User Interface Design

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SCHOOL OF INTERACTIVE ARTS + TECHNOLOGY [SIAT] | WWW.SIAT.SFU.CA
User Modeling
Predicting thoughts and actions
GOMS

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Agenda
 User modeling
– Fitt’s Law
– GOMS

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User Modeling
 Idea: If we can build a model of how a
user works, then we can predict how s/he
will interact with the interface
– Predictive modeling
 Many different modeling techniques exist

4. User Modeling – 2 types
 Stimulus-Response
– Hick’s law
– Practice law
– Fitt’s law
 Cognitive – human as interperter/predictor –
based on Model Human Processor (MHP)
– Key-stroke Level Model
• Low-level, simple
– GOMS (and similar) Models
• Higher-level (Goals, Operations, Methods, Selections)
• Not discussed here
Feb 24, 2011 IAT 334 4

5. Power Law of Practice
 Tn = T1n-a
– Tn to complete the nth trial is T1 on the first trial
times n to the power -a; a is about .4, between .2
and .6
– Skilled behavior - Stimulus-Response and routine
cognitive actions
• Typing speed improvement
• Learning to use mouse
• Pushing buttons in response to stimuli
• NOT learning
Feb 24, 2011 IAT 334 5

6. Power Law of Practice
 How to use it?
– Use measured T1 on the first trial
• Predict whether usability criteria will be met
• How many trials?
– Predict how many practice iterations
needed to reach usability criteria
Feb 24, 2011 IAT 334 6

7. Hick’s Law
 Decision time to choose among n equally
likely alternatives
– T = Ic log2(n+1)
– Ic ~ 150 msec
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8. Hick’s Law
 How to use it?
– Menu selection
– Choose among 64 choices:
• Single 64-item menu
• 2-level menu: 8 choices at each level
• 2-level menu: 4 choices then 16 choices
Feb 24, 2011 IAT 334 8

9. Fitts’ Law
 Models movement times for selection
(reaching) tasks in one dimension
 Basic idea: Movement time for a selection
task
– Increases as distance to target increases
– Decreases as size of target increases
Feb 24, 2011 IAT 334 9

10. Fitts Experiment: 1D
Feb 24, 2011 IAT 334 10
d w

11. Fitts: Index of Difficulty
 ID - Index of difficulty
 ID is an information theoretic quantity
– Based on work of Shannon – larger target => more
information (less uncertainty)
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ID = log2 (d/w + 1.0)
bits
result
width (tolerance)
of target
distance
to move

12. Fitts formula
 MT - Movement time
 MT is a linear function of ID
k1 and k2 are experimental constants
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MT = k1 + k2*ID
MT = k1 + k2 *log2 (d/w + 1.0)

13.  Run empirical tests to determine k1 and k2 in
MT = k1 + k2* ID
 Will get different ones for different input devices
and device uses
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MT
ID = log2(d/w = 1.0)

14. What about 2D
 h x w rect:
one way is ID = log2(d/min(h, w) + 1)
– Should take into account direction of
approach
Feb 24, 2011 IAT 334 14

15. Design implications
 Menu item size
 Icon size
 Put frequenlty used icons together
 Scroll bar target size and placement
– Up / down scroll arrows together or at top
and bottom of scroll bar
Feb 24, 2011 IAT 334 15

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GOMS
 One of the most widely known
 Assumptions
– Know sequence of operations for a task
– Expert will be carrying them out
 Goals, Operators, Methods, Selection
Rules

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GOMS Procedure
 Walk through sequence of steps
 Assign each an approximate time duration
-> Know overall performance time
 (Can be tedious)

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Limitations
 GOMS is not for
– Tasks where steps are not well understood
– Inexperienced users
 Why?
 Good example: Move a sentence in a
document to previous paragraph

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Goal
 End state trying to achieve
 Then decompose into subgoals
Moved sentence
Select sentence
Cut sentence
Paste sentence
Move to new spot
Place it

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Operators
 Basic actions available for performing a
task (lowest level actions)
 Examples: move mouse pointer, drag,
press key, read dialog box, …

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Methods
 Sequence of operators (procedures) for
accomplishing a goal (may be multiple)
 Example: Select sentence
– Move mouse pointer to first word
– Depress button
– Drag to last word
– Release

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Selection Rules
 Invoked when there is a choice of a
method
 Example: Could cut sentence either by
menu pulldown or by ctrl-x

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Further Analysis
 GOMS is often combined with a keystroke
level analysis
– Assigns times to different operators
– Plus: Rules for adding M’s (mental
preparations) in certain spots

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Example
1. Select sentence
Reach for mouse H 0.40
Point to first word P 1.10
Click button down K 0.60
Drag to last word P 1.20
Release K 0.60
3.90 secs
2. Cut sentence
Press, hold ^ Point to menu
Press and release ‘x’ or Press and hold mouse
Release ^ Move to “cut”
Release
3. ...
Move Sentence

25. Keystroke-Level Model
 Simplified GOMS
 KSLM - developed by Card, Moran & Newell, see
their book
– The Psychology of Human-Computer Interaction,
Card, Moran and Newell, Erlbaum, 1983
 Skilled users performing routine tasks
 Assigns times to basic human operations -
experimentally verified
 Based on MHP - Model Human Processor
Feb 24, 2011 IAT 334 25

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User Profiles
 Attributes:
– attitude, motivation, reading level, typing
skill, education, system experience, task
experience, computer literacy, frequency of
use, training, color-blindness, handedness,
gender,…
 Novice, intermediate, expert

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Motivation
 User
– Low motivation,
discretionary use
– Low motivation,
mandatory
– High motivation, due
to fear
– High motivation, due
to interest
 Design goal
– Ease of learning
– Control, power
– Ease of learning,
robustness, control
– Power, ease of use

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Knowledge & Experience
 Experience
 task system
– low low
– high high
– low high
– high low
 Design goals
– Many syntactic and
semantic prompts
– Efficient commands,
concise syntax
– Semantic help facilities
– Lots of syntactic
prompting

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Job & Task Implications
 Frequency of use
– High - Ease of use
– Low - Ease of learning & remembering
 Task implications
– High - Ease of use
– Low - Ease of learning
 System use
– Mandatory - Ease of using
– Discretionary - Ease of learning

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Modeling Problems
 1. Terminology - example
– High frequency use experts - cmd language
– Infrequent novices - menus
 What’s “frequent”, “novice”?

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Modeling Problems (contd.)
 2. Dependent on “grain of analysis”
employed
– Can break down getting a cup of coffee into
7, 20, or 50 tasks
– That affects number of rules and their types

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Modeling Problems (contd.)
 3. Does not involve user per se
– Don’t inform designer of what user wants
 4. Time-consuming and lengthy