Data-Driven Monitoring and Optimization of Classroom Usage in a Smart Campus

摘要

Student enrollments world-wide are increasing each year, while lecture attendance continues to fall, due to diverse demands on student time and easy access to online content. The resulting underutilization of classrooms entails cost penalties, especially in campuses where real-estate is at a premium. This paper outlines our efforts to instrument a University campus with sensors to measure classroom attendance, in a cost-effective and scalable manner without endangering student privacy. We begin by undertaking a lab evaluation of several approaches to measuring class occupancy, and compare them in terms of cost, accuracy, and ease of deployment and operation. We then instrument 9 lecture halls of varying capacity across campus, collect and clean live data on occupancy spanning about 250 courses over 12 weeks during session, and draw insights into attendance patterns, including identification of canceled lectures and class tests; our occupancy data is released openly to the public. Lastly, we show how classroom allocation can be optimized based on attendance rather than enrollments, resulting in potential savings of 52% in room costs.