Student Attention Monitoring System using Deep Learning

K.Salma Khatoon
Computer Science andEngineering(Data Science)Santhiram Engineering College
Nandyal,Indiaksalma273@gmail.com,
T.E.Bramha Koti Goud
Computer Science andEngineering(Data Scienece)
Santhiram Engineering CollegeNandyal,Indiabramhakoti3@gmail.com
B Harish Kumar Reddy
Computer Science andEngineeringK V Subba Reddy Institute of
Technology Kurnool,Indiaharish8099@gmail.com
S.Sajid Mujahid
Computer Science andEngineering(Data Scienece)Santhiram Engineering College
Nandyal,Indiashaiksajid523@gmail.com

T.Mehaboob Basha Computer
Science and Engineering(DataScienece)Santhiram Engineering College
Nandyal,Indiamehaboobbasha3260@gmail.com
S.Mustakheem
Computer Science andEngineering(Data Science)Santhiram Engineering College
Nandyal,Indiamustakheemshaik1@gamil.com

ABSTRACT— Student focus is a significant element that defines the effectiveness of the learning process, but conventional methods of  observation are also subjective and ineffective in cases of virtual learning or when there are many students in the classroom. The proposed project is a real-time student attention monitoring system, the implementation of which is based on deep learning and computer vision, which will automatically identify and analyze student engagement. The system employs a regular web camera to monitor behavioral indicators like openness of the eyes, mouth motions (or speaking or yawning) and the orientation of the head to categorizeeach student into being engaged or not. It integrates face detection of Haar Cascades and head pose estimation of deep learning to provide consistency during work in varying lightning and facial features. Also, in cases whereby a student is realized not to be attentive, the system captures and records the image of the student in real time, and the image is stored safely in the teacher login where he can access and track the student later. Attentive cases are also monitored by the system, and it also offers insights in the form of an interactive dashboard that shows summaries of the engagement and real-time graphs to aid in assessing the effectiveness of the teaching. It is a scalable solution that is designed to be low-priced, non-invasive, and only slightly involves the teacher, minimizing human bias, and encouraging improved engagement and education (physical classroom or online). On balance, the project shows that it is possible to teach smart and data-driven through the application ofdeep learning, which results in better teaching performance and student engagement.