Applying Scruffy Methods to Enable Work-integrated Learning
Stefanie Lindstaedt, Tobias Ley, Peter Scheir, Armin Ulbrich · 2008
This contribution introduces the concept of work-integrated learning which distinguishes itself from traditional eLearning in that it provides learning support (1) during work task execution and tightly contextualized to the work context, (2) within the work environment, and (3) utilizes knowledge artefacts available within the organizational memory for learning. We argue that in order to achieve this highly flexible learning support we need to turn to ‘scruffy’ methods (such as associative retrieval, genetic algorithms, Bayesian and other probabilistic methods) which can provide good results in the presence of uncertainty and the absence of fine-granular models. Hybrid approaches to user context determination, user profile management, and learning material identification are discussed and first results are reported.