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Contributing factors to the improvement of conceptual understanding in a computer-based intervention in Newtonian dynamics

Jannis Weber* and Thomas Wilhelm†

  • *Contact author: weber@physik.uni-frankfurt.de
  • †Contact author: wilhelm@physik.uni-frankfurt.de
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Phys. Rev. Phys. Educ. Res. 20, 020130 – Published 15 October, 2024

DOI: https://doi.org/10.1103/PhysRevPhysEducRes.20.020130

Abstract

Students experience many difficulties learning the fundamental relationships in Newtonian mechanics, partly due to preexisting mental models that originate from their everyday lives. These preconceptions often persist even after instruction in mechanics and lead to a supposed incompatibility between physics lessons in school and personal experiences. This article presents research that focuses on students’ concept knowledge and conceptual understanding in the field of Newtonian dynamics. Interventions are presented that discuss authentic experiments with friction using different software as a tool. Overall, the interventions lead to an increase in conceptual understanding with a large effect size. A hierarchical linear model (HLM) is used to identify factors that lead to a larger or smaller increase in conceptual understanding of the topic. With the HLM, we infer that the pretest score, the students’ interest in how physical properties interrelate (interest in “theoretical physics”), prior physics performance (in kinematics in this case, measured in the physics grade), and the (intrinsic and extraneous) cognitive load influence the post-test score. We discuss possible explanations as to why these factors influence students’ conceptual change.

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References (99)

  1. N. Guicciardini, Reading the Principia. The debate on Newton’s mathematical methods for natural philosophy from 1687 to 1736 (Cambridge University Press, Cambridge, England, 2003).
  2. T. Wilhelm, Verständnis der newtonschen Mechanik bei bayerischen Elftklässlern – Ergebnisse beim Test “Force Concept Inventory” in herkömmlichen Klassen und im Würzburger Kinematik-/Dynamikunterricht, PhyDid A 2, 47 (2005), http://phydid.physik.fu-berlin.de/index.php/phydid/article/view/32/32.
  3. H. Schecker and T. Wilhelm, Schülervorstellungen in der Mechanik, in Schülervorstellungen und Physikunterricht, edited by H. Schecker, T. Wilhelm, M. Hopf, and R. Duit (Springer Berlin Heidelberg, Berlin, Heidelberg, 2018), p. 63, 10.1007/978-3-662-57270-2_4.
  4. P. Potvin, The coexistence claim and its possible implications for success in teaching for conceptual “change”, Eur J. Sci. Math. Educ. 5, 55 (2017).
  5. G. J. Posner et al., Accommodation of a scientific conception: Toward a theory of conceptual change, Sci. Educ. 66, 211 (1982).
  6. I. A. Halloun and D. Hestenes, Common sense concepts about motion, Am. J. Phys. 53, 1056 (1985).
  7. D. Hillmayr et al., The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis, Comput. Educ. 153, 103897 (2020).
  8. Y.-T. Sung, K.-E. Chang, and T.-C. Liu, The effects of integrating mobile devices with teaching and learning on students’ learning performance: A meta-analysis and research synthesis, Comput. Educ. 94, 252 (2016).
  9. E. R. Carnes, J. S. Lindbeck, and C. F. Griffin, Effects of group size and advance organizers on learning parameters when using microcomputer tutorials in kinematics, J. Res. Sci. Teach. 24, 781 (1987).
  10. D. Hillmayr, F. Reinhold, L. Ziernwald, and K. Reiss, Digitale Medien im mathematisch-naturwissenschaftlichen Unterricht der Sekundarstufe. Einsatzmöglichkeiten, Umsetzung und Wirksamkeit (Waxmann, Münster, 2017), 10.25656/01:15482.
  11. J. Weber and T. Wilhelm, The benefit of computational modelling in physics teaching: A historical overview, Eur. J. Phys. 41, 034003 (2020).
  12. H. Schecker, The didactic potential of computer aided modeling for physics education, in Advanced Educational Technologies for Mathematics and Science, edited by D. L. Ferguson (Springer Berlin Heidelberg, Berlin, Heidelberg, 1993), p. 165.
