Razzaq Saad, Shah Babar, Iqbal Farkhund, Ilyas Muhammad, Maqbool Fahad, Rocha Alvaro
Department of Computer Science & IT, University of Sargodha, Sargodha, Pakistan.
College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates.
Neural Comput Appl. 2023;35(11):8017-8026. doi: 10.1007/s00521-021-06754-5. Epub 2022 Jan 7.
A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart classrooms to enhance faculty efficiency based on accumulated learning outcomes and interests. Smart classroom boards, audio-visual aids, and multimedia are directly related to the Smart classroom environment. Along with these facilities, more effort is required to monitor and analyze students' outcomes, teachers' performance, attendance records, and contents delivery in on-campus classrooms. One can achieve more improvement in quality teaching and learning outcomes by developing digital twins in on-campus classrooms. In this article, we have proposed DeepClass-Rooms, a digital twin framework for attendance and course contents monitoring for the public sector schools of Punjab, Pakistan. DeepClassRooms is cost-effective and requires RFID readers and high-edge computing devices at the Fog layer for attendance monitoring and content matching, using convolution neural network for on-campus and online classes.
为了通过课堂监控提高教育水平，人们选择了许多不同的方法。发达国家利用智能教室，根据积累的学习成果和兴趣提高教师效率。智能教室黑板、视听教具和多媒体与智能教室环境直接相关。除了这些设施外，还需要付出更多努力来监控和分析学生成绩、教师表现、出勤记录以及校内课堂的内容传授情况。通过在校内教室开发数字孪生，可以在优质教学和学习成果方面取得更大的进步。在本文中，我们提出了DeepClass-Rooms，这是一个用于巴基斯坦旁遮普省公立学校考勤和课程内容监控的数字孪生框架。DeepClassRooms具有成本效益，在雾层需要射频识别（RFID）阅读器和高端计算设备来进行考勤监控和内容匹配，并使用卷积神经网络来处理校内和在线课程。
