## 5 Experimental Results
### Learning and Results of the Evaluation Model
Compared to the baseline, as before, the proposed method is more accurate and has less variation in prediction, indicating that it is superior in terms of accuracy and \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Dynamics** & **Sharpness** & **Scalability** & **Timing** & **Accuracy** & **Stability** \\ \hline LMA + RF & 0.575 & 0.548 & 0.450 & 0.562 & 0.585 & 0.539 \\ No-reference & 0.858 & 0.791 & 0.837 & 0.838 & 0.756 & 0.825 \\ Reference-guided & **0.874** & **0.880** & **0.873** & **0.885** & **0.908** & **0.920** \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Comparison with and without reference. Figure 20: Radar charts of evaluation model predictions (the three dancers in the test data). The blue line represents the correct score and orange the prediction score. Figure 19: Confusion matrices of evaluation model predictions (the three dancers in the test data, **left**, baseline; **right**, proposed method). The background color of each cell in the matrix is lighter for smaller values and darker for larger values. consistency. Figure 22 shows the prediction results for all evaluation items for 12 dancers' test data. In many of the charts, the blue and orange lines are fairly consistent, indicating that the model makes predictions that are close to the correct score. Figure 21: Confusion matrices of evaluation model predictions (the last count in the test data, **left**, baseline; **right**, proposed method). The background color of each cell in the matrix is lighter for smaller values and darker for larger values. Figure 22: Radar charts of evaluation model predictions (the last count in the test data). These results indicate the usefulness of the reference-guided model, in which a model dance is input as a reference, in the automatic evaluation of dance performance. However, the dataset used in this study was not large, and future work is needed to collect data on different types of dances and choreographies to further validate the versatility of the model. The indicators described above should be sufficient for the evaluation of each item. However, since the purpose of this evaluation model is to select effective feedback items, it is also possible to evaluate the model from other perspectives. In this study, we evaluated the overall performance of the model by calculating the precision and recall in ranking search as the overall evaluation indices of the model. Specifically, evaluation items that fell below the average of the evaluation values for each item were defined as \"items requiring improvement (IRIs)\", and the prediction score and the correct answer score were compared. The precision here is an index that indicates the percentage of items among the predicted IRIs that actually matched the correct IRIs, and the recall indicates the percentage of items among the correct IRIs that were correctly identified as IRIs by the prediction. In addition to these indicators, the accuracy and F1 score were also calculated. Table 20 shows the results. The accuracy indicates how many of the overall predictions were correct, and the F1 score indicates the harmonic mean of the precision and recall. In addition, the percentage of items with the lowest prediction score that were included in the correct IRIs was also calculated and is shown in Table 21. This is an indicator to check whether the lowest-scoring item in the prediction is included in the actual IRIs and to evaluate how accurately the prediction identifies critical areas for improvement. These results confirm that the proposed method demonstrates the ability to select effective feedback items compared to the baseline. Furthermore, the lowest score accuracy, which indicates the percentage of the lowest predictive score included in the actual IRIs, is as high as 0.679 for the CL + AE model compared to 0.500 for the baseline. In particular, the CL + AE model was found to be superior in its ability to accurately identify important movements that need to be improved.