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Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment
Article

, Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong ; , University of Saskatchewan, Canada

International Journal on E-Learning, in Norfolk, VA ISSN 1537-2456 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC USA

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Abstract

In this paper, we proposed an evolving e-learning system which can adapt itself both to the learners and the open the web and pointed out the differences of making recommendations in e-learning and other domains. We propose two pedagogy features in recommendation: learner interest and background knowledge. A description of paper value, similarity, and ordering are presented using formal definitions. We also study two pedagogy-oriented recommendation techniques: content-based and hybrid recommendations. We argue that while it is feasible to apply both of these techniques in our domain, a hybrid collaborative filtering technique is more efficient to make "just-in-time" recommendations. In order to assess and compare these two techniques, we carried out an experiment using artificial learners. Experiment results are encouraging, showing that hybrid collaborative filtering, which can lower the computational costs, will not compromise the overall performance of the RS. In addition, as more and more learners participate in the learning process, both learner and paper models can better be enhanced and updated, which is especially desirable for web-based learning systems. We have tested the recommendation mechanisms with real learners, and the results are very encouraging

Citation

Tang, T. & McCalla, G. (2005). Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment. International Journal on E-Learning, 4(1), 105-129. Norfolk, VA: Association for the Advancement of Computing in Education (AACE). Retrieved August 11, 2024 from .

© 2005 Association for the Advancement of Computing in Education (AACE)

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References

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  1. Basu, C., Hirsh, H., Cohen, W., & Nevill-Manning, C. (2001). Technical paper recommendations: A study in combining multiple information sources. JAIR, 1, 231-252.
  2. Billsus, D., & Pazzani, M. (1999). A hybrid user model for news story classification. Proceedings of UM’99, 99-108.
  3. Boyle, C., & Encarnacion, A. O. (1994). MetaDoc: An adaptive hypertext reading system. UMUAI, 4, 1-19.
  4. Brusilovsky, P. (2001). Adaptive hypermedia. UMUAI, 11(1/2), 87-110.
  5. Chan, T., & Baskin, A.B. (1990). Learning companion systems. ITS 1990, 6-33. Tang and McCalla
  6. Debevc, M., Meyer, B., & Svecko, R. (1997). An adaptive short list for documents on the world wide web. IUI 1997, 209-211. U.S.A.
  7. De Bra, P., & Calvi, L. (1998). AHA! An open adaptive hypermedia architecture. The New Review of Hypermedia and Multimedia, 4, 115-139.
  8. Herlocker, J., Konstan, J., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. SIGIR’99, 230-237.
  9. Jameson, A., Konstan, J., & Riedl, J. (2002). AI techniques for personalized recommendation. Tutorial notes, AAAI-02, Edmonton, Canada.
  10. Joachims, T., Freitag, D., & Mitchell, T. (1997). WebWatcher: A tour guide for the World Wide Web. Proceedings of IJCAI’97, 770-775.
  11. Kaplan, C., Fenwick, J., & Chen, J. (1993). Adaptive hypertext navigation based on user goals and context. UMUAI, 3(3), 193-220.
  12. Kobsa, A., Koenemann, J., & Pohl, W. (2001). Personalized hypermedia presentation techniques for improving online customer relationships. The Knowledge Engineering Review 16(2):111-155. McCalla, G. (2000). The fragmentation of culture, learning, teaching and technology: Implications for the artificial Intelligence in education research agenda in 2010. IJAIED. 11(2): 177-196. 2000.
  13. McNee, S, Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S., Rashid, A., Konstan, J., & Riedl, J. (2002). On the recommending of citations for research papers. ACM CSCW’02, 116-125. Melville, P., Mooney, R., & Nagarajan, R. (2002). Content-boosted collaborative filtering for improved recommendations. AAAI/IAAI 2002, 187-192. Edmonton, Canada.
  14. Paepcke, A., Garcia-Molina, H., Rodriguez-Mula, G., & Cho, J. (2000). Beyond document similarity: Understanding value-based search and browsing technologies. SIGMOD Records, 29(1): 80-92.
  15. Pazzani, M., Muramatsu, J., & Billsus, D. (1996). Syskill and Webert: Identifying interesting Web sites. AAAI’96, 54-61.
  16. Pitkow, J., & Pirolli, P. (1997). Life, death, and lawfulness on the electronic frontier. ACM CHI 1997, 383-390.
  17. Resnick, P., Iacouvou, N., Suchak, N., Bergstrom, P., & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of Netnews. ACM CSCW’94, 175-186.
  18. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Analysis of recommendation algorithms for e-commerce. ACM EC’2000, 158-167. Minneapolis.
  19. Schafer, J., Konstan, J., & Riedl, J. (2001). Electronic commerce recommender applications. Data Mining and Knowledge Discovery, 5, (1/2, 2001), 115-152.
  20. Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating ‘word of mouth’. ACM CHI’1995, 210-217 Denver.
  21. Schein, A., Popescul, A., Ungar, L.H., & Pennock, D. (2002). Proceedings of SIGIR’02. 253-260. Stern, M. K., & Woolf, B.P. (2000). Adaptive content in an online lecture system. AH, 227-238 Tang, T.Y., & Chan, K.C.C. (2002). Feature construction for student group forming based on their browsing behaviors in an e-learning system. PRICAI 2002, LNCS 2417, 512-521.
  22. Tang, T. Y., & McCalla, G. (2003a). Smart recommendations for an evolving e-learning system. Workshop on Technologies for Electronic Documents for Supporting Learning, AIED'2003.
  23. Tang, T. Y. & McCalla, G. (2004a). On the pedagogically guided paper recommendation for an evolving web-based learning system. FLAIRS Conference, 2004. AAAI Press. 86-91.
  24. Tang, T. Y. & McCalla, G. (2004b). Utilizing artificial learners to help overcome the cold-start problem in a pedagogically-oriented paper recommendation system. In Proceedings of AH 2004: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems. August 23-26, Eindhoven, Netherlands, 245-254.
  25. Tang, T Y. & McCalla, G (2004c) Laws of Attraction: In Search of Document Value-ness for Recommendation. In Proceedings of ECDL 2004: the 8th European Conference on Digital Library, Sept. 12-17 2004, Bath, UK. 269-280.
  26. Weber, G., & Brusilovsky, P. (2001). ELM-ART: An adaptive versatile system for web-based instruction. International Journal of AI in Education, 12: 1-35.
  27. Woodruff, A., Gossweiler, R., Pitkow, J., Chi, E., & Card, S. (2000). Enhancing a digital book with a reading recommender. ACM CHI 2000, 153-160.

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