Collaborative Research: Technological and Educational Foundations for Understanding and Improving Large-classroom Learning

大招生课程是许多院校科学、技术、工程和数学(STEM)入门课程的实际需要。创新技术，如观众反应系统，可以加强大规模招生班级的教学，但这些现有技术可以传达的信息仍然相当有限。该项目的目标是为技术在STEM大班中发挥新的作用，利用计算机视觉技术的进步和数字视频的快速扩散。通过建立一个多摄像头阵列，在一个大教室里同时观察所有人，研究人员将在教育和计算机视觉方面进行基础研究。对于计算机视觉，大教室提供了一个方便的社交互动的缩影，其中个人活动受到限制但不受控制；该项目将利用这一特性为大型群体活动开发基于视觉的识别系统。在教育方面，这种新的视觉系统将被用来在大课堂上系统地研究学习-这是以前不可能的。从这两个领域的研究中获得的见解将被用来创造一种全新的计算机辅助协作教学工具。该项目将开发系统，为课程讲师自动检测和汇总实时活动信息，从而提高他们优化利用互动课堂时间的能力。这些系统被视为最终可以在其他机构复制的原型。此外，这项对大课堂学习性质的研究结果将作为改善全国大招生STEM课程教学的基础，无论其技术资产如何。更广泛地说，该项目为教育研究提供了一种新的范式，其中小规模的生态观察通过自动视觉活动识别来放大。

Large-enrollment courses are a practical necessity for introductory courses in science,technology, engineering and mathematics (STEM) at many institutions. Innovative technologies, such as audience response systems, can enhance instruction in large-enrollment classes, but the information that can be conveyed with these existing technologies remains quite limited. The goal of this project is to develop a new role for technology in large STEM classes, one that exploits advances in computer vision technology and the rapid proliferation of digital video. By building a multi-camera array to simultaneously observe all individuals in a large classroom, the investigators will pursue foundational research in both education and computer vision. For computer vision, large classrooms provide a convenient microcosm of social interaction in which individual activities are constrained but not controlled; this project will leverage this property to develop vision-based recognition systems for large group activites. Educationally, this new vision system will be used to systematically study learning in large classrooms---something that has not previously been possible.Insights gained from research in these two areas will be used to create a radically new tool for computer-assisted collaborative instruction. This project will develop systems to automatically detect and summarize real-time activity information for course instructors, thereby enhancing their ability to make optimal use of interactive class time. These systems are viewed as prototypes that can ultimately be replicated at other institutions. In addition, the results of this research into the nature of learning in large classrooms will serve as a basis for improving instruction in large-enrollment STEM courses nationwide, regardless of their technological assets. More broadly, the project offers a new paradigm for education research, in which small-scale ecological observations are scaled up by automated visual activity recognition.