User Modeling
Predicting thoughts and actions
GOMS
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SCHOOL OF INTERACTIVE ARTS + TECHNOLOGY [SIAT] | WWW.SIAT.SFU.CA
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
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different modeling techniques exist
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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
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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
•
•
•
•
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Typing speed improvement
Learning to use mouse
Pushing buttons in response to stimuli
NOT learning
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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
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Hick’s Law
 Decision
time to choose among n equally
likely alternatives
– T = Ic log2(n+1)
– Ic ~ 150 msec
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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
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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
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Fitts Experiment: 1D
d
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w
10
Fitts: Index of Difficulty

ID - Index of difficulty
ID = log2 (d/w + 1.0)
bits
result

distance
to move
width (tolerance)
of target
ID is an information theoretic quantity
– Based on work of Shannon – larger target => more
information (less uncertainty)
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Fitts formula
 MT
- Movement time
MT = k1 + k2*ID
MT = k1 + k2 *log2 (d/w + 1.0)
 MT
is a linear function of ID
k1 and k2 are experimental constants
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Run empirical tests to determine k1 and k2 in
MT = k1 + k2* ID
 Will get different ones for different input devices
and device uses

MT
ID = log2(d/w = 1.0)
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What about 2D
h
x w rect:
one way is ID = log2(d/min(h, w) + 1)
– Should take into account direction of
approach
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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
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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,
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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
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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
Select sentence
Moved sentence
Cut sentence
Move to new spot
Paste sentence
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
method
when there is a choice of a
 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
Move Sentence
1. Select sentence
Reach for mouse
Point to first word
Click button down
Drag to last word
Release
H
P
K
P
K
2. Cut sentence
Press, hold ^
Press and release ‘x’
Release ^
0.40
1.10
0.60
1.20
0.60
3.90 secs
Point to menu
Press and hold mouse
Move to “cut”
Release
or
3. ...
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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

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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,
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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
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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
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
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
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“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
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