Student Activity Monitoring in Online Lectures Using Computer Vision and Internet of Things

Abstract

Since the onset of the coronavirus pandemic, education in schools and colleges have been imparted through different online platforms like Zoom, MS teams, skype and more. Students have found ways to avoid attending lectures by manipulating camera angles, putting up recordings of their faces, fiddling with their phones and laptops, joining late during online classes or even napping during lectures. It is impossible for the faculty members to keep an eye on fifty or more students and teach the class at the same time. It is difficult to track record of how much attention each student is paying during classes. Many students waste plenty of time sitting idle in front of the teachers. They and their parents need to be made aware of how much portion of the class their ward is actually attentive and how much they are not. Teachers need to know and keep track of their students' activities during class and monitor their performances. Through this prototype we aim to solve and tackle the aforementioned problems and barriers that teachers and professors face to properly counsel their students during the pandemic or online lectures as a whole. OpenCV face recognition, tracking, eye detection will be used to read facial textures of the students during online classes. Immediate notification will be sent to student's phone via mail if they are not paying attention in class. IFT TT/ SMTP will be used to send messages through cross-devices. A mobile application on the teacher's phone will keep track of students' activities.