Broadening Participation Research Project: STEP into STEM, Investigating Successful Transitions and Effective Pathways into STEM

北卡罗来纳农工州立大学(North Carolina A&amp；T State University)的研究人员将利用大数据分析进行一项纵向研究，以推进成功战略的知识和证据基础，以鼓励代表性不足的少数族裔走上STEM道路。该项目的目标是描述和了解STEM的特点和途径，根据学生成功的机构系统/项目指标评估全国大学STEM干预的有效性，并建议如何增加攻读STEM学位和职业的人数不足的少数族裔女性。这项研究将审查影响尚未决定的/非STEM学生过渡到STEM的因素，并展示有效的具体系统/方案干预措施。本研究将以社会认知职业理论、学生坚持性理论、人力资本理论和职业预期社会化理论为指导。该项目是一种创新的方法，旨在更好地了解激励学生选择STEM学位课程的因素，并预计将为STEM过渡和途径开发一套核心的最佳实践指标。这项研究的主要数据来源是高中纵向调查和初级中学学生调查。这些数据将被分析以检查学生进入STEM的途径，重点是那些在进入社区大学和四年制学位项目后尚未决定专业的学生。具体的研究问题是：(1)对于代表性不足的少数族裔(URM)和尚未决定的/非STEM主要过渡到STEM，有哪些学术、社会经济和人口支持和障碍？(2)哪些有效的途径和高等教育机构/计划支持URM、尚未决定/非STEM成功过渡到STEM？(3)哪些机构有基于数据的证据来支持他们的最佳实践干预战略？(4)哪些可实施的战略将支持成功过渡和有效途径，特别是对于URM和来自少数族裔服务机构的未决定/非STEM学生？以及(5)建议机构在全国范围内使用哪些共享指标来评估URM向STEM的成功过渡和有效途径？分析技术将包括随机森林、主成分分析、XGBoost和深度学习，以分析大型数据集并对变量进行元研究。研究结果可能会影响高等教育机构的政策和实践。这一奖项反映了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。

Researchers at North Carolina A&T State University will conduct a longitudinal study using big data analytics to advance the knowledge and evidence base for successful strategies to encourage underrepresented minorities to pursue STEM pathways. The goals of the project are to characterize and understand pathways to STEM, assess STEM intervention effectiveness at universities across the nation based on institutional system/program metrics for student success, and recommend ways to increase the number of underrepresented minority women pursuing STEM degrees and careers. The study will examine factors that influence transitions to STEM for undecided/non-STEM students and demonstrate specific systematic/programmatic interventions that are effective. The research will be guided by a triangulation of social cognitive career theory, student persistence theory, human capital theory, and vocational anticipatory socialization theory. The project is an innovative approach to better understanding factors that motivate students to elect STEM degree programs, and it is expected to develop a core set of best practices metrics for STEM transitions and pathways. The primary data sources for the study are the High School Longitudinal Survey and the Beginning Postsecondary Students Survey. The data will be analyzed to examine student pathways into STEM, with a focus on students who are undecided about their majors upon entering community college and four-year degree programs. The specific research questions are (1) what are the academic, socioeconomic, and demographic supports and barriers for underrepresented minorities (URM)and undecided/non-STEM major transitions to STEM?; (2) what effective pathways and higher education institutional systems/programs support URMs, undecided/non-STEM successful transitions to STEM?; (3) which institutions have data-based evidence to support their best practice intervention strategies?; (4) what implementable strategies will support successful transitions and effective pathways into STEM, particularly for URMs and undecided/non-STEM students from minority serving institutions?; and (5) what shared metrics are recommended for institutions to use nationally to assess successful transitions and effective pathways for URMs into STEM? Analytical techniques will include Random Forest, Principal Component Analysis, XGBoost, and Deep Learning to analyze the large datasets and conduct the meta studies on the variables. The study results could impact policy and practice at higher education institutions at large.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.