scholarly journals Use of Deep Multi-Target Prediction to Identify Learning Styles

2020 ◽  
Vol 10 (5) ◽  
pp. 1756 ◽  
Author(s):  
Everton Gomede ◽  
Rodolfo Miranda de Barros ◽  
Leonardo de Souza Mendes
Keyword(s):  
Learning Styles ◽  
Target Prediction ◽  
Learning Processes ◽  
Learning Systems ◽  
Prediction Algorithm ◽  
Traditional Learning ◽  
Multiple Target ◽  
Learning Evaluation ◽  
E Learning ◽  
Target Prediction Algorithm

It is possible to classify students according to the manner they recognize, process, and store information. This classification should be considered when developing adaptive e-learning systems. It also creates a comprehension of the different styles students demonstrate while in the process of learning, which can help adaptive e-learning systems offer advice and instructions to students, teachers, administrators, and parents in order to optimize students’ learning processes. Moreover, e-learning systems using computational and statistical algorithms to analyze students’ learning may offer the opportunity to complement traditional learning evaluation methods with new ones based on analytical intelligence. In this work, we propose a method based on deep multi-target prediction algorithm using Felder–Silverman learning styles model to improve students’ learning evaluation using feature selection, learning styles models, and multiple target classification. As a result, we present a set of features and a model based on an artificial neural network to investigate the possibility of improving the accuracy of automatic learning styles identification. The obtained results show that learning styles allow adaptive e-learning systems to improve the learning processes of students.

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

Obstructions for learners in the use of ICT in KSA institutions

2020 ◽  
Vol 7 (6) ◽  
pp. 414-425
Author(s):  
Omran Alharbi
Keyword(s):  
English Language ◽  
Great Influence ◽  
Learning Systems ◽  
Traditional Learning ◽  
Structured Interviews ◽  
Barriers To Learning ◽  
Learning And Teaching ◽  
Learning Success ◽  
E Learning ◽  
Methods Of Learning

The advancement of digital technology has a great influence on the development of many areas of modern life. Over recent years, e-learning systems have managed to gain a competitive edge over the more traditional methods of learning. The learning and teaching techniques employed by e-learning systems allow more flexibility and provide freedom from the restrictions of time, location, physical presence and other aspects of traditional learning. Nevertheless, e-learning does have its own drawbacks, and research into the barriers to learning will assist in overcoming some of the problems associated with e-learning success. This study attempts to determine the obstacles that can influence the success of ICT within institutions in the Kingdom of Saudi Arabia. This qualitative research examined the obstacles of the use of ICT in the education process from learner’s perspectives and the potential solutions that can help to reduce these obstacles. Semi-structured interviews were conducted with seven e-learning students from one Saudi University. Purposive sample techniques were used with participants, and the results revealed that there were many barriers that hindered learners from benefitting from the use of ICT in education. These obstacles included lack of technical support, technical issues, lack of English language, lack of design e-course materials, and lack of motivation. In addition, a number of solutions were considered in this study.

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 LERNEN UND KOMMUNIKATIVES HANDELN IM ZEITALTER DES INTERNET

2003 ◽  
Vol 4 (4) ◽  
pp. 209-216
Author(s):  
Werner Westphal
Keyword(s):  
Young People ◽  
Information Transfer ◽  
Interdisciplinary Cooperation ◽  
Learning Processes ◽  
Traditional Learning ◽  
Learning Differences ◽  
Teaching Process ◽  
Written Texts ◽  
E Learning ◽  
The Way

The present paper focuses on different aspects of e‐learning. Differences between traditional learning processes on the basis of written texts and e‐learning on the basis of hypertext are discussed in this context. The main differences are to be found in the way of transportation and reception and the kind of presentation of information (multi‐medial). This, of course, has a lot of consequences for both people's socialization and the teaching process itself. Young people in particular need help to find effective ways of using the new possibilities of information transfer. Support should be provided by specially qualified tutors. One of the important conclusions is that e‐learning is a new challenge for studies in different fields as well as a challenge and chance for interdisciplinary cooperation.

scholarly journals E-learning Platforms and E-learning Students: Building the Bridge to Success

2013 ◽  
Vol 1 (2) ◽  
pp. 21-34 ◽  
Author(s):  
Manuel Rodrigues ◽  
Sérgio Gonçalves ◽  
Florentino Fdez-Riverola
Keyword(s):  
Online Learning ◽  
Learning Styles ◽  
Traditional Learning ◽  
Affective States ◽  
Non Invasive ◽  
Learning Platform ◽  
E Learning ◽  
Learning Platforms ◽  
Dynamic Recognition ◽  
Insight Into

