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

EAGER：使用机构数据来确定 STEM 学位完成的预测因素

• 批准号: 1546725
• 负责人: Margaret Blume-Kohout
• 金额: $ 4.98万
• 依托单位: New Mexico Consortium
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2016-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546725&HistoricalAwards=false
• 关键词: EAGER; Use; Institutional; Data; Identify

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.

摘要：西班牙裔目前是四年制学院和大学入学人数最多的少数群体；然而，学位完成率仍然低于非西班牙裔白人和亚裔美国人。 学生在科学、技术、工程和数学 (STEM) 领域攻读本科学位的毅力和成功并不能仅仅通过之前的学术准备和考试成绩来很好地预测，而且，那些提高保留率的经历或品质可能因种族或族裔群体以及不同学科而异。这项研究从一所大型西班牙裔公立大学开发了非常详细的机构数据，该大学拥有大量历史上代表性不足的少数族裔人口。这些数据有可能发现不同种族/族裔在学生意向、保留和完成 STEM 学位课程的相对重要性方面可能存在的差异。该试点项目探讨了利用大学层面现有的大规模机构数据来确定可能有助于代表性不足的少数族裔更多地参与 STEM 领域的因素。包括大学预科准备、经济援助、课程注册和完成以及学生和教师人口统计等多样化数据在内的大规模分析数据集非常罕见。 该项目将从多个数据源中提取、组合和重新编码与学生和课程相关的信息，以生成新的纵向、学生级别的分析数据文件，从而允许检查学生特征、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。利用新的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模型和基于模拟的方法的可行性和适当性，以识别和评估政策干预措施，以提高 STEM 劳动力中代表性不足的少数群体的参与度。