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Modelling Symmetry of Activity as an Indicator of Collocated Group Collaboration

  • Roberto Martinez
  • Judy Kay
  • James R. Wallace
  • Kalina Yacef
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)

Abstract

There are many contexts where it would be helpful to model the collaboration of a group. In learning settings, this is important for classroom teachers and for students learning collaboration skills. Our approach exploits the digital and audio footprints of the users’ actions at collocated settings to automatically build a model of symmetry of activity. This paper describes our theoretical model of collaborative learning and how we implemented it. We use the Gini coefficient as a statistical indicator of symmetry of activity, which is itself an important indicator of collaboration. We built this model from a small-scale qualitative study based on concept mapping at an interactive tabletop. We then evaluated the model using a larger scale study based on a corpus of coded data from a multi-display groupware collocated setting. Our key contributions are the model of symmetry of activity as a foundation for modelling collaboration within groups that should have egalitarian participation, the operationalisation of the model and validation of the approach on both a small-scale qualitative study and a larger scale quantitative corpus of data.

Keywords

tabletop group modelling groupware collaborative learning collocated collaboration clustering 

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References

  1. 1.
    Martín, E., Haya, P.A.: Towards Adapting Group Activities in Multitouch Tabletops, in adj. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 28–30. Springer, Heidelberg (2010)Google Scholar
  2. 2.
    Dillenbourg, P.: What do you mean by ’collaborative learning’? In: Collaborative Learning: Cognitive and Computational Approaches, pp. 1–19. Elsevier Science, Amsterdam (1998)Google Scholar
  3. 3.
    Jeong, H., Hmelo-Silver, C.E.: An Overview of CSCL Methodologies. In: Proc. ICLS 2010, Chicago, USA, pp. 920–921 (2010)Google Scholar
  4. 4.
    Stahl, G.: Collaborative learning through practices of group cognition. In: Proc. CSCL 2009, pp. 33–42 (2009)Google Scholar
  5. 5.
    Upton, K., Kay, J.: Narcissus: Group and individual models to support small group work. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 54–65. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Anaya, A., Boticario, J.: Content-free collaborative learning modeling using data mining. User Modeling and User-Adapted Interaction 1(20), 1–36 (2010)Google Scholar
  7. 7.
    Perera, D., Kay, J., Koprinsca, I., Yacef, K., Zaiane, O.: Clustering and Sequential Pattern Mining of Online Collaborative Learning Data. IEEE TKDE 21, 759–772 (2009)Google Scholar
  8. 8.
    Soller, A., Lesgold, A.: Modeling the process of collaborative learning. The Role of Technology in Proc. CSCL 2007, 63–86 (2007)Google Scholar
  9. 9.
    Casillas, L., Daradoumis, T.: Knowledge extraction and representation of collaborative activity through ontology based and Social Network Analysis technologies. J. Business Intelligence and Data Minining 4(2), 141–158 (2009)CrossRefGoogle Scholar
  10. 10.
    Stahl, G.: Group Cognition: Computer Support for Building Collaborative Knowledge. MIT Press, Cambridge (2006)Google Scholar
  11. 11.
    Bachour, K., Kaplan, F., Dillenbourg, P.: An Interactive Table for Supporting Participation Balance in Face-to-Face Collaborative Learning. IEEE Transactions on Learning Technologies 3(3), 203–213 (2010)CrossRefGoogle Scholar
  12. 12.
    Thomas, V., Wang, Y., Fan, X.: Measuring Education Inequality: Gini Coefficients of Education (2000)Google Scholar
  13. 13.
    Belgiorno, F., Ilaria, M., Giuseppina, P., Vittorio, S.: Free-Riding in Collaborative Diagrams Drawing. In: Sustaining TEL: From Innovation to Learning and Practice, pp. 457–463 (2010)Google Scholar
  14. 14.
    Harris, A., Rick, J., Bonnett, V., Yuill, N., Fleck, R., Marshall, P., Rogers, Y.: Around the table: are multiple-touch surfaces better than single-touch for children’s collaborative interactions? In: Proc. CSCL 2009, pp. 335–344 (2009)Google Scholar
  15. 15.
    Novak, J.: Concept maps and Vee diagrams: two metacognitive tools to facilitate meaningful learning. Instructional Science 19(1), 29–52 (1990)CrossRefGoogle Scholar
  16. 16.
    Martinez, R., Kay, J., Yacef, K.: Collaborative concept mapping at the tabletop. In: Proc. ACM ITS, p. 207 (2010)Google Scholar
  17. 17.
    Martinez, R., Kay, J., Yacef, K.: Visualisations for longitudinal participation, contribution and progress of a collaborative task at the tabletop, pp. 2–11 (2011)Google Scholar
  18. 18.
    Tan, D., et al.: Using job-shop scheduling tasks for evaluating collocated collaboration. Personal and Ubiquitous Computing 12(3), 255–267 (2008)CrossRefGoogle Scholar
  19. 19.
    Wallace, J., Scott, S., Stutz, T., Enns, T., Inkpen, K.: Investigating teamwork and taskwork in single-and multi-display groupware systems. Personal and Ubiquitous Computing 13(8), 569–581 (2009)CrossRefGoogle Scholar
  20. 20.
    Martinez, R., Wallace, J., Kay, J., Yacef, K.: Modelling and identifying collaborative situations in a collocated multi-display groupware setting. In: Proc. AIED 2011 (2011)Google Scholar
  21. 21.
    Anaya, A., Boticario, J.: Clustering learners according to their collaboration. In: Proc. CSCWD 2009, pp. 540–545 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Roberto Martinez
    • 1
  • Judy Kay
    • 1
  • James R. Wallace
    • 2
  • Kalina Yacef
    • 1
  1. 1.School of Information TechnologiesJ12, University of SydneyAustralia
  2. 2.Department of Systems Design EngineeringUniversity WaterlooWaterlooCanada

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