Abstract
In light of recent emphasis on K-12 scientific modeling (e.g., Duschl et al. 2007, Taking science to school: learning and teaching science in grades K-8; Lehrer and Schauble 2015, Handbook of child psychology and developmental science; NRC 2012, A framework for K-12 science education: practices, crosscutting concepts, core ideas), it is important to understand how students’ models and beliefs about modeling shape shared classroom practices, and how, in turn, shared classroom practices influence individual students’ practices. We use co-operative action to consider the ways in which sedimented practices and artifacts become part of the substrate for students’ later actions (Goodwin 2017, Co-operative action (learning in doing: social, cognitive, and computational perspectives)). Lemke (Mind, Culture, and Activity 7(4):273–290, 2000) and Goodwin (2017) describe and provide illustrative examples of the accumulative nature of transformation of materials and practices. However, these examples range in scale from minutes to hours, and there is less guidance about applying these perspectives to consider the transformation of practices longitudinally. In this study, we find that co-operative action is a powerful framework for analyzing classroom practices. We show that new practices rippled through the classroom along three dimensions: (1) immediate transformation, (2) longitudinal reuse and transformation, and (3) transformation through interaction with other practices. These findings confirm that students’ models and practices become part of the substrate for later enactments of practice, and that these practices develop co-operatively and accumulatively within the classroom. Thus, co-operative action provides a unique lens on student progress that complements learning progressions by considering the social dimension of the development of practices. We propose that the extent to which students’ practices are able to sediment into the substrate of shared classroom practices is an important indicator of the health of the classroom as a scientific community.
Similar content being viewed by others
References
Baek, H., & Schwarz, C. V. (2015). The influence of curriculum, instruction, technology, and social interactions on two fifth-grade students’ epistemologies in modeling throughout a model-based curriculum unit. Journal of Science Education and Technology, 24, 216–233.
Berland, L., & Crucet, K. (2016). Epistemological trade-offs: accounting for context when evaluating epistemological sophistication of student engagement in scientific practices. Science Education, 100(1), 5–29.
Berland, L. K., & Hammer, D. (2011). Framing for scientific argumentation. Journal of Research in Science Teaching, 49(1), 68–94.
Berland, L. K., & McNeill, K. L. (2010). A learning progression for scientific argumentation: understanding student work and designing supportive instructional contexts. Science Education, 94, 765–793.
Berland, L. K., Schwarz, C. V., Krist, C., Kenyon, L., Lo, A. S., & Reiser, B. J. (2016). Epistemologies in practice: making scientific practices meaningful for students. Journal of Research in Science Teaching, 53(7), 1082–1112.
Berlin, B., & Kay, P. (1967). University and evolution of basic color terms. University of California, Laboratory for Language-Behavior Research.
Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13.
Duschl, R. (2008). Science education in three-part harmony: balancing conceptual, epistemic, and social learning goals. Review of Research in Education, 32(1), 268–291.
Duschl, R. A., Schweingruber, H. A., & Shouse, A. W. (2007). Taking science to school: learning and teaching science in grades K-8. Washington, DC: National Academies Press.
Elby, A., & Hammer, D. (2001). On the substance of a sophisticated epistemology. Science Education, 85, 554–567.
Erduran, S., & Jiménez-Aleixandre, M. P. (2007). Argumentation in science education: perspectives from classroom-based research. Dordrecht, Netherlands: Springer.
Ford, M. J. (2015). Educational implications of choosing practice to describe science in the next generation science standards. Science Education, 99(6), 1041–1048.
Gogolin, S., & Krüger, D. (2018). Students' understanding of the nature and purpose of models. Journal of Research in Science Teaching, 55(9), 1313–1338.
Goodwin, C. (2017). co-operative action (learning in doing: social, cognitive, and computational perspectives). Cambridge: Cambridge University Press.
Gotwals, A. W., & Alonzo, A. C. (2012). Learning progressions in science: current challenges and future directions. Rotterdam, The Netherlands: Sense Publishers.
Gouvea, J., & Passmore, C. (2017). Models of versus models for. Science & Education, 26(1–2), 49–63.
Hall, R., & Horn, I. S. (2012). Talk and conceptual change at work: adequate representation and epistemic stance in a comparative analysis of statistical consulting and teacher workgroups. Mind, Culture, and Activity, 19(3), 240–258.
Hall, R., & Jurow, A. S. (2015). Changing concepts in activity: descriptive and design studies of consequential learning in conceptual practices. Educational Psychologist, 50(3), 173–189.
Hall, R., Wieckert, K., & Wright, K. (2010). How does cognition get distributed? Case studies of making concepts general in technical and scientific work. In M. T. Banich & D. Caccamise (Eds.), Generalization of knowledge: multidisciplinary perspectives (pp. 225–246). London: Psychology Press.
Hutchins, E. (1995). Cognition in the wild. Cambridge, MA: MIT press.
Jurow, A. S., Hall, R., & Ma, J. Y. (2008). Expanding the disciplinary expertise of a middle school mathematics classroom: re-contextualizing student models in conversations with visiting specialists. The Journal of the Learning Sciences, 17(3), 338–380.
Keifert, D. T., & Marin, A. M. (2018). A commentary on Charles Goodwin’s co-operative action for learning scientists. Retrieved April 30, 2018, from http://cognitionandinstruction.com/goodwinsco-operativeaction/.
Lehrer, R., & Schauble, L. (2006). Scientific thinking and science literacy. In Handbook of Child Psychology (pp. 153–196). Hoboken, NJ: Wiley.
