Адаптивное компьютерное тестирование: принципы и применение.

Form of computer based test that adapts to the examinee's ability level
Computerized adaptive testing (CAT) is a form of computer based test that adapts to the examinee's ability level. For this reason, it has also been called tailored testing. In other words, it is a form of computer administered test in which the next item or set of items selected to be administered depends on the correctness of the test taker's responses to the most recent items administered.

Adaptive tests can provide uniformly precise scores for most test takers. all items must be pretested with a large enough sample to obtain stable item statistics. This sample may be required to be as large as 1,000 examinees. Because of the sophistication, the development of a CAT has a number of prerequisites. The large sample sizes (typically hundreds of examinees) required by IRT calibrations must be present. Items must be scorable in real time if a new item is to be selected instantaneously. Psychometricians experienced with IRT calibrations and CAT simulation research are necessary to provide validity documentation. Finally, a software system capable of true IRT based CAT must be available. In a CAT with a time limit it is impossible for the examinee to accurately budget the time they can spend on each test item and to determine if they are on pace to complete a timed test section. Test takers may thus be penalized for spending too much time on a difficult question which is presented early in a section and then failing to complete enough questions to accurately gauge their proficiency in areas which are left untested when time expires. While untimed CATs are excellent tools for formative assessments which guide subsequent instruction, timed CATs are unsuitable for high stakes summative assessments used to measure aptitude for jobs and educational programs.

In many situations, the purpose of the test is to classify examinees into two or more mutually exclusive and exhaustive categories. This includes the common "mastery test" where the two classifications are "pass" and "fail", but also includes situations where there are three or more classifications, such as "Insufficient", "Basic", and "Advanced" levels of knowledge or competency. The kind of "item level adaptive" CAT described in this article is most appropriate for tests that are not "pass/fail" or for pass/fail tests where providing good feedback is extremely important. Some modifications are necessary for a pass/fail CAT, also known as a computerized classification test (CCT). This formulates the examinee classification problem as a hypothesis test that the examinee's ability is equal to either some specified point above the cutscore or another specified point below the cutscore. Note that this is a point hypothesis formulation rather than a composite hypothesis formulation that is more conceptually appropriate. A composite hypothesis formulation would be that the examinee's ability is in the region above the cutscore or the region below the cutscore. A confidence interval approach is also used, where after each item is administered, the algorithm determines the probability that the examinee's true score is above or below the passing score. For example, the algorithm may continue until the 95% confidence interval for the true score no longer contains the passing score. At that point, no further items are needed because the pass fail decision is already 95% accurate, assuming that the psychometric models underlying the adaptive testing fit the examinee and test. This approach was originally called "adaptive mastery testing" Maximizing information at the ability estimate is more appropriate for the confidence interval approach because it minimizes the conditional standard error of measurement, which decreases the width of the confidence interval needed to make a classification. in which a random number is drawn from U(0,1), and compared to a ki parameter determined for each item by the test user. If the random number is greater than ki, the next most informative item is considered. have advanced an alternative approach called shadow testing which involves creating entire shadow tests as part of selecting items. Selecting items from shadow tests helps adaptive tests meet selection criteria by focusing on globally optimal choices (as opposed to choices that are optimal for a given item).

Given a set of items, a multidimensional computer adaptive test (MCAT) selects those items from the bank according to the estimated abilities of the student, resulting in an individualized test. MCATs seek to maximize the test's accuracy, based on multiple simultaneous examination abilities (unlike a computer adaptive test – CAT – which evaluates a single ability) using the sequence of items previously answered (Piton Gonçalves & Aluisio, 2012).

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