Using Fine-Grained Quantitative and Qualitative Data to Enhance Curricula and Broaden Participation in Computer Science

该项目将通过支持佛罗里达州立大学（卡内基I型研究型大学）中表现出经济需求的高成就低收入学生的保留和毕业，为全国对受过良好教育的科学家、数学家、工程师和技术人员的需求做出贡献。在为期五年的时间里，该项目将为33名攻读计算机学士学位的独特学者提供四年奖学金。项目目标包括：(1)寻找和招收有经济需要和学术才能的学生；(2)通过队列班招生、专职导师和学术支持来提高保留率；(3)为学者提供实习和研究机会；(4)收集反馈意见，完善计算机课程。该项目的一个显著特点是应用自然语言处理、机器学习和传统分析来检查与学生成功相关的细粒度定性和定量数据。这些分析有望对学生保留、计算机课程、当前支持系统的有效性以及如何鼓励妇女和其他代表性不足的群体主修计算机科学提供见解。该项目的总体目标是提高低收入、高成就、有经济需求的本科生的STEM学位完成率。尽管越来越多的工作需要计算机专业知识，但只有10%的STEM毕业生学习计算机科学。这个项目旨在增加低收入、成绩优异的学生在计算机科学方面的参与。为了实现这一目标，项目策略包括高中推广、专门的导师、学生支持系统、队列入学以及用奖学金代替学生贷款。本项目将采用随机对照试验来调查这些活动的有效性。作为本研究的一部分，该项目将使用自然语言处理和机器学习方法来分析来自经验抽样方法调查的数据，以识别和纠正计算机课程中的性别和文化偏见。该项目的预期成果包括确定课程变化，以鼓励多样性和量化有助于学生在计算机科学方面取得成功的因素。项目评估将包括年度数据收集和分析队列人口统计、学术表现、保留、支持系统的使用、离职原因和安置。研究成果将在SIGCSE、ASEE、FIE、AERA等会议上发表。该项目由美国国家科学基金会的科学、技术、工程和数学奖学金项目资助，旨在增加有经济需求的低收入学术天才学生在STEM领域获得学位的人数。它还旨在改善未来STEM工作者的教育，并为低收入学生提供有关学业成功、留校、转学、毕业和学术/职业道路的知识。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。

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Florida State University, a Carnegie I Research University. Over its five- year duration, this project will fund four-year scholarships to 33 unique Scholars who are pursuing bachelor’s degrees in computing. The project objectives include: (1) identifying and recruiting students with financial need and academic talent; (2) improving retention through cohort class enrollments, dedicated tutors, and academic support; (3) providing internship and research opportunities to Scholars; and (4) gathering feedback to refine the computing curriculum. A distinguishing feature of this project is the application of natural language processing, machine learning, and traditional analyses to examine fine-grained qualitative and quantitative data related to student success. These analyses are expected provide insights into student retention, the computing curriculum, the effectiveness of current support systems, and how to encourage women and other underrepresented groups to major in computer science. The overall goal of this project is to increase STEM degree completion of low-income, high- achieving undergraduates with demonstrated financial need. Although a growing number of jobs require expertise in computing, only 10% of STEM graduates study computer science. This project seeks to increase the participation of low-income, high- achieving students in computer science. To achieve this goal, the project strategies include high school outreach, dedicated tutors, student support systems, cohort enrollment, and replacing student loans with scholarships. This project will investigate the effectiveness of these activities using randomized control trial experiments. As part of this study, the project will use natural language processing and machine learning approaches to analyze data from Experience Sampling Method surveys to identify and remediate gender and cultural biases in the computing curriculum. The expected outcomes of the project include identification of curriculum changes to encourage diversity and quantification of factors that contribute to student success in computer science. Project evaluation will include annual data collection and analyses of cohort demographics, academic performance, retention, use of support systems, reasons for separation, and placement. The research findings will be published at conferences such as SIGCSE, ASEE, FIE, and AERA. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income 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.