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

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<ul><li> 1. EdH.Chi PrincipalScientistandAreaManagerAugmentedSocialCognitionArea PaloAltoResearchCenter@edchi echi@parc.com2010-09-13Mensch und Computer 2010 Keynote1 Image from: http://www.flickr.com/photos/ourcommon/480538715/ </li></ul> <p> 2. Earlyfundamentalcontributionsfrom: Computerscientistsinterestedinchanginghowweinteractwithinformation Psychologistsinterestedintheimplicationsofthesechanges TheneedtoestablishHCIasascience Adoptmethodsfrompsychology Dualpurpose:understandnatureofhumanbehaviorandbuildupascienceofHCItechniques. 9/13/10 HCIC "Living Lab"2 3. 2010-09-13 Mensch und Computer 2010 Keynote 3 4. Problem: Intellectualover-specialization TheMemex Extendthepowersofthehumanmind withtechnology Individualscouldattendtogreaterspans Facilecommandofallrecordedknowledge Sharingofknowledgegained 2010-09-13 Mensch und Computer 2010 Keynote 4 5. Graphical User Interface charteredtocreatethearchitectureof Laser Printing information&amp;theoceofthefuture Ethernet inventeddistributedpersonalcomputing - Bit-mapped DisplaysestablishedXeroxslaserprintingbusiness - Distributed File SystemsPage Description Languages createdthefoundationforthedigitalrevolution -First Commercial MouseObject-oriented ProgrammingWYSIWYG EditingDistributed ComputingVLSI Design MethodologiesOptical StorageClient/Server ArchitectureDevice Independent ImagingCedar Programming Language 2010-09-13 Mensch und Computer 2010 Keynote5 6. FittsLaw ModelsofHumanMemory ModelsofHumanAttention Interruptability CognitiveandBehavorialModeling PerceptionandNavigation 2010-09-13Mensch und Computer 2010 Keynote 6 7. Weknowmotionintheperipheryismorenoticeablethaninthefovealregion[DaVinci]. Nowthinkaboutresearchandproductsthatinvolveanimationsorashingicons. 2010-09-13 Mensch und Computer 2010 Keynote 7 8. WeknowthatpeoplecanBlockouttheirrelevantcontentquiteeasily UntilitssemanticallymeaningfulorimportanttoyouHey,Jurgen!UIST 2004 8 9. Characteriza*onModelsEvalua*ons Prototypes Characterizeactivitywithexperiments,ethnography,loganalysis Modelinteractiondynamicsandinterfacevariations Prototypetoolstoincreasebenetsorreducecost Evaluateprototypeswithusers 2010-09-13Mensch und Computer 2010 Keynote9 10. Start with Capturing User Traces 2010-09-13 Mensch und Computer 2010 Keynote 10 11. Scan Skim Decide Action 2010-09-13Mensch und Computer 2010 Keynote 11 12. Characteriza*onModelsEvalua*ons Prototypes Characterizeactivitywithexperiments,ethnography,loganalysis Modelinteractiondynamicsandinterfacevariations Prototypetoolstoincreasebenetsorreducecost Evaluateprototypeswithusers 2010-09-13Mensch und Computer 2010 Keynote12 13. human-informationinteractionisadaptivetotheextent:MAXIMIZE[ Net Knowledge Gained Costs of Interaction]2010-09-13Mensch und Computer 2010 Keynote 13 14. Scent Values: Start users atProbabilities of page with TransitionExamine user patterns some goal Flow users through the network2010-09-13Mensch und Computer 2010 Keynote 14 15. Characteriza*onModelsEvalua*ons Prototypes Characterizeactivitywithexperiments,ethnography,loganalysis Modelinteractiondynamicsandinterfacevariations Prototypetoolstoincreasebenetsorreducecost Evaluateprototypeswithusers 2010-09-13Mensch und Computer 2010 Keynote15 16. Astorethatknowsyourgoal.Over50%reductionintasktime. 2010-09-13Mensch und Computer 2010 Keynote 16 17. Identifytastypages Waftscentbackwardalonglinks Losesintensityasittravels XC4411 copierFeatures:XC4411featuresdigital copiers XC5001remote diagnosticscolor copierscopiers ...backfax machinesother maintenance remotediagnostics... 