Modeling Mobile Learning System Using ANFIS

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 co...

Full description

Saved in:
Bibliographic Details
Main Authors: Al-Hmouz, Ahmed, Jun Shen, Jun Yan, Al-Hmouz, R.
Format: Conference Proceeding
Language:English
Subjects:
Adaptation
Adaptation models
Adaptive systems
ANFIS
Context
Mathematical model
Mobile communication
Mobile Learning
Training
Training data
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2161-3761
2161-377X
DOI:10.1109/ICALT.2011.119