## 4 Methods and Experiment
### Keyframe Extraction
The four pretrained models were Inception-V3, ResNet50-V2, VGG-16, and EfficientNet-B7. We referred to these models as \"IR-BiLSTM-AT\", \"IRV-BiLSTM-AT\", and \"IRVE-BiLSTM-AT\" or \"MFF-BiLSTM-AT\", respectively. The results of these experiments are displayed in Table 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{
\begin{tabular}{c} **Total** \\ **Parameters** \\ \end{tabular} } & \multicolumn{4}{c}{**Classification Overall Accuracy (\%)**} \\ \cline{3-6}  & & **Precision** & **Recall** & **F1** & **Accuracy** \\ \hline Inception-V3 + BiLSTM-AT & 5,393,798 & 0.85 & 0.85 & 0.85 & 84.44 \\ ResNet50-V2 + BiLSTM-AT & 5,393,798 & 0.90 & 0.88 & 0.88 & 87.78 \\ EfficientNet-B7 + BiLSTM-AT & 6,442,374 & 0.88 & 0.88 & 0.88 & 87.78 \\ VGG-16 + BiLSTM-AT & 2,248,070 & **0.90** & **0.89** & **0.89** & **88.89** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of various single pretrained models compared with the BiLSTM-AT network. The bold represents the best value. ### Multiple Pretrained Model Results with BiLSTM-AT

The BiLSTM-AT network uses the keyframe selection with multiple pretrained models for feature extraction. The model has the same hyperparameter tuning and training with the same parameters between MFF-BiLSTM-AT and KMFF-BiLSTM-AT in our overall frameworks. The results are shown in Table 4, and the training and validation accuracy is shown in Figure 11. The sample model prediction results are shown in Figure 12. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{
\begin{tabular}{c} **Total** \\ **Parameters** \\ \end{tabular} } & \multicolumn{4}{c}{**Inference**} & \multicolumn{4}{c}{**Classification Overall Accuracy (\%)**} \\ \cline{3-6}  & **Parameters** & **Time (min)** & **Precision** & **Recall** & **F1** & **Accuracy** \\ \hline S-BiLSTM-AT & 2,248,070 & 04.74 & 0.90 & 0.89 & 0.89 & 88.89 \\ IR-BiLSTM-AT & 9,588,102 & 10.61 & 0.91 & 0.90 & 0.90 & 90.00 \\ IRV-BiLSTM-AT & 10,636,678 & 13.94 & 0.91 & 0.92 & 0.91 & 91.11 \\ MFF-BiLSTM-AT & 15,879,558 & 20.07 & 0.93 & 0.93 & 0.92 & 92.22 \\ KMFF-BiLSTM-AT & 15,879,558 & **21.80** & **0.97** & **0.97** & **0.97** & **96.67** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the overall accuracy results of our dataset with the five models of the proposed framework. The bold represents the best value. Figure 11: Training and validation metrics of (**a**) MFF-BiLSTM-AT and (**b**) KMFF-BiLSTM-AT. Figure 12: Example prediction results of the KMFF-BiLSTM-AT model. We compare the performance of the five models of our proposed framework: S-BiLSTM-AT, IR-BiLSTM-AT, IRV-BiLSTM-AT, MFF-BiLSTM-AT, and KMFF-BiLSTM-AT. The accuracy of the KMFF-BiLSTM-AT model was found to be higher than the accuracy of the other models. However, even though our framework exhibits a high level of performance, there are still some limitations. For example, there is some confusion when making predictions about actual classes; some students turned around, but the model predicted hand-raising behavior because the student also moved one hand. In addition, when students interacted with their friends, the model predicted that they would turn around as they moved their heads to respond to another student. The results show that after fusing the features, the single pretrained model is less efficient than the multiple pretrained models, with an accuracy of 88.89 and 92.22%, respectively. However, when compared with applying the keyframe selection methods and using multi-feature fusion, the KMFF-BiLSTM-AT model was the most efficient, with an accuracy of 96.67%. The results can be found in Table 4.

### Comparison with a Large Dataset

The overall performance of our proposed framework was evaluated using the large public UCF101 and HMDB51 datasets. We selected ten keyframes from each video clip using the SDALK algorithm and then used our proposed novel multi-feature extraction and fusion methodology.