2010-09-13 Mensch und Computer 2010 Keynote17 18. Partial information goal:62 copies/min.remote diagnostic technologyRemainder of information goal:92 copies/min. speed &gt;= 752010-09-13Mensch und Computer 2010 Keynote 18 19. Associated Entries underlined in red2010-09-13 Mensch und Computer 2010 Keynote19 20. Conceptually highlight any relevant User rst type search keywords: passages and keywordsanthrax symptoms Draw user attention2010-09-13 Mensch und Computer 2010 Keynote 20 21. Characteriza*onModelsEvalua*ons Prototypes Characterizeactivitywithexperiments,ethnography,loganalysis Modelinteractiondynamicsandinterfacevariations Prototypetoolstoincreasebenetsorreducecost Evaluateprototypeswithusers 2010-09-13Mensch und Computer 2010 Keynote21 22. (times capped at five minutes)10/12 subjects preferred ScentTrails2010-09-13Mensch und Computer 2010 Keynote 22 23. 2005-10-21 UMN talk 24. 2005-10-21 UMN talk 25. Descriptive:clarifyterms,keyconcepts Explanatory:revealrelationshipsandprocesses Predictive:aboutperformanceandsituations Prescriptive:conveyguidancefordecisionmakingindesignbyrecordingbestpractice Generative:enablepractitionerstocreate,inventordiscoversomethingnew 2010-09-13 Mensch und Computer 2010 Keynote25 26. BongwonSuh,GregorioConvertino,EdH.Chi,Peter Pirolli.TheSingularityisNotNear:SlowingGrowthof Wikipedia.InProc.ofWikiSym2009.Oct,2009.Florida, USA 2010-09-13Mensch und Computer 2010 Keynote 26 27. Number of Articles (Log Scale)http://en.wikipedia.org/wiki/Wikipedia:Modelling_Wikipedias_growth2010-09-13 Mensch und Computer 2010 Keynote27 28. Monthly Edits 2010-09-13 Mensch und Computer 2010 Keynote 28 29. Monthly Edits 2010-09-13 Mensch und Computer 2010 Keynote 29 30. *In thousands Monthly Active Editors2010-09-13Mensch und Computer 2010 Keynote 30 31. *In thousands Monthly Active Editors2010-09-13Mensch und Computer 2010 Keynote 31 32. 2010-09-13 Mensch und Computer 2010 Keynote 32 33. Monthly Ratio of Reverted Edits 2010-09-13 Mensch und Computer 2010 Keynote 33 34. 2010-09-13 Mensch und Computer 2010 Keynote 34 35. PreferentialAttachment:Editsbegetedits morenumberofpreviousedits,morenumberofnewedits Growth rate depends on:N = current populationr = growth rate of the population N(t) = N 0 e rt dN= r N dtGrowth rate Current of population population2010-09-13 Mensch und Computer 2010 Keynote 35 36. Biologicalsystem Competitionincreasesaspopulationhitthelimitsoftheecology Advantagegotomembersofthepopulationthathavecompetitivedominanceoverothers Analogy Limitedopportunitiestomakenovelcontributions Increasedpatternsofconictanddominance2010-09-13 Mensch und Computer 2010 Keynote 36 37. r-Strategist Growthorexploitation dNN Less-crowdedniches/producemany = rN(1 ) ospringdtK K-Strategist Conservation[Gunderson &amp; Holling 2001] Strongcompetitorsincrowdedniches/ investmoreheavilyinfewerospring 2010-09-13Mensch und Computer 2010 Keynote37 38. Ecologicalpopulationgrowthmodel Alsodependonenvironmentalconditions K,carryingcapacity(duetoresourcelimitation) dNN= rN(1 ) dtK2010-09-13 Mensch und Computer 2010 Keynote38 39. Followsalogisticgrowthcurve New Article 2010-09-13 Mensch und Computer 2010 Keynote 39 40. CarryingCapacityasafunctionoftime. 2010-09-13 Mensch und Computer 2010 Keynote 40 41. 2010-09-13 Mensch und Computer 2010 Keynote 41 42. Concepts Topics Users DocumentsNoiseTagsDecodingEncoding T1Tn2010-09-13Mensch und Computer 2010 Keynote42 43. 2010-09-13 Mensch und Computer 2010 Keynote 43 44. 