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The Australian Educational Researcher

, Volume 40, Issue 3, pp 315–337 | Cite as

The architecture of children’s use of language and tools when problem solving collaboratively with robotics

  • Kathy A. MillsEmail author
  • Vinesh Chandra
  • Ji Yong Park
Article

Abstract

This paper demonstrates, following Vygotsky, that language and tool use has a critical role in the collaborative problem-solving behaviour of school-age children. It reports original ethnographic classroom research examining the convergence of speech and practical activity in children’s collaborative problem solving with robotics programming tasks. The researchers analysed children’s interactions during a series of problem solving experiments in which Lego Mindstorms toolsets were used by teachers to create robotics design challenges among 24 students in a Year 4 Australian classroom (students aged 8.5–9.5 years). The design challenges were incrementally difficult, beginning with basic programming of straight line movement, and progressing to more complex challenges involving programming of the robots to raise Lego figures from conduit pipes using robots as pulleys with string and recycled materials. Data collection involved micro-genetic analysis of students’ speech interactions with tools, peers, and other experts, teacher interviews, and student focus group data. Coding the repeated patterns in the transcripts, the authors outline the structure of the children’s social speech in joint problem solving, demonstrating the patterns of speech and interaction that play an important role in the socialisation of the school-age child’s practical intellect.

Keywords

Problem solving Language Cognition Speech Vygotsky Collaboration 

Notes

Acknowledgments

This research was funded by the Australian Research Council Linkage scheme project LP0990289. The views are those of the authors, and are not necessarily those of the Australian Research Council. The authors acknowledge research colleagues Professor Allan Luke and A/Prof Annette Woods et al. and research partners—the Queensland Teachers’ Union and the school staff involved in this project. All students in this study provided consent from caregivers to participate. All names are pseudonyms.

