Discovering Latent Relationships Among Learning Style Dimensions Using Association Rule Mining

Abstract

Learning is the process of acquiring knowledge or skill. It is the process of filtering, storing and organizing information in our brains. Learners differ by the ways in which perceive, process and receive information. Based on the means of processing the information, learners are considered to possess their own style of learning. The learning process tends to be more effective if the knowledge is disseminated to the learners in their own learning styles. In order to disseminate knowledge in different ways, learners should be categorized based on their styles and they should be trained in an appropriate manner. This paper proposes a novel method to detect the relationships between different dimensions in Felder-Silverman learning style model using a data mining technique known as association rules, thereby providing a simpler way to disseminate the knowledge in the context of technology enhanced learning.