Application of Deep Learning on Student Engagement in e-learning environments

Prakhar Bhardwaj; P K Gupta; Harsh Panwar; Mohammad Khubeb Siddiqui; Ruben Morales-Menendez; Anubha Bhaik

doi:10.1016/j.compeleceng.2021.107277

Application of Deep Learning on Student Engagement in e-learning environments

Comput Electr Eng. 2021 Jul:93:107277. doi: 10.1016/j.compeleceng.2021.107277. Epub 2021 Jun 22.

Authors

Prakhar Bhardwaj¹, P K Gupta¹, Harsh Panwar¹, Mohammad Khubeb Siddiqui², Ruben Morales-Menendez², Anubha Bhaik¹

Affiliations

¹ Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.
² School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, N.L, Mexico.

Abstract

The drastic impact of COVID-19 pandemic is visible in all aspects of our lives including education. With a distinctive rise in e-learning, teaching methods are being undertaken remotely on digital platforms due to COVID-19. To reduce the effect of this pandemic on the education sector, most of the educational institutions are already conducting online classes. However, to make these digital learning sessions interactive and comparable to the traditional offline classrooms, it is essential to ensure that students are properly engaged during online classes. In this paper, we have presented novel deep learning based algorithms that monitor the student's emotions in real-time such as anger, disgust, fear, happiness, sadness, and surprise. This is done by the proposed novel state-of-the-art algorithms which compute the Mean Engagement Score (MES) by analyzing the obtained results from facial landmark detection, emotional recognition and the weights from a survey conducted on students over an hour-long class. The proposed automated approach will certainly help educational institutions in achieving an improved and innovative digital learning method.

Keywords: COVID-19; Deep learning; Digital learning; Emotion recognition; Engage Detection; Engagement detection.