USING EDUCATIONAL SPECIFICATIONS AND STANDARDS FOR HYPERMEDIA SYSTEM ADAPTATION ACCORDING TO BCM-INFERRED STUDENT’S LEARNING STYLE

2021 ◽  
Author(s):  
Daiva Goštautaitė ◽  
Jevgenij Kurilov
Keyword(s):  
Learning Style ◽  
Hypermedia System ◽  
System Adaptation

scholarly journals Toward an Adaptive Educational Hypermedia System (AEHS-JS) based on the Overlay Modeling and Felder and Silverman’s Learning Styles Model for Job Seekers

2020 ◽  
Vol 15 (08) ◽  
pp. 235
Author(s):  
Aymane Qodad ◽  
Abdelilah Benyoussef ◽  
Abdallah El Kenz ◽  
Mourad Elyadari
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Job Search ◽  
Social Impact ◽  
Learning Objects ◽  
Educational Systems ◽  
Job Seekers ◽  
Learner Model ◽  
Hypermedia System ◽  
Educational Hypermedia

In this paper we introduce a new design of an adaptive educational hypermedia system for job seekers, this proposal is based, for the part of learning objectives, on a job model which allows adapting the content and the path of education to the intended jobs, and, for the learner model construction, on a specific use of the learning styles of Felder and Silverman. First, we present existing literature to give a general review on adaptive edu-cational hypermedia systems, in that way; we have reported the related items to different notions in the adaptive educational Systems area as the differentiated pedagogy, the learning objects, and the learner profile. Then we argued our choice of the components of our model and we detailed the new ones. As designed, the model can produce a suitable learning path for the user to match the job characteristics and the learning style of the person in order to help the user owning the job sought. With the possibility of linking the required com-petencies to the education skills, we aim to map business tasks to learning activi-ties. Based on this approach, we designed an Adaptive Educational Hypermedia System named AEHS-JS that will help to improve the efficiency and pragmatism of job search activities. In plus of the social impact of this work as it help job seekers to complete their profiles and get the career they are looking for, this work will allow companies to find the candidates that match the job criteria sought.

An Adaptive Predictive Model for Student Modeling

2006 ◽  
pp. 70-92 ◽  
Author(s):  
Gladys Castillo ◽  
João Gama ◽  
Ana M. Breda
Keyword(s):  
Predictive Model ◽  
Learning Style ◽  
Concept Drift ◽  
Statistical Quality Control ◽  
Student Modeling ◽  
Psychometric Instruments ◽  
Hypermedia System ◽  
Modeling Prediction ◽  
Changes Over Time ◽  
Over Time

This chapter presents an adaptive predictive model for a student modeling prediction task in the context of an adaptive educational hypermedia system (AEHS). The task, that consists in determining what kind of learning resources are more appropriate to a particular learning style, presents two issues that are critical. The first is related to the uncertainty of the information about the student’s learning style acquired by psychometric instruments. The second is related to the changes over time of the student’s preferences (concept drift). To approach this task, we propose a probabilistic adaptive predictive model that includes a method to handle concept drift based on statistical quality control. We claim that our approach is able to adapt quickly to changes in the student’s preferences and that it should be successfully used in similar user modeling prediction tasks, where uncertainty and concept drift are presented.

A Model for an Adaptive e-Learning Hypermedia System

2013 ◽  
Vol 9 (4) ◽  
pp. 21-39
Author(s):  
Lamia Mahnane ◽  
Laskri Mohamed Tayeb ◽  
Philippe Trigano
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Personal Characteristics ◽  
Learning Objects ◽  
Thinking Styles ◽  
Pedagogical Strategies ◽  
Learning Content ◽  
Hypermedia System ◽  
E Learning ◽  
Specific Teaching

Recent years have shown increasing awareness for the importance of adaptivity in e-learning. Since the learning style of each learner is different. Adaptive e-learning hypermedia system (AEHS) must fit different learner’s needs. A number of AEHS have been developed to support learning styles as a source for adaptation. However, these systems suffer from several problems, namely: lack of maintenance, adaptation to learning style, less attention was paid to thinking styles and the insertion of specific teaching strategies into learning content. This paper proposes an AEHS model based on thinking styles and knowledge level. On one hand, the developed prototype will assist a learner in accessing and using learning resources which are adapted according to his/her personal characteristics (in this case his/her thinking style and level of knowledge). On the other hand, it will facilitate the learning content teacher in the creation of appropriate learning objects and their application to suitable pedagogical strategies.

