Network Learning: AI-driven Connectivist Framework for E-Learning 3.0

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Network Learning: AI-driven Connectivist Framework for E-Learning 3.0

  1. 1. 国立大学法人   電気通信大学   振興調整費  Unique and Exciting Campus   ネットワーク ラーニング Network Learning AI-driven Connectivist Framework for E-Learning 3.0   Neil Rubens Active Intelligence Group Knowledge Systems Laboratory University of Electro-Communications Tokyo, Japan
  2. 2. Evolution of eLearning: eLearning 1.0eLearning uses technology to enhance Learning ---------‣ eLearning 1.0: --------- --------- --------- --------- ‣ reading: content became easily accessible ‣ logging: user’s activities could be logged and analyzed ‣ Learning Theories: ‣ Behaviorism: learning is manifested by a change in behavior, environment shapes behavior, contiguity ‣ Cognitivism: how human memory works to promote learning
  3. 3. Evolution of eLearning: eLearning 2.0‣ eLearning 2.0: --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- ‣ writing: anybody can easily create content (e.g. blogs, wiki, etc.) ‣ socializing: interaction is easy (e.g. facebook, twitter, etc.) ‣ Learning Theories: ‣ Constructivism: constructing ones own knowledge from ones own experiences (enabled through writing) ‣ Social Learning: people learn from one another (enabled through socializing)
  4. 4. Broken Knowledge Cycle‣ Problem: The current cycle of knowledge creation/utilization is inefficient ! ‣ large portion of created content is never utilized by others* only 0.05% of twitter messages attracts attention (Wu et. al., 2011) only 3% of users look beyond top 3 search results (Infolosopher, 2011) ‣ large parts of created contents are redundant (Drost, 2011) ‣ Peak Social – the point at which we can gain no new advantage from social activity (Siemens 2011) utilize U0lized dge no wle K is ting Ex Redundant create Knowledge Novel *there are some personal benefits e.g. externalization, crystallization, etc.
  5. 5. Information Overload for Computers not a Problem but an Opportunity Tan,  Steinbach,  Kumar;  2004  500  Points   2,000  points   8,000  points  
  6. 6. Messaging NetworksHow can we use computers tolearn in these settings? http://datamining.typepad.com/photos/uncategorized/2007/04/08/twitter20070405.png Citation Network Social Network hp://wiki.ubc.ca/images/f/ff/SocialWeb.jpg9 hp://www.kieranhealy.org/files/misc/SocCoreCites.jpg:
  7. 7. Our  Focus  informa0on  connec0vity   Nova  Spivack©   social  connec0vity  
  8. 8. Connectivism (Learning Theory) Connec0vism:  Knowledge  is  distributed  across  a  network  of   connecTons,  and  therefore  learning  consists  of  the  ability  to   construct  and  traverse  these  networks    (Siemens    Downes,  2008)  Property Behaviourism Cognitivism Constructivism Humanism ConnectivismLearning Thorndike, Pavlov, Watson, Koffka, Kohler, Lewin, Piaget, Maslow, Siemens, Downestheorists Guthrie, Hull, Tolman, Skinner Piaget, Ausubel, Vygotsky Rogers Bruner, GagneHow learning Black box—observable Structured, Social, meaning Reflection on Distributed within a network, social,occurs behaviour main focus computational created by each personal technologically enhanced, learner experience recognizing and interpreting (personal) patternsInfluencing Nature of reward, punishment, Existing schema, Engagement, Motivation, Diversity of network, strength offactors stimuli previous experiences participation, experiences, ties, context of occurrence social, cultural relationshipsRole of Memory