References

  1. ACARA (2012). My School. Retrieved Feb 14 2012 from http://www.myschool.edu.au.
  2. Altheide, D., & Johnson, J. (1994). Criteria for assessing interpretive validity in qualitative research. In N. Denzin & Y. Lincoln (Eds.), Handbook of qualitative research (pp. 485–499). Thousand Oaks, CA: SAGE.Google Scholar
  3. Azmitia, M. (1988). Peer interaction and problem solving: When are two heads better than one? Child Development, 58(1), 87–96.CrossRefGoogle Scholar
  4. Baldo, J. V., Dronkers, N. F., Wilkins, D., Ludy, C., Raskin, P., & Kim, J. (2005). Is problem solving dependent on language? Brain and Language, 92(3), 240–250.CrossRefGoogle Scholar
  5. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  6. Barak, M., & Zadok, Y. (2009). Robotics projects and learning concepts in science, technology and problem solving. International Journal of Technology and Design Education, 19(3), 289–307.CrossRefGoogle Scholar
  7. Behrend, S. & Resnick, L. (1989). Peer scaffolding of cognitive change in a multiple variable experiment. Paper presented at the meeting of the Society for Research in Child Development, Kansas City, Kansas.Google Scholar
  8. Brown, A., Collins, J., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42.CrossRefGoogle Scholar
  9. Bruner, J. (1990). Acts of meaning. Cambridge, MA: Harvard University Press.Google Scholar
  10. Chambers, J., Carbonaro, M., & Rex, M. (2007). Scaffolding knowledge construction through robotic technology: A middle school case study. Electronic Journal for the Integration of Technology in Education, 6, 55–70.Google Scholar
  11. Chandra, V. (2010). Assessing technology and engineering projects using Edward de Bono’s six hats and LEGO 4C’s approach. Paper presented at the Inaugural STEM in Education Conference, 26–27th Nov, Queensland University of Technology, Brisbane, Australia.Google Scholar
  12. Chandra, V., Woods, A., & Levido, A. (2013). Low SES primary school students engaging in an after-school robotics program. In A. Honigsfeld & A. Cohan (Eds.), Breaking the mold of education: Innovative and successful practices for student engagement, empowerment, and motivation. vol 4 (pp. 107–115). Lanham: Rowman & Littlefield Education.Google Scholar
  13. Clark, A. (1998). Magic words: How language augments human computation. In P. Carruthers & J. Boucher (Eds.), Language and thought: Interdisciplinary themes (pp. 162–183). Cambridge: Cambridge University Press.Google Scholar
  14. Cole, M., & Engestrom, Y. (1993). A cultural-historical approach to distributed cognition. In G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations (pp. 1–46). New York: Cambridge University Press.Google Scholar
  15. Cole, M., & Gallego, M. (2000). Success is not enough: Challenges to sustaining new forms of educational activity. Computers in Human Behavior, 16(3), 271–286.CrossRefGoogle Scholar
  16. Dennett, D. (1991). Consciousness Explained. New York: Little Brown and Co.Google Scholar
  17. Emmison, M. (2003). The conceptualisation and analysis of visual data Qualitative analysis: Practice and innovation. Crown Nest, NSW: Allyn and Unwin.Google Scholar
  18. Ertmer, P. A., & Newby, T. J. (1996). The expert learner: Strategic, self-regulated, and reflective. Instructional Science, 24(4), 1–24.CrossRefGoogle Scholar
  19. FitzGerald, E. (2012). Analysing video and audio data: Existing approaches and new innovations. Bristol, UK: The Open University.Google Scholar
  20. Forman, E. A. (1989). The role of peer-interaction in the social construction of mathematical knowledge. International Journal of Educational Research, 13(1), 55–70.CrossRefGoogle Scholar
  21. Forman, E. A., & McPhail, J. (1993). Vygotskian perspective on children’s collaborative problem-solving activities. In E. A. Foreman, N. Minick, & C. A. Stone (Eds.), Contexts for learning: Sociocultural dynamics in children’s development. Oxford University Press: Oxford.Google Scholar
  22. Gauvain, M., & Rogoff, B. (1989). Collaborative problem solving and children’s planning skills. Developmental Psychology, 25(1), 139–151.CrossRefGoogle Scholar
  23. Gee, J. P. (2005). Language in the science classroom: Academic social languages as the heart of school-based literacy. In R. Yerrick & W. M. Roth (Eds.), Establishing scientific classroom discourse communities: Multiple voices of teaching and learning research (pp. 19–37). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  24. Lalonde, J., Vandapel, N., Huber, D. F., & Hebert, M. (2006). Natural terrain classification using three-dimensional ladar data for ground robot mobility. Journal of Field Robotics, 23(10), 839–861.CrossRefGoogle Scholar
  25. Lave, J., & Wenger, E. (Eds.). (1991). Situated learning: Legitimate peripheral participation. Cambridge, UK: Cambridge University Press.Google Scholar
