Conference: A Learning Progression for K-12 Data Science Education

在当今数据日益丰富的世界中，数据科学教育不仅对STEM领域的工作至关重要，而且对所有公民都至关重要。尽管越来越清楚的是，K-12水平的数据科学教育至关重要，但关于它在课堂上应该采取的形式和重点仍然存在很多争论。拟议的研讨会将聚集数据科学教育领域的各种领先研究人员，以制定一个有凝聚力的研究框架，来定义和指导这个快速发展的领域。该框架将侧重于学习者需要知道什么，并能够从最早的学习者开始，直到高中。研讨会的参与者还将确定数据科学教育面临的主要挑战，并为未来的研究提出最有价值的领域。在这样做的过程中，研讨会的成果将支持研究人员，教育工作者，开发人员和政策制定者，加强未来努力的一致性，以支持K-12年级的综合数据科学教育。为了满足这一需求，该项目将K-12数据科学教育领域的利益相关者聚集在一起，首先是一个较小的指导委员会和专注的前期工作组，然后是为期多日的面对面知识建设会议，以解决位于该领域当前需求中心的一系列相关查询。通过有组织的引导讨论，研讨会将提出迄今为止DSE研究所取得进展的总结，确定仍然存在最大差距的地方，并强调为互连提供最有希望的领域。然后，研讨会将利用现有研究的总结，为K-12数据科学教育提出一个学习进展框架，旨在使广泛的利益相关者和应用程序受益。该研讨会将采用一种方法，将数据科学教育所涉及的组件绑定在一起，提供一个框架，阐明包括该领域的学习链，每个确定的链都故意提供跨越K-12年级的切入点。这一框架将足够坚实，可以为研究和开发工作提供信息，但又足够灵活，可以发展和纳入讲习班活动期间肯定会出现的许多新发现。由此产生的框架将作为指导，以确定未来的研究重点和建议，支持者，资助者和实施利益相关者，包括政策和决策者在地方和区域层面。这个项目是由创新技术经验的学生和教师（ITEST）计划资助，该计划支持的项目，建立对实践的理解，计划要素，背景，该奖项反映了NSF的法定使命，并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，被认为值得支持。

In today's increasingly data-rich world, data science education is vital not only for work in STEM fields but also for all citizens. Although it is increasingly clear that data science education at the K-12 level is vital, much debate still exists about the form and focus it should assume in the classroom. The proposed workshop will gather a diverse group of leading researchers in the field of data science education to develop a cohesive research framework to define and guide this quickly growing field. This framework will focus on what learners need to know and be able to do with data starting with the earliest learners and going through high school. Workshop participants will also identify the key grand challenges for data science education and suggest the most valuable areas for future research. In doing so, the outcome of the workshop will support researchers, educators, developers, and policymakers, bolstering the coherence of future efforts towards supporting comprehensive data science education in grades K-12.Establishing and characterizing current research in data science education is critical to guiding all aspects of this quickly emerging field. To address this need, this project brings together stakeholders from across the field of K-12 data science education, first in a smaller steering committee and focused pre-work groups and then for a multiple-day in-person knowledge building session, to grapple with a series of connected queries positioned at the center of the field's current needs. Via organized guided discussions the workshop will propose a summary of the progress made so far in DSE research, identify places where the largest gaps remain, and highlight the areas that provide the most promising ground for interconnection. The workshop will then employ the summary of existing research to suggest a learning progressions framework for K-12 data science education aimed to benefit a broad range of stakeholders and applications. The workshop will adopt an approach that bounds the components involved in data science education, providing a framework elucidating strands of learning that comprise the domain with each identified strand deliberately providing entry points spanning grades K-12. This framework will be solid enough to inform work across both research and development, yet flexible enough to evolve and incorporate the many new findings certain to arise during the workshop activities. The resulting framework will serve as guidance for identifying future research priorities and suggestions to supporters, funders, and implementing stakeholders, including policy- and decision-makers at local and regional levels.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.