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When Simple Harmonic Motion is not That Simple: Managing Epistemological Complexity by Using Computer-based Representations

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Abstract

Many real-world phenomena, even “simple” physical phenomena such as natural harmonic motion, are complex in the sense that they require coordinating multiple subtle foci of attention to get the required information when experiencing them. Moreover, for students to develop sound understanding of a concept or a phenomenon, they need to learn to get the same type of information across different contexts and situations (diSessa and Sherin 1998; diSessa and Wagner 2005). Rather than simplifying complex situations, or creating a linear instructional sequence in which students move from one context to another, this paper demonstrates the use of computer-based representations to facilitate developing understanding of complex physical phenomena. The data is collected from 8 studies in which pairs of students are engaged in an exploratory activity, trying to understand the dynamic behavior of a simulation and, at the same time, to attribute meaning to it in terms of the physical phenomenon it represents. The analysis focuses on three episodes. The first two episodes demonstrate the epistemological complexity involved in attempting to make sense of natural harmonic oscillation. A third episode demonstrates the process by which students develop understanding in this complex perceptual and conceptual territory, through the mediation (Vygotsky 1978) of computer-based representations designed to facilitate understanding in this topic.

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References

  • Ainsworth S (1999) The functions of multiple representations. Comput Educ 33(2–3):131–152

    Article  Google Scholar 

  • Bamberger J (1990) The laboratory for making things: developing multiple representations of knowledge. In: Schön DA (ed) The reflective turn. Teachers College Press, New York

    Google Scholar 

  • Bamberger J, diSessa A (2003) Music as embodied mathematics: a study of a mutually informing affinity. Int J Comput Math Learn 8(2):123–160

    Article  Google Scholar 

  • Brown DE, Hammer D (2008) Conceptual change in physics. In: Vosniadou S (ed) International handbook of research on conceptual change. Routledge, New York, pp 127–154

    Google Scholar 

  • diSessa AA (1993) Toward an epistemology of physics. Cogn Instr 10(2–3):105–225

    Google Scholar 

  • diSessa AA (2008) A bird’s-eye view of the pieces vs. coherence controversy (from the pieces side of the fence). In: Vosniadou S (ed) International handbook of conceptual change. Taylor and Francis, New York, pp 35–60

    Google Scholar 

  • diSessa AA, Sherin BL (1998) What changes in conceptual change? Int J Sci Educ 20(10):1155–1191

    Article  Google Scholar 

  • diSessa AA, Wagner JF (2005) What coordination has to say about transfer. In: Mestre JP (ed) Transfer of learning from a modern multidisciplinary perspective. Information Age Publishing, Greenwich, pp 121–154

    Google Scholar 

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

    Article  Google Scholar 

  • Hammer D (2006). The complexity of complexity. In Levrini, O. (organizer). Why complexity is important for learning? Symposium presented at the annual meeting of the American Educational Research Association, San Francisco, CA

  • Inhelder B, Piaget J (1958) The growth of logical thinking from childhood to adolescence. Basic Books, New York

    Book  Google Scholar 

  • Jacobson MJ, Wilensky U (2006) Complex systems in education: scientific and educational importance and implications for the learning sciences. J Learn Sci 15(1):11–34

    Article  Google Scholar 

  • Jones S, Scaife M (2000) Animated diagrams: an investigation into the cognitive effects of using animation to illustrate dynamic processes. In: Anderson M, Cheng P (eds) Theory & applications of diagrams: lecture notes in artificial intelligence (No. 1889). Springer, Berlin, pp 231–244

    Google Scholar 

  • Kaput J (1989) Linking representations in the symbol systems of algebra. In: Kieran C, Wagner S (eds) Research agenda for mathematics education: research issues in the learning and teaching of algebra. Erlbaum, Hillsdale, pp 167–194

    Google Scholar 

  • Kozma RB, Russell J (1997) Multimedia and understanding: expert and novice responses to different representations of chemical phenomena. J Res Sci Teach 34:949–968

    Article  Google Scholar 

  • Latour B (1986) Visualization and cognition: thinking with eyes and hands. Knowl Soc 6:1–40

    Google Scholar 

  • Latour B (1999) Pandora’s hope: essays on the reality of science studies (Chaps. 1–2). Harvard University Press, Cambridge

    Google Scholar 

  • Levin I, Gardosh R (1993) Everyday concepts and formal concepts: do children distinguish between linear and rotational speed? In: Tirosh D (ed) Implicit and explicit knowledge: an educational approach. Ablex, Norwood, pp 181–203

    Google Scholar 

  • Levrini O, Parnafes O, diSessa AA, Bamberger J, Hammer D (2006) Why complexity is important for learning?. In: Symposium presented at the annual meeting of the American Educational Research Association, San Francisco, CA

  • Linn MC (1995) Designing computer learning environments for engineering and computer science: the scaffolded knowledge integration framework. J Sci Educ Technol 4(2):103–126

    Article  Google Scholar 

  • Linn MC (2008) Teaching for conceptual change: distinguish or extinguish ideas. In: Vosniadou S (ed) International handbook of research on conceptual change. Routledge, New York, pp 694–718

    Google Scholar 

  • Lynch M, Woolgar S (1990) Introduction: sociological orientations to representational practice in science. In Representation in scientific practice. MIT Press, Cambridge

    Google Scholar 

  • Minsky ML (1986) The society of mind. Simon and Schuster, New York

    Google Scholar 

  • Narayanan NH, Hegarty M (2000) Communicating dynamic behaviors: are interactive multimedia presentations better than static mixed-mode presentations? In: Proceedings of the first international conference on the theory and application of diagrams (Diagrams 2000), Springer, pp 178–193

  • Norman DA (1993) Things that make us smart: defending human attributes in the age of the machine. Perseus Books, Cambridge

    Google Scholar 

  • Parnafes O (2005) The development of conceptual understanding through the use of computer-based representations. Unpublished doctoral dissertation, University of California at Berkeley

  • Parnafes O (2007) What does fast mean? Understanding the physical world through representations. J Learn Sci 16(3):415–450

    Google Scholar 

  • Sabelli NH (2006) Complexity, technology, science, and education. J Learn Sci 15(1):5–9

    Article  Google Scholar 

  • Scaife M, Rogers Y (1996) External cognition: how do graphical representations work? Int J Human-Comput Stud 45:185–213

    Article  Google Scholar 

  • Vygotsky LS (1978) Mind in society: the development of higher psychological processes. Harvard University Press, Cambridge

    Google Scholar 

  • Vygotsky LS (1986) Thought and language. MIT Press, Cambridge

    Google Scholar 

  • Yerushalmy M (1991) Student perceptions of aspects of algebraic function using multiple representation software. J Comput Assist Learn 7:42–57

    Article  Google Scholar 

  • Zhang J, Norman D (1994) Representations in distributed cognitive tasks. Cogn Sci 18(1):87–122

    Article  Google Scholar 

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Acknowledgements

I thank Olivia Levrini, David Hammer, Andy diSessa and Jeanne Bamberger for insightful collaboration around the issue of complexity in learning that gave rise to this paper. I am grateful to Mariana Levin and two anonymous reviewers for numerous comments and suggestions on versions of this paper.

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Correspondence to Orit Parnafes.

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Parnafes, O. When Simple Harmonic Motion is not That Simple: Managing Epistemological Complexity by Using Computer-based Representations. J Sci Educ Technol 19, 565–579 (2010). https://doi.org/10.1007/s10956-010-9224-9

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