Skip to main content

Future Research in Learning with, Through and from Scientific Representations

  • Chapter
  • First Online:
Theorizing the Future of Science Education Research

Part of the book series: Contemporary Trends and Issues in Science Education ((CTISE,volume 49))

Abstract

Representations are now broadly recognized as crucial tools students need to use to become scientifically literate. The key question is what experiences and purposes are likely to optimize student interest in and capabilities with these tools? Research over the last 20 years has provided some answers and theories to address this question, but more research is needed to engage with emerging complexities. In this chapter I review how representations as learning tools are currently conceptualized, as well as theories that claim to account for students becoming competent in how to use them. While there has been extensive research on how and why students should learn canonical representations, here I consider research on students constructing their own representations as claims. This approach entails increased challenges for teachers, but there is a growing case for the value of students engaging in this creative activity. In reviewing relevant research, I note current competing theoretical accounts of what and how students learn from this activity and also the need for multi-theoretical perspectives to inform future research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183–198.

    Google Scholar 

  • Ainsworth, S., Prain, V., & Tytler, R. (2011). Drawing to learn in science. Science, 333, 1096–1097.

    Article  Google Scholar 

  • Barsalou, L. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645.

    Article  Google Scholar 

  • Bezemer, J., & Kress, G. (2008). Writing in multimodal texts. A social semiotic account of designs for learning. Written Communication, 25(2), 166–195.

    Article  Google Scholar 

  • Bezemer, J., & Kress, G. (2016). Multimodality, learning and communication. A social-semiotic frame. New York: Routledge.

    Google Scholar 

  • Carolan, J., Prain, V., & Waldrip, B. (2008). Using representations for teaching and learning in science. Teaching Science: The Journal of The Australian Science Teachers Association, 54(1), 18–23.

    Google Scholar 

  • Cox, R. (1999). Representation construction, externalised cognition and individual differences. Learning and Instruction, 9, 343–363.

    Article  Google Scholar 

  • Csikszentmihalyi, M. (1999). Implications of a systems perspective for the study of creativity. In R. Sternber (Ed.), Handbook of creativity (pp. 313–335). Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • De Freitas, E., & Sinclair, N. (2012). Diagrams, gesture, agency: Theorizing embodiment in the mathematics classroom. Educational Studies in Mathematics, 80(1), 133–152.

    Article  Google Scholar 

  • diSessa, A. (2004). Meta-representation: Native competence and targets for instruction. Cognition and Instruction, 22, 293–331.

    Article  Google Scholar 

  • Frigg, R., & Hartmann, S. (2019). Models in science. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Spring 2017 edn). https://plato.stanford.edu/archives/spr2017/entries/models-science. Accessed 15 Jan 2019.

  • Frigg, R., & Nguyen, J. (2019). Scientific representation. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy. https://plato.stanford.edu/archives/win2016/entries/scientific-representation. Accessed 15 Jan 2019.

  • Furtak, E., Hardy, I., & Beinbrech, C. (2010). A framework for analyzing evidence-based reasoning in science classroom discourse. Educational Assessment, 15(3–4), 175–196.

    Article  Google Scholar 

  • Gilbert, J. K., & Treagust, D. (Eds.). (2009). Multiple representations in chemical education. Dordrecht, The Netherlands: Springer.

    Google Scholar 

  • Gillies, R. M., & Baffour, B. (2017). The effects of teacher-introduced multimodal representations and discourse on students’ task engagement and scientific language during cooperative, inquiry-based science. Instructional Science, 45(4), 1–21. https://doi.org/10.1007/s11251-017-9414-4

    Article  Google Scholar 

  • Gooding, D. (2006). From phenomenology to field theory: Faraday’s visual reasoning. Perspectives on Science, 14(1), 40–65.

    Article  Google Scholar 

  • Greene, J. A., Sandoval, W. A., & Braten, I. (Eds.). (2016). Handbook of epistemic cognition. New York: Routledge.

    Google Scholar 

  • Greeno, J. G., & Hall, R. P. (1997). Practicing representation learning with and about representational forms. Phi Delta Kappan, 78(5), 361–368.

    Google Scholar 

  • Hand, B., McDermott, M., & Prain, V. (2016). Using multimodal representations to support learning in the science classroom. Dordrecht, The Netherlands: Springer.

    Book  Google Scholar 

  • Hoban, G., Loughran, J., & Nielsen, W. (2011). Slowmation: Preservice elementary teachers representing science knowledge through creating multimodal digital animations. Journal of Research in Science Teaching, 48(9), 985–1009.

    Article  Google Scholar 

  • Hodges, W. (2005, October). How reasoning depends on representations. Queen Mary: University of London.

    Google Scholar 

  • Hughes, R. (1997). Models and representations. Philosophy of Science, 64, 325–336.

    Article  Google Scholar 

  • Johansson, A.-M., & Wickman, P.-O. (2011). A pragmatist approach to learning progressions. In B. Hudson & M. A. Meyer (Eds.), Beyond fragmentation: Didactics, learning, and teaching (pp. 47–59). Leverkusen, Germany: Barbara Budrich Publishers.

    Chapter  Google Scholar 

  • Kellogg, R. T. (2008). Training writing skills: A cognitive developmental perspective. Journal of Writing Research, 1, 1–26.

