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Strategies for Controlling Item Exposure in Computerized Adaptive Testing With the Generalized Partial Credit Model

Pearson Educational Measurement

Choosing a strategy for controlling item exposure has become an integral part of test development for computerized adaptive testing (CAT). This study investigated the performance of six procedures for controlling item exposure in a series of simulated CATs under the generalized partial credit model. In addition to a no-exposure control baseline condition, the randomesque, modified-within-.10-logits, Sympson-Hetter, conditional Sympson-Hetter, a-stratified with multiple-stratification, and enhanced a-stratified with multiple-stratification procedures were implemented to control exposure rates. Two variations of the randomesque and modified-within-.10-logits procedures were examined, which varied the size of the item group from which the next item to be administered was randomly selected. The results indicate that although the conditional Sympson-Hetter provides somewhat lower maximum exposure rates, the randomesque and modified-within-.10-logits procedures with the six-item group variation have great utility for controlling overlap rates and increasing pool utilization and should be given further consideration.

Key Words: computerized adaptive testing • exposure control • CAT item selection • polytomous IRT • generalized partial credit model

Applied Psychological Measurement, Vol. 28, No. 3, 165-185 (2004)
DOI: 10.1177/0146621604264133


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