Learning styles and cognitive traits - Their relationship and its benefits in web-based educational systems

  • Authors:

  • Sabine Graf;Tzu-Chien Liu; Kinshuk;Nian-Shing Chen;Stephen J. H. Yang

  • Affiliations:

  • School of Computing and Information Systems, Athabasca University, Canada;Graduate Institute of Learning and Instruction, National Central University, Jhongli, Taiwan;School of Computing and Information Systems, Athabasca University, Canada;Department of Information Management, National Sun Yat-sen University, Taiwan;Department of Computer Science & Information Engineering, National Central University, Jhongli, Taiwan

  • Venue:
  • Computers in Human Behavior
  • Year:
  • 2009

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Abstract

Different learners have different needs; they differ, for example, in their learning goals, their prior knowledge, their learning styles, and their cognitive abilities. Adaptive web-based educational systems aim to cater individual learners by customizing courses to suit their needs. In this paper, we investigate the benefits of incorporating learning styles and cognitive traits in web-based educational systems. Adaptivity aspects based on cognitive traits and learning styles enrich each other, enabling systems to provide learners with courses which fit their needs more accurately. Furthermore, consideration of learning styles and cognitive traits can contribute to more accurate student modelling. In this paper, the relationship between learning styles, in particular the Felder-Silverman learning style model (FSLSM), and working memory capacity, a cognitive trait, is investigated. For adaptive educational systems that consider either only learning styles or only cognitive traits, the additional information can be used to provide more holistic adaptivity. For systems that already incorporate both learning styles and cognitive traits, the relationship can be used to improve the detection process of both by including the additional information of learning style into the detection process of cognitive traits and vice versa. This leads to a more reliable student model.