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How to do MobileHCI Research in the Large

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.

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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	
  
	
  
…	
  but	
  lets	
  start	
  with	
  a	
  ques@on:	
  
	
  
Who	
  of	
  you	
  ever	
  par@cipated	
  in	
  a	
  user	
  study?	
  
do	
  you	
  think	
  that	
  any	
  of	
  these	
  guys	
  
 ever	
  did?	
  
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	
  
	
  
 
	
  
User	
  studies	
  at	
  
     MobileHCI	
  2010	
  
       	
  20%	
  acceptance	
  rate	
  
       	
  43	
  short+long	
  papers	
  
	
  
 
                              	
  
                              User	
  studies	
  at	
  
                                   MobileHCI	
  2010	
  
                                 	
  20%	
  acceptance	
  rate	
  
                                 	
  43	
  short+long	
  papers	
  
                                 	
  subjects	
  per	
  paper	
  




hXp://nhenze.net/?p=810	
  

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How to do MobileHCI Research in the Large

  • 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  
  • 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,  ...  
  • 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]  
  • 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]  
  • 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,  and  Other  Wide  Distribu@on  Channels  in  Ubicomp  Research.  Adjunct   Proc.  Ubicomp,  2010.   [Morrison2010RiL]  Alistair  Morrison,  Stuart  Reeves,  Donald  McMillan,  MaXhew  Chalmers:  Experiences  of  Mass   Par@cipa@on  in  Ubicomp  Research,  Proc.  Research  In  The  Large  Workshop  at  Ubicomp,  2010.   [Poppinga2010OMUE]  Benjamin  Poppinga,  Mar@n  Pielot,  Niels  Henze,  Susanne  Boll:  Unsupervised  User  Observa@on  in   the  App  Store:  Experiences  with  the  Sensor-­‐based  Evalua@on  of  a  Mobile  Pedestrian  Naviga@on  Applica@on.   Proc.  OMUE  in  conjunc@on  with  NordiCHI,  2010.  
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