Multidimensional Adaptive Testing with Kullback–Leibler Information Item Selection

2009 ◽  
pp. 77-101 ◽  
Author(s):  
Joris Mulder ◽  
Wim J. van der Linden
Keyword(s):  
Adaptive Testing ◽  
Item Selection ◽  
Information Item ◽  
Multidimensional Adaptive Testing ◽  
Leibler Information

Item Selection Rules in Computerized Adaptive Testing

Methodology ◽  
2009 ◽  
Vol 5 (1) ◽  
pp. 7-17 ◽  
Author(s):  
Juan Ramón Barrada ◽  
Julio Olea ◽  
Vicente Ponsoda ◽  
Francisco José Abad
Keyword(s):  
Fisher Information ◽  
Computerized Adaptive Testing ◽  
Likelihood Function ◽  
Geometric Mean ◽  
Adaptive Testing ◽  
Item Bank ◽  
Item Selection ◽  
Leibler Information ◽  
The One ◽  
Trait Estimation

The item selection rule (ISR) most commonly used in computerized adaptive testing (CAT) is to select the item with maximum Fisher information for the current trait estimation (PFI). Several alternative ISRs have been proposed. Among them, Fisher information considered in an interval (FI*I), Fisher information weighted with the likelihood function (FI*L), Kullback-Leibler information considered in an interval (KL*I) and Kullback-Leibler weighted with the likelihood function (KL*L) have shown a greater precision of trait estimation at the early stages of CAT. A new ISR is proposed, Fisher information by interval with geometric mean (FI*IG), which tries to rectify some detected problems in FI*I. We evaluate accuracy and item bank security for these six ISRs. FI*IG is the only ISR which simultaneously outperforms PFI in both variables. For the other ISRs, there seems to be a trade-off between accuracy and security, PFI being the one with worse accuracy and greater security, and the ISRs using the likelihood function the reverse.

scholarly journals A METHOD FOR SEVERELY CONSTRAINED ITEM SELECTION IN ADAPTIVE TESTING

1992 ◽  
Vol 1992 (1) ◽  
pp. i-32 ◽  
Author(s):  
Martha L. Stocking ◽  
Len Swanson
Keyword(s):  
Adaptive Testing ◽  
Item Selection
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