Comparing CGS and PME on Estimating DCM Attribute Mastery Profiles

• In Event: GSIC Poster Session I

Abstract

Diagnostic Classification Models have emerged as valuable tools for assessing individuals' skills and attributes in various fields. In estimating attribute mastery profiles in DCMs, Collapsed Gibbs Sampling and Posterior Mode Estimation are two reliable methods. This research compares the performance of CGS and PME by conducting simulation studies under various conditions.

Authors

• Jingyang Li, University of Georgia

• Matthew James Madison, UNIVERSITY OF GEORGIA

• Zhenqiu Laura Lu