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Limanauskiene, Virginija; Stuikys, Vytautas – Electronic Journal of e-Learning, 2009
With the expansion of e-learning, the understanding and evaluation of already created e-learning environments is becoming an extremely important issue. One way to dealing with the problem is analysis of case studies, i.e. already created environments, from the reuse perspective. The paper presents a general framework and model to assess UNITE, the…
Descriptors: Electronic Learning, Communication Problems, Computer Assisted Instruction, Editing
Holzinger, Andreas; Kleinberger, Thomas; Muller, Paul – 2001
One of the "hottest" topics in recent information systems and computer science is metadata. Learning Object Metadata (LOM) appears to be a very powerful mechanism for representing metadata, because of the great variety of LOM Objects. This is on of the reasons why the LOM standard is repeatedly cited in projects in the field of eLearning…
Descriptors: Computer System Design, Distance Education, Educational Resources, Educational Technology
Steinacker, Achim; Faatz, Andreas; Seeberg, Cornelia; Rimac, Ivica; Hormann, Stefan; El Saddik, Abdulmotaleb; Steinmetz, Ralf – 2001
This paper gives an overview of MediBook, a joint project of the Medical Department of the University GieBen (Germany) and the Department of Electrical Engineering and Information Technology at Darmstadt University of Technology (Germany) funded by the Hessian Ministry for Science and Arts (HMWK). The basis of MediBook is a formal representation…
Descriptors: Computer System Design, Educational Media, Educational Resources, Educational Technology
Brase, Jan; Painter, Mark – Educational Technology & Society, 2004
Learning Objects Metadata (LOM) aims at describing educational resources in order to allow better reusability and retrieval. In this article we show how additional inference rules allows us to derive additional metadata from existing ones. Additionally, using these rules as integrity constraints helps us to define the constraints on LOM elements,…
Descriptors: Inferences, Metadata, Information Retrieval, Standards