Providing university teachers with high-quality opportunities for professional development cannot happen without data about the classroom environment. Currently, the most effective mechanism is for an expert to observe one or more lectures and provide personalized formative feedback to the instructor. Of course, this is expensive and unscalable, and perhaps most critically, precludes a continuous learning feedback loop for the instructor. In this paper, we present the culmination of two years of research and development on EduSense, a comprehensive sensing system that produces a plethora of theoretically-motivated visual and audio features correlated with effective instruction, which could feed professional development tools in much the same way as a Fitbit sensor reports step count to an end user app. Although previous systems have demonstrated some of our features in isolation, EduSense is the first to unify them into a cohesive, real-time, in-the-wild evaluated, and practically-deployable system. Our two studies quantify where contemporary machine learning techniques are robust, and where they fall short, illuminating where future work remains to bring the vision of automated classroom analytics to reality.

Learn more about the open source project: www.edusense.io

Research Team: Karan Ahuja, Dohyun Kim, Franceska Xhakaj, Virag Varga, Anne Xie, Stanley Zhang, Jay Eric Townsend, Chris Harrison, Amy Ogan, and Yuvraj Agarwal