双通道自适应尺度超图编码器与交叉视图对比学习用于知识追踪
IEEE transactions on neural networks and learning systems
|April 23, 2024
概括
本研究介绍了HyperKT,这是一种新的知识跟踪模型,使用自适应式超图编码器捕捉学习者响应中的复杂,高阶关系. 在智能辅导系统中,HyperKT显著改善了对智能辅导系统未来绩效的预测.
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) 对智能辅导系统至关重要,它可以根据历史反应预测学习者的表现.
- 现有的深度学习方法 (RNN,注意力,GNN) 捕获双向关系,但忽略了更高阶的交互.
- 由于单通道编码,当前的模型难以代表多重细分知识状态.
研究的目的:
- 提出一种新的KT模型,HyperKT,解决现有方法的局限性.
- 纳入非对式,高阶响应信息和多细分知识状态.
- 为了提高智能辅导系统的预测准确度.
主要方法:
- 开发了一个适应性规模的超边缘蒸,用于知识意识和模式意识的超边缘.
- 拟议的双通道超图编码器 (简化和协作超图卷积网络) 用于全球和本地状态捕获.
- 在超图和线图视图之间实施交叉视图对比学习,以加强监督.
主要成果:
- HyperKT有效地捕捉了学习者响应中的非对式,高阶特征.
- 双通道编码器成功地代表了多重细分知识状态.
- 在三个现实数据集上的实验表明,HyperKT的性能优于最先进的方法.
结论:
- 通过建模复杂的关系,HyperKT在知识追踪方面取得了重大进展.
- 提出的方法增强了学习者知识状态的表现.
- HyperKT展示了卓越的性能,为更有效的智能辅导系统铺平了道路.
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