Abstract
Teaching false theories goes against the general pedagogical and philosophical belief that we must only teach and learn what is true. In general, the goal of pedagogy is taken to be epistemic: to gain knowledge and avoid ignorance. In this article, I argue that for realists and antirealists alike, epistemological and pedagogical goals have to come apart. I argue that the falsity of a theory does not automatically make it unfit for being taught. There are several good reasons for teaching false theories in school science. These are (a) false theories can bring about genuine (non-factive) understanding of the world; (b) teaching some false theories from the history of science that line up with children’s ideas can provide students “intellectual empathy” and also aid in better grasp of concepts; (c) teaching false theories from the history of science can sharpen students’ understanding of the nature of science; (d) scientists routinely use false theories and models in their practice and it is good sense for science education to mirror scientific practice; and (e) learning about patently non-scientific and antiscientific ideas will prepare students to face and respond to them. In making arguments for the foregoing five points, I draw upon the work of a variety of philosophers and historians of science, cognitive scientists, science education scholars, and scientists. My goal here is not only to justify theories considered false already being taught, but also to actively endorse the teaching of some theories considered false that are by and large not currently taught.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
See Philip Kitcher (2001) for a related discussion.
In philosophy of science, there have been two prominent views of scientific theory: the syntactic view which takes a theory to be an axiomatized collection of sentences, and the semantic view which takes a theory to be a collection of non-linguistic models. My use of “theory” in this article is I think most aligned with the use of the term in the pragmatic view of scientific theory—that a theory, in Savage’s (1990) words “is an amorphous entity consisting perhaps of sentences and models, but just as importantly of exemplars, problems, standards, skills, practices and tendencies.” (pp. vii–viii) I should clarify though that here I am not defending the pragmatic view—I am in fact not defending any particular philosophical view of scientific theory. And in discussing, for instance, intelligent design or climate change denial as a false “theory” to be taught, I am not claiming that either should qualify as a theory according to any philosophical view—all I am doing here is using the word “theory” as shorthand for “body of views”.
There is a long-standing debate in philosophy of science on the legitimacy of the observable/unobservable divide. See Hempel (1950), Hanson (1958), Feyerabend (1959), Kuhn (1962), Popper (1965), and van Fraassen (1980) for key debates. Issues include entities not considered observable earlier (like microbes) now being considered observable; deciding a standard (human sense organs? hypothetical advanced human sense organs? lab instruments?) with respect to which entities are considered observable; and so on. Here, my larger goal is to show that some theories not favored by antirealists (and some by realists) can be valuable in the classroom, and taking a side in the observable–unobservable debate will not be pressing.
The question of what features—if any—a theory needs besides empirical adequacy to make it true is a long-standing one in philosophy of science. See Duhem (1954), Kuhn (1962), Laudan (2004), and Douglas (2013) for key debates. Antirealists generally maintain that nothing can ever establish that a theory is true, while realists generally take it that based on a theory’s empirical adequacy, although one cannot logically deduce that it is true, there are ampliative arguments for inferring that it is true. And whether such ampliative arguments work is at the core of the realism–antirealism debates.
A common reason for this is that the observable has a privileged epistemic status since it is—at least in principle if not practically—accessible to us with our unaided senses (van Fraassen 1980). Hence, this variety of antirealist argument goes we should only pursue knowledge of the observable.
In this regard, Slater brings to our attention a version of realism—one that van Fraassen (1980) underlined—that only claims that science has an aim of attaining truth, not that our best theories are (approximately) true. And such a realist would also be at loggerheads with my view since I also endorse the teaching of some theories that did not/do not purport to be true; they might be purely instrumental or heuristic. But teaching false theories is not a problem just for realists as we are about to see.
For this reason, according to my arguments, one cannot defend the teaching of false theories in general by downplaying the falsity of certain theories being taught today, like Newtonian mechanics, on the grounds of its being highly empirically adequate/approximately true/partially true. I argue that even theories that according to most would not fit any of these adjectives can be taught for other good reasons. Slater (2008) on the other hand takes the route of arguing that it is not easy or straightforward to argue that Newtonian mechanics is approximately true and defend its teaching on those grounds. He also presses that its empirical adequacy does not make its underlying falsity go away—if we have to defend its teaching, we have to confront its falsity.
It is worth noting that there has been a similar call for reviving a discarded idea in biology by Michael Skinner (2015), who has argued that a unified theory of evolution incorporating both neo-Lamarckian and neo-Darwinian ideas is useful in understanding environmental epigenetics.
We should note here that, although there are few parallels between conceptual development in students and in the history of science at large, Nersessian has shown that there can be important similarities between the conceptual developments of certain individual scientists on the one hand and students of science on the other. A similar point about the obstacles that Charles Darwin faced and how they may be similar to those that students currently face in their learning of evolution is made in K. Kampourakis (2014).
