scholarly journals Adaptive e-learning systems through learning styles: A review of the literature

2021 ◽  
Vol 1 (2) ◽  
pp. 124-145
Author(s):  
Iraklis Katsaris ◽  
◽  
Nikolas Vidakis ◽  
Keyword(s):  
Learning Styles ◽  
Adaptive Learning ◽  
Learning Experience ◽  
Learning Systems ◽  
Model Adaptation ◽  
Review Of The Literature ◽  
Learner Model ◽  
Effective Learning ◽  
Designed Environment ◽  
E Learning

The domain of education has taken great leaps by capitalizing on technology and the utilization of modern devices. Nowadays, the established term "one size fits all" has begun to fade. The research focuses on personalized solutions to provide a specially designed environment on the needs and requirements of the learner. The adaptive platforms usually use Learning Styles to offer a more effective learning experience. This review analyzes the learner model, adaptation module, and domain module, originating from the study of 42 papers published from 2015 to 2020. As more modern techniques for adaptation get incorporated into e-learning systems, such techniques must be compliant with educational theories. This review aims to present the theoretical and technological background of Adaptive E-learning Systems while emphasizing the importance and efficiency of the utilization of Learning Styles in the adaptive learning process. This literature review is designated for the researchers in this field and the future creators and developers of adaptive platforms.

Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type

2019 ◽  
Vol 53 (2) ◽  
pp. 189-200 ◽  
Author(s):  
Aisha Yaquob Alsobhi ◽  
Khaled Hamed Alyoubi
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Learning Experience ◽  
Assistive Technologies ◽  
Learning Systems ◽  
Learning System ◽  
Fixed Cost ◽  
Learning Material ◽  
Content Type ◽  
E Learning

PurposeThrough harnessing the benefits of the internet, e-learning systems provide flexible learning opportunities that can be delivered at a fixed cost at a time and place to suit the user. As such, e-learning systems can allow students to learn at their own pace while also being suitable for both distance and classroom-based learning activities. Adaptive educational hypermedia systems are e-learning systems that employ artificial intelligence. They deliver personalised online learning interventions that extend electronic learning experiences beyond a mere computerised book through the use of intelligence that adapts the content presented to a user according to a range of factors including individual needs, learning styles and existing knowledge. The purpose of this paper is to describe a novel adaptive e-learning system called dyslexia adaptive e-learning management system (DAELMS). For the purpose of this paper, the term DAELMS will be employed to describe the overall e-learning system that incorporates the required functionality to adapt to students’ learning styles and dyslexia type.Design/methodology/approachThe DAELMS is a complex system that will require a significant amount of time and expertise in knowledge engineering and formatting (i.e. dyslexia type, learning styles, domain knowledge) to develop. One of the most effective methods of approaching this complex task is to formalise the development of a DAELMS that can be applied to different learning styles models and education domains. Four distinct phases of development are proposed for creating the DAELMS. In this paper, we will discuss Phase 3 which is the implementation and some adaption algorithms while in future papers will discuss the other phases.FindingsAn experimental study was conducted to validate the proposed generic methodology and the architecture of the DAELMS. The system has been evaluated by group of university students studying a Computer Science related majors. The evaluation results proves that when the system provide the user with learning materials matches their learning style or dyslexia type it enhances their learning outcomes.Originality/valueThe DAELMS correlates each given dyslexia type with its associated preferred learning style and subsequently adapts the learning material presented to the student. The DAELMS represents an adaptive e-learning system that incorporates several personalisation options including navigation, structure of curriculum, presentation, guidance and assistive technologies that are designed to ensure the learning experience is directly aligned with the user's dyslexia type and associated preferred learning style.

scholarly journals Analysing the Outcome of a Learning Process Conducted Within the System ALS_CORR[LP]

2017 ◽  
Vol 12 (03) ◽  
pp. 43 ◽  
Author(s):  
Elghouch Nihad ◽  
En-naimi El Mokhtar ◽  
Yassine Zaoui Seghroucheni
Keyword(s):  
Learning Styles ◽  
Adaptive Learning ◽  
Learning Experience ◽  
Learning Objects ◽  
Learning System ◽  
Learner Model ◽  
Learning Path ◽  
Evaluation Phase ◽  
Behavior Tracking ◽  
Adaptive Learning Systems

This paper presents the results of an experiment, conducted on a sample of computer science students, using the adaptive learning system called ALS_CORR[LP] 1. Indeed, unlike the traditional LMS, the adaptive learning systems provide a personalized learning experience based on the objectives, the prerequisites or even the learning styles generating thereafter a specific learning path. However their main issue remains the fact, that they assume that the generated learning path is necessarily the leading one, which is far from being true, since we can always detect some failure cases during the evaluation phase. In this paper we conduct a learning experience using the system ALS_CORR[LP] which has the ability to correct the generated learning path by recommending the most relevant learning objects, and update the learner model based on a calculation of similarity in behavior between the struggling learner and the succeeding ones. We analyze later the results of behavior tracking within the system.

