@article {71,
	title = {Predicting item exposure parameters in computerized adaptive testing},
	journal = {British Journal of Mathematical and Statistical Psychology},
	volume = {61},
	number = {1},
	year = {2008},
	note = {Chen, Shu-YingDoong, Shing-HwangResearch Support, Non-U.S. Gov{\textquoteright}tEnglandThe British journal of mathematical and statistical psychologyBr J Math Stat Psychol. 2008 May;61(Pt 1):75-91.},
	month = {May},
	pages = {75-91},
	edition = {2008/05/17},
	abstract = {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.},
	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},
	isbn = {0007-1102 (Print)0007-1102 (Linking)},
	author = {Chen, S-Y. and Doong, S. H.}
}
@article {95,
	title = {Strategies for controlling item exposure in computerized adaptive testing with the partial credit model},
	journal = {Journal of Applied Measurement},
	volume = {9},
	number = {1},
	year = {2008},
	note = {Davis, Laurie LaughlinDodd, Barbara GUnited StatesJournal of applied measurementJ Appl Meas. 2008;9(1):1-17.},
	pages = {1-17},
	edition = {2008/01/09},
	abstract = {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.},
	keywords = {*Algorithms, *Computers, *Educational Measurement/statistics \& numerical data, Humans, Questionnaires/*standards, United States},
	isbn = {1529-7713 (Print)1529-7713 (Linking)},
	author = {Davis, L. L. and Dodd, B. G.}
}
