View in EDS

Modeling Demands-Resources Fit in Teacher Education Using Open-Ended Data: A Methodological-Substantive Synergy

Saved in:
Bibliographic Details
Title: Modeling Demands-Resources Fit in Teacher Education Using Open-Ended Data: A Methodological-Substantive Synergy
Language: English
Authors: Fernando Núñez-Regueiro (ORCID 0000-0003-4784-2021), Samuel Falcon (ORCID 0000-0003-3314-1945), Pascal Bressoux (ORCID 0000-0001-8018-5612)
Source: Education and Information Technologies. 2025 30(18):26025-26056.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 32
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Teacher Education, Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Reliability, Student Teachers, Statistics, Data Analysis, Resources, Automation, Responses
DOI: 10.1007/s10639-025-13764-6
ISSN: 1360-2357
1573-7608
Abstract: This study explores the effectiveness of large language models (LLMs) in automatically encoding a large set of open-ended responses to obtain data for use in applied statistics. As a case study, we focus on demands-resources fit processes and engagement in teacher education. To probe the validity of LLMs in investigating these processes, we compare results from measures obtained via ordinary Likert-type items (scale measures), and measures obtained from automatically encoding open-ended questions (LLM measures) for the same sample of student teachers (N = 499, 82% female, M[subscript age]=23.5 years). Results demonstrate the reliability of LLMs in processing and quantifying large amounts of open-ended data quickly and as accurately as scale measures. Moreover, results concur to reveal an "optimal margin" of demands-resources fit in student teacher engagement. Accordingly, study resources surpassing study demands maximizes engagement, whereas insufficient resources minimize it, and moderate levels of both demands and resources lead to intermediate engagement. By contrast, high or low levels of both demands and resources are suboptimal for engagement. Taken together, these findings demonstrate that LLM-derived statistics offer an efficient and reliable approach to extracting data from open-ended responses, enabling the large-scale analysis of qualitative insights while preserving their richness. This method facilitates the integration of qualitative and quantitative approaches, enhancing the study of individual behavior, and holds significant potential for enhancing digital education frameworks by supporting adaptive learning systems and digital assessment practices.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1504248
Database: ERIC
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1504248
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Modeling Demands-Resources Fit in Teacher Education Using Open-Ended Data: A Methodological-Substantive Synergy
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Fernando+Núñez-Regueiro%22">Fernando Núñez-Regueiro</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-4784-2021">0000-0003-4784-2021</externalLink>)<br /><searchLink fieldCode="AR" term="%22Samuel+Falcon%22">Samuel Falcon</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-3314-1945">0000-0003-3314-1945</externalLink>)<br /><searchLink fieldCode="AR" term="%22Pascal+Bressoux%22">Pascal Bressoux</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-8018-5612">0000-0001-8018-5612</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Education+and+Information+Technologies%22"><i>Education and Information Technologies</i></searchLink>. 2025 30(18):26025-26056.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 32
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Teacher+Education%22">Teacher Education</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+Language+Processing%22">Natural Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Reliability%22">Reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Teachers%22">Student Teachers</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Resources%22">Resources</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Responses%22">Responses</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s10639-025-13764-6
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1360-2357<br />1573-7608
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study explores the effectiveness of large language models (LLMs) in automatically encoding a large set of open-ended responses to obtain data for use in applied statistics. As a case study, we focus on demands-resources fit processes and engagement in teacher education. To probe the validity of LLMs in investigating these processes, we compare results from measures obtained via ordinary Likert-type items (scale measures), and measures obtained from automatically encoding open-ended questions (LLM measures) for the same sample of student teachers (N = 499, 82% female, M[subscript age]=23.5 years). Results demonstrate the reliability of LLMs in processing and quantifying large amounts of open-ended data quickly and as accurately as scale measures. Moreover, results concur to reveal an "optimal margin" of demands-resources fit in student teacher engagement. Accordingly, study resources surpassing study demands maximizes engagement, whereas insufficient resources minimize it, and moderate levels of both demands and resources lead to intermediate engagement. By contrast, high or low levels of both demands and resources are suboptimal for engagement. Taken together, these findings demonstrate that LLM-derived statistics offer an efficient and reliable approach to extracting data from open-ended responses, enabling the large-scale analysis of qualitative insights while preserving their richness. This method facilitates the integration of qualitative and quantitative approaches, enhancing the study of individual behavior, and holds significant potential for enhancing digital education frameworks by supporting adaptive learning systems and digital assessment practices.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1504248
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1504248
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10639-025-13764-6
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 32
        StartPage: 26025
    Subjects:
      – SubjectFull: Teacher Education
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Natural Language Processing
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Reliability
        Type: general
      – SubjectFull: Student Teachers
        Type: general
      – SubjectFull: Statistics
        Type: general
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Resources
        Type: general
      – SubjectFull: Automation
        Type: general
      – SubjectFull: Responses
        Type: general
    Titles:
      – TitleFull: Modeling Demands-Resources Fit in Teacher Education Using Open-Ended Data: A Methodological-Substantive Synergy
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Fernando Núñez-Regueiro
      – PersonEntity:
          Name:
            NameFull: Samuel Falcon
      – PersonEntity:
          Name:
            NameFull: Pascal Bressoux
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1360-2357
            – Type: issn-electronic
              Value: 1573-7608
          Numbering:
            – Type: volume
              Value: 30
            – Type: issue
              Value: 18
          Titles:
            – TitleFull: Education and Information Technologies
              Type: main
ResultId 1