scholarly journals INCA: A cognitive multi-agents architecture for designing intelligent & adaptive learning systems

2005 ◽  
Vol 2 (2) ◽  
pp. 99-114 ◽  
Author(s):  
Thierry Nabeth ◽  
Liana Razmerita ◽  
Albert Angehrn ◽  
Claudia Roda
Keyword(s):  
Knowledge Sharing ◽  
Adaptive Learning ◽  
Management Practices ◽  
Learning Systems ◽  
Learning System ◽  
Cognitive Agents ◽  
E Learning ◽  
Personal Agent ◽  
Adaptive Learning Systems ◽  
Multi Agents

This paper presents a cognitive multi-agents architecture called Intelligent Cognitive Agents (InCA) that was elaborated for the design of Intelligent Adaptive Learning Systems. The InCA architecture relies on a personal agent that is aware of the user's characteristics, and that coordinates the intervention of a set of expert cognitive agents (such as story telling agents, assessment agents, stimulation agents or help agents). This InCA architecture has been applied for the design of K"InCA, an e-learning system aimed at helping people to learn and adopt knowledge-sharing management practices.

Acknowledging the Distance Learner

2013 ◽  
pp. 242-254
Author(s):  
Trish Andrews
Keyword(s):  
Distance Learning ◽  
Information And Communication Technologies ◽  
Teaching And Learning ◽  
Learning Experience ◽  
Communication Technologies ◽  
Effective Learning ◽  
Distance Learner ◽  
Information And Communication ◽  
E Learning

The growth of e-learning, particularly distance learning via e-learning, is widely recognised as a significant factor influencing higher education in the 21st century. The rapid and ongoing uptake of Information and Communication Technologies (ICT) for teaching and learning, along with the recognition that increased student engagement can lead to more effective learning, is changing the way in which teaching and learning occurs in universities. This chapter suggests that the distance learner is frequently overlooked in the current climate when it comes to consideration of student needs and that current applications of ICT for distance learning raises questions about the quality of their learning experience. The chapter discusses the role of the student voice in understanding and addressing students’ needs in relation to the quality of their learning experience and suggests that greater attention needs to be paid to the distinct voice of the distance education student. The chapter provides some methodologies for collecting the student’s voice and gives consideration to how addressing the distance learners’ voice to enhance their learning experience might be most effectively accomplished.

scholarly journals Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization

2020 ◽  
Vol 10 (2) ◽  
pp. 42
Author(s):  
Othmar Othmar Mwambe ◽  
Phan Xuan Tan ◽  
Eiji Kamioka
Keyword(s):  
Learning Styles ◽  
Real Time ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Accurate Information ◽  
Interactive Learning Environment ◽  
Time Dynamic ◽  
E Learning ◽  
Time Basis ◽  
User Friendly

Adaptive Educational Hypermedia Systems (AEHS) play a crucial role in supporting adaptive learning and immensely outperform learner-control based systems. AEHS’ page indexing and hyperspace rely mostly on navigation supports which provide the learners with a user-friendly interactive learning environment. Such AEHS features provide the systems with a unique ability to adapt learners’ preferences. However, obtaining timely and accurate information for their adaptive decision-making process is still a challenge due to the dynamic understanding of individual learner. This causes a spontaneous changing of learners’ learning styles that makes hard for system developers to integrate learning objects with learning styles on real-time basis. Thus, in previous research studies, multiple levels navigation supports have been applied to solve this problem. However, this approach destroys their learning motivation because of imposing time and work overload on learners. To address such a challenge, this study proposes a bioinformatics-based adaptive navigation support that was initiated by the alternation of learners’ motivation states on a real-time basis. EyeTracking sensor and adaptive time-locked Learning Objects (LOs) were used. Hence, learners’ pupil size dilation and reading and reaction time were used for the adaption process and evaluation. The results show that the proposed approach improved the AEHS adaptive process and increased learners’ performance up to 78%.

