Teaching, Learning, Policy & Leadership Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1649

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Author: search.filters.author.Weintrop, DavidSubject: search.filters.subject.data science education

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    Preparing K-12 Students to Meet their Data: Analyzing the Tools and Environments used in Introductory Data Science Contexts
    (Association for Computer Machinery (ACM), 2023-06-23) Israel-Fishelson, Rotem; Moon, Peter F.; Tabak, Rachel; Weintrop, David
    Data science education has gained momentum in recent years. Along with the development of curricula to teach data science, the number and diversity of tools for introducing data science to learners are also multiplying. The tools used to teach data science play a central role in shaping the learning experience. Therefore, it is important to carefully choose which tools to use to introduce learners to data science. This article presents a systematic review of 25 data science tools that are, or can be, used in introductory data science education for K-12 students. The identified tools list includes spreadsheets, visual analysis tools, and scripting environments. For each tool, we examine facets of its capabilities, interactions, educational support, and accessibility. This paper advances our understanding of the current state of introductory data science environments and highlights opportunities for creating new tools to better prepare learners to navigate the data-rich world surrounding them.
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    The Tools Being Used to Introduce Youth to Data Science
    (Association for Computer Machinery (ACM), 2023-06-19) Moon, Peter F.; Israel-Fishelson, Rotem; Tabak, Rachel; Weintrop, David
    Data is increasingly shaping the way people interact with each other and the world more broadly. For youth growing up in an increasingly data-driven society, it is critical they have foundational data literacy skills. A central component of data literacy is the ability to collect, analyze, visualize, and make meaning from data. All of these activities are mediated and shaped by the tools that youth use to carry out these data practices. Given the essential role tools play in enabling and supporting youth in engaging with and interpreting data, understanding what tools are used and how they are used in educational contexts will help us understand how youth are being prepared to be data-literate citizens. In this paper, we present the analysis of the data collection and analysis tools used in 4 widely adopted high school data science curricula. The analysis attends to both what tools are used as well as what datasets they are used to analyze. This work contributes to our understanding of the way youth are being introduced to concepts and practices from the field of data science and the role the tools play in shaping those experiences.