论文标题
斯图尔特：对具有自动行为识别和跟踪的学生的个性化课堂观察
StuArt: Individualized Classroom Observation of Students with Automatic Behavior Recognition and Tracking
论文作者
论文摘要
每个学生都很重要，但是教师在课程中几乎没有观察所有学生，并立即为所需的学生提供帮助。在本文中，我们介绍了Stuart，这是一种专为个性化课堂观察而设计的新型自动系统，该系统使教师能够关注每个学生的学习状况。斯图尔特可以认识到与参与度高度相关的五种代表性学生行为（手工锻炼，站立，睡觉，打哈欠和微笑），并在课程中跟踪其变异趋势。为了保护学生的隐私，所有变化趋势均由座位编号索引，而没有任何个人身份信息。此外，Stuart采用了各种用户友好的可视化设计，以帮助教师快速了解个人和整个学习状态。真实教室视频的实验结果证明了嵌入式算法的优越性和鲁棒性。我们希望我们的系统促进学生的大规模个性化指导的发展。更多信息在https://github.com/hhnuzhy/stuart中。
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 https://github.com/hnuzhy/StuArt.
