根据STEAM概念的体育教育教学设计,使用卷积神经网络
1School of Physical Education, Guangzhou Sport University, Guangzhou, 510500, China. 11008@gzsport.edu.cn.
Scientific reports
|July 2, 2025
概括
一个新的深度学习 (DL) 模型,CNN-STEAM,通过显著提高准确性,回忆和F1分数来增强体育 (PE) 教学. 这种模型为PE研究和教育提供了更好的数据处理.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 运动科学 运动科学 运动科学
背景情况:
- 传统的体育教育 (PE) 方法与复杂的现代研究需求作斗争.
- 整合科学,技术,工程,艺术和数学 (STEAM) 对不断发展的教育范式至关重要.
- 深度学习 (DL) 为增强PE教学和研究提供了潜在的解决方案.
研究的目的:
- 在STEAM框架内为体育教育 (PE) 教学设计一个高效的深度学习 (DL) 模型.
- 评估拟议的CNN-STEAM模型与传统的CNN和ResNet模型的性能.
- 为了证明该模型在PE应用程序的数据处理和分析中的有效性.
主要方法:
- 基于卷积神经网络 (CNN) 的新型CNN-STEAM模型的开发.
- 对CNN-STEAM与标准CNN和剩余网络 (ResNet) 模型进行比较分析.
- 使用关键指标进行绩效评估:准确性,回忆力,F1分数和响应时间.
主要成果:
- 与传统的CNN和ResNet相比,CNN-STEAM模型在所有评估指标上表现出卓越的表现.
- 在准确性,回忆力和F1分数方面观察到超过20%的显著改善.
- 减少的响应时间表明CNN-STEAM模型的效率提高.
结论:
- CNN-STEAM模型是用于体育教育 (PE) 教学的有效和高效的深度学习解决方案.
- 该模型为PE研究人员和教育工作者提供了强大的技术支持.
- 该研究提供了新的见解,在STEAM概念下,在PE领域内应用DL.
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