Skip to main content
Log in

Turning Transfer Inside Out: The Affordances of Virtual Worlds and Mobile Devices in Real World Contexts for Teaching About Causality Across Time and Distance in Ecosystems

  • Original research
  • Published:
Technology, Knowledge and Learning Aims and scope Submit manuscript

Abstract

Reasoning about ecosystems includes consideration of causality over temporal and spatial distances; yet learners typically focus on immediate time frames and local contexts. Teaching students to reason beyond these boundaries has met with some success based upon tests that cue students to the types of reasoning required. Virtual worlds offer an opportunity to assess what students actually do in a simulated context. Beyond this, mobile devices make it possible to scaffold and assess learning in the real world. Situating learning outside, in the target contexts, bypasses many of the challenges of transfer. A study investigated the learning of fifth and sixth graders (n = 38) while they used a virtual world called EcoMUVE, designed to support learning of ecosystems concepts and complex causal dynamics, and mobile broadband device (MBDs) components, designed to assess and support learning and transfer in a real pond ecosystem. The experiences of two classes were contrasted as reference populations; one class participated in the MBD experience first, followed by the learning components in EcoMUVE; the other participated in EcoMUVE first, followed by the MBD components. Rich and triangulated data was collected to illuminate how students experienced and responded to the curriculum components. Both classes made learning gains in EcoMUVE. Students who completed EcoMUVE prior to their MBD experience transferred concepts to their pond explorations. Both classes made learning gains at the pond following the MBD support and revealed more expert reasoning about the importance of change over time and distant drivers in ecosystem dynamics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. Accessed at http://gse.harvard.edu/uclab/.

References

  • Atran, S. (1995). Causal constraints on categories and categorical constraints on biological reasoning across cultures. In D. Sperber, D. Premack, & A. J. Premack (Eds.), Causal cognition: A multidisciplinary debate (pp. 205–233). Oxford: Clarendon Press.

    Google Scholar 

  • Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1), 1–14.

    Article  Google Scholar 

  • Chi, M. T. H., & Van Lehn, K. (2012). Seeing deep structure from the interactions of surface features. Educational Psychologist, 47(3), 177–188.

    Article  Google Scholar 

  • Danish, J. A., Peppler, K., Phelps, D., & Washington, D. (2011). Life in the hive: Supporting inquiry into complexity within the zone of proximal development. Journal of Science Education and Technology, 20(5), 454–467.

    Article  Google Scholar 

  • Day, S. B., & Goldstone, R. L. (2012). The import of knowledge export: Connecting findings and theories of transfer of learning. Educational Psychologist, 47(3), 153–176.

    Article  Google Scholar 

  • Dede, C. (2009). Immersive interfaces for engagement and learning. Science, 323(5910), 66–69.

    Article  Google Scholar 

  • Goldstone, R. L., & Sakamoto, Y. (2003). The transfer of abstract principles governing complex adaptive systems. Cognitive Psychology, 46, 414–466.

    Article  Google Scholar 

  • Gopnik, A., & Glymour, C. (2002). Causal maps and Bayes nets: A cognitive and computational account of theory formation. In P. Carruthers, S. Stich, & M. Siegal (Eds.), The cognitive basis of science (pp. 117–132). Cambridge, NY: Cambridge University Press.

    Chapter  Google Scholar 

  • Gopnik, A., & Schulz, L. (2007). Introduction. In A. Gopnik & L. Schulz (Eds.), Causal learning. Oxford: Oxford Press.

    Chapter  Google Scholar 

  • Grotzer, T. A., & Basca, B. B. (2003). How does grasping the underlying causal structures of ecosystems impact students’ understanding? Journal of Biological Education, 38(1), 16–29.

    Article  Google Scholar 

  • Grotzer, T. A., Kamarainen, A., Tutwiler, M. S., 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 

  • Grotzer, T. A., & Solis, S. L. (2015). Action at an attentional distance: A study of children’s reasoning about causes and effects involving spatial and attentional discontinuity. Journal for Research in Science Teaching (accepted, in revision).

  • Grotzer, T. A., Solis, S. L., & Honey, R. B. (2014). The power of comparison when the concept of control doesn’t apply.  (Working Paper, No. 2014-2), Causal Learning in the Classroom Lab, Harvard University, Cambridge, MA: Author.

  • Grotzer, T. A., & Tutwiler, M. S. (2014). Simplifying causal complexity: How interactions between modes of causal induction and information availability lead to heuristic driven reasoning. Mind, Brain, and Education, 8(3), 97–114.

    Article  Google Scholar 

  • Grotzer, T. A., Tutwiler, M. S., Dede, C. Kamarainen, A., & Metcalf, S. (2011). 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, FL.

  • Harp, S. F., & Mayer, R. E. (1998). How seductive details do their damage: A theory of cognitive interest in science learning. Journal of Educational Psychology, 90, 414–434.

