Mapping the Many Pathways (In and Out) of the Postsecondary STEM Pipeline

迫切需要在美国建立一支技术娴熟、多样化的STEM劳动力队伍。对于许多学生来说，确定如何在大学期间把握自己的抱负和能力是一个主要障碍。研究本科生群体的成绩单“故事”可以帮助大专院校和未来的学生(1)了解与目的地专业相关的课程可能性和表现水平的范围，(2)确定某些路径上的学生可能会遇到的关键十字路口。这项研究通过考察选课途径和学生特征之间的关系，有助于揭示完成STEM学位的障碍。该项目建立在关于STEM管道中常见“泄漏”的先验知识的基础上，并提供了一个新的框架，用于评估学术专业之间的整体健康状况，同时考虑到进出STEM的移民模式。利用社会序列分析和网络科学，并结合学生人口统计信息，本研究为STEM教育的政策和实践提供必要的信息。来自公立大学两组本科生连续八个学期的成绩单数据的大学课程序列被用来记录STEM和非STEM专业的迁入和迁出模式。与所有特定于校园的成绩单研究一样，这些数据反映了学生群体的分类：(1)受制于相同的制度政策，(2)在同一班级共同入学时经历相同的课堂环境，以及(3)在课堂表现等级方面构成真正的同龄人。首先使用来源-目的地类型学对每个队列进行描述，该类型法将STEM开始和结束的学生、迁出STEM的学生、迁移到STEM的学生以及非STEM专业的开始和结束的学生进行分类。其次，网络方法被用来量化一个学生相对于其他学生的选课顺序的典型性，这种方法提供了对学术专业的路径分布的洞察。最后，选课途径与学生特征有关，包括性别、种族/民族、第一代学生身份和ACT分数。这些结果将对包括学生、教育工作者和政策制定者在内的许多利益相关者群体产生立竿见影的作用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

There is a pressing need to build a skilled and diverse STEM workforce in the United States. Determining how to navigate aspirations and aptitudes during college is a major hurdle for many students. Examining the transcript "stories" of undergraduate cohorts can help postsecondary institutions and future students (1) contextualize the range of course-taking possibilities and performance levels associated with destination majors, and (2) identify key crossroads that students on certain paths will likely encounter. The study helps uncover barriers to STEM degree completion by examining the relationship between course-taking pathway and student characteristics. This project builds on prior knowledge regarding common "leaks" in the STEM pipeline and offers a new framework for assessing the overall health of academic majors relative to each other, taking into account patterns of migration both in and out of STEM. Drawing on social sequence analysis and network science and incorporating student demographic information, this study provides essential information to inform policies and practices for STEM education. College course sequences from eight consecutive semesters of transcript data for two cohorts of undergraduates at a public university are used to document in- and out-migration patterns through STEM and non-STEM majors. As with all campus-specific transcript studies, these data reflect the sorting of a student population that (1) is subject to the same institutional policies, (2) experiences the same exact classroom environment when co-enrolled in a given class, and (3) constitutes true peers in terms of classroom performance hierarchies. Each cohort is first described using an origin-destination typology, which classifies students who start and finish in STEM, students who migrate out of STEM, students who migrate into STEM, and students who start and finish in non-STEM majors. Second, network methodology is employed to quantify the typicality of a student's course-taking sequence relative to other students, an approach that offers insight into the distribution of pathways through academic majors. Finally, course-taking pathways are linked with student characteristics, including gender, race/ethnicity, first-generation student status and ACT scores. These results will be of immediate utility for many stakeholder groups, including students, educators, and policymakers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.