Utilização de modelos de classificação para mineração de dados relacionados à aprendizagem de matemática e ao perfil de professores do ensino fundamental

• Autor(a) principal: Fonseca, Stella Oggioni da
• Data de Publicação: 2014
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UERJ
• Texto Completo: http://www.bdtd.uerj.br/handle/1/13790
• topic: Data mining; Classification models; Learning of mathematics; Mineração de dados; Modelos de classificação; Aprendizagem de matemática; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

Classification models were applied in this work in order to mine data related to elementary school teachers profiles and students' mathematics learning. More specifically, teacher characteristics which in uence positively and negatively on the Mathematics tests performance of the students in the 9th grade of elementary education in Rio de Janeiro State were addressed. The data used to extract this information are provided by the National Institute of Studies and Educational Research Anisio Teixeira (INEP), which evaluates the Brazilian educational system at various levels and types of education, including Elementary Education. The Knowledge Discovery in Databases (KDD) process was applied comprising the steps of preparation, mining and post processing of data. The patterns were extracted from the classification models generated by decision tree, rule induction and Bayesian classifiers, whose algorithms are implemented in software Weka (Waikato Environment for Knowledge Analysis). In addition, group methods were used as well as a methodology for making uniformly distributed classes in order to improve the accuracy of the models obtained. The results showed important factors that contribute to the learning of mathematics and aspects that negatively compromise the performance of students. The extracted results can provide to educators and public policies makers the support for analysis and decision making.