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Usability and Accessibility Lecture 14 – 09/04/10. Dr. Simeon Keates. Exercise – Part 1. Last week you were asked to prepare your user trial protocols Today – put them into practice Perform a pilot study of the usability of your web-site with at least 1 user - PowerPoint PPT Presentation
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© Simeon Keates 2010
Usability and AccessibilityLecture 14 – 09/04/10Dr. Simeon Keates
© Simeon Keates 2010
Exercise – Part 1
Last week you were asked to prepare your user trial protocols Today – put them into practice
Perform a pilot study of the usability of your web-site with at least 1 user
Remember – the principal aim is to “test the test” • (or “trial the trial” or “evaluate the evaluation”…)
Page 2
© Simeon Keates 2010
Exercise – Part 2
Prepare a progress presentation for the board for Friday Show that good progress is being made
Summarise:• The tasks performed • The data collected• Whether the user liked the site• Whether the user could use the site (e.g. complete the tasks)• What you think is working well in the design• What you think needs to be looked at more closely in the design• Any changes you would like to make to the site and protocol
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© Simeon Keates 2010
Exercise - Practicalities
Remember to print out copies of your protocol
Allow plenty of blank space for adding observation notes
Allocate one person to do the pre-session briefing and debrief
Allocate one person to be the facilitator (the person who directs the user)
The remaining members act as observers
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© Simeon Keates 2010
Cognitive models
Page 5
© Simeon Keates 2010
The Power Law of Practice
Tn = T1 n-α
α = 0.4, T1 = 60s, T2 = 45.5s (24% faster), T10 = 23.9s (60%faster)
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© Simeon Keates 2010
Motor skills – Fitts’ Law
A person wishes to hit this target:
We know that a correction cycle takes:
τp + τc + τm≈ 240 ms
And so n corrections takes n * 240 ms
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Startx0 x1 x2
S
D
© Simeon Keates 2010
Fitts’ Law
Now let xi be the remaining distance after the i-th correction
And let x0 (= D) be the starting point
We will assume that the relative accuracy of movement is constant, i.e.:
Where ε < 1 and is the constant error
On 1st cycle: x1 = ε x0 = ε D
On 2nd cycle: x2 = ε x1 = ε (ε D) = ε2 D
On n-th cycle: xn = εn D
Process stops when: εn D ≤ ½ S
Solving for n gives:
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€
x ix i−1
= ε
€
n = −log2(2D /S)
log2 ε
© Simeon Keates 2010
Fitts’ Law
From:
Total movement time, Tpos is given by:
This can be re-written as:
Where:
ε has been found to be ~ 0.7
Thus IM ≈ -240 / log2(0.7) = 63 ms/bit [27~122 ms/bit]
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€
n = −log2(2D /S)
log2 ε
€
Tpos = n(τ p + τ c + τ m )
€
Tpos = IM log2(2D /S)
€
IM =−(τ p + τ c + τ m )
log2 ε
Fitts’ Law
© Simeon Keates 2010
Fitts’ Law corrections
There are several modifications to Fitts’ Law
Fitt’s Law becomes less accurate for low values of log2(2D / S)
i.e. where the target is quite big compared with the distance
An example correction by Welford (1968):
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€
Tpos = IM log2(D S + 0.5)
© Simeon Keates 2010
Fitts’ Law – Implications for web-site design
Long, thin targets are not good• Small S value => longer acquisition times
Example of long, thin target:• Text-only hyperlinks• e.g. Heinz tomato ketchup
Better to include something large• e.g. an image of a ketchup bottle…
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© Simeon Keates 2010
Merging the models
One basic merged model is the Keystroke Level Model (KLM):
Texecute = TK + TP + TH + TD + TM + TR
Where TK = total time spent keystroking = nk tk (# * time per stroke)
• Time per stroke determined experimentally
TP = total time spent pointing (from Fitts’ Law)
• Assume, say, 1.1 s per pointing action
TH = total time spent homing (moving hands between devices)
• Assume 0.4 s per homing
TD = total time spent drawing = tD (nD, lD) (i.e. f(#, total length))
• Example: 0.9nD + 0.16lD
TM = total time to mentally prepare
