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  • Open Access

Framing intuitive ideas in physics: A proposal grounded in frame semantics

F. Escalante*

F. Vera and R. Rivera

  • *Contact author: fescalante@ucn.cl
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Phys. Rev. Phys. Educ. Res. 22, 020116 – Published 12 August, 2026

DOI: https://doi.org/10.1103/kffj-lvhz

Abstract

The theoretical framework knowledge in pieces (KiP), proposed by Andrea diSessa, holds that intuitive knowledge in physics is organized through small cognitive structures called p-prims (phenomenological primitives). One of the heuristic principles on which the KiP framework is built—the principle of redescription—suggests that the descriptive framework of p-prims can be fine-tuned. However, the theoretical development of p-prim descriptions has remained largely limited to informal linguistic expressions drawn from students’ explanations, without a clearly defined syntactic or semantic structure. This lack of systematicity has led to variations in the naming and conceptualization of p-prims, making their consistent identification and analytical use difficult. In response to this limitation, this paper proposes a formal schematization of p-prims based on Fillmore’s Frame Semantics, supported by tools such as FrameNet. Our proposal aims to make explicit the core and peripheral components of each p-prim and to clarify the conditions under which they are evoked. By providing p-prims with a well-defined linguistic structure, the KiP framework is strengthened, opening new possibilities for the automated analysis of students’ reasoning using artificial intelligence (AI), as well as for the design of visual representations to support the development of instructional materials. Theoretical, methodological, and technological implications of this proposal for future research in physics education research (PER) are also discussed.

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References (71)

  1. A. A. diSessa, Toward an epistemology of physics, Cognit. Instr. 10, 105 (1993).

  2. Intuition and intuitive are terms whose meaning has been extensively discussed in the literature. Fischbein [3] provides several currents of thought from philosophy and psychology concerning the meaning that authors such as Descartes, Kant, and Piaget attribute to the term intuition. For example, for Descartes, intuition was a reliable source of knowledge; Kant defined it as a faculty through which perception of the world and knowledge are connected; and for Piaget, intuition is an early form of reasoning based on what is seen rather than on logical rules.

  3. E. Fischbein, Intuition in Science and Mathematics: An Educational Approach (Springer, Dordrecht, 2002).
  4. A. D. Robertson, L. M. Goodhew, R. E. Scherr, and P. R. L. Heron, University student conceptual resources for understanding forces, Phys. Rev. Phys. Educ. Res. 17, 010121 (2021).
  5. L. A. Barth-Cohen, H. Swanson, and J. Arnell, Methods of research design and analysis for identifying knowledge resources, Phys. Rev. Phys. Educ. Res. 19, 020119 (2023).
  6. T. T. Torrent, E. E. D. S. Matos, F. Belcavello, M. Viridiano, M. A. Gamonal, A. D. D. Costa, and M. C. Marim, Representing context in framenet: A multidimensional, multimodal approach, Front. Psychol. 13, 838441 (2022).
  7. C. Silberer and M. Pinkal, Grounding semantic roles in images, in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, edited by E. Riloff, D. Chiang, J. Hockenmaier, and J. Tsujii (Association for Computational Linguistics, Brussels, Belgium, 2018), pp. 2616–2626.
  8. A. Sadhu, T. Gupta, M. Yatskar, R. Nevatia, and A. Kembhavi, Visual semantic role labeling for video understanding, in Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE Computer Society, Los Alamitos, CA, USA, 2021), pp. 5585–5596.
  9. G. Polverini and B. Gregorcic, How understanding large language models can inform the use of ChatGPT in physics education, Eur. J. Phys. 45, 025701 (2024).
  10. C. J. Fillmore, Frame semantics, in Linguistics in the Morning Calm: Selected Papers from SICOL-1981 (Hanshin Pub. Co., Seoul, 1982), pp. 111–137.
  11. C. J. Fillmore, Frames and the semantics of understanding, Quaderni Di Semantica 6, 222 (1985).
  12. D. Jurafsky and J. H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition With Language Models, 2nd ed. (Prentice Hall, Englewood Cliffs, NJ, 2008).
  13. J. Ruppenhofer, M. Ellsworth, M. Schwarzer-Petruck, C. R. Johnson, and J. Scheffczyk, FrameNet II: Extended Theory and Practice (International Computer Science Institute, Berkeley, CA, 2016).
  14. E. Matos, FrameNet Brasil web annotation tool (2023), https://github.com/FrameNetBrasil/webtool.
  15. S. Carey, Cognitive science and science education, Am. Psychol. 41, 1123 (1986).
  16. J. P. Smith III, A. A. diSessa, and J. Roschelle, Misconceptions reconceived: A constructivist analysis of knowledge in transition, J. Learn. Sci. 3, 115 (1994).
  17. A. A. diSessa and B. L. Sherin, What changes in conceptual change?, Int. J. Sci. Educ. 20, 1155 (1998).
  18. M. McCloskey, A. Washburn, and L. Felch, Intuitive physics: The straight-down belief and its origin, J. Exp. Psychol. Learn. Mem. Cognit. 9, 636 (1983).
  19. M. McCloskey, Naive theories of motion, in Mental Models, edited by D. Gentner and A. L. Stevens (Psychology Press, New York, 1983), 1st ed., p. 26.
  20. K. S. Taber, Conceptual resources for learning science: Issues of transience and grain-size in cognition and cognitive structure, Int. J. Sci. Educ. 30, 1027 (2008).
  21. D. Hammer, Misconceptions or p-prims: How may alternative perspectives of cognitive structure influence instructional perceptions and intentions, J. Learn. Sci. 5, 97 (1996).

