IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v37y2020i3d10.1007_s00357-019-09357-x.html
   My bibliography  Save this article

Cognitive Diagnostic Computerized Adaptive Testing for Polytomously Scored Items

Author

Listed:
  • Xuliang Gao

    (Jiangxi Normal University
    Guizhou Normal University)

  • Daxun Wang

    (Jiangxi Normal University)

  • Yan Cai

    (Jiangxi Normal University)

  • Dongbo Tu

    (Jiangxi Normal University)

Abstract

Cognitive diagnostic computerized adaptive testing (CD-CAT) purports to combine the strengths of both CAT and cognitive diagnosis. Currently, large number of CD-CAT researches focus on the dichotomous data. In our knowledge, there are no researches on CD-CAT for polytomously scored items or data. However, polytomously scored items have been broadly used in a variety of tests for their advantages of providing more information about examinee, and fewer polytomous items can achieve the same precision compared with dichotomous items. Therefore, it is an interesting topic on CD-CAT with polytomously scored items, which need promote the research in polytomous cognitive diagnostic computerized adaptive testing (called PCD-CAT). This study aims to construct a framework of PCD-CAT, including the construction of the item bank, the item selection method, the parameter estimation, and the termination rule.

Suggested Citation

  • Xuliang Gao & Daxun Wang & Yan Cai & Dongbo Tu, 2020. "Cognitive Diagnostic Computerized Adaptive Testing for Polytomously Scored Items," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 709-729, October.
    • Handle: RePEc:spr:jclass:v:37:y:2020:i:3:d:10.1007_s00357-019-09357-x
    DOI: 10.1007/s00357-019-09357-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00357-019-09357-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00357-019-09357-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hong-Yun Liu & Xiao-Feng You & Wen-Yi Wang & Shu-Liang Ding & Hua-Hua Chang, 2013. "The Development of Computerized Adaptive Testing with Cognitive Diagnosis for an English Achievement Test in China," Journal of Classification, Springer;The Classification Society, vol. 30(2), pages 152-172, July.
    2. Ping Chen & Tao Xin & Chun Wang & Hua-Hua Chang, 2012. "Online Calibration Methods for the DINA Model with Independent Attributes in CD-CAT," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 201-222, April.
    3. Curtis Tatsuoka & Thomas Ferguson, 2003. "Sequential classification on partially ordered sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 143-157, February.
    4. 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.
    5. Ying Cheng, 2009. "When Cognitive Diagnosis Meets Computerized Adaptive Testing: CD-CAT," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 619-632, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qingrong Tan & Yan Cai & Fen Luo & Dongbo Tu, 2023. "Development of a High-Accuracy and Effective Online Calibration Method in CD-CAT Based on Gini Index," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 103-141, February.
    2. Wenyi Wang & Lihong Song & Teng Wang & Peng Gao & Jian Xiong, 2020. "A Note on the Relationship of the Shannon Entropy Procedure and the Jensen–Shannon Divergence in Cognitive Diagnostic Computerized Adaptive Testing," SAGE Open, , vol. 10(1), pages 21582440198, January.
    3. 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.
    4. 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.
    5. Ping Chen & Tao Xin & Chun Wang & Hua-Hua Chang, 2012. "Online Calibration Methods for the DINA Model with Independent Attributes in CD-CAT," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 201-222, April.
    6. Pasquale Anselmi & Egidio Robusto & Luca Stefanutti & Debora Chiusole, 2016. "An Upgrading Procedure for Adaptive Assessment of Knowledge," Psychometrika, Springer;The Psychometric Society, vol. 81(2), pages 461-482, June.
    7. Ying Cheng, 2009. "When Cognitive Diagnosis Meets Computerized Adaptive Testing: CD-CAT," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 619-632, December.
    8. Hung-Yu Huang, 2018. "Effects of Item Calibration Errors on Computerized Adaptive Testing under Cognitive Diagnosis Models," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 437-465, October.
    9. Yunxiao Chen & Xiaoou Li & Jingchen Liu & Zhiliang Ying, 2017. "Regularized Latent Class Analysis with Application in Cognitive Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 660-692, September.
    10. 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.
    11. Hong-Yun Liu & Xiao-Feng You & Wen-Yi Wang & Shu-Liang Ding & Hua-Hua Chang, 2013. "The Development of Computerized Adaptive Testing with Cognitive Diagnosis for an English Achievement Test in China," Journal of Classification, Springer;The Classification Society, vol. 30(2), pages 152-172, July.
    12. S. Ferguson, T.Thomas & Tatsuoka, Curtis, 2004. "An optimal strategy for sequential classification on partially ordered sets," Statistics & Probability Letters, Elsevier, vol. 68(2), pages 161-168, June.
    13. Chen, Yunxiao & Li, Xiaoou & Liu, Jingchen & Ying, Zhiliang, 2017. "Regularized latent class analysis with application in cognitive diagnosis," LSE Research Online Documents on Economics 103182, London School of Economics and Political Science, LSE Library.
    14. Hua-Hua Chang, 2015. "Psychometrics Behind Computerized Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 1-20, March.
    15. Matthew S. Johnson & Sandip Sinharay, 2020. "The Reliability of the Posterior Probability of Skill Attainment in Diagnostic Classification Models," Journal of Educational and Behavioral Statistics, , vol. 45(1), pages 5-31, February.
    16. Hans-Friedrich Köhn & Chia-Yi Chiu, 2017. "A Procedure for Assessing the Completeness of the Q-Matrices of Cognitively Diagnostic Tests," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 112-132, March.
    17. Ping Chen & Chun Wang, 2016. "A New Online Calibration Method for Multidimensional Computerized Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 674-701, September.
    18. Miguel A Sorrel & Juan R Barrada & Jimmy de la Torre & Francisco José Abad, 2020. "Adapting cognitive diagnosis computerized adaptive testing item selection rules to traditional item response theory," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-17, January.
    19. Chen-Wei Liu & Björn Andersson & Anders Skrondal, 2020. "A Constrained Metropolis–Hastings Robbins–Monro Algorithm for Q Matrix Estimation in DINA Models," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 322-357, June.
    20. Ping Chen, 2017. "A Comparative Study of Online Item Calibration Methods in Multidimensional Computerized Adaptive Testing," Journal of Educational and Behavioral Statistics, , vol. 42(5), pages 559-590, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jclass:v:37:y:2020:i:3:d:10.1007_s00357-019-09357-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.