Sports Data-Driven Teaching Strategy Optimization Method for University Students Based on Improved Particle Swarm Optimization Algorithm

This study proposes an optimization method for sports teaching strategies based on an improved Particle Swarm Optimization (PSO) algorithm, aiming to enhance the personalization of strategy formulation and teaching effectiveness in physical education. Traditional teaching strategies often struggle to optimize based on the specific needs of individual students, resulting in suboptimal teaching outcomes and low student engagement. To address this issue, the paper introduces an enhanced PSO algorithm that incorporates a local search mechanism and adaptive weight adjustment, presenting an efficient approach for optimizing personalized teaching plans. Experimental results show that the improved PSO algorithm outperforms the traditional PSO algorithm in terms of teaching effectiveness, the implementation of personalized strategies, and student satisfaction, with faster convergence and more significant optimization results. The contribution of this study lies in the novel application of the improved PSO algorithm to the data-driven optimization of sports teaching, providing a new technical pathway for enhancing the quality of physical education and offering a theoretical basis for the realization of personalized teaching strategies.