Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning

  • H. El Fazazi Signals, Distributed Systems and Artificial Intelligence Laboratory, ENSET Mohammedia, University of Hassan II, Mohammedia, Morocco
  • M. Elgarej Signals, Distributed Systems and Artificial Intelligence Laboratory, ENSET Mohammedia, University of Hassan II, Mohammedia, Morocco
  • M. Qbadou Signals, Distributed Systems and Artificial Intelligence Laboratory, ENSET Mohammedia, University of Hassan II, Mohammedia, Morocco
  • K. Mansouri Signals, Distributed Systems and Artificial Intelligence Laboratory, ENSET Mohammedia, University of Hassan II, Mohammedia, Morocco
Volume: 11 | Issue: 1 | Pages: 6637-6644 | February 2021 | https://doi.org/10.48084/etasr.3905

Abstract

Adaptive e-learning systems are created to facilitate the learning process. These systems are able to suggest the student the most suitable pedagogical strategy and to extract the information and characteristics of the learners. A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective. These agents are always in communication and they can be homogeneous or heterogeneous and may or may not have common objectives. The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students’ needs. The agents in these systems collaborate to provide a personalized learning experience. In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. The main objective of this system is the recommendation to the students of a learning path that meets their characteristics and preferences using the Q-learning algorithm. The proposed system is focused on three principal characteristics, the learning style according to the Felder-Silverman learning style model, the knowledge level, and the student's possible disabilities. Three types of disabilities were taken into account, namely hearing impairments, visual impairments, and dyslexia. The system will be able to provide the students with a sequence of learning objects that matches their profiles for a personalized learning experience.

Keywords: adaptative e-learning system, knowledge level, learning path recommendation, learning styles, multi-agent, Q-learning, reinforcement learning, students’ disabilities

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