Um Sistema de recomendação com filtragem híbrida de conteúdos para ambientes virtuais de aprendizagem como instrumento de suporte a alunos e acompanhamento de turmas numerosas

Detalhes bibliográficos
Ano de defesa: 2014
Autor(a) principal: Pequeno, Paulo André Lima
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Não Informado pela instituição
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://www.repositorio.ufc.br/handle/riufc/10848
Resumo: The provision of educational sources on the Web through specific portals or by public libraries has given democratic spaces to both students and teachers to support their educational routine. However, it is always a challenge to make all that diversity of resources useful to each individual having into consideration their needs. This dissertation seeks to contribute providing students and teachers with a computational environment to help in the learning process. This solution connected to a Learning Virtual Environment and an Exercise Virtual Environment allows students to have an automatic tutorial support, which has references and content targeted to their learning level. This solution allows to the teachers not only support their didactic work with the students, but also it permits to view the status of each student against curricular elements that should be addressed in the teacher’s discipline. Such approach can help the teacher in making adjustments and improvements to the course. ESignifica, a recommendation system was developed according to the filtering hybrid techniques, that add a content and a collaborative filter as well. The developed solution was tested with a student group from the Calculus subject that belonged to the Electrical Engineering course from the Federal University of Ceará – UFC, academic years 2012, 2013 and 2014. The Recommendation System developed and the experimental results achieved are presented in this dissertation.