Archive - March 22, 2014

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Quantifying deeper learning dispositions for the future data cloud

Quantifying deeper learning dispositions for the future data cloud

The Big Data in Education workshop in George Mason University, Washington DC, is asking: How might we, from scratch, design digital platforms to model multiple data streams from multiple sources in a generalized ecosystem of learning to make predictions about learning based on changes to instruction? We envision MOORs as digital terrains traversed by learners across formal and informal education (e.g., schooling, museums, the internet), and across the lifespan. Slides from my intro talk, which connected Ruth and Chris’s research at Bristol University and Incept Labs, with emerging concepts of the future learner’s personal data cloud, in which I manage the release of my behavioural and somatic data to boost my learning analytics… Learning Analytics : quantifying deeper learning dispositions? from[…]

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