• Assume 1.35 s per preparation
TR = total system response timePage 12
© Simeon Keates 2010
Using the KLM
[Note: M = mental prep, K = keyboard, P = pointing] Rule 0: Insert Ms in front of all Ks that are not part of argument strings
proper. Place Ms in front of all Ps that select commands Rule 1: If an operator following an M is fully anticipated in an operator
just previous to M, then delete the M (e.g. PMK -> PK) Rule 2: If a string of MKs belongs to a cognitive unit (e.g. name of a
command), then delete all Ms but the first one Rule 3: If a K is a redundant terminator (e.g. terminates a command
immediately following the terminator of its argument), then delete the M in front of it
Rule 4: If a K terminates a constant string (e.g. a command name), then delete the M in front of it, but if the K terminates a variable string (e.g. an argument string) then keep the M in front of it
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© Simeon Keates 2010
An more generic approach - GOMS
The user’s cognitive structure consists of: A set of Goals A set of Operators A set of Methods A set of Selection rules
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© Simeon Keates 2010
GOMS – a quick breakdown
Goals: Symbolic structures that define a state of affairs to be achieved• Examples: GOAL: EDIT-MANUSCRIPT or GOAL: MODIFY-TEXT• Goals can comprise sub-goals
Operators: Elementary perceptual, motor or cognitive acts whose execution is
necessary to change any aspect of the user’s mental state or to affect the task environment• Examples: GET-NEXT-PAGE or GET-NEXT-TASK
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© Simeon Keates 2010
GOMS – a quick breakdown
Methods: Procedures for accomplishing a goal – must be pre-learned at
performance time (i.e. user already knows them)• Contain sets of Operators
Selection rules: Rules for helping the user decide which method to use to accomplish the
goal• Example: if_such_and_such_is_true_then_use_method_M1_else_use_M2
To summarise: Several Operators make up a Method, and Selection rules are used to determine the best Method to reach the Goal
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© Simeon Keates 2010
Using models of interaction
Fundamentally, you need to perform a comprehensive task analysis
The models indicate suggested performance for each sub-task
Those models help you to predict the performance of the interface
This can be used:• In design: Estimate performance using standard parameters to optimise your
design• In usability trials: Estimate the performance and compare with actual
observed data – investigate significant discrepancies
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© Simeon Keates 2010
Extending to Universal Access applications – The MHP
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© Simeon Keates 2010
Experiment I – Testing the MHP for motor-impaired users
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User Description
PJ3 Tetraplegia (from head injury)
PJ4 Muscular Dystrophy
PJ5 Spastic Quadriplegia Cerebral Palsy
PJ6 Athetoid Cerebral Palsy
PJ7 Friedrich’s Ataxia
PJ8 Athetoid Cerebral Palsy
© Simeon Keates 2010
Perceptual response times
Page 20
User Group τp Delay(ms)
τp Smooth(ms)
Card, Moran & Newell: Able-bodied
100[50~200]
100[50~200]
Cambridge:Able-bodied
80[70~100]
80[70~90]
Cambridge:Motion-impaired
100[70~120]
100[70~120]
© Simeon Keates 2010
Cognitive cycle times
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User Group τc (ms)
Card, Moran & Newell: Able-bodied
70[25~170]
Cambridge:Able-bodied
90[90~100]
Cambridge:Motion-impaired
110[100~130]
© Simeon Keates 2010
Motor function times
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User Group τm (ms)
Card, Moran & Newell: Able-bodied
70[30~100]
Cambridge:Able-bodied
70[60~80]
Cambridge:Motion-impaired
210[100~310]
© Simeon Keates 2010
Reaction times
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User Group Predicted(ms)
Observed(ms)
Card, Moran & Newell: Able-bodied
310[100+70+(2*70)]
-
Cambridge:Able-bodied
310[80+90+(2*70)]
320
Cambridge:Motion-impaired
630[100+110+(2*210)]
620
© Simeon Keates 2010
Explaining the observed motor times (100-310 ms)
Theoretical interaction is:• Press the button (motor function)• Release button (motor function)