See Marton et al. (2004) for an argument for the view that a concept is learned more effectively when presented with contrasting concepts.
See Stephen Hartmann (1999) for a discussion of this.
See Richard Dawid (2013) for arguments for valuing and legitimizing string theory as science, on grounds other than empirical adequacy.
See Mary Williams (1982) for a compelling argument that evolutionary biology does make predictions, contrary to common allegations that it does not. She notes that it is just that these predictions may differ from the ones made in physics in some ways. For instance, evolutionary biology often makes predictions not only about individual organisms, but also about patterns found in genuses; and these predictions are not only about the future, but also about the past.
References
Abd-El-Khalick, F., & Lederman, N. (2000). The influence of history of science courses on students’ views of nature of science. Journal of Research in Science Teaching, 37(10), 1057–1095.
Allchin, D. (1997). Rekindling phlogiston: from classroom case study to interdisciplinary relationships. Science & Education, 6, 473–509.
American Association for the Advancement of Science. (1989). Science for all Americans: a Project 2061 report on literacy goals in science, mathematics, and technology. Washington, D.C.
Behe, M. (1996). Darwin’s black box: The biochemical challenge to evolution. Free Press.
Bhakthavatsalam, S., & Cartwright, N. (2017). What’s so special about empirical adequacy? European Journal for Philosophy of Science., 7(3), 445–465.
Cartwright, N. (1989). Nature’s capacities and their measurement. Oxford: Oxford University Press.
Chang, H. (2012). Is water H 2 O? Boston Studies in the Philosophy of Science 293. Dordrecth: Springer.
Clement, J. (1983). A conceptual model discussed by Galileo and used intuitively by physics students. In D. Gentner & A. Stevens (Eds.), Mental models (pp. 325–340). Hillsdale: Lawrence Erlbaum.
Contessa, G. (2007). Scientific representation, interpretation and surrogative reasoning. Philosophy of Science, 74(1), 48–68.
Dawid, R. (2013). String theory and the scientific method. Cambridge: Cambridge University Press.
Douglas, H. (2013). The value of cognitive values. Philosophy of Science, 80(5), 796–806.
Driver, R., & Easly, J. (1978). Pupils and paradigms: a review of literature related to concept development in adolescent science students. Studies in Science Education, 5, 61–84.
Driver, R., Guesne, E., & Tiberghien, A. (1985). Some features of children’s ideas and their implications for teaching. In Children’s ideas in science (pp. 193–201). London: Open University Press.
Duhem, P. (1954). The aim and structure of physical theory. Princeton: Princeton University Press.
Elgin, C. (2009). Is understanding factive? In D. Pritchard, A. Millar, & A. Haddock (Eds.), Epistemic value (pp. 332–330). Oxford: Oxford University Press.
Elgin, C. (2012). Understanding’s tethers. In C. Jäger & W. Löffler (Eds.), Epistemology: contexts, values, and disagreement (pp. 131–146). Heusenstamm: Ontos Verlag.
Feyerabend, P. K. (1959). An attempt at a realistic interpretation of experience. In P. K. Feyerabend (Ed.), Realism, rationalism, and scientific method (philosophical papers I) (Vol. 1985, pp. 17–36). Cambridge: Cambridge University Press.
Galili, I. (2014). Teaching optics: a historico-philosophical perspective. In M. Matthews (Ed.), International handbook of research in history, philosophy, and science teaching (pp. 97–128). Berlin: Springer.
Galili, I., & Hazan, A. (2001). Experts’ views on using history and philosophy of science in the practice of physics instruction. Science & Education, 10(4), 345–367.
Gauld, C. (2014). Using history to teach mechanics. In M. Matthews (Ed.), International handbook of research in history, philosophy, and science teaching (pp. 57–96). Berlin: Springer.
Hanson, N. (1958). Patterns of discovery. Cambridge: Cambridge University Press.
Hartmann, S. (1999). Models and stories in hadron physics. In M. Morrison & M. S. Morgan (Eds.), Models as mediators (pp. 326–346). Cambridge: Cambridge University Press.
Hempel, C. (1950). Problems and changes in the empiricist criterion of meaning. Revue Internationale de Philosophie, 41(11), 41–63.
Kampourakis, K. (2014). Understanding evolution. Cambridge: Cambridge University Press.
Kampourakis, K., & Nehm, R. (2014). History and philosophy of science and the teaching of evolution: students’ conceptions and explanations. In M. Matthews (Ed.), International handbook of research in history, philosophy and science teaching (pp. 377–399). Berlin: Springer.
Kitcher, P. (2001). Science, truth, and democracy. New York: Oxford University Press.
