Towards to a predictive model of academic performance using data mining in the UTN-FRRe

Autores
La Red Martínez, David Luis; Karanik, Marcelo; Giovaninni, Mirta Eve; Scappini, Reinaldo
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Students completing the courses required to become an Engineer in Information Systems in the Resistencia Regional Faculty, National Technological University, Argentine (UTN-FRRe), face the challenge of attending classes and fulfilling course regularization requirements, often for correlative courses. Such is the case of freshmen's course Algorithms and Data Structures: it must be regularized in order to be able to attend several second and third year courses. Based on the results of the project entitled “Profiling of students and academic performance through the use of data mining”, 25/L059 - UTI1719, implemented in the aforementioned course (in 2013-2015), a new project has started, aimed to take the descriptive analysis (what happened) as a starting point, and use advanced analytics, trying to explain the why, the what will happen, and how we can address it. Different data mining tools will be used for the study: clustering, neural networks, Bayesian networks, decision trees, regression and time series, etc. These tools allow different
Fil: La Red Martínez, David Luis. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Karanik, Marcelo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Giovaninni, Mirta Eve. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Scappini, Reinaldo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Peer Reviewed
Materia
academic performance
data warehouses
data mining
predictive models
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-nd/3.0/us/
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/1031
Acceder
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network_acronym_str RIAUTN
repository_id_str a
network_name_str Repositorio Institucional Abierto (UTN)
spelling Towards to a predictive model of academic performance using data mining in the UTN-FRReLa Red Martínez, David LuisKaranik, MarceloGiovaninni, Mirta EveScappini, Reinaldoacademic performancedata warehousesdata miningpredictive modelsStudents completing the courses required to become an Engineer in Information Systems in the Resistencia Regional Faculty, National Technological University, Argentine (UTN-FRRe), face the challenge of attending classes and fulfilling course regularization requirements, often for correlative courses. Such is the case of freshmen's course Algorithms and Data Structures: it must be regularized in order to be able to attend several second and third year courses. Based on the results of the project entitled “Profiling of students and academic performance through the use of data mining”, 25/L059 - UTI1719, implemented in the aforementioned course (in 2013-2015), a new project has started, aimed to take the descriptive analysis (what happened) as a starting point, and use advanced analytics, trying to explain the why, the what will happen, and how we can address it. Different data mining tools will be used for the study: clustering, neural networks, Bayesian networks, decision trees, regression and time series, etc. These tools allow differentFil: La Red Martínez, David Luis. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; ArgentinaFil: Karanik, Marcelo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; ArgentinaFil: Giovaninni, Mirta Eve. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; ArgentinaFil: Scappini, Reinaldo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; ArgentinaPeer Reviewed2016-09-28T12:58:09Z2016-09-28T12:58:09Z2016-05-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdf1690-4524http://hdl.handle.net/20.500.12272/1031enghttp://www.iiisci.org/journal/CV$/sci/pdfs/SA751ET16.pdfinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/3.0/us/Acceso AbiertoAttribution-NonCommercial-NoDerivs 3.0 United Statesreponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2025-10-23T11:18:35Zoai:ria.utn.edu.ar:20.500.12272/1031instacron: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:a2025-10-23 11:18:35.86Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Towards to a predictive model of academic performance using data mining in the UTN-FRRe
title Towards to a predictive model of academic performance using data mining in the UTN-FRRe
spellingShingle Towards to a predictive model of academic performance using data mining in the UTN-FRRe
La Red Martínez, David Luis
academic performance
data warehouses
data mining
predictive models
title_short Towards to a predictive model of academic performance using data mining in the UTN-FRRe
title_full Towards to a predictive model of academic performance using data mining in the UTN-FRRe
title_fullStr Towards to a predictive model of academic performance using data mining in the UTN-FRRe
title_full_unstemmed Towards to a predictive model of academic performance using data mining in the UTN-FRRe
title_sort Towards to a predictive model of academic performance using data mining in the UTN-FRRe
dc.creator.none.fl_str_mv La Red Martínez, David Luis
Karanik, Marcelo
Giovaninni, Mirta Eve
Scappini, Reinaldo
author La Red Martínez, David Luis
author_facet La Red Martínez, David Luis
Karanik, Marcelo
Giovaninni, Mirta Eve
Scappini, Reinaldo
author_role author
author2 Karanik, Marcelo
Giovaninni, Mirta Eve
Scappini, Reinaldo
author2_role author
author
author
dc.subject.none.fl_str_mv academic performance
data warehouses
data mining
predictive models
topic academic performance
data warehouses
data mining
predictive models
dc.description.none.fl_txt_mv Students completing the courses required to become an Engineer in Information Systems in the Resistencia Regional Faculty, National Technological University, Argentine (UTN-FRRe), face the challenge of attending classes and fulfilling course regularization requirements, often for correlative courses. Such is the case of freshmen's course Algorithms and Data Structures: it must be regularized in order to be able to attend several second and third year courses. Based on the results of the project entitled “Profiling of students and academic performance through the use of data mining”, 25/L059 - UTI1719, implemented in the aforementioned course (in 2013-2015), a new project has started, aimed to take the descriptive analysis (what happened) as a starting point, and use advanced analytics, trying to explain the why, the what will happen, and how we can address it. Different data mining tools will be used for the study: clustering, neural networks, Bayesian networks, decision trees, regression and time series, etc. These tools allow different
Fil: La Red Martínez, David Luis. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Karanik, Marcelo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Giovaninni, Mirta Eve. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Fil: Scappini, Reinaldo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Peer Reviewed
description Students completing the courses required to become an Engineer in Information Systems in the Resistencia Regional Faculty, National Technological University, Argentine (UTN-FRRe), face the challenge of attending classes and fulfilling course regularization requirements, often for correlative courses. Such is the case of freshmen's course Algorithms and Data Structures: it must be regularized in order to be able to attend several second and third year courses. Based on the results of the project entitled “Profiling of students and academic performance through the use of data mining”, 25/L059 - UTI1719, implemented in the aforementioned course (in 2013-2015), a new project has started, aimed to take the descriptive analysis (what happened) as a starting point, and use advanced analytics, trying to explain the why, the what will happen, and how we can address it. Different data mining tools will be used for the study: clustering, neural networks, Bayesian networks, decision trees, regression and time series, etc. These tools allow different
publishDate 2016
dc.date.none.fl_str_mv 2016-09-28T12:58:09Z
2016-09-28T12:58:09Z
2016-05-02
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 1690-4524
http://hdl.handle.net/20.500.12272/1031
identifier_str_mv 1690-4524
url http://hdl.handle.net/20.500.12272/1031
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.iiisci.org/journal/CV$/sci/pdfs/SA751ET16.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/3.0/us/
Acceso Abierto
Attribution-NonCommercial-NoDerivs 3.0 United States
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/3.0/us/
Acceso Abierto
Attribution-NonCommercial-NoDerivs 3.0 United States
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv 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
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score 12.471625

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