  A central goal of both knowledge tracing and traditional assessment is to
quantify student knowledge and skills at a given point in time. Deep knowledge
tracing flexibly considers a student's response history but does not quantify
epistemic uncertainty, while IRT and CDM compute measurement error but only
consider responses to individual tests in isolation from a student's past
responses. Elo and BKT could bridge this divide, but the simplicity of the
underlying models limits information sharing across skills and imposes strong
inductive biases. To overcome these limitations, we introduce Dynamic LENS, a
modeling paradigm that combines the flexible uncertainty-preserving properties
of variational autoencoders with the principled information integration of
Bayesian state-space models. Dynamic LENS allows information from student
responses to be collected across time, while treating responses from the same
test as exchangeable observations generated by a shared latent state. It
represents student knowledge as Gaussian distributions in high-dimensional
space and combines estimates both within tests and across time using Bayesian
updating. We show that Dynamic LENS has similar predictive performance to
competing models, while preserving the epistemic uncertainty - the deep
learning analogue to measurement error - that DKT models lack. This approach
provides a conceptual bridge across an important divide between models designed
for formative practice and summative assessment.
