College Smart Classroom Attendance Management System Based on Internet of Things

Since entering the information age, educational informatization reform has become the inevitable trend of the development of colleges and universities. The traditional education management methods, especially the classroom attendance methods, not only need to rely on a large number of manpower for d...

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Autores principales: Zhao, Mingtao, Zhao, Gang, Qu, Meihong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9276505/
https://www.ncbi.nlm.nih.gov/pubmed/35837210
http://dx.doi.org/10.1155/2022/4953721
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author Zhao, Mingtao
Zhao, Gang
Qu, Meihong
author_facet Zhao, Mingtao
Zhao, Gang
Qu, Meihong
author_sort Zhao, Mingtao
collection PubMed
description Since entering the information age, educational informatization reform has become the inevitable trend of the development of colleges and universities. The traditional education management methods, especially the classroom attendance methods, not only need to rely on a large number of manpower for data collection and analysis but also cannot dynamically monitor students' attendance and low efficiency. The development of Internet of things technology provides technical support for the informatization reform of education management in colleges and universities and makes the classroom attendance management in colleges and universities have a new development direction. In this study, a college smart classroom attendance management system based on RFID technology and face recognition technology is constructed under the architecture of the Internet of things, and the corresponding simulation experiments are carried out. The experimental results show that the smart classroom attendance management system based on RFID technology can accurately identify the absence and substitution of students and has the advantages of fast response and low cost. However, its recognition is easily affected by obstructions, which requires students to place identification cards uniformly. The smart classroom attendance management system based on face recognition technology can accurately record and identify the situation of students entering and leaving the classroom and identify the situations of being late and leaving early, absenteeism, and substitute classes. The experimental results are basically consistent with the sample results, and the error rate is low. However, the system is easily affected by environmental light, students' sitting posture, expression, and other factors, so it cannot be recognized. Generally speaking, both can meet the needs of classroom attendance in colleges and universities and have high accuracy and efficiency.
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spelling pubmed-92765052022-07-13 College Smart Classroom Attendance Management System Based on Internet of Things Zhao, Mingtao Zhao, Gang Qu, Meihong Comput Intell Neurosci Research Article Since entering the information age, educational informatization reform has become the inevitable trend of the development of colleges and universities. The traditional education management methods, especially the classroom attendance methods, not only need to rely on a large number of manpower for data collection and analysis but also cannot dynamically monitor students' attendance and low efficiency. The development of Internet of things technology provides technical support for the informatization reform of education management in colleges and universities and makes the classroom attendance management in colleges and universities have a new development direction. In this study, a college smart classroom attendance management system based on RFID technology and face recognition technology is constructed under the architecture of the Internet of things, and the corresponding simulation experiments are carried out. The experimental results show that the smart classroom attendance management system based on RFID technology can accurately identify the absence and substitution of students and has the advantages of fast response and low cost. However, its recognition is easily affected by obstructions, which requires students to place identification cards uniformly. The smart classroom attendance management system based on face recognition technology can accurately record and identify the situation of students entering and leaving the classroom and identify the situations of being late and leaving early, absenteeism, and substitute classes. The experimental results are basically consistent with the sample results, and the error rate is low. However, the system is easily affected by environmental light, students' sitting posture, expression, and other factors, so it cannot be recognized. Generally speaking, both can meet the needs of classroom attendance in colleges and universities and have high accuracy and efficiency. Hindawi 2022-07-05 /pmc/articles/PMC9276505/ /pubmed/35837210 http://dx.doi.org/10.1155/2022/4953721 Text en Copyright © 2022 Mingtao Zhao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhao, Mingtao
Zhao, Gang
Qu, Meihong
College Smart Classroom Attendance Management System Based on Internet of Things
title College Smart Classroom Attendance Management System Based on Internet of Things
title_full College Smart Classroom Attendance Management System Based on Internet of Things
title_fullStr College Smart Classroom Attendance Management System Based on Internet of Things
title_full_unstemmed College Smart Classroom Attendance Management System Based on Internet of Things
title_short College Smart Classroom Attendance Management System Based on Internet of Things
title_sort college smart classroom attendance management system based on internet of things
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9276505/
https://www.ncbi.nlm.nih.gov/pubmed/35837210
http://dx.doi.org/10.1155/2022/4953721
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