MOOCs: the power of collaborative learning and communities of knowledge
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  • 1.     MOOCS:  THE  POWER  OF   COLLABORATIVE  LEARNING  AND   THE  COMMUNITIES  OF   KNOWLEDGE     Marta  Cáceres-­‐Piñuel   UNED  PhD  Researcher   GO-­‐GN  Member   OCWC  Lujbljana  23th  April  2014    
  • 2. PhD  AIM  &  HYPOTHESIS   •  AIM:       •  To  analize  MOOCs  and  Beyond  (MOOCs  as  New  online  spaces  for  Open  Knowledge;    New   Methodologies,  innovaYve  social  dynamics,  and  renewed  insYtuYonals  designs  associated  for   creaYng  Open  CommuniYes  of  Knowledge)       •  HYPOTHESIS   1.  MOOCs  enable  much  more  than  massive  spaces  for  scalable  online  courses.-­‐  MOOCs  create  new   online  spaces  for  CommuniYes  of  Knowledge.     2.  MOOCs  can  permeate  remove  borders  in  (formal/non  formal)EducaYon  and  enlarge  open   knowledge  sharing  in  two  direcYons.   –   They  offer  soluYons  for  EducaYonal  InsYtuYons  (Secondary  Schools/UniversiYes/Colleges…)  to  offer  Open   and  Massive  educaYon  (formal/non  formal).   –  They  offer  Open  Knowledge  Networks  to  different  stakeholders  (companies,  insYtuYons/  individuals…).   Networks  to  access  knowledge,  to  organise  knowledge,  to  share/exchange  knowledge  with  others.     3.  MOOCs  are  a  powerful  tool  in  combinaYon  with  other  social  tools  (P2P  acYviYes,  fora,  Q&A…)  for   Global  Open  Knowledge  Networks.  Knowledge  networks  (academic,  professional,  communiYes   of  pracYce)  can  be  connected  in  a  region  or  even  globally.    
  • 3. THEORETICAL  FRAMEWORK     •  Networking  Analisys  Theory    (Sociological  ProspecJve)     •  Networkig    analysis  is  based  on  an  assumpYon  of  the  importance  of  relaYonships   among  interacYng  units.  The  social  network  perspecYve  encompasses  theories,   models,  and  applicaYons  that  are  expressed  in  terms  of  relaYonal  concepts  or   processes.  Actors  and  their  acYons  are  viewed  as  interdependent  rather  than   independent,  autonomous  units(…)(1).(Wasserman,  S.  and  K.  Faust,  1994,  Social   Network  Analysis.Cambridge:  Cambridge  University  Press.   •  ConnecJvism  (  EducaJonal  ProspecJve)   •  ConnecYvism  is  a  hypothesis  of  learning  which  emphasizes  the  role  of  social  and   cultural  context(1).    The  relaYonship  between  work  experience,  learning,  and   knowledge,  as  expressed  in  the  concept  of  ‘connecYvity,  is  central  to  connecYvism,   moYvaYng  the  theory's  name.The  phrase  "a  learning  theory  for  the  digital  age”(2)   indicates  the  emphasis  that  connecYvism  gives  to  technology's  effect  on  how   people  live,  communicate  and  learn.(  George  Siemens.  ConnecYvism:  A  Learning   Theory  for  the  Digital  Age)   •  (1)ConecYvism  and  Previous  Theories:  ConnecYvism  is  oien  associated  with  and  proposes  a  perspecYve   similar  to  Vygotsky's  'zone  of  proximal  development'  (ZPD),  an  idea  later  transposed  into  Engeström's   (2001)  AcYvity  theory.  It  is  somewhat  similar  to  Bandura's  Social  Learning  Theory  that  proposes  that  people   learn  through  contact.  
  • 4. DATA  ANALYSIS   MOOC  survey  to  Students  and  Learning   AnalyYcs           TOOLS:   Survey   to   Students   and   Learning   AnalyYcs   from   2   different   MOOC  Plamorms                                                                                                                                                                                                                                                                                                    UNED  COMA:       The  MOOC  strategy  of  the  NaYonal  Distance  University.             Spain.Removing  borders  of    Distance  EducaYon  in  Spanish                  UNX:  The  open  LaYnamerican  Plaoorm  for  Entrepreneurship                                                                  Training  and  CreaYng  Networworking  for  new  Entrepreneurs   RATIONAL  OF  THE  MOOC  DATA  ANALYSIS   •  MOOCs  collect  valuable  data  on  student  learning  behavior;  essenYally  complete   records  of  all  student  interacYons  in  a  self-­‐contained  learning  environment,  and   analize  their  opinions  and  moYvaYon  with  the  benefit  of  large  sample  sizes.     •  Our   main   objecYve   here   is   to   show   how   the   huge   amount   of   data   available   in   MOOCs   offers   a   unique   research   opportunity,     permirng   not   only   to   study   detailed   student   profile   ,   interest   and   behavior,   but   also   analize   how   these   students  interact  with  other  peers    in  a  new  online  environment  creaYng  a  new   space  for  Knowledge  ang  Networking.    
