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Comparative Efficiency of Classification of VARK Learning Style Using Data Mining Techniques
This research aimed to compare efficiency of VARK learning style classification that are Bayes, Decision Tree and Rules-Based. A questionnaire was used for data collection from 900 students in bachelor degree at Chiang Mai Rajabhat University in academic year 1/2013. The data was analyzed by using WEKA software with data mining technique on 10-fold cross validation for this model showed that the Decision tree classification have high accuracy with more than 80% accuracy (Decision tree C4.5=82.78%, NBTree=81.78%). That meaned the Decision tree algorithm showed better accuracy than Rule-Based and Bayes respectively.
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