Evaluating adaptive hypermedia authoring while teaching adaptive systems

2004 ◽  
Author(s):  
Alexandra Cristea
Keyword(s):  
Adaptive Systems ◽  
Adaptive Hypermedia ◽  
Hypermedia Authoring

Adaptive Hypermedia Systems

2019 ◽  
pp. 217-228
Author(s):  
Ana Carolina Tomé Klock ◽  
Isabela Gasparini ◽  
Marcelo Soares Pimenta ◽  
José Palazzo M. de Oliveira
Keyword(s):  
Data Mining ◽  
Adaptive Systems ◽  
User Profile ◽  
User Model ◽  
Social Web ◽  
Adaptive Hypermedia ◽  
Individual Traits ◽  
Adaptive Hypermedia Systems ◽  
Methods And Techniques ◽  
Web Device

Adaptive hypermedia systems are systems that modify the different visible aspects based on the user profile. To provide this adaptation, the system is modeled according to a user model, which stores the information about each user. This information can include knowledge, interests, goals and tasks, background and skills, behavior, interaction preferences, individual traits, and context of the user. This chapter's goal is to introduce adaptive hypermedia systems fundamentals and trends. In this context, this chapter identifies some methods and techniques used to adapt the content, the presentation, and the navigation of the system. In the end, some applications (ELM-ART, Interbook, AHA!, AdaptWeb®) and trends (standardization, data mining, social web, device adaptation, and gamification) are exposed. As a result, this chapter highlights the importance of the improvement and the use of adaptive systems.

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.

Adaptive Hypermedia Systems

2018 ◽  
pp. 6424-6434 ◽  
Author(s):  
Ana Carolina Tomé Klock ◽  
Isabela Gasparini ◽  
Marcelo Soares Pimenta ◽  
José Palazzo M. de Oliveira
Keyword(s):  
Data Mining ◽  
Adaptive Systems ◽  
User Profile ◽  
User Model ◽  
Social Web ◽  
Adaptive Hypermedia ◽  
Individual Traits ◽  
Adaptive Hypermedia Systems ◽  
Methods And Techniques ◽  
Web Device

Adaptive Hypermedia Systems are systems that modify the different visible aspects based on the user profile. To provide this adaptation, the system is modeled according to a user model, which stores the information about each user. This information can include knowledge, interests, goals and tasks, background and skills, behavior, interaction preferences, individual traits and context of the user. This chapter goal is to introduce Adaptive Hypermedia Systems fundamentals and trends. In this context this chapter identifies some methods and techniques used to adapt the content, the presentation and the navigation of the system. In the end, some applications (ELM-ART, Interbook, AHA!, AdaptWeb®) and trends (standardization, data mining, social web, device adaptation and gamification) are exposed. As a result, this chapter highlights the importance of the improvement and the use of adaptive systems.

Adaptive control of time-varying non-linear plants by speed-gradient algorithms

2019 ◽  
pp. 37-44 ◽  
Author(s):  
O. P. Tomchina ◽  
D. N. Polyakhov ◽  
O. I. Tokareva ◽  
A. L. Fradkov
Keyword(s):  
Adaptive Control ◽  
Adaptive Systems ◽  
Reference Model ◽  
Maximum Deviation ◽  
Model Parameters ◽  
Time Varying ◽  
System Solution ◽  
Non Linear ◽  
Initial Uncertainty ◽  
Speed Gradient

Introduction: The motion of many real world systems is described by essentially non-linear and non-stationary models. A number of approaches to the control of such plants are based on constructing an internal model of non-stationarity. However, the non-stationarity model parameters can vary widely, leading to more errors. It is only assumed in this paper that the change rate of the object parameters is limited, while the initial uncertainty can be quite large.Purpose: Analysis of adaptive control algorithms for non-linear and time-varying systems with an explicit reference model, synthesized by the speed gradient method.Results: An estimate was obtained for the maximum deviation of a closed-loop system solution from the reference model solution. It is shown that with sufficiently slow changes in the parameters and a small initial uncertainty, the limit error in the system can be made arbitrarily small. Systems designed by the direct approach and systems based on the identification approach are both considered. The procedures for the synthesis of an adaptive regulator and analysis of the synthesized system are illustrated by an example.Practical relevance: The obtained results allow us to build and analyze a broad class of adaptive systems with reference models under non-stationary conditions.

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