Skip to main content

Towards Automatic Competence Assignment of Learning Objects

  • Conference paper
21st Century Learning for 21st Century Skills (EC-TEL 2012)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 7563))

Included in the following conference series:

Abstract

Competence-annotations assist learners to retrieve and better understand the level of skills required to comprehend learning objects. However, the process of annotating learning objects with competence levels is a very time consuming task; ideally, this task should be performed by experts on the subjects of the educational resources. Due to this, most educational resources available online do not enclose competence information. In this paper, we present a method to tackle the problem of automatically assigning an educational resource with competence topics. To solve this problem, we exploit information extracted from external repositories available on the Web, which lead us to a domain independent approach. Results show that automatically assigned competences are coherent and may be applied to automatically enhance learning objects metadata.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Diaz-Aviles, E., Georgescu, M., Stewart, A., Nejdl, W.: Lda for on-the-fly auto tagging. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 309–312. ACM, New York (2010)

    Chapter  Google Scholar 

  2. Niemann, K., Schwertel, U., Kalz, M., Mikroyannidis, A., Fisichella, M., Friedrich, M., Dicerto, M., Ha, K.-H., Holtkamp, P., Kawase, R., Parodi, E., Pawlowski, J., Pirkkalainen, H., Pitsilis, V., Vidalis, A., Wolpers, M., Zimmermann, V.: Skill-Based Scouting of Open Management Content. In: Wolpers, M., Kirschner, P.A., Scheffel, M., Lindstaedt, S., Dimitrova, V. (eds.) EC-TEL 2010. LNCS, vol. 6383, pp. 632–637. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  3. Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)

    MATH  Google Scholar 

  4. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kawase, R., Siehndel, P., Pereira Nunes, B., Fisichella, M., Nejdl, W. (2012). Towards Automatic Competence Assignment of Learning Objects. In: Ravenscroft, A., Lindstaedt, S., Kloos, C.D., Hernández-Leo, D. (eds) 21st Century Learning for 21st Century Skills. EC-TEL 2012. Lecture Notes in Computer Science, vol 7563. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33263-0_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33263-0_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33262-3

  • Online ISBN: 978-3-642-33263-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics