What happens when you push the button? Analyzing the functional dynamics of concept development in computer supported science inquiry

Article

Abstract

In this article we analyze how the joint cognitive system of teacher and student actions mediated by cultural tools develops sense making of science concepts, and the use of concepts as tools for explaining phenomena and processes related to energy and energy transformation. We take a sociocultural approach to the analysis of how material and digital learning resources become tools for thinking and reasoning. We combined ethnographic descriptions with analysis of video records of classroom interactions in a high school and examined how a teacher and a group of students engaged in a computer-supported collaborative inquiry. Our results show that students through inquiry are enabled to make sense of concepts and their experiences with resources and also to use science concepts as explanatory tools. However, this is mediated by the teachers’ practices for supporting students, such as providing relevant clues for them to continue their inquiry, eliciting their initial understanding of concepts thereby making them available for further development, pressing for explanations, and reformulating their explanations. The teacher is continuously alternating between withdrawing and making students inquire by themselves and supporting their inquiry. In and through such social interactions, materials and digital tools become tools for thinking. We argue that one of the practical implications of our study is that it is crucial that teachers explicitly draw students into their system of activity throughout the entire learning trajectory and that the teachers and students together make sense of science concepts for explaining energy transformation.

Keywords

Collaborative learning Digital learning resources Science learning CSCL Functional systems Multi-representational learning settings 

