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Exploiting Semantic Information for Graph-Based Recommendations of Learning Resources

  • Mojisola Anjorin
  • Thomas Rodenhausen
  • Renato Domínguez García
  • Christoph Rensing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7563)

Abstract

Recommender systems in e-learning have different goals as compared to those in other domains. This brings about new requirements such as the need for techniques that recommend learning resources beyond their similarity. It is therefore an ongoing challenge to develop recommender systems considering the particularities of e-learning scenarios like CROKODIL. CROKODIL is a platform supporting the collaborative acquisition and management of learning resources. It supports collaborative semantic tagging thereby forming a folksonomy. Research shows that additional semantic information in extended folksonomies can be used to enhance graph-based recommendations. In this paper, CROKODIL’s folksonomy is analysed, focusing on its hierarchical activity structure. Activities help learners structure their tasks and learning goals. AScore and AInheritScore are proposed approaches for recommending learning resources by exploiting the additional semantic information gained from activity structures. Results show that this additional semantic information is beneficial for recommending learning resources in an application scenario like CROKODIL.

Keywords

ranking resource recommendation folksonomy tagging 

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References

  1. 1.
    Abel, F.: Contextualization, User Modeling and Personalization in the Social Web. PhD Thesis, Gottfried Wilhelm Leibniz Universitšt Hannover (2011)Google Scholar
  2. 2.
    Abel, F., Frank, M., Henze, N., Krause, D., Plappert, D., Siehndel, P.: GroupMe! - Where Semantic Web Meets Web 2.0. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 871–878. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Anjorin, M., Rensing, C., Bischoff, K., Bogner, C., Lehmann, L., Reger, A., Faltin, N., Steinacker, A., Lüdemann, A., Domínguez García, R.: CROKODIL - A Platform for Collaborative Resource-Based Learning. In: Kloos, C.D., Gillet, D., Crespo García, R.M., Wild, F., Wolpers, M. (eds.) EC-TEL 2011. LNCS, vol. 6964, pp. 29–42. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Anjorin, M., Rensing, C., Steinmetz, R.: Towards ranking in folksonomies for personalized recommender systems in e-learning. In: Proc. of the 2nd Workshop on Semantic Personalized Information Management: Retrieval and Recommendation. CEUR-WS, vol. 781, pp. 22–25 (October 2011)Google Scholar
  5. 5.
    Böhnstedt, D., Scholl, P., Rensing, C., Steinmetz, R.: Collaborative Semantic Tagging of Web Resources on the Basis of Individual Knowledge Networks. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 379–384. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Cantador, I., Konstas, I., Jose, J.: Categorising Social Tags to Improve Folksonomy-Based Recommendations. Web Semantics: Science, Services and Agents on the World Wide Web 9, 1–15 (2011)CrossRefGoogle Scholar
  7. 7.
    Desrosiers, C., Karypis, G.: A Comprehensive Survey of Neighborhood-Based Recommendation Methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 107–144. Springer (2011)Google Scholar
  8. 8.
    Drachsler, H., Pecceu, D., Arts, T., Hutten, E., Rutledge, L., van Rosmalen, P., Hummel, H.G.K., Koper, R.: ReMashed – Recommendations for Mash-Up Personal Learning Environments. In: Cress, U., Dimitrova, V., Specht, M. (eds.) EC-TEL 2009. LNCS, vol. 5794, pp. 788–793. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Hannafin, M., Hill, J.: Resource-Based Learning. In: Handbook of Research on Educational Communications and Technology, pp. 525–536 (2008)Google Scholar
  10. 10.
    Hintze, J., Nelson, R.: Violin plots: A box plot-density trace synergism. The American Statistician 52(2), 181–184 (1998)Google Scholar
  11. 11.
    Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: BibSonomy: A Social Bookmark and Publication Sharing System. In: Proc. of the Conceptual Structures Tool Interoperability Workshop (2006)Google Scholar
  12. 12.
    Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information Retrieval in Folksonomies: Search and Ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag Recommendations in Folksonomies. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 506–514. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Manning, C., Raghavan, P., Schÿtze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)Google Scholar
  15. 15.
    Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R.: Recommender Systems in Technology Enhanced Learning. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 387–415. Springer (2011)Google Scholar
  16. 16.
    Peters, I.: Folksonomies: Indexing and Retrieval in Web 2.0. De Gruyter Saur (2010)Google Scholar
  17. 17.
    Rakes, G.: Using the Internet as a Tool in a Resource-Based Learning Environment. Educational Technology 36, 52–56 (1996)Google Scholar
  18. 18.
    Romero Zaldivar, V.A., Crespo García, R.M., Burgos, D., Kloos, C.D., Pardo, A.: Automatic Discovery of Complementary Learning Resources. In: Kloos, C.D., Gillet, D., Crespo García, R.M., Wild, F., Wolpers, M. (eds.) EC-TEL 2011. LNCS, vol. 6964, pp. 327–340. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mojisola Anjorin
    • 1
  • Thomas Rodenhausen
    • 1
  • Renato Domínguez García
    • 1
  • Christoph Rensing
    • 1
  1. 1.Multimedia Communications LabTechnische Universität DarmstadtGermany

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