A Bayesian Network Approach To Modeling Learning Progressions
Patti West, Daisy Wise Rutstein, Robert J. Mislevy, Junhui Liu, Roy Levy, Kristen Eignor DiCerbo, Aaron Crawford, Younyoung Choi, Kristina Chapple, John T. Behrens · SensePublishers eBooks · 2012
A central challenge in using learning progressions (LPs) in practice is modeling the relationships that link student performance on assessment tasks to students’ levels on the LP. On the one hand, there is a progression of theoretically defined levels, each defined by a configuration of knowledge, skills, and/or abilities (KSAs). On the other hand, there are observed performances on assessment tasks, associated with levels but only imperfectly and subject to inconsistencies. What is needed is a methodology that can be used to map assessment performance onto the levels, to combine information across multiple tasks measuring similar and related KSAs, to support inferences about students, and to study how well actual data exhibit the relationships posited by the LP.