Title: Improving smart learning experience quality through the use of extracted data from social networks

Authors: Kenza Sakout Andaloussi; Laurence Capus; Ismail Berrada; Karim Boubouh

Addresses: Department of Computer Science and Software Engineering, Laval University, Quebec, Canada ' Department of Computer Science and Software Engineering, Laval University, Quebec, Canada ' LIMS LAB, Faculty of Science, Sidi Mohamed Ben Abdellah University, Fez, Morocco ' LIMS LAB, Faculty of Science, Sidi Mohamed Ben Abdellah University, Fez, Morocco

Abstract: With the development of smart cities applications, the need for smart learning is increasing. Although current systems are supposed supporting smart learning, they do not really meet the expectations. In fact, for learner models, data are still mostly gathered through questionnaires and are not predicted, then standards are not satisfied. Regarding the domain model, it appears that collaborative learning is not considered. Finally, for the adaptation process, it depends on two main criteria namely the learning style and the knowledge level in addition, it concerns only one or two of these aspects: content, navigation or presentation. This paper explores the feasibility of learner modelling based on data extracted and inferred from social networks, according to the IMS-LIP specification. An adaptive learning system has been developed on the basis of this innovative approach. The first results confirm that this new way can improve the smart learning experience.

Keywords: smart learning; educational hypermedia system; adaptation; learner model; Felder & Silverman learning style; big five personality traits; social networks.

DOI: 10.1504/IJIE.2019.101134

International Journal of Intelligent Enterprise, 2019 Vol.6 No.2/3/4, pp.311 - 340

Received: 17 Jan 2018
Accepted: 14 Jan 2019
Published online: 24 Jul 2019 *

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