Overview	
  of	
  emerging	
  technologies	
  to	
  
define,	
  enhance,	
  and	
  measure	
  health	
  
literacy	
  
	
  
...
About	
  me	
  (and	
  CHAI)	
  …	
  HCI-­‐techo
	
  
•  Inven1ng	
  future	
  technology	
  to	
  tackle	
  
important	
 ...
Mental	
  models
	
  

chai::	
  

Computer	
  human	
  adapted	
  interac1on	
  research	
  group	
  
Mental	
  models
	
  
A	
  set	
  of	
  beliefs	
  that	
  the	
  user	
  holds
	
  

chai::	
  

Computer	
  human	
  ada...
Mental	
  models
	
  
A	
  set	
  of	
  beliefs	
  that	
  the	
  user	
  holds
	
  
eg.	
  It	
  is	
  healthier	
  not	
...
Mental	
  models
	
  
A	
  set	
  of	
  beliefs	
  that	
  the	
  user	
  holds
	
  
eg.	
  It	
  is	
  healthier	
  not	
...
Mental	
  models
	
  
A	
  set	
  of	
  beliefs	
  that	
  the	
  user	
  holds
	
  
eg.	
  It	
  is	
  healthier	
  not	
...
Ra1onality?
	
  

chai::	
  

Computer	
  human	
  adapted	
  interac1on	
  research	
  group	
  
“Be	
  able	
  to	
  keep	
  two	
  completely	
  
contradictory	
  ideas	
  alive	
  and	
  well	
  inside	
  
your	
  he...
“Four	
  out	
  of	
  five	
  voices	
  in	
  my	
  head	
  say-­‐	
  
"Eat	
  the	
  Chocolate”.
	
  
PhD	
  Student	
  T-...
Complexity?
	
  

chai::	
  

Computer	
  human	
  adapted	
  interac1on	
  research	
  group	
  
“I	
  know	
  that	
  you	
  should	
  eat	
  a	
  lot	
  of	
  the	
  
Indian	
  spice	
  turmeric,	
  as	
  it	
  fights	...
Mental	
  models	
  come	
  from:
	
  
•  Formal	
  educa1on	
  
•  And	
  so	
  much	
  else	
  
– 
– 
– 
– 
– 

Experien...
Why	
  do	
  mental	
  models	
  maZer	
  
for	
  interface	
  designers?
	
  

chai::	
  

Computer	
  human	
  adapted	
...
Why	
  do	
  mental	
  models	
  maZer	
  
for	
  interface	
  designers?
	
  
They	
  define	
  	
  
•  what	
  a	
  user	...
Mental	
  models	
  for	
  Health	
  literacy	
  
soware	
  and	
  systems
	
  
•  Design	
  based	
  on	
  each	
  user’s...
User	
  models
	
  
And	
  personalisa1on
	
  

chai::	
  

Computer	
  human	
  adapted	
  interac1on	
  research	
  grou...
User	
  model
	
  
•  Computer	
  systems	
  “beliefs”	
  about	
  the	
  user	
  
–  eg	
  User	
  cannot	
  read	
  grap...
Example…	
  dangerous	
  filter	
  bubbles
	
  
User	
  belief:	
  vaccina1on	
  is	
  dangerous
	
  

chai::	
  

Computer...
But	
  personalisa1on	
  is	
  everywhere
	
  
And	
  does	
  help	
  cope	
  with	
  complexity
	
  

chai::	
  

Compute...
Accountable	
  personalisa1on?
	
  
PuLng	
  users	
  in	
  control…
	
  
	
  
chai::	
  

Computer	
  human	
  adapted	
 ...
User	
  models,	
  personal	
  data,	
  
exploi1ng	
  digital	
  footprints….
	
