EAGER: The Use of Institutional Data to Identify Predictors of STEM Degree Completion

摘要：西班牙裔现在是四年制学院和大学最大的少数民族群体;然而，学位完成率仍然低于非西班牙裔白色和亚裔美国人。 学生在科学，技术，工程和数学（STEM）领域追求本科学位的坚持和成功并不能简单地通过先前的学术准备和考试成绩来预测，此外，那些提高保留率的经验或品质可能会因种族或民族而异，跨学科。这项研究开发了非常详细的机构数据，从一个大型的西班牙裔服务的公立大学与大量人口的历史上代表性不足的少数民族。这些数据有可能发现可能的差异种族/民族的各种因素的相对重要性，在学生的意图，保留，并完成STEM学位课程。这个试点项目探讨了潜在的使用现有的，大规模的机构数据在大学一级，以确定可能有助于更多的参与，在STEM领域的代表性不足的少数民族的因素。大规模的分析数据集，包括大学预科，财政援助，课程注册和完成，以及学生和教师的人口统计数据等各种数据是罕见的。 该项目将从多个数据源中提取，联合收割机和重新编码学生和课程相关信息，以产生新的纵向，学生级分析数据文件，允许检查学生特征，课程准备和排序以及教学环境如何相互作用，以促进STEM学位的完成。利用新分析文件进行探索性描述性分析将评估应用更复杂的计量经济学建模和基于模拟的方法的可行性和适当性，以确定和评估政策干预措施，以增加代表性不足的少数族裔在STEM劳动力中的参与。

Abstract: Hispanics are now the largest minority group enrolling at four-year colleges and universities; however, degree completion rates remain lower than among non-Hispanic White and Asian Americans. Student persistence and success in pursuit of undergraduate degrees in science, technology, engineering and mathematics (STEM) fields are not well-predicted simply by prior academic preparation and test scores, and furthermore, those experiences or qualities which enhance retention may differ both across racial or ethnic groups, and across disciplines. This study develops highly detailed institutional data from a large Hispanic-Serving public university with a substantial population of historically underrepresented minorities. These data have potential to detect possible differences by race/ethnicity in the relative importance of various factors in student intentions, retention, and completion of STEM degree programs.This pilot project explores the potential for using existing, large-scale institutional data at the university level to identify factors that may contribute to greater participation of underrepresented minorities in STEM fields. Large-scale analytic datasets including such diverse data on pre-college preparation, financial aid, course enrollments and completions, and demographics of students and faculty are rare. This project will extract, combine, and recode student- and course-related information from multiple data sources to produce new longitudinal, student-level analytic data files, permitting examination of how student characteristics, course preparation and sequencing, and instructional environments interact to facilitate STEM degree completion. Exploratory descriptive analyses utilizing the new analytic files will assess feasibility and appropriateness of applying more sophisticated econometric modeling and simulation-based approaches, towards identifying and evaluating policy interventions to increase participation of underrepresented minorities in the STEM workforce.