Department of Biomedical Engineering, City College of New York, New York, NY 10031;
Department of Biomedical Engineering, City College of New York, New York, NY 10031.
Proc Natl Acad Sci U S A. 2021 Feb 2;118(5). doi: 10.1073/pnas.2016980118.
Experienced teachers pay close attention to their students, adjusting their teaching when students seem lost. This dynamic interaction is missing in online education. We hypothesized that attentive students follow videos similarly with their eyes. Thus, attention to instructional videos could be assessed remotely by tracking eye movements. Here we show that intersubject correlation of eye movements during video presentation is substantially higher for attentive students and that synchronized eye movements are predictive of individual test scores on the material presented in the video. These findings replicate for videos in a variety of production styles, for incidental and intentional learning and for recall and comprehension questions alike. We reproduce the result using standard web cameras to capture eye movements in a classroom setting and with over 1,000 participants at home without the need to transmit user data. Our results suggest that online education could be made adaptive to a student's level of attention in real time.
有经验的教师密切关注学生，如果学生看起来困惑不解，他们会调整教学。这种动态互动在在线教育中是缺失的。我们假设，专心的学生会用眼睛跟随视频。因此，可以通过跟踪眼球运动来远程评估对教学视频的注意力。在这里，我们表明，在视频呈现过程中，注意力集中的学生的眼球运动的主体间相关性要高得多，并且同步的眼球运动可以预测视频中呈现的材料的个体测试分数。对于各种制作风格的视频、偶然学习和有意学习以及回忆和理解问题，这些发现都是可以复制的。我们使用标准的网络摄像头在课堂环境中捕捉眼球运动，并在家中使用超过 1000 名参与者复制了该结果，而无需传输用户数据。我们的研究结果表明，在线教育可以实时适应学生的注意力水平。
