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Modelling academic dropout in computer engineering using arti cial neural networks

Publication . Camelo, Diogo; Santos, João C.C.; Martins, Maria Prudência; Gouveia, Paulo D.F.

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.

2021Comunicação em conferência

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Entidade financiadora

Fundação para a Ciência e a Tecnologia

Programa de financiamento

6817 - DCRRNI ID

Número da atribuição

UIDB/04131/2020