Evaluation Algorithm of Teaching Work Quality in Colleges and Universities Based on Deep Denoising Autoencoder Network
One of the most significant components of the teaching department’s evaluation of teaching quality is evaluating teachers’ performance. With the acceleration of educational informatization, modern information processing technology can be used effectively to evaluate teachers’ teaching quality in tra...
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| Veröffentlicht in: | Mobile information systems Jg. 2021; S. 1 - 7 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Amsterdam
Hindawi
2021
John Wiley & Sons, Inc |
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| ISSN: | 1574-017X, 1875-905X |
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| Abstract | One of the most significant components of the teaching department’s evaluation of teaching quality is evaluating teachers’ performance. With the acceleration of educational informatization, modern information processing technology can be used effectively to evaluate teachers’ teaching quality in traditional teaching. In this context, combined with some computational intelligence algorithms, it is critical to developing a targeted teaching quality evaluation system. This paper studies teacher teaching evaluation’s characteristics and existing problems and analyzes the fundamental theories and methods of teacher teaching evaluation in colleges and universities. A novel combination of deep denoising autoencoder and support vector machine was proposed for evaluating teacher’s teaching quality. Moreover, support vector regression is used to predict the model’s output layer to achieve supervised assessment prediction. To capture the data’s key properties, the model comprises numerous hidden layers and conducts various feature transformations during unsupervised training to minimize the mean square error between the reconstructed output data and the original input data. As a result, the proposed model achieved the highest recognition accuracy of 85.23% and convergence compared to other models. Thus, the method can be employed to evaluate and forecast the quality of university teaching activity successfully. |
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| AbstractList | One of the most significant components of the teaching department’s evaluation of teaching quality is evaluating teachers’ performance. With the acceleration of educational informatization, modern information processing technology can be used effectively to evaluate teachers’ teaching quality in traditional teaching. In this context, combined with some computational intelligence algorithms, it is critical to developing a targeted teaching quality evaluation system. This paper studies teacher teaching evaluation’s characteristics and existing problems and analyzes the fundamental theories and methods of teacher teaching evaluation in colleges and universities. A novel combination of deep denoising autoencoder and support vector machine was proposed for evaluating teacher’s teaching quality. Moreover, support vector regression is used to predict the model’s output layer to achieve supervised assessment prediction. To capture the data’s key properties, the model comprises numerous hidden layers and conducts various feature transformations during unsupervised training to minimize the mean square error between the reconstructed output data and the original input data. As a result, the proposed model achieved the highest recognition accuracy of 85.23% and convergence compared to other models. Thus, the method can be employed to evaluate and forecast the quality of university teaching activity successfully. |
| Author | Liu, Yu |
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| Cites_doi | 10.2478/cait-2014-0037 10.1016/j.caeai.2021.100009 10.1155/2021/9962057 10.1016/j.eswa.2012.07.059 10.1177/0022487118755699 10.1109/JBHI.2021.3069629.2021 10.52810/tc.2021.100024 10.3991/ijet.v15i01.12533 10.3390/s21041129 10.1080/10508406.2019.1573730 10.52810/tiot.2021.100035 10.4300/jgme-d-17-00128.1 10.1186/s41239-017-0041-6 10.1016/j.knosys.2017.11.010 10.1007/s12528-018-9197-x 10.1007/s00521-021-05933-8.2021 10.1166/jctn.2016.5939 10.1609/aaai.v34i07.6991 10.1016/j.learninstruc.2017.08.001 10.1109/compcomm.2016.7924899 |
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| Copyright | Copyright © 2021 Yu Liu. Copyright © 2021 Yu Liu. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| References | 23 24 25 10 Y. Zeng (11) 2020; 15 13 14 C. Dede (17) 15 16 Z. Wang (21) 19 G. Dongjun (12) 2021; 16 X. Ning (22) 2021 1 2 Y. Zhang (18) 2020; 31 3 4 5 6 7 8 9 20 |
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| SubjectTerms | Algorithms Artificial intelligence Colleges & universities Data processing Noise reduction Performance evaluation Quality assessment Quality of education Sentiment analysis Students Support vector machines Teacher evaluations Teachers Teaching Teaching methods |
| Title | Evaluation Algorithm of Teaching Work Quality in Colleges and Universities Based on Deep Denoising Autoencoder Network |
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