Workplace Learning - Providing Recommendations of Experts and Learning Resources in a Context-sensitive and Personalized Manner

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Autor:in (Körperschaft)
Publikationsdatum
2016
Typ der Arbeit
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Typ
04B - Beitrag Konferenzschrift
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Herausgeber:in (Körperschaft)
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Übergeordnetes Werk
Proceedings of Special Session on Learning Modeling in Complex Organizations (LCMO) at MODELSWARD'16
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Zusammenfassung
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.
Schlagwörter
Personalized learning, Recommender system, Public administration, Workplace learning, Ontology supported learning
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Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review des Abstracts
Open Access-Status
Lizenz
Zitation
EMMENEGGER, Sandro, Emanuele LAURENZI, Barbara THÖNSSEN, Congyu ZHANG SPRENGER, Knut HINKELMANN und Hans Friedrich WITSCHEL, 2016. Workplace Learning - Providing Recommendations of Experts and Learning Resources in a Context-sensitive and Personalized Manner. In: Proceedings of Special Session on Learning Modeling in Complex Organizations (LCMO) at MODELSWARD′16. Rom. 2016. Verfügbar unter: https://doi.org/10.26041/fhnw-1012