  Few models have been more ubiquitous in their respective fields than Bayesian
knowledge tracing and item response theory. Both of these models were developed
to analyze data on learners. However, the study designs that these models are
designed for differ; Bayesian knowledge tracing is designed to analyze
longitudinal data while item response theory is built for cross-sectional data.
This paper illustrates a fundamental connection between these two models.
Specifically, the stationary distribution of the latent variable and the
observed response variable in Bayesian knowledge Tracing are related to an item
response theory model. This connection between these two models highlights a
key missing component: the role of education in these models. A research agenda
is outlined which answers how to move forward with modeling learner data.
%Furthermore, recent advances in network psychometrics demonstrate how this
relationship can be exploited and generalized to a network model.
