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. We have released our code at https://github.com/lucky7-code/CORE.

翻译：知识追踪（KT）旨在通过学生与概念相关问题的学习交互来监控其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。在本文中，我们观察到普遍存在答题偏见现象，即每个问题正确与错误答案的分布高度不平衡。现有模型倾向于将答题偏见作为知识追踪中获得高预测性能的捷径加以记忆，从而未能充分理解学生的知识状态。为解决这一问题，我们从因果关系角度探讨知识追踪任务。首先建立知识追踪的因果图，从中识别出答题偏见的影响在于问题对学生回答的直接因果效应。进一步提出了一种新颖的知识追踪反事实推理（CORE）框架，该框架在训练过程中分别捕获总因果效应和直接因果效应，并在测试中通过从前者中减去后者来缓解答题偏见。CORE框架可适用于多种现有知识追踪模型，我们分别基于主流的DKT、DKVMN和AKT模型实现了该框架。在三个基准数据集上的大量实验证明了CORE在知识追踪中实现无偏推断的有效性。我们已在https://github.com/lucky7-code/CORE上发布代码。