Clustering learning objects in the IEEE-LOM standard considering learning styles to support customized recommendation systems in educational environments

Adapting an educational environment to students considering its features and individuals is a necessity due to the large amount of learning objects in the repositories. Thus, organizing learning objects so that they can be efficiently recommended is a real need. In this way, this work presents a proposal for clustering learning objects in repositories considering the learning styles they support, in order to facilitate the content recommendation process based on students' learning styles. For this, a comparative analysis of clustering techniques was performed, and the most efficient was used in the implementation of this approach. Experiments were conducted and promising results were obtained.