发现深度知识追踪模型的核心能力是捕捉知识点间的因果关系。
Extracting Causal Relations in Deep Knowledge Tracing
- 用因果图替代双向关联图，验证模型预测性能与因果结构匹配
- 在Assistments数据集上，因果子集使模型表现显著提升
- 适合教育数据挖掘、可解释性建模的研究者阅读
计算教育学长期目标是构建可解释的知识追踪（KT）模型。深度知识追踪（DKT）利用循环神经网络（RNN）预测学生对习题的知识掌握情况，相较于传统方法有显著提升。现有研究认为其优势源于对课程内知识点（KCs）间双向关系的建模，从而通过一个知识点的表现推断另一个。本文挑战这一观点，提出DKT的真正优势在于隐式建模知识点间的先决依赖关系，即因果结构。通过将练习关系图剪枝为有向无环图（DAG），并在Assistments数据集的因果子集上训练DKT，我们发现其预测能力与这些因果结构高度一致。此外，我们提出一种基于DKT学习表征提取练习关系DAG的方法，并提供实证支持。结果表明，DKT的有效性主要源于其对知识点间因果依赖的近似能力，而非简单的关联映射。
原文摘要 · Abstract (English)
A longstanding goal in computational educational research is to develop explainable knowledge tracing (KT) models. Deep Knowledge Tracing (DKT), which leverages a Recurrent Neural Network (RNN) to predict student knowledge and performance on exercises, has been proposed as a major advancement over traditional KT methods. Several studies suggest that its performance gains stem from its ability to model bidirectional relationships between different knowledge components (KCs) within a course, enabling the inference of a student's understanding of one KC from their performance on others. In this paper, we challenge this prevailing explanation and demonstrate that DKT's strength lies in its implicit ability to model prerequisite relationships as a causal structure, rather than bidirectional relationships. By pruning exercise relation graphs into Directed Acyclic Graphs (DAGs) and training DKT on causal subsets of the Assistments dataset, we show that DKT's predictive capabilities align strongly with these causal structures. Furthermore, we propose an alternative method for extracting exercise relation DAGs using DKT's learned representations and provide empirical evidence supporting our claim. Our findings suggest that DKT's effectiveness is largely driven by its capacity to approximate causal dependencies between KCs rather than simple relational mappings.
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