On initial item selection in cognitive diagnostic computerized adaptive testing

When computerized adaptive testing (CAT) is under stringent item exposure control, the precision of trait estimation will substantially decrease. A new item selection method, the dynamic Stratification method based on Dominance Curves (SDC), which is aimed at improving trait estimation, is proposed to mitigate this problem. The objective function of the SDC in item selection is to maximize the sum of test information for all examinees rather than maximizing item information for individual examinees at a single-item administration, as in conventional CAT. To achieve this objective, the SDC uses dominance curves to stratify an item pool into strata with the number being equal to the test length to precisely and accurately increase the quality of the administered items as the test progresses, reducing the likelihood that a high-discrimination item will be administered to an examinee whose ability is not close to the item difficulty. Furthermore, the SDC incorporates a dynamic process for on-the-fly item–stratum adjustment to optimize the use of quality items. Simulation studies were conducted to investigate the performance of the SDC in CAT under item exposure control at different levels of severity. According to the results, the SDC can efficiently improve trait estimation in CAT through greater precision and more accurate trait estimation than those generated by other methods (e.g., the maximum Fisher information method) in most conditions.

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

SAGE Open ◽

10.1177/2158244019899046 ◽

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.

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

Acta Psychologica Sinica ◽

10.3724/sp.j.1041.2011.00203 ◽

2012 ◽

Vol 43 (2) ◽

pp. 203-212 ◽

Cited By ~ 1

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

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