Publications & Resources
Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments
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. A Bayesian approach to model construction, calibration, and use in facilitating inferences about students on the fly is described.
Levy, R. (2014). Dynamic Bayesian network modeling of game based diagnostic assessments (CRESST Report 837). Los Angeles: University of California, Los Angeles, National Center for Research on Evaluation, Standards, and Student Testing (CRESST).