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 Intelligent and Adaptive Hypermedia System Based on Thinking Style (IAHS-TS)

2016 ◽  
pp. 111-130
Author(s):  
Mahnane M. L. Lamia ◽  
Hafidi Mohamed
Keyword(s):  
Personal Characteristics ◽  
Learning Objects ◽  
Learning System ◽  
Thinking Style ◽  
Brain Dominance ◽  
Pedagogical Strategies ◽  
Learning Content ◽  
Hypermedia System ◽  
E Learning ◽  
Level Of Knowledge

In this paper, an adaptive and intelligent hypermedia system, AIHS was designed, developed and implemented. This e-learning system was intended for bachelor degree program that is offered in all Algerian public universities, where the studied subjects are: “ORL”, “Dermatology”, “Ophthalmology” and “Language”. Content which was transformed into learning objects in four different ways in accordance with Herrmann Brain Dominance Instrument (HBDI). 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). It will facilitate the learning content teacher in the creation of appropriate learning objects and applying them to suitable pedagogical strategies.

A Model for an Adaptive Hypermedia Learning System Based on Data Mining Technique

2017 ◽  
pp. 65-85
Author(s):  
Mahnane Lamia ◽  
Mohamed Hafidi
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Personal Characteristics ◽  
Learning Objects ◽  
Learning System ◽  
Learning Performance ◽  
Adaptive Hypermedia ◽  
Data Mining Technique ◽  
Pedagogical Strategies ◽  
Hypermedia Learning

Since the learning style of each learner is different. Adaptive hypermedia learning system (AHLS) must fit different learner's needs. A number of AHLS have been developed to support learning styles as a source for adaptation. However, these systems suffer from several problems, namely: less attention was paid to the relationship between learning styles and learning performance. This paper proposes an AHLS model based on learning styles and learning performance. 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 learning 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.

Development and Testing of New E-Learning Hypermedia System

2015 ◽  
Vol 10 (2) ◽  
pp. 1-15
Author(s):  
Lamia Mahnane ◽  
Mohamed Hafidi
Keyword(s):  
Learning Styles ◽  
Learning Process ◽  
Learning Experience ◽  
Innovative Approach ◽  
Thinking Styles ◽  
New Approach ◽  
Learning Content ◽  
Hypermedia System ◽  
E Learning ◽  
Web Learning

Adaptive E-learning Hypermedia Systems (AEHS) are an innovative approach to a web learning experience delivery. They try to solve main shortcomings of classical hypermedia e-learning applications: “one-size-fits-all” approach and “lost-in-hyperspace” phenomena by adapting the learning content and its presentation to needs, goals, thinking styles and learning styles of every individual learner. This paper outlines a new approach to automatically detect learner's thinking and learning styles, and taking into account that thinking and learning styles may change during the learning process in an unexpected and unpredictable way. The authors' approach is based on the Felder Learning Styles Model and Hermann thinking styles model.

Domain Ontology and Hermann Brain Dominance Instrument Model for Personalized E-Learning Hypermedia System

2012 ◽  
Vol 7 (4) ◽  
pp. 1-12 ◽  
Author(s):  
Mahnane Lamia ◽  
Laskri Mohamed Tayeb
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Domain Ontology ◽  
Thinking Styles ◽  
Second Phase ◽  
Brain Dominance ◽  
Post Test ◽  
E Learning ◽  
Specific Teaching ◽  
And Control

A number of adaptive e-learning hypermedia systems (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 domain ontology. The experiment was completed in three phases for both experimental and control groups. In the first phase all the students were informed that they will participate in an experimental process. The students received a short introduction on how to use the system and to create a user account for login purposes into the system. Then, information about thinking styles categories were given to the experimental group and were asked to complete the questionnaire. In the second phase, the students followed regularly the lessons until the completion of the course; meanwhile taking a quiz at the end of each lesson. In the third phase, learners followed a link to do the post-test.

scholarly journals Development & Evaluation of E-Learning Module Based on Visual and Global Preferences Using a User-Centered Design Approach

2021 ◽  
Vol 16 (15) ◽  
pp. 139
Author(s):  
Harry Budi Santoso ◽  
Panca O. Hadi Putra ◽  
Febrian Fikar Farras Hendra S
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Interaction Design ◽  
Interface Design ◽  
Learning Objects ◽  
User Centered Design ◽  
Alternative Design ◽  
Learning Module ◽  
E Learning ◽  
User Centered