  • 5.   CASE  STUDY:     MOOC:  Transversal  Competencies  for  Entrepreneurship.   (Course    offered  in  both  plaoorms)      •  Total  Students:261.480   •  In  the  Course:  5602     •  Sample:210  surveys   •  DescripYon:  Clasic  xMOOC    Academic   Plamorm  (Language,Spainish)   •  Features:  (OpenMOOC)   –  (Video,  test,  Self-­‐assessment,  online  and   presenYal  cerYficaYon)   –  Some  social  tools      (Fora,  Curator,  Facilitator    P2P   acYviYes,  P2P  Assesment,    open  badges)   •  Total  Studens:38.881   •  In  the  Course  2262   •  Sample:  82  surveys   •  DescripYon:  ThemaYc  cMOOC  Plamorm   focused  on  Entrepreneurship  (MOOC  layar +Community    layar)  (Languages  Spanish   &Portuguese)   •   Features:  (WeMOOC)   •  MOOC  Plamorm  (video,test,  self-­‐assessment,   online  cerYficaYon)   •  Social  Layar:   (Karma,  system  of  voYng,  fora,  Q&A,  Tool  for   friendship,  Content  upload  by  users  ,  Supply  &   demand  ads,  events,  Facebook,  twiuer,…)  
  • 6. QUESTIONS  FOR  ANALYSIS   1.  Who  is  behind  the  MOOC?  Sociodemographic  Profile  of  students   2.  Why  do  they  register  in  a  MOOC?  MoJvaJon   3.  What  is  the  level  of  Drop  out?  CompleJon  Rate  and  CerJficaJon   availability   4.  What  is  the  level  of  Social  parJcipaJon  in  the  online   environment?.InteracJon  with  peers.   5.  How  do  students  learn  in  a  MOOC  environment?  Tools  Used  and   learning  style   6.  How  to  measure  MOOC  success?  Beyond  the  scalability  of    the   online  learning  experience;    Level  of  saJsfacJon  and  outputs  (For   students  and  InsJtuJons)   7.  Where  are  the  MOOC  borders?  Language  and  internaJonalizaJon   8.  Lessons  Learned      
  • 7. SOCIODEMOGRAPHIC  PROFILE   ¿What  is  the  profile  of  the  students  in   the  Case  Study?  (I)   •  Note:  NO    significant  sociodemographic  differences  between  plamorms,  or  with   other  MOOC.   0%   35,37%   46,34%   13,41%   4,88%   0%   5%   10%   15%   20%   25%   30%   35%   40%   45%   50%   <15  years   15-­‐30   31-­‐45   46-­‐55   >55  years   Age  of  MOOC  students  (%)   Age  of  MOOC  students   (%)   36,59%   63,41%   Sex  of    MOOC  Students  %   Women   Men   AGE   GENRE  
  • 8.   ¿What  is  the  profile  of  the  students  in  the   Case  Study?  (II)   11,11%   88,89%   Place  of  residence  %   Rural   Urban   0%   7,32%   14,63%   37,80%   40,24%   0%   5%   10%   15%   20%   25%   30%   35%   40%   45%   EducaJon  %   EducaYon  %   SOCIODEMOGRAPHIC  PROFILE    
  • 9. SOCIODEMOGRAPHIC  PROFILE   ¿What  is  the  profile  of  the  students  in   the  Case  Study?  (III)   10,98   32,93   37,8   12,2   6,1   0   5   10   15   20   25   30   35   40   Job  SituaJon  (%)   Job  SituaYon  (%)   10,98   70,33   12,2   6,1   Student   Worker/Self-­‐employed   Unemployed     Other  situaYon  
  • 10. MOTIVATION   (To  Register  in  the  MOOC)     92,68   91,46   53,66   48,78   0   10   20   30   40   50   60   70   80   90   100   InteresYng  Subject   To  Entrepreneur   To  find  a  job   To  Improve  my  current  job   MoJvaJon  %     78   4   6   1   10   0   50   100   IntesYng  Subject   Profesional(to  find  a   job)   Curiosity   CerYficaYon   Other  moYvaYon  (to   entrepreneur)   MoJvaJon  %  
