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Collaborative and Differential Utterances, Pivotal Moments, and Polyphony

Part of the Computer-Supported Collaborative Learning Series book series (CULS,volume 15)

Abstract

This chapter presents a multivocal analysis method of collaborative learning and its application on the origami fractions data set, considering several dimensions: spoken dialogue, body language, visual dimension, internal dialogue (at an intramental level), and echoes. The analysis is performed starting from the polyphonic model, which was previously used for instant messenger conversations and discussion forums and was extended for the face-to-face classroom interactions in this data set. The analysis includes the identification of the voices, in an extended sense, interanimation patterns among them, collaborative and differential utterances, changes in the rhythm (the chronotopes), and pivotal moments of the interactions.

Keywords

  • Collaborative Learning
  • Natural Language Processing
  • Body Language
  • Differential Pattern
  • Internal Dialogue

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Fig. 6.1

Notes

  1. 1.

    In our approach, we consider a chronotope as “a genre of movement or pacing in the space that participants adopt over the temporal duration of an activity” (Ligorio & Ritella, 2010).

  2. 2.

    The indicated times are those from the video file, not those in the transcription or subtitling, which are slightly different.

  3. 3.

    Of course such a tool loses many useful details of the face-to-face origami data set because it is not able to identify nonverbal utterances. However, as it will be seen below, it still provides some useful representation for analyzing rhythm and pivotal moments.

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Acknowledgements

I would like to thank Kris Lund and Dan Suthers for their very useful comments and suggestions. I also want to thank Hajime Shirouzu for the very interesting data set; Hajime and Ming Ming Chiu for the very interesting discussions on the analysis of the learning sessions; and Costin Chiru, Traian Rebedea, and Mihai Dascalu for their help on the automated support development. The work in this chapter was supported partially by the EU FP7 projects LTfLL and ERRIC.

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Trausan-Matu, S. (2013). Collaborative and Differential Utterances, Pivotal Moments, and Polyphony. In: Suthers, D., Lund, K., Rosé, C., Teplovs, C., Law, N. (eds) Productive Multivocality in the Analysis of Group Interactions. Computer-Supported Collaborative Learning Series, vol 15. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-8960-3_6

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