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Publicação
Modelling academic dropout in computer engineering using arti cial neural networks
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Orientador(es)
Resumo(s)
School dropout in higher education is an academic, economic, political and social problem, which has a great impact and is difficult to resolve. In order to mitigate this problem, this paper proposes a predictive model of classification, based on artificial neural networks, which allows the prediction, at the end of the first school year, of the propensity that the computer engineering students of a polytechnic institute in the interior of the country have for dropout. A differentiating aspect of this study is that it considers the classifications obtained in the course units of the first academic year as potential predictors of dropout. A new approach in the process of selecting the factors that foreshadow the dropout allowed isolating 12 explanatory variables, which guaranteed a good predictive capacity of the model (AUC = 78.5%). These variables reveal fundamental aspects for the adoption of management strategies that may be more assertive in the combat to academic dropout.
Descrição
Palavras-chave
Educational data mining Artificial neural network Academic dropout Predictive model
Contexto Educativo
Citação
Camelo, Diogo; Santos, João; Martins, Maria Prudência; Gouveia, Paulo D.F. (2021). Modelling academic dropout in computer engineering using arti cial neural networks. In Rocha, Álvaro [et al.] (eds.) Trends and applications in information systems and technologies. Springer, Cham. 1366. p. 141-150
Editora
Springer International Publishing