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What is Multi-Entity Bayesian Networks

Cognitive Computing in Technology-Enhanced Learning
A logic system that integrates first order logic (FOL) with Bayesian probability theory.
Published in Chapter:
Managing the Learner Model With Multi-Entity Bayesian Networks in Adaptive Hypermedia Systems
Mouenis Anouar Tadlaoui (Abdelmalek Essaâdi University, Morocco), Rommel Novaes Carvalho (University of Brasília, Brazil), and Mohamed Khaldi (Abdelmalek Essaâdi University, Morocco)
Copyright: © 2019 |Pages: 20
DOI: 10.4018/978-1-5225-9031-6.ch005
Abstract
Modeling the learner in adaptive systems involves different information. There are several methods to manage the learner model. They do not handle the uncertainty in the dynamic modeling of the learner. The main hypothesis of this chapter is the management of the learner model based on multi-entity Bayesian networks. This chapter focuses on modeling the learner model in a dynamic and probabilistic way. The authors propose in this work the use of the notion of fragments and m-theory to lead to a Bayesian multi-entity network. The use of this Bayesian method can handle the whole course of a learner as well as all of its shares in an adaptive educational hypermedia.
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Combining the Overlay Model and Bayesian Networks to Determine Learning Styles in AHES
A logic system that integrates first order logic (FOL) with Bayesian probability theory.
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