## 4 Experiments and Analysis
### Performance Evaluation of the Emotion Encoding Network  
As shown in Step 2 of Figure 1, the research team utilized an emotion encoding network based on multimodal information fusion, conducting emotion recognition experiments on the multimodal emotional dataset created from both IEMOCAP and TTS-based multimodal data. Ultimately, the experimental dataset included 5998 group data records, and we chose the F1 score and unweighted accuracy as the evaluation metrics. Table 4 presents the experimental results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Module** & **Batch Size** & **Learning Rate** & **Dropout** & **Number of Layer** & **Layer Size** \\ \hline Emotion & & & & & \\ Classification & 10 & 0.00025 & 0.5 & 2 (MLP) & (100, 6) \\ Module & & & & & \\ Emotional & & & & & \\ Evaluation & 10 & 0.0002 & - & 3 (KAN) & (1024, 512, 6) \\ Module & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Table of model-training hyperparameters. The emotion recognition method designed in this paper obtained 68.02% in the accuracy metric, exceeding the 67.04% of COGMEN [35]. Meanwhile, the F1 score of this paper achieved 67.11%, indicating that the algorithm is robust and can adapt to the various discrepancies in students' language expressions. The UA and F1 scores of this paper's six-category emotion categories outperform the four-category categories of citation [26], showing the effective improvement this paper has achieved on previous work. The recognition results for each emotion category are shown in Figure 5. Referring to the table of experimental results, the emotion recognition model shows a significant improvements in accuracy and F1 scores across all emotion categories after incorporating TTS data. Specifically, the most notable increases in accuracy are seen in the \"sadness\" and \"frustration\" categories, which improved by 0.242 and 0.265, respectively. In terms of F1 scores, the \"sadness\" category shows a particularly significant increase, rising from 0.573 to 0.790. This research method utilizes video, audio, and text as inputs and employs multimodal feature fusion for feature encoding, demonstrating that the method in this paper has reached SOTA standards in emotion recognition tasks. The aim of this part of the study is to capture emotional features in multimodal data for the score computations of the evaluation network. The results show an excellent performance of this emotion encoding network in capturing multimodal emotional features, proving the availability of the feature extraction network. The extracted features can be used as inputs to the subsequent emotion evaluation network, thus laying the foundation for a more accurate evaluation of language learners' emotional expressions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Number of Categories** & **F1\(\uparrow\) (\%)** & **Accuracy\(\uparrow\) (\%)** \\ \hline bc-LSTM [32] & 6 & 59.58 & 59.10 \\ MMGCN [33] & 6 & 65.71 & 65.56 \\ DilogueCRN [34] & 6 & 65.34 & 65.31 \\ [PERSON] et al. [26] & 4 & 64.10 & 66.60 \\ COGMEN [35] & 6 & **67.27** & 67.04 \\ Ours & 6 & 67.11 & **68.02** \\ \hline \hline \end{tabular} Optimal values are bolded and sub-optimal values are italicized in the table.  
\end{table} 
Table 4: Table of multimodal emotion recognition results for the IEMOCAP dataset. Figure 5: Graph of the results of the emotion recognition model: (**a**) Comparison of accuracy before and after adding TTS data based on IEMOCAP dataset; (**b**) comparison of F1 score before and after adding TTS data based on IEMOCAP dataset. The ablation experiments of the emotion classification network on the multimodal dataset of this paper are shown in Table 5. When data of only a single modality is input, the model gives the best classification results for text data and the worst classification results for video data.