ispot analysed: participatory learning and reputation
DESCRIPTION
Talk given to the 1st Learning Analytics and Knowledge Conference, 2011, in Banff, Canada. Liveblog notes available here http://t.co/6bg6JuqTRANSCRIPT
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iSpot Analysed: Participatory Learning & ReputationLAK11, Banff, 28 February 2011Doug Clow and Elpida Makriyannis,
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• 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?
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• 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
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Take a photo
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iSpot ecosystem
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underpinning theory
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Fairy Rings of Participation (Makriyannis & De Liddo, 2010)
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general analytics
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Visits to iSpot, from Google Analytics
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Observations posted to iSpot, by month posted, from iSpot database
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Observations posted by month observed, from iSpot database
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reputation and learning
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• ‘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
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reputation analytics
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Invertebrates: Observations per user, ordered by number of observations
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it’s not a power law
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Invertebrates: observations per user, ordered by number of observations, log-log plot, showing power law (dotted line) with exponent of -1.3
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The Learning Analytics
Cycle
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• 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?
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Invertebrates: log plot of reputation received, ranked by reputation received, showing clear discontinuity at reputation < 1.0.
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Invertebrates: reputation received, log-log plot, first 618 users (reputation score 1.0), showing power law (dotted line) with an exponent of -1.4.
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it’s not a power law
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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.
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it’s not a power law(but it might be logarithmic)
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Invertebrates: Reputation given ordered by reputation given, log-log plot, showing power law (dotted line) with exponent of -3.6.
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it’s not a power law
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Agreements received against agreements given for Invertebrates, log-log plot, showing fitted power law (dotted line) with exponent 0.57 and R2 = 0.47.
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Reputation received against reputation given for Invertebrates, log-log plot, showing fitted power law (dotted line) with exponent 0.345 and R2 = 0.62.
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it’s not a power law
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what have we learned?
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• 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
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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
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future work
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• Adapt reputation system to other domains• More sophisticated fitting• Social network analysis• Identifying learning (e.g. reputation vs formal course)• More qualitative research
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• 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
[email protected]@dougclowhttp://dougclow.wordpress.com