, it was analyzed which academic data can be related to each one of the factors and, therefore, possibly suggest if the student tend to dropout. After analyzing the data present in school records, the results indicated that the undergraduate course has a dropout rate of 62.3%. Also, it has higher failure rate in the first and second semesters, 45% and 27% of disapproval respectively. The objective was reached, first because the literature survey identified what factors motivate students to dropout their courses. Then, a correspondence was established between these factors and possible academic student data, and also suggested in which university documents and systems such data could be obtained. As a final result, based on the identified factors and correspondent academic data, a study of the academic data of students from an undergraduate technological course contributed to the formulation dropout indicators as well as some parameters for their analysis. Such parameters guide educational program managers through the analysis of academic information intended to reduce dropout rates.