Consequently, either • very slow motor function times
or• extra steps being inserted
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© Simeon Keates 2010
Identifying the delays
c & p calculated as for Experiment I
m button-down and button-up times separated
Motor function and reaction time tasks performed Range of input devices used• mouse• touchpad button• space bar• EasyBall
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© Simeon Keates 2010
The MS EasyBall
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© Simeon Keates 2010
User descriptions
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User Description τp (ms)
τc (ms)
PI3 Athetoid Cerebral Palsy
100 120
PI5 Athetoid Cerebral Palsy
100 100
PI6 Athetoid Cerebral Palsy
90 110
PI7 Friedrich’s Ataxia 90 110
© Simeon Keates 2010
Results
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User Input Device
Button down Button up
Mean time (ms)
SD Mean time (ms)
SD
PI3 Mouse 230 35 210 34
PI5 MouseSpace barTrackpad
1708090
425
14
150115100
262219
PI6 MouseSpace barTrackpad
1008080
281417
90110110
231320
PI7 MouseSpace barTrackpadEasyBall
210120200220
71394367
180240230310
43865880
© Simeon Keates 2010
Results
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© Simeon Keates 2010
Results
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τm
?
© Simeon Keates 2010
PI7 results – Motor function task
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0%
5%
10%
15%
20%
25%
30%0-
25
50-7
5
100-
125
150-
175
200-
225
250-
275
300-
325
350-
375
400-
425
450-
475
Time (ms)
Fre
qu
en
cy o
f o
ccu
ren
ce (
%) Button-
down
Button-up
© Simeon Keates 2010BackgroundPage 32
The MHP results
0
50
100
150
200
250
300
350
Mo
tor
resp
on
se t
ime
(m
s)
A B C D E PI4 PI6 PI3 PI7 PI5 PI8
User
Able-bodied
Motion-impaired
~c
© Simeon Keates 2010
Conclusions
Extra cognitive cycles are being inserted Interaction process is:
• Decide to press button (cognitive) - OPTIONAL• Press the button (motor) - REQUIRED• Decide to release button (cognitive) - OPTIONAL• Release button (motor) - REQUIRED
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© Simeon Keates 2010
Sources of extra cognitive steps
Users always in learning mode?
Users being overly careful?
Extra cognitive load from impairment?
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© Simeon Keates 2010
Implications for use of user models
Individual components were comparable
However• method of combination was not
Therefore• need to verify user model assumptions before use
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© Simeon Keates 2010
Implications for design
Users “add” own extra cognitive load
Need to support users by:• Minimising user uncertainty• Minimising cognitive load from program• Maximising interface intuitiveness• Maximising useful feedback
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© Simeon Keates 2010
Extending to Universal Access applications – Cursor control
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© Simeon Keates 2010
Symptoms associated with ageing and Parkinson’s
Symptoms: Essential tremor Restricted motion Reduced strength Poor hand-eye co-ordination Fatigue
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© Simeon Keates 2010
Examples of motor-impaired cursor control
0
100
200
300
400
500
600
700
0 200 400 600 800 1000
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© Simeon Keates 2010
Cursor movement theories
Fitts’ Law• Relates target distance and width to time
Movement Optimization Model• Initial, pre-planned ballistic move• (Optional) Secondary corrective submovements• Submovements based on visual feedback
Analysis of movement paths• Describes effect of changes in distance, width and height of target• Longer distances => higher peak velocity• Smaller target => longer deceleration phase
Initial studies [Hwang et al., 2004] suggest NOT universally applicable
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© Simeon Keates 2010
Cursor measures(MacKenzie et al - CHI 2001)
Target Re-Entry (TRE)
Task Axis Crossing (TAC)
Movement Direction Change (MDC)
Orthogonal Direction Change (ODC)
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© Simeon Keates 2010
Cursor measures (cont.)
