Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their future performance on new questions based on their past response records. While existing deep learning knowledge tracing (DLKT) methods have significantly improved prediction accuracy and achieved state-of-the-art results, they often suffer from a lack of interpretability. To address this limitation, current approaches have explored incorporating psychological influences to achieve more explainable predictions, but they tend to overlook the potential influences of historical responses. In fact, understanding how models make predictions based on response influences can enhance the transparency and trustworthiness of the knowledge tracing process, presenting an opportunity for a new paradigm of interpretable KT. However, measuring unobservable response influences is challenging. In this paper, we resort to counterfactual reasoning that intervenes in each response to answer \textit{what if a student had answered a question incorrectly that he/she actually answered correctly, and vice versa}. Based on this, we propose RCKT, a novel response influence-based counterfactual knowledge tracing framework. RCKT generates response influences by comparing prediction outcomes from factual sequences and constructed counterfactual sequences after interventions. Additionally, we introduce maximization and inference techniques to leverage accumulated influences from different past responses, further improving the model's performance and credibility. Extensive experimental results demonstrate that our RCKT method outperforms state-of-the-art knowledge tracing methods on four datasets against six baselines, and provides credible interpretations of response influences.

翻译：知识追踪（KT）在计算机辅助教育和智能导学系统中扮演着关键角色，其目标是通过学生过往的答题记录预测其在新题目上的未来表现，从而评估学生的知识掌握程度。尽管现有的深度学习知识追踪（DLKT）方法显著提升了预测精度并取得了最先进的成果，但这些方法通常缺乏可解释性。为解决这一局限，当前研究尝试通过融入心理学影响因素来实现更具可解释性的预测，但往往忽视了历史答题记录可能产生的影响。事实上，理解模型如何基于答题影响进行预测，能够增强知识追踪过程的透明度和可信度，这为构建新型可解释知识追踪范式提供了契机。然而，测量不可观测的答题影响具有挑战性。本文采用反事实推理方法，通过对每次答题进行干预来回答“如果学生实际答对的题目被假设为答错（反之亦然）会产生何种影响”。基于此，我们提出RCKT——一种新颖的基于响应影响的反事实知识追踪框架。RCKT通过比较原始事实序列与干预后构建的反事实序列的预测结果，生成答题影响度量。此外，我们引入最大化与推断技术，以利用来自不同历史答题的累积影响，进一步提升模型的性能与可信度。大量实验结果表明，我们的RCKT方法在四个数据集上对比六种基线模型，均优于当前最先进的知识追踪方法，并能提供可信的答题影响解释。