  
Open	
  user/learner	
  models	
  (OLMs...
Visible	
  digital	
  footprints	
  so	
  I	
  can	
  
compare	
  myself	
  with	
  others
	
  

chai::	
  

Computer	
  h...
This user’s footprints

Overall population footprints
Patina: Dynamic Heatmaps for Visualizing Application Usage (CHI2013)...
MOOCs	
  
SPOCS	
  
Self-­‐paced	
  simula1ons	
  

Discussion	
  board	
  

New	
  online	
  learning	
  tools
	
  
Platf...
Model of learner

Gamification element

Kahn Academy, what a student sees after the pre-test
Short video + self-test
Learning	
  Analy1cs	
  and	
  
Educa1onal	
  Data	
  Mining
	
  
Popula1on	
  level
	
  
Classroom
	
  
Teacher
	
  
Indi...
SIV	
  
Lots of green means
learner doing well

Weak aspects
visible as red

Overview
visualisation

chai::	
  

Computer	...
Little detail
User	
  models,	
  personal	
  data,	
  
exploi1ng	
  digital	
  footprints….
	
  
Open	
  user/learner	
  models	
  (OLMs...
Technologies	
  to	
  help	
  track	
  and	
  
discover	
  personal	
  “reality”
	
  

chai::	
  

Computer	
  human	
  ad...
Sensors in our home – like Withings scales
Sensors we wear – like Fitbit, Nike FuelBand,....
Desktop sensors like slife, RSI-prevention sensors,.......
Pervasive	
  displays	
  that	
  help	
  us	
  
see	
  “reality”
	
  
Lots	
  of	
  displays,	
  some	
  calmer	
  than	
 ...
Opportunity
Ubiquitous Devices: personal, wearable, portable, pervasive.
Example:	
  pill	
  taking	
  aid
	
  

chai::	
  

Computer	
  human	
  adapted	
  interac1on	
  research	
  group	
  
Red glow – time to take
medication

Ambient displays, with subtle meaning,
perhaps known only to the owner
Green glow .. All on track
Also	
  has	
  mobile-­‐phone	
  reminder
	
  

chai::	
  

Computer	
  human	
  adapted	
  interac1on	
  research	
  grou...
The	
  inac1vity	
  problem
	
  
Too	
  much	
  siLng
	
  
For	
  too	
  long	
  without	
  breaks
	
  

chai::	
  

Compu...
Blue… active
Inactive > 30 mins
Sharing	
  data
	
  
Peers	
  to	
  support	
  each	
  other
	
  
And	
  compete
	
  

chai::	
  

Computer	
  human	
  ad...
The power of peers
Reality is relative!!!

chai::	
  

Computer	
  human	
  adapted	
  interac1on	
  research	
  group	
  
Some	
  new	
  ways	
  to	
  learn
	
  

chai::	
  

Computer	
  human	
  adapted	
  interac1on	
  research	
  group	
  
Interactive walls for engaging health education
Older	
  users	
  too
	
  

T. Apted, J. Kay, and A. Quigley. Tabletop sharing of digital photographs for the elderly. In ...
externalisa1on	
  
affec1on	
  
building	
  on	
  others	
  

argumenta1on	
  

Collabora1ve	
  learning
	
  
P. Dillenbour...
chai::	
  

Computer	
  human	
  adapted	
  interac1on	
  research	
  group	
  
chai::	
  

Computer	
  human	
  adapted	
  interac1on	
  research	
  group	
  
Summary	
  from	
  an	
  HCI-­‐techo
	
  
•  User-­‐centred	
  design	
  
–  Understanding	
  users’	
  mental	
  models	
...
Acknowledgements	
  

chai::	
  

Computer	
  human	
  adapted	
  interac1on	
  research	
  group	
  
Interac1ve	
  surfaces	
  

Interfaces	
  to	
  user	
  model	
  

Data	
  mining	
  

Acknowledgements	
  

Soware	
  inf...
Influences…
	
  
•  Human-­‐Computer	
  Interac1on	
  
–  Mental	
  models	
  
–  User	
  models	
  
–  Explicit	
  assump1...
User	
  models,	
  personal	
  data,	
  
exploi1ng	
  digital	
  footprints….
	
