Decoding Student Sentiments: Academic Emotion Prediction with CNN-ResNet

Student emotions significantly influence the learning process, yet traditional methods of monitoring emotional well-being are challenging. This research explores the potential of technology to understand and respond to student emotions in real-time. By analyzing facial expressions, body language, and physiological signals, the study aims to develop computer models capable of accurately predicting student emotions. This information can empower teachers to adapt their teaching methods to address the unique emotional needs of each student, creating a more engaging and effective learning environment. Machine learning and deep learning models, such as CNN and ResNet, are employed to analyze student data and predict emotions. The models are trained on a dataset of student facial expressions and body language to enhance accuracy and effectiveness. This research contributes to the development of innovative tools for improving student engagement and well-being in the classroom. By understanding and addressing student emotions, educators can create more personalized and supportive learning environments.