Computerized Adaptive Testing: Theory and Practice by Wim J. van der Linden, Cees A.W. Glas

By Wim J. van der Linden, Cees A.W. Glas

This e-book deals a complete advent to the most recent advancements within the concept and perform of CAT. it may be used either as a easy reference and a invaluable source on try out concept. It covers such subject matters as merchandise choice and skill estimation, merchandise pool improvement and upkeep, merchandise calibration and version healthy, and testlet-based adaptive trying out, in addition to the operational elements of latest large-scale CAT programs.

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Let denote the probability of selecting item conditional on the ability and A the event of administering the item. It holds that: The exposure rates are controlled by setting the values at appropriate levels. However, the probabilities of selecting the items, can only be determined by simulating adaptive test administrations from the item pool. Moreover, the event of selecting item is dependent on the presence of the other items in the pool. Therefore, the values of the control parameters can be set only through an iterative process with cycles of: (1) simulating the test; (2) estimating the probabilities of selection; and (3) adjusting the values for the control parameters.

Use of these methods does assume explicit coding of all relevant item attributes. Both in the testlet-based CAT and multi-stage testing approaches, groups of items are selected sequentially. Hence, adaptation of item selection to ability estimation is suboptimal. On the other hand, the constraints are realized simultaneously when assembling the testlets or subtests prior to the test administration, pro- 32 VAN DER LINDEN vided formal methods and adequate item coding are used to assemble the pool of testlets or the multi-stage testing system.

W. ), Computerized Adaptive Testing: Theory and Practice, 27–52. © 2000 Kluwer Academic Publishers. Printed in the Netherlands. 28 VAN DER LINDEN of the examinee is known to be a monotonically increasing function of the guessing parameter, Both test theoreticians and practitioners immediately adopted the information measure in (1) as their favorite criterion of test assembly. The fact that item information additively contributes to the test precision has greatly enhanced its popularity. Though other criteria of item selection have been introduced later (for a review see van der Linden and Pashley, this volume), the most frequently used criterion in computerized adaptive testing (CAT) has also been the one based on the information measure in (1).

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