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Using Student Action Recognition to Enhance the Efficiency of Tele-education

Due to the COVID-19 pandemic, many schools worldwide are using tele-education for class delivery. However, this causes a problem related to students' active class participation. We propose to address the problem with a system that recognizes student's actions and informs the teacher accordingly, while preserving the privacy of students. In the proposed action recognition system, seven typical actions performed by students attending online courses, are recognized using Convolutional Neural Network (CNN) architectures. The actions considered were defined by considering the relevant literature and educator's views, and ensure that they provide information about the physical presence, active participation, and distraction of students, that constitute important pedagogical aspects of class delivery. The action recognition process is performed locally on the device of each student, thus it is imperative to use classification methods that require minimal computational load and memory requirements. Initial experimental results indicate that the proposed action recognition system provides promising classification results, when dealing with new instances of previously enrolled students or when dealing with previously unseen students.