课题基金 / 基金详情

Scholarships To Improve Undergraduate Students' Academic Achievement, Retention, and Career Success in Computer Science and Artificial Intelligence

Scholarships To Improve Undergraduate Students' Academic Achievement, Retention, and Career Success in Computer Science and Artificial Intelligence
奖学金旨在提高本科生在计算机科学和人工智能领域的学业成绩、保留率和职业成功
批准号:
2030581
负责人:
Anagha Kulkarni
金额:
$100.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-03-01 至 2025-02-28

项目摘要

项目成果

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中文摘要
翻译
该项目将通过支持旧金山州立大学有经济需求的高成就、低收入学生的留校和毕业,来促进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。这所大学是加州州立大学系统的23个校区之一,被指定为拉美裔服务机构。该项目为期四年,将为80名攻读计算机科学学士学位的全日制一年级学生提供一年奖学金。该项目将支持一个三管齐下的全方位支持系统,确保学生在第一年取得稳定的学业、社交和职业进步。这一支持系统将包括学术咨询、寒暑假计划、联合课程活动和职业发展活动。该项目有可能扩大低收入学生在人工智能和计算机科学领域的参与度,并提高美国人工智能行业的经济竞争力。该项目的结果可能对其他高等教育机构寻求在其人工智能/计算机科学项目中留住不同社会经济背景的学生具有价值。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。该项目的目标是:(1)通过密集和量身定制的学术和专业咨询来改善信息公平;(2)通过联合课程活动提高学术和专业自我效能,这些活动提供早期接触、职业指导以及人工智能方面的研究和行业经验;(3)通过帮助学生成为计算机科学界的一部分,并通过展示榜样和社会公益项目使计算机科学和人工智能人性化,培养学生作为计算机科学家的归属感和认同感。学生作为计算机科学家的认同感已被证明是他们在该领域坚持不懈和取得成功的关键,特别是对于来自代表性不足人群的学生。然而,人们对学生培养积极的计算机科学认同感的机制知之甚少。该项目将调查低收入家庭学生的计算机科学认同感是否以及如何在他们本科计算机科学学习的第一年发生变化,以及计算机科学认同感的增强是否预示着计算机科学专业的留校率更高。还将研究早期人工智能暴露对学生成绩和保持能力的影响。将使用包括形成性和总结性部分的混合方法来评估项目的可接受性、可行性和有效性。作为该项目一部分开发的资源将通过项目网站传播,项目成果将通过期刊出版物和人工智能和计算机科学教育会议传播。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。
英文摘要
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 San Francisco State University. This university is one of the 23 campuses of the California State University system and is designated as a Hispanic-serving institution. Over its four-year duration, this project will provide one-year scholarships to 80 first-year, full-time students who are pursuing bachelor’s degrees in computer science. The project will support a three-pronged wrap-around support system that ensures students make steady academic, social, and professional progress in the first year. This support system will include academic advising, winter and summer programs, co-curricular activities, and professional development activities. The project has the potential to broaden the participation of low-income students in the fields of artificial intelligence and computer science and to increase the economic competitiveness of the US artificial intelligence sector. The results from this project may be valuable for other higher education institutions seeking to increase retention of socioeconomically diverse students in their artificial intelligence/computer science programs. The overall goal of this project is to increase STEM degree completion of low-income, high achieving undergraduates with demonstrated financial need. The objectives through which the project goal will be realized are: (1) to improve information equity through intensive and tailored academic and professional advising; (2) to increase academic and professional self-efficacy through co-curricular activities that provide early exposure to, career coaching in, and research and industry experiences in artificial intelligence; (3) to develop students’ sense of belonging and identity as computer scientists by helping them become part of the computer science community and by humanizing computer science and artificial intelligence via showcasing role models and social good projects. Students’ sense of identity as computer scientists has been shown to be critical to their persistence and success in the field, especially for students from underrepresented populations. However, little is known about the mechanisms by which students develop a positive sense of computer science identity. This project will investigate if and how low-income students’ sense of computer science identity changes during their first year of undergraduate computer science study, and if a stronger sense of computer science identity predicts greater retention in the computer science major. The impact of early artificial intelligence exposure on student’s achievement and retention will also be studied. A mixed-methods approach including both formative and summative components will be used to evaluate the acceptability, feasibility, and effectiveness of the project. The resources developed as part of this project will be disseminated through the project website, and the results from the project will be disseminated through publications in journals and at conferences in artificial intelligence and computer science education. 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.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
Foundational Strategies to Support Students with Diverse Backgrounds and Interests in Early Programming
支持具有不同背景和兴趣的学生进行早期编程的基本策略
DOI: --
发表时间: 2023
期刊: ASEE Annual Conference proceedings
影响因子: --
作者: [Gautam, A., Ihorn, S., Yoon, I., Savvides, M., Kulkarni, A.]
通讯作者: Kulkarni, A.
海外基金