Karan Ahuja
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Classroom sensing is an important and active area of research with great potential to improve instruction. Complementing professional observers - the current best practice - automated pedagogical professional development systems can attend every class and capture fine-grained details of all occupants. One particularly valuable facet to capture is class gaze behavior. For students, certain gaze patterns have been shown to correlate with interest in the material, while for instructors, student-centered gaze patterns have been shown to increase approachability and immediacy. Unfortunately, prior classroom gaze-sensing systems have limited accuracy and often require specialized external or worn sensors. In this work, we developed a new computer-vision-driven system that powers a 3D “digital twin” of the classroom and enables whole-class, 6DOF head gaze vector estimation without instrumenting any of the occupants. We describe our open source implementation, and results from both controlled studies and real-world classroom deployments.
https://doi.org/10.1145/3411764.3445711
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)