Knowledge Services for Work-Integrated Learning

Authors:
Stefanie N. Lindstaedt;Peter Scheir;Robert Lokaiczyk;Barbara Kump;Günter Beham;Viktoria Pammer
Affiliations:
Know-Center Graz, Austria and Graz University of Technology, Austria;Know-Center Graz, Austria and Graz University of Technology, Austria;SAP Research, Germany;Graz University of Technology, Austria;Graz University of Technology, Austria;Know-Center Graz, Austria and Graz University of Technology, Austria
Venue:
EC-TEL '08 Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts
Year:
2008

Citing 4
Cited 3

Knowledge Spaces

Knowledge Spaces
YAWL: yet another workflow language

Information Systems
A storyboard of the APOSDLE vision

EC-TEL'06 Proceedings of the First European conference on Technology Enhanced Learning: innovative Approaches for Learning and Knowledge Sharing
Exploiting context information for identification of relevant experts in collaborative workplace-embedded e-learning environments

EC-TEL'07 Proceedings of the Second European conference on Technology Enhanced Learning: creating new learning experiences on a global scale

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

EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Knowledge Maturing Services: Supporting Knowledge Maturing in Organisational Environments

KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Studying the factors influencing automatic user task detection on the computer desktop

EC-TEL'10 Proceedings of the 5th European conference on Technology enhanced learning conference on Sustaining TEL: from innovation to learning and practice

Quantified Score

Hi-index	0.00

Visualization

Abstract

In order to support work-integrated learning scenarios task- and competency-aware knowledge services are needed. In this paper we introduce three key knowledge services of the APOSDLE system and illustrate how they interact. The context determination daemon observes user interactions and infers the current work task of the user. The user profile service uses the identified work tasks to determine the competences of the user. And finally, the associative retrieval service utilizes both the current work task and the inferred competences to identify relevant (learning) content. All of these knowledge services improve through user feedback.