References

  1. Ainsworth, S. (1999). The functions of multiple representations. Computers & Education, 33(2–3), 131–152.CrossRefGoogle Scholar
  2. Arnseth, H. C., & Ludvigsen, S. (2006). Approaching institutional contexts: systemic versus dialogic research in CSCL. International Journal of Computer-Supported Collaborative Learning, 1(2), 167–185.CrossRefGoogle Scholar
  3. Bakhurst, D. (2007). Vygotsky's demons. In H. Daniels, M. Cole, & J. V. Wertsch (Eds.), The Cambridge companion to Vygotsky (pp. 50–76). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  4. Bereiter, C. (1985). Toward a solution of the learning paradox. Review of Educational Research, 55(2), 201–226.CrossRefGoogle Scholar
  5. Braaten, M., & Windschitl, M. (2011). Working toward a stronger conceptualization of scientific explanation for science education. Science Education, 95(4), 639–669. doi:10.1002/sce.20449.CrossRefGoogle Scholar
  6. Bransford, J., Brown, A., & Cocking, R. R. (Eds.) (2000). How people learn : brain, mind, experience, and school. Washington, D.C.: National Academy Press.Google Scholar
  7. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. doi:10.1191/1478088706qp063oa.CrossRefGoogle Scholar
  8. Clark, A. (1997). Being there: putting brain, body and world together again. Cambridge, MA: MIT Press.Google Scholar
  9. Cole, M. (1996). Cultural psychology: a once and future discipline. Cambridge, MA: The Belknap Press of Harvard University Press.Google Scholar
  10. Donnelly, D. F., Linn, M. C., & Ludvigsen, S. (2014). Impacts and characteristics of computer-based science inquiry learning environments for precollege students. Review of Educational Research, 84(4), 572–608. doi:10.3102/0034654314546954.CrossRefGoogle Scholar
  11. Enyedy, N., & Stevens, R. (2015). Analyzing collaboration. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences. New York: Cambridge University Press.Google Scholar
  12. Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12(2), 219–245. doi:10.1177/1077800405284363.CrossRefGoogle Scholar
  13. Furberg, A. (2016). Teacher support in computer-supported lab work: bridging the gap between lab experiments and students’ conceptual understanding. International Journal of Computer-Supported Collaborative Learning, 11(1), 89–113. doi:10.1007/s11412-016-9229-3.CrossRefGoogle Scholar
  14. Furberg, A., Ludvigsen, S., & Kluge, A. (2013). Students' sense making with science diagrams in a computer-based setting. International Journal of Computer-Supported Collaborative Learning, 8(1), 41–64.CrossRefGoogle Scholar
  15. Gillen, J., Littleton, K., Twiner, A., Staarman, J. K., & Mercer, N. (2008). Using the interactive whiteboard to resource continuity and support multimodal teaching in a primary science classroom. Journal of Computer Assisted Learning, 24(4), 348–358. doi:10.1111/j.1365-2729.2007.00269.x.CrossRefGoogle Scholar
  16. Greiffenhagen, C. (2012). Making rounds: the routine work of the teacher during collaborative learning with computers. International Journal of Computer-Supported Collaborative Learning, 7(1), 11–42. doi:10.1007/s11412-011-9134-8.CrossRefGoogle Scholar
  17. John-Steiner, V., Meehan, T. M., & Mahn, H. (1998). A functional systems approach to concept development. Mind, Culture, and Activity, 5(2), 127–134. doi:10.1207/s15327884mca0502_6.CrossRefGoogle Scholar
  18. Jornet, A., & Roth, W. M. (2015). The work of connecting multiple (Re) presentational forms in science classrooms. Science Education, 99(2), 378–403.CrossRefGoogle Scholar
  19. Jornet, A., Roth, W.-M., & Krange, I. (2016). A transactional approach to transfer episodes. The Journal of the Learning Sciences, 25(2), 285–330.CrossRefGoogle Scholar
  20. Krange, I., & Ludvigsen, S. (2008). What does it mean? Students’ procedural and conceptual problem solving in a CSCL environment designed within the field of science education. International Journal of Computer-Supported Collaborative Learning, 3(25–51).Google Scholar
  21. Linn, M. C., & Eylon, B. S. (2011). Science learning and instruction. Taking advantage of technology to promote knowledge integration. New York: Routledge.Google Scholar
  22. Linn, M. C., Davis, E. A., & Eylon, B. S. (2004). The scaffolded knowledge integration framework for instruction. In M. C. Linn, E. A. Davis, & P. Bell (Eds.), Internet environments for science education (pp. 47–72). Mahwah, NJ: Lawrence Erlbaum Associates, Inc..Google Scholar
  23. Luria, A. R. (1932). The nature of human conflicts: or emotion, conflict and will. New York: Liveright.Google Scholar
  24. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52.CrossRefGoogle Scholar
  25. Mercer, N. (2000). Words & minds. How we use language to think together. London & New York: Routledge.CrossRefGoogle Scholar
  26. Newman, D., Griffin, P., & Cole, M. (1989). The construction zone: working for cognitive change in school. New York, NY: Cambridge University Press.Google Scholar
  27. Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., et al. (2004). A scaffolding design framework for software to support Sceince inquiry. The Journal of Leaning Sciences, 13(3), 337–386.CrossRefGoogle Scholar
  28. Rogers, Y. (2008). Using external visualizations to extend and integrate learning in mobile and classroom settings. In J. K. Gilbert, M. Reiner, & M. Nakleh (Eds.), Visualization: theory and practice in science education: Springer.Google Scholar
  29. Roschelle, J. (1992). Learning by collaborating: convergent conceptual change. The Journal of the Learning Sciences, 2(3), 235–276.CrossRefGoogle Scholar
  30. Schaeffer, J. H. (1995). Videotape: New Techniques of Observation and Analysis in Anthropology. In P. Hockings (Ed.), Principles of Visual Anthropology (pp. 255–284): De Gruyter.Google Scholar
  31. Schwartz, D. L. (1995). The emergence of abstract representations in dyad problem solving. The Journal of the Learning Sciences, 4(3), 321–354.CrossRefGoogle Scholar
  32. Stahl, G. (2006). Group cognition : computer support for building collaborative knowledge. Cambridge, Mass: MIT Press.Google Scholar
  33. Tabak, I. (2004). Synergy: a complement to emerging patterns of distributed scaffolding. The Journal of the Learning Sciences, 13(3), 305–335.CrossRefGoogle Scholar
  34. van der Meij, J., & de Jong, T. (2006). Learning with multiple representations. Supporting students' learning with multiple representations in a dynamic simulation-based learning environment. Learning and Instruction, 16(3), 199–212.CrossRefGoogle Scholar
  35. van der Pol, J., Volman, M., & Beishuizen, J. (2010). Scaffolding in teacher-student interaction: a decade of research. Educational Psychology Review, 22, 271–296.CrossRefGoogle Scholar
  36. Van Joolingen, W. R., De Jong, T., & Dimitrakopoulou, A. (2007). Issues in computer supported inquiry learning in science. Journal of Computer Assisted Learning, 23(2), 111–119. doi:10.1111/j.1365-2729.2006.00216.x.CrossRefGoogle Scholar
  37. Vygotsky, L. (1986). Thought and language (A Kozulin ed.). Cambridge: MIT Press.Google Scholar
  38. Wertsch, J. (1998). Mind as action. New York: Oxford University Press.Google Scholar
  39. Windschitl, M., Thompson, J., Braaten, M., & Stroupe, D. (2012). Proposing a core set of instructional practices and tools for teachers of science. Science Education, 96(5), 878–903. doi:10.1002/sce.21027.CrossRefGoogle Scholar
  40. Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89–100.CrossRefGoogle Scholar

Copyright information

© International Society of the Learning Sciences, Inc. 2016

Authors and Affiliations

  1. 1.Department of EducationUniversity of OsloOsloNorway
  2. 2.Department of Creativity and InnovationKristiania University CollegeOsloNorway

Personalised recommendations