Students develop various learning styles based on their preferences and learning habits. To serve different learning styles in a class with a number of students using the conventional face-to-face teaching method is not practical; therefore, the idea of personalized e-Learning to accommodate differences in learning style has arisen. Building on this idea, this research intends to provide an alternative interaction design for e-Learning modules by developing content based on user needs using the User-Centered Design methodology. Due to a lack of e-Learning content for visual and global preferences in the Felder-Silverman learning styles, User-Centered Design is chosen as the basis to design the e-Learning module. The result consists of an alternative design and a proposed interface design. The alternative design describes learning objects and navigation of the e-Learning module. The proposed interface design is a prototype of an interactive e-Learning module. After being evaluated, the prototype satisfies the user's expectations in terms of content translation, content navigation, and interactivity throughout the module.

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.

Towards Best Practices in Web-Based Learning and Teaching

2016 ◽  
pp. 317-332
Author(s):  
Mercy A. Iroaganachi
Keyword(s):  
Best Practices ◽  
Learning Environment ◽  
Learning Styles ◽  
Learning Style ◽  
Learning Objects ◽  
Web Based ◽  
Learning And Teaching ◽  
Web Based Learning ◽  
E Learning ◽  
Level Of Knowledge

The chapter explored best practices in web-based learning and teaching with a view to discover trends and provide valuable information for all in the e-learning environment. It affirms that paradigms in Web-based education have shifted from teacher-centered to learner-centered but basically it remains synchronous or asynchronous. This requires Learning Objects (LOs) to be pedagogically efficient, designed to standard (Multimodal) with designers bearing in mind the varied population and learning styles. LOs are to be personalized thereby creating adaptive content based on learner's abilities, learning style, level of knowledge and preferences. It is recommended that educators have requisite background knowledge and competencies in technology such as hardware, software, and course management systems etcetera. Instructors, designers and all interested persons should consult a checklist of best practices, for assessing learning object repositories. More so, there is need to incorporate hands-on component into the e-learning environment. The chapter provides Indicators for best practices.

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

scholarly journals From Cognitive Landscapes to Digital Hyperscapes

2003 ◽  
Vol 4 (2) ◽  
Author(s):  
Jose Bidarra ◽  
Ana Dias
Keyword(s):  
Learning Styles ◽  
Learning Management Systems ◽  
Learning Objects ◽  
Management Systems ◽  
Digital Information ◽  
Cognitive Profiles ◽  
E Learning ◽  
Learning Management ◽  
Key Terms ◽  
Massive Information

<P> The widespread diffusion of e-Learning in organizations has encouraged the discovery of more effective ways for conveying digital information to learners, for instance, via the commonly called Learning Management Systems (LMS). A problem that we have identified is that cognitive variables and pedagogical processes are rarely taken into consideration and sometimes are confused with the mere use by learners of “diversified” hypermedia resources. Within the context of widespread dissemination of multimedia content that has followed the emergence of massive information resources, we discuss the need for more powerful and effective learner-centered tools capable of handling all kinds of design configurations and learning objects. </p> <P class=abstract><B>Key Terms: </B>cognitive profiles, learning styles, mind mapping, multimedia and hypermedia content, hyperscapes, e-Learning, learning objects, Learning Management Systems (LMS).</P>

Development of Adaptive Social Network Based on Learners' Thinking and Learning Styles

2018 ◽  
pp. 90-107
Author(s):  
Mahnane Lamia ◽  
Hafidi Mohamed
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Learning Styles ◽  
Learning Experience ◽  
Social Network Sites ◽  
Innovative Approach ◽  
Thinking Styles ◽  
New Approach ◽  
Learning Content ◽  
Web Learning

Adaptive social network sites (ASNS) are an innovative approach to a web learning experience delivery. They try to solve the main shortcomings of classical social networks—“one-size-fits-all” approach and “lost-in-hyperspace” phenomena—by adapting the learning content and its presentation to needs, goals, thinking styles, and learning styles of every individual learner. This chapter outlines a new approach to automatically detect learners' thinking and learning styles, and takes into account that thinking and learning styles may change during the learning process in unexpected and unpredictable ways. The approach is based on the Felder learning styles model and Hermann thinking styles model.

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