Cognitive style, hypermedia navigation and learning
Computers & Education
What affect student cognitive style in the development of hypermedia learning system?
Computers & Education
Evaluating Bayesian networks' precision for detecting students' learning styles
Computers & Education
ACM Transactions on Computer-Human Interaction (TOCHI)
A learning style classification mechanism for e-learning
Computers & Education
The Top Ten Algorithms in Data Mining
The Top Ten Algorithms in Data Mining
Learning Styles and Behavioral Differences in Web-Based Learning Settings
ICALT '09 Proceedings of the 2009 Ninth IEEE International Conference on Advanced Learning Technologies
User models for adaptive hypermedia and adaptive educational systems
The adaptive web
Learners' navigation behavior identification based on trace analysis
User Modeling and User-Adapted Interaction
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Several education hypermedia systems (EHS) use learning styles (LS) as a criterion for adaptation and tracking. To measure these styles, EHS are generally based on the questionnaires provided by the used LS model, and that learners should answer before the first session. This approach has a major drawback: learners' LS are defined only once. To overcome this limitation, recent researches are currently being done on the detection of LS based on learner's interaction traces. Their general criticism is related to the use of a specific environment, and therefore specific traces and indicators. Therefore, we aim to identify the learner's LS automatically, based on simple navigation traces. In this paper, we present experimental results of identification of sequential/global and active/reflective LS, for 45 students, using supervised classification. The findings provide initial evidence that LS can be automatically identified based on learners' navigation behaviours.