Integrating Content and Skills from the Humanities into Data Science Education

该项目旨在通过使用以人为本的方法来教授数据科学基础，从而改善数据科学教育，从而为国家利益服务。这个项目的目的是了解致力于扩大数据科学领域的招聘和保留的努力的影响。这一努力对于来自目前在数据科学领域代表性不足的人口统计学群体的学生尤为重要。这些未来的数据科学家不仅需要分析数字，还需要分析它们的人类背景和后果，以防止有意或无意地滥用数据科学，并有效地沟通结果。该项目旨在测试是否可以通过将人文学科的内容和技能整合到数据科学教育中来实现这些目标。团队授课的跨学科方法将用于创建和提供本科学生的入门数据科学课程。该课程将利用现实世界的社会问题，教授重要的统计和编码技能，以及人文学科的思维方式。这种思维的例子包括对数据来源的分析以及数据收集和分析的危害和益处。它还包括那些在政治和政策制定中使用数据的人的修辞目的和策略。课程在提高学生学习方面的有效性将被评估，以确定如何改进、适应并最终在其他学院和大学实施这种教育模式。该项目有潜力打造一种更具包容性和以人为本的数据科学基础教学方法。通过开发一种可以在全国范围内推广的数据科学教育的新型协作模式，该项目旨在对STEM教育产生积极影响，从而使国家劳动力更加多样化、更具创造性和创新性，并使公众更加了解STEM。该项目的目标是开发和评估新的教学方法，以有效地将STEM和人文学科的观点结合起来，协同教授数据科学。由此产生的数据科学入门课程将为未来的数据科学和STEM专业学生提供传统上在人文学科中教授的定性推理技能，为未来的人文专业学生提供进一步研究数据科学的入口，并为所有学生提供统计和计算技能，他们可以在未来的课程和劳动力中应用。本项目将收集证据来回答四个研究问题：(1)以人文学科为重点的数据科学入门课程以何种方式和在多大程度上改变了人们对STEM、数据科学和人文学科的态度？(2)该课程在帮助学生实现数据科学和人文学科的学习成果方面效果如何？(3)同行评议制度的有效性如何？(4)该项目在多大程度上增加了数据科学、STEM和人文学科的多元化学生的招聘和保留？这些问题将通过调查学生对STEM和人文学科的态度、评估跨学科数据科学课程的学生学习成果、开发和评估同行评估项目、对教学效果的多重评估以及对学生途径和表现的纵向研究来解决。最终，这些问题的答案将为如何教育更多的数据科学家，提高学生的学习能力，并为非科学家提供理解和解释数据科学基础概念的能力提供见解。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。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 improving data science education using a human-centered approach to teaching the foundations of data science. The purpose of this project is to understand the impact of efforts dedicated to broaden recruitment and retention in data science. This effort is particularly important for students coming from demographic groups that are currently underrepresented in data science. These future data scientists will need to analyze not just numbers, but their human contexts and consequences, to prevent intentional or unintentional misuse of data science, and to communicate results effectively. This project has been designed to test if these goals might be achieved by integrating content and skills from the humanities into data science education. A team-taught interdisciplinary approach will be used to create and deliver an introductory data science course for undergraduate students. The course will use real-world social issues to teach important statistics and coding skills alongside ways of thinking from the humanities. Examples of such thinking include analysis of the source of data and the harms and benefits of data collection and analysis. It also includes the rhetorical aims and strategies of those who use data in politics and policymaking. The effectiveness of the course in improving student learning will be assessed to determine how this education model can be improved, adapted, and ultimately implemented at other colleges and universities. This project has potential to craft a more inclusive and human-centered approach to teaching the foundations of data science. By developing a new collaborative model of data science education that can be adapted nationwide, this project aims to positively impact STEM education, leading to a more diverse, creative, and innovative national workforce and a more STEM-literate public.This project’s goal is to develop and assess new pedagogical approaches to collaboratively teaching data science that effectively incorporate perspectives of both STEM and humanities disciplines. The resulting introductory data science course will provide future data science and STEM majors with qualitative reasoning skills that are traditionally taught in the humanities, provide future humanities majors with an on-ramp to further study of data science, and provide all students with statistical and computational skills they can apply in future courses and in the workforce. This project will collect evidence to answer four research questions: (1) In what ways and to what degree does the humanities-focused introductory data science course change attitudes about STEM, data science, and the humanities? (2) How effective is the course in helping students achieve student learning outcomes in both data science and the humanities? (3) How effective is the proposed peer assessment system? (4) To what extent does the project increase the recruitment and retention of diverse students in data science, STEM, and the humanities? These questions will be addressed using surveys of students’ attitudes toward STEM and the humanities, assessments of student learning outcomes from an interdisciplinary data science course, the development and evaluation of a peer assessment program, multiple assessments of teaching effectiveness, and a longitudinal study of student pathways and performance. Ultimately, the answers to these questions will provide insights on how to educate more data scientists, improve student learning, and provide non-scientists with the ability to understand and interpret foundational concepts in data science. The NSF IUSE: EHR 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.