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Content Provider | IEEE Xplore Digital Library |
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Author | Juan Zhang Changjun Zhu |
Copyright Year | 2009 |
Description | Author affiliation: College of Urban Construction, Hebei University of Engineering, Handan, 056038, China (Juan Zhang; Changjun Zhu) |
Abstract | At present, with the popularization of higher education and acceleration of college students every year, colleges and universities are increasing scale. Expansion of the scale for colleges and universities on the one hand, broadens the scope for development, on the other hand it also brings many problems, including the issue of the quality of teaching which is particularly prominent. Owing to the problems existing in the previous system of teaching quality, based on the teaching characteristics, a new GA-based teaching quality evaluation model is set up by means of GA theory and neural network method. And the application processes of the model are illuminated in detail. The model is applied into the evaluation of teaching quality. By analyzing a lot of practical examples, the experiment result indicates that this mathematical model has better appraisal effect than other appraisal model, which can overcome the complexity of traditional evaluation model. Compared with other methods, this method is scientific, simple and operable. And its structure and method will have a bright future. |
Starting Page | 33 |
Ending Page | 35 |
File Size | 884252 |
Page Count | 3 |
File Format | |
ISBN | 9781424445448 |
DOI | 10.1109/PEITS.2009.5406870 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-12-19 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Genetic mutations teaching quality Educational institutions Appraisal Intelligent transportation systems Fuzzy logic evaluation BP neural network Neural networks Education Fuzzy neural networks GA Mathematical model Acceleration |
Content Type | Text |
Resource Type | Article |
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