在高等教育中使用机器学习和基于决策的模糊的弗兰克权力聚合运营商模型的大规模开放在线课程的影响
1Education Research, Guangdong Polytechnic University of Light Industry, Guangzhou, 510000, Guangdong, China. 13602314027@163.com.
Scientific reports
|August 8, 2025
概括
本研究引入了使用循环直觉模糊集的新数学方法,以提高专家在选择大规模开放在线课程 (MOOCs) 的判断力. 这些技术解决了多标准组决策中的模糊信息,以更好地选择教育平台.
科学领域:
- 模糊的集合理论 模糊的集合理论
- 人工智能的人工智能
- 教育技术的教育技术
背景情况:
- 大规模开放在线课程 (MOOC) 已经使教育民主化,但选择最佳平台仍然具有挑战性.
- 机器学习 (ML) 集成提高了MOOC的有效性,但专家的判断可能是模糊的.
- 现有的决策模式在教育环境中与不精确和相互矛盾的标准作斗争.
研究的目的:
- 在专家评价中解决MOOC选择的信息不足和模糊的问题.
- 为多标准组决策 (MCGDM) 问题开发新的数学方法.
- 加强MOOC平台的选择过程,以改善高等教育.
主要方法:
- 循环直觉模糊集合理论 (Cir-IFS) 的修改.
- 强大的功率聚合运营商的推导 (加权平均和几何).
- 将决策算法和数学模型应用于数值示例.
主要成果:
- 开发并验证了新的电力聚合运营商来处理相互冲突的标准.
- 证明了拟议的数学方法的效率和可行性.
- 根据专家的判断,成功地应用了该模型来对MOOC平台进行排名.
结论:
- 提出的基于Cir-IFS的MCGDM方法有效地处理模糊和相互矛盾的信息.
- 新型聚合运营商为教育平台的选择提供了一个强大的框架.
- 这种方法提高了通过MOOC提高高等教育的决策准确性.
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