Effective Features of Algorithm Visualizations. Saraiya, P., Shaffer, C. A., McCrickard, D. S., & North, C. In Proceedings of the 35th SIGCSE Technical Symposium on Computer Science Education, of SIGCSE '04, pages 382–386, New York, NY, USA, 2004. ACM. ![Effective Features of Algorithm Visualizations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{saraiya_effective_2004,
address = {New York, NY, USA},
series = {{SIGCSE} '04},
title = {Effective {Features} of {Algorithm} {Visualizations}},
isbn = {978-1-58113-798-9},
url = {http://doi.acm.org/10.1145/971300.971432},
doi = {10.1145/971300.971432},
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.},
urldate = {2017-12-07},
booktitle = {Proceedings of the 35th {SIGCSE} {Technical} {Symposium} on {Computer} {Science} {Education}},
publisher = {ACM},
author = {Saraiya, Purvi and Shaffer, Clifford A. and McCrickard, D. Scott and North, Chris},
year = {2004},
keywords = {algorithm visualization, courseware, heapsort},
pages = {382--386}
}
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