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  • 1. Lightning  Presenta-ons!  
  • 2. Simon  Buckingham-­‐Shum  
  • 3. Visualizing  and  filtering  social  8es  in   SocialLearn  by  topic  and  type  Visualising  Social  Learning  in  the  SocialLearn  Environment.    Bieke  Schreurs  and  Maarten  de  Laat  (Open  University,  The  Netherlands),  Chris  Teplovs  (ProblemshiB  Inc.  and  University  of  Windsor),  Rebecca  Ferguson  and  Simon  Buckingham  Shum  (Open  University  UK),  SoLAR  Storm  webinar,  Open  University  UK.  hGp://  
  • 4. Disposi8onal  Learning  Analy8cs   for  C21/LLL   Ques8oning  and   Different  social   challenging   network  paGerns  as   behaviours  as   proxies  for  Learning   proxies  for  CriKcal   RelaKonships   Curiosity   Cross-­‐contextual   Persevering   behaviours  as  proxies   behaviours  as  proxies   for  Meaning  Making   for  Resilience  Shaofu Huang: Prototyping Learning Power Modelling in SocialLearn
  • 5. Nicola  Avery  
  • 6. Medicines  &  Healthcare  Products  Regulatory  Agency  regulate :applications : Life  cycle     Pre-­‐clinical    Phase  1    Phase  2    Phase  3      MA  Approval   management   Clinical  Trials   Licence  &  Varia-ons   ▼900+ staff, agency internal systems, laptops/tablets, workload: 10-300 submissionsLearning & performance - procedures, assessment, consultation, committees, timescales, data & document management / literacy
  • 7. Learning analytics focus group projectsPerformance Support Data Assurance & Transparency Agency BI strategycurrently looking at feedback -  text analysis of existing feedback from training, develop examples -  ratings & recommendations for procedures – useful, accurate, up-to-dateevaluation report January 2013“Well  done  youve  used  really  nice  language  in  that  email”   “you  seem  to  have  been  working  on  this  report  for  7  years”   “8  out  of  10  assessors  said  they  prefer…”  
  • 8. Doug  Clow  
  • 9.    •  Data  Wrangling   – Demographics,     VLE  usage,     course  characteris8cs,     student  feedback   – Human  sense-­‐making   Doug  Clow  
  • 10. Joseph  Corneli  
  • 11. Adam  Cooper  
  • 12. Exponen8al  Random  Graph  Models   A dFirst  Experiments  with   Mutuality     a     m     Transi8vity   C   o     o     p Homophily   er   (JI S C  
  • 13. e.g.  JISC  and  CETIS  Teams   •  Showing  our  colours?   ●  Main  effect   ●  Homophily   ●  Mixing  edges  +  sender(base=c(-­‐4,-­‐21,-­‐29,-­‐31))  +  receiver(base=c(-­‐14,-­‐19,-­‐23,-­‐28))  +  nodematch("team",  diff=TRUE,  keep=c(1,3,4))  +  mutual   All  images  and  text  CC-­‐By:  Adam  Cooper,  2012  
  • 14. Martyn  Cooper  
  • 15. John  Doove  
  • 16. Exploring  Learning  AMore  paossibili8es  of   naly8cs   the   wareness   Enthusiasm!   Lessons  Learned  Vak  voor   Vak   User   Needs   UvAnaly-­‐ 8cs   PinPoint   MAIS   ProF   Curri    Analy8cs   M   hGp://   Seven  tangible  examples  to   refer  to   Community  of  various   Areas  of  work  to  be   experts   done…  
  • 17. Cath  Ellis  
  • 18. Unlikely   Very  unlikely   Neither  Likely  or   Unlikely   Very  unlikely   7%   2%   Unlikely   0%   2%   Neither  Likely  or   5%   Unlikely   11%  Before   Very  Likely   How  likely   AEer   32%   are  you  to   Likely   29%   use  this   Very  Likely   feedback?   64%   Likely   48%   Clearer  sense  of  where  they  sit  in  comparison  to  their  cohort  which  mo8vates  them  to  want  to  do  more  to  improve   Shining  aGen8on  to  important  areas  that  they  tend  to  neglect   Mo8va8ng  high  achieving  students   Seeing  a  bigger  picture   For  some  this  is  emo8onally  challenging  and  sensi8ve  but  for  others  it’s  not  
  • 19. Rebecca  Ferguson  
