首頁 演講摘要 演講詳細內容 相關資料 相關網站 相 關 資 料 信息和通訊技術(ICT) Bayesian network Evidence-based design ETC CAF CAT A computer-adaptive test (CAT) is a method for administering tests that adapts to the examinee's ability level. For this reason, it has also been called tailored testing. 1 How CAT works 2 Advantages 3 Disadvantages 4 Other Issues 4.1 Pass-Fail CAT 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).[2] For example, a new termination criterion and scoring algorithm must be applied that classifies the examinee into a category rather than providing a point estimate of ability. There are two primary methodologies available for this. The more prominent of the two is the sequential probability ratio test (SPRT).[3][4] 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 hypthesis formulation rather than a composite hypothesis formulation[5] 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[6][7] . 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). For examinees with true-scores very close to the passing score, this algorithm will result in long tests while those with true-scores far above or below the passing score will have shortest exams. As a practical matter, the algorithm is generally programmed to have a minimum and a maximum test length (or a minimum and maximum administration time). This approach was originally called "adaptive mastery testing"[6] but it can be applied to non-adaptive item selection and classification situations of two or more cutscores (the typical mastery test has a single cutscore).[7] The item selection algorithm utilized depends on the termination criterion. Maximizing information at the cutscore is more appropriate for the SPRT because it maximizes the difference in the probabilities used in the likelihood ratio.[8] 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.[7] 4.2 Constraints of Adaptivity 5 References 5.1 Additional sources 6 See also 7 External links