Evaluation Of Student Focus Level In A Virtual Classroom Using Computer Vision

Abstract

"The gap between physical and virtual classrooms have grown thin in the recent years due to the advancement of technology, remote learning and convenience. Even having many advantages over a physical classroom in many ways, the leading reason for teachers and students prefer a real classroom is the teacher’s inability to monitor student focus level in a virtual classroom. Almost all of the existing solutions tend to use video or image classification methods varying from binary to multi-class approaches or regression. Presented here a novel approach for student focus evaluation for virtual classrooms using Computer Vision involving a Spatiotemporal Convolutional Autoencoder - Pose estimator hybrid model by considering the evaluation problem as a video anomaly detection problem. The paper offers a Deep Learning models can be implemented in a system student focus levels and present them to the teacher allowing the teacher to be more aware of the student focus levels, dynamically shifting teaching methods to keep more students engaged with the learning materials, increasing the effectiveness of the class. Observed from the tests conducted for the EmotiW dataset, the system can achieve area under the curve of the receiver operating characteristic curve value of ~0.8, area under the curve of the precision–recall curve value of 0.53 and a False Alarm rate of 0.02 which is an improvement on the existing systems, suggesting that considering the focus evaluation problem as a video anomaly detection problem is a success and should be researched further. Although there is room for improvement by using different video anomaly detection methods and integrating active learning to the system."