@article {2242,
	title = {A Knowledge-Based Approach for Item Exposure Control in Computerized Adaptive Testing},
	journal = {Journal of Educational and Behavioral Statistics},
	volume = {34},
	number = {4},
	year = {2009},
	pages = {530-558},
	abstract = {<p>The purpose of this study is to investigate a functional relation between item exposure parameters (IEPs) and item parameters (IPs) over parallel pools. This functional relation is approximated by a well-known tool in machine learning. Let P and Q be parallel item pools and suppose IEPs for P have been obtained via a Sympson and Hetter\&ndash;type simulation. Based on these simulated parameters, a functional relation k = fP (a, b, c) relating IPs to IEPs of P is obtained by an artificial neural network and used to estimate IEPs of Q without tedious simulation. Extensive experiments using real and synthetic pools showed that this approach worked pretty well for many variants of the Sympson and Hetter procedure. It worked excellently for the conditional Stocking and Lewis multinomial selection procedure and the Chen and Lei item exposure and test overlap control procedure. This study provides the first step in an alternative means to estimate IEPs without iterative simulation.</p>
},
	doi = {10.3102/1076998609336667},
	url = {http://jeb.sagepub.com/cgi/content/abstract/34/4/530},
	author = {Doong, S. H.}
}
@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.}
}
