Towards an adaptive model to personalise open learning environments using learning styles

Abstract

Open learning represents a new form of online learning. It is based on providing courses, learning materials for free to be taken by any interested learner. The current model of open learning has certain limitations which provide potential for improvement. One such area is personalization in learning environments. One avenue to enhance learning experience in open learning environments is giving consideration to learning principles and cognitive science. This paper aims to introduce a proposal for an adaptive model to personalize the open learning environments based on the theory of learning styles and particularly the Felder and Silverman Learning Style Model (FSLSM). This model consists of two main agents to perform its functionalities. First, the identification agent which is responsible of identifying the learners' learning styles by monitoring certain determined patterns of learners' behaviors with learning objects while the learner interact with learning materials. Second, the recommender agent which is responsible of providing an adaptable navigational support based on the identified learning styles and preferences. The paper presents a description of the model and its functionalities including the patterns that can be monitored in open learning environments to identify the learning styles and also how the adaptation support can be provided based on the identified styles. Future implementation will test and verify this proposed model.