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

Israel-Fishelson, Rotem; Moon, Peter F.; Tabak, Rachel E.; Weintrop, David

doi:10.1145/3594781.3594796

Cited by 1 publication

(1 citation statement)

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“…Teachers and curriculum designers have at their disposal a variety of up-to-date and free-to-use databases, starting with K-12 data science tools that have built-in data sets (Israel-Fishelson et al, 2023) Similarly, the selection of the size of the data sets, the programming language, and the tool/environment for processing and analyzing the data are essential for designing the learning experiences and achieving the educational goals. Our findings showed that the curricula mainly use small data sets and limited tools and programming languages.…”

Section: Discussionmentioning

confidence: 99%

Understanding the Data in K-12 Data Science

Israel-Fishelson,

Moon,

Tabak

et al. 2024

Harvard Data Science Review

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Our increasingly data-driven world is amplifying the need for everyone to develop foundational data literacy skills. In response, a growing number of K-12 data science curricula are being designed to introduce all students to data. These curricula define what data science is at the high school level and directly shape how students are introduced to and understand the discipline. Ensuring these curricula are effective, engaging, and, most critically, equitable is of paramount importance. This article presents a qualitative analysis of four curricula, focusing on the data used to introduce learners to the field of data science. The analysis uses a series of analytical lenses to evaluate the 296 distinct data sets used across the curricula and identifies trends and best practices in data set selection. The analysis includes using data collected from high school students about their interests and experiences with data to understand if and how contemporary data science curricula are tapping into students' lived experiences to situate data science learning experiences. The findings show that the curricula use relatively recent and small data sets covering a range of topics and that learner involvement in data set selection is limited. Further, the analysis reveals gaps between the data sets used and students' selfreported interests. This work highlights the importance of data set selection, especially as it relates to supporting learners from historically excluded populations in technology fields. Finally, this article provides practical implications to assess existing curricula and advances our understanding of how to situate the field of data science in the interests, ideas, and values of today's students.

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Section: Discussionmentioning

confidence: 99%