Ferramenta de recomendação híbrida de objetos de aprendizagem com predição das necessidades personalizadas de cada estudante

• Ano de defesa: 2023
• Autor(a) principal: Schrammel, Lucca Alexandre
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Ciência da Computação; UFSM; Programa de Pós-Graduação em Ciência da Computação; Centro de Tecnologia
• Palavras-chave em Português: Recomendação; Objetos de aprendizagem; Predição; Desempenho; Recommendation; Learning object; Predict; Performance; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/30895

With a large amount of data available, it is increasingly difficult to identify information that will contribute to the student's learning process. In this scenario, the present study seeks to apply a hybrid model framework to recommend learning objects based on the preferences and needs of each student. To identify students' needs, the framework carries out a performance prediction process to identify any future difficulties that a student may present. In this way, the recommendations generated precede a possible need for more specific learning objects for the needs of each student. Recommendations are generated from collaborative, content, and knowledge-based filtering methods. With the framework implemented, the validation of the algorithms made a significant contribution to the recommendations generated, considering that it was evident that the framework was capable of integrating filtering methods and data prediction algorithms, for recommending learning objects. The framework generated recommendations with accuracy above 80% in all test scenarios. Furthermore, it generated a low number of recommendations, which highlights that even if several possibilities of proposals were considered, only the learning objects with the highest probability of acceptance by students were recommended.