  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.
