Fuzzy Student Modeling for Personalization of e-Learning Courses

  • Carla Limongelli
  • Filippo Sciarrone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8523)


In the context of e-learning courses, personalization is a more and more studied issue, being its advantage in terms of time and motivations widely proved. Course personalization basically means to understand student’s needs: to this aim several Artificial Intelligence methodologies have been used to model students for tailoring e-learning courses and to provide didactic strategies, such as planning, case based reasoning, or fuzzy logic, just to cite some of them. Moreover, in order to disseminate personalised e-learning courses, the use of known and available Learning Management System is mandatory.

In this paper we propose a fine-grained student model, embedded into an Adaptive Educational Hypermedia, LS_Plan provided as plug-in for Moodle. In this way we satisfy the two most important requirements: a fine-grained personalization and a large diffusion. In particular, the substantial modification proposed in this contribution regards the methodology to evaluate the knowledge of the single student which currently has a low granularity level. The experiments showed that the new system has improved the evaluation mechanism by adding information that students and teachers can use to keep track of learning progress.


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  1. 1.
    Biancalana, C., Flamini, A., Gasparetti, F., Micarelli, A., Millevolte, S., Sansonetti, G.: Enhancing traditional local search recommendations with context-awareness. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 335–340. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Capuano, N., Marsella, M., Salerno, S.: Abits: An agent based intelligent tutoring system for distance learning. In: Proceedings of the International Workshop in Adaptative and Intelligent Web-based Educational Systems, pp. 17–28 (2000)Google Scholar
  3. 3.
    Chrysafiadi, K., Virvou, M.: Dynamically personalized e-training in computer programming and the language c. IEEE Transactions on Education 56(4), 385–392 (2013)CrossRefGoogle Scholar
  4. 4.
    De Marsico, M., Sterbini, A., Temperini, M.: A strategy to join adaptive and reputation-based social-collaborative e-learning, through the zone of proximal development. International Journal of Distance Education Technologies 19(2), 105–121 (2012)Google Scholar
  5. 5.
    De Marsico, M., Sterbini, A., Temperini, M.: The definition of a tunneling strategy between adaptive learning and reputation-based group activities, pp. 498–500 (2011)Google Scholar
  6. 6.
    De Marsico, M., Temperini, M.: Average effort and average mastery in the identification of the zone of proximal development (2013)Google Scholar
  7. 7.
    Gasparetti, F., Micarelli, A., Sansonetti, G.: Exploiting web browsing activities for user needs identification. In: Proceedings of CSCI 2014. IEEE Computer Society, Conference Publishing Services (March 2014)Google Scholar
  8. 8.
    Gentili, G., Micarelli, A., Sciarrone, F.: Infoweb: An adaptive information filtering system for the cultural heritage domain. Applied Artificial Intelligence 17(8-9), 715–744 (2003)CrossRefGoogle Scholar
  9. 9.
    Graf, S.: Kinshuk: Providing adaptive courses in learning management systems with respect to learning styles. In: e-Learn 2007 (2007)Google Scholar
  10. 10.
    Kavcic, A.: Fuzzy user modeling for adaptation in educational hypermedia. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 34(4), 439–449 (2004)CrossRefGoogle Scholar
  11. 11.
    Kosba, E.: Generating Computer-Based Advice in Web-Based Distance Education Environments. PhD thesis, University of Leeds School of Computing (2004)Google Scholar
  12. 12.
    Kosba, E., Dimitrova, V., Boyle, R.: Using fuzzy techniques to model students in web-based learning environments. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2774, pp. 222–229. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Limongelli, C., Lombardi, M., Marani, A., Sciarrone, F.: A teacher model to speed up the process of building courses. In: Kurosu, M. (ed.) HCII/HCI 2013, Part II. LNCS, vol. 8005, pp. 434–443. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Limongelli, C., Lombardi, M., Marani, A., Sciarrone, F.: A teaching-style based social network for didactic building and sharing. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 774–777. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Limongelli, C., Miola, A., Sciarrone, F., Temperini, M.: Supporting teachers to retrieve and select learning objects for personalized courses in the moodle-ls environment. In: Giovannella, C., Sampson, D.G., Aedo, I. (eds.) ICALT, pp. 518–520. IEEE (2012)Google Scholar
  16. 16.
    Limongelli, C., Mosiello, G., Panzieri, S., Sciarrone, F.: Virtual industrial training: Joining innovative interfaces with plant modeling. In: ITHET, pp. 1–6. IEEE (2012)Google Scholar
  17. 17.
    Limongelli, C., Sciarrone, F., Starace, P., Temperini, M.: An ontology-driven olap system to help teachers in the analysis of web learning object repositories. Information System Management 27(3), 198–206 (2010)CrossRefGoogle Scholar
  18. 18.
    Limongelli, C., Sciarrone, F., Temperini, G., Vaste, M.: The lecomps5 framework for personalized web-based learning: A teacher’s satisfaction perspective. Computers in Human Beaviour 27(4) (2011)Google Scholar
  19. 19.
    Limongelli, C., Sciarrone, F., Vaste, G.: LS-plan: An effective combination of dynamic courseware generation and learning styles in web-based education. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 133–142. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  20. 20.
    Limongelli, C., Sciarrone, F., Vaste, G.: Personalized e-learning in moodle: The moodle_ls system. Journal of e-Learning and Knowledge Society 7(1), 49–58 (2011)Google Scholar
  21. 21.
    Sciarrone, F.: An extension of the q diversity metric for information processing in multiple classifier systems: a field evaluation. International Journal of Wavelets, Multiresolution and Information Processing, IJWMIP 11(6) (2013)Google Scholar
  22. 22.
    Sterbini, A., Temperini, M.: Collaborative projects and self evaluation within a social reputation-based exercise-sharing system, vol. 3, pp. 243–246 (2009)Google Scholar
  23. 23.
    Sterbini, A., Temperini, M.: Selection and sequencing constraints for personalized courses, pp. T2C1–T2C6 (2010); cited by (since 1996)Google Scholar
  24. 24.
    Warendorf, K., Tsao, S.J.: Application of fuzzy logic techniques in the bss1 tutoring system. J. Artif. Intell. Educ. 8(1), 113–146 (1997)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Carla Limongelli
    • 1
  • Filippo Sciarrone
    • 1
  1. 1.Engineering DepartmentRoma Tre UniversityRome

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