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"It is argued that the analysis of the learner’s generated log files during interactions with a learning environment is necessary to produce interpretative views of their activities. The analysis of these log files, or traces, provides ""knowledge"" about the activity we call indicators. Our work is related to this research field. We are particularly interested in automatically identifying learners’ learning styles from learning indicators. This concept, used in several Educational Hypermedia Systems (EHS) as a criterion for adaptation and tracking, belongs to a set of behaviors and strategies in how to manage and organize information. In this paper, we validate our approach of auto-detection of student's learning styles based on their navigation behavior using machine- learning classifiers."
1. LS Classification results. Through Table1, we notice that for the information processing LS’ attribute, all the classifiers learn the active style better than the reflective one, except for Neural Networks. This is due to the stronger presence of active learners than reflective ones. Concerning the understanding LS’ attribute, the global style was better learned by all classifiers than the sequential one for the same reason as the first attribute, where neural networks give the best total results. We observe that the total results are all over 50%. Thus, we can strengthen the hypothesis of the possibility to deduce information about learner preferences using simple navigational information that we can apply on any learning environment on the Web, without having to consider evaluation scores or the communication tool traces that allow us to give more details. We plan to continue the development of other indicators to improve the LS’ identification results.
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