Mineração de Dados Educacionais na Predição da Evasão Estudantil: Tendências, Oportunidades e Desafios
DOI:
https://doi.org/10.5753/rbie.2024.3559Keywords:
Evasão Estudantil, Predição de Evasão, Mineração de Dados Educacionais, Revisão Sistemática da LiteraturaAbstract
Atualmente, enfrentamos prejuízos acadêmicos, sociais e econômicos associados à evasão estudantil. Vários estudos têm aplicado técnicas de mineração de dados a conjuntos de dados educacionais para entender os perfis de evasão e reconhecer alunos em risco. Para identificar características contextuais (níveis, modalidades e sistemas educacionais), técnicas (tarefas, categorias de algoritmos e ferramentas) e de dados (tipos, cobertura e volume) relacionadas a esses trabalhos, realizou-se uma revisão sistemática da literatura, considerando a evasão institucional e de curso. A partir de repositórios reconhecidos internacionalmente, artigos foram selecionados e demonstraram, entre outras características, uma maior exploração de dados acadêmicos, demográficos e econômicos de estudantes de graduação, a partir de técnicas de classificação de comitês de árvores de decisão. Além de não ter sido identificado nenhum estudo de países subdesenvolvidos entre os selecionados, foram observadas carências na aplicação dos modelos preditivos e na disponibilização de suas previsões aos gestores acadêmicos, o que sugere uma subutilização dos esforços e do potencial da maioria desses estudos na prática educacional.
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Copyright (c) 2024 Miriam Pizzatto Colpo, Tiago Thompsen Primo, Marilton Sanchotene de Aguiar, Cristian Cechinel
Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
