Study on the Application of Error Back-Propagation Algorithm Applied to the Student Status Management in Higher Education Institutions

The objective of this work is to predict the employment rate of students based on the information in the SSM (student status management) in colleges and universities. Firstly, the relevant content of SSM is introduced. Secondly, the BP (Back Propagation) neural network, the LM (Levenberg Marquardt)...

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Bibliographic Details
Published in:International journal of information and communication technology education Vol. 20; no. 1; pp. 1 - 14
Main Author: Yang, XinXiu
Format: Journal Article
Language:English
Published: Hershey IGI Global 01.01.2024
Subjects:
Algorithms
Back propagation
Back propagation networks
Bayesian analysis
Big Data
Brain
College graduates
College students
Colleges & universities
Communication
Communication (Thought Transfer)
Computer Use
Data
Data analysis
Educational Technology
Educational Trends
Employment
Error analysis
Graduates
Higher education
Higher education institutions
Influence of Technology
Information management
Information technology
Networks
Neural networks
Periodicals
Prediction models
Predictions
Propagation
Regularization
Social Development
Students
Technology assessment
Technology education
Trend Analysis
Work
ISSN:1550-1876, 1550-1337
Online Access:Get full text
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Summary:The objective of this work is to predict the employment rate of students based on the information in the SSM (student status management) in colleges and universities. Firstly, the relevant content of SSM is introduced. Secondly, the BP (Back Propagation) neural network, the LM (Levenberg Marquardt) algorithm, and the BR (Bayesian Regularization) algorithm are introduced. In addition, the LM algorithm is combined with the BR algorithm to optimize the BP neural network, so as to establish a prediction model of the employment rate of college graduates based on the LM-BP neural network (the established prediction model). Finally, the established prediction model is verified after the historical data of college students in the SSM are managed and processed using the big data analysis technology. It suggests that the established prediction model shows higher prediction accuracy, more stable prediction performance, more ideal prediction effect, and higher practical application value.
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content type line 14
ISSN:1550-1876
1550-1337
DOI:10.4018/IJICTE.348960