知识追踪是一项评估学生学习过程中知识状态演变情况的任务。现有大多数方法都致力于探索不同的知识状态评估方法。然而,答题过程中更为基础的题目表征受到的关注相对较少。因此,该文提出了一种融合通用题目表征学习的神经知识追踪框架。具体地,该文首先设计了一种通用的题目表征方法,通过知识点、难度和题目独有特征来区分题目。然后,采用现有知识追踪方法同时捕捉知识状态演变并学习题目表征。最后,利用知识状态和待回答题目表征的内积来模拟回答过程。在三个真实数据集上的实验结果表明,该文方法可以在知识追踪过程中学习精确有效的题目表征,并且显著提升了基线知识追踪方法的性能,使其能够超过现有最优方法。
Abstract
Knowledge tracing (KT) is the task of assessing students’ evolving knowledge states in their learning process. Most existing methods have devoted significant efforts to explore different means to assess students’ knowledge state. However, the more fundamental issue of exercises representation receives relatively little attention. This paper proposes a Neural Knowledge Tracing Framework (NKTF) that integrates general exercises representation learning. Specifically, we first design a type of general exercises representation, which distinguishes exercises by their knowledge concepts, difficulty levels and other individual features. We then adapt several existing KT models to simultaneously capture the evolution of students’ knowledge state and learn the exercises representation. Finally, to predict students' future performance, we utilize the inner product of knowledge state and the representation of exercises to be answered to model the answering process. Extensive experimental results on three real-world datasets demonstrate that the NKTF can automatically learn precise and effective exercises representations. NKTF also effectively improves the performance of baseline KT models to outperform state-of-the-art methods.
关键词
题目表征 /
知识追踪 /
深度学习 /
数据挖掘
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Key words
exercises representation /
knowledge tracing /
deep learning /
data mining
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基金
国家重点研究与发展计划项目(2018YFB1005105),国家自然科学基金(61922073,U20A20229,62106244)
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