• DocumentCode
    1166641
  • Title

    Evaluating Learning Style Personalization in Adaptive Systems: Quantitative Methods and Approaches

  • Author

    Brown, Elizabeth J. ; Brailsford, Timothy J. ; Fisher, Tony ; Moore, Adam

  • Author_Institution
    Sch. of Comput. Sci., Univ. of Nottingham, Nottingham
  • Volume
    2
  • Issue
    1
  • fYear
    2009
  • Firstpage
    10
  • Lastpage
    22
  • Abstract
    It is a widely held assumption that learning style is a useful model for quantifying user characteristics for effective personalized learning. We set out to challenge this assumption by discussing the current state of the art, in relation to quantitative evaluations of such systems and also the methodologies that should be employed in such evaluations. We present two case studies that provide rigorous and quantitative evaluations of learning-style-adapted e-learning environments. We believe that the null results of both these studies indicate a limited usefulness in terms of learning styles for user modeling and suggest that alternative characteristics or techniques might provide a more beneficial experience to users.
  • Keywords
    adaptive systems; computer aided instruction; human computer interaction; adaptive systems; learning style personalization; learning-style-adapted e-learning environments; user characteristics; Data mining; Decision support systems; Probability density function; Adaptive hypermedia; Artificial Intelligence; Computer-assisted instruction; Computing Methodologies; Evaluation/methodology; Human information processing; Hypertext/Hypermedia; Information Interfaces and Representation (HCI); Information Technologies; Miscellaneous; User issues;
  • fLanguage
    English
  • Journal_Title
    Learning Technologies, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1939-1382
  • Type

    jour

  • DOI
    10.1109/TLT.2009.11
  • Filename
    4785451