Design of a New Mobile-Optimized Remote Laboratory Application Architecture for M-Learning

Ning Wang, Xuemin Chen, Gangbing Song, Qianlong Lan, Hamid Parsaei

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

As mobile learning (M-Learning) has demonstrated increasing impacts on online education, more and more mobile applications are designed and developed for the M-Learning. In this paper, a new mobile-optimized application architecture using Ionic framework is proposed to integrate the remote laboratory into mobile environment for the M-Learning. With this mobile-optimized application architecture, remote experiment applications can use a common codebase to deploy native-like applications on many different mobile platforms such as iOS, Android, Windows Mobile, and Blackberry. To demonstrate the effectiveness of the proposed new architecture for M-Learning, an innovative remote networked proportional-integral-derivative control experiment has been successfully implemented based on this new application architecture. The performance is validated by the Baidu mobile cloud testing bed.

Original languageEnglish
Article number7605517
Pages (from-to)2382-2391
Number of pages10
JournalIEEE Transactions on Industrial Electronics
Volume64
Issue number3
DOIs
Publication statusPublished - 1 Mar 2017

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Keywords

  • Ionic framework
  • mobile learning (Mlearning)
  • mobile-optimized application architecture
  • remote laboratory
  • unified framework

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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