OpenScout

OpenScout members from the L3S Research Center, Leibniz University Hannover, won the best paper award at EC-TEL 2011. The authors propose an annotation method that automatically enhances online learning objects with tags. These additional annotations support users to find and structure the information more easily; moreover it can facilitate the system to automatically recommend useful resources. User evaluations of the method show that automatically generated tags were preferred 35% more than the original author’s annotations. Furthermore, they were nearly 18% more relevant in terms of recall for users.The method will be applied in the project OpenScout (http://www.openscout.net).

OpenScout provides a single access point for searching in multiple high-quality business and management learning repositories (http://learn.openscout.net). To support end-users to find open resources relevant to their needs OpenScout allows registered end-users to add social metadata (tags, ratings, comments) and expert users to add competence metadata. In both areas, OpenScout also faces the ‘cold-start’ problem mentioned in the paper and can therefore benefit from the proposed auto-tagging method. The method will also be investigated to give semi-automatic support for competence tagging of OpenScout’s learning objects.

More about the paper:

Ernesto Diaz-Aviles, Marco Fisichella, Ricardo Kawase, Wolfgang Nejdl, and Avaré Stewart. Unsupervised Auto-Tagging for Learning Object Enrichment. In Karlos Delgado Kloos, Denis Gillet, Raquel M. Crespo García, Fridolin Wild, and Martin Wolpers, editors, Towards Ubiquitous Learning, Proceedings of 6th European Conference on Technology Enhanced Learning, EC-TEL 2011, volume 6964 of LNCS. Springer, September 2011. [More]