用知识路径信息提升个性化学习预测准确率
Advancing Personalized Learning Analysis via an Innovative Domain Knowledge Informed Attention-based Knowledge Tracing Method
- 引入课程知识路径构建注意力机制，捕捉概念间依赖关系
- 在XES3G5M数据集上优于7个主流模型，显著提升预测性能
- 适合教育大数据、智能教学系统研究者参考
新兴的知识追踪（KT）模型，尤其是基于深度学习和注意力机制的方法，在基于学生历史交互行为预测未来表现方面展现出巨大潜力。现有方法主要关注近期交互或单一知识点，未充分考虑知识点间的依赖关系，即知识路径，而这一因素对理解学习成效至关重要。为此，本文提出一种创新的注意力机制方法，有效融合课程中给定的知识路径领域知识。此外，我们采用包含丰富辅助信息的知识路径基准数据集XES3G5M，对所提方法与七个前沿深度学习模型进行评估和对比。
原文摘要 · Abstract (English)
Emerging Knowledge Tracing (KT) models, particularly deep learning and attention-based Knowledge Tracing, have shown great potential in realizing personalized learning analysis via prediction of students' future performance based on their past interactions. The existing methods mainly focus on immediate past interactions or individual concepts without accounting for dependencies between knowledge concept, referred as knowledge concept routes, that can be critical to advance the understanding the students' learning outcomes. To address this, in this paper, we propose an innovative attention-based method by effectively incorporating the domain knowledge of knowledge concept routes in the given curriculum. Additionally, we leverage XES3G5M dataset, a benchmark dataset with rich auxiliary information for knowledge concept routes, to evaluate and compare the performance of our proposed method to the seven State-of-the-art (SOTA) deep learning models.
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