Mineração de dados educacionais aplicada à busca de perfis de alunos em casos de evasão ou retenção: uma abordagem através de Redes Bayesianas

Abstract

This work investigates the profiles of undergraduate students at the University of Federal University of Pará prone to two problems faced in several universities evasion and retention. These problems stimulated the study of methodologies that detect patterns that lead to extrapolation or the premature end of the studies. The tool chosen for this purpose, the Bayesian Network is powerful in providing reasoning about uncertainties, especially in causes and effects diagnoses. Assumption of the relationship of the variables and their probability of occurrence and marginal. Another aspect inherent in the structure of Bayesian Networks is the comprehensibility of representation and results, which generate specialists and users entered into the domain. Considering such placements, these potential of the methodology in question strengthened its application in this research. So, academic records containing tens of thousands of samples from students immersed in presential teaching environments belonging to undergraduate students at the Federal University of Pará until the year 2016 were submitted to the of Knowledge Discovery in the Database, specifically in Data Mining the desired patterns were extracted using the classification task. In addition, several performance analyzes were performed during Data Mining stage The Bayesian Network together with other classic algorithms of supervised learning, and which revealed its great accuracy and efficiency, rising from the best solutions found, its use has been certified on the selected database. In three Study of Case, the results shows classifier’s quality based on Bayesian Networks, which presented an accuracy of more than 82%, a condition that its usefulness in the researched domain. Thus, the results achieved were satisfactory and strong influences of some variables on the propensity of evasion or retention.