Collaborative Research: Researching Early Access to Computing and Higher Education (REACH): Understanding CS pathways with a focus on Black women

这项研究项目旨在调查长期以来在获得和参与计算机科学(CS)教育方面的不平等现象。几十年的研究表明，某些亚群(例如，妇女、残疾学生、代表不足的少数族裔学生)在参加CS课程和方案方面往往面临巨大障碍。随着计算机教育在K-12教育体系中的不断扩大，了解计算机教育的早期经验与大学计算机课程和程序的注册之间的关系是很重要的。通过将大规模入学数据的量化分析和学生调查与重点小组和访谈的定性数据相结合，该项目旨在为扩大和多样化参与计算机教育的努力提供信息。具体地说，这个项目试图系统地回答K-12计算经验如何影响他们追求高等教育的学生，以及这种影响是否以及如何在不同的学生群体中不同。这个混合方法项目的目标是通过调查K-12学生的计算经验与高等教育之间的关系来检查计算机科学(CS)教育的公平性，重点关注黑人女性的经验。指导本研究的理论框架是黑人女权主义思想，特别是交叉性，以及社会资本理论。该项目跨越了三个层面的数据收集和分析：国家、机构和个人体验。这些级别不是指分析单位(所有三个级别都使用学生级别的数据)，而是指数据的组织方式。全州教育数据的聚类分析将被用来确定初中和高中的计算机科学课程选择模式。然后，将使用多水平建模来调查这些选课模式与大学计算机课程和程序的参与之间的关系。重点将放在了解这些关系对于不同的学生群体是如何不同的。这些分析结果将与大学生调查分析相结合，以更好地了解学生在K-12阶段的经历如何影响他们在大学接受计算机教育的机会、挑战和决定。最后，主修CS的黑人女性将参与数据解释、焦点小组和访谈，以更好地了解她们在CS生态系统中的独特经历。这些分析将有助于概念化扩大K-16课程沿途计算的参与，以支持学生，特别是黑人女性，应用计算技能和知识解决各种学科的问题。该项目由NSF的EHR核心研究(ECR)计划支持。ECR计划强调基础STEM教育研究，以产生该领域的基础知识。在基本、广泛和持久的关键领域进行投资：STEM学习和STEM学习环境，扩大STEM的参与，以及STEM劳动力发展。该计划支持积累强有力的证据，为理解、构建理论以解释和建议干预措施和创新来解决持续问题提供信息。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research project seeks to examine longstanding inequities in access to and participation in computer science (CS) education. Decades of research have shown that certain subgroups (e.g., women, students with disabilities, underrepresented minority students) tend to face substantial barriers to participating in CS courses and programs. As computing education continues to expand in K-12 education systems, it is important to understand how early experiences in computing education relate to enrollment in computing courses and programs in college. By combining quantitative analyses of large-scale enrollment data and student surveys with qualitative data from focus groups and interviews, this project aims to inform efforts to broaden and diversify participation in computing education. Specifically, this project seeks to systematically answer how K-12 computing experiences influence students as they pursue higher education and whether and how that influence differs for distinct subpopulations of students.The goal of this mixed-methods project is to examine equity in computer science (CS) education by investigating the relationship between students’ computing experiences in K-12 and higher education with a focus on the experiences of Black women. The theoretical frameworks guiding this research are Black Feminist Thought, specifically intersectionality, and social capital theory. The project spans three levels of data collection and analysis: state, institution, and individual experience. These levels have reference not to the unit of analysis (all three levels utilize student-level data) but rather to the way the data are organized. Cluster analysis of statewide education data will be used to identify computer science course taking patterns in middle and high school. Multilevel modeling will then be employed to investigate how these course taking patterns are related to participation in computing courses and programs in college. Focus will be placed on understanding how these relationships differ for distinct groups of students. Findings from these analyses will be coupled with analyses of college student surveys to better understand how students’ experiences in K-12 influenced their opportunities, challenges, and decisions regarding computing education in college. Finally, Black women who are majoring in CS will be engaged in data interpretation, focus groups, and interviews to better understand their unique experiences within the CS ecosystem. These analyses will help conceptualize broadening participation in computing along the K-16 pathway in a way that supports students, particularly Black women, in applying computing skills and knowledge to solve problems in a variety of disciplines.This project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad, and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest interventions and innovations to address persistence.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.