Transformers para previsão de desempenho acadêmico no ensino Fundamental e Médio
DOI:
https://doi.org/10.5753/rbie.2024.3661Palavras-chave:
Desempenho acadêmico, Transformers, EDMResumo
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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Copyright (c) 2024 Lorran Santos Rodrigues, Marcos Santos, Carlos Francisco Simoes Gomes, Ricardo Choren, Ronaldo Goldschmidt, Saulo Barbará
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