通过动态因果异质图神经网络对学习资源进行细粒度因果效应估计
Yuan Ren1, Zhanfang Chen2, Xiaoming Jiang1
1Changchun University of Science and Technology, Changchun, 130000, China.
Scientific reports
|January 8, 2026
概括
我们开发了一个动态因果异质图神经网络 (DCHGNN),以评估在线教育中的学习资源效应. 这种方法准确地估计了因果关系的影响,改善了教育决策.
科学领域:
- 教育技术的教育技术
- 因果推理因果推理
- 图形神经网络的神经网络
背景情况:
- 评估学习资源对于个性化的在线教育至关重要.
- 传统的方法面临着复杂的学生资源互动,时间动态和偏见的挑战.
- 需要对资源有效性的准确因果鉴定.
研究的目的:
- 为学习资源的强有力的因果效应估计提出一个新的框架.
- 解决现有方法在处理异质,动态教育数据方面的局限性.
- 为了实现教学策略的数据驱动优化.
主要方法:
- 开发了一个动态因果异质图神经网络 (DCHGNN) 框架.
- 模拟学生-资源-评估交互使用动态异质图.
- 集成图表表示学习,具有两倍可靠的估计,以减轻偏差.
主要成果:
- DCHGNN证明了比传统基线更准确和更强大的因果效应估计.
- 该框架成功地确定了各种学习资源类型的差异性因果影响.
- 在真实世界数据上的实验结果验证了拟议的方法.
结论:
- DCHGNN框架为评估学习资源有效性提供了一个有希望的解决方案.
- 它促进了数据驱动的教育决策和资源分配.
- 这种方法提高了在线学习环境中的整体教学效率.
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