Managing the Learner Model With Multi-Entity Bayesian Networks in Adaptive Hypermedia Systems

Managing the Learner Model With Multi-Entity Bayesian Networks in Adaptive Hypermedia Systems

Mouenis Anouar Tadlaoui, Rommel Novaes Carvalho, Mohamed Khaldi
Copyright: © 2019 |Pages: 20
DOI: 10.4018/978-1-5225-9031-6.ch005
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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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Learner Modeling In Adaptive Hypermedia

In this section, we will present the categories of a learner model in adaptive hypermedia educational systems. And then will present the main functionalities of the learner model.

Key Terms in this Chapter

Multi-Entity Bayesian Networks: A logic system that integrates first order logic (FOL) with Bayesian probability theory.

Learner Profile: A part of the learner model that only contain the static information of the learner that could be gathered before developing a learner model.

Adaptive Hypermedia Systems: Online information and help systems, as well as institutional information systems that provide hyperlinks that are most relevant to the user in an effort to shape the user's cognitive load.

E-Learning: A concept that describes the cognitive science principles of effective multimedia learning using electronic educational technology.

Learner Situation: A part of a learning path that the learner takes to achieve a diploma or a certification.

Bayesian Networks: Probabilistic graphical model or a type of statistical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.

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