@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 {16,
	title = {Maximum information stratification method for controlling item exposure in computerized adaptive testing},
	journal = {Psicothema},
	volume = {18},
	number = {1},
	year = {2006},
	note = {Barrada, Juan RamonMazuela, PalomaOlea, JulioResearch Support, Non-U.S. Gov{\textquoteright}tSpainPsicothemaPsicothema. 2006 Feb;18(1):156-9.},
	month = {Feb},
	pages = {156-159},
	edition = {2007/02/14},
	abstract = {The proposal for increasing the security in Computerized Adaptive Tests that has received most attention in recent years is the a-stratified method (AS - Chang and Ying, 1999): at the beginning of the test only items with low discrimination parameters (a) can be administered, with the values of the a parameters increasing as the test goes on. With this method, distribution of the exposure rates of the items is less skewed, while efficiency is maintained in trait-level estimation. The pseudo-guessing parameter (c), present in the three-parameter logistic model, is considered irrelevant, and is not used in the AS method. The Maximum Information Stratified (MIS) model incorporates the c parameter in the stratification of the bank and in the item-selection rule, improving accuracy by comparison with the AS, for item banks with a and b parameters correlated and uncorrelated. For both kinds of banks, the blocking b methods (Chang, Qian and Ying, 2001) improve the security of the item bank.M{\'e}todo de estratificaci{\'o}n por m{\'a}xima informaci{\'o}n para el control de la exposici{\'o}n en tests adaptativos informatizados. La propuesta para aumentar la seguridad en los tests adaptativos informatizados que ha recibido m{\'a}s atenci{\'o}n en los {\'u}ltimos a{\~n}os ha sido el m{\'e}todo a-estratificado (AE - Chang y Ying, 1999): en los momentos iniciales del test s{\'o}lo pueden administrarse {\'\i}tems con bajos par{\'a}metros de discriminaci{\'o}n (a), increment{\'a}ndose los valores del par{\'a}metro a admisibles seg{\'u}n avanza el test. Con este m{\'e}todo la distribuci{\'o}n de las tasas de exposici{\'o}n de los {\'\i}tems es m{\'a}s equilibrada, manteniendo una adecuada precisi{\'o}n en la medida. El par{\'a}metro de pseudoadivinaci{\'o}n (c), presente en el modelo log{\'\i}stico de tres par{\'a}metros, se supone irrelevante y no se incorpora en el AE. El m{\'e}todo de Estratificaci{\'o}n por M{\'a}xima Informaci{\'o}n (EMI) incorpora el par{\'a}metro c a la estratificaci{\'o}n del banco y a la regla de selecci{\'o}n de {\'\i}tems, mejorando la precisi{\'o}n en comparaci{\'o}n con AE, tanto para bancos donde los par{\'a}metros a y b correlacionan como para bancos donde no. Para ambos tipos de bancos, los m{\'e}todos de bloqueo de b (Chang, Qian y Ying, 2001) mejoran la seguridad del banco.},
	keywords = {*Artificial Intelligence, *Microcomputers, *Psychological Tests, *Software Design, Algorithms, Chi-Square Distribution, Humans, Likelihood Functions},
	isbn = {0214-9915 (Print)},
	author = {Barrada, J and Mazuela, P. and Olea, J.}
}
