Reducing Racial and Gender Achievement Gaps in STEM: Use of Natural Language Processing to Understand Why Affirmation Interventions Improve Performance

缩小 STEM 中的种族和性别成就差距：利用自然语言处理来理解为什么肯定干预可以提高绩效

• 批准号: 1420446
• 负责人: Valerie Purdie-Greenaway
• 金额: $ 103.6万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1420446&HistoricalAwards=false
• 关键词: Reducing; Racial; Gender; Achievement; Gaps

Addressing issues related to reducing the size of the achievement gaps in STEM disciplines among subpopulations of students is important to helping the Nation meet its 21st Century science and technology needs. Research shows that causes of achievement gaps in STEM arise from reciprocal interactions between societal, social, and environmental factors that might suppress students' true academic potential in challenging academic STEM domains. This project focuses on environmental factors (identified as social identity threats) that devalue, marginalize, or discriminate against students based on a social identity like race, gender, disability status, or socioeconomic status; such factors can eventually lead students to withdraw and disengage in STEM learning and careers. The objectives of this research are to: (1) synthesize and systematically analyze data from interventions (affirmation writing essays) shown to help reduce the impact of social identity threats on student participation in STEM; and (2) apply results of the synthesis and analyses to enhance existing interventions (e.g., maximize impact on subpopulations of students whose achiement in STEM fields is below their potential).The research project will proceed in two phases. First, the investigators will create an encrypted online repository of data from more than 2,500 affirmation writing essays, previously collected through randomized double-blind experiments involving approximately 1,400 students who vary by race, ethnicity, age, gender, and social class. The researchers will link this online repository of information to academic and psychological outcomes for middle school and college students. Using natural language processing (NLP), topic modeling, and other methods the investigators will identify sematic content and essay structure processes that mediate affirmation effects and highlight meaning of the effectiveness of the essay writing interventions. Results of these analyses will be used to develop and test a more robust intervention for reducing social identity threats involving African Americans, White, and female students. One hundred eighty (180) students (90 females and 90 males) will participate in two separate laboratory studies. One, conducted at Columbia University, will focus on race as a social factor; the second, conducted at Penn State University, will focus on gender. The ultimate goal of this work is to uncover and address psychological factors that might otherwise hinder students' participation in STEM careers.

解决与缩小学生群体在STEM学科方面的成就差距有关的问题，对于帮助国家满足21世纪的科学和技术需求非常重要。研究表明，STEM成绩差距的原因是社会、社会和环境因素之间的相互作用，这些因素可能会抑制学生在具有挑战性的STEM学术领域的真正学术潜力。该项目侧重于基于种族、性别、残疾状况或社会经济地位等社会身份贬低、边缘化或歧视学生的环境因素（被确定为社会身份威胁）；这些因素最终会导致学生退出STEM学习和职业生涯。本研究的目的是：(1)综合和系统地分析干预措施（肯定写作论文）的数据，这些数据显示有助于减少社会身份威胁对学生参与STEM的影响；(2)应用综合和分析的结果来增强现有的干预措施（例如，最大限度地影响在STEM领域的成就低于其潜力的学生亚群体）。该研究项目将分两个阶段进行。首先，研究人员将从2500多篇肯定写作论文中创建一个加密的在线数据存储库，这些论文之前是通过随机双盲实验收集的，涉及大约1400名不同种族、民族、年龄、性别和社会阶层的学生。研究人员将把这个在线信息库与中学生和大学生的学术和心理结果联系起来。使用自然语言处理（NLP）、主题建模和其他方法，研究人员将确定语义内容和论文结构过程，这些过程介导肯定效应，并突出论文写作干预措施的有效性。这些分析的结果将用于开发和测试一种更有效的干预措施，以减少涉及非裔美国人、白人和女学生的社会身份威胁。180名学生（90名女性和90名男性）将参加两个独立的实验室研究。其中一项在哥伦比亚大学进行，将把重点放在种族这个社会因素上；第二项研究将在宾夕法尼亚州立大学进行，重点关注性别。这项工作的最终目标是发现和解决可能阻碍学生参与STEM职业的心理因素。