A Case Study of Applying the Classification Task for Students Performance Prediction

Detalhes bibliográficos
Autor(a) principal: Guerra, M. S.
Data de Publicação: 2018
Outros Autores: Neto, H. Asseiss, Oliveira, S. A. [UNESP]
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/TLA.2018.8291470
http://hdl.handle.net/11449/221018
Resumo: In this study the application of data mining in an academic database is presented, aiming the identification of the reasons of student dropouts by preventing failures in Programming Language class from Internet Systems course of Federal Institute of Mato Grosso do Sul (IFMS). The classification task is used, as well as decision tree technique and J4.8 algorithm, wich is run with three different options and, in each option pruned and unpruned decision trees are generated. The results show that the most realistic test is Cross-validation, with a success rate of 75.8% in classification, and that it is possible to prevent student failure in specific cases and as a consequence, to lower the number of student dropouts by failure.
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spelling A Case Study of Applying the Classification Task for Students Performance Predictionacademic databaseclassification taskData miningIn this study the application of data mining in an academic database is presented, aiming the identification of the reasons of student dropouts by preventing failures in Programming Language class from Internet Systems course of Federal Institute of Mato Grosso do Sul (IFMS). The classification task is used, as well as decision tree technique and J4.8 algorithm, wich is run with three different options and, in each option pruned and unpruned decision trees are generated. The results show that the most realistic test is Cross-validation, with a success rate of 75.8% in classification, and that it is possible to prevent student failure in specific cases and as a consequence, to lower the number of student dropouts by failure.Instituto Federal de Mato Grosso Do sulUniversidade Estadual Paulista Júlio de Mesquita FilhoUniversidade Estadual Paulista Júlio de Mesquita FilhoInstituto Federal de Mato Grosso Do sulUniversidade Estadual Paulista (UNESP)Guerra, M. S.Neto, H. AsseissOliveira, S. A. [UNESP]2022-04-28T19:08:43Z2022-04-28T19:08:43Z2018-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article172-177http://dx.doi.org/10.1109/TLA.2018.8291470IEEE Latin America Transactions, v. 16, n. 1, p. 172-177, 2018.1548-0992http://hdl.handle.net/11449/22101810.1109/TLA.2018.82914702-s2.0-85042303231Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporIEEE Latin America Transactionsinfo:eu-repo/semantics/openAccess2022-04-28T19:08:43Zoai:repositorio.unesp.br:11449/221018Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:33:02.908074Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv A Case Study of Applying the Classification Task for Students Performance Prediction
title A Case Study of Applying the Classification Task for Students Performance Prediction
spellingShingle A Case Study of Applying the Classification Task for Students Performance Prediction
Guerra, M. S.
academic database
classification task
Data mining
title_short A Case Study of Applying the Classification Task for Students Performance Prediction
title_full A Case Study of Applying the Classification Task for Students Performance Prediction
title_fullStr A Case Study of Applying the Classification Task for Students Performance Prediction
title_full_unstemmed A Case Study of Applying the Classification Task for Students Performance Prediction
title_sort A Case Study of Applying the Classification Task for Students Performance Prediction
author Guerra, M. S.
author_facet Guerra, M. S.
Neto, H. Asseiss
Oliveira, S. A. [UNESP]
author_role author
author2 Neto, H. Asseiss
Oliveira, S. A. [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Instituto Federal de Mato Grosso Do sul
Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Guerra, M. S.
Neto, H. Asseiss
Oliveira, S. A. [UNESP]
dc.subject.por.fl_str_mv academic database
classification task
Data mining
topic academic database
classification task
Data mining
description In this study the application of data mining in an academic database is presented, aiming the identification of the reasons of student dropouts by preventing failures in Programming Language class from Internet Systems course of Federal Institute of Mato Grosso do Sul (IFMS). The classification task is used, as well as decision tree technique and J4.8 algorithm, wich is run with three different options and, in each option pruned and unpruned decision trees are generated. The results show that the most realistic test is Cross-validation, with a success rate of 75.8% in classification, and that it is possible to prevent student failure in specific cases and as a consequence, to lower the number of student dropouts by failure.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
2022-04-28T19:08:43Z
2022-04-28T19:08:43Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/TLA.2018.8291470
IEEE Latin America Transactions, v. 16, n. 1, p. 172-177, 2018.
1548-0992
http://hdl.handle.net/11449/221018
10.1109/TLA.2018.8291470
2-s2.0-85042303231
url http://dx.doi.org/10.1109/TLA.2018.8291470
http://hdl.handle.net/11449/221018
identifier_str_mv IEEE Latin America Transactions, v. 16, n. 1, p. 172-177, 2018.
1548-0992
10.1109/TLA.2018.8291470
2-s2.0-85042303231
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv IEEE Latin America Transactions
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 172-177
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808129084226535424

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