Personalized Adaptive Learner Model in E-Learning System Using FCM and Fuzzy Inference System

Personalized Adaptive Learner Model in E-Learning System Using FCM and Fuzzy Inference System Each learner has unique learning style in which one learns easily. It is aimed to individualize the learning experiences for each learner in e-learning. Therefore, it is important to diagnose complete learners’ learning style and behaviour to provide suitable learning paths and automated personalized contents as per their choices. This paper proposes some new dimensions of adaptivity like automatic and dynamic detection of learning styles and provides personalization accordingly. It has advantages in terms of precision and time spent. It is a literature-based approach in which a personalized adaptive learner model (PALM) was constructed. This proposed learner model mines learner’s navigational accesses data and finds learner’s behavioural patterns which individualize each learner and provide personalization according to their learning styles in the learning process. Fuzzy cognitive maps and fuzzy inference system a soft computing techniques were introduced to implement PALM. Result shows that personalized adaptive e-learning system is better and promising than the non-adaptive in terms of benefits to the learners and improvement in overall learning process. Thus, providing adaptivity as per learner’s needs is an important factor for enhancing the efficiency and effectiveness of the entire learning process. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Fuzzy Systems Springer Journals

Personalized Adaptive Learner Model in E-Learning System Using FCM and Fuzzy Inference System

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Operations Research, Management Science
ISSN
1562-2479
eISSN
2199-3211
D.O.I.
10.1007/s40815-017-0309-y
Publisher site
See Article on Publisher Site

Abstract

Each learner has unique learning style in which one learns easily. It is aimed to individualize the learning experiences for each learner in e-learning. Therefore, it is important to diagnose complete learners’ learning style and behaviour to provide suitable learning paths and automated personalized contents as per their choices. This paper proposes some new dimensions of adaptivity like automatic and dynamic detection of learning styles and provides personalization accordingly. It has advantages in terms of precision and time spent. It is a literature-based approach in which a personalized adaptive learner model (PALM) was constructed. This proposed learner model mines learner’s navigational accesses data and finds learner’s behavioural patterns which individualize each learner and provide personalization according to their learning styles in the learning process. Fuzzy cognitive maps and fuzzy inference system a soft computing techniques were introduced to implement PALM. Result shows that personalized adaptive e-learning system is better and promising than the non-adaptive in terms of benefits to the learners and improvement in overall learning process. Thus, providing adaptivity as per learner’s needs is an important factor for enhancing the efficiency and effectiveness of the entire learning process.

Journal

International Journal of Fuzzy SystemsSpringer Journals

Published: Mar 20, 2017

References

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