    Article  Google Scholar 

  • Kirsh, D. (2010). Thinking with external representations. AI and Society, 25, 441–454.

    Article  Google Scholar 

  • Klein, P. (2006). The challenges of scientific literacy: From the viewpoint of second-generation cognitive science. International Journal of Science Education, 28(2–3), 143–178.

    Article  Google Scholar 

  • Kress, G., Jewitt, C., Ogborn, J., & Tsatsarelis, C. (2001). Multimodal teaching and learning: The rhetorics of the science classroom. London: Continuum.

    Google Scholar 

  • Latour, B. (2014). The more manipulations, the better. In E. Coopmans, J. Vertesi, M. Lynch, & S. Woolgar (Eds.), Representation in scientific practice revisited (pp. 347–350). Cambridge, MA: MIT Press.

    Chapter  Google Scholar 

  • Lehrer, R., & Schauble, L. (2017). Children’s conception of sampling in local ecosystems investigations. Science Education, 101, 968–984. https://doi.org/10.1002/sce.21297

    Article  Google Scholar 

  • Lehrer, R., Schauble, L., & Sawyer, K. (2006). Cultivating model-based reasoning in science education. In The Cambridge handbook of the learning sciences (pp. 371–387). New York: Cambridge University Press.

    Google Scholar 

  • Magnani, L. (2015). Naturalizing logic: Errors of reasoning vindicated: Logic reapproaches cognitive science. Journal of Applied Logic, 13(1), 13–36.

    Article  Google Scholar 

  • Mercier, H., & Sperber, D. (2011). Why do humans reason? Arguments for an argumentative theory. Behavioral & Brain Sciences, 34(2), 57–74.

    Article  Google Scholar 

  • Paavola, S., & Hakkarainen, K. (2005). Three abductive solutions to the Meno paradox – With instinct, inference and distributed cognition. Studies in Philosophy and Education, 24, 235–253.

    Article  Google Scholar 

  • Pande, P., & Chandrasekharan, S. (2017). Representational competence: Towards a distributed and embodied cognition account. Studies in Science Education, 53(2), 1–43.

    Article  Google Scholar 

  • Peirce, C. (1931–1958). Collected papers of Charles Sanders Peirce, 8 Volumes (C. Hartshorne, P. Weiss, A. W. Burks, Eds., Vols 1–6), (Arthur W. Burks, Ed., Vols 7–8). Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Prain, V., & Hand, B. (2016). Coming to know more through and from writing. Educational Researcher, 45(7), 430–434.

    Article  Google Scholar 

  • Prain, V., & Tytler, R. (2012). Learning through constructing representations in science: A framework of representational construction affordances. International Journal of Science Education, 34, 2751–2773.

    Article  Google Scholar 

  • Prain, V., & Waldrip, B. (2006). An exploratory study of teachers’ and students’ use of multi-modal representations of concepts in primary science. International Journal of Science Education, 28(15), 1843–1866.

    Article  Google Scholar 

  • Roth, W.-M., & Jornet, A. G. (2013). Situated cognition. WIREs Cognitive Science, 4, 463–478.

    Article  Google Scholar 

  • Rowlands, S. (2011). Disciplinary boundaries for creativity. Creative Education, 2(1), 47–55.

    Article  Google Scholar 

  • Sennett, R. (2008). The craftsman. New Haven, CT/London: Yale University Press.

    Google Scholar 

  • Starbuck, W. (2016). 60th anniversary essay: How journals could improve research practices in social science. Administrative Science Quarterly, 61(2), 165–183.

    Article  Google Scholar 

  • Tang, K. S., Delgrado, C., & Moje, E. (2014). An integrative framework for the analysis of multiple and multimodal representations for meaning-making in science education. Science Education, 98(2), 305–326.

    Article  Google Scholar 

  • Tytler, R., Murcia, K., Hsiung, C., & Ramseger, J. (2017). Reasoning from representations. In M. Hackling, J. Ramseger, & H. L. S. Chen (Eds.), Quality teaching in primary science education; cross-cultural perspectives (pp. 149–179). Cham, Switzerland: Springer.

    Chapter  Google Scholar 

  • Tytler, R., Prain, V., Hubber, P., & Waldrip, B. (Eds.). (2013). Constructing representations to learn in science. Rotterdam, The Netherlands: Sense Publishers.

    Google Scholar 

  • Waldrip, B., Prain, V., & Sellings, P. (2013). Explaining Newton’s laws of motion: Using student reasoning through representations to develop conceptual understanding. Instructional Science, 41, 165–189.

    Article  Google Scholar 

  • Watson, J. D. (1968). The double helix: Being a personal account of the discovery of the structure of DNA. New York: Atheneum.

    Google Scholar 

  • Weick, K. (1998). Introductory essay—Improvisation as a mindset for organizational analysis. Organization Science, 9(5), 543–555.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vaughan Prain .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Prain, V. (2019). Future Research in Learning with, Through and from Scientific Representations. In: Prain, V., Hand, B. (eds) Theorizing the Future of Science Education Research. Contemporary Trends and Issues in Science Education, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-030-24013-4_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24013-4_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24012-7

  • Online ISBN: 978-3-030-24013-4

  • eBook Packages: EducationEducation (R0)

Publish with us

Policies and ethics