Abstract
In distance education environments, collaborative activities such as wikis, forums and chats play an important role in the e-learning experience because they promote communication among students and so allow cooperative learning settings to be implemented. Nevertheless, it could be difficult for learners to pick out the most interesting and appropriate collaborative activities to meet their learning needs. Recommender systems integrated in e-learning platforms are usually used mainly to help learners choose teaching resources, but they can also be useful to suggest the collaborative activities that best fit their learning objectives from a pedagogical point of view. In this context, the paper presents a recommendation approach able to suggest collaborative activities such as forums, chats, wikis and blogs, that combines dynamic clustering and prediction calculus on the basis of the learners’ profiles and needs.
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Wang, P.Y.: The Analysis and Design of Educational Recommender Systems. In: Carlsen, R., et al. (eds.) Proceedings of Society for Information Technology & Teacher Education International Conference 2007, pp. 2134–2140. AACE, Chesapeake (2007)
Castro-Herrera, C., Cleland-Huang, J., Mobasher, B.: A recommender system for dynamically evolving online forums. In: Proceedings of the Third ACM Conference on Recommender systems (RecSys 2009), pp. 213–216. ACM, New York (2009)
Tang, T., McCalla, G.: Smart recommendation for an evolving e-learning system. International Journal on E-Learning 4(1), 105–129 (2005)
Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining, vol. 400, pp. 525–526 (2000)
Jaafar, A., Sareni, B., Roboam, X.: Clustering analysis of railway driving missions with niching. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 31(3), 920–931 (2012)
Chiang, M.M.-T., Mirkin, B.: Experiments for the Number of Clusters in K-Means. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 395–405. Springer, Heidelberg (2007)
Ben Schafer, J., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)
Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 257–298. Springer (2011)
Adomavicius, G., Kwon, Y.: New Recommendation Techniques for Multi-Criteria Rating Systems. IEEE Intelligent Systems 22, 48–55 (2007)
Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002) ISSN: 0924-1868, doi:10.1023/A:1021240730564
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer Science + Business Media, New York (2011) ISBN/ISSN: 9780387858197 0387858199, doi: 10.1007/978-0-387-85820-3_2, LLC 201
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Walker, A., Recker, M., Lawless, K., Wiley, D.: Collaborative information filtering: A review and an educational application. International Journal of ArtificialIntelligence and Education 14, 3–28 (2004)
Ben Schafer, J., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)
Anderson, M., Ball, M., Boley, H., Greene, S., Howse, N., Lemire, D., McGrath, S.: RACOFI: a Rule-Applying Collaborative Filtering System. IEEE/WIC, Halifax (2003)
Drachsler, H., Hummel, H.G.K., Van den Berg, B., Eshuis, J., Berlanga, A.J., Nadolski, R.J., Waterink, W., Boers, N., Koper, R.: Recommendation strategies for e-learning: preliminary effects of a personal recommender system for lifelong learners, Maastricht (2007)
Burke, R.: Knowledge-based Recommender Systems. In: Kent, A. (ed.) Encyclopedia of Library and Information Systems, Marcel Dekker, New York (2000)
Zaıane, O.: Building a recommender agent for e-learning systems. In: ICCE, pp. 55–59 (2002)
Wang, K., Zhang, J., Li, D., Zhang, X., Guo, T.: Adaptive Affinity Propagation Clustering. Acta Automatica Sinica 33(12)
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Di Bitonto, P., Laterza, M., Roselli, T., Rossano, V. (2013). Recommendation of Collaborative Activities in E-learning Environments. In: Kurosu, M. (eds) Human-Computer Interaction. Applications and Services. HCI 2013. Lecture Notes in Computer Science, vol 8005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39262-7_55
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DOI: https://doi.org/10.1007/978-3-642-39262-7_55
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