---
_id: '58885'
abstract:
- lang: eng
  text: 'There have been several attempts to conceptualize and operationalize pedagogical
    content knowledge (PCK) in the context of teachers'' professional competencies.
    A recent and popular model is the Refined Consensus Model (RCM), which proposes
    a framework of dispositional competencies (personal PCK—pPCK) that influence more
    action-related competencies (enacted PCK—ePCK) and vice versa. However, descriptions
    of the internal structure of pPCK and possible knowledge domains that might develop
    independently are still limited, being either primarily theoretically motivated
    or strictly hierarchical and therefore of limited use, for example, for formative
    feedback and further development of the RCM. Meanwhile, a non-hierarchical differentiation
    for the ePCK regarding the plan-teach-reflect cycle has emerged. In this study,
    we present an exploratory computational approach to investigate pre-service teachers''
    pPCK for a similar non-hierarchical structure using a large dataset of responses
    to a pPCK questionnaire (N=846). We drew on theoretical foundations and previous
    empirical findings to achieve interpretability by integrating this external knowledge
    into our analyses using the Computational Grounded Theory (CGT) framework. The
    results of a cluster analysis of the pPCK scores indicate the emergence of prototypical
    groups, which we refer to as competency profiles: (1) a group with low performance,
    (2) a group with relatively advanced competency in using pPCK to create instructional
    elements, (3) a group with relatively advanced competency in using pPCK to assess
    and analyze described instructional elements, and (4) a group with high performance.
    These groups show tendencies for certain language usage, which we analyze using
    a structural topic model in a CGT-inspired pattern refinement step. We verify
    these patterns by demonstrating the ability of a machine learning model to predict
    the competency profile assignments. Finally, we discuss some implications of the
    results for the further development of the RCM and their potential usability for
    an automated formative assessment.'
article_type: original
author:
- first_name: Jannis
  full_name: Zeller, Jannis
  id: '99022'
  last_name: Zeller
  orcid: 0000-0002-1834-5520
- first_name: Josef
  full_name: Riese, Josef
  id: '429'
  last_name: Riese
  orcid: 0000-0003-2927-2619
citation:
  ama: 'Zeller J, Riese J. Competency Profiles of PCK Using Unsupervised Learning:
    What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses?
    <i>Journal of Research in Science Teaching</i>. Published online 2025. doi:<a
    href="https://doi.org/10.1002/tea.70001">10.1002/tea.70001</a>'
  apa: 'Zeller, J., &#38; Riese, J. (2025). Competency Profiles of PCK Using Unsupervised
    Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical
    Analyses? <i>Journal of Research in Science Teaching</i>. <a href="https://doi.org/10.1002/tea.70001">https://doi.org/10.1002/tea.70001</a>'
  bibtex: '@article{Zeller_Riese_2025, title={Competency Profiles of PCK Using Unsupervised
    Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical
    Analyses?}, DOI={<a href="https://doi.org/10.1002/tea.70001">10.1002/tea.70001</a>},
    journal={Journal of Research in Science Teaching}, author={Zeller, Jannis and
    Riese, Josef}, year={2025} }'
  chicago: 'Zeller, Jannis, and Josef Riese. “Competency Profiles of PCK Using Unsupervised
    Learning: What Implications for the Structures of PPCK Emerge From Non-Hierarchical
    Analyses?” <i>Journal of Research in Science Teaching</i>, 2025. <a href="https://doi.org/10.1002/tea.70001">https://doi.org/10.1002/tea.70001</a>.'
  ieee: 'J. Zeller and J. Riese, “Competency Profiles of PCK Using Unsupervised Learning:
    What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses?,”
    <i>Journal of Research in Science Teaching</i>, 2025, doi: <a href="https://doi.org/10.1002/tea.70001">10.1002/tea.70001</a>.'
  mla: 'Zeller, Jannis, and Josef Riese. “Competency Profiles of PCK Using Unsupervised
    Learning: What Implications for the Structures of PPCK Emerge From Non-Hierarchical
    Analyses?” <i>Journal of Research in Science Teaching</i>, 2025, doi:<a href="https://doi.org/10.1002/tea.70001">10.1002/tea.70001</a>.'
  short: J. Zeller, J. Riese, Journal of Research in Science Teaching (2025).
date_created: 2025-03-04T08:08:37Z
date_updated: 2025-03-04T08:08:42Z
department:
- _id: '299'
doi: 10.1002/tea.70001
keyword:
- computational grounded theory
- language analysis
- machine learning
- pedagogical content knowledge
- unsupervised learning
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://onlinelibrary.wiley.com/doi/epdf/10.1002/tea.70001
oa: '1'
publication: Journal of Research in Science Teaching
publication_identifier:
  eissn:
  - 1098-2736
  issn:
  - 0022-4308
publication_status: published
status: public
title: 'Competency Profiles of PCK Using Unsupervised Learning: What Implications
  for the Structures of pPCK Emerge From Non-Hierarchical Analyses?'
type: journal_article
user_id: '99022'
year: '2025'
...