Learning Powered Learning Analytics

Ruth and I just gave this talk here in Vancouver, introducing the learning analytics community to ELLI as an approach to modelling dispositions and attitudes in a computationally tractable form, and different rationalities for validating learning analytics. This is also the first publication (see below) which details the functionality of the Learning Warehouse platform, and how it changes traditional educational research.

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Buckingham Shum, S. and Deakin Crick, R. (2012). Learning Dispositions and Transferable Competencies: Pedagogy, Modelling and Learning Analytics. Proceedings 2nd International Conference on Learning Analytics & Knowledge, 29 Apr – 02 May 2012, Vancouver, British Columbia, CA. ACM Press: New York. Eprint: http://oro.open.ac.uk/32823

Abstract: Theoretical and empirical evidence in the learning sciences substantiates the view that deep engagement in learning is a function of a complex combination of learners’ identities, dispositions, values, attitudes and skills. When these are fragile, learners struggle to achieve their potential in conventional assessments, and critically, are not prepared for the novelty and complexity of the challenges they will meet in the workplace, and the many other spheres of life which require personal qualities such as resilience, critical thinking and collaboration skills. To date, the learning analytics research and development communities have not addressed how these complex concepts can be modelled and analysed, and how more traditional social science data analysis can support and be enhanced by learning analytics.  We report progress in the design and implementation of learning analytics based on a research validated multidimensional construct termed “learning power”. We describe, for the first time, a learning analytics infrastructure for gathering data at scale, managing stakeholder permissions, the range of analytics that it supports from real time summaries to exploratory research, and a particular visual analytic which has been shown to have demonstrable impact on learners. We conclude by summarising the ongoing research and development programme and identifying the challenges of integrating traditional social science research, with learning analytics and modelling.

About the author

Simon Buckingham Shum

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