@article {75,
	title = {Computerized adaptive testing using the nearest-neighbors criterion},
	journal = {Applied Psychological Measurement},
	volume = {27},
	number = {3},
	year = {2003},
	pages = {204-216},
	abstract = {Item selection procedures designed for computerized adaptive testing need to accurately estimate every taker{\textquoteright}s trait level (θ) and, at the same time, effectively use all items in a bank. Empirical studies showed that classical item selection procedures based on maximizing Fisher or other related information yielded highly varied item exposure rates; with these procedures, some items were frequently used whereas others were rarely selected. In the literature, methods have been proposed for controlling exposure rates; they tend to affect the accuracy in θ estimates, however. A modified version of the maximum Fisher information (MFI) criterion, coined the nearest neighbors (NN) criterion, is proposed in this study. The NN procedure improves to a moderate extent the undesirable item exposure rates associated with the MFI criterion and keeps sufficient precision in estimates. The NN criterion will be compared with a few other existing methods in an empirical study using the mean squared errors in θ estimates and plots of item exposure rates associated with different distributions. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)},
	keywords = {(Statistical), Adaptive Testing, Computer Assisted Testing, Item Analysis, Item Response Theory, Statistical Analysis, Statistical Estimation computerized adaptive testing, Statistical Tests},
	author = {Cheng, P. E. and Liou, M.}
}
@article {68,
	title = {The relationship between item exposure and test overlap in computerized adaptive testing},
	journal = {Journal of Educational Measurement},
	volume = {40},
	number = {2},
	year = {2003},
	pages = {129-145},
	abstract = {The purpose of this article is to present an analytical derivation for the mathematical form of an average between-test overlap index as a function of the item exposure index, for fixed-length computerized adaptive tests (CATs). This algebraic relationship is used to investigate the simultaneous control of item exposure at both the item and test levels. The results indicate that, in fixed-length CATs, control of the average between-test overlap is achieved via the mean and variance of the item exposure rates of the items that constitute the CAT item pool. The mean of the item exposure rates is easily manipulated. Control over the variance of the item exposure rates can be achieved via the maximum item exposure rate (r-sub(max)). Therefore, item exposure control methods which implement a specification of r-sub(max) (e.g., J. B. Sympson and R. D. Hetter, 1985) provide the most direct control at both the item and test levels. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {(Statistical), Adaptive Testing, Computer Assisted Testing, Human Computer, Interaction computerized adaptive testing, Item Analysis, Item Analysis (Test), Test Items},
	author = {Chen, S-Y. and Ankemann, R. D. and Spray, J. A.}
}
@article {308,
	title = {A comparison of item selection techniques and exposure control mechanisms in CATs using the generalized partial credit model},
	journal = {Applied Psychological Measurement},
	volume = {26},
	number = {2},
	year = {2002},
	pages = {147-163},
	abstract = {The use of more performance items in large-scale testing has led to an increase in the research investigating the use of polytomously scored items in computer adaptive testing (CAT). Because this research has to be complemented with information pertaining to exposure control, the present research investigated the impact of using five different exposure control algorithms in two sized item pools calibrated using the generalized partial credit model. The results of the simulation study indicated that the a-stratified design, in comparison to a no-exposure control condition, could be used to reduce item exposure and overlap, increase pool utilization, and only minorly degrade measurement precision. Use of the more restrictive exposure control algorithms, such as the Sympson-Hetter and conditional Sympson-Hetter, controlled exposure to a greater extent but at the cost of measurement precision. Because convergence of the exposure control parameters was problematic for some of the more restrictive exposure control algorithms, use of the more simplistic exposure control mechanisms, particularly when the test length to item pool size ratio is large, is recommended. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)},
	keywords = {(Statistical), Adaptive Testing, Algorithms computerized adaptive testing, Computer Assisted Testing, Item Analysis, Item Response Theory, Mathematical Modeling},
	author = {Pastor, D. A. and Dodd, B. G. and Chang, Hua-Hua}
}
@article {74,
	title = {Estimation of trait level in computerized adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {24},
	number = {3},
	year = {2000},
	pages = {257-265},
	abstract = {Notes that in computerized adaptive testing (CAT), a examinee{\textquoteright}s trait level (θ) must be estimated with reasonable accuracy based on a small number of item responses. A successful implementation of CAT depends on (1) the accuracy of statistical methods used for estimating θ and (2) the efficiency of the item-selection criterion. Methods of estimating θ suitable for CAT are reviewed, and the differences between Fisher and Kullback-Leibler information criteria for selecting items are discussed. The accuracy of different CAT algorithms was examined in an empirical study. The results show that correcting θ estimates for bias was necessary at earlier stages of CAT, but most CAT algorithms performed equally well for tests of 10 or more items. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {(Statistical), Adaptive Testing, Computer Assisted Testing, Item Analysis, Statistical Estimation computerized adaptive testing},
	author = {Cheng, P. E. and Liou, M.}
}
