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MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
MOOCs, Myths and Misconseptions
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MOOCs, Myths and Misconseptions

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Talk Presented via videoconference to Fast Forward Language Educator Symposium- University of Pennsyvania. Dec 14, 2013

Talk Presented via videoconference to Fast Forward Language Educator Symposium- University of Pennsyvania. Dec 14, 2013

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  • I particularly like the part on Unbundling...

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    That what we exactly do with SkillAcademy - mix-n-match the best courses into skillcamps focused on a specific skillset needed in the job market.

    Here are examples:
    MBA: http://www.skillacademy.com/bundle/11%7CThe%20Best%20from%20MBAs

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    We have like 60+ other skillcamps, would love to hear your feedback

    Twitter @brfayek @skillacademy
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  • 1. FAST  FORWARD:        LANGUAGE  ONLINE     Saturday,  December  14,  2013     Language  Educator  Symposium   University  of  Pennsylvania   MOOCS,  MYTHS  AND   MISCONCEPTIONS  
  • 2. Values   •  We  can  (and  must)  conRnuously  improve  the   quality,  effecRveness,  appeal,  cost  and  Rme   efficiency  of  the  learning  experience.   •  Student  control  and  freedom  is  integral  to  21st   century  life-­‐long  educaRon  and  learning.   •  ConRnuing  educaRon  opportunity  is  a  basic   human  right.  
  • 3. E-­‐Learning  is  not  the  same  
  • 4. Learning  as  Dance     (Anderson,  2008)   •  Technology   sets  the   beat  and   the  Rming.   •   Pedagogy   defines  the   moves.     “A  learning  technology,  by  definiRon,  is  an   orchestraRon  of  technologies,  necessarily   including  pedagogies,  whether  implicit  or   explicit.”  Jon  Dron  
  • 5. Gardiner  Hype  Cycle  
  • 6. What  is  a  MOOC?   •  •  •  •  •  •  •  •  •  MOOC  is  a  course   Defined  Curriculum  or  content?   “Big  Data”  mining  potenRal   SubsRtute  of  student-­‐content  and  perhaps  student-­‐student   for  student-­‐teacher  interacRon     May  be  asynchronous,  synchronous,  mixed   Paced  or  self-­‐paced   May  be  open  content  or  not  –  as  in  using  open  resources   Up-­‐sell  of  auxiliary  products   Emerging  credenRal  opRons   »  Invigilated  exams,  badges,  private  cerRficaRon  
  • 7. Different  Types  of  MOOCs   By  Mathieu  Plourde  {(Mathplourde  on  Flickr)  [CC-­‐BY-­‐2.0  
  • 8. Different  Types  of  MOOCs   •   “Our  cMOOC  model  emphasizes  creaRon,   creaRvity,  autonomy,  and  social  networked   learning.  The  Coursera  model  emphasizes  a  more   tradiRonal  learning  approach  through  video   presentaRons  and  short  quizzes  and  tesRng.     •  Put  another  way,  cMOOCs  focus  on  knowledge   creaRon  and  generaRon  whereas  xMOOCs  focus   on  knowledge  duplicaRon.”  George  Siemens  
  • 9. Pedagogy  of  Moocs  and  Other  forms   of  higher  EducaRon   •  xMOOCs  –  Cogni&ve  Behavioural  Pedagogy,   disseminaRon  of  knowledge,     •  sMOOCs  –  Social  construc&vist  pedagogy,   small  groups,  cohorts,  model  of  most  online   educaRon  today   •  xMOOCs  –  Connec&vist  pedagogy,  building   networks  and  persistent  arRfacts,  net-­‐naRve   Anderson,  T.,  &  Dron,  J.  (2011).  Three  generaRons  of  distance   educaRon  pedagogy.  Interna'onal  Review  of  Research  on  Distance  and   Open  Learning,  12(3),  80-­‐97.     hEp://www.irrodl.org/index.php/irrodl/ar'cle/view/890/1826.  
