A Reinforcement Learning-Based Adaptive Learning System

With the plethora of educational and e-learning systems and the great variation in students' personal and social factors that affect their learning behaviors and outcomes, it has become mandatory for all educational systems to adapt to the variability of these factors for each student. Since there is a large number of factors that need to be taken into consideration, the task is very challenging. In this paper, we present an approach that adapts to the most influential factors in a way that varies from one learner to another, and in different learning settings, including individual and collaborative learning. The approach utilizes reinforcement learning for building an intelligent environment that, not only provides a method for suggesting suitable learning materials, but also provides a methodology for accounting for the continuously-changing students' states and acceptance of technology. We evaluate our system through simulations. The obtained results are promising and show the feasibility of the proposed approach.