Quality Evaluation Method of a Mathematics Teaching Model Reform Based on an Improved Genetic Algorithm
The poor comprehensiveness of the evaluation indexes of quality evaluation methods for the traditional college mathematics teaching model reform results in low accuracy of the evaluation outcomes. In this paper, aiming at this problem, a quality evaluation method for the college mathematics teaching...
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| Published in: | Scientific programming Vol. 2021; pp. 1 - 10 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Hindawi
2021
John Wiley & Sons, Inc |
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| ISSN: | 1058-9244, 1875-919X |
| Online Access: | Get full text |
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| Abstract | The poor comprehensiveness of the evaluation indexes of quality evaluation methods for the traditional college mathematics teaching model reform results in low accuracy of the evaluation outcomes. In this paper, aiming at this problem, a quality evaluation method for the college mathematics teaching model reform, based on the genetic algorithm, is proposed. The simulated annealing algorithm uses the weighted comprehensive objective evaluation multiobjective optimization effect that can effectively improve the accuracy of the evaluation results. In the training process, the gradient descent back-propagation training method is used to obtain new weights for the quality evaluation of college mathematics teaching mode reforms and to score various indicators and evaluate the indicators. The mean value of the outcomes is the result of mathematics teaching quality evaluation. The experimental results show that the training error of the convolutional network of the proposed method is significantly small. Based on the genetic algorithm that improves the convolutional network training process, the obtained quality evaluation outcomes are higher in accuracy, better in goodness of fitness function, and considerably lower than other state-of-the-art methods. We observed that the improved genetic algorithm has a more than 90% goodness of fit and the error is significantly lower, that is, 0.01 to 0.04, than the classical genetic algorithm. |
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| AbstractList | The poor comprehensiveness of the evaluation indexes of quality evaluation methods for the traditional college mathematics teaching model reform results in low accuracy of the evaluation outcomes. In this paper, aiming at this problem, a quality evaluation method for the college mathematics teaching model reform, based on the genetic algorithm, is proposed. The simulated annealing algorithm uses the weighted comprehensive objective evaluation multiobjective optimization effect that can effectively improve the accuracy of the evaluation results. In the training process, the gradient descent back-propagation training method is used to obtain new weights for the quality evaluation of college mathematics teaching mode reforms and to score various indicators and evaluate the indicators. The mean value of the outcomes is the result of mathematics teaching quality evaluation. The experimental results show that the training error of the convolutional network of the proposed method is significantly small. Based on the genetic algorithm that improves the convolutional network training process, the obtained quality evaluation outcomes are higher in accuracy, better in goodness of fitness function, and considerably lower than other state-of-the-art methods. We observed that the improved genetic algorithm has a more than 90% goodness of fit and the error is significantly lower, that is, 0.01 to 0.04, than the classical genetic algorithm. |
| Author | Yang, Yun |
| Author_xml | – sequence: 1 givenname: Yun orcidid: 0000-0002-8147-6507 surname: Yang fullname: Yang, Yun organization: School of Physical and Mathematical SciencesNanjing Tech UniversityNanjingJiangsu 211800Chinanjtech.edu.cn |
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| Cites_doi | 10.1016/j.solener.2020.10.032 10.1002/cae.22349 10.1155/2021/5577167 10.1007/s00500-020-05069-2 10.1177/1524838020916257 10.1016/j.compedu.2020.103965 10.1002/int.22109 10.1007/s10489-020-01833-x 10.1109/TPWRS.2020.2986710 10.1109/TSMC.2019.2958550 10.3233/jifs-189357 10.1109/TFUZZ.2019.2957263 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 Yun Yang. Copyright © 2021 Yun Yang. 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 | 11 13 14 15 Y. Li (5) 2020; 43 16 17 Y. Zhang (4) 2019; 42 B. Deng (2) 2020; 40 3 6 7 L. Boulton (1) 2021; 31 8 9 A. Abdelhadi (12) 2019; 35 10 |
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| SubjectTerms | Accuracy Active learning Addition & subtraction Back propagation Classrooms College students Colleges & universities Data mining Educational technology Genetic algorithms Goodness of fit Indicators Innovations Mathematical analysis Mathematics education Multiple objective analysis Mutation Neural networks Optimization Population Quality assessment Simulated annealing Social change Teaching Training |
| Title | Quality Evaluation Method of a Mathematics Teaching Model Reform Based on an Improved Genetic Algorithm |
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