A Learner Ontology Based on Learning Style Models for Adaptive E-Learning

Abstract

Learning style models are used as indicators of individual differences of learners based on observations during learning processes. Numerous learning style models have been developed to model the individual differences of learners. Among these models, Felder-Silverman, Honey-Mumford and Kolb learning style models are the most-widely used ones in the literature. Learning style models are frequently used to provide personalization in adaptive e-learning systems. On the other hand, with the advancements on Semantic Web technologies in the last decade, ontologies have been used to represent domain knowledge and user information in the e-learning field, too. Ontological learner models have been developed and learners have been modeled based on their individual differences, usually based on their learning styles. In this regard, we examined how learning style models have been modeled with ontologies in different adaptive e-learning systems for personalization. Then, we proposed a learner modeling ontology based on three learning style models; Felder-Silverman, Honey-Mumford and Kolb; for personalized e-learning. Initial usage of the proposed learner ontology in a multi-agent based e-learning system is also discussed with current limitations and future work directions.