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Enfoque de análisis de datos visual - predictivo para el desempeño académico de los estudiantes de una universidad peruana
dc.contributor.advisor | López Gonzales, Javier Linkolk | |
dc.contributor.author | Orrego Granados, David Leandro | |
dc.date.accessioned | 2021-10-25T17:14:20Z | |
dc.date.available | 2021-10-25T17:14:20Z | |
dc.date.embargoEnd | 2023-10-12 | |
dc.date.issued | 2021-10-12 | |
dc.description.abstract | 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 |
dc.description.escuela | Escuela de Posgrado | en_ES |
dc.description.lineadeinvestigacion | Inteligencia de Negocios | en_ES |
dc.description.sede | LIMA | en_ES |
dc.format | application/pdf | en_ES |
dc.identifier.uri | http://repositorio.upeu.edu.pe/handle/20.500.12840/4892 | |
dc.language.iso | eng | |
dc.publisher | Universidad Peruana Unión | en_ES |
dc.publisher.country | PE | en_ES |
dc.publisher.country | PE | |
dc.rights | info:eu-repo/semantics/embargoedAccess | en_ES |
dc.rights | Attribution-NonCommercial-ShareAlike 3.0 Spain | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ | * |
dc.subject | Students | en_ES |
dc.subject | Performances | en_ES |
dc.subject | Learning analytics | en_ES |
dc.subject | Educational Data Mining | en_ES |
dc.subject | Business intelligence in education | en_ES |
dc.subject | Machine learning | en_ES |
dc.subject.ocde | http://purl.org/pe-repo/ocde/ford#2.02.04 | en_ES |
dc.title | Enfoque de análisis de datos visual - predictivo para el desempeño académico de los estudiantes de una universidad peruana | en_ES |
dc.title.alternative | Visual-predictive data analysis approach for the academic performance of students from a Peruvian university | en_ES |
dc.type | info:eu-repo/semantics/masterThesis | en_ES |
renati.advisor.dni | 46071566 | |
renati.advisor.orcid | https://orcid.org/0000-0003-0847-0552 | en_ES |
renati.author.dni | 42870733 | |
renati.discipline | 612467 | en_ES |
renati.juror | Valladares Castillo, Sergio Omar | |
renati.juror | Saboya Rios, Nemias | |
renati.juror | Acuña Salinas, Erika Inés | |
renati.juror | Loaiza Jara, Omar Leonel | |
renati.juror | Lopez Gonzáles, Javier Linkolk | |
renati.level | http://purl.org/pe-repo/renati/nivel#maestro | en_ES |
renati.type | http://purl.org/pe-repo/renati/type#tesis | en_ES |
thesis.degree.discipline | Maestría en Ingeniería de Sistemas con Mención en Dirección y Gestión en Tecnologías de Información | en_ES |
thesis.degree.grantor | Universidad Peruana Unión. Unidad de Posgrado de Ingeniería y Arquitectura | en_ES |
thesis.degree.name | Maestro en Ingeniería de Sistemas con Mención en Dirección y Gestión en Tecnologías de Información | en_ES |
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