Foundations of Data Science for Students in Grades K-12 Proceedings of a Workshop (2023) / Chapter Skim
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Pages 58-62
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Selection of Tools Hollylynne Lee (North Carolina State University) asked panelists about selecting tools for various data science education purposes.
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Dorsey agreed that data literacy is essential and argued that data literacy cannot be separated from data science; data exploration is a key part of developing these skills and understandings. Biehler gave an example from Germany, in which students discussed news stories about the gender pay gap and then were given the data to explore themselves.
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Current State of Teacher Preparation What do teachers already know about data science education, asked H Lee, and how did they develop their knowledge, skills, and dispositions?
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When focusing on teacher preparation, said Bargagliotti, it is important to build these skills and give teachers these experiences, with data that are relevant to them. She noted that just as data science education needs to be engaging and relevant for students, the same is true for teachers.
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From page 62... ...
Perez added that part of teacher preparation is creating awareness around where and how data science can be brought to the classroom. She noted that domains are often siloed in elementary school; for example, students are looking at bar graphs in math class and six months later are looking at the same types of data visualizations in a science unit on the weather.
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