Advertisement

Semantic-Based Automated Composition of Distributed Learning Objects for Personalized E-Learning

  • Simona Colucci
  • Tommaso Di Noia
  • Eugenio Di Sciascio
  • Francesco M. Donini
  • Azzurra Ragone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3532)

Abstract

Recent advances in e-learning techonologies and web services make realistic the idea that courseware for personalized e-learning can be built by dynamic composition of distributed learning objects, available as web-services. To be assembled in an automated way, learning objects metadata have to be exploited, associating unambiguous and semantically rich descriptions, to be used for such an automated composition. To this aim, we present a framework and algorithms for semantic-based learning objects composition, fully compliant with Semantic Web technologies. In particular our metadata refer to ontologies built on a subset of OWL-DL, and we show how novel inference services in Description Logics can be used to compose dynamically, in an approximated –but computationally tractable– way learning resources, given a requested courseware description.

Keywords

Background Knowledge Learn Object Description Logic International World Wide Automate Composition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    IEEE Standard for Learning Object Metadata, std 1484.12.1-2002 edition (2002)Google Scholar
  2. 2.
    IEEE standard for learning technology-learning technology systems architecture (LTSA), std 1484.1-2003 edition (2003)Google Scholar
  3. 3.
    Advanced Distributed Systems (ADL) Lab, Sharable Content Object Reference Model (SCORM), http://www.adlnet.org/index.cfm?fuseaction=scormabt
  4. 4.
    Ajami, K.: Specifying and implementing interoperable and reusable learning objects: one step beyond. In: Proc. of Intl. Conf. on Information and Communication Technologies: From Theory to Applications, pp. 111–112 (2004)Google Scholar
  5. 5.
    Baader, F., Calvanese, D., Mc Guinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2002)Google Scholar
  6. 6.
    Baader, F.: Least common subsumers and most specific concepts in a description logic with existential restrictions and terminological cycles. In: Proc. International Joint Conference on Artificial Intelligence (IJCAI 2003), pp. 319–324 (2003)Google Scholar
  7. 7.
    Baader, F., Horrocks, I., Sattler, U.: Description logics as ontology languages for the semantic web. In: Hutter, D., Stephan, W. (eds.) Festschrift in honor of Jörg Siekmann. LNCS (LNAI), pp. 228–248. Springer, Heidelberg (2003)Google Scholar
  8. 8.
    Bennacer, N., Bourda, Y., Doan, B.: Formalizing for querying learning objects using OWL. In: Proc. of Intl. Conf. on Advanced Learning Technologies, pp. 321–325. IEEE, Los Alamitos (2004)CrossRefGoogle Scholar
  9. 9.
    Cabezuelo, A.S., Beardo, J.M.D.: Towards a model of quality for learning objects. In: Proc. of Intl. Conf. on Advanced Learning Technologies, pp. 822–825. IEEE, Los Alamitos (2004)CrossRefGoogle Scholar
  10. 10.
    Calvanese, D., De Giacomo, G., Lenzerini, M.: On the Decidability of Query Containment under Constraints. In: Proceedings of the Seventeenth ACM SIGACT SIGMOD SIGART Symposium on Principles of Database Systems (PODS 1998), pp. 149–158 (1998)Google Scholar
  11. 11.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. The Massachusetts Institute of Technology (1990)Google Scholar
  12. 12.
    DAML+OIL Specifications (2001), http://www.daml.org/2001/03/daml+oil-index.html
  13. 13.
    Di Noia, T., Di Sciascio, E., Donini, F.M., Mongiello, M.: Abductive matchmaking using description logics. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI 2003), Acapulco, Messico, pp. 337–342. Morgan Kaufmann, Los Altos (2003)Google Scholar
