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Methodology and Epistemology of Computer Simulations and Implications for Science Education

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Abstract

While computer simulations are a key element in understanding and doing science today, their nature and implications for science education have not been adequately explored in the relevant literature. In this article, (1) we provide an analysis of the methodology and epistemology of computer simulations, aiming to contribute to a sound and comprehensive account of the nature of computer simulations in science education, and (2) examine certain implications for science education, particularly in terms of contemporary educational goals relating to scientific literacy. We describe methodological elements relating to processes, techniques, and skills required for the construction and evaluation of scientific simulations, and we discuss epistemological views of their reliability and epistemic status based on the relevant philosophical views. We then examine implications of these elements for the use of simulations and especially for supporting scientific practices in the classroom and the corresponding educational goals. Concretely, we compare educational simulations with those used in scientific research and with laboratory experiments, we discuss the question of the reliability of simulations used in teaching or in public information, and we give examples of their use for supporting NOS understanding and reasoning abilities. Finally, in the context of the philosophical discourse about scientific realism, we examine implications of the epistemology of models that concern the conception of the relation between scientific claims and the real world, which constitutes a fundamental epistemological basis for teaching the nature of science.

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Notes

  1. The research on modelling-based teaching suggests that modelling activities can enhance the acquiring of knowledge, abilities, and epistemologies that reflect real science (e.g. Schwarz & White 2005; Gilbert & Justi 2016) and notes the conditions for the success of model-based teaching, such as the the correct conception of models and modelling (e.g. Justi § Gilbert 2003; Oh & Oh 2011) and the appropriate learning environments, e.g. the use of simulation-based software (e.g. Dorn 1975; Andaloro et al. 1991; Mellar et al. 1994; Develaki 2017).

  2. The model-based view (in its original version, the semantic view) based its analysis on the central role of models in scientific research and highlighted many aspects of their nature and functions. It followed the statement view predominating at the beginning of the twentieth century, which sees scientific theories as sets of lingustic statements (principles, axioms) that directly descibe the real world (see, e.g. Losee 1990; Giere 1999). The model-based view sees scientific theories not as sets of statements but as sets of models that mediate the application of the theories to complex real-world systems, which means that the theories are absolutely valid only for the models, the simplified versions of the real systems, because the real systems are too complex and the theories too general and abstract for immediate application to them. (Some basic accounts of this view are given, e.g. by Suppe 1977; van Fraasen1980; Cartwright 1983; Morrison and Morgan 1999; Giere 1988, 1999; Knuuttila 2011.)

  3. Giere (1988, 1999) interpreted this conception for the case of classical mechanics: for example, the fundamental equation F = ma is valid for all the theoretical kinematic models of Newtonian mechanics, but the function of the force is specialized differently in each model, that is, as F = ct, or F = k/r2, or F = − Dx for the models of rectilinear and curvilinear motion and harmonic oscillation respectively. (F is the force, m is the mass, a is the acceleration, r is the radial distance, and k and D are constants).

  4. The core logic of forward Euler discretization is in general outline also followed, although with mathematically more advanced and complicated discretization schemes, in the solution of the very complex differential equations (which contain derivatives of the variables also with respect to space, i.e. they are partial differential equations) contained in the models of complex states/phenomena, such as turbulent flows in the atmosphere or the astrophysical plasma in solar flares or the progress of a forest fire.

  5. Scientific computer simulations are essentially written in the mathematical form described in the previous subsection ‘Numerical Solutions of Differential Equations—the Basic Idea and Structure’, which is very productive because the model can thus easily be improved or expanded with further processing. The mathematical form of programming (as in Figs. 1, 2, and 3) is more appropriate for senior high school and college level students. For younger students, graphically oriented computer-based programming software has been developed, which provides a microworld environment and a programming language that is either in text form (the program is then a set of textual orders) or in graphical form (in which case the program is a set of behaviour rules for the objects/items designated to represent the phenomenon or system modeled. For a comparative and research-based study of these programming environments (characteristics, ways of use, possibilities and limitations), see, e.g. in Louca 2004; Luca & Zacharia 2008; Sherrin et al. 1993.

  6. It has though been argued (e.g. Morrison 2009) that, like traditional experiments on material systems, computer simulations also have materiality, in that during the running of a simulation there is experimentation with a material entity, that is, with a programmed computer (the object of investigation in this case) that undergoes the intervention of the simulation program and falls into different states during the running of the simulation, yielding information about the evolution of the target system. (For more details as regards the explanations, the objections and the arguments relating to this view, see, e.g. Morrison 2009; Norton & Suppe 2001; Hughes 1999; Giere 2009; Winsberg 2010).

  7. Logical Empiricism interprets deductive reasoning in a formal way, considering that the agreement or non-agreement of the predictions derived from hypotheses and theories with the empirical data leads to the unequivocal acceptance or rejection of the theory. This is a strong scheme in logic and mathematics but is insufficient to interpret in all cases the complexity of judging and choosing the empirical theories of science (see, e.g. Duhem 1978; Lakatos 1974; Kuhn 1989; Giere 2001).

  8. Such successful applications and predictions, e.g. when they are based on theories about entities of the microscopic world, would then appear as a miracle if the existence of theoretical entities is not accepted: the non-miracles argument (see in Boyd 1983; Devitt 1991).

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Acknowledgements

The author thanks the anonymous reviewers for their helpful comments and suggestions, and Dr Heinz Isliker for useful discussions on computer simulations in scientific research.

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    Maria Develaki

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Develaki, M. Methodology and Epistemology of Computer Simulations and Implications for Science Education. J Sci Educ Technol 28, 353–370 (2019). https://doi.org/10.1007/s10956-019-09772-0

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