Multidimensional Computerized Adaptive Testing for Classifying Examinees

Abstract

Multidimensional computerized classification testing can be used when classification decisions are required for constructs that have a multidimensional structure. Here, two methods for making those decisions are included for two types of multidimensionality. In the case of between-item multidimensionality, each item is intended to measure just one dimension. In the case of within-item multidimensionality, items are intended to measure multiple or all dimensions. Wald's (1947) sequential probability ratio test and Kingsbury and Weiss (1979) confidence interval method can be applied to multidimensional classification testing. Three methods are included for selecting the items: random item selection, maximization at the current ability estimate, and the weighting method. The last method maximizes information based on a combination of the cutoff points weighted by their distance to the ability estimate. Two examples illustrate the use of the classification and item selection methods. 14.1 Introduction Computerized classification tests, like computerized adaptive tests, adapt some test elements to the student. Test length and/or item selection is tailored during test administration. The goal of a computerized classification test is to classify the examinee into one of two levels (e.g., master/nonmaster) or into one of multiple levels (e.g., basic/proficient/advanced). Classification methods stop testing when enough confi

Empowering higher education students to monitor their learning progress: opportunities of computerised classification testing

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Improving test efficiency for a grid multidimensional computerized classification test by the application of a conditional latent-trait distribution to a sequential probability ratio test

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