Student profiles to improve searching in e-learning systems
by Oriana Licchelli, Giovanni Semeraro
International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), Vol. 17, No. 4/5, 2007

Abstract: European countries have accumulated an enormous quantity of information in Digital Libraries (DLs). Offering seamless universal access to those collections will have a formidable impact on citizens' activities. Students could use information in DLs for improving their curricula, but it is difficult to find the exact chunk of material that solves a specific problem. A possible solution is to develop technologies that learn user preferences for customising information search. This paper focuses on a system based on Machine Learning techniques, the Profile Extractor, which automatically builds student models. An experimental session has been performed, evaluating the accuracy of the system.

Online publication date: Thu, 06-Sep-2007

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