scholarly journals cdcatR: An R Package for Cognitive Diagnostic Computerized Adaptive Testing

Psych ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 386-403
Author(s):  
Miguel A. Sorrel ◽  
Pablo Nájera ◽  
Francisco J. Abad
Keyword(s):  
Latent Class ◽  
Computerized Adaptive Testing ◽  
R Package ◽  
Adaptive Testing ◽  
Latent Class Models ◽  
Cognitive Diagnosis ◽  
Generation Model ◽  
Data Generation ◽  
Item Exposure ◽  
Fine Grained

Cognitive diagnosis models (CDMs) are confirmatory latent class models that provide fine-grained information about skills and cognitive processes. These models have gained attention in the last few years because of their usefulness in educational and psychological settings. Recently, numerous developments have been made to allow for the implementation of cognitive diagnosis computerized adaptive testing (CD-CAT). Despite methodological advances, CD-CAT applications are still scarce. To facilitate research and the emergence of empirical applications in this area, we have developed the cdcatR package for R software. The purpose of this document is to illustrate the different functions included in this package. The package includes functionalities for data generation, model selection based on relative fit information, implementation of several item selection rules (including item exposure control), and CD-CAT performance evaluation in terms of classification accuracy, item exposure, and test length. In conclusion, an R package is made available to researchers and practitioners that allows for an easy implementation of CD-CAT in both simulation and applied studies. Ultimately, this is expected to facilitate the development of empirical applications in this area.

scholarly journals Stratified Item Selection Methods in Cognitive Diagnosis Computerized Adaptive Testing

2019 ◽  
Vol 44 (5) ◽  
pp. 346-361
Author(s):  
Jing Yang ◽  
Hua-Hua Chang ◽  
Jian Tao ◽  
Ningzhong Shi
Keyword(s):  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Cognitive Diagnosis ◽  
Item Selection ◽  
Data Generation ◽  
Selection Methods ◽  
Calibration Sample ◽  
Key Factor ◽  
Item Quality ◽  
Better Than

Cognitive diagnostic computerized adaptive testing (CD-CAT) aims to obtain more useful diagnostic information by taking advantages of computerized adaptive testing (CAT). Cognitive diagnosis models (CDMs) have been developed to classify examinees into the correct proficiency classes so as to get more efficient remediation, whereas CAT tailors optimal items to the examinee’s mastery profile. The item selection method is the key factor of the CD-CAT procedure. In recent years, a large number of parametric/nonparametric item selection methods have been proposed. In this article, the authors proposed a series of stratified item selection methods in CD-CAT, which are combined with posterior-weighted Kullback–Leibler (PWKL), nonparametric item selection (NPS), and weighted nonparametric item selection (WNPS) methods, and named S-PWKL, S-NPS, and S-WNPS, respectively. Two different types of stratification indices were used: original versus novel. The performances of the proposed item selection methods were evaluated via simulation studies and compared with the PWKL, NPS, and WNPS methods without stratification. Manipulated conditions included calibration sample size, item quality, number of attributes, number of strata, and data generation models. Results indicated that the S-WNPS and S-NPS methods performed similarly, and both outperformed the S-PWKL method. And item selection methods with novel stratification indices performed slightly better than the ones with original stratification indices, and those without stratification performed the worst.

Evaluating a Computerized Adaptive Testing Version of a Cognitive Ability Test Using a Simulation Study

2021 ◽  
pp. 073428292110277
Author(s):  
Ioannis Tsaousis ◽  
Georgios D. Sideridis ◽  
Hannan M. AlGhamdi
Keyword(s):  
Cognitive Ability ◽  
Simulation Study ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Estimation Methods ◽  
Item Pool ◽  
Sequential Approach ◽  
Ability Test ◽  
Promising Alternative ◽  
Item Exposure

