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

Abstract

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.