An Adaptive Predictive Model for Student Modeling

2008 ◽  
pp. 562-578 ◽  
Author(s):  
Gladys Castillo ◽  
João Gama ◽  
Ana M. Breda
Keyword(s):  
Predictive Model ◽  
Learning Style ◽  
Concept Drift ◽  
Statistical Quality Control ◽  
Student Modeling ◽  
Psychometric Instruments ◽  
Hypermedia System ◽  
Modeling Prediction ◽  
Changes Over Time ◽  
Over Time

This chapter presents an adaptive predictive model for a student modeling prediction task in the context of an adaptive educational hypermedia system (AEHS). The task, that consists in determining what kind of learning resources are more appropriate to a particular learning style, presents two issues that are critical. The first is related to the uncertainty of the information about the student’s learning style acquired by psychometric instruments. The second is related to the changes over time of the student’s preferences (concept drift). To approach this task, we propose a probabilistic adaptive predictive model that includes a method to handle concept drift based on statistical quality control. We claim that our approach is able to adapt quickly to changes in the student’s preferences and that it should be successfully used in similar user modeling prediction tasks, where uncertainty and concept drift are presented.

An Adaptive Predictive Model for Student Modeling

2011 ◽  
pp. 1307-1324
Author(s):  
Gladys Castillo ◽  
João Gama ◽  
Ana M. Breda
Keyword(s):  
Predictive Model ◽  
Learning Style ◽  
Concept Drift ◽  
Statistical Quality Control ◽  
Student Modeling ◽  
Psychometric Instruments ◽  
Hypermedia System ◽  
Modeling Prediction ◽  
Changes Over Time ◽  
Over Time

This chapter presents an adaptive predictive model for a student modeling prediction task in the context of an adaptive educational hypermedia system (AEHS). The task, that consists in determining what kind of learning resources are more appropriate to a particular learning style, presents two issues that are critical. The first is related to the uncertainty of the information about the student’s learning style acquired by psychometric instruments. The second is related to the changes over time of the student’s preferences (concept drift). To approach this task, we propose a probabilistic adaptive predictive model that includes a method to handle concept drift based on statistical quality control. We claim that our approach is able to adapt quickly to changes in the student’s preferences and that it should be successfully used in similar user modeling prediction tasks, where uncertainty and concept drift are presented.

The Optimization by Using the Learning Styles in the Adaptive Hypermedia Applications

2018 ◽  
Vol 13 (2) ◽  
pp. 16-31 ◽  
Author(s):  
Lamia Hamza ◽  
Guiassa Yamina Tlili
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Optimization Problem ◽  
Adaptive Hypermedia ◽  
Adaptive Hypermedia Systems ◽  
Hypermedia System ◽  
Methods And Techniques ◽  
Hypermedia Applications ◽  
Selection Of ◽  
Adaptive Content

This article addresses the learning style as a criterion for optimization of adaptive content in hypermedia applications. First, the authors present the different optimization approaches proposed in the area of adaptive hypermedia systems whose goal is to define the optimization problem in this type of system. Then, they present the architecture of their proposed system. The first step involves choosing a learning style model. The selection of this style is done by using a dedicated questionnaire answered by a learner. Then a modeling of the learner is completed based on his learning style. Finally, content that is to be presented to the learner is managed by a content generator module, depending on the model of the learner. Built on methods and techniques proposed for modeling and adaptation, the adaptive hypermedia system based on learning styles provides optimized adaptations. The authors' approach has been experimentally validated and the results are encouraging.

A Web-based Learner-Controlled Adaptive Group Formation Technique

2014 ◽  
Vol 10 (1) ◽  
pp. 14-34 ◽  
Author(s):  
Alexandros Papadimitriou ◽  
Maria Grigoriadou ◽  
Georgios Gyftodimos
Keyword(s):  
Collaborative Learning ◽  
Learning Style ◽  
Group Formation ◽  
Knowledge Level ◽  
Web Based ◽  
Priority List ◽  
Hypermedia System ◽  
Formation Technique ◽  
Collaboration Agreement ◽  
The Web

This paper presents the learner-controlled adaptive group formation technique offered by the web-based adaptive educational hypermedia system MATHEMA. More specifically, this paper describes why we take into consideration the group effectiveness, concerning the concrete-abstract dimension of learners' learning style, in forming learner groups and how the adaptive group formation algorithm generates a priority list of possible matching candidate collaborators for a certain student, taking into account the abstract or concrete dimension of his/her learning style and his/her possible candidate collaborators' learning style and knowledge level on the current learning goal as well. Moreover, the paper describes how the algorithm supports the learners in selecting the most suitable collaborator, and how it automatically links them up via a chat tool with the aim of negotiating a collaboration agreement. An evaluation of the adaptive group formation of our system indicated that it is usable and useful enough tool for collaborative learning.

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