Context Separation Using Structured Knowledge Models For Reusable Interactive Computer Assisted Learning Resources

In this paper, we describe a novel approach to the generation of reusable computer-based assets to support the production and customis ation of learning materials for user and environment constrained learning scenarios. We present a framework for learning which proposes that navigating within a space of many models overcomes limitations inherent in mono-model approaches to learning contextualised knowledge. The approach is based on work carried out by the authors in knowledge base research, user modelling, training systems for industry, and learning environment development. Work is now under way on a project to develop both the technological solution and the pedagogic strategy necessary to produce an adaptive learning system based on this approach. How part of a professional’s knowledge, for industrial process control, can be structured for use by a learner for one particular approach to learning is discussed to illustrate different elements of context. This combination of different knowledge types allows a variety of learning situations to be accommodated.