Recommendation of Educational Content to Improve Student Performance: An Approach based on Learning Styles

Virtual learning environments are a powerful tool in the teaching-learning process and can provide a variety of utilization data that can be explored by data mining techniques to improve the understanding of student behavior and performance. By using Learning Analytics, it is possible to identify potential problems, such as student dropout or failures before they become irreversible, and indicate corrective actions to be taken by teachers. In this context, content recommendation plays a prominent role since choosing the proper content for a certain audience may motivate them to become more involved in the learning process. However, in distance education settings nowadays, teachers do not know their students, thus it becomes difficult to select the content most suitable to their needs. In this paper, we propose a content recommendation architecture that takes into account the learning profile of students enrolled in an LMS to customize content recommendations to each learner's style. A profile assessment tool, based on the Honey-Mumford learning style taxonomy was implemented and some preliminary data obtained. We devised a recommendation scheme that considers the euclidean distance between students' learning styles when suggesting content to be studied. Our preliminary results indicate this approach may be beneficial to improve the teaching-learning process and student performance as a whole.