- Open Access
When skill meets struggle: Mapping students’ physmatic difficulties to physmatic skills in physics lessons
Phys. Rev. Phys. Educ. Res. 22, 010113 – Published 3 February, 2026
DOI: https://doi.org/10.1103/s3wr-sbqb
Abstract
The interplay of mathematics within physics—termed here as physmatics—presents unique cognitive challenges for students, requiring coordination between mathematical reasoning and physical sensemaking. While previous studies have documented common physmatic difficulties, capturing these challenges as they emerge in classroom instruction is rare. This study analyzed 273 teacher–student interactions from 23 video-recorded physics lessons taught across a diverse range of classrooms, aiming to identify and categorize physmatic difficulties as they naturally arose during instruction. Twenty-four codes of difficulties were identified through content analysis and grouped into five overarching clusters: (i) questions about model components, (ii) questions about the model, (iii) difficulties applying mathematics in a physical context, (iv) interrepresentational fluency, and (v) unit-related difficulties. A key finding of this study was the identification of systematic associations between the modeling skills teachers aimed to develop (i.e., mathematization, manipulation, interpretation, and validation) and the corresponding student difficulties. These findings underscore the centrality of modeling as a pedagogical framework for physics instruction and suggest that teachers who are aware of the links between skills and difficulties may be better prepared to anticipate student struggles, respond to them in real time, and even leverage them as part of their instructional practice.
Physics Subject Headings (PhySH)
Article Text
References (48)
- E. Zaslow, Physmatics, arXiv:physics/0506153.
- E. F. Redish and E. Kuo, Language of physics, language of math: Disciplinary culture and dynamic epistemology, Sci. Educ. 24, 561 (2015).
- J. D. Gifford and N. D. Finkelstein, Categorical framework for mathematical sense making in physics, Phys. Rev. Phys. Educ. Res. 16, 020121 (2020).
- B. Modir, J. D. Thompson, and E. C. Sayre, Framing difficulties in quantum mechanics, Phys. Rev. Phys. Educ. Res. 15, 020146 (2019).
- A. Heck and O. van Buuren, Students’ understanding of algebraic concepts, in Mathematics in Physics Education, edited by G. Pospiech, M. Michelini, and B.-S. Eylon (Springer International Publishing, Cham, 2019), pp. 53–74.
- M. Am and E. Istiyono, Multiple representation ability of high school students in physics: A study of modern response theory, THABIEA J. Nat. Sci. Teach. 5, 85 (2022).
- M. Planinic, A. Susac, L. Ivanjek, and Ž. Milin Šipuš, Comparing student understanding of graphs in physics, and mathematics, in Mathematics in Physics Education, edited by G. Pospiech, M. Michelini, and B.-S. Eylon (Springer International Publishing, Cham, 2019), pp. 233–246.
- B. K. Prahani, U. A. Deta, N. A. Lestari, M. Yantidewi, M. N. RJauhariyah, V. P. Kelelufna, J. Siswanto, M. Misbah, S. Mahtari, and S. Suyidno, A profile of physics multiple representation ability of senior high school students on heat material, J. Phys. Conf. Ser. 1760, 012020 (2021).
- N. S. Mumthas and S. U. Abdulla, Substandard performance in mathematical problem solving in physics among higher secondary school students in Kerala—An investigation on teacher perceptions and student difficulties, Issues Ideas Educ. 7, 35 (2019).
- K. Meli, K. Zacharos, and D. Koliopoulos, The integration of mathematics in physics problem solving: A case study of Greek upper secondary school students, Can. J. Sci. Math. Technol. Educ. 16, 48 (2016).
- Mathematics in Physics Education, edited by G. Pospiech, M. Michelini, and B.-S. Eylon (Springer International Publishing, Cham, 2019).
- M. Carli, S. Lippiello, O. Pantano, M. Perona, and G. Tormen, Testing students ability to use derivatives, integrals, and vectors in a purely mathematical context and in a physical context, Phys. Rev. Phys. Educ. Res. 16, 010111 (2020).
- A. Macuca, B. Poljančić, R. Jurdana-Šepić, and K. Lončarić, Relationship between mathematical competencies and achievements in physics of 14-year-old students, presented at the GIREP 2022 Conference, Ljubljana, Slovenia (2022).
- H. Levi, A. Merzel, Y. Lehavi, and B. Schwarz, Diagnosing the cognitive source of students’ difficulties within the physics-mathematics interplay context, in Teaching and Learning Physics Effectively in Challenging Times, edited by S. Faletič and J. Pavlin (Springer Nature Switzerland, Cham, 2024), pp. 175–187.
- H. Levi, A. Merzel, and Y. Lehavi, Physmatic difficulties and students’ thinking approaches, Eurasia J. Math. Sci. Technol. Educ. 21, em2663 (2025).
- Y. Lehavi, E. Bagno, B.-S. Eylon, R. Mualem, G. Pospiech, U. Böhm, and O. Krey, Towards a PCK of physics and mathematics interplay, in Proceedings of the GIREP MPTL 2014 Conference (Università degli Studi di Palermo, Italy, 2015), pp. 843–853.
- Y. Gingras, What did mathematics do to physics?, Hist. Sci. 39, 383 (2001).
- D. Hestenes, Toward a modeling theory of physics instruction, Am. J. Phys. 55, 440 (1987).
- R. Karam, G. Pospiech, and M. Pietrocola, Mathematics in physics lessons: Developing structural skills, in Proceedings of the GIREP-EPEC & PHEC 2009 International Conference (GIREP, Reims, France, 2011), pp. 17–21.
- M. Pietrocola, Mathematics as structural language of physical thought, in Connecting Research in Physics Education with Teacher Education (International Commission on Physics Education, 2008), Vol. 2.
