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

在美国，迫切需要建立一支熟练和多样化的STEM劳动力队伍。在大学期间，如何把握自己的志向和能力是许多学生面临的主要障碍。研究本科群体的成绩单“故事”可以帮助高等教育机构和未来的学生(1)了解与目标专业相关的课程选修可能性和表现水平的范围，以及(2)确定学生在某些道路上可能遇到的关键十字路口。该研究通过研究课程选择途径与学生特征之间的关系，有助于揭示STEM学位完成的障碍。该项目建立在对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，用于评估学术专业相对于彼此的整体健康状况，同时考虑到STEM内外的迁移模式。利用社会序列分析和网络科学，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了必要的信息。一所公立大学的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通过STEM和非STEM专业的迁入和迁出模式。与所有校园特定的成绩单研究一样，这些数据反映了学生群体的分类：(1)服从相同的制度政策，(2)在同一班级共同注册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。每个队列首先使用起源-目的地类型学进行描述，该类型学对开始和完成STEM的学生，从STEM迁移的学生，迁移到STEM的学生以及开始和完成非STEM专业的学生进行分类。其次，采用网络方法来量化学生相对于其他学生的课程选择顺序的典型性，这种方法可以深入了解学术专业的路径分布。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直接的效用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.