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Model-based Research in Human-Computer Interaction (HCI): Keynote at Mensch und Computer 2010
 

Model-based Research in Human-Computer Interaction (HCI): Keynote at Mensch und Computer 2010

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keynote given at the Mensch und Computer 2010 conference in Duisburg, Germany

keynote given at the Mensch und Computer 2010 conference in Duisburg, Germany

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    Model-based Research in Human-Computer Interaction (HCI): Keynote at Mensch und Computer 2010 Model-based Research in Human-Computer Interaction (HCI): Keynote at Mensch und Computer 2010 Presentation Transcript

    • Ed  H.  Chi   Principal  Scientist  and  Area  Manager   Augmented  Social  Cognition  Area   Palo  Alto  Research  Center   @edchi   echi@parc.com   2010-09-13 Mensch und Computer 2010 Keynote 1 Image from: http://www.flickr.com/photos/ourcommon/480538715/
    •   Early  fundamental  contributions  from:   –  Computer  scientists  interested  in  changing   how  we  interact  with  information   –  Psychologists  interested  in  the  implications   of  these  changes     The  need  to  establish  HCI  as  a  science   –  Adopt  methods  from  psychology   –  Dual  purpose:  understand  nature  of  human   behavior  and  build  up  a  science  of  HCI   techniques.   9/13/10 HCIC "Living Lab" 2
    • 2010-09-13 Mensch und Computer 2010 Keynote 3
    •   Problem:     –  Intellectual  over-­‐specialization     The  Memex     Extend  the  powers  of  the  human  mind   with  technology   –  Individuals  could  attend  to  greater  spans   –  Facile  command  of  all  recorded  knowledge   –  Sharing  of  knowledge  gained   2010-09-13 Mensch und Computer 2010 Keynote 4
    • Graphical User Interface chartered  to  create  the  architecture  of   Laser Printing information  &  the  office  of  the  future   Ethernet  invented  distributed  personal  computing   -­‐  Bit-mapped Displays  established  Xerox’s  laser  printing  business     -­‐  Distributed File Systems Page Description Languages  created  the  foundation  for  the  digital  revolution   -­‐  First Commercial Mouse Object-oriented Programming WYSIWYG Editing Distributed Computing VLSI Design Methodologies Optical Storage Client/Server Architecture Device Independent Imaging Cedar Programming Language 2010-09-13 Mensch und Computer 2010 Keynote 5
    •   Fitts’  Law     Models  of  Human  Memory     Models  of  Human  Attention     Interruptability     Cognitive  and  Behavorial  Modeling     Perception  and  Navigation     …   2010-09-13 Mensch und Computer 2010 Keynote 6
    •   We  know  motion  in  the  periphery  is  more  noticeable   than  in  the  foveal  region  [DaVinci].       Now  think  about  research  and  products  that  involve   animations  or  flashing  icons.   2010-09-13 Mensch und Computer 2010 Keynote 7
    •   We  know  that  people   can  Block  out  the   irrelevant  content   quite  easily     Until  it’s  semantically   meaningful  or   important  to  you   Hey, Jurgen! UIST 2004 8
    • Characteriza*on   Models   Evalua*ons   Prototypes     Characterize  activity  with  experiments,  ethnography,  log  analysis     Model  interaction  dynamics  and  interface  variations     Prototype  tools  to  increase  benefits  or  reduce  cost     Evaluate  prototypes  with  users   2010-09-13 Mensch und Computer 2010 Keynote 9
    • Start with Capturing User Traces 2010-09-13 Mensch und Computer 2010 Keynote 10
