Since the introduction of application stores for mobile devices there has been an increasing interest to use this distribution platform to collect user feedback. Mobile application stores can make research prototypes widely available and enable to conduct user studies "in the wild" with participants from all over the world. Using apps as an apparatus goes beyond just distributing research prototypes. Consider apps as a tool for research means distributing specifically designed prototypes in order to extend our understanding of mobile HCI. In this tutorial we will provide an overview about recent research in this domain. It will be shown that stringent tasks and users´ motivation are crucial aspects. We will discuss how to design app-based experiments, what kind of users one can expect, and how to avoid ethical and legal issues.

1. How
to
do
Mobile
HCI
Research
in
the
large?
Niels
Henze
University
of
Oldenburg
Media
Informa@cs
and
Mul@media
Systems
OFFIS
-­‐
Ins@tute
for
Informa@on
Technology
Intelligent
User
Interface
Group

2. …
but
lets
start
with
a
ques@on:
Who
of
you
ever
par@cipated
in
a
user
study?

3. do
you
think
that
any
of
these
guys
ever
did?

4. Outline
1. Limita@ons
of
common
studies
2. Into
the
large
3. Types
of
studies
4. What
is
so
special?
5. What
works
for
me
6. Wrap
up

5.
User
studies
at
MobileHCI
2010
20%
acceptance
rate
43
short+long
papers

6.
User
studies
at
MobileHCI
2010
20%
acceptance
rate
43
short+long
papers
subjects
per
paper
hXp://nhenze.net/?p=810

7.
User
studies
at
MobileHCI
2010
20%
acceptance
rate
43
short+long
papers
subjects
per
paper
subject’s
gender
hXp://nhenze.net/?p=810

8. all
with
a
university
degree,
recruited
in
the
Ins@tute
community
students
or
employees
at
our
university
User
studies
at
recruited
through
flyers,
posters
and
various
mailing
lists
at
the
university
MobileHCI
2010
20%
acceptance
rate
10
university
students
and
2
par@cipants
43
short+long
papers
are
marke@ng
professionals
subjects
per
paper
undergraduate
or
graduate
students
at
subject’s
gender
the
local
university
studying
a
variety
of
majors
o]en
a
biased
sample
university
students
most
subjects
were
students
with
a
background
in
computer
sciences
most
par@cipants
were
students
studying
or
working
in
the
University
of
Glasgow
members
in
a
joint
research
project
hXp://nhenze.net/?p=810

9. Some
male
students
from
the
lab
took
part
in
our
study...
Small
sample
size
isn’t
necessarily
an
issue
for
a
study
Not
every
study
needs
a
perfect
sample
of
the
popula@on
Focussing
on
studies
with
few
subjects
prevents
finding
subtle
differences
We
stew
in
our
own
juices
if
using
our
own
students
by
default

10. Some
mo@va@on
Large
numbers
are
expensive
in
the
lab
– 1,000
subjects
for
an
hour
-­‐>
10,000€
– 1,000
subjects
for
an
hour
-­‐>
6
month
– 1,000
subjects
from
around
the
world
-­‐>
impossible
Different
contexts
are
hard
to
address
– We
have
no
subway
in
Oldenburg
– Don’t
want
to
pay
the
flight
for
my
par@cipant
– And
what
are
the
relevant
contexts
anyway?

11.
Example
of
gehng
large…
Target
selec@on
on
mobile
phones
thirty
right-­‐handed
subjects
different
target
loca@ons
and
sizes
[Park2008MobileHCI]

12.
Target
selec@on
on
mobile
phones
thirty
right-­‐handed
subjects
different
target
loca@ons
and
sizes
Taps
are
skewed
fixed
posture
single
device
Korean
students
vague
results
[Park2008MobileHCI]

13.
…same
thing
in
the
large
game
published
on
the
Android
Market

14.
…same
thing
in
the
large
game
published
on
the
Android
Market
we
inform
the
player
about
the
study

15.
…same
thing
in
the
large
game
published
on
the
Android
Market
we
inform
the
player
about
the
study
just
looks
like
an
ordinary
game

16.
…same
thing
in
the
large
game
published
on
the
Android
Market
we
inform
the
player
about
the
study
just
looks
like
an
ordinary
game
par@cipants
get
some
introduc@on

17.
…same
thing
in
the
large
game
published
on
the
Android
Market
we
inform
the
player
about
the
study
just
looks
like
an
ordinary
game
par@cipants
get
some
introduc@on

18.
…same
thing
in
the
large
game
published
on
the
Android
Market
we
inform
the
player
about
the
study
just
looks
like
an
ordinary
game
par@cipants
get
some
introduc@on
they
tap
the
targets

