A Novel Approach for Selection of Learning Objects for Personalized Delivery of E-learning Content

Personalized E-learning, as an intelligent package of technology enhanced education tends to overrule the traditional practices of static web based E-learning systems. Delivering suitable learning objects according to the learners’ knowledge, preferences and learning styles makes up the personalized E-learning. This paper proposes a novel approach for classifying and selecting learning objects for different learning styles proposed by Felder and Silverman The methodology adheres to the IEEE LOM standard and maps the IEEE LO Metadata to the identified learning styles based on rule based classification of learning objects. A pilot study on the research work is performed and evaluation of the system gives an encouraging result.