Overview of emerging technologies to define, enhance, and measure health literacy. Professor Judy Kay
•
1 gefällt mir•488 views
School of Information Technologies, University of Sydney. Presentation given at "Health Literacy Network: Crossing Disciplines, Bridging Gaps", November 26, 2013. The University of Sydney.
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Andere mochten auch
Andere mochten auch (10)
Ähnlich wie Overview of emerging technologies to define, enhance, and measure health literacy. Professor Judy Kay
Ähnlich wie Overview of emerging technologies to define, enhance, and measure health literacy. Professor Judy Kay (20)
Mehr von Charles Perkins Centre, University of Sydney
Mehr von Charles Perkins Centre, University of Sydney (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Overview of emerging technologies to define, enhance, and measure health literacy. Professor Judy Kay
- 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. 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. Mental models chai:: Computer human adapted interac1on research group
- 4. Mental models A set of beliefs that the user holds chai:: Computer human adapted interac1on research group
- 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. 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. 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. Ra1onality? chai:: Computer human adapted interac1on research group
- 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. “Four out of five voices in my head say-‐ "Eat the Chocolate”. PhD Student T-‐shirt chai:: Computer human adapted interac1on research group
- 11. Complexity? chai:: Computer human adapted interac1on research group
- 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. 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. Why do mental models maZer for interface designers? chai:: Computer human adapted interac1on research group
- 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. 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. User models And personalisa1on chai:: Computer human adapted interac1on research group
- 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. Example… dangerous filter bubbles User belief: vaccina1on is dangerous chai:: Computer human adapted interac1on research group
- 21. But personalisa1on is everywhere And does help cope with complexity chai:: Computer human adapted interac1on research group
- 23. Accountable personalisa1on? PuLng users in control… chai:: Computer human adapted interac1on research group
- 24. User models, personal data, exploi1ng digital footprints…. Open user/learner models (OLMs) chai:: Computer human adapted interac1on research group
- 25. Visible digital footprints so I can compare myself with others chai:: Computer human adapted interac1on research group
- 26. 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
- 27. 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
- 28. Model of learner Gamification element Kahn Academy, what a student sees after the pre-test
- 29. Short video + self-test
- 30. Learning Analy1cs and Educa1onal Data Mining Popula1on level Classroom Teacher Individual chai:: Computer human adapted interac1on research group
- 31. SIV Lots of green means learner doing well Weak aspects visible as red Overview visualisation chai:: Computer human adapted interac1on research group
- 32. Little detail
- 33. User models, personal data, exploi1ng digital footprints…. Open user/learner models (OLMs) chai:: Computer human adapted interac1on research group
- 34. Technologies to help track and discover personal “reality” chai:: Computer human adapted interac1on research group
- 35. Sensors in our home – like Withings scales
- 36. Sensors we wear – like Fitbit, Nike FuelBand,....
- 37. Desktop sensors like slife, RSI-prevention sensors,.......
- 38. Pervasive displays that help us see “reality” Lots of displays, some calmer than others chai:: Computer human adapted interac1on research group
- 39. Opportunity Ubiquitous Devices: personal, wearable, portable, pervasive.
- 40. Example: pill taking aid chai:: Computer human adapted interac1on research group
- 41. Red glow – time to take medication Ambient displays, with subtle meaning, perhaps known only to the owner
- 42. Green glow .. All on track
- 43. Also has mobile-‐phone reminder chai:: Computer human adapted interac1on research group
- 44. The inac1vity problem Too much siLng For too long without breaks chai:: Computer human adapted interac1on research group
- 45. Blue… active Inactive > 30 mins
- 46. Sharing data Peers to support each other And compete chai:: Computer human adapted interac1on research group
- 47. The power of peers Reality is relative!!! chai:: Computer human adapted interac1on research group
- 48. Some new ways to learn chai:: Computer human adapted interac1on research group
- 49. Interactive walls for engaging health education
- 51. 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
- 52. 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
- 53. chai:: Computer human adapted interac1on research group
- 54. chai:: Computer human adapted interac1on research group
- 55. 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
- 56. Acknowledgements chai:: Computer human adapted interac1on research group
- 57. Interac1ve surfaces Interfaces to user model Data mining Acknowledgements Soware infrastructure user control, scrutability chai:: Computer human adapted interac1on research group
- 58. 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
- 60. User models, personal data, exploi1ng digital footprints…. Open user/learner models (OLMs) chai:: Computer human adapted interac1on research group
- 61. 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