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Using Computer Simulations for Promoting Model-based Reasoning

Epistemological and Educational Dimensions

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Abstract

Scientific reasoning is particularly pertinent to science education since it is closely related to the content and methodologies of science and contributes to scientific literacy. Much of the research in science education investigates the appropriate framework and teaching methods and tools needed to promote students’ ability to reason and evaluate in a scientific way. This paper aims (a) to contribute to an extended understanding of the nature and pedagogical importance of model-based reasoning and (b) to exemplify how using computer simulations can support students’ model-based reasoning. We provide first a background for both scientific reasoning and computer simulations, based on the relevant philosophical views and the related educational discussion. This background suggests that the model-based framework provides an epistemologically valid and pedagogically appropriate basis for teaching scientific reasoning and for helping students develop sounder reasoning and decision-taking abilities and explains how using computer simulations can foster these abilities. We then provide some examples illustrating the use of computer simulations to support model-based reasoning and evaluation activities in the classroom. The examples reflect the procedure and criteria for evaluating models in science and demonstrate the educational advantages of their application in classroom reasoning activities.

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Notes

  1. Causal-mechanistic reasoning explains phenomena on the basis of causal relations between their key entities and characteristics, i.e., on the basis of possible mechanisms underlying the phenomena (see, e.g., White 1993; Besson 2010; Zangori et al. 2015). Theoretical-mathematical reasoning means explaining phenomena based on the principles and basic equations of the relevant theories about them (see, e.g., Giere 1999b, 2010; Boon 2011). (Causal-mechanistic reasoning and theoretical-mathematical reasoning appear to be the two basic types of scientific reasoning (Boon 2011)) Analogical reasoning is based on the use of analogies (see, e.g., Gilbert & Justi 2016), and systems thinking is based on considering the systemic character and interaction of particular (complex) systems (see, e.g., Joyner et al. 2013).

  2. Induction in empirical sciences is the inference of general conclusions about a phenomenon from observations and experiments with a limited number of systems (e.g., inferring the “laws” of gases from relevant observations/experiments with some gases). Deduction means deriving specific proposals or predictions about a phenomenon from a general principle/hypothesis/model, which are then compared to empirical data to check the validity of the initial principle/hypothesis/model (e.g., the value of the lunar period, which is calculated (deduced) from the law of universal gravitation, is compared to the value given by observations with a telescope) (see, e.g., Popper 1959). Abduction is the process of generating hypotheses in which a known successful explanation for one situation is transferred and applied to a new situation as a tentative explanation of it (see, e.g., Lawson 2003, 2009; Kwon et al. 2006).

  3. Τhe semantic view (or non-statement or predicate or model-theoretic view (see Suppe 1977; Ariza et al. 2016), understands theories not as sets of (linguistic) statements that directly describe the real world, as in the statement view, but as sets of models, non-linguistic entities that mediate the application of theories to the real complex systems. In the semantic view, however, so named in contrast to the linguistic-syntactic approach to theories, the initial conceptions of models and their relation to theory were also expressed in the formal logic language of the statement view. These conceptions then became progressively more concrete by interpretations of models and modeling from a perspective closer to scientific practice developed in the philosophy of science (mainly by Giere, e.g., Giere 1988, 1999a, 2010), the sociology of science, and the cognitive sciences. The term “model-based view” is now used more broadly for this version of the original semantic view.

  4. A considerable body of science education literature has been published, arguing the potential of a science education resting on a model-based foundation for the achievement of contemporary educational aims and noting the conditions for the success of model-based teaching, such as the correct conceptualization of the models and modeling and the necessary revisions to the curricula, teachers’ education and textbooks (e.g., Ogborn 1994; Adúriz-Bravo & Izquierdo-Aymerich 2005; Matthews 2007; Develaki 2007; Adúriz-Bravo 2013; Ariza et al. 2016).

  5. Giere et al. (2006) cite the case of the model for the structure of DNA to exemplify the cases of positive and negative evidence and their role in the assessment of a model’s suitability. Specifically, the initial triple helix model was rejected because it did not correctly predict the amount of water that the DNA molecules were observed to retain in Franklin’s experiments with DNA samples (negative evidence). The double helix model that was proposed in its stead was selected not only because it gave a correct prediction for the quantity of water but chiefly because it correctly predicted the x-ray image of DNA (positive evidence), which could not be predicted with any other possible alternative model, while the prediction for the correct quantity of water could have been extracted from alternative models (e.g., from appropriately adjusted triple helix models).

  6. For moderate velocities, the frictional resistance of the air is F fr = 1/2ρcAv 2, where ρ is the density of the air (1.29 kg/m3), A is the maximum cross-section of the falling body perpendicular to the air flow, and c is the coefficient of friction of the air, which depends on the shape of the body.

  7. The Framework for K-12 Science Education and the NGSS articulate science education in three dimensions which should be integrated into students’ learning experiences and provide the basis for student assessment, expressed as ‘Performance Expectations’ (PES) in the NGSS. These dimensions are: some core ideas (as regards content knowledge), crosscutting concepts (for the linking of knowledge), and basic science and engineering practices. The transformation and implementation of the (PES) at classroom level requires additional guidance for teachers and the provision of concrete examples, strategies and instructional curricular materials (see Krajcik et al. 2014).

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Acknowledgements

The author thanks the anonymous reviewers for their substantial comments and feedback on earlier versions of the article. The author also thanks Heinz Isliker for useful discussions on computer simulations in scientific research.

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    Maria Develaki

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Develaki, M. Using Computer Simulations for Promoting Model-based Reasoning. Sci & Educ 26, 1001–1027 (2017). https://doi.org/10.1007/s11191-017-9944-9

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