View in EDS HTML Full Text PDF Full Text

A general dynamic learning model framework for cognitive diagnosis.

Saved in:
Bibliographic Details
Title: A general dynamic learning model framework for cognitive diagnosis.
Authors: Liu, Zichu (AUTHOR), Wang, Shiyu (AUTHOR), Xiao, Houping (AUTHOR), Zhang, Shumei (AUTHOR), Qiu, Tao (AUTHOR)
Source: British Journal of Mathematical & Statistical Psychology. Nov2025, Vol. 78 Issue 3, p856-888. 33p.
Subjects: Cognitive testing, Bayes' estimation, Cognitive development, Stimulus & response (Psychology), Computer adaptive testing, Instructional systems, Achievement gains (Education), Markov chain Monte Carlo
Abstract: Understanding students' learning trajectories is crucial for educators to effectively monitor and enhance progress. With the rise of computer‐based testing, researchers now have access to rich datasets that provide deeper insights into student performance. This study introduces a general dynamic learning model framework that integrates response accuracy and response times to capture different test‐taking behaviors and estimate learning trajectories related to polytomous attributes over time. A Bayesian estimation method is proposed to estimate model parameters. Rigorous validation through simulation studies confirms the effectiveness of the MCMC algorithm in parameter recovery and highlights the model's utility in understanding learning trajectories and detecting different test‐taking behaviors in a learning environment. Applied to real data, the model demonstrates practical value in educational settings. Overall, this comprehensive and validated model offers educators and researchers nuanced insights into student learning progress and behavioral dynamics. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 188632972
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A general dynamic learning model framework for cognitive diagnosis.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Zichu%22">Liu, Zichu</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Shiyu%22">Wang, Shiyu</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xiao%2C+Houping%22">Xiao, Houping</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Shumei%22">Zhang, Shumei</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qiu%2C+Tao%22">Qiu, Tao</searchLink> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Mathematical+%26+Statistical+Psychology%22">British Journal of Mathematical & Statistical Psychology</searchLink>. Nov2025, Vol. 78 Issue 3, p856-888. 33p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Cognitive+testing%22">Cognitive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Bayes'+estimation%22">Bayes' estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+development%22">Cognitive development</searchLink><br /><searchLink fieldCode="DE" term="%22Stimulus+%26+response+%28Psychology%29%22">Stimulus & response (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+adaptive+testing%22">Computer adaptive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br /><searchLink fieldCode="DE" term="%22Achievement+gains+%28Education%29%22">Achievement gains (Education)</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+chain+Monte+Carlo%22">Markov chain Monte Carlo</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Understanding students' learning trajectories is crucial for educators to effectively monitor and enhance progress. With the rise of computer‐based testing, researchers now have access to rich datasets that provide deeper insights into student performance. This study introduces a general dynamic learning model framework that integrates response accuracy and response times to capture different test‐taking behaviors and estimate learning trajectories related to polytomous attributes over time. A Bayesian estimation method is proposed to estimate model parameters. Rigorous validation through simulation studies confirms the effectiveness of the MCMC algorithm in parameter recovery and highlights the model's utility in understanding learning trajectories and detecting different test‐taking behaviors in a learning environment. Applied to real data, the model demonstrates practical value in educational settings. Overall, this comprehensive and validated model offers educators and researchers nuanced insights into student learning progress and behavioral dynamics. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=188632972
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/bmsp.12384
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 33
        StartPage: 856
    Subjects:
      – SubjectFull: Cognitive testing
        Type: general
      – SubjectFull: Bayes' estimation
        Type: general
      – SubjectFull: Cognitive development
        Type: general
      – SubjectFull: Stimulus & response (Psychology)
        Type: general
      – SubjectFull: Computer adaptive testing
        Type: general
      – SubjectFull: Instructional systems
        Type: general
      – SubjectFull: Achievement gains (Education)
        Type: general
      – SubjectFull: Markov chain Monte Carlo
        Type: general
    Titles:
      – TitleFull: A general dynamic learning model framework for cognitive diagnosis.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Liu, Zichu
      – PersonEntity:
          Name:
            NameFull: Wang, Shiyu
      – PersonEntity:
          Name:
            NameFull: Xiao, Houping
      – PersonEntity:
          Name:
            NameFull: Zhang, Shumei
      – PersonEntity:
          Name:
            NameFull: Qiu, Tao
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 00071102
          Numbering:
            – Type: volume
              Value: 78
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: British Journal of Mathematical & Statistical Psychology
              Type: main
ResultId 1