Intelligent evaluation of teaching based on multi-networks integration
It is of great significance to develop a scientific and reasonable evaluation system for improving the teaching quality in colleges or universities. Traditional teaching evaluation systems are always limited to subjectivity and injustice for single dimensional evaluation index, subjective weight set...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2020-06-01
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| Series: | International Journal of Cognitive Computing in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666307420300024 |
| _version_ | 1797976446602313728 |
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| author | Guannan Li Lin Xiang Zhengxing Yu Hui Li |
| author_facet | Guannan Li Lin Xiang Zhengxing Yu Hui Li |
| author_sort | Guannan Li |
| collection | DOAJ |
| description | It is of great significance to develop a scientific and reasonable evaluation system for improving the teaching quality in colleges or universities. Traditional teaching evaluation systems are always limited to subjectivity and injustice for single dimensional evaluation index, subjective weight setting, and inefficient integration. In this paper, neural networks are introduced to evaluate teaching quality in an objective way. Firstly, three sub-networks S-NET, P-NET and L-NET are trained on the evaluation sets of students, peers, and leaders. To boost the performance of a single network, we offer an integrating way to synthesize the results of three sub-networks. The integrated network I-NET is trained on the multi-dimensional data sets to acquaint comprehensive evaluations. The experimental results demonstrate the feasibility of three sub-networks with the results that the average error rate of S-NET is 1.457%, P-NET is 1.003% and L-NET is 1.528%. To be further, the prominent evaluation ability of I-NET is also verified with the accuracy of 98.59% on the verification data sets. Finally, online teaching evaluating system based on the SSM framework is designed to provide a convenient and friendly interface. |
| first_indexed | 2024-04-11T04:50:59Z |
| format | Article |
| id | doaj.art-5d725015dd4c468f90a684636fb5bc4a |
| institution | Directory Open Access Journal |
| issn | 2666-3074 |
| language | English |
| last_indexed | 2024-04-11T04:50:59Z |
| publishDate | 2020-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Cognitive Computing in Engineering |
| spelling | doaj.art-5d725015dd4c468f90a684636fb5bc4a2022-12-27T04:37:08ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742020-06-011917Intelligent evaluation of teaching based on multi-networks integrationGuannan Li0Lin Xiang1Zhengxing Yu2Hui Li3Huaiyin Institute of Technology, Huai'an, Jiangsu 223003, ChinaCorresponding author.; Huaiyin Institute of Technology, Huai'an, Jiangsu 223003, ChinaHuaiyin Institute of Technology, Huai'an, Jiangsu 223003, ChinaHuaiyin Institute of Technology, Huai'an, Jiangsu 223003, ChinaIt is of great significance to develop a scientific and reasonable evaluation system for improving the teaching quality in colleges or universities. Traditional teaching evaluation systems are always limited to subjectivity and injustice for single dimensional evaluation index, subjective weight setting, and inefficient integration. In this paper, neural networks are introduced to evaluate teaching quality in an objective way. Firstly, three sub-networks S-NET, P-NET and L-NET are trained on the evaluation sets of students, peers, and leaders. To boost the performance of a single network, we offer an integrating way to synthesize the results of three sub-networks. The integrated network I-NET is trained on the multi-dimensional data sets to acquaint comprehensive evaluations. The experimental results demonstrate the feasibility of three sub-networks with the results that the average error rate of S-NET is 1.457%, P-NET is 1.003% and L-NET is 1.528%. To be further, the prominent evaluation ability of I-NET is also verified with the accuracy of 98.59% on the verification data sets. Finally, online teaching evaluating system based on the SSM framework is designed to provide a convenient and friendly interface.http://www.sciencedirect.com/science/article/pii/S2666307420300024Teaching evaluation in colleges and universitiesMulti-networks integratedTeaching evaluation index |
| spellingShingle | Guannan Li Lin Xiang Zhengxing Yu Hui Li Intelligent evaluation of teaching based on multi-networks integration International Journal of Cognitive Computing in Engineering Teaching evaluation in colleges and universities Multi-networks integrated Teaching evaluation index |
| title | Intelligent evaluation of teaching based on multi-networks integration |
| title_full | Intelligent evaluation of teaching based on multi-networks integration |
| title_fullStr | Intelligent evaluation of teaching based on multi-networks integration |
| title_full_unstemmed | Intelligent evaluation of teaching based on multi-networks integration |
| title_short | Intelligent evaluation of teaching based on multi-networks integration |
| title_sort | intelligent evaluation of teaching based on multi networks integration |
| topic | Teaching evaluation in colleges and universities Multi-networks integrated Teaching evaluation index |
| url | http://www.sciencedirect.com/science/article/pii/S2666307420300024 |
| work_keys_str_mv | AT guannanli intelligentevaluationofteachingbasedonmultinetworksintegration AT linxiang intelligentevaluationofteachingbasedonmultinetworksintegration AT zhengxingyu intelligentevaluationofteachingbasedonmultinetworksintegration AT huili intelligentevaluationofteachingbasedonmultinetworksintegration |