模型无意识的反事实推理用于识别和减轻知识追踪中的答案偏差
Chaoran Cui1, Hebo Ma1, Xiaolin Dong1
1School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China.
概括
本研究引入了知识追踪 (KT) 的因果关系方法,以克服教育数据中的答案偏差. 新的反事实推理 (CORE) 框架有效地消除了学生的知识状态预测.
科学领域:
- 人工智能的人工智能
- 教育技术的教育技术
- 机器学习 机器学习
背景情况:
- 知识跟踪 (KT) 模型学生的知识状态从学习互动.
- 现有的KT模型通常依赖于答案偏差,阻碍了准确的知识状态评估.
- 答案偏差,正确/不正确答案的不平衡分布,是教育数据集中的一个常见问题.
研究的目的:
- 为了解决知识追踪中答案偏差的问题.
- 开发一种方法,防止模型利用答案偏差作为捷径.
- 提高学生知识状态监测的准确性.
主要方法:
- 建立了知识追踪的因果图.
- 提出了一个新的反事实推理 (CORE) 框架.
- CORE将总和直接的因果关系分开,以减轻推断过程中的答案偏差.
主要成果:
- 在多个基准数据集中证明了CORE框架的有效性.
- 核心成功地降低了KT推断,导致更准确的学生知识状态估计.
- 该框架成功地与DKT,DKVMN和AKT等现有模型集成.
结论:
- 因果视角提供了一个强大的解决方案,以回答知识追踪中的偏见.
- 核心框架提高了知识追踪模型的可靠性和可解释性.
- 这种方法为个性化学习系统提供了重大进步.
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