Abstract
In the past years learning has evolved from face-to-face to computer-supported learning, and we are now entering yet a new phase. The (r)evolution that yielded the knowledge transforming the Web 1.0 into Web 2.0 is now coming to e-learning contexts. Social media are the technologies most widely used to share educational contents, to find colleagues, discussion groups, and so on. But while in the Web 1.0 the most “time-spending” activity was to find suitable learning content, in the Web 2.0 era the search process is focused on different types of resources. This paper proposes a recommendation method that, by using a clustering algorithm, is able to support users during the selection steps. The recommendation is based on the tags defined by the network learners and the items to be recommended include not only contents but also social connections that could enrich the user’s learning process.
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References
Giurgiu, L., Bârsan, G.: The prosumer – core and consequence of the web 2.0 era. Revista de Informatica Sociala V(9) (June 2008)
Chatti, M.A., Jarke, M., Frosch-Wilke, D.: The future of e-learning: a shift to knowledge networking and social software. Int. J. Knowledge and Learning 3(4/5), 404–420 (2007)
Naeve, A.: The human semantic web – shifting from knowledge push to knowledge pull. International Journal of Semantic Web and Information Systems (IJSWIS) 1(3), 1–30 (2005)
Tian, S.W., Yu, A.Y., Vogel, D., Chi-Wai Kwok, R.: The impact of online social networking on learning: a social integration perspective. International Journal of Networking and Virtual Organisations 8(3/4), 264–280 (2011)
Frosch-Wilke, D., Amine Chatti, M., Jarke, M.: The future of e-learning: a shift to knowledge networking and social software. Int. J. Knowledge and Learning 3(4/5) (2007)
Cho, H., Gay, G., Davidson, B., Ingraffea, A.: Social networks, communication styles, and learning performance in a CSCL community. Computers and Education 49(2), 309–329 (2007)
Drachsler, H., Hummel, H.G.K., Koper, R.: Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model. Int. J. Learning Technology 3(4), 404–423 (2008)
Chen, C.-M., Hong, C.-M., Chang, C.-C.: Mining interactive social network for recommending appropriate learning partners in a Web-based cooperative learning environment. In: IEEE Conference on Cybernetics and Intelligent Systems, pp. 642–647. IEEE Press (2008)
Yang, F., Han, P., Shen, R.-M., Kraemer, B.J., Fan, X.: Cooperative Learning in Self-Organizing E-Learner Communities Based on a Multi-Agents Mechanism. In: Gedeon, T(T.) D., Fung, L.C.C. (eds.) AI 2003. LNCS (LNAI), vol. 2903, pp. 490–500. Springer, Heidelberg (2003)
Ounnas, A., Liccardi, I., Davis, H.C., Millard, D., White, S.A.: Towards a semantic modeling of learners for social networks. In: SW-EL, Workshop at the AH 2006, Dublin, Ireland, pp. 1–6 (2006)
Friend-of-a-Friend project, http://www.foaf-project.org/
ELGG, Social Network Engine Open Source, http://www.elgg.org/
Roselli, T., Rossano, V.: Describing learning scenarios to share teaching experiences. In: Int. Conference on Information Technology Based Higher Education and Training (ITHET), July 10-13, pp. 180–186. IEEE Computer Society Press, Sydney (2006)
Ertoz, L., Steinback, M., Kumar, V.: Finding Clusters of Different Sizes, Shapes, and Density in Noisy, High Dimensional Data. In: Second SIAM International Conference on Data Mining, San Francisco, CA, USA (2003)
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Di Bitonto, P., Roselli, T., Rossano, V. (2011). Recommendation in E-Learning Social Networks. In: Leung, H., Popescu, E., Cao, Y., Lau, R.W.H., Nejdl, W. (eds) Advances in Web-Based Learning - ICWL 2011. ICWL 2011. Lecture Notes in Computer Science, vol 7048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25813-8_36
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DOI: https://doi.org/10.1007/978-3-642-25813-8_36
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