Proposing a novel approach for classification and sequencing of Learning Objects in E-learning systems based on learning style

Abstract

Abstract

Learning is a cognitive activity which differs from person to person and hence needs personalization. When learning is performed online, the system needs to understand various traits of learner and deliver Learning Objects (LO) suitable for them to achieve personalization. Many research works have been developing personalization strategies based on various learner traits such as knowledge level, learner characteristics and preferences. However, there are quite few works which consider the relationship between the learner traits and the attributes of LOs that are defined by major standards. This paper focuses on personalization strategy based on Felder and Silverman learning model and proposes a novel approach for classifying and sequencing learning objects according to the learning styles adhering to IEEE LOM metadata standard. The learning styles of the learners are identified and a fuzzy classification scheme maps the IEEE LOM Metadata to the identified learning styles. A pilot study on the research work is performed and the evaluation of the system has given an encouraging result.