Investigação da Evasão Estudantil por meio da Mineração de Dados e Aprendizagem de Máquina: Um Mapeamento Sistemático
DOI:
https://doi.org/10.5753/rbie.2024.3466Palavras-chave:
Predição, Classificação, Evasão do estudante, Aprendizagem de máquina, Mineração de dadosResumo
A evasão dos alunos nas escolas e universidades é um problema recorrente na educação, tanto é danoso para o aluno em termos de aprendizagem, como gera prejuízos financeiros para as instituições, sejam públicas ou privadas. Estudos que utilizam técnicas de mineração de dados (MD) e aprendizado de máquina (AM) para investigar problemas na educação estão em ascensão. A evasão estudantil é um desses problemas. Por meio dessas técnicas, é possível identificar padrões em indivíduos ou grupos que possam vir a abandonar os estudos. Este artigo tem como objetivo mapear sistematicamente artigos no estado da arte sobre a aplicação de DM e ML na classificação de dados em estudos sobre evasão escolar. A busca foi realizada em 5 bases de dados bibliográficas, ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect e Web of Science, e retornou um total de 336 estudos primários. Após a aplicação dos critérios de exclusão e inclusão, restaram 71 estudos relevantes. Após a extração de dados desses estudos, identificou-se que, as experiências com estudantes do ensino superior e na modalidade presencial são as mais recorrentes nesses artigos, o ano que mais se destacou em termos de publicação foi 2020, e os algoritmos mais frequentemente utilizados para construção dos modelos de classificação são algoritmos baseados em árvores de decisão.
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