TY - JOUR T1 - Predicting item exposure parameters in computerized adaptive testing JF - British Journal of Mathematical and Statistical Psychology Y1 - 2008 A1 - Chen, S-Y. A1 - Doong, S. H. KW - *Algorithms KW - *Artificial Intelligence KW - Aptitude Tests/*statistics & numerical data KW - Diagnosis, Computer-Assisted/*statistics & numerical data KW - Humans KW - Models, Statistical KW - Psychometrics/statistics & numerical data KW - Reproducibility of Results KW - Software AB - 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. VL - 61 SN - 0007-1102 (Print)0007-1102 (Linking) N1 - Chen, Shu-YingDoong, Shing-HwangResearch Support, Non-U.S. Gov'tEnglandThe British journal of mathematical and statistical psychologyBr J Math Stat Psychol. 2008 May;61(Pt 1):75-91. ER - TY - JOUR T1 - Strategies for controlling item exposure in computerized adaptive testing with the partial credit model JF - Journal of Applied Measurement Y1 - 2008 A1 - Davis, L. L. A1 - Dodd, B. G. KW - *Algorithms KW - *Computers KW - *Educational Measurement/statistics & numerical data KW - Humans KW - Questionnaires/*standards KW - United States AB - Exposure control research with polytomous item pools has determined that randomization procedures can be very effective for controlling test security in computerized adaptive testing (CAT). The current study investigated the performance of four procedures for controlling item exposure in a CAT under the partial credit model. In addition to a no exposure control baseline condition, the Kingsbury-Zara, modified-within-.10-logits, Sympson-Hetter, and conditional Sympson-Hetter procedures were implemented to control exposure rates. The Kingsbury-Zara and the modified-within-.10-logits procedures were implemented with 3 and 6 item candidate conditions. The results show that the Kingsbury-Zara and modified-within-.10-logits procedures with 6 item candidates performed as well as the conditional Sympson-Hetter in terms of exposure rates, overlap rates, and pool utilization. These two procedures are strongly recommended for use with partial credit CATs due to their simplicity and strength of their results. VL - 9 SN - 1529-7713 (Print)1529-7713 (Linking) N1 - Davis, Laurie LaughlinDodd, Barbara GUnited StatesJournal of applied measurementJ Appl Meas. 2008;9(1):1-17. ER -