Abstract
This chapter outlines a guided inquiry approach, called representation construction, which was successfully developed within an Australian Research Council (ARC) project that links student learning and engagement with the knowledge production practices of science. This approach involves challenging students to generate and negotiate the representations (text, graphs, models, diagrams) that constitute the discursive practices of science, rather than focusing on the text-based, definitional versions of concepts. The representation construction approach is based on sequences of representational challenges which involve students constructing representations to actively explore and make claims about phenomena. It thus represents a more active view of knowledge than traditional structural approaches and encourages visual as well as the traditional text-based literacies. The approach has been successful in demonstrating enhanced outcomes for students, in terms of sustained engagement with ideas, and quality learning, and for teachers enhanced pedagogical knowledge and understanding of how knowledge in science is developed and communicated. This chapter draws on specific examples of how the approach was implemented in a variety of topics, such as energy, forces, astronomy and ideas about matter within junior secondary science classrooms. It will also draw on the issues associated with the adoption of the approach in laptop/tablet classrooms where part of the curriculum is delivered in the cloud.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Pseudonym for the school
- 2.
Pseudonyms are given for all teacher names.
References
Ainsworth, S. (2006). DEFT: A conceptual framework for learning with multiple representations. Learning and Instruction, 16(3), 183–198.
Ainsworth, S. (2008). The educational value of multiple representations when learning complex scientific concepts. In J. K. Gilbert, M. Reiner, & M. Nakhlel (Eds.), Visualization: Theory and practice in science education (pp. 191–208). New York: Springer.
Ainsworth, S., Prain, V., & Tytler, R. (2011). Drawing to learn in science. Science, 333(26), 1096–1097.
Chi, M. (2009). Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1, 73–105.
Chubb, I. (2014). Office of the Chief Scientist: Science, Technology, Engineering and Mathematics: Australia’s Future. Australian Government, Canberra, Australia. Retrieved from http://www.chiefscientist.gov.au/wp- content/uploads/STEM_AustraliasFuture_Sept2014_Web.pdf
Cox, R. (1999). Representation construction, externalized cognition and individual differences. Learning and Instruction, 9, 343–363.
diSessa, A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22(3), 293–331.
Dreher, A., Kuntze, S., & Lerman, S. (2016). Why use multiple representations in the mathematics classroom? Views of English and German preservice teachers. International Journal of Science and Mathematics Education, 14(2), 363–381. doi:10.1007/s10763-015-9633-6.
Duval, R. (2006). A cognitive analysis of problems of comprehension in a learning of mathematics. Educational Studies in Mathematics, 61, 103–131. doi:10.1007/s10649-006-0400-z.
Elkins, J. (2011). Visual practices across the University: A report. In O. Grau (Ed.), Imagery in the 21st century. Cambridge, MA: MIT Press.
Furtak, E., Seidel, T., Iverson, H., & Briggs, D. (2012). Experimental and quasi-experimental studies of inquiry-based science teaching: A meta-analysis. Review of Educational Research, 82(3), 300–329.
Gibson, J. (1977). The theory of affordances. In R. Shaw & J. Bransford (Eds.), Perceiving, acting, and knowing: Toward an ecological psychology (pp. 67–82). Hillsdale, NJ: Erlbaum.
Gilbert, J. K. (2005). Visualization in science education. New York: Springer.
Gooding, D. (2004). Visualization, inference and explanation in the sciences. In G. Malcolm (Ed.), Studies in Multidisciplinarity (Vol. 2, pp. 1–25). Elsevier.
Gooding, D. (2006). From phenomenology to field theory: Faraday’s visual reasoning. Perspectives on Science, 14(1), 40–65.
Goodrum, D., Druhan, A., & Abbs, J. (2012). The status and quality of year 11 and 12 science in Australian schools. Canberra, Australia: Australian Academy of Science. Retrieved April 2016 from http://www.science.org.au/publications/research-reports-and-policy.html
Greeno, J. G., & Hall, R. P. (1997). Practicing representation: Learning with and about representational forms. Phi Delta Kappan, 78(5), 361–368.
Hackling, M., & Prain, V. (2005). Primary connections: Stage 2 trial. Canberra, Australia: Australian Academy of Science.
Honey, M., Pearson, G., & Schweingruber, H. (Eds.). (2014). STEM Integration in K-12 education: Status, prospects and an Agenda for research. Washington, DC: The National Academies Press.
Hubber, P., Tytler, R., Chittleborough, G., Campbell, C., & Jobling, W. (2012). Evaluation of delivery of the switched on secondary science professional learning (SOSSPL) program (2011–2012). Waurn Ponds, Victoria: Deakin University.
Hubber, P., Tytler, R., & Haslam, F. (2010). Teaching and learning about force with a representational focus: Pedagogy and teacher change. Research in Science Education, 40(1), 5–28.
Johri, A., Roth, W., & Olds, B. (2013). The role of representations in engineering practices: Taking a turn towards inscriptions. Journal of Engineering Education, 102(1), 2–19. doi:10.1002/jee.20005.
Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424. doi:10.1080/07370000802212669.
Kozma, R., & Russell, J. (2005). Students becoming chemists: Developing representational competence. In J. Gilbert (Ed.), Visualization in Science Education (pp. 121–145). Springer.
Latour, B. (1999). Pandora’s hope: Essays on the reality of science studies. Cambridge, MA: Harvard University Press.
Lehrer, R., & Chazan, D. (1998). Designing learning environments for developing understanding of geometry and space. Mahwah, NJ: Lawrence Erlbaum Associates.
Lehrer, R., & Schauble, L. (2006a). Cultivating model-based reasoning in science education. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 371–388). Cambridge, MA: Cambridge University Press.
Lehrer, R., & Schauble, L. (2006b). Scientific thinking and science literacy. In W. Damon & R. Lerner (Eds.), Handbook of child psychology (6th ed., Vol. 4).
Lemke, J. L. (1990). Talking science: Language, learning and values. Greenwood Publishing Group.
Lemke, J. (2004). The literacies of science. In E. W. Saul (Ed.), Crossing borders in literacy and science instruction: Perspectives on theory and practice (pp. 33–47). Newark, NJ: International Reading Association/National Science Teachers Association.
Leviston, Z., Price, J., Malkin, S., & McCrea, R. (2014). Fourth annual survey of Australian attitudes to climate change: Interim report. CSIRO: Perth, Australia.
Linn, M., Lewis, C., Tsuchida, I., & Songer, N. (2000). Beyond fourth grade science: Why do U.S. and Japanese students diverge? Educational Researcher, 29(3), 4–14.
Moje, E. (2007). Developing socially just subject-matter instruction: A review of the literature on disciplinary literacy learning. Review of Research in Education, 31, 1–44.
Osborne, J. (2006). Towards a science education for all: The role of ideas, evidence and argument. In Proceedings of the ACER research conference: Boosting science learning –What will it take? (pp. 2–5). Retrieved from http://research.acer.edu.au/cgi/viewcontent.cgi?article=1000&context=research_conference_2006
Prain, V., & Tytler, R. (2012). Learning through constructing representations in science: A framework of representational construction affordances. International Journal of Science Education, 34(17), 2751–2773.
Schwab, J. J. (1962). The teaching of science as enquiry. Cambridge, MA: Harvard University Press.
Schwartz, D. L. (1995). The emergence of abstract representations in dyad problem solving. The Journal of the Learning Sciences, 4(3), 321–354.
Stieff, M. (2011). Improving representational competence using molecular simulations embedded in inquiry activities. Journal of Research in Science Teaching, 48(10), 1137–1158.
Stieff, M., & DeSutter, D. (2016, June). Drawing from dynamic visualisations. Paper presented at the International Conference of the Learning Sciences, Singapore.
Tytler, R., Ferguson, J., Aranda, G., Gorur, R., & Prain, V. (2016, June). Drawing within experimental exploration as part of core epistemological and epistemic practices in science. Paper presented at the International Conference of the Learning Sciences, Singapore.
Tytler, R., Haslam, F., Prain, V., & Hubber, P. (2009). An explicit representational focus for teaching and learning about animals in the environment. Teaching Science, 55(4), 21–27.
Tytler, R., Peterson, S., & Prain, V. (2006). Picturing evaporation: Learning science literacy through a particle representation. Teaching Science, the Journal of the Australian Science Teachers Association, 52(1), 12–17.
Tytler, R., Prain, V., Hubber, P., & Waldrip, B. (Eds.). (2013). Constructing representations to learn science. Rotterdam, The Netherlands: Sense Publishers.
Van Meter, P., Aleksic, M., Schwartz, A., & Garner, J. (2006). Learner-generated drawing as a strategy for learning from content area text. Contemporary Educational Psychology, 31, 142–166.
Van Meter, P., & Garner, J. (2005). The promise and practice of learner-generated drawing: Literature review and synthesis. Educational Psychology Review, 17(4), 285–325.
Vygotsky, L. (1981). Thought and language (Rev. and Ed. by A. Kozulin). Cambridge, MA: MIT Press.
Waldrip, B., Prain, V., & Carolan, J. (2010). Using multi-modal representations to improve learning in junior secondary science. Research in Science Education, 40(1), 65–80.
Zhang, Z., & Linn, M. (2008). Using drawings to support learning from dynamic visualizations. In Proceedings of the 8th international conference on the learning sciences (Vol. 3, pp. 161–162).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Hubber, P., Tytler, R., Chittleborough, G. (2018). Representation Construction: A Guided Inquiry Approach for Science Education. In: Jorgensen, R., Larkin, K. (eds) STEM Education in the Junior Secondary. Springer, Singapore. https://doi.org/10.1007/978-981-10-5448-8_5
Download citation
DOI: https://doi.org/10.1007/978-981-10-5448-8_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5447-1
Online ISBN: 978-981-10-5448-8
eBook Packages: EducationEducation (R0)