Analysis of a Simple, Low-cost Intervention's Impact on Retention of Women in Computer Science
分析简单、低成本的干预措施对计算机科学领域女性保留的影响
基本信息
- 批准号:2021330
- 负责人:
- 金额:$ 17.49万
- 依托单位:
- 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:2020-07-01 至 2023-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This project aims to serve the national interest in high-quality STEM education by studying an intervention designed to improve persistence of women in computer science. Engaging all people in learning about STEM, particularly computer science, is important for innovation, global competitiveness, and preparation for future careers that increasingly rely on computing. Women occupy just 28% of STEM jobs and account for only 17% of computer science majors and 21% of engineering majors. These differences in career choices arise partially from gender differences in self-assessment of STEM ability. This project will test whether educational institutions can use a simple intervention to increase the persistence of women in computer science. This intervention consists of email messages to students in introductory computer science courses. The messages contain contextual information about the student’s performance in class and encouragement regarding their effort and potential. For example, a message might reveal that the student is a top performer in the course and suggest that the student should explore getting involved in undergraduate computer science research. A pilot study found that changing the wording of a single email could increase women's intentions to stay in computer science by 18%. This project aims to evaluate the effectiveness of the intervention on a larger scale, and determine how to maximize its efficacy. If successful, the project will provide educational institutions with a simple, easily implemented tool to increase the number of women in computer science fields. This intervention could have positive effects on other groups that face negative stereotypes about their intellectual capacity and ability to succeed in computer sciences, and might be applicable to other STEM disciplines, as well. This project will use both a field experiment and an online experiment to test the intervention. The field experiment will be conducted across all the introductory computer science courses at a large research-based university. Multivariate analysis will be conducted to determine both the immediate and longitudinal effects of the intervention on self-assessed computer science ability and on persistence in computer science. Qualitative interviews will be conducted with some students in the field experiment to obtain nuanced data about students’ experience with the intervention. The online experiment will use short programming activities and will explore which pieces of contextual information are most effective. This knowledge will be critical for crafting efficient messages that can be used at scale, and for determining methods (other than emails) through which institutions can boost students’ self-assessment and persistence in computer science. Mediation analyses will be used in both the field and online experiments to illuminate the process through which the intervention impacts self-assessed computer science ability, and in turn, persistence. This research is expected to contribute to theoretical and practical knowledge of how institutions can intervene to promote gender equity and to increase knowledge about the social-psychological processes around self-assessments of ability and career choice that reproduce existing gender inequalities. This project is supported by the NSF Improving Undergraduate STEM Education Program: Education and Human Resources Program, which supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.
该项目旨在通过研究旨在提高妇女在计算机科学领域的持久性的干预措施,为高质量STEM教育的国家利益服务。让所有人参与学习STEM,特别是计算机科学,对于创新,全球竞争力以及为越来越依赖计算的未来职业做准备非常重要。女性仅占STEM工作的28%,仅占计算机科学专业的17%和工程专业的21%。职业选择上的这些差异部分源于STEM能力自我评估的性别差异。该项目将测试教育机构是否可以使用简单的干预措施来提高妇女在计算机科学领域的持久性。这种干预包括向计算机科学入门课程的学生发送电子邮件。 这些消息包含有关学生在课堂上的表现的上下文信息以及有关他们的努力和潜力的鼓励。例如,一条消息可能会显示学生在课程中表现最好,并建议学生应该探索参与本科计算机科学研究。 一项试点研究发现,改变一封电子邮件的措辞可以使女性留在计算机科学领域的意愿增加18%。该项目旨在更大规模地评估干预措施的有效性,并确定如何最大限度地发挥其功效。 如果成功,该项目将为教育机构提供一个简单、易于实施的工具,以增加计算机科学领域的妇女人数。这种干预可能会对其他群体产生积极影响,这些群体面临着对他们的智力能力和在计算机科学领域取得成功的能力的负面刻板印象,并且可能也适用于其他STEM学科。 该项目将使用现场实验和在线实验来测试干预措施。现场实验将在一所大型研究型大学的所有计算机科学入门课程中进行。将进行多变量分析,以确定即时和纵向影响的干预自我评估的计算机科学能力和计算机科学的持久性。定性访谈将进行一些学生在现场实验,以获得细致入微的数据,学生的经验与干预。在线实验将使用简短的编程活动,并将探索哪些上下文信息最有效。这些知识对于制作可大规模使用的有效信息以及确定机构可以提高学生自我评估和计算机科学坚持性的方法(电子邮件除外)至关重要。中介分析将用于现场和在线实验,以阐明干预影响自我评估的计算机科学能力的过程,进而影响持久性。预计这项研究将有助于从理论和实践上了解各机构如何进行干预,以促进两性平等,并增加对围绕能力自我评估和职业选择的社会心理过程的了解,这些过程重现了现有的两性不平等现象。 该项目由NSF改善本科STEM教育计划:教育和人力资源计划支持,该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NSF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。
项目成果
期刊论文数量(0)
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专利数量(0)
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Bita Akram其他文献
Detecting ChatGPT-Generated Code in a CS1 Course
在 CS1 课程中检测 ChatGPT 生成的代码
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Muntasir Hoq;Yang Shi;Juho Leinonen;Damilola Babalola;Collin Lynch;Bita Akram - 通讯作者:
Bita Akram
AI in Computing Education from Research to Practice
人工智能在计算教育中从研究到实践
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Bita Akram;Juho Leinonen;Narges Norouzi;J. Prather;Lisa Zhang - 通讯作者:
Lisa Zhang
Assessment of Students' Computer Science Focal Knowledge, Skills, and Abilities in Game-Based Learning Environments.
- DOI:
- 发表时间:
2019-07 - 期刊:
- 影响因子:0
- 作者:
Bita Akram - 通讯作者:
Bita Akram
CINAPACT-Splines: A Family of Infinitely Smooth, Accurate and Compactly Supported Splines
CINAPACT-Splines:一系列无限平滑、精确且紧支撑的样条曲线
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Bita Akram;U. Alim;F. Samavati - 通讯作者:
F. Samavati
Investigation of Students’ Learning, Interest, and Career Aspirations in an Integrated Science and Artificial Intelligence Learning Environment (i-SAIL)
在综合科学和人工智能学习环境(i-SAIL)中调查学生的学习、兴趣和职业愿望
- DOI:
10.1145/3568812.3603488 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Bita Akram;Shiyan Jiang - 通讯作者:
Shiyan Jiang
Bita Akram的其他文献
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{{ truncateString('Bita Akram', 18)}}的其他基金
Transforming Introductory Computer Science Instruction with an AI-Driven Classroom Assistant
利用人工智能驱动的课堂助手改变计算机科学入门教学
- 批准号:
2236195 - 财政年份:2023
- 资助金额:
$ 17.49万 - 项目类别:
Continuing Grant
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Analysis of a Simple, Low-cost Intervention's Impact on Retention of Women in Computer Science
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