View in EDS

A Polytomous Extension of the Higher-Order, Hidden Markov Model with Covariates and Hierarchical Learning Trajectories

Saved in:
Bibliographic Details
Title: A Polytomous Extension of the Higher-Order, Hidden Markov Model with Covariates and Hierarchical Learning Trajectories
Language: English
Authors: Xin Xu (ORCID 0000-0002-0791-111X), Shixiu Ren, Xintong Shan, Danhui Zhang
Source: Journal of Educational and Behavioral Statistics. 2026 51(1):206-247.
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: 42
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Markov Processes, Item Response Theory, Models, Cognitive Measurement, Simulation, Educational Assessment, Learning Trajectories, Longitudinal Studies, Data Analysis
DOI: 10.3102/10769986251313769
ISSN: 1076-9986
1935-1054
Abstract: In practice, constructed-response items are commonly used to diagnose students' performance using polytomous scores. Existing longitudinal cognitive diagnosis models (CDMs) primarily focus on dichotomizing the data, unsuitable for polytomous scores. This article introduces a longitudinal CDM for handling polytomous responses over time. The proposed model expands the capabilities of learning models to handle polytomous data and account for hierarchies among attributes within the CDMs. For estimation, a Gibbs formulation was proposed to estimate parameters in the measurement part, while a Metropolis-Hastings sampler was employed for the transition part. An empirical study was conducted to showcase the practical application and advantages of the proposed model. Additionally, two simulation studies demonstrated that parameters can be well recovered under various conditions.
Abstractor: As Provided
Entry Date: 20260101
Accession Number: EJ1514329
Database: ERIC
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1514329
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Polytomous Extension of the Higher-Order, Hidden Markov Model with Covariates and Hierarchical Learning Trajectories
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Xin+Xu%22">Xin Xu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0791-111X">0000-0002-0791-111X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Shixiu+Ren%22">Shixiu Ren</searchLink><br /><searchLink fieldCode="AR" term="%22Xintong+Shan%22">Xintong Shan</searchLink><br /><searchLink fieldCode="AR" term="%22Danhui+Zhang%22">Danhui Zhang</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Educational+and+Behavioral+Statistics%22"><i>Journal of Educational and Behavioral Statistics</i></searchLink>. 2026 51(1):206-247.
– Name: Avail
  Label: Availability
  Group: Avail
  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
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 42
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2026
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Markov+Processes%22">Markov Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Measurement%22">Cognitive Measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation%22">Simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Assessment%22">Educational Assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Trajectories%22">Learning Trajectories</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+Studies%22">Longitudinal Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.3102/10769986251313769
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1076-9986<br />1935-1054
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In practice, constructed-response items are commonly used to diagnose students' performance using polytomous scores. Existing longitudinal cognitive diagnosis models (CDMs) primarily focus on dichotomizing the data, unsuitable for polytomous scores. This article introduces a longitudinal CDM for handling polytomous responses over time. The proposed model expands the capabilities of learning models to handle polytomous data and account for hierarchies among attributes within the CDMs. For estimation, a Gibbs formulation was proposed to estimate parameters in the measurement part, while a Metropolis-Hastings sampler was employed for the transition part. An empirical study was conducted to showcase the practical application and advantages of the proposed model. Additionally, two simulation studies demonstrated that parameters can be well recovered under various conditions.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 20260101
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1514329
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1514329
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3102/10769986251313769
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 42
        StartPage: 206
    Subjects:
      – SubjectFull: Markov Processes
        Type: general
      – SubjectFull: Item Response Theory
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Cognitive Measurement
        Type: general
      – SubjectFull: Simulation
        Type: general
      – SubjectFull: Educational Assessment
        Type: general
      – SubjectFull: Learning Trajectories
        Type: general
      – SubjectFull: Longitudinal Studies
        Type: general
      – SubjectFull: Data Analysis
        Type: general
    Titles:
      – TitleFull: A Polytomous Extension of the Higher-Order, Hidden Markov Model with Covariates and Hierarchical Learning Trajectories
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Xin Xu
      – PersonEntity:
          Name:
            NameFull: Shixiu Ren
      – PersonEntity:
          Name:
            NameFull: Xintong Shan
      – PersonEntity:
          Name:
            NameFull: Danhui Zhang
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 1076-9986
            – Type: issn-electronic
              Value: 1935-1054
          Numbering:
            – Type: volume
              Value: 51
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Journal of Educational and Behavioral Statistics
              Type: main
ResultId 1