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

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4 Author(s)
Brown, E.J. ; Sch. of Comput. Sci., Univ. of Nottingham, Nottingham ; Brailsford, T.J. ; Fisher, T. ; Moore, A.

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.

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Learning Technologies, IEEE Transactions on  (Volume:2 ,  Issue: 1 )