HOW DOES ALGORITHM VISUALIZATION AFFECT COLLABORATION? - Video Analysis of Engagement and Discussions

Ari Korhonen, Mikko-Jussi Laakso, Niko Myller

2009

Abstract

In this paper, we report a study on the use of Algorithm Visualizations (AV) in collaborative learning. Our previous results have confirmed the hypothesis that students’ higher engagement has a positive effect on learning outcomes. Thus, we now analyze the students’ collaborative learning process in order to find phenomena that explain the learning improvements. Based on the study of the recorded screens and audio during the learning, we show that the amount of collaboration and discussion increases during the learning sessions when the level of engagement increases. Furthermore, the groups that used visualizations on higher level of engagement, discussed the learned topic on different levels of abstraction whereas groups that used visualizations on lower levels of engagement tended to concentrate more on only one aspect of the topic. Therefore, we conclude that the level of engagement predicts, not only the learning performance, but also the amount of on-topic discussion in collaboration. Furthermore, we claim that the amount and quality of discussions explain the learning performance differences when students use visualizations in collaboration on different levels of engagement.

References

  1. Beck, L. L. and Chizhik, A. W. (2008). An experimental study of cooperative learning in CS1. In SIGCSE 7808: Proceedings of the 39th SIGCSE technical symposium on Computer science education, pages 205-209, New York, NY, USA. ACM.
  2. Ben-Bassat Levy, R., Ben-Ari, M., and Uronen, P. A. (2003). The Jeliot 2000 program animation system. Computers & Education, 40(1):15-21.
  3. Berkowitz, M. W. and Gibbs, J. C. (1983). Measuring the development of features in moral discussion. MerillPalmer Quarterly, 29:399-410.
  4. Evans, C. and Gibbons, N. J. (2007). The interactivity effect in multimedia learning. Computers & Education, 49(4):1147-1160.
  5. Gall, M. D., Gall, J. P., and Borg, W. R. (2006). Educational Research: An Introduction (8th Edition). Allyn & Bacon.
  6. Garrison, D. R. (1993). A cognitive constructivist view of distance education: An analysis of teaching-learning assumptions. Distance Education, 14:199-211.
  7. Grissom, S., McNally, M., and Naps, T. L. (2003). Algorithm visualization in CS education: Comparing levels of student engagement. In Proceedings of the First ACM Symposium on Software Visualization, pages 87-94. ACM Press.
  8. Hundhausen, C. D. (2002). Integrating algorithm visualization technology into an undergraduate algorithms course: Ethnographic studies of a social constructivist approach. Computers & Education, 39(3):237-260.
  9. Hundhausen, C. D. (2005). Using end-user visualization environments to mediate conversations: A 'Communicative Dimensions' framework. Journal of Visual Languages and Computing, 16(3):153-185.
  10. Hundhausen, C. D. and Brown, J. L. (2008). Designing, visualizing, and discussing algorithms within a CS 1 studio experience: An empirical study. Computers & Education, 50(1):301-326.
  11. Hundhausen, C. D., Douglas, S. A., and Stasko, J. T. (2002). A meta-study of algorithm visualization effectiveness. Journal of Visual Languages and Computing, 13(3):259-290.
  12. Korhonen, A., Malmi, L., and Silvasti, P. (2003). TRAKLA2: a framework for automatically assessed visual algorithm simulation exercises. In Proceedings of Kolin Kolistelut / Koli Calling - Third Annual Baltic Conference on Computer Science Education, pages 48-56, Joensuu, Finland.
  13. Laakso, M.-J., Myller, N., and Korhonen, A. (2009). Comparing learning performance of students using algorithm visualizations collaboratively on different engagement levels. Accepted to the Journal of Educational Technology & Society.
  14. Malmi, L., Karavirta, V., Korhonen, A., Nikander, J., Seppälä, O., and Silvasti, P. (2004). Visual algorithm simulation exercise system with automatic assessment: TRAKLA2. Informatics in Education, 3(2):267 - 288.
  15. McMahon, M. (1997). Social constructivism and the world wide web - a paradigm for learning. In Proceedings of the ASCILITE conference, Perth, Australia.
  16. Moore, M. G. (1989). Editorial: Three types of interaction. The American Journal of Distance Education, page 16.
  17. Myller, N., Bednarik, R., Ben-Ari, M., and Sutinen, E. (2008). Applying the Extended Engagement Taxonomy to Collaborative Software Visualization. Accepted to the ACM Transactions on Computing Education.
  18. Myller, N., Laakso, M., and Korhonen, A. (2007). Analyzing engagement taxonomy in collaborative algorithm visualization. In Hughes, J., Peiris, D. R., and Tymann, P. T., editors, ITiCSE 7807: Proceedings of the 12th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education, pages 251-255, New York, NY, USA. ACM Press.
  19. Naps, T. L. and Grissom, S. (2002). The effective use of quicksort visualizations in the classroom. Journal of Computing Sciences in Colleges, 18(1):88-96.
  20. Naps, T. L., Rößling, G., Almstrum, V., Dann, W., Fleischer, R., Hundhausen, C., Korhonen, A., Malmi, L., McNally, M., Rodger, S., and Velázquez-Iturbide, J. Í. (2002). 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. ACM Press.
  21. Palincsar, A. S. (1998). Social constructivist perspectives on teaching and learning. Annual Review of Psychology, 49:345-375.
  22. Piaget, J. (1977). The Development of Thought: Equilibration of Cognitive Structures. Viking, New York.
  23. Suthers, D. D. and Hundhausen, C. D. (2003). An experimental study of the effects of representational guidance on collaborative learning processes. Journal of the Learning Sciences, 12(2):183-219.
  24. Teague, D. and Roe, P. (2008). Collaborative learning: towards a solution for novice programmers. In ACE 7808: Proceedings of the tenth conference on Australasian computing education, pages 147-153, Darlinghurst, Australia, Australia. Australian Computer Society, Inc.
  25. Teasley, S. (1997). Talking about reasoning: How important is the peer in peer collaboration. In Resnick, L., Säljö, R., Pontecorvo, C., and Burge, B., editors, Discourse, Tools and Reasoning: Essays on Situated Cognition, pages 361-384. Springer, New York.
  26. Valdivia, R. and Nussbaum, M. (2007). Face-to-face collaborative learning in computer science classes. International Journal of Engineering Education, 23:434- 440(7).
  27. Vygotsky, L. S. (1978). In Cole, M., John-Steiner, V., Scribner, S., and Souberman, E., editors, Mind in Society. Harvard University Press, Cambridge, Mass.
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Paper Citation


in Harvard Style

Korhonen A., Laakso M. and Myller N. (2009). HOW DOES ALGORITHM VISUALIZATION AFFECT COLLABORATION? - Video Analysis of Engagement and Discussions . In Proceedings of the Fifth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8111-81-4, pages 479-488. DOI: 10.5220/0001825104790488


in Bibtex Style

@conference{webist09,
author={Ari Korhonen and Mikko-Jussi Laakso and Niko Myller},
title={HOW DOES ALGORITHM VISUALIZATION AFFECT COLLABORATION? - Video Analysis of Engagement and Discussions},
booktitle={Proceedings of the Fifth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2009},
pages={479-488},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001825104790488},
isbn={978-989-8111-81-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fifth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - HOW DOES ALGORITHM VISUALIZATION AFFECT COLLABORATION? - Video Analysis of Engagement and Discussions
SN - 978-989-8111-81-4
AU - Korhonen A.
AU - Laakso M.
AU - Myller N.
PY - 2009
SP - 479
EP - 488
DO - 10.5220/0001825104790488