SIMS 213: User Interface Design & Development

1. SIMS 213: User Interface Design & Development Marti Hearst Thurs, March 3, 2005

2. Today • Content Layout Technique: Wireframing • Content Expression Normalization: Card Sorting

3. Wireframing • What is the main idea? • Separate content from layout and display • Graphic Design: • Use the page layout to signal the flow of interaction • Group conceptually related items together • Nielsen: • What does the layout communicate? • Test if a page of content becomes more usable because of the layout • A template (for a home page) should contain what items?

4. How WireFraming Fits In Kelly Goto & Eric Ott of Macromedia http://www.gotomedia.com/macromedia/monterey/architecture/

5. From http://www.gotomedia.com/macromedia/monterey/architecture/

6. From http://www.gotomedia.com/macromedia/monterey/architecture/

7. How to Test A Layout • Study conducted by Thomas S. Tullis from Fidelity Investments in 1998 • Assessed the usability of five alternative template designs for their intranets. • Method: • Show templates with “greeked” text • Draw labeled boxes around each page corresponding to 9 elements • No overlapping allowed • Indicate if something appears not to be there

8. The Elements • Main content selections for this page • Page title • Person responsible for this page • Intranet-wide navigation (e.g., intranet home, search) • Last updated date • Intranet identifier/logo • Site navigation (e.g, major sections of this section of the intranet) • Confidentiality/security (e.g, Public, Confidential, etc.) • Site news items

9. From http://www.useit.com/alertbox/980517.html

10. From http://www.useit.com/alertbox/980517.html

11. From http://www.useit.com/alertbox/980517.html

12. From http://www.useit.com/alertbox/980517.html

13. Nielsen Wireframing Example • Different parts of the designs scored better • Best parts were taken from each design and combined • Resulted in an overall better score

14. Results of Study

15. The Vocabulary Problem • If you ask a set of people to describe a set of things there is little overlap in the results. • Some nice studies have been done on this: • Furnas, Landauer, Gomez, Dumais: The Vocabulary Problem in Human-System Communication. Commun. ACM 30(11): 964-971 (1987)

16. The Vocabulary Problem • Idea: see how often people agree on word choice • Data sets examined (# of participants): • Main verbs used by typists to describe edit types (48) • Commands for a hypothetical “message decoder” computer program (100) • First word used to describe 50 common objects (337) • Categories for 64 classified ads (30) • First keywords for a each of a set of recipes (24) Furnas, Landauer, Gomez, Dumais: The Vocabulary Problem in Human-System Communication. Commun. ACM 30(11): 964-971 (1987)

17. The Vocabulary Problem • We get really bad results • If one person assigns the name, the probability of it NOT matching with another person’s is about 80% Furnas, Landauer, Gomez, Dumais: The Vocabulary Problem in Human-System Communication. Commun. ACM 30(11): 964-971 (1987)

18. The Vocabulary Problem • What if we pick the most commonly chosen words as the standard? • Still not good: Furnas, Landauer, Gomez, Dumais: The Vocabulary Problem in Human-System Communication. Commun. ACM 30(11): 964-971 (1987)

19. The Solution? Card Sorting • Content-focused • Two main purposes • Determine how to organize a set of information • Determine a collective mental model of the collection’s contents • Determine good labels for the items in that organization • How is it done? • User-centered • Write topics on cards • Ask different participants to organize them for a given task • Need strategies for “difficult” cards • Consolidate the results • Do another round for labels

20. Consolidating Card Sorting Results • At root of all categorization techniques is question: “How far is A from B?” • Proximity / similarity matrix can organize the results • Each cell shows how often 2 items were in the same category Slide adapted from Rashmi Sinha's, www.rashmisinha.com Self correlation

21. Analyzing Results viaHierarchical Clustering • Cluster Analysis for dataset about cars • Advantages • Suggests a structural solution • Disadvantages • Prescriptive • Averages over different dimensions Continental Buick Cadillac Mercedes Corvette Jaguar Firebird Camaro Monte Carlo Capri Chevy Vega Dart Volkswagen Slide adapted from Rashmi Sinha's, www.rashmisinha.com

22. Analyzing Results via Multidimensional Scaling (MDS) Slide adapted from Rashmi Sinha's, www.rashmisinha.com

23. Advantages of Multidimensional Scaling (MDS) • Hints at possible solutions rather than prescribes. Tells you the possibilities, leaves specifics of solution to you. • Dimensions (axes) can suggest facets. • Similarity maps are easy to understand • Helps identify which dimensions are important • Do cluster analysis using the axes identified by MDS • Cluster Analysis and MDS are complementary Slide adapted from Rashmi Sinha's, www.rashmisinha.com

24. How Many Participants forCard Sorting? • How many participants? • Tullis and Wood ’04, discussed by Nielsen: • http://home.comcast.net/%7Etomtullis/publications/UPA2004CardSorting.pdf • http://www.useit.com/alertbox/20040719.html • Study included 168 participants, 46 cards • Nielson concludes: • 5 for usability studies • Evaluating and reacting to an existing design • Fewer potential responses • 15 participants for card sorting • (to get 90% agreement) • Generating something, so more diversity in responses • (Numbers based on only one site!) • (Interface design should interweave generating and testing)

25. Let’s Do Some Sorting! • There are many tools out there • Let’s try WebSort • http://websort.net