Aplicação de máquinas de vetores de suporte na identificação de perfis de alunos de acordo com características da teoria das inteligências múltiplas
Arquivos
Data
2016-05-31
Autores
Lázaro, Diego Henrique Emygdio [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Estadual Paulista (Unesp)
Resumo
Nesta dissertação foi desenvolvido um mecanismo de classificação capaz de identificar o perfil de um aluno de acordo com características da teoria das inteligências múltiplas, baseado em Support Vector Machines (SVMs, sigla em inglês para Máquinas de Vetores de Suporte), métodos de agrupamento e balanceamento de classes. O objetivo dessa classificação consiste em permitir que os tutores responsáveis por gerar o material para aulas em ferramentas de apoio ao ensino à distância possam utilizar este método de classificação para direcionar o conteúdo ao aluno de forma a explorar sua inteligência múltipla predominante. Para realização dos experimentos, duas SVMs foram criadas, utilizando o método de classificação baseado em k problemas binários, que reduzem o problema de múltiplas classes a um conjunto de problemas binários. Os resultados obtidos durante as fases de treino e teste das SVMs foram apresentados em percentuais por meio de um algoritmo de agrupamento particionado. Esses percentuais ajudam a interpretar a classificação do perfil de acordo com as inteligências predominantes. Além disso, com o uso de métodos de balanceamento de classes, obteve-se melhora no desempenho do classificador, assim, aumentando a eficácia do mecanismo, pois, suas taxas de incorreções foram baixas.
In this work, it was developed a mechanism in order to classify students’ profiles according to the Theory of Multiple Intelligences, based on Support Vector Machines (SVMs), cluster methods and classes balancing. By using these classifications, tutors, who prepare materials for classes in specific tools for distance education purposes, are able to suggest contents for students so that they are able to explore their predominant multiple intelligence. To perform these experiments, SVMs were created by using classification methods based on binary problems that reduce multiple classes problems into a set of binary problems. The results generated during the training and the SVM test stages were presented in percentages by using partitioning clustering algorithm. These percentages are helpful for analysis of profiles classifications according to multiple intelligences. Besides that, by using classes balancing methods, it was possible to obtain improvements on the classifier performance and, consequently, the mechanism efficiency was increased as well, considering the fact that inaccuracy rates were low.
In this work, it was developed a mechanism in order to classify students’ profiles according to the Theory of Multiple Intelligences, based on Support Vector Machines (SVMs), cluster methods and classes balancing. By using these classifications, tutors, who prepare materials for classes in specific tools for distance education purposes, are able to suggest contents for students so that they are able to explore their predominant multiple intelligence. To perform these experiments, SVMs were created by using classification methods based on binary problems that reduce multiple classes problems into a set of binary problems. The results generated during the training and the SVM test stages were presented in percentages by using partitioning clustering algorithm. These percentages are helpful for analysis of profiles classifications according to multiple intelligences. Besides that, by using classes balancing methods, it was possible to obtain improvements on the classifier performance and, consequently, the mechanism efficiency was increased as well, considering the fact that inaccuracy rates were low.
Descrição
Palavras-chave
Máquina de vetores de suporte, Métodos de classificação, Métodos de agrupamento particionado, Balanceamento de classes, Aprendizado de máquina, Support vector machines, Classification methods, Partitioning clustering method, Classes balancing, Machine learning