Information Architecture

1
Information Architecture

• Organization Systems
• Labeling Systems
• Kathryn Summers 2006

2
First Principles
Wodtke, chapter 2

• Design for way finding
• Set expectations and provide feedback
• Design Ergonomically
• Be consistent and consider standards
• Provide error support prevent, protect inform
• Rely on recognition, not recall
• Support varying skill levels
• Provide meaningful contextual help
  documentation

3
Why organize information?

• To understand, explain control
• Reflect social political perspectives
• Support casual browsing and directed searching
• Provide the visitor with a mental model of what
  the website is about and contains

4
Why is organizing information so difficult?

• Ambiguity
• Heterogeneity
• Differences in perspectives
• Internal politics

5
Organization systems

• Schemes
• Grouping shared characteristics of content items
  that influence the logical grouping of items

• Structures
• The logical grouping of items or the relationship
  of items to each other
• Defines the primary ways the user can navigate
• Orient users show the range of options

6
Organization schemes

• Exact schemes
• Well-defined mutually exclusive sections
• No ambiguity
• Supports known-item searching well
• Most frequently used exact schemes
• Alphabetical
• Chronological
• Geographical

7
Organization schemes

• Ambiguous schemes
• Needed when you dont know know the exact label
• Based in associative learning theory
• Ongoing need for maintenance of these schemes
• Items may be found in more than one
  classification

8
Organization Schemes

• Common ambiguous schemes may be used
  simultaneously
• Topic define breath of coverage
• Task collections of processes, functions or
  tasks
• Audience when more than one definable user
  group
• Metaphor relate to the familiar limiting
• Hybrids multiple schemes at the same level of
  navigation inhibits mental model formation

9
Organization Structure (taxonomies)

• Hierarchytop/down
• Databasebottom/up
• Hypertext

10
Organization Structure (taxonomies)

• Hierarchy top/down model the foundation
• Characteristics
• Mutually exclusive units normally
• Parent child relationships
• Cross listing polyhierarchial/multiple facets
• Issues of breath depth
• What can be visually scanned on a single page
• Group items for easy scanning
• Global items on every page
• Local items on this page
• Primary taxonomies (views by product, audience,
  task)
• Logical groupings (resources, contacts,
  downloads, etc.)
• 2-3 levels avoid major restructuring in expansion

11
Organization structures (taxonomies)

• Database model bottom/up
• Good for homogeneous content
• Relational database structure
• Provide definition of metadata
• Requires use of a controlled vocabulary
• Enables
• Automatic generation of alphabetical indexes
• Dynamic presentation of associative see also
  links
• Fielded searching
• Advanced filtering and storing of search results
• Faceted classification
• User perceived attribute values, not the
  attribute itself

12
Organization structures (taxonomies)

• Hypertext
• Items or chunks of information that are linked,
  and the links between them
• Hypermedia systems to content text, data, image,
  video and audio chunks
• Can be hierarchical or not
• Easy to get lost
• Use as supportive structure to creatively link
  hierarchical items and areas

13
Labeling
14
Why care about labeling?

• To speak the visitors language
• To reflect the website content
• To educate visitors about new concepts

15
Labels should

• Differentiate and represent
• Be user-centric and not jargon
• Not intrude on the user experience
• Is the user having a huh? experience
• Make a good impression (reflect the reputation
  youre trying to build)

16
Forces that influence label naming

• The organizations brand (3)
• Common conventions (2)
• Space available for displaying the label
• What the user will understand (1)
• Designer boredom

17
Kinds of labels

• Contextual links
• Headings
• Navigation system choices
• Index terms
• Labels are not mutually exclusive
• Labels can do double duty

18
A continuum of labels
Contextual Links
Navigation Systems
Headings
Index terms
Consistency and discipline in application
Iconic Labels?
19
Labels as contextual links

• Generally, are
• Not developed systematically
• Heterogeneous and personal
• Draw meaning from the surrounding text
• Straight forward and clear is best
• Do your visitors have contextual knowledge?
• Trust must precede the use of non-representational
  links
• Labels have meaning by being part of a set of
  labels or labeling system
• Ask what kind of information will the user
  expect to be taken to?

20
Labels as headings

• Used to establish hierarchy within text (e.g. an
  organization system
• Established visually through consistent use of
• numbering
• font sizes
• colors and styles
• white space
• indentations
• combinations

21
Labels within navigation systems

• Must be consistent in application
• What is familiar, what is used on related/other
  websites
• Page 87 standard labeling
• Augment labels with brief descriptions (e.g
  rollovers, alt tags and scripted mouse over
  effects)

22
Labels as index terms

• Many invisible to users
• Kinds of index labels
• Keywords
• Descriptive meta data
• Taxonomies
• Controlled vocabularies
• Thesauri
• Sets of terms

23
Iconic labels

• Much more limited language than text
• Can add aesthetic quality
• International standards?
• What about people with disabilities using a
  website with iconic labels?

