加强了职业学院教师团队评估系统
Qiming Tian1, Wanle Chi1, Dafeng Gong1
1Department of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China.
Scientific reports
|November 10, 2025
概括
一个新的人工智能模型,CLDMAO,增强了artemisinin优化算法,以准确预测职业学院的教学团队评估. 这种人工智能驱动的方法提高了预测准确性,并确定了强大的评估系统的关键因素.
科学领域:
- 人工智能的人工智能
- 优化算法 优化算法
- 教育技术的教育技术
背景情况:
- 有效的教师团队评估对于职业学院改革至关重要.
- 现有的人工智能系统在预测教学评估方面缺乏准确性.
- 艺术素优化 (AO) 算法显示出有前途的结果,但与局部优化扎.
研究的目的:
- 开发一个增强的人工智能算法,用于准确的教学团队评估.
- 改进AO算法的全球优化能力.
- 确定构建一个全面的教学评估系统的关键因素.
主要方法:
- 通过全面学习和分散的食机制增强了AO算法,创建了CLDMAO.
- 与使用CEC 2017测试函数的最先进算法对比基准CLDMAO.
- 为教师团队评估系统开发了一个二进制的CLDMAO-KNN模型.
主要成果:
- CLDMAO表现出卓越的表现,在大多数CEC 2017测试功能中排名第一.
- 二进制CLDMAO-KNN模型在错误率,适应性和特征选择方面取得了出色的结果.
- 该模型成功地确定了构建完整评估系统的因素.
结论:
- 在基于AI的优化中,CLDMAO算法提供了显著的进步.
- CLDMAO-KNN模型为预测教学团队评估提供了有效的解决方案.
- 这项研究有助于开发更准确,更有洞察力的教育评估系统.
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