Collaborative learning with multiple cases

Professional training usually includes some form of learning with case problems. Exposure to problems in a context enables the critical acquisition of experiential and tacit knowledge of specialist domains. Often this form of training is within groups, where discursive processes become the basis for individual learning. In this paper we present a cognitive model of case based learning which characterizes this accumulation of experiential knowledge from exposure to multiple problem cases. In the model, the details of cases are retained whilst immediate abstractions from those details are often incomplete. The mapping of cases to subsequent problems leads to a more complete understanding of the retained cases, hence knowledge of a domain is acquired progressively through reuse. We illustrate the model with observations drawn from our studies of collaborative learning with multiple programming problems that have been matched using software patterns.