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- Modelling academic dropout in computer engineering using arti cial neural networksPublication . 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.
- Previsão do abandono académico numa instituição de ensino superior com recurso a data miningPublication . Martins, Maria Prudência; Migueis, Vera L.; Fonseca, D.S.B.; Gouveia, Paulo D.F.Este estudo propõe dois modelos preditivos de classificação que permitem identificar, logo no final do 1º e do 2º semestres escolares, os estudantes de licenciatura de uma instituição de ensino superior mais propensos ao abandono académico. A metodologia proposta, que combina 3 algoritmos populares de data mining, como são as random forest, as máquinas de vetores de suporte e as redes neuronais artificiais, para além de contribuir para a assertividade da previsão, permite identificar por ordem de relevância os principais fatores que prenunciam o abandono académico. Os resultados empíricos demonstram ser possível reduzir para cerca de 1/4 as 4 dezenas de potenciais preditores do abandono, e mostram serem essencialmente dois, do contexto curricular do estudante, a explicarem essa propensão. Esse conhecimento revela-se de importância primordial para que os agentes de gestão possam adotar as medidas e decisões estratégicas mais propícias à diminuição dos índices de evasão discente.
- Spinning rough disc moving in a rarefied mediumPublication . Plakhov, Alexander; Tchemisova, Tatiana; Gouveia, Paulo D.F.We study the Magnus effect: deflection of the trajectory of a spinning body moving in a gas. It is well known that in rarefied gases, the inverse Magnus effect takes place, which means that the transversal component of the force acting on the body has opposite signs in sparse and relatively dense gases. The existing works derive the inverse effect from nonelastic interaction of gas particles with the body. We propose another (complementary) mechanism of creating the transversal force owing to multiple collisions of particles in cavities of the body surface. We limit ourselves to the two-dimensional case of a rough disc moving through a zero-temperature medium on the plane, where reflections of the particles from the body are elastic and mutual interaction of the particles is neglected. We represent the force acting on the disc and the moment of this force as functionals depending on ‘shape of the roughness’, and determine the set of all admissible forces. The disc trajectory is determined for several simple cases. The study is made by means of billiard theory, Monge–Kantorovich optimal mass transport and by numerical methods