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Automating Assessment of Web Site Usability

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  1. Automating Assessment of Web Site Usability Marti Hearst Melody Ivory Rashmi Sinha University of California, Berkeley

  2. 196M new Web sites in the next 5 years [Nielsen99] ~20,000 user interface professionals [Nielson99] The Usability Gap ASIS IA Summit, Feb 2001

  3. A shortage of user interface professionals [Nielson99] The Usability Gap 196M new Web sites in the next 5 years [Nielsen99] Most sites have inadequate usability [Forrester, Spool, Hurst] (users can’t find what they want 39-66% of the time) ASIS IA Summit, Feb 2001

  4. The Problem • NON-professionals need to create websites • Guidelines are helpful, but • Sometimes imprecise • Sometimes conflict • Usually not empirically founded ASIS IA Summit, Feb 2001

  5. Ultimate Goal: Tools to Help Non-Professional Designers • Examples: • A “grammar checker” to assess guideline conformance • Imperfect • Only suggestions – not dogma • Automatic comparison to highly usable pages/sites • Automatic template suggestions ASIS IA Summit, Feb 2001

  6. A View of Web Site Structure (Newman et al. 00) • Information design • structure, categories of information • Navigation design • interaction with information structure • Graphic design • visual presentation of information and navigation (color, typography, etc.) ASIS IA Summit, Feb 2001 Courtesy of Mark Newman

  7. A View of Web Site Design(Newman et al. 00) • Information Architecture • includes management and more responsibility for content • User Interface Design • includes testing and evaluation Courtesy of Mark Newman ASIS IA Summit, Feb 2001

  8. The Goal • Eventually want to assess navigation structure and graphic design at the page and site level. • Farther down the line: information design and scent • Note: we are NOT suggesting we can characterize: • Aesthetics • Subjective preferences ASIS IA Summit, Feb 2001

  9. The Investigation • Can we place web design guidelines onto an empirical foundation? • Can we build models of good design by looking at existing designs? ASIS IA Summit, Feb 2001

  10. Example Empirical Investigation Is it all about the content? ASIS IA Summit, Feb 2001

  11. Webby Awards 2000 • 6 criteria • 27 categories • We used finance, education, community, living, health, services • 100 judges • International Academy of Digital Arts & Sciences • 3 rounds of judging • 2000 sites initially ASIS IA Summit, Feb 2001

  12. Webby Awards 2000 • 6 criteria • Content • Structure & navigation • Visual design • Functionality • Interactivity • Overall experience • Scale: 1-10 (highest) • Nearly normally distributed across judged sites • What are Webby judgements about? ASIS IA Summit, Feb 2001

  13. Webby Awards 2000 • The best predictor of the overall score is the score for content • The worst predictor is visual design ASIS IA Summit, Feb 2001

  14. So … Webbys focus on content! ASIS IA Summit, Feb 2001

  15. Comparing Two Categories news arts ASIS IA Summit, Feb 2001

  16. Guidelines • There are MANY usability guidelines • A survey of 21 sets of web guidelines found little overlap (Ratner et al. 96) • Why? • Our hypothesis: not empirically validated • So … let’s figure out what works! ASIS IA Summit, Feb 2001

  17. Web Page Metrics • Web metric analysis tools report on what is easy to measure • Predicted download time • Depth/breadth of site • We want to worry about • Content • User goals/tasks • We also want to compare alternative designs. ASIS IA Summit, Feb 2001

  18. Another Empirical Study: Which features distinguish well-designed web pages? ASIS IA Summit, Feb 2001

  19. Quantitative Metrics • Identified 42 attributes from the literature • Roughly characterized: • Page Composition (e.g., words, links, images) • Page Formatting (e.g., fonts, lists, colors) • Overall Page Characteristics (e.g., information & layout quality, download speed) ASIS IA Summit, Feb 2001

  20. Word Count Body Text Percentage Emphasized Body Text Percentage Text Positioning Count Text Cluster Count Link Count Page Size Graphic Percentage Graphics Count Color Count Font Count Metrics Used in Study ASIS IA Summit, Feb 2001

  21. Data Collection • Collected data for 1898 pages from 163 sites • Attempted to collect from 3 levels within each site • Six Webby categories • Health, Living, Community, Education, Finance, Services • Data constraints • At least 30 words • No pages with forms • Exhibit high self-containment (i.e., no style sheets, scripts, applets, etc.) ASIS IA Summit, Feb 2001

  22. Method • Collect metrics • from sites evaluated for Webby Awards 2000 • Two comparisons • Top 33% of sites vs. the rest (using the overall Webby score) • Top 33% of sites vs. bottom 33% (using the Webby factor) • Goal: see if we can use the metrics to predict membership in top vs. other groups. ASIS IA Summit, Feb 2001

  23. Questions: • Can we use the metrics to predict membership in top vs. other groups? • Do we see a difference in how the metrics behave in different content categories? ASIS IA Summit, Feb 2001

  24. Findings • We can accurately classify web pages • Linear discriminant analysis • For top vs. rest • 67% correct for overall • 73% correct when taking categories into account • For top vs. bottom • 65% correct for overall • 80% correct using categories ASIS IA Summit, Feb 2001

  25. Why does this work? • Content is most important predictor of overall score • BUT there is some predictive power in the visual design / navigation criteria • Also, it may just be that good design is good design all over • Film making analogy • This happens in other domains – automatic essay grading for one ASIS IA Summit, Feb 2001

  26. Deeper Analysis • Which metrics matter? • All played a role • To get more insight: • We noticed that small, medium, and large pages behave differently • We subdivided pages according to size and category to find out which metrics matter and if they should have high or low values ASIS IA Summit, Feb 2001

  27. Small pages (66 words on average) • Good pages have slightly more content, smaller page sizes, less graphics and employ more font variations • The smaller page sizes and graphics count suggests faster download times for these pages (corroborated by a download time metric, not discussed in detail here). • Correlations between font count and body text suggest that good pages vary fonts used between header and body text. ASIS IA Summit, Feb 2001

  28. Medium pages (230 words on average) • Good pages emphasize less of the body text • Text positioning and text cluster count indicate medium-sized good pages appear to organize text into clusters (e.g., lists and shaded table areas). • Negative correlations between body text and color count suggests that good medium-sized pages use colors to distinguish headers. ASIS IA Summit, Feb 2001

  29. Large pages (827 words on average) • Good pages have less body text and more colors (suggesting pages have more headers and text links) • Good pages are larger but have fewer graphics ASIS IA Summit, Feb 2001

  30. Future work • Distinguish according to page role • Home page vs. content vs. index … • Better metrics • Separate info design, nav design, graphic design • Site level as well as page level • Compare against results of live user studies ASIS IA Summit, Feb 2001

  31. Future work • Category-based profiles • Can use clustering to create profiles of good and poor sites for each category • These can be used to suggest alternative designs • More information: CHI 2001 paper ASIS IA Summit, Feb 2001

  32. Ramifications • It is remarkable that such simple metrics predict so well • Perhaps good design is good overall • There may be other factors • A foundation for a new methodology • Empirical, bottom up • But, there is no one path to good design! ASIS IA Summit, Feb 2001

  33. In Summary • Automated Usability Assessment should help close the Web Usability Gap • We can empirically distinguish between highly rated web pages and other pages • Empirical validation of design guidelines • Can build profiles of good vs. poor sites • Are validating expert judgements with usability assessments via a user study • Eventually want to build tools to help end-users assess their designs ASIS IA Summit, Feb 2001

  34. More information: • http://webtango.berkeley.edu • http://www.sims.berkeley.edu/~hearst ASIS IA Summit, Feb 2001