Lightning	  Presenta-ons!	  
Simon	  Buckingham-­‐Shum	  
Visualizing	  and	  filtering	  social	  8es	  in	               SocialLearn	  by	  topic	  and	  type	  Visualising	  Soci...
Disposi8onal	  Learning	  Analy8cs	            for	  C21/LLL	         Ques8oning	  and	                                   ...
Nicola	  Avery	  
Medicines	  &	  Healthcare	  Products	  Regulatory	  Agency	  regulate :applications :                                    ...
Learning analytics focus group                                                      projectsPerformance Support           ...
Doug	  Clow	  
                                                    	  •  Data	  Wrangling	     – Demographics,	  	       VLE	  usage,	  	...
Joseph	  Corneli	  
Adam	  Cooper	  
Exponen8al	  Random	  Graph	  Models	                                                                A                    ...
e.g.	  JISC	  and	  CETIS	  Teams	                                                              •  Showing	  our	  colours...
Martyn	  Cooper	  
John	  Doove	  
Exploring	  Learning	  AMore	  paossibili8es	  of	                                                naly8cs	                ...
Cath	  Ellis	  
Unlikely	   Very	  unlikely	                                          Neither	  Likely	  or	                  Unlikely	   ...
Rebecca	  Ferguson	  
Social	  learning	  analy-cs:	  discourse	  Challenge: Locate the exploratory dialogue                                    ...
Self-­‐training	  framework	  for	  automa-c	       exploratory	  discourse	  detec-on	  •  Framework	  uses	  cue	  phras...
Dai	  Griffiths	  
Mar8n	  Hawksey	  
c   MOOC Architecture	          Blogs                               Daily Alert                                           ...
c   MOOC Analytics	  Opportunity•  Open (ish) dataIssues•  Time limited•  "analytically cloaked"•  Darksocial•  Infrastruc...
Jean	  MuGon	  
         Engagement	  	  AnalyKcs	                  	  Jean	  MuGon,	  Project	  Manager	  	                      	       ...
Jonathan	  San	  Diego	  •  hGp://infiniterooms.co.uk/poster/	  
Mark	  Stubbs	  
1.    Uniview	  -­‐	  Oracle-­‐based	  data	  warehouse	  /	  BI	  repor8ng	  since	  2009	  2.    Used	  R	  randomForest...
Annika	  Wolff	  
students	       Data	  sources	        VLE	         TMA	      Demographic	        Other..	                                ...
BUILDING	  THE	  PREDICTIVE	  MODELS	  	  Developed	  and	  tested	  on	  3	  historic	  data	  sets	  Compared:	  decisio...
Labs	                                                          www.triballabs.net	           Learning	  Analy8cs	  R&D	  P...
Labs	                                                                       www.triballabs.net	                           ...
SoLAR-FlareUK-2012.11.19-lightningtalks
SoLAR-FlareUK-2012.11.19-lightningtalks
SoLAR-FlareUK-2012.11.19-lightningtalks
SoLAR-FlareUK-2012.11.19-lightningtalks
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SoLAR-FlareUK-2012.11.19-lightningtalks

  1. 1. Lightning  Presenta-ons!  
  2. 2. Simon  Buckingham-­‐Shum  
  3. 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://bit.ly/LearningAnaly8csOU  
  4. 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 SocialLearnhttp://www.open.ac.uk/blogs/SocialLearnResearch/2012/06/20/social-learning-analytics-symposium
  5. 5. Nicola  Avery  
  6. 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. 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. 8. Doug  Clow  
  9. 9.    •  Data  Wrangling   – Demographics,     VLE  usage,     course  characteris8cs,     student  feedback   – Human  sense-­‐making   Doug  Clow  
  10. 10. Joseph  Corneli  
  11. 11. Adam  Cooper  
  12. 12. Exponen8al  Random  Graph  Models   A dFirst  Experiments  with   Mutuality     a     m     Transi8vity   C   o     o     p Homophily   er   (JI S C  
  13. 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. 14. Martyn  Cooper  
  15. 15. John  Doove  
  16. 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://youtu.be/Xs3MsGPVivg   Seven  tangible  examples  to   refer  to   Community  of  various   Areas  of  work  to  be   experts   done…  
  17. 17. Cath  Ellis  
  18. 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. 19. Rebecca  Ferguson  
  20. 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. 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. 22. Dai  Griffiths  
  23. 23. Mar8n  Hawksey  
  24. 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. 25. c MOOC Analytics  Opportunity•  Open (ish) dataIssues•  Time limited•  "analytically cloaked"•  Darksocial•  Infrastructure/messy data
  26. 26. Jean  MuGon  
  27. 27.   Engagement    AnalyKcs    Jean  MuGon,  Project  Manager       TwiGer  @myderbi         www.derby.ac.uk/ssis/JISC-­‐projects            
  28. 28. Jonathan  San  Diego  •  hGp://infiniterooms.co.uk/poster/  
  29. 29. Mark  Stubbs  
  30. 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  |  m.stubbs@mmu.ac.uk  |  twiGer.com/thestubbs  
  31. 31. Annika  Wolff  
  32. 32. students   Data  sources   VLE   TMA   Demographic   Other..   Who  is   struggling?   RETAIN  predic8ve  models   Why  are  they  Dashboard  visualisa8ons   struggling?  
  33. 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. 34. Labs   www.triballabs.net   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. 35. Labs   www.triballabs.net   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  

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