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.
Description
CAT successively selects questions (test items) for the purpose of maximizing the precision of the exam based on what is known about the examinee from previous questions. From the examinee's perspective, the difficulty of the exam seems to tailor itself to their level of ability. For example, if an examinee performs well on an item of intermediate difficulty, they will then be presented with a more difficult question. Or, if they performed poorly, they would be presented with a simpler question. Compared to static tests that nearly everyone has experienced, with a fixed set of items administered to all examinees, computer-adaptive tests require fewer test items to arrive at equally accurate scores.
- The pool of available items is searched for the optimal item, based on the current estimate of the examinee's ability
- The chosen item is presented to the examinee, who then answers it correctly or incorrectly
- The ability estimate is updated, based on all prior answers
- Steps 1–3 are repeated until a termination criterion is met
Nothing is known about the examinee prior to the administration of the first item, so the algorithm is generally started by selecting an item of medium, or medium-easy, difficulty as the first item.
As a result of adaptive administration, different examinees receive quite different tests. Although examinees are typically administered different tests, their ability scores are comparable to one another (i.e., as if they had received the same test, as is common in tests designed using classical test theory). The psychometric technology that allows equitable scores to be computed across different sets of items is item response theory (IRT). IRT is also the preferred methodology for selecting optimal items which are typically selected on the basis of information rather than difficulty, per se.
Examples
CAT has existed since the 1970s, and there are now many assessments that utilize it.
- Graduate Management Admission Test
- MAP test from NWEA
- SAT (beginning outside of the US in 2023 and in the US in 2024)
- National Council Licensure Examination
- Armed Services Vocational Aptitude Battery
Additionally, a list of active CAT exams is found at International Association for Computerized Adaptive Testing, along with a list of current CAT research programs and a near-inclusive bibliography of all published CAT research.
Advantages
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.
Components
There are five technical components in building a CAT (the following is adapted from Weiss & Kingsbury, 1984
Other issues
Pass-fail
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 k<sub>i</sub> parameter determined for each item by the test user. If the random number is greater than k<sub>i</sub>, 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).
Multidimensional
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 .
See also
- Bayesian knowledge tracing
- Linear-on-the-fly testing
- Active learning
- Elo system