The Determination of Learning Styles in a Learner Model Using the Combination of Bayesian Network and the Overlay Model

The Determination of Learning Styles in a Learner Model Using the Combination of Bayesian Network and the Overlay Model

DOI: 10.4018/978-1-5225-7413-2.ch005

Abstract

This chapter aims to treat the problem of dynamic modeling in an adaptive educational system construed as computational modeling of the learner. Modeling the learner in adaptive systems involves different information such as knowledge of the domain, the performance of the learning goals, background, learning styles, etc. Although there are several methods to manage the learner model, like the stereotype model or learner profiles, they do not handle the uncertainty in the dynamic modeling of the learner. The main purpose of this chapter is to show the link between the structure of the learner model and the characteristics of a learning profile and the learning style of a learning situation. This chapter shows how the combination of these two approaches to learner modeling can address the dynamic aspect of the problem in the modeling of the learner. The experiments and results presented in this work are arguments in favor of the hypothesis and can also promote reusing the modeling obtained through different systems and similar modeling situations.
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Learner Modelling In Adaptive Educational Hypermedias

The learner modeling is the modeling of all the important features that affect the learner (knowledge, preferences, goals, etc.). The purpose of modeling the learner is to identify relevant information, to structure, to initialize, to update, and to exploit them. By replacing the word “learning” with the term “user” this definition is also applicable to the model of the user. In the case of an application other than the learner's educational model, it is called the user model.

The learner model can be an integral part of Adaptive Hypermedia Systems as it can be shared with multiple systems. In the latter case we speak of user modeling servers (Yudelson et al., 2007). This type of server areused in distributed environments whereadaptive systems can access this server to query or update user information. Cumulate (Brusilovsky et al., 2005) is one of the most known and used systems for user modeling servers.

Key Terms in this Chapter

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

Learning Path: A collection of learning situations that the learner takes in a certain period of time; it could be composed of a pretest, a learning activity, or an evaluation.

Adaptive Hypermedia Systems: On-line 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.

Learning 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.

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.

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

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