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IT Monitoring and Management of Learning Quality of Online Courses for College Students Based on Machine Learning
Mobile Information Systems ( IF 1.863 ) Pub Date : 2022-9-28 , DOI: 10.1155/2022/5501322
Jinpeng Guo, Ting Xu

Network information technology and distance technology learning provide convenience for college students to learn online courses, but some problems have also been found in practice, and schools need to pay attention to improving students’ learning quality and supervision. The cross-spatial nature of the study can be used to study how to detect students’ learning fatigue and learning concentration in online classrooms. This paper first designs a lightweight convolutional neural network model for eye state classification and verifies the performance of the model. The designed model has a compact structure and a high recognition rate. Combined with the human eye positioning algorithm, the recognition of the opening and closing state of the eyes is realized. Finally, the feasibility of using the PERCLOS value for fatigue detection, Euler pitch angle, and yaw is verified by experiments. Corners can be used to detect student attention. The method can enhance the synergistic supervision of other cooperative methods, thus improving the quality and effectiveness of online learning for college students, promoting the development of digital modern teaching and learning management models, and exploring possible future technologies and corresponding changes in teaching methods and management models.

中文翻译:

基于机器学习的大学生在线课程学习质量的IT监控与管理

网络信息技术和远程技术学习为大学生学习网络课程提供了便利,但在实践中也发现了一些问题,学校需要注意提高学生的学习质量和监督。该研究的跨空间特性可用于研究如何检测学生在在线课堂中的学习疲劳和学习集中度。本文首先设计了一种用于眼状态分类的轻量级卷积神经网络模型,并验证了模型的性能。设计的模型结构紧凑,识别率高。结合人眼定位算法,实现对眼睛开合状态的识别。最后,使用PERCLOS值进行疲劳检测的可行性,欧拉俯仰角,偏航通过实验验证。角落可以用来检测学生的注意力。该方法可以增强其他合作方式的协同监督,从而提高大学生在线学习的质量和效果,促进数字化现代教学管理模式的发展,探索未来可能的技术和教学方法和管理的相应变革楷模。
更新日期:2022-09-28
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