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Application of Deep Learning on Student Engagement in e-learning environments
Computers & Electrical Engineering ( IF 4.3 ) Pub Date : 2021-06-22 , DOI: 10.1016/j.compeleceng.2021.107277
Prakhar Bhardwaj 1 , P K Gupta 1 , Harsh Panwar 1 , Mohammad Khubeb Siddiqui 2 , Ruben Morales-Menendez 2 , Anubha Bhaik 1
Affiliation  

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.



中文翻译:

深度学习在电子学习环境中对学生参与的应用

COVID-19 大流行的严重影响在我们生活的方方面面都可见,包括教育。随着电子学习的显着兴起,由于 COVID-19,教学方法正在数字平台上远程进行。为减少疫情对教育行业的影响,大部分教育机构已经开始在线授课。然而,为了使这些数字学习课程具有交互性并与传统的线下课堂相媲美,必须确保学生在在线课程中适当参与。在本文中,我们提出了新颖的基于深度学习的算法,可以实时监控学生的情绪,例如愤怒、厌恶、恐惧、快乐、悲伤和惊讶。这是通过所提出的最先进的算法来完成的,该算法通过分析从面部标志检测、情感识别和一个多小时的学生调查中获得的权重来计算平均参与分数 (MES)班级。拟议的自动化方法肯定会帮助教育机构实现改进和创新的数字学习方法。

更新日期:2021-06-22
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