Random Interval Attendance Management System (RIAMS): A Novel Multimodal Approach for Post-COVID Virtual Learning

The exceptional circumstance caused by the COVID-19 pandemic demands substantial modifications in the teaching-learning processes across the globe. Teachers and students are making use of online learning in virtual classrooms as an alternative for face-to-face learning in physical classrooms. However, students' attendance management during virtual learning is a challenging problem. It is quite difficult to identify students' disengagement and even to know whether they are in front of their smart devices or not. In this paper, we introduce the 'Random Interval Attendance Management System' (RIAMS), which is an innovative solution for attendance monitoring issues, students' disengagement, and attendance faking during virtual learning. In RIAMS, we employed a face recognition module built using the Dlib open-source software library. In order to improve the efficiency of the system, we introduced two ancillary modalities - verifying students' responses to CAPTCHAs and UIN (Unique Identification Number) queries. Both the face recognition and ancillary modalities operate at random intervals of time. This distinctive feature of randomness in our design ensures that students' attention and engagement in virtual learning are enhanced. Furthermore, the RIAMS' multimodal architecture and its sub-modalities' adaptive weight system enable teachers to customize their attendance strategy for every course. The output analysis of each of the RIAMS modalities and the combined results emphasize the effectiveness and reliability of our system in the attendance management for virtual learning. The novel RIAMS model has the potential to be extensively deployed for virtual learning in post-COVID settings.