  Knowledge tracing aims to trace students' evolving knowledge states by
predicting their future performance on concept-related exercises. Recently,
some graph-based models have been developed to incorporate the relationships
between exercises to improve knowledge tracing, but only a single type of
relationship information is generally explored. In this paper, we present a
novel Dual Graph Ensemble learning method for Knowledge Tracing (DGEKT), which
establishes a dual graph structure of students' learning interactions to
capture the heterogeneous exercise-concept associations and interaction
transitions by hypergraph modeling and directed graph modeling, respectively.
To ensemble the dual graph models, we introduce the technique of online
knowledge distillation, due to the fact that although the knowledge tracing
model is expected to predict students' responses to the exercises related to
different concepts, it is optimized merely with respect to the prediction
accuracy on a single exercise at each step. With online knowledge distillation,
the dual graph models are adaptively combined to form a stronger teacher model,
which in turn provides its predictions on all exercises as extra supervision
for better modeling ability. In the experiments, we compare DGEKT against eight
knowledge tracing baselines on three benchmark datasets, and the results
demonstrate that DGEKT achieves state-of-the-art performance.