Movement Offset (MO)• mean deviation of points from task axis ( y )• signed
Movement Error (ME)• average deviation of points from task axis• unsigned
Movement Variability (MV)• standard deviation of points from task axis
Missed Click (MCL)
Path Length / Task Axis Length (PL/TA)
Additional measures
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© Simeon Keates 2010
Cursor measures (cont.)
Can distinguish between motor impaired and able-bodied users• As “groups”• Keates et al. ASSETS 2002
Can they do more?
Designed to explain why differences exist
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© Simeon Keates 2010
User trials - The users
4 groups of users
• IBM interns (Y) – mean age 23, SD = 2.0• IBM regulars (A) – mean age 47, SD= 9.4• Older adults (OA) – mean age 79, SD = 4.5• APDA members (P) – mean age 57, SD = 5.2
6 users per group
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© Simeon Keates 2010
User trials - The experimental methodology
Fitts’ Law type task• 3 target sizes• 3 target distances• 36 target acquisitions per target session
• 4 of each size/distance combination
• Random angle of approach to target
4 target sessions per user session (144 target acquisitions)
Interviews between each target session
Post-session debrief
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© Simeon Keates 2010
User trials – Qualitative results
21 difficulties reported with mouse use, e.g.:• Keeping hand steady when navigating• Slipping off menus• Losing the cursor• Moving in the desired direction• Running out of room on the mouse pad• Mouse ball getting stuck (and/or dirty)
12 compensatory strategies, e.g.:• Avoid use of menus• Switch hands• Consciously go slower• Pause before clicking
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© Simeon Keates 2010Page 47
User trials – Quantitative results
0
0.5
1
1.5
2
2.5
3
3.5
Group OA Group P Group Y Group A
Target activation times
0
20
40
60
80
100
120
Group OA Group P Group Y Group A
No. of incorrect clicks
Peak velocities
0
1
2
3
4
5
Group OA Group P Group Y Group A
© Simeon Keates 2010
User trials – Cursor measures (cont.)
Cursor measure Group OA Group P Group Y Group A p()
Path length / task axis length (PL/TA)
1.79 1.21 1.38 1.41 0.036
Missed clicks (MCL) 0.13 0.04 0.06 0.02 0.010
Task axis crossings (TAC) 3.32 2.55 2.71 2.49 0.018
Target re-entries (TRE) 0.88 0.46 0.58 0.59 0.913
Movement direction changes (MDC)
11.94 10.80 8.40 7.48 0.267
Orthogonal direction changes (ODC)
9.34 4.61 4.36 4.46 0.006
Movement error (ME) 33.00 21.40 24.61 28.78 0.152
Movement offset (MO) 26.74 18.22 20.76 24.34 0.258
Movement variability (MV) 30.53 18.93 23.31 27.45 0.200
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© Simeon Keates 2010Page 49
User trials – Nature of movement observed
Differences in peak velocity do not explain all of target activation time differences
Theory: Target user movements are like able-bodied movements only more of them needed to complete the task
00.5
11.5
22.5
33.5
44.5
5
Group OA Group P Group Y Group A
No. of pauses >100 msec
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Group OA Group P Group Y Group A
No. of pauses >250 msec
© Simeon Keates 2010
User trials – Nature of movement observed
Submovements can distinguish between user groups (p<0.01)• Submovements are significantly related to:
• Path length / task axis length (PL/TA)• Missed/incorrect clicks (MCL)• Task axis crossings (TAC)• Target re-entries (TRE)• Movement direction changes (MDC)• Orthogonal direction changes (ODC)
• Submovement not significantly related to:• Movement error (ME)• Movement offset (MO)• Movemenet variability (MV)
Cumulative measures
Normalised measures
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© Simeon Keates 2010
User trials – Pauses and cursor measures
Cursor measure # of 100 msec pauses
# of 250 msec pauses
Path length / task axis length (PL/TA) 1% 5%
Missed clicks (MCL) 1% 5%
Task axis crossings (TAC) 1% n.s.
