Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning
This addresses the problem of scalable and reusable adaptive learning systems for educational technology, though it is incremental as it builds on existing machine learning approaches.
The paper tackles learning path personalization by formalizing it as a reinforcement learning problem and using a graph neural network-based sequential recommender system, achieving good recommendations in small-data regimes with simulated learners.
Adaptive learning is an area of educational technology that consists in delivering personalized learning experiences to address the unique needs of each learner. An important subfield of adaptive learning is learning path personalization: it aims at designing systems that recommend sequences of educational activities to maximize students' learning outcomes. Many machine learning approaches have already demonstrated significant results in a variety of contexts related to learning path personalization. However, most of them were designed for very specific settings and are not very reusable. This is accentuated by the fact that they often rely on non-scalable models, which are unable to integrate new elements after being trained on a specific set of educational resources. In this paper, we introduce a flexible and scalable approach towards the problem of learning path personalization, which we formalize as a reinforcement learning problem. Our model is a sequential recommender system based on a graph neural network, which we evaluate on a population of simulated learners. Our results demonstrate that it can learn to make good recommendations in the small-data regime.