Studying Relations Between E-learning Resources to Improve the Quality of Searching and Recommendation

Nguyen Ngoc Chan, Azim Roussanaly, Anne Boyer


Searching and recommendation are basic functions that effectively assist learners to approach their favorite learning resources. Several searching and recommendation techniques in the Information Retrieval (IR) domain have been proposed to apply in the Technology Enhanced Learning (TEL) domain. However, few of them pay attention on particular properties of e-learning resources, which potentially improve the quality of searching and recommendation. In this paper, we propose an approach that studies relations between e-learning resources, which is a particular property existing in online educational systems, to support resource searching and recommendation. Concretely, we rank e-learning resources based on their relations by adapting the Google's PageRank algorithm. We integrate this ranking into a text-matching search engine to refine the search results. We also combine it with a content-based recommendation technique to compute the similarity between user profile and e-learning resources. Experimental results on a shared dataset showed the efficiency of our approach.


  1. Avancini, H. and Straccia, U. (2005). User recommendation for collaborative and personalised digital archives. Int. J. Web Based Communities, 1(2):163-175.
  2. Brin, S. and Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. In Proceedings of the Seventh International Conference on World Wide Web 7, WWW7, pages 107-117, Amsterdam, The Netherlands, The Netherlands. Elsevier Science Publishers B. V.
  3. Chan, N., Roussanaly, A., and Boyer, A. (2014). Learning resource recommendation: An orchestration of content-based filtering, word semantic similarity and page ranking. In Rensing, C., de Freitas, S., Ley, T., and Mun˜oz-Merino, P., editors, Open Learning and Teaching in Educational Communities, volume 8719 of Lecture Notes in Computer Science, pages 302- 316. Springer International Publishing.
  4. Drachsler, H., Pecceu, D., Arts, T., Hutten, E., Rutledge, L., Rosmalen, P., Hummel, H., and Koper, R. (2009). Remashed - recommendations for mash-up personal learning environments. In Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines, EC-TEL 7809, pages 788-793, Berlin, Heidelberg. Springer-Verlag.
  5. Engelhardt, M., Hildebrand, A., Lange, D., and Schmidt, T. C. (2006). Reasoning about elearning multimedia objects. In First International Workshop on Semantic Web Annotations for Multimedia (SWAMM), joint with the 15th World Wide Web (WWW) Conference, Edinburgh, Scotland.
  6. Hendez, M. and Achour, H. (2014). Keywords extraction for automatic indexing of e-learning resources. In Computer Applications Research (WSCAR), 2014 World Symposium on, pages 1-5.
  7. Huang, Y.-M., Huang, T.-C., Wang, K.-T., and Hwang, W.- Y. (2009). A markov-based recommendation model for exploring the transfer of learning on the web. Educational Technology & Society, 12(2):144-162.
  8. Hummel, H. G. K., van den Berg, B., Berlanga, A. J., Drachsler, H., Janssen, J., Nadolski, R., and Koper, R. (2007). Combining social-based and informationbased approaches for personalised recommendation on sequencing learning activities. IJLT, 3(2):152-168.
  9. Janssen, J., Tattersall, C., Waterink, W., van den Berg, B., van Es, R., Bolman, C., and Koper, R. (2007). Selforganising navigational support in lifelong learning: How predecessors can lead the way. Comput. Educ., 49(3):781-793.
  10. Khribi, M. K., Jemni, M., and Nasraoui, O. (2009). Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval. Educational Technology & Society, 12(4):30-42.
  11. Koutrika, G., Ikeda, R., Bercovitz, B., and Garcia-Molina, H. (2008). Flexible recommendations over rich data. In Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 7808, pages 203-210, New York, NY, USA. ACM.
  12. Lemire, D., Boley, H., McGrath, S., and Ball, M. (2005). Collaborative filtering and inference rules for contextaware learning object recommendation. International Journal of Interactive Technology and Smart Education, 2(3).
  13. Manning, C. D., Raghavan, P., and Schutze, H. (2008). Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA.
