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International Journal of Innovation and Applied Studies
ISSN: 2028-9324     CODEN: IJIABO     OCLC Number: 828807274     ZDB-ID: 2703985-7
 
 
Saturday 26 May 2018

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  Call for Papers - May 2018     |     Now IJIAS is indexed in EBSCO, ResearchGate, ProQuest, Chemical Abstracts Service, Index Copernicus, IET Inspec Direct, Ulrichs Web, Google Scholar, CAS Abstracts, J-Gate, UDL Library, CiteSeerX, WorldCat, Scirus, Research Bible and getCited, etc.  
 
 
 

Identifying and tracking learning styles in MOOCs: A neural networks approach


Volume 19, Issue 2, February 2017, Pages 267–275

 Identifying and tracking learning styles in MOOCs: A neural networks approach

Brahim HMEDNA1, Ali El Mezouary2, Omar Baz3, and Driss Mammass4

1 IRF-SIC Laboratory FSA, Ibn Zohr University Agadir, Morocco
2 IRF-SIC Laboratory FSA, Ibn Zohr University Agadir, Morocco
3 IRF-SIC Laboratory FSA, Ibn Zohr University Agadir, Morocco
4 IRF-SIC Laboratory FSA, Ibn Zohr University Agadir, Morocco

Original language: English

Received 20 July 2016

Copyright © 2017 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


Learning styles identification using learners’ behavior and the actions they perform on a MOOC environment constitute in our opinion not just an interesting research issue but also an important solution to improve MOOC effectiveness. Indeed, providing learners with learning resources and activities that suit to their preferences and learning styles increases their satisfaction improve learning performances and save time (efficiency). In this paper, we propose an approach that uses neural networks to identify and track learners learning styles, then to provide them the appropriate resources, activities, etc. through adaptive recommendation system. The purpose of this paper is to examine the point of view of literature on MOOCs, learning styles and their use in MOOCs environment and our proposed solution to integrate an adaptive recommendation system with MOOC taking into accounts the plurality of participants’ learning styles.

Author Keywords: MOOC, TEL, Learning style, Machine learning, Neural networks, Adaptation.


How to Cite this Article


Brahim HMEDNA, Ali El Mezouary, Omar Baz, and Driss Mammass, “Identifying and tracking learning styles in MOOCs: A neural networks approach,” International Journal of Innovation and Applied Studies, vol. 19, no. 2, pp. 267–275, February 2017.