Examining Educational Climate Change Technology: How Group Inquiry Work with Realistic Scientific Technology Alters Classroom Learning
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.
KeywordsClimate change Education Inquiry-based learning Global climate modeling
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.
- 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
- American Association for the Advancement of Science (AAAS). (1990). The liberal art of science. Science, 248(4959), 1137–1137.Google Scholar
- 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
- 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
- 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
- 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
- Dewey, J. (1938). Logic: The theory of inquiry. New York: Holt, Rinehart & Wiston.Google Scholar
- 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
- 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
- GoNorth! (2006). GoNorth!: Arctic National Wildlife Refuge (ANWR) http://www.polarhusky.com/2006/home2006.asp. Accessed 9 Dec 2016.
- Harkness, L. M. (2014). Incorporating real science into the classroom: Aerosols and climate change (Masters Thesis). Houghton, MI: Michigan Technological University.Google Scholar
- 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
- Hubble, D. (2009). Improving student participation in e-learning activities. Paper presented at Fourth international Blended Learning Conference, Hatfield: University of Hertfordshire.Google Scholar
- 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
- 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
- Leiserowitz, A., Smith, N., & Marlon, J. R. (2010). Americans’ knowledge of climate change. New Haven: Yale Project on Climate Change Communication.Google Scholar
- 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
- Merton, R. K. (1942). Note on science and democracy. Journal of Legal and Policy Sociology, 1, 115.Google Scholar
- NGSS Lead States. (2013). Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press.Google Scholar
- Nunnaly, J. (1978). Psychometric theory. New York: McGraw-Hill.Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Zohar, A., & Dori, Y. J. (2011). Metacognition in science education: Trends in current research. New York: Springer.Google Scholar