A Learner Model Based on Bayesian Networks in Adaptive Educational Hypermedia Systems

A Learner Model Based on Bayesian Networks in Adaptive Educational Hypermedia Systems

DOI: 10.4018/978-1-5225-7413-2.ch004

Abstract

The work presented in this chapter lies within learner modeling in an adaptive educational system construed as a computational modeling of the learner. All actions of the learner in a learning situation on an adaptive hypermedia system are not limited to valid or invalid actions (true and false), but they are a set of actions that characterize the learning path of formation. Thus, one cannot represent the information from the system of each learner using relative data. It requires putting the work in a probabilistic context due to the changes in the learner model information during formation. In this chapter, the authors propose to use Bayesian networks as a probabilistic framework to resolve the issue of dynamic management and update of the learner model. 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.
Chapter Preview
Top

The Learner Mode In Adaptive Hypermedia Educational Systems

The purpose of this section is to bring to the readers the knowledge required in the fields of Learner modeling. We will come back to in this section on definitions and terminology of each of the main key words in our chapter.

Concepts and Definition

The learner modeling is the modeling of all the important features that affect the learner (knowledge, preferences, goals, etc.). It comes to identify relevant information, to structure, to initialize them, update them and 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 is called the user model.

A learner model allows keeping the learner information, for example his level of knowledge on a given topic (performance), his frequent mistakes/misunderstandings, psychological characteristics, etc.

A learner model can be defined as a set of structured information about the learning process, and this structure contains values on the characteristics of the learner. It provides the necessary data to the other modules to achieve the adaptation of teaching to the learner (Zaitseva et al., 2005)

Many studies emphasize the uncertainty of the information contained in the student model and the importance of the intention behind the creation of this model. Thus, a student model represents the belief system about learners' beliefs (the system's beliefs about the learner's beliefs) accumulated during the diagnostic process (Beck et al., 1996)

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

Key Terms in this Chapter

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.

User Model: Is the subdivision of human-computer interaction that describes the process of building up and modifying a conceptual understanding of the user.

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

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 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.

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.

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

Complete Chapter List

Search this Book:
Reset