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/macrom edia/monterey/architecture/

5. 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 1.Main content selections for this page 2.Page title 3.Person responsible for this page 4.Intranet-wide navigation (e.g., intranet home, search) 5.Last updated date 6.Intranet identifier/logo 7.Site navigation (e.g, major sections of this section of the intranet) 8.Confidentiality/security (e.g, Public, Confidential, etc.) 9.Site news items

9. 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 Correct Page Elements Subjective Appeal (-3 to +3) Template 152%+1.3 Template 367%+0.9 Final Design72%+2.1

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. Slide adapted from Rashmi Sinha's, www.rashmisinha.com 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 Self correlation

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

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

23. Slide adapted from Rashmi Sinha's, www.rashmisinha.com 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

24. How Many Participants for Card 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.nethttp://websort.net