STUDENT ATTITUDE IDENTIFICATION TOWARDS E-LEARNING COURSE BASED ON BIOSENSOR INFORMATION

Providing attractive and interesting e-learning course materials as well as delivering normal lectures in class are important issues in an e-learning society. Knowing about this issue, first, it is essential to investigate student interest about topics and types of e-learning courses delivered by measuring the students‘ responses. Thus, this study engages in evaluation of students‘ responses initiated by two distinct e-learning materials; one is characterized by interactive material and the other by non-interactive material based on biosignals—i.e., Blood Pressure (BP), Galvanic Skin Response (GSR), and Electrocardiogram (ECG) signals. Use of a multilinear principal component analysis (MPCA) was proposed in this study to extract the representative features of student responses. Finally, the classification was performed by a support vector machine (SVM) to discriminate between responses of students.