加强教育和教师培训的关键维度使用模糊的基于决策算法和CRITIC WASPAS方法
1Department of Endocrinology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China.
Scientific reports
|August 6, 2025
概括
本研究介绍了一种新的循环毕达哥拉斯模糊集 (Cr-PyFS) 方法,用于教育质量和教师培训的决策. 它增强了排名方法,以管理不确定性并改善教育政策的制定.
科学领域:
- 教育决策 - - 教育决策
- 模糊的集合理论 模糊的集合理论
- 数学优化的数学优化
背景情况:
- 优质教育和教师培训对于社会发展至关重要,需要在教育政策中做出有效的决策.
- 现有的决策框架与复杂的教育环境固有的不确定性和模糊性作斗争.
- 在教育环境中,需要强大的数学方法来汇总偏好和排名替代方案是显而易见的.
研究的目的:
- 开发一种新的决策框架,使用循环毕达哥拉斯模糊集 (Cr-PyFS) 来解决教育质量和教师培训的不确定性.
- 在圆形毕达哥拉斯模糊 (Cr-PyF) 环境中制定灵活的运算和聚合运算符.
- 根据教师技能发展标准,建立一个全面的排名培训机构的方法.
主要方法:
- 修改模糊的框架和决策技术.
- 探索圆形毕达哥拉斯模糊集合 (Cr-PyFS) 和它们的运算 (弗兰克t-norm和t-conorm).
- 开发了Cr-PyF聚合运营商 (Cr-PyFFWA,Cr-PyFFWG),用于标准加权的CRITIC方法,以及用于替代排名的WASPAS.
主要成果:
- 一个数值示例证明了该框架在选择适当的培训机构以提高教师技能方面的有效性.
- 拟议的Cr-PyF方法有效地管理决策过程中的不确定性和模糊性.
- 对比分析证实了先进方法在现有技术上的优越性.
结论:
- 开发的Cr-PyF决策方法为提高教育质量和教师培训提供了强大而灵活的工具.
- 这种方法提供了一个可靠的机制,在复杂和不确定的条件下对替代品进行排名.
- 该研究强调了先进的模糊集合理论在优化教育政策和实践方面的潜力.
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