A Personalised Hybrid Learning Object Recommender System
Samuel Stallin Kapembe, José Ghislain Quenum · 2019
In this work, we present a hybrid Recommender System (RS) for prescribing learning objects to students in a Personalised Learning Environment (PLE). This RS for Learning Objects (LOs) uses explicit and implicit student profiling to filter learning material for recommendation to a student. Further, the student profile consists of learning preferences, the student's confidence level in the required topics as well as the courses she enrolled in. Finally, we factor in implicit and explicit learning object ratings to enhance the recommendations.