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Forming Homogeneous Classes for e-Learning in a Social Network Scenario

  • Antonello Comi
  • Lidia Fotia
  • Fabrizio MessinaEmail author
  • Giuseppe Pappalardo
  • Domenico Rosaci
  • Giuseppe M. L. Sarné
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 616)

Abstract

The use of network technology to provide online courses is the latest trend in the training and development industry and has been defined as the “e-Learning revolution”. On the other hand, Online Social Networks (OSNs) represent today an effective possibility to have common and easy-to-use platforms for supporting e-Learning activities. However, as underlined by previous studies, many of the proposed e-Learning systems can result in confusion and decrease the learner’s interest. In this paper, we introduce the possibility to form e-Learning classes in the context of OSNs. At the best of our knowledge, any of the approaches proposed in the past considers the evolution of on-line classes as a problem of matching the individual users’ profiles with the profiles of the classes. In this paper, we propose an algorithm that exploits a multi-agent system to suitably distribute such a matching computation on all the user devices. The good effectiveness and the promising efficiency of our approach is shown by the experimental results obtained on simulated On-line Social Networks data.

Notes

Acknowledgments

This work has been supported by project PRISMA PON04a2 A/F funded by the Italian Ministry of University and NeCS Laboratory of the Department DICEAM, University Mediterranea of Reggio Calabria.

