Evaluating the impact of China's educational policy on career development
A comprehensive analysis of university education's role using machine learning (ML) modeling
Bibliographic Data
| ID | 7145186 |
|---|---|
| Authors | Qiankun Yang (0009-0003-7624-1505, Hefei University of Technology, corresponding author), Changyong Liang (0000-0003-2076-5345, Hefei University of Technology) |
| Year | 2025 |
| Volume | 260 |
| Pages | 105398-105398 |
| Publication date | 2025-09-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Acta Psychologica (JOURNAL) |
| Journal identifiers | ISSN: 0001-6918 • E-ISSN: 1873-6297 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.actpsy.2025.105398 |
| PMID | 40902493 |
| OpenAlex | W4413911517 |
| Language | EN |
| Citations received | 1 |
| References cited | 32 |
The impact of university education on the comprehensive development of students in higher education is a crucial area of research. This study shows the effect of China's recently released Guideline of Educational Policy (GCEP) on the post-graduation career development of students. Using linear regression analysis, the study first explored the relationship between well-rounded educational development and students' subsequent career progression. Additionally, a three-stage model was proposed to investigate the influence of moral, intellectual, physical, aesthetic, and vocational education on the long-term career development of university students. The results indicate that the development of moral, intellectual, physical, aesthetic, and vocational education had a statistically significant positive impact on students' career progression. Differentiated effects were observed across the three stages of career development, namely the survival stage, the growth stage, and the self-realization stage. Based on our findings, some specific suggestions of practical significance and academic value are proposed for education policy-makers at the national, university, enterprise, and student levels. In this article, an artificial neural network (ANN) was used to better understand the relationships between various parameters. Neural networks are a practical method for learning complex functions, and are immune to training data errors, making them useful for problems like speech and image recognition. We used the neural network to estimate the survival, growth, and self-improvement stages by adjusting factors like education and work. The neural network's predictions were then validated against experimental results using linear regression, confirming acceptable error levels
China · Machine learning · Mathematics education · Policy learning · Political science · Sociology · Computer Science · Educational Innovations and Challenges · Online Learning and Analytics · Psychology · Resilience and Mental Health
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |