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:每个学生都关心每个学生,但教师很难在课程期间观察所有学生,并立即为需要的学生提供帮助。在本文中,我们介绍StuArt(StuArt),这是一个为个性化课堂观察设计的新型自动系统,使教师能够关注每个学生的学习状况。StuArt可以识别五个与参与关系高度相关的具有代表性的学生行为(手艺、站立、睡觉、打哈欠和微笑),并跟踪课程中学生的不同趋势。为了保护学生的隐私,所有变异趋势都用座位编号索引,没有任何个人身份识别信息。此外,StuArt采用各种方便用户的视觉化设计,帮助教员快速了解个人和整个学习状况。真实课堂视频的实验结果显示了嵌入式算法的优越性和稳健性。我们期望我们的系统能够促进学生大规模个人化指导的发展。更多信息见:https://github.com/hnuzhy/STUart}。