LEVERAGING DEEP LEARNING TO ENHANCE MUSIC-DRIVEN COGNITIVE AND PHYSICAL TRAINING IN ATHLETES: AN EVALUATION OF ONLINE NATIONAL MUSIC TEACHING METHODS

Abstract

Evaluating national music online teaching poses a complex, high-dimensional classification challenge, where traditional methods fall short in accurately quantifying key performance indicators. This paper presents a deep learning-based evaluation model tailored to address this issue by incorporating elements specifically beneficial in sports science. The model analyzes various dimensions of online teaching effectiveness, including teaching support, interactive feedback, instructional outcomes, and student satisfaction, which are critical for cognitive and physical training in athletes. Utilizing these dimensions as input variables, the model defines expected outputs and establishes a mapping relationship between the inputs and the predicted effectiveness of the music teaching. The development of this evaluation model involves refining parameters within a feedforward neural network, optimizing training parameters, and assessing the model's accuracy using backpropagation algorithms. Preliminary tests demonstrate the model's efficacy, showcasing low Root Mean Square Error (RMSE) values, indicating precise assessments of online national music teaching quality. The results suggest that this deep learning approach not only enhances the evaluation of online national music teaching methods but also offers insights into how such educational tools can be optimized to support cognitive and physical training regimes in athletes. This application of technology is promising for developing training programs that leverage musical education to boost mental agility and physical coordination, ultimately contributing to improved athletic performance.