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.

Adaptive E-Learning Using Soft Computing Techniques

2020 ◽  
Vol 17 (5) ◽  
pp. 2057-2059
Author(s):  
S. Muralidharan ◽  
Latha Parthiban
Keyword(s):  
Learning Environment ◽  
Soft Computing ◽  
Education System ◽  
Personalized Learning ◽  
Slow Learners ◽  
Learning Path ◽  
Soft Computing Techniques ◽  
E Learning

E-Learning is gaining more importance in the present education system and methodology of learning are moving from instructor-orientation to learner-orientation thereby providing learner with flexible, efficient and personalized learning environment. In this paper, adaptive e-learning using various soft computing techniques needed for achieving adaptation in learning path in e-learning is discussed. Adaptive e-learning becomes necessary for slow learners and challenged people who take their own time for learning due to their impairment.

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 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.

One-step models for soft computing techniques. Industrial application to image processing in quality assurance process

2015 ◽  
Vol 81 (5-8) ◽  
pp. 771-778 ◽  
Author(s):  
Pascual Noradino Montes Dorantes ◽  
Marco Aurelio Jiménez Gómez ◽  
Gerardo Maximiliano Méndez ◽  
Juan Pablo Nieto González ◽  
Jesús de la Rosa Elizondo
Keyword(s):  
Image Processing ◽  
Quality Assurance ◽  
Soft Computing ◽  
Industrial Application ◽  
Quality Assurance Process ◽  
Soft Computing Techniques ◽  
One Step ◽  
Assurance Process
Sign in / Sign up

Export Citation Format

Share Document