Exploring Ecosystems from the Inside: How Immersive Multi-user Virtual Environments Can Support Development of Epistemologically Grounded Modeling Practices in Ecosystem Science Instruction

Abstract

Recent reform efforts and the next generation science standards emphasize the importance of incorporating authentic scientific practices into science instruction. Modeling can be a particularly challenging practice to address because modeling occurs within a socially structured system of representation that is specific to a domain. Further, in the process of modeling, experts interact deeply with domain-specific content knowledge and integrate modeling with other scientific practices in service of a larger investigation. It can be difficult to create learning experiences enabling students to engage in modeling practices that both honor the position of the novice along a spectrum toward more expert understanding and align well with the practices and reasoning used by experts in the domain. In this paper, we outline the challenges in teaching modeling practices specific to the domain of ecosystem science, and we present a description of a curriculum built around an immersive virtual environment that offers unique affordances for supporting student engagement in modeling practices. Illustrative examples derived from pilot studies suggest that the tools and context provided within the immersive virtual environment helped support student engagement in modeling practices that are epistemologically grounded in the field of ecosystem science.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. Barab SA, Hay KE, Barnett M, Keating T (2000) Virtual solar system project: building understanding through model building. J Res Sci Teach 37(7):719–756

    Article  Google Scholar 

  2. Barnett M, Yamagata-Lynch L, Keating T, Barab SA, Hay KE (2005) Using virtual reality computer models to support student understanding of astronomical concepts. J Comput Math Sci Teach 24(4):812–856

    Google Scholar 

  3. Berland LK, McNeill KL (2009). Using a learning progression to inform scientific argumentation in talk and writing. Learning Progressions in Science Conference, Iowa City, IA

  4. Berland LK, Reiser BJ (2010) Classroom communities’ adaptations of the practice of scientific argumentation. Sci Educ 95(2):191–216

    Article  Google Scholar 

  5. Brizuela BM, Gravel BE (2013) Introduction. In: Brizuela BM, Gravel BE (eds) Show me what you know: exploring student representations across stem disciplines. Teachers College Press

  6. Canham CDW, Cole J, Lauenroth WK (eds) (2003) Models in ecosystem science. Princeton University Press, Princeton

    Google Scholar 

  7. Capon N, Kuhn D (2004) What’s so good about problem-based learning? Cognit Instr 22(1):61–79

    Article  Google Scholar 

  8. Chaiklin S, Lave (eds) (1993) Understanding practice: perspectives on activity and context. Cambridge University Press, Cambridge

  9. Chinn C, Hmelo-Silver C (2002) Authentic inquiry: introduction to the special section. Sci Educ 86(2):171–174

  10. Chinn C, Malhotra B (2002) Epistemologically authentic inquiry in schools: a theoretical framework for evaluating inquiry tasks. Sci Educ 86(2):175–218. doi:10.1002/sce.10001

    Article  Google Scholar 

  11. Clement J (2000) Model based learning as a key research area for science education. Int J Sci Educ 22(9):1041–1053. doi:10.1080/095006900416901

    Article  Google Scholar 

  12. Coll RK, France B, Taylor I (2005) The role of models/and analogies in science education: implications from research. Int J Sci Educ 27(2):183–198. doi:10.1080/0950069042000276712

    Article  Google Scholar 

  13. Dede C (1999) The multiple-media difference. Technos 8(1):16–18

    Google Scholar 

  14. Duschl R (2008) Science education in three-part harmony: balancing conceptual, epistemic, and social learning goals. Rev Res Educ 32(1):268–291. doi:10.3102/0091732X07309371

    Article  Google Scholar 

  15. Fishman B, Dede C, Means B (in press) Teaching and technology: new tools for new times. In: Handbook of research on teaching, 5th ed. American Educational Research Association, Washington

  16. Gilbert JK (2004) Models and modelling: routes to more authentic science education. Int J Sci Math Educ 2(2):115–130. doi:10.1007/s10763-004-3186-4

    Article  Google Scholar 

  17. Goldin GA (2013) Forward. In: Brizuela BM, Gravel BE (eds) Show me what you know: exploring student representations across stem disciplines. Teachers College Press

