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Skill Transmittance in Science Education

Studying the Skills of Scientific Expertise
  • Brandon Boesch
Article

Abstract

It is widely argued that the skills of scientific expertise are tacit, meaning that they are difficult to study. In this essay, I draw on work from the philosophy of action about the nature of skills to show that there is another access point for the study of skills—namely, skill transmission in science education. I will begin by outlining Small’s Aristotelian account of skills, including a brief exposition of its advantages over alternative accounts of skills. He argues that skills exist in a sort of life cycle between learning, practicing, and transmitting, which provides reasons to think that we should pay close attention to the way skills are transmitted in teaching and learning. To demonstrate how a study of skill transmittance can be revealing about the nature of skills in expertise, I explore an example—what I identify as the skill of tension-balancing in model-building. After describing the skill, I briefly examine two case studies from the science education literature that reveal insights about the skill of tension-balancing as it functions in the practice of model-building.

Notes

Acknowledgements

I am grateful to Tarja Knuuttila, Jennifer Frey, Michael Dickson, and four anonymous reviewers for helpful comments during the writing of this article. Valuable feedback was also provided by attendees of the Society for Philosophy of Science in Practice Meeting in 2016, as well as by attendees of a philosophy of science seminar at the University of Helsinki.

Funding Information

I am grateful for financial support from the University of South Carolina Office of the Vice President for Research and the Department of Philosophy, which funded research visits to the Academy of Finland Center of Excellence in the Philosophy of the Social Sciences at the University of Helsinki and the Complutense University of Madrid, during which the bulk of this article was written. I am also grateful to the Russell J. and Dorothy S. Bilinski Foundation, for fellowship funding, under which I finished this article.

Compliance with Ethical Standards

Conflict of Interest

The author declares no conflict of interest.