  14. Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., and Koper, R. (2011). Recommender Systems in Technology Enhanced Learning. In Ricci, F., Rokach, L., Shapira, B., and Kantor, P. B., editors, Recommender Systems Handbook, pages 387-415. Springer US.
  15. Manouselis, N., Vuorikari, R., and Assche, F. V. (2007). Simulated analysis of maut collaborative filtering for learning object recommendation. In In Workshop proceedings of the EC-TEL conference: SIRTEL07 (ECTEL 7807, pages 17-20.
  16. Nadolski, R. J., van den Berg, B., Berlanga, A. J., Drachsler, H., Hummel, H. G., Koper, R., and Sloep, P. B. (2009). Simulating light-weight personalised recommender systems in learning networks: A case for pedagogy-oriented and rating-based hybrid recommendation strategies. Journal of Artificial Societies and Social Simulation, 12(1):4.
  17. Nilsson, M., Johnston, P., Naeve, A., and Powell, A. (2006). The future of learning object metadata interoperability. In Koohang, A., editor, Principles and Practices of the Effective Use of Learning Objects. Informing Science Press.
  18. Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. Technical Report 1999-66, Stanford InfoLab. Previous number = SIDL-WP-1999-0120.
  19. Saini, P., Ronchetti, M., and Sona, D. (2006). Automatic generation of metadata for learning objects. In Advanced Learning Technologies, 2006. Sixth International Conference on, pages 275-279.
  20. Salton, G., Wong, A., and Yang, C. S. (1975). A vector space model for automatic indexing. Commun. ACM, 18(11):613-620.
  21. Seeberg, C., Steinacker, A., and Steinmetz, R. (2000). Coherence in modularly composed adaptive learning documents. In Brusilovsky, P., Stock, O., and Strapparava, C., editors, Adaptive Hypermedia and Adaptive Web-Based Systems, volume 1892 of Lecture Notes in Computer Science, pages 375-379. Springer Berlin Heidelberg.
  22. Shen, L.-p. and Shen, R.-m. (2004). Learning content recommendation service based-on simple sequencing specification. In Liu, W., Shi, Y., and Li, Q., editors, Advances in Web-Based Learning - ICWL 2004, volume 3143 of Lecture Notes in Computer Science, pages 363-370. Springer Berlin Heidelberg.
  23. Tang, T. and McCalla, G. (2005). Smart Recommendation for an Evolving e-Learning System: Architecture and Experiment. International Journal on e-Learning, 4(1):105-129.
  24. Tsai, K. H., Chiu, T. K., Lee, M. C., and Wang, T. I. (2006). A learning objects recommendation model based on the preference and ontological approaches. In Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies, ICALT 7806, pages 36-40, Washington, DC, USA. IEEE Computer Society.
  25. Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., and Duval, E. (2012). Context-aware recommender systems for learning: A survey and future challenges. IEEE Trans. Learn. Technol., 5(4):318-335.
  26. Wills, R. S. (2006). Google's pagerank: The math behind the search engine. Math. Intelligencer, pages 6-10.
  27. Yen, N. Y., Shih, T. K., Chao, L. R., and Jin, Q. (2010). Ranking metrics and search guidance for learning object repository. IEEE Trans. Learn. Technol., 3(3):250-264.
  28. Zhuhadar, L., Nasraoui, O., and Wyatt, R. (2008). Metadata domain-knowledge driven search engine in ”hypermanymedia” e-learning resources. In Proceedings of the 5th International Conference on Soft Computing As Transdisciplinary Science and Technology, CSTST 7808, pages 363-370, New York, NY, USA. ACM.

Paper Citation

in Harvard Style

Ngoc Chan N., Roussanaly A. and Boyer A. (2015). Studying Relations Between E-learning Resources to Improve the Quality of Searching and Recommendation . In Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-107-6, pages 119-129. DOI: 10.5220/0005454301190129

in Bibtex Style

author={Nguyen Ngoc Chan and Azim Roussanaly and Anne Boyer},
title={Studying Relations Between E-learning Resources to Improve the Quality of Searching and Recommendation},
booktitle={Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU,},

in EndNote Style

JO - Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Studying Relations Between E-learning Resources to Improve the Quality of Searching and Recommendation
SN - 978-989-758-107-6
AU - Ngoc Chan N.
AU - Roussanaly A.
AU - Boyer A.
PY - 2015
SP - 119
EP - 129
DO - 10.5220/0005454301190129