Abstract—The objective of this research is to study the results
of learning style classification and compare the efficiency of
David Kolb's learning style classification of students in the
Department of Computer Information System, Rajamangala
University of Technology Lanna (Tak Campus). Thereby, the
algorithms used in this research include J48, NBTree and
NaiveBayes. The 10-fold Cross Validation was used to create
and test the model, and the data was analyzed by the WAKA
program. The data was collected by means of questionnaire
from 502 students in the 1st semester of academic year 2013. The
results show that the efficiency of classification by means of J48
technique had the highest value of Correct at 85.65% and it
could be applied to develop David Kolb's learning style, which
was correct and precise to classify the learning style.
Index Terms—Comparative, data mining technique, David Kolb's experiential learning style, classification.
The authors are with the Department of Computer Education, Faculty of Technical Education, King Mongkut's University of Technology North Bangkok, Bangsue, Thailand (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
Cite: Phanthipha Petchboonmee, Duangkamol Phonak, and Monchai Tiantong, "A Comparative Data Mining Technique for David Kolb's Experiential Learning Style Classification," International Journal of Information and Education Technology vol. 5, no. 9, pp. 672-675, 2015.