2016, Emmenegger, Sandro, Laurenzi, Emanuele, Thönssen, Barbara, Zhang Sprenger, Congyu, Hinkelmann, Knut, Witschel, Hans Friedrich

Support of workplace learning is increasingly important as change in every form determines today's working world in industry and public administrations alike. Adapt quickly to a new job, a new task or a new team is a major challenge that must be dealt with ever faster. Workplace learning differs significantly from school learning as it should be strictly aligned to business goals. In our approach we support workplace learning by providing recommendations of experts and learning resources in a context-sensitive and personalized manner. We utilize user s' workplace environment, we consider their learning preferences and zone of proximal development, and compare required and acquired competencies in order to issue the best suited recommendations. Our approach is part of the European funded project Learn PAd. Applied research method is Design Science Research. Evaluation is done in an iterative process. The recommender system introduced here is evaluated theoretically based on user requirements and practically in an early evaluation process conducted by the Learn PAd application partner.

2016, Emmenegger, Sandro, Thönssen, Barbara, Hinkelmann, Knut, Witschel, Hans Friedrich, Ana, Fred, Aveiro, David

Enterprises and Public Administrations alike need to ensure that newly hired employees are able to learn the ropes fast. Employers also need to support continuous workplace learning. Work-place learning should be strongly related to business goals and thus, learning goals should direct-ly add to business goals. To measure achievement of both learning and business goals we pro-pose augmented Key Performance Indicators (KPI). In our research we applied model driven engineering. Hence we developed a model for a Learning Scorecard comprising of business and learning goals and their KPIs represented in an ontology. KPI performance values and scores are calculated with formal rules based on the SPARQL Inferencing Notation. Results are presented in a dashboard on an individual level as well as on a team/group level. Requirements, goals and KPIs as well as performance measurement were defined in close co-operation with Marche Region, business partner in Learn PAd.

2012-04-04T00:00:00Z, Thönssen, Barbara, Emmenegger, Sandro

Um Risiken in der Supply Chain früh zu entdecken, genügt es nicht, eigene ERP-Daten für Lieferanten auszuwerten. Vielmehr muss das gesamte Netzwerk betrachtet und Daten aus externen Quellen mit internen Daten verknüpft werden. Mit Hilfe semantischer Technologien können Daten integriert, automatisch analysiert und so Risiken früher identifiziert werden.