  22. DiSessa [1] proposes a series of heuristic principles about p-prims. Among them, the principle of continuity stands out. It states that p-prims evolve over time depending on the knowledge that a person acquires and associates with their prior knowledge. This implies that it is not only important to identify the p-prims that emerge at a given moment in the learning process, but also to track their evolution in terms of structure and activation circumstances, whether individually or in groups.

  23. M. Chiu and J. Gutwill, The architecture of intuition: Converging views from physics education and linguistics, in Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society (Routledge, New York, 1994).
  24. A. A. diSessa, Knowledge in pieces: An evolving framework for understanding knowing and learning, in Converging Perspectives on Conceptual Change: Mapping an Emerging Paradigm in the Learning Sciences, edited by Tamer G. Amin and Olivia Levrini 1st ed. (Routledge, London, 2017).
  25. D. Hammer, Student resources for learning introductory physics, Am. J. Phys. 68, S52 (2000), https://pubs.aip.org/aapt/ajp/article-pdf/68/S1/S52/7527642/s52_1_online.pdf.
  26. D. Hammer and A. Elby, Tapping epistemological resources for learning physics, J. Learn. Sci. 12, 53 (2003).
  27. A. J. Richards, D. C. Jones, and E. Etkina, How students combine resources to make conceptual breakthroughs, Res. Sci. Educ. 50, 1119 (2020).
  28. B. W. Harrer, V. J. Flood, and M. C. Wittmann, Productive resources in students’ ideas about energy: An alternative analysis of Watts’ original interview transcripts, Phys. Rev. ST Phys. Educ. Res. 9, 023101 (2013), 10.1103/PhysRevSTPER.9.023101.
  29. A. D. Robertson, L. M. Goodhew, L. C. Bauman, B. Hansen, and A. T. Alesandrini, Identifying student conceptual resources for understanding physics: A practical guide for researchers, Phys. Rev. Phys. Educ. Res. 19, 020138 (2023).
  30. A. A. diSessa, Conceptual change in a microcosm: Comparative learning analysis of a learning event, Hum. Dev. 60, 1 (2017), https://karger.com/hde/article-pdf/60/1/1/2898308/000469693.pdf.

