Analytic Representations and Affordances for Productive Multivocality

  • Gregory Dyke
  • Kristine Lund
  • Daniel D. Suthers
  • Chris Teplovs
Chapter
Part of the Computer-Supported Collaborative Learning Series book series (CULS, volume 15)

Abstract

This chapter describes and reflects upon the analytic representations used in the analyses presented in this book, and the roles they played in multivocal analysis. As shown in other chapters, multivocality across analyses based on shared datasets can be productive in a variety of ways and for a variety of reasons. From a pragmatic perspective this productivity is also dependent on the ability of analysts to share datasets, perform analyses, inscribe new analytic knowledge into representations and use these representations as a basis for discussion. In this chapter, we examine how representations are used and given meaning in analysis. We catalogue the types of entities and attributes inscribed in representations, the notational systems by which they are encoded, and the kinds of moves that result in the creation of new representations. We then discuss the opportunities for multivocality afforded by the representations present in the different data sections, and discuss the properties desirable in a framework for coordinating analytic representations. We describe instances of representation-based productive multivocality found in this volume, presenting nine strategies for researchers seeking to engage in productive multivocality. This chapter will be of interest to tool designers, but also provide guidance to researchers in reflectively choosing representations (and their affordances for interpretation and manipulation) so as to maximize their ability to engage in productive multivocality.

Notes

Acknowledgements

 The authors are grateful to the ASLAN project (ANR-10-LABX-0081) of Université de Lyon, for its financial support within the program “Investissements d’Avenir” (ANR-11-IDEX-0007) of the French government operated by the National Research Agency (ANR).

