TY - JOUR T1 - Time-Efficient Adaptive Measurement of Change JF - Journal of Computerized Adaptive Testing Y1 - 2019 A1 - Matthew Finkelman A1 - Chun Wang KW - adaptive measurement of change KW - computerized adaptive testing KW - Fisher information KW - item selection KW - response-time modeling AB -

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.

VL - 7 UR - http://iacat.org/jcat/index.php/jcat/article/view/73/35 IS - 2 ER - TY - CONF T1 - The Use of Decision Trees for Adaptive Item Selection and Score Estimation T2 - Annual Conference of the International Association for Computerized Adaptive Testing Y1 - 2011 A1 - Barth B. Riley A1 - Rodney Funk A1 - Michael L. Dennis A1 - Richard D. Lennox A1 - Matthew Finkelman KW - adaptive item selection KW - CAT KW - decision tree AB -

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.

JF - Annual Conference of the International Association for Computerized Adaptive Testing ER -