Designing item pools for computerized adpative testing

In existing computerized adaptive testing (CAT) programs, each successive item in the test is chosen to optimize an objective function. Examples of well-known objectives in CAT are maximizing the information in the test at the ability estimate for the examinee and minimizing the deviation of the information in the test from a target value at this estimate. In addition, item selection is required to realize a set of content specifications for the test. For example, item content may be required to follow a certain taxonomy, or, if the items have a multiple-choice format, their answer key distribution should deviate not too much from uniformity. Content specifications are generally defined in terms of combinations of attributes the items in the test should have. They are typically realized by imposing a set of constraints on the item-selection process. The presence of an objective function and constraints in CAT leads to the notion of CAT as constrained (sequential) optimization. For a more formal introduction to this notion, see van der Linden (2000).