References

  1. Ainsworth, S. E. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183–198.CrossRefGoogle Scholar
  2. Amarel, S. (1968). On representations of problems of reasoning about actions. Machine Intelligence, 3, 131–171.Google Scholar
  3. Chiu, M. M. (this volume). Social metacognition, micro-creativity and justifications: Statistical discourse analysis of a mathematics classroom conversation. In D. D. Suthers, K. Lund, C. P. Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 7. New York, NY: Springer.Google Scholar
  4. Chiu, M. M. (this volume). Statistical discourse analysis of an online discussion: Cognition and social metacognition. In D. D. Suthers, K. Lund, C. P. Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 23. New York, NY: Springer.Google Scholar
  5. Collins, A., & Ferguson, W. (1993). Epistemic forms and epistemic games: Structures and strategies to guide inquiry. Educational Psychologist, 28(1), 25–42.CrossRefGoogle Scholar
  6. Dyke, G., Lund, K., & Girardot, J.-J. (2009). Tatiana: An environment to support the CSCL analysis process. Proc. CSCL 2009, Rhodes, Greece (pp. 58–67).Google Scholar
  7. Dyke, G., Lund, K., Jeong, H., Medina, R., Suthers, D. D., van Aalst, J., et al. (2011). Technological affordances for productive multivocality in analysis. In H. Spada, G. Stahl, N. Miyake & N. Law (Eds.), Connecting Computer-Supported Collaborative Learning to Policy and Practice: Proceedings of the 9th International Conference on Computer-Supported Collaborative Learning (CSCL 2011) (Vol. I, pp. 454–461). Hong Kong: International Society of the Learning Sciences.Google Scholar
  8. Fisher, C., & Sanderson, P. (1996). Exploratory sequential data analysis: Exploring continuous observational data. Interactions, 3(2), 25–34.CrossRefGoogle Scholar
  9. Fujita, N. (this volume). Online graduate education course using knowledge forum. In D. D. Suthers, K. Lund, C. P. Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 20. New York, NY: Springer.Google Scholar
  10. Goggins, S. P., & Dyke, G. (this volume). Network analytic techniques for online chat. In D. D. Suthers, K. Lund, C. P. Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 29. New York, NY: Springer.Google Scholar
  11. Harrer, A., Zeini, S., Kahrimanis, G., Avouris, N., Marcos, J. A., Martinez-Mones, A., et al. (2007). Towards a flexible model for computer-based analysis and visualisation of collaborative learning activities. Proc. CSCL 2007, New Jersey, USA.Google Scholar
  12. Hmelo-Silver, C. E. (2003). Analyzing collaborative knowledge construction: Multiple methods for integrated understanding. Computers and Education, 41, 397–420.CrossRefGoogle Scholar
  13. Howley, I. K., Kumar, R., Mayfield, E., Dyke, G., & Rosé, C. P. (this volume). Gaining insights from sociolinguistic style analysis for redesign of conversational agent based support for collaborative learning. In D. D. Suthers, K. Lund, C. P. Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 26. New York, NY: Springer.Google Scholar
  14. Jeong, H. (this volume). Development of group understanding via the construction of physical and technological artifacts. In D. D. Suthers, K. Lund, C. P. Rose, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 18. New York, NY: Springer.Google Scholar
  15. Koedinger, K. R. (1991). On the design of novel notations and actions to facilitate thinking and learning. In Proceedings of the International Conference on the Learning Sciences (pp. 266–273). Charlottesville, VA.Google Scholar
  16. Koschmann, T., & Zemel, A. (2009). Optical pulsars and black arrows: Discoveries as occasioned productions. The Journal of the Learning Sciences, 18, 200–246.CrossRefGoogle Scholar
  17. Lajoie, S. P., & Derry, S. J. (Eds.). (1993). Computers as cognitive tools. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  18. Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11, 65–99.CrossRefGoogle Scholar
  19. Latour, B. (1990). Postmodern? No simply amodern. Steps towards an anthropology of science. An essay review. Studies in the History and Philosophy of Science, 21, 145–171.CrossRefGoogle Scholar
  20. Law, N., & Wong, O.-W. (this volume). Exploring pivotal moments in students’ knowledge building progress using participation and discourse marker indicators as heuristic guides. In D. D. Suthers, K. Lund, C. P. Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 22. New York, NY: SpringerGoogle Scholar
  21. Looi, C.-K., Song, Y., Wen, Y., & Chen, W. (this volume). Identifying pivotal contributions for group progressive inquiry in a multi-modal interaction environment. In D. D. Suthers, K. Lund, C. P. Rose, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 15. New York, NY: Springer.Google Scholar
  22. Lund, K., & Bécu-Robinault, K. (this volume). Conceptual change and sustainable coherency of concepts across modes of interaction. In D. D. Suthers, K. Lund, C. P. Rose, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 17. New York, NY: Springer.Google Scholar
  23. Medina, R. (this volume). Cascading inscriptions and practices: Diagramming and experimentation in the group scribbles classroom. In D. D. Suthers, K. Lund, C. P. Rose, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 16. New York, NY: Springer.Google Scholar
