Academic emotions in educational games

Academic emotions refer to facial expressions that students display along with their academic performance in a learning process. Understanding students’ academic emotions in online learning environments. By checking students’ academic emotions, teachers can provide the most emotion-appropriate teaching material to improve their performance and students’ motivation. The results can also be subsequently applied to adaptive learning. To solve this problem, a method of continuous recognition of facial emotional patterns based on deep learning to analyze academic emotions is proposed in this study. This method combines the convolutional neural network (CNN) and the long-term memory network (LSTM) for deep learning to recognize and analyze students’ continuous academic facial emotional patterns and thus recognize academic emotions. Through this method, the educational game can understand students’ learning progress quickly and accurately, and provide students with appropriate teaching materials to improve their academic performance and motivation.