Understanding the Data in K-12 Data Science

Abstract

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.

Developing Standards for Educational Datasets by School Level: A Framework for Sustainable K-12 Education

“…However, our framework extends beyond these principles to provide a comprehensive, developmentally appropriate approach tailored to the specific needs of learners at each grade level from K-12. While existing research highlights the importance of understanding data in K-12 data science education [28], our framework goes further by proposing a structured progression of data preprocessing skills and techniques across different developmental stages. By aligning the complexity of data preprocessing tasks with students' cognitive abilities at each stage, our framework ensures that students can gradually build their data literacy and computational thinking skills in a manner appropriate to their grade level.…”

“…However, despite the growing recognition of the importance of data science education, there is limited research on effective strategies for integrating data science into K-12 curric-ula. Existing studies have primarily focused on specific aspects of data science education, such as teaching data literacy, developing computational thinking skills, or using data visualization tools [27,28]. There is a need for more comprehensive research that examines the challenges and opportunities of implementing data science across different subject areas and grade levels.…”