    •   Scan     Skim     Decide     Action   2010-09-13 Mensch und Computer 2010 Keynote 11
    • Characteriza*on   Models   Evalua*ons   Prototypes     Characterize  activity  with  experiments,  ethnography,  log  analysis     Model  interaction  dynamics  and  interface  variations     Prototype  tools  to  increase  benefits  or  reduce  cost     Evaluate  prototypes  with  users   2010-09-13 Mensch und Computer 2010 Keynote 12
    •   human-­‐information  interaction  is  adaptive  to  the  extent:   MAXIMIZE [ Net Knowledge Gained Costs of Interaction ] 2010-09-13 Mensch und Computer 2010 Keynote 13
    • Scent Values: Start users at Probabilities of page with Transition Examine user patterns some goal Flow users through the network 2010-09-13 Mensch und Computer 2010 Keynote 14
    • Characteriza*on   Models   Evalua*ons   Prototypes     Characterize  activity  with  experiments,  ethnography,  log  analysis     Model  interaction  dynamics  and  interface  variations     Prototype  tools  to  increase  benefits  or  reduce  cost     Evaluate  prototypes  with  users   2010-09-13 Mensch und Computer 2010 Keynote 15
    •   A  store  that  knows  your  goal.     Over  50%  reduction  in  task  time.   2010-09-13 Mensch und Computer 2010 Keynote 16
    •   Identify  tasty  pages     Waft  scent  backward  along  links   –  Loses  intensity  as  it  travels   XC4411 copier Features: XC4411 features digital copiers XC5001 remote diagnostics color copiers copiers ... back fax machines other maintenance remote diagnostics ... 2010-09-13 Mensch und Computer 2010 Keynote 17
    • Partial information goal: 62 copies/min. “remote diagnostic technology” Remainder of information goal: 92 copies/min. “speed >= 75” 2010-09-13 Mensch und Computer 2010 Keynote 18
    • Associated Entries underlined in red 2010-09-13 Mensch und Computer 2010 Keynote 19
    • Conceptually highlight any relevant User first type search keywords: passages and keywords “anthrax symptoms” Draw user attention 2010-09-13 Mensch und Computer 2010 Keynote 20
    • Characteriza*on   Models   Evalua*ons   Prototypes     Characterize  activity  with  experiments,  ethnography,  log  analysis     Model  interaction  dynamics  and  interface  variations     Prototype  tools  to  increase  benefits  or  reduce  cost     Evaluate  prototypes  with  users   2010-09-13 Mensch und Computer 2010 Keynote 21
    • (times capped at five minutes) 10/12 subjects preferred ScentTrails 2010-09-13 Mensch und Computer 2010 Keynote 22
    • 2005-10-21 UMN talk
    • 2005-10-21 UMN talk
    •   Descriptive:  clarify  terms,  key  concepts     Explanatory:  reveal  relationships  and  processes     Predictive:  about  performance  and  situations     Prescriptive:  convey  guidance  for  decision   making  in  design  by  recording  best  practice     Generative:  enable  practitioners  to  create,   invent  or  discover  something  new   2010-09-13 Mensch und Computer 2010 Keynote 25
    • Bongwon  Suh,  Gregorio  Convertino,  Ed  H.  Chi,  Peter   Pirolli.  The  Singularity  is  Not  Near:  Slowing  Growth  of   Wikipedia.  In  Proc.  of  WikiSym  2009.  Oct,  2009.  Florida,   USA   2010-09-13 Mensch und Computer 2010 Keynote 26
    • Number of Articles (Log Scale) http://en.wikipedia.org/wiki/Wikipedia:Modelling_Wikipedia’s_growth 2010-09-13 Mensch und Computer 2010 Keynote 27
    • Monthly Edits 2010-09-13 Mensch und Computer 2010 Keynote 28
    • Monthly Edits 2010-09-13 Mensch und Computer 2010 Keynote 29
