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