Interpretable Machine Learning Techniques in School Dropout Prediction: a review Técnicas de Aprendizado de Máquina Interpretáveis na Predição de Evasão Escolar: uma revisão
Tipo
Artigo de revisão
Data de publicação
2024
Periódico
RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
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0
Autores
Camargos R.C.
Silveira I.F.
Silveira I.F.
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Resumo
© 2024, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.School dropout is an issue that has impacted educational institutions and been the subject of research in the field of Artificial Intelligence due to its complex nature and its association with multiple factors. The reasons leading students to dropout are varied and multifaceted and understanding them can help adopt measures to prevent their consequences. Machine learning techniques have been applied with the intent of both identifying the potential causes of dropout and mitigating them through monitoring students’ academic performance. This review seeks to go beyond listing studies that utilize these techniques in the context of school dropout; its primary goal is to identify predictive techniques that are explainable to members of the school community without prior knowledge in Computer Science or related fields.