Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning - Université de Paris - Faculté des Sciences Access content directly
Conference Papers Year : 2023

Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning

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.
Fichier principal
Vignette du fichier
article_EDM2023.pdf (2.12 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04108408 , version 1 (27-05-2023)

Identifiers

Cite

Jean Vassoyan, Jill-Jênn Vie, Pirmin Lemberger. Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning. EDM 2023 - 16th International Conference on Educational Data Mining, Jul 2023, Bangalore, India. ⟨hal-04108408⟩
116 View
41 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More