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

Loading next page...
 
/lp/springer_journal/personalized-adaptive-learner-model-in-e-learning-system-using-fcm-and-0POEKqRxcv
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

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off