LAUSR

Computer adaptive testing (CAT) is a kind of tailored testing, in that it is a form of computer-based testing that is adaptive to each test-taker’s ability level. In this review, the impacts of CAT are discussed from different perspectives in order to illustrate crucial points to keep in mind during the development and implementation of CAT. Test developers and psychometricians often emphasize the efficiency and accuracy of CAT in comparison to traditional linear tests. However, many test-takers report feeling discouraged after taking CATs, and this feeling can reduce learning self-efficacy and motivation. A trade-off must be made between the psychological experiences of test-takers and measurement efficiency. From the perspective of educators and subject matter experts, nonstatistical specifications, such as content coverage, content balance, and form length are major concerns. Thus, accreditation bodies may be faced with a discrepancy between the perspectives of psychometricians and those of subject matter experts. In order to improve test-takers’ impressions of CAT, the author proposes increasing the target probability of answering correctly in the item selection algorithm even if doing so consequently decreases measurement efficiency. Two different methods, CAT with a shadow test approach and computerized multistage testing, have been developed in order to ensure the satisfaction of subject matter experts. In the shadow test approach, a full-length test is assembled that meets the constraints and provides maximum information at the current ability estimate, while computerized multistage testing gives subject matter experts an opportunity to review all test forms prior to administration.