KAJAL : an algorithm for workflow navigation in informal learning

The more information a knowledge worker has to handle, the more (informal) learning is needed right at the workplace. This paper describes an algorithm that enables users to explore their current workflow, jump and navigate through their own tasks (even future ones), for informal learning or help purposes. This is done by overlaying the real workflow with a cloned one, providing an interface to access additional information, like prerequisites, associated documents or expert contacts. In particular, we developed KAJAL, which is a Petri net formalism to cope with the question of determining the states after a jump to a future task was triggered during the user’s navigation. -- Informal learning ; workflow ; workplace-embedded learning