  13. S. Mikelskis-Seifert and L. Kasper, Modellieren in der Physik, im Alltag und im Unterricht. Hintergründe und unterrichtliche Orientierung zum Thema Modelle, NiU-Phys. 22, 4 (2011).
  14. M. Suleder, Eine kurze Geschichte der Videoanalyse, Plus Lucis 28, 4 (2020), https://www.pluslucis.org/ZeitschriftenArchiv/2020-1_PL.pdf.
  15. P. Potvin et al., Models of conceptual change in science learning: establishing an exhaustive inventory based on support given by articles published in major journals, Stud. Sci. Educ. 56, 157 (2020).
  16. P. Potvin et al., Coexistence of misconceptions and scientific conceptions in chemistry professors: A mental chronometry and fMRI study, Front. Educ. 5 (2020).
  17. P. Potvin et al., Persistence of the intuitive conception that heavier objects sink more: A reaction time study with different levels of interference, Int. J. Sci. Math. Educ. 13, 21 (2015).
  18. A. A. diSessa, Toward an epistemology of physics, Cognit. Instr. 10, 105 (1993).
  19. S. Vosniadou, Capturing and modeling the process of conceptual change, Learn. Instr. 4, 45 (1994).
  20. C. Chinn, A critique of social constructivist explanations of knowledge change, in Perspectives on Conceptual Change. Multiple Ways to Understand Knowing and Learning in a Complex World, edited by B. J. Guzzetti (Taylor and Francis, Mahwah, 2013).
  21. R. Driver et al., Constructing scientific knowledge in the classroom, Educ. Res. 23, 5 (1994).
  22. P. Cobb, Where is the mind? Constructivist and sociocultural perspectives on mathematical development, Educ. Res. 23, 13 (1994).
  23. M. Gregoire, Is It a challenge or a threat? A dual-process model of teachers’ cognition and appraisal processes during conceptual change, Educ. Psychol. Rev. 15, 147 (2003).
  24. P. R. Pintrich, R. W. Marx, and R. A. Boyle, Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change, Rev. Educ. Res. 63, 167 (1993).
  25. L. S. Vygostky et al., Mind in Society (Harvard University Press, Cambridge, MA, 1980), 10.2307/j.ctvjf9vz4.
  26. L. Mason, Introduction: Bridging the cognitive and sociocultural approaches in research on conceptual change: Is it feasible? Educ. Psychol. 42, 1 (2007).
  27. A. Sfard, On two metaphors for learning and the dangers of choosing just one, Educ. Res. 27, 4 (1998).
  28. J. V. Wertsch, Mind as Action (Oxford University Press, New York, 1998), 10.1093/acprof:oso/9780195117530.001.0001.
  29. J. Lave, Cognition in Practice Mind, Mathematics and Culture in Everyday (Cambridge University Press, Cambridge, England, 1988), 10.1017/CBO9780511609268.
  30. H. Schecker, Physik—Modellieren. Grafikorientierte Modellbildungssysteme im Physikunterricht (Klett, Stuttgart, 1998).
  31. L. Hucke, Handlungsregulation und Wissenserwerb in traditionellen und computergestützten Experimenten des physikalischen Praktikums, Dissertation, Logos-Verlag, Berlin, 2000, Vol. 8, https://d-nb.info/960396934/34.
  32. F. Sander, H. Schecker, and H. Niedderer, Wirkungen des Einsatzes grafikorientierter Modellbildung im physikalischen Praktikum, Z. Didakt. Naturwiss. 7, 147 (2002).
  33. H. Schecker et al., Abschlussbericht zum DFG-Projekt—Physiklernen mit Modellbildungssystemen. Förderung physikalischer Kompetenz und systemischen Denkens durch computergestützte Modellbildungssysteme, 1999.
  34. J. Benacka, Projectile general motion in a vacuum and a spreadsheet simulation, Phys. Educ. 50, 58 (2015).
  35. J. Benacka, Spreadsheet application showing the proper elevation angle, points of shot and impact of a projectile, Phys. Educ. 50, 342 (2015).
  36. J. Benacka, Numerical modelling with spreadsheets as a means to promote STEM to high school students, Eurasia J. Math. Sci. Technol. Educ. 12 (2016).
