201106 G4C

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201106 G4C

  1. 1. COVERT AND OVERTmeasures   of   engagement   within   an   educaNonal   mulNmedia   environment.     This  research  was  supported  by  Office  of  Naval  Research  under   Grant  N00014-­‐10-­‐1-­‐0143  awarded  to  Dr.  Robert  Atkinson  Robert  M.  Christopherson,  Javier  Gonzalez-­‐Sanchez,  Mustafa  Baydogan,  Maria  Elena  Chavez-­‐Echeagaray,  David-­‐Gibson  Robert  Atkinson   lsrl.lab.asu.edu  
  2. 2. Games  can  change  the  way  we  learn   Empirical  research  can   change  the  way  we  game  
  3. 3. Learning   Gaming   surprise   happiness   flow/engagement   anger   delight   boredom   frustraNon   confusion   curiosity  anxiety   fear  
  4. 4. Goals  EDUCATION:    Engagement  =  Learning   GAMES:    Engagement  =  Fun    
  5. 5. Overt  -­‐  observable     Covert  -­‐  hidden  
  6. 6. Overt  and  Covert  Facial expressions Cognition Verbalization Motivation Behavior Flow Engagement Attitude Performance Emotion Physical interactions Posture
  7. 7. What  is  engagement?   GAMING  “concerned  with  all  the  qualiNes  of  an  experience  that  really  pull  people  in  –  whether  this  is  a  sense  of  immersion  that  one  feels  when  reading  a  good  book,  or  a  challenge  one  feels  when  playing  a  good  game,  or  the  fascinaNng  unfolding  of  a  radio  drama”  Benyon  and  colleagues  (2005)  
  8. 8. What  is  engagement?   LEARNING  “the  nexus  of  intrinsic  knowledge  and  interest  and  external  sNmuli  that  promote  the  iniNal  interest  in,  and  use  of  a  computer-­‐based  learning  environment”  (Jones,  1998)  
  9. 9. Measuring  Engagement   OBJECTIVE   ObservaNonal   Physiological   CogniNve            Analysis   data   walkthrough   Think-­‐aloud  QUALITATIVE   HeurisNc   EvaluaNon   QUANTITATIVE       Surveys    &   Interviews  &         QuesNonnaires   Focus  Groups   SUBJECTIVE  
  10. 10. Use  of  Physiological  Data  1.  Decide  what  you  want  to  measure  2.  Choose  the  appropriate  sensors  3.  Control  your  task  and  environment  4.  Process  the  data  according  to  which   sensors  were  chosen  5.  Make  inferences,  evaluate  and  revise  
  11. 11. 1.  Decide  what  you  want  to  measure   •  engagement   •  arousal   •  mental  effort   •  ajenNon   •  excitement   •  boredom   •  meditaNon   •  frustraNon  
  12. 12. 2.  Choose  the  appropriate  sensors   PHYSICAL   PSYCHOLOGICAL   skin  conducNvity  (GSR)   arousal   pressure  on  controller   frustraNon,  arousal   posiNve  vs  negaNve  emoNons,   pupil  dilaNon   mental  effort   engagement,  mental  effort,   brain  waves  (EEG)   frustraNon,  boredom   gaze  locaNon   ajenNon  
  13. 13. 3.  Control  your  task  and  environment   Con  •  20  Users  play   Guitar  HeroTM  •  easy  and  hard  song  •  15  mins  of  pracNce  •  Skin/Eye/Head/ Guitar  sensors  
  14. 14. Real  Time  Monitoring  
  15. 15. 4.  Process  the  data  according  to   which  sensors  were  chosen  
  16. 16. 5.  Make  inferences  and  iterate  on  game/ instrucNonal  design   VisualizaNon   InterpretaNon   Data   StaNsNcs   mining  
  17. 17. High  Scoring  Performance  in  Guitar  HeroTM  –––––  Raw  Engagement  –––––  Median  Engagement  –––––  Normalized  Performance  
  18. 18. Low  Scoring  Performance  in  Guitar  HeroTM  –––––  Raw  Engagement  –––––  Median  Engagement  –––––  Normalized  Performance  
  19. 19. Engagement  
  20. 20. Boredom  
  21. 21. MeditaNon  
  22. 22. FrustraNon  
  23. 23. Long  Term  Excitement  
  24. 24. Why  measure  engagement?  •  Dynamic  Difficulty  Adjustment  •  Expand  Demographics  •  Longer  Time  on  Task  
  25. 25. Dynamic  Difficulty  Adjustment   CHALLENGE   Anxiety   Flow  Zone   Boredom   ABILITY  
  26. 26. Expand  Demographics  CHALLENGE   Expert   Flow  Zone   Novice   ABILITY  
  27. 27. Time  on  Task  FLOW   TIME  
  28. 28. ROI  in  Games  •  CompeNNve  edge    •  Broader  appeal  •  Micro  and  macro  evaluaNon  •  PersonalizaNon  •  Improve  gameplay  
  29. 29. ROI  in  EducaNon  •  Increase  Performance  •  RetenNon  •  Time  on  task  •  Antude  toward  learning  
  30. 30. Ongoing  Work  •  SeducNve  Details  (InstrucNonal  Design)  •  Videogames  and  Engagement  (Guitar  Hero)  •  EmoNons  and  Working  Memory  Capacity   (puzzles)  •  3D  InstrucNonal  training  (US  Navy,  Save   Science)  •  AffecNve  Meta  Tutor  
  31. 31. QuesNons?  lsrl.lab.asu.edu  

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