# A Diffusion Modeling-Based System for Teaching Dance to Digital Human
## 4 Implementation of Digital Human Dance Movement Teaching System
### Results Analysis of Digital Human Dance Teaching System
#### 4.3.2 Evaluation of Digital Human Dance Teaching System  
To assess the applicability and advantages of this system for dance movement teaching, we conducted a comparative experiment with three existing dance movement teaching systems: A (ChoreoMaster), B (TM2D), and C (AlxDance). We recruited 20 students with no prior dance experience to learn four different dance sequences generated from the same music. Professional dance instructors were then invited to evaluate the effectiveness of the learning outcomes.  
The assessment results are depicted in Figure 19. Significant differences were observed in the teaching effectiveness among the four different dance teaching methods. The system proposed in this study achieved the highest evaluation score, with a maximum of 98 points. Notably, volunteers No. 5 and No. 19 excelled, scoring 80, 91, and 93 points respectively with the dance movements generated by the other three methods. This indicates a substantial advantage of the proposed method. The average teaching effectiveness score for the proposed system was 95.23, underscoring its efficacy in teaching dance. This result demonstrates that beginners can successfully utilize the dance teaching system developed in this study, achieving commendable results.  
As illustrated in Figure 20 we categorized the dataset into five music styles: Break, Pop, Locking, Hip-hop, and Ballet Jazz. The fluency of the generated dance movements was evaluated by a professional dance instructor, with scores ranging from 0 to 100. The results indicate that our system exhibits a significant advantage in fluency. The fluency  
Figure 19: Comparison of the teaching effects of four types of dance.  
Figure 18: Intensity characteristics.  
scores for all five dance styles remain consistently high, with an average score of 93.6. In contrast, Methods A, B, and C demonstrate potential issues with movement coordination.  
In order to better validate the applicability of the research method, we selected 20 beginners, intermediate, and advanced dancers to use the system for learning and recorded their performance changes before and after training. We collected dancers' performance scores through evaluations by dance teachers and compared the score differences before and after learning. As shown in Figure 21, dancers of all skill levels improved their performance after using the system, especially beginners.  
As shown in Figure 21, dancers of all skill levels (including beginners, intermediate, and advanced) saw score improvements after learning with the system. This indicates that the system is effective for dancers at all levels. Among them, beginners experienced the most significant improvement, with their scores increasing from around 45 points to nearly 70 points, demonstrating the system's substantial benefit for beginners. Intermediate dancers also saw an increase in scores, though the magnitude was smaller, rising from around 65 points to about 80 points. This may suggest that intermediate dancers already have a solid foundation, and the system helps them further enhance their skills. Advanced dancers experienced smaller improvements, but the increase was still noticeable, with scores rising from around 80 points to about 85 points, indicating that even advanced dancers can benefit from the system.  
Figure 21: Comparison of effects at different skill levels.  
Figure 20: Comparison of fluency of dance images generated by four methods.