Advanced learning and adaptive problem solving techniques:
practical lessons from cognitive science
FACILITATOR: Dr David Delany, School of Mathematics
NUMBER OF SESSIONS: Six
This practical course will focus on advanced deep learning and adaptive
problem solving techniques based on research in cognitive science into
the development of superior mental performance. Particular attention
will be paid to the application of these techniques to improving both
research and teaching skills. These techniques will be introduced
within the context of creating an ongoing Reflective Teaching and
Research Portfolio for Professional Development.
Over six sessions’ four key areas will be covered:
I The Foundations of Superior Performance
Current understanding of the nature and development of superior
performance, as revealed by studies of expert performers by cognitive
scientists, will be described. The Advanced Critical Thinking (ACT)
Framework - a model of the fundamental elements of adaptive expertise will be introduced and its implications for research and teaching
practice discussed.
II Knowledge Engineering - Building Expert Understanding
Participants will be introduced to Knowledge Engineering, a novel and
powerful skilled meaningful learning tool. The corollary ACT Study
method and ACT Structured Writing methods will be explained. The
Advanced Concept Mapping tool will be introduced.
III Logical Thinking Skills - Components of Knowledge Engineering
The core logical thinking skills that underpin the construction of
expert 'connected understanding' and the development of adaptive
problem solving skills will be explored.
IV Reverse Engineering of Expertise - Learning from Geniuses
Participants will practice using the advanced Knowledge Engineering
techniques of Deep Structure Analysis and Heuristic Analysis to
'reverse engineer' the deep-level understanding and adaptive problem
solving strategies of elite experts.