Tan Chen, Lin Jianzhong
Shanghai Jiao Tong University, Shanghai, 200040 China.
Soft comput. 2023;27(3):1699-1713. doi: 10.1007/s00500-021-05932-w. Epub 2021 Jun 10.
Today, emerging technologies such as 5G Internet of things (IoT), virtual reality and cloud-edge computing have enhanced and upgraded higher education environments in universities, colleagues and research centers. Computer-assisted learning systems with aggregating IoT applications and smart devices have improved the e-learning systems by enabling remote monitoring and screening of the behavioral aspects of teaching and education scores of students. On the other side, educational data mining has improved the higher education systems by predicting and analyzing the behavioral aspects of teaching and education scores of students. Due to an unexpected and huge increase in the number of patients during coronavirus (COVID-19) pandemic, all universities, campuses, schools, research centers, many scientific collaborations and meetings have closed and forced to initiate online teaching, e-learning and virtual meeting. Due to importance of behavioral aspects of teaching and education between lecturers and students, prediction of quality of experience (QoE) in virtual education systems is a critical issue. This paper presents a new prediction model to detect technical aspects of teaching and e-learning in virtual education systems using data mining. Association rules mining and supervised techniques are applied to detect efficient QoE factors on virtual education systems. The experimental results described that the suggested prediction model meets the proper accuracy, precision and recall factors for predicting the behavioral aspects of teaching and e-learning for students in virtual education systems.
如今，诸如5G物联网（IoT）、虚拟现实和云边缘计算等新兴技术已经提升和升级了大学、学院及研究中心的高等教育环境。结合物联网应用和智能设备的计算机辅助学习系统，通过实现对学生教学行为方面和教育成绩的远程监控与筛选，改进了电子学习系统。另一方面，教育数据挖掘通过预测和分析学生教学行为方面和教育成绩，改进了高等教育系统。由于在冠状病毒病（COVID-19）大流行期间患者数量意外大幅增加，所有大学、校园、学校、研究中心、许多科研合作及会议都已关闭，并被迫启动在线教学、电子学习和虚拟会议。鉴于教师与学生之间教学行为方面的重要性，虚拟教育系统中的体验质量（QoE）预测是一个关键问题。本文提出一种新的预测模型，用于使用数据挖掘来检测虚拟教育系统中教学和电子学习的技术方面。应用关联规则挖掘和监督技术来检测虚拟教育系统中的有效QoE因素。实验结果表明，所提出的预测模型在预测虚拟教育系统中学生教学和电子学习行为方面时，满足适当的准确率、精确率和召回率因素。