  24. Medina, R., & Suthers, D. D. (2013). Juxtaposing practice: Uptake as modal transposition. Proceedings of the 10th International Conference on Computer Supported Collaborative Learning (CSCL '13), Madison, WI. Google Scholar
  25. Norman, D. A. (1999 May–June). Affordance, conventions, and design. ACM Interactions, 6, 38–42.Google Scholar
  26. Oshima, J., Matsuzawa, Y., Oshima, R., & Niihara, Y. (this volume). Application of social network analysis to collaborative problem solving discourse: An attempt to capture dynamics of collective knowledge advancement. In D. D. Suthers, K. Lund, C. P. Rose, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 12. New York, NY: Springer.Google Scholar
  27. Puntambekar, S. (2006). Analyzing collaborative interactions: Divergence, shared understanding and construction of knowledge. Computers and Education, 47, 332–351.CrossRefGoogle Scholar
  28. Rosé, C. P. (this volume). A multivocal analysis of the emergence of leadership in chemistry study groups. In D. D. Suthers, K. Lund, C. P. Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 13. New York, NY: Springer.Google Scholar
  29. Roth, W.-M. (2003). Toward an anthropology of graphing: Semiotic and activity-theoretic perspectives. Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar
  30. Sanderson, P., & Fisher, C. (1994). Exploratory sequential data analysis: Foundations. Human-Computer Interaction, 9(4), 251–317.CrossRefGoogle Scholar
  31. Shirouzu, H. (this volume). Focus-based constructive interaction. In D. D. Suthers, K. Lund, C. P. Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 5. New York, NY: Springer.Google Scholar
  32. Shirouzu, H. (this volume). Learning fractions through folding in an elementary face-to-face classroom. In D. D. Suthers, K. Lund, C. P. Rose, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 4. New York, NY: Springer.Google Scholar
  33. Stahl, G. (this volume). Interaction analysis of a biology chat. In D. D. Suthers, K. Lund, C. P. Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 28. New York, NY: Springer.Google Scholar
  34. Suthers, D. (2000). Initial Evidence for Representational Guidance of Learning Discourse. In Proceedings of International Conference on Computers in Education, Taipei, Taiwan, 21–24.Google Scholar
  35. Suthers, D. D. (1999, January 5–8). Representational support for collaborative inquiry. In Proceedings of the Hawai‘i International Conference on System Sciences (HICSS-32), Maui, Hawai‘i (CD-ROM). Maui, Hawai‘i: Institute of Electrical and Electronics Engineers, Inc. (IEEE).Google Scholar
  36. Suthers, D. D. (2001). Towards a systematic study of representational guidance for collaborative learning discourse. Journal of Universal Computer Science, 7(3). http://www.jucs.org/jucs_7_3/ towards_a_systematic_study.
  37. Suthers, D. D. (2006). Technology affordances for intersubjective meaning making: A research agenda for CSCL. International Journal of Computer-Supported Collaborative Learning, 1(3), 315–337.CrossRefGoogle Scholar
  38. Suthers, D. D. (this volume). Agency and modalities in multimediated interaction. In D. D. Suthers, K. Lund, C. P. Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 19. New York, NY: Springer.Google Scholar
  39. Suthers, D. D., Dwyer, N., Medina, R., & Vatrapu, R. (2010). A framework for conceptualizing, representing, and analyzing distributed interaction. International Journal of Computer-Supported Collaborative Learning, 5(1), 5–42.CrossRefGoogle Scholar
  40. Suthers, D. D., & Hundhausen, C. (2003). An experimental study of the effects of representational guidance on collaborative learning. The Journal of the Learning Sciences, 12(2), 183–219.CrossRefGoogle Scholar
  41. Suthers, D. D., Vatrapu, R., Medina, R., Joseph, S., & Dwyer, N. (2008). Beyond threaded discussion: Representational guidance in asynchronous collaborative learning environments. Computers and Education, 50(4), 1103–1127.CrossRefGoogle Scholar
  42. Teplovs, C., & Fujita, N. (this volume). Socio-dynamic latent semantic learner models. In D. D. Suthers, K. Lund, C. P. Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 21. New York, NY: SpringerGoogle Scholar
  43. Thomas, J. J., & Cook, K. A. (Eds.). (2005). Illuminating the path: The research and development agenda for visual analytics. Los Alamitos, CA: IEEE Press.Google Scholar
  44. Utgoff, P. (1986). Shift of bias for inductive concept learning. In R. Michalski, J. Carbonell, & T. Mitchell (Eds.), Machine learning: An artificial intelligence approach (Vol. II, pp. 107–148). Los Altos, CA: Morgan Kaufmann.Google Scholar
  45. Zhang, J. (1997). The nature of external representations in problem solving. Cognitive Science, 21(2), 179–217.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Gregory Dyke
    • 1
  • Kristine Lund
    • 1
  • Daniel D. Suthers
    • 2
  • Chris Teplovs
    • 3
  1. 1.ICAR Research LabCNRS—University of LyonLyonFrance
  2. 2.Department of Information and Computer SciencesUniversity of Hawai‘i at ManoaHonoluluUSA
  3. 3.Problemshift Inc.WindsorCanada

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