By Wim J. van der Linden, Cees A.W. Glas
Modern laptop know-how has spread out numerous new probabilities for optimizing the management of academic and mental exams. In machine adaptive checking out (CAT), checks are immediately adapted to the talent point of the person examinees. at the moment, approximately all large-scale trying out courses within the western international are already adaptive or within the strategy of turning into so. Written by means of energetic CAT researchers from Europe and North the US, the chapters supply a entire creation to the most recent advancements within the idea and perform of CAT. The e-book can be utilized either as a uncomplicated reference at the cutting-edge in CAT and a priceless source in graduate classes on try out idea. The theoretical chapters during this ebook conceal such issues as merchandise choice and skill estimation, merchandise pool improvement and upkeep, merchandise calibration and version healthy, and testlet-based adaptive trying out. the sensible chapters describe the operational facets of latest large-scale CAT programs.
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Additional resources for Computerized Adaptive Testing: Theory and Practice
As shadow tests are full-size linear tests, 40 VAN DER LINDEN in principle any feature possible for linear tests can also be realized for an adaptive test. The only thing needed is the formulation of the constraints as a set of linear (in) equalities for insertion into the model in (6)-(23). The principle is illustrated with four applications each addressing a different aspect of adaptive testing. In the first application, the practicality of the shadow test approach is assessed for a CAT program with an extremely large number of content constraints of varying nature.
Note that each attribute involves a different partition. For simplicity, only the case of one attribute is discussed; adding more constraints is straightforward. The model below requires the number of items in the test from class to be between and Likewise, the set of stimuli in the pool is partitioned by a categorical attribute and the number of stimuli from class is required to be between and In addition, the items and stimuli are assumed to be described by categorical attributes, such as a word count or an item difficulty parameter.
Bayes modal estimation in item response models. Psychometrika, 51, 177-195. Mislevy, R. , & Bock, R. D. (1983). BILOG: Item and test scoring with binary logistic models [Computer program and manual]. Mooresville, IN: Scientific Software. Mislevy, R. -K. (1988). Inferring examinee ability when some items response are missing (Research Report 88-48-ONR). Princeton, NJ: Educational Testing Service. ITEM SELECTION AND ABILITY ESTIMATION 25 Owen, R. J. (1969). A Bayesian approach to tailored testing (Research Report 6992).
Computerized Adaptive Testing: Theory and Practice by Wim J. van der Linden, Cees A.W. Glas