2010-09-13 Mensch und Computer 2010 Keynote 44 45. Source: Hypertext 2008 study on del.icio.us (Chi &amp; Mytkowicz)2010-09-13 Mensch und Computer 2010 Keynote45 46. 2010-09-13 Mensch und Computer 2010 Keynote 46 47. Jointworkwith RowanNairn,LawrenceLeeKammerer,Y.,Nairn,R.,Pirolli,P.,andChi,E.H.2009.Signpostfromthe masses:learningeectsinanexploratorysocialtagsearchbrowser.In Proceedingsofthe27thinternationalConferenceonHumanFactorsin ComputingSystems(Boston,MA,USA,April04-09,2009).CHI'09.ACM,New York,NY,625-634. 2010-09-13 Mensch und Computer 2010 Keynote47 48. Semantic Similarity Graph WebTools ReferenceGuideHowtoTutorial TipsHelp TipTutorials Tricks2010-09-13 Mensch und Computer 2010 Keynote 48 49. Tags URLsP(URL|Tag) P(Tag|URL) SpreadingActivationinabi-graph Computationoveraverylargedataset 150Million+bookmarks 2010-09-13Mensch und Computer 2010 Keynote49 50. 2010-09-13 Mensch und Computer 2010 Keynote 50 51. 2010-09-13 Mensch und Computer 2010 Keynote 51 52. 2010-09-13 Mensch und Computer 2010 Keynote 52 53. Dellarocas,MITSloanManagementReview 2010-09-13 Mensch und Computer 2010 Keynote 53 54. (1)Generatenewtoolsandsystems,newtechniques (2)Generatedatathatlookslikerealbehavioraldata 2010-09-13 Mensch und Computer 2010 Keynote54 55. externally-motivated self-motivatedframing the contextBefore Searchsearcherssearchers 31% 69% Social InteractionsGATHER REQUIREMENTS refining the requirements FORMULATE REPRESENTATION 28%13% 59% During Searchnavigational transactional informationalFORAGINGstep Astep Asearch processstep Bstep B evidence fileTRANSACTIONSENSEMAKINGsearch product /end product After Search28%72%DO NOTHING TAKE ACTIONORGANIZE DISTRIBUTE to self 15% to proximate 87%to public 2% othersothers 56. externally-motivated self-motivated framingthe contextBefore Searchsearcherssearchers 31% 69% 43% users engaged in pre-search social Social Interactions interactions. GATHER REQUIREMENTSrefiningthe reasons for interacting: to get advice, guidelines, feedback, FORMULATE REPRESENTATIONrequirements or search tips28%13% 59% During Search navigational transactional informationalFORAGINGstep Astep 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 stepprocess evidence fileTRANSACTIONSENSEMAKINGsearch product /end product After Search28%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 obligationto self 15% to proximate 87% to public 2% others others 57. externally-motivated self-motivatedframing the contextBefore Searchsearcherssearchers instant 31% messaging69% to personal social (IM)Social Interactions connections near the search boxrefiningGATHER REQUIREMENTS the requirements FORMULATE REPRESENTATION 28%13% 59% During Search navigational transactional informational step A clouds from domain FORAGINGtag step Aexpertssearch step B users search trails process feedback)other (forstep B related search terms (for feedback) Similar to: Glance; Smyth" evidence fileTRANSACTIONSENSEMAKINGsearch product /end product After Search28%72%DO NOTHING TAKE ACTION sharing tools built-in to (search) site Spartag.us" collective tag clouds (for feedback)ORGANIZEDISTRIBUTE Mr. Taggy"to self 15%to proximate 87% to public 2% others others 58. Allmodelsarewrong! Somearemorewrongthanothers! Sowhataretheoriesandmodelsgoodfor? Theyreasummaryofwhatwethinkishappening Waystodescribeandexplainwhatwehavelearned Predictsuserandgroupbehavior Helpsgeneratenewnoveltoolsandsystems 2010-09-13 Mensch und Computer 2010 Keynote58 59. 