Prediction of academic dropout in a higher education institution using data mining [Previsão do abandono académico numa instituição de ensino superior com recurso a data mining]

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Martins, MPG (Author) Other; Vera L. Miguéis (Author) FEUP; Fonseca, DSB (Author) Other; Gouveia, PDF (Author) Other

Journal

Title: RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação

Vol. 2020

Pages: 188-203

ISSN: 1646-9895

Publisher: Associação Ibérica de Sistemas e Tecnologias de Informação (AISTI)

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00R-VCS

Abstract (EN): 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. This knowledge is useful for decision-makers to adopt the most appropriate strategic measures and decisions in order to reduce student dropout rates.

Language: Portuguese

Type (Professor's evaluation): Scientific