基于自适应模糊算法的高等教育改革绩效评估方法的设计.
Dakun Yang1, Muhammad Sheraz Arshad Malik2
1School of Marxism, Anyang Institute of Technology, Anyang, China.
PeerJ. Computer science
|September 24, 2025
概括
本研究介绍了一种适应性神经模糊推理系统 (ANFIS),用于评估大学教师的表现. 与传统方法相比,ANFIS模型提供了更准确和客观的评估.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 绩效管理系统 绩效管理系统
背景情况:
- 大学教师绩效评估对于学术质量和机构管理至关重要.
- 现有的评估方法往往缺乏客观性,全面性和适应性.
- 需要采用科学和数据驱动的方法来评估教学和研究贡献.
研究的目的:
- 开发和验证基于自适应神经模糊推理系统 (ANFIS) 的框架,用于大学教师绩效评估.
- 提高绩效评估的客观性,准确性和适应性.
- 为机构管理和决策支持提供科学机制.
主要方法:
- 开发一个全面的评估指数系统,包括16个教学,研究和教师信息的子指标.
- 应用因子分析来优化数据处理和减少指标冗余.
- 实现自适应神经模糊推理系统 (ANFIS),这是一个混合模型,结合了模糊逻辑和神经网络.
主要成果:
- 在ANFIS模型中,比起传统的方法,如反向传播 (BP) 神经网络和支持矢量机器 (SVM),ANFIS模型表现出了更高的性能.
- 经验验证证证实了ANFIS模型在绩效评估中提高了准确性,精度和整体有效性.
- 该系统通过从训练数据中不断学习,动态优化其参数和规则基础.
结论:
- 拟议的基于ANFIS的框架为评估大学教师绩效提供了一种新而实用的解决方案.
- 这种方法可以更准确地反映教学和研究成果.
- 该系统为学术管理提供了有价值的决策支持,提高了机构的有效性.
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