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.

翻译：自适应学习是教育技术领域的一个方向，旨在通过提供个性化学习体验来满足每位学习者的独特需求。学习路径个性化是该领域的重要子领域：其目标是设计能够推荐序列化教育活动以最大化学生学习成果的系统。许多机器学习方法已在与学习路径个性化相关的多种场景中展现出显著成效。然而，大多数方法仅针对特定场景设计，缺乏复用性。这一局限因模型通常依赖非可扩展架构而加剧——这些模型在基于特定教育资源集训练后，无法整合新元素。本文针对学习路径个性化问题提出了一种灵活且可扩展的解决方案，并将其形式化为强化学习问题。我们的模型是基于图神经网络的序列推荐系统，通过模拟学习者群体进行评估。结果表明，该模型在小数据场景下能够学习生成优质推荐。