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Recommendation agent behaviour with Dynamic Fuzzy Petri Net in e-learning environment

Recommendation agent behaviour with Dynamic Fuzzy Petri Net in e-learning environment This paper presents an agent-based e-learning environment to facilitate learners achieving his learning target. The agent can generate a recommendation sequence to the learner based on Dynamic Fuzzy Petri Net (DFPN). Besides this, we suggest that each course should define the study intensity function to normalise different exercise-grade criteria. In DFPN, each learner should receive different learning suggestions based on evaluation degree of truth of any proposition. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Applied Systemic Studies Inderscience Publishers

Recommendation agent behaviour with Dynamic Fuzzy Petri Net in e-learning environment

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1751-0589
eISSN
1751-0597
DOI
10.1504/IJASS.2010.034112
Publisher site
See Article on Publisher Site

Abstract

This paper presents an agent-based e-learning environment to facilitate learners achieving his learning target. The agent can generate a recommendation sequence to the learner based on Dynamic Fuzzy Petri Net (DFPN). Besides this, we suggest that each course should define the study intensity function to normalise different exercise-grade criteria. In DFPN, each learner should receive different learning suggestions based on evaluation degree of truth of any proposition.

Journal

International Journal of Applied Systemic StudiesInderscience Publishers

Published: Jan 1, 2010

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