Framework for identifying and visualising emotional atmosphere in online learning environments in the COVID-19 Era.

Fei Yan, Nan Wu, Abdullah M Iliyasu, Kazuhiko Kawamoto, Kaoru Hirota
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引用次数: 4

Abstract

In addition to the almost five million lives lost and millions more than that in hospitalisations, efforts to mitigate the spread of the COVID-19 pandemic, which that has disrupted every aspect of human life deserves the contributions of all and sundry. Education is one of the areas most affected by the COVID-imposed abhorrence to physical (i.e., face-to-face (F2F)) communication. Consequently, schools, colleges, and universities worldwide have been forced to transition to different forms of online and virtual learning. Unlike F2F classes where the instructors could monitor and adjust lessons and content in tandem with the learners' perceived emotions and engagement, in online learning environments (OLE), such tasks are daunting to undertake. In our modest contribution to ameliorate disruptions to education caused by the pandemic, this study presents an intuitive model to monitor the concentration, understanding, and engagement expected of a productive classroom environment. The proposed apposite OLE (i.e., AOLE) provides an intelligent 3D visualisation of the classroom atmosphere (CA), which could assist instructors adjust and tailor both content and instruction for maximum delivery. Furthermore, individual learner status could be tracked via visualisation of his/her emotion curve at any stage of the lesson or learning cycle. Considering the enormous emotional and psychological toll caused by COVID and the attendant shift to OLE, the emotion curves could be progressively compared through the duration of the learning cycle and the semester to track learners' performance through to the final examinations. In terms of learning within the CA, our proposed AOLE is assessed within a class of 15 students and three instructors. Correlation of the outcomes reported with those from administered questionnaires validate the potential of our proposed model as a support for learning and counselling during these unprecedentedtimes that we find ourselves.

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新冠肺炎时代在线学习环境情感氛围识别和可视化框架
除了近500万人丧生和数百万多人住院之外,缓解COVID-19大流行的努力也值得所有人做出贡献。COVID-19大流行扰乱了人类生活的方方面面。教育是受新冠疫情影响最严重的领域之一,人们对身体(即面对面)交流感到厌恶。因此,世界各地的学校、学院和大学被迫过渡到不同形式的在线和虚拟学习。在在线学习环境(OLE)中,教师可以根据学习者的感知情绪和参与程度来监控和调整课程和内容,而在在线学习环境(F2F)中,这些任务是令人望而生畏的。本研究提出了一个直观的模型,用于监测富有成效的课堂环境所期望的注意力、理解和参与度,这是我们为改善疫情对教育造成的干扰所做的微薄贡献。拟议的相应OLE(即AOLE)提供了教室氛围(CA)的智能3D可视化,这可以帮助教师调整和定制内容和教学,以最大限度地交付。此外,个体学习者的状态可以通过可视化他/她在课程或学习周期的任何阶段的情绪曲线来跟踪。考虑到COVID造成的巨大情绪和心理损失以及随之而来的OLE转变,可以在学习周期和学期的持续时间内逐步比较情绪曲线,以跟踪学习者的表现直到期末考试。就CA内的学习而言,我们建议的AOLE是在一个由15名学生和3名教师组成的班级中进行评估的。报告的结果与管理问卷的结果的相关性验证了我们提出的模型在我们发现自己处于前所未有的时期支持学习和咨询的潜力。
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