    • *In thousands Monthly Active Editors 2010-09-13 Mensch und Computer 2010 Keynote 30
    • *In thousands Monthly Active Editors 2010-09-13 Mensch und Computer 2010 Keynote 31
    • 2010-09-13 Mensch und Computer 2010 Keynote 32
    • Monthly Ratio of Reverted Edits 2010-09-13 Mensch und Computer 2010 Keynote 33
    • 2010-09-13 Mensch und Computer 2010 Keynote 34
    •   Preferential  Attachment:  Edits  beget  edits   –  more  number  of  previous  edits,  more  number  of  new  edits   Growth rate depends on: N = current population r = growth rate of the population N(t) = N 0 ⋅ e rt dN = r⋅ N dt Growth rate Current of population € population € 2010-09-13 Mensch und Computer 2010 Keynote 35
    •   Biological  system   –  Competition  increases  as   population  hit  the  limits  of  the   ecology   –  Advantage  go  to  members  of  the   population  that  have  competitive   dominance  over  others     Analogy   –  Limited  opportunities  to  make   novel  contributions   –  Increased  patterns  of  conflict  and   dominance     2010-09-13 Mensch und Computer 2010 Keynote 36
    •   r-­‐Strategist   –  Growth  or  exploitation   dN N –  Less-­‐crowded  niches  /  produce  many   = rN(1− ) offspring   dt K   K-­‐Strategist   –  Conservation   [Gunderson & Holling 2001] –  Strong  competitors  in  crowded  niches  /   invest  more  heavily  in  fewer  offspring   € 2010-09-13 Mensch und Computer 2010 Keynote 37
    •   Ecological  population  growth  model   –  Also  depend  on  environmental  conditions   –  K,  carrying  capacity  (due  to  resource  limitation)   dN N = rN(1− ) dt K € 2010-09-13 Mensch und Computer 2010 Keynote 38
    •   Follows  a  logistic  growth  curve   New Article 2010-09-13 Mensch und Computer 2010 Keynote 39
    •   Carrying  Capacity  as  a  function  of  time.   2010-09-13 Mensch und Computer 2010 Keynote 40
    • 2010-09-13 Mensch und Computer 2010 Keynote 41
    • Concepts   Topics   Users   Documents   Noise   Tags   Decoding   Encoding   T1…Tn   2010-09-13 Mensch und Computer 2010 Keynote 42
    • 2010-09-13 Mensch und Computer 2010 Keynote 43
    • 2010-09-13 Mensch und Computer 2010 Keynote 44
    • Source: Hypertext 2008 study on del.icio.us (Chi & Mytkowicz) 2010-09-13 Mensch und Computer 2010 Keynote 45
    • 2010-09-13 Mensch und Computer 2010 Keynote 46
    • Joint  work  with     Rowan  Nairn,  Lawrence  Lee   Kammerer,  Y.,  Nairn,  R.,  Pirolli,  P.,  and  Chi,  E.  H.  2009.  Signpost  from  the   masses:  learning  effects  in  an  exploratory  social  tag  search  browser.  In   Proceedings  of  the  27th  international  Conference  on  Human  Factors  in   Computing  Systems  (Boston,  MA,  USA,  April  04  -­‐  09,  2009).  CHI  '09.  ACM,  New   York,  NY,  625-­‐634.     2010-09-13 Mensch und Computer 2010 Keynote 47
    • Semantic Similarity Graph Web Tools Reference Guide Howto Tutorial Tips Help Tip Tutorials Tricks 2010-09-13 Mensch und Computer 2010 Keynote 48
    • Tags URLs P(URL|Tag) P(Tag|URL)   Spreading  Activation  in  a  bi-­‐graph     Computation  over  a  very  large  data  set   –  150  Million+  bookmarks   2010-09-13 Mensch und Computer 2010 Keynote 49
    • 2010-09-13 Mensch und Computer 2010 Keynote 50
    • 2010-09-13 Mensch und Computer 2010 Keynote 51
    • 2010-09-13 Mensch und Computer 2010 Keynote 52
    • Dellarocas,  MIT  Sloan  Management  Review   2010-09-13 Mensch und Computer 2010 Keynote 53
    • (1)  Generate  new  tools  and  systems,  new  techniques   (2)  Generate  data  that  looks  like  real  behavioral  data   2010-09-13 Mensch und Computer 2010 Keynote 54
