Improve the Assessment Model of Personnel Develop Level in Higher Education Based on Machine Learning

• Published in: Wireless communications and mobile computing Vol. 2022; pp. 1 - 9
• Main Authors: Zhu, Rui, Li, Yong
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 05.10.2022; John Wiley & Sons, Inc
• Subjects: 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
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/3834456

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.