Strategies for controlling item exposure in computerized adaptive testing with polytomously scored items
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Choosing a strategy for controlling the exposure of items to examinees has become an integral part of test development for computerized adaptive testing (CAT). Item exposure can be controlled through the use of a variety of algorithms which modify the CAT item selection process. This may be done through a randomization, conditional selection, or stratification approach. The effectiveness of each procedure as well as the degree to which measurement precision is sacrificed has been extensively studied with dichotomously scored item pools. However, only recently have researchers begun to examine these procedures in polytomously scored item pools. The current study investigated the performance of six different exposure control mechanisms under three polytomous IRT models in terms of measurement precision, test security, and ease of implementation. The three models examined in the current study were the partial credit, generalized partial credit, and graded response models. In addition to a no exposure control baseline condition, the randomesque, within .10 logits, Sympson-Hetter, conditional Sympson-Hetter, aStratified, and enhanced a-Stratified procedures were implemented to control item exposure rates. The a-Stratified and enhanced a-Stratified procedures were not evaluated with the partial credit model. Two variations of the randomesque and within .10 logits procedures were also examined which varied the size of the item group from which the next item to be administered was randomly selected. The results of this study were remarkably similar for all three models and indicated that the randomesque and within .10 logits procedures, when implemented with the six item group variation, provide the best option for controlling exposure rates when impact to measurement precision and ease of implementation are considered. The three item group variations of the procedures were, however, ineffective in controlling exposure, overlap, and pool utilization rates to desired levels. The Sympson-Hetter and conditional Sympson-Hetter procedures were difficult and time consuming to implement, and while they did control exposure rates to the target level, their performance in terms of item overlap (for the SympsonHetter) and pool utilization were disappointing. The a-Stratified and enhanced aStratified procedures both turned in surprisingly poor performances across all variables.