@article {2717, title = {Time-Efficient Adaptive Measurement of Change}, journal = {Journal of Computerized Adaptive Testing}, volume = {7}, year = {2019}, pages = {15-34}, abstract = {

The adaptive measurement of change (AMC) refers to the use of computerized adaptive testing (CAT) at multiple occasions to efficiently assess a respondent\’s improvement, decline, or sameness from occasion to occasion. Whereas previous AMC research focused on administering the most informative item to a respondent at each stage of testing, the current research proposes the use of Fisher information per time unit as an item selection procedure for AMC. The latter procedure incorporates not only the amount of information provided by a given item but also the expected amount of time required to complete it. In a simulation study, the use of Fisher information per time unit item selection resulted in a lower false positive rate in the majority of conditions studied, and a higher true positive rate in all conditions studied, compared to item selection via Fisher information without accounting for the expected time taken. Future directions of research are suggested.

}, keywords = {adaptive measurement of change, computerized adaptive testing, Fisher information, item selection, response-time modeling}, issn = {2165-6592}, doi = {10.7333/1909-0702015}, url = {http://iacat.org/jcat/index.php/jcat/article/view/73/35}, author = {Matthew Finkelman and Chun Wang} } @conference {2107, title = {The Use of Decision Trees for Adaptive Item Selection and Score Estimation}, booktitle = {Annual Conference of the International Association for Computerized Adaptive Testing}, year = {2011}, month = {10/2011}, abstract = {

Conducted post-hoc simulations comparing the relative efficiency, and precision of decision trees (using CHAID and CART) vs. IRT-based CAT.

Conclusions

Decision tree methods were more efficient than CAT

But,...

Conclusions

CAT selects items based on two criteria: Item location relative to current estimate of theta, Item discrimination

Decision Trees select items that best discriminate between groups defined by the total score.

CAT is optimal only when trait level is well estimated.
Findings suggest that combining decision tree followed by CAT item selection may be advantageous.

}, keywords = {adaptive item selection, CAT, decision tree}, author = {Barth B. Riley and Rodney Funk and Michael L. Dennis and Richard D. Lennox and Matthew Finkelman} } @article {2202, title = {A Conditional Exposure Control Method for Multidimensional Adaptive Testing}, journal = {Journal of Educational Measurement}, volume = {46}, number = {1}, year = {2009}, pages = {84{\textendash}103}, abstract = {

In computerized adaptive testing (CAT), ensuring the security of test items is a crucial practical consideration. A common approach to reducing item theft is to define maximum item exposure rates, i.e., to limit the proportion of examinees to whom a given item can be administered. Numerous methods for controlling exposure rates have been proposed for tests employing the unidimensional 3-PL model. The present article explores the issues associated with controlling exposure rates when a multidimensional item response theory (MIRT) model is utilized and exposure rates must be controlled conditional upon ability. This situation is complicated by the exponentially increasing number of possible ability values in multiple dimensions. The article introduces a new procedure, called the generalized Stocking-Lewis method, that controls the exposure rate for students of comparable ability as well as with respect to the overall population. A realistic simulation set compares the new method with three other approaches: Kullback-Leibler information with no exposure control, Kullback-Leibler information with unconditional Sympson-Hetter exposure control, and random item selection.

}, issn = {1745-3984}, doi = {10.1111/j.1745-3984.2009.01070.x}, url = {http://dx.doi.org/10.1111/j.1745-3984.2009.01070.x}, author = {Matthew Finkelman and Nering, Michael L. and Roussos, Louis A.} }