Towards Personalized Adaptive Learning in e-Learning Recommender Systems

An adaptive e-learning scenario not only allows people to remain motivated and engaged in the learning process, but it also helps them expand their awareness of the courses they are interested in. e-Learning systems in recent years had to adjust with the advancement of the educational situation. Therefore many recommender systems have been presented to design and provide educational resources. However, some of the major aspects of the learning process have not been explored quite enough; for example, the adaptation to each learner. In learning, and in a precise way in the context of the lifelong learning process, adaptability is necessary to provide adequate learning resources and learning paths that suit the learners’ characteristics, skills, etc. e-Learning systems should allow the learner to benefit the most from the presented learning resources content taking into account her/his learning experience. The most relevant resources should be recommended matching her/his profile and knowledge background not forgetting the learning goals she/he would like to achieve and the spare time she/he has in order to adjust the learning session with her/his goals whether it is to acquire or reinforce a certain skill. This paper proposes a personalized e-learning system that recommends learning paths adapted to the users profile.

@article{Sabeima2022,
  title     = {Towards Personalized Adaptive Learning in e-Learning Recommender Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {8},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Massra Sabeima and Myriam Lamolle and Mohamedade Farouk Nanne},
  doi       = {10.14569/IJACSA.2022.0130803},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130803}
}