Abstract
We report a study on collaborative learning with Algorithm Visualizations (AV). We have previously confirmed the hypothesis that students’ higher engagement has a positive effect on learning outcomes when they learn collaboratively. Thus, in this paper the analysis is targeted on students’ collaborative learning process in order to find phenomena that explain the learning improvements. In the video and audio analysis of the learning sessions, we have identified that the amount of collaboration and discussion increase 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, one of our conclusions is that the level of engagement indicates, not only the learning performance, but also the amount of on-topic discussions in collaboration. Furthermore, based on previous literature, 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.
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References
Hundhausen, C.D., Douglas, S.A., Stasko, J.T.: A meta-study of algorithm visualization effectiveness. Journal of Visual Languages and Computing 13(3), 259–290 (2002)
Naps, T.L., Rößling, G., Almstrum, V., Dann, W., Fleischer, R., Hundhausen, C., Korhonen, A., Malmi, L., McNally, M., Rodger, S., Velázquez-Iturbide, J.Á.: 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, pp. 131–152. ACM Press, New York (2002)
Garrison, D.R.: A cognitive constructivist view of distance education: An analysis of teaching-learning assumptions. Distance Education 14(2), 199–211 (1993)
Piaget, J.: The Development of Thought: Equilibration of Cognitive Structures. Viking, New York (1977)
Myller, N., Bednarik, R., Sutinen, E., Ben-Ari, M.: Extending the engagement taxonomy: Software visualization and collaborative learning. Trans. Comput. Educ. 9(1), 1–27 (2009)
Palincsar, A.S.: Social constructivist perspectives on teaching and learning. Annual Review of Psychology 49, 345–375 (1998)
McMahon, M.: Social constructivism and the world wide web – a paradigm for learning. In: Proceedings of the ASCILITE conference, Perth, Australia (1997)
Vygotsky, L.S.: Mind in society. Harvard University Press, Cambridge (1978)
Beck, L.L., Chizhik, A.W.: An experimental study of cooperative learning in CS1. In: SIGCSE 2008: Proceedings of the 39th SIGCSE technical symposium on Computer science education, pp. 205–209. ACM, New York (2008)
Teague, D., Roe, P.: Collaborative learning: towards a solution for novice programmers. In: ACE 2008: Proceedings of the tenth conference on Australasian computing education, Darlinghurst, Australia, pp. 147–153. Australian Computer Society, Inc. (2008)
Valdivia, R., Nussbaum, M.: Face-to-face collaborative learning in computer science classes. International Journal of Engineering Education 23(3), 434–440 (2007)
Suthers, D.D., Hundhausen, C.D.: An experimental study of the effects of representational guidance on collaborative learning processes. Journal of the Learning Sciences 12(2), 183–219 (2003)
Laakso, M.J., Myller, N., Korhonen, A.: Comparing learning performance of students using algorithm visualizations collaboratively on different engagement levels. Journal of Educational Technology & Society 12(2), 267–282 (2009)
Myller, N., Laakso, M., Korhonen, A.: Analyzing engagement taxonomy in collaborative algorithm visualization. In: Hughes, J., Peiris, D.R., Tymann, P.T. (eds.) ITiCSE 2007: Proceedings of the 12th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education, pp. 251–255. ACM Press, New York (2007)
Moore, M.G.: Editorial: Three types of interaction. The American Journal of Distance Education 3, 16 (1989)
Grissom, S., McNally, M., Naps, T.L.: Algorithm visualization in CS education: Comparing levels of student engagement. In: Proceedings of the First ACM Symposium on Software Visualization, pp. 87–94. ACM Press, New York (2003)
Naps, T.L., Grissom, S.: The effective use of quicksort visualizations in the classroom. Journal of Computing Sciences in Colleges 18(1), 88–96 (2002)
Hundhausen, C.D.: Integrating algorithm visualization technology into an undergraduate algorithms course: Ethnographic studies of a social constructivist approach. Computers & Education 39(3), 237–260 (2002)
Hundhausen, C.D., Brown, J.L.: Designing, visualizing, and discussing algorithms within a CS 1 studio experience: An empirical study. Computers & Education 50(1), 301–326 (2008)
Ben-Bassat Levy, R., Ben-Ari, M., Uronen, P.A.: The Jeliot 2000 program animation system. Computers & Education 40(1), 15–21 (2003)
Laakso, M.J., Salakoski, T., Korhonen, A.: The feasibility of automatic assessment and feedback. In: Proceedings of Cognition and Exploratory Learning in Digital Age (CELDA 2005), Porto, Portugal, December 2005, pp. 113–122 (2005)
Evans, C., Gibbons, N.J.: The interactivity effect in multimedia learning. Computers & Education 49(4), 1147–1160 (2007)
Hundhausen, C.D.: Using end-user visualization environments to mediate conversations: A ‘Communicative Dimensions’ framework. Journal of Visual Languages and Computing 16(3), 153–185 (2005)
Korhonen, A., Malmi, L., Silvasti, P.: TRAKLA2: a framework for automatically assessed visual algorithm simulation exercises. In: Proceedings of Koli Calling – Third Annual Baltic Conference on Computer Science Education, Joensuu, Finland, pp. 48–56 (2003)
Malmi, L., Karavirta, V., Korhonen, A., Nikander, J., Seppälä, O., Silvasti, P.: Visual algorithm simulation exercise system with automatic assessment: TRAKLA2. Informatics in Education 3(2), 267–288 (2004)
Gall, M.D., Gall, J.P., Borg, W.R.: Educational Research: An Introduction, 8th edn. Allyn & Bacon, Boston (2006)
Teasley, S.: Talking about reasoning: How important is the peer in peer collaboration. In: Resnick, L., Säljö, R., Pontecorvo, C., Burge, B. (eds.) Discourse, Tools and Reasoning: Essays on Situated Cognition, pp. 361–384. Springer, New York (1997)
Berkowitz, M.W., Gibbs, J.C.: Measuring the development of features in moral discussion. Merill-Palmer Quarterly 29, 399–410 (1983)
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Laakso, MJ., Myller, N., Korhonen, A. (2010). Interaction Promotes Collaboration and Learning: Video Analysis of Algorithm Visualization Use during Collaborative Learning. In: Cordeiro, J., Filipe, J. (eds) Web Information Systems and Technologies. WEBIST 2009. Lecture Notes in Business Information Processing, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12436-5_15
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