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Dynamics of collaborative modeling in an ill-structured real-world problem

Tom Reshef-Israeli* and Shulamit Kapon†

  • *Contact author: tomis@campus.technion.ac.il
  • †Contact author: skapon@technion.ac.il
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Phys. Rev. Phys. Educ. Res. 22, 010141 – Published 13 May, 2026

DOI: https://doi.org/10.1103/44fz-2y3x

Abstract

Collaborative mathematical modeling of real-world problems requires learners not only to engage in multiple epistemic practices but also to remain aligned around shared physical models as assumptions and solution paths evolve. However, few empirical studies have traced how such coordination unfolds moment by moment during authentic work. This study examined the in vivo discourse of a group of preservice physics teachers solving an ill-structured mechanics problem by tracking their collaborative reasoning turn by turn. We analyzed the group’s work by coding their epistemic modeling phases and the operational physical models in use, alongside a time-resolved measure of discourse model coherence (DMC) that indexes alignment around shared model assumptions. By combining turn-level qualitative coding with time-resolved quantitative visualizations, we traced how the group’s shared problem space evolved over time and identified moments of convergence and divergence. The analysis revealed that the group progressed through six epistemic phases of modeling while iterating across multiple operational physics models of increasing complexity. Periods of heightened complexity were marked by slower progression and frequent fluctuations between epistemic phases. Cross-model comparisons functioned as a key evaluative resource, in which simpler models served as benchmarks for judging more complex ones. Instructor prompts played a significant role: moves that narrowed the modeling scope tended to stabilize coherence, whereas prompts that reopened assumptions often increased epistemic uncertainty and temporarily disrupted alignment that produced measurable drops in DMC. These findings provide a time-resolved account of collaborative modeling dynamics and offer methodological and instructional insights for supporting modeling-rich physics learning.

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Phys. Rev. Phys. Educ. Res. 22, 010141– Published 13 May, 2026
  • Received 29 January 2026
  • Accepted 13 April 2026
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