  31. See Appendix B in [1] for a summary of the listed p-prims.

  32. A. A. diSessa, Phenomenology and the evolution of intuition, in Mental Models, edited by D. Gentner and A. L. Stevens (Psychology Press, New York, 1983) 1st ed., pp. 15–33.
  33. P. A. White, The causal asymmetry, Psychol. Rev. 113, 132 (2006).

  34. We are not considering the vector nature of Newton’s second law. In p-prims like Force as a mover, Force as a deflector, and Force as a spinner, greater emphasis is placed on the direction of the force and the object’s acceleration.

  35. N. Didiş Körhasan, Knowledge elements used by pre-service primary teachers to explain free fall, J. Turk. Sci. Educ 18, 574 (2021), https://www.tused.org/index.php/tused/article/view/798.
  36. A. A. diSessa, What do “just plain folk” know about physics?, in The Handbook of Education and Human Development (Wiley & Sons, Ltd, 1998) Chap. 30, pp. 681–700.
  37. D. C. Jones. Cognitive resources of physics experts, Ph.D. thesis, Rutgers, The State University of New Jersey, 2015, 10.7282/T33J3FSC.
  38. A. diSessa, Knowledge in pieces, Constructivism in the Computer Age, edited by G. Forman and P. B. Pufall (Psychology Press, Hillsdale, NJ, 1988).
  39. S. Gross, Origins of human communication—by Michael Tomasello, Mind & Language 25, 237 (2010).
  40. A. Paivio, Language comprehension and production, in Mental Representations: A Dual Coding Approach (Oxford University Press, New York, 1990), ch. 10.
  41. V. Evans and M. Green, Cognitive Linguistics: An Introduction (Lawrence Erlbaum Associates Publishers, Mahwah, NJ, 2006).
  42. A. Paivio, Dual coding theory, in Mental Representations: A Dual Coding Approach (Oxford University Press, New York, 1990), ch. 4.
  43. Cognitive Linguistics: Basic Readings, edited by D. Geeraerts (De Gruyter Mouton, Berlin, New York, 2006).
  44. G. Lakoff, Women, Fire, and Dangerous Things: What Categories Reveal About the Mind (University of Chicago Press, Chicago, 1987).
  45. R. W. Langacker, Cognitive Grammar: A Basic Introduction (Oxford University Press, Oxford, 2008).
  46. L. Talmy, Toward a Cognitive Semantics, Vol. 1: Concept Structuring Systems (MIT Press, Cambridge, MA, 2000).
  47. R. Fincher-Kiefer, How the Body Shapes Knowledge: Empirical Support for Embodied Cognition (American Psychological Association, Washington, D.C., DC, 2019).
  48. K. Sullivan, Three levels of framing, WIREs Cognit. Sci. 14, e1651 (2023).
  49. S. J. Flusberg, K. J. Holmes, P. H. Thibodeau, R. L. Nabi, and T. Matlock, The psychology of framing: How everyday language shapes the way we think, feel, and act, Psychol. Sci. Publ. Interest 25, 105 (2024).
  50. C. J. Fillmore and C. Baker, A frames approach to semantic analysis, in The Oxford Handbook of Linguistic Analysis, edited by B. Heine and H. Narrog (Oxford University Press, Oxford, 2015), pp. 313–339.
  51. T. Van Leeuwen, Introducing social semiotics, illustrated, reprinted edition ed. (Psychology Press, Routledge, London, 2005), p. 301.
  52. M. R. L. Petruck, Frame semantics, in Handbook of Pragmatics, edited by J. Verschueren, J.-O. Östman, J. Blommaert, and C. Bulcaen (John Benjamins, Amsterdam, 1996).
  53. H. C. Boas, J. Ruppenhofer, and C. Baker, Framenet at 25, Int. J. Lexicography 37, 263 (2024).
  54. https://framenet.icsi.berkeley.edu/.
  55. https://www2.ufjf.br/framenetbr-en/.
  56. https://framenet.icsi.berkeley.edu/WhatIsFrameNet.
  57. C. F. Baker, Framenet: Frame semantic annotation in practice, in Handbook of Linguistic Annotation edited by N. Ide and J. Pustejovsky (Springer Netherlands, Dordrecht, 2017), pp. 771–811.
  58. C. J. Fillmore, C. R. Johnson, and M. R. Petruck, Background to framenet, Int. J. Lexicography 16, 235 (2003).
  59. P. Wulff, Physics language and language use in physics—what do we know and how AI might enhance language-related research and instruction, Eur. J. Phys. 45, 023001 (2024).
  60. R. G. Fuller, R. Karplus, and A. E. Lawson, Can physics develop reasoning?, Phys. Today. 30, No. 2, 23 (1977). https://pubs.aip.org/physicstoday/article-pdf/30/2/23/8283396/23_1_online.pdf.

