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 -