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López Gonzales, Javier Linkolk
Orrego Granados, David Leandro
2023-10-12 (embargoEnd)
2021-10-25T17:14:20Z
2021-10-25T17:14:20Z
2021-10-25T17:14:20Z
2021-10-25T17:14:20Z
2021-10-12
http://hdl.handle.net/20.500.12840/4892
Escuela de Posgrado (escuela)
LIMA (sede)
Inteligencia de Negocios (lineadeinvestigacion)
The academic success of university students is a result that depends in a multi-factorial way on the aspects related to the student and the career itself. In this work, we carry out a visual analysis of the data to obtain relevant information regarding the academic performance of students from a Peruvian university. This study was complemented with the construction of machine learning models to provide a predictive model of the students’ academic success. In specific, the XGBoost Machine Learning method achieved a performance of up to 91.5% of Accuracy. In this sense, this study offers a novel visual-predictive data analysis approach as a valuable tool for developing and targeting policies to support students with lower academic performance or to stimulate advanced students. The results obtained allow us to identify the relevant variables associated with the students’ academic performances. Moreover, we were able to give some insight into the academic situation of the different careers of the University. (en_ES)
application/pdf (en_ES)
eng
Universidad Peruana Unión (en_ES)
info:eu-repo/semantics/embargoedAccess (en_ES)
Attribution-NonCommercial-ShareAlike 3.0 Spain (*)
http://creativecommons.org/licenses/by-nc-sa/3.0/es/ (*)
Machine learning (en_ES)
Educational Data Mining (en_ES)
Business intelligence in education (en_ES)
Students (en_ES)
Performances (en_ES)
Learning analytics (en_ES)
Enfoque de análisis de datos visual - predictivo para el desempeño académico de los estudiantes de una universidad peruana (en_ES)
Visual-predictive data analysis approach for the academic performance of students from a Peruvian university (en_ES)
info:eu-repo/semantics/masterThesis (en_ES)
Universidad Peruana Unión. Unidad de Posgrado de Ingeniería y Arquitectura (en_ES)
Maestría en Ingeniería de Sistemas con Mención en Dirección y Gestión en Tecnologías de Información (en_ES)
Maestro en Ingeniería de Sistemas con Mención en Dirección y Gestión en Tecnologías de Información (en_ES)
PE
PE (en_ES)
http://purl.org/pe-repo/ocde/ford#2.02.04 (en_ES)
http://purl.org/pe-repo/renati/nivel#maestro (en_ES)
46071566
https://orcid.org/0000-0003-0847-0552 (en_ES)
42870733
612467 (en_ES)
Saboya Rios, Nemias
Valladares Castillo, Sergio Omar
Acuña Salinas, Erika Inés
Loaiza Jara, Omar Leonel
Lopez Gonzáles, Javier Linkolk
http://purl.org/pe-repo/renati/type#tesis (en_ES)
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