  26. Luke, A., Comber, B., & Grant, H. (2003). Critical literacies and cultural studies. In M. Anstey & G. Bull (Eds.), The literacy lexicon (2nd ed., pp. 15–35). Frenchs Forest, NSW: Primary Education Publications Pty Ltd.Google Scholar
  27. Mason, L. (2007). Introduction. Bridging the cognitive and socio-cultural approaches in research on conceptual change: Is it feasible? Educational Psychologist, 42(1), 1–7.CrossRefGoogle Scholar
  28. Morgan, D. (1988). Focus groups as qualitative research. Newbury Park, CA: SAGE.Google Scholar
  29. Murphy, R., Kravitz, J., Stover, S., & Shoureshi, R. (2009). Mobile robots in mine rescue and recovery. Robotics and Automation Magazine, 16(2), 91–103.CrossRefGoogle Scholar
  30. Newman, D., Griffin, P., & Cole, M. (1989). The construction zone: Working for cognitive change in schools. New York: Cambridge University Press.Google Scholar
  31. Norris, S. P., & Phillips, L. M. (2003). How literacy in its fundamental sense is central to scientific literacy Science Education, 87(2), 224–240.Google Scholar
  32. Norton, S., McRobbie, C., & Ginns, I. (2007). Problem solving in a middle school robotics design classroom. Research in Science Education, 37(3), 261–277.CrossRefGoogle Scholar
  33. Phelps, E., & Damon, W. (1989). Problem solving with equals: Peer collaboration as a context for learning mathematics and spatial concepts. Journal of Educational Psychology, 81(4), 639–646.CrossRefGoogle Scholar
  34. Portz, S. (2002). LEGO League: Bringing robotics training to your middle school. Tech Directions, 61(10), 17–19.Google Scholar
  35. QSA (2007). Technology essential learnings by the end of year 5. Brisbane: QLD Studies Authority. Retrieved March 4 2013 from http://www.qsa.qld.edu.au/7300.html.
  36. Schoenfeld, A. (1989). Problem-solving in context. In R. Charles & E. Silver (Eds.), The teaching and assessing of mathematical problem-solving. Reston, VA: National Council of Teachers of Mathematics.Google Scholar
  37. Scribner, S. (1984). Studying working intelligence. In B. Rogoff & J. Lave (Eds.), Everyday cognition: Its development in social context. Cambridge, MA: Harvard University Press.Google Scholar
  38. Siegler, R. (1994). Cognitive variability: A key to understanding cognitive development. Current Directions in Psychological Science, 3(1), 1–5.CrossRefGoogle Scholar
  39. Silverman, D. (2001). Interpreting qualitative data: Methods for analysing talk, text, and interaction (2nd ed.). London: Sage.Google Scholar
  40. Sinatra, G. M., & Broughton, S. H. (2011). Bridging reading comprehension and conceptual change in science education: The promise of refutation text. Reading Research Quarterly, 46(4), 374–393.Google Scholar
  41. Spradley, J. (1979). The ethnographic interview. New York: Bergin.Google Scholar
  42. Sullivan, F. R. (2011). Serious and playful inquiry: Epistemological aspects of collaborative creativity. Educational Technology & Society, 14(1), 55–65.Google Scholar
  43. Tedlock, B. (2000). Ethnography and ethnographic representation. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (2nd ed., pp. 455–486). London, UK: SAGE.Google Scholar
  44. Tomasello, M., Kruger, A. C., & Ratner, H. H. (1993). Cultural learning. Behavioral and Brain Sciences, 16(3), 495–552.CrossRefGoogle Scholar
  45. Vygotsky, L. (1962). Thought and language. Cambridge, MA: Massachusetts Institute of Technology.CrossRefGoogle Scholar
  46. Vygotsky, L. (1978). Mind in society: The development of higher psychological processes. London: Harvard University Press.Google Scholar
  47. Vygotsky, L. (1981). The genesis of higher mental functions. In J. V. Wertsch (Ed.), The concept of activity in Soviet psychology. New York: Armonk.Google Scholar
  48. Wertsch, J. V. (1981). The concept of activity in Soviet psychology. Armonk, NY: Sharpe.Google Scholar
  49. Wertsch, J. V. (1985). Vygotsky and the social formation of mind. Cambridge, MA: Harvard University Press.Google Scholar
  50. Yore, L. D. (2000). Enhancing science literacy for all students with embedded reading instructive and writing-to-learn activities. Journal of Deaf Studies and Deaf Education, 5(1), 105–122.CrossRefGoogle Scholar

Copyright information

© The Australian Association for Research in Education, Inc. 2013

Authors and Affiliations

  • Kathy A. Mills
    • 1
    Email author
  • Vinesh Chandra
    • 2
  • Ji Yong Park
    • 3
  1. 1.Faculty of Education, School of CurriculumQueensland University of TechnologyKelvin GroveAustralia
  2. 2.Faculty of Education, School of CurriculumQueensland University of TechnologyKelvin GroveAustralia
  3. 3.Faculty of Education, School of CurriculumQueensland University of TechnologyKelvin GroveAustralia

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