面向可扩展自适应学习：基于图神经网络与强化学习
Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning
AI总结：
本文提出一种基于图神经网络和强化学习的灵活可扩展序列推荐系统，用于学习路径个性化，在模拟学习者上验证了其在小数据场景下的良好推荐能力。
摘要：
自适应学习是教育技术领域中的一个方向，其核心在于提供个性化的学习体验，以满足每位学习者的独特需求。自适应学习的一个重要子领域是学习路径个性化：它旨在设计系统，推荐一系列教育活动，以最大化学生的学习成果。许多机器学习方法已经在与学习路径个性化相关的多种情境中展现了显著成果。然而，这些方法大多是为非常特定的场景设计的，可复用性不强。这一情况因它们往往依赖于不可扩展的模型而更加突出，这些模型在针对特定教育资源集合训练后，无法整合新的元素。在本文中，我们提出了一种灵活且可扩展的方法来解决学习路径个性化问题，并将其形式化为一个强化学习问题。我们的模型是一个基于图神经网络的序列推荐系统，我们在模拟学习者群体上对其进行了评估。结果表明，该模型能够在数据量较小的情境下学会做出良好的推荐。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
