S3DCN-OLSR:一种浅层的3D CNN方法,用于在线学习状态识别
Jing Bai1, Xiaohong Yang1, Qi Li1
1Northwest Normal University, Lanzhou, 730070, China.
Heliyon
|October 23, 2023
概括
这项研究引入了一种通过分析微表达式来识别在线学生参与的新方法. 浅层3D卷积方法准确识别学习状态,提高在线教育质量.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 教育技术的教育技术
背景情况:
- 由于COVID-19的破坏,需要有效的在线学习解决方案.
- 在线教学在课堂管理和监测学生参与度方面存在挑战.
- 准确识别学生的学习状态对于有效的在线教育至关重要.
研究的目的:
- 通过微表达式分析,提出和评估一种用于识别在线学生学习状态的新方法.
- 为了应对监测学生参与在线学习环境的挑战.
- 通过提高学习状态识别来提高在线教学的有效性.
主要方法:
- 开发了一个浅层3D卷积神经网络 (S3DC-OLSR) 用于在线学习状态识别.
- 使用数据增强技术将视频数据分解为光流组件 (水平,垂直) 和光幅.
- 应用了S3DC-OLSR模型来分析微表达式以识别学生的学习状态.
主要成果:
- 与最先进的方法相比,拟议的S3DC-OLSR方法表现出优越的性能.
- 在CASME II和SMIC数据集上实现了高识别精度,UF1和UAR分数.
- 验证了微表达式分析用于在线学习状态检测的有效性.
结论:
- 在识别学生的在线学习状态方面,S3DC-OLSR方法非常有效.
- 微表达式分析为改善在线教育监测提供了一个有希望的途径.
- 这种方法可以大大促进更好的课堂管理和在数字学习环境中为学生提供支持.
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