Rough Set Approach for Classifying Student’s Learning Style : A Comparative Analysis

The student’s interaction in e-learning which were captured in the log file can be intelligently examined to diagnose students’ learning style. This is important since a student’s behaviour while learning online is among the significant parameters for adaptation in e-learning system. Currently, Felder Silverman (FS) is a common learning style model that is frequently used by many researchers for personalizing learning materials based on learning style. There are four learning style dimensions in FS model and most researches need to develop four classifiers to map the characteristics into the dimensions. Such approach is quite tedious in terms of data pre-processing and it also time consuming when it comes to classification. Therefore, this study propose mapping the students’ characteristics into Integrated Felder Silverman (IFS) learning styles, by combining the four learning dimensions in FS model into sixteen learning styles. However, the most crucial problem for IFS model is the difficulties in identifying the significant pattern for the classifier that has high dimension and large number of classes. In this study, fifteen features have been identified as the granule learning features for IFS. Comparative analysis of the Rough Set performance between IFS classifier and the conventional four classifiers shows that the proposed IFS gives higher classification accuracy and rule coverage in identifying student’s learning style. However, Rough Sets generate very large rules for IFS compared to the conventional FS four classifiers.