1. SIMS 213: User Interface Design & Development
Marti Hearst
Tues, April 18, 2006

2. Today Evaluation based on Cognitive Modeling Keystroke-Level Model
low-level description of what users must do to perform a task.
GOMS
structured, multi-level description of what users must do to perform a task
Fitts’ Law
Used to predict time needed to select a target

3. Keystroke-level Model
Another “discount” usability method
Main idea:
Walk through the interface, counting how many operations it would take an expert user to perform
Look for ways to optimize
Look for potential sources of error
KLM is very low-level (tiny operations)

4. Keystroke-Level Model
How to make a KLM
List specific actions user does to perform task
Keystrokes and button presses
Mouse movements
Hand movements between keyboard & mouse
System response time (if it makes user wait)
Add Mental operators
Assign execution times to steps
Sum execution times
Only provides execution time and operator sequence
Slide adapted from Chris Long

5. KLM Example Replace all instances of a 4-letter word.
(example from Hochstein)

6. Slide adapted from Chris Long
What is GOMS?
A family of user interface modeling techniques
Goals, Operators, Methods, and Selection rules
Higher-level than KLM
Input: detailed description of UI and task(s)
Output: various qualitative and quantitative measures
Slide adapted from Chris Long

7. Applications of GOMS analysis
Compare UI designs
Profiling
Building a help system
GOMS modelling makes user tasks and goals explicit
Can suggest questions users will ask and the answers
Slide adapted from Chris Long

8. Slide adapted from Chris Long
What GOMS can model
Task must be goal-directed
Some activities are more goal-directed than others
Even creative activities contain goal-directed tasks
Task must a routine cognitive skill
Can include serial and parallel tasks
Slide adapted from Chris Long

9. Slide adapted from Chris Long
GOMS Output
Functionality coverage and consistency
Does UI contain needed functions?
Are similar tasks performed similarly? (NGOMSL only?)
Operator sequence
In what order are individual operations done?
Abstraction of operations may vary among models
Slide adapted from Chris Long

10. Slide adapted from Chris Long
Output (cont’d)
Execution time
By expert
Error recovery
Procedure learning time (NGOMSL only)
Accurate for relative comparison only
Does not include time for learning domain knowledge
Slide adapted from Chris Long

11. How to do (CMN-)GOMS Analysis
Generate task description
Pick high-level user Goal
Write Method for accomplishing Goal - may invoke subgoals
Write Methods for subgoals
This is recursive
Stops when Operators are reached
Evaluate description of task
Apply results to UI
Iterate
Slide adapted from Chris Long

12. Slide adapted from Chris Long
Operators vs. Methods
Operator: the most primitive action
Method: requires several Operators or subgoal invocations to accomplish
Level of detail determined by
KLM level - keypress, mouse press
Higher level - select-Close-from-File-menu
Different parts of model can be at different levels of detail
Slide adapted from Chris Long

13. GOMS Example 1: PDA Text Entry
goal: enter-text-PDA
move-pen-to-text-start
goal: enter-word-PDA
...repeat until no more words
write-letter ...repeat until no more letters
[select: goal: correct-misrecognized-word] ...if incorrect
expansion of correct-misrecognized-word goal:
move-pen-to-incorrect-letter
write-letter
Slide adapted from Chris Long

14. GOMS Example 2: Graph Drawer
goal: draw-graph
goal: draw-node ...repeat until no more nodes
goal: draw-circle
draw-circle-gesture
goal: verify-circle-gesture
[select: goal: correct-gesture] ...if misrecognized or drawn incorrectly
goal: connect-node ...repeat until no more connections
draw-line-gesture
move-pen-to-node-just-drawn
goal: name-node
make-naming-gesture
goal:enter-text
Slide adapted from Chris Long

15. Slide adapted from Chris Long
GOMS Example 2
expansion of correct-gesture goal
move-pen-to-undo-button
tap-undo-button
goal: copy-node
move-pen-to-node
draw-copy-gesture
drag-pen-to-destination
Slide adapted from Chris Long

16. GOMS Example 3
Move text in a word processor
(example from Hochstein)

17. GOMS Example 3
Move text in a word processor
(example from Hochstein)

18. GOMS Example 3
Move text in a word processor
(example from Hochstein)

19. Slide adapted from Chris Long
Members of GOMS Family
Keystroke-Level Model (KLM) –
Card, Moran, Newell (1983)
CMN-GOMS
Card, Moran, Newell GOMS
Natural GOMS Language (NGOMSL)
-Kieras (1988+)
Critical Path Method or Cognitive, Perceptual, and Motor GOMS (CPM-GOMS)
John (1990+)
Slide adapted from Chris Long

