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Automatic Tagging of Learning Objects Based on Their Usage in Web Portals

  • Katja NiemannEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9307)

Abstract

Data sets coming from the educational domain often suffer from sparsity. Hence, many learning objects are not accessible by the users as they are not able to find these objects using for example a text-based search. Furthermore, the lack of information makes it difficult or even impossible to recommend such hidden learning resources. In order to address the data sparsity problem, this paper presents a new way to enhance the objects’ semantic representations. This is done by automatically assigning tags and classifications to learning objects offered by educational web portals. This way, we aim to increase the accessibility of the learning objects as well as to enable their recommendation. In contrast to popular tagging approaches that usually base the tagging of a learning object on its content or on the tags already assigned to it, the approach proposed in this paper is solely based on the objects’ usage. Therefore, tags and classifications can be exchanged between the objects and also previously un-tagged objects that do not hold any textual content can be automatically assigned with tags and classifications.

Keywords

Automatic tagging Data mining Educational web portals Findability Information retrieval Technology enhanced learning 

Notes

Acknowledgments

This work has been partly supported by the project Open Discovery Space project that is funded by the European Commission’s CIP-ICT Policy Support Program (Project Number: 297229).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Fraunhofer Institute for Applied Information Technology (FIT)Schloss BirlinghovenSankt AugustinGermany

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