skip to main content
research-article

Extending the Engagement Taxonomy: Software Visualization and Collaborative Learning

Published: 01 March 2009 Publication History

Abstract

As collaborative learning in general, and pair programming in particular, has become widely adopted in computer science education, so has the use of pedagogical visualization tools for facilitating collaboration. However, there is little theory on collaborative learning with visualization, and few studies on their effect on each other. We build on the concept of the engagement taxonomy and extend it to classify finer variations in the engagement that result from the use of a visualization tool. We analyze the applicability of the taxonomy to the description of the differences in the collaboration process when visualization is used. Our hypothesis is that increasing the level of engagement between learners and the visualization tool results in a higher positive impact of the visualization on the collaboration process. This article describes an empirical investigation designed to test the hypothesis. The results provide support for our extended engagement taxonomy and hypothesis by showing that the collaborative activities of the students and the engagement levels are correlated.

References

[1]
Ben-Bassat Levy, R., Ben-Ari, M., and Uronen, P. A. 2003. The Jeliot 2000 program animation system. Comput. Ed. 40, 1, 15--21.
[2]
Berkowitz, M. W. and Gibbs, J. C. 1983. Measuring the development of features in moral discussion. Merill-Palmer Quar. 29, 399--410.
[3]
Bryant, S., Romero, P., and du Boulay, B. 2005. Pair programming and the reappropriation of individual tools for collaborative programming. In Proceedings of the International ACM SIGGROUP Conference on Supporting Group Work (SIGGROUP’05), M. Pendergast, K. Schmidt, G. Mark, and M. Ackerman Eds. ACM Press, 332--333.
[4]
Ebel, G. and Ben-Ari, M. 2006. Affective effects of program visualization. In Proceedings of the 2nd International Computing Education Research Workshop (ICER’06). ACM Press, 1--5.
[5]
Evans, C. and Gibbons, N. J. 2007. The interactivity effect in multimedia learning. Comput. Ed. 49, 4, 1147--1160.
[6]
Gall, M. D., Gall, J. P., and Borg, W. R. 2006. Educational Research: An Introduction 8th Ed. Allyn & Bacon.
[7]
Green, T. R. G. and Petre, M. 1996. Usability analysis of visual programming environments: A “cognitive dimensions” framework. J. Vis. Lang. Comput. 7, 131--174.
[8]
Grissom, S., McNally, M., and Naps, T. L. 2003. Algorithm visualization in CS education: Comparing levels of student engagement. In Proceedings of the 1st ACM Symposium on Software Visualization (SOFTVIS’03). ACM Press, 87--94.
[9]
Haaster, K. V. and Hagan, D. 2004. Teaching and learning with BlueJ: An evaluation of a pedagogical tool. In Proceedings of Informing Science + IT Education Conference (InSITE’04). Informing Science Institute, 455--470.
[10]
Hübscher-Younger, T. and Narayanan, N. H. 2003. Constructive and collaborative learning of algorithms. SIGCSE Bull. 35, 1, 6--10.
[11]
Hundhausen, C. D. 2002. Integrating algorithm visualization technology into an undergraduate algorithms course: Ethnographic studies of a social constructivist approach. Comput. Ed. 39, 3, 237--260.
[12]
Hundhausen, C. D. 2005. Using end-user visualization environments to mediate conversations: A “communicative dimensions” framework. J. Vis. Lang. Comput. 16, 3, 153--185.
[13]
Hundhausen, C. D. and Brown, J. L. 2005. Personalizing and discussing algorithms within CS1 studio experiences: An observational study. In Proceedings of the International Workshop on Computing Education Research (ICER’05). ACM Press, 45--56.
[14]
Hundhausen, C. D. and Brown, J. L. 2007. What you see is what you code: A “live” algorithm development and visualization environment for novice learners. J. Vis. Lang. Comput. 18, 1, 22--47.
[15]
Hundhausen, C. D. and Brown, J. L. 2008. Designing, visualizing, and discussing algorithms within a CS 1 studio experience: An empirical study. Comput. Ed. 50, 1, 301--326.
[16]
Hundhausen, C. D., Douglas, S. A., and Stasko, J. T. 2002. A meta-study of algorithm visualization effectiveness. J. Vis. Lang. Comput. 13, 3, 259--290.
[17]
Janssen, J., Erkens, G., Kanselaar, G., and Jaspers, J. 2007. Visualization of participation: Does it contribute to successful computer-supported collaborative learning? Comput. Ed. 49, 4, 1037--1065.
[18]
Jehng, J.-C. J. and Chan, T.-W. 1998. Designing computer support for collaborative visual learning in the domain of computer programming. Comput. Hum. Behav. 14, 3, 429--448.
[19]
Kölling, M., Quig, B., Patterson, A., and Rosenberg, J. 2003. The BlueJ system and its pedagogy. Comput. Science Ed. 13, 4, 249--268.
[20]
Korhonen, A., Laakso, M., and Myller, N. 2008. How does algorithm visualization affect collaboration? Video analysis of engagement and discussions. 5th International Conference on Web Information Systems and Technologies (WEBIST’09). Submitted.
[21]
Laakso, M.-J., Myller, N., and Korhonen, A. 2008. Analyzing the extended engagement taxonomy in collaborative algorithm visualization. J. Ed. Technol. Soc. To appear.
[22]
Landis, J. R. and Koch, G. G. 1977. The measurement of observer agreement for categorical data. Biometrics 33, 159--174.
[23]
McDowell, C., Werner, L., Bullock, H. E., and Fernald, J. 2006. Pair programming improves student retention, confidence, and program quality. Comm. ACM 49, 8, 90--95.
