Examining Educational Climate Change Technology: How Group Inquiry Work with Realistic Scientific Technology Alters Classroom Learning

  • Drew Bush
  • Renee Sieber
  • Gale Seiler
  • Mark Chandler
Article

Abstract

This study with 79 students in Montreal, Quebec, compared the educational use of a National Aeronautics and Space Administration (NASA) global climate model (GCM) to climate education technologies developed for classroom use that included simpler interfaces and processes. The goal was to show how differing climate education technologies succeed and fail at getting students to evolve in their understanding of anthropogenic global climate change (AGCC). Many available climate education technologies aim to convey key AGCC concepts or Earth systems processes; the educational GCM used here aims to teach students the methods and processes of global climate modeling. We hypothesized that challenges to learning about AGCC make authentic technology-enabled inquiry important in developing accurate understandings of not just the issue but how scientists research it. The goal was to determine if student learning trajectories differed between the comparison and treatment groups based on whether each climate education technology allowed authentic scientific research. We trace learning trajectories using pre/post exams, practice quizzes, and written student reflections. To examine the reasons for differing learning trajectories, we discuss student pre/post questionnaires, student exit interviews, and 535 min of recorded classroom video. Students who worked with a GCM demonstrated learning trajectories with larger gains, higher levels of engagement, and a better idea of how climate scientists conduct research. Students who worked with simpler climate education technologies scored lower in the course because of lower levels of engagement with inquiry processes that were perceived to not actually resemble the work of climate scientists.

Keywords

Climate change Education Inquiry-based learning Global climate modeling 

Notes

Acknowledgements

This research was supported by a McGill University Richard H. Tomlinson Fellowship in University Science Teaching.

Compliance with Ethical Standards

All procedures in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study, pre-tests, and pilot research were each approved by the appropriate McGill University Research Ethics Board (File Numbers 347-0214, 288-0113, and 321-0312). Informed consent was obtained from all individual participants in the study.

Conflict of Interest Statement

The authors declare that they have no conflict of interest.

Supplementary material

10956_2017_9714_MOESM1_ESM.docx (460 kb)
ESM 1 (DOCX 460 kb)

