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VARK Learning Style Classification Using Decision Tree with Physiological Signals


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.

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Mae Fah Luang University supports this study, and the author remains grateful for the materials, grants, and tools provided to him by the noble university. Also, will like to thank Dr. Pradorn Sureephong, Mrs. Baratu, and Dr. Irshad Ansari for their endless advice during this paper. Finally, the kind staff and students of Huay Ploo Pittaya primary school Chiang Rai and the university student of Mae Fah Luang University for a cooperative data collection experience.

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Correspondence to Punnarumol Temdee.

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Dutsinma, L.I.F., Temdee, P. VARK Learning Style Classification Using Decision Tree with Physiological Signals. Wireless Pers Commun (2020).

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  • VARK model
  • Physiological signal
  • Learning style
  • Blood pressure
  • Heart rate
  • Decision tree