iSpot Analysed: Participatory Learning & Reputation LAK11, Banff, 28 February 2011 Doug Clow and Elpida Makriyannis,
<ul><li>Millions interested in nature </li></ul><ul><li>They watch nature TV programmes </li></ul><ul><li>Vast wealth of <...
<ul><li>Lower barriers to identification </li></ul><ul><li>Open to all </li></ul><ul><li>Provide identification checking t...
Take a photo
 
 
iSpot ecosystem
 
underpinning theory
Fairy Rings of Participation (Makriyannis & De Liddo, 2010)
 
general analytics
Visits to iSpot, from Google Analytics
Observations posted to iSpot, by month posted, from iSpot database
Observations posted by month observed, from iSpot database
reputation and learning
<ul><li>‘ Reputation’ as proxy measure of learning </li></ul><ul><ul><li>not (just) social approval </li></ul></ul><ul><li...
 
 
reputation analytics
Invertebrates: Observations per user, ordered by number of observations
it’s not a power law
Invertebrates: observations per user, ordered by number of observations,  log-log plot, showing power law (dotted line) wi...
The  Learning  Analytics  Cycle
<ul><li>Who’s learning anything? </li></ul><ul><li>‘ Reputation’ as proxy measure of learning </li></ul><ul><li>Main feedb...
Invertebrates: log plot of reputation received, ranked by reputation received, showing clear discontinuity at reputation <...
Invertebrates: reputation received, log-log plot, first 618 users (reputation score    1.0), showing power law ( dotted l...
it’s not a power law
Invertebrates – reputation received for users with reputation < 1.0, showing logarithmic curve fit ( solid line ) giving y...
it’s not a power law (but it might be logarithmic)
Invertebrates: Reputation given ordered by reputation given, log-log plot, showing power law ( dotted line ) with exponent...
it’s not a power law
Agreements received against agreements given for Invertebrates, log-log plot, showing fitted power law ( dotted line ) wit...
Reputation received against reputation given for Invertebrates, log-log plot, showing fitted power law ( dotted line ) wit...
it’s not a power law
what have we learned?
<ul><li>Observations and reputation received (learning) are highly unequally distributed – ‘fat tailed’ </li></ul><ul><li>...
iSpot reputation <ul><li>informal learning context </li></ul><ul><li>feedback is direct to other learners </li></ul><ul><u...
future work
<ul><li>Adapt reputation system to other domains </li></ul><ul><li>More sophisticated fitting </li></ul><ul><li>Social net...
<ul><li>iSpot Team: Jonathan Silvertown, Doug Clow, Richard Greenwood, Richard Lovelock, Mike Dodd, Martin Harvey, Donal O...
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iSpot Analysed: Participatory Learning and Reputation

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Talk given to the 1st Learning Analytics and Knowledge Conference, 2011, in Banff, Canada.
Liveblog notes available here http://t.co/6bg6Juq

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  • This is a model of the user-driven aspect of the Biodiversity Observatory and how it will help people with a casual interest into informal, and ultimately formal, learning about biodiversity.
  • This is a model of the user-driven aspect of the Biodiversity Observatory and how it will help people with a casual interest into informal, and ultimately formal, learning about biodiversity.
  • Wave paper document!
  • iSpot Analysed: Participatory Learning and Reputation