Kuhn, T. S. (1962). The structure of scientific revolutions (p. 1996). Chicago: University of Chicago Press, reprinted.
Laudan, L. (2004). The epistemic, the cognitive and the social. In P. Machamer & G. Wolters (Eds.), Science, values, and objectivity (pp. 14–23). Konstanz: University of Pittsburgh Press Pittsburgh, and Universitatsverlag.
Lin, H., & Chen, C. (2002). Promoting preservice chemistry teachers’ understanding about the nature of science through history. Journal of Research in Science Teaching, 39(9), 773–792.
Lipton, P. (2009). Understanding without explanation. In H. de Regt, S. Leonelli, & K. Eigner (Eds.), Scientific understanding: philosophical perspectives (pp. 43–63). Pittsburgh: University of Pittsburgh Press.
Manne, K. (2017). Down girl: the logic of misogyny. Oxford: Oxford University Press.
Marton, F., Runesson, U., & Tsui, A. B. (2004). The space of learning. In F. Marton & A. B. Tsui (Eds.), Classroom learning and the space of learning (pp. 3–40). Mahwah, NJ: Lawrence Erlbaum and Associates.
Matthews, M. (1994). Science teaching: the role of history and philosophy of science. Abingdon: Routledge.
Mayr, E. (1982). The growth of biological thought. Cambridge: Harvard University Press.
McComas, W. (2014). Nature of science in the science curriculum and in teacher education programs in the United States’ assessments. In M. Matthews (Ed.), International handbook of research in history, philosophy, and science teaching (pp. 1993–2024). Berlin: Springer.
McKagan, S. B., Perkins, K. K., & Wieman, C. E. (2008). Why we should teach the Bohr model and how to teach it effectively. Physical Review Special Topics – Physical Education Research, 4, 010103–1–10.
Miller, K. R. (2010). The flagellum unspun: the collapse of “irreversible complexity”. In A. Rosenberg & R. Arp (Eds.), Philosophy of biology: an anthology (pp. 439–448). Hoboken: Wiley-Blackwell.
National Reasearch Council. (1996). National science education standards. Washington D.C.: No. National Academy Press.
Nersessian, N. (1989) Conceptual change in science and in science education Synthese, Vol. 80, No. 1, History, Philosophy, and Science Teaching. Springer. 163–183.
NGSS Lead States (2013). Next Generation Science Standards: for states, by states. http://www.nextgenscience.org/search-standards. Accessed 01 Feb 2019.
Passmore, C., Gouvea, J. S., & Giere, R. (2014). Models in science and in learning science: focusing scientific practice on sense making. In M. Matthews (Ed.), International handbook of research in history, philosophy, and science teaching (pp. 1171–1202). Springer.
Popper, K. (1965). The logic of scientific discovery. New York, NY: Harper & Row.
Potochnick, A. (2017). Idealization and the aims of science. Chicago: The University of Chicago Press.
Savage, C. W. (1990). Preface in C.W. Savage (Ed.) Scientific theories. Minnesota Studies in the philosophy of science. Volume 14. University of Minnesota Press, vii–ix.
Shanks, N., & Joplin, K. (1999). Redundant complexity: a critical analysis of intelligent design in biochemistry. Philosophy of Science, 66(2), 268–282.
Skinner, M. (2015). Environmental epigenetics and a unified theory of the molecular aspects of evolution: a neo-Lamarckian concept that facilitates neo-Darwinian evolution. Genome Biology and Evolution, 7(5), 1296–1302.
Slater, M. (2008). How to justify teaching false science. Science Education, 92(3), 526–542.
Solomon, J., Duveen, J., Scot, L., & McCarthy, S. (1992). Teaching about the nature of science through history: action research in the classroom. Journal of Research in Science Teaching, 29(4), 409–421.
Swoyer, C. (1991). Structural representation and surrogative reasoning. Synthese, 87, 449–508.
van Fraassen, B. (1980). The scientific image. The Clanderon Library of logic and philosophy. Oxford: Oxford University Press.
Williams, M. (1982). The importance of prediction testing in evolutionary biology. Erkenntnis, 17(3), 291–306.
Wimsatt, W. (2006). False models as means to truer theories. In Re-engineering philosophy for limited beings: piecewise approximations to reality (pp. 94–132). Cambridge, MA: Harvard University Press.
Funding
This work was supported by the CSUN College of Humanities’ Faculty Fellowship (Fall 2016) and by the CSUN Faculty Development’s Probationary Faculty Support Award (Spring 2017).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The author declares no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bhakthavatsalam, S. The Value of False Theories in Science Education. Sci & Educ 28, 5–23 (2019). https://doi.org/10.1007/s11191-019-00028-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11191-019-00028-2