Abstract
Teachers' visual attention and its distribution across the students in classrooms can constitute important implications for student engagement, achievement, and professional teacher training. Despite that, inferring the information about where and which student teachers focus on is not trivial. Mobile eye tracking can provide vital help to solve this issue; however, the use of mobile eye tracking alone requires a significant amount of manual annotations. To address this limitation, we present an automated processing pipeline concept that requires minimal manually annotated data to recognize which student the teachers focus on. To this end, we utilize state-of-the-art face detection models and face recognition feature embeddings to train face recognition models with transfer learning in the classroom context and combine these models with the teachers' gaze from mobile eye trackers. We evaluated our approach with data collected from four different classrooms, and our results show that while it is possible to estimate the visually focused students with reasonable performance in all of our classroom setups, U-shaped and small classrooms led to the best results with accuracies of approximately 0.7 and 0.9, respectively. While we did not evaluate our method for teacher-student interactions and focused on the validity of the technical approach, as our methodology does not require a vast amount of manually annotated data and offers a non-intrusive way of handling teachers' visual attention, it could help improve instructional strategies, enhance classroom management, and provide feedback for professional teacher development.
Abstract (translated)
教师在教室中的视觉注意力及其对学生的分配对于学生参与度、学业成就和专业教师培训具有重要的影响。然而，推断出教师关注的是哪位学生以及他们关注的位置并不容易。移动眼动追踪技术可以为解决这一问题提供重要帮助；然而，仅使用移动眼动追踪需要大量的手动标注数据。为了克服这个限制，我们提出了一种自动化处理流水线的概念，该概念只需要少量的手动标注数据就能识别出教师关注的是哪位学生。 为此，我们利用最先进的面部检测模型和面部识别特征嵌入，在课堂环境中通过迁移学习训练面部识别模型，并将这些模型与来自移动眼动追踪器的教师注视信息结合在一起。我们在四个不同教室收集的数据上评估了我们的方法，结果表明，在所有教室布置中都能合理地估计出视觉关注的学生，而U形和小型教室的表现最佳，准确率分别为大约0.7和0.9。 虽然我们没有评估这种方法在教师与学生互动方面的效果，并且主要验证了技术方法的有效性，但我们的方法不依赖大量手动标注数据，并提供了一种非侵入式的处理教师视觉注意力的方式。因此，它可以帮助改进教学策略、提高课堂管理效率以及为专业教师发展提供反馈。
URL
https://arxiv.org/abs/2505.07552