References

  1. Boesch, B. (2017a). The means-end account of scientific representation. Synthese.  https://doi.org/10.1007/s11229-017-1537-2.
  2. Boesch, B. (2017b). There Is a special problem of scientific representation. Philosophy of Science, 84(5), 970–981.CrossRefGoogle Scholar
  3. Boesch, B. (2018). Representing in the student the laboratory. Transversal 5, 34–49. Google Scholar
  4. Bokulich, A. (2011). How scientific models can explain. Synthese, 180(1), 33–45.CrossRefGoogle Scholar
  5. Bolinska, A. (2016). Successful Visual Epistemic Representation. Studies in History and Philosophy of Science Part A, 56(April), 153–160.CrossRefGoogle Scholar
  6. Brenni, P. (2012). The evolution of teaching instruments and their use between 1800 and 1930. Science & Education, 21(2), 191–226.CrossRefGoogle Scholar
  7. Chi, M. (2006). Two approaches to the study of experts’ characteristics. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 21–30). New York: Cambridge University Press.CrossRefGoogle Scholar
  8. Chiel, H. J., McManus, J. M., & Shaw, K. M. (2010). From biology to mathematical models and Back: teaching modeling to biology students, and biology to math and engineering students. CBE-Life Sciences Education, 9(3), 248–265.CrossRefGoogle Scholar
  9. Cianciolo, A., Cynthia M., Sternberg, R., Wagner, R. (2006). Tacit knowledge, practical intelligence and expertise. In K. A. Ericsson, N.Charness, R. Hoffman, & P. Feltovich (Eds.), In The Cambridge handbook of expertise and expert performance. New York: Cambridge University Press.Google Scholar
  10. Cohen, S. M. (2016). Aristotle’s metaphysics. In Edward N. Zalta Winter (Ed.), The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University. https://plato.stanford.edu/archives/win2016/entries/aristotle-metaphysics/.
  11. Collins, H. (1974). The TEA set: tacit knowledge and scientific networks. Science Studies, 4(2), 165–185.CrossRefGoogle Scholar
  12. Collins, H., & Evans, R. (2002). The third wave of science studies: studies of expertise and experience. Social Studies of Science, 32(2), 235–296.CrossRefGoogle Scholar
  13. Collins, H., & Evans, R. (2015). Expertise revisited, part I—Interactional expertise. Studies in History and Philosophy of Science Part A, 54, 113–123.CrossRefGoogle Scholar
  14. Collins, H., Evans, R., & Weinel, M. (2016). Expertise revisited, part II: Contributory expertise. Studies in History and Philosophy of Science Part A, 56, 103–110.CrossRefGoogle Scholar
  15. Elliott, K. C., & McKaughan, D. J. (2014). Nonepistemic values and the multiple goals of science. Philosophy of Science, 81(1), 1–21.CrossRefGoogle Scholar
  16. Engel, P. J. H. (2008). Tacit knowledge and visual expertise in medical diagnostic reasoning: implications for medical education. Medical Teacher, 30(7), e184–e188.CrossRefGoogle Scholar
  17. Fang, W. (2018). An inferential account of model explanation. Philosophia, March, 1–18.  https://doi.org/10.1007/s11406-018-9958-9.
  18. Fantl, J. (2008). Knowing-how and knowing-that. Philosophy Compass, 3(3), 451–470.CrossRefGoogle Scholar
  19. Fantl, J. (2012). Knowledge how. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophyhttps://plato.stanford.edu/archives/fall2017/entries/knowledge-how/.
  20. Frey, C. (2015). From blood to flesh: homonymy, unity, and ways of being in Aristotle. Ancient Philosophy, 35(2), 375–394.CrossRefGoogle Scholar
  21. Frigg, R., & Nguyen, J. (2017). Models and representation. In Springer Handbook of Model-Based Science, 49–102. Springer.Google Scholar
  22. Gamble, J. (2001). Modelling the invisible: the pedagogy of craft apprenticeship. Studies in Continuing Education, 23(2), 185–200.CrossRefGoogle Scholar
  23. Gilbert, J. K. (2004). Models and modelling: routes to more authentic science education. International Journal of Science and Mathematics Education, 2(2), 115–130.CrossRefGoogle Scholar
  24. Glass, R. J. (2013). Tacit beginnings towards a model of scientific thinking. Science & Education, 22(10), 2709–2725.CrossRefGoogle Scholar
  25. Goddiksen, M. (2014). Clarifying interactional and contributory expertise. Studies in History and Philosophy of Science Part A, 47(September), 111–117.CrossRefGoogle Scholar
  26. Halloun, I. A. (2007). Modeling theory in science education, vol. 24. Springer Science & Business Media.Google Scholar
  27. Hoffman, R. R. (1998). How can expertise be defined? Implications of research from cognitive psychology. In Exploring expertise (pp. 81–100). London: Palgrave Macmillan.CrossRefGoogle Scholar
  28. Humphreys, P. (2004). Extending ourselves: computational science, empiricism, and scientific method. New York; Oxford: Oxford University Press.Google Scholar
  29. Justi, R. S., & Gilbert, J. K. (2002). Modelling, teachers’ views on the nature of modelling, and implications for the education of Modellers. International Journal of Science Education, 24(4), 369–387.CrossRefGoogle Scholar
  30. Knuuttila, T. (2005). Models, representation, and mediation. Philosophy of Science, 72(5), 1260–1271.  https://doi.org/10.1086/508124.CrossRefGoogle Scholar
  31. Knuuttila, T. (2011). Modelling and representing: an artefactual approach to model-based representation. Studies in History and Philosophy of Science Part A, 42(2), 262–271.CrossRefGoogle Scholar
  32. Knuuttila, T., & García Deister, V. (2018). Modelling gene regulation: (De)compositional and template-based strategies. Studies in History and Philosophy of Science Part A, January.  https://doi.org/10.1016/j.shpsa.2017.11.002.
  33. Knuuttila, T., & Loettgers, A. (2012). The productive tension: mechanisms vs. templates in modeling the phenomena. Representations, Models, and Simulations, 2–24.Google Scholar
  34. Knuuttila, T., & Loettgers, A. (2014). Varieties of noise: analogical reasoning in synthetic biology. Studies in History and Philosophy of Science Part A, 48, 76–88.CrossRefGoogle Scholar
  35. Kuhn, T. S. (1977). The essential tension: selected studies in scientific tradition and change. University of Chicago Press.Google Scholar
  36. Lundgren, A. (2006). The transfer of chemical knowledge: the case of chemical technology and its textbooks. Science & Education, 15(7–8), 761–778.CrossRefGoogle Scholar
  37. Mattila, E. (2006). Struggle between specificity and generality: how do infectious disease models become a simulation platform? in Simulation, 125–138. Springer.Google Scholar
  38. Mattila, E. (2007). Struggle between specificity and generality: how do infectious disease models become a simulation platform? In G. Kuppers, J. Lenhard, & T. Shinn (Eds.), Simulation: Pragmatic Constructions of Reality (pp. 125–138). Dordecht: Springer.Google Scholar
  39. Morgan, M. (2014). Resituating knowledge: generic strategies and case studies. Philosophy of Science, 81(5), 1012–1024.CrossRefGoogle Scholar
  40. Morgan, M. & Morrison, M. (1999). Models as mediating instruments. in Models as Mediators: Perspectives on Natural and Social Science, 10–37.Google Scholar
  41. Oh, P. S., & Oh, S. J. (2011). What teachers of science need to know about models: an overview. International Journal of Science Education, 33(8), 1109–1130.CrossRefGoogle Scholar
  42. Polanyi, M. (1962). Rev. Mod. Phys. 34, 601 (1962) - tacit knowing: its bearing on some problems of philosophy. Review of Modern Physics, 34(4), 601–616.CrossRefGoogle Scholar
  43. Reyes-Galindo, L. I., & Duarte, T. R. (2015). Bringing tacit knowledge back to contributory and interactional expertise: a reply to Goddiksen. Studies in History and Philosophy of Science Part A, 49, 99–102.Google Scholar
  44. Rice, C., & Smart, J. (2011). Interdisciplinary modeling: a case study of evolutionary economics. Biology and Philosophy, 26(5), 655–675.CrossRefGoogle Scholar
  45. Rudolph, J. L. (2008). Historical writing on science education: a view of the landscape. Studies in Science Education, 44(1), 63–82.CrossRefGoogle Scholar
  46. Ryle, G. (2009). The concept of mind. Routledge.Google Scholar
  47. Small, W. (2014). The transmission of skill. Philosophical Topics, 42(1), 85–111.CrossRefGoogle Scholar
  48. Stanley, J., & Williamson, T. (2001). Knowing how. The Journal of Philosophy, 98(8), 411–444.CrossRefGoogle Scholar
  49. Tala, S. (2011). Enculturation into Technoscience: analysis of the views of novices and experts on modelling and learning in nanophysics. Science & Education, 20(7–8), 733–760.CrossRefGoogle Scholar
  50. Tala, S. (2013). Knowledge building expertise: nanomodellers’ education as an example. Science & Education, 22(6), 1323–1346.CrossRefGoogle Scholar
  51. Trumper, R. (2003). The physics laboratory – a historical overview and future perspectives. Science & Education, 12(7), 645–670.CrossRefGoogle Scholar
  52. Volterra, V. (1928). Variations and fluctuations of the number of individuals in animal species living together. ICES Journal of Marine Science, 3(1), 3–51.CrossRefGoogle Scholar
  53. Weisberg, M. (2007). Who is a modeler? The British Journal for the Philosophy of Science, 58(2), 207–233.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of HumanitiesMorningside CollegeSioux CityUSA

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