Lehrer, R., & Schauble, L. (2015). The development of scientific thinking. In R. M. Lerner (Ed.), Handbook of child psychology and developmental science (pp. 1–44). Hoboken, NJ: Wiley.
Lehrer, R., Carpenter, S., Schauble, L., & Putz, A. (2000). Designing classrooms that support inquiry. In J. Minstrell & E. van Zee (Eds.), Inquiring into inquiry learning and teaching in science (pp. 80–99). Washington, DC: American Association for the Advancement of Science.
Lemke, J. L. (1995). Taking towers, making withs. Paper presented at National Association for Research in Science Teaching, San Francisco, April 1995. VA: Arlington.
Lemke, J. L. (1998). Multimedia demands of the scientific curriculum. Linguistics and Education, 10(3), 1–25.
Lemke, J. L. (2000). Across the scales of time: artifacts, activities, and meanings in ecosocial systems. Mind, Culture, and Activity, 7(4), 273–290.
Lemke, J. L. (2001). Articulating communities: sociocultural perspectives on science education. Journal of Research in Science Teaching, 38(3), 296–316.
Manz, E. (2012). Understanding the codevelopment of modeling practice and ecological knowledge. Science Education, 96(6), 1071–1105 Mahwah, New Jersey: Lawrence Erlbaum Associates.
Manz, E. (2015). Representing student argumentation as functionally emergent from scientific activity. Review of Educational Research 85(4), 553–590.
Maskiewicz, A. C., & Winters, V. A. (2012). Understanding the co-construction of inquiry practices: a case study of a responsive teaching environment. Journal of Research in Science Teaching, 49(4), 429–464.
McNeill, K. L., & Pimentel, D. S. (2009). Scientific discourse in three urban classrooms: the role of the teacher in engaging high school students in argumentation. Science Education, 27(8), 203–229.
National Research Council. (2012). A framework for K-12 science education. In A framework for K-12 science education: practices, crosscutting concepts, core ideas. Washington, DC: National Academies Press.
Nersessian, N. (2017). Hybrid devices: embodiments of culture in biomedical engineering. In K. Chemla & E. F. Keller (Eds.), Culture without culturalism (pp. 117–144). Durham, NC: Duke University Press.
Nerssessian, N. (1992). How do scientists think? Capturing the dynamics of conceptual changes in science. In R. Giere (Ed.), The Minnesota studies in the philosophy of science (Vol. XV, pp. 3–44). Minneapolis, MN: University of Minnesota Press.
NGSS Lead States. (2013). Next generation science standards: for states, by states. The Next Generation Science Standards. Washington, DC. Retrieved from www.nextgenscience.org/next-generation-science-standards.
Oliveira, A. W., Akerson, V. L., Colak, H., Pongsanon, K., & Genel, A. (2012). The implicit communication of nature of science and epistemology during inquiry discussion. Science Education, 96(4), 652–684.
Östman, L., & Wickman, P.-O. (2014). A pragmatic approach on epistemology, teaching, and learning. Science Education, 98(3), 375–382.
Pahl, K. (2003). Artefacts, timescales and kinetic design: the semiotic affordances of popular culture in children’s home communicative practices’ (pp. 2002–2004) Presented at the ESRC Research Seminar Series, Children’s Literacy and Popular Culture.
Pierson, A. E., & Clark, D. B. (2018). Engaging students in computational modeling: The role of an external audience in shaping conceptual learning, model quality, and classroom discourse. Science Education, 102(6), 1336–1362.
Pierson, A. E., Clark, D. B., & Sherard, M. K. (2017). Learning progressions in context: Tensions and insights from a semester‐long middle school modeling curriculum. Science Education, 101(6), 1061–1088.
Pickering, A. (1995). The mangle of practice: time, agency and science. In American journal of sociology. Chicago: University of Chicago Press.
Rouse, J. (2007). Practice theory. In S. Turner & M. Risjord (Eds.), Handbook of the philosophy of science Vol 15: philosophy of anthropology and sociology (pp. 630–681). Dordrecht: Elsevier.
Rowsell, J., & Pahl, K. (2007). Sedimented identities in texts: instances of practice. Reading Research Quarterly, 42(3), 388–404.
Sandoval, W. (2014). Conjecture mapping: an approach to systematic educational design research. Journal of the Learning Sciences, 23(1), 18–36.
Saxe, G. B. (2012). Cultural development of mathematical ideas: Papua New Guinea studies. New York, NY: Cambridge University Press.
Scherr, R. E., & Hammer, D. (2009). Student behavior and epistemological framing: examples from collaborative active-learning activities in physics. Cognition and Instruction, 27(2), 147–174.
Schwarz, C. V., & White, B. Y. (2005). Meta-modeling knowledge: developing students’ understanding of scientific modeling. Cognition and Instruction, 23(2), 165–205.
Schwarz, C., Reiser, B. J., Acher, A., Kenyon, L., & Fortus, D. (2012). MoDeLS: Challenges in defining a learning progression for scientific modeling. In A. Alonzo & A. W. Gotwals (Eds.), Learning progressions in science: current challenges and future directions. Rotterdam: Sense Publishers.
Strauss, A., & Corbin, J. (1990). Basics of qualitative research (Vol. 15). Newbury Park, CA: Sage.
Funding
This study was supported by the National Science Foundation through grants 1119290 and 1742138 to Vanderbilt University.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Disclaimer
The opinions expressed are those of the authors and do not represent views of the National Science Foundation.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pierson, A.E., Clark, D.B. Sedimentation of Modeling Practices. Sci & Educ 28, 897–925 (2019). https://doi.org/10.1007/s11191-019-00050-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11191-019-00050-4