TY - JOUR
T1 - Infeasibility in automated test assembly models: A comparison study of different methods
JF - Journal of Educational Measurement
Y1 - 2005
A1 - Huitzing, H. A.
A1 - Veldkamp, B. P.
A1 - Verschoor, A. J.
KW - Algorithms
KW - Item Content (Test)
KW - Models
KW - Test Construction
AB - Several techniques exist to automatically put together a test meeting a number of specifications. In an item bank, the items are stored with their characteristics. A test is constructed by selecting a set of items that fulfills the specifications set by the test assembler. Test assembly problems are often formulated in terms of a model consisting of restrictions and an objective to be maximized or minimized. A problem arises when it is impossible to construct a test from the item pool that meets all specifications, that is, when the model is not feasible. Several methods exist to handle these infeasibility problems. In this article, test assembly models resulting from two practical testing programs were reconstructed to be infeasible. These models were analyzed using methods that forced a solution (Goal Programming, Multiple-Goal Programming, Greedy Heuristic), that analyzed the causes (Relaxed and Ordered Deletion Algorithm (RODA), Integer Randomized Deletion Algorithm (IRDA), Set Covering (SC), and Item Sampling), or that analyzed the causes and used this information to force a solution (Irreducible Infeasible Set-Solver). Specialized methods such as the IRDA and the Irreducible Infeasible Set-Solver performed best. Recommendations about the use of different methods are given. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)
VL - 42
ER -
TY - JOUR
T1 - Optimal stratification of item pools in α-stratified computerized adaptive testing
JF - Applied Psychological Measurement
Y1 - 2003
A1 - Chang, Hua-Hua
A1 - van der Linden, W. J.
KW - Adaptive Testing
KW - Computer Assisted Testing
KW - Item Content (Test)
KW - Item Response Theory
KW - Mathematical Modeling
KW - Test Construction computerized adaptive testing
AB - 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)
VL - 27
ER -