Classification of learning style based on Kolb's Learning Style Inventory and EEG using cluster analysis approach

Abstract

The learning style eloquence had garnered increase influence in education field. Simultaneously educationist also taking great interest on blending neuroscience research in their work, hence the Brain-based Learning and Brain-based Education are among the terms which had been geared into limelight recently. By taking into account, that learners' learning style is crucial in succeeding the learning process and the importance of brain as “The Organ of Learning”, we believe that the brain's fingerprint should be put under the research radar in order to discover the reciprocal relation between both entities. In this research, we used electroencephalography (EEG) technology to record a brain signal of our participants (N=41) at resting baseline state of Eyes-Open and Eyes-Closed. Prior to that, we deployed the Kolb's Learning Style Inventory to conduct a learning style classification onto them. By using a cluster analysis approach which examined the brain signals Centroids, the learning style are successfully classified into a 4 unique clusters which put a way forward towards other related classification work in future.