Model-based reasoning to foster environmental and socio-scientific literacy in higher education

  • Amanda E. Sorensen
  • Rebecca C. Jordan
  • Rachel Shwom
  • Diane Ebert-May
  • Cindy Isenhour
  • Aaron M. McCright
  • Jennifer Meta Robinson
Article
  • 190 Downloads

Abstract

The American public’s environmental, scientific, and civic literacies are generally low. While environmental science courses often recognize the human dimensions of environmental problems and solutions, they typically treat such phenomena as matters of opinion and rarely engage with social scientific ways of knowing. Recently, there has been a push in higher education to advance broader scientific literacy, but little attention has been paid to helping students gain an understanding of how socio-scientific evidence and claims are generated. Our work here aims to develop the knowledge framework that facilitates the integration of knowledge across biophysical and social science domains. In this research brief, we report on a project in which an interdisciplinary team developed a model of climate adaptation and mitigation to help teach undergraduates about the coupled human-climate system. The research team found this process to be integral to both thinking and learning about a system with biophysical and social variables. This project is unique in that we then used this model to develop not just curricula but also a framework that can be used to guide and assess interdisciplinary instruction at the collegiate level. This framework allows learners to make sense of complex socio-environmental issues and reason with scientific information from the social and biophysical sciences.

Keywords

Environmental Literacy Climate Change Modeling Socio-scientific Science education 

