This study aims to evaluate student physiological response towards the e-learning materials. The experiments were conducted by introducing two contracting e-learning materials, i.e., the one is characterized as interactive material and the other is non-interactive one. During the experiment physiological sensor, i.e., galvanic skin response (GSR) sensor was attached to the participant. Furthermore, GSR data were extracted by feature generator, LDA. The purpose of feature extraction is to find preferably small number of features that are particularly distinguishing or informative for the classification process and that are invariant to irrelevant transformations of the data. Finally, several classifiers were performed discriminating student attitude towards e-learning course materials response using GSR sensor data. The results showed that discriminant analysis (DA) and support vector machine (SVM) give high accuracy rate, while the k-nearest neighbor (KNN) give moderate accuracy rate.