Dissertações em Computação Aplicada (Mestrado) - PPCA/NDAE/Tucuruí
URI Permanente para esta coleçãohttps://repositorio.ufpa.br/handle/2011/9399
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Navegando Dissertações em Computação Aplicada (Mestrado) - PPCA/NDAE/Tucuruí por Assunto "Aprendizado do computador"
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Item Acesso aberto (Open Access) Um modelo bayesiano que auxilia na identificação de alunos com dificuldades na aprendizagem de programação de computadores(Universidade Federal do Pará, 2019-05-10) CAMPOS, Willys do Socorro Almeida de; TEIXEIRA, Otávio Noura; http://lattes.cnpq.br/5784356232477760; https://orcid.org/0000-0002-7860-5996; REIS, Rodrigo Quites; http://lattes.cnpq.br/9839778710074372; https://orcid.org/0000-0002-3657-4175The learning of computer programming subjects has always brought challenges to any class of computer students, due to the difficulties linked to its use. This scenario greatly contributes to the demotivation of the student and, thus, the increased dropout of courses. Generally, teachers who work in these disciplines have signs about which students will become good programmers, but it is difficult to detect which need help in the learning process. This article proposes the use of a Bayesian model that helps in the identification of students with difficulties in the computer programming subjects. The research uses a mixed approach, quantitative and qualitative. An experiment, with an informal character, was carried out with students who were studying programming subjects. This set of students was presented to five specialist teachers in order to identify which ones would need help with the learning of programming. The same set was presented to the Bayesian model. The results showed that the model can help in the identification of students who present difficulties, with the potential to contribute to the learning process.Item Acesso aberto (Open Access) Predição de comportamento de usuários oriundos do marketing digital por meio de redes neurais artificiais e aprendizado supervisionado(Universidade Federal do Pará, 2019) ALVES, Vitor Pinheiro; TEIXEIRA, Otávio Noura; http://lattes.cnpq.br/5784356232477760; https://orcid.org/0000-0002-7860-5996Success in attracting customers using marketing techniques creates a billionare problem wich is one of the most difficult that is selecting among the many prospects, which are more likely to become a customer. This work uses artificial neural networks to analyze the dataset generated from digital marketig techniques and classify which prospects have a greater chance to become a customer and which ones should be discarded. The Neural Network scores approximately 70% of cases among 3,541 records processed.