  • 20. Social  learning  analy-cs:  discourse  Challenge: Locate the exploratory dialogue Manual analysis identifies indicatorsCategory   Indicator  Challenge   But  if,  have  to  respond,  my  view  Cri8que   However,  I’m  not  sure,  maybe  Discussion  of  resources   Have  you  read,  more  links  Evalua8on   Good  example,  good  point  Explana8on   Means  that,  our  goals  Explicit  reasoning   Next  step,  relates  to,  that’s  why  Jus8fica8on   I  mean,  we  learned,  we  observed  Reflec8on  of  perspec8ves  of  others   Agree,  here  is  another,  take  your  point   23  
  • 21. Self-­‐training  framework  for  automa-c   exploratory  discourse  detec-on  •  Framework  uses  cue  phrases  to  make   use  of  discourse  features  for   classifica8on  •  Uses  a  k-­‐nearest  neighbours  instance   selec8on  approach  to  draw  on   topical  features    
  • 22. Dai  Griffiths  
  • 23. Mar8n  Hawksey  
  • 24. c MOOC Architecture   Blogs Daily Alert (email/RSS) LMS “ Central store filter Black box Social “ (aggregator) Bookmarking Twitter & Comments Social media Adapted from Siemens, 2012
  • 25. c MOOC Analytics  Opportunity•  Open (ish) dataIssues•  Time limited•  "analytically cloaked"•  Darksocial•  Infrastructure/messy data
  • 26. Jean  MuGon  
  • 27.   Engagement    AnalyKcs    Jean  MuGon,  Project  Manager       TwiGer  @myderbi­‐projects            
  • 28. Jonathan  San  Diego  •  hGp://  
  • 29. Mark  Stubbs  
  • 30. 1.  Uniview  -­‐  Oracle-­‐based  data  warehouse  /  BI  repor8ng  since  2009  2.  Used  R  randomForest  for  learning  tech  review  &  NSS  analysis  since  2010  3.  Consistent  student  sa8sfac8on  data  collec8on,  10,770  respondents  2011  4.  Star8ng  major  Analy8cs  project  (SQL  Server,  SSAS,  SSRS,  SP2010)   A League  table  rankings   Marke)ng  &   Recruitment   Reputa)on   Processes   C   B Learning,  Teaching,  Assessment     Student  Intake   Student  Reten)on   &  Personal  Development   (Aspira)ons,  A8tude   Success  &     Processes,  Facili)es   &  Abili)es)   Sa)sfac)on   &  Resources   Resource  alloca)on   All  Year  Numbers   A Recruit  to  target   B Improve  sa8sfac8on,  reten8on  &  success   C   Inform  decision-­‐makers  Prof  Mark  Stubbs  |  Head  of  Learning  &  Research  Tech  |  |  
  • 31. Annika  Wolff  
  • 32. students   Data  sources   VLE   TMA   Demographic   Other..   Who  is   struggling?   RETAIN  predic8ve  models   Why  are  they  Dashboard  visualisa8ons   struggling?  
  • 33. BUILDING  THE  PREDICTIVE  MODELS    Developed  and  tested  on  3  historic  data  sets  Compared:  decision  trees  and  SVM’s.  Compared:  VLE  only,  TMA  and  combined    MAIN  FINDINGS    •  No  overall  clicking  measure  correlated  with  pass/fail:  focus  on  change  in  student   behaviour  instead  •  High  precision  can  be  achieved  in  predic8ng  both  performance  drop  and  final   outcome  (pass/fail)  for  all  3  modules,  using  combined  VLE  and  TMA  data  •  Demographic  data  can  improve  performance,  but  in  early  stages  the  VLE  ac8vity  is   the  most  informa8ve  data  source.  •  Successfully  applied  2010  model  to  2011  data.  Even  some  success  across  modules.  
  • 34. Labs   Learning  Analy8cs  R&D  Project  •  Partnership  with  a  university  to  develop  a  Learning   Analy8cs  PoC:   –  Predic8ve  model  which  can  predict  student  success   –  Combine  data  from  mul8ple  administra8ve  and  ac8vity   sources   –  Test  how  support  staff  can  interact  with  the  model  and   correctly  interpret  predic8ons   –  Bring  together  visualisa8on  and  ac8on  –  onen  a  missing   element   @chrisaballard  
  • 35. Labs   Mapping  Success  Factors   Academic  Integra-on   Engagement   Circumstances  Grades   VLE  Ac8vity   Social  Background   Library  Ac8vity   Proximity   Finance   Social  Integra-on   Prepara-on  for  HE  Forum  interac8on   Demographics   Qualifica8ons   @chrisaballard