%0 Journal Article
	%A Stephen Akuma and  Timothy Ndera
	%D 2021
	%J International Journal of Educational and Pedagogical Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 170, 2021
	%T Adaptive Educational Hypermedia System for High School Students Based on Learning Styles
	%U https://publications.waset.org/pdf/10011860
	%V 170
	%X Information seekers get “lost in hyperspace” due to the voluminous documents updated daily on the internet. Adaptive Hypermedia Systems (AHS) are used to direct learners to their target goals. One of the most common AHS designed to help information seekers to overcome the problem of information overload is the Adaptive Education Hypermedia System (AEHS). However, this paper focuses on AEHS that adopts the learning preference of high school students and deliver learning content according to this preference throughout their learning experience. The research developed a prototype system for predicting students’ learning preference from the Visual, Aural, Read-Write and Kinesthetic (VARK) learning style model and adopting the learning content suitable to their preference. The predicting strength of several classifiers was compared and we found Support Vector Machine (SVM) to be more accurate in predicting learning style based on users’ preferences.

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