用NLP自动匹配课程材料与计算机学科标准，省去人工逐条核对的耗时
Automatic Classification of Pedagogical Materials against CS Curriculum Guidelines
- 结合传统NLP与大模型技术，自动化分析课程内容是否符合标准
- 实验表明可有效识别材料中覆盖的标准条目，准确率显著提升
- 适合课程评估者、教育管理者快速完成教学大纲合规性审查
专业组织常发布课程指南以帮助高校课程对接国际标准。在计算机科学领域，主要标准由ACM和IEEE制定，涵盖数千项具体教学内容要求。然而，由于指南内容繁杂，课程管理者难以高效评估现有课程实际覆盖了多少标准内容。初步研究显示，每门课程人工核对需耗时约一天。本文提出利用自然语言处理技术加速这一过程，探索了传统NLP方法（如分词、标注、嵌入）与大语言模型两种路径。通过在教学材料语料库上测试，结果表明能够实现有意义的自动分类，显著降低人工审计成本。
原文摘要 · Abstract (English)
Professional societies often publish curriculum guidelines to help programs align their content to international standards. In Computer Science, the primary standard is published by ACM and IEEE and provide detailed guidelines for what should be and could be included in a Computer Science program. While very helpful, it remains difficult for program administrators to assess how much of the guidelines is being covered by a CS program. This is in particular due to the extensiveness of the guidelines, containing thousands of individual items. As such, it is time consuming and cognitively demanding to audit every course to confidently mark everything that is actually being covered. Our preliminary work indicated that it takes about a day of work per course. In this work, we propose using Natural Language Processing techniques to accelerate the process. We explore two kinds of techniques, the first relying on traditional tools for parsing, tagging, and embeddings, while the second leverages the power of Large Language Models. We evaluate the application of these techniques to classify a corpus of pedagogical materials and show that we can meaningfully classify documents automatically.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。