A methodology to design, develop, and evaluate machine learning models for predicting dropout in school systems: the case of Chile.

Rodríguez, Patricio; Villanueva, Alexis; Dombrovskaia, Lioubov; Valenzuela, Juan Pablo

Rodríguez, Patricio; Villanueva, Alexis; Dombrovskaia, Lioubov; Valenzuela, Juan Pablo.

Afiliação

Rodríguez P; Institute of Education and Center for Advanced Research in Education, Universidad de Chile, Periodista José Carrasco Tapia 75, 8330014 Santiago, Región Metropolitana Chile.
Villanueva A; Center for Advanced Research in Education, Universidad de Chile, Santiago, Chile.
Dombrovskaia L; Department of Informatics, Universidad Técnica Federico Santa María, Santiago, Chile.
Valenzuela JP; Institute of Education and Center for Advanced Research in Education, Universidad de Chile, Periodista José Carrasco Tapia 75, 8330014 Santiago, Región Metropolitana Chile.

Educ Inf Technol (Dordr) ; : 1-47, 2023 Jan 23.

Article em En | MEDLINE | ID: mdl-36714447

Palavras-chave

Data science applications in education; Learning analytics; Machine learning; Student retention

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Qualitative_research / Risk_factors_studies País/Região como assunto: America do sul / Chile Idioma: En Revista: Educ Inf Technol (Dordr) Ano de publicação: 2023 Tipo de documento: Article

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