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
- Institución
- Universidad Tecnológica Nacional
- OAI Identificador
- oai:ria.utn.edu.ar:20.500.12272/1031
Ver los metadatos del registro completo
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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 Nacional2025-09-29T14:29:29Zoai: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:a2025-09-29 14:29:30.063Repositorio 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) |
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Repositorio Institucional Abierto (UTN) |
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Universidad Tecnológica Nacional |
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Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional |
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gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar |
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12.559606 |