Collaborative Research: Investigating Inclusive Data Science Tools to Overcome Statistics Anxiety

该合作项目的目标是设计、创建和评估相关在线可访问数据科学（ROADS）平台。ROADS旨在让本科生和研究生更容易理解在线数据科学，重点是克服统计焦虑。统计焦虑的原因包括有限的数学和计算背景、自尊、性别、种族和残疾，以及统计平台的设计。数据科学素养是在高等教育科学、技术、工程和数学（STEM）领域取得成功的必要条件，也是培养关键的、与行业相关的计算思维技能的必要条件。该项目将与涉及高等教育、无障碍和数据科学的伙伴组织合作，对ROADS进行评估，以降低成功参与的障碍。ROADS将在开发的所有阶段使用形成性和总结性经验评估进行迭代和包容性设计，以告知最终用户体验。该项目将研究一个新的平台，该平台使用相关的数据科学语言，可以通过网络随时在线获取，并且可以通过视觉、听觉和触觉反馈为残疾学生提供帮助。它汇集了计算机科学、机械工程、教育和认知神经科学的研究人员，研究三个关键领域：(1)数据科学表示的清晰度和人类理解；(2)减少焦虑；(3)残疾人无障碍。为了评估，计划进行一系列研究，涉及180名无残疾大学生和120名残疾大学生。此外，研究人员计划研究数据科学工具的输出，结合焦虑，涉及大约1000名本科生或研究生，有残疾和没有残疾。该项目将纳入一个顾问委员会的定期反馈，该委员会包括一个数据科学治理小组（ACM数据科学工作组）、高等教育和残疾协会以及四所机构的残疾服务办公室。项目成果将包括开发数据科学工具的包容性设计的设计含义，以及本科生和研究生阶段的教学方法，特别侧重于减少与数据科学相关的焦虑。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。

The objective of this collaborative project is to design, create, and evaluate the Relatable Online Accessible Data Science (ROADS) platform. ROADS is designed to make online data science easier to understand by undergraduate and graduate students, with a focus on overcoming statistics anxiety. Reasons for statistical anxiety include limited math and computing background, self-esteem, gender, ethnicity, and disability, in addition to the design of statistics platforms. Data science literacy is integral for success in post-secondary science, technology, engineering, and math (STEM) fields, as well as for developing critical, industry-relevant, computational thinking skills. In collaboration with partner organizations, which span higher education, accessibility, and data science, the project will evaluate ROADS to lower barriers for successful participation. ROADS will be iteratively and inclusively designed, using both formative and summative empirical evaluations, at all stages of development, to inform the end user experience.This project will investigate a new platform that uses relatable data science language, is readily available online through the web, and is accessible through visual, auditory, and touch feedback to students with disabilities. It brings together investigators in Computer Science, Mechanical Engineering, Education, and Cognitive Neuroscience to investigate three critical areas: (1) clarity and human comprehension of data science representations; (2) anxiety reduction; and (3) accessibility to people with disabilities. For evaluation, a series of studies is planned, involving 180 undergraduate students without disabilities and 120 with disabilities. Further, the researchers plan to study the outputs of data science tools, in conjunction with anxiety, involving approximately 1,000 undergraduate or graduate students, with and without disabilities. The project will incorporate regular feedback from an engaged advisory board, including a data science governance group (ACM Data Science Task Force), the Association on Higher Education and Disability, and disabilities services offices at four institutions. Project outcomes will include the development of design implications for inclusive design of data science tools and pedagogy at the undergraduate and graduate level, with a specific focus on reducing anxiety associated with data science.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.