Undergraduate Data Science Education at Scale

大规模的本科数据科学教育

• 批准号: 1915714
• 负责人: David Harding
• 金额: $ 300万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915714&HistoricalAwards=false
• 关键词: Undergraduate; Data; Science; Education; Scale; Undergraduate; Data; Science; Education; Scale

With support from the NSF Improving Undergraduate STEM Education Program: Education and Human Resources (IUSE: EHR), this project aims to serve the national interest by improving undergraduate data science education for STEM and non-STEM majors. It plans to achieve this goal by implementing, refining, and expanding an innovative prototype data science program at the University of California Berkeley (an R1 university), the University of Maryland, Baltimore County (an R2 university) and Mills College (a primarily women's liberal arts college). The prototype program serves as an entry point into data science for students with limited previous experience in statistics or data science. It is built around a zero-prerequisites data science course, with concurrent connector courses that introduce how data science is used in different fields. It includes modules that "push" data science into existing courses and Discovery Projects that enable students to apply data science skills in real-world settings. It also incorporates a Data Science Scholars program to support student success, particularly students from groups underrepresented in STEM. The prototype program uses a peer instruction model to support student learning, build community, provide mentoring, and co-create course materials with faculty. The project will produce a set of open source curricular materials and the technical infrastructure to facilitate successful implementation of the prototype program at other institutions. It is expected that the models and materials developed through this project will support the teaching of data science at scale to a diverse set of students in diverse types of institutions. Because data science is a comparatively new field, much work needs to be done to investigate how pedagogical and curricular approaches function in this domain. This project aims to generate new knowledge about how to best design data science curricula and pedagogy to promote learning among diverse undergraduate students, including students from underrepresented groups in STEM. The project's research objectives include evaluation of how specific components of the prototype program impact student outcomes; and assessment of whether and how the prototype can broaden participation in data science. The project's mixed-methods evaluation will include formative evaluation to enable continuous quality improvement, as well as summative evaluation to measure project outcomes. The project will develop curricular and pedagogical data science materials and technical infrastructure that can be efficiently tailored and scaled at different institutions with diverse student bodies and disparate resources. The materials and research findings will be widely disseminated, to help drive a community transformation in undergraduate data science education that can scale with student demand and ultimately broaden participation in data science across multiple, diverse institutional settings. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is in the Institutional and Community Transformation track, which supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities.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.

在NSF改善本科STEM教育计划的支持下：教育和人力资源（IUSE：EHR），该项目旨在通过改善针对STEM和非STEM专业的本科数据科学教育来服务国家利益。 它计划通过在加利福尼亚大学伯克利分校（一所R1大学），马里兰大学，巴尔的摩县（R2大学）和米尔斯学院（主要是女子自由艺术学院）的加​​州大学伯克利分校（一所R1大学），马里兰大学（马里兰州大学），马里兰大学（马里兰州大学），马里兰大学（马里兰州大学）实施创新的原型数据科学计划来实现这一目标。 原型计划是对统计或数据科学经验有限的学生的数据科学的切入点。 它围绕零前提数据科学课程建立，并通过并发连接器课程介绍了如何在不同领域使用数据科学。 它包括将数据科学推向现有课程和发现项目的模块，使学生能够在现实世界中应用数据科学技能。 它还结合了一个数据科学学者计划，以支持学生成功，尤其是来自STEM中代表性不足的团体的学生。 原型计划使用同行指导模型来支持学生学习，建立社区，提供指导并与教师共同创建课程材料。 该项目将生产一套开源课程材料和技术基础架构，以促进在其他机构中成功实施原型计划。 预计通过该项目开发的模型和材料将支持数据科学的教学，以向各种类型的机构中的各种学生进行大规模的教学。 由于数据科学是一个相对较新的领域，因此需要做很多工作，以研究该领域的教学和课程方法如何起作用。该项目旨在生成有关如何最好地设计数据科学课程和教育学的新知识，以促进多样化的本科生的学习，包括来自STEM中代表性不足的群体的学生。 该项目的研究目标包括评估原型计划的特定组成部分如何影响学生的成果；以及评估原型以及如何扩大数据科学的参与。 该项目的混合方法评估将包括形成性评估，以实现持续的质量改进以及汇总评估以衡量项目结果。该项目将开发课程和教学数据科学材料和技术基础设施，这些基础架构可以在不同的机构中有效地定制和缩放，并具有不同的学生身体和不同的资源。 材料和研究结果将被广泛传播，以帮助推动本科数据科学教育的社区转型，该教育可以随着学生的需求而扩展，并最终扩大了多种不同机构环境的数据科学的参与。 NSF IUSE：EHR计划支持研发项目，以提高所有学生STEM教育的有效性。该项目是在机构和社区转型的轨道上，该奖项支持努力在高等教育和学科社区的机构之间进行转型和改善STEM教育。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准通过评估来获得支持的。