## 5 Conclusions and Future Work  
This study provides an in-depth exploration of student emotion recognition and analysis in primary and secondary school classrooms, with the main goal of achieving accurate object detection and emotion recognition in natural classroom environments using deep learning techniques. The main contributions of the study are improving the YOLOv8 model so that it can find emotions more quickly and accurately in complicated classroom settings; creating a multi-channel classroom emotion dataset with information about facial expressions, skeletal structures, and background; and suggesting the MultiEmoNet emotion recognition model. The fusion of multiple information sources enhances the accuracy and robustness of emotion recognition. In addition, this study provides a visual view of classroom student emotion analysis, which provides teachers with data-based teaching feedback and optimization suggestions, enriching the application scenarios of educational technology.  
Despite the results, this study still has some limitations. Firstly, although the MultiEmoNet model improves the accuracy of emotion recognition, it also brings higher computational complexity and resource consumption, which may affect its efficiency in real-time applications. Second, the study primarily concentrates on primary and secondary school classroom scenarios, without the extensive verification of its applicability in other educational scenarios and different age groups.  
Future research will require further psychological validation, especially with neurodegenerative populations, to ensure the robustness and inclusivity of the system across diverse user groups. Future research will focus on model optimization and lightweighting to further enhance the real-time processing capability of the system, especially in resource-limited environments. In addition, the research also plans to add an analysis of misclassification and challenging scenarios to provide directions for the further optimization and performance improvement of the model, thereby enhancing the robustness and adaptability of the system. Future research will also explore the application of emotion recognition technology in more educational scenarios and age groups, aiming to promote the realization of personalized and highly interactive educational environments.  
Conceptualization, Y.H., W.D. and T.X.; methodology, Y.H., W.D. and T.X.; software, Y.H., W.D. and T.X.; validation, W.D. and T.X.; visualization, Y.H.; writing--original draft, Y.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by The Humanities and Social Science Research Planning Fund of the Ministry of Education (No. 20 YJAS80009), and the 2024 \"AI + Examination and Evaluation\" Special Teaching Reform Project of Central China Normal University (CCNU24 GJ17), the Fundamental Research Funds for the Central Universities under Grant CCNU22 JC011.  
This study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Central China Normal University, Ethic Committee, EC (protocol code CCNU-IRB-202305004a and 24 May 2023).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study.  
The data presented in this study are openly available in the CrowdHuman dataset at [[https://www.crowdhuman.org/download.html](https://www.crowdhuman.org/download.html)]([https://www.crowdhuman.org/download.html](https://www.crowdhuman.org/download.html)) (accessed on 22 May 2024). Relevant data from this study are available from the corresponding author upon reasonable request.  
The authors would like to thank the Hubei Key Laboratory of Digital Education, Central China Normal University, for providing the research facilities.  
The authors declare no conflicts of interest.