基于模糊-DEMATEL-BP的教师评估指标系统的优化
JiDong Qian1, GuoHui Zhou1, Wei He1
1School of Computer Science and Information, Harbin Normal University, Harbin, 150000, China.
Heliyon
|July 29, 2024
概括
本研究引入了一种结合Fuzzy-DEMATEL和逆向传播神经网络 (BP) 的新方法,以优化教师评估指标. 这种方法简化了复杂的系统,提高了准确性,并将指标数量减少了高达30%.
科学领域:
- 教育测量和统计学
- 教育中的人工智能
- 数据科学数据科学数据科学
背景情况:
- 教师评估指标系统对于教育质量至关重要.
- 现有系统经常受到复杂性和模糊不确定性的困扰.
- 简化和优化指标对于准确和高效的评估至关重要.
研究的目的:
- 提出一种用于优化教师评价指标系统的新方法.
- 解决指标选择中的复杂因果关系和模糊不确定性.
- 建立一个精确和合理的教师评价指数系统.
主要方法:
- 组合三角模糊决策试验和评估实验室模型 (模糊-DEMATEL) 与逆向传播神经网络 (BP).
- 使用DEMATEL来分析因果关系和模糊逻辑来处理不确定性.
- 使用BP神经网络进行客观数据培训,以减轻主观错误.
主要成果:
- 提出的方法有效地简化了复杂的评估系统,并确定了关键指标.
- 对TIMSS 2019数据的实证分析显示,指标减少了28%-30%.
- 与多标准决策 (MCDM) 相比,实现了更高的准确性和更少的指标量.
结论:
- Fuzzy-DEMATEL 和 BP 神经网络组合提供了一个强大的方法来优化教师评估指标.
- 这种方法提高了教师评估系统的可解释性和准确性.
- 优化的指标系统导致更准确,更有效的教师评估.
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