  • 10. CoursEra-­‐  Northwestern-­‐  Case  Study   •  Media  studies  “Understanding  Media  by   Understanding  Google”   •  6  weeks,  video  lectures   •  Book  excerpts,  80  background  arRcles/blogs/youtube   •  12  machine  marked  quizzes   •  5  short  essays  –  peer  reviewed   •  25,000  discussion  posts   •  55,000  registered,  19,000  logged  in,  2400  handed  in   homework,  1,196  from  87  countries  “passed”   •  90%  of  grads  had  a  4  year  degree   Owen  Youngman  professor  of  digital  media  strategy  in  the  Medill  School  at   Northwestern  University.  MOOC  
  • 11. EducaRon  is  InteracRon   Anderson,  T.,  &  Garrison,  D.  R.  (1998).  Learning  in  a  networked  world  
  • 12. InteracRon  Equivalency  Theorem   (Anderson,  2004)   •  Thesis  1.  Deep  and  meaningful  formal  learning  is   supported  as  long  as  one  of  the  three  forms  of   interacRon  (student–teacher;  student–student;   student–content)  is  at  a  high  level.  The  other  two  may   be  offered  at  minimal  levels,  or  even  eliminated,   without  degrading  the  educaRonal  experience.   •  Thesis  2.  High  levels  of  more  than  one  of  these  three   modes  will  likely  provide  a  more  saRsfying  educaRonal   experience,  although  these  experiences  may  not  be  as   cost-­‐  or  Rme  effecRve  as  less  interacRve  learning   sequences.   hop://equivalencytheorem.info/  
  • 13. xMOOC  Pedagogy   •  DrasRcally  reduce  (by  subsRtuRon)  student   teacher  interacRon  by  student-­‐content   (videos)  and  student-­‐student  (discussion/peer   assessment)     •  This  affords  scalability  and  cost  reducRon.  
  • 14. •  “The  students  who  drop  out  early   do  not  add  substanRally  to  the   cost  of  delivering  the  course”.  The   most  expensive  students  are  the   ones  who  sRck  around  long   enough  to  take  the  final,  and  those   are  the  ones  most  likely  to  pay  for   a  cerRficate.  Daphne  Koller,   Founder  Coursera  
  • 15. MisconcepRons:  Drop  out  rates  are   higher  in  MOOCs  and  online  because   the  instrucRon  is  poor   •  Tinto’s  Model  of  academic  and  social   integraRon   •  MOOC  users  are  busy  adults   •  50%  of  MOOC  registrants  don’t  login  even   once   •  How  much  work  would  your  student  do   without  credit??  
  • 16. Penn/CoursEra  results   •  16  MOOCs  from  110,000  to  13,000  registrants   •  Course  compleRon  rates  are  very  low,  averaging   4%  across  all  courses  and  ranging  from  2%  to  14%   •  compleRon  rates  are  somewhat  higher  for   courses  with  lower  workloads  for  students  (about   6%  versus  2.5%).   •  VariaRons  in  compleRon  rates  based  on  other   course  characterisRcs  (e.g.,  course  length,   availability  of  live  chat)  were  not  staRsRcally   significant.  
  • 17. How  Massive  are  MOOCs?  (Katy  Jordon,   2013)   (N  =  220;  Median  =  18941;  Minimum  =  95;  Maximum  =  226,652).  75%  courses  in  the   <10,000  range.  
  • 18. Size  Maoers  (Katy  Jordon,  2013)  
  • 19. Length  Maoers  (Katy  Jordon,  2013)  
  • 20. Moocs  or  learners  are  getng  beoer   (Katy  Jordon,  2013)  
  • 21. DifferenRated  MOOC     ParRcipaRon  Paoerns   Blended  online  Student   Unaffiliated  Student   Blue:  -­‐  Video  lecture   Green/Red/Brown:  -­‐  Automated  assessment   Yellow:  -­‐  Discussions  Groups   Rethinking  Online  Community  in  MOOCs  Used  for  Blended  Learning   by  Michael  Caulfield,  Amy  Collier,  and  Sherif  Halawa   hop://www.educause.edu/ero/arRcle/rethinking-­‐online-­‐community-­‐moocs-­‐ used-­‐blended-­‐learning  
  • 22. Commercial  MOOC  DisrupRons   •  •  •  •  •  Intellectual  ownership?   Plauorm  ownership?   CompeRRve  and  due  process  for  partnering?   Data  Mining?   Re-­‐selling  and  mashing?   ©Coursera-­‐  All  Rights  reserved  
  • 23. Is  there  a  digital  dividend     for  Students?   George  Siemens  2013  
  • 24. Myth:  UniversiRes  cannot  be   Unbundled   •  Unbundling:   –  provision  from  accreditaRon   –  research  from  teaching   –  residence  from  learning   –  football  teams  from  mission   –  teaching  from  tenure   Anderson,  T.,  &  McGreal,  R.  (2012).  DisrupRve  Pedagogies  and  Technologies  in   UniversiRes.  Educa'on,  Technology  and  Society,  15(4),  380-­‐389.    
