Towards Adaptive E-Learning using Decision Support Systems
The significance of personalization towards learners’ needs has recently been agreed by all web-based instructional researchers. This study presents a novel ontology semantic-based approach to design an e-learning Decision Support System (DSS) which includes major adaptive
features. The ontologically modelled learner, learning domain and content are separately designed to support personalized adaptive learning. The proposed system utilise captured learners’ models during the registration phase to determine learners’ characteristics. The system also tracks learner’s activities and tests during the learning process. Test results are analysed according to the Item Response Theory in order to calculate learner’s abilities. The learner model is updated based on the results of test and learner’s abilities for use in the adaptation process. Updated learner models are used to generate different learning paths for individual learners. In this study, the proposed system is implemented on the “Fraction topic” of the mathematics domain. Experimental test results indicated that the proposed system improved learning effectiveness and learner’s satisfaction, particularly in its adaptive capabilities.
International Journal of Emerging Technologies in Learning
