Fuzzy Neural Network for the Online Course Quality Assessment System
Under the influence of COVID-19, online office and online education has ushered in a golden period of development. The teaching quality of online education has been a controversial issue. Our study takes online course teaching quality assessment as the starting point, explores the influencing factor...
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| Veröffentlicht in: | Mathematical problems in engineering 2022-11, Vol.2022, p.1-10 |
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| creator | Bai, Xue Bai, Yongguo |
| description | Under the influence of COVID-19, online office and online education has ushered in a golden period of development. The teaching quality of online education has been a controversial issue. Our study takes online course teaching quality assessment as the starting point, explores the influencing factors of online course quality assessment with online courses as the research object, and analyzes the latest research proposal for an online course quality index. To make the online course quality assessment more intelligent, we propose an online course quality assessment method based on a fuzzy neural network. The method uses fuzzy rules as the baseline and adds a TSK perception mechanism to expand the perception domain of the fuzzy neural network and improve the course quality index prediction accuracy. At the input side of the fuzzy neural network, we preclassify the online course data into four parts, and each part of the data represents a different assessment domain. Due to the large data cost, we expanded the collective amount of data using data augmentation methods. In addition, we parse the structure of the fuzzy neural network hierarchy and introduce the construction and role of the TSK perception mechanism in the fuzzy rules. An optimal learning strategy is proposed in the fuzzy neural network training. Finally, in the experimental session, we verify the effectiveness of data augmentation and explore the distribution of course quality assessment weights. In the comparison of the model prediction results with the actual assessment results, our method achieves an excellent matching rate, which proves the high efficiency of our method in the online course quality assessment system. |
| doi_str_mv | 10.1155/2022/4865027 |
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The teaching quality of online education has been a controversial issue. Our study takes online course teaching quality assessment as the starting point, explores the influencing factors of online course quality assessment with online courses as the research object, and analyzes the latest research proposal for an online course quality index. To make the online course quality assessment more intelligent, we propose an online course quality assessment method based on a fuzzy neural network. The method uses fuzzy rules as the baseline and adds a TSK perception mechanism to expand the perception domain of the fuzzy neural network and improve the course quality index prediction accuracy. At the input side of the fuzzy neural network, we preclassify the online course data into four parts, and each part of the data represents a different assessment domain. Due to the large data cost, we expanded the collective amount of data using data augmentation methods. In addition, we parse the structure of the fuzzy neural network hierarchy and introduce the construction and role of the TSK perception mechanism in the fuzzy rules. An optimal learning strategy is proposed in the fuzzy neural network training. Finally, in the experimental session, we verify the effectiveness of data augmentation and explore the distribution of course quality assessment weights. In the comparison of the model prediction results with the actual assessment results, our method achieves an excellent matching rate, which proves the high efficiency of our method in the online course quality assessment system.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2022/4865027</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Artificial neural networks ; CAI ; Classrooms ; Computer assisted instruction ; Coronaviruses ; COVID-19 ; Data augmentation ; Domains ; Education ; Educational materials ; Efficiency ; Fuzzy logic ; Learning ; Medical research ; Neural networks ; Online instruction ; Perception ; Quality assessment ; Researchers ; Students ; Teachers ; Teaching</subject><ispartof>Mathematical problems in engineering, 2022-11, Vol.2022, p.1-10</ispartof><rights>Copyright © 2022 Xue Bai and Yongguo Bai.</rights><rights>Copyright © 2022 Xue Bai and Yongguo Bai. 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. 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| subjects | Artificial neural networks CAI Classrooms Computer assisted instruction Coronaviruses COVID-19 Data augmentation Domains Education Educational materials Efficiency Fuzzy logic Learning Medical research Neural networks Online instruction Perception Quality assessment Researchers Students Teachers Teaching |
| title | Fuzzy Neural Network for the Online Course Quality Assessment System |
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