人工多宇宙优化用于预测意识形态和政治理论课程的效果
Xingzhong Zhuang1, Zhaodi Yi1, Yuqing Wang2
1College of Marxism, Wenzhou University, Wenzhou, 325035, China.
Heliyon
|May 6, 2024
概括
本研究介绍了一种人工多节优化器 (AMVO),用于提高意识形态和政治理论课程的教学效率. 该AMVO模型准确预测学生的课程评估,提高教育效率.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 课程评估 课程评估
背景情况:
- 低教学效率是意识形态和政治理论课程的持续挑战.
- 需要有效的评估方法来提高课程交付和学生参与度.
研究的目的:
- 开发一种智能方法来预测意识形态和政治理论课程的有效性.
- 通过数据分析,提高教学充分性和完善课程评估.
主要方法:
- 通过新生学生 (2022年队列) 的问卷收集的数据.
- 数据被组织并缩小用于分析.
- 开发和应用一个人工多节优化器 (AMVO) 用于预测.
- 用于性能评估的二进制AMVO增强的k-最近邻近分类器.
主要成果:
- AMVO方法表现出卓越的性能,在23个基准测试函数中排名第一.
- 增强的分类器实现了卓越的准确性,特异性和灵敏性,错误率低.
- 该模型成功地预测了学生对课程的整体评估态度.
结论:
- 开发的AMVO模型提供了一个强大的工具来预测意识形态和政治理论课程的有效性.
- 这种方法可以识别影响课程内容的因素,并提高教育成果.
- 该模型支持教师和学生的精细评估,与道德培养的目标保持一致.
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