Mineração de Dados Educacionais na Predição da Evasão Estudantil: Tendências, Oportunidades e Desafios

Authors

  • Miriam Pizzatto Colpo Programa de Pós-Graduação em Computação Universidade Federal de Pelotas (UFPel) Diretoria de Tecnologia da Informação Instituto Federal Farroupilha (IFFar) https://orcid.org/0000-0002-6477-3227
  • Tiago Thompsen Primo Programa de Pós-Graduação em Computação Universidade Federal de Pelotas (UFPel) https://orcid.org/0000-0003-3870-097X
  • Marilton Sanchotene de Aguiar Programa de Pós-Graduação em Computação Universidade Federal de Pelotas (UFPel) https://orcid.org/0000-0002-5247-6022
  • Cristian Cechinel Programa de Pós-Graduação em Computação Universidade Federal de Pelotas (UFPel) Centro de Ciências, Tecnologias e Saúde Universidade Federal de Santa Catarina (UFSC) https://orcid.org/0000-0001-6384-409X

DOI:

https://doi.org/10.5753/rbie.2024.3559

Keywords:

Evasão Estudantil, Predição de Evasão, Mineração de Dados Educacionais, Revisão Sistemática da Literatura

Abstract

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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Published

2024-05-20

Como Citar

COLPO, M. P.; THOMPSEN PRIMO, T.; AGUIAR, M. S. de; CECHINEL, C. Mineração de Dados Educacionais na Predição da Evasão Estudantil: Tendências, Oportunidades e Desafios. Revista Brasileira de Informática na Educação, [S. l.], v. 32, p. 220–256, 2024. DOI: 10.5753/rbie.2024.3559. Disponível em: https://journals-sol.sbc.org.br/index.php/rbie/article/view/3559. Acesso em: 23 jul. 2024.

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