is the hardwiring of Encoding, storage, Prior knowledge Holds changing Adaptive patterns, representativememory repeated experiences—where retrieval remixed to concept of self of current state, existing in reward and punishment are current context networks most influentialHow transfer Stimulus, response Duplicating knowledge Socialization Facilitation, Connecting to (adding) nodes andoccurs constructs of “knower” openness growing the network (social/ conceptual/biological)Types of Task-based learning Reasoning, clear Social, vague Self-directed Complex learning, rapid changinglearning best objectives, problem (“ill defined”) core, diverse knowledge sourcesexplained solving
  9. 9. ConnecTvism:  Nice  Theory  Need:  Tools    Frameworks  To  make  it  Prac0cal   hFp://imgs.sfgate.com/c/pictures/2011/12/19/ba-­‐BRIDGE20_SFC0105724887.jpg  
  10. 10. Methods  
  11. 11. Conceptual Framework network Analysis Layer --------- --------- --------- --------- connections --------- AI --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- Aggregation Layer links use AI to: Linking Layer §  connect contents §  connect people nodes §  connect people contents §  connect models Extraction Layer documents
  12. 12. Modules Concept concept concept Extraction concept concept concept concept concept concept concepts Group Formation documents tasks concept concept context (documents) Semantic concept concept Influence Mapping Estimationconcept concept concept concept concept concept concept concept concept concept interaction log concepts network Analysis Layer concept concept connections Aggregation Layer Knowledge Level concept concept Estimation links concept concept concept concept concept concept concept concept concept concept Linking Layer nodes concepts Extraction Layer documents
  13. 13. Analysis Layer Link LayerModulePipeline docs want to learn about: concept Search Engine(Example) Concept concept concept docs Extractor concept concept concept concept concept concept concept concept concept concept Semantic concept concept concept concept concept concept Mapping concept concept concept concept concept concept concept concept Search Engine concept concept concept concept … discussions … concept … concept … concept … concept concept
  14. 14. Sequence Diagram (Example)user system user system user system I want to know about term t_i I think t_i and t_k are similar .. I want to know about term t_i and t_k social semantics semantics t_i t_i u_i: I think t_i is same as t_j … t_i t_i u_j: no t_is is more like t_p ... u_j: no t_is is more like t_p ... t_i t_i t_i t_i u_m: I think t_i and t_k are similar .. u_i: you are right t_i t_i t_i t_i t_i semantics t_i t_i contents contents t_i t_i t_i t_i contents t_i social social social u_i: I think t_i is same as t_j … u_i: I think t_i is same as t_j … u_j: no t_is is more like t_p ... u_j: no t_is is more like t_p ... u_k: you are both wrong t_i is ... u_j: no t_is is more like t_p ... u_i: I think t_i is same as t_j … u_j: no t_is is more like t_p ... u_j: no t_is is more like t_p ... u_m: I think t_i and t_k are similar .. u_i: you are right
