Abstract
Collaborative tagging is becoming a popular practice to annotate resources on the web, which has even reached e-learning initiatives. The aim of this chapter is exporting this technology to the field of learning through Interactive Digital TV (IDTV) (t-learning). In previous research, we have exposed our solution for creating learning experiences for t-learning, based on combining TV programmes and learning elements in order to lure viewers into education and make these experiences more entertaining. At the beginning of this research, we suggested reasoning over ontologies for the combination of the different elements. However, this approach did not take into account the user’s point of view towards the contents. In this chapter, we go a step further and present a proposal that includes collaborative tagging techniques, complementing ontologies with folksonomies to establish the relationships between the contents linked to create the learning experiences.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Advanced Distributed Learning (ADL): Sharable Content Object Reference Model (SCORM ®) 2004, 3rd edn. (2006), http://www.adlnet.org
Advanced Distributed Learning (ADL): Sharable Content Object Reference Model (SCORM®) 2004, 3rd edn. Run-Time Environment Version 1.0. (2006), http://www.cen-ltso.net/
Golder, S.A., Huberman, B.A.: The structure of collaborative tagging systems. Journal of Information Science 32(2), 198–208 (2006)
Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information Retrieval in Folksonomies: Search and Ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006)
IEEE Learning Technology Standards Committee (LTSC): Learning Object Metadata. IEEE Standard 1484.12.1 (2002)
Michlmayr, E., Cayzer, S., Shabajee, P.: Add-A-Tag: Learning Adaptive User Profiles from Bookmark Collections. In: 1st International Conference on Weblogs and Social Media (ICWSM 2007), Boulder, USA, Colorado (2007)
Niwa, S., Doi, T., Honiden, S.: Web Page Recommender System based on Folksonomy Mining for ITNG 2006 Submissions. In: Society, I.C. (ed.) Third International Conference on Information Technology: New Generations (ITNG 2006), Washington, DC, USA, pp. 388–393 (2006)
Rey-López, M., Fernández-Vilas, A., Díaz-Redondo, R.P.: New trends for personalised t-learning. In: Arias, J.J.P. (ed.) Personalization of Interactive Multimedia Services: A Research and Development Perspective, pp. 149–162. Nova Science Publishers, Inc., Bombay (2008)
Rey-López, M., Díaz-Redondo, R.P., Fernández-Vilas, A., Pazos-Arias, J.J.: Entercation: Engaging Viewers in Education through TV. ACM Computers in Entertainment 5(2) (2007)
Specia, L., Motta, E.: Integrating folksonomies with the semantic web. In: The Semantic Web: Research and Applications, pp. 624–639 (2007)
Szomszor, M., Cattuto, C., Alani, H., O’Hara, K., Baldassarri, A., Loreto, V., Servedio, V.D.: Folksonomies, the Semantic Web, and Movie Recommendation. In: 4th European Semantic Web Conference, Bridging the Gap between Semantic Web and Web 2.0, Innsbruck, Austria, pp. 71–84 (2007)
The TV-Anytime Forum: Broadcast and On-line Services: Search, select and rightful use of content on personal storage systems. European Standard ETSI TS 102 822 (2004)
Vander Wal, T.: Folksonomy Coinage and Definition (2007), http://www.vanderwal.net/folksonomy.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Rey-López, M., Díaz-Redondo, R.P., Fernández-Vilas, A., Pazos-Arias, J.J. (2010). T-Learning 2.0: A Personalised Hybrid Approach Based on Ontologies and Folksonomies. In: Xhafa, F., Caballé, S., Abraham, A., Daradoumis, T., Juan Perez, A.A. (eds) Computational Intelligence for Technology Enhanced Learning. Studies in Computational Intelligence, vol 273. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11224-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-11224-9_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11223-2
Online ISBN: 978-3-642-11224-9
eBook Packages: EngineeringEngineering (R0)