Unobtrusive Students' Engagement Analysis in Computer Science Laboratory Using Deep Learning Techniques

Abstract

Nowadays, analysing the students' engagement using non-verbal cues is very popular and effective. There are several web camera based applications for predicting the students' engagement in an e-learning environment. But there are very limited works on analyzing the students' engagement using the video surveillance cameras in a teaching laboratory. In this paper, we propose a Convolutional Neural Networks based methodology for analysing the students' engagement using video surveillance cameras in a teaching laboratory. The proposed system is tested on five different courses of computer science and information technology with 243 students of NITK Surathkal, Mangalore, India. The experimental results demonstrate that there is a positive correlation between the students' engagement and learning, thus the proposed system outperforms the existing systems.