Análise associativa: identificação de padrões de associação entre o perfil socioeconômico dos alunos do ensino básico e os resultados nas provas de matemática

• Ano de defesa: 2014
• Autor(a) principal: Aloquio, Lyvia
• Orientador(a): Namen, Anderson Amendoeira
• Banca de defesa: Platt, Gustavo Mendes , Tamariz, Annabell Del Real
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade do Estado do Rio de Janeiro
• Programa de Pós-Graduação: Programa de Pós-Graduação em Modelagem Computacional
• Departamento: Centro de Tecnologia e Ciências::Instituto Politécnico
• País: BR
• Palavras-chave em Português: Mineração de dados; Regras de associação; Algoritmo Apriori; Aprendizagem de matemática
• Palavras-chave em Inglês: Data mining; Association rules; Apriori Algorithm; Learning of Mathematics
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::MATEMATICA::MATEMATICA APLICADA
• Link de acesso: http://www.bdtd.uerj.br/handle/1/13791

Nowadays, most of the transactions made by companies and organizations is stored in databases that can be explored by researchers in order to obtain useful information to aid decision making. Due to the large volume involved, the extraction and analysis of data is not a simple task. The general process of converting raw data into useful information is called Knowledge Discovery in Databases (KDD). One step in this process is the Data Mining, which involves the application of algorithms and statistical techniques to exploit information contained implicitly in large databases. Many areas use the KDD process to facilitate the recognition of patterns or models on their bases of information. This work presents a practical application of KDD process using the database of students in the 9th grade of elementary education in the State of Rio de Janeiro, available in INEP site, with the aim of finding interesting patterns between the socioeconomic profile of the student and his/her performance obtained in Mathematics. The tool called Weka was used and the Apriori algorithm was applied to extracting association rules. This study revealed, for example, that students who have been reproved once tend to get a lower score on the math test, as well as students who had never been disapproved have had superior performance. Other factors like student future perspectives, ethnic group, parent's schooling, satisfaction in mathematics studying, and the frequency of access to Internet also affect positively or negatively the students learning. An analysis related to the schools infrastructure was made, with the conclusion that patterns do not change regardless of the student studying in good or bad infrastructure schools. The results obtained can be used to trace the students profiles which have a better or a worse performance in mathematics and to the development of public policies in education, aimed at elementary education.