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）是一项通过观察学生历史学习过程来预测其未来表现的关键技术。得益于深度神经网络的强大表示能力，利用深度学习技术解决知识追踪问题已取得显著进展。现有方法大多基于同质问题假设，即若问题共享相同的知识组件，则其贡献等价。然而，这一假设在真实教育场景中并不准确。此外，现有基于深度学习的知识追踪模型的预测结果极难解释。因此，本文提出QIKT——一种以问题为中心的可解释知识追踪模型，以应对上述挑战。所提出的QIKT方法通过联合学习问题中心的知识获取模块与问题中心的问题求解模块，以问题敏感的认知表示在细粒度层面显式建模学生的知识状态变化。同时，QIKT采用基于项目反应理论的预测层生成可解释的预测结果。我们在三个公开的真实教育数据集上评估了所提出的QIKT模型。结果表明，该方法在知识追踪预测任务中表现优异，并在预测准确性和模型可解释性方面优于多种基于深度学习的知识追踪模型。为促进结果可复现，我们已在\url{https://pykt.org/}提供了全部数据集与代码。