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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Bibliographic Details
Published in:Mathematical problems in engineering 2022-11, Vol.2022, p.1-10
Main Authors: Bai, Xue, Bai, Yongguo
Format: Article
Language:English
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
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/4865027