Personalizing open learning environments through the adaptation to learning styles

Abstract

Open learning represents a new form of online learning. It is based on providing Massive Open Online Courses (MOOCs) for free to be taken by any interested learner. It has been found that the current model of open learning suffers from some limitations, and one of these limitations is the lack of personalization. It has also been found that the consideration of learning principles and cognitive science is able to enhance the learning experience in open learning environments. Therefore, this paper aims to introduce an approach to enhance open learning environments and provide personalization based on the consideration of cognitive science. The learning style theory is considered and, specifically, the Felder and Silverman model is selected to identify the learning styles and provide the required adaptation. The paper presents the patterns that can be monitored in the open learning environment to identify the learning styles, and also a description of how the adaptation support can be provided based on the identified patterns