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Analyzing engagement taxonomy in collaborative algorithm visualization

Published:25 June 2007Publication History
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Abstract

More collaborative use of visualizations is taking place in the classrooms due to the introduction of pair programming and collaborative learning as teaching and learning methods. This introduces new challenges to the visualization tools, and thus, research and theory to support the development of collaborative visualization tools is needed. We present an empirical study in which the learning outcomes of students were compared when students were learning in collaboration and using materials which contained visualizations on different engagement levels. Results indicate that the level of engagement has an effect on students' learning results although the difference is not statistically significant. Especially, students without previous knowledge seem to gain more from using visualizations on higher engagement level.

References

  1. R. Ben-Bassat Levy, M. Ben-Ari, and P. A. Uronen. The Jeliot 2000 program animation system. Computers & Education, 40(1):15--21, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. J. Cohen. Statistical power analysis for the behavioral sciences. Academic Press, New York, 1977.Google ScholarGoogle Scholar
  3. C. Evans and N. J. Gibbons. The Interactivity Effect in Multimedia Learning. Accepted to Computers & Education, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Grissom, M. McNally, and T. L. Naps. Algorithm visualization in CS education: comparing levels of student engagement. In Proceedings of the First ACM Symposium on Software Visualization, pages 87--94, June 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. T. Hübscher-Younger and N. H. Narayanan. Constructive and collaborative learning of algorithms. SIGCSE Bulletin, 35(1):6--10, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. C. D. Hundhausen. Integrating Algorithm Visualization Technology into an Undergraduate Algorithms Course: Ethnographic Studies of a Social Constructivist Approach. Computers & Education, 39(3):237--260, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. C. D. Hundhausen and J. L. Brown. Designing, Visualizing, and Discussing Algorithms within a CS 1 Studio Experience: An Empirical Study. Computers & Education, In press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. C. D. Hundhausen, S. A. Douglas, and J. T. Stasko. A Meta-Study of Algorithm Visualization Effectiveness. Journal of Visual Languages and Computing, 13(3):259--290, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  9. A. Korhonen, L. Malmi, P. Silvasti, V. Karavirta, J. Lnnberg, J. Nikander, K. Stålnacke, and P. Ihantola. Matrix - a framework for interactive software visualization. Research Report TKO-B 154/04, Laboratory of Information Processing Science, Department of Computer Science and Engineering, Helsinki University of Technology, 2004.Google ScholarGoogle Scholar
  10. N. Nagappan, L. Williams, M. Ferzli, E. Wiebe, K. Yang, C. Miller, and S. Balik. Improving the CS1 experience with pair programming. In Proceedings of the 34th SIGCSE technical symposium on Computer science education, pages 359--362. ACM Press, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. T. L. Naps and S. Grissom. The effective use of quicksort visualizations in the classroom. Journal of Computing Sciences in Colleges, 18(1):88--96, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. T. L. Naps, G. Rößling, V. Almstrum, W. Dann, R. Fleischer, C. Hundhausen, A. Korhonen, L. Malmi, M. McNally, S. Rodger, and J. Á. Velázquez-Iturbide. Exploring the Role of Visualization and Engagement in Computer Science Education. In Working Group Reports from ITiCSE on Innovation and Technology in Computer Science Education, pages 131--152, New York, NY, USA, 2002. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. J. Roschelle. Designing for cognitive communication: Epistemic fidelity or mediating collaborating inquiry. In D. L. Day and D. K. Kovacs, editors, Computers, Communication & Mental Models, pages 13--25. Taylor & Francis, London, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. M. Scaife and Y. Rogers. External cognition: how do graphical representations work? International Journal of Human-Computer Studies, 45(2):185--213, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. D. D. Suthers and C. D. Hundhausen. An experimental study of the effects of representational guidance on collaborative learning processes. Journal of the Learning Sciences, 12(2):183--219, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  16. L. Williams, R. R. Kessler, W. Cunningham, and R. Jeffries. Strengthening the case for pair programming. IEEE Software, 17(4):19--25, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM SIGCSE Bulletin
      ACM SIGCSE Bulletin  Volume 39, Issue 3
      Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education (ITiCSE'07)
      September 2007
      366 pages
      ISSN:0097-8418
      DOI:10.1145/1269900
      Issue’s Table of Contents
      • cover image ACM Conferences
        ITiCSE '07: Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education
        June 2007
        386 pages
        ISBN:9781595936103
        DOI:10.1145/1268784

      Copyright © 2007 ACM

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      Publication History

      • Published: 25 June 2007

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