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Reseach Article

A Hybrid Recommender Method for Learning Objects

Published on None 2011 by Alfredo Zapata, Victor H. Menendez, Manuel E. Prieto, Cristobal Romero
Design and Evaluation of Digital Content for Education
Foundation of Computer Science USA
DEDCE - Number 1
None 2011
Authors: Alfredo Zapata, Victor H. Menendez, Manuel E. Prieto, Cristobal Romero
1d32ac84-f4e1-4071-b1d4-599574446a1d

Alfredo Zapata, Victor H. Menendez, Manuel E. Prieto, Cristobal Romero . A Hybrid Recommender Method for Learning Objects. Design and Evaluation of Digital Content for Education. DEDCE, 1 (None 2011), 1-7.

@article{
author = { Alfredo Zapata, Victor H. Menendez, Manuel E. Prieto, Cristobal Romero },
title = { A Hybrid Recommender Method for Learning Objects },
journal = { Design and Evaluation of Digital Content for Education },
issue_date = { None 2011 },
volume = { DEDCE },
number = { 1 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 1-7 },
numpages = 7,
url = { /proceedings/dedce/number1/2806-dece001/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Design and Evaluation of Digital Content for Education
%A Alfredo Zapata
%A Victor H. Menendez
%A Manuel E. Prieto
%A Cristobal Romero
%T A Hybrid Recommender Method for Learning Objects
%J Design and Evaluation of Digital Content for Education
%@ 0975-8887
%V DEDCE
%N 1
%P 1-7
%D 2011
%I International Journal of Computer Applications
Abstract

Searching for and retrieval of Learning Objects in e-learning environments is a complex assignment for instructors and students. Generally, the search results include several Learning Objects and the ranking criteria are not always clear (keyword frequency, date updating, user profile similarity, etc.). This means that selection of the best Learning Object for a specific use requires a lot of effort and time. This paper proposes a hybrid recommendation method to assist users in the search and selection processes in Learning Objects Repositories. The intended method uses a combination of different filtering techniques, such as content comparison, and collaborative and demographic searches. To achieve this goal, metadata information, management activities of resources and user profiles are used. The hybrid recommendation method has been implemented in a search system called DELPHOS.

References
  1. Sicilia, M. A., Garcia, E. 2003. On the Concepts of Usability and Reusability of Learning Objects. International Review of Open and Distance Learning, 4(2)
  2. Polsani, P. Use and Abuse of Reusable Learning Objects. Journal of Digital Information, 3(4), 1-10 (2003)
  3. ADL. 2002. Emerging and Enabling Technologies for the design of Learning Object Repositories Report. Advanced Distributed Learning Initiative.
  4. ARIADNE. 2006. Alliance of Remote Instructional Authoring and Distribution Networks for Europe. http://www.ariadne-eu.org
  5. Schell, G. P., and Burns, M. 2002. Merlot. A Repository of e-Learning Objects for Higher Education. e-Service Journal, vol. 1(2), 53-64.
  6. Stefaner, M., Vecchia, E. D., Condotta, M., Wolpers, M., Specht, M., Apelt, S., et al. 2007. MACE - Enriching Architectural Learning Objects for Experience Multiplication. Lecture Notes, in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),vol. 4753, 322-336.
  7. Prieto, M. E., Menendez, V., Segura, A. and Vidal, C. 2008. A Recommender System Architecture for Instructional Engineering. In Emerging Technologies and Information Systems for Knowledge Society. LNCS, Vol. 5288, 314-321.
  8. Burke, R. 2007. Hybrid Web Recommender Systems. In: The Adaptive Web, 377-408. Springer Berlin / Heidelberg.
  9. Mahmood, T., Ricci, F. 2009. Improving Recommender Systems with Adaptive Conversational Strategies. In: C. Cattuto, G. Ruffo, F. Menczer (eds.) Hypertext, 73-82. ACM.
  10. Resnick, P., Varian, H.R. 1997. Recommender systems. Communications of the ACM 40(3), 56-58.
  11. García, E., Romero, C., Ventura, S., Castro, C. 2008. Sistema Recomendador Colaborativo Usando Minería de Datos Distribuida para la Mejora Continua de Cursos E-learning. IEEE Rita: Revista Iberoamericana de Tecnologías del Aprendizaje. Vol. 3(1), 19-30.
  12. Khribi, M. K., Jemni, M., Nasraoui, O. 2008. Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval. In Eighth IEEE International Conference on Advanced Learning Technologies (ICALT '08), vol. 12, 241-245.
  13. Ruiz-iniesta, A., Jiménez-díaz, M., Gómez-albarrán, G. 2010. Personalización en Recomendadores Basados en Contenido y su Aplicación a Repositorios de Objetos de Aprendizaje. IEEE-RITA, vol. 5(1), 31-38.
  14. Zhuhadar, L., Nasraoui, O. 2010. Personalized Search Based on a User-Centered Recommender Engine. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 200-203.
  15. Manouselis, N., Vourikari, R. and Van Asschet, F. 2010. Collaborative Recommendation of e-learning Resources: an Experimental Investigation. Journal of computer Assisted Learning, 227-242.
  16. Bozo, J., Alarc, R. and Iribarra, S. 2010. Recommending Learning Objects According to a Teachers’ Contex Model. Learning. In: EC-TEL 2010, Barcelona, Spain, 470-475.
  17. IEEE. Learning Technology Standards Committee. 2002. Draft Standard for Learning Object Metadata (IEEE-LOM). http://ltsc.ieee.org/wg12/files/LOM_1484_12_1_v1_Final_Draft.pdf
  18. Menendez, V. and Prieto, M.E. 2010. La Similitud Borrosa en la Generación de Metadatos de Objetos de Aprendizaje. In III Simposio Sobre Lógica Fuzzy y Soft Computing LFSC2010, Grupo Editorial Gaceta, Valencia, Spain, 369-376.
  19. Zapata, A. Menéndez, V., Y. Eguigure and Prieto, M.E. 2009. Quality Evaluation Model for Learning Objects from Pedagogical Perspective. A Case Study. In: International Conference of Education, Research and Innovation, ICERI2009, Madrid, Spain, 2228-2238.
  20. Brooke, J. 1996. SUS: A 'Quick and Dirty' Usability Scale. Usability Evaluation in Industry, P. W. Jordan, B. Thomas, B. A. Weerdmeester, A. L. McClelland (eds), Taylor y Francis: London, 189-194.
Index Terms

Computer Science
Information Sciences

Keywords

Learning Object IEEE-LOM Pedagogical Quality