  61. In accordance with the principle of Scavenging data, in which diSessa states that “There is no need for highly refined and idiosyncratic experimental setups. Thus, data from large numbers of reported experiments, if reinterpreted, can contribute to the analysis of p-prims.” [ [1], p. 124].

  62. M. Yatskar, L. Zettlemoyer, and A. Farhadi, Situation recognition: Visual semantic role labeling for image understanding, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA (2016), pp. 5534–5542.
  63. W. Alhoshan, R. Batista-Navarro, and L. Zhao, A framenet-based approach for annotating natural language descriptions of software requirements, in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Paris, France, edited by Tiago Timponi Torrent, Lars Borin, and Collin F. Baker (European Language Resources Association (ELRA), 2018).
  64. M. Alizadeh and B. Di Eugenio, Augmenting visual question answering with semantic frame information in a multitask learning approach, in Proceedings of the 2020 IEEE 14th International Conference on Semantic Computing (ICSC), San Diego, CA, USA, (2020), pp. 37–44. 10.1109/ICSC.2020.00013
  65. F. Escalante, F. Vera, and R. Rivera, Semantic p-prims: Using cognitive linguistics to unveil the hidden syntactic and semantic structure of disessa’s phenomenological primitives (to be published).
  66. C. J. Fillmore and B. T. Atkins, Toward a frame-based lexicon: The semantics of RISK and its neighbors, in Frames, Fields, and Contrasts: New Essays in Semantic and Lexical Organization, edited by A. Lehrer and E. F. Kittay (Lawrence Erlbaum Associates, Hillsdale, NJ, 1992), pp. 75–102.
  67. R. Kahaleh and V. Lopez, Evaluating large language models in high school physics education: Addressing misconceptions and fostering conceptual understanding, Phys. Educ. 60, 025013 (2025).
  68. B. Coyne, O. Rambow, J. Hirschberg, and R. Sproat, Frame semantics in text-to-scene generation, in Knowledge-Based and Intelligent Information and Engineering Systems, edited by R. Setchi, I. Jordanov, R. J. Howlett, and L. C. Jain (Springer Berlin Heidelberg, Berlin, Heidelberg, 2010), pp. 375–384.
  69. K. R. Glass and S. Bangay, Automating the creation of 3D animation from annotated fiction text, in IADIS International Conference on Computer Graphics and Visualization, Amsterdam, Holland (2008), Vol. 3, pp. 3–10.
  70. S. Dupuy, A. Egges, V. Legendre, and P. Nugues, Generating a 3D simulation of a car accident from a written description in natural language: The carsim system, in Proceedings of the Workshop on Temporal and Spatial Information Processing—Volume 13, TASIP ’01 (Association for Computational Linguistics, USA, 2001) 10.3115/1118238.1118239.
  71. R. Johansson, P. Nugues, and D. Williams, Carsim: A system to convert written accident reports into animated 3D scenes, in Proceedings of the 2nd Joint SAIS/SSLS Workshop Artificial Intelligence and Learning Systems, AILS-04, edited by Lund University (Department of Computer Science, Lund University, Lund, Sweden, 2004), pp. 76–86.

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Phys. Rev. Phys. Educ. Res. 22, 020116– Published 12 August, 2026
  • Received 25 July 2025
  • Accepted 23 June 2026
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