基于知识图的认知学习与多事实推理推理
Chengfeng Liu1, Jianrui Chen2, Zhihui Wang1
1School of Artificial Inteligence and Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
概括
这项研究引入了一种新的认知诊断框架 (CD-SKG),通过分析学生-练习-概念相互作用来改善个性化学习. 该模型捕捉了复杂的,更高阶的关系,以更准确地诊断认知状态和性能预测.
科学领域:
- 教育中的人工智能
- 教育数据挖掘教育数据挖掘
- 认知科学 认知科学
背景情况:
- 智能教育系统通过分析学生,任务和概念交互来个性化学习.
- 认知诊断 (CD) 模型预测学生的表现,但与复杂的学生-练习-概念关系和更高层次的相互作用作斗争.
研究的目的:
- 提出基于多事实推理 (CD-SKG) 的签名知识图表的新型认知诊断框架.
- 解决现有的CD模型中关于学生,练习和概念之间的复杂和更高阶交互的局限性.
主要方法:
- 模拟学生,练习和概念作为签名的事实来编码响应价值并捕捉三方交互.
- 在签名认知超图上使用双视图超图卷积网络来学习响应特征.
- 通过整合三方认知因素,在两个诊断层面分析了高阶关系.
主要成果:
- CD-SKG框架在四个现实数据集上实现了最佳性能.
- 超过了八个最先进的认知诊断模型.
- 证明了复杂和高阶相互作用的有效捕获.
结论:
- 拟议的CD-SKG框架显著提高了智能教育系统中的认知诊断准确性.
- 它提供了一种强大的方法来建模教育数据中的复杂关系.
- 这些发现为更复杂的个性化学习体验铺平了道路.
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