A comparative analysis of mining techniques for automatic detection of student's learning style

Abstract

This paper compares performance of several classifiers provided in WEKA such as Bayes, decision tree and classification rules in classifying student's learning style. The student's preferences and behavior while using e-learning system have been observed and analyzed and twenty attributes have been selected to map into Felder Silverman learning style model. There are four learning dimensions in Felder Silverman model and this research integrates the dimensions to map the student's characteristics into sixteen learning styles. A 10-fold cross validation was used to evaluate the classifiers. Among parameters being observed in the performance of the classifiers are classification accuracy, Kappa statistics, training errors and time taken to build the model. The experiment showed that the tree classifiers have high accuracy with more than 91% accuracy. The sizes of the tree and the number of leaves among the tree classifier techniques have also been observed.