Knowledge tracing (KT) is a crucial technique to predict students' future performance by observing their historical learning processes. Due to the powerful representation ability of deep neural networks, remarkable progress has been made by using deep learning techniques to solve the KT problem. The majority of existing approaches rely on the \emph{homogeneous question} assumption that questions have equivalent contributions if they share the same set of knowledge components. Unfortunately, this assumption is inaccurate in real-world educational scenarios. Furthermore, it is very challenging to interpret the prediction results from the existing deep learning based KT models. Therefore, in this paper, we present QIKT, a question-centric interpretable KT model to address the above challenges. The proposed QIKT approach explicitly models students' knowledge state variations at a fine-grained level with question-sensitive cognitive representations that are jointly learned from a question-centric knowledge acquisition module and a question-centric problem solving module. Meanwhile, the QIKT utilizes an item response theory based prediction layer to generate interpretable prediction results. The proposed QIKT model is evaluated on three public real-world educational datasets. The results demonstrate that our approach is superior on the KT prediction task, and it outperforms a wide range of deep learning based KT models in terms of prediction accuracy with better model interpretability. To encourage reproducible results, we have provided all the datasets and code at \url{https://pykt.org/}.

翻译：知识追踪（KT）是一项关键的技术，通过观察学生的历史学习过程来预测其未来表现。由于深度神经网络的强大表征能力，利用深度学习技术解决KT问题取得了显著进展。现有方法大多基于“同质问题”假设，即如果问题共享相同的知识组件集，则其贡献等同。然而，这一假设在真实教育场景中并不准确。此外，基于深度学习的KT模型对其预测结果的解释极具挑战。因此，本文提出QIKT——一种面向问题的可解释知识追踪模型，以应对上述挑战。所提出的QIKT方法通过问题敏感型认知表征，在细粒度层面显式建模学生的知识状态变化；这些表征由面向问题的知识获取模块和面向问题的问题求解模块联合学习得出。同时，QIKT利用基于项目反应理论的预测层生成可解释的预测结果。我们基于三个公开的真实教育数据集评估所提出的QIKT模型。结果表明，该方法在KT预测任务中表现优异，在预测准确度和模型可解释性方面均优于多种基于深度学习的KT模型。为促进结果可复现，我们已在\url{https://pykt.org/}提供所有数据集与代码。