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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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iSpot Analysed: Participatory Learning & ReputationLAK11, Banff, 28 February 2011Doug Clow and Elpida Makriyannis,
• Millions interested in nature• They watch nature TV programmes
• Vast wealth of• OER on nature• more formal learning
• How can we help people to learn?
• Lower barriers to identification• Open to all• Provide identification checking
through a social network
= social networking for natural history
• Use thrill of observing nature & sense of achievement when you identify it
• BUT identification is difficult
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
• ‘Reputation’ as proxy measure of learning• not (just) social approval
• Assessment hugely important in learning• Expensive to provide • Very hard to provide in informal learning contexts
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) with exponent of -1.3
The Learning Analytics
Cycle
• Who’s learning anything?• ‘Reputation’ as proxy measure of learning• Main feedback cycle is of ‘reputation’ to the users• What can we see in the pattern of feedback?
Invertebrates: log plot of reputation received, ranked by reputation received, showing clear discontinuity at reputation < 1.0.
Invertebrates: reputation received, log-log plot, first 618 users (reputation score 1.0), showing power law (dotted line) with an exponent of -1.4.
it’s not a power law
Invertebrates – reputation received for users with reputation < 1.0, showing logarithmic curve fit (solid line) giving y = -0.197ln(x) + 0.8317, R2 = 0.98. NB Not log plot.
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 of -3.6.
it’s not a power law
Agreements received against agreements given for Invertebrates, log-log plot, showing fitted power law (dotted line) with exponent 0.57 and R2 = 0.47.
Reputation received against reputation given for Invertebrates, log-log plot, showing fitted power law (dotted line) with exponent 0.345 and R2 = 0.62.
it’s not a power law
what have we learned?
• Observations and reputation received (learning) are highly unequally distributed – ‘fat tailed’
• Reputation given is even more highly unequal• experts are having an amplified effect
• Any correlation between• agreements given and received• reputation given and received
is weak, highly nonlinear, and distinct
• They’re not power laws
iSpot reputation• informal learning context• feedback is direct to other learners
• not mediated by analysts or faculty
• participation pattern typical of social software• highly unequal effect of expert opinion on reputation
• effective informal learning assessment by social networking
future work
• Adapt reputation system to other domains• More sophisticated fitting• Social network analysis• Identifying learning (e.g. reputation vs formal course)• More qualitative research
• 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
• Photos: Mike Dodd, Jonathan Silvertown, Martin Harvey
d.j.clow@open.ac.uk@dougclowhttp://dougclow.wordpress.com
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