A Testlet Selection Method for Cognitive Diagnostic–Computerized Adaptive Testing

• In Event: 68.062 - Poster Session 15
  In Poster Session: 68.062-4 - Poster Session: Innovative Research on Diverse Methodological Approaches

Abstract

The paper presents a novel method for selecting item sets, or testlets, in Cognitive Diagnostic-Computerized Adaptive Testing (CD-CAT). A new testlet selection algorithm is introduced, followed by a simulation study comparing the performance of the proposed method with existing item selection methods without taking into account the testlet effects. It is expected that by taking into account the testlet effects in item selection and in attribute pattern estimation, the proposed method would improve the estimation accuracy and efficiency in CD-CAT with the presence of testlets.

Authors

• Susu Zhang, University of Illinois at Urbana-Champaign

• Hua-Hua Chang, University of Illinois at Urbana-Champaign