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

• Published in: Computers & electrical engineering Vol. 93; p. 107277
• Main Authors: Bhardwaj, Prakhar, Gupta, P.K., Panwar, Harsh, Siddiqui, Mohammad Khubeb, Morales-Menendez, Ruben, Bhaik, Anubha
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.07.2021; Elsevier BV
• Subjects: Algorithms; Coronaviruses; COVID-19; Deep learning; Digital learning; Distance learning; Emotion recognition; Engage Detection; Engagement detection; Machine learning; Online instruction; Pandemics; Students; Teaching methods; Deep learning; COVID-19; Engage Detection; Emotion recognition; Digital learning; Engagement detection
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2021.107277

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.