AI-Powered Student Monitoring for Smart Classrooms using ML

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G.Manisha; Busala Yashwanth Kumar; E. Karthik Sai; C. Nikhil; Mr. K. Suresh Babu

AI-Powered Student Monitoring for Smart Classrooms using ML

Authors: G.Manisha, Busala Yashwanth Kumar, E. Karthik Sai, C. Nikhil, Mr. K. Suresh Babu

Unique Paper ID: 179890
Volume: 11
Issue: 12
PageNo: 8622-8630

Keywords: Computer vision Deep learning Real-time notifications FaceNet MediaPipe.

Abstract:
Student behavior plays a vital role in improving and maintaining student involvement and discipline for better academic outcomes. The current study proposes an integrated framework that detects activities like sleeping, eating, and using mobiles during class hours and notifies the faculty in real time. It also tracks student attendance. The integration of Artificial Intelligence has become essential in classrooms. This system integrates computer vision algorithms, which includes YOLOv8 for object detection, MediaPipe for facial and hand movement recognition and Twilio WhatsApp API, which sends notifications directly to the faculty whenever a student is detected doing any inappropriate activity like above mentioned activities. In this study, we collected classroom footage and applied techniques like frame filtering, face alignment and activity annotation. We compared deep learning models like YOLOv7, YOLOv8 and EfficientDet for the highest performance; among these, YOLOv8 has the highest detection accuracy of 94.87%, recall of 95.76% and an F1-score of 94.01%. Face recognition models like FaceNet and Dlib are used for attendance tracking, with FaceNet achieving an accuracy of 96.45%. Compared to old manual monitoring techniques, our system delivers better accuracy and enables faculty response faster. This system covers the gap in traditional classroom monitoring and provides an innovative and automatic solution to improve student discipline and engagement. Supporting swift action while lightening the load of faculty, this solution combines deep learning and real-time notifications. This research makes a noteworthy contribution to the development of intelligent educational environments and encourages the use of ethical AI to develop more accurate smart analysis approach models on data up to October 2023.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 8622-8630

AI-Powered Student Monitoring for Smart Classrooms using ML

Available:https://ijirt.org/article?manuscript=179890

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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