CAREER: Situating Computational Learning Opportunities in the Digital Lives of High School Students

职业：在高中生的数字生活中定位计算学习机会

• 批准号: 2141655
• 负责人: David Weintrop
• 金额: $ 104.01万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2141655&HistoricalAwards=false
• 关键词: CAREER; Situating; Computational; Learning; Opportunities

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This project is also funded through the CS for All: Research and RPPs program.Computer science and data science are important topics with applications in many STEM disciplines. Data science can bring together computer programming and the data students produce and use in their daily lives as a way to help them understand the impacts of computing on the world. The project will design and study a curricular unit that introduces learners to the powerful ideas of computing by having students pose questions based on their interests and answer them by using programming to gather and analyze large data sets from publicly available platforms. The curriculum will be designed through a partnership with an urban school district to integrate programming in a new data science course. Teachers and researchers will collaborate to develop the curriculum so that it can be shared with other schools. This new course provides a unique and timely opportunity to situate innovative computer science instruction in a district-wide initiative specifically designed to reach students from populations who have historically had little opportunity to learn computer science. This project will conduct a multi-year design-based research study in high school classrooms and use participatory design, classroom case studies, and a multi-school implementation to develop and research this new CS curricular unit. There is a particular focus on the experience of learners from populations historically excluded from computing. The research is guided by questions about the design of the curriculum and the experiences of the students. For example, how can a curricular unit designed to have learners pose questions based on their interests and answer them using publicly available datasets produce the co-expression of interest and computer science knowledge? How do such educational experiences help learners understand the impact of computing on their lives? How do such educational experiences increase learners’ knowledge of and abilities in computer science? How do such educational experiences impact confidence and self-efficacy for learners historically excluded from computing? The project will gather data from teachers and students using qualitative and quantitative sources. For instance, to capture students’ learning experience, the project will gather video of students’ working and the written artifacts they produce. Participatory design sessions with the teachers will be used to design and revise the resources for classroom use and understand teachers’ perspectives. Field notes and artifacts will be gathered from the participatory design sessions with teachers. Overall, the project will develop knowledge about how to create data science learning experiences for students using a participatory process with teachers.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.

该奖项的全部或部分资金根据《2021 年美国救援计划法案》（公法 117-2）提供。该项目还通过 CS for All：研究和 RPP 计划资助。计算机科学和数据科学是许多 STEM 学科中应用的重要主题。数据科学可以将计算机编程和学生在日常生活中产生和使用的数据结合起来，帮助他们了解计算对世界的影响。该项目将设计和研究一个课程单元，通过让学生根据自己的兴趣提出问题，并通过使用编程从公共平台收集和分析大型数据集来回答问题，向学习者介绍强大的计算思想。该课程将通过与城市学区的合作来设计，将编程整合到新的数据科学课程中。教师和研究人员将合作开发课程，以便与其他学校共享。这门新课程提供了一个独特而及时的机会，将创新的计算机科学教学纳入全学区范围内的计划中，该计划专门为那些历史上几乎没有机会学习计算机科学的人群而设计。该项目将在高中课堂进行多年的基于设计的研究，并利用参与式设计、课堂案例研究和多学校实施来开发和研究这个新的计算机科学课程单元。特别关注历史上被排除在计算之外的人群中的学习者的体验。该研究以有关课程设计和学生经历的问题为指导。例如，一个旨在让学习者根据自己的兴趣提出问题并使用公开数据集回答这些问题的课程单元如何产生兴趣和计算机科学知识的共同表达？这样的教育经历如何帮助学习者了解计算对他们生活的影响？这些教育经历如何提高学习者的计算机科学知识和能力？对于历史上被排除在计算之外的学习者来说，这样的教育经历如何影响他们的信心和自我效能？该项目将使用定性和定量来源收集教师和学生的数据。例如，为了捕捉学生的学习体验，该项目将收集学生工作的视频和他们制作的书面作品。与教师的参与式设计会议将用于设计和修改课堂使用的资源并了解教师的观点。现场笔记和工件将从与教师的参与式设计会议中收集。总体而言，该项目将通过教师的参与过程，开发有关如何为学生创造数据科学学习体验的知识。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。

项目成果

What Data is in K-12 Data Science? An Analytic Approach to Understanding the Data Used in K-12 Data Science Courses

K-12 数据科学中有哪些数据？

• 发表时间: 2023
• 期刊: Proceedings of the International Society of the Learning Sciences Annual Conference 2023
• 作者: Israel-Fishelson, R.

Preparing K-12 Students to Meet their Data: Analyzing the Tools and Environments used in Introductory Data Science Contexts

让 K-12 学生做好迎接数据的准备：分析入门数据科学背景中使用的工具和环境

• DOI: 10.1145/3594781.3594796
• 发表时间: 2023
• 期刊: Design and Technology (LDT '23
• 作者: Israel-Fishelson, Rotem;Moon, Peter F.;Tabak, Rachel E.;Weintrop, David
• 通讯作者: Weintrop, David

The Tools Being Used to Introduce Youth to Data Science

用于向年轻人介绍数据科学的工具

• DOI: 10.1145/3585088.3589363
• 发表时间: 2023
• 期刊: Proceedings of the 22nd Annual ACM Interaction Design and Children Conference
• 作者: Moon, Peter F;Israel-Fishelson, Rotem;Tabak, Rachel;Weintrop, David
• 通讯作者: Weintrop, David