Aligning open educational resources (OER) to skill taxonomies is a common task in the education field and helps teachers better locate material that aligns with the standards of their curriculum. When taxonomies change, as they periodically do, re-tagging the increasing mass of open educational resources is needed. The process of manual tagging is, however, exceedingly labor intensive. We propose and evaluate a novel combination of machine learning methods to help automate tagging open educational resources with skills from an existing taxonomy as well as skills from any newly introduced taxonomy. We collected text, image figures, and videos from tens of thousands of educational resources from two major digital learning platforms to answer the research questions of: how effective are machine learning models in automatically updating OER classification to reflect a new taxonomy (RQ1), and which models may be of practical use in different scenarios (RQ2)? Using several taxonomies, including the US Common Core, we find that while full automation is not practically viable, our most generalizable model can reach non-expert human labeling performance requiring only 100 labeled examples and near expert level with 5000. We believe these novel findings may have immediate utility for practitioners and policymakers and better ready the growing landscape of open educational resources for the advent of new taxonomies ahead. We publicly release our pre-trained US Common Core and new taxonomy tagging models, providing guidance on their viability in various real-world scenarios.

中文翻译：

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将开放教育资源与新分类法相结合：人工智能技术如何提供帮助以及在哪些场景中提供帮助

将开放教育资源 (OER) 与技能分类保持一致是教育领域的一项常见任务，可帮助教师更好地找到符合其课程标准的材料。当分类法发生变化时（就像它们定期发生的那样），需要重新标记越来越多的开放教育资源。然而，手动标记的过程非常耗费人力。我们提出并评估了一种新颖的机器学习方法组合，以帮助使用现有分类法中的技能以及任何新引入的分类法中的技能自动标记开放教育资源。我们从两个主要数字学习平台的数万个教育资源中收集了文本、图像和视频，以回答以下研究问题：机器学习模型在自动更新开放教育资源分类以反映新分类法（RQ1）方面的有效性如何，以及哪些模型可以在不同场景中实际使用（RQ2）？使用包括美国通用核心在内的多种分类法，我们发现虽然完全自动化实际上并不可行，但我们最通用的模型可以达到非专家人类标记性能，仅需要 100 个标记示例，并且需要 5000 个接近专家水平。我们相信这些新颖的发现可能对从业者和政策制定者有直接的效用，并为未来新分类法的出现更好地准备开放教育资源的不断发展。我们公开发布了预先训练的美国通用核心和新的分类标记模型，为其在各种现实场景中的可行性提供指导。