Collaborative recommender system using distributed rule mining for improving web-based adaptive courses;Sistema recomendador colaborativo usando minería de datos distribuida para la mejora continua de cursos e-learning

Revista Iberoamericana de Tecnologias del Aprendizaje

Volumn 3, Issue 1, 2008, Pages 19-30

  García Salcines, Enrique ^a   Romero Morales, Cristóbal ^a   Ventura Soto, Sebastián ^a   De Castro, Carlos Lozano ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

Data mining; Elearning; Recommender systems; Web based education

Indexed keywords

ASSOCIATION RULE ALGORITHM; COLLABORATIVE RECOMMENDER SYSTEMS; DATA MINING TECHNIQUES; EDUCATIONAL SYSTEMS; EVALUATION MEASURES; RULE MINING; WEB BASED; WEB COURSE; WEB-BASED COURSE; WEB-BASED EDUCATION;

DATA MINING; RECOMMENDER SYSTEMS; TEACHING;

E-LEARNING;

EID: 79954576999     PISSN: None     EISSN: 19328540     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (39)

1. Itmazi, J.A.S., "Sistema Flexible de gestión del e-learning para soportar el aprendizaje en las universidades tradicionales y abiertas". PhD Thesis. University of Granada, Spain.
2. Brusilovsky, P. "Adaptive and Intelligent Web-based Educational Systems", International Journal of Artificial Intelligence in Education, 2003, pp. 156-169, IO Press.
3. Brusilovsky, P., "Methods and techniques of adaptive hypermedia", User Modeling and User-Adapted Interaction, 6(2-3), 1996, pp. 87- 129 (Pubitemid 126716208)
4. De Bra, P. & Calvi, L., "AHA! An open Adaptive Hipermedia Architecture", The New Review of Hipermedia and Multimedia, 4, 1998, pp. 115-139.
5. Brusilovsky, P., Schwarz, E., & Weber, G., "ELM-ART: An intelligent tutoring system on World Wide Web", Third International Conference on Intelligent Tutoring Systems, ITS-96 (Vol. 1086, 1996, pp. 261-269). Berlin: Springer Verlag (Pubitemid 126086764)
6. Heift, T., & Nicholson, D., "Web delivery of adaptive and interactive language tutoring", International Journal of Artificial Intelligence in Education, 12(4), 2001, pp. 310-324.
7. Costaguta R., "Una Revisión de Desarrollos Inteligentes para Aprendizaje Colaborativo Soportado por Computadora". Revista Ingeniería Informática, edición 13, Disponible en Abril de 2008 en http://www.inf.udec.cl/revista.
8. Herín, D., Sala M., Pompidor, P. "Evaluating and Revising Courses from Web Resources Educational". ITS 2002, LNCS 2363, pp. 208- 218. 2002.
9. Zaïne, O.R., Han, J. "Implementation of a Web Usage Mining Framework for Web Activity Evaluation". TeleLearning Conference 2000. pp. 13-21.
10. Zhang C., Zhang S., Association Rule Mining. Berlin: Springer, 2002, ch. 7.
11. W. Klösgen, J.M. Zytkow. Handbook of Data Mining and Knowledge Discovery. Oxford University Press. 2002.
12. Srivastava, J.; Mobasher, B.; Cooley, R., "Automatic Personalization Based on Web Usage Mining", Communications of the Association of Computing Machinery, 2000, pp. 142-151.
13. Barnett, V.; Lewis, T.; Outliers in Statistical Data. John Wiley & Sons, 1994.
14. Romero, C., Ventura, S., Bra, P. D., & de Castro, C., "Discovering prediction rules in AHA! Courses", En 9th International User Modeling Conference (Vol. 2702, pp. 25-34), 2003, Berlin: Springer Verlag. (Pubitemid 36768545)
15. Li, J.; Zaiane, O.R. "Combining Usage, Content and Structure Data to Improve Web Site Recommendation", Int. Conf. on Electronic Commerce and Web Technologies, Spain, 2004.
16. Agrawal, R., et al, "Fast discovery of association rules", In Advances in Knowledge Discovery and Data Mining, Menlo Park, CA: AAAI Press, 1996, pp. 307-328.
17. Zheng Z., et al, "Real world performance of association rules". In Proceedings of the Sixth ACM-SIGKDD, 2001.
18. Tobias S., "Finding Association Rules That Trade Support Optimally against Confidence", Lecture Notes in Computer Science, Vol. 2168, 2001, 424+
