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

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.

Development of Bayesian Networks From Use Case Diagrams for Managing the Learner Model

First of all, and to clarify the purpose, it seems important to say that the work presented in this chapter lies within the framework of learner modeling in an adaptive system understood as computational modeling of the learner. One must also state that Bayesian networks are effective tools for learner modeling under uncertainty. They have been successfully used in many systems, with different objectives, from the assessment of knowledge of the learner to the recognition of the plan followed in problem solving. The main objective of this chapter is to develop a Bayesian networks for modeling the learner from the use case diagram of the unified modeling language. The prototypes and diagrams presented in this chapter are arguments in favor of the objective. The network obtained also promotes reusing learner modeling through similar systems.

A Comparative Study of the Modeling Techniques and Models of a Learner Model in Adaptive Hypermedia Educational Systems

The main objective of the learner model is to modify the interaction between the system and the learner in a dynamic way to address the needs of each learner on an individual basis. To obtain a complete learner model, we need the proper techniques and methods to initialize it and update it. This chapter present a comparative study of different adaptive hypermedia systems and the methods and techniques used in with them. This study lies within the range of modeling the learner in adaptive educational system as a conceptual modeling of the learner. Although there are several methods that deal with the learner model, like stereotypes methods or learner profile, they are likely unable to handle the uncertainty embedded in the dynamic modeling of the learner. The chapter aims studies different models and approaches to model the learner in an adaptive educational system and comes up with the most appropriate method based on the dynamic aspect of this model.

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

This chapter presents a probabilistic and dynamic learner model based on multi-entity Bayesian networks and artificial intelligence. There are several methods for modelling the learner in AHES, but they're based on the initial profile of the learner created in his entry into the learning situation. They do not handle the uncertainty in the dynamic modelling of the learner based on the actions of the learner. The main purpose of this chapter is the management of the learner model based on MEBN and artificial intelligence, taking into account the different actions that the learner could take during his/her whole learning path. The approach that the authors followed in this chapter is marked initially by modelling the learner model in three levels: they started with the conceptual level of modelling with the unified modelling language, followed by the model based on Bayesian networks to be able to achieve probabilistic modelling in the three phases of learner modelling.

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

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.

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

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.

A Comparative Study of the Functionalities and Characteristics of a Learner Model in Adaptive Hypermedia Educational Systems

Today's adaptive hypermedia systems are putting more and more emphasis on the intelligence of the system. One of the most important factors in assessing the quality and usability of the system is the level to meet the needs of the user, the learner. So, the learner model, the component that backs up and manages learner information, becomes more important. The learner model is an essential component for adaptive e-learning systems. The term adaptation in e-learning systems involves the selection and manner of presentation of each learning activity as a function that examines the entity of knowledge, skills, and other information given by each subject taught. The chapter aims at studying the functionalities of the learner model in different adaptive hypermedia educational systems in the three stages of developing and managing this model. The authors present in this comparative study a full analysis of the learner model used in 10 major hypermedia to come up with most appropriate method to treat the dynamic aspect of this model.