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

合作研究：研究包容性数据科学工具以克服统计焦虑

• 批准号: 2106393
• 负责人: Nicholas Giudice
• 金额: $ 34.56万
• 依托单位: University of Maine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106393&HistoricalAwards=false
• 关键词: Collaborative; Research; Investigating; Inclusive; Data

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.

这个协作项目的目的是设计，创建和评估相关的在线访问数据科学（ROADS）平台。 道路旨在使在线数据科学更容易被大学生和研究生理解，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，除了统计平台的设计外。数据科学素养是大专科学，技术，工程和数学（STEM）领域以及发展关键，与行业相关的计算思维技能的成功的重要组成部分。与涵盖高等教育，可访问性和数据科学的合作伙伴组织合作，该项目将评估较低障碍的道路以成功参与。道路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设计，以告知最终用户体验。该项目将调查一个新的平台，使用相关数据科学语言，可以通过网络在线使用相关的数据科学语言，并且可以通过视觉，听觉来访问，并可以通过视觉，听觉来访问障碍学生的反馈。 它汇集了计算机科学，机械工程，教育和认知神经科学领域的调查人员，以研究三个关键领域：（1）清晰和人类对数据科学表示的理解； （2）减轻焦虑； （3）对残疾人的可及性。为了进行评估，计划进行一系列研究，涉及180名没有残疾的本科生和120个残疾人。此外，研究人员计划研究数据科学工具的输出以及焦虑，涉及大约1,000名本科生或研究生，有和没有残疾。该项目将结合参与顾问委员会的定期反馈，包括数据科学治理小组（ACM数据科学工作组），高等教育和残疾协会以及四个机构的残疾服务办公室。项目成果将包括在本科和研究生级别的数据科学工具和教学法的包容性设计的发展，并特别关注减少与数据科学相关的焦虑。这项奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来通过评估来提供支持的。