View in EDS PDF Full Text

Flow with an Intelligent Tutor: A Latent Variable Modeling Approach to Tracking Flow during Artificial Tutoring

Saved in:
Bibliographic Details
Title: Flow with an Intelligent Tutor: A Latent Variable Modeling Approach to Tracking Flow during Artificial Tutoring
Language: English
Authors: Kang, Hyeon-Ah (ORCID 0000-0003-4496-6467), Sales, Adam, Whittaker, Tiffany A.
Source: Grantee Submission. 2023.
Peer Reviewed: Y
Page Count: 24
Publication Date: 2023
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305D210036
Document Type: Reports - Research
Descriptors: Intelligent Tutoring Systems, Algebra, Educational Technology, Learning Processes, Mathematics Skills, Markov Processes, Models, Measurement
DOI: 10.3758/s13428-022-02041-w
Abstract: Increasing use of intelligent tutoring systems in education calls for analytic methods that can unravel students' learning behaviors. In this study, we explore a latent variable modeling approach for tracking learning flow during computer-interactive artificial tutoring. The study considers three models that give discrete profiles of a latent process: the (i) latent class model, (ii) latent transition model, and (iii) hidden Markov model. We illustrate application of each model using example log data from Cognitive Tutor Algebra I and suggest analytic procedures of drawing learning flow. Through experimental application, we show that the models can reveal substantive information about students' learning behaviors and have potential utility for describing the learning flow. The models differed in the assumptions and data constraints but yielded consistent findings on the flow states and interaction modalities. Based on our experiential analyses, we discuss strengths and limitations of the models and illuminate areas of future development. [This is the online version of an article published in "Behavior Research Methods."]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2023
Accession Number: ED628263
Database: ERIC
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwHNX_YIFmhjpmYn1dvF7vXiAAAA6DCB5QYJKoZIhvcNAQcGoIHXMIHUAgEAMIHOBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDPY6PRE5tDuhsAdvPwIBEICBoJ43NBK_VzbQLPSuD4uXydd68yzNpAs70NT84af5TOSfUiftuQDv3Gn16CPI8pftM22vXstMplTCscUFLvwpi7VnHnj3B9npuA5hWOq3tACqfAC1Gb_W3M7CZyB_o0cG1pd848d1NdtqnfFqzGm-Zhg0yDENG4n-ndP2lFhfma25z82pSkLNsXaqA-3GOm2Ujw5EjEFopVlNIOPEvktQ-r0=
Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: ED628263
AccessLevel: 3
PubType: Report
PubTypeId: report
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Flow with an Intelligent Tutor: A Latent Variable Modeling Approach to Tracking Flow during Artificial Tutoring
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Kang%2C+Hyeon-Ah%22">Kang, Hyeon-Ah</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-4496-6467">0000-0003-4496-6467</externalLink>)<br /><searchLink fieldCode="AR" term="%22Sales%2C+Adam%22">Sales, Adam</searchLink><br /><searchLink fieldCode="AR" term="%22Whittaker%2C+Tiffany+A%2E%22">Whittaker, Tiffany A.</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2023.
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 24
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: Institute of Education Sciences (ED)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: R305D210036
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Intelligent+Tutoring+Systems%22">Intelligent Tutoring Systems</searchLink><br /><searchLink fieldCode="DE" term="%22Algebra%22">Algebra</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Technology%22">Educational Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Skills%22">Mathematics Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+Processes%22">Markov Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement%22">Measurement</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.3758/s13428-022-02041-w
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Increasing use of intelligent tutoring systems in education calls for analytic methods that can unravel students' learning behaviors. In this study, we explore a latent variable modeling approach for tracking learning flow during computer-interactive artificial tutoring. The study considers three models that give discrete profiles of a latent process: the (i) latent class model, (ii) latent transition model, and (iii) hidden Markov model. We illustrate application of each model using example log data from Cognitive Tutor Algebra I and suggest analytic procedures of drawing learning flow. Through experimental application, we show that the models can reveal substantive information about students' learning behaviors and have potential utility for describing the learning flow. The models differed in the assumptions and data constraints but yielded consistent findings on the flow states and interaction modalities. Based on our experiential analyses, we discuss strengths and limitations of the models and illuminate areas of future development. [This is the online version of an article published in "Behavior Research Methods."]
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: CodeSource
  Label: IES Funded
  Group: SrcInfo
  Data: Yes
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2023
– Name: AN
  Label: Accession Number
  Group: ID
  Data: ED628263
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED628263
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3758/s13428-022-02041-w
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
    Subjects:
      – SubjectFull: Intelligent Tutoring Systems
        Type: general
      – SubjectFull: Algebra
        Type: general
      – SubjectFull: Educational Technology
        Type: general
      – SubjectFull: Learning Processes
        Type: general
      – SubjectFull: Mathematics Skills
        Type: general
      – SubjectFull: Markov Processes
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Measurement
        Type: general
    Titles:
      – TitleFull: Flow with an Intelligent Tutor: A Latent Variable Modeling Approach to Tracking Flow during Artificial Tutoring
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kang, Hyeon-Ah
      – PersonEntity:
          Name:
            NameFull: Sales, Adam
      – PersonEntity:
          Name:
            NameFull: Whittaker, Tiffany A.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 07
              M: 02
              Type: published
              Y: 2023
          Titles:
            – TitleFull: Grantee Submission
              Type: main
ResultId 1