Towards Association Rule-Based Item Selection Strategy in Computerized Adaptive Testing

2021 ◽  
pp. 27-54
Author(s):  
Josué Pacheco-Ortiz ◽  
Lisbeth Rodríguez-Mazahua ◽  
Jezreel Mejía-Miranda ◽  
Isaac Machorro-Cano ◽  
Ulises Juárez-Martínez
Keyword(s):  
Association Rule ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Item Selection ◽  
Selection Strategy ◽  
Rule Based

scholarly journals Towards Association Rule-based Item Selection Strategy in Computerized Adaptive Testing

2020 ◽  
Vol 7 (2-1) ◽  
pp. 19-30
Author(s):  
Josué Pacheco Ortiz ◽  
Lisbeth Rodríguez Mazahua ◽  
Jezreel Mejía miranda ◽  
Isaac Machorro Cano ◽  
Giner Alor Hernández ◽  
...  
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Computerized Adaptive Testing ◽  
Selection Criterion ◽  
Adaptive Testing ◽  
Item Selection ◽  
Selection Strategy ◽  
Rule Mining ◽  
Data Set ◽  
Advantages And Disadvantages

One of the most important stages of Computerized Adaptive Testing is the selection of items, in which various methods are used, which have certain weaknesses at the time of implementation. Therefore, in this paper, it is proposed the integration of Association Rule Mining as an item selection criterion in a CAT system. We present the analysis of association rule mining algorithms such as Apriori, FP-Growth, PredictiveApriori and Tertius into two data set with the purpose of knowing the advantages and disadvantages of each algorithm and choose the most suitable. We compare the algorithms considering number of rules discovered, average support and confidence, and velocity. According to the experiments, Apriori found rules with greater confidence, support, in less time.

scholarly journals A Dynamic Stratification Method for Improving Trait Estimation in Computerized Adaptive Testing Under Item Exposure Control

2019 ◽  
Vol 44 (3) ◽  
pp. 182-196
Author(s):  
Jyun-Hong Chen ◽  
Hsiu-Yi Chao ◽  
Shu-Ying Chen
Keyword(s):  
Computerized Adaptive Testing ◽  
Item Difficulty ◽  
Adaptive Testing ◽  
Item Selection ◽  
Exposure Control ◽  
Item Exposure ◽  
Stratification Method ◽  
High Discrimination ◽  
Item Exposure Control ◽  
Trait Estimation

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.

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.

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