## 5 Discussion  
Although this study achieved significant results in detecting student classroom behavior, several limitations still need to be addressed.  
1. Dataset diversity and scale: The diversity and scale of the dataset are still insufficient. Although the fusion dataset (FSCB-dataset) combines AI-generated data and actual observation data, enriching the data sources, the scenes and behavior types included in the dataset may not fully cover all possible situations in actual classrooms. The data generated using advanced text-to-image generation techniques (such as Stable Diffusion and DALL-E 3) may not completely reflect the complexity and variability of real scenes in some cases.  
2. Real-time performance: Although the RT-DETR model proposed in this paper incorporates the MobileNetV3 backbone network, learned positional encoding (LPE), and dynamic upsampling (DySample) techniques, significantly improving computational efficiency, further optimization is needed to ensure real-time performance, especially in resource-constrained mobile devices or embedded systems.  
3. Model explainability: The black-box nature of deep learning models makes their decision-making processes difficult to interpret, which is particularly important in educational settings. Future research can further explore the explainability of behavior detection  
Figure 8: Heatmap visualization of different algorithms.  
models, using attention maps and visualization tools to help teachers and researchers better understand the model's decision-making process, thereby improving the system's transparency and acceptability.  
To further enhance the performance and practicality of the student classroom behavior detection system, future research can explore the following directions:  
1. Real-time inference optimization: Investigate methods to further optimize real-time inference, such as edge computing and model compression techniques, to improve deployment efficiency and performance on mobile devices. Techniques such as quantization, pruning, and distillation are feasible approaches [41].  
2. Multimodal data fusion: Combine video, audio, and sensor data to build a multimodal behavior detection system. Multimodal fusion not only provides more comprehensive behavior analysis but also enhances detection robustness and accuracy in various environments. For example, integrating microphone data to detect students' vocal behaviors, combined with visual data, can improve overall detection performance [42].  
3. Explainability in behavior detection**: Further study the explainability of deep learning models, especially in interpreting detected student behaviors. Using explainability techniques such as attention maps and visualization tools can help teachers and researchers better understand the model's decision-making process, enhancing the system's transparency and acceptability [31].  
These research directions not only offer new technical pathways for student behavior detection but also lay a solid foundation for further development in the field of educational technology. Future research can continuously optimize algorithms, enhance the model's generalization capabilities, and integrate more practical application scenarios, advancing the development and application of intelligent education systems.