  • 11. RATE  OF  COMPLETION   ¿  PotenYal  link  with  cerYficaYon?   •  37´8%    complete  the  course   •  1,23%  cerYficaYon  apply   (cerYficaYon  fee  for  ECTs  credits)   •  23´5%  Complete  the  course   •  No  cerYficaYon     •  DROP  OUT  PATH   0   500   1000   1500   2000   2500   CerYficaYon  
  • 12. PARTICIPATION   (InteracYon  with  peers)   •  Level  of  ParJcipaJon  (Fora)   78,51%   0,65%   20,81%   Level  of  ParJcipaJon  (Karma)   Low  ParYcipaYon   High  ParYcipaYon   No  parYcipaYon   (observers)   4,60   8,25   87,85   ParJcipaJon  %   Low  ParYcipaYon   High  ParYcipaYon   No  parYcipaYon   (Observer)  
  • 13. USE  OF  TOOLS    FOR  LEARNING   0   10   20   30   40   50   60   70   80   Videos   Contents   Test   Selfassesments   P2P  Asessment   Facilitator  Support   Curator  Support   Peer  Support   Use  of  Social  Tools   0   20   40   60   80   100   120   Videos   Test   Selfassesment   Contents   P2P  AcYviYes     Facebook  (followers)   Twiuer  (followers)   Supply/demands  ads   Q&A  regarding  MOOCs   P2P  Direct  Contact   Fora  comments   VoYng  tool   Wrirng  in  personal  walls   Sharing  contents   Use  of  Social  Tools      
  • 14. LEVEL  OF  SUCCESS     SaYsfacYon  and  Outputs   •  Percentage  of  SaJsfacJon  95%   0   25,61   29,27   45,12   0   10   20   30   40   50   No   ParYally  capable   Very  capable   Totally  Capable   Do  you  feel  capable  to   entrepreneur  aeer  the  MOOC  (%)?   Percentage  of  SaJsfacJon  98%   OUTPUT  OUTPUT   0   10   20   30   40   50   60   70   80   I  konw  beuer  UNED  University   I  would  like  to  parYcipate  in  other   UNED  MOOC   I  woull  like  to  parYcipate  in  Formal   Courses  in  UNED   I  will  not  parYcipate  in  formal  course   in  UNED   Aeer  the  MOOC  Experience…  
  • 15. 13.640  4.080   1.632   1.089   711   518   372   328  262   214   Visits  per  Country   Spain   Brazil   Mexico   Colombia   ArgenYna   Perú    Student  per  Country   Spain   Colombia   Mexico   ArgenYna   Peru   INTERNATIONALIZATION   Removing  Borders  with  Languages  
  • 16. PRELIMINARY  FINDINGS     1.  Typical  sociodemographic  Profile:  man,  25-­‐35  years  old,high  educaYon,   worker  and  urban.   2.  MoJvaJon:  Interest  in  academic  subject  and  job  purposes  (LLL)   3.  The  level  of  drop  out  in  a  MOOC  could  be  linked  with  cerYficaYon  availability;   More  CompleYon  Rate  if  there  is  a  CerJficaJon  (specifically  in  academic   MOOC  environments)   4.  The  tradiYonal  MOOC  environment  (xMOOCs)  promotes  less  interacJon   between  peers  than  communiYes  of  knowledge  (cMOOCs)  .   5.   Learning  style  in  MOOCs  is  Autonomous  but  ICT  and  Social  Tools  can  enable   more  collaboraJve  learning  style.   6.  MOOC  success  must  be  measured  beyond  the  scalability  of    the  online   learning  experience;    Level  of  saJsfacJon  and  outputs  for  students  and   InsJtuJons  must  be  take  in  account.     7.  Language  is  the  main  enabler  for  internaJonalizaJon  of  MOOC   8.  Lessons  Learned  :  More  Learning  AnalyYcs  are  needed  and  a  standardized   framework  for  assessment.      
  • 17.   “IntuiYons  without  data  are  just  opinions”                                                                                                                                                                                      THANK  YOU!!!  
  • 18.         Marta  Cáceres-­‐Piñuel   UNED  PhD  Researcher   Mail:  martacaceres@hotmail.com