References

  1. Abbasi, D. (2006). Americans and climate change––closing the gap between science and action: A synthesis of insights and recommendations from the 2005 Yale conference. New Haven, CT: Yale School of Forestry and Environmental Studies.Google Scholar
  2. Ainsworth, S., & Van Labeke, N. (2004). Multiple forms of dynamic representation. Learn Instr, 14, 241–225.CrossRefGoogle Scholar
  3. American Association for the Advancement of Science (AAAS). (1990). The liberal art of science. Science, 248(4959), 1137–1137.Google Scholar
  4. Bell, T., Urhahne, D., Schanze, S., & Ploetzner, R. (2010). Collaborative inquiry learning: Models, tools, challenges. Int J Sci Educ, 32(3), 349–377.CrossRefGoogle Scholar
  5. Bliss, J. (1994). From mental models to modeling. In H. Mellar, J. Bliss, R. Boohan, J. Ogborn, & C. Tompsett (Eds.), Learning with artificial worlds: Computer based modeling in the curriculum (pp. 27–32). London: The Falmer Press.Google Scholar
  6. Bush, D., Sieber, R., Seiler, G., & Chandler, M. (2016). The teaching of anthropogenic climate change and earth science via technology-enabled inquiry education. J Geosci Educ, 64(3), 159–174.CrossRefGoogle Scholar
  7. Campbell, T., & Oh, P. S. (2015). Engaging students in modeling as an epistemic practice of science: An introduction to the special issue of the journal of science education and technology. J Sci Educ Technol, 24(2–3), 125–131.CrossRefGoogle Scholar
  8. Carey, C. C., & Gougis, R. D. (2016). Simulation modeling of lakes in undergraduate and graduate classrooms increases comprehension of climate change concepts and experience with computational tools. J Sci Educ Technol, 26(1), 1–11.CrossRefGoogle Scholar
  9. Chambers, L. H., Alston, E. J., Phelps, C. S., Moore, S. W., Diones, D. D., Oots, P. C., Fisher, J. D., & Mims III, F. M. (2008). The My NASA data project. Bull Am Meteorol Soc, 89(4), 437.CrossRefGoogle Scholar
  10. Chandler, M. A., Richards, S. J. & Shopsin, M. J. (2005). EdGCM: Enhancing climate science education through climate modeling research projects. Paper presented at The 85th Annual Meeting of the American Meteorological Society: 14th Symposium on Education, San Diego.Google Scholar
  11. Chandler, M. A., Sohl, L. E., Zhou, J., & Sieber, R. (2011). EdGCM: Research tools for training the climate change generation. Paper presented at the Fall Meeting of the American Geophysical Union, San Francisco.Google Scholar
  12. Clark, J. J. (2015). “Hands-on” remote sensing of physical models in exploration of surficial processes. In K. Crosby, C. Thompson. Proceedings of the 25th Annual Wisconsin Space Conference: Innovations in Flight, Oshkosh: WI (pp. 1-9). Kenosha: Wisconsin Space Grant Consortium.Google Scholar
  13. Cox, H., Kelly, K., & Yetter, L. (2014). Using remote sensing geospatial technology for climate change education. J Geosci Educ, 62(4), 609–620.CrossRefGoogle Scholar
  14. Dewey, J. (1938). Logic: The theory of inquiry. New York: Holt, Rinehart & Wiston.Google Scholar
  15. Dommenget, D. (2015). The Monash Simple Climate Model: An interactive climate model for teaching. Paper presented at the European Geosciences Union General Assembly Conference, Vienna.Google Scholar
  16. Feurtzeig, W., & Roberts, N. (1999). Modeling and simulations in science and mathematics education. New York: Springer.CrossRefGoogle Scholar
  17. Fraedrich, K., Jansen, H., Kirk, E., Luksch, U., & Lunkeit, F. (2005). The planet simulator: Towards a user-friendly model. Meteorol Z, 14(3), 299–304.CrossRefGoogle Scholar
  18. Gautier, C., & Solomon, R. (2005). A preliminary study of students’ asking quantitative scientific questions for inquiry-based climate model experiments. J Geosci Educ, 53(4), 432–443.CrossRefGoogle Scholar
  19. Gerjets, P., Imhof, B., Kuhl, T., Pfeiffer, V., Scheiter, K., & Gemballa, S. (2010). Using static and dynamic visualizations to support the comprehension of complex dynamic phenomena in the natural sciences. In L. Verschaffel, E. de Corte, T. de Jong, & J. Elen (Eds.), Use of external representations in reasoning and problem solving: Analysis and improvement (pp. 153–168). London: Routledge.Google Scholar
  20. GoNorth! (2006). GoNorth!: Arctic National Wildlife Refuge (ANWR) http://www.polarhusky.com/2006/home2006.asp. Accessed 9 Dec 2016.
  21. Hansen, J., Russell, G., Rind, D., Stone, P., Lacis, A., Lebedeff, S., Ruedy, R., & Travis, L. (1983). Efficient three-dimensional global models for climate studies: Models I and II. Mon Weather Rev, 111(4), 609–662.CrossRefGoogle Scholar
  22. Harkness, L. M. (2014). Incorporating real science into the classroom: Aerosols and climate change (Masters Thesis). Houghton, MI: Michigan Technological University.Google Scholar
