Name
Title | ||
|---|---|---|
Determining the Learner’s Profile and Context Profile in Order to Propose Adaptive Mobile Interfaces Based on Machine Learning |
Abstract | ||
|---|---|---|
Sociological studies show that the use of mobile phones in the whole worldwide has attained a record level. This phenomenon has influenced the field of education. The integration of mobile technologies into teaching can improve learning conditions and the way in which learners receive educational content.Changing attitudes and behaviors in the education field, developing appropriate pedagogical models, good design (pedagogical and visual), providing methods to control the students to allow for uninterrupted mobile learning activities, seem the challenges of Mobile Learning.Though the mobile devices are important in the daily lives of learners and trainers, the use of these technologies in distance learning remains weak. To carry out our project, we used tools such Moodle and Open edX databases and Google Analytics for data collection. In this project, we try to propose an approach based on machine learning algorithms in order to personalize the mobile display and the pedagogical content on mobile devices in an online learning scenario. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICECOCS50124.2020.9314518 | 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS) |
Keywords | DocType | ISBN |
Mobile Learning,Learners’ profile,Context Profile,Machine Learning,Mobile Device | Conference | 978-1-7281-6922-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Daoudi | 1 | 0 | 0.34 |
| Nada Lebkiri | 2 | 0 | 0.34 |
| Ilham Oumaira | 3 | 0 | 0.34 |