使用循环直觉模糊的MAIRCA CRITIC模型对体育教育课程进行智能数据驱动评估
1Physical Education, Handan University, Handan, Hebei, 056004, China. feiaiping1983@126.com.
Scientific reports
|October 10, 2025
概括
本研究介绍了一个智能框架,用于评估体育教育 (PE) 系统,使用循环直觉模糊集和混合MAIRCA-CRITIC方法. 它在复杂的教育环境中提高了决策准确性和灵活性.
科学领域:
- 教育技术的教育技术
- 决策科学 决策科学 决策科学
- 模糊逻辑系统 模糊逻辑系统
背景情况:
- 评估体育教育 (PE) 计划是具有挑战性的,因为数据模糊和矛盾.
- 传统的评估模型与PE系统固有的不确定性和不准确性作斗争.
- 需要一个强大的框架来有效地处理犹和相互矛盾的信息.
研究的目的:
- 开发PE系统的智能多标准评估框架.
- 整合循环直觉模糊集 (CIFS) 来建模不确定性.
- 结合MAIRCA和CRITIC方法进行全面的评估.
主要方法:
- 利用循环直觉模糊集 (CIFS) 来表示犹和相互矛盾的数据.
- 采用MAIRCA (多属性理想-真实比较分析) 方法来对替代品进行排名.
- 应用了CRITIC (通过标准之间的相关性来确定标准的重要性) 方法来对客观标准进行加权.
主要成果:
- 拟议的框架准确地排名PE程序,即使含有模糊的信息.
- 混合方法在评估中平衡主观和客观的测量.
- 与传统模型相比,在处理不确定性方面表现出更大的灵活性.
结论:
- 该框架为评估PE系统提供了更准确,更灵活的方法.
- 它有效地协助教育政策制定者在资源利用和计划改进方面.
- 将CIFS与MAIRCA-CRITIC集成,为复杂的评估提供了一个强大的决策工具.
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