通过创新的领域知识引导的基于注意力的知识追踪方法推进个性化学习分析
Advancing Personalized Learning Analysis via an Innovative Domain Knowledge Informed Attention-based Knowledge Tracing Method
AI总结：
针对现有知识追踪忽视知识概念依赖的问题，提出融合课程中知识概念路径领域知识的注意力方法，并在 XES3G5M 上与七个 SOTA 深度学习模型比较。
摘要：
新兴的知识追踪（KT）模型，尤其是深度学习和基于注意力的知识追踪，已展现出通过依据学生过去的交互预测其未来表现来实现个性化学习分析的巨大潜力。现有方法主要关注最近的过去交互或单个概念，而没有考虑知识概念之间的依赖关系，即知识概念路径；这种依赖关系对于增进理解学生的学习成果可能至关重要。为解决这一问题，本文提出一种创新的基于注意力的方法，有效纳入给定课程中知识概念路径的领域知识。此外，我们利用 XES3G5M 数据集（一个包含丰富知识概念路径辅助信息的基准数据集），评估所提方法并将其性能与七个最先进（SOTA）深度学习模型进行比较。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
