用GPT自动提取课程概念及其关系，省去人工标注
Examining GPT's Capability to Generate and Map Course Concepts and Their Relationship
- 设计多级提示词，让GPT根据不同详细程度的课程信息生成概念
- 实验验证了生成的概念与关系质量高，可支持课程推荐
- 适合教育科技开发者和课程设计者参考
从课程信息与材料中提取关键概念及其关系，有助于为学习者提供可视化展示和课程推荐。然而，人工识别与提取主题耗时费力。以往基于机器学习的方法依赖详细的课程材料，需大量前期准备。本文探讨大模型如GPT在自动生成课程概念及其关系方面的潜力。我们设计了一套提示词，将不同详细程度的课程信息输入GPT，生成高质量课程概念并识别其关联。通过大规模实验全面评估生成结果的质量。结果表明，大模型可作为支持教育内容选择与交付的有效工具。
原文摘要 · Abstract (English)
Extracting key concepts and their relationships from course information and materials facilitates the provision of visualizations and recommendations for learners who need to select the right courses to take from a large number of courses. However, identifying and extracting themes manually is labor-intensive and time-consuming. Previous machine learning-based methods to extract relevant concepts from courses heavily rely on detailed course materials, which necessitates labor-intensive preparation of course materials. This paper investigates the potential of LLMs such as GPT in automatically generating course concepts and their relations. Specifically, we design a suite of prompts and provide GPT with the course information with different levels of detail, thereby generating high-quality course concepts and identifying their relations. Furthermore, we comprehensively evaluate the quality of the generated concepts and relationships through extensive experiments. Our results demonstrate the viability of LLMs as a tool for supporting educational content selection and delivery.
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