scholarly journals Some New Item Selection Criteria for Adaptive Testing

1997 ◽  
Vol 22 (2) ◽  
pp. 203-226 ◽  
Author(s):  
Wim J. J. Veerkamp ◽  
Martijn P. F. Berger
Keyword(s):  
Selection Criteria ◽  
Information Criterion ◽  
Good Alternative ◽  
Adaptive Testing ◽  
Item Selection ◽  
A Posteriori ◽  
Likelihood Estimator ◽  
Maximum Information ◽  
Ability Estimates ◽  
Special Cases

In this study some alternative item selection criteria for adaptive testing are proposed. These criteria take into account the uncertainty of the ability estimates. A general weighted information criterion of which the usual maximum information criterion and the proposed alternative criteria are special cases is suggested. A small simulation study was conducted to compare the different criteria. The results showed that the likelihood weighted information criterion is a good alternative to the maximum information criterion. Another good alternative is a maximum information criterion with the maximum likelihood estimator of ability replaced by the Bayesian expected a posteriori estimator.

Using a Response Time–Based Expected A Posteriori Estimator to Control for Differential Speededness in Computerized Adaptive Test

2021 ◽  
pp. 014662162110146
Author(s):  
Justin L. Kern ◽  
Edison Choe
Keyword(s):  
Response Times ◽  
Real Data ◽  
Joint Estimation ◽  
Item Selection ◽  
A Posteriori ◽  
Computerized Adaptive Test ◽  
Maximum Information ◽  
Adaptive Test ◽  
Estimation And Control ◽  
And Control

This study investigates using response times (RTs) with item responses in a computerized adaptive test (CAT) setting to enhance item selection and ability estimation and control for differential speededness. Using van der Linden’s hierarchical framework, an extended procedure for joint estimation of ability and speed parameters for use in CAT is developed following van der Linden; this is called the joint expected a posteriori estimator (J-EAP). It is shown that the J-EAP estimate of ability and speededness outperforms the standard maximum likelihood estimator (MLE) of ability and speededness in terms of correlation, root mean square error, and bias. It is further shown that under the maximum information per time unit item selection method (MICT)—a method which uses estimates for ability and speededness directly—using the J-EAP further reduces average examinee time spent and variability in test times between examinees above the resulting gains of this selection algorithm with the MLE while maintaining estimation efficiency. Simulated test results are further corroborated with test parameters derived from a real data example.

scholarly journals Some New Item Selection Criteria for Adaptive Testing

1997 ◽  
Vol 22 (2) ◽  
pp. 203 ◽  
Author(s):  
Wim J. J. Veerkamp ◽  
Martijn P. F. Berger
Keyword(s):  
Selection Criteria ◽  
Adaptive Testing ◽  
Item Selection ◽  
New Item

scholarly journals A Note on the Relationship of the Shannon Entropy Procedure and the Jensen–Shannon Divergence in Cognitive Diagnostic Computerized Adaptive Testing

SAGE Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 215824401989904
Author(s):  
Wenyi Wang ◽  
Lihong Song ◽  
Teng Wang ◽  
Peng Gao ◽  
Jian Xiong
Keyword(s):  
Shannon Entropy ◽  
Selection Criteria ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Item Selection ◽  
Item Parameters ◽  
Post Hoc ◽  
Relationship Of ◽  
Jensen Shannon Divergence ◽  
The Relationship

The purpose of this study is to investigate the relationship between the Shannon entropy procedure and the Jensen–Shannon divergence (JSD) that are used as item selection criteria in cognitive diagnostic computerized adaptive testing (CD-CAT). Because the JSD itself is defined by the Shannon entropy, we apply the well-known relationship between the JSD and Shannon entropy to establish a relationship between the item selection criteria that are based on these two measures. To understand the relationship between these two item selection criteria better, an alternative way is also provided. Theoretical derivations and empirical examples have shown that the Shannon entropy procedure and the JSD in CD-CAT have a linear relation under cognitive diagnostic models. Consistent with our theoretical conclusions, simulation results have shown that two item selection criteria behaved quite similarly in terms of attribute-level and pattern recovery rates under all conditions and they selected the same set of items for each examinee from an item bank with item parameters drawn from a uniform distribution U(0.1, 0.3) under post hoc simulations. We provide some suggestions for future studies and a discussion of relationship between the modified posterior-weighted Kullback–Leibler index and the G-DINA (generalized deterministic inputs, noisy “and” gate) discrimination index.

New Item Selection Criteria of Computerized Adaptive Testing with Exposure-Control Factor

2012 ◽  
Vol 43 (2) ◽  
pp. 203-212 ◽  
Author(s):  
Xiao-Yang CHENG ◽  
Shu-Liang DING ◽  
Shen-Hai YAN ◽  
Long-Yin ZHU
Keyword(s):  
Selection Criteria ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Item Selection ◽  
Control Factor ◽  
Exposure Control ◽  
New Item

Bayesian item selection criteria for adaptive testing

Psychometrika ◽  
1998 ◽  
Vol 63 (2) ◽  
pp. 201-216 ◽  
Author(s):  
Wim J. van der Linden
Keyword(s):  
Selection Criteria ◽  
Adaptive Testing ◽  
Item Selection

scholarly journals Item Selection Criteria With Practical Constraints in Cognitive Diagnostic Computerized Adaptive Testing

2018 ◽  
Vol 79 (2) ◽  
pp. 335-357 ◽  
Author(s):  
Chuan-Ju Lin ◽  
Hua-Hua Chang
Keyword(s):  
Selection Criteria ◽  
Computerized Adaptive Testing ◽  
Selection Index ◽  
Selection Criterion ◽  
Adaptive Testing ◽  
Item Selection ◽  
Item Pool ◽  
Single Item ◽  
Estimation Precision ◽  
Practical Constraints

For item selection in cognitive diagnostic computerized adaptive testing (CD-CAT), ideally, a single item selection index should be created to simultaneously regulate precision, exposure status, and attribute balancing. For this purpose, in this study, we first proposed an attribute-balanced item selection criterion, namely, the standardized weighted deviation global discrimination index (SWDGDI), and subsequently formulated the constrained progressive index (CP_SWDGDI) by casting the SWDGDI in a progressive algorithm. A simulation study revealed that the SWDGDI method was effective in balancing attribute coverage and the CP_SWDGDI method was able to simultaneously balance attribute coverage and item pool usage while maintaining acceptable estimation precision. This research also demonstrates the advantage of a relatively low number of attributes in CD-CAT applications.

Sign in / Sign up

Export Citation Format

Share Document