Mineração de dados educacionais para apoio à gestão acadêmica na formulação de prognóstico de perfil de aluno ingressante em cursos superiores

• Ano de defesa: 2023
• Autor(a) principal: Moura, Amanda Ferreira de
• Orientador(a): Gaspar, Marcos Antônio
• Banca de defesa: Gaspar, Marcos Antônio , Costa, Ivanir , Ohashi, Fábio Kazuo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Nove de Julho
• Programa de Pós-Graduação: Programa de Pós-Graduação em Informática e Gestão do Conhecimento
• Departamento: Informática
• País: Brasil
• Palavras-chave em Português: inteligência artificial; mineração de dados; mineração de dados educacionais; gestão acadêmica; ensino superior
• Palavras-chave em Inglês: artificial intelligence; data mining; educational data mining; academic management; university education
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: http://bibliotecatede.uninove.br/handle/tede/3236

Educational institutions are organizations that generate large amounts of data in their routine procedures. The management of these educational data is an important task to provide student performance monitoring, as well as providing prognoses for actions to be taken by the manager. Educational data mining is a branch of data mining that aims to extract knowledge from data generated in educational institutions. The objective of this research was to develop an automated educational data mining solution to support academic management in the formulation of a prognostic profile for students entering higher education courses. To achieve this objective, applied experimental research of a quantitative nature was carried out. Therefore, computational experiments were performed with the application of intelligent data mining techniques aimed at grouping (clustering) educational data from a database of postgraduate students. This solution was developed in four stages, with the respective tools, databases, operations and necessary actions being indicated in each stage. The results produced by the experiments carried out in the application of data mining proved the efficiency of the conceived solution, that is, the educational data mining solution developed in this research has the capacity to establish the prognosis of the freshman student profile that is more adherent and aligned with the available courses by the higher education institution. It should be noted that the developed solution is based on free or low-cost tools, which makes it accessible to institutions that want to implement this application, as well as create solutions based on educational data mining.