24
Designing labels

• Narrow scope whenever possible
• Simplicity, modularization, focus
• Develop consistent labeling systems, not
  individual labels
• Predictability
• Easy to remember easy to learn

25
Designing labels

• Consistency is affected by
• Grammatical style
• Presentation (font, color, font size)
• Syntax (verb, noun based or question)
• Granularity (level of specificity hierarchy)
• Comprehensive scope (is something missing?)
• Audience (reading levels orientation)

26
Sources of labeling systems

• What is already in existence at the website
• Label destination heading label destination
  title label
• What is used on comparable or competitive
  websites
• What exists in controlled vocabularies in
  professional thesauri

27
Creating new labeling systems

• Content analysis
• Content authors
• User advocates and subject matter experts
• Top-down structure approach
• Category, simple labels, scientific labels
• Card sort exercise (open and closed sorts)
• Search log analysis
• Tweaking and tuning
• Sort alphabetically, then remove duplicates
• Review list for consistency of usage,
  punctuation, letter case, etc.
• Group into categories

28
Creating new labeling systems

• Categorization has consequences (can enhance or
  inhibit access to important info)
• Categorization provides context
• Lampfrom camping category vs. from lighting
  category tells you about type of lamp youll get
• Labeling systems send messages

29
(No Transcript)
30
Sources

• Vogue
• Fortune
• Salon
• Epicurious
• ESPN
• Architectural digest
• Gourmet magazine

31
Cardsorting

• Content adapted from Jason Withrow

32
Open Card Sort Process

• 1. Collect all labels or possible names of
  things, make into cards
• 2. The user groups related cards into piles
• 3. The user assigns one label to each pile
• 4. Can the piles be subdivided further?
• 5. Label each of the smaller sub-piles
• 6. Sometimes further subdivision is needed
• 7. Record the groupings and labels (document
  results)
• 8. Repeat with another user

33
Card Sorting Example Election Website

• Candidates bio Election issues Press releases
• Campaign events Speeches Campaign donations
• Media coverage Campaign timeline Voter
  registration
• Website feedback Newsletter
  Endorsements
• Ask the candidate a question

On-the-road journal
Let a friend know about this website
Candidate comparison
Privacy policy Related links Sitemap
Frequently asked questions
Volunteering Campaign staff and openings
Candidates record and accomplishments
34
Create Primary Groups
Privacy policy
Candidates bio
Website feedback
Candidates record and accomplishments
Campaign staff and openings
Sitemap
Related links
Ask the candidate a question
Press releases
Election issues
On-the-road journal
Candidate comparison
Campaign timeline
Frequently asked questions
Volunteering
Media coverage
Endorsements
Speeches
Voter registration
Newsletter
Campaign donations
Campaign events
Let a friend know about this website
35
Label Primary Groups
Privacy policy
About the Candidate
Website feedback
Candidates bio
Sitemap
Related links
Candidates record and accomplishments
Campaign staff and openings
News Events
Ask the candidate a question
Press releases
On The Issues
On-the-road journal
Getting Involved
Election issues
Campaign timeline
Candidate comparison
Volunteering
Media coverage
Endorsements
Frequently asked questions
Speeches
Voter registration
Newsletter
Campaign donations
Campaign events
Let a friend know about this website
36
Create Secondary Groups
News Events
Campaign timeline
Media coverage
Campaign events
Press releases
Newsletter
On-the-road journal
Speeches
37
Label Secondary Groups
News Events
Events
In the Media
Campaign timeline
Media coverage
Campaign events
Press releases
Newsletter
News from the Candidate
On-the-road journal
Speeches
38
Analyzing the Data

• Eyeball the data for common groupings and
  number of top level categories
• Use a program for analysis (as well as
  administration of the card sort)
• EZSort/USort
• WebCAT
• (Withrows Excel spreadsheet)
• Cluster analysis, a statistical technique, is
  useful for identifying groupings

39
Closed Card Sorting (confirmatory)

• Conducted after the site architecture has been
  developed
• Asks the question Do users expect to find
  content under the right label?
• If users sort content under the wrong label (or
  cannot place the content at all), that strongly
  suggests scent issues with the current labeling

40
Provide Global Navigation Cards
Getting Involved
About the Candidate
Related Links
Sitemap
On The Issues
News Events
Privacy Policy
Website Feedback
41
First Pass at Dividing Cards
Getting Involved
About the Candidate
Related Links
Sitemap
Candidates bio
Endorsements
Candidates record and accomplishments
Voter registration
Campaign staff and openings
Campaign donations
Ask the candidate a question
Let a friend know about this website
On The Issues
News Events
Privacy Policy
Website Feedback
Election issues
Press releases
On-the-road journal
Candidate comparison
Campaign timeline
Frequently asked questions
Media coverage
Speeches
Newsletter
Campaign events
42
Breadth and Similarity Matching

• User rates on a scale of 1-10 the similarity of
  every possible pairing of content cards
• Cluster analysis creates the groups by crunching
  the numbers and seeing which items are rated as
  being most similar
• No labels are suggested for each cluster of
  content items, but hopefully a clear label
  emerges from examining the groupings