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

使用细粒度的定量和定性数据来增强课程并扩大计算机科学的参与

• 批准号: 2030070
• 负责人: An-I Wang
• 金额: $ 99.98万
• 依托单位: Florida State 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: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030070&HistoricalAwards=false
• 关键词: Using; Fine; Grained; Quantitative; Qualitative

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.

该项目将为国家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，通过支持高成就、低收入、有经济需求的学生在佛罗里达州立大学留校和毕业，佛罗里达州立大学是卡内基一世研究大学。在五年的时间里，该项目将为33名正在攻读计算机学士学位的独特学者提供为期四年的奖学金。该项目的目标包括：(1)识别和招收有经济需要和学术天赋的学生；(2)通过分组注册班级、专门的导师和学术支持来提高留校率；(3)为学者提供实习和研究机会；以及(4)收集反馈以完善计算课程。这个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。这些分析有望为留学生、计算机课程、当前支持系统的有效性以及如何鼓励女性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增加低收入、高成就、有经济需求的本科生的STEM学位毕业率。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。该项目旨在增加低收入、成绩优异的学生对计算机科学的参与。为了实现这一目标，项目战略包括高中外展、专职导师、学生支持系统、队列招生和用奖学金取代学生贷款。该项目将使用随机对照试验实验来调查这些活动的有效性。作为这项研究的一部分，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法调查的数据，以确定和纠正计算机课程中的性别和文化偏见。该项目的预期结果包括确定课程改革以鼓励多样性，并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，包括队列人口统计、学业成绩、留存、支持系统的使用、分离原因和安置。研究结果将在SIGCSE、ASEE、FIE和AERA等会议上发表。该项目由NSF的科学、技术、工程和数学奖学金项目资助，该项目旨在增加在STEM领域获得学位的低收入学术天才学生的数量。它还旨在改善未来STEM工作者的教育，并产生关于低收入学生的学业成功、留住、转移、毕业和学术/职业道路的知识。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。

项目成果

Students’ Perceptions of their Engineering Identity Development and REU Summer Program Experiences: An Equity-Centered Analysis

学生对其工程身份发展和 REU 暑期项目经历的看法：以公平为中心的分析

• 发表时间: 2023
• 期刊: ASEE Annual Conference proceedings
• 作者: Fluker, C.

How Do Institutional Type and Transfer Affect Contemporary College Students’ Degree Attainment?

院校类型和转学如何影响当代大学生的学位获得？

• DOI: 10.1080/10668926.2022.2156633
• 发表时间: 2022
• 期刊: Community College Journal of Research and Practice
• 影响因子: 1
• 作者: Holton-Thomas, Amber;Perez-Felkner, Lara;Templeton, Da’Shay Portis
• 通讯作者: Templeton, Da’Shay Portis

Gender Differences in Motivational and Curricular Pathways Towards Postsecondary Computing Majors

中学后计算机专业动机和课程途径的性别差异

• DOI: 10.1007/s11162-023-09751-w
• 发表时间: 2023
• 期刊: Research in Higher Education
• 影响因子: 2.1
• 作者: Chen, Jinjushang;Perez-Felkner, Lara;Nhien, Chantra;Hu, Shouping;Erichsen, Kristen;Li, Yang
• 通讯作者: Li, Yang

Facilitating the Bootstrapping of a New ISA

• DOI: 10.1145/3589610.3596282
• 发表时间: 2023-06
• 期刊: Proceedings of the 24th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems
• 作者: Abigail Mortensen;Scott Pomerville;D. Whalley;Soner Önder;Gang-Ryung Uh