Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University

Vista sencilla de ítem
dc.contributor.authorOrrego Granados, David
dc.contributor.authorUgalde, Jonathan
dc.contributor.authorSalas, Rodrigo
dc.contributor.authorTorres, Romina
dc.contributor.authorLópez Gonzales, Javier Linkolk
dc.date.accessioned2023-06-02T16:46:38Z
dc.date.available2023-06-02T16:46:38Z
dc.date.issued2022-11
dc.descriptionIndexación: Scopus.es
dc.description.abstractThe academic success of university students is a problem that depends in a multi-factorial way on the aspects related to the student and the career itself. A problem with this level of complexity needs to be faced with integral approaches, which involves the complement of numerical quantitative analysis with other types of analysis. This study uses a novel visual-predictive data analysis approach to obtain relevant information regarding the academic performance of students from a Peruvian university. This approach joins together domain understanding and data-visualization analysis, with the construction of machine learning models in order to provide a visual-predictive model of the students’ academic success. Specifically, a trained XGBoost Machine Learning model achieved a performance of up to 91.5% Accuracy. The results obtained alongside a visual data analysis allow us to identify the relevant variables associated with the students’ academic performances. In this study, this novel approach was found to be a valuable tool for developing and targeting policies to support students with lower academic performance or to stimulate advanced students. Moreover, we were able to give some insight into the academic situation of the different careers of the university. © 2022 by the authors.es
dc.description.urihttps://www.mdpi.com/2076-3417/12/21/11251
dc.identifier.citationApplied Sciences (Switzerland), Volume 12, Issue 21, November 2022, Article number 11251es
dc.identifier.doi10.3390/app122111251
dc.identifier.issn2076-3417
dc.identifier.urihttps://repositorio.unab.cl/xmlui/handle/ria/50286
dc.language.isoenes
dc.publisherMDPIes
dc.rights.licenseAtribución 4.0 Internacional (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subjectBusiness intelligence in educationes
dc.subjectEducational data mininges
dc.subjectLearning analyticses
dc.subjectMachine learninges
dc.subjectStudents’ performanceses
dc.titleVisual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian Universityes
dc.typeArtículoes
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Orrego_Visual_Predictive_data_analysis_approach.pdf
Tamaño:
1.43 MB
Formato:
Adobe Portable Document Format
Descripción:
TEXTO COMPLETO EN INGLES
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar
Colecciones
FIng - Artículos de Revista