Personalized Learning Path Generation in E-Learning Systems using Reinforcement Learning and Generative Adversarial Networks

Accelerated by the pandemic and the resulting increased adoption of online classes by universities, E-learning and the challenge to increase its efficiency in terms of conveying content effectively to the individual learner has gained interest and importance. To achieve this customization, personalization of the learning path and the learning objects based on the characteristics of the learner could form an important component that is currently largely absent from the most used e-learning modalities. This paper introduces an approach to personalization of the learning path that attempts to optimize the learner's performance using Reinforcement Learning based on implicit feedback while minimizing the need for actual interaction with the learning system during training. The proposed system adopts the concepts of the Felder and Silverman Learning Style Model and Differentiated Pedagogy and builds an architecture using two interacting machine learning components, one using Reinforcement Learning to optimize the learning path and learning objects, and one using Conditional Generative Adversarial Networks to rapidly adapt a model of the learner's characteristics. The model strives to minimize student interactions to learn about the learner's performance characteristics and to generate personalized learning paths, thus reducing negative experiences during training. For this, a Conditional Generative Adversarial Networks is used that can rapidly adapt to provide realistic simulations of the student's performance to use in the training of the e-learning strategy and thus to help reduce the need for actual learner feedback. In a set of base experiments with synthetic data, the model is shown to be effective at learning personalization for different learner types and efficient compared to models without Generative Adversarial Networks.