E-learning platforms are becoming more and more common in education and with organisations. They are seen as a complementary tool to support learning or, as in many cases, as the primary tool to do it (possibly the only one). In traditional learning, teachers can easily get an insight into how their students work and learn, and how they interact in the classroom. However, in online learning, it is more difficult for teachers to see how individual students behave. Affective states and learning styles are determinant in students’ performance. Together with stress, these are crucial factor to success. It is believed that the sole use of an E-learning platform can in itself be a cause of stress for students. Estimating, in a non-invasive way, such parameters, and taking measures to deal with them, are then the goal of this paper. We do not consider the use of dedicated sensors (invasive) such as special gloves or wrist bracelets since we intend not to be dependent on specific hardware and also because we believe that such specific hardware can induce for itself some alteration in the parameters being analysed. Our work focuses on the development of a new module (Dynamic Recognition Module) to incorporate in Moodle E-learning platform, to accommodate individualized support to E-learning students.

Trend of E-Learning

2010 ◽  
Vol 8 (1) ◽  
pp. 69-88
Author(s):  
Neil Y. Yen ◽  
Timothy K. Shih ◽  
Qun Jin ◽  
Hui-Huang Hsu ◽  
Louis R. Chao
Keyword(s):  
Distance Learning ◽  
Learning Styles ◽  
Traditional Learning ◽  
Learning Tools ◽  
Web Based ◽  
Learning Standards ◽  
Multimedia Resources ◽  
Research Organizations ◽  
Technical Architecture ◽  
E Learning

With the improvement of internet technologies and multimedia resources, traditional learning has been replaced by distance learning, web-based learning or others’ e-learning learning styles. According to distance learning, there are many research organizations and companies who make efforts in developing the relevant systems. But they lack interoperability. The only way to reuse these applications is to redevelop them for specific purposes. In order to solve this situation and norm the various learning resources, IMS proposes a new e-learning standard named “Common Cartridge”. This standard not only integrates the past e-learning standards like LOM, SCORM and QTI but also proposes a technical architecture called Learning Tools Interoperability to allow applications to reuse different systems without reprogramming. In this paper, we firstly introduce the current e-learning environment. Then we pay attention on the usage of Common Cartridge standards and discuss the architecture of Learning Tools Interoperability. According to these standards, we will point out the e-learning standard that might be widely utilized in the future.

Discovering Learners' Characteristics Through Cluster Analysis for Recommendation of Courses in E-Learning Environment

2019 ◽  
Vol 15 (1) ◽  
pp. 42-66
Author(s):  
Bhupesh Rawat ◽  
Sanjay K. Dwivedi
Keyword(s):  
Cluster Analysis ◽  
Academic Performance ◽  
Learning Environment ◽  
Learning Process ◽  
Recommendation System ◽  
Learning Systems ◽  
Traditional Learning ◽  
Huge Number ◽  
E Learning ◽  
The Web

With the emergence of the web, traditional learning has changed significantly. Hence, a huge number of ‘e-learning systems' with the advantages of time and space have been created. Currently, many e-learning systems are being used by a large number of academic institutions worldwide which allow different users of the system to perform various tasks based on their goals. However, most of these systems follow a ‘one size fits all' approach where same resources are offered to learners irrespective of their unique learning requirements. Therefore, personalization is required as a part of e-learning systems which offers resources to learners based on their profile. This research aims to perform cluster analyses in order to validate clusters created through a k-means algorithm. The clusters will be used to classify a new learner into its appropriate class and recommend relevant courses. Finally, the accuracy of the recommendation is evaluated using various evaluation metrics. The proposed recommendation system helps learners to improve their academic performance and hence overall learning process as well.

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.

The Preliminary Investigation of the Factors that Influence the E-Learning Adoption in Higher Education Institutes

2010 ◽  
Vol 8 (4) ◽  
pp. 1-11 ◽  
Author(s):  
Maen Al-hawari ◽  
Sanaa Al-halabi
Keyword(s):  
High Performance ◽  
Preliminary Investigation ◽  
Learning Systems ◽  
Educational Institutions ◽  
Traditional Learning ◽  
Web Content ◽  
Analysis Software ◽  
E Learning ◽  
And Performance ◽  
Learning Factors

Creativity and high performance in learning processes are the main concerns of educational institutions. E-learning contributes to the creativity and performance of these institutions and reproduces a traditional learning model based primarily on knowledge transfer into more innovative models based on collaborative learning. In this paper, the authors focus on the preliminary investigation of factors that influence e-learning adoption in Jordan. As a pioneer country for e-learning systems in the Middle East, an investigation has been completed for one of Jordan’s universities that has implemented e-learning. Factors are defined through the analysis of unstructured interviews with developers and users of the e-learning systems, and Leximancer content analysis software is used to analyze the interview’s content. Main factors include Internet, legislations, human factors, and Web content.

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