Dynamic Bayesian Network Modeling of Game-Based Diagnostic Assessments

Abstract

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. Drawing from and advancing methods in dynamic Bayesian networks, cognitive diagnostic modeling, and analysis of process data, a Bayesian approach to model construction, calibration, and use in facilitating inferences about students on the fly is described, and implemented in the context of an educational video game.