  At present, the educational data mining community lacks many tools needed for
ensuring equitable ability estimation for Neurodivergent (ND) learners. On one
hand, most learner models are susceptible to under-estimating ND ability since
confounding contexts cannot be held accountable (eg consider dyslexia and
text-heavy assessments), and on the other, few (if any) existing datasets are
suited for appraising model and data bias in ND contexts. In this paper we
attempt to model the relationships between context (delivery and response
types) and performance of ND students with zero-inflated learner models. This
approach facilitates simulation of several expected ND behavioural traits,
provides equitable ability estimates across all student groups from generated
datasets, increases interpretability confidence, and can significantly increase
the quality of learning opportunities for ND students. Our approach
consistently out-performs baselines in our experiments and can also be applied
to many other learner modelling frameworks.
