EDUCATIONAL SYSTEMS: Settings Yielding Science, Technology, Engineering, & Math Success (ED-SYSTEMS)

社区学院和少数族裔服务机构，包括西班牙裔服务机构（HSI），提供了通往STEM管道的多种途径和联系，并且是代表性不足的少数族裔（URM）接受中学后教育的关键接入点，特别是非裔美国人、美洲印第安人和拉丁裔。学生的流动性和跨机构的过渡是评估STEM学位途径时要考虑的关键因素。本研究旨在探讨STEM途径的学生谁开始在社区学院，特别强调URM学生，学生跨多个机构的流动性，以及HSIs的作用。研究结果将提供一个更好地了解如何评估干成果，并保持潜力，以改变结构和过程的基础干中学后的轨迹，以促进种族平等和扩大参与干综合混合方法的方法，利用多成员随机效应建模，社会网络分析，和数字叙事，与国家和地区的数据，将提供一个细粒度和细致入微的检查的障碍和机会，以干由个人特点和机构环境URM学生谁可以浏览STEM途径在几个机构提出。将讨论以下研究目的：1）确定STEM学生流动模式，显示机构之间的联系以及这些联系如何影响学生，2）利用先进的统计技术，说明学生跨学院的流动性，以检查学生的特点，大学的经验，以及有助于STEM成果的机构环境，以及3）定性地深化和澄清大规模数据分析的结果，以产生对学生生活经验的见解。

Community colleges and Minority Serving Institutions, including Hispanic Serving Institutions (HSIs), provide several pathways and linkages to the STEM pipeline and are key access points to postsecondary education for underrepresented racial minorities (URM), specifically African Americans, American Indians, and Latinos. Student mobility and transitions across institutions are key factors to consider in assessing STEM degree pathways. This study seeks to examine STEM pathways for students who begin at community colleges, with a specific emphasis on URM students, student mobility across multiple institutions, and the role of HSIs. The research findings will provide a better understanding of how to assess STEM outcomes and hold potential to transform the structure and processes that underlie STEM postsecondary trajectories to promote racial equity and broaden participation in STEM.An integrated mixed methods approach utilizing multiple membership random effect modeling, social network analysis, and digital narratives, with both national and regional data, will provide a fine-grained and nuanced examination of the barriers and opportunities to STEM presented by individual characteristics and institutional contexts for URM students who may navigate STEM pathways across several institutions. The following research purposes will be addressed: 1) identify STEM student mobility patterns, showing the links between institutions and how these links impact students, 2) utilize advanced statistical techniques that account for student mobility across colleges to examine the student characteristics, college experiences, and institutional contexts that contribute to STEM outcomes, and 3) qualitatively deepen and clarify findings from the large scale data analysis to generate insights on students lived experiences.