Author(s)Yarandi, Maryam; School of Architecture, Computing and Engineering, University of East London
Jahankhani, Hossein; School of Architecture, Computing and Engineering, University of East London
Tawil, Abdel-Rahman H.; School of Architecture, Computing and Engineering, University of East London
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AbstractThe significance of personalization towards learners�?? 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 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 pro-posed system improved learning effectiveness and learner�??s satisfaction, particularly in its adaptive capabilities.