Academic performance problems : a predictive data mining-based model

Autores
La Red Martínez, David Luis; Giovannini, Mirtha; Báez, María Eugenia; Torre, Juliana; Yaccuzzi, Nelson
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Often times, universities are not able to deal with the variety of factors that may affect the academic performance of students. This kind of situation generates the need for tools that establish academic performance patterns, setting profiles as a basis to detect potential cases of underachieving students who need support in their academic activities. This paper proposes the use of Data Warehousing and Data Mining techniques on performance, social, economic, demographic and cultural data from students who took “Algorithms and Data Structures”, which is a subject in the Information Systems Engineering curricula at UTN-FRRe (Resistencia, Chaco, Argentina) in an attempt to establish generic academic performance profiles. From the descriptive analysis obtained during the 2013 to 2015 period from the subject aforementioned, a predictive model was used. It establishes the possibility of students' academic failure, taking into account the factors earlier mentioned.
Fil: La Red Martínez, David Luis. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Giovannini, Mirta Eve. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Báez, María Eugenia. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Torre, Juliana. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Yaccuzzi, Nelson. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Peer Reviewed
Fuente
Academia Journal of Educational Research 5(4), 061-075. (2017)
Materia
academic performance
educational data mining
predictive data mining
higher education
course assessment
student assessment
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2020-05-29T13:16:40Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/4437
Acceder
id RIAUTN_2fcc4612e52dbd9aa00be01a0edef3a5
oai_identifier_str oai:ria.utn.edu.ar:20.500.12272/4437
network_acronym_str RIAUTN
repository_id_str a
network_name_str Repositorio Institucional Abierto (UTN)
spelling Academic performance problems : a predictive data mining-based modelLa Red Martínez, David LuisGiovannini, MirthaBáez, María EugeniaTorre, JulianaYaccuzzi, Nelsonacademic performanceeducational data miningpredictive data mininghigher educationcourse assessmentstudent assessmentOften times, universities are not able to deal with the variety of factors that may affect the academic performance of students. This kind of situation generates the need for tools that establish academic performance patterns, setting profiles as a basis to detect potential cases of underachieving students who need support in their academic activities. This paper proposes the use of Data Warehousing and Data Mining techniques on performance, social, economic, demographic and cultural data from students who took “Algorithms and Data Structures”, which is a subject in the Information Systems Engineering curricula at UTN-FRRe (Resistencia, Chaco, Argentina) in an attempt to establish generic academic performance profiles. From the descriptive analysis obtained during the 2013 to 2015 period from the subject aforementioned, a predictive model was used. It establishes the possibility of students' academic failure, taking into account the factors earlier mentioned.Fil: La Red Martínez, David Luis. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; ArgentinaFil: Giovannini, Mirta Eve. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; ArgentinaFil: Báez, María Eugenia. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; ArgentinaFil: Torre, Juliana. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; ArgentinaFil: Yaccuzzi, Nelson. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; ArgentinaPeer Reviewed2020-05-29T13:16:40Z2020-05-29T13:16:40Z2017-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdf2315-7704http://hdl.handle.net/20.500.12272/4437Academia Journal of Educational Research 5(4), 061-075. (2017)reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica NacionalengengDiseño de un modelo predictivo de rendimiento académico mediante la utilización de minería de datos. Director del proyecto: Dr. David L. La Red Martínezinfo:eu-repo/semantics/openAccess2020-05-29T13:16:40ZAtribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/Acceso abierto2026-09-24T12:47:36Zoai:ria.utn.edu.ar:20.500.12272/4437instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:47:36.717Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Academic performance problems : a predictive data mining-based model
title Academic performance problems : a predictive data mining-based model
spellingShingle Academic performance problems : a predictive data mining-based model
La Red Martínez, David Luis
academic performance
educational data mining
predictive data mining
higher education
course assessment
student assessment
title_short Academic performance problems : a predictive data mining-based model
title_full Academic performance problems : a predictive data mining-based model
title_fullStr Academic performance problems : a predictive data mining-based model
title_full_unstemmed Academic performance problems : a predictive data mining-based model
title_sort Academic performance problems : a predictive data mining-based model
dc.creator.none.fl_str_mv La Red Martínez, David Luis
Giovannini, Mirtha
Báez, María Eugenia
Torre, Juliana
Yaccuzzi, Nelson
author La Red Martínez, David Luis
author_facet La Red Martínez, David Luis
Giovannini, Mirtha
Báez, María Eugenia
Torre, Juliana
Yaccuzzi, Nelson
author_role author
author2 Giovannini, Mirtha
Báez, María Eugenia
Torre, Juliana
Yaccuzzi, Nelson
author2_role author
author
author
author
dc.subject.none.fl_str_mv academic performance
educational data mining
predictive data mining
higher education
course assessment
student assessment
topic academic performance
educational data mining
predictive data mining
higher education
course assessment
student assessment
dc.description.none.fl_txt_mv Often times, universities are not able to deal with the variety of factors that may affect the academic performance of students. This kind of situation generates the need for tools that establish academic performance patterns, setting profiles as a basis to detect potential cases of underachieving students who need support in their academic activities. This paper proposes the use of Data Warehousing and Data Mining techniques on performance, social, economic, demographic and cultural data from students who took “Algorithms and Data Structures”, which is a subject in the Information Systems Engineering curricula at UTN-FRRe (Resistencia, Chaco, Argentina) in an attempt to establish generic academic performance profiles. From the descriptive analysis obtained during the 2013 to 2015 period from the subject aforementioned, a predictive model was used. It establishes the possibility of students' academic failure, taking into account the factors earlier mentioned.
Fil: La Red Martínez, David Luis. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Giovannini, Mirta Eve. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Báez, María Eugenia. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Torre, Juliana. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Yaccuzzi, Nelson. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Peer Reviewed
description Often times, universities are not able to deal with the variety of factors that may affect the academic performance of students. This kind of situation generates the need for tools that establish academic performance patterns, setting profiles as a basis to detect potential cases of underachieving students who need support in their academic activities. This paper proposes the use of Data Warehousing and Data Mining techniques on performance, social, economic, demographic and cultural data from students who took “Algorithms and Data Structures”, which is a subject in the Information Systems Engineering curricula at UTN-FRRe (Resistencia, Chaco, Argentina) in an attempt to establish generic academic performance profiles. From the descriptive analysis obtained during the 2013 to 2015 period from the subject aforementioned, a predictive model was used. It establishes the possibility of students' academic failure, taking into account the factors earlier mentioned.
publishDate 2017
dc.date.none.fl_str_mv 2017-04-01
2020-05-29T13:16:40Z
2020-05-29T13:16:40Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv 2315-7704
http://hdl.handle.net/20.500.12272/4437
identifier_str_mv 2315-7704
url http://hdl.handle.net/20.500.12272/4437
dc.language.none.fl_str_mv eng
eng
language eng
dc.relation.none.fl_str_mv Diseño de un modelo predictivo de rendimiento académico mediante la utilización de minería de datos. Director del proyecto: Dr. David L. La Red Martínez
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2020-05-29T13:16:40Z
Atribución-NoComercial-CompartirIgual 4.0 Internacional
http://creativecommons.org/licenses/by-nc-sa/4.0/
Acceso abierto
eu_rights_str_mv openAccess
rights_invalid_str_mv 2020-05-29T13:16:40Z
Atribución-NoComercial-CompartirIgual 4.0 Internacional
http://creativecommons.org/licenses/by-nc-sa/4.0/
Acceso abierto
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv Academia Journal of Educational Research 5(4), 061-075. (2017)
reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
_version_ 1877230941887266816
score 13.24418

Publicaciones similares