Transformers para previsão de desempenho acadêmico no ensino Fundamental e Médio

Authors

DOI:

https://doi.org/10.5753/rbie.2024.3661

Keywords:

Desempenho acadêmico, Transformers, EDM

Abstract

A previsão de desempenho acadêmico apresenta um potencial grande no trabalho pró-ativo das escolas na identificação de alunos em risco de reprovação. de duas redes distintas, permitindo a comparação entre diferentes anos escolares, anos letivos e redes de ensino. Contrastaram-se os desempenhos de modelos baseados na arquitetura Transformers com modelos mais estabelecidos, como o XGBoost e um modelo de rede neural mais simples. Os resultados mostraram que os Transformers tiveram um desempenho interessante na tarefa de previsão de desempenho acadêmico, especialmente com um número maior de avaliações. No entanto, o XGBoost conseguiu alcançar um alto desempenho mais cedo no período letivo. Uma vantagem dos Transformers é sua flexibilidade no treinamento, permitindo lidar com conjuntos de dados semi-estruturados sem a necessidade de pré-processamento. Em última análise, esta pesquisa contribui para o desenvolvimento de métodos que podem identificar precocemente alunos em risco de reprovação, oferecendo a oportunidade de intervenção e apoio adequados. Isso pode ter um impacto positivo na formação dos alunos e na sociedade como um todo, mitigando prejuízos e promovendo a educação de qualidade.

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Published

2024-04-13

Como Citar

RODRIGUES, L. S.; SANTOS, M.; GOMES, C. F. S.; CHOREN, R.; GOLDSCHMIDT, R.; BARBARÁ, S. Transformers para previsão de desempenho acadêmico no ensino Fundamental e Médio. Revista Brasileira de Informática na Educação, [S. l.], v. 32, p. 213–241, 2024. DOI: 10.5753/rbie.2024.3661. Disponível em: https://journals-sol.sbc.org.br/index.php/rbie/article/view/3661. Acesso em: 23 jul. 2024.

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