This study evaluated the psychometric quality of a computerized adaptive testing (CAT) version of the general cognitive ability test (GCAT), using a simulation study protocol put forth by Han, K. T. (2018a). For the needs of the analysis, three different sets of items were generated, providing an item pool of 165 items. Before evaluating the efficiency of the GCAT, all items in the final item pool were linked (equated), following a sequential approach. Data were generated using a standard normal for 10,000 virtual individuals ( M = 0 and SD = 1). Using the measure’s 165-item bank, the ability value (θ) for each participant was estimated. maximum Fisher information (MFI) and maximum likelihood estimation with fences (MLEF) were used as item selection and score estimation methods, respectively. For item exposure control, the fade away method (FAM) was preferred. The termination criterion involved a minimum SE ≤ 0.33. The study revealed that the average number of items administered for 10,000 participants was 15. Moreover, the precision level in estimating the participant’s ability score was very high, as demonstrated by the CBIAS, CMAE, and CRMSE). It is concluded that the CAT version of the test is a promising alternative to administering the corresponding full-length measure since it reduces the number of administered items, prevents high rates of item exposure, and provides accurate scores with minimum measurement error.

scholarly journals Mutual Information Reliability for Latent Class Analysis

2018 ◽  
Vol 42 (6) ◽  
pp. 460-477 ◽  
Author(s):  
Yunxiao Chen ◽  
Yang Liu ◽  
Shuangshuang Xu
Keyword(s):  
Mutual Information ◽  
Latent Variables ◽  
Goodness Of Fit ◽  
Latent Class ◽  
Computerized Adaptive Testing ◽  
Diagnostic System ◽  
General Concept ◽  
Latent Class Models ◽  
Information Reliability ◽  
Class Models

Latent class models are powerful tools in psychological and educational measurement. These models classify individuals into subgroups based on a set of manifest variables, assisting decision making in a diagnostic system. In this article, based on information theory, the authors propose a mutual information reliability (MIR) coefficient that summaries the measurement quality of latent class models, where the latent variables being measured are categorical. The proposed coefficient is analogous to a version of reliability coefficient for item response theory models and meets the general concept of measurement reliability in the Standards for Educational and Psychological Testing. The proposed coefficient can also be viewed as an extension of the McFadden’s pseudo R-square coefficient, which evaluates the goodness-of-fit of logistic regression model, to latent class models. Thanks to several information-theoretic inequalities, the MIR coefficient is unitless, lies between 0 and 1, and receives good interpretation from a measurement point of view. The coefficient can be applied to both fixed and computerized adaptive testing designs. The performance of the MIR coefficient is demonstrated by simulated examples.

scholarly journals Some Alternatives to Sympson-Hetter Item-Exposure Control in Computerized Adaptive Testing

2003 ◽  
Vol 28 (3) ◽  
pp. 249-265 ◽  
Author(s):  
Wim J. van der Linden
Keyword(s):  
Iterative Process ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Law School ◽  
Exposure Control ◽  
Item Exposure ◽  
Item Exposure Control ◽  
Admission Test ◽  
Formal Properties ◽  
Ability Parameter

The Hetter and Sympson (1997 ; 1985 ) method is a method of probabilistic item-exposure control in computerized adaptive testing. Setting its control parameters to admissible values requires an iterative process of computer simulations that has been found to be time consuming, particularly if the parameters have to be set conditional on a realistic set of values for the examinees’ ability parameter. Formal properties of the method are identified that help us explain why this iterative process can be slow and does not guarantee admissibility. In addition, some alternatives to the SH method are introduced. The behavior of these alternatives was estimated for an adaptive test from an item pool from the Law School Admission Test (LSAT). Two of the alternatives showed attractive behavior and converged smoothly to admissibility for all items in a relatively small number of iteration steps.

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.

Application of Computerized Adaptive Testing in Cognitive Diagnosis

2013 ◽  
Vol 20 (4) ◽  
pp. 616-626
Author(s):  
Xiao-Juan TANG ◽  
Shu-Liang DING ◽  
Zong-Huo YU
Keyword(s):  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Cognitive Diagnosis
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