Improve the Assessment Model of Personnel Develop Level in Higher Education Based on Machine Learning
In order to improve the ability to manage the quality constraint index parameters of higher education talent training, an evaluation model of higher education talent training quality based on improved machine learning is proposed. Combined with the characteristic analysis method of higher education...
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| Published in | Wireless communications and mobile computing Vol. 2022; pp. 1 - 9 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Hindawi
05.10.2022
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | In order to improve the ability to manage the quality constraint index parameters of higher education talent training, an evaluation model of higher education talent training quality based on improved machine learning is proposed. Combined with the characteristic analysis method of higher education talent training quality constraint index parameters, an autocorrelation feature matching model of higher education talent training quality constraint index parameters is established, and fuzzy association rule scheduling is used to realize the feature extraction and fuzzy clustering of higher education talent training quality constraint index parameters. The mutual information coupling parameter analysis of the quality constraint index parameters of higher education talent training, combined with the generalized association rules and periodic association rules, realizes the feature clustering and global candidate item set analysis of the quality constraint index parameters of higher education talent training and realizes the dynamic evaluation of the quality of higher education talent training according to the machine learning analysis results. The test results show that this method has good clustering of data characteristics, good reliability of the evaluation process, and good convergence of machine learning process, which improves the dynamic and quantitative management ability of talent training quality in higher education. |
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| AbstractList | In order to improve the ability to manage the quality constraint index parameters of higher education talent training, an evaluation model of higher education talent training quality based on improved machine learning is proposed. Combined with the characteristic analysis method of higher education talent training quality constraint index parameters, an autocorrelation feature matching model of higher education talent training quality constraint index parameters is established, and fuzzy association rule scheduling is used to realize the feature extraction and fuzzy clustering of higher education talent training quality constraint index parameters. The mutual information coupling parameter analysis of the quality constraint index parameters of higher education talent training, combined with the generalized association rules and periodic association rules, realizes the feature clustering and global candidate item set analysis of the quality constraint index parameters of higher education talent training and realizes the dynamic evaluation of the quality of higher education talent training according to the machine learning analysis results. The test results show that this method has good clustering of data characteristics, good reliability of the evaluation process, and good convergence of machine learning process, which improves the dynamic and quantitative management ability of talent training quality in higher education. |
| Author | Li, Yong Zhu, Rui |
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| Copyright | Copyright © 2022 Rui Zhu and Yong Li. Copyright © 2022 Rui Zhu and Yong Li. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Algorithms Artificial intelligence Big Data Clustering Data analysis Data mining Education Evaluation Feature extraction Higher education Interdisciplinary subjects Knowledge representation Machine learning Model matching Parameter identification Parameters Reliability analysis Training |
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| Title | Improve the Assessment Model of Personnel Develop Level in Higher Education Based on Machine Learning |
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