2010-09-13 Mensch und Computer 2010 Keynote 59 60. Word connectivityHuman Movement Study: Fitts law MT = a + b Log2(Dsi/Wi + 1) 18000 English Letter Corpus 16000140001200010000(News, chat etc) 800060004000 [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 27Pij Dij t = Log2 +1 W ( A B) = eEkTif E &gt;0 i=1 j =1 IP Wi =1 if E 0Metropolis random walkoptimization Alphabetical tuningUIST 200460 61. Betweenjustgettingthingsdone vs.ndingoutthescience 2010-09-13 Mensch und Computer 2010 Keynote 61 62. A B Bucket Testing or A/B Testing [Kohavi et al] 63. Characteriza*onModelsEvalua*ons PrototypesEvalua*ons PrototypesDesign,Prototype,Learn; Ifyoucan,youshouldcodifyyour ndingssothatotherscan ThenRe-design,Prototype,Learn replicateit,learnfromit,predict Sometimesthatsallyoucando. behaviorfromit. Thebasisofatruescienticeld2010-09-13Mensch und Computer 2010 Keynote63 64. 2010-09-13 Mensch und Computer 2010 Keynote 64 65. ResearchVision:Understandhowsocialcomputingsystemscanenhancetheabilityofagroupofpeopletoremember,think,andreason.http://asc-parc.blogspot.com http://www.edchi.net echi@parc.com WikiDashboard.comMrTaggy.comZerozero88.com 66. 2010-09-13 Mensch und Computer 2010 Keynote 66 67. Appropriatefortheoccasion 2010-09-13Mensch und Computer 2010 Keynote 67 68. Poor heuristic Good heuristic 2010-09-13Mensch und Computer 2010 Keynote 68 69. SoloCooperative (good hints) 2010-09-13 Mensch und Computer 2010 Keynote 69 70. Social Tagging Creates Noise Synonyms Misspellings Morphologies People use different tagwords to express similarconcepts.2010-09-13 Mensch und Computer 2010 Keynote70 71. Database Lucene Delicious P(URL|Tag) Serve up search Ma.gnolia P(Tag|URL) resultsTuples of Pre-computed Other social cuesbookmarksBayesian Network patterns in a fastWell defined APIs[User, URL, Tags,InferenceindexTime]Crawling MapReduce Web Server WebServerUISearch Frontend Results MapReduce:monthsofcomputa*ontoasingleday Developmentofnovelscoringfunc*on2010-09-13 Mensch und Computer 2010 Keynote 71 72. framingBefore Searchexternally-motivated self-motivatedsearcherssearchersthe context 31% 69%Social InteractionsGATHER REQUIREMENTS refining the requirements FORMULATE REPRESENTATION 28%13% 59% During Searchnavigational transactional informationalFORAGINGstep Astep Asearch processstep Bstep B evidence fileTRANSACTIONSENSEMAKINGsearch product /end product After Search28%72%DO NOTHING TAKE ACTIONORGANIZE DISTRIBUTE to self 15% to proximate 87% to public 2% others others 73. externally-motivated self-motivated framingthe context Before Searchsearcherssearchers 31% 69%Social InteractionsGATHER REQUIREMENTS refining the requirements FORMULATE REPRESENTATION 28%13% 59% During Searchnavigational transactional informationalFORAGINGstep Astep Asearch processstep Bstep B evidence fileTRANSACTIONSENSEMAKINGsearch product /end product After Search28%72%DO NOTHING TAKE ACTIONORGANIZE DISTRIBUTE to self 15% to proximate 87% to public 2% others others 74. externally-motivated self-motivated framingthe context Before Searchsearcherssearchers 31% 69%Social InteractionsGATHER REQUIREMENTS refining the requirements FORMULATE REPRESENTATION 28%13% 59% During Searchnavigational transactional informationalFORAGINGstep Astep Asearch processstep Bstep B evidence fileTRANSACTIONSENSEMAKINGsearch product /end product 28%72% After SearchDO NOTHING TAKE ACTIONORGANIZE DISTRIBUTE to self 15% to proximate 87% to public 2% others others 75. Forexample,forinformationdiusion,itstheoryofinuentials[Gladwell,etc.] reachasmallgroupofinuentialpeople,andyoullreacheveryoneelseFigure From: Kleinberg, ICWSM2009 2010-09-13 Mensch und Computer 2010 Keynote75 76. From: Sun et al, ICWSM2009 2010-09-13 Mensch und Computer 2010 Keynote 76 </p>