@article {275,
	title = {A Bayesian method for the detection of item preknowledge in computerized adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {27},
	number = {2},
	year = {2003},
	pages = {121-137},
	abstract = {With the increased use of continuous testing in computerized adaptive testing, new concerns about test security have evolved, such as how to ensure that items in an item pool are safeguarded from theft. In this article, procedures to detect test takers using item preknowledge are explored. When test takers use item preknowledge, their item responses deviate from the underlying item response theory (IRT) model, and estimated abilities may be inflated. This deviation may be detected through the use of person-fit indices. A Bayesian posterior log odds ratio index is proposed for detecting the use of item preknowledge. In this approach to person fit, the estimated probability that each test taker has preknowledge of items is updated after each item response. These probabilities are based on the IRT parameters, a model specifying the probability that each item has been memorized, and the test taker{\textquoteright}s item responses. Simulations based on an operational computerized adaptive test (CAT) pool are used to demonstrate the use of the odds ratio index. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {Adaptive Testing, Cheating, Computer Assisted Testing, Individual Differences computerized adaptive testing, Item, Item Analysis (Statistical), Mathematical Modeling, Response Theory},
	author = {McLeod, L. and Lewis, C. and Thissen, D.}
}
@article {57,
	title = {Optimal stratification of item pools in α-stratified computerized adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {27},
	number = {4},
	year = {2003},
	pages = {262-274},
	abstract = {A method based on 0-1 linear programming (LP) is presented to stratify an item pool optimally for use in α-stratified adaptive testing. Because the 0-1 LP model belongs to the subclass of models with a network flow structure, efficient solutions are possible. The method is applied to a previous item pool from the computerized adaptive testing (CAT) version of the Graduate Record Exams (GRE) Quantitative Test. The results indicate that the new method performs well in practical situations. It improves item exposure control, reduces the mean squared error in the θ estimates, and increases test reliability. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)},
	keywords = {Adaptive Testing, Computer Assisted Testing, Item Content (Test), Item Response Theory, Mathematical Modeling, Test Construction computerized adaptive testing},
	author = {Chang, Hua-Hua and van der Linden, W. J.}
}
@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 {232,
	title = {Lagrangian relaxation for constrained curve-fitting with binary variables: Applications in educational testing},
	journal = {Dissertation Abstracts International Section A: Humanities and Social Sciences},
	volume = {61},
	number = {3-A},
	year = {2000},
	pages = {1063},
	abstract = {This dissertation offers a mathematical programming approach to curve fitting with binary variables. Various Lagrangian Relaxation (LR) techniques are applied to constrained curve fitting. Applications in educational testing with respect to test assembly are utilized. In particular, techniques are applied to both static exams (i.e. conventional paper-and-pencil (P\&P)) and adaptive exams (i.e. a hybrid computerized adaptive test (CAT) called a multiple-forms structure (MFS)). This dissertation focuses on the development of mathematical models to represent these test assembly problems as constrained curve-fitting problems with binary variables and solution techniques for the test development. Mathematical programming techniques are used to generate parallel test forms with item characteristics based on item response theory. A binary variable is used to represent whether or not an item is present on a form. The problem of creating a test form is modeled as a network flow problem with additional constraints. In order to meet the target information and the test characteristic curves, a Lagrangian relaxation heuristic is applied to the problem. The Lagrangian approach works by multiplying the constraint by a "Lagrange multiplier" and adding it to the objective. By systematically varying the multiplier, the test form curves approach the targets. This dissertation explores modifications to Lagrangian Relaxation as it is applied to the classical paper-and-pencil exams. For the P\&P exams, LR techniques are also utilized to include additional practical constraints to the network problem, which limit the item selection. An MFS is a type of a computerized adaptive test. It is a hybrid of a standard CAT and a P\&P exam. The concept of an MFS will be introduced in this dissertation, as well as, the application of LR as it is applied to constructing parallel MFSs. The approach is applied to the Law School Admission Test for the assembly of the conventional P\&P test as well as an experimental computerized test using MFSs. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {Analysis, Educational Measurement, Mathematical Modeling, Statistical},
	author = {Koppel, N. B.}
}
