Detection of Affective States of the Students in a Blended Learning Environment Comprising of Smartphones

Abstract

ABSTRACT Consideration of affective states of students in the teaching-learning process is very important. However, it is cumbersome for a teacher to detect the states in real-time, especially in a large classroom. Although literature contains many techniques for systematic detection of the affective states, most of them either are expensive both in terms of computational and economical aspects or do not fit in the classroom environment. Assuming a blended learning environment comprising smartphones, we have proposed a process model to detect the affective states of the students. Empirical studies affirm that the model is able to detect the states with high accuracy. Apart from training and testing the model with affective data collected through a novel game designed by us, the model has been additionally validated with EEG signals of thirty six participants.