Evidence centered design framework and dynamic bayesian network for modeling learning progression in online assessment system - Choi, Younyoung; Mislevy, Robert J. (Frontiers in Psychology, 2022)
Formative assessments are increasingly of interest in the field of education. Few assessment design frameworks and statistical analytic models have been studied to characterize the relationship between student performance and levels on learning progressions under a formative assessment system. In this study, we review a coherent assessment design framework for modeling learning progression under a formative assessment system using an evidence-centered design framework. Then we describe how Dynamic Bayesian Networks can be used to addresses the question of how a learner’s current, past, and future levels in learning progressions are inferred under a formative assessment. Finally, we conduct an application study of Dynamic Bayesian Networks using real data from the domain of beginning computer network engineering drawn from an online formative assessment in the Cisco Networking Academy. Consequently, this study describes a design framework and learning analytics method for measuring students’ advancement along learning progression in a formative assessment system.