@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 {69,
	title = {Effects of practical constraints on item selection rules at the early stages of computerized adaptive testing},
	journal = {Journal of Educational Measurement},
	volume = {41},
	number = {2},
	year = {2004},
	pages = {149-174},
	publisher = {Blackwell Publishing: United Kingdom},
	abstract = {The purpose of this study was to compare the effects of four item selection rules--(1) Fisher information (F), (2) Fisher information with a posterior distribution (FP), (3) Kullback-Leibler information with a posterior distribution (KP), and (4) completely randomized item selection (RN)--with respect to the precision of trait estimation and the extent of item usage at the early stages of computerized adaptive testing. The comparison of the four item selection rules was carried out under three conditions: (1) using only the item information function as the item selection criterion; (2) using both the item information function and content balancing; and (3) using the item information function, content balancing, and item exposure control. When test length was less than 10 items, FP and KP tended to outperform F at extreme trait levels in Condition 1. However, in more realistic settings, it could not be concluded that FP and KP outperformed F, especially when item exposure control was imposed. When test length was greater than 10 items, the three nonrandom item selection procedures performed similarly no matter what the condition was, while F had slightly higher item usage. (PsycINFO Database Record (c) 2007 APA, all rights reserved)},
	keywords = {computerized adaptive testing, item selection rules, practical constraints},
	isbn = {0022-0655 (Print)},
	author = {Chen, S-Y. and Ankenmann, R. D.}
}