  15. 15. Connecting Representations ⇤ V (d1 )Semantic + Network ⇤ V (d2 ) ⇤ Type! Network (N)! Content (C)! Proposed Hybrid! V (d1 ) ⇤ V (d3 ) ⇤ V (d2 ) Characteristics! This is a Title of a ! tfi,j = P ni,j Research Paper ⇤ (d V (d )1 ) V k nk,j Joe Fakeman Jane Noman ! 3 Analysis! V (d1 ) Nowhere University {fakeman, noman}@nowhereuni.edu |D| Abstract idfi = log ~~~~~~~~~~~~~~~~~~~~~ ni,j |{d : ti ⌅ d}| tfi,j = P !#$%$()%*+(,( !#$%$()%*+(-( ~~~~~~~~~~~~~~~~~~~~~ ! ~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~ k nk,jIntroduction~~~~~~~~~~~~~~~~-~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ V (d2 ) tf-idf i,j = tfi,j ⇥ idfi same as C part of N~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~ |D| ⇤ ! V (d1 )~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~ idfi = log !~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~ V (d2 )~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~ |{d : ti ⌅ d}| w (d1 ) V = tf-idf i,j i,j User Interface! ! ⇤ V (d2 ) ./+$(/.(,( V (d3 ) This is a Title of a tf-idf i,j = tfi,j ⇥ idfi 2 3 Research Paper w1,j 6 w2,j 7 Joe Fakeman Jane Noman ! ⇤ ! 6 7 V (d3 ) V (dj ) = 6 . 7 Nowhere University wi,j = tf-idf i,j V (d ) 4 . 5 ⇤ {fakeman, noman}@nowhereuni.edu Abstract . V (d3 ) ~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~ 2 wt,j ~~~~~~~~~~~~~~~~~~~~~ 2 3Introduction~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~ 6 w1,j 7 ni,j Deployment!~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~ ⇤ 6 w2,j 7 tfi,j = P~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~ V (dj ) = 6 . 7 4 . 5 ./+$(/.(,(~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~ . ! k nk,j~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~ wt,j V (d3 ) This is a Title of a ⇤ ⇤ |D| Research Paper V (di ) · V (dj ) idfi = log sim(di , dj ) = ⇤ ⇤ |{d : ti ⌅ d}| Joe Fakeman Jane Noman Nowhere University {fakeman, noman}@nowhereuni.edu V (di ) V (dj ) Execution
 Abstract ~~~~~~~~~~~~~~~~~~~~~ ⇤ Speed! ./+$(/.(,( V (d1 ) tf-idf i,j = tfi,j ⇥ idfi ~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~Introduction~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~ ⇤ V (d2 ) wi,j = tf-idf i,j~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- ~~~~~~~~~~~~~~~~~~ Reverse could also be done: i.e. converting textual representation to a network one. ⇤ 2 3 term-space V (d3 ) 6 w1,j 7 1 ⇤ 6 w2,j 7 V (dj ) = 6 . 7 . tfi,j = n P i,j 4 . 5 ⇥ ! k nk,j w|T |,j w1,j V (d1 ) ⇧ ⌃ 1 ! ⇤ ⇤ ⇧ w2,j ⌃ V (d1 ) |D| idfi = log sim(di , dj ) = V (di ) · V (dj ) ⇧ . . ⌃ ! |{d : ti ⌅ d}| ⇤ ⇤ V (di ) V (dj ) ⇧ . ⌃ V (d1 ) ! ⇧ ⌃ ! — V (d2 ) ⇧ w|T |,j ⌃ V (d1 ) —- ! Network Content based ⇥ idfi tf-idf i,j = tfi,j ⇥ ⇧ ⌃ V (d2 ) V (dj ) = ⇧ ⇧ ⌃ ⌃ ! wi,j i,j = (i, j) j) w = dist(i, ⇧ w1,j ⌃ V (d2 ) != wV (d tf-idf i,j ⇧ ⌃ ! —- i,j 3 ) 2 3 ⇧ w2,j ⌃ V (d2 ) Generalized Assignment Problem ! w1,j ⇧ ⌃ V (d3 ) 2 Objective: given a paper p, 1,j ⇤ 3 6 choose a 6 w2,jof 7 group 7 experts ⇧ . . ⌃ ! ! V (d1 ) w V (d ) = 6 M ⇥ M (of a fixed size6s) 2,j 7jcollectively (d ) ⇤ w that 4 ! possesses . . V 5 . 7 ⇤ . ⌅ V (d3 ) V (d ) = 6 6 7 1 ! the most expertise about p: . 7 j 4 . 5. w|N |,j w|N |,j V (d3 ) wt,j P maximize R(M, p) = m2M r (m, p) (1) ! subject to |M | =!(d ) s (2) V (d2 ) ⇤ ⇤ V (di ) · V (dj ) V 2 Challenge sim(di , dj ) = ⇤ ⇤ V (di ) V (dj ) Expertise is not additive: 1 ! X ! V (d3 ) R(M, p) ⇤= V (d3 . r (m, p)) (3) m2M – Group Expertise Estimation Assumptions Mp are athors of paper p; so R(Mp , p) = 1, and ⇤ ⇤ network-space R(M, p) = 0, where M ⌅ Mp = Ø. ⇤ term-network-space M ⌅ M⇤