    • externally-motivated self-motivated framing the context Before Search searchers searchers 31% 69% Social Interactions GATHER REQUIREMENTS refining the requirements FORMULATE REPRESENTATION 28% 13% 59% During Search navigational transactional informational FORAGING step A step A search process step B step B “evidence file” TRANSACTION SENSEMAKING search product /end product After Search 28% 72% DO NOTHING TAKE ACTION ORGANIZE DISTRIBUTE to self 15% to proximate 87% to public 2% others others
    • externally-motivated self-motivated framing the context Before Search searchers searchers 31% 69% 43% users engaged in pre-search social Social Interactions interactions. GATHER REQUIREMENTS refining the reasons for interacting: to get advice, guidelines, feedback, FORMULATE REPRESENTATION requirements or search tips 28% 13% 59% During Search navigational transactional informational FORAGING step A step A search 3 types of search: informational search provides a 150 reports of unique search experiences compelling caseBfor social search support. mapped to a canonical model of social search. step B step process “evidence file” TRANSACTION SENSEMAKING search product /end product After Search 28% 72% DO NOTHING TAKE ACTION 59% users engaged in post-search sharing. ORGANIZE DISTRIBUTE reasons for interacting: thought others might be interested, to get feedback, out of obligation to self 15% to proximate 87% to public 2% others others
    • externally-motivated self-motivated framing the context Before Search searchers searchers •  instant 31% messaging69% to personal social (IM) Social Interactions connections near the search box refining GATHER REQUIREMENTS the requirements FORMULATE REPRESENTATION 28% 13% 59% During Search navigational transactional informational •  step A clouds from domain FORAGING tag step A experts search •  step B users’ search trails process feedback) other (for step B •  related search terms (for feedback) Similar to: Glance; Smyth" “evidence file” TRANSACTION SENSEMAKING search product /end product After Search 28% 72% DO NOTHING TAKE ACTION •  sharing tools built-in to (search) site Spartag.us" •  collective tag clouds (for feedback) ORGANIZE DISTRIBUTE Mr. Taggy" to self 15% to proximate 87% to public 2% others others
    •   All  models  are  wrong!   –  Some  are  more  wrong  than  others!     So  what  are  theories  and  models  good  for?     They’re  a  summary  of  what  we  think  is  happening   –  Ways  to  describe  and  explain  what  we  have  learned   –  Predicts  user  and  group  behavior   –  Helps  generate  new  novel  tools  and  systems   2010-09-13 Mensch und Computer 2010 Keynote 58
    • 2010-09-13 Mensch und Computer 2010 Keynote 59
    • Word connectivity Human Movement Study: Fitts’ law MT = a + b Log2(Dsi/Wi + 1) 18000 English Letter Corpus 16000 14000 12000 10000 (News, chat etc) 8000 6000 4000 [Zhai et al., 2000, 2002] 2000 0 sp E T A H O N S R I D L U W M C G Y F B P K V J X Q Z Slide adopted from Mary Czerwinski Keynote UIST 2004 “Fitts-digraph energy” 27 27 Pij ⎡ ⎛ Dij ⎞ ⎤ t = ∑ ∑ ⎢ Log2 ⎜ +1⎟ ⎥ W ( A →B) = e −ΔE kT if ΔE >0 i=1 j =1 IP ⎣ ⎝ Wi ⎠ ⎦ =1 if ΔE ≤ 0 Metropolis “random walk” optimization Alphabetical tuning UIST 2004 60 € €
    • Between  just  getting  things  done     vs.  finding  out  the  science   2010-09-13 Mensch und Computer 2010 Keynote 61
    • A B Bucket Testing or A/B Testing [Kohavi et al]
