An association rule based recommender system for learning materials recommendation
Soulef Benhamdi, Abdesselam Babouri, Raja Chiky · 2021
The classic learning environments are based on the "one size fits all" approach, that is to propose the same contents to all learners without considering their preferences and abilities. This work aims to develop a learning environment that provides personalized contents to learners. For this, a new association rule based recommendation approach (A_RS) is proposed and integrated into this environment. A_RS recommends learning materials taking into account learners' preferences, prior knowledge and memory capacity.