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

chai::  Computer  human  adapted  interac1on  research  group  

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  

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  

Mental  models  

chai::  Computer  human  adapted  interac1on  research  group  

Mental  models  

A  set  of  beliefs  that  the  user  holds  

chai::  Computer  human  adapted  interac1on  research  group  

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  

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  

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  

Ra1onality?  

chai::  Computer  human  adapted  interac1on  research  group  

“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  

“Four  out  of  five  voices  in  my  head  say-­‐  "Eat  the  Chocolate”.  

PhD  Student  T-­‐shirt  

chai::  Computer  human  adapted  interac1on  research  group  

Complexity?  

chai::  Computer  human  adapted  interac1on  research  group  

“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  

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  

Why  do  mental  models  maZer  for  interface  designers?  

chai::  Computer  human  adapted  interac1on  research  group  

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  

Mental  models  for  Health  literacy  so\ware  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  

User  models  

And  personalisa1on  

chai::  Computer  human  adapted  interac1on  research  group  

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  

Example…  dangerous  filter  bubbles  

User  belief:  vaccina1on  is  dangerous  

chai::  Computer  human  adapted  interac1on  research  group  

But  personalisa1on  is  everywhere  

And  does  help  cope  with  complexity  

chai::  Computer  human  adapted  interac1on  research  group  

Accountable  personalisa1on?  

PuLng  users  in  control…    

chai::  Computer  human  adapted  interac1on  research  group  

User  models,  personal  data,  exploi1ng  digital  footprints….  

Open  user/learner  models  (OLMs)    

chai::  Computer  human  adapted  interac1on  research  group  

Visible  digital  footprints  so  I  can  compare  myself  with  others  

chai::  Computer  human  adapted  interac1on  research  group  

Patina: Dynamic Heatmaps for Visualizing Application Usage (CHI2013) Justin Matejka, Tovi Grossman, and George Fitzmaurice

This user’s footprints

Overall population footprints

chai::  Computer  human  adapted  interac1on  research  group  

Platforms that will give excellent foundations for individuals to learn

Community  forma1on  

MOOCs  

Can  create  many…    Different  strokes  for  different  folks  

SPOCS  

Lots  of  learning  data  so  we  can  learn  to  improve  learning  

Self-­‐paced  simula1ons  Discussion  board  

New  online  learning  tools  

Kahn Academy, what a student sees after the pre-test

Model of learner

Gamification element

Short video + self-test

chai::  Computer  human  adapted  interac1on  research  group  

Learning  Analy1cs  and  Educa1onal  Data  Mining  

Popula1on  level  Classroom  Teacher  Individual  

chai::  Computer  human  adapted  interac1on  research  group  

SIV  Lots of green means

learner doing well

Weak aspects visible as red

Overview visualisation

Little detail

chai::  Computer  human  adapted  interac1on  research  group  

User  models,  personal  data,  exploi1ng  digital  footprints….  

Open  user/learner  models  (OLMs)    

chai::  Computer  human  adapted  interac1on  research  group  

Technologies  to  help  track  and  discover  personal  “reality”  

Sensors in our home – like Withings scales

Sensors we wear – like Fitbit, Nike FuelBand,....

Desktop sensors like slife, RSI-prevention sensors,.......

chai::  Computer  human  adapted  interac1on  research  group  

Pervasive  displays  that  help  us  see  “reality”  

Lots  of  displays,  some  calmer  than  others  

Opportunity

39

Ubiquitous Devices: personal, wearable, portable, pervasive.

chai::  Computer  human  adapted  interac1on  research  group  

Example:  pill  taking  aid  

Ambient displays, with subtle meaning, perhaps known only to the owner

Red glow – time to take medication

Green glow .. All on track

chai::  Computer  human  adapted  interac1on  research  group  

Also  has  mobile-­‐phone  reminder  

chai::  Computer  human  adapted  interac1on  research  group  

The  inac1vity  problem  

Too  much  siLng  For  too  long  without  breaks  

Blue… active

Inactive > 30 mins

chai::  Computer  human  adapted  interac1on  research  group  

Sharing  data  

Peers  to  support  each  other  And  compete  

chai::  Computer  human  adapted  interac1on  research  group  

The power of peers

Reality is relative!!!

chai::  Computer  human  adapted  interac1on  research  group  

Some  new  ways  to  learn  

Interactive walls for engaging health education

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

Older  users  too  

chai::  Computer  human  adapted  interac1on  research  group  

P. Dillenbourg. What  do  you  mean  by  'collabora1ve  learning'?

discussion?  

externalisa1on  

diverse  exper1se  

affec1on  

Two  hands  are  beZer  than  one  

building  on  others  argumenta1on  

Collabora1ve  learning  

chai::  Computer  human  adapted  interac1on  research  group  

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  – Crea1ng  personalised  so\ware  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  

Acknowledgements  

chai::  Computer  human  adapted  interac1on  research  group  

Interac1ve  surfaces  

So\ware  infrastructure  user  control,  scrutability  

Interfaces  to  user  model  

Acknowledgements  Data  mining  

chai::  Computer  human  adapted  interac1on  research  group  

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  

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 Luis Santos • Frans Van Assche •

Gonzalo Parra • Joris Klerkx Pers Ubiquit Comput, 2013