Deep learning optimization of teaching schedules in sports dance education.

The increasing complexity of scheduling in sports dance education presents challenges such as conflicts in instructor availability, inefficient class assignments, and the need for personalized training plans. Traditional scheduling methods, which rely on manual adjustments or rule-based heuristics, often fail to handle dynamic constraints effectively. This study proposes a deep learning-powered scheduling framework that integrates historical scheduling data, instructor availability, and student performance metrics to generate optimal class schedules. The model leverages Recurrent Neural Networks (RNNs) for sequential learning and Reinforcement Learning (RL) for adaptive decision-making, ensuring conflict-free scheduling while maintaining flexibility for modifications. Experimental validation was conducted using five years of real-world sports dance class data from educational institutions. The proposed model achieves a 95% conflict resolution rate, significantly outperforming traditional heuristic-based scheduling, which resolves only 55% of conflicts. Additionally, instructor workload balancing efficiency improved to 92%, ensuring fairer distribution of teaching hours, while student schedule continuity reached 94%, reducing class fragmentation and optimizing learning progression. The model also demonstrates superior computational efficiency, reducing scheduling execution time to 40 seconds per iteration, compared to 120 seconds in traditional methods. The findings highlight the scalability and adaptability of AI-driven scheduling optimization in structured educational settings.