通过卷积神经网络和混合学习增强舞蹈教育
1College of Education, HanJiang Normal University, Shiyan, Hubei, China.
PeerJ. Computer science
|December 9, 2024
概括
技术通过在线平台和混合学习来增强舞蹈教育. 用卷积神经网络 (CNN) 识别面部情绪和捕捉运动,提供客观的绩效反.
科学领域:
- 舞蹈教育 技术 舞蹈教育
- 计算性能分析 计算性能分析
- 人与计算机在艺术中的互动
背景情况:
- 传统舞蹈教学正在随着互联网和数字技术的整合而发展.
- 在线平台和混合学习模式在舞蹈教育中提供了更大的灵活性和可访问性.
- 需要客观的评估方法来评估学生在舞蹈中的复杂表现.
研究的目的:
- 探索技术对舞蹈教学方法的影响.
- 通过使用先进的算法和技术,引入用于客观舞蹈表演评估的新方法.
- 通过综合性评估,结合情绪表达和运动分析,增强舞蹈教育.
主要方法:
- 利用双翼和 (DWH) 多视图度量学习 (MVML) 算法进行面部情绪识别.
- 集成的移动捕捉技术与卷积神经网络 (CNN) 进行精确的舞蹈运动分析.
- 结合情绪表达评估与运动分析,以进行全面的绩效评估.
主要成果:
- 实验结果表明,对情绪表达和舞蹈动作都具有很高的识别准确性.
- 综合方法为学生表现和教学效率提供了有价值的见解.
- 对舞蹈教育成功建立了客观评估指标.
结论:
- 技术进步,包括DWH-MVML和带有运动捕捉的CNN,为客观的舞蹈评估提供了有效的工具.
- 这种整体的评估方法提高了舞蹈教育中的学习成果和教学实践.
- 在舞蹈教学中采用技术为创新和有效的学生发展开辟了新的途径.
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