  With the rapid development in online education, knowledge tracing (KT) has
become a fundamental problem which traces students' knowledge status and
predicts their performance on new questions. Questions are often numerous in
online education systems, and are always associated with much fewer skills.
However, the previous literature fails to involve question information together
with high-order question-skill correlations, which is mostly limited by data
sparsity and multi-skill problems. From the model perspective, previous models
can hardly capture the long-term dependency of student exercise history, and
cannot model the interactions between student-questions, and student-skills in
a consistent way. In this paper, we propose a Graph-based Interaction model for
Knowledge Tracing (GIKT) to tackle the above probems. More specifically, GIKT
utilizes graph convolutional network (GCN) to substantially incorporate
question-skill correlations via embedding propagation. Besides, considering
that relevant questions are usually scattered throughout the exercise history,
and that question and skill are just different instantiations of knowledge,
GIKT generalizes the degree of students' master of the question to the
interactions between the student's current state, the student's history related
exercises, the target question, and related skills. Experiments on three
datasets demonstrate that GIKT achieves the new state-of-the-art performance,
with at least 1% absolute AUC improvement.
