Application of Deep Learning on Student Engagement in e-learning environments.
Comput Electr Eng
; 93: 107277, 2021 Jul.
Article
in English
| MEDLINE | ID: covidwho-1275234
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Observational study
/
Prognostic study
Language:
English
Journal:
Comput Electr Eng
Year:
2021
Document Type:
Article
Affiliation country:
J.compeleceng.2021.107277
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