scholarly journals Comparison of E-Learning Personalization Systems: Protus and PLeMSys

2017 ◽  
Vol 12 (01) ◽  
pp. 57 ◽  
Author(s):  
Natasha Blazheska-Tabakovska ◽  
Mirjana Ivanovic ◽  
Aleksandra Klasnja-Milicevic ◽  
Jovana Ivkovic
Keyword(s):  
Learning Styles ◽  
Cognitive Abilities ◽  
Personalized Learning ◽  
Knowledge Level ◽  
Collaborative Tagging ◽  
Tutoring System ◽  
E Learning ◽  
Knowledge Levels ◽  
Individual Needs ◽  
Contemporary Education

E-learning is becoming more and more important in contemporary education. It allows learners to learn at their own pace, when their schedule permits it. However, learners have individual needs and disparate traits such as learning styles, knowledge levels, motivation and cognitive abilities. So, a need for personalized learning has been made clear. Two ways of personalized learning are discussed in this paper: the first is Protus 2.1. - a tutoring system that allows personalization based on learning styles and collaborative tagging and the second one is PLeMSys - a model of a Moodle plug-in where personalization is based on learning styles and knowledge level.

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 Mining the Students’ Chat Conversations in a Personalized e-Learning Environment

2019 ◽  
Vol 14 (23) ◽  
pp. 98
Author(s):  
Amal Alabri ◽  
Zuhoor Al-Khanjari ◽  
Yassine Jamoussi ◽  
Naoufel Kraiem
Keyword(s):  
Learning Environment ◽  
Learning Styles ◽  
Learning Style ◽  
Domain Model ◽  
Semantic Relations ◽  
Knowledge Level ◽  
Social Collaboration ◽  
Mining Technique ◽  
E Learning ◽  
Mining Model

Providing personalized e-learning environment is normally relying on a domain model representing the knowledge to be acquired by learners and learners’ characteristics to be used in the personalization process. Therefore, constructing the domain model and understanding the characteristics of the learners are very crucial in such an environment. With the inclusion of social collaboration tools for collaborative learning activities, the generated data during conversations enrich with valuable information to be used for personalization. However, when considering chat conversations as a source for constructing the domain model, there is a need to perform a mining technique for chat conversations in order to extract the semantic relations from the user-generated contents hidden inside these conversations. As well as the learner’s characteristics like learning style and knowledge level expressed during conversations. Thus in this paper, we are aiming for the best utilization of chat conversation by proposing a model containing a rule-based technique as a form of mining technique. This mining aims at extracting the semantic relations to build the domain model as an ontology-based depiction. In addition, the mining model is proposed to perform some collaborative filtering techniques to identify the learning styles and knowledge level of the learners.

Pedagogical Software Agents for Personalized E-Learning Using Soft Computing Techniques

2019 ◽  
pp. 319-338
Author(s):  
Mukta Goyal ◽  
Rajalakshmi Krishnamurthi
Keyword(s):  
Learning Styles ◽  
Soft Computing ◽  
Software Agents ◽  
Personalized Learning ◽  
Human Voice ◽  
Intelligent Tutors ◽  
Soft Computing Techniques ◽  
E Learning

Due to the emerging e-learning scenario, there is a need for software agents to teach individual users according to their skill. This chapter introduces software agents for intelligent tutors for personalized learning of English. Software agents teach a user English on the aspects of reading, translation, and writing. Software agents help user to learn English through recognition and synthesis of human voice and helps users to improve on handwriting. Its main objective is to understand what aspect of the language users wants to learn. It deals with the intuitive nature of users' learning styles. To enable this feature, intelligent soft computing techniques have been used.

Automatic Detection of Tutoring Styles Based on Tutors' Behavior

2016 ◽  
Vol 14 (2) ◽  
pp. 79-97
Author(s):  
Safia Bendjebar ◽  
Yacine Lafifi ◽  
Amina Zedadra
Keyword(s):  
Learning Process ◽  
Automatic Detection ◽  
Learning Systems ◽  
Knowledge Level ◽  
Tutoring System ◽  
Critical Step ◽  
E Learning ◽  
On Line

In e-learning systems, tutors have a significant impact on learners' life to increase their knowledge level and to make the learning process more effective. They are characterized by different features. Therefore, identifying tutoring styles is a critical step in understanding the preference of tutors on how to organize and help the learners. In this context, the authors address the problem of extracting tutoring styles from tutors' behavior. According to this later, tutors are classified automatically into their styles. This technique will be helpful to provide a suitable advice to learners. In the first step, a set of indicators are defined to characterize a tutoring style. In the second one, the accuracy between the tutoring styles obtained from the proposed approach and those defined from a simple questionnaire is investigated. To validate this approach, the authors have collected data from an on line tutoring system (LETline, http://www.labstic.com/letline). They present the results of their analysis and discuss some limitations that can be helpful to the researchers working in the tutoring field.

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