Knowledge tracing (KT) aims to monitor students' evolving knowledge states through their learning interactions with concept-related questions, and can be indirectly evaluated by predicting how students will perform on future questions. In this paper, we observe that there is a common phenomenon of answer bias, i.e., a highly unbalanced distribution of correct and incorrect answers for each question. Existing models tend to memorize the answer bias as a shortcut for achieving high prediction performance in KT, thereby failing to fully understand students' knowledge states. To address this issue, we approach the KT task from a causality perspective. A causal graph of KT is first established, from which we identify that the impact of answer bias lies in the direct causal effect of questions on students' responses. A novel COunterfactual REasoning (CORE) framework for KT is further proposed, which separately captures the total causal effect and direct causal effect during training, and mitigates answer bias by subtracting the latter from the former in testing. The CORE framework is applicable to various existing KT models, and we implement it based on the prevailing DKT, DKVMN, and AKT models, respectively. Extensive experiments on three benchmark datasets demonstrate the effectiveness of CORE in making the debiased inference for KT.

知识追踪（KT）旨在通过学生与概念相关问题的学习互动来监控他们不断变化的知识状态，并可通过预测学生在未来问题上的表现来间接评估。这篇论文观察到了存在一个普遍的答案偏差现象，即每个问题的正确和错误答案呈高度不平衡的分布。针对这个问题，作者从因果关系的角度处理了KT任务，提出了一种新颖的核心（CORE）框架来减轻答案偏差，并通过从总因果效应中减去直接因果效应来进行debias推理。该CORE框架适用于现有的多种KT模型，并在三个基准数据集上的大量实验证明了它在进行知识追踪时的有效性。

我们对学生知识状态的理解是否充分？识别和减轻知识跟踪中的答案偏倚