Academic performance profiles : an intelligent predictive model based on data mining

Autores
La Red Martínez, David Luis; Giovannini, Mirtha; Karanik, Marcelo
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
It is well known that academic achievement is one of the key aspects in the development of educational activities and it strongly determines the chances of success during and after a university career. It is therefore important to try and effectively monitor students’ performance in order to prevent problems from emerging, as well as, to be able to provide academic coaching when the performance is not adequate. The aforementioned problem-anticipation possibility is closely related to the ability to predict the most probable situation based on concrete information. In an academic achievement framework, it is desirable to be able to predict students’ performance considering concrete individual parameters. This work outlines the results obtained by an academicachievement prediction model based on data mining algorithms which uses socioeconomic information as well as, students’ grades. The tests were carried out at National Technological University, Resistencia Regional Faculty (UTN-FRRe), during the AED-Algoritmos y Estructuras de Datos (Algorithms and Data Structures) class throughout the 2013, 2014, 2015 and 2016 terms. The results obtained confirmed adequate behaviour of the model which has been validated for both description and prediction of academic achievement profiles.
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: Karanik, Marcelo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Peer Reviewed
Fuente
Academia Journal of Educational Research 6(12), 279-289. (2018)
Materia
academic achievement
student profiles
data mining
machine learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2020-05-29T15:06:43Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/4439
Acceder
id RIAUTN_5366b9c0aed4e5c900d6c8a97a621cfd
oai_identifier_str oai:ria.utn.edu.ar:20.500.12272/4439
network_acronym_str RIAUTN
repository_id_str a
network_name_str Repositorio Institucional Abierto (UTN)
spelling Academic performance profiles : an intelligent predictive model based on data miningLa Red Martínez, David LuisGiovannini, MirthaKaranik, Marceloacademic achievementstudent profilesdata miningmachine learningIt is well known that academic achievement is one of the key aspects in the development of educational activities and it strongly determines the chances of success during and after a university career. It is therefore important to try and effectively monitor students’ performance in order to prevent problems from emerging, as well as, to be able to provide academic coaching when the performance is not adequate. The aforementioned problem-anticipation possibility is closely related to the ability to predict the most probable situation based on concrete information. In an academic achievement framework, it is desirable to be able to predict students’ performance considering concrete individual parameters. This work outlines the results obtained by an academicachievement prediction model based on data mining algorithms which uses socioeconomic information as well as, students’ grades. The tests were carried out at National Technological University, Resistencia Regional Faculty (UTN-FRRe), during the AED-Algoritmos y Estructuras de Datos (Algorithms and Data Structures) class throughout the 2013, 2014, 2015 and 2016 terms. The results obtained confirmed adequate behaviour of the model which has been validated for both description and prediction of academic achievement profiles.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: Karanik, Marcelo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; ArgentinaPeer Reviewed2020-05-29T15:06:43Z2020-05-29T15:06:43Z2018-12-01info: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/20.500.12272/4439Academia Journal of Educational Research 6(12), 279-289. (2018)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/openAccess2020-05-29T15:06:43Zhttp://creativecommons.org/licenses/by-nc-sa/4.0/Atribución-NoComercial-CompartirIgual 4.0 InternacionalAcceso abierto2026-09-24T12:45:42Zoai:ria.utn.edu.ar:20.500.12272/4439instacron: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 12:45:43.154Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Academic performance profiles : an intelligent predictive model based on data mining
title Academic performance profiles : an intelligent predictive model based on data mining
spellingShingle Academic performance profiles : an intelligent predictive model based on data mining
La Red Martínez, David Luis
academic achievement
student profiles
data mining
machine learning
title_short Academic performance profiles : an intelligent predictive model based on data mining
title_full Academic performance profiles : an intelligent predictive model based on data mining
title_fullStr Academic performance profiles : an intelligent predictive model based on data mining
title_full_unstemmed Academic performance profiles : an intelligent predictive model based on data mining
title_sort Academic performance profiles : an intelligent predictive model based on data mining
dc.creator.none.fl_str_mv La Red Martínez, David Luis
Giovannini, Mirtha
Karanik, Marcelo
author La Red Martínez, David Luis
author_facet La Red Martínez, David Luis
Giovannini, Mirtha
Karanik, Marcelo
author_role author
author2 Giovannini, Mirtha
Karanik, Marcelo
author2_role author
author
dc.subject.none.fl_str_mv academic achievement
student profiles
data mining
machine learning
topic academic achievement
student profiles
data mining
machine learning
dc.description.none.fl_txt_mv It is well known that academic achievement is one of the key aspects in the development of educational activities and it strongly determines the chances of success during and after a university career. It is therefore important to try and effectively monitor students’ performance in order to prevent problems from emerging, as well as, to be able to provide academic coaching when the performance is not adequate. The aforementioned problem-anticipation possibility is closely related to the ability to predict the most probable situation based on concrete information. In an academic achievement framework, it is desirable to be able to predict students’ performance considering concrete individual parameters. This work outlines the results obtained by an academicachievement prediction model based on data mining algorithms which uses socioeconomic information as well as, students’ grades. The tests were carried out at National Technological University, Resistencia Regional Faculty (UTN-FRRe), during the AED-Algoritmos y Estructuras de Datos (Algorithms and Data Structures) class throughout the 2013, 2014, 2015 and 2016 terms. The results obtained confirmed adequate behaviour of the model which has been validated for both description and prediction of academic achievement profiles.
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: Karanik, Marcelo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Educativa sobre Ingeniería; Argentina
Peer Reviewed
description It is well known that academic achievement is one of the key aspects in the development of educational activities and it strongly determines the chances of success during and after a university career. It is therefore important to try and effectively monitor students’ performance in order to prevent problems from emerging, as well as, to be able to provide academic coaching when the performance is not adequate. The aforementioned problem-anticipation possibility is closely related to the ability to predict the most probable situation based on concrete information. In an academic achievement framework, it is desirable to be able to predict students’ performance considering concrete individual parameters. This work outlines the results obtained by an academicachievement prediction model based on data mining algorithms which uses socioeconomic information as well as, students’ grades. The tests were carried out at National Technological University, Resistencia Regional Faculty (UTN-FRRe), during the AED-Algoritmos y Estructuras de Datos (Algorithms and Data Structures) class throughout the 2013, 2014, 2015 and 2016 terms. The results obtained confirmed adequate behaviour of the model which has been validated for both description and prediction of academic achievement profiles.
publishDate 2018
dc.date.none.fl_str_mv 2018-12-01
2020-05-29T15:06:43Z
2020-05-29T15:06:43Z
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/20.500.12272/4439
identifier_str_mv 2315-7704
url http://hdl.handle.net/20.500.12272/4439
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
2020-05-29T15:06:43Z
http://creativecommons.org/licenses/by-nc-sa/4.0/
Atribución-NoComercial-CompartirIgual 4.0 Internacional
Acceso abierto
eu_rights_str_mv openAccess
rights_invalid_str_mv 2020-05-29T15:06:43Z
http://creativecommons.org/licenses/by-nc-sa/4.0/
Atribución-NoComercial-CompartirIgual 4.0 Internacional
Acceso abierto
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv Academia Journal of Educational Research 6(12), 279-289. (2018)
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_ 1877230907392262144
score 13.24418

Publicaciones similares