Uma Análise Detalhada do Desempenho de Aprendizagem ensinando Machine Learning na Educação Básica aplicando a Teoria de Resposta ao Item
DOI:
https://doi.org/10.5753/rbie.2023.3442Palavras-chave:
Avaliação da aprendizagem, Machine Learning, Teoria de Resposta ao Item, TRI, Educação BásicaResumo
A atual inserção de Machine Learning (ML) no dia-a-dia demonstra a importância de introduzir o ensino de conceitos de ML desde a Educação Básica. Acompanhando esta tendência surge a necessidade de avaliar essa aprendizagem. Neste artigo apresentamos o projeto, desenvolvimento e implementação de um modelo de avaliação da aprendizagem de ML, com destaque para avaliação da validade e da confiabilidade da rubrica resultante. Essa rubrica visa avaliar a aprendizagem pelo desempenho do aluno com base nos resultados da aprendizagem da aplicação de conceitos de ML por alunos dos anos finais do Ensino Fundamental e do Ensino Médio. Adotando a Teoria de Resposta ao Item apresentamos uma proposta preliminar da construção de uma escala para o nível de aprendizagem dos estudantes. Os resultados da análise detalhada mostram que foi possível calibrar os parâmetros da Teoria de Resposta ao Item com índices satisfatórios de confiabilidade e validade, o que demonstra o potencial de utilização da rubrica de modo a auxiliar tanto alunos quanto pesquisadores e professores a promover o desenvolvimento do ensino de ML na Educação Básica.
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Copyright (c) 2023 Marcelo Fernando Rauber, Christiane Gresse von Wangenheim, Adriano Ferreti Borgatto, Ramon Mayor Martins
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