Identify Learning Style Using a Fuzzy System Based on Network Behaviors

In the e-learning environment, there are various learners with varying learning characteristics, including prior knowledge, experience, motivation, and learning objective, and each learner is responsible for their own learning. In such environments, there would not be an effective and efficient learning, unless adaptive approaches are considered. Thus, the ultimate goal of adaptive learning is delivering courses, programs, and educational resources tailored to the learning characteristics of individual learner. The most important step in adaptive learning is to identify and select appropriate indicator based on which adapt learning would be performed. Researchers have selected a variety of indicators in their studies, and due to the fact that learning style model is one of the most significant indicators in recognizing individual differences in the learning process in order to adapt to the e-learning environment, in this study, "Kolb’s learning style model" was considered as the selected indicator. However, given the fact that there is uncertainty in determining this indicator, it is very complex, thus it cannot accurately described and defined. In this research, fuzzy sets theory was used to model the uncertainty and inherent ambiguity in the learning style model by creating a set of rules which was able to increase the precision of identifying dimensions of the learning style. To achieve this, a fuzzy system utilizing learners’ network behaviors in the environment to identifying and modeling their learning style was designed. In this system, the precision of the measurement in identifying individuals’ learning style compared to the results of the questionnaire that was previously completed by learners is 89.07%, showing that this method has increased the precision compared to other methods.