19.
…same
thing
in
the
large
game
published
on
the
Android
Market
we
inform
the
player
about
the
study
just
looks
like
an
ordinary
game
par@cipants
get
some
introduc@on
they
tap
the
targets
we
vary
targets’
size
and
posi@on

20.
…same
thing
in
the
large
game
published
on
the
Android
Market
we
inform
the
player
about
the
study
just
looks
like
an
ordinary
game
par@cipants
get
some
introduc@on
they
tap
the
targets
we
vary
targets’
size
and
posi@on
there
is
even
a
high
score
list

21.
published
on
the
Android
Market
100,000
installa@ons
in
three
months
120
million
touch
events
more
than
hundred
different
devices
players
from
all
over
the
world

22. [Park2008MobileHCI]

23. [Henze2011MobileHCI]

24. Types
of
work
Proof
of
concept
– Showing
that
an
idea/concept/product
works
– Lots
of
users,
good
ra@ngs,
posi@ve
comments,
...
App
stores
as
research
tool
– Experience
report
– Ethical
and
legal
issues
Inves@ga@ng
app-­‐specific
aspects
– How
a
specific
app
is
used
– Compare
different
visualiza@ons
Observing
general
aspects
– Learn
about
how
people
and
devices
behave
– How
are
apps
how,
how
people
touch
the
screen,
...

25. Proof
of
concept

26.
Smule’s
iPhone
Ocarina
music
instrument
for
the
iPhone
million
installa@ons
[Wang2009NIME]

27.
Shapewriter
developed
gesture-­‐based
keyboard
+
notepad
qualita@ve
feedback
from
App
Store
comments
[Zhai2009CHI]

28. App
stores
as
research
tool

29.
Into
the
wild
with
Hungry
Yoshi
loca@on
based
game
for
the
iPhone
94,642
unique
downloader
inves@gated
how
to
get
subjec@ve
feedback
[McMillan2010Pervasive]

30.
100%
83,68%
81,31%
80%
Experience
from
60%
54,76%
5
Studies
40%
compare
amount
of
collected
data
20%
experience
with
collec@ng
7,32%
qualita@ve
data
0,46%
discuss
internal
and
0%
external
validity
[Henze2011IJMHCI]

31. Inves@ga@ng
app-­‐specific
aspects

32.
Ra@ngs
for
Mobile
Applica@ons
compare
amount
of
collected
data
experience
with
collec@ng
qualita@ve
data
discuss
internal
and
external
validity
[Girardello2010DSZ]

33.
Compare
off-­‐screen
visualisa@ons
using
repeated
measures
using
a
tutorial
for
a
map
applica@on
[Henze2010MobileHCI]
[Henze2010MobileHCI]

34.
Compare
off-­‐screen
visualisa@ons
using
repeated
measures
using
a
tutorial
for
a
map
applica@on
and
using
a
simple
game
(which
worked
much
beXer!)
[Henze2010MobileHCI]
[Henze2010MobileHCI]

35. Observing
general
aspects

36. Falling
Asleep
with
…
appazaar
[Böhmer2011MobileHCI]

37. A
Study
of
BaXery
Life
[Ferreira2011Pervasive]

38. app
stores
as
a
inves@ga@ng
app-­‐ inves@ga@ng
proof
of
concept
research
tool
specific
aspects
general
aspects
[Wang2009NIME]
[McMillan2010RiL]
[Girardello2010DSZ]
[Hood2011IJTR]
[McMillan2010Pervasive]
[Zhai2009CHI]
[Riccamboni2010IB]
[Henze2011MobileHCIa]
[Henze2011IJMHCI]
[Gilbertson2008CiE]
[Kuhn2010MM]
[Henze2011MobileHCIb]
[Miluzzo2010RiL]
[Watzdorf2010LocWeb]
[Poppinga2010OMUE]
[Yan2011MobiSys]
[Ferreira2011Pervasive]
[Oliver2010HotPlanet]
[Budde2010IoT]
[Morrison2010RiL]
[Buddharaju2010CHI]
[Karpischek2011RiL]
[Sahami2011CHI]
[Henze2010MobileHCI]
[Verkasalo2010MB]
[Pielot2011ELV]
[Henze2010NordiCHI]
[Böhmer2011MobileHCI]
[Cramer2010UbiComp]
[Morrison2011CHI]
[Henderson2009HotPlanet]
[Norcie2011ELV]
Ethics
and
legal
issues

39. but
what
is
special
about
app
store
studies?