  37. D. Scherer, P. Dubois, and B. Sherwood, VPython: 3D interactive scientific graphics for students, Comput. Sci. Eng. 2, 56 (2000).
  38. M. D. Caballero, M. A. Kohlmyer, and M. F. Schatz, Implementing and assessing computational modeling in introductory mechanics, Phys. Rev. ST Phys. Educ. Res. 8 (2012).
  39. M. D. Caballero et al., Integrating numerical computation into the modeling instruction curriculum, Phys. Teach. 52, 38 (2014).
  40. P. W. Laws and H. Pfister, Using digital video analysis in introductory mechanics projects, Phys. Teach. 36, 282 (1998).
  41. T. Wilhelm and T. Trefzger, Erhebung zum Computereinsatz bei Physik-Gymnasiallehrern, PhyDid B - Didaktik der Physik - Beiträge zur DPG-Frühjahrstagung (2010), https://ojs.dpg-physik.de/index.php/phydid-b/article/view/109/119.
  42. M. Wenzel, Computereinsatz in Schule und Schülerlabor, Dissertation, Logos-Verlag, Berlin, 2018, Vol. 251, https://www.logos-verlag.de/ebooks/OA/978-3-8325-4659-5.pdf.
  43. R. J. Beichner, The impact of video motion analysis on kinematics graph interpretation skills, Am. J. Phys. 64, 1272 (1996).
  44. P. Hockicko, B. Trpišová, and J. Ondruš, Correcting Students’ Misconceptions about Automobile Braking Distances and Video Analysis Using Interactive Program Tracker, J. Sci. Educ. Technol. 23, 763 (2014).
  45. L. Escalada, R. Grabhorn, and D. Zollman, Applications of interactive digital video in a physics classroom, J. Educ. Multimed. Hypermedia 5, 73 (1996).
  46. S. Becker et al., Förderung von Konzeptverständnis und Repräsentationskompetenz durch Tablet-PC-gestützte Videoanalyse, Z. Didakt. Naturwiss. 25, 1 (2019).
  47. T. Wilhelm, Konzeption und Evaluation eines Kinematik/Dynamik-Lehrgangs zur Veränderung von Schülervorstellungen mit Hilfe dynamisch ikonischer Repräsentationen und graphischer Modellbildung, Dissertation, Logos-Verlag, Berlin, 2005, Vol. 46, https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/3310.
  48. K. Hochberg et al., Using smartphones as experimental tools—a follow-up: Cognitive effects by video analysis and reduction of cognitive load by multiple representations, J. Sci. Educ. Technol. 29, 303 (2020).
  49. J. Sweller, Cognitive load during problem solving: Effects on learning, Cogn. Sci. 12, 257 (1988).
  50. J. J. G. van Merriënboer and J. Sweller, Cognitive load theory and complex learning: Recent developments and future directions, Educ. Psychol. Rev. 17, 147 (2005).
  51. J. Leppink and A. van den Heuvel, The evolution of cognitive load theory and its application to medical education, Perspect. Med. Educ. 4, 119 (2015).
  52. S. Becker et al., Investigating dynamic visualizations of multiple representations using mobile video analysis in physics lessons, Z. Didakt. Naturwiss. 26, 123 (2020).
  53. J. Weber, Mathematische Modellbildung und Videoanalyse zum Lernen der Newtonschen Dynamik im Vergleich, Dissertation, Logos-Verlag, Berlin, 2022, Vol. 345, 10.30819/5566.
  54. K. Backhaus, B. Erichson, W. Plinke, and R. Weiber, Multivariate Analysemethoden. Eine anwendungsorientierte Einführung (Springer Gabler, Berlin, Heidelberg, 2016), 10.1007/978-3-662-46076-4.
  55. J. M. Graham, Congeneric and (essentially) tau-equivalent estimates of score reliability, Educ. Psychol. Meas. 66, 930 (2006).