  
Open	
  user/learner	
  models	
  (OLMs...
Learning dashboards: an overview and future research opportunities
Katrien Verbert • Sten Govaerts • Erik Duval • Jose Lui...
Overview of emerging technologies to define, enhance, and measure health literacy. Professor Judy Kay
Overview of emerging technologies to define, enhance, and measure health literacy. Professor Judy Kay
Overview of emerging technologies to define, enhance, and measure health literacy. Professor Judy Kay
Overview of emerging technologies to define, enhance, and measure health literacy. Professor Judy Kay
Overview of emerging technologies to define, enhance, and measure health literacy. Professor Judy Kay
Overview of emerging technologies to define, enhance, and measure health literacy. Professor Judy Kay
Overview of emerging technologies to define, enhance, and measure health literacy. Professor Judy Kay
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Overview of emerging technologies to define, enhance, and measure health literacy. Professor Judy Kay

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School of Information Technologies, University of Sydney.
Presentation given at "Health Literacy Network: Crossing Disciplines, Bridging Gaps", November 26, 2013. The University of Sydney.

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Overview of emerging technologies to define, enhance, and measure health literacy. Professor Judy Kay

  1. 1. Overview  of  emerging  technologies  to   define,  enhance,  and  measure  health   literacy     Judy  Kay     Human  Centred  Technology  Group,  Engineering  and  IT,  University  of  Sydney   SyReNs:  Science  and  Technology  of  Learning   SyReNs:  PLANET…  Physical  Ac;vity     chai::   Computer  human  adapted  interac1on  research  group  
  2. 2. About  me  (and  CHAI)  …  HCI-­‐techo   •  Inven1ng  future  technology  to  tackle   important  problems   •  Personalisa1on   •  Personal  data  and  its  management   •  PuLng  people  in  control   •  Interac1ve  surfaces…  walls,  tables…   chai::   Computer  human  adapted  interac1on  research  group  
  3. 3. Mental  models   chai::   Computer  human  adapted  interac1on  research  group  
  4. 4. Mental  models   A  set  of  beliefs  that  the  user  holds   chai::   Computer  human  adapted  interac1on  research  group  
  5. 5. Mental  models   A  set  of  beliefs  that  the  user  holds   eg.  It  is  healthier  not  to  take  medica1ons   chai::   Computer  human  adapted  interac1on  research  group  
  6. 6. Mental  models   A  set  of  beliefs  that  the  user  holds   eg.  It  is  healthier  not  to  take  medica1ons   chai::   Computer  human  adapted  interac1on  research  group  
  7. 7. Mental  models   A  set  of  beliefs  that  the  user  holds   eg.  It  is  healthier  not  to  take  medica1ons   Vaccina1ons  are  dangerous   Sta1ns  are  dangerous  and  useless   chai::   Computer  human  adapted  interac1on  research  group  
  8. 8. Ra1onality?   chai::   Computer  human  adapted  interac1on  research  group  
  9. 9. “Be  able  to  keep  two  completely   contradictory  ideas  alive  and  well  inside   your  heart  and  head  at  all  1mes”.   Bruce  Springsteen   (on  37signals)   chai::   Computer  human  adapted  interac1on  research  group  
  10. 10. “Four  out  of  five  voices  in  my  head  say-­‐   "Eat  the  Chocolate”.   PhD  Student  T-­‐shirt   chai::   Computer  human  adapted  interac1on  research  group  
  11. 11. Complexity?   chai::   Computer  human  adapted  interac1on  research  group  
  12. 12. “I  know  that  you  should  eat  a  lot  of  the   Indian  spice  turmeric,  as  it  fights  cancer.       Also  that  you  should  avoid  the  Indian  spice   turmeric,  as  it  might  contain  dangerous   levels  of  lead.       One  or  the  other.”.   A.J.  Jacobs,   Drop  Dead  Healthy:  One  Man's  Humble  Quest  for  Bodily  PerfecFon   chai::   Computer  human  adapted  interac1on  research  group  
