A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents

TL;DR

This paper presents a data-efficient, pre-trained graph-based model for personalized learning path sequencing in MOOCs, reducing reliance on extensive data or expert annotation and improving adaptability to new educational materials.

Contribution

Introduces a novel pre-trained, reinforcement learning-based framework for adaptive learning path personalization that operates without expert annotation and enhances data efficiency.

Findings

Pre-training improves data-efficiency in adaptive learning scenarios.

The model effectively adapts to new educational materials.

Reinforcement learning enhances personalization without extensive data.

Abstract

Massive Open Online Courses (MOOCs) have greatly contributed to making education more accessible. However, many MOOCs maintain a rigid, one-size-fits-all structure that fails to address the diverse needs and backgrounds of individual learners. Learning path personalization aims to address this limitation, by tailoring sequences of educational content to optimize individual student learning outcomes. Existing approaches, however, often require either massive student interaction data or extensive expert annotation, limiting their broad application. In this study, we introduce a novel data-efficient framework for learning path personalization that operates without expert annotation. Our method employs a flexible recommender system pre-trained with reinforcement learning on a dataset of raw course materials. Through experiments on semi-synthetic data, we show that this pre-training stage substantially improves data-efficiency in a range of adaptive learning scenarios featuring new educational materials. This opens up new perspectives for the design of foundation models for adaptive learning.