Metadata Generation for Learning Objects An Experimental Comparison of Automatic and Collaborative Solutions Matthias Bauer, Ronald Maier, Stefan Thalmann University of Passau University of Innsbruck Production and Logistics Information Systems Innstrasse 39 Universitaetsstrasse 15 94032 Passau (Germany) 6020 Innsbruck (Austria) Matthias.Bauer@uni-passau.de; Ronald.Maier@uibk.ac.at; Stefan.Thalmann@uibk.ac.at 1 Introduction For administration and exchange of learning objects (LOs), meaningful metadata are required. Typically, learning material is not limited to text, but includes multimedia content, such as images, audio and video. Meta- data not only describe the content, but also refer to e.g., didactical meth- ods, domain of usage and relationships to other LOs (Motelet et al. 2006). Many authors agree on the fact that dealing with metadata cannot be en- tirely left to humans (Duval and Hodgins 2004; Cardinaels et al. 2005; Ochoa et al. 2005). It is argued that creation of structured metadata is too difficult, complicated and time-consuming for authors of LOs. Thus, tradi- tionally, a small group of experts categorizes or indexes resources on the basis of an agreed, structured catalogue of keywords, a taxonomy, in order to make resources accessible (McGregor and McCulloch 2006). With the rapidly increasing amount of resources, here LOs, time and cost required for professional metadata creators are unsustainable for many organiza- tions. Furthermore, experts find it challenging to describe LOs for all kinds of application areas, due to the fact that they cannot be experts in all do- mains that LOs are developed for (Shipman and McCall 1994). Therefore, metadata generation generally remains the responsibility of authors of LOs. While learners or educational professionals may benefit from metadata, the authors rarely take advantage (Motelet et al. 2007). So it is not surprising that one of the most often heard critical remarks about LO metadata is that LO authors are not willing to spend additional effort to add metadata to their LOs (Duval and Hodgins 2003). Automatic processes can resolve the problem in part by reducing the number of metadata ele- ments which have to be humanly edited (Duval and Hodgins 2004).