  Monitoring student knowledge states or skill acquisition levels known as
knowledge tracing, is a fundamental part of intelligent tutoring systems.
Despite its inherent challenges, recent deep neural networks based knowledge
tracing models have achieved great success, which is largely from models'
ability to learn sequential dependencies of questions in student exercise data.
However, in addition to sequential information, questions inherently exhibit
side relations, which can enrich our understandings about student knowledge
states and has great potentials to advance knowledge tracing. Thus, in this
paper, we exploit side relations to improve knowledge tracing and design a
novel framework DTKS. The experimental results on real education data validate
the effectiveness of the proposed framework and demonstrate the importance of
side information in knowledge tracing.
