Attentiveness Recognition System for Online Classes using OpenCV

During the pandemic, education was challenging for many institutions. While some of them even to closed, and were subjected to criticisms from multiple stakeholders, others persevered and embraced “Online-Education”. This seemed to work well for some institutes though others were struggling to cope with the new culture that was not well understood by both sides; the instructors as well as the students. Our sole objective of this undertaking is to ease the pressure on teachers and students, and help create a healthier ecosystem that further promotes progress in education. We have designed a tool that essentially picks up each student's emotions throughout the whole online class and calculate the degree of attentiveness of the student basing on multiple parameters. At the end of the class, the teacher gets an auto-generated report about their students. With the help of this report, teachers shall be able to come up with a counter measure to adjust accordingly by either fixing their teaching patterns or spending extra time with particular students. We used OpenCV and Convolutional Neural Networks for this system.