Towards Adaptive E-Learning using Decision Support Systems

Abstract

The significance of personalization towards learnersâ?? needs has recently been agreed by all web-based instructional researchers. This study presents a novel ontol-ogy semantic-based approach to design an e-learning Deci-sion Support System (DSS) which includes major adaptive features. The ontologically modelled learner, learning do-main and content are separately designed to support per-sonalized adaptive learning. The proposed system utilise captured learnersâ?? models during the registration phase to determine learnersâ?? characteristics. The system also tracks learnerâ??s activities and tests during the learning process. Test results are analysed according to the Item Response Theory in order to calculate learnerâ??s abilities. The learner model is updated based on the results of test and learnerâ??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 â??Fraction topicâ? of the mathematics domain. Experimental test results indicated that the pro-posed system improved learning effectiveness and learnerâ??s satisfaction, particularly in its adaptive capabilities.