In the realm of Intelligent Tutoring System (ITS), the accurate assessment of students' knowledge states through Knowledge Tracing (KT) is crucial for personalized learning. However, due to data bias, $\textit{i.e.}$, the unbalanced distribution of question groups ($\textit{e.g.}$, concepts), conventional KT models are plagued by cognitive bias, which tends to result in cognitive underload for overperformers and cognitive overload for underperformers. More seriously, this bias is amplified with the exercise recommendations by ITS. After delving into the causal relations in the KT models, we identify the main cause as the confounder effect of students' historical correct rate distribution over question groups on the student representation and prediction score. Towards this end, we propose a Disentangled Knowledge Tracing (DisKT) model, which separately models students' familiar and unfamiliar abilities based on causal effects and eliminates the impact of the confounder in student representation within the model. Additionally, to shield the contradictory psychology ($\textit{e.g.}$, guessing and mistaking) in the students' biased data, DisKT introduces a contradiction attention mechanism. Furthermore, DisKT enhances the interpretability of the model predictions by integrating a variant of Item Response Theory. Experimental results on 11 benchmarks and 3 synthesized datasets with different bias strengths demonstrate that DisKT significantly alleviates cognitive bias and outperforms 16 baselines in evaluation accuracy.

翻译：在智能导学系统（ITS）领域，通过知识追踪（KT）准确评估学生的知识状态对于实现个性化学习至关重要。然而，由于数据偏差（即问题组（如概念）分布不均衡），传统KT模型普遍受到认知偏差的困扰，这往往导致表现优异者认知负荷不足，而表现欠佳者认知负荷过载。更严重的是，这种偏差会随着ITS的习题推荐而被放大。通过深入分析KT模型中的因果关系，我们发现其主要原因在于学生对不同问题组的历史正确率分布作为混淆因子，对学生的表征和预测分数产生了干扰效应。为此，我们提出了一种解耦知识追踪（DisKT）模型，该模型基于因果效应分别建模学生的熟悉能力与不熟悉能力，并在模型内部消除了混淆因子对学生表征的影响。此外，为屏蔽学生偏差数据中的矛盾心理（如猜测与失误），DisKT引入了矛盾注意力机制。同时，通过整合项目反应理论的变体，DisKT增强了模型预测的可解释性。在11个基准数据集和3个具有不同偏差强度的合成数据集上的实验结果表明，DisKT能显著缓解认知偏差，并在评估准确性上优于16个基线模型。