Title: Artificial nero fuzzy inference model for location and time aware m-learning system - an empirical investigation

Authors: Sudhindra Balwant Deshpande; Shrinivas R. Mangalwede

Addresses: Department of Information Science and Engineering, Gogte Institute of Technology, Belagavi, India ' Department of Computer Science and Engineering, Gogte Institute of Technology, Belagavi, India

Abstract: Use of smart devices in education has facilitating anywhere and anytime learning, providing access to the contents through mobile devices, is called as 'mobile learning (m-learning)'. Learning styles have been evolved with advancements in mobile technology and mobile networks. Personalisation and context awareness of a learner play a vital role in delivering suitable content, based on the context of a learner. There is a need of a content delivery system that takes care of dynamically changing learner needs. Artificial nero fuzzy inference system (ANFIS) can be used to model such an adaptive content delivery system for an m-learning environment. In this paper, two models have been built and compared for their performance. The first model uses grid partitioning algorithm and second, subtractive clustering. Firstly, in both models the behaviour of the two optimisation methods-hybrid and back propagation; against various membership functions are compared for their exclusive performance against various datasets. Secondly, behaviours of both models are compared to find out which model performs better against various datasets.

Keywords: m-learning; ANFIS; fuzzy system; neural networks; context awareness; personalisation; content adaption; e-learning.

DOI: 10.1504/IJISC.2021.113321

International Journal of Intelligence and Sustainable Computing, 2021 Vol.1 No.2, pp.115 - 127

Received: 05 Dec 2018
Accepted: 05 Mar 2019
Published online: 26 Feb 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article