01923nas a2200241   4500008004100000245009400041210006900135300001200204490000700216520110100223653002101324653001301345653003001358653005701388653000901445653003201454653002601486653002001512100001401532700001301546700001501559856010701574       2003                        eng d00aA Bayesian method for the detection of item preknowledge in computerized adaptive testing0 aBayesian method for the detection of item preknowledge in comput  a121-1370 v273 aWith 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'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 )10aAdaptive Testing10aCheating10aComputer Assisted Testing10aIndividual Differences computerized adaptive testing10aItem10aItem Analysis (Statistical)10aMathematical Modeling10aResponse Theory1 aMcLeod, L1 aLewis, C1 aThissen, D  uhttp://www.iacat.org/content/bayesian-method-detection-item-preknowledge-computerized-adaptive-testing01436nas a2200205   4500008004100000245008800041210007000129300001200199490000700211520067700218653002100895653003000916653002400946653002500970653002600995653005201021100001901073700002301092856011501115       2003                        eng d00aOptimal stratification of item pools in α-stratified computerized adaptive testing0 aOptimal stratification of item pools in αstratified computerized  a262-2740 v273 aA 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)10aAdaptive Testing10aComputer Assisted Testing10aItem Content (Test)10aItem Response Theory10aMathematical Modeling10aTest Construction computerized adaptive testing1 aChang, Hua-Hua1 avan der Linden, WJ  uhttp://www.iacat.org/content/optimal-stratification-item-pools-%CE%B1-stratified-computerized-adaptive-testing02085nas a2200229   4500008004100000245012900041210006900170300001200239490000700251520124000258653001801498653002101516653004501537653003001582653001801612653002501630653002601655100001601681700001401697700001901711856012501730       2002                        eng d00aA comparison of item selection techniques and exposure control mechanisms in CATs using the generalized partial credit model0 acomparison of item selection techniques and exposure control mec  a147-1630 v263 aThe 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)10a(Statistical)10aAdaptive Testing10aAlgorithms computerized adaptive testing10aComputer Assisted Testing10aItem Analysis10aItem Response Theory10aMathematical Modeling1 aPastor, D A1 aDodd, B G1 aChang, Hua-Hua  uhttp://www.iacat.org/content/comparison-item-selection-techniques-and-exposure-control-mechanisms-cats-using-generalized02718nas a2200169   4500008004100000245011500041210006900156300000900225490000700234520208500241653001302326653002802339653002602367653001602393100001602409856012302425       2000                        eng d00aLagrangian relaxation for constrained curve-fitting with binary variables: Applications in educational testing0 aLagrangian relaxation for constrained curvefitting with binary v  a10630 v613 aThis 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 )10aAnalysis10aEducational Measurement10aMathematical Modeling10aStatistical1 aKoppel, N B  uhttp://www.iacat.org/content/lagrangian-relaxation-constrained-curve-fitting-binary-variables-applications-educational