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.