Educational DataMining: A Study of the Factors That Cause School Dropout in Higher Education Institutions in Brazil

Autores

  • Marília N. C. A. Lima Departamento de engenharia da Computação – Universidade de Pernambuco Recife – Pernambuco – Brasil
  • Roberta A. de A. Fagundes Departamento de engenharia da Computação – Universidade de Pernambuco Recife – Pernambuco – Brasil

DOI:

https://doi.org/10.22456/1679-1916.105950

Palavras-chave:

Educational DataMining, Mineração de Dados Educacionais

Resumo

Context:In Brazil, there is a high dropout rate in higher education institutions. Thus, it is clear that evasion is a frequent problem and that it is necessary to analyze the factors that cause it to enable solutions that can mitigate/ reduce this problem. Objetive: (1)perform a correlation analysis (Pearson and Spearman) of the educational factores of the School Census; (2)propose school dropout prediction models taking into account educational and economic factors using regression methods (linear, robust, ridge, lasso, clusterwise regression). Methodology: used the phases of the CRISP-DM methodology. Results: the factors related to not allowing financial assistance are related to as evasion, namely: food, permanence, didactic material, transportation. There are also factors related to the study period. The regression robust and linear regression show fewer errors. Conclusion: the correlations used present the selection of factors in a similar way, thus following a linear distribution. This study can help to create more investment in public policies, as it ratifies factors are related to this dropout problem.

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Publicado

2020-07-31

Como Citar

N. C. A. LIMA, M.; A. DE A. FAGUNDES, R. Educational DataMining: A Study of the Factors That Cause School Dropout in Higher Education Institutions in Brazil. Revista Novas Tecnologias na Educação, Porto Alegre, v. 18, n. 1, 2020. DOI: 10.22456/1679-1916.105950. Disponível em: https://seer.ufrgs.br/index.php/renote/article/view/105950. Acesso em: 29 mar. 2024.

Edição

Seção

Mineração de dados educacionais