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 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.

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 and Development of a Web-based Student Screening Module as Part of a Personalized Learning System

TEM Journal ◽  
2021 ◽  
pp. 1454-1460
Author(s):  
Pavel Zlatarov ◽  
Ekaterina Ivanova ◽  
Galina Ivanova ◽  
Julia Doncheva
Keyword(s):  
Personalized Learning ◽  
Learning Systems ◽  
Learning System ◽  
Practical Implementation ◽  
Design Development ◽  
Web Based ◽  
The Past ◽  
Development Level ◽  
Recent Developments ◽  
E Learning

Various researchers, institutions and companies have been increasingly working on and using e-learning systems in the past. However, with the recent developments, the demand for learning systems that can adapt to learners’ need and development level has risen considerably. A lot of learning from a distance requires new approaches in teaching, It is more important than ever for teachers to be able to accurately test students’ knowledge, determine the appropriate level of difficulty and adjust content accordingly. This paper describes the design, development and use of a web-based application used to prepare tests for students and determine their level as a module of an integrated personalized learning system. Results from a practical implementation of the system are also discussed.

A Unified Quality Control Model for E-Learning Systems

2017 ◽  
Vol 7 (3) ◽  
pp. 1355
Author(s):  
Khalid Hamed Allehaibi ◽  
Nasser Nammas Albaqami
Keyword(s):  
Quality Control ◽  
Quality Management ◽  
Learning Process ◽  
Control Model ◽  
Learning Systems ◽  
Learning System ◽  
Quality Requirements ◽  
Learning Platform ◽  
E Learning

Defining, measuring, and achieving quality of e-learning systems are not an easy task. Accordingly, one of the most essential goals for the higher educational institutes is how to reach a high and satisfied level of quality in their learning systems. Achieving such level needs adequate and continuous improvements for the whole e-learning environment elements. Therefore, we aim in our work to construct a unified framework for total quality management system (TQMS) that attempt to satisfy the quality requirements, needs, and standards. The objective of this paper is to present a quality control model for e-learning system that adopts the e-learning platform according to the on-line determination of both user's requirements and global standards. This paper proposed software architecture of quality Management framework for e-learning that could be adopted by different higher education institutes to control the quality of the e-learning process, and assure the quality of the e-learning process outcome. The proposed framework is based on a tri-dimensions quality model. The three dimensions are set of quality requirements for e-learning environment represented in Quality Assurance (QA) policies that will be formalized by using policy based approach, the specifications of e-learning platform that provide learning and teaching activities, and quality control process loop. The architecture for monitor and ensure quality control of the QA policies for e-learning system will deliver the whole learning services in an optimal way. It is also flexible and can be implemented over any e-learning system.

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.

Personalized Recommendation Research in E-Learning Systems

2013 ◽  
Vol 433-435 ◽  
pp. 603-606
Author(s):  
Bing Wu ◽  
Ping Ping Chen
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Learning Systems ◽  
Learning System ◽  
Personalized Recommendation ◽  
Important Research ◽  
Research Areas ◽  
Communication Efficiency ◽  
E Learning ◽  
Explicit Study

The purpose of this paper is to review the literatures which have made an explicit study on personalized recommendation in E-Learning systems. By identifying the important research areas, which are in different perspectives, firstly, filtering recommendation is introduced before the illustration of how it has been developed in E-Learning systems. Then personalized recommendation is proposed for E-Learning system. Although social network is the basic way to improve the communication efficiency with others in E-Learning system, previous studies pay less attention on this. Therefore social network analysis should be taken into consideration for the recommendation in E-Learning system for further research.

scholarly journals Studi Pustaka Tentang Kerentanan Keamanan E-Learning dan Penanganannya

2020 ◽  
Vol 4 (2) ◽  
pp. 309
Author(s):  
Reza Pramudita ◽  
Syifaul Fuada ◽  
Nuur Wachid Abdul Majid
Keyword(s):  
Higher Education ◽  
Information Technology ◽  
Learning Process ◽  
Research Method ◽  
Learning Systems ◽  
High Potential ◽  
Learning System ◽  
Security Issues ◽  
Literature Reviews ◽  
E Learning

E-learning is an educational media that use ICT in the learning process. Nowadays, most of the higher education (universities) in Indonesia have integrated the learning process with e-learning systems. By using this system, learning can be done outside the classroom, anytime and anywhere. Based on the literature reviews, e-learning has many positive impacts on higher education components and its ecosystems, i.e., students, lecturers, higher education staff, and campus officials. Although it offers many advantages, similar to the information technology in general, e-learning systems are also vulnerable to security issues. Because, several security holes in the system can be penetrated easily by hackers, where the essential data contained in e-learning has high potential to be spoofed even modified by irresponsible parties. The security aspect of the e-learning system must be considered to minimize risks. Thus, the higher education can run their business as well. This paper aims to elaborate on the e-Learning system, opportunities, challenges, and several offered solutions on it. The research method used in this paper is literature studies from the latest references

The Role of Multi-Agent Social Networking Systems in Ubiquitous Education

2010 ◽  
pp. 72-88 ◽  
Author(s):  
Jonathan Bishop
Keyword(s):  
Social Networking ◽  
Information Science ◽  
Learning Systems ◽  
Learning System ◽  
Future Research ◽  
System A ◽  
E Learning ◽  
Multi Agent ◽  
Social Networking Technology ◽  
Networks Of Practice

Knowledge it could be argued is constructed from the information actors pick up from the environments they are in. Assessing this knowledge can be problematic in ubiquitous e-learning systems, but a method of supporting the critical marking of e-learning exercises is the Circle of Friends social networking technology. Understanding the networks of practice in which these e-learning systems are part of requires a deeper understanding of information science frameworks. The Ecological Cognition Framework (ECF) provides a thorough understanding of how actors respond to and influence their environment. Forerunners to ecological cognition, such as activity theory have suggested that the computer is just a tool that mediates between the actor and the physical environment. Utilising the ECF it can be seen that for an e-learning system to be an effective teacher it needs to be able to create five effects in the actors that use it, with those being the belonging effect, the demonstration effect, the inspiration effect, the mobilisation effect, and the confirmation effect. In designing the system a developer would have to consider who the system is going to teach, what it is going to teach, why it is teaching, which techniques it is going to use to teach and finally whether it has been successful. This chapter proposes a multi-agent e-learning system called the Portable Assistant for Intelligently Guided Education (PAIGE), which is based around a 3D anthropomorphic avatar for educating actors ubiquitously. An investigation into the market for PAIGE was carried out. The data showed that those that thought their peers were the best form of support were less likely to spend more of their free time on homework. The chapter suggests that future research could investigate the usage of systems like PAIGE in educational settings and the effect they have on learning outcomes.

scholarly journals Enhanced Education by Using Intelligent Agents in Multi-Agent Adaptive e-Learning Systems

2015 ◽  
Vol 24 (1) ◽  
Author(s):  
Adriana ALEXANDRU ◽  
Eugenia TÎRZIU ◽  
Eleonora TUDORA ◽  
Ovidiu BICA
Keyword(s):  
Intelligent Agents ◽  
Learning Systems ◽  
E Learning ◽  
Multi Agent
Sign in / Sign up

Export Citation Format

Share Document