Equity and Fairness of Bayesian Knowledge Tracing

We consider the equity and fairness of curricula derived from Knowledge Tracing models. We begin by defining a unifying notion of an equitable tutoring system as a system that achieves maximum possible knowledge in minimal time for each student interacting with it. Realizing perfect equity requires tutoring systems that can provide individualized curricula per student. In particular, we investigate the design of equitable tutoring systems that derive their curricula from Knowledge Tracing models. We first show that the classical Bayesian Knowledge Tracing (BKT) model and their derived curricula can fall short of achieving equitable tutoring. To overcome this issue, we then propose a novel model, Bayesian-Bayesian Knowledge Tracing (BBKT), that naturally allows online individualization. We demonstrate that curricula derived from our model are more effective and equitable than those derived from existing models. Furthermore, we highlight that improving models with a focus on the fairness of next-step predictions can be insufficient to develop equitable tutoring systems.