Exploring the Development of Endorsed Learning Resources Profiles in the Connexions Repository

  • Cristian Cechinel
  • Salvador Sánchez-Alonso
  • Miguel-Ángel Sicilia
  • Priscyla Waleska Targino de Azevedo Simões
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 240)

Abstract

Existing learning object repositories are adopting strategies for quality assessment and recommendation of materials that rely on information provided by their community of users, such as ratings, comments, and tags. In this direction, Connexions has implemented an innovative approach for quality assurance where resources are socially endorsed by distinct members and organizations through the use of the so-called lenses. This kind of evaluative information constitutes a referential body of knowledge that can be used to create profiles of endorsed learning resources that, in their turn, can be further used in the process of automated quality assessment. The present paper explores the development of endorsed learning resources profiles based on intrinsic features of the resources, and initially evaluates the use of these profiles on the creation of automated models for quality evaluation.

Keywords

Learning objects Connexions automated assessment endorsement mechanisms repository 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Blumenstock, J.E.: Size matters: word count as a measure of quality on wikipedia. Paper Presented at the Proceedings of the 17th International Conference on World Wide Web, Beijing, China (2008)Google Scholar
  2. Brosnan, K.: Developing and sustaining a national learning-object sharing network: A social capital theory perspective. In: Williams, J.B., Goldberg, M.A. (eds.) Proceedings of the ASCILITE 2005 Conference, pp. 105–114. ASCILITE, Brisbane (2005)Google Scholar
  3. Cechinel, C., Sánchez-Alonso, S.: Analyzing Associations between the Different Ratings Dimensions of the MERLOT Repository. Interdisciplinary Journal of E-Learning and Learning Objects 7, 1–9 (2011)Google Scholar
  4. Cechinel, C., Sánchez-Alonso, S., García-Barriocanal, E.: Statistical profiles of highly-rated learning objects. Computers & Education 57(1), 1255–1269 (2011)CrossRefGoogle Scholar
  5. Cechinel, C., Sánchez-Alonso, S., Sicilia, M.Á.: Empirical Analysis of Errors on Human-Generated Learning Objects Metadata. In: Sartori, F., Sicilia, M.Á., Manouselis, N. (eds.) Metadata and Semantic Research, vol. 46, pp. 60–70. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. Custard, M., Sumner, T.: Using Machine Learning to Support Quality Judgments. D-Lib Magazine 11(10) (2005)Google Scholar
  7. García-Barriocanal, E., Sicilia, M.Á.: Preliminary Explorations on the Statistical Profiles of Highly-Rated Learning Objects. In: Sartori, F., Sicilia, M.Á., Manouselis, N. (eds.) Metadata and Semantic Research, vol. 46, pp. 108–117. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  9. Ivory, M.Y., Hearst, M.A.: Statistical profiles of highly-rated web sites. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems: Changing our World, Changing Ourselves, pp. 367–374. ACM, Minneapolis (2002)CrossRefGoogle Scholar
  10. Kelty, C.M., Burrus, C.S., Baraniuk, R.G.: 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. Monge, S., Ovelar, R., Azpeitia, I.: Repository 2.0: Social Dynamics to Support Community Building in Learning Object Repositories. Interdisciplinary Journal of E-Learning and Learning Objects 4, 191–204 (2008)Google Scholar
  12. Ochoa, X.: Connexions: a Social and Successful Anomaly among Learning Object Repositories. Journal of Emerging Technologies in Web Intelligence 2(1) (2010)Google Scholar
  13. Ochoa, X., Duval, E.: Relevance Ranking Metrics for Learning Objects. IEEE Transactions on Learning Technologies 1(1), 34–48 (2008)CrossRefGoogle Scholar
  14. Sánchez-Alonso, S., Sicilia, M.-A., García-Barriocanal, E., Pagés-Arévalo, C., Lezcano, L.: Social models in open learning object repositories: A simulation approach for sustainable collections. Simulation Modelling Practice and Theory 19(1), 110–120 (2011)CrossRefGoogle Scholar
  15. Sanz-Rodríguez, D.J.M., Sánchez-Alonso, S.: Ranking Learning Objects through Integration of Different Quality Indicators. IEEE Transactions on Learning Technologies 3(4), 358–363 (2010)CrossRefGoogle Scholar
  16. Sicilia, M.-A., Garcia, E., Pages, C., Martinez, J.J., Gutierrez, J.M.: Complete metadata records in learning object repositories: some evidence and requirements. International Journal of Learning Technology (IJLT) 1(4), 411–424 (2005)CrossRefGoogle Scholar
  17. Stvilia, B., Twidale, M.B., Smith, L.C., Gasser, L.: Assessing information quality of a community-based encyclopedia. Paper Presented at the Proceedings of the International Conference on Information Quality - ICIQ 2005 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cristian Cechinel
    • 1
  • Salvador Sánchez-Alonso
    • 2
  • Miguel-Ángel Sicilia
    • 2
  • Priscyla Waleska Targino de Azevedo Simões
    • 3
  1. 1.Computer Engineering CourseFederal University of PampaBagéBrazil
  2. 2.Information Engineering Research Unit, Computer Science Dept.University of AlcaláAlcalá de Henares (Madrid)Spain
  3. 3.Applied Computational Intelligence Research GroupUniversity of the Extreme South of Santa CatarinaCriciúmaBrazil

Personalised recommendations