NDLI: Modeling Mobile Learning System Using ANFIS

Content Provider	IEEE Xplore Digital Library
Author	Al-Hmouz, A. Jun Shen Jun Yan Al-Hmouz, R.
Copyright Year	2011
Abstract	Personalisation is becoming more important in the area of mobile learning. Learner model is logically partitioned into smaller elements or classes in the form of learner profiles, which can represent the entire learning process. Machine learning techniques have the ability to detect patterns from complicated data and learn how to perform activities based on learner profiles. This paper focuses on a systematic approach in reasoning the learner contexts to deliver adaptive learning content. A fuzzy rule base model that has been proposed in related work is found insufficient in deciding all possible conditions. To tackle this problem, this paper adopts the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to determine all possible conditions. ANFIS uses the hybrid (least-squares method and the back propagation gradient descent method) as learning mechanism for the Neural Network to determine the incompleteness in the decision made by human experts. The simulating results by Matlab indicate that the performance of ANFIS approach is valuable and easy to implement.
Starting Page	378
Ending Page	380
File Size	147536
Page Count	3
File Format	PDF
ISBN	9781612842097
ISSN	2161377X
DOI	10.1109/ICALT.2011.119
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mobile communication Adaptation models Training Context Training data Mathematical model Adaptive systems Adaptation Mobile Learning ANFIS
Content Type	Text
Resource Type	Article

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