  23. Horwitz, P., & White, B. Y. (1988). Computer microworlds and conceptual change: A new approach to science education. In P. Ramsden (Ed.), Improving learning: New perspectives (pp. 69–80). London: Kogan Page.Google Scholar
  24. Hubble, D. (2009). Improving student participation in e-learning activities. Paper presented at Fourth international Blended Learning Conference, Hatfield: University of Hertfordshire.Google Scholar
  25. Johnson, R. M., Henderson, S., Gardiner, L., Russell, R., Ward, D., Foster, S., Meymaris, K., Hatheway, B., Carbone, L., & Eastburn, T. (2008). Lessons learned through our climate change professional development program for middle and high school teachers. Phys Geogr, 29(6), 500–511.CrossRefGoogle Scholar
  26. Kahan, D. (2010). Fixing the communications failure. Nature, 463(7279), 296–297.CrossRefGoogle Scholar
  27. Kahan, D. M., Jenkins-Smith, H., & Braman, D. (2011). Cultural cognition of scientific consensus. Journal of Risk Research, 14(2), 147–174.CrossRefGoogle Scholar
  28. Kahan, D. M., Peters, E., Wittlin, M., Slovic, P., Ouellette, L. L., Braman, D., & Mandel, G. (2012). The polarizing impact of science literacy and numeracy on perceived climate change risks. Nat Clim Chang, 2(10), 732–735.CrossRefGoogle Scholar
  29. Kearney, A. (1994). Understanding global change: A cognitive perspective on communicating through stories. Clim Chang, 27(4), 419–441.CrossRefGoogle Scholar
  30. Kerr, R. A. (2005). How hot will the greenhouse world be? Science, 309(5731), 100–100.CrossRefGoogle Scholar
  31. Lahti, D. (2013). Does attainment of Piaget’s formal operational level of cognitive development predict student understanding of scientific models (Doctoral Thesis). Missoula: University of Montana.Google Scholar
  32. Ledley, T. S., Dahlman, L., McAuliffe, C., Haddad, N., Taber, M. R., Domenico, B., Lynds, S., & Grogan, M. (2011). Making Earth science data accessible and usable in education. Science, 333(6051), 1838–1839.CrossRefGoogle Scholar
  33. Leiserowitz, A., Smith, N., & Marlon, J. R. (2010). Americans’ knowledge of climate change. New Haven: Yale Project on Climate Change Communication.Google Scholar
  34. Lewandowsky, S., Ecker, U. K., Seifert, C. M., Schwarz, N., & Cook, J. (2012). Misinformation and its correction: Continued influence and successful debiasing. Psychol Sci Public Interest, 13(3), 106–131.CrossRefGoogle Scholar
  35. Löhner, S., Van Joolingen, W. R., Savelsbergh, E. R., & Van Hout-Wolters, B. (2005). Students’ reasoning during modeling in an inquiry learning environment. Comput Hum Behav, 21(3), 441–461.CrossRefGoogle Scholar
  36. Lueddecke, S. B., Pinter, N., & McManus, S. A. (2001). Greenhouse effect in the classroom: A project and laboratory-based curriculum. J Geosci Educ, 49(3), 274–279.CrossRefGoogle Scholar
  37. Maibach, E., Roser-Renouf, C., & Leiserowitz, A. (2009). Global warming’s six americas 2009: An audience segmentation analysis. New Haven: Yale Project on Climate Change and George Mason Center for Climate Change Communication.Google Scholar
  38. McCright, A. M., & Dunlap, R. E. (2011). The politicization of climate change and polarization in the American public's views of global warming, 2001–2010. Sociol Q, 52(2), 155–194.CrossRefGoogle Scholar
  39. McNeal, K. S., Libarkin, J. C., Ledley, T. S., Bardar, E., Haddad, N., Ellins, K., & Dutta, S. (2014). The role of research in online curriculum development: The case of EarthLabs climate change and Earth system modules. J Geosci Educ, 62(4), 560–577.CrossRefGoogle Scholar
  40. Merton, R. K. (1942). Note on science and democracy. Journal of Legal and Policy Sociology, 1, 115.Google Scholar
  41. NGSS Lead States. (2013). Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press.Google Scholar
  42. Nunnaly, J. (1978). Psychometric theory. New York: McGraw-Hill.Google Scholar
  43. Nyhan, B., & Reifler, J. (2010). When corrections fail: The persistence of political misperceptions. Polit Behav, 32(2), 303–330.CrossRefGoogle Scholar
  44. Oh, P. S. (2010). How can teachers help students formulate scientific hypotheses? Some strategies found in abductive inquiry activities of earth science. Int J Sci Educ, 32(4), 541–560.CrossRefGoogle Scholar
  45. Oh, P. S. (2011). Characteristics of abductive inquiry in earth science: An undergraduate case study. Sci Educ, 95(3), 409–430.CrossRefGoogle Scholar
  46. Oreskes, N. (2007). The scientific consensus on climate change: How do we know we’re not wrong? In J. F. C. DiMento & P. Doughman (Eds.), Climate change: What it means for us, our children, and our grandchildren (pp. 65–99). Cambridge: MIT Press.Google Scholar
