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Advisor(s)
Abstract(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.
Description
Keywords
Educational data mining Artificial neural network Academic dropout Predictive model
Citation
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
Publisher
Springer International Publishing