## 4 Results
### Recognition Results
#### 4.1.2 Analysis of Teacher's Action Recognition  
Table 7 shows the recognition results of nine schemes. From the comparison of Schemes 1 and 2, it can be concluded that RGB information has more noise than bone information, so a feature extraction method is needed to extract effective information from it. However, it is obvious that the feature extracted by VGG16 is not as effective as the bone information. It can be seen from Schemes 2 and 3 that the bone information (BCI) itself has very important discriminant information. After the artificial design, the accuracy rate of nine bone features reached 0.8, which shows that these feature designs are effective, but some key information is still missing, so the effect is not as good as the original bone coordinate information. Therefore, it is a direction to dig more discriminating bone features. The comparison of Schemes 1 and 4 directly shows the excellent performance of FPN technology. Compared with Schemes 4 and 5, it is found that the recognition rate of multi-layer features is not as good as that of one-layer features. It may be that multi-layer features bring more information but also noise.  
From the comparison between Scheme 6 and Scheme 1, it can be seen that the attitude information does have an impact on the performance of the action. In Scheme 6, action recognition shares the characteristics of attitude recognition, and the performance is slightly improved. From the comparison between Scheme 7 and Scheme 6, it is found that, after adopting FPN and CBAM technology, the recognition of motion is improved, but the recognition of attitude is not. This shows that the CBAM module has a certain function, but the effect of attitude recognition is not significantly improved due to its single-layer prediction. In addition, there seems to be a relationship between different channels. When one kind of attitude recognition rate increases, the other kind will decrease. The effect of Scheme 8 is the best among all schemes, which shows the effectiveness of the model design. From the comparison of Schemes 8 and 9, it is found that decision fusion (action only) has no better effect. This is because there is little difference in the recognition effect between  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \cline{3-10}  & Raising hands & **0.828** & 0 & 0.109 & 0 & 0 & 0.063 & 0.828 & **0.453** & 0.586 \\ \cline{2-10}  & Speaking & 0 & **0.931** & 0 & 0 & 0 & 0.069 & 0.931 & 0.73 & 0.818 \\ \cline{2-10}  & Listening & 0.038 & 0.006 & **0.934** & 0.01 & 0.012 & 0 & 0.934 & 0.954 & 0.944 \\ \cline{2-10}  & Writing & 0.027 & 0.003 & 0.199 & **0.756** & 0.014 & 0 & 0.756 & 0.94 & 0.838 \\ \cline{2-10}  & Reading & 0 & 0 & 0 & 0 & **1** & 0 & 1 & **0.475** & 0.644 \\ \cline{2-10}  & Discussion & 0.005 & 0 & 0 & 0 & 0 & **0.995** & 0.995 & 0.973 & 0.984 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mixed matrix of student behavior recognition.  
the two schemes, and the complementary information has no obvious improvement effect on the wrongly classified samples. More fusion methods are worth exploring.  
In addition, we find that the recognition effect of the head (up and down direction) is obviously lower than that of the other three local poses, which indicates that the features of this local pose are not as discriminating as those of the other three, and it is more difficult for the model to capture the effective feature information of this pose. On the whole, the recognition results of all schemes still can not reach the ideal results. Too few training samples and similar information among action categories may be the main reasons for the poor recognition results.