Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


Ley Tobias, Lindstaedt Stefanie , Schöfegger Karin, Seitlinger Paul, Weber Nicolas, Hu Bo, Riss Uwe, Brun Roman, Hinkelmann Knut, Thönssen Barbara, Maier Ronald, Schmidt Andreas

Maturing Services Definition



Schoefegger K., Weber Nicolas, Lindstaedt Stefanie , Ley Tobias

KNOWLEDGE MATURING SERVICES: Supporting Knowledge Maturing in Organisational Environments

Knowledge Science, Engineering and Management, Third International Conference, KSEM 2009, Karagiannis, D., Jinpeng, Z., Springer, 2009

The changes in the dynamics of the economy and thecorresponding mobility and fluctuations of knowledge workers within organizationsmake continuous social learning an essential factor for an organization.Within the underlying organizational processes, KnowledgeMaturing refers to the the corresponding evolutionary process in whichknowledge objects are transformed from informal and highly contextualizedartifacts into explicitly linked and formalized learning objects.In this work, we will introduce a definition of Knowledge (Maturing)Services and will present a collection of sample services that can be dividedinto service functionality classes supporting Knowledge Maturingin content networks. Furthermore, we developed an application of thesesample services, a demonstrator which supports quality assurance withina highly content based organisational context.

Schoefegger K., Seitlinger Paul, Ley Tobias

Temporal Patterns in Collaborative Tagging: Analyzing Maturing of Semantic Knowledge Structures

Second Stellar Alpine Rendez-Vous 2009, It´s about time: exploring temporality in group learning - persistent worlds and long lasting simulations, STELLAR, 2009


Schmidt A., Hinkelmann K., Ley Tobias, Lindstaedt Stefanie , Maier R., Riss U.

Conceptual Foundations for a Service-oriented Knowledge and Learning Architecture: Supporting Content, Process, and Ontology Maturing

Networked Knowledge - Networked Media Integrating Knowledge Management, New Media Technologies and Semantic Systems, Studies in Computational Intelligence, Pellegrini, T., Auer, S., Tochtermann, K., Schaffert, S., Springer, 2009

Effective learning support in organizations requires a flexible and personalizedtoolset that brings together the individual and the organizational perspectiveon learning. Such toolsets need a service-oriented infrastructure of reusable knowledgeand learning services as an enabler. This contribution focuses on conceptualfoundations for such an infrastructure as it is being developed within the MATUREIP and builds on the knowledge maturing process model on the one hand, and theseeding-evolutionary growth-reseeding model on the other hand. These theories areused to derive maturing services, for which initial examples are presented.

Weber Nicolas, Ley Tobias, Lindstaedt Stefanie , Schoefegger K., Bimrose J., Brown A., Barnes S.

Knowledge Maturing in the Semantic MediaWiki: A design study in career guidance

Lecture Notes in Computer Science 5794, Cress, U., Dimitrova, V., Specht, M., Springer, 2009


Lindstaedt Stefanie , Beham Günter, Ley Tobias, Kump Barbara

Getting to Know Your User: Unobtrusive User Model Maintenance within Work-Integrated Learning Environments

Learning in the Synergy of Multiple Disciplines: Proceedings of the 4th European Conference on Technology Enhanced Learning, ECTEL 2009, Nice, France, September/October 2009 , Cress, U., Dimitrova, V., Specht, M., Springer, 2009

Work-integrated learning (WIL) poses unique challenges for usermodel design: on the one hand users’ knowledge levels need to be determinedbased on their work activities – testing is not a viable option; on the other handusers do interact with a multitude of different work applications – there is nocentral learning system. This contribution introduces a user model and correspondingservices (based on SOA) geared to enable unobtrusive adaptabilitywithin WIL environments. Our hybrid user model services interpret usage datain the context of enterprise models (semantic approaches) and utilize heuristics(scruffy approaches) in order to determine knowledge levels, identify subjectmatter experts, etc. We give an overview of different types of user model services(logging, production, inference, control), provide a reference implementationwithin the APOSDLE project, and discuss early evaluation results.

Ley Tobias, Kump Barbara, Maiden N., Albert D., Maas A.

Evaluating the Adaptation of a Learning System when the Prototype is not Ready: A Paper-based Lab Study

User Modelling, Adaptation, and Personalization 17th International Conference UMAP 2009, Houben, G., McCalla, G., Pianesi, F., Zancanaro, M., Springer, 2009

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