K–12 Data Science Learning

• In Event: Foundations of K–12 Data Science Education

Abstract

Objectives. Given the ubiquity of data, students are already faced with interacting with data on a daily basis. Through these interactions they have preconceived notions of what data is, how to interact with data through various different platforms, and do so in a variety of different contexts and settings (Clegg, et al., 2020; Wilkerson & Polman, 2020). Despite an increasing call for students to engage with data, the opportunities to learn about data and acquire data-related skills at the K-12 level are limited. This paper will (a) review known literature about student thinking and learning about data; (b) examine what is known about current opportunities for learning about data within the typical K-12 curriculum; and (c) report on common outside-of-school experiences (e.g. at museums, in everyday interactions, within particular cultural practices, etc.) that can especially contribute to students’ familiarity and learning with data.

Perspective. This review builds on foundational work emerging from the science and mathematics education communities on learning progressions (e.g., Duncan & Rivet, 2013). Similar to those efforts our goals are to identify core ideas in the field; develop coherent views of how those ideas may develop over the course of schooling; and emphasize the deepening of data-oriented practices over time, rather than covering a set of conceptually disjointed topics.

Methods. This review paper will focus on the empirical (qualitative and quantitative) work on K-12 data science learning including the expectations for student learning about data and the construction of the learning environments. As applicable, the review will consider how data science learning unfolds across K-12 settings, how it is guided/scaffolded, as well as the opportunities and challenges of learners' data science engagement. Where possible, we draw from existing research on learning trajectories related to data analysis, statistics, and visualization (e.g., Kim, et al., 2020; Lehrer et al., 2014; Mayes et al., 2014).

Data sources. This paper is a review of the literature with an explicit focus on what is happening in school-aged youth. Although much of the data reviewed will focus on classroom experiences, as needed, information from out-of-school contexts will be considered.

Results. This overview will illustrate what is anticipated that students will be able to do with data including their preconceived notions of data and how the data is processed (e.g., as rows and columns, or multidimensional? Are data sources and potential bias considered?). Through these examples of how students engage with data, the nature of the experiences will be explored to include the stumbling blocks that students experience along their developmental trajectories. Recommendations for what can be done to address these stumbling blocks/barriers are offered.

Significance. As the last presentation in this session, this paper will offer an empirically-based conceptual backbone around which previous considerations of equity and justice; tools; and curriculum implementation can be woven together. Considering learning across ages, school subjects, and contexts allows for development of a vision of coherent and robust data experiences throughout the K-12 experience.

Authors

• Joshua M. Rosenberg, University of Tennessee
  Presenting Author

• Ryan Seth Jones, Middle Tennessee State University
  Presenting Author