Context-Aware Recommender Systems for Learning

  • Inssaf EL GUABASSI Faculty of Sciences, Tetouan, Morocco
  • Mohammed Al Achhab National School of Applied Sciences, Tetouan, Morocco
  • Ismail JELLOULI Faculty of Sciences, Tetouan, Morocco
  • Badr Eddine EL Mohajir Faculty of Sciences, Tetouan, Morocco

Abstract

Ubiquitous learning is a set of methods using new technologies to enhance learning and expand the traditional perspective of the learning process itself. In a broad sense, one of the main objectives of ubiquitous learning is to provide learners the right resource at the right time and in the best way. In order to provide learners with adequate learning experience, factors such learner’s characteristics and context should be considered. Managing the learner context can help delivering the best resource adaptation services. Learning object proposed to the learner is obtained from learner context using the decision tree model. On  the  present  paper, a recommender system for ubiquitous learning using learner context and a decision tree model is presented, and k-fold cross validation is used in the experiment for estimating and validating the performance of our recommender system for U-learning.
Published
Mar 4, 2018
How to Cite
EL GUABASSI, Inssaf et al. Context-Aware Recommender Systems for Learning. International Journal of Information Science and Technology, [S.l.], v. 1, n. 1, p. pp. 17-25, mar. 2018. ISSN 2550-5114. Available at: <https://innove.org/ijist/index.php/ijist/article/view/11>. Date accessed: 02 oct. 2022.
Section
Special Issue : Learning Systems and Innovation in Education