IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v81y2016i2d10.1007_s11336-016-9498-9.html
   My bibliography  Save this article

An Upgrading Procedure for Adaptive Assessment of Knowledge

Author

Listed:
  • Pasquale Anselmi

    (University of Padua)

  • Egidio Robusto

    (University of Padua)

  • Luca Stefanutti

    (University of Padua)

  • Debora Chiusole

    (University of Padua)

Abstract

In knowledge space theory, existing adaptive assessment procedures can only be applied when suitable estimates of their parameters are available. In this paper, an iterative procedure is proposed, which upgrades its parameters with the increasing number of assessments. The first assessments are run using parameter values that favor accuracy over efficiency. Subsequent assessments are run using new parameter values estimated on the incomplete response patterns from previous assessments. Parameter estimation is carried out through a new probabilistic model for missing-at-random data. Two simulation studies show that, with the increasing number of assessments, the performance of the proposed procedure approaches that of gold standards.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:psycho:v:81:y:2016:i:2:d:10.1007_s11336-016-9498-9
    DOI: 10.1007/s11336-016-9498-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11336-016-9498-9
    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/s11336-016-9498-9?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. Rolf Langeheine & Jeroen Pannekoek & Frank Van De Pol, 1996. "Bootstrapping Goodness-of-Fit Measures in Categorical Data Analysis," Sociological Methods & Research, , vol. 24(4), pages 492-516, May.
    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. Jimmy de la Torre & Jeffrey Douglas, 2008. "Model Evaluation and Multiple Strategies in Cognitive Diagnosis: An Analysis of Fraction Subtraction Data," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 595-624, December.
    4. Jürgen Heller & Luca Stefanutti & Pasquale Anselmi & Egidio Robusto, 2016. "Erratum to: On the Link between Cognitive Diagnostic Models and Knowledge Space Theory," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 250-251, March.
    5. Jürgen Heller & Luca Stefanutti & Pasquale Anselmi & Egidio Robusto, 2015. "On the Link between Cognitive Diagnostic Models and Knowledge Space Theory," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 995-1019, December.
    6. Pasquale Anselmi & Egidio Robusto & Luca Stefanutti, 2012. "Uncovering the Best Skill Multimap by Constraining the Error Probabilities of the Gain-Loss Model," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 763-781, October.
    7. Jürgen Heller & Luca Stefanutti & Pasquale Anselmi & Egidio Robusto, 2016. "Erratum to: On the Link between Cognitive Diagnostic Models and Knowledge Space Theory," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 250-251, March.
    8. Jimmy Torre & Jeffrey Douglas, 2004. "Higher-order latent trait models for cognitive diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 69(3), pages 333-353, September.
    9. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luca Stefanutti & Debora Chiusole & Pasquale Anselmi & Andrea Spoto, 2020. "Extending the Basic Local Independence Model to Polytomous Data," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 684-715, September.

    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. Luca Stefanutti & Debora Chiusole & Pasquale Anselmi & Andrea Spoto, 2020. "Extending the Basic Local Independence Model to Polytomous Data," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 684-715, September.
    2. 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.
    3. 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.
    4. Jimmy de la Torre, 2011. "The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 179-199, April.
    5. 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.
    6. Jürgen Heller & Luca Stefanutti & Pasquale Anselmi & Egidio Robusto, 2015. "On the Link between Cognitive Diagnostic Models and Knowledge Space Theory," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 995-1019, December.
    7. 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.
    8. 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.
    9. Yinghan Chen & Ying Liu & Steven Andrew Culpepper & Yuguo Chen, 2021. "Inferring the Number of Attributes for the Exploratory DINA Model," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 30-64, March.
    10. Yinghan Chen & Steven Andrew Culpepper & Yuguo Chen & Jeffrey Douglas, 2018. "Bayesian Estimation of the DINA Q matrix," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 89-108, March.
    11. Yinghan Chen & Shiyu Wang, 2023. "Bayesian Estimation of Attribute Hierarchy for Cognitive Diagnosis Models," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 810-841, December.
    12. Meng-Ta Chung & Shui-Lien Chen, 2021. "A Deterministic Learning Algorithm Estimating the Q-Matrix for Cognitive Diagnosis Models," Mathematics, MDPI, vol. 9(23), pages 1-11, November.
    13. 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.
    14. Hans-Friedrich Köhn & Chia-Yi Chiu, 2018. "How to Build a Complete Q-Matrix for a Cognitively Diagnostic Test," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 273-299, July.
    15. 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.
    16. Kazuhiro Yamaguchi & Kensuke Okada, 2020. "Variational Bayes Inference for the DINA Model," Journal of Educational and Behavioral Statistics, , vol. 45(5), pages 569-597, October.
    17. Chen, Yunxiao & Liu, Jingchen & Xu, Gongjun & Ying, Zhiliang, 2015. "Statistical analysis of Q-matrix based diagnostic classification models," LSE Research Online Documents on Economics 103183, London School of Economics and Political Science, LSE Library.
    18. 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.
    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. Steven Andrew Culpepper, 2019. "An Exploratory Diagnostic Model for Ordinal Responses with Binary Attributes: Identifiability and Estimation," Psychometrika, Springer;The Psychometric Society, vol. 84(4), pages 921-940, December.

    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:psycho:v:81:y:2016:i:2:d:10.1007_s11336-016-9498-9. 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.