Development of Inclusive STEM Curriculum Using Data Science Innovations of W.E.B. Du Bois to Promote Diversity in Science

利用 W.E.B. 的数据科学创新开发包容性 STEM 课程

• 批准号: 2236270
• 负责人: Frederick Wherry
• 金额: $ 59.93万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236270&HistoricalAwards=false
• 关键词: Development; Inclusive; STEM; Curriculum; Using

This project aims to serve the national interest by developing, evaluating, and disseminating novel curricular materials that aim to increase engaged student learning and promote diversity and inclusion in undergraduate science courses. The new materials will be of broad importance as they are intended to teach the process of scientific reasoning and data visualization across a range of science, technology, engineering, and math courses. Curriculum materials will feature the early 20th century work of the foundational Black social scientist, W.E.B. Du Bois who pioneered data visualization techniques that are still considered state of the art across science disciplines. Additionally, centering the scholarly work of Du Bois is of importance as he investigated the social causes of racial inequality and promoted empowerment of Black scholars in ways that were accessible for broad audiences. The project team plans to include faculty and students from multiple institutions and science disciplines to contribute to curriculum development and participate in testing its impact. The project intends to measure how the curriculum influences undergraduate student learning, sense of belonging in science, and persistence in science. Curricular materials and study results will be widely shared in an open, online repository, which could benefit large numbers of students and faculty in undergraduate science courses across the country. The project aims to develop, implement, and assess the Du Bois curriculum module that could be implemented in a variety of science courses during three, 50-minute class or laboratory sessions, along with three homework assignments. Undergraduate students will be engaged in exploring the scholarly work of Du Bois in data science, using data analysis and visualization techniques employed by Du Bois with domain-appropriate data for their particular disciplinary course, and completing metacognitive written reflections on their learning. The curriculum will be developed in collaboration with science faculty and students at each of the three partner institutions – a Hispanic Serving Institution, a Historically Black College and University where Du Bois earned a degree, and a top-ranked, Ivy League and Primarily White Institution – each of which has unique student populations. Developed curriculum will be piloted in 40 courses reaching over 1,600 students across the three collaborating institutions. The project will test the hypothesis that implementation of this inclusive curriculum can promote science learning and sense of belonging among undergraduate students, in particular fostering positive science associations and reducing stigma associated with scientists from marginalized backgrounds. Investigation of curriculum impact will use a pre/post-assessment design and published assessment tools that have evidence of validity for measuring constructs such as: 1) belonging, 2) positive (stereotype boost) vs stigmatized (stereotype threat) associations with racial and ethnic identity and scientist identity, stereotypes, and relatability, and 3) self-efficacy. Further, the project will collect assessment evidence in course offerings that use the module intervention as well as in course offerings that do not, enabling direct analyses to test for shifts in student assessments over time that are associated with the intervention. The project has strong potential to advance understanding of how development and implementation of curriculum highlighting the accomplishments of scientists from marginalized groups might promote student success and retention, while also integrating the teaching of science concepts and social justice. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its 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.

该项目旨在通过开发，评估和传播新的课程材料来服务于国家利益，这些材料旨在提高学生的学习参与度，促进本科科学课程的多样性和包容性。这些新材料将具有广泛的重要性，因为它们旨在教授科学推理和数据可视化的过程，涉及一系列科学，技术，工程和数学课程。课程材料将以基础黑人社会科学家W.E.B.的世纪早期的工作为特色。杜波依斯是数据可视化技术的先驱，这些技术仍然被认为是跨科学学科的最先进技术。此外，以杜波依斯的学术工作为中心是重要的，因为他调查了种族不平等的社会原因，并促进了黑人学者的赋权方式，为广大观众。该项目团队计划包括来自多个机构和科学学科的教师和学生，以促进课程开发并参与测试其影响。该项目旨在衡量课程如何影响本科生的学习，科学的归属感，并坚持科学。课程材料和研究成果将在一个开放的在线存储库中广泛共享，这将使全国本科科学课程的大量学生和教师受益。该项目的目的是开发，实施和评估杜波依斯课程模块，可以在各种科学课程中实施三个，50分钟的课堂或实验室会议，沿着三个家庭作业。本科生将参与探索杜波依斯在数据科学方面的学术工作，使用杜波依斯采用的数据分析和可视化技术，为特定学科课程提供适合领域的数据，并完成元认知书面反思。该课程将与三个合作机构中的每一个的科学教师和学生合作开发-一个西班牙裔服务机构，一个历史上的黑人学院和大学，杜波依斯获得了学位，以及一个排名第一的常春藤联盟和Priestion白色机构-每个机构都有独特的学生群体。开发的课程将在40门课程中试行，覆盖三个合作机构的1 600多名学生。该项目将检验这样一种假设，即实施这一包容性课程可以促进本科生的科学学习和归属感，特别是培养积极的科学协会，减少与边缘化背景的科学家有关的耻辱感。课程影响的调查将使用评估前/后设计和已公布的评估工具，这些工具具有有效性的证据，可用于测量以下结构：1）归属感，2）与种族和民族身份以及科学家身份，刻板印象和相关性的积极（刻板印象增强）与污名化（刻板印象威胁）关联，以及3）自我效能。此外，该项目将收集使用模块干预的课程以及不使用模块干预的课程中的评估证据，从而进行直接分析，以测试与干预相关的学生评估随时间的变化。该项目有很大的潜力，以促进了解如何制定和实施课程，突出来自边缘化群体的科学家的成就可能会促进学生的成功和保留，同时也整合了科学概念和社会正义的教学。NSF IUSE：EDU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。