Each student matters, but it is hardly for instructors to observe all the students during the courses and provide helps to the needed ones immediately. In this paper, we present StuArt, a novel automatic system designed for the individualized classroom observation, which empowers instructors to concern the learning status of each student. StuArt can recognize five representative student behaviors (hand-raising, standing, sleeping, yawning, and smiling) that are highly related to the engagement and track their variation trends during the course. To protect the privacy of students, all the variation trends are indexed by the seat numbers without any personal identification information. Furthermore, StuArt adopts various user-friendly visualization designs to help instructors quickly understand the individual and whole learning status. Experimental results on real classroom videos have demonstrated the superiority and robustness of the embedded algorithms. We expect our system promoting the development of large-scale individualized guidance of students. More information is in \url{https://github.com/hnuzhy/StuArt}.

翻译：每个学生都很重要，但教师难以在课程中观察所有学生并及时提供帮助。本文提出StuArt，一种专为个体化课堂观察设计的新型自动化系统，使教师能够关注每位学生的学习状态。StuArt可识别五种与课堂参与度高度相关的代表性学生行为（举手、站立、睡觉、打哈欠、微笑），并追踪其在整个课程中的变化趋势。为保护学生隐私，所有变化趋势均以座位号索引，不包含任何个人身份信息。此外，StuArt采用多种用户友好的可视化设计，帮助教师快速理解个体及整体学习状态。在真实课堂视频上的实验结果表明，所嵌入的算法具有优越性和鲁棒性。我们期望该系统能推动大规模学生个体化指导的发展。更多信息请参见\url{https://github.com/hnuzhy/StuArt}。