Computer-Supported Collaborative Drawing in Primary School Education – Technical Realization and Empirical Findings

  • Lars Bollen
  • Hannie Gijlers
  • Wouter van Joolingen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7493)


Self-constructed external representation, especially when embedded in peer inter-actions, are supposed to be beneficial in learning and teaching and can positively affect the course and type of reasoning for various reasons, e.g. by providing a ground for explanations and self-explanations, by helping to disambiguate learners’ mental models of phenomena, by reducing working memory load, and by increasing and sharing the task focus. This paper reports on the results of research efforts in investigating conditions that are advantageous in collaborative drawing activities in learning scenarios for young students. We describe the design, technical implementation and empirical results of a study with 94 primary school students working on a collaborative drawing task in various conditions that include awareness information, prompting and scripted activities.


external representations collaboration shared workspace primary school education scripted collaboration awareness support 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    van Meter, P., Garner, J.: The Promise and Practice of Learner-Generated Drawing: Literature Review and Synthesis. Educational Psychology Review 17, 285–325 (2005)CrossRefGoogle Scholar
  2. 2.
    Leopold, C., Leutner, D.: Science text comprehension: Drawing, main idea selection, and summarizing as learning strategies. Learning and Instruction 22, 16–26 (2012)CrossRefGoogle Scholar
  3. 3.
    Schwamborn, A., Mayer, R.E., Thillmann, H., Leopold, C., Leutner, D.: Drawing as a Generative Activity and Drawing as a Prognostic Activity. Journal of Educational Psychology 102, 872–879 (2010)CrossRefGoogle Scholar
  4. 4.
    van Meter, P.: Drawing construction as a strategy for learning from text. Journal of Educational Psychology 93, 129–140 (2001)CrossRefGoogle Scholar
  5. 5.
    Brooks, M.: Drawing, Visualisation and Young Children’s Exploration of “Big Ideas”. International Journal of Science Education 31, 319–341 (2009)CrossRefGoogle Scholar
  6. 6.
    Buder, J., Bodemer, D.: Supporting controversial CSCL discussions with augmented group awareness tools. International Journal of Computer-Supported Collaborative Learning 3, 123–139 (2008)CrossRefGoogle Scholar
  7. 7.
    Weinberger, A., Fischer, F.: A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education 46, 71–95 (2006)CrossRefGoogle Scholar
  8. 8.
    Dillenbourg, P.: Over-scripting CSCL: The risks of blending collaborative learning with instructional design. In: Kirschner, P.A. (ed.) Three Worlds of CSCL. Can We Support CSCL, pp. 61–91. Open Universiteit Nederland, Heerlen (2002)Google Scholar
  9. 9.
    Anjewierden, A., Gijlers, H., Kolloffel, B., Saab, N., de Hoog, R.: Examining the relation between domain-related communication and collaborative inquiry learning. Computers & Education 57, 1741–1748 (2011)CrossRefGoogle Scholar
  10. 10.
    Berkowitz, M.W., Gibbs, J.C.: Measuring the developmental features of moral discussion. Merrill-Palmer Quarterly 29, 399–410 (1983)Google Scholar
  11. 11.
    Teasley, S.D.: Talking About Reasoning: How Important Is the Peer in Peer Collaboration? In: Resnick, L.B., Saljo, R., Pontecorvo, C., Burge, B. (eds.) Discourse, Tools, and Reasoning: Essays on Situated Cognition, pp. 361–384. Springer (1997)Google Scholar
  12. 12.
    Buschmann, F., Meunier, R., Rohnert, H., Sommerlad, P., Stal, M.: Pattern-Oriented Software Architecture: A System of Patterns. John Wiley & Sons Ltd., West Sussex (1996)Google Scholar
  13. 13.
    Gelernter, D.: Generative communication in Linda. ACM Transactions on Programming Languages and Systems 7, 80–112 (1985)zbMATHCrossRefGoogle Scholar
  14. 14.
    Weinbrenner, S., Giemza, A., Hoppe, H.U.: Engineering Heterogeneous Distributed Learn-ing Environments Using Tuple Spaces as an Architectural Platform. In: Proceedings of Seventh IEEE International Conference on Advanced Learning Technologies, ICALT 2007, pp. 434–436 (2007)Google Scholar
  15. 15.
    Weinbrenner, S.: Entwicklung und Erprobung einer Architektur für heterogene verteilte Systeme mit TupleSpaces und Prolog. Faculty of Engineering, Department of Computational and Cognitive Sciences. Diploma thesis. University of Duisburg-Essen (2006)Google Scholar
  16. 16.
    Bollen, L., Giemza, A., Hoppe, H.U.: Flexible Analysis of User Actions in Heterogeneous Distributed Learning Environments. In: Dillenbourg, P., Specht, M. (eds.) EC-TEL 2008. LNCS, vol. 5192, pp. 62–73. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Bollen, L., van Joolingen, W.R., Leenaars, F.: Towards Modeling with Inaccurate Drawings. In: Proceedings of International Workshop on Intelligent Support for Exploratory Environments at the International Conference on Artificial Intelligence in Education, AIED 2009 (2009)Google Scholar
  18. 18.
    Leenaars, F.: Facilitating Model Construction during Inquiry Learning with Self-Generated Drawings. Faculty of Behavioural Sciences, Department of Instructional Technology. Master thesis. University of Twente (2009)Google Scholar
  19. 19.
    van Joolingen, W.R., Bollen, L., Leenaars, F.A.J.: Using Drawings in Knowledge Modeling and Simulation for Science Teaching. In: Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds.) Advances in Intelligent Tutoring Systems. SCI, vol. 308, pp. 249–264. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Proceedings of 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), pp. 935–940. ACM (2006)Google Scholar
  21. 21.
    Hammond, T., Davis, R.: LADDER: A Language to Describe Drawing, Display, and Edit-ing in Sketch Recognition. In: Proceedings of International Joint Conference on Artificial Intelligence (2003)Google Scholar
  22. 22.
    Hammond, T., Davis, R.: LADDER, a sketching language for user interface developers. Computers & Graphics 29, 518–532 (2005)CrossRefGoogle Scholar
  23. 23.
    Weinberger, A., Fischer, F.: A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education 46, 71–95 (2006)CrossRefGoogle Scholar
  24. 24.
    Weinberger, A., Stegmann, K., Fischer, F.: Learning to argue online: Scripted groups surpass individuals (unscripted groups do not). Computers in Human Behavior 26, 506–515 (2010)CrossRefGoogle Scholar
  25. 25.
    Bollen, L.: Activity Structuring and Activity Monitoring in Heterogeneous Learning Scenarios with Mobile Devices. Verlag Dr. Kovac, Hamburg (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lars Bollen
    • 1
  • Hannie Gijlers
    • 1
  • Wouter van Joolingen
    • 1
  1. 1.Dept. of Instructional TechnologyUniversity of TwenteThe Netherlands

Personalised recommendations