01539nas a2200229 4500008004100000020002200041245006400063210006300127260002600190300001200216490000700228520081800235653003401053653003001087653002801117653001301145100001901158700001501177700001601192700001701208856008401225 2004 eng d a0146-6216 (Print)00aComputerized adaptive testing with multiple-form structures0 aComputerized adaptive testing with multipleform structures bSage Publications: US a147-1640 v283 aA multiple-form structure (MFS) is an ordered collection or network of testlets (i.e., sets of items). An examinee's progression through the network of testlets is dictated by the correctness of an examinee's answers, thereby adapting the test to his or her trait level. The collection of paths through the network yields the set of all possible test forms, allowing test specialists the opportunity to review them before they are administered. Also, limiting the exposure of an individual MFS to a specific period of time can enhance test security. This article provides an overview of methods that have been developed to generate parallel MFSs. The approach is applied to the assembly of an experimental computerized Law School Admission Test (LSAT). (PsycINFO Database Record (c) 2007 APA, all rights reserved)10acomputerized adaptive testing10aLaw School Admission Test10amultiple-form structure10atestlets1 aArmstrong, R D1 aJones, D H1 aKoppel, N B1 aPashley, P J uhttp://iacat.org/content/computerized-adaptive-testing-multiple-form-structures02714nas a2200169 4500008004100000245011500041210006900156300000900225490000700234520208500241653001302326653002802339653002602367653001602393100001602409856011902425 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://iacat.org/content/lagrangian-relaxation-constrained-curve-fitting-binary-variables-applications-educational