Activity Number:
|
477
- SPEED: Bayesian Methods and Applications in the Life and Social Sciences
|
Type:
|
Contributed
|
Date/Time:
|
Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract #328418
|
Presentation
|
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
|
Abstract:
|
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.