40. App-­‐based
vs.
other
studies
Common
con-­‐ Mining
exis@ng
App-­‐based
trolled
studies
data
studies
Few
par@cipants
Many
par@cipants
Many
par@cipants
Ar@ficial
context
Natural
context
Natural
context
Defined
tasks
Defined
task
No
tasks
(if
needed)
Total
control
over
Weak
control
over
No
control
par@cipants
par@cipants
Heavily
biased
Biased
to
unbiased
Unbiased
sample
sample
sample

41. You
have
to
“sell”
your
study
The
study
has
a
goal
– Collect
informa@on
about
specific
behaviour
– Performance
for
a
specific
task
Users
have
to
install
the
app
on
their
own
will
– App
needs
a
purpose
– Good
ra@ngs,
high
ranking
Find
a
compromise
– Maintain
the
goals
of
the
study
– AXract
sufficient
par@cipants

42. Types
of
apps
Applica@ons
Games
Widgets

43.
100.000
90.000
80.000
Par@cipants
70.000
How
do
we
count
the
60.000
number
of
par@cipant?
50.000
40.000
30.000
20.000
10.000
0
installa@ons
opt-­‐in
ac@ve
users
[McMillan2010Pervasive]
[Morrison2010RiL]

44.
US
Android
users
US
popula@on
60%
Par@cipants
50%
How
do
we
count
the
number
of
par@cipant?
40%
A
good
sample
of
the
30%
popula@on?
20%
10%
0%
18-­‐34
35-­‐44
45-­‐54
55-­‐64
65+
[Nielsen2011]
[USCensusBureau2008]

45. Collec@ng
informa@on
Objec@ve
data
– As
early
as
possible
[Henze2011IJMHCI]
– More
than
just
the
task
performance
• All
aspects
that
affect
the
results
• E.g.
device
type,
local,
@me,
screen
size,
resolu@on,
...
• In
par@cular:
a
version
number
– Compromise
between
permissions
and
data
to
collect

46. Collec@ng
informa@on
Subjec@ve
data
– App
Store
comments
can
provide
informa@on
• but
usually
don't
[Henze2011IJMHCI]
• Might
help
to
claim
an
app
is
great
(e.g.
[Zhai2009CHI])
• Ra@ngs
without
baseline
are
meaningles
– Inves@gated
how
to
get
subjec@ve
feedback
[McMillan2010Pervasive]
• In-­‐game
“tasks”
with
dynamically
loaded
ques@ons
• Integra@on
with
Facebook
• Interviewed
10
people
over
VoIP
for
$25

47. Realy
stupid
hope
Stupid
waste
of
@me!!!
cailan
FC
the
rabbit....
uninstalled
Godimus
Prime
Ready
for
prime
Its
ok
Stupid
waste
of
@me.
erika
lance
@me
boring
and
dumb.
Users
don’t
care
if
it’s
a
Beba
research
prototype
Stupid
and
offincive
to
my
pet
rabbit
bayleigh
Logan
1
word......
dumb!
josue
5
stars
if
there
is
a
way
to
turn
the
music
off.
Doesnt
go
to
well
with
slipknot
Allen
What
the
hell
is
this??
Boo!
Luci
Cullen
Girl
Examples
from
one
of
my
games

48.
Ready
for
prime
@me
Users
don’t
care
if
it’s
a
research
prototype
Low
quality
results
in
low
ra@ngs

49.
Ready
for
prime
@me
users
don’t
care
if
it’s
a
research
prototype
low
quality
results
in
low
ra@ngs
and
few
install
installa@ons

50. Ethical
and
legal
issues
“One
should
treat
others
as
one
would
like
others
to
treat
oneself”
[Flew1979Dic@onary]
“Primum
non
nocere”/”First,
do
no
harm”
(Thomas
Sydenham)

51. 6.96%
57.28%
Informed
consent
Presenta@on
highly
affects
the
conversion
rate
67.42%
87.57%
[Pielot2011ELV]

52.
Informed
consent
Presenta@on
highly
affects
the
conversion
rate
Par@cipants
aren't
aware
what
data
is
collected
[Morrison2011CHI]

53.
Regula@ons
Which
rules
to
follow?

54.
“any
informa+on
rela+ng
to
an
iden+fied
or
iden+fiable
natural
person”
Regula@ons
• Transparency:
the
persons
whose
data
Which
rules
to
follow?
are
being
collected
or
accessed
have
the
right
to
be
informed
when
such
data
e.g.
EU
Data
Protec@on
processing
is
taking
place.
Direc@ve
• Legi+mate
purpose:
data
can
only
be
collected
for
specific
purposes
• Propor+onality :
data
should
be
processed
in
a
fashion
that
is
not
excessive
beyond
the
purposes
for
which
they
were
collected
[Henderson2009HotPlanet]

55. …
or
what
works
for
me

56.
number
of
installa+ons
400
350
Games
vs.
Apps
Thousands
300
our
games
are
more
250
successful
200
150
100
50
0

57.
games
15,6%
Games
vs.
Apps
our
games
are
more
successful
there
are
more
apps
than
games
apps
84,4%
available
in
the
Android
Market
hXp://www.androlib.com/appstatstype.aspx

