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CHI2014 Workshop - Leveraging Video Interaction Data and Content Analysis to Improve Video Learning

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Video has emerged as a dominant medium for online
education, as witnessed by millions of students learning
from educational videos on Massive Open Online
Courses (MOOCs), Khan Academy, and YouTube. The
large-scale data collected from students’ interactions
with video provide a unique opportunity to analyze and
improve the video learning experience. We combine
click-level interaction data, such as pausing, resuming,
or navigating between points in the video, and video
content analysis, such as visual, text, and speech, to
analyze peaks in viewership and student activity. Such
analysis can reveal points of interest or confusion in the
video, and suggest production and editing
improvements. Furthermore, we envision novel video
interfaces and learning platforms that automatically
adapt to learners’ collective watching behaviors.

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CHI2014 Workshop - Leveraging Video Interaction Data and Content Analysis to Improve Video Learning

  1. 1. Juho  Kim  (MIT  CSAIL)   Shang-­‐Wen  (Daniel)  Li  (MIT  CSAIL)   Carrie  J.  Cai  (MIT  CSAIL)   Krzysztof  Z.  Gajos  (Harvard  EECS)   Robert  C.  Miller  (MIT  CSAIL)   2014.04.27     CHI  2014    |    Workshop  on   Learning  Innovation  at  Scale   Leveraging     Video  Interaction  and  Content  to   Improve  Video  Learning  
  2. 2. Video  Lectures  in  MOOCs  
  3. 3. Classrooms:  rich,  natural  interaction  data   armgov  on  Flickr  |  CC  by-­‐nc-­‐sa  Maria  Fleischmann  /  Worldbank  on  Flickr    |    CC  by-­‐nc-­‐nd   Love  Krittaya  |  public  domain   unknown  author  |  from  pc4all.co.kr  
  4. 4. liquidnight  on  Flickr  |  CC  by-­‐nc-­‐sa  
  5. 5. How  do  learners  use  videos?   Data-­‐Driven  Approach:   Analyze  learners’  interaction     with  the  video  player    
  6. 6. Why  does  data  matter?   •  detailed  understanding  of  video  usage   •  design  implications  for     – Instructors   – Video  editors   – Platform  designers   •  new  video  interfaces  and  formats   Improved  video  learning  experience  
  7. 7. ~40M  video  interaction  events   from  4  edX  courses   Learners   Videos   Mean  Video   Length   Processed   Events   127,839   862   7:46   39.3M   Courses:  Computer  science,  Statistics,  Chemistry  
  8. 8. How  do  learners  use  videos?   •  Watch  sequentially   •  Pause   •  Re-­‐watch     •  Skip  /  Skim  
  9. 9. Collective  Interaction  Traces   video  'me   Learner  #1   Learner  #2   Learner  #3   Learner  #4   .  .  .  .  .  .   Learner  #7888   Learner  #7887  
  10. 10. Collective  Interaction  Traces   into  Interaction  Patterns   video  'me   interac'on   events   second-­‐by-­‐second  in-­‐video  activity  
  11. 11. Data-­‐Driven  Analysis  and  Design     for  Educational  Videos  
  12. 12. 1.  Analyze  interaction  patterns   scalable  and  automatic  methods  to     interpret  interaction  data   2.  Improve  video  learning   video  interfaces  that  adapt  to     collective  learner  interaction  patterns   Research  Directions:  Data-­‐Driven     Analysis  and  Design  for  Educational  Videos  
  13. 13. 1.  Analyze  interaction  patterns   scalable  and  automatic  methods  to     interpret  interaction  data   2.  Improve  video  learning   video  interfaces  that  adapt  to     collective  learner  interaction  patterns   Research  Directions:  Data-­‐Driven     Analysis  and  Design  for  Educational  Videos  
