Opportunities for personalization in modeling students as Bayesian learners

The following paper is a proof-of-concept demonstration of a novel Bayesian framework for making inferences about individual students and the context in which they are learning. It has implications for both efforts to automate personalized instruction and to probabilistically model educational context. By modelling students as Bayesian learners, individuals who weigh their prior belief against current circumstantial data to reach conclusions, it becomes possible to both generate estimates of performance and the impact of the educational environment in probabilistic terms. This framework is tested through a Bayesian algorithm that can be used to characterize student prior knowledge in course material and predict student performance. This is demonstrated using both simulated data. The algorithm generates estimates that behave qualitatively as expected on simulated data and predict student performance substantially better than chance. A discussion of the results and the conceptual benefits of the framework follow.