  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/}.
