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.

翻译：大规模开放在线课程（MOOCs）极大地促进了教育的普及。然而，许多MOOCs保持着僵化的“一刀切”结构，未能满足不同学习者的多样化需求和背景。学习路径个性化旨在通过定制教育内容的序列来优化个体学生的学习成果，从而解决这一局限。然而，现有方法通常需要大量的学生互动数据或广泛的专家标注，这限制了其广泛应用。在本研究中，我们提出了一种新颖的数据高效学习路径个性化框架，该框架无需专家标注即可运行。我们的方法采用了一个灵活的推荐系统，该系统通过在原始课程材料数据集上进行强化学习预训练。通过在半合成数据上的实验，我们表明，在涉及新教育材料的一系列自适应学习场景中，这种预训练阶段显著提高了数据效率。这为设计自适应学习的基础模型开辟了新的视角。