Modeling mobile learning system using ANFIS

Ahmed Al-Hmouz*, Jun Shen, Jun Yan, Rami Al-Hmouz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 2011 11th IEEE International Conference on Advanced Learning Technologies, ICALT 2011
Pages378-380
Number of pages3
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 11th IEEE International Conference on Advanced Learning Technologies, ICALT 2011 - Athens, GA, United States
Duration: Jul 6 2011Jul 8 2011

Publication series

NameProceedings of the 2011 11th IEEE International Conference on Advanced Learning Technologies, ICALT 2011

Conference

Conference2011 11th IEEE International Conference on Advanced Learning Technologies, ICALT 2011
Country/TerritoryUnited States
CityAthens, GA
Period7/6/117/8/11

ASJC Scopus subject areas

  • Software
  • Education

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