Procedimiento de agrupación de estudiantes según riesgo de abandono para mejorar la gestión estudiantil en educación superior
No hay miniatura disponible
Archivos
Fecha
2022
Profesor/a Guía
Facultad/escuela
Idioma
es
Título de la revista
ISSN de la revista
Título del volumen
Editor
Lundiana
Nombre de Curso
Licencia CC
Attribution 4.0 International
CC BY 4.0
Deed
Licencia CC
https://creativecommons.org/licenses/by/4.0/
Resumen
The complex problem of student dropout represents an opportunity for the application of data mining technology and methods in higher education. The objective of this research is to obtain the profile of students at risk of dropping out and thus generate student management plans that impact on the variables that explain this situation. For this, it is proposed to use a CRISP-DM methodological structure, applying statistical tools and unsupervised machine learning. The cross-sectional analysis was carried out on a universe of freshmen day students at a private Chilean university. The sociodemographic and behavioural variables used were based on attrition theory and expert judgment, and the data were obtained from the historical records available at the Institution. To obtain the variables that most influenced dropout, correlation and principal component analyses were performed. The application of agglomerative hierarchical clustering and rough sets technique produced four profiles of students with their respective association rules and five academic variables that allowed the design of a support system to reduce dropout and promote retention. © 2022 Universidade Federal de Minas Gerais. All rights reserved.
Notas
Indexación: Scopus.
Palabras clave
Agglomerative hierarchical clustering, CRISP-DM, Principal component analysis, Rough sets, Student dropout
Citación
Texto Livre, Volume 15, 2022 Article number e37275
DOI
10.35699/1983-3652.2022.37275