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Features of Modeling-Based Abductive Reasoning as a Disciplinary Practice of Inquiry in Earth Science

Cases of Novice Students Solving a Geological Problem

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Abstract

The purpose of this study was to investigate the features of modeling-based abductive reasoning as a disciplinary practice of inquiry in the domain of earth science. The study was based on an undergraduate course of a university of education, Korea, offered for preservice elementary teachers majoring in science as their specialty. The course enrollees participated in an inquiry project in which they were asked to abductively generate models representing past geologic events in order to explain how two units in a sedimentary rock outcrop had been formed. Three students were selected as major informants for the study, and multiple types of qualitative data were collected, including the students’ sketchbook records, audio-recording of whole-class presentation and discussion, and interviews with the students. The data were analyzed according to the method of analytical induction, which yielded three assertions as the findings of the study. First, while an explanatory model can be generated by combining resource models, the combination of resource models does not necessarily result in a scientifically sound explanatory model. Second, a systemic approach can help activate a critical resource model which can in turn lead to a scientifically sound explanatory model. Third, simulations with a model can enhance the plausibility of the model. Based on these findings, causal combinations of resource models, a systemic approach to earth scientific problem-solving, and simulations with a model are suggested and discussed as implications for science education and relevant research.

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References

  • Ault, C. R., Jr. (1998). Criteria of excellence for geological inquiry: the necessity of ambiguity. Journal of Research in Science Teaching, 35, 189–212.

    Article  Google Scholar 

  • Ault, C. R., Jr. (2008). Achieving querencia: integrating a sense of place with disciplined thinking. Curriculum Inquiry, 38(5), 605–637.

    Article  Google Scholar 

  • Ault, C. R., Jr., & Dodick, J. (2010). Tracking the footprint puzzle: the problematic persistence of science-as-process in teaching the nature and culture of science. Science Education, 94, 1092–1122.

    Article  Google Scholar 

  • Bailer-Jones, D. M. (2002). Scientists’ thoughts on scientific models. Perspectives on Science, 10(3), 275–301.

    Article  Google Scholar 

  • Baker, V. R. (1996). Hypotheses and geomorphological reasoning. In B. L. Rhoads & C. E. Thorn (Eds.), The scientific nature of geomorphology (pp. 57–85). New York: John Wiley & Sons.

    Google Scholar 

  • Ben-Zvi-Assaraf, O., & Orion, N. (2005). Development of system thinking skills in the context of earth system education. Journal of Research in Science Teaching, 42(5), 518–560.

    Article  Google Scholar 

  • Ben-Zvi-Assaraf, O., & Orion, N. (2010). Four case studies, six years later: developing system thinking skills in junior high school and sustaining them over time. Journal of Research in Science Teaching, 47(10), 1253–1280.

    Article  Google Scholar 

  • Brown, D. E., & Hammer, D. (2013). Conceptual change in physics. In S. Vosniadou (Ed.), International handbook of research on conceptual change (2nd ed., pp. 127–154). New York: Routledge.

    Google Scholar 

  • Cheng, M.-F., & Brown, D. E. (2010). Conceptual resources in self-developed explanatory models: the importance of integrating conscious and intuitive knowledge. International Journal of Science Education, 32(17), 2367–2392.

    Article  Google Scholar 

  • Chevallard, Y., & Bosch, M. (2014). Didactic transposition in mathematics education. In S. Lerman (Ed.), Encyclopedia of mathematics education (pp. 170–174). Berlin: Springer.

    Google Scholar 

  • Cleland, C. E. (2011). Prediction and explanation in historical natural science. The British Journal for the Philosophy of Science, 62(3), 551–582.

    Article  Google Scholar 

  • Clement, J. J. (2008). Creative model construction in scientists and students: the role of imagery, analogy, and mental simulation. Dordrecht: Springer.

    Book  Google Scholar 

  • Clement, J. J. (2013). Roles for explanatory models and analogies in conceptual change. In S. Vosniadou (Ed.), International handbook of research on conceptual change (2nd ed., pp. 412–446). New York: Routledge.

    Google Scholar 

  • Clement, J. J., & Steinberg, M. S. (2002). Step-wise evolution of mental models of electric circuits: a “learning-aloud” case study. The Journal of the Learning Sciences, 11(4), 389–452.

    Article  Google Scholar 

  • Craig, D. L., Nersessian, N. J., & Catrambone, R. (2002). Perceptual simulation in analogical problem solving. In L. Magnani & N. J. Nersessian (Eds.), Model-based reasoning: science, technology, values (pp. 167–189). New York: Kluwer Academic/Plenum Publishers.

    Chapter  Google Scholar 

  • DeBoer, G. E. (2004). Historical perspectives on inquiry teaching in schools. In L. B. Flick & N. G. Lederman (Eds.), Scientific inquiry and nature of science (pp. 17–35). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Dodick, J., & Orion, N. (2003). Geology as an historical science: its perception within science and the education system. Science & Education, 12, 197–211.

    Article  Google Scholar 

  • Dodick, J., Argamon, S., & Chase, P. (2009). Understanding scientific methodology in the historical and experimental science via language analysis. Science & Education, 18, 985–1004.

