Collaborative Research: Equity of Access to Computer Science: Factors Impacting the Characteristics and Success of Undergraduate CS Majors

合作研究：获得计算机科学的公平性：影响本科计算机科学专业特征和成功的因素

• 批准号: 2031907
• 负责人: Anna Bargagliotti
• 金额: $ 34.4万
• 依托单位: Loyola Marymount University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-15 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031907&HistoricalAwards=false
• 关键词: Collaborative; Research; Equity; Access; Computer

This project aims to serve the national interest by improving undergraduate computer science education. It will do so by completing a research study that can reveal potential systemic limitations in access to computer science education by all students. This research study will examine ten-years of undergraduate student application, admissions, and retention data from four institutions. Analysis of these data will describe how students of varying demographics and pre-college preparation are present throughout the computer science talent pipeline. This study will fill an important research gap about factors that affect the flow of students into and through the computer science major. It is well documented that the demographic characteristics of computer science students are highly skewed toward males versus females and have skewed racial/ethnic distributions. What is not yet understood is at what point in the talent pipeline these imbalances are greatest and the degree to which they change as students progress through computer science undergraduate programs. In addition, the current educational disruption caused by COVID-19 provides the important and unique opportunity to determine what effect, if any, the resulting educational changes have had on participation of different groups of students in computer science. Students from underrepresented groups appear to have encountered greater difficulty accessing distance learning and being connected to the full range of educational opportunities presented by these unique circumstances, which are very strongly related to technological know-how. There is legitimate cause for concern that the pandemic will further divide the advantaged from the disadvantaged, further marginalizing the underrepresented groups that the project is studying from opportunities to advance into computer science majors and progress successfully through them. Computer science is an area of critical strategic importance for the nation, and a field in which cultivating domestic talent can have enormous impact. Thus, examining pre- and post- pandemic patterns of participation in computer science have the potential to help the nation meet its growing needs for talent in computer science and related fields, such as cybersecurity and artificial intelligence. This study will use a large, rich data set compiled from ten years of undergraduate application, admissions, and course-level data from four institutions: Loyola Marymount University, Cal State University Long Beach, the University of California Riverside, and the University of California San Diego. Analysis of these longitudinal data will improve understanding of who has access, who applies, who is admitted, and who succeeds in computer science. Using classical statistical approaches and modern machine learning based approaches to analysis of large data sets, the study seeks to understand how to improve the inclusion of all students in computer science. It will supplement this large-scale quantitative analysis with qualitative analysis of results from targeted focus-groups and interviews. The qualitative analysis, coupled with the quantitative analysis of longitudinal data from four institutions with different student demographics and other characteristics, will provide a deeper analysis of access to and success in computer science than any previous study. The resulting extension of knowledge has the potential to lay the foundation for achieving equitable access to computer science education for all students. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students.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.

本项目旨在通过改善本科计算机科学教育服务于国家利益。它将通过完成一项研究来实现这一目标，该研究可以揭示所有学生获得计算机科学教育的潜在系统性限制。本研究将调查四所院校十年来的本科生申请、录取和留校数据。对这些数据的分析将描述不同人口统计数据和大学预科的学生如何在整个计算机科学人才管道中呈现。本研究将填补一个重要的研究空白，关于影响学生进入和通过计算机科学专业的因素。有充分的证据表明，计算机科学专业学生的人口统计学特征高度偏向于男性而不是女性，并且有扭曲的种族/民族分布。目前尚不清楚的是，在人才储备的哪个阶段，这些不平衡是最大的，以及随着学生在计算机科学本科课程中的进步，这些不平衡会发生多大程度的变化。此外，当前由COVID-19造成的教育中断提供了一个重要而独特的机会，可以确定由此产生的教育变化对不同群体的学生参与计算机科学产生了什么影响（如果有的话）。来自代表性不足群体的学生似乎在获得远程学习和获得这些与技术诀窍密切相关的独特环境所提供的各种教育机会方面遇到了更大的困难。我们有理由担心，疫情将进一步分化优势群体和弱势群体，使该项目正在研究的代表性不足的群体进一步边缘化，使他们没有机会进入计算机科学专业，并通过这些专业成功取得进步。计算机科学是一个对国家至关重要的战略领域，也是一个培养国内人才可以产生巨大影响的领域。因此，研究大流行前后参与计算机科学的模式有可能帮助国家满足其对计算机科学和相关领域（如网络安全和人工智能）人才日益增长的需求。这项研究将使用一个庞大而丰富的数据集，这些数据集是从洛约拉玛丽蒙特大学、加州州立大学长滩分校、加州大学河滨分校和加州大学圣地亚哥分校这四所大学10年来的本科申请、录取和课程水平数据中汇编而成的。对这些纵向数据的分析将提高对谁有访问权限、谁申请、谁被录取以及谁在计算机科学领域取得成功的理解。使用经典的统计方法和基于现代机器学习的方法来分析大型数据集，该研究旨在了解如何提高所有学生在计算机科学中的包容性。它将用对目标焦点小组和访谈结果的定性分析来补充这种大规模定量分析。定性分析，再加上对四所院校的纵向数据进行定量分析，这些院校的学生人口统计和其他特征不同，将比以往任何研究都更深入地分析计算机科学的获取途径和成功。由此产生的知识扩展有可能为实现所有学生公平获得计算机科学教育奠定基础。NSF IUSE: EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。