A Comparative Data Mining Technique for David Kolb's Experiential Learning Style Classification

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.