在教育VR环境中使用多模式特征对内部和外部干扰进行分类
IEEE transactions on visualization and computer graphics
|September 10, 2024
概括
这项研究使用眼睛跟踪和EEG数据来检测虚拟现实 (VR) 课堂中的内部和外部分心. 随机森林模型实现了超过83%的准确性,识别了关键的大脑区域和与分心相关的眼睛跟踪特征.
科学领域:
- 教育技术的教育技术
- 神经科学是一个神经科学.
- 人与计算机的交互
背景情况:
- 虚拟现实 (VR) 提供了增强学生参与和记忆保留的潜力.
- 干扰,无论是内部还是外部,在VR环境中对学习构成重大挑战.
- 由于个体的变化,单模特的功能不足以准确检测各种干扰.
研究的目的:
- 调查多模特特征 (眼睛跟踪和EEG) 在教育VR环境中分类内部和外部分心的有效性.
- 为了比较各种机器学习模型在检测这些干扰方面的性能.
- 识别有助于分心检测的关键特征.
主要方法:
- 为多模式数据收集 (眼睛跟踪和EEG) 建立了一个教育VR环境.
- 实现了包括kNN,随机森林 (RF),1D-CNN-LSTM和2D-CNN在内的机器学习模型.
- 模型使用跨主题,跨会话和基于性别的分组测试进行了评估.
主要成果:
- 随机森林分类器表现出最高的准确性,在跨主题测试中超过83%.
- 跨会话准确度在68%至78%之间,而基于性别的分组达到约90%的准确度.
- SHAP分析强调了脑后和前额前脑区域,视角,视线来源和头部旋转在分心检测中的重要性.
结论:
- 多模式数据融合,特别是与眼睛跟踪和EEG,是有效的分类分心在教育VR.
- 随机森林模型在识别学生分心状态方面表现强.
- 了解特定神经生理和眼动特征的贡献,可以为设计更具吸引力,更少分散注意力的VR学习体验提供信息.
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