  14. 14. Interaction  Peaks   Temporal  peaks  in  the  number  of  interaction   events,  where  a  significant  number  of  learners   show  similar  interaction  patterns   video  'me   interac'on   events   Understanding  In-­‐Video  Dropouts  and  Interaction  Peaks  in  Online  Lecture  Videos.   Juho  Kim,  Philip  J.  Guo,  Daniel  T.  Seaton,  Piotr  Mitros,  Krzysztof  Z.  Gajos,  Robert  C.  Miller.   Learning  at  Scale  2014.  
  15. 15. What  causes  an  interaction  peak?  
  16. 16. Video  interaction  log  data       Video  content  analysis   – Visual  content   – Text  from  transcript   – Speech  &  acoustic  stream  
  17. 17. Observation:  Visual  transitions  in  the   video  often  coincide  with  a  peak.  
  18. 18. Type  1.  Returning  to  content   interaction  
  19. 19. Type  2.  Beginning  of  new  material   interaction  
  20. 20. Text  Analysis   •  Topic  transitions  &  interaction  peaks   – Topic  modeling   •  Linguistic  patterns  &  interaction  peaks   – N-­‐gram  analysis  
  21. 21. node,  optimal,  goal   cost,  equal,  path   expand   mean   topic  transition     likelihood  over  time  
  22. 22. N-­‐gram  Analysis   “<start-­‐of-­‐sentence>  So”   •  initiating  an  explanation      “So  let  me  spend  a  second  on  that,”  “So  that  means,”     •  arriving  at  the  take-­‐home  message     “So  we  can  get  lots  of  information  just  from  these  five   number  summaries.”  
  23. 23. N-­‐gram  Analysis   “this  is”   •  visual  explanation   “this  is  the  double  bonds  here  on  this  oxygen”   •  naming  of  a  particular  concept   “and  this  is  called  a  dislocation”,     “so  this  is  sometimes  called  the  first  quartile”  
  24. 24. Acoustic  Analysis   Speaking  rate   Pitch  
  25. 25. Automatic,  multi-­‐channel,  scalable   peak  detection  and  classification  
  26. 26. Video  Analytics:     “debugging”  interface  for  instructors  &  editors  
  27. 27. 1.  Analyze  interaction  patterns   scalable  and  automatic  methods  to     interpret  interaction  data   2.  Improve  video  learning   video  interfaces  that  adapt  to     collective  learner  interaction  patterns   Research  Directions:  Data-­‐Driven     Analysis  and  Design  for  Educational  Videos  
  28. 28. Data-­‐Driven  Interaction  Techniques   to  Support  Video  Navigation  
  29. 29. For  learners:  Data-­‐Driven  Video  UI  
  30. 30. Rollercoaster  Timeline   •  Embedded  visualization  of  collective  interactions   •  Visual  &  physical  emphasis  on  interaction  peaks  
  31. 31. Automatic  Summarization   •  Keyword  summary  with  word  cloud   •  Visual  summary  with  captured  highlights  
  32. 32. Automatic  Side-­‐by-­‐Side  View   Pinned  slide   Video  stream  
  33. 33. Lab  Study:  edX  &  On-­‐Campus  Students   “It’s  not  like  cold-­‐watching.     It  feels  like  watching  with  other  students.”     “[interaction  data]  makes  it  seem  more  classroom-­‐y,   as  in  you  can  compare  yourself  to     what  how  other  students  are  learning    and  what  they  need  to  repeat.”  
  34. 34. Rethinking     Educational  Videos  
  35. 35. Are  behind-­‐the-­‐encoding-­‐wall   videos  the  best  format?   •  Hard  to  edit  once  published   •  Only  a  single  stream  is  published   •  Lack  of  useful  metadata  (concepts,  difficulty…)   •  Hard  to  comment  on,  point  to  specific  parts  
  36. 36. Toward  More  Direct  &  Social   Interaction  for  Video  Learning   •  Alternative  explanations  from  learners     •  Synchronous  watching  with  other  learners   •  Linking  relevant  resources  with  different   levels  of  scaffolding   •  Experimenting  with  in-­‐video  examples  &  data  
  37. 37. Leveraging  Video  Interaction  and  Content  to   Improve  Video  Learning   Juho  Kim   MIT  CSAIL     juhokim@mit.edu     juhokim.com  

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