Target re-entries (TRE) 1% 1%
Movement direction changes (MDC) 1% 1%
Orthogonal direction changes (ODC) 1% 1%
Movement error (ME) n.s. n.s.
Movement offset (MO) n.s. n.s.
Movement variability (MV) n.s. n.s.
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© Simeon Keates 2010
User Trials – Where the pauses occur
0
50
100
150
200
250
300
3505
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
10
0
% of movement
No
. of
pa
us
es
Young
Adult
Senior
PD
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© Simeon Keates 2010
User trials – Verification pauses
0
50
100
150
200
250
300
475 425 375 325 275 225 175 125
msec before mouse down
No
. of
pa
us
es
Young
Adult
Senior
PD
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© Simeon Keates 2010
User trials – Peak velocities
Peak velocity & Distance to target – strong +ve correlation Peak velocity & Target size – weak –ve correlation Peak velocity & User group:
0
1
2
3
4
5
Group OA Group P Group Y Group A
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© Simeon Keates 2010
User trials – Incorrect click results
No. of incorrect clicks as target session progresses No. of incorrect clicks as target size No. of incorrect clicks is not related to target distance No. of incorrect clicks is related to user group:
0
20
40
60
80
100
120
Group OA Group P Group Y Group A
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© Simeon Keates 2010
User trials – Incorrect click results (cont.)
Question – Can we tell the difference between correct and incorrect clicks?
No significant difference in:• Pause before• Duration of press
Significant difference in:• Distance moved while button pressed • Number of events while button pressed
Note: these are across ALL incorrect clicks• Across user groups• Across types of incorrect clicks
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© Simeon Keates 2010
User trials – Observed types of incorrect clicks
210 incorrect clicks observed Near misses – within 50% of target radius (110) Not-so-near misses – >50% and <100% of target radius (35) Slipped clicks (32) Accidental clicks – >200% of target radius (9) Two button press (5)• User presses left button• While button down, user also presses right button• User releases right button, then left button• Not predicted!
Middle button press (2) Unclear (17)
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© Simeon Keates 2010
Conclusions – Movement behaviour of older adults
Lower peak velocities• Do not explain all of longer target acquisition times
Increased # of pauses• Lack of confidence? Lack of expertise?
Most likely – difficulty getting on to target• High correlation of pauses with TRE, MDC and ODC• # of pauses towards (but not only at) end of movement
Do not follow single large ballistic / single homing phase Many (smaller) submovements towards target
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© Simeon Keates 2010
Conclusions – Movement behaviour of users with PD
Many measures consistent with age of group• Which is more dominant – effects of age or PD?
Initiating movement can be difficult• “Bump” at start of pauses graph• Shared feature with older adults
Tend to be very deliberate• Lowest peak velocity• Second lowest error rate
Slight movement of mouse when pressing button• Multiple pauses in last half second of button press• Increased verification pause time
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© Simeon Keates 2010Page 60
Summarising the differences
Younger adults (IBM interns)• Shortest (1), fastest (1), more errors (3) - slapdash
• “I can fix it”
• Games culture?
Adults (IBM regulars)• Shorter (2), faster (2), fewest errors (1)
• Best compromise between speed and accuracy?
Parkinson’s users• Longer (3), slowest (4), fewer errors (2)
• Slow, but sure
Older adults• Longest (4), slower (3), most errors (4)
• Lack of expertise
• Difficulty acquiring target
© Simeon Keates 2010
Exercise
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© Simeon Keates 2010
Exercise
On Tuesday(-ish) you performed a pilot study
Today, make any changes you identified to your usability protocol
Also, make any changes to your web-site based on the feedback that you obtained
Please mail your finalised protocols to Susanne and me
Page 62