Um modelo para apoiar a gestão educacional das IES com descoberta de conhecimento baseado no processo de autoavaliação institucional (SINAES)

• Ano de defesa: 2012
• Autor(a) principal: Oliveira, Leopoldo Ramos de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Alagoas; Brasil; Programa de Pós-Graduação em Modelagem Computacional de Conhecimento; UFAL
• Palavras-chave em Português: Mineração de dados (Computação); Sistemas de suporte de decisão; Data mining (computing); Decision support systems; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://www.repositorio.ufal.br/handle/riufal/2116

The Institutional Self-evaluation is a matter of great current relevance in the context of higher education in Brazil. The problem investigated in this research: How to help managers IES Detecting weaknesses in promoting self-assessment and corrective actions in the short, medium and long term. In attempting to answer this question the ultimate goal of this work is to develop a model to enable the systematic application of the policy of the National Assessment of Higher Education (SINAES). In developing the theoretical framework of this research sought to contextualize the policy defined by SINAES to implement self-evaluation and show how the combined resources of Statistical Process Knowledge Discovery (KDD) for collection, analysis and discovery of new knowledge can contribute to the process IES.A of evaluative methodology used was the construction of assessment tools for learners and managers to generate statistics and through this database to perform these mining and obtain the relevance of the attributes of a database by the software Rapid miner. The next step was the elaboration of the relevance of the attributes of the base by a specialist in institutional assessment based on official documents IES.Os results of the three assessments (students, managers and specialist) were consolidated in a graphic for analysis and identification of differences of problems to generate a model of recommendation to minimize the weaknesses detected. After that a seminar was held with the leaders of the college of IES to assess impacts in managing against the new knowledge obtained through data mining and new knowledge they considered important as a tool to support decision making of IES.