Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Supporting sensemaking of physics equations: An intervention study in undergraduate courses

Julia Hofmann1,*, Pascal Klein1, Andreas Müller2, and Josefine Neuhaus1

  • 1Faculty of Physics, Physics Education Research, University of Göttingen, Friedrich-Hund-Platz 1, 37077, Göttingen, Germany
  • 2Faculty of Sciences, Department of Physics and Institute of Teacher Education, University of Geneva, Boulevard du Pont d’Arve 40, 1211, Genève, Switzerland

  • *Contact author: julia.hofmann@uni-goettingen.de

Phys. Rev. Phys. Educ. Res. 22, 010144 – Published 22 May, 2026

DOI: https://doi.org/10.1103/mb7q-kf2s

Abstract

A deep understanding of mathematical equations and their physical meaning is an important skill for physicists and therefore a central learning goal in university physics education. However, many students approach mathematical expressions in physics in a purely procedural manner, using them as calculation tools without critically evaluating their physical plausibility. Making sense of equations, that is, being able to interpret and evaluate equations within a physical context, is essential for the development of expertlike reasoning in physics. Frequently employed strategies that assist in the sensemaking process are dimensional, covariational, special, and limiting case analysis. Despite their importance, these strategies are rarely taught or practiced explicitly in university physics courses. The present intervention study systematically implemented sensemaking strategies within a university course for first-year physics students covering topics of electro- and magnetostatics as well as electrodynamics. Traditional exercises were enriched with tasks requiring students to critically evaluate the plausibility of self-derived equations. The tasks were integrated into recitation sessions and homework assignments. A total of N=67 physics students participated in the study. A quasiexperimental pre-post design with two groups was employed to evaluate the effectiveness of the intervention approach. Results show that students highly valued all four strategies equally and reported an increased perceived competence in their application after the intervention. Significant improvements in performance were observed for dimensional (d=0.67) as well as for special case analysis (d=0.50). Students’ performance in analyzing covariations only showed a tendency favoring the intervention group. No intervention-related impact on the performance of limiting case analysis was found, suggesting that this strategy may require more extensive practice. The findings underscore the importance and usefulness of explicitly teaching strategies to support students’ sensemaking of physics equations.

View figure in article

Physics Subject Headings (PhySH)

Article Text

Supplemental Material

References (54)

