Non-Intrusive Classroom Attention Tracking System (NiCATS)

• Published in: Proceedings - Frontiers in Education Conference pp. 1 - 9
• Main Authors: Sanders, Andrew, Boswell, Bradley, Walia, Gursimran Singh, Allen, Andrew
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.10.2021
• Subjects: Attention; Convolutional neural networks; Correlation; Education; Engagement; Eye Metrics; Eyetracking; Measurement; Real-time systems; Technological innovation; Webcams
• ISSN: 2377-634X
• DOI: 10.1109/FIE49875.2021.9637411

This Innovative Practice Full-Paper presents a system for real-time accurate detection of classroom attentiveness using monitor-mounted webcams and eye trackers. Academic institutions and instructors cannot accurately assess the moment-to-moment attentiveness of students in classrooms where students' faces are obscured by computer monitors. This can cause the lectures of Computer Science, Information Technology, or other lab-based courses to be incorrectly paced, which leads to students having overall poorer grasps of the subject material. We present a system for accurate detection of classroom attentiveness using monitor-mounted webcams and eye trackers. To determine correlations for the attentiveness judging system, we compare an initial attentiveness score produced by trained labelers using an image of the student's face with a series of calculated eye metrics to determine a final attentiveness score. Because the student webcam images and eye coordinates are synchronously collected with the lecture, this final attentiveness score is used to provide post-hoc feedback to instructors on the status of their students via time-series graphs displayed on the instructor's computer monitor. The proposed system is invaluable for institutions seeking to improve student education, instructors striving to improve the flow of lectures, and students seeking a more accommodating learning environment. The primary source of innovation from this system comes from the correlation of extracted eye metrics with the face images labeled for attentiveness. Research exists about determining attentiveness using a convolutional neural network trained on face images and even determining attentiveness by correlating face-image-trained outputs, each of which we plan to incorporate to make our system real-time in the future. This novel research could prove helpful for the field of education.