A COMPARATIVE STUDY OF DIFFERENT ONTOLOGY-BASED LEARNER MODELS FOR PERSONALIZED E-LEARNING SYSTEMS

Abstract

In recent years, a number of learning styles which are incorporated in various learning methods around the world. Learning styles are one of the foremost vital attributes of individuals, with each individual having variations in their learning styles. Adaptive e-learning plays a significant role in curating learning materials for individual users to enable flexible learning. Nowadays, adaptive e-learning systems with radically different learning styles are following the current web-based learning environments which are more innovative. This article focuses mainly on the content analysis of the new Personalized E-Learning research using the semantic web. Most of the prior research focuses on the result of academic accomplishment, learning satisfaction and outputs, whereas other research has offered the architecture/framework or proposed a model for personalized e-learning. This work analyses the present trends and gaps in the literature and discusses the potential future research.