  1. D. J. Griffiths, Introduction to Electrodynamics, 4th ed. (Pearson, Boston, 2013).
  2. K. T. Hahn, P. J. Emigh, M. Lenz, and E. Gire, Student sense-making on homework in a sophomore mechanics course, presented at PER Conf. 2018, Cincinnati, OH, pp. 160–163, 10.1119/perc.2017.pr.035.
  3. M. Lenz, Sensemaking throughout the physics curriculum: Understanding expert and student ideas about sensemaking in a physics context, Ph.D. thesis, Oregon State University, 2020.
  4. E. F. Redish and E. Kuo, Language of physics, language of math: Disciplinary culture and dynamic epistemology, Sci. Educ. 24, 561 (2015).
  5. B. R. Wilcox, M. D. Caballero, R. E. Pepper, and S. J. Pollock, Upper-division student understanding of Coulomb’s law: Difficulties with continuous charge distributions, AIP Conf. Proc. 1513, 418 (2013).
  6. O. Uhden, R. Karam, M. Pietrocola, and G. Pospiech, Modelling mathematical reasoning in physics education, Sci. Educ. 21, 485 (2012).
  7. A. R. Akinyemi, M. E. Loverude, and J. R. Thompson, Solution evaluation strategies used by first-year physics students, Phys. Rev. Phys. Educ. Res. 21, 020114 (2025).
  8. E. W. Burkholder, O. C. Miller, and L. F. Blackmon, Investigating the epistemology of physics students while reflecting on solutions, Eur. J. Phys. 44, 045701 (2023).
  9. G. White, T.-R. Sikorski, J. Landay, and M. Ahmed, Limiting case analysis in an electricity and magnetism course, Phys. Rev. Phys. Educ. Res. 19, 010125 (2023).
  10. M. Lenz, K. Hahn, P. Emigh, and E. Gire, Student perspective of and experience with sense-making: a case study, presented at PER Conf. 2017, Cincinnati, OH, pp. 240–243, 10.1119/perc.2017.pr.055.
  11. E. Burkholder, L. Blackmon, and C. Wieman, Characterizing the mathematical problem-solving strategies of transitioning novice physics students, Phys. Rev. Phys. Educ. Res. 16, 020134 (2020).
  12. T. O. B. Odden and R. S. Russ, Defining sensemaking: Bringing clarity to a fragmented theoretical construct, Sci. Educ. 103, 187 (2019).
  13. A. Karelina and E. Etkina, Acting like a physicist: Student approach study to experimental design, Phys, Rev. ST Phys. Educ. Res. 3, 020106 (2007).
  14. M. E. Loverude, Mathematization and the “boas course”, presented at PER Conf. 2017, Cincinnati, OH, pp. 15–18, 10.1119/perc.2017.juried.004.
  15. A. R. Akinyemi. An investigation of students’ use and understanding of evaluation strategies, Ph.D. thesis, University of Maine, 2021.
  16. A. R. Warren, Impact of teaching students to use evaluation strategies, Phys. Rev. ST Phys. Educ. Res. 6, 020103 (2010).
  17. M. E. Loverude, Quantitative reasoning skills in math methods, presented at PER Conf. College Park, MD, pp. 203–206, 10.1119/perc.2015.pr.046.
  18. J. J. Clement, Creative Model Construction in Scientists and Students: The Role of Imagery, Analogy, and Mental Simulation, 1st ed. (Springer, Dordrecht, 2009).
  19. E. F. Redish, Using math in physics: 2. Estimation, Phys. Teach. 59, 525 (2021).
  20. B. Sherin. The symbolic basis of physical intuition : A study of two symbol systems in physics instruction, Ph.D. thesis, University of California, 1996.
  21. K. Plicht, Ein Physikübungskonzept Zur Förderung Der Problemlösekompetenz: Entwicklung Und Empirische Evaluation Eines Strategietrainings Auf Der Basis Von Expertisemerkmalen (Logos-Verlag Berlin, 2024).
  22. M. Carlson, S. Jacobs, E. Coe, S. Larsen, and E. Hsu, Applying covariational reasoning while modeling dynamic events: A framework and a study, J. Res. Math. Educ. 33, 352 (2002).
  23. L. Saldanha and P. Thompson, Re-thinking co-variation from a quantitative perspective: Simultaneous continuous variation, in Proceedings of the Annual Meeting of the Psychology of Mathematics Education—North America (1998), pp. 298–304, https://www.academia.edu/110758966/Proceedings_of_the_Annual_Meeting_of_the_North_American_Chapter_of_the_International_Group_for_the_Psychology_of_Mathematics_Education_20th_Raleigh_NC_October_31_November_3_1998_Volume_1.
  24. E. F. Redish, Using math in physics: Overview, Phys. Teach. 59, 314 (2021).
  25. S. White Brahmia, A. Olsho, T. I. Smith, A. Boudreaux, P. Eaton, and C. Zimmerman, Physics inventory of quantitative literacy: A tool for assessing mathematical reasoning in introductory physics, Phys. Rev. Phys. Educ. Res. 17, 020129 (2021).
  26. A. Zietsman and J. Clement, The role of extreme case reasoning in instruction for conceptual change, J. Learn. Sci. 6, 61 (1997).
