Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning

TL;DR

This paper presents a scalable, flexible reinforcement learning approach using graph neural networks for personalized learning path recommendation, effective even with limited data, addressing scalability and reusability issues in adaptive learning systems.

Contribution

It introduces a novel graph neural network-based reinforcement learning model for scalable, reusable learning path personalization in adaptive education.

Findings

Effective in small-data regimes

Demonstrates scalability and flexibility

Outperforms traditional models in simulations

Abstract

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.