Presenters:
Nancy M. Smith, Learning Strategist, Trent University (Retired)
Session Description:
We find ourselves with a gap between best practices for learning pre-ChatGPT and exaptive practices for learning in a world of burgeoning technology, complexity, and change.
For example, we use the term “learning strategies” with humans; we also refer to “machine learning” with computers. This can create confusion. A computer does not change in the way that a human brain does. Similarly, with humans, learning and memory are often confounded. We compare computer memory with analogous references to human memory. And we have yet to distinguish between the brain and the mind.
Our mental models serve to structure our approach to human learning and learning strategies. What repurposing of learning strategies for humans is needed in a world of AI?
Learning Outcomes:
- “Re-Cognize” their assumptions of learner understanding and the limits of proffered learning strategies that are intended to guide, encourage, and support learner metacognition.
- Operationalize opportunities that self-reflection provides to mobilize, enrich, and close the learning feedback loop.
- Discover and demonstrate the power of play and its inherent paradoxes for learning.