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]

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Martins, MPG (Autor) Outra; Vera L. Miguéis (Autor) FEUP; Fonseca, DSB (Autor) Outra; Gouveia, PDF (Autor) Outra

Revista

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

Vol. 2020

Páginas: 188-203

ISSN: 1646-9895

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

Indexação

Scopus - 2 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Português

Tipo (Avaliação Docente): Científica