Context-aware factorization machine for recommendation in Massive Open Online Courses(MOOCs)
Abdessamad Chanaa, Nour-eddine El Faddouli · 2019
Personalization in the field of e-learning is a topic that receives a lot of interest from researchers. In the same time, the Massive Open Online Course(MOOCs) have witnessed fast development in recent years due to their high flexibility. In this paper, we aim to create a contextual modelling recommender system in a MOOC platform that relies on analysing cognitive acquisition of learners across the learning period. Using context-aware factorization machine algorithm, our approach is designed to be sensitive to the characteristics of each individual learner since this last have different intellectual capabilities, skills, modes of learning, preferences, and needs; in order to make the suitable decision corresponding to each learner's profile.