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.
Highlights
The success of web technologies has led to a growing attention on e-learning activities
To evaluate learner’s satisfaction with the adaptive learning system, a questionnaire was designed to measure whether or not the proposed learning system with adaptive features satisfies the real requirements of most learners
This study presents a novel ontology-based approach to design an elearning Decision support system which recommends adaptive learning paths personalized to particular learners
Summary
The success of web technologies has led to a growing attention on e-learning activities. Most current e-learning systems provide static web-based learning so that learners access the same learning content through the internet, irrespective of individual learner’s profile. These learners may have very different learning backgrounds, knowledge levels, learning styles, and abilities. There are no systems that suggest to learners the appropriate learning content, activities and sequences based on learner’s characteristics and analysis of previous learning steps. We present an innovative adaptive e-learning decision support system capable of suggesting learners’ appropriate learning contents, activities and sequences by analysing user’s profile model in an adaptive engine. We conclude the paper with directions for future research
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