Computerized classification test: Difference between revisions

Revision as of 20:17, 18 December 2006

Revision as of 17:41, 19 December 2006

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== Overview ==

A '''computerized classification test''' (CCT) refers to, as its name would suggest, a [[test]] that is administered by [[computer]] for the purpose of classifying examinees. The most common CCT is a mastery test where the test classifies examinees as "Pass" or "Fail," but the term also includes tests that classify examinees into more than two categories. While the term may generally be considered to refer to all computer-administered tests for classification, it is usually used to refer to tests that are interactively administered or of variable-length, similar to [[computerized adaptive testing]] (CAT). Like CAT, variable-length CCTs can accomplish the goal of the test (accurate classification) with a fraction of the number of items used in a conventional fixed-form test.

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4. A termination criterion and scoring procedure

The starting point is not a topic of contention; research on CCT primarily investigates the application of different methods for the other three components. ''Note:'' The termination criterion and scoring procedure are separate in CAT, but the same in CCT. Therefore, there are five components that must be specified to design a CAT.

ThisMore articleinformation provides a broad overview. A more detailed overview of IRT-basedon CCT is found in Thompson (2006) and Eggen and Straetmans (2000). IfA youbibliography are interested inof CCT, a bibliographyresearch is providedfound below.

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In a CCT, items are selected for administration throughout the test, unlike the traditional method of administering a fixed set of items to all examinees. While this is usually done by individual item, it can also be done in groups of items known as testlets (Leucht & Nungester, 1996; Vos & Glas, 2000).

Methods of item selection fall into two categories: cutscore-based and estimate-based. Cutscore-based methods (also known as sequential selection) maximize the [[information]] provided by the item at the cutscore, or cutscores if there are more than one, regardless of the ability of the examinee. Estimate-based methods (also known as adaptive selection) maximize information at the current estimate of examinee [[ability]], regardless of the ___location of the cutscore. Both work efficiently, but the efficiency depends in part on the termination criterion employed. Because the [[sequential probability ratio test]] only evaluates probabilities near the cutscore, cutscore-based item selection is more appropriate. Because the [[confidence interval]] termination criterion is centered around the examinees ability estimate, estimate-based item selection is more appropriate. This is because the test will make a classification when the confidence interval is small enough to be completely above or below the cutscore (see below). The confidence interval will be smaller when the standard error of measurement is smaller, and the standard error of measurement will be smaller when there is more information at the theta level of the examinee.

== Termination criterion ==

There are three termination criteria commonly used for CCTs. [[Bayesian]] decision theory methods offer great flexibility by presenting an infinite choice of loss/utility structures and evaluation considerations, but also introduce greater arbitrariness. A [[confidence interval]] approach calculates a confidence interval around the examinee's current theta estimate at each point in the test, and classifies the examinee when the interval falls completely within a region of theta that defines a classification. This was originally known as adaptive mastery testing (Kingsbury & Weiss, 1983), but does not necessarily require adaptive item selection, nor is it limited to the two-classification mastery testing situation. The [[sequential probability ratio test]] (Reckase, 1983) defines the classification problem as a [[hypothesis test]] that the examinee's theta is equal to a specified point above the cutscore or a specified point below the cutscore.

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CAT Central by David J. Weiss [http://www.psych.umn.edu/psylabs/catcentral/]

[[Category: Psychometrics]]

[[Category: Educational assessment and evaluation]]

[[Category: Educational technology]]

[[Category:Psychometrics Educational psychology]]

[[Category: School examinations]]