Adaptation to Learners' Learning Styles in a Multi-Agent E-Learning System

Abstract

Adaptation to learners’ learning styles can help education systems improve learning efficiency and effectiveness. This research orientation has been studied by many researchers lately, but most of the existing education systems lack adaptation in which every learner is delivered the same learning content. Moreover, many researchers concluded that it is worth applying automatic identification of learning style because of its advantages in precision and time savings. In our study, we concentrate on two main technologies to implement adaptation in education systems: semantic web and intelligent agents. Using ontology with the Semantic Web services makes it faster and more convenient to query and retrieve educational materials. Intelligent agents can provide the learners with personal assistants to carry out learning activities according to their learning styles and knowledge level. In this paper, we present a domain ontology that is suitable for adaptive e-learning environments. The ontology describes the learning objects that compose a course as well as the learners and their learning styles. We also present a multi-agent e-learning system that supports pre-defining and re-examining students’ learning styles during the course for better personalization. In the system, the learning style of each learner can be identified automatically and dynamically. We used a new literature-based method that uses learners’ behaviors on learning objects as indicators for this task. The evaluation showed a high precision in detecting learning styles and in delivering learning materials. Together with the mentioned benefits, this result indicates that our e-learning system is capable of wide use. !