  47. Pallant, A., & Lee, H. S. (2015). Constructing scientific arguments using evidence from dynamic computational climate models. J Sci Educ Technol, 24(2–3), 378–395.CrossRefGoogle Scholar
  48. Pallant, A., & Tinker, R. (2004). Reasoning with atomic-scale molecular dynamics models. J Sci Educ Technol, 13(1), 51–66.CrossRefGoogle Scholar
  49. Pandya, R., Charlevoix, D., Cordero, E., Smith, D., & Yald, S. (2012). Trends in the AMS education symposium and highlights from 2012. Bulletin of the American Meteorlogical Society, 93(12), 1917–1920.CrossRefGoogle Scholar
  50. Ramamurthy, K. N., Hinnov, L. A., & Spanias, A. S. (2014). Teaching Earth signals analysis using the Java-DSP Earth Systems edition: Modern and past climate change. J Geosci Educ, 62(4), 621–630.CrossRefGoogle Scholar
  51. Riebeek, H., Chambers, L.H., Yuen, K., & Herring, D. (2009). Bringing terra science to the people: 10 years of education and public outreach. Paper presented at the Fall Meeting of the American Geophysical Union, San Francisco.Google Scholar
  52. Schwarz, C. V., & White, B. Y. (2005). Metamodeling knowledge: Developing students’ understanding of scientific modeling. Cogn Instr, 23(2), 165–205.CrossRefGoogle Scholar
  53. Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Acher, A., Fortus, D., Shwartz, Y., Hug, B., & Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. J Res Sci Teach, 46(6), 632–654.CrossRefGoogle Scholar
  54. Shepard, A. L. (2008). The role of assessment in a learning culture. Educ Res, 29(7), 4–14.CrossRefGoogle Scholar
  55. Sinha, S., Rogat, T. K., Adams-Wiggins, K. R., & Hmelo-Silver, C. E. (2015). Collaborative group engagement in a computer-supported inquiry learning environment. International Journal Computer-Supported Collaborative Learning, 10(3), 273–307.CrossRefGoogle Scholar
  56. Slater, S. J., Slater, T. F., & Olsen, J. K. (2009). Survey of K-12 science teachers’ educational product needs from planetary scientists. Astron Educ Rev, 8(1), 1–20.CrossRefGoogle Scholar
  57. Sohl, L. E. (2012). Enhancing Earth system science education through paleoclimate modeling with EdGCM. Paper presented at the 124th Annual Meeting of the Geological Society of America, Charlotte.Google Scholar
  58. Sohl, L. E., Chandler, M. A., & Zhou, J. (2013). Meeting the Next Generation Science Standards through “rediscovered” climate model experiments. Paper presented at the Fall Meeting of the American Geophysical Union, San Francisco.Google Scholar
  59. Sterman, J. D., & Sweeney, L. B. (2002). Cloudy skies: Assessing public understanding of global warming. Syst Dyn Rev, 18(2), 207–240.CrossRefGoogle Scholar
  60. Sterman, J., Franck, T., Fiddaman, T., Jones, A., McCauley, S., Rice, P., Sawin, E., Siegel, L., & Rooney-Varga, J. N. (2015). World climate: A role-play simulation of climate negotiations. Simulation and Gaming, 46(3–4), 348–382.CrossRefGoogle Scholar
  61. Stewart, J., & Rudolph, J. (2001). Considering the nature of scientific problems when designing science curricula. Sci Educ, 85(3), 207–222.CrossRefGoogle Scholar
  62. Stewart, J., Cartier, J., & Passmore, C. (2005). Developing understanding through model-based inquiry. In M. S. Donavan & J. D. Bransford (Eds.), How students learn (pp. 515–565). Washington, DC: National Research Council.Google Scholar
  63. Swarat, S., Ortony, A., & Revelle, W. (2012). Activity matters: Understanding student interest in school science. J Res Sci Teach, 49(4), 515–537.CrossRefGoogle Scholar
  64. Tochon, F. V. (2007). From video cases to video pedagogy: A framework for video feedback and reflection in pedagogical research praxis. In R. Goldman, R. Pea, B. Barron, & S. Derry (Eds.), Video research in the learning sciences (pp. 53–65). Mahwah: Lawrence Erlbaum Associates.Google Scholar
  65. Visintainer, T., & Linn, M. (2015). Sixth-grade students progress in understanding the mechanisms of global climate change. J Sci Educ Technol, 24(2–3), 287–310.CrossRefGoogle Scholar
  66. Weber, E. U., & Stern, P. C. (2011). Public understanding of climate change in the United States. Am Psychol, 66(4), 315–328.CrossRefGoogle Scholar
  67. Zohar, A., & Dori, Y. J. (2011). Metacognition in science education: Trends in current research. New York: Springer.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Drew Bush
    • 1
    • 2
  • Renee Sieber
    • 1
    • 2
  • Gale Seiler
    • 3
  • Mark Chandler
    • 4
  1. 1.Department of GeographyMcGill UniversityQCCanada
  2. 2.McGill School of EnvironmentMcGill UniversityQCCanada
  3. 3.School of EducationIowa State UniversityAmesUSA
  4. 4.Center for Climate Systems ResearchColumbia UniversityNew YorkUSA

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