    1. 1. iSpot Analysed: Participatory Learning & Reputation LAK11, Banff, 28 February 2011 Doug Clow and Elpida Makriyannis,
    2. 2. <ul><li>Millions interested in nature </li></ul><ul><li>They watch nature TV programmes </li></ul><ul><li>Vast wealth of </li></ul><ul><ul><li>OER on nature </li></ul></ul><ul><ul><li>more formal learning </li></ul></ul><ul><li>How can we help people to learn? </li></ul>
    3. 3. <ul><li>Lower barriers to identification </li></ul><ul><li>Open to all </li></ul><ul><li>Provide identification checking through a social network </li></ul>= social networking for natural history <ul><li>Use thrill of observing nature & sense of achievement when you identify it </li></ul><ul><li>BUT identification is difficult </li></ul>
    4. 4. Take a photo
    5. 7. iSpot ecosystem
    6. 9. underpinning theory
    7. 10. Fairy Rings of Participation (Makriyannis & De Liddo, 2010)
    8. 12. general analytics
    9. 13. Visits to iSpot, from Google Analytics
    10. 14. Observations posted to iSpot, by month posted, from iSpot database
    11. 15. Observations posted by month observed, from iSpot database
    12. 16. reputation and learning
    13. 17. <ul><li>‘ Reputation’ as proxy measure of learning </li></ul><ul><ul><li>not (just) social approval </li></ul></ul><ul><li>Assessment hugely important in learning </li></ul><ul><li>Expensive to provide </li></ul><ul><li>Very hard to provide in informal learning contexts </li></ul>
    14. 20. reputation analytics
    15. 21. Invertebrates: Observations per user, ordered by number of observations
    16. 22. it’s not a power law
    17. 23. Invertebrates: observations per user, ordered by number of observations, log-log plot, showing power law (dotted line) with exponent of -1.3
    18. 24. The Learning Analytics Cycle
    19. 25. <ul><li>Who’s learning anything? </li></ul><ul><li>‘ Reputation’ as proxy measure of learning </li></ul><ul><li>Main feedback cycle is of ‘reputation’ to the users </li></ul><ul><li>What can we see in the pattern of feedback? </li></ul>
    20. 26. Invertebrates: log plot of reputation received, ranked by reputation received, showing clear discontinuity at reputation < 1.0.
    21. 27. Invertebrates: reputation received, log-log plot, first 618 users (reputation score  1.0), showing power law ( dotted line ) with an exponent of -1.4.
    22. 28. it’s not a power law
    23. 29. Invertebrates – reputation received for users with reputation < 1.0, showing logarithmic curve fit ( solid line ) giving y = -0.197ln(x) + 0.8317, R 2 = 0.98. NB Not log plot.
    24. 30. it’s not a power law (but it might be logarithmic)
    25. 31. Invertebrates: Reputation given ordered by reputation given, log-log plot, showing power law ( dotted line ) with exponent of -3.6.
    26. 32. it’s not a power law
    27. 33. Agreements received against agreements given for Invertebrates, log-log plot, showing fitted power law ( dotted line ) with exponent 0.57 and R 2 = 0.47.
    28. 34. Reputation received against reputation given for Invertebrates, log-log plot, showing fitted power law ( dotted line ) with exponent 0.345 and R 2 = 0.62.
    29. 35. it’s not a power law
    30. 36. what have we learned?
    31. 37. <ul><li>Observations and reputation received (learning) are highly unequally distributed – ‘fat tailed’ </li></ul><ul><li>Reputation given is even more highly unequal </li></ul><ul><ul><li>experts are having an amplified effect </li></ul></ul><ul><li>Any correlation between </li></ul><ul><ul><li>agreements given and received </li></ul></ul><ul><ul><li>reputation given and received </li></ul></ul><ul><li>is weak, highly nonlinear, and distinct </li></ul><ul><li>They’re not power laws </li></ul>
    32. 38. iSpot reputation <ul><li>informal learning context </li></ul><ul><li>feedback is direct to other learners </li></ul><ul><ul><li>not mediated by analysts or faculty </li></ul></ul><ul><li>participation pattern typical of social software </li></ul><ul><li>highly unequal effect of expert opinion on reputation </li></ul><ul><li>effective informal learning assessment by social networking </li></ul>
    33. 39. future work
    34. 40. <ul><li>Adapt reputation system to other domains </li></ul><ul><li>More sophisticated fitting </li></ul><ul><li>Social network analysis </li></ul><ul><li>Identifying learning (e.g. reputation vs formal course) </li></ul><ul><li>More qualitative research </li></ul>
    35. 41. <ul><li>iSpot Team: Jonathan Silvertown, Doug Clow, Richard Greenwood, Richard Lovelock, Mike Dodd, Martin Harvey, Donal O’Donnell, Jenny Worthington, Marion Edwards, Jon Rosewell, Janice Ansine, iSpot Mentors </li></ul><ul><li>Photos: Mike Dodd, Jonathan Silvertown, Martin Harvey </li></ul>[email_address] @dougclow http://dougclow.wordpress.com

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