TitlePredicting item exposure parameters in computerized adaptive testing
Publication TypeJournal Article
Year of Publication2008
AuthorsChen, S-Y, Doong, SH
JournalBritish Journal of Mathematical and Statistical Psychology
Date PublishedMay
Publication Languageeng
ISBN Number0007-1102 (Print)0007-1102 (Linking)
Accession Number18482476
Keywords*Algorithms, *Artificial Intelligence, Aptitude Tests/*statistics & numerical data, Diagnosis, Computer-Assisted/*statistics & numerical data, Humans, Models, Statistical, Psychometrics/statistics & numerical data, Reproducibility of Results, Software

The purpose of this study is to find a formula that describes the relationship between item exposure parameters and item parameters in computerized adaptive tests by using genetic programming (GP) - a biologically inspired artificial intelligence technique. Based on the formula, item exposure parameters for new parallel item pools can be predicted without conducting additional iterative simulations. Results show that an interesting formula between item exposure parameters and item parameters in a pool can be found by using GP. The item exposure parameters predicted based on the found formula were close to those observed from the Sympson and Hetter (1985) procedure and performed well in controlling item exposure rates. Similar results were observed for the Stocking and Lewis (1998) multinomial model for item selection and the Sympson and Hetter procedure with content balancing. The proposed GP approach has provided a knowledge-based solution for finding item exposure parameters.