Modeling semantic context for active e-learning in the workplace

Abstract

How to improve the competence and productivity of knowledge workers becomes an important task in the knowledge society. Despite the gradually increasing practices of e-learning in the workplace, most of the applications perform poorly in motivating workers to learn and fail to adapt learning materials to the workers' individual situation, characteristics and needs. Therefore, by encapsulating learning resources as self-represented and evolving learning cells, this paper proposes three types of ontologies for semantic context modeling in terms of personal information, business flow, and social interaction. Based on semantic context, two learning modes, i.e., modular learning and just-in-time learning, are provided to support contextual learning in a more active and adaptive manner. The utility and functionality is illustrated along a real world application scenario at the research and development department of an education technology company. © 2012 Springer-Verlag.