基于姿势估计和运动识别的基础上,为中小学体育课堂构建智能评估模型
1Faculty of Education, Shaanxi Normal University, Xi'an, 710061, Shaanxi, China. iwhts9670111@outlook.com.
Scientific reports
|January 19, 2026
概括
本研究介绍了KINEVAL与教学上下文化策略 (PCS) 进行适应性课堂分析. 它使用人工智能增强学生绩效评估,提高智能教育系统的准确性和公平性.
科学领域:
- 智能教育系统 智能教育系统
- 以人为中心的人工智能
- 多式联动互动 多式联动互动
背景情况:
- 现有的体育活动评估工具缺乏适用于教育环境的可扩展性和上下文敏感性.
- 传统的方法过于简化了教学动态,导致了静态或模两可的反.
- 需要适应性,透明性和强大的课堂分析.
研究的目的:
- 开发一种综合解决方案,将语义对齐和层次关注结合起来,用于课堂行为分析.
- 创建一个融合了运动轨迹嵌入,指令状态编码和环境意识调制的系统.
- 创建结构化,可解释的学生绩效评估,与教学目标保持一致.
主要方法:
- 使用了KINEVAL架构和教学上下文化策略 (PCS).
- 员工分层注意力建模用于空间时间模式捕获和自适应上下文化.
- 集成的基于注意力的扩展GRU,基于变压器的教学建模和同行意识的规范化.
主要成果:
- 与传统基线相比,在准确性,公平性和与专家注释一致性方面取得了实质性的改进.
- 展示了评估系统的强大可靠性和可解释性.
- 在不同的学校环境中验证了跨领域的概括能力.
结论:
- 提出了一个可扩展和教学知情的方法来进行课堂行为分析.
- 支持教育中的智能传感,自适应性学习和多式联络系统设计.
- 在智能教育系统中提供了一种更有意义和更具背景意识的反解决方案.
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