1. Towards Automated Web Design Advisors Melody Y. Ivory Marti A. Hearst School of Information Management & Systems UC Berkeley IBM Make IT Easy Conference June 4, 2002

2. 2 The Problem: Poor Website Design by Non-Professionals

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4. 4 A Solution  Automatic recommendations and context-specific guidelines.  “Grammar checkers” for web design –Create good templates to incorporate into web design tools –Compare current design to high-quality designs and show differences

5. 5 The WebTango Goal Predictions Similarities Differences Suggestions Modification Quality Checker User’s Design Profiles High Quality Designs

6. 6 The Approach Develop Statistical Profiles 1. Create a large set of measures to assess various design attributes 2. Obtain a large set of evaluated sites 3. Create models of good vs. avg. vs. poor sites Take into account the context and type of site 4. Use models to evaluate other sites 5. Use models to suggest improvements Idea: Reverse engineer design patterns from high-quality sites and use to assess the quality of other sites

7. 7 Step 1: Measuring Web Design Aspects  Identified key aspects from the literature –Extensive survey of Web design literature: texts from recognized experts; user studies amount of text on a page, text alignment, fonts, colors, consistency of page layout in the site, use of frames, … –Example guidelines Use 2–4 words in text links [Nielsen00]. Use links with 7–12 useful words [Sawyer & Schroeder00]. Consistent layout of graphical interfaces result in a 10–25% speedup in performance [Mahajan & Shneiderman96]. –There are no theories about what to measure

8. 8 157 Web Design Measures (Metrics Computation Tool)  Text Elements (31) # words, type of words  Link Elements (6) # graphic links, type of links  Graphic Elements (6) # images, type of images  Text Formatting (24) # font styles, colors, alignment, clustering  Link Formatting (3) # colors used for links, standard colors  Graphics Formatting (7) max width of images, page area  Page Formatting (27) quality of color combos, scrolling  Page Performance (37) download time, accessibility  Site Architecture (16) consistency, breadth, depth TELEGE TFLFGF PF PP SA information, navigation, & graphic design experience design

9. 9 Word Count: 157

10. 10 Content Word Count: 81

11. 11 Body Word Count: 94

12. 12 Step 2: Obtaining a Sample of Evaluated Sites  Webby Awards 2000 –Only large corpus of rated Web sites  3000 sites initially –27 topical categories Studied sites from informational categories –Finance, education, community, living, health, services  100 judges –International Academy of Digital Arts & Sciences Internet professionals, familiarity with a category –3 rounds of judging (only first round used) Scores are averaged from 3 or more judges Converted scores into good (top 33%), average (middle 34%), and poor (bottom 33%)

13. 13 Webby Awards 2000  6 criteria –Content –Structure & navigation –Visual design –Functionality –Interactivity –Overall experience  Scale: 1 – 10 (highest)  Nearly normally distributed

14. 14 Example Page from Good Site

15. 15 Example Page from Avg. Site

16. 16 Example Page from Poor Site

17. 17 The Data Set  Downloaded pages from sites –Downloads informational pages at multiple levels of the site  Computed measures for the sample –Processes static HTML, English pages Measures for 5346 pages Measures for 333 sites –Categorize by Topic: education, health, finance, … Page Type: content, homepage, link page, …

18. 18 Step 3: Creating Prediction Models  Statistical analysis of quantitative measures –Methods Classification & regression tree, linear discriminant classification, & K- means clustering analysis –Context sensitive models Content category, page style, etc. –Models identify a subset of measures relevant for each prediction ? Good Average Poor

19. 19 Page-Level Models (5346 Pages) ModelMethodAccuracy GoodAvg.Poor Overall page quality ~1782 pgs/class C&RT96%94%93% Content category quality ~297 pgs/class & cat LDC92%91%94% ANOVAs showed that all differences in measures were significant (good vs. avg, good vs. poor, etc.)

20. 20 Page-Level Models (5346 Pages) ModelMethodAccuracy GoodAvg.Poor Page type quality ~356 pgs/class & type LDC84%78%84% Overall page qualityC&RT96%94%93% Content category qualityLDC92%91%94% ANOVAs showed that all differences in measures were significant (good vs. avg, good vs. poor, etc.) Page Type Classifier (decision tree) Home page, content, form, link, other 1770 manually-classified pages, 84% accurate

21. 21 Clustering Good Pages  K-means clustering to identify 3 subgroups  ANOVAs revealed key differences –# words on page, HTML bytes, table count  Characterize clusters as: –Small-page cluster (1008 pages) –Large-page cluster (364 pages) –Formatted-page cluster (450 pages)  Use for detailed analysis of pages Small page Large page Formatted page

22. 22 Step 4: Evaluate Other Sites  Make predictions for an existing design –good, average, poor –How do the scores on th emetrics vary from good pages?

