@article {2604,
	title = {Factors Affecting the Classification Accuracy and Average Length of a Variable-Length Cognitive Diagnostic Computerized Test},
	journal = {Journal of Computerized Adaptive Testing},
	volume = {6},
	year = {2018},
	pages = {1-14},
	doi = {10.7333/1802-060101},
	url = {http://iacat.org/jcat/index.php/jcat/article/view/55/30},
	author = {Huebner, Alan and Finkelman, Matthew D. and Weissman, Alexander}
}
@article {2145,
	title = {A Feedback Control Strategy for Enhancing Item Selection Efficiency in Computerized Adaptive Testing},
	journal = {Applied Psychological Measurement},
	volume = {30},
	number = {2},
	year = {2006},
	pages = {84-99},
	abstract = {<p>A computerized adaptive test (CAT) may be modeled as a closed-loop system, where item selection is influenced by trait level (\&theta;) estimation and vice versa. When discrepancies exist between an examinee\&$\#$39;s estimated and true \&theta; levels, nonoptimal item selection is a likely result. Nevertheless, examinee response behavior consistent with optimal item selection can be predicted using item response theory (IRT), without knowledge of an examinee\&$\#$39;s true \&theta; level, yielding a specific reference point for applying an internal correcting or feedback control mechanism. Incorporating such a mechanism in a CAT is shown to be an effective strategy for increasing item selection efficiency. Results from simulation studies using maximum likelihood (ML) and modal a posteriori (MAP) trait-level estimation and Fisher information (FI) and Fisher interval information (FII) item selection are provided.</p>
},
	doi = {10.1177/0146621605282774},
	url = {http://apm.sagepub.com/content/30/2/84.abstract},
	author = {Weissman, Alexander}
}
