Towards a user model for personalized recommendations in work-integrated learning: A report on an experimental study with a collaborative tagging system

The informal setting of learning at work give rise for unique challenges to the field of technology enhanced learning systems. Personalized recommendations taking into account the current context of the individual knowledge worker are a powerful approach to overcome those challenges and effectively support the knowledge workers to meet their individual information needs. Basis for these recommendations to adopt to the current context of a knowledge worker can be provided by user models which reflects the topics knowledge workers are dealing with and their corresponding knowledge levels, but research has only focused on user modeling in settings with a static underlying domain model so far. We suggest to model the users’ context based on the emergent topics they are dealing with and their individual current knowledge levels within these topics by extracting the necessary information from the user’s past activities within the system. Based on data from an experiment with students learning a new topic with the help of a collaborative tagging system, we started to evaluate this approach and report on first results.