解码学生的认知能力:对教育数据挖掘中的可解释AI算法进行比较研究
Tianyue Niu1, Ting Liu2, Yiming Taclis Luo2
1Xiamen Academy of Arts and Design, Fuzhou University, Xiamen, 361021, China.
Scientific reports
|July 23, 2025
概括
人工智能 (AI) 模型显示,自我认知和父母的期望影响学生的认知能力. 不同的人工智能可解释性算法为教育数据挖掘提供了独特的见解,突出了可解释的人工智能在教育中的重要性.
科学领域:
- 教育数据挖掘教育数据挖掘
- 教育中的人工智能
- 认知科学 认知科学
背景情况:
- 了解影响学生认知能力的因素对于教育进步至关重要.
- 教育数据挖掘 (EDM) 为分析学习过程提供了强大的工具.
- 在教育领域,人工智能模型的可解释性仍然是一个重大挑战.
研究的目的:
- 使用人工智能模型调查影响学生认知能力的因素.
- 在教育数据分析中比较各种可解释性算法的性能.
- 探索自我认知和父母期望对认知发展的影响.
主要方法:
- 在教育数据中使用了五种数据驱动的人工智能 (AI) 模型.
- 利用四个可解释的AI算法 (特征重要性,莫里斯灵敏度,SHAP,LIME) 来进行全球模型解释.
- 进行倾向分数匹配 (PSM) 因果测试以确定影响因素.
主要成果:
- 在所有测试的算法中,自我感知和父母的期望始终被确定为影响认知能力的重要因素.
- 不同的可解释性算法 (莫里斯灵敏度,SHAP,特征重要性,LIME) 表现出不同的特征重要性排名,表明不同的分析视角.
- 突出特征的差异强调了每个可解释性方法提供的细微解释.
结论:
- 可解释的人工智能算法为教育数据挖掘提供了宝贵的见解.
- 选择可解释性算法可以影响影响影响学生认知能力的因素的解释.
- 这项研究证明了可解释的人工智能的实用实用性,用于改进教育应用和研究.
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