Journal of Science Education and Technology

, Volume 20, Issue 5, pp 454–467 | Cite as

Life in the Hive: Supporting Inquiry into Complexity Within the Zone of Proximal Development

  • Joshua A. Danish
  • Kylie Peppler
  • David Phelps
  • DiAnna Washington
Article

Abstract

Research into students’ understanding of complex systems typically ignores young children because of misinterpretations of young children’s competencies. Furthermore, studies that do recognize young children’s competencies tend to focus on what children can do in isolation. As an alternative, we propose an approach to designing for young children that is grounded in the notion of the Zone of Proximal Development (Vygotsky 1978) and leverages Activity Theory to design learning environments. In order to highlight the benefits of this approach, we describe our process for using Activity Theory to inform the design of new software and curricula in a way that is productive for young children to learn concepts that we might have previously considered to be “developmentally inappropriate”. As an illuminative example, we then present a discussion of the design of the BeeSign simulation software and accompanying curriculum which specifically designed from an Activity Theory perspective to engage young children in learning about complex systems (Danish 2009a, b). Furthermore, to illustrate the benefits of this approach, we will present findings from a new study where 40 first- and second-grade students participated in the BeeSign curriculum to learn about how honeybees collect nectar from a complex systems perspective. We conclude with some practical suggestions for how such an approach to using Activity Theory for research and design might be adopted by other science educators and designers.

