Improving smart learning experience quality through the use of extracted data from social networks. (15th July 2019) - Andaloussi, Kenza Sakout; Capus, Laurence; Berrada, Ismail; Boubouh, Karim
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.