An Automatic Indicator of the Reusability of Learning Objects Based on Metadata That Satisfies Completeness Criteria

  • Javier Sanz-Rodríguez
  • Merkourios Margaritopoulos
  • Thomas Margaritopoulos
  • Juan Manuel Dodero
  • Salvador Sánchez-Alonso
  • Athanasios Manitsaris
Part of the Communications in Computer and Information Science book series (CCIS, volume 73)


The search for learning objects in open repositories is currently a tedious task, owing to the vast amount of resources available and the fact that most of them do not have associated ratings to help users make a choice. In order to tackle this problem, we propose a reusability indicator, which can be calculated automatically using the metadata that describes the objects, allowing us to select those materials most likely to be reused. In order for this reusability indicator to be applied, metadata records must reach a certain amount of completeness, guaranteeing that the material is adequately described. This reusability indicator is tested in two studies on the Merlot and eLera repositories, and results obtained offer evidence to support their effectiveness.


learning object reusability metadata completeness 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Boyle, T.: Design principles for authoring dynamic, reusable learning objects. Australian Journal of Educational Technology 19(1), 46–58 (2003)Google Scholar
  2. 2.
    Brownfield, G., Oliver, R.: Factors influencing the discovery and reusability of digital resources for teaching and learning. In: Crisp, G., Thiele, D., Scholten, I., Barker, S., Baron, J. (eds.) Interact, Integrate, Impact: Proceedings of the 20th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education, Adelaide, ASCILITE, pp. 74–83 (2003)Google Scholar
  3. 3.
    Currier, S., Campbell, L.: Evaluating learning resources for reusability: the “dner & learning objects” study. In: Proceeding of The Australasian Society for Computers in Learning in Tertiary Education (ASCILITE) Auckland, New Zealand (2002)Google Scholar
  4. 4.
    Daniel, B., Mohan, P.: A Model for evaluating learning objects. In: Proceeding of the IEEE International Conference on Advanced Learning Technologies, pp. 50–60 (2004)Google Scholar
  5. 5.
    Downes, S.: Models for sustainable open educational resources. Interdisciplinary Journal of Knowledge and Learning Objects 3, 29–44 (2007)Google Scholar
  6. 6.
    Duval, E., Warkentyne, K., Haenni, F., Forte, E., Cardinaels, K., Verhoeven, B., Durm, R.V., Hendrikx, K., Forte, M.W., Ebel, N., Macowicz, M.: The ariadne knowledge pool system. Communications of the ACM 44(5), 72–78 (2001)CrossRefGoogle Scholar
  7. 7.
    Gray, A., MacDonell, S.: A comparison of techniques for developing predictive models of software metrics. Information and Software Technology 39(6), 425–437 (2003)CrossRefGoogle Scholar
  8. 8.
    Huddlestone, J., Pike, J.: Learning object reuse – A four tier model. People and systems - who are we designing for (November 2005)Google Scholar
  9. 9.
    Kay, R., Knaack, L.: Evaluating the learning in learning objects. Open Learning: The Journal of Open and Distance Learning 22(1), 5–28 (2007)CrossRefGoogle Scholar
  10. 10.
    Kelty, C., Burrus, C., Baraniuk, R.: Peer review anew: Three principles and a case study in postpublication quality assurance. Proceedings of the IEEE 96(6), 1000–1011 (2008)CrossRefGoogle Scholar
  11. 11.
    Margaritopoulos, T., Margaritopoulos, M., Mavridis, I., Manitsaris, A.: A fine-grained metric system for the completeness of metadata. In: Proceedings of 3rd International Conference, Metadata and Semantic Research 2009, Milan, Italy, October 1-2, pp. 83–94 (2009)CrossRefGoogle Scholar
  12. 12.
    Ochoa, X., Duval, E.: Quality metrics for learning object metadata. In: Duval, E., Klamma, R., Wolpers, M. (eds.) Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications, pp. 1004–1011 (2006)Google Scholar
  13. 13.
    Ochoa, X., Duval, E.: Measuring learning object reuse. In: Dillenbourg, P., Specht, M. (eds.) EC-TEL 2008. LNCS, vol. 5192, pp. 322–325. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Ochoa, X., Duval, E.: Relevance ranking metrics for learning objects. IEEE Transactions on Learning Technologies 1(1), 34–48 (2008b)CrossRefGoogle Scholar
  15. 15.
    Palmer, K., Richardson, P.: Learning Object Reusability – Motivation, Production and Use. In: 11th International Conference of the Association for Learning Technology (ALT). University of Exeter, Devon (2004)Google Scholar
  16. 16.
    Sánchez-Alonso, S., Sicilia, M.A.: Normative specifications of learning objects and processes. International Journal of Instructional Technology and Distance Learning 2(3), 3–12 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Javier Sanz-Rodríguez
    • 1
  • Merkourios Margaritopoulos
    • 2
  • Thomas Margaritopoulos
    • 2
  • Juan Manuel Dodero
    • 3
  • Salvador Sánchez-Alonso
    • 4
  • Athanasios Manitsaris
    • 2
  1. 1.University Carlos III of MadridSpain
  2. 2.University of MacedoniaGreece
  3. 3.University of CádizSpain
  4. 4.University of Alcalá de HenaresSpain

Personalised recommendations