Identificação de alunos com tendência à evasão nos cursos de graduação a distância por meio de mineração de dados educacionais
Ano de defesa: | 2018 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Santa Maria
Centro de Educação |
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Tecnologias Educacionais em Rede
|
Departamento: |
Educação
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://repositorio.ufsm.br/handle/1/14229 |
Resumo: | The evasion is one of the great constant challenges in the educational context in all the modalities. On the other hand, the increase in the amount of data generated by systems such as Virtual Environment for Teaching Learning (AVEA in Portuguese) has been highlighting the area of Educational Data Mining (EDM). This fact can be verified through several studies that have been developed in this area with the purpose of predicting students with tendency to evasion. However, the vast majority detain on the technical aspects pertaining to mining. In this perspective, the work aims provide to educational manager’s strategic data, through the application of EDM, so that they can evaluate, reflect and generate actions to mitigate the evasion process. In order to do so, a bibliographical research was carried out with the intention of comprehending in detail themes related to distance education, evasion as well as its causes. Soon afterwards a Systematic Literature Review (SLR) was carried out aiming to know the technological approaches applied in the prediction of evasion. In the sequence, the aspects related to the discovery of knowledge in databases, related works and methodological aspects were approached. The development was carried out through two experiments covering three undergraduate courses in which data were used of student interactions in AVEA and data from the institution's academic management system. As results, good indexes of correct answers were obtained, thus allowing the conclusion that the proposed approach is feasible for the detection of students with a tendency to evasion in undergraduate courses. Lastly, an application for mobile devices was developed for the availability of mining data. In the assessment of the application, along with the coordinators of the evaluated courses, it was found that the data discovered are of paramount importance for management because it facilitates the follow-up of students with a tendency to evasion to carry out interventions with them in order to avoid their dropping out. The dissertation presented is part of the research line Development of Educational Softwares, of the Post-Graduate Program in Educational Technologies in Network, and generated as products the text presented here, as well as the EDM strategy created and the application developed. |
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2018-09-06T18:43:27Z2018-09-06T18:43:27Z2018-06-12http://repositorio.ufsm.br/handle/1/14229The evasion is one of the great constant challenges in the educational context in all the modalities. On the other hand, the increase in the amount of data generated by systems such as Virtual Environment for Teaching Learning (AVEA in Portuguese) has been highlighting the area of Educational Data Mining (EDM). This fact can be verified through several studies that have been developed in this area with the purpose of predicting students with tendency to evasion. However, the vast majority detain on the technical aspects pertaining to mining. In this perspective, the work aims provide to educational manager’s strategic data, through the application of EDM, so that they can evaluate, reflect and generate actions to mitigate the evasion process. In order to do so, a bibliographical research was carried out with the intention of comprehending in detail themes related to distance education, evasion as well as its causes. Soon afterwards a Systematic Literature Review (SLR) was carried out aiming to know the technological approaches applied in the prediction of evasion. In the sequence, the aspects related to the discovery of knowledge in databases, related works and methodological aspects were approached. The development was carried out through two experiments covering three undergraduate courses in which data were used of student interactions in AVEA and data from the institution's academic management system. As results, good indexes of correct answers were obtained, thus allowing the conclusion that the proposed approach is feasible for the detection of students with a tendency to evasion in undergraduate courses. Lastly, an application for mobile devices was developed for the availability of mining data. In the assessment of the application, along with the coordinators of the evaluated courses, it was found that the data discovered are of paramount importance for management because it facilitates the follow-up of students with a tendency to evasion to carry out interventions with them in order to avoid their dropping out. The dissertation presented is part of the research line Development of Educational Softwares, of the Post-Graduate Program in Educational Technologies in Network, and generated as products the text presented here, as well as the EDM strategy created and the application developed.A evasão é um dos grandes desafios