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

这个合作项目的目标是设计，创建和评估相关的在线可扩展数据科学（ROADS）平台。 ROADS旨在使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。数据科学素养是中学后科学，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，行业相关的计算思维技能的必要条件。该项目将与跨越高等教育、无障碍环境和数据科学的合作伙伴组织合作，评估ROADS，以降低成功参与的障碍。ROADS将在开发的各个阶段使用形成性和总结性的经验评估进行迭代和包容性的设计，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，可以通过网络在线访问，并通过视觉，听觉和触觉反馈访问残疾学生。 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，以调查三个关键领域：（1）数据科学表示的清晰度和人类理解;（2）减少焦虑;（3）残疾人的可访问性。为了进行评估，计划进行一系列研究，涉及180名非残疾大学生和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1,000名本科生或研究生，有残疾和没有残疾。该项目将纳入一个参与咨询委员会的定期反馈，包括一个数据科学治理小组（ACM数据科学工作队）、高等教育和残疾协会以及四个机构的残疾人服务办公室。项目成果将包括为本科生和研究生阶段的数据科学工具和教学法的包容性设计开发设计影响，特别关注减少与数据科学相关的焦虑。该奖项反映了NSF的法定使命，并被认为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来支持。

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.