Full Program »
Modelling Academic Dropout In Computer Engineering Using Articial Neural Networks
School dropout in higher education is an academic, economic, political and social problem, which has a great impact and is difcult to resolve. In order to mitigate this problem, this paper proposes a predictive model of classication, based on articial neural networks, which allows the prediction, at the end of the rst school year, of the propensity that the informatics engineering students of a polytechnic institute in the interior of the country have for dropout. A diferentiating aspect of this study is that it considers the classications obtained in the course units of the rst 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.