Attentiveness monitoring and user record maintenance in virtual classrooms

Abstract

Virtual classrooms rely on video conferencing tools like Google Meet, Microsoft Teams or Zoom, to carry out lecture sessions. Although previously it was an additional tool, the pandemic scenario made it the primary mode of taking classes. It is observed that lecturers find it hard to monitor and look after every student in such a scenario due to divided webcam feeds, simultaneous presentation and replying on chats. An automated monitoring system can aid in such a scenario where the user's local system can be used for localized calculation of attentiveness using face and eye detection. A simulated back-end architecture is developed to propose a proof-of-concept for the same using a path compressed master-Trie structure. © 2021 IEEE.