VARK Learning Style Classification Using Decision Tree with Physiological Signals

Abstract

Learning style is deemed crucial for different types of age groups. It is essential, especially for individual learning achievement. Learning is a part of cognitive processes affecting the human central nervous system, which can be monitored by using the physiological signals. In this study, physiological signals thus are proposed as key attributes for the classification of learning styles to avoid biased data from completing the questionnaire and promote the real-time response in the classroom environment. More specifically, heart rate and blood pressure signals are chosen for this study. Following the VARK model, the physiological signals of learners are classified with the decision tree into four different types, including visual, aural, read and write, and kinesthetic learners. There are 40 primary school children and 30 university students involved in the whole study. The results show that the proposed factors obtain 85% and 90% classification accuracy for children and university students, respectively. Both heart rate and blood pressure are thus reasonably impacted as the classification attributes.