Prediction of school dropout risk group using neural network
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/227468 |
Resumo: | Dropping out of school is one of the most complex and crucial problems in education, causing social, economic, political, academic and financial losses. In order to contribute to solve the situation, this paper presents the potentials of an intelligent, robust and innovative system, developed for the prediction of risk groups of student dropout, using a Fuzzy-ARTMAP Neural Network, one of the techniques of artificial intelligence, with possibility of continued learning. This study was conducted under the Federal Institute of Education, Science and Technology of Mato Grosso, with students of the Colleges of Technology in Automation and Industrial Control, Control Works, Internet Systems, Computer Networks and Executive Secretary. The results showed that the proposed system is satisfactory, with global accuracy superior to 76% and significant degree of reliability, making possible the early identification, even in the first term of the course, the group of students likely to drop out. © 2013 Polish Information Processing Society. |
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Repositório Institucional da UNESP |
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Prediction of school dropout risk group using neural networkDropping out of school is one of the most complex and crucial problems in education, causing social, economic, political, academic and financial losses. In order to contribute to solve the situation, this paper presents the potentials of an intelligent, robust and innovative system, developed for the prediction of risk groups of student dropout, using a Fuzzy-ARTMAP Neural Network, one of the techniques of artificial intelligence, with possibility of continued learning. This study was conducted under the Federal Institute of Education, Science and Technology of Mato Grosso, with students of the Colleges of Technology in Automation and Industrial Control, Control Works, Internet Systems, Computer Networks and Executive Secretary. The results showed that the proposed system is satisfactory, with global accuracy superior to 76% and significant degree of reliability, making possible the early identification, even in the first term of the course, the group of students likely to drop out. © 2013 Polish Information Processing Society.Department of Electro-Electronic Federal Institute of Mato Grosso, Rua Zulmira Canavarros, no. 95, CEP: 78000-000, Cuiabá, MTDepartment of Informatics Federal Institute of Mato Grosso, Rua Zulmira Canavarros, no. 95, CEP: 78000-000, Cuiabá, MTLaboratory of Intelligent Systems Electrical Engineering Department Campus of Ilha Solteira UNESP, Av. Brasil 56, PO Box 31, CEP: 153 85-000, Ilha Solteira, SPLaboratory of Intelligent Systems Electrical Engineering Department Campus of Ilha Solteira UNESP, Av. Brasil 56, PO Box 31, CEP: 153 85-000, Ilha Solteira, SPFederal Institute of Mato GrossoUniversidade Estadual Paulista (UNESP)Martinho, Valquiria R. C.Nunes, ClodoaldoMinussi, Carlos Roberto [UNESP]2022-04-29T07:13:24Z2022-04-29T07:13:24Z2013-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject111-1142013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013, p. 111-114.http://hdl.handle.net/11449/2274682-s2.0-84892496898Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013info:eu-repo/semantics/openAccess2024-07-04T19:11:49Zoai:repositorio.unesp.br:11449/227468Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:20:12.970152Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Prediction of school dropout risk group using neural network |
title |
Prediction of school dropout risk group using neural network |
spellingShingle |
Prediction of school dropout risk group using neural network Martinho, Valquiria R. C. |
title_short |
Prediction of school dropout risk group using neural network |
title_full |
Prediction of school dropout risk group using neural network |
title_fullStr |
Prediction of school dropout risk group using neural network |
title_full_unstemmed |
Prediction of school dropout risk group using neural network |
title_sort |
Prediction of school dropout risk group using neural network |
author |
Martinho, Valquiria R. C. |
author_facet |
Martinho, Valquiria R. C. Nunes, Clodoaldo Minussi, Carlos Roberto [UNESP] |
author_role |
author |
author2 |
Nunes, Clodoaldo Minussi, Carlos Roberto [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Federal Institute of Mato Grosso Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Martinho, Valquiria R. C. Nunes, Clodoaldo Minussi, Carlos Roberto [UNESP] |
description |
Dropping out of school is one of the most complex and crucial problems in education, causing social, economic, political, academic and financial losses. In order to contribute to solve the situation, this paper presents the potentials of an intelligent, robust and innovative system, developed for the prediction of risk groups of student dropout, using a Fuzzy-ARTMAP Neural Network, one of the techniques of artificial intelligence, with possibility of continued learning. This study was conducted under the Federal Institute of Education, Science and Technology of Mato Grosso, with students of the Colleges of Technology in Automation and Industrial Control, Control Works, Internet Systems, Computer Networks and Executive Secretary. The results showed that the proposed system is satisfactory, with global accuracy superior to 76% and significant degree of reliability, making possible the early identification, even in the first term of the course, the group of students likely to drop out. © 2013 Polish Information Processing Society. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-12-01 2022-04-29T07:13:24Z 2022-04-29T07:13:24Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013, p. 111-114. http://hdl.handle.net/11449/227468 2-s2.0-84892496898 |
identifier_str_mv |
2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013, p. 111-114. 2-s2.0-84892496898 |
url |
http://hdl.handle.net/11449/227468 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
111-114 |
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_ |
1808129310896160768 |