Evaluating Bayesian networks' precision for detecting students' learning styles
Computers & Education
ICALT '08 Proceedings of the 2008 Eighth IEEE International Conference on Advanced Learning Technologies
ICALT '08 Proceedings of the 2008 Eighth IEEE International Conference on Advanced Learning Technologies
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
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Efficiency and effectiveness of learning process can be improved by adaptations to learners' learning styles. But for the time being, most of existing education systems lack of adaptation or personalization; every learner is delivered the same learning contents. Many researchers have been studying to find out an efficient way of students' learning style identification for a better personalization. In our study, we concentrate on intelligent agents that can provide the learners with personal assistants to carry out learning activities according to their learning styles and knowledge level. In this paper, we present a new literature-based method that uses learners' behaviours on learning objects as indicators for estimating students' learning styles during an online course conducted in our POLCA learning management system. The evaluation of learning style estimation and adaptation from our experiment show a high precision. Together with the mentioned benefits of learning style adaptation, this result indicates that our method is capable for wide use.