References

  1. Bandura A (1977) Social learning theory. Englewood Cliffs, NJGoogle Scholar
  2. Boud D, Feletti G (1998) The challenge of problem-based learning. Psychology PressGoogle Scholar
  3. Brown JS, Collins A, Duguid P (1989) Situated cognition and the culture of learning. Educ Res 18:32–42CrossRefGoogle Scholar
  4. Brundiers K, Wiek A, Redman CL (2010) Real-world learning opportunities in sustainability: from classroom into the real world. Int J Sustain High Educ 11(4):308–324CrossRefGoogle Scholar
  5. Castree N (2014) Changing the Intellectual Climate. Nat Clim Chang 4:763–768CrossRefGoogle Scholar
  6. Coyle K (2005) Environmental literacy in America: what ten years of NEETF/Roper research and related studies say about environmental literacy in the U.S. The National Environmental Education & Training Foundation.Google Scholar
  7. Crawford B, Jordan RC (2013) Inquiry, models, and complex reasoning to transform learning in environmental education. In: Krasny and Dillon (eds.) Transdisciplinary Research in Environmental Education. Ithaca: Cornell University Press.Google Scholar
  8. Dauer JT, Long TM (2015) Long-term conceptual retrieval by college biology majors following model-based instruction. J Res Sci Teach DOI:. doi:10.1002/tea.21258 Google Scholar
  9. Dietz T (2013) New Essays in Risk, Energy, and Sustainability. In: Dietz T, Jorgenesen A (eds) Chapter 3: Epistemology, ontology, and the practice of structural human ecology pp. 31–52 in Structural Human Ecology. Washington State University Press, Pullman, WAGoogle Scholar
  10. Dunlap RE, Catton Jr W (1994) Struggling with human exemptionalism: the rise, decline, and revitalization of environmental sociology. Am Sociol 25:5–30CrossRefGoogle Scholar
  11. Duschl RA, Schweingruber HA, Shouse AW (2007) Taking science to school: Learning and teaching science in grades K-8. National Academy Press, Washington, DCGoogle Scholar
  12. Fiske SJ, Crate SA, Crumley CL, Galvin K, Lazrus H, Lucero L, Oliver-Smith A, Orlove B, Strauss S, Wilk R (2014) Changing the atmosphere. Anthropology and climate change. Final report of the AAA Global Climate Change Task Force: 137. Arlington, VA: American Anthropological AssociationGoogle Scholar
  13. Flyvbjerg B (2001) Making social science matter: why social inquiry fails and how it can succeed again. Cambridge University PressGoogle Scholar
  14. Hmelo-Silver CE (2004) Problem-based learning: what and how do students learn? Educ Psychol Rev 16(3):235–266CrossRefGoogle Scholar
  15. Jordan RC, Duncan RG (2009) Preservice teachers’ image of science in ecology when compared to genetics. J Biol Educ 43:62–69CrossRefGoogle Scholar
  16. Jordan RC, Singer F, Vaughan J, Berkowitz A (2009) What should every citizen know about ecology? Front Ecol Environ 7:495–500Google Scholar
  17. Jordan RC, Sorensen AE, Hmelo-Silver C (2014) A conceptual representation to support ecological systems learning. Nat Sci Educ 43:141–146Google Scholar
  18. Keen M, Brown VA, Dyball R (2005a) Social learning in environmental management: towards a sustainable future. Earthscan, LondonGoogle Scholar
  19. Keen M, Brown VA, Dyball R (2005b) Fostering ecoliteracy through model-based instruction. Front Ecol Environ 12:138–139Google Scholar
  20. Long TM, Dauer JT, Kostelnik KM, Momsen JL, Wyse SA, Speth EB, Ebert-May D (2014) Fostering ecoliteracy through model-based instruction. Front Ecol Environ 12:138–139CrossRefGoogle Scholar
  21. Meyer JHF, Land R (2005) Threshold concepts and troublesome knowledge (2): epistemological considerations and a conceptual framework for teaching and learning. High Educ 49:373–388CrossRefGoogle Scholar
  22. Middendorf J, Pace D (2004) Decoding the disciplines: a model for helping students learn disciplinary ways of thinking. New Dir Teach Learn 98:1–12CrossRefGoogle Scholar
  23. Miller JD (2002) Civic scientific literacy. FAS Public Interest Report. J Fed Am Sci 55:3–6Google Scholar
  24. Mistry RS, Brown CS, Chow KA, Collins GS (2012) Increasing the complexity of young adolescents’ beliefs about poverty and inequality: results of an 8th grade social studies curriculum intervention. J Youth Adoles 41(6):704–716CrossRefGoogle Scholar
  25. National Research Council (2011) Board on Science Education and Board on Testing and Assessment, Division of Behavioral and Social Sciences and Education. In: Successful K-12 STEM education: identifying effective approaches in science, technology, engineering, and mathematics. Committee on Highly Successful Science Programs for K-12 Science Education. The National Academies Press, Washington, DCGoogle Scholar
  26. NGSS Lead States (2013) Next generation science standards: for states, by states. The National Academies Press, Washington, DCGoogle Scholar
  27. Pennington D, Bammer G, Danielson A, Gosselin D, Gouvea J, Habron G, Hawthorne D, Parnell R, Thompson K, Vincent S, Wei C (2015) The EMBeRS project: employing model-based reasoning in socio-environmental synthesis. J Environ Sci Stud in pressGoogle Scholar
  28. Pickett STA, Kolasa J, Jones CG (2007) Ecological understanding: the nature of theory and the theory of nature, 2nd edn. Springer, New YorkGoogle Scholar
  29. Robelia B, Murphy T (2012) What do people know about key environmental issues? A review of environmental knowledge surveys. Environ Ed Res 18(3) 2012:299–321CrossRefGoogle Scholar
  30. Rosenblueth A, Wiener N (1945) The role of models in science. Philos Sci 12(4):316–321CrossRefGoogle Scholar
  31. Sadler TD, Zeidler DL (2005) Patterns of informal reasoning in the context of socioscientific decision making. J Res Sci Teach 42:112–138CrossRefGoogle Scholar
  32. Sadler TD, Barab SA, Scott B (2007) What do students gain by engaging in socioscientific inquiry? Res Sci Educ 37:371–391CrossRefGoogle Scholar
  33. Schwartz DL, Martin T (2004) Inventing to prepare for future learning: the hidden efficiency of encouraging student production in statistics instruction. Cognition Instruct 222:129–184CrossRefGoogle Scholar
  34. Sterman JD (1994) Learning in and about complex systems. Syst Dynam Rev 10:291–330CrossRefGoogle Scholar
  35. U.S. Global Change Research Program (USGCRP) (2009) Climate literacy: the essential principles of climate science. Available athttp://www.globalchange.gov/browse/educators (accessed 9 6 2015).
  36. Vincent S, Focht W (2009) US higher education environmental program managers’ perspectives on curriculum design and core competencies: implications for sustainability as a guiding framework. International Journal of Sustainability in Higher Education 10(2):164–183CrossRefGoogle Scholar
  37. Wilensky U, Reisman K (2006) Thinking like a wolf, a sheep, or a firefly: learning biology through constructing and testing computational theories—an embodied modeling approach. Cognition Instruct 24:171–209CrossRefGoogle Scholar
  38. Wilson TD, Gilbert DT (2003) Affective forecasting. Adv Exp Soc Psychol 35:345–411CrossRefGoogle Scholar
  39. York R (2013) New Essays in Risk, Energy, and Sustainability. In: Dietz T, Jorgenesen A (eds) Metatheoretical foundations of post-normal prediction pp. 19–30 in Structural Human Ecology. Washington State University Press, Pullman, WAGoogle Scholar

Copyright information

© AESS 2015

Authors and Affiliations

  • Amanda E. Sorensen
    • 1
  • Rebecca C. Jordan
    • 2
  • Rachel Shwom
    • 2
  • Diane Ebert-May
    • 3
  • Cindy Isenhour
    • 4
  • Aaron M. McCright
    • 5
  • Jennifer Meta Robinson
    • 6
  1. 1.Department of Ecology, Evolution and Natural ResourcesRutgers UniversityNew BrunswickUSA
  2. 2.Department of Human EcologyRutgers UniversityNew BrunswickUSA
  3. 3.Department of Plant BiologyMichigan State UniversityEast LansingUSA
  4. 4.Department of AnthropologyUniversity of MaineOronoUSA
  5. 5.Department of SociologyMichigan State UniversityEast LansingUSA
  6. 6.Department of Communication and CultureIndiana UniversityBloomingtonUSA

Personalised recommendations