Cognitive Diagnostic Computerized Adaptive Testing for Polytomously Scored Items

Gao, Xuliang; Wang, Daxun; Cai, Yan; Tu, Dongbo

doi:10.1007/s00357-019-09357-x

Cited by 7 publications

(8 citation statements)

References 23 publications

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“…It is hoped that this development will allow realistic contexts in applied practice to also be explored. The study by Gao et al (2020) has pioneered this [53]. Fourth, there has been recent interest in hybrid selection rules that seek to simultaneously classify a general latent trait score that is continuous and that explains the relationships between discrete attributes modeled in CDM and the attributes (e.g., [30]).…”

Section: Discussionmentioning

confidence: 99%

cdcatR: An R Package for Cognitive Diagnostic Computerized Adaptive Testing

Sorrel

Nájera

Abad

2021

Psych

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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.

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Section: Discussionmentioning

confidence: 99%

cdcatR: An R Package for Cognitive Diagnostic Computerized Adaptive Testing

Sorrel

Nájera

Abad

2021

Psych

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“…Furthermore, the dichotomous item response is only often used in multiple-choice or fill-in-the-blank items. For polytomous scoring items (Gao et al, 2020 ), the adaptive designs remain to be further studied.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

An Adaptive Design for Item Parameter Online Estimation and Q-Matrix Online Calibration in CD-CAT

Wang

Song

et al. 2021

Front. Psychol.

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The implementation of cognitive diagnostic computerized adaptive testing often depends on a high-quality item bank. How to online estimate the item parameters and calibrate the Q-matrix required by items becomes an important problem in the construction of the high-quality item bank for personalized adaptive learning. The related previous research mainly focused on the calibration method with the random design in which the new items were randomly assigned to examinees. Although the way of randomly assigning new items can ensure the randomness of data sampling, some examinees cannot provide enough information about item parameter estimation or Q-matrix calibration for the new items. In order to increase design efficiency, we investigated three adaptive designs under different practical situations: (a) because the non-parametric classification method needs calibrated item attribute vectors, but not item parameters, the first study focused on an optimal design for the calibration of the Q-matrix of the new items based on Shannon entropy; (b) if the Q-matrix of the new items was specified by subject experts, an optimal design was designed for the estimation of item parameters based on Fisher information; and (c) if the Q-matrix and item parameters are unknown for the new items, we developed a hybrid optimal design for simultaneously estimating them. The simulation results showed that, the adaptive designs are better than the random design with a limited number of examinees in terms of the correct recovery rate of attribute vectors and the precision of item parameters.

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“…The item with the minimized expected SHE should be chosen as the next item (Tatsuoka, 2002) because it would lead to minimal uncertainty, which allows the estimated attribute profile to better approximate the true attribute profile. Since the SHE method does not rely on the current estimated attribute profile, it is less impacted by the measurement error of the estimated attribute profile (Gao et al, 2020). In addition, several previous studies indicated that the SHE method might slightly outperform the PWKL methods in some conditions (Chang et al, 2019; Chen et al, 2011; Dai et al, 2016; Gao et al, 2020; Xu et al, 2016).…”

Section: Item Selection Methods For Cd-catmentioning

confidence: 99%

“…Since the SHE method does not rely on the current estimated attribute profile, it is less impacted by the measurement error of the estimated attribute profile (Gao et al, 2020). In addition, several previous studies indicated that the SHE method might slightly outperform the PWKL methods in some conditions (Chang et al, 2019; Chen et al, 2011; Dai et al, 2016; Gao et al, 2020; Xu et al, 2016).…”

Section: Item Selection Methods For Cd-catmentioning

confidence: 99%

Dual-Objective Item Selection Methods in Computerized Adaptive Test Using the Higher-Order Cognitive Diagnostic Models

Cai

2022

Applied Psychological Measurement

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To efficiently obtain information about both the general abilities and detailed cognitive profiles of examinees from a single model that uses a single-calibration process, higher-order cognitive diagnostic computerized adaptive testing (CD-CAT) that employ higher-order cognitive diagnostic models have been developed. However, the current item selection methods used in higher-order CD-CAT adaptively select items according to only the attribute profiles, which might lead to low precision regarding general abilities; hence, an appropriate method was proposed for this CAT system in this study. Under the framework of the higher-order models, the responses were affected by attribute profiles, which were governed by general abilities. It is reasonable to hold that the item responses were affected by a combination of general abilities and attribute profiles. Based on the logic of Shannon entropy and the generalized deterministic, inputs, noisy “and” gate (G-DINA) model discrimination index (GDI), two new item selection methods were proposed for higher-order CD-CAT by considering the above combination in this study. The simulation results demonstrated that the new methods achieved more accurate estimations of both general abilities and cognitive profiles than the existing methods and maintained distinct advantages in terms of item pool usage.

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Cognitive Diagnostic Computerized Adaptive Testing for Polytomously Scored Items

Cited by 7 publications

References 23 publications

cdcatR: An R Package for Cognitive Diagnostic Computerized Adaptive Testing

cdcatR: An R Package for Cognitive Diagnostic Computerized Adaptive Testing

An Adaptive Design for Item Parameter Online Estimation and Q-Matrix Online Calibration in CD-CAT

Dual-Objective Item Selection Methods in Computerized Adaptive Test Using the Higher-Order Cognitive Diagnostic Models

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