Collaborative Research: Creating and testing data science learning tools for secondary students with disabilities

合作研究：为残疾中学生创建和测试数据科学学习工具

• 批准号: 2048428
• 负责人: Jenna Gorlewicz
• 金额: $ 30.48万
• 依托单位: Saint Louis University
• 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: 2021-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048428&HistoricalAwards=false
• 关键词: Collaborative; Research; Creating; testing; data

The main goal of this collaborative project is to create and evaluate a universally accessible data science infrastructure for high-school-aged learners, with a focus on students with disabilities. Data science is critical in the development of industry-relevant computational thinking skills. Computing initiatives, including data science, are rapidly growing at the preschool-12th grade level because of the compelling career pathways that data science skills provide. A careful investigation into already-at-scale data science initiatives shows that such tools and curriculum are largely not accessible to individuals with disabilities, nor do they have a strong foundation of human factors evidence supporting their designs. These issues are crucial and must be resolved for workforce equity and a diverse science, technology, engineering, and mathematics (STEM) pipeline. This project will bring together investigators in computer science, mechanical engineering, education, social science, and cognitive neuroscience to rethink the tools that support the teaching and learning of data science at the high school level. The overarching goal will be to create and evaluate data science tools and curriculum that are not just in legal compliance for accessibility, but that carefully take into account the needs of learners, including those with disabilities. This project will involve numerous strategic partnerships including two schools for students with disabilities, the Disabilities, Opportunities, Internetworking, and Technology Center, and an engaged advisory board representing computing accessibility (AccessComputing), data science industry (RStudio), and data science governance (Association for Computing Machinery Data Science Task Force). The rigorous research plan will include iterative, user-focused development, empirical quantitative investigations, qualitative focus groups, and a culminating in-classroom field study, targeting an estimated total of 385 students, 105 teachers, and 30 industry professionals as participants across all studies. By creating, deploying, and rigorously evaluating the first data science tool and curriculum that is accessible to all, the project intends to help create equitable pathways for all students to enter the field of data science. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.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.

该合作项目的主要目标是为高中年龄学习者创建和评估普遍可访问的数据科学基础设施，重点关注残疾学生。数据科学对于行业相关计算思维技能的发展至关重要。由于数据科学技能提供了令人信服的职业道路，包括数据科学在内的计算项目在学前至 12 年级迅速发展。对已经大规模的数据科学计划的仔细调查表明，残疾人士基本上无法使用此类工具和课程，也没有支持其设计的坚实的人为因素证据基础。这些问题至关重要，必须解决才能实现劳动力公平和多样化的科学、技术、工程和数学 (STEM) 渠道。该项目将汇集计算机科学、机械工程、教育、社会科学和认知神经科学领域的研究人员，重新思考支持高中数据科学教学的工具。总体目标是创建和评估数据科学工具和课程，这些工具和课程不仅符合法律的可访问性，而且仔细考虑学习者（包括残疾人）的需求。该项目将涉及众多战略合作伙伴关系，包括两所残疾学生学校、残疾、机会、互联网和技术中心，以及一个代表计算可访问性 (AccessComputing)、数据科学行业 (RStudio) 和数据科学治理（计算机器协会数据科学任务组）的咨询委员会。严格的研究计划将包括迭代、以用户为中心的开发、实证定量调查、定性焦点小组和最终的课堂实地研究，所有研究的参与者估计共有 385 名学生、105 名教师和 30 名行业专业人士。通过创建、部署和严格评估第一个所有人都可以使用的数据科学工具和课程，该项目旨在帮助所有学生创造进入数据科学领域的公平途径。该项目由学生和教师创新技术体验 (ITEST) 计划资助，该计划支持旨在加深对实践、计划要素、背景和流程的理解的项目，有助于增加学生对科学、技术、工程和数学 (STEM) 以及信息和通信技术 (ICT) 职业的知识和兴趣。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进行评估，被认为值得支持。 以及更广泛的影响审查标准。