Authors:
Benedikt Engelbert
1
;
Karsten Morisse
1
and
Oliver Vornberger
2
Affiliations:
1
University of Applied Sciences Osnabrueck, Germany
;
2
University of Osnabrueck, Germany
Keyword(s):
Social Tagging, Recommender System, Learning Material.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Collaborative Learning
;
Computer-Supported Education
;
e-Learning
;
e-Learning Hardware and Software
;
e-Learning Platforms
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Intelligent Tutoring Systems
;
Learning Analytics
;
Simulation and Modeling
;
Simulation Tools and Platforms
;
Social Context and Learning Environments
;
Virtual Learning Environments
Abstract:
With the variety of Learning Materials (LM) available in Learning Management Systems and the Internet,
the time a student requires to select the most appropriate content increases. Especially the use of the Internet
to find new LM is time consuming and not necessarily successful. A study accomplished at our university
shows, that students mainly look for alternative explanations, content related exercises and examples, which
can be used in addition to the existing LM. In this paper we describe the System Learning Assistance
Osnabrueck (LAOs), which is based on a collaborative tagging approach with the main goals to give content
related assistance for available LM, but also recommend content in further LM e.g. from the Internet.