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

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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.