  Tracing a student's knowledge is vital for tailoring the learning experience.
Recent knowledge tracing methods tend to respond to these challenges by
modelling knowledge state dynamics across learning concepts. However, they
still suffer from several inherent challenges including: modelling forgetting
behaviours and identifying relationships among latent concepts. To address
these challenges, in this paper, we propose a novel knowledge tracing model,
namely \emph{Deep Graph Memory Network} (DGMN). In this model, we incorporate a
forget gating mechanism into an attention memory structure in order to capture
forgetting behaviours dynamically during the knowledge tracing process.
Particularly, this forget gating mechanism is built upon attention forgetting
features over latent concepts considering their mutual dependencies. Further,
this model has the capability of learning relationships between latent concepts
from a dynamic latent concept graph in light of a student's evolving knowledge
states. A comprehensive experimental evaluation has been conducted using four
well-established benchmark datasets. The results show that DGMN consistently
outperforms the state-of-the-art KT models over all the datasets. The
effectiveness of modelling forgetting behaviours and learning latent concept
graphs has also been analyzed in our experiments.
