Detalhes bibliográficos
Ano de defesa: |
2021 |
Autor(a) principal: |
Oliveira, João Lucas dos Santos
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Orientador(a): |
Brancher, Jacques Duílio
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Banca de defesa: |
Brancher, Jacques Duílio,
Silv, Nádia Félix Felipe da,
Barros, Rodolfo Miranda de |
Tipo de documento: |
Dissertação
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Tipo de acesso: |
Acesso aberto |
Idioma: |
por |
Instituição de defesa: |
Universidade Federal de Goiás
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Programa de Pós-Graduação: |
Programa de Pós-graduação em Ciência da Computação (INF)
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Departamento: |
Instituto de Informática - INF (RG)
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País: |
Brasil
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Palavras-chave em Português: |
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Palavras-chave em Inglês: |
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Área do conhecimento CNPq: |
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Link de acesso: |
http://repositorio.bc.ufg.br/tede/handle/tede/11453
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Resumo: |
Student evasion and retention is a recurring problem in all areas of education. In Area ssuch as Educational Data Mining (MDE) have been used to mitigate such problems. In particular, the area of Graph-based Educational Data Mining (G-EDM) uses unconventional data mining techniques to represent student behavior. This analysis of students can be done both in physical and virtual environments, through complex networks and graphs. The students’ behavior shown by the graphs can express dimensional patterns that would not be expressed by tabular and statistical analyzes. The present work investigated three different techniques of representing student history to investigate the possible causes of retention and dropout in computer courses. The results show that it is possible to identify retention problems in curriculum and that the modeling of the curriculum in the form of agraph can show patterns that would not be possible to describe in tabular representation. |