Skill Transmittance in Science Education
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.
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.
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.
- Boesch, B. (2017a). The means-end account of scientific representation. Synthese. https://doi.org/10.1007/s11229-017-1537-2.
- Boesch, B. (2018). Representing in the student the laboratory. Transversal 5, 34–49. Google Scholar
- 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
- 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/.
- Fang, W. (2018). An inferential account of model explanation. Philosophia, March, 1–18. https://doi.org/10.1007/s11406-018-9958-9.
- Fantl, J. (2012). Knowledge how. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/fall2017/entries/knowledge-how/.
- Frigg, R., & Nguyen, J. (2017). Models and representation. In Springer Handbook of Model-Based Science, 49–102. Springer.Google Scholar
- Halloun, I. A. (2007). Modeling theory in science education, vol. 24. Springer Science & Business Media.Google Scholar
- Humphreys, P. (2004). Extending ourselves: computational science, empiricism, and scientific method. New York; Oxford: Oxford University Press.Google Scholar
- 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.
- Knuuttila, T., & Loettgers, A. (2012). The productive tension: mechanisms vs. templates in modeling the phenomena. Representations, Models, and Simulations, 2–24.Google Scholar
- Kuhn, T. S. (1977). The essential tension: selected studies in scientific tradition and change. University of Chicago Press.Google Scholar
- Mattila, E. (2006). Struggle between specificity and generality: how do infectious disease models become a simulation platform? in Simulation, 125–138. Springer.Google Scholar
- 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
- Morgan, M. & Morrison, M. (1999). Models as mediating instruments. in Models as Mediators: Perspectives on Natural and Social Science, 10–37.Google Scholar
- 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
- Ryle, G. (2009). The concept of mind. Routledge.Google Scholar