20. Slide adapted from Chris Long
Other GOMS techniques
NGOMSL
Regularized level of detail
Formal syntax, so computer interpretable
Gives learning times
CPM-GOMS
Closer to level of Model Human Processor
Much more time consuming to generate
Can model parallel activities
Slide adapted from Chris Long

21. Real-world GOMS Applications
KLM
Mouse-based text editor
Mechanical CAD system
NGOMSL
TV control system
Nuclear power plant operator’s associate
CPM-GOMS
Telephone operator workstation
Slide adapted from Chris Long

22. Engineering Model of Human Performance
From The Psychology of Human-Computer Interaction, by Card, Moran, and Newell
Quantitative predictions
Usefully approximate
Based on the “Model Human Processor”
Slide adapted from Chris Long

23. Slide adapted from Chris Long
Advantages of GOMS
Gives several qualitative and quantitative measures
Model explains why the results are what they are
Less work than user study
Easy to modify when interface is revised
Research ongoing for tools to aid modeling process
Slide adapted from Chris Long

24. Slide adapted from Chris Long
Disadvantages of GOMS
Not as easy as heuristic analysis, guidelines, or cognitive walkthrough
Only works for goal-directed tasks
Assumes tasks are performed by expert users
Evaluator must pick users’ tasks/goals
Does not address several important UI issues, such as
readability of text
memorability of icons, commands
Does not address social or organizational impact
Slide adapted from Chris Long

25. Slide adapted from Chris Long
GOMS Summary
Provides info about many important UI properties
But does not tell you most of what you want to know about a UI
Substantial effort to do initial model, but still (potentially) easier than user testing
Changing later is much less work than initial generation
Slide adapted from Chris Long

26. Slide adapted from Newstetter & Martin, Georgia Tech
Fitts’ Law
Models movement time for selection tasks
The movement time for a well-rehearsed selection task
increases as the distance to the target
increases
decreases as the size of the target
Slide adapted from Newstetter & Martin, Georgia Tech

27. Slide adapted from Newstetter & Martin, Georgia Tech
Fitts’ Law
Time (in msec) = a + b log2(D/S+1)
where
a, b = constants (empirically derived)
D = distance
S = size
ID is Index of Difficulty = log2(D/S+1)
Slide adapted from Newstetter & Martin, Georgia Tech

28. Slide adapted from Pourang Irani
Fitts’ Law
Time = a + b log2(D/S+1)
Target 1
Target 2
Same ID → Same Difficulty
Slide adapted from Pourang Irani

29. Slide adapted from Pourang Irani
Fitts’ Law
Time = a + b log2(D/S+1)
Target 1
Target 2
Smaller ID → Easier
Slide adapted from Pourang Irani

30. Slide adapted from Pourang Irani
Fitts’ Law
Time = a + b log2(D/S+1)
Target 1
Target 2
Larger ID → Harder
Slide adapted from Pourang Irani

31. Determining Constants for Fitts’ Law
To determine a and b
design a set of tasks with varying values for D and S (conditions)
For each task condition
multiple trials conducted and the time to execute each is recorded and stored electronically for statistical analysis
Accuracy is also recorded
either through the x-y coordinates of selection or
through the error rate — the percentage of trials selected with the cursor outside the target
Slide adapted from Pourang Irani

32. A Quiz Designed to Give You Fitts
Microsoft Toolbars offer the user the option of displaying a label below each tool. Name at least one reason why labeled tools can be accessed faster. (Assume, for this, that the user knows the tool.)
Slide adapted from Pourang Irani

33. A Quiz Designed to Give You Fitts
The label becomes part of the target. The target is therefore bigger. Bigger targets, all else being equal, can always be acccessed faster, by Fitt's Law.
When labels are not used, the tool icons crowd together.
Slide adapted from Pourang Irani

34. A Quiz Designed to Give You Fitts
You have a palette of tools in a graphics application that consists of a matrix of 16x16-pixel icons laid out as a 2x8 array that lies along the left-hand edge of the screen. Without moving the array from the left-hand side of the screen or changing the size of the icons, what steps can you take to decrease the time necessary to access the average tool?
Slide adapted from Pourang Irani

35. A Quiz Designed to Give You Fitts
Change the array to 1X16, so all the tools lie along the edge of the screen.
Ensure that the user can click on the very first row of pixels along the edge of the screen to select a tool. There should be no buffer zone.
Slide adapted from Pourang Irani

36. Summary
We can use Cognitive Modeling to make predictions about interface usability
Complementary to Usability Studies
In practice:
GOMS not used often
Fitts law often used for determining best case for new kinds of input methods