This research explores an approach to learning types of usability concerns
considered useful for the management of Web sites and to identifying usability concerns
based on these learned models. By having one or more Web site managers rate a subset
of pages in a site based on a number of usability criteria, the approach builds models that
determine what automatically measurable characteristics are correlated to issues
identified. To test this, the approach collected usability assessments from twelve
students pursuing advanced degrees in the area of computer-human interaction. These
students were divided into two groups and given different scenarios of use of a Web site.
They assessed the usability of Web pages from the site, and their data was divided into a
training set, used to find models, and a prediction set, used to evaluate the relative
quality of models. Results show that the learned models predicted remaining data for
one scenario in more categories of usability than did the single model found under the alternate scenario. Results also show how systems may prioritize usability problems for
Web site managers by probability of occurrence under context rather than by merely
listing pages that break specific rules, as provided by some current tools.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-12-8825 |
| Date | 2010 December 1900 |
| Creators | Davis, Paul |
| Contributors | Shipman, Frank M., Simmons, Dick B. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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