References

  1. 1.
    De Meo, P., Nocera, A., Quattrone, G., Rosaci, D., Ursino, D.: Finding reliable users and social networks in a social internetworking system. In: Proceeding of the 2009 International Database Engineering and Applications Symposium, pp. 173–181. ACM, (2009)Google Scholar
  2. 2.
    De Meo, P., Nocera, A., Rosaci, D., Ursino, D.: Recommendation of reliable users, social networks and high-quality resources in a social internetworking system. AI Commun. 24(1), 31–50 (2011)Google Scholar
  3. 3.
    De Meo, P., Quattrone, G., Rosaci, D., Ursino, D.: Dependable recommendations in social internetworking. In: Web Intelligence and Intelligent Agent Technologies, IAT, pp. 519–522 (2009)Google Scholar
  4. 4.
    De Meo, P., Messina, F., Rosaci, D., Sarné, G.M.L.: Improving the compactness in social network thematic groups by exploiting a multi-dimensional user-to-group matching algorithm. In: 2014 International Conference on Intelligent Networking and Collaborative Systems (INCoS), IEEE, pp. 57–64 (2014)Google Scholar
  5. 5.
    De Meo, P., Messina, F., Rosaci, D., Sarné, G.M.L.: Recommending users in social networks by integrating local and global reputation. In: Internet and Distributed Computing Systems, pp. 437–446. Springer International Publishing (2014)Google Scholar
  6. 6.
    De Meo, P., Messina, F., Rosaci, D., Sarné, G.M.L.: 2d-socialnetworks: away to virally distribute popular information avoiding spam. In: Intelligent Distributed Computing VIII, pp. 369–375. Springer International Publishing (2015)Google Scholar
  7. 7.
    Garruzzo, S., Rosaci, D., Sarné, G.M.L.: Isabel: A multi agent e-learning system that supports multiple devices. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT’07, pp. 485–488. IEEE (2007)Google Scholar
  8. 8.
    Hui, P., Buchegger, S.: Groupthink and peer pressure: social influence in online social network groups. In: ASONAM’09 International Conference on Advances in Social Network Analysis and Mining, pp. 53–59. IEEE, (2009)Google Scholar
  9. 9.
    Hummel, J., Lechner, U.: Social profiles of virtual communities. In: HICSS Proceeding of the 35th Annual Hawaii International Conference on System Sciences, pp. 2245–2254. IEEE, (2002)Google Scholar
  10. 10.
    Kasavana, M.L., Nusair, K., Teodosic, K.: Online social networking: redefining the human web. J. Hospitality Tour. Technol. 1(1), 68–82 (2010)CrossRefGoogle Scholar
  11. 11.
    Kim, J.K., Kim, H.K., Oh, H.Y., Ryu, Y.U.: A group recommendation system for online communities. Int. J. Inf. Manag. 30(3), 212–219 (2010)CrossRefGoogle Scholar
  12. 12.
    Messina, F., Pappalardo, G., Rosaci, D., Santoro, C., Sarné, G.M.L.: A distributed agent-based approach for supporting group formation in p2p e-learning. In: AI* IA 2013: Advances in Artificial Intelligence, pp. 312–323. Springer International Publishing (2013)Google Scholar
  13. 13.
    Messina, F., Pappalardo, G., Rosaci, D., Santoro, C., Sarné, G.M.L.: Hyson: A distributed agent-based protocol for group formation in online social networks. In: Multiagent System Technologies, pp. 320–333. Springer Berlin Heidelberg (2013)Google Scholar
  14. 14.
    Messina, F., Pappalardo, G., Santoro, C.: Complexsim: An smp-aware complex network simulation framework. In: 2012 Sixth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), pp. 861–866. IEEE (2012)Google Scholar
  15. 15.
    Messina, F., Pappalardo, G., Santoro, C.: Complexsim: a flexible simulation platform for complex systems. Int. J. Simul. Process Model. 6 8(4), 202–211 (2013)Google Scholar
  16. 16.
    Moore, J., Dickson-Deane, C., Galyen, K.: e-learning, online learning, and distance learning environments:are they the same? Internet High. Educ. 14(2), 129–135 (2011)CrossRefGoogle Scholar
  17. 17.
    Palopoli, L., Rosaci, D., Sarné, G.M.L.: A multi-tiered recommender system architecture for supporting e-commerce. In: Studies in Computational Intelligence 446, Intelligent Distributed Computing VI, pp. 71–81 (2013)Google Scholar
  18. 18.
    Pearson, R.K., Zylkin, T., Schwaber, J.S., Gonye, G.E.: Quantitative evaluation of clustering results using computational negative controls. In: Proceeding of 2004 SIAM International Conference on Data Mining, pp. 188–199 (2004)Google Scholar
  19. 19.
    Rosaci, D., Sarné, G.M.L.: Efficient personalization of e-learning activities using a multi-device decentralized recommender system. Comput. Intell. 26(2), 121–141 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Rosaci, D., Sarné, G.M.L.: A multi-agent recommender system for supporting device adaptivity in e-Commerce. J. Intell. Inf. Syst. 38(2), 393–418 (2012)CrossRefGoogle Scholar
  21. 21.
    Rosaci, D., Sarné, G.M.L.: Recommending multimedia web services in a multi-device environment. Inf. Syst. 38(2), 198–212 (2013)CrossRefGoogle Scholar
  22. 22.
    Ruiz, J.G., Mintzer, M.J., Leipzig, R.M.: The impact of e-learning in medical education. Acad. Med. 81(3), 207–212 (2006)CrossRefGoogle Scholar
  23. 23.
    Welsh, E.T., Wanberg, C.R., Brown, K.G., Simmering, M.J.: e-learning: emerging uses, empirical results and future directions. Int. J. Train. Dev. 7(4), 245–258 (2003)CrossRefGoogle Scholar
  24. 24.
    Zhang, D., Zhao, J.L., Zhou, L., Nunamaker, J.F. Jr.: Can e-learning replace classroom learning? Commun. ACM 47(5), 75–79 (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Antonello Comi
    • 1
  • Lidia Fotia
    • 1
  • Fabrizio Messina
    • 3
    Email author
  • Giuseppe Pappalardo
    • 3
  • Domenico Rosaci
    • 1
  • Giuseppe M. L. Sarné
    • 2
  1. 1.DIIES, University Mediterranea of Reggio CalabriaReggio CalabriaItaly
  2. 2.DICEAM, University Mediterranea of Reggio CalabriaReggio CalabriaItaly
  3. 3.Department of Mathematics and Computer ScienceUniversity of CataniaCataniaItaly

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