Research on Smart Classroom Attention Monitoring System Combining IoT Sensing and Machine Learning

DOI: https://doi.org/10.62517/jike.202604113

Author(s)

Liu Yan, Long Yanbin*

Affiliation(s)

Liaoning University of Science and Technology, Anshan, China *Corresponding Author

Abstract

This paper focuses on a smart classroom attention monitoring system combining IoT sensing and machine learning, elucidating its research background and significance, analyzing the theoretical basis of attention monitoring and the role mechanism of IoT sensing and machine learning in it. The system design is described in detail, including hardware architecture and software algorithms, and the system performance is verified through experiments. The results show good performance in terms of accuracy and real-time performance. This system can effectively improve teaching quality and provide new ideas for the development of smart education.

Keywords

IoT Sensing; Machine Learning; Smart Classroom; Attention Monitoring System

References

[1] Zhou Wen; Jia Wenbo. AIGC-driven VR image intelligent narrative: an innovative form. Audiovisual Theory and Practice, 2024(04)12-13 [2] Liu Huicong; Wang Fengxia; Li Dongsheng; Chi Wenzheng; Sun Lining. Research status and development trend of robot perception technology based on MEMS sensors. Intelligent Sensing Engineering, 2024(01)20-34 [3] Song Aiguo; Lai Jianwei; Wu Peiyang. Research progress on human-computer interaction technology of soft hand rehabilitation robot based on intelligent perception. Intelligent Perception Engineering, 2025(01)45-47 [4] Wang Yuan; Wang Cuixiu. Application and research progress of machine learning in dynamic monitoring of labor. Chinese Journal of Obstetrics and Gynecology, 2025(06)3-4 [5] Zhang Siyu; Li Ri. Application of machine learning in the research of lightweight alloys. Progress in Materials, 2025(11)12-17 [6] Kang Jinzheng; Mo Shaoxing; Kang Xueyuan; Dang Jingxuan; Cheng Chijitai; Xu Peijie; Shi Xiaoqing. Frontier progress and challenges of machine learning in groundwater science. Earth Science Frontiers, 2026(01)4-6 [7] Wang Rui; Wang Zhikai; Zhong Yiming; Sun Hui; Yang Kaixin. Research on demand prediction of aircraft spare parts based on machine learning. Computer Science, 2025(S2)22-23 [8] Yang Qizhong; Ma Beili Machine learning based identification and motivation mining of the phenomenon of "greenwashing" in enterprises Statistics and Decision making, 2025 (21) 17- [9] Zhang Dongli; Rong Qing Exploration and explanation of factors influencing online self disclosure frequency based on machine learning Computer Programming Skills and Maintenance, 2025 (11) 45-46 [10] Zhu Yuanyuan; Luo Yong. Development of Software for Economic and Environmental Coordination Analysis in Wuhan Based on Machine Learning. Software, 2025(11)33-34