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Advances in CD-CAT: The General Nonparametric Item Selection Method

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  • Chia-Yi Chiu

    (Rutgers, The State University of New Jersey)

  • Yuan-Pei Chang

    (Rutgers, The State University of New Jersey)

Abstract

Computerized adaptive testing (CAT) is characterized by its high estimation efficiency and accuracy, in contrast to the traditional paper-and-pencil format. CAT specifically for cognitive diagnosis (CD-CAT) carries the same advantages and has been seen as a tool for advancing the use of cognitive diagnosis (CD) assessment for educational practice. A powerful item selection method is the key to the success of a CD-CAT program, and to date, various parametric item selection methods have been proposed and well-researched. However, these parametric methods all require large samples, to secure high-precision calibration of the items in the item bank. Thus, at present, implementation of parametric methods in small-scale educational settings, such as classroom, remains challenging. In response to this issue, Chang, Chiu, and Tsai (Appl Psychol Meas 43:543–561, 2019) proposed the nonparametric item selection (NPS) method that does not require parameter calibration and outperforms the parametric methods for settings with only small or no calibration samples. Nevertheless, the NPS method is not without limitations; extra assumptions are required to guarantee a consistent estimator of the attribute profiles when data conform to complex models. To remedy this shortcoming, the general nonparametric item selection (GNPS) method that incorporates the newly developed general NPC (GNPC) method (Chiu et al. in Psychometrika 83:355–375, 2018) as the classification vehicle is proposed in this study. The inclusion of the GNPC method in the GNPS method relaxes the assumptions imposed on the NPS method. As a result, the GNPS method can be used with any model or multiple models without abandoning the advantage of being a small-sample technique. The legitimacy of using the GNPS method in the CD-CAT system is supported by Theorem 1 proposed in the study. The efficiency and effectiveness of the GNPS method are confirmed by the simulation study that shows the outperformance of the GNPS method over the compared parametric methods when the calibration samples are small.

Suggested Citation

  • Chia-Yi Chiu & Yuan-Pei Chang, 2021. "Advances in CD-CAT: The General Nonparametric Item Selection Method," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 1039-1057, December.
    • Handle: RePEc:spr:psycho:v:86:y:2021:i:4:d:10.1007_s11336-021-09792-z
    DOI: 10.1007/s11336-021-09792-z
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    References listed on IDEAS

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    1. Jingchen Liu & Zhiliang Ying & Stephanie Zhang, 2015. "A Rate Function Approach to Computerized Adaptive Testing for Cognitive Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 468-490, June.
    2. Curtis Tatsuoka, 2002. "Data analytic methods for latent partially ordered classification models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(3), pages 337-350, July.
    3. Chia-Yi Chiu & Hans-Friedrich Köhn, 2019. "Consistency Theory for the General Nonparametric Classification Method," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 830-845, September.
    4. Chia-Yi Chiu & Jeff Douglas, 2013. "A Nonparametric Approach to Cognitive Diagnosis by Proximity to Ideal Response Patterns," Journal of Classification, Springer;The Classification Society, vol. 30(2), pages 225-250, July.
    5. Jimmy de la Torre, 2011. "The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 179-199, April.
    6. Chia-Yi Chiu & Yan Sun & Yanhong Bian, 2018. "Cognitive Diagnosis for Small Educational Programs: The General Nonparametric Classification Method," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 355-375, June.
    7. Chia-Yi Chiu & Jeffrey Douglas & Xiaodong Li, 2009. "Cluster Analysis for Cognitive Diagnosis: Theory and Applications," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 633-665, December.
    8. Jimmy Torre, 2011. "Erratum to: The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(3), pages 510-510, July.
    9. Shiyu Wang & Jeff Douglas, 2015. "Consistency of Nonparametric Classification in Cognitive Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 85-100, March.
    10. Ying Cheng, 2009. "When Cognitive Diagnosis Meets Computerized Adaptive Testing: CD-CAT," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 619-632, December.
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    Cited by:

    1. Yu Wang & Chia-Yi Chiu & Hans Friedrich Köhn, 2023. "Nonparametric Classification Method for Multiple-Choice Items in Cognitive Diagnosis," Journal of Educational and Behavioral Statistics, , vol. 48(2), pages 189-219, April.
    2. Yan Li & Chao Huang & Jia Liu, 2023. "Diagnosing Primary Students’ Reading Progression: Is Cognitive Diagnostic Computerized Adaptive Testing the Way Forward?," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 842-865, December.

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