IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0312747.html
   My bibliography  Save this article

Introducing a blocked procedure in nonparametric CD-CAT

Author

Listed:
  • Jiahui Zhang
  • Yuqing Yuan
  • Ziying Qiu
  • Feng Li

Abstract

Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT), in conjunction with nonparametric methodologies, is an adaptive assessment tool utilized for diagnosing students’ knowledge mastery within smaller educational contexts. Expanding upon this framework, this study introduces the blocked procedure previously used in the parametric CD-CAT, enhancing the flexibility of nonparametric CD-CAT by enabling within-block item review and answer modification. A simulation study was conducted to evaluate the performance of this blocked procedure within the context of nonparametric CD-CAT across varied conditions. With increasing block size, there was a marginal reduction in pattern correct classification rate; however, such differences diminished as item quality or test length augmented. Overall, under a majority of conditions, the blocked procedure, characterized by block sizes of 2 or 4 items, allows item review within-block while attaining satisfactory levels of classification accuracy. The integration of within-block item review and answer modification with nonparametric CD-CAT fosters a more adaptive and learner-centric testing environment.

Suggested Citation

  • Jiahui Zhang & Yuqing Yuan & Ziying Qiu & Feng Li, 2024. "Introducing a blocked procedure in nonparametric CD-CAT," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-16, December.
  • Handle: RePEc:plo:pone00:0312747
    DOI: 10.1371/journal.pone.0312747
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0312747
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0312747&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0312747?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Jimmy de la Torre, 2011. "The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 179-199, April.
    3. Jimmy Torre, 2011. "Erratum to: The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(3), pages 510-510, July.
    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. James Joseph Balamuta & Steven Andrew Culpepper, 2022. "Exploratory Restricted Latent Class Models with Monotonicity Requirements under PÒLYA–GAMMA Data Augmentation," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 903-945, September.
    2. Xin Xu & Guanhua Fang & Jinxin Guo & Zhiliang Ying & Susu Zhang, 2024. "Diagnostic Classification Models for Testlets: Methods and Theory," Psychometrika, Springer;The Psychometric Society, vol. 89(3), pages 851-876, September.
    3. Peida Zhan & Wen-Chung Wang & Xiaomin Li, 2020. "A Partial Mastery, Higher-Order Latent Structural Model for Polytomous Attributes in Cognitive Diagnostic Assessments," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 328-351, July.
    4. 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.
    5. Chun Wang & Jing Lu, 2021. "Learning Attribute Hierarchies From Data: Two Exploratory Approaches," Journal of Educational and Behavioral Statistics, , vol. 46(1), pages 58-84, February.
    6. Hans Friedrich Köhn & Chia-Yi Chiu, 2021. "A Unified Theory of the Completeness of Q-Matrices for the DINA Model," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 500-518, October.
    7. Chenchen Ma & Jimmy Torre & Gongjun Xu, 2023. "Bridging Parametric and Nonparametric Methods in Cognitive Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 51-75, March.
    8. Kazuhiro Yamaguchi, 2023. "Bayesian Analysis Methods for Two-Level Diagnosis Classification Models," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 773-809, December.
    9. Motonori Oka & Kensuke Okada, 2023. "Scalable Bayesian Approach for the Dina Q-Matrix Estimation Combining Stochastic Optimization and Variational Inference," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 302-331, March.
    10. Yuqi Gu, 2023. "Generic Identifiability of the DINA Model and Blessing of Latent Dependence," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 117-131, March.
    11. Ying Liu & Steven Andrew Culpepper & Yuguo Chen, 2023. "Identifiability of Hidden Markov Models for Learning Trajectories in Cognitive Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 361-386, June.
    12. Steven Andrew Culpepper, 2023. "A Note on Weaker Conditions for Identifying Restricted Latent Class Models for Binary Responses," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 158-174, March.
    13. Junhuan Wei & Liufen Luo & Yan Cai & Dongbo Tu, 2024. "A Multistrategy Cognitive Diagnosis Model Incorporating Item Response Times Based on Strategy Selection Theories," Journal of Educational and Behavioral Statistics, , vol. 49(4), pages 658-686, August.
    14. Shiyu Wang & Jeff Douglas, 2015. "Consistency of Nonparametric Classification in Cognitive Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 85-100, March.
    15. Pablo Nájera & Francisco J. Abad & Chia-Yi Chiu & Miguel A. Sorrel, 2023. "The Restricted DINA Model: A Comprehensive Cognitive Diagnostic Model for Classroom-Level Assessments," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 719-749, December.
    16. Yuqi Gu & Gongjun Xu, 2019. "The Sufficient and Necessary Condition for the Identifiability and Estimability of the DINA Model," Psychometrika, Springer;The Psychometric Society, vol. 84(2), pages 468-483, June.
    17. Mengqi Lin & Gongjun Xu, 2024. "Sufficient and Necessary Conditions for the Identifiability of DINA Models with Polytomous Responses," Psychometrika, Springer;The Psychometric Society, vol. 89(2), pages 717-740, June.
    18. Yinyin Chen & Steven Culpepper & Feng Liang, 2020. "A Sparse Latent Class Model for Cognitive Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 121-153, March.
    19. Ying Liu & Steven Andrew Culpepper, 2024. "Restricted Latent Class Models for Nominal Response Data: Identifiability and Estimation," Psychometrika, Springer;The Psychometric Society, vol. 89(2), pages 592-625, June.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0312747. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.