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Effective features of algorithm visualizations
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Source Technical Symposium on Computer Science Education archive
Proceedings of the 35th SIGCSE technical symposium on Computer science education table of contents
Norfolk, Virginia, USA
SESSION: Visualization table of contents
Pages: 382 - 386  
Year of Publication: 2004
ISBN:1-58113-798-2
Also published in ...
Authors
Purvi Saraiya  Virginia Tech, Blacksburg, VA
Clifford A. Shaffer  Virginia Tech, Blacksburg, VA
D. Scott McCrickard  Virginia Tech, Blacksburg, VA
Chris North  Virginia Tech, Blacksburg, VA
Sponsors
ACM: Association for Computing Machinery
SIGCSE: ACM Special Interest Group on Computer Science Education
Publisher
ACM  New York, NY, USA
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ABSTRACT

Many algorithm visualizations have been created, but little is known about which features are most important to their success. We believe that pedagogically useful visualizations exhibit certain features that hold across a wide range of visualization styles and content. We began our efforts to identify these features with a review that attempted to identify an initial set of candidates. We then ran two experiments that attempted to identify the effectiveness for a subset of features from the list. We identified a small number of features for algorithm visualizations that seem to have a significant impact on their pedagogical effectiveness, and found that several others appear to have little impact. The single most important feature studied is the ability to directly control the pace of the visualization. An algorithm visualization having a minimum of distracting features, and which focuses on the logical steps of an algorithm, appears to be best for procedural understanding of the algorithm. Providing a good example for the visualization to operate on proved significantly more effective than letting students construct their own data sets. Finally, a pseudocode display, a series of questions to guide exploration of the algorithm, or the ability to back up within the visualization did not show a significant effect on learning.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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V. Colaso, A. Kamal, P. Saraiya, C. North, S. McCrickard, and C. Shaffer. Learning and retention in data structures: A comparison of visualization, text, and combined methods. In Proceedings of the World Conference on Educational Multimedia/Hypermedia and Educational Telecommunications (ED-MEDIA 2002) June 2002.
 
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B. Price, R. Baecker, and I. Small. A principled taxonomy of software visualization. Journal of Visual Languages and Computing 4:211--266, 1993.
 
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P. Saraiya. Effective features of algorithm visualizations. Master's thesis, Department of Computer Science,Virginia Tech, July 2002.
 
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J. Stasko and D. McCrickard. Real clock time animation support for developing software visualizations. Australian Computer Journal 27(3):118--128, No. 1995.
 
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P. Wright, A. Lickorish, A. Hull, and N. Umellen. Graphics in written directions: Appreciated by readers not by writers. Applied Cognitive Psychology 9:41--59, 1995.


Collaborative Colleagues:
Purvi Saraiya: colleagues
Clifford A. Shaffer: colleagues
D. Scott McCrickard: colleagues
Chris North: colleagues

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