IMS Compliant Ontological Learner Model for Adaptive E-Learning Environments

Abstract

It has been proven that adopting the “one size fits one” approach has better learning outcomes than the “one size fits all” one. A customized learning experience is attainable with the use of learner models, the main source of variability, in adaptive educational hypermedia systems or any intelligent learning environment. While such a model includes a large number of characteristics which can be difficult to incorporate and use, several standards that were developed to overcome these complexities. In this paper, the proposed work intents to improve learner’s model representation to meet the requirements and needs of adaptation. We took IMS-LIP, IMS-ACCLIP and IMS-RDCEO standards into consideration and incorporated their characteristics to our proposed learner model so that it conforms to international standards. Moreover, the suggested learner model takes advantage of the semantic web technologies that offer a better data organization, indexing and management and ensures the reusability, the interoperability and the extensibility of this model. Furthermore, due to the use of ontologies, the metadata about a learner can be used by a wide range of personalization techniques to provide more accurate customization.