从使用深度学习的面部动态特征来实时估计过度注意力
概括
一个人工智能模型仅使用面部动态预测学生的注意力,消除了对组数据或手动标签的需求. 这项技术为远程教育提供客观的实时参与监控.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 教育技术的教育技术
背景情况:
- 由于缺乏视觉反,远程学习对监控学生参与提出了挑战.
- 传统的注意力评估方法是主观的,劳动密集的.
- 大脑活动或目光的学科间相关性 (ISC) 显示出对客观注意力测量有希望.
研究的目的:
- 开发一种AI模型,仅从面部动态来预测个体学生的注意力.
- 消除对注意力评估中的参考组或手动标签的需求.
- 创建一个可扩展,客观和保护隐私的工具来监测远程教育的参与.
主要方法:
- 使用眼动的学科间相关性 (ISC) 作为注意力指数.
- 训练了一个深度神经网络,从单个主体的面部动态中预测注意力.
- 对83名参与者进行了3项实验.
主要成果:
- 人工智能模型解释了已知受试者数据 (R2=0.38) 中高达38%的差异,以及新受试者数据 (R2=0.26-0.30) 中的26-30%差异.
- 该模型捕捉了时间解析的公开注意力,并与视频后测试分数相关 (r=0.41-0.49).
- 眼睛和头部运动被确定为驱动模型预测的关键特征.
结论:
- 面部动态,特别是眼睛和头部的运动,可以可靠地预测学生的注意力.
- 拟议的AI方法为实时参与监控提供了一个客观,可扩展和保护隐私的解决方案.
- 这种方法通过提供对学生关注度和表现的可操作见解来增强远程教育.
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