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

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：教育和人力资源（IUSE：EHR）的支持下，该项目旨在通过改善STEM和非STEM专业的本科数据科学教育来服务于国家利益。 它计划通过在加州伯克利大学（R1大学）、马里兰州大学、巴尔的摩县大学（R2大学）和米尔斯学院（主要是女子文理学院）实施、完善和扩展一个创新的原型数据科学项目来实现这一目标。 原型程序作为一个切入点，进入数据科学的学生在统计或数据科学经验有限。 它是围绕零先决条件的数据科学课程构建的，同时连接器课程介绍了数据科学如何在不同领域中使用。 它包括将数据科学“推入”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。 它还包括一个数据科学学者计划，以支持学生的成功，特别是来自STEM代表性不足的群体的学生。 原型程序使用同伴教学模式来支持学生学习，建立社区，提供指导，并与教师共同创建课程材料。 该项目将制作一套开源课程材料和技术基础设施，以促进在其他机构成功实施原型计划。 预计通过该项目开发的模型和材料将支持向不同类型机构的不同学生大规模教授数据科学。 由于数据科学是一个相对较新的领域，需要做很多工作来研究教学和课程方法在这一领域的作用。该项目旨在产生关于如何最好地设计数据科学课程和教学法的新知识，以促进不同本科生的学习，包括来自STEM代表性不足群体的学生。 该项目的研究目标包括评估原型计划的具体组成部分如何影响学生的成绩;以及评估原型是否以及如何扩大对数据科学的参与。 该项目的混合方法评价将包括形成性评价，以实现持续的质量改进，以及总结性评价，以衡量项目成果。该项目将开发课程和教学数据科学材料和技术基础设施，可以在具有不同学生群体和不同资源的不同机构中有效地定制和扩展。 这些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，该教育可以根据学生需求进行扩展，并最终在多个不同的机构环境中扩大对数据科学的参与。NSF IUSE：EHR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。该项目是在机构和社区转型轨道，支持努力改造和改善高等教育机构和学科社区的STEM教育.这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持.

项目成果

ADOPTING DATA SCIENCE CURRICULA: A STUDENT CENTRIC EVALUATION

采用数据科学课程：以学生为中心的评估

• DOI: 10.21125/inted.2023.2276
• 发表时间: 2023
• 期刊: INTED Proceedings
• 作者: Wang, Susan;Janeja, Vandana;Harding, David;Von Vacano, Claudia;Lobo, Daniel
• 通讯作者: Lobo, Daniel

RETHINKING DATA SCIENCE PEDAGOGY WITH EMBEDDED ETHICAL CONSIDERATIONS

重新思考具有嵌入式道德考虑的数据科学教学法

• DOI: 10.21125/edulearn.2022.1964
• 发表时间: 2022
• 期刊: EDULEARN Proceedings
• 作者: Janeja, Vandana;Sanchez, Maria
• 通讯作者: Sanchez, Maria