研究生教育满意度的行为分析:揭示贝叶斯网络和特征重要性的关键影响因素
Sheng Li1, Ting Wang2, Hanqing Yin1
1Graduate School, Northwestern Polytechnical University, Xi'an 710072, China.
Behavioral sciences (Basel, Switzerland)
|April 26, 2025
概括
这项研究引入了一种先进的系统来评估研究生教育满意度,确定学术弹性和指导等关键驱动因素. 这些发现提供了数据驱动的策略,以提高高等教育课程的质量.
科学领域:
- 行为科学 行为科学
- 教育管理的教育管理.
- 数据科学数据科学数据科学
背景情况:
- 评估研究生满意度的传统方法在捕捉复杂的因素相互作用方面是有限的.
- 准确的评估对于提高高等教育质量和管理至关重要.
- 需要一个新的框架来整合行为互动,并确定关键的满意度驱动器.
研究的目的:
- 为研究生教育提出一个创新的满意度指标系统框架.
- 通过先进的数据分析,识别和量化研究生满意度的关键驱动因素.
- 为改善研究生教育质量提供数据驱动的建议.
主要方法:
- 开发了一个整合两阶段特征优化和树增强天真贝叶斯 (TAN) 模型的框架.
- 使用确认因素分析 (CFA) 对8903个响应进行可靠性验证.
- 采用统计预选和XGBoost进行特征选择,改进49至29个指标,并使用Birnbaum重要性指标.
主要成果:
- TAN模型构建了一个因果网络,其性能优于其他算法,AUC为91.01%.
- 确定了关键的满意度驱动因素:学术性,愿望,奉献精神,创造力,标准和独立研究能力.
- 两阶段的优化将49个特征改进为29个核心指标,提高了模型的准确性.
结论:
- 拟议的框架有效地通过揭示因素之间的动态关系来评估研究生满意度.
- 加强学术支持,指导和跨学科学习是管理的关键建议.
- 结果为优化指标和改善研究生教育提供数据驱动的见解,为行为科学做出贡献.
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