Equity and Fairness of Bayesian Knowledge Tracing
This work addresses the problem of ensuring equitable educational outcomes for students by improving tutoring systems, representing an incremental advancement in the field of educational technology.
The paper tackles the problem of equity and fairness in tutoring systems derived from Knowledge Tracing models, showing that existing models like BKT and DKT fall short, and proposes a new model, BBKT, which enables online individualization and results in more effective and equitable curricula.
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 many existing models, including classical Bayesian Knowledge Tracing (BKT) and Deep Knowledge Tracing (DKT), 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 enables online individualization and, thereby, more equitable tutoring. We demonstrate that curricula derived from our model are more effective and equitable than those derived from classical BKT models. Furthermore, we highlight that improving models with a focus on the fairness of next-step predictions might be insufficient to develop equitable tutoring systems.