- O. Uhden, R. Karam, M. Pietrocola, and G. Pospiech, Modelling mathematical reasoning in physics education, Sci. Educ. 21, 485 (2012).
- K. S. Taber, Finding the optimum level of simplification: The case of teaching about heat and temperature, Phys. Educ. 35, 320 (2000).
- J. Tuminaro and E. F. Redish, Student use of mathematics in the context of physics problem solving: A cognitive model, University of Maryland preprint, 2005.
- A. Olsho, C. Zimmerman, and S. W. Brahmia, A framework for characterizing covariational reasoning in physics, arXiv:2310.06941.
- S. White Brahmia, Quantification and its importance to modeling in introductory physics, Eur. J. Phys. 40, 044001 (2019).
- A. Redfors, L. Hansson, Ö. Hansson, and K. Juter, The role of mathematics in the teaching and learning of physics, in Proceedings of the ESERA 2013 Conference: Science Education Research for Evidence-based Teaching and Coherence in Learning (European Science Education Research Association, Nicosia, Cyprus, 2014), pp. 376–383.
- E. Kuo, M. M. Hull, A. Gupta, and A. Elby, How students blend conceptual and formal mathematical reasoning in solving physics problems, Sci. Educ. 97, 32 (2013).
- B. L. Sherin, How students understand physics equations, Cognit. Instr. 19, 479 (2001).
- Y. Lehavi, E. Bagno, B.-S. Eylon, R. Mualem, G. Pospiech, U. Böhm, O. Krey, and R. Karam, Classroom evidence of teachers’ PCK of the interplay of physics and mathematics, in Key Competences in Physics Teaching and Learning, edited by (Springer International Publishing, Cham, 2017), Vol. 190, pp. 95–104.
- E. Etkina and G. Planinsic, The Investigative Science Learning Environment: A Guide for Teacher Preparation and Professional Development (IOP Publishing, Bristol, UK, 2024).
- M. E. Arseneault, The effects of modeling instruction in a high school physics classroom, Master’s thesis, Louisiana State University and Agricultural & Mechanical College, 2014.
- I. Halloun, Schematic modeling for meaningful learning of physics, J. Res. Sci. Teach. 33, 1019 (1996).
- B. M. Zwickl, D. Hu, N. Finkelstein, and H. J. Lewandowski, Model-based reasoning in the physics laboratory: Framework and initial results, Phys. Rev. ST Phys. Educ. Res. 11, 020113 (2015).
- S. Magnusson, J. Krajcik, and H. Borko, Nature, sources, and development of pedagogical content knowledge for science teaching, in Examining Pedagogical Content Knowledge: The Construct and Its Implications for Science Education, edited by J. Gess-Newsome and N. G. Lederman (Springer Netherlands, Dordrecht, 1999), pp. 95–132.
- E. Etkina, Pedagogical content knowledge and preparation of high school physics teachers, Phys. Rev. ST Phys. Educ. Res. 6, 020110 (2010).
- A. Acevedo Nistal, W. Van Dooren, and L. Verschaffel, What counts as a flexible representational choice? An evaluation of students’ representational choices to solve linear function problems, Instr. Sci. 40, 999 (2012).
- A. C. Pratama, Supahar, Warsono, and Jumadi, The development physics essay test to measure vector and mathematics representation ability in senior high school, J. Phys. Conf. Ser. 1097, 012013 (2018).
- L. Bollen, P. van Kampen, C. Baily, M. Kelly, and M. De Cock, Student difficulties regarding symbolic and graphical representations of vector fields, Phys. Rev. Phys. Educ. Res. 13, 020109 (2017).
- M. Niss, Obstacles related to structuring for mathematization encountered by students when solving physics problems, Int. J. Sci. Math. Educ. 15, 1441 (2017).
- P. Fehlinger, S. Becker-Genschow, and B. Watzka, Gaze behavior as a key to revealing strategies for identifying indirectly proportional graphs in thermodynamic and mathematical context, Phys. Rev. Phys. Educ. Res. 21, 020129 (2025).
- O. T. Badmus and L. C. Jita, Physics difficulty and problem-solving: Exploring the role of mathematics and mathematical symbols, Interdiscip. J. Educ. Res. 6, 1 (2024).
- S. Friese, Qualitative Data Analysis with ATLAS.ti (SAGE Publications Ltd., London, 2019).
- Y. Lehavi, A. Merzel, R. Segal, A. Baram, and B.-S. Eylon, Using self-video-based discourse in training physics teachers, in Concepts, Strategies and Models to Enhance Physics Teaching and Learning, edited by E. McLoughlin and P. van Kampen (Springer International Publishing, Cham, 2019), pp. 159–169.
- R. Segal, A. Merzel, and Y. Lehavi, Improving the professional awareness of mathematics teachers and teacher instructors using video-based curiosity-driven discourse—A case study, Int. J. Sci. Math. Educ. 22, 1083 (2024).
- R. Jutkowitz, C. S. C. Asterhan, Y. Lehavi, and A. Merzel, Preparing for peer-led productive professional discourse around classroom video: In-service and pre-service teachers (to be published).
- M. T. H. Chi, Quantifying qualitative analyses of verbal data: A practical guide, J. Learn. Sci. 6, 271 (1997).
- J. W. Creswell, Qualitative Inquiry and Research Design: Choosing Among Five Approaches (SAGE Publications, Thousand Oaks, CA, 2013).
- J. Mason and B. Davis, The importance of teachers’ mathematical awareness for in-the-moment pedagogy, Can. J. Sci. Math. Technol. Educ. 13, 182 (2013).