Towards an Adaptive Approach that Combines Semantic Web Technologies and Metaheuristics to Create and Recommend Learning Objects

摘要

This work aims to present a proposal that combines semantic web and metaheuristic strategies to recommend web-based Learning Objects (LO) according to learners' preferences. The idea is to create a content recommendation process that uses existing resources from the Web to recommend LO in virtual learning environments. In this approach, knowledge level and Learning Styles are considered in the student model. Preliminary results have shown the possibility of creation and personalized recommendation using Wikipedia contents. At the end, some questions according to the recommendation process and the web content should be answered.