预测私立大学教师流动:基于10年的数据和满意度因素的机器学习方法
Wang Jingwen1,2, Liu Yi1, Yang Xiaohong2
1General Office of the Party and Government, Xi'an Fanyi University, Xi'an, China.
Frontiers in psychology
|November 24, 2025
概括
中国私立大学的教师周转率是使用机器学习 (ML) 和心理因素来预测的. K-最接近邻居 (KNN) 模型显示高准确度,确定补偿和发展作为关键驱动因素.
科学领域:
- 教育管理的教育管理.
- 数据科学数据科学数据科学
- 心理学 心理学 心理学
背景情况:
- 教师流动是中国私立大学的一个关键问题.
- 现有的机器学习 (ML) 模型用于营业额预测,往往忽视了心理因素和纵向数据.
- 这项研究通过结合十年的数据和心理维度来解决这些差距.
研究的目的:
- 开发和评估用于预测中国私立大学教师流动的机器学习模型.
- 确定影响教师流动的关键心理因素.
- 提出一种混合方法,将探索因素分析 (EFA) 和机器学习 (ML) 结合起来,以提高预测和解释性.
主要方法:
- 利用了来自中国西部一所私立大学的10年纵向数据集.
- 雇员探索性因素分析 (EFA) 以确定影响营业额的关键维度.
- 构建并评估了三个机器学习模型:K-最近邻居 (KNN),天真贝叶斯 (NB) 和逆向传播神经网络 (BPNN),使用准确性,F1分数和AUC指标.
主要成果:
- 该K-最近邻居 (KNN) 模型显示出卓越的预测性能,准确度为83.64%,F1得分为84.16%,AUC为0.901.
- "薪酬,福利和发展"因素是最重要的预测因素,解释了教师流动率变化的25.41%.
- 混合"EFA + ML"方法提高了预测特征的解释性和模型的稳定性.
结论:
- 拟议的"EFA + ML"混合方法通过将因子分析与机器学习相结合,提供了一种可靠的方法来预测教师流动.
- 薪酬,福利和发展是影响私立高等教育教师保留的关键因素.
- 这些发现为人力资源管理战略提供了可操作的见解,旨在减少私立大学教师流动.
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