  18. Goodwin C (1994) Professional vision. Am Anthropol 96(3):606–633

  19. Greca IM, Moreira MA (2001) Mental, physical, and mathematical models in the teaching and learning of physics. Sci Educ 86(1):106–121. doi:10.1002/sce.10013

    Article  Google Scholar 

  20. Greeno JG (1998) The situativity of knowing, learning, and research. Am Psychol 53(1):5–26. doi:10.1037//0003-066X.53.1.5

    Article  Google Scholar 

  21. Griesemer JR (1990, January). Material models in biology. In PSA: proceedings of the biennial meeting of the philosophy of science association. Philosophy of Science Association, pp 79–93

  22. Grosslight L, Unger C, Jay E, Smith CL (1991) Understanding models and their use in science: conceptions of middle and high school students and experts. J Res Sci Teach 28(9):799–822. doi:10.1002/tea.3660280907

    Article  Google Scholar 

  23. Grotzer TA, Tutwiler MS, Dede C, Kamarainen A, Metcalf S (2011, April). Helping students learn more expert framing of complex causal dynamics in ecosystems using EcoMUVE. Presented at the national association of research in science teaching (NARST) conference, Orlando, April 4, 2011

  24. Grotzer TA, Kamarainen AM, Tutwiler MS, Metcalf S, Dede C (2013) Learning to reason about ecosystems dynamics over time: the challenges of an event-based causal focus. Bioscience 63(4):288–296

    Article  Google Scholar 

  25. Harrison AG, Treagust DF (2000) A typology of school science models. Int J Sci 37–41. doi:10.1080/095006900416884

  26. Hmelo CE, Holton DL, Kolodner JL (2000) Designing to learn about complex systems. J Learn Sci 9(3):247–298

    Article  Google Scholar 

  27. Hofstein A, Lunetta VN (2004) The laboratory in science education: foundations for the twenty-first century. Sci Educ 88(1):28–54

    Article  Google Scholar 

  28. Kelly RA, Jakeman AJ, Barreteau O, Borsuk ME, Elsawah S, Hamilton SH, Voinov AA (2013) Environmental modelling & software selecting among five common modelling approaches for integrated environmental assessment and management. Environ Model Softw 47:159–181. doi:10.1016/j.envsoft.2013.05.005

    Article  Google Scholar 

  29. Kuhn D, Pearsall S (2000) Developmental origins of scientific thinking. J Cognit Dev 1(1):113–129. doi:10.1207/S15327647JCD0101N_11

    Article  Google Scholar 

  30. Lauenroth WK, Burke IC, Berry JK (2003) The status of dynamic quantitative modeling in ecology. In: Canham CDW, Cole J, Lauenroth WK (eds) Models in ecosystem science. Princeton University Press, Princeton, pp 32–48

  31. Lehrer R, Schauble L (2000) Developing model-based reasoning in mathematics and science. J Appl Dev Psychol 21(1):39–48

    Article  Google Scholar 

  32. Lehrer R, Horvath J, Schauble L (1994) Developing model-based reasoning. Interact Learn Environ 4(3):218–232. doi:10.1080/1049482940040304

    Article  Google Scholar 

  33. Lehrer R, Schauble L, Lucas D (2008) Supporting development of the epistemology of inquiry. Cognit Dev 23(4):512–529. doi:10.1016/j.cogdev.2008.09.001

    Article  Google Scholar 

  34. Lesh R, Lehrer R (2003) Models and modeling perspectives on the development of students and teachers. Math Think Learn 5(2–3):109–129. doi:10.1080/10986065.2003.9679996

    Article  Google Scholar 

  35. Manz EVE (2012) Understanding the codevelopment of modeling practice and ecological. Sci Educ 96(6):1071–1105. doi:10.1002/sce.21030

    Article  Google Scholar 

  36. NGSS Lead States (2013) Next generation science standards: for states, by states. The National Academies Press, Washington

    Google Scholar 

  37. Pace ML, Groffman PM (1998) Successes, limitations, and frontiers in ecosystem science: reflections on the seventh cary conference OR. Ecosystems 1:137–142

  38. Palincsar AS (1998) Keeping the metaphor of scaffolding fresh—a response to C. Addison Stone’s “The metaphor of scaffolding its utility for the field of learning disabilities”. J Lear Disabil 31(4):370–373

