A Personalised Hybrid Learning Object Recommender System

作者: Samuel Stallin Kapembe , José Ghislain Quenum

DOI: 10.1145/3297662.3365810

关键词:

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

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