Sistemas de recomendação para o AVA moodle: uma abordagem baseada em filtragem colaborativa e na taxonomia revisada de bloom

• Ano de defesa: 2020
• Autor(a) principal: Santos, Tarsio Gomides Lima dos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Informática; Programa de Pós-Graduação em Informática; UFPB
• Palavras-chave em Português: Educação adaptativa; Filtragem colaborativa; Sistemas de aprendizagem adaptativos; Sistemas de recomendação; Taxonomia de bloom revisada; Adaptive education; Adaptive learning systems; Collaborative filtering; Recommender systems; Revised bloom taxonomy; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/18269

In the current scenario, distance and even face-to-face education has been using virtual learning environments as a tool. However, generally these tools do not adapt according to the student, as is the case with Moodle. In order to solve the problem for the so-called Moodle evaluation questions, this work proposes and develops a REST API to generate recommendations using Bloom’s Revised Taxonomy, collaborative user-based filtering and also an efficiency module. The recommender used data from an LC-EAD course at UFPB and the results obtained were recommendations for a target user. Therefore, a use of this service can bring benefits to students, who use suggested activities according to their profile and cognitive level, and can help in the learning and and consequently have a reduction in dropout.