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A tool for creating predictive performance models from user interface demonstrations
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Source Symposium on User Interface Software and Technology archive
Proceedings of the 12th annual ACM symposium on User interface software and technology table of contents
Asheville, North Carolina, United States
Pages: 93 - 102  
Year of Publication: 1999
ISBN:1-58113-075-9
Authors
Scott E. Hudson  Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA
Bonnie E. John  Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA
Keith Knudsen  Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA
Michael D. Byrne  Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 12,   Downloads (12 Months): 60,   Citation Count: 10
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ABSTRACT

A central goal of many user interface development tools has been to make the construction of high quality interfaces easy enough that iterative design approaches could be a practical reality. In the last 15 years significant advances in this regard have been achieved. However, the evaluation portion of the iterative design process has received relatively little support from tools. Even though advances have also been made in usability evaluation methods, nearly all evaluation is still done “by hand,” making it more expensive and difficult than it might be. This paper considers a partial implementation of the CRITIQUE usability evaluation tool that is being developed to help remedy this situation by automating a number of evaluation tasks. This paper will consider techniques used by the system to produce predictive models (keystroke level models and simplified GOMS models) from demonstrations of sample tasks in a fraction of the time needed by conventional handcrafting methods. A preliminary comparison of automatically generated models with models created by an expert modeler show them to produce very similar predictions (within 2%). Further, because they are automated, these models promise to be less subject to human error and less affected by the skill of the modeler.


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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CITED BY  10

Collaborative Colleagues:
Scott E. Hudson: colleagues
Bonnie E. John: colleagues
Keith Knudsen: colleagues
Michael D. Byrne: colleagues