Transcript slides for part 1

User Modeling of
Assistive
Technology
Rich Simpson
The Problem…
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The most challenging aspect of designing a
computer access system for a client is predicting
and accommodating a client’s performance in six
months based on two hours of interaction with
that client.
The Problem…
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Clients may only see the clinician once, and that
visit only lasts for a few hours
There may be multiple potential solutions
Each potential solution may have multiple
configuration options
The client has little or no experience with
assistive technology upon which to base decisions
The Problem…
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Often, the assistive technology that’s easiest to
use at first will be less efficient in the long run
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Morse Code vs Row-Column Scanning
The Problem…
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What we want:
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We want to know how well each potential solution
would work for a client if the client had six months
to practice
What we have:
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Observations in the clinic
Assistive Technology Lending Library
Keystroke-Level Modeling
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“A simple model for the time it takes [an expert]
user to perform a task with a given method on an
interactive computer system.”
Predictive rather than descriptive or explanatory
Based on intuition rather than observation
Intended to allow comparisons between two or
more designs without having to run user trials
Keystroke-Level Modeling
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What does “expert” mean?
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Knows how to do the task
Doesn’t make mistakes
Consistent time for each action
Keystroke-Level Modeling
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Operators
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K - Keystroking
P - Pointing
H - Homing
D - Drawing
M - Thinking
R - System Responding
Keystroke-Level Modeling
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Keystroking (K)
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Typing speed
Can range between 0.08 and 1.20 seconds for ablebodied adults using a standard keyboard
Keystroke-Level Modeling
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Pointing (P)
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Based on Fitts’ Law
d
t p  a  blog 2 ( 1)
s

Keystroke-Level Modeling
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Mental Operations (M)
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The time to mentally prepare to execute physical
operators
In front of the first K of a string
In front of all Ps that select commands
Keystroke-Level Modeling
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An example: saving a file
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Move mouse to File menu
Press mouse button
Move mouse to “Save” option
Press mouse button
Type in the name of the file
Press the enter button
Keystroke-Level Modeling
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An example: saving a file
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Decide what to do (M)
Move mouse to File menu (P)
Press mouse button (K)
Decide what to do (M)
Move mouse to “Save” option (P)
Press mouse button (K)
Pick a name for the file (M)
Type in the name of the file (K x length of name)
Decide what to do (M)
Press the enter key (K)
Keystroke-Level Modeling
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Simplifications
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Fitts’ Law vs Steering Law
All movements (P, K) take the same amount of time
No actions overlap
The Problem…
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The most challenging aspect of designing a
computer access system for a client is predicting
and accommodating a client’s performance in six
months based on two hours of interaction with
that client.
What is Word Prediction?
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Word prediction is used to
reduce the number of
keystrokes required to generate
text.
The computer supplies a list of
“best guesses” for the word the
user is currently entering, and
when the word appears it may
be selected from the list with a
single keystroke.
What is Word Prediction?
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Word prediction is used to
reduce the number of
keystrokes required to generate
text.
The computer supplies a list of
“best guesses” for the word the
user is currently entering, and
when the word appears it may
be selected from the list with a
single keystroke.
What is Word Prediction?
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Word prediction is used to
reduce the number of
keystrokes required to generate
text.
The computer supplies a list of
“best guesses” for the word the
user is currently entering, and
when the word appears it may
be selected from the list with a
single keystroke.
What is Word Prediction?
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Word prediction is used to
reduce the number of
keystrokes required to generate
text.
The computer supplies a list of
“best guesses” for the word the
user is currently entering, and
when the word appears it may
be selected from the list with a
single keystroke.
What is Word Prediction?
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Word prediction is used to
reduce the number of
keystrokes required to generate
text.
The computer supplies a list of
“best guesses” for the word the
user is currently entering, and
when the word appears it may
be selected from the list with a
single keystroke.
Why doesn’t Word Prediction always increase
text entry rate?
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Word Prediction doesn’t necessarily increase the
speed with which a person can enter text because
it trades off physical effort for cognitive effort.
The configuration of a word prediction system
can have a significant effect on a user’s
performance.
Configuring Word Prediction
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Show: Number of keystrokes entered before list appears
Hide: The number of keystrokes entered after list appears before it
disappears
Llen: Maximum number of words in list
MWS: Minimum number of letters in each word in list
The Questions…
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Will word prediction increase text entry rate for a
client?
How should word prediction be configured to
maximize text entry rate?
Koester’s Model of Word
Prediction
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Search word prediction list
Decide what key to press
Press Key
Repeat…
Koester’s Model of Word
Prediction
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Search word prediction list (ts)
Decide what key to press (d)
Press Key (tk)
Repeat…
Koester’s Model of Word
Prediction
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S=number of searches/number of characters
K=number of keystrokes/number of characters
Twp=(S)(ts) + (K)(tk+M)
So the question is…
how do these…
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Show: Number of keystrokes entered before list appears
Hide: The number of keystrokes entered after list appears before it
disappears
Llen: Maximum number of words in list
MWS: Minimum number of letters in each word in list
influence S, ts, K and tk?
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Number of searches (S)
When does the list appear? (Show)
 When does the list disappear? (Hide)
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List search time (ts)
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Length of list (Llen)
Size of words in list (MWS)
Number of keystrokes (K)
When does the list appear? (Show)
 When does the list disappear? (Hide)
 Length of list (Llen)
 Size of words in list (MWS)
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Since you can’t set S and K,
what good are these models?
Since you can’t set S and K,
what good are these models?
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You can measure ts and tk
It’s hard to measure M (which Koester calls d)
You can simulate user performance over a range
of values for Show, Hide, Llen and MWS
The most promising configurations can be
compared in trials with the client
Experimental Validation
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Six subjects with disabilities
ABA design
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A was a “default” condition: list always displayed,
six words in list, no minimum number of letters
B was chosen using the model and observations
during the first A phase
For three subjects, B was 61% faster than A
For the other three subjects, B was 20% faster