Keywords

Science education Inquiry Zone of proximal development Complex systems 

References

  1. Chaiklin S (2003) The zone of proximal development in Vygotsky’s analysis of learning and instruction. In: Kozulin A, Gindis B, Ageyev VS, Miller SM (eds) Vygotsky’s educational theory in cultural context. Cambridge University Press, CambridgeGoogle Scholar
  2. Cole M (1996) Cultural psychology: a once and future discipline. Belknap Press of Harvard University Press, CambridgeGoogle Scholar
  3. Cole M, Engeström Y (1993) A cultural-historical approach to distributed cognition. In: Salomon G (ed) Distributed cognitions: psychological and educational considerations. Cambridge University Press, New York, pp 47–87Google Scholar
  4. Danish JA (2009a) BeeSign: a computationally-mediated intervention to examine K-1 students’ representational activities in the context of teaching complex systems concepts. Unpublished Dissertation, University of California at Los Angeles, Los AngelesGoogle Scholar
  5. Danish JA (2009b) BeeSign: a design experiment to teach kindergarten and first grade students about honeybees from a complex systems perspective. Paper presented at the annual meeting of the American Educational Research AssociationGoogle Scholar
  6. Danish JA (under review) BeeSign: the role of activity in shaping kindergarten and first-grade students’ engagement with honeybees collecting nectar as a complex systemGoogle Scholar
  7. Engeström Y (1987) Learning by expanding: an activity—theoretical approach to developmental research. Orienta-Konsultit Oy, HelsinkiGoogle Scholar
  8. Engeström Y (1990) Learning, working and imagining: twelve studies in activity theory. Orienta-Konsultit Oy, HelsinkiGoogle Scholar
  9. Engeström Y (1999) Activity theory and individual and social transformation. Cambridge University Press, CambridgeGoogle Scholar
  10. Erickson F (2006) Definition and analysis of data from videotape: some research procedures and their rationales. In: Green J, Camilli G, Elmore P (eds) Handbook of complementary methods in educational research, 3rd edn. American Educational Research Association, Washington, DCGoogle Scholar
  11. Griffin P, Cole M (1984) Current activity for the future: the Zo-ped. New Dir Child Dev 23:45–64CrossRefGoogle Scholar
  12. Hmelo-Silver CE, Azevedo R (2006) Understanding complex systems: some core challenges. J Learn Sci 15(1):53–62CrossRefGoogle Scholar
  13. Hmelo-Silver CE, Pfeffer MG (2004) Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cogn Sci 28(1):127–138CrossRefGoogle Scholar
  14. Hmelo-Silver CE, Marathe S, Liu L (2007) Fish swim, rocks sit, and lungs breathe: expert-novice understanding of complex systems. J Learn Sci 16(3):307–331CrossRefGoogle Scholar
  15. Jacobson MJ, Wilensky U (2006) Complex systems in education: scientific and educational importance and implications for the learning sciences. J Learn Sci 15(1):11–34CrossRefGoogle Scholar
  16. Kaptelinin V, Nardi BA (2006) Acting with technology: activity theory and interaction design. MIT Press, CambridgeGoogle Scholar
  17. Metz KE (1995) Reassessment of developmental constraints on children’s science instruction. Rev Educ Res 65(2):93–127Google Scholar
  18. Metz KE (1997) On the complex relation between cognitive developmental research and children’s science curricula. Rev Educ Res 67(1):151–163Google Scholar
  19. NRC (1996) National science education standards. National Academy Press, Washington, DCGoogle Scholar
  20. Peppler K, Danish JA, Zaitlen B, Glosson D, Jacobs A, Phelps D (2010). BeeSim: leveraging wearable computers in participatory simulations with young children. In: Proceedings of the 9th international conference on interaction design and children. ACM, Barcelona, pp 246–249Google Scholar
  21. Resnick M (1996) Beyond the centralized mindset. J Learn Sci 5(1):1–22CrossRefGoogle Scholar
  22. Resnick M (1999) Decentralized modeling and decentralized thinking. In: Feurzeig W, Roberts N (eds) Modeling and simulation in precollege science and mathematics. Springer, New York, pp 114–137CrossRefGoogle Scholar
  23. Resnick M, Massachusetts Institute of Technology. Epistemology & Learning Research Group (1990) Overcoming the centralized mindset: towards an understanding of emergent phenomena. Epistemology and Learning Group, MIT Media Laboratory, Cambridge, MAGoogle Scholar
  24. Roth W-M (2007) On mediation: toward a cultural-historical understanding. Theory Psychol 17(5):655–680CrossRefGoogle Scholar
  25. Sabelli NH (2006) Complexity, technology, science, and education. J Learn Sci 15(1):5–9CrossRefGoogle Scholar
  26. Sandoval WA (2004) Developing learning theory by refining conjectures embodied in educational designs. Educ Psychol 39(4):213–223CrossRefGoogle Scholar
  27. Seeley TD (1995) The wisdom of the hive: the social physiology of honey bee colonies. Harvard University Press, CambridgeGoogle Scholar
  28. Vygotsky LS (1978) Mind in society: the development of higher psychological processes. Harvard University Press, CambridgeGoogle Scholar
  29. Wertsch JV (1981) The concept of activity in soviet psychology: an introduction. In: Wertsch JV (ed) The concept of activity in soviet psychology. M.E. Sharpe, Armonk, pp 3–36Google Scholar
  30. White B (1993) Thinker tools: causal models, conceptual change, and science education. Cogn Instruc 10(1):1–100CrossRefGoogle Scholar
  31. 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. Cogn Instruc 24(2):171–209CrossRefGoogle Scholar
  32. Wilensky U, Resnick M (1999) Thinking in levels: a dynamic systems perspective to making sense of the world. J Sci Educ Technol 8(1):3–19CrossRefGoogle Scholar
  33. Wilensky U, Stroup W (2000) Networked gridlock: students enacting complex dynamic phenomena with the HubNet architecture. Paper presented at the fourth annual international conference of the learning sciences, Ann ArborGoogle Scholar
  34. Witte SP, Haas C (2005) Research in activity: an analysis of speed bumps as mediational means. Written Commun 22(2):127–165CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Joshua A. Danish
    • 1
  • Kylie Peppler
    • 2
  • David Phelps
    • 1
  • DiAnna Washington
    • 1
  1. 1.Learning SciencesIndiana UniversityBloomingtonUSA
  2. 2.Learning SciencesIndiana UniversityBloomingtonUSA

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