  As the rapid development of Intelligent Tutoring Systems (ITS) in the past
decade, tracing the students' knowledge state has become more and more
important in order to provide individualized learning guidance. This is the
main idea of Knowledge Tracing (KT), which models students' mastery of
knowledge concepts (KCs, skills needed to solve a question) based on their past
interactions on platforms. Plenty of KT models have been proposed and have
shown remarkable performance recently. However, the majority of these models
use concepts to index questions, which means the predefined skill tags for each
question are required in advance to indicate the KCs needed to answer that
question correctly. This makes it pretty hard to apply on large-scale online
education platforms where questions are often not well-organized by skill tags.
In this paper, we propose Q-matrix-based Attentive Knowledge Tracing (QAKT), an
end-to-end style model that is able to apply the attentive method to scenes
where no predefined skill tags are available without sacrificing its
performance. With a novel hybrid embedding method based on the q-matrix and
Rasch model, QAKT is capable of modeling problems hierarchically and learning
the q-matrix efficiently based on students' sequences. Meanwhile, the
architecture of QAKT ensures that it is friendly to questions associated with
multiple skills and has outstanding interpretability. After conducting
experiments on a variety of open datasets, we empirically validated that our
model shows similar or even better performance than state-of-the-art KT
methods. Results of further experiments suggest that the q-matrix learned by
QAKT is highly model-agnostic and more information-sufficient than the one
labeled by human experts, which could help with the data mining tasks in
existing ITSs.
