Intelligent Personalized E-Learning Platform using Evolutionary Algorithms

The aim of adaptive learning is to personalize the structure of course content according to the learner profiles. The idea is to propose a suitable learning resource for the student. In this work, we study the efficiency of evolutionary algorithms to generate a rule-based model for course personalization. In fact, we consider the course personalization as a search-based optimization problem where the goal is to maximize the accuracy of recommended learning resources and to minimize the complexity of the generated individual. We conducted a comparative study of four multi-objective evolutionary algorithms (NSGAII, IBEA, PESA2, SPEA2) in terms of their efficiency. The obtained results confirm the efficiency of the NSGAII algorithm to extract appropriate personalization rules for course adaptation. The obtained results prove the efficiency of the rule-based model with 93 % of accuracy rate.