  Deep learning based knowledge tracing model has been shown to outperform
traditional knowledge tracing model without the need for human-engineered
features, yet its parameters and representations have long been criticized for
not being explainable. In this paper, we propose Deep-IRT which is a synthesis
of the item response theory (IRT) model and a knowledge tracing model that is
based on the deep neural network architecture called dynamic key-value memory
network (DKVMN) to make deep learning based knowledge tracing explainable.
Specifically, we use the DKVMN model to process the student's learning
trajectory and estimate the student ability level and the item difficulty level
over time. Then, we use the IRT model to estimate the probability that a
student will answer an item correctly using the estimated student ability and
the item difficulty. Experiments show that the Deep-IRT model retains the
performance of the DKVMN model, while it provides a direct psychological
interpretation of both students and items.
