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Activity Number: 539 - SPEED: Bayesian Methods and Applications in the Life and Social Sciences
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 11:35 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #332580
Title: A Multivariate Probit Model for Learning Trajectories with Application to Classroom Assessment
Author(s): Yinghan Chen* and Steven Culpepper
Companies: University of Nevada, Reno and University of Illinois at Urbana-Champaign
Keywords: cognitive diagnosis model; classroom assessment; growth curve model

We propose a new dynamic cognitive diagnosis model that tracks learning process over time and incorporates external covariates. In particular, we model the changes in skill profiles through a multivariate probit model and estimate the model parameters in a Bayesian approach. We also apply our method to an educational intervention study to provide a fine-grained assessment of the experimental intervention.

Authors who are presenting talks have a * after their name.

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