An adaptive e-learning Decision support system

Abstract

The success of web technologies has led to a growing attention on e-learning activities. However, 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. The `one size fit all' in an e-Learning systems is clearly a typical problem. To overcome this limitation and increase effective learning, adaptive and personalised learning is currently an active research area. This study presents a novel ontology-based approach to design an e-learning Decision support system which includes major adaptive features. The ontological learner, domain and content model are separately designed to support adaptive learning. The proposed system utilise the captured learner's model during the registration phase for determining learners' characteristics. The system also tracks learners' 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 result of activities, results of test and learner's ability for use in the adaptation process. The updated learner model is used to generate different learning paths for individual learners. In this study, the proposed system is implemented on the “Fraction topic” of mathematics domain. After the system was tested, the results indicate that the proposed system improved learning effectiveness and learner satisfaction, particularly in its adaptive capabilities.