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
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;
Journal_Title :
Learning Technologies, IEEE Transactions on
DOI :
10.1109/TLT.2009.11