Department of Physical Education and Military Education, Jingdezhen Ceramic University, Xianghu Town, Jingdezhen City, Jiangxi Province, China.
PLoS One. 2024 Aug 7;19(8):e0307882. doi: 10.1371/journal.pone.0307882. eCollection 2024.
The research analyzed the effect of weekly training plans, physical training frequency, AI-powered coaching systems, virtual reality (VR) training environments, wearable sensors on developing technical tennis skills, with and personalized learning as a mediator. It adopted a quantitative survey method, using primary data from 374 young tennis players. The model fitness was evaluated using confirmatory factor analysis (CFA), while the hypotheses were evaluated using structural equation modeling (SEM). The model fitness was confirmed through CFA, demonstrating high fit indices: CFI = 0.924, TLI = 0.913, IFI = 0.924, RMSEA = 0.057, and SRMR = 0.041, indicating a robust model fit. Hypotheses testing revealed that physical training frequency (β = 0.198, p = 0.000), AI-powered coaching systems (β = 0.349, p = 0.000), virtual reality training environments (β = 0.476, p = 0.000), and wearable sensors (β = 0.171, p = 0.000) significantly influenced technical skills acquisition. In contrast, the weekly training plan (β = 0.024, p = 0.834) and personalized learning (β = -0.045, p = 0.81) did not have a significant effect. Mediation analysis revealed that personalized learning was not a significant mediator between training methods/technologies and acquiring technical abilities. The results revealed that physical training frequency, AI-powered coaching systems, virtual reality training environments, and wearable sensors significantly influenced technical skills acquisition. However, personalized learning did not have a significant mediation effect. The study recommended that young tennis players' organizations and stakeholders consider investing in emerging technologies and training methods. Effective training should be given to coaches on effectively integrating emerging technologies into coaching regimens and practices.
研究分析了每周训练计划、体能训练频率、人工智能教练系统、虚拟现实 (VR) 训练环境、可穿戴传感器对发展技术网球技能的影响，以及个性化学习作为中介。它采用了定量调查方法，使用了 374 名年轻网球运动员的原始数据。模型拟合度采用验证性因子分析 (CFA) 进行评估，假设采用结构方程建模 (SEM) 进行评估。通过 CFA 确认了模型的拟合度，表现出较高的拟合指数：CFI=0.924，TLI=0.913，IFI=0.924，RMSEA=0.057，SRMR=0.041，表明模型拟合良好。假设检验表明，体能训练频率（β=0.198，p=0.000）、人工智能教练系统（β=0.349，p=0.000）、虚拟现实训练环境（β=0.476，p=0.000）和可穿戴传感器（β=0.171，p=0.000）对技术技能的获取有显著影响。相比之下，每周训练计划（β=0.024，p=0.834）和个性化学习（β=-0.045，p=0.81）没有显著影响。中介分析表明，个性化学习在训练方法/技术和技术能力获取之间不是一个显著的中介。研究结果表明，体能训练频率、人工智能教练系统、虚拟现实训练环境和可穿戴传感器对技术技能的获取有显著影响。然而，个性化学习没有显著的中介作用。研究建议年轻网球运动员的组织和利益相关者考虑投资新兴技术和培训方法。应该为教练提供有效的培训，让他们有效地将新兴技术整合到教练方案和实践中。