[24]
Meier, A., Spada, H., and Rummel, N. 2007. A rating scheme for assessing the quality of computer-supported collaboration processes. International J. Comput. Support. Collab. Learn. 2, 1, 63--86.
[25]
Moreno, A., Myller, N., Sutinen, E., and Ben-Ari, M. 2004. Visualizing program with Jeliot 3. In Proceedings of the International Working Conference on Advanced Visual Interfaces (AVI’04). ACM Press, 373--380.
[26]
Myller, N. 2007. Automatic generation of prediction questions during program visualization. Electron. Notes Theor. Comput. Sci. 178, 43--49.
[27]
Myller, N., Bednarik, R., and Moreno, A. 2007a. Integrating dynamic program visualization into BlueJ: The Jeliot 3 extension. In Proceedings of the 7th IEEE International Conference on Advanced Learning Technologies, J. M. Spector, D. G. Sampson, T. Okamoto, Kinshuk, S. A. Cerri, M. Ueno, and A. Kashihara Eds. IEEE Computer Society, 505--506.
[28]
Myller, N., Laakso, M., and Korhonen, A. 2007b. Analyzing engagement taxonomy in collaborative algorithm visualization. In Proceedings of the 12th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE’07), J. Hughes, D. R. Peiris, and P. T. Tymann Eds. ACM Press, 251--255.
[29]
Myller, N. and Nuutinen, J. 2006. JeCo: Combining program visualization and story weaving. Informatics Ed. 5, 2, 267--276.
[30]
Nagappan, N., Williams, L., Ferzli, M., Wiebe, E., Yang, K., Miller, C., and Balik, S. 2003. Improving the CS1 experience with pair programming. In Proceedings of the 34th SIGCSE Technical Symposium on Computer Science Education (SIGCSE’03). ACM Press, 359--362.
[31]
Naps, T. L. 2005. Jhavé -- Addressing the need to support algorithm visualization with tools for active engagement. IEEE Comput.- Graph. Appl. 25, 5, 49--55.
[32]
Naps, T. L. and Grissom, S. 2002. The effective use of quicksort visualizations in the classroom. J. Comput. Sci. Coll 18, 1, 88--96.
[33]
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. Á. 2002. Exploring the role of visualization and engagement in computer science education. In ITiCSE on Innovation and Technology in Computer Science Education (ITiCSE’02). (Working Groups Report). ACM Press, 131--152.
[34]
Oechsle, R. and Morth, T. 2007. Peer review of animations developed by students. Electron. Notes Theor. Comput. Sci. 178, 181--186.
[35]
Ragonis, N. and Ben-Ari, M. 2005. On understanding the statics and dynamics of object-oriented programs. SIGCSE Bull. 37, 1, 226--230.
[36]
Roschelle, J. 1996. Designing for cognitive communication: Epistemic fidelity or mediating collaborative inquiry. In Computers, Communication & Mental Models, D. L. Day and D. K. Kovacs Eds. Taylor & Francis, London, UK. 13--25.
[37]
Rößling, G. and Naps, T. L. 2002. A testbed for pedagogical requirements in algorithm visualizations. In Proceedings of the Innovation and Technology in Computer Science Education (ITiCSE’02). ACM Press, 96--100.
[38]
Scaife, M. and Rogers, Y. 1996. External cognition: how do graphical representations work? Int. J. Hum.-Comput. Stud. 45, 2, 185--213.
[39]
Simon, B., Anderson, R., Hoyer, C., and Su, J. 2004. Preliminary experiences with a tablet PC based system to support active learning in computer science courses. In Proceedings of the 9th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE’04). ACM, 213--217.
[40]
Spada, H., Meier, A., Rummel, N., and Hauser, S. 2005. A new method to assess the quality of collaborative process in CSCL. In Computer Supported Collaborative Learning 2005: The Next 10 Years, T. Koschmann, D. Suthers, and T. W. Chan Eds. Lawrence Erlbaum, Mahwah, NJ. 622--631.
[41]
Suthers, D. D. and Hundhausen, C. D. 2003. An experimental study of the effects of representational guidance on collaborative learning processes. J. Learn. Sciences 12, 2, 183--219.
[42]
Suthers, D. D., Hundhausen, C. D., and Girardeau, L. E. 2003. Comparing the roles of representations in face-to-face and online computer supported collaborative learning. Comput. Ed. 41, 4, 335--351.
[43]
Teasley, S. 1997. Talking about reasoning: How important is the peer in peer collaboration. In Discourse, Tools and Reasoning: Essays on Situated Cognition, L. Resnick, R. Säljö, C. Pontecorvo, and B. Burge Eds. Springer, Berlin, Germany. 361--384.
[44]
Williams, L., Kessler, R. R., Cunningham, W., and Jeffries, R. 2000. Strengthening the case for pair programming. IEEE Softw. 17, 4, 19--25.

Cited By

View all

Index Terms

  1. Extending the Engagement Taxonomy: Software Visualization and Collaborative Learning

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Computing Education
    ACM Transactions on Computing Education  Volume 9, Issue 1
    March 2009
    167 pages
    EISSN:1946-6226
    DOI:10.1145/1513593
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 01 March 2009
    Accepted: 01 November 2008
    Revised: 01 September 2008
    Received: 01 January 2008
    Published in TOCE Volume 9, Issue 1

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Program visualization
    2. collaborative learning
    3. engagement taxonomy

    Qualifiers

    • Research-article
    • Research
    • Refereed

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)41
    • Downloads (Last 6 weeks)3
    Reflects downloads up to 27 Dec 2024

    Other Metrics

    Citations

    Cited By

    View all

    View Options

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media