用自动提问生成知识图谱，让学习更个性化。
Automated Domain Question Mapping (DQM) with Educational Learning Materials
- 以学习目标为导向生成问题，构建层级化问答地图
- 能识别问题间的层次关系，生成结构化知识网络
- 适合教育AI、智能辅导系统开发者参考
概念图在教育中被广泛用于描绘知识结构及学科概念之间的关联。然而，从非结构化教学材料中自动构建概念图面临挑战，主要源于教育内容的复杂性与多样性。本文聚焦两大难题：（1）缺乏适用于低阶到高阶思维训练的学科概念体系；（2）学科概念及其相互关系的标注数据稀缺。为此，本研究提出一种新方法，构建领域问答地图（Domain Question Maps, DQMs），而非传统概念图。通过设计与学习目标对齐的具体问题，DQMs增强知识表征并提升学习者参与度。实验表明，该方法能有效生成教育类问题，并准确识别其层级关系，形成结构化的问答网络，为下游个性化与自适应学习应用提供支持。
原文摘要 · Abstract (English)
Concept maps have been widely utilized in education to depict knowledge structures and the interconnections between disciplinary concepts. Nonetheless, devising a computational method for automatically constructing a concept map from unstructured educational materials presents challenges due to the complexity and variability of educational content. We focus primarily on two challenges: (1) the lack of disciplinary concepts that are specifically designed for multi-level pedagogical purposes from low-order to high-order thinking, and (2) the limited availability of labeled data concerning disciplinary concepts and their interrelationships. To tackle these challenges, this research introduces an innovative approach for constructing Domain Question Maps (DQMs), rather than traditional concept maps. By formulating specific questions aligned with learning objectives, DQMs enhance knowledge representation and improve readiness for learner engagement. The findings indicate that the proposed method can effectively generate educational questions and discern hierarchical relationships among them, leading to structured question maps that facilitate personalized and adaptive learning in downstream applications.
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