  • 25. Who/What  Should  Accredit?   •   Accredit  the  Learner,  or  the  Course  not  the   InsRtuRon.   •  “The  tradiRonal  accrediRng  agencies,  which  were   founded  long  ago  to  serve  the  needs  of  the   tradiRonal  insRtuRons,  are  not  well-­‐suited  to  lead   technological  and  social  innovaRons  that  are   alternaRves  to  the  tradiRonal  system”  David   Bergeron  &  Steven  Klinsky,  2013   hop://www.insidehighered.com/views/2013/10/28/essay-­‐ need-­‐new-­‐innovaRon-­‐focused-­‐accreditor#ixzz2n7Fanb00   Inside  Higher  Ed  “  
  • 26. New  Forms  of  AccrediRng   Challenge  Exams  for  Credit  
  • 27. Myth:  Classroom  Learners   outperform  online  Learners  
  • 28. Myths:  Good  Teachers  are  Good   Researchers   •  A  meta-­‐analysis  of  58  studies  demonstrates   that  the  relaRonship  is  zero.   •   "instead  of  looking  for  even  more  mediators   and  moderators  ....  we  should  accept  the   conclusion  that  teaching  and  research   (however  conceived)  are  unrelated  and  move   on  to  asking  how  we  can  enhance  this   relaRon"  p.  632   Hate,  J.,  &  Marsh,  H.  W.  (1996).  The  relaRonship  between  research  and   teaching:  A  meta-­‐analysis.  Review  of  Educa'onal  Research,  66(4),  507-­‐542.    
  • 29. Big  Data:  Savior  or  Just  Scary?  
  • 30. Big  Data  &EducaRon   1)  Technology:  maximizing  computaRon  power  and   algorithmic  accuracy  to  gather,  analyze,  link,  and   compare  large  data  sets.     2)  Analysis:  drawing  on  large  data  sets  to  idenRfy   paoerns  in  order  to  make  economic,  social,  technical,   and  legal  claims  and  design  intervenRons.   3)  Mythology:  the  widespread  belief  that  large  data  sets   offer  a  higher  form  of  intelligence  and  knowledge  that   can  generate  insights  that  were  previously  impossible,   with  the  aura  of  truth,  objecRvity,  and  accuracy.   Boyd,  d.  &  Crawford,  K.  (2013) .  CriRcal  QuesRons  for  Big  Data:  ProvocaRons  for  a  Cultural,  Technological,  and   Scholarly  Phenomenon  
  • 31. The  dialecRc  of  surveillance  and   recogniRon-­‐  Boellstorff,  T.  (2013)   •  “if  a  surveillance   program  produces   informaRon  of  value,  it   legiRmizes  it...  .  In  one   step,  we’ve  managed   to  jusRfy  the  operaRon   of  the  PanopRcon.”   Michel  Foucault:    
  • 32. •  MOOCs  just  one  component  of  Open   Scholarship   Open  PublicaRon   Open  Data   Open  Science   Open  Texts   Open  EducaRonal  Resources   Open  Review   Weller,  M.  (2103)  The  baole  for  open  -­‐  a   perspecRve.  JIME  
  • 33. Why  get  Involved  in     Open  Scholarship  &  MOOCs?   •  Public  service  in  a  Rme  of  public  distrust  and   weakening  support   •  PromoRons,  branding     •  TesRng  of  more  cost  and  learning  effecRve   models   •  TesRng  of  flipped  classroom  model   •  “first  one  free”  markeRng   •  Good  scholarship  is  open  scholarship  
  • 34. •  John  Dewey  “Consider  the   history  of  any  significant   invenRon  or  discovery,  and   you  will  find  a  period  when   there  was  enough  knowledge   to  make  a  new  mode  of   acRon  or  observaRon   possible  but  no  definite   informaRon  or  instrucRon  as   to  how  to  make  it  actual.     (EducaRon  as  Engineering,   1922,  p.  3)  
  • 35. Conclusion   •  “We  think  there’s  as  much   opportunity  as  threat.  If  universiRes   and  governments  take  up  these   opportuniRes  there  could  be  a   golden  age  ahead.  The  big  dangers   are  complacency,  Rmidity  and  risk   aversion.”  (Michael  Barber  advisor   to  Pearson  Publishing  in  Warrell,   2013).   •  Or  are  MOOCs  part  of  the  Neo-­‐ liberal  aoack  on  higher  educaRon??  
  • 36. Your comments and questions most welcomed! Terry Anderson terrya@athabascau.ca Blog: terrya.edublogs.org Skype: @terguy

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