    Article  Google Scholar 

  39. Passmore C, Stewart J (2002) A modeling approach to teaching evolutionary biology in high schools. J Res Sci Teach 39(3):185–204. doi:10.1002/tea.10020

    Article  Google Scholar 

  40. Passmore CM, Svoboda J (2012) Exploring opportunities for argumentation in modeling classrooms. Int J Sci Educ 34(10):1535–1554. doi:10.1080/09500693.2011.577842

    Article  Google Scholar 

  41. Passmore C, Stewart J, Cartier J (2009) Model-based inquiry and school science: creating connections. Sch Sci Math 109(7):394–402. doi:10.1111/j.1949-8594.2009.tb17870.x

    Article  Google Scholar 

  42. Penner DE, Giles ND, Lehrer R, Schauble L (1997) Building functional models: designing an elbow. J Res Sci Teach 34(2):125–143. doi:10.1002/(SICI)1098-2736(199702)34:2<125:AID-TEA3>3.0.CO;2-V

    Article  Google Scholar 

  43. Quintana C, Reiser BJ, Davis EA, Krajcik J, Fretz E, Duncan RG, Soloway E (2004) A scaffolding design framework for software to support science inquiry. J Learn Sci 13(3):337–386

    Article  Google Scholar 

  44. Reiser B, Tabak I, Sandoval WA, Smith BK, Steinmuller F, Leone AJ (2001) BGuILE: strategic and conceptual scaffolds for scientific inquiry in biology classrooms. In: Carver SM, Klahr D (eds) Cognition and instruction: twenty-five years of progress. Lawrence Erlbaum Associates, Mahwah, NJ

  45. Salzman M, Dede C, Loftin B (1999) Virtual reality’s frames of reference: a visualization technique for mastering abstract information spaces. In: Proceedings of CHI’99, pp 489–495

  46. Sandoval W (2014) Science education’s need for a theory of epistemological development. Sci Educ 98(3):383–387. doi:10.1002/sce.21107

    Article  Google Scholar 

  47. Sandoval WA, Reiser BJ (2004) Explanation-driven inquiry: integrating conceptual and epistemic scaffolds for scientific inquiry. Sci Educ 88(3):345–372

    Article  Google Scholar 

  48. Schwarz CV, Meyer J, Sharma A (2007) Technology, pedagogy, and epistemology: opportunities and challenges of using computer modeling and simulation tools in elementary science methods. 243–269. doi:10.1007/s10972-007-9039-6

  49. Singer SR, Hilton ML, Schweingruber HA (eds) (2005) America’s lab report: investigations in high school science. National Academies Press, Washington, DC

  50. Squire K (2010) From information to experience: place-based augmented reality games as a model for learning in a globally networked society. Teach Coll Rec 112(10):2565–2602

    Google Scholar 

  51. Stratford SJ (1997) A review of computer-based model research in precollege science classrooms. J Comput Math Sci Teach 16:3–23

  52. Tairab HH, Khalaf Al-Naqbi AK (2004) How do secondary school science students interpret and construct scientific graphs? J Biol Educ 38(3):127–132. doi:10.1080/00219266.2004.9655920

    Article  Google Scholar 

  53. Wenger E (1998) Communities of practice: learning, meaning, and identity. Cambridge university press, Cambridge

    Google Scholar 

  54. Windschitl M, Thompson J, Braaten M (2008) Beyond the scientific method: model-based inquiry as a new paradigm of preference for school science investigations. Sci Educ 92(5):941–967

    Article  Google Scholar 

Download references

Acknowledgments

The EcoMUVE project was supported by a grant from the U.S. Department of Education Insitute of Education Sciences under award number (R305A080141).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Amy M. Kamarainen.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kamarainen, A.M., Metcalf, S., Grotzer, T. et al. Exploring Ecosystems from the Inside: How Immersive Multi-user Virtual Environments Can Support Development of Epistemologically Grounded Modeling Practices in Ecosystem Science Instruction. J Sci Educ Technol 24, 148–167 (2015). https://doi.org/10.1007/s10956-014-9531-7

Download citation

Keywords

  • Model
  • Ecosystem
  • Immersion
  • Inquiry
  • Epistemology