Your browser doesn't support javascript.
Application of Deep Learning on Student Engagement in e-learning environments.
Bhardwaj, Prakhar; Gupta, P K; Panwar, Harsh; Siddiqui, Mohammad Khubeb; Morales-Menendez, Ruben; Bhaik, Anubha.
  • Bhardwaj P; Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.
  • Gupta PK; Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.
  • Panwar H; Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.
  • Siddiqui MK; School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, N.L, Mexico.
  • Morales-Menendez R; School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, N.L, Mexico.
  • Bhaik A; Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.
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.
Keywords

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

Similar

MEDLINE

...
LILACS

LIS


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