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Modeling Partial Knowledge in Multiple-Choice Cognitive Diagnostic Assessment

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Title: Modeling Partial Knowledge in Multiple-Choice Cognitive Diagnostic Assessment
Language: English
Authors: Kentaro Fukushima (ORCID 0000-0002-7439-9312), Nao Uchida (ORCID 0000-0001-8515-3820), Kensuke Okada (ORCID 0000-0003-1663-5812)
Source: Journal of Educational and Behavioral Statistics. 2025 50(1):5-43.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 39
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Diagnostic Tests, Cognitive Measurement, Multiple Choice Tests, Educational Assessment, Objective Tests, Psychometrics, Test Theory, Testing
DOI: 10.3102/10769986241245707
ISSN: 1076-9986
1935-1054
Abstract: Diagnostic tests are typically administered in a multiple-choice (MC) format due to their advantages of objectivity and time efficiency. The MC-deterministic input, noisy "and" gate (DINA) family of models, a representative class of cognitive diagnostic models for MC items, efficiently and parsimoniously estimates the mastery profiles of examinees. However, the existing models often overestimate the latent traits of examinees when they respond with partial knowledge, which is often observed in educational assessment. Therefore, the novel models of the MC-DINA family that can appropriately handle such responses were developed in this study. Unlike the existing models, the proposed models placed no restrictions on the Q-vector, which represents attribute specifications. Simulation and empirical studies verified that the proposed approach could resolve the overestimation problem.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1457142
Database: ERIC
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  Data: Modeling Partial Knowledge in Multiple-Choice Cognitive Diagnostic Assessment
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  Data: <searchLink fieldCode="AR" term="%22Kentaro+Fukushima%22">Kentaro Fukushima</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7439-9312">0000-0002-7439-9312</externalLink>)<br /><searchLink fieldCode="AR" term="%22Nao+Uchida%22">Nao Uchida</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8515-3820">0000-0001-8515-3820</externalLink>)<br /><searchLink fieldCode="AR" term="%22Kensuke+Okada%22">Kensuke Okada</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1663-5812">0000-0003-1663-5812</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Educational+and+Behavioral+Statistics%22"><i>Journal of Educational and Behavioral Statistics</i></searchLink>. 2025 50(1):5-43.
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: <searchLink fieldCode="DE" term="%22Diagnostic+Tests%22">Diagnostic Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Measurement%22">Cognitive Measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+Choice+Tests%22">Multiple Choice Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Assessment%22">Educational Assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Objective+Tests%22">Objective Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Psychometrics%22">Psychometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Theory%22">Test Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Testing%22">Testing</searchLink>
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  Data: 10.3102/10769986241245707
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  Data: Diagnostic tests are typically administered in a multiple-choice (MC) format due to their advantages of objectivity and time efficiency. The MC-deterministic input, noisy "and" gate (DINA) family of models, a representative class of cognitive diagnostic models for MC items, efficiently and parsimoniously estimates the mastery profiles of examinees. However, the existing models often overestimate the latent traits of examinees when they respond with partial knowledge, which is often observed in educational assessment. Therefore, the novel models of the MC-DINA family that can appropriately handle such responses were developed in this study. Unlike the existing models, the proposed models placed no restrictions on the Q-vector, which represents attribute specifications. Simulation and empirical studies verified that the proposed approach could resolve the overestimation problem.
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  Data: 2025
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        Value: 10.3102/10769986241245707
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      – Text: English
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        PageCount: 39
        StartPage: 5
    Subjects:
      – SubjectFull: Diagnostic Tests
        Type: general
      – SubjectFull: Cognitive Measurement
        Type: general
      – SubjectFull: Multiple Choice Tests
        Type: general
      – SubjectFull: Educational Assessment
        Type: general
      – SubjectFull: Objective Tests
        Type: general
      – SubjectFull: Psychometrics
        Type: general
      – SubjectFull: Test Theory
        Type: general
      – SubjectFull: Testing
        Type: general
    Titles:
      – TitleFull: Modeling Partial Knowledge in Multiple-Choice Cognitive Diagnostic Assessment
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            NameFull: Kentaro Fukushima
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            NameFull: Nao Uchida
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            NameFull: Kensuke Okada
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              Type: published
              Y: 2025
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