  13. 13. Mental  models  come  from:   •  Formal  educa1on   •  And  so  much  else   –  –  –  –  –  Experience   Cultural  expecta1ons   Context   Emo1onal  state     ….   •  Determining  what  the  user   –  –  –  –  Believes  to  be  true   Trusts   Feels  permiZed  to  consider  and  do   Feeling  of  competence     chai::   Computer  human  adapted  interac1on  research  group  
  14. 14. Why  do  mental  models  maZer   for  interface  designers?   chai::   Computer  human  adapted  interac1on  research  group  
  15. 15. Why  do  mental  models  maZer   for  interface  designers?   They  define     •  what  a  user  can  “see”  and  “hear”   •  How  they  interpret  informa;on   Clashes  between  user,  programmer,  expert  MMs   chai::   Computer  human  adapted  interac1on  research  group  
  16. 16. Mental  models  for  Health  literacy   soware  and  systems   •  Design  based  on  each  user’s  mental  models   –  Q:  Will  this  user  be  able  to  find  the  informa1on  that  is   relevant  to  them  (given  their  mental  model)?   –  Q:  Will  they  understand  that  informa1on  (given  their   mental  model)?   •  Systems  that  help  people     –  Build  awareness  of  their  own  mental  model   –  And  of  alternate  views   –  Be  sa1sfied  with  their  interac1on  experience   chai::   Computer  human  adapted  interac1on  research  group  
  17. 17. User  models   And  personalisa1on   chai::   Computer  human  adapted  interac1on  research  group  
  18. 18. User  model   •  Computer  systems  “beliefs”  about  the  user   –  eg  User  cannot  read  graphs   –  eg.  User  believes  vaccina1on  is  dangerous   •  Data  about  a  person  …  big  personal  data   •  Drives  personalisa1on   –  Personalisa1on  is  pervasive  in  search  engines  and   web  sites   –  can  be  dangerous  …“filter  bubbles”  …   confirma1on  and  valida1on  of  personal  beliefs   chai::   Computer  human  adapted  interac1on  research  group  
  19. 19. Example…  dangerous  filter  bubbles   User  belief:  vaccina1on  is  dangerous   chai::   Computer  human  adapted  interac1on  research  group  
  20. 20. But  personalisa1on  is  everywhere   And  does  help  cope  with  complexity   chai::   Computer  human  adapted  interac1on  research  group  
  21. 21. Accountable  personalisa1on?   PuLng  users  in  control…     chai::   Computer  human  adapted  interac1on  research  group  
  22. 22. User  models,  personal  data,   exploi1ng  digital  footprints….   Open  user/learner  models  (OLMs)     chai::   Computer  human  adapted  interac1on  research  group  
  23. 23. Visible  digital  footprints  so  I  can   compare  myself  with  others   chai::   Computer  human  adapted  interac1on  research  group  
  24. 24. This user’s footprints Overall population footprints Patina: Dynamic Heatmaps for Visualizing Application Usage (CHI2013) Justin Matejka, Tovi Grossman, and George Fitzmaurice chai::   Computer  human  adapted  interac1on  research  group  
  25. 25. MOOCs   SPOCS   Self-­‐paced  simula1ons   Discussion  board   New  online  learning  tools   Platforms that will give excellent foundations for individuals to learn Can  create  many…     Different  strokes  for  different  folks   Community  forma1on   Lots  of  learning  data  so  we  can  learn  to  improve  learning   chai::   Computer  human  adapted  interac1on  research  group  
  26. 26. Model of learner Gamification element Kahn Academy, what a student sees after the pre-test
  27. 27. Short video + self-test
  28. 28. Learning  Analy1cs  and   Educa1onal  Data  Mining   Popula1on  level   Classroom   Teacher   Individual     chai::   Computer  human  adapted  interac1on  research  group  
  29. 29. SIV   Lots of green means learner doing well Weak aspects visible as red Overview visualisation chai::   Computer  human  adapted  interac1on  research  group  
  30. 30. Little detail
