###### Abstract  
Emotion recognition is critical to understanding students' emotional states. However, problems such as crowded classroom environments, changing light, and occlusion often affect the accuracy of recognition. This study proposes an emotion recognition algorithm specifically for classroom environments. Firstly, the study adds the self-made MCC module and the Wise-IoU loss function to make object detection in the YOLOv8 model more accurate and efficient. Compared with the native YOLOv8, it reduces the parameters by 16% and accelerates the inference speed by 20%. Secondly, in order to address the intricacies of the classroom setting and the specific requirements of the emotion recognition task, a multi-channel emotion recognition network (MultiEmoNet) has been developed. This network fuses skeletal, environmental, and facial information, and introduces a central loss function and an attention module AAM to enhance the feature extraction capability. The experimental results show that MultiEmoNet achieves a classification accuracy of 91.4% on a homemade classroom student emotion dataset, which is a 10% improvement over the single-channel classification algorithm. In addition, this study also demonstrates the dynamic changes in students' emotions in the classroom through visual analysis, which helps teachers grasp students' emotional states in real time. This paper validates the potential of multi-channel information-fusion deep learning techniques for classroom teaching analysis and provides new ideas and tools for future improvements to emotion recognition techniques.  
YOLOv8; object detection; emotion recognition; pedagogical analysis +
Footnote †: journal: Applied Sciences  
However, there are challenges to applying deep learning and emotion recognition technology in primary and secondary classrooms. Firstly, the changing positions and postures of students and teachers in classroom environments increase the difficulty of emotion recognition and place higher demands on the robustness of the techniques. Secondly, environmental disturbances, such as classroom background noise [4] and lighting intensity and distribution [5], can weaken the accuracy of emotion recognition. In addition, occlusion problems [6] prevent the full capture of students' facial expressions and body features, which in turn affects the recognition accuracy. In order to effectively address these issues, it is essential to enhance the adaptability and precision of the technique.  
Based on this, the main objectives of this study are as follows:  
* Improving deep learning-based object detection algorithms to meet the special needs of teaching scenarios for the difficulty of student object detection due to the large number of people in complex classroom environments.
* To develop a multi-channel emotion recognition network aimed at enhancing the accuracy and stability of emotion recognition, addressing the current gap in research attributed to the lack of students' facial expression features within classroom environments.
* This study will provide a visual analysis of student emotions within the classroom, offering comprehensive and accurate data for the continuous monitoring of classroom emotions.