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Learning Analytics for Self-Regulated Learning: Frameworks, Methods & Future Work (Dr. Phil Winne)
This webinar sketches a fusion of learning science with learning analytics using a model of self-regulated learning (SRL) as glue. Seven challenging questions are explored: (1) What are learning analytics and what they are for? (2) Why is traditional experimental research inadequate to guide SRL? (3) What key facets are needed to model SRL? (4) What are ambient trace data and how can trace data reflect SRL? (5) What methods for analyzing trace data set a stage for learning analytics about SRL? (6) What does a learning ecology look like in which learning and learning science thrive in symbiosis? (7) What are some targets needing attention in future research?

About the Speaker: Phil Winne (PhD, Stanford) is Distinguished SFU Professor of Education and, previously, a 2-term Tier I Canada Research Chair. He is a Fellow of the Royal Society of Canada, the American Educational Research Association, the American Psychological Association, the Association for Psychological Science and the Canadian Psychological Association. Phil researches self-regulated learning, metacognition and learning analytics; and develops software technologies to support learners and generate big data for learning science. Author of 195 scholarly publications, his honors include the Robbie Case Memorial Award, the Barry J. Zimmerman Award, and the Canadian Society for the Study of Education Mentorship Award.

Sep 30, 2020 12:30 PM in Central Time (US and Canada)

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