    Article  Google Scholar 

  • Harris, P. L. (2002). What do children learn from testimony? In P. Carruthers, S. Stich, & M. Siegal (Eds.), The cognitive basis of science (pp. 316–334). Cambridge, NY: Cambridge University Press.

    Chapter  Google Scholar 

  • Hmelo-Silver, C. E., & Azevedo, R. (2006). Understanding complex systems: Some core challenges. The Journal of the Learning Sciences, 15(1), 53–61.

    Article  Google Scholar 

  • Hmelo-Silver, C. E., & Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 28, 127–138.

    Article  Google Scholar 

  • Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. Journal of the Learning Sciences, 16, 307–331.

    Article  Google Scholar 

  • Hogan, K., & Fisherkeller, J. (1996). Representing students’ thinking about nutrient cycling in ecosystems: Bi-dimensional coding of a complex topic. Journal of Research in Science Teaching, 33(9), 941–970.

    Article  Google Scholar 

  • Jacobson, M. J. (2001). Problem-solving, cognition, and complex systems: Differences between experts and novices. Complexity, 6(3), 41–49.

    Article  Google Scholar 

  • Keil, F. C. (1994). The birth and nurturance of concepts by domains. In L. Hirschfield & S. Gelman (Eds.), Domain specificity in cognition and culture (pp. 234–254). Cambridge, NY: Cambridge University Press.

    Chapter  Google Scholar 

  • Klopfer, E., & Yoon, S. (2005). Using palm technology in participatory simulations of complex systems: A new take on ubiquitous and accessible mobile computing. Journal of Science Education and Technology, 14(3), 287–295.

    Article  Google Scholar 

  • Metcalf, S. J., Kamarainen, A., Tutwiler, M. S., Grotzer, T. A., & Dede, C. J. (2011). Ecosystem science learning via multi-user virtual environments. International Journal of Gaming and Computer-Mediated Simulations, 3(1), 86–90.

    Article  Google Scholar 

  • Penner, D. (2000). Explaining systems: Investigating middle school students’ understanding of emergent phenomena. Journal of Research in Science Teaching, 37(8), 784–806.

    Article  Google Scholar 

  • Perkins, D. N., Jay, E., & Tishman, S. (1993). Beyond abilities: A dispositional theory of thinking. The Merrill-Palmer Quarterly, 39, 1–21.

    Google Scholar 

  • Perkins, D. N., & Salomon, G. (2012). Knowledge to go: A motivational and dispositional view of transfer. Educational Psychologist, 47(3), 248–258.

    Article  Google Scholar 

  • Raia, F. (2008). Causality in complex dynamic systems: A challenge in earth systems science education. Journal of Geoscience Education, 56(1), 81–94.

    Google Scholar 

  • Ratterman, M. J., & Gentner, D. (1998). The effect of language on similarity: The use of relational labels improves young children’s performance in a mapping task. In K. Holyoak, D. Gentner, & B. Kokinov (Eds.), Advances in analogy research: Integration of theory and data from the cognitive, computational, and neural sciences (pp. 274–282). Sophia: New Bulgarian University.

    Google Scholar 

  • Shepardson, D. P., Wee, B., Priddy, M., Schellenberger, L., & Harbor, J. (2007). What is a watershed? Implications of student conceptions for environmental science education and the national science education standards. Science Education, 91, 544–578.

    Article  Google Scholar 

  • Tal, T., Alon, N. L., & Morag, O. (2014). Exemplary practices in field trips to natural environments. Journal of Research in Science Teaching, 51, 430–461.

    Article  Google Scholar 

  • Weathers, K., Strayer, D., & Likens, G. (2013). Fundamentals of ecosystem science. Waltham, MA: Academic Press.

    Google Scholar 

Download references

Acknowledgments

We would like to express our appreciation to the teachers and students who contributed their time to this project and enabled us to collect data on their reasoning patterns. We also thank Maya Bialik, Amber K. Boyd, Maleka Donaldson Gramling, Couger Jimenez Jaramillo, Tim Johnson, Miles Malbrough, Daniel Oh, S. Lynneth Solis and M. Shane Tutwiler for their assistance in data collection, transcribing, and analysis. Thank you to Daniel Hackett, Brandon Pousley, and the Cambridge Water Department for their assistance in developing components for EcoMOBILE. This work is supported by the National Science Foundation, Grant No. DRK12 1118530 to Chris Dede and Tina Grotzer. All opinions, findings, conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the National Science Foundation. EcoMUVE was supported by the Institute of Education Sciences, U.S. Department of Education, Grant No. R305A080514 to Chris Dede and Tina Grotzer. All opinions, findings, conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the Institute for Education Sciences.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tina A. Grotzer.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Grotzer, T.A., Powell, M.M., M. Derbiszewska, K. et al. Turning Transfer Inside Out: The Affordances of Virtual Worlds and Mobile Devices in Real World Contexts for Teaching About Causality Across Time and Distance in Ecosystems. Tech Know Learn 20, 43–69 (2015). https://doi.org/10.1007/s10758-014-9241-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10758-014-9241-5

Keywords

Navigation