    • Characteriza*on   Models   Evalua*ons   Prototypes   Evalua*ons   Prototypes     Design,  Prototype,  Learn;       If  you  can,  you  should  codify  your   findings  so  that  others  can     Then  Re-­‐design,  Prototype,  Learn   replicate  it,  learn  from  it,  predict     Sometimes  that’s  all  you  can  do.   behavior  from  it.     The  basis  of  a  true  scientific  field   2010-09-13 Mensch und Computer 2010 Keynote 63
    • 2010-09-13 Mensch und Computer 2010 Keynote 64
    •   Research  Vision:  Understand  how  social  computing   systems  can  enhance  the  ability  of  a  group  of   people  to  remember,  think,  and  reason.   http://asc-­‐parc.blogspot.com   http://www.edchi.net   echi@parc.com   WikiDashboard.com   MrTaggy.com   Zerozero88.com  
    • 2010-09-13 Mensch und Computer 2010 Keynote 66
    •   Appropriate  for   the  occasion   2010-09-13 Mensch und Computer 2010 Keynote 67
    • Poor heuristic Good heuristic 2010-09-13 Mensch und Computer 2010 Keynote 68
    • Solo Cooperative (“good hints”) 2010-09-13 Mensch und Computer 2010 Keynote 69
    • Social Tagging Creates Noise •  Synonyms •  Misspellings •  Morphologies People use different tag words to express similar concepts. 2010-09-13 Mensch und Computer 2010 Keynote 70
    • Database Lucene • Delicious • P(URL|Tag) • Serve up search • Ma.gnolia • P(Tag|URL) results • Tuples of • Pre-computed • Other social cues bookmarks • Bayesian Network patterns in a fast • Well defined APIs • [User, URL, Tags, Inference index Time] Crawling MapReduce Web Server Web Server UI Search Frontend Results •  MapReduce:  months  of  computa*on  to  a  single  day   •  Development  of  novel  scoring  func*on     2010-09-13 Mensch und Computer 2010 Keynote 71
    • framing Before Search externally-motivated self-motivated searchers searchers the context 31% 69% Social Interactions GATHER REQUIREMENTS refining the requirements FORMULATE REPRESENTATION 28% 13% 59% During Search navigational transactional informational FORAGING step A step A search process step B step B “evidence file” TRANSACTION SENSEMAKING search product /end product After Search 28% 72% DO NOTHING TAKE ACTION ORGANIZE DISTRIBUTE to self 15% to proximate 87% to public 2% others others
    • externally-motivated self-motivated framing the context Before Search searchers searchers 31% 69% Social Interactions GATHER REQUIREMENTS refining the requirements FORMULATE REPRESENTATION 28% 13% 59% During Search navigational transactional informational FORAGING step A step A search process step B step B “evidence file” TRANSACTION SENSEMAKING search product /end product After Search 28% 72% DO NOTHING TAKE ACTION ORGANIZE DISTRIBUTE to self 15% to proximate 87% to public 2% others others
    • externally-motivated self-motivated framing the context Before Search searchers searchers 31% 69% Social Interactions GATHER REQUIREMENTS refining the requirements FORMULATE REPRESENTATION 28% 13% 59% During Search navigational transactional informational FORAGING step A step A search process step B step B “evidence file” TRANSACTION SENSEMAKING search product /end product 28% 72% After Search DO NOTHING TAKE ACTION ORGANIZE DISTRIBUTE to self 15% to proximate 87% to public 2% others others
    •   For  example,  for  information  diffusion,  it’s  theory  of   influentials  [Gladwell,  etc.]   –  reach  a  small  group  of  influential  people,  and  you’ll  reach   everyone  else   Figure From: Kleinberg, ICWSM2009 2010-09-13 Mensch und Computer 2010 Keynote 75
    • From: Sun et al, ICWSM2009 2010-09-13 Mensch und Computer 2010 Keynote 76