  16. 16. #pouf #noel #deco #mobilier #facebook Connecting Concepts #sustainable #evenementiel #savings #twitter #cadeau #reduce #buzz #design #reuse #money Modeling of: #inception #fuelcell #web #prop23 #hydrogen #recycle #cleantech #mobile #diy #new #florida •  topics #pr #innovation #california #news #global #home #biofuels #biofuel #greenroofs #publicrelations #boerse #asia #blog #thermal #frugal #biz #windenergy #aktien #hot #business #greenit •  lexicon #gossip #improvements #budget #cleanthinking #nw #change #construction #lightingdesign #warming #socent #benefits #finland #tudelft #eco #ff #green #audit #finance #ca #aia #doe #electric #cofely #scriptie •  semantics #duurzaam #architecture #health #india #efficient #greenbuilding #ecomonday #stock #footprint #renewableenergy #energy #tips #tip #carbon #ev #ecofriendly #ac #cancun •  dynamics #technology #homes #israel #china #greentip #climate #energyefficiency #delonghi #pv #solar #canada #co2 #csr #building #economist #alternative #education #btu #45 #dd45p #pt #dd50p #50 #science #re #cleanenergy #clean #electronics #cre #10000 #lg #art #africa #coal #dehumidifiers #novosti #climatechange #conservation #electricity #cooperative #emissions #edgestar #star #frigidaire #animals #nasa #environment #cop16 #efficiency #cville #datacenter #cdw #dehumidifier #rated #earth #dlr #pint #space #nature #sustainability #windpower #cars #65 #microwave #energysaving #it #70 #b #yahoo #sandiego #economy #soleus #c #renewables #greenenergy #oil #sd #peakoil #ontario #startup #dp1 #a #un #d #neweconomy #wef #encell #vc #dimmer #air #agw #water #gobeyondoil #wcs #pakistan #seattle #ecosmart #30 #sunpentown #globalwarming #unfccc #energyrevolution #iea #solarpower #ft #investment #us #uk #renewable #pkfloods #eecbg #platts #lamp #hybrid #011013 #epa #transport #solpwr10 #pentair#intelliflo #climategate #socialmedia #energystar #wind #products #led #vs #enviro #climaterealism #turbines #living #power #car #leaf #offshore #housing #sun #greenjobs #nissan #p2 #politics #cnet #google #career #meter #sunpower #utility #lighting #tech #london #greentech #tv #europe #dems #apple #organic #mass #topprog #gadgets #travel #greenliving #nuclear #eu #becktips #gadget #greenbuild #smartgrid #texas #dhilipsiva #baby #leed #cisco #rural #free #gop #tcot #obama #technews #tfb #taf #tcn #cloud #media #gridweek #gas #victoria #3 #fail #pledge #livewithless #glam #privacy #save #lx #streetlights #ftrs #p21 #careers #saving #r #hhrs #youcut #thepowerofwaiting #policy #leds #rs #winterhomeprep #football #veterans #smartmeter #ledstreetlights #ocra #lampe #windworks #ghg #sp1580 #momentive #motiongraphics #tlot #sgp #timelyadvice #win #underoath #licht #clothdiapers #arm #jobs #ebc #utilities #fuel #environmental #siemens #light #glennbeck #tpp #disambiguation #affiliate #linux #employment #hayward #2 #9 #twisters #reddit #hiring #46201 #912 #geospatial #marketing #fossil #teaparty #rulez #engineering #silver #6 #4 #heatpumps #heathrow #1 #capandtrade #gis #saveenergy #uksnow #job #digguser #fuels #realestate #pump #jobdanmark #in #digg #gold #sale #sp2610x15 #5 #8 #ontpoli #hohoho #mlfeeds #cdnprog #trhug #super #7 #stopglobalwarming #starbucks #architekt #cdnpoli #ondp #tweetmyjobs #calau #vt #cookies #aps #99 #indee #getinthegame #photography #indeed #credits #haier #pod #sfo #tax #esncm053e #management #jobangels #solarthermal #grid #meters #breakingnews #accenture #fb #smart #wpccc #dms #cochabamba #dsm #stocks #usa #cmpcc #noc #bolivia #cfd #mkt #webhosting #webhost #greenwebhostingFigure 7: Visualization (zoomable) of 416 Hashtags in Energy-Related Tweets, September 2010 ThroughJanuary 2011; created with Network Explorer (Rubens et al., 2011).proaches that help understand the large scale conversations enhanced word-of-mouth to stimulate user-generated per-

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