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Presentation
Modeling mobile learning system using ANFIS
Faculty of Informatics - Papers (Archive)
  • Ahmed Al-Hmouz, University of Wollongong
  • Jun Shen, University of Wollongong
  • Jun Yan, University of Wollongong
  • Rami Al-Hmouz, Isra Private University
RIS ID
35632
Publication Date
1-1-2011
Publication Details

Al-Hmouz, A., Shen, J., Yan, J. & Al-Hmouz, R. (2011). Modeling mobile learning system using ANFIS. ICALT '11 Proceedings of the 2011 IEEE International Conference on Advanced Learning Technologies (pp. 378-380). USA: IEEE CS Press.

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.

Citation Information
Ahmed Al-Hmouz, Jun Shen, Jun Yan and Rami Al-Hmouz. "Modeling mobile learning system using ANFIS" (2011) p. 378 - 380
Available at: http://works.bepress.com/jyan/25/