Determining information relevance based on personalization techniques to meet specific user needs

Abstract

The support of workplace learning is becoming increasingly important as change in every form determines today’s working world in the industry and public administrations alike. Adapting quickly to any kind of change is just one aspect. Another is dealing with the information relevant to this change. A recommender system for workplace learning was developed within the European funded project Learn PAd. Even if the information is filtered based on a learner’s context with the help of the recommender, information overload remains a problem. It is not only the sheer amount of information but also the (often little) time for processing it that adds to the problem, time needed to assess the quality of the information according to its level of novelty, ambiguity, etc. Therefore, we enhanced the Learn PAd’s recommender by implementing a personalization strategy to filter (recommended) information based on a learner’s context. Our research work follows a design science research strategy and is evaluated in an iterative manner, first by comparing it to previously elicited user requirements and then through practical application in a test process conducted by the project application partner.