Creating Diverse Data Science Learning Pathways

该项目旨在通过为教师开发教学方法和专业发展来加强本科生的数据素养和技能，从而为国家利益服务。随着组织生成和分析越来越多的丰富数据集，数据科学正在重塑美国的劳动力和整个社会。如果不确保大量参与数据科学教育，美国就无法满足对数据素养日益增长的需求。为了构建灵活的解决方案，满足最关键的需求，服务于大量不同的人群，数据科学团队需要包含多个视角。越来越多的社区学院、学院、大学和培训公司正在为本科生开设数据科学课程和证书，但这些课程往往吸引的是已经在STEM领域工作的学生。为了增加stem劳动力，让那些还没有计划将数据科学技能融入职业生涯的学生参与进来是至关重要的。该项目旨在建立数据科学体验，吸引、留住、支持和授权学习者，他们将能够将数据技能和实践带到STEM内外的领域。目前，为来自不同背景和学科的学生提供数据科学学习经验的努力面临两个主要差距：(1)需要可复制的模型来支持更广泛的实施；(2)缺乏研究证明模型如何支持数据科学身份发展，同时照顾具有不同学科和社会政治背景的学生。该项目的目标是让学生和教师参与到基于项目的、与劳动力相关的数据科学教育模型的研究、测试和改进中来。该模型基于一个假设，即学生在一个灵活的、基于项目的学习环境中的选择和代理将降低进入门槛，并鼓励具有不同背景的学生在本科职业生涯中探索数据科学。项目研究将利用多种定性和定量数据流来产生新的知识，了解学生身份的考虑和对学生选择和代理的关注如何支持数据科学社区的身份发展。NSF IUSE: EDU项目支持研究和开发项目，以提高所有学生STEM教育的有效性。通过参与学生学习轨道，该计划支持有前途的实践和工具的创建，探索和实施。该奖项反映了美国国家科学基金会的法定使命，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。

This project aims to serve the national interest by developing instructional methods and professional development for instructors that will strengthen undergraduate students’ data literacy and skills. Data science is reshaping the US workforce and society at large as organizations generate and look to analyze increasing collections of rich data. The US cannot meet an increasing demand for data literacy without ensuring significant participation in data science education. The inclusion of multiple perspectives in the data science workforce is needed to build flexible solutions that address the most critical needs and serve large, diverse populations. An increasing number of community colleges, colleges, universities and training companies are building data science courses and credentials for undergraduates, but these courses often attract students already in the STEM fields. In order to increase the STEM-enabled workforce, it is critical to involve students who are not yet planning to incorporate data science skills into their careers. This project aims to build data science experiences that attract, retain, support, and empower learners who will be able to bring data skills and practices to fields within or beyond STEM.Current efforts to bring data science learning experiences to students from different backgrounds and disciplines face two major gaps: (1) a need for replicable models to support broader implementation; and (2) a lack of research that demonstrates how models can support data science identity development while attending to students with diverse disciplinary and sociopolitical backgrounds. The goal of this project is to engage students and faculty in the study, testing, and improvement of a model for project-based, workforce-relevant data science education. The model is based on the hypothesis that student choice and agency in a flexible, project-based learning environment will lower barriers to entry and encourage students with diverse backgrounds to explore data science during their undergraduate careers. Project research will draw upon multiple qualitative and quantitative data streams to generate new knowledge about how consideration of students’ identities and attention to student choice and agency can support identity development in data science communities. The NSF IUSE: EDU Program 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.