Using Decision Tree C4.5 Algorithm to Predict VARK Learning Styles

Abstract

This research has an objective to classify VARK learning styles of learners by using Decision Tree C4.5 algorithm. Data concerning learning styles of learners were collected via a questionnaire responded by 1,205 students. The collected data, which included sex, age, major, year of education, GPA, previous educational background, and VARK learning styles of learners, were then classified by using Decision Tree C4.5 algorithm run on Weka software with 10-fold cross validation technique. The study results revealed that the learners’ VARK learning style classification based on Decision Tree C4.5 algorithm yielded an accuracy at 83.40%, and a total of 108 rules were obtained. It can be concluded that Decision Tree C4.5 algorithm can be used to classify VARK learning styles of learners efficiently.