1. Automating Assessment of WebSite Design Melody Y. Ivory and Marti A. Hearst UC Berkeley 1999 - 2002

2. 2 Usability affects the bottom line IBM case study [1999] Spent $millions to redesign site 84% decrease in help usage 400% increase in sales Attributed to improvements in information architecture Creative Good Study [1999] Studied 10 e-commerce sites 59% attempts failed If 25% of these had succeeded -> estimated additional $3.9B in sales

3. 3 Problem Statement  Non-professionals need help designing high-quality Web sites –Design guidelines conflict; are not empirically-validated; and ignore context  One solution –Empirically-validated, automated analysis of Web sites

4. 4 The WebTango Approach Predictions Similarities Differences Suggestions Design Modification Quality Checker Web Site Design Profiles Quality Designs

5. 5 Developing Statistical Profiles: The WebTango Approach 1. Create a large set of measures to assess various design attributes (benchmark) 2. Obtain a large set of evaluated sites 3. Create models of good vs. avg. vs. poor sites (guidelines) Take into account the context and type of site 4. Use models to evaluate other sites (guideline review) 5. Validate models Idea: Reverse engineer design patterns from high-quality sites and use to check the quality of other sites Measures Data ModelsEvaluate Validate

6. 6 WebTango Architecture

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 the 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]. Use several layouts (e.g., one for each page style) for variation within the site [Sano96]. Adhere to accessibility principles in order to create sites that serve a broad user community [Cooper99; Nielsen00] Avoid using ‘Click Here’ for link text [Nielsen00] Use left-justified, ragged-right margins for text [Schriver97] –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, scent quality  Site Architecture (16) –consistency, breadth, depth information, navigation, & graphic design experience design

9. 9 Page-Level Measures

10. 10 Word Count: 157

11. 11 Good Word Count: 81

12. 12 Body Word Count: 94

13. 13 Link Count: 34

14. 14 Page Title Hits: 3

15. 15 Visible Link Text Hits: 25

16. 16 Site-Level Measures

17. 17 Text Element Variation: 119% Good Word Count = 81 Average Link Words = 3 … Good Word Count = 733 Average Link Words = 2 … Good Word Count = 240 Average Link Words = 2 … Good Word Count = 292 Average Link Words = 2 … Good Word Count = 236 Average Link Words = 2 … Good Word Count = 142 Average Link Words = 2 … Good Word Count = 72 Average Link Words = 2 … Good Word Count = 29 Average Link Words = 2 … Good Word Count = 785 Average Link Words = 2 … Good Word Count = 294 Average Link Words = 2 … Good Word Count = 363 Average Link Words = 2 … Good Word Count = 1350 Average Link Words = 2 …

18. 18 Page Title Variation: 185% Page Title Hits = 3 Page Title Score = 3 Page Title Hits = 3 Page Title Score = 3 Page Title Hits = 0 Page Title Score = 0 Page Title Hits = 2 Page Title Score = 2

19. 19 Webby Awards Data

20. 20 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%)

21. 21 Example Page from Good Site

22. 22 Example Page from Avg. Site

23. 23 Example Page from Poor Site

24. 24 Webby Awards 2000  6 criteria –Content –Structure & navigation –Visual design –Functionality –Interactivity –Overall experience  Scale: 1 – 10 (highest)  Nearly normally distributed

25. 25 Which criteria contribute most to overall rating?

26. 26 Summary of Analysis of Webby Awards Data  The specific ratings do explain overall experience.  The best predictor of overall score is content.  The second best predictor is interactivity.  The worst predictor is visual design.  These results varied by type of site –Art vs health, for example.

27. 27 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

28. 28 Building the Data Set  Downloaded pages from sites using a Site Crawler Tool –Downloads informational pages at multiple levels of the site  Used a Metrics Computation Tool to compute measures for the sample –Processes static HTML, English pages Measures for 5346 pages Measures for 333 sites –No discussion of site-level models

29. 29 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

30. 30 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.)

31. 31 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) Types: home page, content, form, link, other 1770 manually-classified pages, 84% accurate

32. 32 Characteristics of 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

33. 33 The Models in More Detail

34. 34 Step 4: Evaluate Other Sites  Embed prediction profiles into an Analysis Tool –For each model Prediction: good, average, poor, mapped cluster Rationale: decision tree rule, deviant measures, etc. –Example page-level feedback Overall page quality model –Predicted quality: poor –Rationale: if (Italicized Body Word Count is not missing AND (Italicized Body Word Count > 2.5)) Good page cluster model –Mapped cluster: small-page, Cluster distance: 22.74 –Similar measures: Word Count;Good Word Count … –Deviant measures: Link Count [12.0] out of range (12.40-- 41.24);Text Link Count [2.0] out of range (4.97--27.98)… –Limitation: no suggestions for improvement or examples

35. 35 Assessment of GVU Home Page Predicted page style: link (average) Overall Quality: Average Rationale: min graphic width > 8.5 Cluster: Small page Differences: word counts Education Quality: Average

36. 36 Assessment of the School Home Page Take away: example of when the system fails due to extensive use of scripts Predicted page style: home Home Page Quality: poor Rationale: too few redundant links, interactive objects; too many scripts, italicized body text Overall Quality: poor Rationale: use of italicized body text Cluster: Formatted page Education Quality: poor

37. 37 Example Assessment  Demonstrate use of profiles to assess site quality and identify areas for improvement  Site drawn from Yahoo Education/Health –Discusses training programs on numerous health issues –Not in original study –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

38. 38 Sample Page (Before)

39. 39 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]

40. 40 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 page

41. 41 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)

42. 42 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

43. 43 Sample Page (After) Added linked menu that mirrors image menu. Removed colored and italicized body words. Added an accent color.

44. 44 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

45. 45 Before & After Pages  Participants improved pages based on overall page quality measures and closest good-page cluster models.

46. 46 Step 5: Validating the Prediction Models  Small study –Hypothesis: pages and sites modified based on the profiles are preferred over original versions –5 sites modified based on profiles (including the example site) Modifications by 2 undergraduate (Deep Debroy & Toni Wadjiji) and 1 graduate student (Wai-ling Ho-Ching) –Students had little to no design experience –Same procedure as in the example assessment –Minimal changes based on overall page quality and good page cluster models –13 participants 4 professional, 3 non-professional, and 6 non Web designers

47. 47 Profile Evaluation –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

48. 48 Summary of the Approach  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 user studies Measures Data ModelsEvaluate Validate

49. 49 Conclusions  Let’s hear from you!