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

合作研究：理解和改进大课堂学习的技术和教育基础

• 批准号: 0835394
• 负责人: Rachel Scherr
• 金额: $ 8.25万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0835394&HistoricalAwards=false
• 关键词: Collaborative; Research; Technological; Educational; Foundations

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.

大招生课程是许多机构科学、技术、工程和数学 (STEM) 入门课程的实际需要。创新技术，例如观众反应系统，可以增强大招生班级的教学，但这些现有技术可以传达的信息仍然相当有限。该项目的目标是在大型 STEM 课程中为技术开发一个新角色，利用计算机视觉技术的进步和数字视频的快速扩散。通过构建多摄像头阵列来同时观察大教室中的所有个人，研究人员将在教育和计算机视觉方面进行基础研究。对于计算机视觉来说，大教室提供了一个方便的社会互动的缩影，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团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用于系统地研究大型教室中的学习——这在以前是不可能的。从这两个领域的研究中获得的见解将用于创建一种全新的计算机辅助协作教学工具。该项目将开发自动检测和总结课程讲师实时活动信息的系统，从而提高他们充分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，这项关于大教室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，其中通过自动视觉活动识别来扩大小规模的生态观察。