  31. 31. User  models,  personal  data,   exploi1ng  digital  footprints….   Open  user/learner  models  (OLMs)     chai::   Computer  human  adapted  interac1on  research  group  
  32. 32. Technologies  to  help  track  and   discover  personal  “reality”   chai::   Computer  human  adapted  interac1on  research  group  
  33. 33. Sensors in our home – like Withings scales
  34. 34. Sensors we wear – like Fitbit, Nike FuelBand,....
  35. 35. Desktop sensors like slife, RSI-prevention sensors,.......
  36. 36. Pervasive  displays  that  help  us   see  “reality”   Lots  of  displays,  some  calmer  than   others   chai::   Computer  human  adapted  interac1on  research  group  
  37. 37. Opportunity Ubiquitous Devices: personal, wearable, portable, pervasive.
  38. 38. Example:  pill  taking  aid   chai::   Computer  human  adapted  interac1on  research  group  
  39. 39. Red glow – time to take medication Ambient displays, with subtle meaning, perhaps known only to the owner
  40. 40. Green glow .. All on track
  41. 41. Also  has  mobile-­‐phone  reminder   chai::   Computer  human  adapted  interac1on  research  group  
  42. 42. The  inac1vity  problem   Too  much  siLng   For  too  long  without  breaks   chai::   Computer  human  adapted  interac1on  research  group  
  43. 43. Blue… active Inactive > 30 mins
  44. 44. Sharing  data   Peers  to  support  each  other   And  compete   chai::   Computer  human  adapted  interac1on  research  group  
  45. 45. The power of peers Reality is relative!!! chai::   Computer  human  adapted  interac1on  research  group  
  46. 46. Some  new  ways  to  learn   chai::   Computer  human  adapted  interac1on  research  group  
  47. 47. Interactive walls for engaging health education
  48. 48. Older  users  too   T. Apted, J. Kay, and A. Quigley. Tabletop sharing of digital photographs for the elderly. In CHI '06: SIGCHI Conf on Human Factors in Computing Systems, pp 781-790, New York, NY, USA, 2006. ACM Press
  49. 49. externalisa1on   affec1on   building  on  others   argumenta1on   Collabora1ve  learning   P. Dillenbourg. What  do  you  mean  by  'collabora1ve  learning'? diverse  exper1se   discussion?   Two  hands  are  beZer  than  one   chai::   Computer  human  adapted  interac1on  research  group  
  50. 50. chai::   Computer  human  adapted  interac1on  research  group  
  51. 51. chai::   Computer  human  adapted  interac1on  research  group  
  52. 52. Summary  from  an  HCI-­‐techo   •  User-­‐centred  design   –  Understanding  users’  mental  models   –  Crea1ng  personalised  soware  to  aid   communica1on,  based  on  user  models   –  Exploi1ng  user  models:  OLMs,  gamifica1on   –  Learning  analy1cs  and  data  mining   •  Pervasive  sensing  and  displays   –  Capturing  “reality”   –  New  learning  contexts   chai::   Computer  human  adapted  interac1on  research  group  
  53. 53. Acknowledgements   chai::   Computer  human  adapted  interac1on  research  group  
  54. 54. Interac1ve  surfaces   Interfaces  to  user  model   Data  mining   Acknowledgements   Soware  infrastructure  user  control,  scrutability   chai::   Computer  human  adapted  interac1on  research  group  
  55. 55. Influences…   •  Human-­‐Computer  Interac1on   –  Mental  models   –  User  models   –  Explicit  assump1ons     •  Open  Learner  Models  (OLMs)   •  Technology  for  learning   –  Pervasive  devices  for  lifelong  awareness,  self-­‐ monitoring   –  New  places  to  learn,  embedded  everywhere   –  Personalisa1on,  Learning  Analy1cs,  Data  Mining   chai::   Computer  human  adapted  interac1on  research  group  
  56. 56. User  models,  personal  data,   exploi1ng  digital  footprints….   Open  user/learner  models  (OLMs)     chai::   Computer  human  adapted  interac1on  research  group  
  57. 57. Learning dashboards: an overview and future research opportunities Katrien Verbert • Sten Govaerts • Erik Duval • Jose Luis Santos • Frans Van Assche • Gonzalo Parra • Joris Klerkx Pers Ubiquit Comput, 2013
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