Integrating Bayesian Explanatory IRT Models and CAT Algorithms for Small Sample Settings

• In Event: Bayesian Techniques

Abstract

This study investigates the integration of Bayesian explanatory item response models and computerized adaptive testing algorithms to address limited sample size challenges in classroom assessments. The study introduces novel algorithms for item selection and ability estimation with a fully Bayesian approach. Explanatory IRT models are utilized to efficiently estimate item properties.

Authors

• Ae Kyong Jung, University of Iowa

• Jonathan Templin, UNIVERSITY OF IOWA

• A. Corinne Huggins-Manley, University of Florida

• Tae Yeon Kwon

• Jiehan Li, SELECT OR ENTER