  27. N. Nersessian, How do scientists think? Capturing the dynamics of conceptual change in science, in Cognitive Models of Science (University of Minnesota Press, Minneapolis, 1992), pp. 3–45.
  28. M. Lenz, P. J. Emigh, and E. Gire, Surprise! Students don’t do special-case analysis when unaware of it, presented at PER Conf. 2018, Washington, DC, 10.1119/perc.2018.pr.Lenz.
  29. R. Robinett, Quantum fields from dimensional analysis: Applications in condensed matter systems, Am. J. Phys. 93, 344 (2025).
  30. K. Gifford, G. S. Ehrlich, E. Bumbacher, and E. Kuo, “I forgot the formula:” How students can use coherence to reconstruct a (partially) forgotten equation, arXiv:2506.19641.
  31. E. Buckingham, On physically similar systems; illustrations of the use of dimensional equations, Phys. Rev. 4, 345 (1914).
  32. E. Etkina, A. Van Heuvelen, S. White-Brahmia, D. T. Brookes, M. Gentile, S. Murthy, D. Rosengrant, and A. Warren, Scientific abilities and their assessment, Phys. Rev. ST Phys. Educ. Res. 2, 020103 (2006).
  33. T.-R. Sikorski, G. D. White, and J. Landay, Uptake of solution checks by undergraduate physics students, presented at PER Conf. 2017, Cincinnati, OH, pp. 368–371, 10.1119/perc.2017.pr.087.
  34. W. Demtröder, Experimentalphysik 2: Elektrizität Und Optik, Springer-Lehrbuch (Springer, Berlin, Heidelberg, 2013).
  35. Kultusministerkonferenz, Abiturnoten im Ländervergleich, https://www.kmk.org/dokumentation-statistik/statistik/schulstatistik/abiturnoten.html (2024) [accessed November 6, 2024].
  36. A. Renkl, Toward an instructionally oriented theory of example-based learning, Cognit. Sci. 38, 1 (2014).
  37. J. Wittwer and A. Renkl, How effective are instructional explanations in example-based learning? A meta-analytic review, Educ. Psychol. Rev. 22, 393 (2010).
  38. See Supplemental Material at http://link.aps.org/supplemental/10.1103/mb7q-kf2s for intervention tasks, performance tests, and questionanaire.
  39. N. D. Finkelstein and S. J. Pollock, Replicating and understanding successful innovations: Implementing tutorials in introductory physics, Phys. Rev. ST Phys. Educ. Res. 1, 010101 (2005).
  40. Intrinsic motivation inventory (IMI), https://selfdeterminationtheory.org/intrinsic-motivation-inventory/ (2025) [accessed November 2024, 6].
  41. L. Ding and R. Beichner, Approaches to data analysis of multiple-choice questions, Phys. Rev. ST Phys. Educ. Res. 5, 020103 (2009).
  42. J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. (L. Erlbaum Associates, Hillsdale, NJ, 1988).
  43. S. B. Morris and R. P. DeShon, Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs., Psychol. Methods 7, 105 (2002).
  44. M. J. Blanca, R. Alarcón, J. Arnau, J. García-Castro, and R. Bono, How to proceed when normality and sphericity are violated in the repeated measures ANOVA, An. Psicol. 40, 466 (2024).
  45. P. Mayring and T. Fenzl, Qualitative inhaltsanalyse, in Handbuch Methoden Der Empirischen Sozialforschung (Springer Fachmedien, Wiesbaden, 2019), pp. 633–648.
  46. M. Wirtz and F. Caspar, Beurteilerübereinstimmung Und Beurteilerreliabilität: Methoden Zur Bestimmung Und Verbesserung Der Zuverlässigkeit Von Einschätzungen Mittels Kategoriensystemen Und Ratingskalen (Hogrefe, Göttingen, 2002).
  47. M. Lenz and E. Gire, Faculty views of and expectations for dimensional analysis, presented at PER Conf. 2016, Sacramento, CA, pp. 196–199, 10.1119/perc.2016.pr.044.
  48. G. Greefrath, R. Oldenburg, H.-S. Siller, V. Ulm, and H.-G. Weigand, Didaktik Der Analysis (Springer, Berlin Heidelberg, 2016).
  49. J. A. Fernández-Plaza and A. Simpson, Three concepts or one? Students’ understanding of basic limit concepts, Educ. Stud. Math. 93, 315 (2016).
  50. T. de Jong and M. G. M. Ferguson-Hessler, Types and qualities of knowledge, Educ. Psychol. 31, 105 (1996).
  51. C. Loretan, A. Müller, M. Delaval, S. Roch, and L. Weiss, Understanding of size and scale and order-of-magnitude reasoning in secondary science: A teaching experiment with worked examples as educational scaffold, arXiv:2506.19641.
  52. D. R. M. Edge and M. K. Dirks, Problem solving, Enrico Fermi and the bull moose, School Sci. Math. 83, 601 (1983).
  53. U. Heublein, J. Ebert, C. Hutzsch, S. Isleib, R. König, J. Richter, and A. Woisch, Zwischen Studienerwartungen Und Studienwirklichkeit. Ursachen Des Studienabbruchs, Beruflicher Verbleib Der Studienabbrecherinnen Und Studienabbrecher Und Entwicklung Der Studienabbruchquote (Forum Hochschule, Hannover, 2017).
  54. L. J. Cronbach, Coefficient alpha and the internal structure of tests, Psychometrika 16, 297 (1951).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation