Mutual Information Item Selection in Adaptive Classification Testing  

Oleh:

Weissman, Alexander

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Educational and Psychological Measurement vol. 67 no. 01 (Feb. 2007), page 41-58.
Topik: computerized adaptive testing; item selection; mutual information; sequential probability ratio test; classification
Fulltext: 41.pdf (491.6KB)

Isi artikel

Ageneral approach for item selection in adaptive multiple-category classification tests is provided. The approach uses mutual information (MI), a special case of the Kullback- Leibler distance, or relative entropy. MIworks efficiently with the sequential probability ratio test and alleviates the difficulties encountered with using other local- and globalinformation measures in the multiple-category classification setting. Results from simulation studies using three item selection methods, Fisher information (FI), posteriorweighted FI (FIP), and MI, are provided for an adaptive four-category classification test. Both across and within the four classification categories, it is shown that in general, MI item selection classifies the highest proportion of examinees correctly and yields the shortest test lengths. The next best performance is observed for FIP item selection, followed by FI.

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