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IMPROVING WEB NAVIGATION USABILITY BY COMPARING
ACTUAL AND ANTICIPATED USAGE
ABSTRACT
We present a new method to identify navigationrelated Web usability problems based on
comparing actual and anticipated usage patterns. The actual usage patterns can be extracted from
Web server logs routinely recorded for operational websites by first processing the log data to
identify users, user sessions, and user task-oriented transactions, and then applying an usage
mining algorithm to discover patterns among actual usage paths. The anticipated usage,
including information about both the path and time required for user-oriented tasks, is captured
by our ideal user interactive path models constructed by cognitive experts based on their
cognition of user behavior. The comparison is performed via the mechanism of test oracle for
checking results and identifying user navigation difficulties. The deviation data produced from
this comparison can help us discover usability issues and suggest corrective actions to improve
usability. A software tool was developed to automate a significant part of the activities involved.
With an experiment on a small service-oriented website, we identified usability problems, which
were cross-validated by domain experts, and quantified usability improvement by the higher task
success rate and lower time and effort for given tasks after suggested corrections were
implemented. This case study provides an initial validation of the applicability and effectiveness
of our method.

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Mechanical Engineering on AAI Summer Training Report-003.pdf
 

IMPROVING WEB NAVIGATION USABILITY BY COMPARING ACTUAL AND ANTICIPATED USAGE

  • 1. IMPROVING WEB NAVIGATION USABILITY BY COMPARING ACTUAL AND ANTICIPATED USAGE ABSTRACT We present a new method to identify navigationrelated Web usability problems based on comparing actual and anticipated usage patterns. The actual usage patterns can be extracted from Web server logs routinely recorded for operational websites by first processing the log data to identify users, user sessions, and user task-oriented transactions, and then applying an usage mining algorithm to discover patterns among actual usage paths. The anticipated usage, including information about both the path and time required for user-oriented tasks, is captured by our ideal user interactive path models constructed by cognitive experts based on their cognition of user behavior. The comparison is performed via the mechanism of test oracle for checking results and identifying user navigation difficulties. The deviation data produced from this comparison can help us discover usability issues and suggest corrective actions to improve usability. A software tool was developed to automate a significant part of the activities involved. With an experiment on a small service-oriented website, we identified usability problems, which were cross-validated by domain experts, and quantified usability improvement by the higher task success rate and lower time and effort for given tasks after suggested corrections were implemented. This case study provides an initial validation of the applicability and effectiveness of our method.