scholarly journals Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

10.28945/4459 ◽  
2019 ◽  
Vol 18 ◽  
pp. 529-547 ◽  
Author(s):  
Mohammad T Alshammari ◽  
Amjad Qtaish
Keyword(s):  
Computer Security ◽  
Learning Style ◽  
Student Characteristics ◽  
Learning Systems ◽  
Learning System ◽  
Learning Material ◽  
Knowledge Level ◽  
Learning Gains ◽  
Adaptive Approach ◽  
E Learning

Aim/Purpose: Effective e-learning systems need to incorporate student characteristics such as learning style and knowledge level in order to provide a more personalized and adaptive learning experience. However, there is a need to investigate how and when to provide adaptivity based on student characteristics, and more importantly, to evaluate its value in learning enhancement. This study aims to bridge that gap by examining the effect of different modes of learning material adaptation and their sequences to the learning style and knowledge level of students in e-learning systems. Background: E-learning systems aim to provide acceptability and interactivity between students, instructors, and learning content anytime and anywhere. However, traditional systems are typically designed for generic students irrespective of individual requirements. Successful e-learning systems usually consider student characteristics such as learning style and knowledge level to provide more personalized and adaptive student-system interaction. Methodology: A controlled experiment was conducted in a learning context with 174 subjects to evaluate the learning effectiveness of adaptivity in e-learning systems. Contribution: The main contributions of the paper are threefold. First, a novel adaptive approach is proposed based on a specific learning style model and knowledge level. Second, the approach is implemented in an e-learning system to teach computer security, the application domain. Third, a rigorous experimental evaluation of the learning effect of the adaptive approach is offered. Findings: The results indicate that adaptation according to the combination of learning style and knowledge level produces significantly better learning gains, both in the short-term and medium-term, than adaptation according to either trait individually. Recommendations for Practitioners: Practitioners should consider the combination of learning style and knowledge level when delivering and presenting learning material to their students. Recommendation for Researchers: Researchers should consider sound educational models when designing adaptive e-learning systems. Also, rigorous and carful experimental design evaluations should be taken into account. Impact on Society: Universities and e-learning industries can benefit from the proposed adaptive approach and the findings in designing and developing more personalized and adaptive e-learning systems. The incorporation of student characteristics, especially learning style and knowledge level, may be used to enhance learning. Future Research: The experiment might be duplicated with a focus on longer-term learning gains by including more subjects and more learning resources. Also, the study might be expanded to application domains other than computer security. Moreover, other variables such as student satisfaction, motivation, and affective state might be explored to further the understanding of the effect of adaptivity on learning gains.

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.

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 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 ANALISIS DAN EVALUASI REKOMENDASI E-LEARNING BERBASIS OPEN SOURCE (STUDI KASUS: SMKN XYZ BANDUNG)

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Arief Samuel Gunawan ◽  
Herastia Maharani ◽  
Karen K Tatuh
Keyword(s):  
Open Source ◽  
Online Discussion ◽  
Learning Systems ◽  
Learning System ◽  
Learning Activities ◽  
Learning Material ◽  
Web Based ◽  
Analysis And Evaluation ◽  
System Recommendation ◽  
E Learning

SMKN XYZ is a vocational institution located in Bandung. In performing learning activities, SMKN XYZ implemented e-learning systems, namely Edmodo and school website, to complement and support daily learning activities. E-learning system is used as a solution when teachers could not attend the class. But in reality, it was found that both Edmodo and school website have relatively similar features and functions in use. Furthermore, it was found from interviews and questionnaire results that both existing applications were lacking of several necessary features such as chatting and math equation features. Therefore, an analysis and evaluation of e-learning system recommendation will be conducted in this research. There are 5 main requirements of SMKN XYZ regarding e-learning implementation, which were found from the interviews and questionnaire, namely learning material online sharing, online discussion, online assignment, online quiz and online announcement. Moodle is a web-based open source e-learning application. Using Moodle could support online learning activities and meet the 5 main requirements of SMKN XYZ, by providing features such as access to learning materials, assignment, quiz, discussion and other important informations, including chatting and math equation features which were not available in Edmodo and school website.

Virtual Co Learner

2010 ◽  
pp. 41-54 ◽  
Author(s):  
Dimitrios Georgiou ◽  
Sotirios Botsios ◽  
Georgios Tsoulouhas
Keyword(s):  
Collaborative Learning ◽  
Learning Environments ◽  
Learning Style ◽  
Learning Systems ◽  
Learning System ◽  
Cognitive Characteristics ◽  
Learning Procedure ◽  
E Learning ◽  
On Line ◽  
And Behavior

Adaptation and personalization of the information and instruction offered to the users in on-line e-learning environments are considered to be the turning point of recent research efforts. Collaborative learning may contribute to adaptive and personalized asynchronous e-learning. In this chapter authors intend to introduce the Virtual co Learner (VcL) that is a system designed on a basis of distributed architecture able to imitate the behavior of a learning companion who has suitable to the user’s cognitive and learning style and behavior. To this purpose an asynchronous adaptive collaborating e-learning system is proposed in the sense of reusing digitized material which deployed before by the users of computer supported collaborating learning systems. Matching real and simulated learners who have cognitive characteristics of the same type, one can find that learning procedure becomes more efficient and productive. Aiming to establish such VcL, one faces a number of questions. An important question is related to the user’s cognitive or learning characteristics diagnosis. Other questions are examined too.

