A national longitudinal dataset of skills taught in U.S. higher education curricula

Higher education plays a critical role in driving an innovative economy by equipping students with knowledge and skills demanded by the workforce. While researchers and practitioners have developed data systems to track detailed occupational skills, such as those established by the U.S. Department of Labor (DOL), much less effort has been made to document skill development in higher education at a similar granularity.Here, we fill this gap by presenting a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions.To construct this dataset, we apply natural language processing to extract from course descriptions detailed workplace activities (DWAs) used by the DOL to describe occupations. We then aggregate these DWAs to create skill profiles for institutions and academic majors. Our dataset offers a large-scale representation of college-educated workers and their role in the economy.To showcase the utility of this dataset, we use it to 1) compare the similarity of skills taught and skills in the workforce according to the US Bureau of Labor Statistics, 2) estimate gender differences in acquired skills based on enrollment data, 3) depict temporal trends in the skills taught in social science curricula, and 4) connect college majors' skill distinctiveness to salary differences of graduates.Overall, this dataset can enable new research on the source of skills in the context of workforce development and provide actionable insights for shaping the future of higher education to meet evolving labor demands especially in the face of new technologies.<b>Citation</b>:The preprint is available here:https://arxiv.org/abs/2404.13163 If you are using the dataset, please use the following citation:<pre>@article{sabet2024national,<br> title={A national longitudinal dataset of skills taught in US higher education curricula},<br> author={Sabet, Alireza Javadian and Bana, Sarah H and Yu, Renzhe and Frank, Morgan R},<br> journal={arXiv preprint arXiv:2404.13163},<br> year={2024}<br>}<br></pre>

高等教育在驱动创新型经济发展中扮演关键角色，可为学生赋能劳动力市场所需的知识与技能。尽管研究者与从业者已开发出多套数据系统以追踪细分职业技能——例如美国劳工部（U.S. Department of Labor, DOL）搭建的相关体系，但针对高等教育阶段技能培养情况、以类似精细粒度进行记录的研究却寥寥无几。
为此，本研究填补了这一研究空白：我们发布一套纵向数据集，该数据集从美国近3000所高等教育机构开设的逾300万份课程大纲中推断得到相关技能信息。
为搭建该数据集，我们运用自然语言处理技术，从课程描述中提取美国劳工部用于描述职业的细分工作活动（Detailed Workplace Activities, DWAs）；随后对这些细分工作活动进行聚合，为各高等教育机构及学术专业生成技能画像。本数据集实现了对受过高等教育劳动者及其在经济中作用的大规模刻画。
为展示本数据集的应用价值，我们开展了四项实证分析：1）对比美国劳工统计局（U.S. Bureau of Labor Statistics）发布的岗位技能与高校传授技能之间的相似度；2）结合招生数据估算学习者在技能习得层面的性别差异；3）描绘社会科学课程体系中传授技能的时间演变趋势；4）关联学术专业的技能独特性与毕业生薪资差距。
总体而言，本数据集可为劳动力发展背景下的技能来源相关研究提供全新的研究视角，并可为优化高等教育发展以适配不断变化的劳动力需求（尤其是在新技术涌现的背景下）提供可落地的决策参考。
<b>引用说明</b>：该预印本可通过以下链接获取：https://arxiv.org/abs/2404.13163。若您在研究中使用本数据集，请采用如下引用格式：<pre>@article{sabet2024national,
title={美国高等教育课程传授技能的全国性纵向数据集},
author={Sabet, Alireza Javadian and Bana, Sarah H and Yu, Renzhe and Frank, Morgan R},
journal={arXiv预印本 arXiv:2404.13163},
year={2024}
}</pre>