The Combination of Bayesian Networks and Stereotypes to Initialize the Learner Model in Adaptive Educational Hypermedia Systems

The Combination of Bayesian Networks and Stereotypes to Initialize the Learner Model in Adaptive Educational Hypermedia Systems

DOI: 10.4018/978-1-5225-7413-2.ch006
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This chapter aims to propose a new way to initialize a learner model in adaptive educational hypermedia systems. Learner modelling in adaptive systems contains several indicators. Even if there are several methods for initializing the learner model, they do not manage the side of uncertainty in the dynamic modeling of the learner. The main purpose of this chapter is the initialization of the learner model based on the combination of the Bayesian networks and the stereotypes method. In order to carry out a complete initialization of this model, the authors propose to use a combination of the stereotype method to process the content of the specific domain of information and the Bayesian networks to process the contents of the independent domain of information. The experiments and results presented in this work are arguments in favor of the hypothesis and can promote also reusing the modeling obtained through different systems and similar modeling situations.
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The Initialization Of The Learner Model In The Ahes

We will concern this part to the initialization of the model of the learner in the AHES, begin by describing the process of initialization of the model that we will propose by combining the Bayesian networks as we presented in the preceding chapters and the stereotypes in order to achieve a complete initialization in all aspects are understood.

The Process of Initializing the Learner Model

The initialization of the learner model represents the process of gathering information about the learner and transferring this information to the model. This process of initialization represents a major problem for adaptive systems.

In this section, we will present our process of collecting data about the learner. According to Self (1991), the learner model can be initialized in three ways, using explicit questions, initial tests, or the method of stereotypes.

Figure 1 shows our proposed process for collecting learner data. We propose in this chapter the use of two methods simultaneously to gather all the information related to the learner, which will give us a more complete learner model, and this will be reflected on the adaptation of the system to the needs of the learner in a more precise way.

Figure 1.

The process of initializing the learner model


To achieve this goal, we will use two methods for data collection: explicit questions, and initial tests.

In order to gather specific information about the learner that makes up the learner's profile, such as personal information, goals, cognitive skills, preferences ... we will base this process on explicit questions, which will guide us in assigning each learner to a well-defined stereotype.

And for independent learner information, which reflects its level of knowledge and skills for each module in the system, and which represent the key element for system adaptation. We base this process on initial tests, using Bayesian networks as a formalism to properly represent and manage this information in a probabilistic way.

Key Terms in this Chapter

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.

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

Stereotypes: A widely held but fixed and oversimplified image or idea of a particular type of person or thing.

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.

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

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