Course-Skill Atlas: 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 which of these skills are being developed in higher education at a similar granularity.Here, we fill this gap by presenting Course-Skill Atlas -- a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions. To construct Course-Skill Atlas, we apply natural language processing to quantify the alignment between course syllabi and detailed workplace activities (DWAs) used by the DOL to describe occupations. We then aggregate these alignment scores to create skill profiles for institutions and academic majors. Our dataset offers a large-scale representation of college education's role in preparing students for the labor market.Overall, Course-Skill Atlas 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.高等教育通过赋能学生掌握劳动力市场所需的知识与技能，在驱动创新型经济发展中发挥着至关重要的作用。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——例如由美国劳工部（U.S. Department of Labor, DOL）搭建的相关体系，但针对高等教育领域中以同等细分粒度对上述技能的开发情况进行记录的相关工作却寥寥无几。为此，本研究推出课程-技能图谱（Course-Skill Atlas）以填补这一研究空白：该数据集为纵向数据集，涵盖了美国近3000所高等教育机构开设的超过300万份课程教学大纲中提取得到的技能相关信息。为构建课程-技能图谱，我们采用自然语言处理（Natural Language Processing, NLP）技术，量化课程教学大纲与美国劳工部用于描述职业的细分工作活动（Detailed Workplace Activities, DWAs）之间的匹配程度。随后我们对这些匹配得分进行聚合，以此生成各高等教育机构与学科专业的技能画像。本数据集从大规模维度展现了高等教育在为学生适配劳动力市场需求方面所发挥的作用。总体而言，课程-技能图谱可为劳动力开发背景下的技能来源相关研究开辟全新方向，并可为优化高等教育发展路径以适配不断演变的劳动力需求（尤其是在新技术蓬勃涌现的背景下）提供切实可行的决策参考。