constantes no contexto educacional em todas as modalidades. Por outro lado, o crescente aumento na quantidade de dados gerados por sistemas como Ambientes Virtuais de Ensino e Aprendizagem (AVEA) vem trazendo destaque para a área de Mineração de Dados Educacionais (MDE). Esse fato pode ser constatado por meio de diversos trabalhos que vêm sendo desenvolvidos nesta área com o propósito de prever alunos com tendência à evasão. No entanto, a grande maioria se detém nos aspectos técnicos pertinentes à mineração. Nessa perspectiva, o trabalho visa propiciar aos gestores educacionais dados estratégicos por meio de aplicação de MDE, para que eles possam avaliar, refletir e gerar ações para mitigar o processo de evasão. Para tanto, foi realizada uma pesquisa bibliográfica com a intenção de compreender em detalhes temas relacionados à EaD, evasão, bem como suas causas. Logo em seguida foi realizada uma Revisão Sistemática de Literatura (RSL) visando conhecer as abordagens tecnológicas aplicadas na previsão de evasão. Na sequência, foram abordados os aspectos relacionados a descoberta de conhecimento em base de dados, trabalhos correlatos e aspectos metodológicos. O desenvolvimento foi realizado por meio de dois experimentos abrangendo três cursos de graduação em que foram empregados dados de interações dos alunos no AVEA e dados do sistema de gestão acadêmico da instituição. Como resultados foram obtidos bons índices de acertos, permitindo assim, a conclusão de que a abordagem proposta é factível para detecção de alunos com tendência à evasão em cursos de graduação EaD. Por fim, para a disponibilização dos dados oriundos da mineração foi desenvolvido um aplicativo para dispositivos móveis. Na avaliação do aplicativo, junto aos coordenadores dos cursos avaliados, constatou-se que os dados descobertos são de suma importância para gestão, pois facilita o acompanhamento dos alunos com tendência à evasão, para realização de intervenções junto a eles a fim de evitar a sua desistência. A dissertação de mestrado apresentada está inserida na linha de pesquisa Desenvolvimento de Softwares Educacionais, do Programa de Pós-Graduação em Tecnologias Educacionais em Rede, e gerou como produtos o próprio texto aqui apresentado, bem como a estratégia de MDE criada e o aplicativo desenvolvido.porUniversidade Federal de Santa MariaCentro de EducaçãoPrograma de Pós-Graduação em Tecnologias Educacionais em RedeUFSMBrasilEducaçãoAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessMineração de dados educacionaisPrevisão de evasão na EaDGestão Educacional na EaDEducational data miningPrediction of evasion in distance educationEducational management in distance educationCNPQ::CIENCIAS HUMANAS::EDUCACAOIdentificação de alunos com tendência à evasão nos cursos de graduação a distância por meio de mineração de dados educacionaisIdentification of students with evasion trend in the distance graduation courses by means of educational data mininginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisBernardi, Gilianehttp://lattes.cnpq.br/8988734339185408Kantorski, Gustavo Zaninihttp://lattes.cnpq.br/0721839644753258Pertile, Solange de Lurdeshttp://lattes.cnpq.br/5597581688504821Falkembach, Gilse Antoninha Morgentalhttp://lattes.cnpq.br/5167203367542704http://lattes.cnpq.br/6357832506369650Schmitt, Jáder Adiél700800000006600168635cb-5373-46c8-ac1e-45c44c10c2eba1f1d17e-fd74-45b6-8ae7-b3405a8c40788a6182d6-5f93-4b3e-8d98-2578f0665ddee79b5958-2509-46e0-bbcc-4fe1bcc03c6eb8861c30-3c0e-4b61-a3ee-4a2bc8d79c9areponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALDIS_PPGTER_2018_SCHMITT_JADER.pdfDIS_PPGTER_2018_SCHMITT_JADER.pdfDissertação de Mestradoapplication/pdf3238409http://repositorio.ufsm.br/bitstream/1/14229/1/DIS_PPGTER_2018_SCHMITT_JADER.pdf877c5b0f058ffb847f5781f66786fe2fMD51LICENSElicense.txtlicense.txttext/plain; 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dc.title.por.fl_str_mv |
Identificação de alunos com tendência à evasão nos cursos de graduação a distância por meio de mineração de dados educacionais |
dc.title.alternative.eng.fl_str_mv |
Identification of students with evasion trend in the distance graduation courses by means of educational data mining |
title |
Identificação de alunos com tendência à evasão nos cursos de graduação a distância por meio de mineração de dados educacionais |
spellingShingle |
Identificação de alunos com tendência à evasão nos cursos de graduação a distância por meio de mineração de dados educacionais Schmitt, Jáder Adiél Mineração de dados educacionais Previsão de evasão na EaD Gestão Educacional na EaD Educational data mining Prediction of evasion in distance education Educational management in distance education CNPQ::CIENCIAS HUMANAS::EDUCACAO |
title_short |
Identificação de alunos com tendência à evasão nos cursos de graduação a distância por meio de mineração de dados educacionais |
title_full |
Identificação de alunos com tendência à evasão nos cursos de graduação a distância por meio de mineração de dados educacionais |
title_fullStr |
Identificação de alunos com tendência à evasão nos cursos de graduação a distância por meio de mineração de dados educacionais |
title_full_unstemmed |
Identificação de alunos com tendência à evasão nos cursos de graduação a distância por meio de mineração de dados educacionais |
