Smart Lifelong Learning System Based on Q-Learning

The majority of current web-based learning systems are closed learning environments where courses and learning materials are fixed and the only dynamic aspect is the organization of the material that can be adapted to allow a relatively individualized learning environment. In this paper, we propose an evolving web-based learning system which can adapt itself to its learners. More specifically, the novelty with respect to the system lies in its ability to find relevant content on the web, and its ability to personalize and adapt this content based on the system´s observation of its learners and the accumulated ratings given by the learners. Hence, although learners do not have direct interaction with the open Web, the system can retrieve relevant information related to them and their situated learning characteristics. Lifelong learning scenarios have particular differences in their need for personalized recommendations that make not possible reusing existing general approaches of recommender systems. The paper describes those challenges and we propose a hybrid technique based on machine learning to recognize learner preferences and predict theirs required contents with high accuracy.