Name
Abstract | ||
|---|---|---|
E-learning systems have benefited greatly from the concepts of the social web, where learners and tutors are living and interacting mainly in social networks. Our goal is to show that this technology can be adopted to improve the e-learning user experience and to provide a full automatic learning platform called Netlearn. We present a learning environment formulated as a social network, including the interactions between users as well as their relationships with the provided learning resources. In particular, we propose an automatic learning environment based on the analysis of the social interactions that takes place between users-users and users-resources. The analysis is based on the history of interactions made by learners within the environment to deduce their interests in relation to a module. Learners with similar interests will then be assigned to the same learning group in order to propose recommendations regarding their preferences, interests and needs. This system ensures that these recommendations will certainly improve the learning process by providing students with the best learning practices, the desirable collaborators, and the relevant resources that fit better their needs. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SNAMS.2019.8931848 | 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS) |
Keywords | Field | DocType |
Social network,E-learning,community detection,k-mean,learner interests | k-means clustering,World Wide Web,User experience design,E learning,Social network,Social web,Computer science,Automatic learning,Learning environment,Learning group | Conference |
ISBN | Citations | PageRank |
978-1-7281-2947-1 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samira Aouidi | 1 | 0 | 0.34 |
| Mahnane Lamia | 2 | 0 | 0.34 |
| Mohamed Hafidi | 3 | 5 | 5.84 |