An Improved Genetic Algorithm and Neural Network-Based Evaluation Model of Classroom Teaching Quality in Colleges and Universities
Research on educational quality has gotten a lot of attention as the current higher education teaching reform continues to deepen and grow. The key to improving education quality is to improve teaching quality, and teacher evaluation is an important tool for doing so. As a result, educational manage...
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| Published in | Wireless communications and mobile computing Vol. 2021; no. 1 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Hindawi
2021
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1530-8669 1530-8677 1530-8677 |
| DOI | 10.1155/2021/2602385 |
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| Abstract | Research on educational quality has gotten a lot of attention as the current higher education teaching reform continues to deepen and grow. The key to improving education quality is to improve teaching quality, and teacher evaluation is an important tool for doing so. As a result, educational management requires the development and refinement of a system for evaluating teaching quality. Traditional approaches to assessing teaching quality, on the other hand, are problematic due to their limitations. As a result, a scientific and reasonable model for evaluating the teaching quality of college undergraduate teachers must be developed. We present a unique model for evaluating the quality of classroom teaching in colleges and universities, which is based on improved genetic algorithms and neural networks. The basic idea is to use adaptive mutation genetic algorithms to refine the initial weights and thresholds of the BP neural network. The teaching quality evaluation findings were improved by improving the neural network’s prediction accuracy and convergence speed, resulting in a more practical scheme for evaluating college and university teaching quality. We have conducted simulation experiments and comparative analysis, and the mean square error of the results of the proposed model is very low, which proves the effectiveness and superiority of the algorithm. |
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| AbstractList | Research on educational quality has gotten a lot of attention as the current higher education teaching reform continues to deepen and grow. The key to improving education quality is to improve teaching quality, and teacher evaluation is an important tool for doing so. As a result, educational management requires the development and refinement of a system for evaluating teaching quality. Traditional approaches to assessing teaching quality, on the other hand, are problematic due to their limitations. As a result, a scientific and reasonable model for evaluating the teaching quality of college undergraduate teachers must be developed. We present a unique model for evaluating the quality of classroom teaching in colleges and universities, which is based on improved genetic algorithms and neural networks. The basic idea is to use adaptive mutation genetic algorithms to refine the initial weights and thresholds of the BP neural network. The teaching quality evaluation findings were improved by improving the neural network’s prediction accuracy and convergence speed, resulting in a more practical scheme for evaluating college and university teaching quality. We have conducted simulation experiments and comparative analysis, and the mean square error of the results of the proposed model is very low, which proves the effectiveness and superiority of the algorithm. |
| Author | Zhang, Huaying Li, Jinqiong Hou, Min Xiao, Bin |
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| Cites_doi | 10.1080/02602930601117191 10.1109/TCBB.2019.2963873 10.31209/2019.100000138 10.1177/105268461202200307 10.52810/TPRIS.2021.100019 10.32604/csse.2019.34.231 10.1166/jctn.2017.6145 10.32604/csse.2020.35.237 10.1155/2021/9962057 10.1109/TCSVT.2020.3043026 10.32604/iasc.2020.013915 10.1097/01.NEP.0000000000000020 10.1142/S0218001418500428 10.32604/iasc.2020.012663 10.52810/TPRIS.2021.100009 10.1177/1469787404040463 10.1080/13538320600685024 10.1016/j.compedu.2018.07.016 10.1300/J082v22n03_03 10.2753/CED1061-1932420212 10.32604/iasc.2020.013926 10.31209/2018.100000067 10.32604/iasc.2020.010106 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 Huaying Zhang et al. Copyright © 2021 Huaying Zhang et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Adaptive algorithms Back propagation networks Classrooms Colleges & universities Education Error analysis Genetic algorithms Mutation Quality assessment Quality of education Teacher evaluations Teachers Teaching |
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| Title | An Improved Genetic Algorithm and Neural Network-Based Evaluation Model of Classroom Teaching Quality in Colleges and Universities |
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