Abstract:
The traditional classroom learning approach has been replaced with an online learning system in the educational field due to the COVID-19 pandemic. During this pandemic, a lot of students suffered from depression, academic stress, or anxiety in e-learning because they could not adapt to the new norm. Thus, a system that can recognize the students' emotions is beneficial to evaluate the students' emotions during e-learning. Most of the current student emotion recognition systems do not focus on classifying academic emotions. Hence, this project proposed a system that can classify multi-class student emotions based on facial expressions such as "bored", "confused", "engaged", and "frustrated using a deep learning approach. The dataset used in this project is Dataset for Affective States in E-Environments (DAiSEE). Based on the results obtained in the research experiment, it was reported that the CNN model had the highest accuracy (88.92%) compared to LSTM (79.17%) and ConvLSTM (85.33%). This project is expected to develop a real-time system where the student emotions can be evaluated during online learning, and the result of student emotion classification will be shown.