    Article  Google Scholar 

  • Elby, A., & Hammer, D. (2010). Epistemological resources and framing: a cognitive framework for helping teachers interpret and respond to their students’ epistemologies. In L. D. Bendixen & F. C. Feucht (Eds.), Personal epistemology in the classroom: theory, research, and implications for practice (pp. 409–434). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Frodeman, R. (1995). Geological reasoning: geology as an interpretive and historical science. GSA Bulletin, 107(8), 960–968.

    Article  Google Scholar 

  • Giere, R. N. (1999). Science without laws. Chicago, IL: University of Chicago Press.

    Google Scholar 

  • Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: strategies for qualitative research. New Brunswick, NJ: Aldine Transaction.

    Google Scholar 

  • Gray, R. (2014). The distinction between experimental and historical sciences as a framework for improving classroom inquiry. Science Education, 98, 327–341.

    Article  Google Scholar 

  • Haig, B. D. (2005). An abductive theory of scientific method. Psychological Methods, 10(4), 371–388.

    Article  Google Scholar 

  • Hammer, D. (2004). The variability of student reasoning, lecture 3: manifold cognitive resources. In E. Redish & M. Vicentini (Eds.), Proceedings of the Enrico Fermi Summer School, Course CLVI (pp. 321–340). Bologna, Italy: Italian Physical Society.

    Google Scholar 

  • Hammer, D., Elby, A., Scherr, R. E., & Redish, E. F. (2005). Resources, framing, and transfer. In J. Mestre (Ed.), Transfer of learning from a modern multidisciplinary perspective (pp. 89–120). Greenwich, CT: Information Age Publishing.

    Google Scholar 

  • Hanson, N. R. (1958). Patterns of discovery. London: Cambridge University Press.

    Google Scholar 

  • Hmelo-Silver, C. E., & Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 28, 127–138.

    Article  Google Scholar 

  • Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. The Journal of the Learning Sciences, 16(3), 307–331.

    Article  Google Scholar 

  • Hsu, Y.-S., Lin, L.-F., Wu, H.-K., Lee, D.-Y., & Hwang, F.-K. (2012). A novice-expert study of modeling skills and knowledge structures about air quality. Journal of Science Education and Technology, 21(5), 588–606.

    Article  Google Scholar 

  • Jaber, L. Z., & Hammer, D. (2016). Engaging in science: a feeling for the discipline. The Journal of the Learning Sciences, 25(2), 156–202.

    Article  Google Scholar 

  • Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: scientific and educational importance and implications for the learning science. The Journal of the Learning Sciences, 15(1), 11–34.

    Article  Google Scholar 

  • Kee, W.-S., Kim, B. C., & Lee, Y.-N. (2006). Sedimentary environments and structural evolution of the cretaceous Namyang Basin, Korea. Journal of the Geological Society of Korea, 42(3), 329–351 in Korean with an English abstract.

    Google Scholar 

  • Kleinhans, M. G., Buskes, C. J. J., & de Regt, H. W. (2005). Terra incognita: explanation and reduction in earth science. International Studies in the Philosophy of Science, 19(3), 289–317.

    Article  Google Scholar 

  • Koponen, I. T. (2007). Models and modelling in physics education: a critical re-analysis of philosophical understandings and suggestions for revisions. Science & Education, 16, 751–773.

    Article  Google Scholar 

  • Krogh, L. B., & Nielsen, K. (2013). Introduction: how science works – and how to teach it. Science & Education, 22, 2055–2065.

    Article  Google Scholar 

  • Lehrer, R., & Schauble, L. (2012). Seeding evolutionary thinking by engaging children in modeling its foundations. Science Education, 96, 701–724.

    Article  Google Scholar 

  • Magnani, L. (2001). Abduction, reason, and science: process of discovery and explanation. New York: Kluwer Academic/Plenum Publishers.

    Book  Google Scholar 

  • Magnani, L. (2002). Epistemic mediators and model-based discovery in science. In L. Magnani & N. J. Nersessian (Eds.), Model-based reasoning: science, technology, values (pp. 305–329). New York: Kluwer Academic/Plenum Publishers.

    Chapter  Google Scholar 

  • Magnani, L. (2004). Model-based and manipulative abduction in science. Foundation of Science, 9, 219–247.

    Google Scholar 

  • Magnani, L. (2014). Understanding abduction: inference, perception, and instinct. In L. Magnani (Ed.), Model-based reasoning in science and technology: theoretical and cognitive issues (pp. 173–205). Berlin: Springer.

    Chapter  Google Scholar 

  • National Research Council. (2012). A framework for K-12 science education: practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press.

    Google Scholar 

  • Nersessian, N. J. (2008). Creating scientific concepts. Cambridge, MA: Massachusetts Institute of Technology.

    Book  Google Scholar 

  • NGSS Lead States. (2013). Next generation science standards: for states, by states. Washington, DC: The National Academies Press.

    Google Scholar 

  • Oh, P. S. (2010). How can teachers help students formulate scientific hypotheses? Some strategies found in abductive inquiry activities of earth science. International Journal of Science Education, 32(4), 541–560.

