Fig. 3
From: Deep learning optimization of teaching schedules in sports dance education
Detailed performance metrics and evaluation criteria. The figure presents a structured evaluation framework for the AI-powered scheduling model, categorizing key performance indicators into five primary areas: (i) Scheduling efficiency: conflict resolution rate (CRR) and schedule stability index (SSI); (ii) computational performance: execution time (ET) and latency in adjustments (LA); (iii) Workload balancing: instructor workload variance (WV) and instructor utilization score (IUS); (iv) adaptability and generalization: adaptability score (AS) and retraining frequency (RF); (v) scalability and robustness: scalability index (SI) and generalization capability (GC). These performance metrics ensure optimal conflict resolution, balanced workload distribution, dynamic scheduling adaptability, and large-scale applicability.
