Advanced prediction of learner's profile based on Felder-Silverman learning styles using web usage mining approach and fuzzy c-means algorithm

Abstract

Automatic prediction of learner's profile is an important requirement for personalised e-learning. This can be provided based on the learning behaviours of the learners. In this work, the learning behaviour is captured using the web usage mining technique, preprocessed and converted into the XML format based on sequences of accessing contents. These sequences are mapped to the eight categories of Felder-Silverman learning style model (FSLSM) using fuzzy c-means (FCM) algorithm. A gravitational search based back propagation neural network (GSBPNN) algorithm is used for the prediction of learning styles of a new learner. In this algorithm, the neural network approach is modified by calculating the weights using gravitational search algorithm. The accuracy of the prediction model is compared with the basic back propagation neural network (BPNN) algorithm. The result shows that the captured data is labelled as per FSLSM and the accuracy is more in GSBPNN as compare to BPNN.