58.
Games
vs.
Apps
our
games
are
more
successful
there
are
more
apps
than
games
players
execute
the
strangest
tasks

59.
Games
vs.
Apps
our
games
are
more
successful
there
are
more
apps
than
games
players
execute
the
strangest
tasks
widgets
and
background
services
are
perfect
for
longitudinal
observa@ons

60.
Informing
the
user
provide
informa@on
in
the
Market

61.
Informing
the
user
provide
informa@on
in
the
Market
show
a
modal
dialog
at
the
first
start

62.
Informing
the
user
provide
informa@on
in
the
Market
show
a
modal
dialog
at
the
first
start
provide
more
informa@on
and
a
link
in
an
about
page

63.
Publishing
fancy
screenshots
and
icon
(that
the
first
thing
someone
sees)
@tle
&
descrip@on
contain
words
users
search
for
of
course
I
don’t
want
to
miss
a
single
user
prepare
a
dedicated
webpage
for
each
app

64.
Playing
with
the
market
weekly
updates

65.
Playing
with
the
market
weekly
updates
rate
your
app
as
soon
as
it
becomes
available
(and
force
everyone
you
know
to
do
it
as
well)

66.
Keep
it
simple
focused
and
specialized
studies

67.
Keep
it
simple
focused
and
specialized
studies
learning
by
doing

68.
Keep
it
simple
focused
and
specialized
studies
learning
by
doing
release
early,
o]en,
and
try
it
again
if
it
doesn’t
work

69.
Logging
use
hXp
to
transmit
data

70.
Logging
use
hXp
to
transmit
data
store
unaggregated
measures

71.
Logging
use
hXp
to
transmit
data
store
unaggregated
measures
consider
limited
resources
in
total:
392,401
files
27,331,383,646
bytes
Examples
from
one
of
my
games

72.
How
to
do
Mobile
HCI
Research
in
Wrap
up
the
large?
ethnography,
controlled
Niels
Henze
experiments,
observa@ons,
…
can
all
work
in
the
large
University
of
Oldenburg
Media
Informa@cs
and
collect
data
early,
Mul@media
Systems
release
o]en,
be
flexible
OFFIS
-­‐
Ins@tute
for
Informa@on
respect
ethics,
Technology
ser
Interface
Group
Intelligent
U
consider
regula@ons

73. References
[Wang2009NIME]
Ge
Wang:
Designing
Smule’s
iPhone
Ocarina.
Proc.
NIME,
2009.
[Zhai2009CHI]
Zhai,
S.,
Kristensson,
P.O.,
Gong,
P.,
Greiner,
M.,
Peng,
S.,
Liu,
L.
Dunnigan,
A.,
Shapewriter
on
the
iPhone:
from
the
laboratory
to
the
real
world.
Adjunct
Proc.
CHI,
2009.
[Gilbertson2008CiE]
Paul
Gilbertson,
Paul
Coulton,
Fadi
Chehimi,
Tamas
Vajk:
Using
'Tilt'
as
an
Interface
to
control
'No
BuXon'
3-­‐D
Mobile
Games.
ACM
Computers
in
Entertainment,
2008.
[Oliver2010HotPlanet]
Earl
Oliver.
The
Challenges
in
Large-­‐Scale
Smartphone
User
Studies.
Invited
talk
@
HotPlanet,
2010.
[McMillan2010RiL]
Donald
McMillan:
iPhone
So]ware
Distribu@on
for
Mass
Par@cipa@on.
Proc.
Research
in
the
Large
Workshop
@
UbiComp,
2010.
[Miluzzo2010RiL]
Emiliano
Miluzzo,
Nicholas
D.
Lane,
Hong
Lu,
Andrew
T.
Campbell:
Research
in
the
App
Store
Era:
Experiences
from
the
CenceMe
App
Deployment
on
the
iPhone.
Proc.
Research
in
the
Large
Workshop
@
UbiComp,
2010.
[Henze2011IJMHCI]
Niels
Henze,
Mar@n
Pielot,
Benjamin
Poppinga,
Torben
Schinke,
Susanne
Boll:
My
App
is
an
Experiment:
Experience
from
User
Studies
in
Mobile
App
Stores,
accepted
by
the
Interna@onal
Journal
of
Mobile
Human
Computer
Interac@on
(IJMHCI),
2011
[McMillan2010Pervasive]
Donald
McMillan,
Alistair
Morrison,
Owain
Brown,
Malcolm
Hall
&
MaXhew
Chalmers:
Further
into
the
Wild:
Running
Worldwide
Trials
of
Mobile
Systems,
Proc.
Pervasive
2010.
[Cramer2010UbiComp]
HenrieXe
Cramer,
Mahas
Rost,
Nicolas
Belloni,
Didier
Chincholle,
Frank
Bentley:
Research
in
the
Large.
Using
App
Stores,
Markets,
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