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The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used by such adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism efficiently combines distributed learner models without the need to exchange internal structure of local Bayesian networks, nor local evidence between the involved platforms.
|Title of host publication||ELEARN 2006 : World Conference on E-Learning in Corporate, Government, Health Care, and Higher Education|
|Number of pages||8|
|Publisher||AACE - Association for the Advancement of Computing in Education|
|Publication status||Published - 2006|
|Event||ELEARN 2006 - Honolulu, Hawai, United States|
Duration: 19 May 2010 → …
|Period||19/05/2010 → …|
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- 1 Finished
EU/IST FP6 NoE Prolearn
Information Sociaty Technologies
01/09/2006 → 31/12/2007