Detalhes bibliográficos
| Ano de defesa: |
2021 |
| Autor(a) principal: |
Oliveira, Michelle Christiane da Silva
 |
| Orientador(a): |
Brancher, Jacques Duílio
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| Banca de defesa: |
Brancher, Jacques Duílio,
Ferreira, Deller James,
Barros , Rodolfo Miranda de |
| Tipo de documento: |
Dissertação
|
| 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
|
| 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/11767
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Resumo: |
Through the computerized systems of universities, it is possible to have access to a lot of student data, from demographic, socioeconomic, admission, egress and performance data. Transforming these data into useful information for the academic society, both management and students, is a challenge. One of the ways to identify the impact factors on the academic performance of higher-level students is Educational Data Mining. Based on the results, it is possible to make academic, managerial and administrative decisions based on evidence. This study aims, through the use of Educational Data Mining techniques, to identify which factors impact the performance of higher education students in computing courses, having as a case study, the computing courses of the Instituto de Informática da Universidade Federal de Goiás, with a database of 2.501 incoming students between the years 2009 to 2019. Through Systematic Literature Review, the main algorithms used for educational data mining (analysis and prediction) were identified. The data base went through the data mining process (selection, pre-processing, data transformation, datamining), where a data set was initially defined, which allowed the generation of graphical views of various aspects of the profile of the data students. This dataset was then adjusted to be applied to the algorithms identified in the SLR, where it was possible to define a data model. With the application of these algorithms to the data model, it was possible to identify the algorithms that had the best performance (accuracy). And also analyze, through feature importance techniques, such as SHAP and correlation maps between Heatmaps attributes, which factors had the greatest impact on student performance. |
