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

该合作项目的目标是设计、创建和评估可关联的在线可访问数据科学(ROADS)平台。Roads旨在使在线数据科学更容易被本科生和研究生理解，重点是克服统计焦虑。统计焦虑的原因除了统计平台的设计外，还包括有限的数学和计算背景、自尊、性别、种族和残疾。数据科学素养对于在中学后科学、技术、工程和数学(STEM)领域取得成功以及培养关键的、与行业相关的计算思维技能是不可或缺的。该项目将与跨越高等教育、可访问性和数据科学的伙伴组织合作，评估降低成功参与门槛的途径。道路将在开发的所有阶段使用形成性和终结性经验评估进行迭代和包容的设计，以告知最终用户体验。该项目将调查一个新平台，该平台使用可关联的数据科学语言，可通过网络随时获得，并可通过视觉、听觉和触觉反馈访问残疾学生。它将计算机科学、机械工程、教育和认知神经科学的研究人员聚集在一起，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)残疾人的可访问性。为了进行评估，计划进行一系列研究，包括180名非残疾本科生和120名残疾本科生。此外，研究人员计划研究数据科学工具的输出，并结合焦虑，涉及大约1000名本科生或研究生，包括有残疾和无残疾的学生。该项目将纳入一个积极参与的咨询委员会的定期反馈，咨询委员会包括数据科学治理小组(ACM数据科学工作队)、高等教育和残疾人协会以及四个机构的残疾服务办公室。项目成果将包括为本科生和研究生水平的数据科学工具和教育学的包容性设计开发设计影响，并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，并通过使用基金会的智力优势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.