SVM and PCA Based Learning Feature Classification Approaches for E-Learning System

2018 ◽  
Vol 13 (2) ◽  
pp. 32-45 ◽  
Author(s):  
Aditya Khamparia ◽  
Babita Pandey
Keyword(s):  
Online Education ◽  
Student Learning ◽  
Learning Style ◽  
Least Square ◽  
Learning System ◽  
Support Vector ◽  
Learning Material ◽  
Feed Forward Neural Network ◽  
Dynamic Learning ◽  
E Learning

E-learning and online education has made great improvements in the recent past. It has shifted the teaching paradigm from conventional classroom learning to dynamic web based learning. Due to this, a dynamic learning material has been delivered to learners, instead of static content, according to their skills, needs and preferences. In this article, the authors have classified eight different types of student learning attributes based on National Centre for Biotechnical Information (NCBI) e-learning database. The eight types of attributes are Anxiety (A), Personality (P), Learning style (L), Cognitive style (C), Grades from previous sem (GP), Motivation (M), Study level (SL) and Student prior knowledge (SPK). In this article the authors have proposed an approach which uses principal components of student learning attributes and have later independently classified these attributes using feed forward neural network (NN) and Least Square –Support Vector Machine (LS-SVM).

scholarly journals Student modelling in adaptive e-learning systems

2011 ◽  
pp. 342-355 ◽  
Keyword(s):  
Online Courses ◽  
Learning Systems ◽  
Domain Model ◽  
Learning System ◽  
Knowledge Level ◽  
Student Model ◽  
Java Programming ◽  
E Learning ◽  
The Right ◽  
And Behavior

Most e-Learning systems provide web-based learning so that students can access the same online courses via the Internet without adaptation, based on each student's profile and behavior. In an e-Learning system, one size does not fit all. Therefore, it is a challenge to make e-Learning systems that are suitably “adaptive”. The aim of adaptive e-Learning is to provide the students the appropriate content at the right time, means that the system is able to determine the knowledge level, keep track of usage, and arrange content automatically for each student for the best learning result. This study presents a proposed system which includes major adaptive features based on a student model. The proposed system is able to initialize the student model for determining the knowledge level of a student when the student registers for the course. After a student starts learning the lessons and doing many activities, the system can track information of the student until he/she takes a test. The student’s knowledge level, based on the test scores, is updated into the system for use in the adaptation process, which combines the student model with the domain model in order to deliver suitable course contents to the students. In this study, the proposed adaptive e-Learning system is implemented on an “Introduction to Java Programming Language” course, using LearnSquare software. After the system was tested, the results showed positive feedback towards the proposed system, especially in its adaptive capability.

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.

Factors Associated with students’ Intention to use E-Learning System: A Study from Public Universities of Lahore

2021 ◽  
Vol 9 (2) ◽  
pp. 167-173
Author(s):  
Shagufta Shaheen ◽  
Mubasher Muhammad Kamran ◽  
Saira Naeem ◽  
Tahir Mahmood
Keyword(s):  
Undergraduate Students ◽  
Public Universities ◽  
Positive Impact ◽  
Cross Sectional Study ◽  
Learning Systems ◽  
Learning System ◽  
Policy Implications ◽  
Intention To Use ◽  
Cross Sectional ◽  
E Learning

The study's primary purpose is to explore the factors affecting the students' intention to use e-learning systems in the COVID pandemic. The model of the “Unified theory of acceptance and use of technology” (UTAUT) was used as a theoretical underpinning. The Independent variables include “performance expectancy, effort expectancy, social influence, facilitating condition,” and the dependent variable is the intention to use e-learning systems. The quantitative data were collected from the postgraduate and undergraduate students of the public universities of Lahore. A total of n=411 students were approached, out of which the responses of only 399 were considered valid and were used for Multiple linear regression through SPSS 25. It was a cross-sectional study. It was found that almost all constructs of the model have a significant positive impact on intention to use e-learning systems.  The study's main contribution is exposing the factors that affect the acceptance and use of e-learning systems. This study has several policy implications for policy experts of higher education”.

scholarly journals POSSIBILITIES OF DEVELOPING INTERACTIVE E-LEARNING SYSTEM FOR BORDER GUARDS TRAINING IN THE STATE BORDER GUARD COLLEGE OF THE REPUBLIC OF LATVIA

2020 ◽  
Vol 3 (8) ◽  
pp. 45-53
Author(s):  
Mārtiņš Spridzāns ◽  
Jans Pavlovičs ◽  
Diāna Soboļeva
Keyword(s):  
Scientific Literature ◽  
Online Communication ◽  
The State ◽  
Learning Systems ◽  
Learning System ◽  
Digital Learning ◽  
E Learning ◽  
Research Findings ◽  
The Republic ◽  
Learning Development

Efficient use of educational technology and digital learning possibilities has always been the strategic area of high importance in border guards training at the State Border Guard College of Latvia. Recently, issues related to training during the Covid-19, have spurred and revived the discussion, topicality and practical need to use the potential of e-learning opportunities which brought up unexpected, additional, previously unsolved, unexplored, challenges and tasks to border guards training. New opportunities and challenges for trainers, learners and administration of training process both in online communication and learning administration contexts. In order to find out and define further e-learning development possibilities at the State Border Guard College the authors of this research explore the scientific literature on the current research findings, methodologies, approaches on developing interactive e-learning systems in educational contexts, particularly within the sphere of law enforcement. Based on scientific literature research findings authors put forward suggestions on improving the e-learning systems for border guards training.

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