Reducing the Misclassification Costs of Cognitive Diagnosis Computerized Adaptive Testing: Item Selection With Minimum Expected Risk.

ABSTRACT: Cognitive diagnosis computerized adaptive testing (CD-CAT) aims to identify each examinee's strengths and weaknesses on latent attributes for appropriate classification into an attribute profile. As the cost of a CD-CAT misclassification differs across user needs (e.g., remedial program vs. scholarship eligibilities), item selection can incorporate such costs to improve measurement efficiency. This study proposes such a method, minimum expected risk (MER), based on Bayesian decision theory. According to simulations, using MER to identify examinees with no mastery (MER-U0) or full mastery (MER-U1) showed greater classification accuracy and efficiency than other methods for these attribute profiles, especially for shorter tests or low quality item banks. For other attribute profiles,

SUBMITTER: Hsu CL 

PROVIDER: S-EPMC9073635 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature