Capturing Multi-Perspective Knowledge of Job Activities for Training

Using simulated environments for experiential learning gains a growing popularity in professional training. However, simulated context cannot capture the complexity of real world activities, hindering the adaptation to individual learning needs and real world experiences. On the other hand, there is a vast amount of user contributed content about real world activity which represents different viewpoints and contexts. Although this content can be a useful source for enriching the experiential learning experience in simulated environments, it has not been exploited to date. The main limitations are the poor structure and the lack of approaches to retrieve the knowledge nuggets embedded in the existing digital content (e.g. videos or user stories) and to relate them to the simulated context. The aim of this doctoral project is to develop a novel approach for capturing multi-perspective knowledge of job related activities from existing digital records and personal experiences. The proposed approach aims at extracting an advanced context model which will augment digital records of job activities with semantics, and will provide intelligent search to augment simulated context with real life experiences.