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Epistemological Issues Concerning Computer Simulations in Science and Their Implications for Science Education

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Abstract

Computers and simulations represent an undeniable aspect of daily scientific life, the use of simulations being comparable to the introduction of the microscope and the telescope, in the development of knowledge. In science education, simulations have been proposed for over three decades as useful tools to improve the conceptual understanding of students and the development of scientific capabilities. However, various epistemological aspects that relate to simulations have received little attention. Although the absence of this discussion is due to various factors, among which the relatively recent interest in the analysis of longstanding epistemological questions concerning the use of simulations, the inclusion of this discussion on the research agenda in science education appears relevant, if we wish to educate scientifically literate students in a vision of the nature of science closer to the work conducted by researchers today. In this paper we review some contemporary thoughts emerging from philosophy of science about simulations in science and set out questions that we consider of relevance for discussion in science education, in particular related with model-based learning and experimental work.

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Notes

  1. As in the literature, we shall also use the following terms interchangeably, throughout the text: simulations, computer simulations, computational models and computational modeling (that is, a model running on a digital computer, with special characteristics that differentiate it from more traditional modeling, a point discussed in Sect. 2.3).

  2. See de Jong and Njoo (1992); Hsu and Thomas (2002); Huppert and Lazarowitz (2002); Kaput (1995); Tao and Gunstone (1999); Zacharia (2003); Zacharia and Anderson (2003) and a recent critical review on the topic by Smetana and Bell (2012).

  3. Although this, as Humphreys (2004) has highlighted, is no slight matter: a great part of the success of physics is due to the development of better methods of calculation.

  4. We recall that the term “computer” referred to people whose job it was to make calculations, in general women; it was only later that it came to refer to an electronic device (Galison 1996).

  5. This definition includes the simulation of mathematical objects, because B is not required to be real.

  6. In the Monte Carlo method, a formal isomorphism is established between differential equations with certain equations in probabilistic theory, using the probabilistic relations to resolve the differential equations and replacing the calculation of all combinatorial possibilities for an entire sequence of events, by an estimation of the results obtained for a “sample” of attempts. The general idea of the finite-element method is the division of a continuum by a series of points known as nodes into a set of small interconnected elements, based on the idea that the equations that govern the behaviour of the continuum will also govern that of the element. Thus, it is possible to pass from a continuous system (infinite degrees of freedom), governed by one or by a system of differential equations, to a system with finite degrees of freedom, the behaviour of which is modelled by a system of either linear or non-linear equations. Visually, it is like dividing the space into a reticular mesh, seeking the solution at the points that are determined by the mesh.

  7. The series of dynamic images constitute the animations.

  8. It is important to highlight that we are focusing only on the debate surrounding the epistemology of science. The lively discussion among mathematicians concerning epistemological issues (among others, the notion of proof) relating to the use of computers is beyond the scope of this study.

  9. Similar ideas have been proposed by some theoreticians in the field of quantum information (Wheeler 1990; Brukner and Zeilinger 2005).

  10. The status of models in relation to theory and experimentation is not, in fact, an epistemological problem specific to simulations, but a general problem (Frigg and Reiss 2009); however the reappraisal of models in relation to theory coincided with the generalization of the use of simulations in all scientific areas, which may not be coincidental.

  11. Durán (2013, pp. 107–108) divides the systematic errors in simulations into three kinds: physical errors (related to the malfunctioning of any physical component of the computer), logical errors (related to coding errors or a part of a faulty compiler or a computer language, leading to instabilities in the behavior of the computer program) and representational errors (the most common ones, located at the level of the mathematical model or the specification, as, for example, a grid too big for precise results, bad approximations, unacceptable mean square errors, etc.).

  12. The preferential vocabulary among those that use simulations is full of experimental metaphors.

  13. Theoretical knowledge to which we refer does not necessarily imply having a well-established theory of the phenomenological dynamics of the system of interest, but having some knowledge about its dynamics, as in the case of simulations in the area of social sciences.

  14. In a recent work, Durán (2013) points that, although neither software nor hardware can be fully verified nor validated, researchers are developing methods for reducing the possibility of errors in order to increase the credibility of the model.

  15. Other programs that allow modelling in K12 science teaching are LOGO (Papert 1980); Model-It (Jackson et al. 1996); ThinkerTools (White 1984); and BioLogicaTM (Buckley et al. 2004).

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The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.

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  1. Dpto. de Didácticas Específicas, Universidad de Burgos, Burgos, Spain

    Ileana M. Greca

  2. Fac. de Cs. Exactas - ECienTec, Universidad Nacional del Centro de la Provincia de Buenos Aires, Pinto 399 - (7000) Tandil, Buenos Aires, Argentina

    Eugenia Seoane

  3. Fac. de Cs. Exactas - ECienTec, Universidad Nacional del Centro de la Provincia de Buenos Aires - CONICET, Pinto 399 - (7000) Tandil, Buenos Aires, Argentina

    Irene Arriassecq

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Greca, I.M., Seoane, E. & Arriassecq, I. Epistemological Issues Concerning Computer Simulations in Science and Their Implications for Science Education. Sci & Educ 23, 897–921 (2014). https://doi.org/10.1007/s11191-013-9673-7

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