Mutual Information Item Selection Method in Cognitive Diagnostic Computerized Adaptive Testing With Short Test Length

2013 ◽  
Vol 73 (6) ◽  
pp. 1017-1035 ◽  
Author(s):  
Chun Wang
Keyword(s):  
Mutual Information ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Selection Method ◽  
Item Selection ◽  
Test Length ◽  
Information Item ◽  
Short Test

scholarly journals A New Method to Balance Measurement Accuracy and Attribute Coverage in Cognitive Diagnostic Computerized Adaptive Testing

2021 ◽  
pp. 014662162110404
Author(s):  
Xiaojian Sun ◽  
Björn Andersson ◽  
Tao Xin
Keyword(s):  
Measurement Accuracy ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Selection Method ◽  
Item Selection ◽  
Major Theme ◽  
Selection Methods ◽  
Research Areas ◽  
Single Attribute ◽  
Multiple Attributes

As one of the important research areas of cognitive diagnosis assessment, cognitive diagnostic computerized adaptive testing (CD-CAT) has received much attention in recent years. Measurement accuracy is the major theme in CD-CAT, and both the item selection method and the attribute coverage have a crucial effect on measurement accuracy. A new attribute coverage index, the ratio of test length to the number of attributes (RTA), is introduced in the current study. RTA is appropriate when the item pool comprises many items that measure multiple attributes where it can both produce acceptable measurement accuracy and balance the attribute coverage. With simulations, the new index is compared to the original item selection method (ORI) and the attribute balance index (ABI), which have been proposed in previous studies. The results show that (1) the RTA method produces comparable measurement accuracy to the ORI method under most item selection methods; (2) the RTA method produces higher measurement accuracy than the ABI method for most item selection methods, with the exception of the mutual information item selection method; (3) the RTA method prefers items that measure multiple attributes, compared to the ORI and ABI methods, while the ABI prefers items that measure a single attribute; and (4) the RTA method performs better than the ORI method with respect to attribute coverage, while it performs worse than the ABI with long tests.

scholarly journals The Impact of Item Calibration Error on Variable-Length Cognitive Diagnostic Computerized Adaptive Testing

2020 ◽  
Vol 11 ◽  
Author(s):  
Xiaojian Sun ◽  
Yanlou Liu ◽  
Tao Xin ◽  
Naiqing Song
Keyword(s):  
Adverse Effects ◽  
Measurement Accuracy ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Unified Model ◽  
Variable Length ◽  
Calibration Error ◽  
Test Length ◽  
Test Efficiency ◽  
Item Parameters

Calibration errors are inevitable and should not be ignored during the estimation of item parameters. Items with calibration error can affect the measurement results of tests. One of the purposes of the current study is to investigate the impacts of the calibration errors during the estimation of item parameters on the measurement accuracy, average test length, and test efficiency for variable-length cognitive diagnostic computerized adaptive testing. The other purpose is to examine the methods for reducing the adverse effects of calibration errors. Simulation results show that (1) calibration error has negative effect on the measurement accuracy for the deterministic input, noisy “and” gate (DINA) model, and the reduced reparameterized unified model; (2) the average test lengths is shorter, and the test efficiency is overestimated for items with calibration errors; (3) the compensatory reparameterized unified model (CRUM) is less affected by the calibration errors, and the classification accuracy, average test length, and test efficiency are slightly stable in the CRUM framework; (4) methods such as improving the quality of items, using large calibration sample to calibrate the parameters of items, as well as using cross-validation method can reduce the adverse effects of calibration errors on CD-CAT.

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