Uploaded May 2021 | Updated September 2026, 2 hours ago
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.
Learn more: edusense.io/digital-twins
Citation:
Karan Ahuja, Deval Shah, Sujeath Pareddy, Franceska Xhakaj, Amy Ogan, Yuvraj Agarwal, and Chris Harrison. 2021. Classroom Digital Twins with Instrumentation-Free Gaze Tracking. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 484, 1–9. DOI:doi.org/10.1145/3411764.3445711
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.
Learn more: edusense.io/digital-twins
Citation:
Karan Ahuja, Deval Shah, Sujeath Pareddy, Franceska Xhakaj, Amy Ogan, Yuvraj Agarwal, and Chris Harrison. 2021. Classroom Digital Twins with Instrumentation-Free Gaze Tracking. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 484, 1–9. DOI:doi.org/10.1145/3411764.3445711
![TouchTools [CMU/Qeexo]](https://i.ytimg.com/vi/N8s8NJf34fM/mqdefault.jpg "TouchTools [CMU/Qeexo]
Gestures on todays touch devices are simplistic, solely relying on the number of fingers (1-finger pan, 2-finger zoom, 3-finger swipe, etc). We propose instead that gesture design be inspired by the manipulation of real world artifacts specifically tools. The result is a set of rich gestures for touch interaction that leverages user familiarity and fluency with real world objects.")
