E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Honolulu, Hawaii, USA ISBN 978-1-880094-60-0 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA
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.
Tedesco, R., Dolog, P., Nejdl, W. & Allert, H. (2006). Distributed Bayesian Networks for User Modeling. In T. Reeves & S. Yamashita (Eds.), Proceedings of E-Learn 2006--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 292-299). Honolulu, Hawaii, USA: Association for the Advancement of Computing in Education (AACE). Retrieved December 16, 2018 from https://www.learntechlib.org/primary/p/23699/.
© 2006 Association for the Advancement of Computing in Education (AACE)