Real Time Attention Monitoring System in Classrooms

A real-time attention monitoring system in classrooms focuses on understanding how attentive students are during lectures using modern technology. It combines computer vision and machine learning to observe student behavior without interrupting the class. Cameras capture live video, and the system analyzes facial expressions, eye movement, and posture to detect attention levels. Instead of manual observation, the system automatically identifies whether students are focused or distracted. The collected data is processed instantly, allowing teachers to get real-time feedback on classroom engagement. This helps in identifying students who may be struggling to concentrate. A dashboard provides a simple visualization of attention levels, making it easier for teachers to adjust their teaching methods. At the same time, privacy and data protection are carefully considered while designing the system. It can be implemented in different classroom sizes and supports smart classroom environments. Overall, this system helps create a more interactive and effective learning experience by bridging the gap between teaching and student engagement.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-18
DOI
https://doi.org/10.5281/zenodo.20266406
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
0.00
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article

Real Time Attention Monitoring System in Classrooms

Miss.Yukta Gaikwad, Mr. Arjun Waman, Mr. D. B. Ghorpade, Mr.Akash Dange et al.
Zenodo (CERN European Organization for Nuclear Research)
Gaze Tracking and Assistive Technology
article

Real Time Attention Monitoring System in Classrooms

Miss.Yukta Gaikwad, Mr. Arjun Waman, Mr. D. B. Ghorpade, Mr.Akash Dange, Miss. Snehal Phad
article en

Abstract

A real-time attention monitoring system in classrooms focuses on understanding how attentive students are during lectures using modern technology. It combines computer vision and machine learning to observe student behavior without interrupting the class. Cameras capture live video, and the system analyzes facial expressions, eye movement, and posture to detect attention levels. Instead of manual observation, the system automatically identifies whether students are focused or distracted. The collected data is processed instantly, allowing teachers to get real-time feedback on classroom engagement. This helps in identifying students who may be struggling to concentrate. A dashboard provides a simple visualization of attention levels, making it easier for teachers to adjust their teaching methods. At the same time, privacy and data protection are carefully considered while designing the system. It can be implemented in different classroom sizes and supports smart classroom environments. Overall, this system helps create a more interactive and effective learning experience by bridging the gap between teaching and student engagement.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 4%
Gaze Tracking and Assistive Technology
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Real Time Attention Monitoring System in Classrooms — Miss.Yukta Gaikwad, Mr. Arjun Waman, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS