用预训练图模型自适应排序课件，无需专家标注也能高效个性化学习路径。
A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents
- 基于强化学习预训练图模型，从原始课程材料中学习内容关系。
- 在半合成数据上验证，新课件适配时仅需少量交互数据即见效。
- 适合缺乏标注资源但需个性化教学的在线教育场景。
大规模开放在线课程（MOOCs）极大提升了教育可及性，但多数仍采用固定统一的内容结构，难以满足学习者个体差异。学习路径个性化旨在通过定制内容顺序来优化学习效果。现有方法常依赖大量用户交互数据或专家标注，限制了广泛应用。本文提出一种新型数据高效框架，无需专家标注即可实现学习路径个性化。该方法利用强化学习在原始课程材料上进行预训练，构建灵活推荐系统。在半合成数据上的实验表明，此预训练阶段显著提升了多种新课件情境下的数据效率，为自适应学习领域基础模型的设计开辟了新方向。
原文摘要 · Abstract (English)
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.
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