BP Neural Network-Enhanced System for Employment and Mental Health Support for College Students

• Published in: International journal of information and communication technology education Vol. 20; no. 1; pp. 1 - 19
• Main Authors: Cheng, Hanlie, Qin, Qiang, Deng, Zhengrong, Xiang, Hong, Tang, Weijun
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 01.01.2024
• Subjects: Academic Achievement; Algorithms; Artificial neural networks; Back propagation networks; Career and Technical Education; College students; Colleges & universities; Communication (Thought Transfer); Course Evaluation; Curriculum Evaluation; Educational Change; Educational Needs; Educational Quality; Educational Technology; Entrepreneurship education; Genetic algorithms; Individualized Instruction; Influence of Technology; Innovation; Innovations; Instructional Effectiveness; Instructional Improvement; Literature Reviews; Mental health; Networks; Periodicals; Student Characteristics; Student Evaluation; Student Records; Students; Teacher Effectiveness; Technology Education; Vocational Evaluation
• ISSN: 1550-1876; 1550-1337
• DOI: 10.4018/IJICTE.348334

This paper employs BP Neural Network (BPNN) theory to evaluate innovation and entrepreneurship education in universities. It utilizes students' evaluation indexes as input vectors and determines the number of hidden layer neurons. Experimental results serve as output vectors. The BPNN method proves reasonable and feasible for vocational education course evaluation, exhibiting a 14.96% higher accuracy than traditional genetic algorithms. The paper discusses the model, configuration, characteristics, training process, algorithm enhancement, and limitations of neural networks, followed by an introduction to genetic algorithms. Through analysis of principles, basic operations, and common operators, it establishes a theoretical foundation for subsequent discussions.