  56. E. Cho, Making reliability reliable, Organ. Res. Meth. 19, 651 (2016).
  57. J. C. Nunnally and I. H. Bernstein, Psychometric Theory (McGraw-Hill, New York, NY, 2008).
  58. D. Hestenes, M. Wells, and G. Swackhamer, Force concept inventory, Phys. Teach. 30, 141 (1992).
  59. A. Savinainen and P. Scott, The Force Concept Inventory: A tool for monitoring student learning, Phys. Educ. 37, 45 (2002).
  60. R. R. Hake, Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses, Am. J. Phys. 66, 64 (1998).
  61. D. Huffman and P. Heller, What does the force concept inventory actually measure? Phys. Teach. 33, 138 (1995).
  62. J. Gerdes and H. Schecker, Der Force Concept Inventory. Ein diagnostischer Test zu Schülervorstellungen in der Mechanik, MNU 52, 283 (1999).
  63. A. M. Just, C. von Aufschnaiter, and A. Vorholzer, Effects of conceptual and contextual task characteristics on students’ activation of mechanics conceptions, Eur. J. Phys. 42, 025702 (2021).
  64. T. Wilhelm, Vektorverständnis von Studienanfängern, in Didaktik der Physik. Regensburg 2007, edited by V. Nordmeier, A. Oberländer, and H. Grötzebauch (Lehmanns Media LOB.de, Berlin, 2007).
  65. S. Flores, S. E. Kanim, and C. H. Kautz, Student use of vectors in introductory mechanics, Am. J. Phys. 72, 460 (2004).
  66. J. Warren, Understanding Force (John Murray, London, 1979).
  67. R. K. Thornton and D. R. Sokoloff, Assessing student learning of Newton’s laws: The force and motion conceptual evaluation and the evaluation of active learning laboratory and lecture curricula, Am. J. Phys. 66, 338 (1998).
  68. D. Heuer and T. Wilhelm, Aristoteles siegt immer noch über Newton, MNU 50, 280 (1997).
  69. H. Schecker and J. Gerdes, Messung der Konzeptualisierungsfähigkeit in der Mechanik - Zur Aussagekraft des Force Concept Inventory, Z. Didakt. Naturwiss. 5, 75 (1999).
  70. M. Laukenmann et al., Eine Untersuchung zum Einfluss emotionaler Faktoren auf das Lernen im Physikunterricht, Z. Didakt. Naturwiss. 6, 139 (2000).
  71. J. Leppink et al., Development of an instrument for measuring different types of cognitive load, Behav. Res. Methods 45, 1058 (2013).
  72. S. Becker-Genshow, Lernwirksamkeit einer Tablet-PC-gestützten Videoanalyse im Mechanikunterricht der Sekundarstufe - Ergebnisse quasi-experimenteller Feldstudien zu Konzeptverständnis, kognitiver Belastung und induzierten Emotionen, Dissertation, Technische Universität Kaiserslautern, 2020.
  73. J. Leppink et al., Effects of pairs of problems and examples on task performance and different types of cognitive load, Learn. Instr. 30, 32 (2014).
  74. M. Thees et al., Comparing two subjective rating scales assessing cognitive load during technology-enhanced STEM laboratory courses, Front. Educ. 6 (2021).
  75. R. Eisinga, M. t. Grotenhuis, and B. Pelzer, The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown? Int. J. Public Health 58, 637 (2013).
  76. A. Helmke, Determinanten der Schulleistung: Forschungsstand und Forschungsdefizite, in Empirische Pädagogik 1970-1990. Eine Bestandsaufnahme der Forschung in der Bundesrepublik Deutschland., edited by K. Ingenkamp, R. Jäger, H. Petillon, and B. Wolf (Deutscher Studien Verlag, Weinheim, 1992), p. 595.
  77. M. Kunter et al., PISA 2000: Dokumentation der Erhebungsinstrumente (Max-Planck-Institut für Bildungsforschung, Berlin, 2002), Vol. 72.
  78. C. Brell, Lernmedien und Lernerfolg—reale und virtuelle Materialien im Physikunterricht. Empirische Untersuchungen in achten Klassen an Gymnasien (Laborstudie) zum Computereinsatz mit Simulation und IBE, Dissertation Logos-Verlag, Berlin, 2008, Vol. 74.
