A literature-based method to automatically detect learning styles in learning management systems

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.