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I’ve Fallen and I Can’t Get Up: Can High-Ability Students Recover From Early Mistakes in CAT?

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Kelly L. Rulison, Assistant Professor (Creator)
Institution
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: A difficult result to interpret in Computerized Adaptive Tests (CATs) occurs when an ability estimate initially drops and then ascends continuously until the test ends, suggesting that the true ability may be higher than implied by the final estimate. This study explains why this asymmetry occurs and shows that early mistakes by high ability students can lead to considerable underestimation, even in tests with 45 items. The opposite response pattern, where low-ability students start with lucky guesses, leads to much less bias. The authors show that using Barton and Lord’s four-parameter model (4?M) and a less Lord’s four-parameter model (4?M) and a less informative prior can lower bias and root mean square error (RMSE) for high-ability students with a poor start, as the CAT algorithm ascends more quickly after initial underperformance. Results also show that the 4?M slightly outperforms a CAT in which less discriminating items are initially used. The practical implications and relevance for psychological measurement more generally are discussed.

Additional Information

Publication
Applied Psychological Measurement, 33, 83-101.
Language: English
Date: 2009
Keywords
Computerized adaptive testing, Bayesian, Item response theory, Achievement testing, High-stakes assessment