  79. J. Chen et al., The role of collaboration, computer use, learning environments, and supporting strategies in CSCL: A meta-analysis, Rev. Educ. Res. 88, 799 (2018).
  80. H. Schecker and J. Gerdes, Interviews über Experimente zu Bewegungsvorgängen, Z. Didakt. Naturwiss. 4, 61 (1998).
  81. S. Becker, P. Klein, and J. Kuhn, Video analysis on tablet computers to investigate effects of air resistance, Phys. Teach. 54, 440 (2016).
  82. J. Leisen and M. Neffgen, Modellbildungspraktikum: Fall von Körpern in Luft, PdN-PhiS 48, 7 (1999).
  83. J. Cohen, Statistical Power Analysis for the Behavioral Sciences (L. Erlbaum Associates, Hillsdale, NJ, 1988), 10.4324/9780203771587.
  84. J. Kuhn, Mehrebenenanalyse am Beispiel der Lernwirkung von Aufgaben, in Methoden in der naturwissenschaftsdidaktischen Forschung, edited by D. Krüger, I. Parchmann, and H. Schecker (Springer Spektrum, Berlin, Heidelberg, 2014), p. 297, 10.1007/978-3-642-37827-0_24.
  85. S. W. Raudenbush and A. S. Bryk, Hierarchical Linear Models. Applications and Data Analysis Methods (SAGE Publ., Thousand Oaks, CA, 2010), Vol. 1.
  86. H. Ditton, Mehrebenenanalyse. Grundlagen und Anwendungen des hierarchisch linearen Modells (Juventa-Verl., Weinheim, 1998).
  87. J. J. Hox, Multilevel Analysis. Techniques and Applications (Routledge, New York, NY, 2010).
  88. M. Gollwitzer et al., Effectiveness of the Vienna social competence training on explicit and implicit aggression, Eur. J. Psychol. Assess. 23, 150 (2007).
  89. C. J. M. Maas and J. J. Hox, The influence of violations of assumptions on multilevel parameter estimates and their standard errors, Comput. Stat. Data Anal. 46, 427 (2004).
  90. C. J. M. Maas and J. J. Hox, Sufficient sample sizes for multilevel modeling, Methodology 1, 86 (2005).
  91. J.-P. Burde, Konzeption und Evaluation eines Unterrichtskonzepts zu einfachen Stromkreisen auf Basis des Elektronengasmodells, Dissertation Logos-Verlag, Berlin, 2018, Vol. 259, 10.30819/4726.
  92. G. Rasch, Probabilistic Models for Some Intelligence and Attainment Tests (University of Chicago Press, Chicago, 1980).
  93. H. Wu and S.-O. Leung, Can Likert scales be treated as interval scales?—A simulation study, J. Soc. Serv. Res. 43, 527 (2017).
  94. W. J. Browne, Applying MCMC methods to multi-level models, Dissertation, University of Bath, 1998.
  95. A. Field, Discovering Statistics Using SPSS (and Sex, Drugs and Rock’n’Roll) (SAGE Publication, London, 2007).
  96. J. Hartig and K. Rakoczy, Mehrebenenanalyse - multilevel analysis, in Handbuch Statistik, Methoden und Evaluation, edited by H. Holling (Hogrefe, Göttingen, 2010), p. 538.
  97. P. W. Irving, M. J. Obsniuk, and M. D. Caballero, P3: a practice focused learning environment, Eur. J. Phys. 38, 055701 (2017).
  98. F. Sander, Verbindung von Theorie und Experiment im physikalischen Praktikum. Eine empirische Untersuchung zum handlungsbezogenen Vorverständnis und dem Einsatz grafikorientierter Modellbildung im Praktikum, Zugl.: Bremen, Univ., Diss., 1999 (Logos-Verl., Berlin, 2000), Vol. 13.
  99. P. Klein, J. Kuhn, and A. Müller, Förderung von Repräsentationskompetenz und Experimentbezug in den vorlesungsbegleitenden Übungen zur Experimentalphysik, Z. Didakt. Naturwiss. 24, 17 (2018).

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Phys. Rev. Phys. Educ. Res. 20, 020130– Published 15 October, 2024
  • Received 3 October 2023
  • Accepted 19 August 2024
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