19. Tan P., Kumar V., "Interesting Measures for Association Patterns: A Perspectiva", Technical Report TR00-036. Department of Computer Science. University of Minnnesota, 2000.
20. Silberschatz, A., Tuzhilin, A.. "What makes pattterns interesting in Knoledge discovery systems"., IEEE Trans. on Knowledge and Data Engineering. 8(6), 1996, pp.970-974. (Pubitemid 126774153)
21. Liu B., Wynne H., Shu C. Yiming M., "Analyzing the Subjective Interestingness of Association Rules", IEEE Inteligent System, 2000.
22. A. Savasere, E. Omiecinski, and S. B. Navathe, "An efficient algorithm for mining association rules in large databases," in Proceedings of 21st International Conference on Very Large Data Bases. VLDB, Sept. 11-15 1995, pp. 432-444. [Online]. Disponible en Abril de 2008 en http://www.vldb.org/ dblp/db/conf/vldb/.
23. P. Scheuermann, "Distributed web log mining using maximal large itemsets," Knowledge and Information Systems, vol. 3, no. 4, Nov. 2001, pp. 389-404.
24. D. B. Skillicorn and Y. Wang, "Parallel and sequential algorithms for data mining using inductive logic," Knowledge and Information Systems, vol. 3, no. 4, pp. 405-421, Nov. 2001.
25. D. W.-L. Cheung, V. Ng, A. W.-C. Fu, and Y. Fu, "Efficient mining of association rules in distributed databases," IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 911-922, Dec. 1996. (Pubitemid 126774146)
26. Zan, H. et al, "A graph model for E-commerce Recomendador systems," Journal of the American Society of Information Science and Technology, 55(3), 2004, pp.259-274.
27. Eliassi-Rad, T. and Shavlik, J., "A System for Building Intelligent Agents that Learn to Retrieve and Extract Information," International Journal of User Modeling and User-Adapted Interaction, special issue User Modeling and Intelligent Agents. 13 (4), No. 1-2, 2003, pp.. 35-88.
28. Geyer-Schulz, A. et al., "An Architecture for Behavior-Based Library Recomendador Systems," Information Technology and Libraries. 22(4), 2003, pp.165-174. (Pubitemid 38153360)
29. Rosta F., Brusilovsky, P., "Social navigation support in a course recommendation system", Adaptive Hypermedia and Adaptive Web- Based Systems: 4th International Conference, AH 2006, 2006, pp. 91-100. (Pubitemid 44077598)
30. Tang T., McCalla, G., "Smart Recommendation for an Evolving ELearning System: Architecture and Experiment". International Journal on E-Learning, 4(1), 2005, pp. 105-129.
31. Terveen, L. and Hill, W., "Beyond Recomendador Systems: Helping People Help Each Other". In J. M. Carroll (Ed.) Human-Computer Interaction in the New Millennium, Addison-Wesley. ACM Press, New York, ch 22, 2001, pp. 487-509.
32. Burke, R., "Semantic ratings and heuristic similarity for collaborative filtering", In Proceedings of the Seventeenth National Conference on Artificial Intelligence, Austin, Texas, July 30th-August 3rd, 2000.
33. Burke, R,. "Knowledge-based Recomendador Systems". In A. Kent (ed.), Encyclopedia of Library and Information Systems. Vol. 69, Supplement 32. New York: Marcel Dekker. 2000.
34. Data Mining Group. Predictive Model Markup Language (PMML). Disponible en Abril de 2008 en http://www.dmg.org/pmml-v3-0.html .
35. García, E., Romero, C. et al, "Using Rules Discovery for the Continuous Improvement of e-Learning Courses", en Proc. of the 7th International Conference on Intelligent Data Engineering and Automated Learning- IDEAL 2006, LNCS 4224, 2006, pp. 887-895 (Pubitemid 44670453)
36. Good I., Probability and the weighting of evidence. Charles Griffin, London, 1950.
37. Paquete de minería de datos Weka. Disponible en Abril de 2008 en http://www.cs.waikato.ac.nz/ml/weka/.
38. De Castro, C., García, E., Romero, C., Ventura, S. "Herramienta autor INDESAHC para la creación de cursos hipermedia adaptativos". Revista latinoamericana de tecnología educativa. Vol. 3, 1, 2004.
39. H. Liu, F. Hussain, C.L. Tan, and M. Dash. "Discretization: An enabling technique". Journal of Data Mining and Knowledge Discovery, 2002, pp. 393-423. (Pubitemid 37113864)