title_sort |
Identificação de alunos com tendência à evasão nos cursos de graduação a distância por meio de mineração de dados educacionais |
author |
Schmitt, Jáder Adiél |
author_facet |
Schmitt, Jáder Adiél |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Bernardi, Giliane |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/8988734339185408 |
dc.contributor.advisor-co1.fl_str_mv |
Kantorski, Gustavo Zanini |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/0721839644753258 |
dc.contributor.referee1.fl_str_mv |
Pertile, Solange de Lurdes |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/5597581688504821 |
dc.contributor.referee2.fl_str_mv |
Falkembach, Gilse Antoninha Morgental |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/5167203367542704 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/6357832506369650 |
dc.contributor.author.fl_str_mv |
Schmitt, Jáder Adiél |
contributor_str_mv |
Bernardi, Giliane Kantorski, Gustavo Zanini Pertile, Solange de Lurdes Falkembach, Gilse Antoninha Morgental |
dc.subject.por.fl_str_mv |
Mineração de dados educacionais Previsão de evasão na EaD Gestão Educacional na EaD |
topic |
Mineração de dados educacionais Previsão de evasão na EaD Gestão Educacional na EaD Educational data mining Prediction of evasion in distance education Educational management in distance education CNPQ::CIENCIAS HUMANAS::EDUCACAO |
dc.subject.eng.fl_str_mv |
Educational data mining Prediction of evasion in distance education Educational management in distance education |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS HUMANAS::EDUCACAO |
description |
The evasion is one of the great constant challenges in the educational context in all the modalities. On the other hand, the increase in the amount of data generated by systems such as Virtual Environment for Teaching Learning (AVEA in Portuguese) has been highlighting the area of Educational Data Mining (EDM). This fact can be verified through several studies that have been developed in this area with the purpose of predicting students with tendency to evasion. However, the vast majority detain on the technical aspects pertaining to mining. In this perspective, the work aims provide to educational manager’s strategic data, through the application of EDM, so that they can evaluate, reflect and generate actions to mitigate the evasion process. In order to do so, a bibliographical research was carried out with the intention of comprehending in detail themes related to distance education, evasion as well as its causes. Soon afterwards a Systematic Literature Review (SLR) was carried out aiming to know the technological approaches applied in the prediction of evasion. In the sequence, the aspects related to the discovery of knowledge in databases, related works and methodological aspects were approached. The development was carried out through two experiments covering three undergraduate courses in which data were used of student interactions in AVEA and data from the institution's academic management system. As results, good indexes of correct answers were obtained, thus allowing the conclusion that the proposed approach is feasible for the detection of students with a tendency to evasion in undergraduate courses. Lastly, an application for mobile devices was developed for the availability of mining data. In the assessment of the application, along with the coordinators of the evaluated courses, it was found that the data discovered are of paramount importance for management because it facilitates the follow-up of students with a tendency to evasion to carry out interventions with them in order to avoid their dropping out. The dissertation presented is part of the research line Development of Educational Softwares, of the Post-Graduate Program in Educational Technologies in Network, and generated as products the text presented here, as well as the EDM strategy created and the application developed. |
publishDate |
2018 |
dc.date.accessioned.fl_str_mv |
2018-09-06T18:43:27Z |
dc.date.available.fl_str_mv |
2018-09-06T18:43:27Z |
dc.date.issued.fl_str_mv |
2018-06-12 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/masterThesis |
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masterThesis |
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http://repositorio.ufsm.br/handle/1/14229 |
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http://repositorio.ufsm.br/handle/1/14229 |
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por |
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por |
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700800000006 |
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600 |
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Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Educação |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Tecnologias Educacionais em Rede |
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UFSM |
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Brasil |
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Educação |
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Universidade Federal de Santa Maria Centro de Educação |
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