Evaluating Letters of Reference to Engineering Doctoral Programs for Racial and Gender Bias

评估工程博士课程的推荐信是否存在种族和性别偏见

• 批准号: 2225209
• 负责人: Allyson Flaster
• 金额: $ 33.04万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225209&HistoricalAwards=false
• 关键词: Evaluating; Letters; Reference; Engineering; Doctoral

Advanced training and education in STEM are of critical importance for the nation’s workforce and economic status. Equitable access to graduate level training reduces barriers for individuals from minoritized groups. Standardized graduate exams are becoming less important in the graduate admissions process, which places a higher weight on letters of recommendation for the selection of graduate students for a program. This project focuses on examining the language used by mentors in describing students in letters of recommendations and seeks to identify racial and gender bias in the application of key words or phrases that may perpetuate enrollment disparities. The professional development plan will build capacity in engineering education research through development of skills, methods, and text-based data analyses.The goals of the project are to identify differences in the way an applicant’s potential for success in engineering is described in letters of recommendation based on their gender and race/ethnicity and develop a deeper understanding of disciplinary practices, values, and norms while building skills in text-based data analysis. Founded on role congruity theory and stereotype content model theory, the project will use qualitative methods, content analysis, and natural language processing techniques to identify and compare language used in letters of recommendation. The project aims to produce a model engineering graduate admissions landscape that is free of race and gender biases than can be used for similar investigations of other disciplines. The project is supported by NSF's EHR Core Research Building Capacity in STEM Education Research (ECR:BCSER) program, which is designed to build investigators’ capacity to carry out high-quality STEM education research.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.

STEM的高级培训和教育对国家的劳动力和经济地位至关重要。公平获得研究生水平的培训减少了少数群体个人面临的障碍。标准化的研究生考试在研究生招生过程中变得越来越不重要，这使得推荐信在为一个项目选择研究生时变得越来越重要。该项目的重点是检查导师在推荐信中描述学生时使用的语言，并试图确定在应用可能使入学差异永久化的关键词或短语时存在的种族和性别偏见。该专业发展计划将通过发展技能、方法和基于文本的数据分析来建设工程教育研究的能力。该项目的目标是根据申请人的性别和种族/民族，确定推荐信中描述申请人在工程方面取得成功的潜力的方式的差异，并加深对学科实践、价值观、和规范，同时建立基于文本的数据分析技能。基于角色一致性理论和刻板印象内容模型理论，该项目将使用定性方法，内容分析和自然语言处理技术来识别和比较推荐信中使用的语言。该项目旨在建立一个没有种族和性别偏见的工程研究生入学模式，而不是用于其他学科的类似调查。该项目由NSF的EHR Core Research Building Capacity in STEM Education Research（ECR：BCSER）项目提供支持，该项目旨在培养研究人员开展高质量STEM教育研究的能力。该奖项反映了NSF的法定使命，通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。