Science & Education

, Volume 23, Issue 2, pp 465–484 | Cite as

Determinism and Underdetermination in Genetics: Implications for Students’ Engagement in Argumentation and Epistemic Practices

  • María Pilar Jiménez-Aleixandre


In the last two decades science studies and science education research have shifted from an interest in products (of science or of learning), to an interest in processes and practices. The focus of this paper is on students’ engagement in epistemic practices (Kelly in Teaching scientific inquiry: Recommendations for research and implementation. Sense Publishers, Rotterdam, pp 99–117, 2008), or on their practical epistemologies (Wickman in Sci Educ 88(3):325–344, 2004). In order to support these practices in genetics classrooms we need to take into account domain-specific features of the epistemology of genetics, in particular issues about determinism and underdetermination. I suggest that certain difficulties may be related to the specific nature of causality in genetics, and in particular to the correspondence between a given set of factors and a range of potential effects, rather than a single one. The paper seeks to bring together recent developments in the epistemology of biology and of genetics, on the one hand, with science education approaches about epistemic practices, on the other. The implications of these perspectives for current challenges in learning genetics are examined, focusing on students’ engagement in epistemic practices, as argumentation, understood as using evidence to evaluate knowledge claims. Engaging in argumentation in genetics classrooms is intertwined with practices such as using genetics models to build explanations, or framing genetics issues in their social context. These challenges are illustrated with studies making part of our research program in the USC.


Science Learning Bovine Spongiform Encephalopathy Scrapie Genetic Knowledge Genetic Determinism 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Work supported by the Spanish Ministry of Science and Innovation (Ministerio de Ciencia e Innovación), code EDU2009-13890-C02-01. The author gratefully acknowledges the work from the doctoral dissertations of Ánxela Bugallo-Rodríguez, Marta Federico-Agraso and Blanca Puig, discussed in the paper. Thanks also for the helpful suggestions of Kostas Kampourakis, Thomas Reydon and Science & Education’s anonymous reviewers to the first draft.


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© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.University of Santiago de CompostelaSantiago de CompostelaSpain

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