An approach to detect and analyze continuous emotions in a real-time online classroom

In this work, a computational framework for detecting and analyzing the continuous students' emotions in a real-time online classroom was proposed. A video conferencing system suitable for the online classroom was developed using WebRTC (Web Real-Time Communication). Following this, a CNN (convolutional neural network) model was proposed to predict the emotional valence and arousal of the students. The AffectNet dataset containing labeled emotions of two lakhs ninety-one thousand six hundred fifty-one images were used to develop and test the proposed model. The predicted valence and arousal values were used to determine students' emotional states. Five emotional states — neutral, bored, confused, hopeful, and curious — suitable for online classroom teaching were selected. It was found that our proposed CNN-based model outperforms the current baseline model.