automating assessment of website design melody y. ivory and marti a. hearst uc berkeley 1999 - 2002

Automating Assessment of WebSite Design Melody Y. Ivory and Marti A. HearstUC Berkeley

1999 - 2002

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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 architectureCreative Good Study [1999]

Studied 10 e-commerce sites59% attempts failedIf 25% of these had succeeded ->

estimated additional $3.9B in sales

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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

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The WebTango Approach

•Predictions•Similarities•Differences•Suggestions•Design Modification

Quality Checker

Web Site Design

Profiles

Quality Designs

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Developing Statistical Profiles:The WebTango Approach1. 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

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WebTango Architecture

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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

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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,& graphicdesign

experiencedesign

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Page-Level Measures

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Word Count: 157

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Good Word Count: 81

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Body Word Count: 94

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Link Count: 34

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Page Title Hits: 3

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Visible Link Text Hits: 25

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Site-Level Measures

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Text Element Variation: 119%

Good Word Count = 81Average Link Words = 3…












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Page Title Variation: 185%

Page Title Hits = 3Page Title Score = 3




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Webby Awards Data

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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%)

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Example Page from Good Site

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Example Page from Avg. Site

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Example Page from Poor Site

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Webby Awards 2000 6 criteria

– Content– Structure &

navigation– Visual design– Functionality– Interactivity– Overall experience

Scale: 1–10 (highest)

Nearly normally distributed

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Which criteria contribute most to overall rating?

Figure 2a. Review StageContribution of Specific Criteria to Overall Site

Rating

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Content Navigation VisualDesign Interactivity Functionality

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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.

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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

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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

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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

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Page-Level Models (5346 Pages)

Model Method Accuracy

Good

Avg.

Poor

Overall page quality~1782 pgs/class

C&RT 96% 94%

93%

Content category quality~297 pgs/class & cat

LDC 92% 91%

94%

ANOVAs showed that all differences in measures were significant (good vs. avg, good vs. poor, etc.)

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Page-Level Models (5346 Pages)

Model Method Accuracy

Good

Avg.

Poor

Page type quality~356 pgs/class & type

LDC 84% 78%

84%

Overall page quality C&RT 96% 94% 93%

Content category quality LDC 92% 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, other1770 manually-classified pages, 84% accurate

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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

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The Models in More Detail

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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

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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

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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

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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

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Sample Page (Before)

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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]

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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 CountSans Serif Word Count

Display Word Count

Top deviant measures for content page

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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)

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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

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Sample Page (After)

Added linked menu that mirrors image menu.Removed colored and italicized body words.Added an accent color.

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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

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Before & After Pages Participants improved pages based on overall page

quality measures and closest good-page cluster models.

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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

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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

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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

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Conclusions

Let’s hear from you!

automating assessment of website design melody y. ivory and marti a. hearst uc berkeley 1999 - 2002

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