A robust classification to predict learning styles in adaptive E-learning systems

2019 ◽  
Vol 25 (1) ◽  
pp. 437-448 ◽  
Author(s):  
Ibtissam Azzi ◽  
Adil Jeghal ◽  
Abdelhay Radouane ◽  
Ali Yahyaouy ◽  
Hamid Tairi
Keyword(s):  
Learning Styles ◽  
Learning Systems ◽  
Robust Classification ◽  
E Learning

Replicating Human Interaction to Support E-Learning

2006 ◽  
pp. 494-503
Author(s):  
William A. Janvier ◽  
Claude Ghaoui
Keyword(s):  
Learning Styles ◽  
Text Messaging ◽  
Learning Experience ◽  
Human Interaction ◽  
Learning Tools ◽  
Tutoring Systems ◽  
Language Patterns ◽  
E Learning ◽  
Communication Preference ◽  
The Way

HCI-related subjects need to be considered to make e-learning more effective; examples of such subjects are: psychology, sociology, cognitive science, ergonomics, computer science, software engineering, users, design, usability evaluation, learning styles, teaching styles, communication preference, personality types, and neuro-linguistic programming language patterns. This article discusses the way some components of HI can be introduced to increase the effectiveness of e-learning by using an intuitive interactive e-learning tool that incorporates communication preference (CP), specific learning styles (LS), neurolinguistic programming (NLP) language patterns, and subliminal text messaging. The article starts by looking at the current state of distance learning tools (DLTs), intelligent tutoring systems (ITS) and “the way we learn”. It then discusses HI and shows how this was implemented to enhance the learning experience.

scholarly journals Multi-Agent Adaptive e-Learning System Based on Learning Styles

2021 ◽  
Vol 4 (1) ◽  
pp. 1-12
Author(s):  
Faith Ngami Kivuva ◽  
Elizaphan Maina ◽  
Rhoda Gitonga
Keyword(s):  
Learning Styles ◽  
Learning Process ◽  
Intelligent Agents ◽  
Personalized Learning ◽  
Learning Systems ◽  
Learning System ◽  
Personalized Recommendation ◽  
Information Agent ◽  
E Learning ◽  
Multi Agent

Most traditional e-learning system fails to provide the intelligence that a learner may require during their learning process. Different learners have different learning styles but the current e-learning systems are not able to provide personalized learning. In this paper, we discuss how intelligent agents can aid learners in their learning process. Three agents have been developed namely, learner agent, information agent, and tutor agents that will be integrated into a learning management system (Moodle). Learners are provided with a personalized recommendation based on the learning styles.

scholarly journals Learning Styles and Dyslexia Types – Understanding Their Relationship and its Benefits in Adaptive E-learning Systems

2020 ◽  
Vol 14 (15) ◽  
pp. 25
Author(s):  
Aisha Y Alsobhi ◽  
Khaled H Alyoubi
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Learning Systems ◽  
Learning System ◽  
Knowledge Level ◽  
Educational Establishment ◽  
E Learning ◽  
User Learning ◽  
The Relationship ◽  
Better Than

Learning is a fundamental element of people’s everyday lives. Learning experiences can take the form of our interactions with others, through attending an educational establishment, etc. Not everyone learns in the same way, and even people who are considered to have a similar standard of abilities or proficiency will exhibit different learning styles. This does not necessarily mean that some students are better than others; it means that students are different from one another. Adaptive e-learning system should be capable of adapting the content to the user learning style, abilities and knowledge level. In this paper, we investigate the benefits of incorporating learning styles and dyslexia type in adaptive e-learning systems. Adaptivity aspects based on dyslexia type and learning styles enrich each other, enabling systems to provide learners with materials which fit their needs more accurately. Besides, consideration of learning styles and dyslexia type can contribute to more accurate student modelling. In this paper, the relationship between learning styles, the Felder–Silverman learning style model (FSLSM), and dyslexia type, is investigated. These relationships will lead to a more reliable student model.

scholarly journals Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 6637-6644
Author(s):  
H. El Fazazi ◽  
M. Elgarej ◽  
M. Qbadou ◽  
K. Mansouri
Keyword(s):  
Reinforcement Learning ◽  
Learning Style ◽  
Learning Process ◽  
Learning Experience ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning System ◽  
E Learning ◽  
Multi Agent

Adaptive e-learning systems are created to facilitate the learning process. These systems are able to suggest the student the most suitable pedagogical strategy and to extract the information and characteristics of the learners. A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective. These agents are always in communication and they can be homogeneous or heterogeneous and may or may not have common objectives. The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students’ needs. The agents in these systems collaborate to provide a personalized learning experience. In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. The main objective of this system is the recommendation to the students of a learning path that meets their characteristics and preferences using the Q-learning algorithm. The proposed system is focused on three principal characteristics, the learning style according to the Felder-Silverman learning style model, the knowledge level, and the student's possible disabilities. Three types of disabilities were taken into account, namely hearing impairments, visual impairments, and dyslexia. The system will be able to provide the students with a sequence of learning objects that matches their profiles for a personalized learning experience.

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