Towards a Personalized Learning Experience Using Reinforcement Learning
Cognitive computing has become one of the most promising fields, especially in education, where building adaptive learning systems that provide different learning paths and material based on learners' states and needs are developed. One of the most challenging issues in designing such systems is to correctly identify the factors that will influence the learning experience, especially when these factors highly differ from one learner to another. In addition, for one particular learner, the values of these factors change with time. In this paper, we present an approach that adapts to the most influential factors in learning 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 (RL) for building an intelligent environment that, not only does it provide a method for suggesting suitable learning materials, but it also provides a methodology for accounting for the continuously changing students' states and acceptance of the technology. We evaluate our system through simulations. The obtained results are promising and show the feasibility of the proposed approach. In addition, we propose a "rich" personalized learning system that relies on RL as its backend, while utilizing big data tools and learning analytics to continuously feed the system with new generated states.
Machine Learning Paradigms