An integrated model for learning style classification in university students using data mining techniques

Different people may have a different learning styles and it is important to provide the most suitable content and course materials for learning. However, determining the learning style may be difficult due to limited information about the learner and lack of a learner profile. The learner has to complete a questionnaire form based on educational theory in order to determine the learning style. Moreover, it is necessary to know the learner's study record related to other factors such as demographic factors. Therefore, an enhanced model to identify learning style is needed. This research aims to address the problem of identifying the learning style for a new student. A learning style classification model was proposed and ensemble classification techniques were implemented. The results showed that the proposed ensemble classification techniques performed better compared to other classification techniques used in the experiment.