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

在当今数据日益丰富的世界里，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。尽管越来越明显的是，K-12阶段的数据科学教育是至关重要的，但关于它应该在课堂上承担的形式和重点，仍然存在很多争论。拟议的研讨会将汇集数据科学教育领域的一批领先研究人员，以开发一个连贯的研究框架，以定义和指导这一快速增长的领域。这一框架将侧重于学习者需要了解和能够利用数据做些什么，从最早的学习者开始，一直到高中。研讨会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。通过这样做，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，加强未来支持K-12年级全面数据科学教育的努力的一致性。建立和确定数据科学教育的当前研究对于指导这一迅速崛起的领域的所有方面至关重要。为了满足这一需求，该项目将来自K-12数据科学教育领域的利益相关者聚集在一起，首先是在一个较小的指导委员会和专注的预工作小组中，然后进行为期数天的面对面知识构建会议，以解决位于该领域当前需求中心的一系列相互关联的问题。通过有组织的有指导的讨论，研讨会将总结到目前为止在DSE研究方面取得的进展，找出仍然存在最大差距的地方，并突出提供最有希望的互联基础的领域。然后，讲习班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，旨在使广泛的利益攸关方和应用程序受益。讲习班将采用一种界定数据科学教育所涉及的组成部分的方法，提供一个框架，阐明构成该领域的学习线索，每一条已确定的线索都有意提供跨越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，可以发展和纳入研讨会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确定未来研究重点和向支持者、资助者和实施利益相关者提出建议的指导，包括地方和地区层面的政策和决策者。该项目由学生和教师创新技术体验(ITEST)计划资助，该计划支持一些项目，这些项目建立对实践、计划要素、背景和过程的理解，有助于提高学生对科学、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.