Previsão do abandono académico numa instituição de ensino superior com recurso a data mining

This study proposes two predictive models of classification that allow to identify, at the end of the 1st and 2nd semesters, the undergraduate students of a higher education institution more prone to academic dropout. The proposed methodology, which combines 3 popular data mining algorithms, such as...

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Bibliographic Details
Published in:RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação 2020-04 (E28), p.188-203
Main Authors: Martins, Maria P G, Migueis, Vera L, Fonseca, D S B, Gouveia, Paulo D F
Format: Article
Language:Portuguese
Subjects:
Algorithms
Artificial neural networks
Curricula
Data mining
Education
Higher education
Neural networks
Performance prediction
Prediction models
Support vector machines
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Summary:This study proposes two predictive models of classification that allow to identify, at the end of the 1st and 2nd semesters, the undergraduate students of a higher education institution more prone to academic dropout. The proposed methodology, which combines 3 popular data mining algorithms, such as random forest, support vector machines and artificial neural networks, in addition to contributing to predictive performance, allows to identify the main factors behind academic dropout. The empirical results show that it is possible to reduce to about 1/4 the 4 tens potential predictors of dropout, and show that there are essentially two predictors, concerning student's curriculum context, that explain this propensity. Keywords: Educational data mining; prediction academic dropout; random forest; support vector machines; artificial neural networks. 1.
ISSN:1646-9895