Ứng dụng sự thích nghi cộng đồng trong các hệ thống đào tạo thông minh
Corressponding author's email:
tapchikhgkdt@hcmute.edu.vnKeywords:
Intelligent Tutoring Systems, User Profile, Collaborative Filtering, and Rule-based In- ductionAbstract
Nowadays, personalized systems are increasingly applied in diverse fields including e-Learning. In general, Intelligent Tutoring Systems build learners’ profiles in order to give them adaptive access to information and other learning resources. Recently, some researchers propose using Collaborative Filtering as an alternative approach for personalizing information access in Intelligent Tutoring Systems. With this technique, a user receives recommendations on the basis of the evaluation of his/her community. In this paper, we present a new approach of multi-criteria communities for Collaborative Filtering in Intelligent Tutoring Systems in which criteria can be extracted from features/ characteristics in learner profiles.
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References
Amato, G., & Straccia, U. (1999). User Profile Modeling and Applications to Digital Libraries. In Proceedings of the 3rd European Conference on Research and Advanced Technology for Digital Libraries (ECDL‘99), LNCS, 1696, France (pp. 184197).
Breese, J.-S., Heckerman, D., & Kadie C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the 14th Conference on Uncertainty In Artificial Intelligence (UAI’98), Wisconsin, USA (pp. 43-52).
Brooks, C., Greer, J., Melis, E., & Ullrich, C. (2006). Combining ITS and eLearning Technologies: Opportunities and Challenges. In Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS 2006), Jhongli, Taiwan (pp. 278-287).
Brusilovsky, P. (2001). Adaptive Hypermedia. User Modeling and UserAdapted Interaction, 11, 87-110.
Clark, P., & Niblett, T. (1989). The CN2 induction algorithm. Machine Learning, 3 (4), 261-284.
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., & Sartin M. (1999). Combining ContentBased and Collaborative Filters in an Online Newspaper. In Proceedings of the 22nd International ACM Conference on Research and Development in Information Retrieval (SIGIR’99), USA.
Grzymala-Busse, J.-W., & Hu, M. (2000). A Comparison of Several Approaches to Missing Attribute Values in Data Mining. In Proceedings of the 2nd Conference on RS and Current Trends in Computing (RSCTC’00), Canada.
Herlocker, J.-L. (2002). Understanding and Improving Automated Collaborative Filtering Systems. Ph.D Dissertation, University of Minnesota, USA.
Jain, A.-K., Murty, M.-N., & Flynn P.-J. (1999). Data Clustering: A Review. ACM Computing Surveys, 31 (3), 264323.
Kiared, S.-A., Razek, M.-A., & Frasson, C. (2006). The Pyramid Collaborative Filtering Method: Toward an Efficient E-Course. In Proceedings of the Intelligent Tutoring Systems (IASTED’06), Puerto Vallarta, Mexico (pp. 248-257).
Kumar, V.-S. (1996). ComputerSupported Collaborative Learning: Issues for Research. In Proceedings of the 8th Annual Graduate Symposium on Computer Science, Canada (Retrieved August 2004).
Madhour, H., & Forte, M.-W. (2006). Semantic learning and extended student model: Towards an AHAM-based adaptive system. In Proceedings of the 5th IASTED International Conference on Webbased Education (IASTED’06), Puerto Vallarta, Mexico (pp. 349-354).
Miyahara, K., & Pazzani, M.-J. (2000). Collaborative Filtering with the Simple Bayesian Classifier. In Proceedings of the 6th Pacific Rim International Conference on Artificial Intelligence (PRICAI’00), Australia (pp. 679-689).
Montaner, M., López, B., & De La Rosa, J.-L. (2003). A Taxonomy of Recommender Agents on the Internet. Artificial Intelligence Review, 19, 285330. Kluwer Publishers.
Nguyen, A.-T., Denos, N., & Berrut, C. (2005). Cartes de communautés pour l’adaptation interactive de profils dans un système de filtrage d’information. Actes du 23ème Congrès annuel de l’Informatique des Organisations và Systèmes d’Information và de Décision (INFORSID’05), France (pp. 253-268).
Nguyen, A.-T., Denos, N., & Berrut, C. (2007). Improving New User Recommendations with Rule-based Induction on Cold User Data. In Proceedings of the ACM Conference on Recommender Systems 2007 (RecSys 2007), Minnesota, USA (to appear).
Pawlak, Z. (1982). Rough Sets. International Journal of Computer and Information Sciences, 11 (5), 341-356. Plenum Publishing Corporation.
Pawlak, Z. (2004). Some Issues on Rough Sets. Transaction on Rough Sets I, LNCS 3100.
Pintwart, N., Aleven, V., Ashley, K., & Lynch, C. (2006). Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation. In Proceedings of Workshops held at the 4th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, Ireland (pp. 542-551).
Rashid, A., Albert, I., Cosley, D., Lam, S.-K., McNee, S.-M., Konstan, J.A., & Riedl, J. (2002). Getting to Know You: Learning New User Preferences in Recommender Systems. In Proceedings of the 7th International Conference on Intelligent User Interfaces (IUI’02), California, USA (pp. 127-134).
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In Proceedings of the Conference on Computer Supported Cooperative Work (CSCW’94), NC, USA.
Skowron, A., & Rauszer, C. (1992). The Discernibility Matrices and Functions in Information Systems. Intelligent Decision Support: Handbook of Applications and Advances of Rough Set Theory, Series: Theory and Decision Library, 11, 331-362. Kluwer “Công chức” Publishers, Dordrecht.
Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning: An historical perspective. Cambridge Handbook of the Learning Sciences. Cambridge, UK: Cambridge University Press.
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