    Article  Google Scholar 

  • Oh, P. S. (2011). Characteristics of abductive inquiry in earth science: an undergraduate case study. Science Education, 95, 409–430.

    Article  Google Scholar 

  • Oh, P. S. (2016). Roles of models in abductive reasoning: a schematization through theoretical and empirical studies. Journal of the Korean Association for Science Education, 36(4), 551–561 In Korean with an English abstract.

    Article  Google Scholar 

  • Oh, P. S. (2017). The roles and importance of critical evidence (CE) and critical resource models (CRMs) in abductive reasoning for earth scientific problem solving. Journal of Science Education, 41(3), 426–446 In Korean with an English abstract.

    Article  Google Scholar 

  • Oh, P. S., & Oh, S. J. (2011). What teachers of science need to know about models: an overview. International Journal of Science Education, 33(8), 1109–1130.

    Article  Google Scholar 

  • Oh, P. S., & Oh, S. J. (2013). Modeling sunspots. The Science Teacher, 80(6), 51–56.

    Article  Google Scholar 

  • 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). Dordrecht: Springer.

    Google Scholar 

  • Raia, F. (2005). Students’ understanding of complex dynamic systems. Journal of Geoscience Education, 53(3), 297–308.

    Article  Google Scholar 

  • Rhoads, B. L., & Thorn, C. E. (1993). Geomorphology as science: the role of theory. Geomorphology, 6, 287–307.

    Article  Google Scholar 

  • Roth, W.-M. (2002). From action to discourse: the bridging function of gesture. Cognitive Systems Research, 3, 535–554.

    Article  Google Scholar 

  • Santini, J., Bloor, T., & Sensevy, G. (2018). Modeling conceptualization and investigating teaching effectiveness: a comparative case study of earthquakes studied in classroom practice and in science. Science & Education, 27, 921–961.

    Article  Google Scholar 

  • Schumm, S. A. (1991). To interpret the earth: ten ways to be wrong. Cambridge: Cambridge University Press.

    Google Scholar 

  • Simpson, G. G. (1963). Historical science. In C. C. Albritton Jr. (Ed.), The fabric of geology (pp. 24–48). Reading, MA: Addison-Wesley.

    Google Scholar 

  • Singer, M., Radinsky, J., & Goldman, S. R. (2008). The role of gesture in meaning construction. Discourse Processes, 45, 365–386.

    Article  Google Scholar 

  • Stillings, N. (2012). Complex systems in the geosciences and in geoscience learning. In K. A. Kastens & C. A. Manduca (Eds.), Earth and mind II: a synthesis of research on thinking and learning in the geoscience (pp. 97–111). Boulder, CO: The Geological Society of America.

    Chapter  Google Scholar 

  • Sung, J. Y., & Oh, P. S. (2018). Sixth grade students’ content-specific competencies and challenges in learning the seasons through modeling. Research in Science Education, 48(4), 839–864.

    Article  Google Scholar 

  • Taylor, S. J., Bogdan, R., & DeVault, M. L. (2016). Introduction to qualitative research methods: a guidebook and resource (4th ed.). Hoboken, NJ: Wiley.

    Google Scholar 

  • Thagard, P. (2012). The cognitive science of science: explanation, discovery, and conceptual change. Cambridge, MA: The MIT Press.

    Book  Google Scholar 

  • Tytler, R., & Peterson, S. (2003). Tracing young children’s scientific reasoning. Research in Science Education, 33(4), 433–465.

    Article  Google Scholar 

  • van der Valk, T., van Driel, J. H., & de Vos, W. (2007). Common characteristics of models in present-day scientific practice. Research in Science Education, 37, 469–488.

    Article  Google Scholar 

  • Visintainer, T., & Linn, M. (2015). Sixth-grade students’ progress in understanding the mechanisms of global climate change. Journal of Science Education and Technology, 24, 287–310.

    Article  Google Scholar 

  • von Engelhardt, W., & Zimmermann, J. (1988). Theory of earth science (trans: L. Fisher). Cambridge: Cambridge University Press.

    Google Scholar 

  • Walton, D. (2004). Abductive reasoning. Tuscaloosa, AL: The University of Alabama Press.

    Google Scholar 

  • Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a firefly: Learning biology through constructing and testing computational theories – an embodied modeling approach. Cognition and Instruction, 24(2), 171–209.

    Article  Google Scholar 

  • Xiang, L., & Passmore, C. (2015). A framework for model-based inquiry through agent-based programming. Journal of Science Education and Technology, 24, 311–329.

    Article  Google Scholar 

  • Yoon, S. A., & Hmelo-Silver, C. (2017). Introduction to special issue: models and tools for systems learning and instruction. Instructional Science, 45, 1–4.

    Article  Google Scholar 

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Correspondence to Phil Seok Oh.

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Oh, P.S. Features of Modeling-Based Abductive Reasoning as a Disciplinary Practice of Inquiry in Earth Science. Sci & Educ 28, 731–757 (2019). https://doi.org/10.1007/s11191-019-00058-w

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