  14. 14.
    Di Noia, T., Di Sciascio, E., Donini, F.M., Mongiello, M.: A system for principled Matchmaking in an electronic marketplace. In: Proc. International World Wide Web Conference (WWW 2003), Budapest, Hungary, pp. 321–330. ACM, New York (2003)CrossRefGoogle Scholar
  15. 15.
    Dolog, P., Henze, N., Nejdl, W., Sintek, M.: Student tracking and personalization: Personalization in distributed e-learning environments. In: Proc. International World Wide Web Conference (WWW 2004) (2004); Alternate track papers and postersGoogle Scholar
  16. 16.
    Dublin Core Metadata Element Set, Version 1.1: Reference Description, http://dublincore.org/documents/1999/07/02/dces/
  17. 17.
    Gasevic, D., Jovanovic, J., Devedzic, V.: Enhancing learning object content on the semantic web. In: Proc. of Intl. Conf. on Advanced Learning Technologies, pp. 714–716. IEEE, Los Alamitos (2004)CrossRefGoogle Scholar
  18. 18.
    Hacid, M.-S., Leger, A., Rey, C., Toumani, F.: Computing Concept Covers: a Preliminary Report. In: Proc. of the 15th Intl. Workshop on Description Logics (DL 2002), CEUR Workshop Proceedings, vol. 53 (2002)Google Scholar
  19. 19.
    Hatala, M., Richards, G., Eap, T., Willms, J.: Sharing educational resources: The interoperability of learning object repositories and services: standards, implementations and lessons learned. In: Proc. International World Wide Web Conference (WWW 2004); Alternate track papers and postersGoogle Scholar
  20. 20.
    IMS, Learning Resource Meta-data Best Practices and Implementation Guide Version 1.1 - Final Specification, http://www.imsproject.org/metadata/mdbestv1p1.html
  21. 21.
    Ip, A., Young, A., Morrison, I.: Learning objects - Whose are they. In: Proc. of 15th Conf. of the National Advisory Committee on Computing Qualifications, pp. 315–320 (2002)Google Scholar
  22. 22.
    Katia, S., Massimo, P., Anupriya, A., Srinivasan, N.: Automated Discovery, Interaction and Composition of Semantic Web Services. Journal of Web Semantics, 1 (December 2003)Google Scholar
  23. 23.
    McGuinness, D.L., Fikes, R., Hendler, J., Stein, L.A.: DAML+OIL: An Ontology Language for the Semantic Web. IEEE Intelligent Systems 17(5), 72–80 (2002)CrossRefGoogle Scholar
  24. 24.
  25. 25.
    Pahl, C., Barrett, R.: A web services architecture for learning object discovery and assembly. In: Proc. International World Wide Web Conference (WWW 2004) (2004); Alternate track papers and postersGoogle Scholar
  26. 26.
    Sanchez, S., Sicilia, M.: On the semantics of aggregation and generalization in learning object contracts. In: Proc. of Intl. Conf. on Advanced Learning Technologies, pp. 425–429. IEEE, Los Alamitos (2004)CrossRefGoogle Scholar
  27. 27.
    Teege, G.: Making the difference: A subtraction operation for description logics. In: Proceedings of the Fourth International Conference on the Principles of Knowledge Representation and Reasoning (KR 1994), MK, pp. 540–550 (1994)Google Scholar
  28. 28.
    The OWL Services Coalition (2004), http://www.daml.org/services/owl-s/1.0/owl-s.html
  29. 29.
    Hendler, J., Berners-Lee, T., Lassila, O.: The semantic web. Scientific American 248(4), 34–43 (2001)Google Scholar
  30. 30.
    Vossen, G., Jaeschke, P.: Learning objects as a uniform foundation for e-learning platforms. In: Proc. of Intl. Symp. on Database Engineering and Applications Symposium, pp. 278–287 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Simona Colucci
    • 1
    • 3
  • Tommaso Di Noia
    • 1
  • Eugenio Di Sciascio
    • 1
  • Francesco M. Donini
    • 2
  • Azzurra Ragone
    • 1
  1. 1.Politecnico di BariBariItaly
  2. 2.Università della TusciaViterboItaly
  3. 3.Knowledge Media InstituteThe Open UniversityUnited Kingdom

Personalised recommendations