scholarly journals A learner model based on multi-entity Bayesian networks and artificial intelligence in adaptive hypermedia educational systems

2018 ◽  
Vol 8 (37) ◽  
pp. 148-160 ◽  
Author(s):  
Mouenis Anouar Tadlaoui ◽  
Rommel Novaes Carvalho ◽  
Mohamed Khaldi
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Networks ◽  
Educational Systems ◽  
Adaptive Hypermedia ◽  
Learner Model ◽  
Model Based

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

2018 ◽  
pp. 149-174
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Networks ◽  
Dynamic Modelling ◽  
Educational Systems ◽  
Probabilistic Modelling ◽  
Adaptive Hypermedia ◽  
Modelling Language ◽  
Learner Model ◽  
Model Based ◽  
Learning Path

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.

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

2019 ◽  
pp. 89-108
Author(s):  
Mouenis Anouar Tadlaoui ◽  
Rommel Novaes Carvalho ◽  
Mohamed Khaldi
Keyword(s):  
Bayesian Networks ◽  
Dynamic Modeling ◽  
Adaptive Systems ◽  
Bayesian Method ◽  
Adaptive Hypermedia ◽  
Main Hypothesis ◽  
Learner Model ◽  
Model Based ◽  
Adaptive Hypermedia Systems ◽  
M Theory

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.

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

2018 ◽  
pp. 65-94
Keyword(s):  
Computational Modeling ◽  
Bayesian Networks ◽  
Educational System ◽  
Probabilistic Framework ◽  
Adaptive Hypermedia ◽  
Learner Modeling ◽  
Learner Model ◽  
Learning Path ◽  
Hypermedia System ◽  
Probabilistic Context

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.

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

2018 ◽  
pp. 24-47
Keyword(s):  
Comparative Study ◽  
Educational System ◽  
Conceptual Modeling ◽  
Educational Systems ◽  
Dynamic Aspect ◽  
Adaptive Hypermedia ◽  
Learner Model ◽  
Adaptive Hypermedia Systems ◽  
Methods And Techniques ◽  
Learner Profile

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.

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

2018 ◽  
pp. 124-148
Keyword(s):  
Bayesian Networks ◽  
Dynamic Modeling ◽  
Adaptive Systems ◽  
Specific Domain ◽  
Learner Model ◽  
Model Based ◽  
Learner Modelling ◽  
Independent Domain ◽  
Educational Hypermedia

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

2018 ◽  
pp. 1-23
Keyword(s):  
Comparative Study ◽  
Learning Systems ◽  
Learning Activity ◽  
Educational Systems ◽  
Adaptive Hypermedia ◽  
Learner Model ◽  
Other Information ◽  
E Learning ◽  
Full Analysis ◽  
To Come

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.

Implementing a Probabilistic Learner Model Into a Course Creation Application

2020 ◽  
pp. 244-258
Author(s):  
Mouenis Anouar Tadlaoui ◽  
Fauzi El Moudden ◽  
Mohamed Khaldi
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Networks ◽  
Internal Representation ◽  
Learning Systems ◽  
Learner Model ◽  
Different Types ◽  
E Learning ◽  
Adaptable Systems

In the context of e-learning systems, we can distinguish between two different types of settings. First, we have adaptable systems, which refer to the property of changing the system settings. The learner can change the behavior of the system. Then, the learner is able to customize the system in a specified way to fit the needs of users. The learner model is the key element to generate the adaptation of the system to each specific user. It is an internal representation of the user's properties through which the system is based in order to adapt to the needs of each user. The authors present in this chapter the implementation of a probabilistic learner model developed based on multi-entities Bayesian networks and artificial intelligence into a course creation application (COPROLINE) compatible with LMS-LD. The results presented in this work are arguments in our favor for the implementation of a learner model to endorse the adaptation into some learning situations that the learners have followed during a year of testing.

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