  Knowledge tracing (KT) which aims at predicting learner's knowledge mastery
plays an important role in the computer-aided educational system. In recent
years, many deep learning models have been applied to tackle the KT task, which
have shown promising results. However, limitations still exist. Most existing
methods simplify the exercising records as knowledge sequences, which fail to
explore rich information that existed in exercises. Besides, the existing
diagnosis results of knowledge tracing are not convincing enough since they
neglect prior relations between exercises. To solve the above problems, we
propose a hierarchical graph knowledge tracing model called HGKT to explore the
latent hierarchical relations between exercises. Specifically, we introduce the
concept of problem schema to construct a hierarchical exercise graph that could
model the exercise learning dependencies. Moreover, we employ two attention
mechanisms to highlight the important historical states of learners. In the
testing stage, we present a K&S diagnosis matrix that could trace the
transition of mastery of knowledge and problem schema, which can be more easily
applied to different applications. Extensive experiments show the effectiveness
and interpretability of our proposed models.
