The Cathedral and the Bazaar
On the Design of Collective Applications
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
iSpot analysed: participatory learning and reputation
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
Proceedings of the Third International Conference on Learning Analytics and Knowledge
MOOCs and the funnel of participation
Proceedings of the Third International Conference on Learning Analytics and Knowledge
Supporting action research with learning analytics
Proceedings of the Third International Conference on Learning Analytics and Knowledge
Issues, challenges, and lessons learned when scaling up a learning analytics intervention
Proceedings of the Third International Conference on Learning Analytics and Knowledge
Designing pedagogical interventions to support student use of learning analytics
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
Data wranglers: human interpreters to help close the feedback loop
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
Student explorer: a tool for supporting academic advising at scale
Proceedings of the first ACM conference on Learning @ scale conference
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This paper develops Campbell and Oblinger's [4] five-step model of learning analytics (Capture, Report, Predict, Act, Refine) and other theorisations of the field, and draws on broader educational theory (including Kolb and Schön) to articulate an incrementally more developed, explicit and theoretically-grounded Learning Analytics Cycle. This cycle conceptualises successful learning analytics work as four linked steps: learners (1) generating data (2) that is used to produce metrics, analytics or visualisations (3). The key step is 'closing the loop' by feeding back this product to learners through one or more interventions (4). This paper seeks to begin to place learning analytics practice on a base of established learning theory, and draws several implications from this theory for the improvement of learning analytics projects. These include speeding up or shortening the cycle so feedback happens more quickly, and widening the audience for feedback (in particular, considering learners and teachers as audiences for analytics) so that it can have a larger impact.