23. 23 Example Site drawn from Yahoo Education/Health –Discusses training programs on numerous health issues –Chose one that looked good at first glance, but on further inspection seemed to have problems. –Only 9 pages were available, at level 0 and 1 –Not present in the original study

24. 24 Sample Content Page (Before)

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26. 26 Page-Level Assessment  Decision tree predicts: all 9 pages consistent with poor pages –Content page does not have accent color; has colored, bolded body text words Avoid mixing text attributes (e.g., color, bolding, and size) [Flanders & Willis98] Avoid italicizing and underlining text [Schriver97]

27. 27 Page-Level Assessment  Cluster mapping –All pages mapped into the small-page cluster –Deviated on key measures, including text link, link cluster, interactive object, content link word, ad Most deviations can be attributed to using graphic links without corresponding text links –Use corresponding text links [Flanders & Willis98,Sano96] Link Count Text Link Count Good Link Word Count Font Count Sans Serif Word Count Display Word Count Top deviant measures for content pagecontent page

28. 28 Page-Level Assessment  Compared to models for health and education categories –All pages found to be poor for both models  Compared to models for the 5 page styles –All 9 pages were considered poor pages by page style (after correcting predicted types)

29. 29 Improving the Site  Eventually want to automate the translation from differences to recommendations  Revised the pages by hand as follows: –To improve color count and link count: Added a link text cluster that mirrors the content of the graphic links –To improve text element and text formatting variation Added headings to break up paragraphs Added font variations for body text and headings and made the copyright text smaller –Several other changes based on small-page cluster characteristics

30. 30 Sample Content Page (After)

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32. 32 After the Changes  All pages now classified correctly by style  All pages rated good overall  All pages rated good health pages  Most pages rated as average education pages  Most pages rated as average by style

33. 33 Profile Evaluation  Small user study –Page-level comparisons (15 page pairs) Participants preferred modified pages (57.4% vs. 42.6% of the time, p =.038) –Site-level ratings (original and modified versions of 2 sites) Participants rated modified sites higher than original sites (3.5 vs. 3.0., p=.025) Non Web designers had difficulty gauging Web design quality –Freeform Comments Subtle changes result in major improvements

34. 34 Summary  Goal: –Provide automated, context-sensitive suggestions for improving web design.  Approach: –Compute statistics over large collection of rated web sites –From these build models of good sites –Use these to suggest changes. Measures Data ModelsEvaluate Validate

35. 35 Advantages and Limitations  Advantages –Derived from empirical data –Context-sensitive –More insight for improving designs –Evolve over time –Applicable to other types of Uis  Limitations –Based on expert ratings –Correlation, not causality –Not a substitute for usability studies

36. 36 Next Steps  Update the profiles (Webby 02 data)  Develop tool to facilitate interpretation of predictions  Examine the profiles in more detail –Factor analysis to highlight design patterns –See which guidelines are valid empirically (studies) Moving from predictions to recommendations  Incorporate assessments of content quality (text analysis & studies)  Improve site-level measures and models –Incorporate page-level predictions  New page-level measures (spatial properties)  Develop interactive Web design tool

37. 37 Thank You  For more information –http://webtango.berkeley.edu Research supported by the following grants: Hellman Faculty Fund, Microsoft Research Grant, Gates Millennium Fund, GAANN Fellowship, Lucent Cooperative Research Fellowship Program Thanks to: Webby Awards (Maya Draisin & Tiffany Shlain) Rashmi Sinha

38. 38 Do Webby Ratings Reflect Usability?  Do the profiles assess usability or something else?  User study (30 participants) –Usability ratings (WAMMI scale) for 57 sites Two conditions – actual and perceived usability –Contrast to judges’ ratings  Results –Some correlation between users’ and judges’ ratings –Not a strong finding –Virtually no difference between actual and perceived usability ratings Participants thought it would be easier to find info in the perceived usability condition